diff --git a/CHANGELOG.md b/CHANGELOG.md
index 034aa5ab..c690721c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,11 +9,9 @@
   
 ## Changelog
   
-### **HEAD -> main** 2022/07/19 mandic00@live.com
-
-
-### **origin/main** 2022/07/18 mandic00@live.com
+### **HEAD -> main** 2022/07/21 mandic00@live.com
 
+- tflite experiments
 - add load monitor test
 - beta for upcoming major release
 - swtich to release version of tfjs
@@ -25,9 +23,7 @@
 - fix npm v7 compatibility
 - add getmodelstats method
 - rebuild
-
-### **release: 2.8.1** 2022/06/08 mandic00@live.com
-
+- release build
 
 ### **2.8.1** 2022/06/08 mandic00@live.com
 
@@ -46,6 +42,7 @@
 ### **2.7.2** 2022/05/12 mandic00@live.com
 
 - fix demo when used with video files
+- major release
 
 ### **2.7.1** 2022/05/09 mandic00@live.com
 
diff --git a/dist/human.d.ts b/dist/human.d.ts
new file mode 100644
index 00000000..fed34cea
--- /dev/null
+++ b/dist/human.d.ts
@@ -0,0 +1,2643 @@
+/// <reference types="@webgpu/types/dist" />
+/// <reference types="offscreencanvas" />
+
+/** meta-function that performs draw for: canvas, face, body, hand */
+declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
+
+/** Defines all possible canvas types */
+export declare type AnyCanvas = HTMLCanvasElement | OffscreenCanvas;
+
+/** Defines all possible image types */
+export declare type AnyImage = HTMLImageElement | typeof Image;
+
+/** Defines all possible video types */
+export declare type AnyVideo = HTMLMediaElement | HTMLVideoElement;
+
+/** @docalias number[] */
+declare interface ArrayMap {
+    R0: number;
+    R1: number[];
+    R2: number[][];
+    R3: number[][][];
+    R4: number[][][][];
+    R5: number[][][][][];
+    R6: number[][][][][][];
+}
+
+/** Possible TensorFlow backends */
+export declare type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];
+
+/** draw detected bodies */
+declare function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+export declare type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;
+
+export declare type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';
+
+export declare type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';
+
+/** Configures all body detection specific options */
+export declare interface BodyConfig extends GenericConfig {
+    /** maximum number of detected bodies */
+    maxDetected: number;
+    /** minimum confidence for a detected body before results are discarded */
+    minConfidence: number;
+}
+
+/** body gesture type */
+export declare type BodyGesture = `leaning ${'left' | 'right'}` | `raise ${'left' | 'right'} hand` | 'i give up';
+
+/** Body Result keypoints */
+export declare interface BodyKeypoint {
+    /** body part name */
+    part: BodyLandmark;
+    /** body part position */
+    position: Point;
+    /** body part position normalized to 0..1 */
+    positionRaw: Point;
+    /** body part position relative to body center in meters */
+    distance?: Point;
+    /** body part detection score */
+    score: number;
+}
+
+export declare type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;
+
+export declare type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle' | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';
+
+export declare type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';
+
+export declare type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+export declare type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+/** Body results */
+export declare interface BodyResult {
+    /** body id */
+    id: number;
+    /** body detection score */
+    score: number;
+    /** detected body box */
+    box: Box;
+    /** detected body box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected body keypoints */
+    keypoints: Array<BodyKeypoint>;
+    /** detected body keypoints combined into annotated parts */
+    annotations: Record<BodyAnnotation, Point[][]>;
+}
+
+/** generic box as [x, y, width, height] */
+export declare type Box = [number, number, number, number];
+
+/**
+ * Creates an IOHandler that loads model artifacts from user-selected files.
+ *
+ * This method can be used for loading from files such as user-selected files
+ * in the browser.
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * // Note: This code snippet won't run properly without the actual file input
+ * //   elements in the HTML DOM.
+ *
+ * // Suppose there are two HTML file input (`<input type="file" ...>`)
+ * // elements.
+ * const uploadJSONInput = document.getElementById('upload-json');
+ * const uploadWeightsInput = document.getElementById('upload-weights');
+ * const model = await tf.loadLayersModel(tf.io.browserFiles(
+ *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+ * ```
+ *
+ * @param files `File`s to load from. Currently, this function supports only
+ *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+ *   which an `Array` of `File`s is expected (in that order):
+ *   - A JSON file containing the model topology and weight manifest.
+ *   - Optionally, One or more binary files containing the binary weights.
+ *     These files must have names that match the paths in the `weightsManifest`
+ *     contained by the aforementioned JSON file, or errors will be thrown
+ *     during loading. These weights files have the same format as the ones
+ *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+ *     Python PIP package. If no weights files are provided, only the model
+ *     topology will be loaded from the JSON file above.
+ * @returns An instance of `Files` `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function browserFiles(files: File[]): IOHandler;
+
+/**
+ * Deprecated. Use `tf.io.http`.
+ * @param path
+ * @param loadOptions
+ */
+declare function browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** draw processed canvas */
+declare function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas): Promise<void>;
+
+/**
+ * Concatenate a number of ArrayBuffers into one.
+ *
+ * @param buffers A number of array buffers to concatenate.
+ * @returns Result of concatenating `buffers` in order.
+ */
+declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+
+/**
+ * Configuration interface definition for **Human** library
+ * Contains all configurable parameters
+ * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+ */
+export declare interface Config {
+    /** Backend used for TFJS operations
+     * valid build-in backends are:
+     * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`
+     * - NodeJS: `cpu`, `wasm`, `tensorflow`
+     * default: `humangl` for browser and `tensorflow` for nodejs
+     */
+    backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+    /** Path to *.wasm files if backend is set to `wasm`
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPath: string;
+    /** Force WASM loader to use platform fetch
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPlatformFetch: boolean;
+    /** Print debug statements to console
+     *
+     * default: `true`
+     */
+    debug: boolean;
+    /** Perform model loading and inference concurrently or sequentially
+     *
+     * default: `true`
+     */
+    async: boolean;
+    /** What to use for `human.warmup()`
+     * - warmup pre-initializes all models for faster inference but can take significant time on startup
+     * - used by `webgl`, `humangl` and `webgpu` backends
+     *
+     * default: `full`
+     */
+    warmup: '' | 'none' | 'face' | 'full' | 'body';
+    /** Base model path (typically starting with file://, http:// or https://) for all models
+     * - individual modelPath values are relative to this path
+     *
+     * default: `../models/` for browsers and `file://models/` for nodejs
+     */
+    modelBasePath: string;
+    /** Cache models in IndexDB on first sucessfull load
+     * default: true if indexdb is available (browsers), false if its not (nodejs)
+     */
+    cacheModels: boolean;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+     *
+     * default: 0.7
+     */
+    cacheSensitivity: number;
+    /** Perform immediate garbage collection on deallocated tensors instead of caching them */
+    deallocate: boolean;
+    /** Internal Variable */
+    skipAllowed: boolean;
+    /** Filter config {@link FilterConfig} */
+    filter: Partial<FilterConfig>;
+    /** Gesture config {@link GestureConfig} */
+    gesture: Partial<GestureConfig>;
+    /** Face config {@link FaceConfig} */
+    face: Partial<FaceConfig>;
+    /** Body config {@link BodyConfig} */
+    body: Partial<BodyConfig>;
+    /** Hand config {@link HandConfig} */
+    hand: Partial<HandConfig>;
+    /** Object config {@link ObjectConfig} */
+    object: Partial<ObjectConfig>;
+    /** Segmentation config {@link SegmentationConfig} */
+    segmentation: Partial<SegmentationConfig>;
+}
+
+/**
+ * Copy a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Copying within a storage medium, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Copying between two storage mediums, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Copy the model, from Local Storage to IndexedDB.
+ * await tf.io.copyModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove both models.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of copying.
+ * @param destURL Destination URL of copying.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * We wrap data id since we use weak map to avoid memory leaks.
+ * Since we have our own memory management, we have a reference counter
+ * mapping a tensor to its data, so there is always a pointer (even if that
+ * data is otherwise garbage collectable).
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
+ * Global_Objects/WeakMap
+ */
+declare type DataId = object;
+
+declare type DataToGPUOptions = DataToGPUWebGLOption;
+
+declare interface DataToGPUWebGLOption {
+    customTexShape?: [number, number];
+}
+
+/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
+declare type DataType = keyof DataTypeMap;
+
+declare interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+
+/**
+ * Decode flat ArrayBuffer as weights.
+ *
+ * This function does not handle sharding.
+ *
+ * This function is the reverse of `encodeWeights`.
+ *
+ * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+ *   concatenated in the order specified in `specs`.
+ * @param specs Specifications of the names, dtypes and shapes of the tensors
+ *   whose value are encoded by `buffer`.
+ * @return A map from tensor name to tensor value, with the names corresponding
+ *   to names in `specs`.
+ * @throws Error, if any of the tensors has unsupported dtype.
+ */
+declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+
+/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
+export declare const defaults: Config;
+
+/** Face descriptor type as number array */
+export declare type Descriptor = Array<number>;
+
+/** Calculates distance between two descriptors
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ */
+declare function distance(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare namespace draw {
+    export {
+        person,
+        canvas,
+        all,
+        options,
+        face,
+        body,
+        hand,
+        object,
+        gesture
+    }
+}
+export { draw }
+
+/** Draw Options
+ * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter
+ */
+export declare type DrawOptions = {
+    /** draw line color */
+    color: string;
+    /** alpha value used for lines */
+    alpha: number;
+    /** label color */
+    labelColor: string;
+    /** label shadow color */
+    shadowColor: string;
+    /** label font */
+    font: string;
+    /** line spacing between labels */
+    lineHeight: number;
+    /** line width for drawn lines */
+    lineWidth: number;
+    /** size of drawn points */
+    pointSize: number;
+    /** draw rounded boxes by n pixels */
+    roundRect: number;
+    /** should points be drawn? */
+    drawPoints: boolean;
+    /** should labels be drawn? */
+    drawLabels: boolean;
+    /** should face attention keypoints be highlighted */
+    drawAttention: boolean;
+    /** should detected gestures be drawn? */
+    drawGestures: boolean;
+    /** should draw boxes around detection results? */
+    drawBoxes: boolean;
+    /** should draw polygons from detection points? */
+    drawPolygons: boolean;
+    /** should draw gaze arrows? */
+    drawGaze: boolean;
+    /** should fill polygons? */
+    fillPolygons: boolean;
+    /** use z-coordinate when available */
+    useDepth: boolean;
+    /** should lines be curved? */
+    useCurves: boolean;
+};
+
+export declare type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';
+
+/**
+ * Encode a map from names to weight values as an ArrayBuffer, along with an
+ * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+ *
+ * This function does not perform sharding.
+ *
+ * This function is the reverse of `decodeWeights`.
+ *
+ * @param tensors A map ("dict") from names to tensors.
+ * @param group Group to which the weights belong (optional).
+ * @returns A `Promise` of
+ *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+ *     concatenated.
+ *   - An `Array` of `WeightManifestEntry`s, carrying information including
+ *     tensor names, `dtype`s and shapes.
+ * @throws Error: on unsupported tensor `dtype`.
+ */
+declare function encodeWeights(tensors: NamedTensorMap | NamedTensor[], group?: WeightGroup): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+
+/** Env class that holds detected capabilities */
+export declare class Env {
+    /** Running in Browser */
+    browser: boolean;
+    /** Running in NodeJS */
+    node: boolean;
+    /** Running in WebWorker thread */
+    worker: boolean;
+    /** Detected platform */
+    platform: string;
+    /** Detected agent */
+    agent: string;
+    /** List of supported backends */
+    backends: string[];
+    /** Has any work been performed so far */
+    initial: boolean;
+    /** Are image filters supported? */
+    filter: boolean | undefined;
+    /** TFJS instance details */
+    tfjs: {
+        version: undefined | string;
+    };
+    /** Is offscreenCanvas supported? */
+    offscreen: undefined | boolean;
+    /** Are performance counter instant values or additive */
+    perfadd: boolean;
+    /** WASM detected capabilities */
+    wasm: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        simd: undefined | boolean;
+        multithread: undefined | boolean;
+    };
+    /** WebGL detected capabilities */
+    webgl: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        version: undefined | string;
+        renderer: undefined | string;
+    };
+    /** WebGPU detected capabilities */
+    webgpu: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        adapter: undefined | string;
+    };
+    /** CPU info */
+    cpu: {
+        model: undefined | string;
+        flags: string[];
+    };
+    /** List of supported kernels for current backend */
+    kernels: string[];
+    /** MonkeyPatch for Canvas */
+    Canvas: undefined;
+    /** MonkeyPatch for Image */
+    Image: undefined;
+    /** MonkeyPatch for ImageData */
+    ImageData: undefined;
+    constructor();
+    /** update backend information */
+    updateBackend(): Promise<void>;
+    /** update cpu information */
+    updateCPU(): Promise<void>;
+}
+
+export declare const env: Env;
+
+/** Events dispatched by `human.events`
+ * - `create`: triggered when Human object is instantiated
+ * - `load`: triggered when models are loaded (explicitly or on-demand)
+ * - `image`: triggered when input image is processed
+ * - `result`: triggered when detection is complete
+ * - `warmup`: triggered when warmup is complete
+ */
+export declare type Events = 'create' | 'load' | 'image' | 'result' | 'warmup' | 'error';
+
+/** Defines possible externally defined canvas */
+export declare type ExternalCanvas = typeof env.Canvas;
+
+/** draw detected faces */
+declare function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Anti-spoofing part of face configuration */
+export declare interface FaceAntiSpoofConfig extends GenericConfig {
+}
+
+/** Attention part of face configuration */
+export declare interface FaceAttentionConfig extends GenericConfig {
+}
+
+/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */
+export declare interface FaceConfig extends GenericConfig {
+    detector: Partial<FaceDetectorConfig>;
+    mesh: Partial<FaceMeshConfig>;
+    attention: Partial<FaceAttentionConfig>;
+    iris: Partial<FaceIrisConfig>;
+    description: Partial<FaceDescriptionConfig>;
+    emotion: Partial<FaceEmotionConfig>;
+    antispoof: Partial<FaceAntiSpoofConfig>;
+    liveness: Partial<FaceLivenessConfig>;
+    gear: Partial<FaceGearConfig>;
+}
+
+/** Description or face embedding part of face configuration
+ * - also used by age and gender detection
+ */
+export declare interface FaceDescriptionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Detector part of face configuration */
+export declare interface FaceDetectorConfig extends GenericConfig {
+    /** is face rotation correction performed after detecting face?
+     * used to correctly analyze faces under high angles
+     */
+    rotation: boolean;
+    /** maximum number of detected faces */
+    maxDetected: number;
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected faces before one is discarded */
+    iouThreshold: number;
+    /** should child models perform on masked image of a face */
+    mask: boolean;
+    /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?
+     * if enabled it must be manually deallocated to avoid memory leak */
+    return: boolean;
+}
+
+/** Emotion part of face configuration */
+export declare interface FaceEmotionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Gear part of face configuration */
+export declare interface FaceGearConfig extends GenericConfig {
+    /** minimum confidence for a detected race before results are discarded */
+    minConfidence: number;
+}
+
+/** face gesture type */
+export declare type FaceGesture = `facing ${'left' | 'center' | 'right'}` | `blink ${'left' | 'right'} eye` | `mouth ${number}% open` | `head ${'up' | 'down'}`;
+
+/** Iris part of face configuration */
+export declare interface FaceIrisConfig extends GenericConfig {
+}
+
+export declare type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette' | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner' | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris' | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris' | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';
+
+/** Liveness part of face configuration */
+export declare interface FaceLivenessConfig extends GenericConfig {
+}
+
+/** Mesh part of face configuration */
+export declare interface FaceMeshConfig extends GenericConfig {
+    /** Keep detected faces that cannot be verified using facemesh */
+    keepInvalid: boolean;
+}
+
+/** Face results
+ * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
+ * - Some values may be null if specific model is not enabled
+ */
+export declare interface FaceResult {
+    /** face id */
+    id: number;
+    /** overall face score */
+    score: number;
+    /** detection score */
+    boxScore: number;
+    /** mesh score */
+    faceScore: number;
+    /** detected face box */
+    box: Box;
+    /** detected face box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected face mesh */
+    mesh: Array<Point>;
+    /** detected face mesh normalized to 0..1 */
+    meshRaw: Array<Point>;
+    /** face contours as array of 2d points normalized to 0..1 */
+    /** face contours as array of 2d points */
+    /** mesh keypoints combined into annotated results */
+    annotations: Record<FaceLandmark, Point[]>;
+    /** detected age */
+    age?: number;
+    /** detected gender */
+    gender?: Gender;
+    /** gender detection score */
+    genderScore?: number;
+    /** detected emotions */
+    emotion?: Array<{
+        score: number;
+        emotion: Emotion;
+    }>;
+    /** detected race */
+    race?: Array<{
+        score: number;
+        race: Race;
+    }>;
+    /** face descriptor */
+    embedding?: Array<number>;
+    /** face iris distance from camera */
+    iris?: number;
+    /** face anti-spoofing result confidence */
+    real?: number;
+    /** face liveness result confidence */
+    live?: number;
+    /** face rotation details */
+    rotation?: {
+        angle: {
+            roll: number;
+            yaw: number;
+            pitch: number;
+        };
+        matrix: [number, number, number, number, number, number, number, number, number];
+        gaze: {
+            bearing: number;
+            strength: number;
+        };
+    } | null;
+    /** detected face as tensor that can be used in further pipelines */
+    tensor?: Tensor;
+}
+
+/** Run input through image filters before inference
+ * - available only in Browser environments
+ * - image filters run with near-zero latency as they are executed on the GPU using WebGL
+ */
+export declare interface FilterConfig {
+    /** are image filters enabled? */
+    enabled: boolean;
+    /** perform image histogram equalization
+     * - equalization is performed on input as a whole and detected face before its passed for further analysis
+     */
+    equalization: boolean;
+    /** resize input width
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    width: number;
+    /** resize input height
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    height: number;
+    /** return processed canvas imagedata in result */
+    return: boolean;
+    /** flip input as mirror image */
+    flip: boolean;
+    /** range: -1 (darken) to 1 (lighten) */
+    brightness: number;
+    /** range: -1 (reduce contrast) to 1 (increase contrast) */
+    contrast: number;
+    /** range: 0 (no sharpening) to 1 (maximum sharpening) */
+    sharpness: number;
+    /** range: 0 (no blur) to N (blur radius in pixels) */
+    blur: number;
+    /** range: -1 (reduce saturation) to 1 (increase saturation) */
+    saturation: number;
+    /** range: 0 (no change) to 360 (hue rotation in degrees) */
+    hue: number;
+    /** image negative */
+    negative: boolean;
+    /** image sepia colors */
+    sepia: boolean;
+    /** image vintage colors */
+    vintage: boolean;
+    /** image kodachrome colors */
+    kodachrome: boolean;
+    /** image technicolor colors */
+    technicolor: boolean;
+    /** image polaroid camera effect */
+    polaroid: boolean;
+    /** range: 0 (no pixelate) to N (number of pixels to pixelate) */
+    pixelate: number;
+}
+
+export declare type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';
+
+export declare type FingerCurl = 'none' | 'half' | 'full';
+
+export declare type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandler` that simply loads the provided data.
+ */
+declare function fromMemory(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler;
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
+ */
+declare function fromMemorySync(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandlerSync;
+
+export declare type Gender = 'male' | 'female' | 'unknown';
+
+/** Generic config type inherited by all module types */
+export declare interface GenericConfig {
+    /** is module enabled? */
+    enabled: boolean;
+    /** path to model json file (relative to `modelBasePath` */
+    modelPath: string;
+    /** how many max frames to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipFrames: number;
+    /** how many max milliseconds to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipTime: number;
+}
+
+/** draw detected gestures */
+declare function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Controlls gesture detection */
+export declare interface GestureConfig {
+    /** is gesture detection enabled? */
+    enabled: boolean;
+}
+
+/** Gesture combined results
+ * Each result has:
+ * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`
+ * - gesture: gesture detected
+ */
+export declare type GestureResult = {
+    'face': number;
+    gesture: FaceGesture;
+} | {
+    'iris': number;
+    gesture: IrisGesture;
+} | {
+    'body': number;
+    gesture: BodyGesture;
+} | {
+    'hand': number;
+    gesture: HandGesture;
+};
+
+declare const getLoadHandlers: (url: string | string[], loadOptions?: LoadOptions) => IOHandler[];
+
+/**
+ * Create `ModelArtifacts` from a JSON file.
+ *
+ * @param modelJSON Object containing the parsed JSON of `model.json`
+ * @param loadWeights Function that takes the JSON file's weights manifest,
+ *     reads weights from the listed path(s), and returns a Promise of the
+ *     weight manifest entries along with the weights data.
+ * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+ */
+declare function getModelArtifactsForJSON(modelJSON: ModelJSON, loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[WeightsManifestEntry[], /* weightData */ ArrayBuffer]>): Promise<ModelArtifacts>;
+
+/**
+ * Populate ModelArtifactsInfo fields for a model with JSON topology.
+ * @param modelArtifacts
+ * @returns A ModelArtifactsInfo object.
+ */
+declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
+
+declare const getModelStats: (instance: Human) => ModelStats;
+
+declare const getSaveHandlers: (url: string | string[]) => IOHandler[];
+
+declare interface GPUData {
+    tensorRef: Tensor;
+    texture?: WebGLTexture;
+    buffer?: GPUBuffer;
+    texShape?: [number, number];
+    bufSize?: number;
+}
+
+/**
+ * A `tf.GraphModel` is a directed, acyclic graph built from a
+ * SavedModel GraphDef and allows inference execution.
+ *
+ * A `tf.GraphModel` can only be created by loading from a model converted from
+ * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+ * the command line converter tool and loaded via `tf.loadGraphModel`.
+ *
+ * @doc {heading: 'Models', subheading: 'Classes'}
+ */
+export declare class GraphModel<ModelURL extends Url = string | io.IOHandler> implements InferenceModel {
+    private modelUrl;
+    private loadOptions;
+    private executor;
+    private version;
+    private handler;
+    private artifacts;
+    private initializer;
+    private resourceManager;
+    private signature;
+    readonly modelVersion: string;
+    readonly inputNodes: string[];
+    readonly outputNodes: string[];
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
+    readonly metadata: {};
+    readonly modelSignature: {};
+    /**
+     * @param modelUrl url for the model, or an `io.IOHandler`.
+     * @param weightManifestUrl url for the weight file generated by
+     * scripts/convert.py script.
+     * @param requestOption options for Request, which allows to send credentials
+     * and custom headers.
+     * @param onProgress Optional, progress callback function, fired periodically
+     * before the load is completed.
+     */
+    constructor(modelUrl: ModelURL, loadOptions?: io.LoadOptions);
+    private findIOHandler;
+    /**
+     * Loads the model and weight files, construct the in memory weight map and
+     * compile the inference graph.
+     */
+    load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync ? boolean : Promise<boolean>;
+    /**
+     * Synchronously construct the in memory weight map and
+     * compile the inference graph. Also initialize hashtable if any.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    loadSync(artifacts: io.ModelArtifacts): boolean;
+    /**
+     * Save the configuration and/or weights of the GraphModel.
+     *
+     * An `IOHandler` is an object that has a `save` method of the proper
+     * signature defined. The `save` method manages the storing or
+     * transmission of serialized data ("artifacts") that represent the
+     * model's topology and weights onto or via a specific medium, such as
+     * file downloads, local storage, IndexedDB in the web browser and HTTP
+     * requests to a server. TensorFlow.js provides `IOHandler`
+     * implementations for a number of frequently used saving mediums, such as
+     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+     * for more details.
+     *
+     * This method also allows you to refer to certain types of `IOHandler`s
+     * as URL-like string shortcuts, such as 'localstorage://' and
+     * 'indexeddb://'.
+     *
+     * Example 1: Save `model`'s topology and weights to browser [local
+     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+     * then load it back.
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     *
+     * const saveResults = await model.save('localstorage://my-model-1');
+     *
+     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+     * console.log('Prediction from loaded model:');
+     * model.predict(zeros).print();
+     * ```
+     *
+     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+     * scheme-based string shortcut for `IOHandler`.
+     * @param config Options for saving the model.
+     * @returns A `Promise` of `SaveResult`, which summarizes the result of
+     * the saving, such as byte sizes of the saved artifacts for the model's
+     *   topology and weight values.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+     * inputs params should be in either `tf.Tensor`[] if the input order is
+     * fixed, or otherwise NamedTensorMap format.
+     *
+     * For model with multiple inputs, we recommend you use NamedTensorMap as the
+     * input type, if you use `tf.Tensor`[], the order of the array needs to
+     * follow the
+     * order of inputNodes array. @see {@link GraphModel.inputNodes}
+     *
+     * You can also feed any intermediate nodes using the NamedTensorMap as the
+     * input type. For example, given the graph
+     *    InputNode => Intermediate => OutputNode,
+     * you can execute the subgraph Intermediate => OutputNode by calling
+     *    model.execute('IntermediateNode' : tf.tensor(...));
+     *
+     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+     * state needs to be fed manually.
+     *
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size and
+     * output node names. Currently the batch size option is ignored for graph
+     * model.
+     *
+     * @returns Inference result tensors. The output would be single `tf.Tensor`
+     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+     * will be returned for model with multiple outputs.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    private normalizeInputs;
+    private normalizeOutputs;
+    /**
+     * Executes inference for the model for given input tensors.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no
+     * outputs are specified, the default outputs of the model would be used.
+     * You can inspect intermediate nodes of the model by adding them to the
+     * outputs array.
+     *
+     * @returns A single tensor if provided with a single output or no outputs
+     * are provided and there is only one default output, otherwise return a
+     * tensor array. The order of the tensor array is the same as the outputs
+     * if provided, otherwise the order of outputNodes attribute of the model.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
+    /**
+     * Executes inference for the model for given input tensors in async
+     * fashion, use this method when your model contains control flow ops.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no outputs
+     * are specified, the default outputs of the model would be used. You can
+     * inspect intermediate nodes of the model by adding them to the outputs
+     * array.
+     *
+     * @returns A Promise of single tensor if provided with a single output or
+     * no outputs are provided and there is only one default output, otherwise
+     * return a tensor map.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
+    /**
+     * Get intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    getIntermediateTensors(): NamedTensorsMap;
+    /**
+     * Dispose intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    disposeIntermediateTensors(): void;
+    private convertTensorMapToTensorsMap;
+    /**
+     * Releases the memory used by the weight tensors and resourceManager.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    dispose(): void;
+}
+
+/** draw detected hands */
+declare function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all hand detection specific options */
+export declare interface HandConfig extends GenericConfig {
+    /** should hand rotation correction be performed after hand detection? */
+    rotation: boolean;
+    /** minimum confidence for a detected hand before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected hands before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected hands */
+    maxDetected: number;
+    /** should hand landmarks be detected or just return detected hand box */
+    landmarks: boolean;
+    detector: {
+        /** path to hand detector model json */
+        modelPath?: string;
+    };
+    skeleton: {
+        /** path to hand skeleton model json */
+        modelPath?: string;
+    };
+}
+
+/** hand gesture type */
+export declare type HandGesture = `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward` | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up` | 'victory' | 'thumbs up';
+
+/** Hand results */
+export declare interface HandResult {
+    /** hand id */
+    id: number;
+    /** hand overal score */
+    score: number;
+    /** hand detection score */
+    boxScore: number;
+    /** hand skelton score */
+    fingerScore: number;
+    /** detected hand box */
+    box: Box;
+    /** detected hand box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected hand keypoints */
+    keypoints: Array<Point>;
+    /** detected hand class */
+    label: HandType;
+    /** detected hand keypoints combined into annotated parts */
+    annotations: Record<Finger, Array<Point>>;
+    /** detected hand parts annotated with part gestures */
+    landmarks: Record<Finger, {
+        curl: FingerCurl;
+        direction: FingerDirection;
+    }>;
+}
+
+export declare type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';
+
+/**
+ * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+ *
+ * An HTTP request of the `multipart/form-data` mime type will be sent to the
+ * `path` URL. The form data includes artifacts that represent the topology
+ * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+ * blobs (files) exist in form-data:
+ *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+ *   - A binary weights file consisting of the concatenated weight values.
+ * These files are in the same format as the one generated by
+ * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+ *
+ * The following code snippet exemplifies the client-side code that uses this
+ * function:
+ *
+ * ```js
+ * const model = tf.sequential();
+ * model.add(
+ *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+ *
+ * const saveResult = await model.save(tf.io.http(
+ *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+ * console.log(saveResult);
+ * ```
+ *
+ * If the default `POST` method is to be used, without any custom parameters
+ * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+ *
+ * ```js
+ * const saveResult = await model.save('http://model-server:5000/upload');
+ * ```
+ *
+ * The following GitHub Gist
+ * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+ * implements a server based on [flask](https://github.com/pallets/flask) that
+ * can receive the request. Upon receiving the model artifacts via the requst,
+ * this particular server reconsistutes instances of [Keras
+ * Models](https://keras.io/models/model/) in memory.
+ *
+ *
+ * @param path A URL path to the model.
+ *   Can be an absolute HTTP path (e.g.,
+ *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+ *   './model-upload').
+ * @param requestInit Request configurations to be used when sending
+ *    HTTP request to server using `fetch`. It can contain fields such as
+ *    `method`, `credentials`, `headers`, `mode`, etc. See
+ *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+ *    for more information. `requestInit` must not have a body, because the
+ * body will be set by TensorFlow.js. File blobs representing the model
+ * topology (filename: 'model.json') and the weights of the model (filename:
+ * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+ * `body`, an Error will be thrown.
+ * @param loadOptions Optional configuration for the loading. It includes the
+ *   following fields:
+ *   - weightPathPrefix Optional, this specifies the path prefix for weight
+ *     files, by default this is calculated from the path param.
+ *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+ *     the `fetch` from node-fetch can be used here.
+ *   - onProgress Optional, progress callback function, fired periodically
+ *     before the load is completed.
+ * @returns An instance of `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function http(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** **Human** library main class
+ *
+ * All methods and properties are available only as members of Human class
+ *
+ * - Configuration object definition: {@link Config}
+ * - Results object definition: {@link Result}
+ * - Possible inputs: {@link Input}
+ *
+ * @param userConfig - {@link Config}
+ * @returns instance of {@link Human}
+ */
+declare class Human {
+    #private;
+    /** Current version of Human library in *semver* format */
+    version: string;
+    /** Current configuration
+     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+     */
+    config: Config;
+    /** Last known result of detect run
+     * - Can be accessed anytime after initial detection
+     */
+    result: Result;
+    /** Current state of Human library
+     * - Can be polled to determine operations that are currently executed
+     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
+     */
+    state: string;
+    /** currenty processed image tensor and canvas */
+    process: {
+        tensor: Tensor | null;
+        canvas: AnyCanvas | null;
+    };
+    /** Instance of TensorFlow/JS used by Human
+     *  - Can be embedded or externally provided
+     * [TFJS API](https://js.tensorflow.org/api/latest/)
+     */
+    tf: any;
+    /** Object containing environment information used for diagnostics */
+    env: Env;
+    /** Draw helper classes that can draw detected objects on canvas using specified draw
+     * - canvas: draws input to canvas
+     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
+     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
+     */
+    draw: {
+        canvas: typeof draw.canvas;
+        face: typeof draw.face;
+        body: typeof draw.body;
+        hand: typeof draw.hand;
+        gesture: typeof draw.gesture;
+        object: typeof draw.object;
+        person: typeof draw.person;
+        all: typeof draw.all;
+        options: DrawOptions;
+    };
+    /** Currently loaded models
+     * @internal
+     * {@link Models}
+     */
+    models: models.Models;
+    /** Container for events dispatched by Human
+     * Possible events:
+     * - `create`: triggered when Human object is instantiated
+     * - `load`: triggered when models are loaded (explicitly or on-demand)
+     * - `image`: triggered when input image is processed
+     * - `result`: triggered when detection is complete
+     * - `warmup`: triggered when warmup is complete
+     * - `error`: triggered on some errors
+     */
+    events: EventTarget | undefined;
+    /** Reference face triangualtion array of 468 points, used for triangle references between points */
+    faceTriangulation: number[];
+    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
+    faceUVMap: [number, number][];
+    /** Performance object that contains values for all recently performed operations */
+    performance: Record<string, number>;
+    /** WebGL debug info */
+    gl: Record<string, unknown>;
+    /** Constructor for **Human** library that is futher used for all operations
+     * @param userConfig - user configuration object {@link Config}
+     */
+    constructor(userConfig?: Partial<Config>);
+    /** internal function to measure tensor leaks */
+    analyze: (...msg: string[]) => void;
+    /** Reset configuration to default values */
+    reset(): void;
+    /** Validate current configuration schema */
+    validate(userConfig?: Partial<Config>): {
+        reason: string;
+        where: string;
+        expected?: string;
+    }[];
+    /** Exports face matching methods {@link match#similarity} */
+    similarity: typeof match.similarity;
+    /** Exports face matching methods {@link match#distance} */
+    distance: typeof match.distance;
+    /** Exports face matching methods {@link match#match} */
+    match: typeof match.match;
+    /** Utility wrapper for performance.now() */
+    now(): number;
+    /** Process input as return canvas and tensor
+     *
+     * @param input - any input {@link Input}
+     * @param getTensor - should image processing also return tensor or just canvas
+     * Returns object with `tensor` and `canvas`
+     */
+    image(input: Input, getTensor?: boolean): Promise<{
+        tensor: Tensor<Rank> | null;
+        canvas: AnyCanvas | null;
+    }>;
+    /** Segmentation method takes any input and returns processed canvas with body segmentation
+     *  - Segmentation is not triggered as part of detect process
+     * @param input - {@link Input}
+     * @param background - {@link Input}
+     *  - Optional parameter background is used to fill the background with specific input
+     *  Returns:
+     *  - `data` as raw data array with per-pixel segmentation values
+     *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
+     *  - `alpha` as grayscale canvas that represents segmentation alpha values
+     */
+    segmentation(input: Input, background?: Input): Promise<{
+        data: number[] | Tensor;
+        canvas: AnyCanvas | null;
+        alpha: AnyCanvas | null;
+    }>;
+    /** Enhance method performs additional enhacements to face image previously detected for futher processing
+     *
+     * @param input - Tensor as provided in human.result.face[n].tensor
+     * @returns Tensor
+     */
+    enhance(input: Tensor): Tensor | null;
+    /** Compare two input tensors for pixel simmilarity
+     * - use `human.image` to process any valid input and get a tensor that can be used for compare
+     * - when passing manually generated tensors:
+     *  - both input tensors must be in format [1, height, width, 3]
+     *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
+     * - return value is pixel similarity score normalized by input resolution and rgb channels
+     */
+    compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
+    /** Explicit backend initialization
+     *  - Normally done implicitly during initial load phase
+     *  - Call to explictly register and initialize TFJS backend without any other operations
+     *  - Use when changing backend during runtime
+     */
+    init(): Promise<void>;
+    /** Load method preloads all configured models on-demand
+     * - Not explicitly required as any required model is load implicitly on it's first run
+     *
+     * @param userConfig - {@link Config}
+     */
+    load(userConfig?: Partial<Config>): Promise<void>;
+    /** emit event */
+    emit: (event: string) => void;
+    /** Runs interpolation using last known result and returns smoothened result
+     * Interpolation is based on time since last known result so can be called independently
+     *
+     * @param result - {@link Result} optional use specific result set to run interpolation on
+     * @returns result - {@link Result}
+     */
+    next(result?: Result): Result;
+    /** get model loading/loaded stats */
+    getModelStats(): ModelStats;
+    /** Warmup method pre-initializes all configured models for faster inference
+     * - can take significant time on startup
+     * - only used for `webgl` and `humangl` backends
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    warmup(userConfig?: Partial<Config>): Promise<Result | undefined>;
+    /** Run detect with tensorflow profiling
+     * - result object will contain total exeuction time information for top-20 kernels
+     * - actual detection object can be accessed via `human.result`
+     */
+    profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>>;
+    /** Main detection method
+     * - Analyze configuration: {@link Config}
+     * - Pre-process input: {@link Input}
+     * - Run inference for all configured models
+     * - Process and return result: {@link Result}
+     *
+     * @param input - {@link Input}
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    detect(input: Input, userConfig?: Partial<Config>): Promise<Result>;
+}
+export { Human }
+export default Human;
+
+/** Defines all possible image objects */
+export declare type ImageObjects = ImageData | ImageBitmap;
+
+/**
+ * Common interface for a machine learning model that can do inference.
+ */
+declare interface InferenceModel {
+    /**
+     * Return the array of input tensor info.
+     */
+    readonly inputs: ModelTensorInfo[];
+    /**
+     * Return the array of output tensor info.
+     */
+    readonly outputs: ModelTensorInfo[];
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size.
+     *
+     * @returns Inference result tensors. The output would be single Tensor if
+     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
+     * be returned for model with multiple outputs.
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    /**
+     * Single Execute the inference for the input tensors and return activation
+     * values for specified output node names without batching.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     *
+     * @param outputs string|string[]. List of output node names to retrieve
+     * activation from.
+     *
+     * @returns Activation values for the output nodes result tensors. The return
+     * type matches specified parameter outputs type. The output would be single
+     * Tensor if single output is specified, otherwise Tensor[] for multiple
+     * outputs.
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
+
+/** Defines all possible input types for **Human** detection */
+export declare type Input = Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas;
+
+declare namespace io {
+    export {
+        copyModel,
+        listModels,
+        moveModel,
+        removeModel,
+        browserFiles,
+        browserHTTPRequest,
+        concatenateArrayBuffers,
+        decodeWeights,
+        encodeWeights,
+        fromMemory,
+        fromMemorySync,
+        getLoadHandlers,
+        getModelArtifactsForJSON,
+        getModelArtifactsInfoForJSON,
+        getSaveHandlers,
+        http,
+        IOHandler,
+        IOHandlerSync,
+        isHTTPScheme,
+        LoadHandler,
+        LoadOptions,
+        loadWeights,
+        ModelArtifacts,
+        ModelArtifactsInfo,
+        ModelJSON,
+        ModelStoreManager,
+        OnProgressCallback,
+        registerLoadRouter,
+        registerSaveRouter,
+        RequestDetails,
+        SaveConfig,
+        SaveHandler,
+        SaveResult,
+        TrainingConfig,
+        WeightGroup,
+        weightsLoaderFactory,
+        WeightsManifestConfig,
+        WeightsManifestEntry,
+        withSaveHandler,
+        withSaveHandlerSync
+    }
+}
+
+/**
+ * Interface for a model import/export handler.
+ *
+ * The `save` and `load` handlers are both optional, in order to allow handlers
+ * that support only saving or loading.
+ */
+declare interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+
+declare type IOHandlerSync = {
+    [K in keyof IOHandler]: Syncify<IOHandler[K]>;
+};
+
+declare type IORouter = (url: string | string[], loadOptions?: LoadOptions) => IOHandler;
+
+/** iris gesture type */
+export declare type IrisGesture = 'facing center' | `looking ${'left' | 'right' | 'up' | 'down'}` | 'looking center';
+
+declare function isHTTPScheme(url: string): boolean;
+
+/**
+ * List all models stored in registered storage mediums.
+ *
+ * For a web browser environment, the registered mediums are Local Storage and
+ * IndexedDB.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @returns A `Promise` of a dictionary mapping URLs of existing models to
+ * their model artifacts info. URLs include medium-specific schemes, e.g.,
+ *   'indexeddb://my/model/1'. Model artifacts info include type of the
+ * model's topology, byte sizes of the topology, weights, etc.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+
+/** Load method preloads all instance.configured models on-demand */
+declare function load(instance: Human): Promise<void>;
+
+/**
+ * Type definition for handlers of loading operations.
+ */
+declare type LoadHandler = () => Promise<ModelArtifacts>;
+
+/** @innamespace io */
+declare interface LoadOptions {
+    /**
+     * RequestInit (options) for HTTP requests.
+     *
+     * For detailed information on the supported fields, see
+     * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
+     *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
+     */
+    requestInit?: RequestInit;
+    /**
+     * Progress callback.
+     */
+    onProgress?: OnProgressCallback;
+    /**
+     * A function used to override the `window.fetch` function.
+     */
+    fetchFunc?: Function;
+    /**
+     * Strict loading model: whether extraneous weights or missing
+     * weights should trigger an `Error`.
+     *
+     * If `true`, require that the provided weights exactly match those
+     * required by the layers. `false` means that both extra weights
+     * and missing weights will be silently ignored.
+     *
+     * Default: `true`.
+     */
+    strict?: boolean;
+    /**
+     * Path prefix for weight files, by default this is calculated from the
+     * path of the model JSON file.
+     *
+     * For instance, if the path to the model JSON file is
+     * `http://localhost/foo/model.json`, then the default path prefix will be
+     * `http://localhost/foo/`. If a weight file has the path value
+     * `group1-shard1of2` in the weight manifest, then the weight file will be
+     * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
+     * if you provide a `weightPathPrefix` value of
+     * `http://localhost/foo/alt-weights`, then the weight file will be loaded
+     * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
+     */
+    weightPathPrefix?: string;
+    /**
+     * Whether the module or model is to be loaded from TF Hub.
+     *
+     * Setting this to `true` allows passing a TF-Hub module URL, omitting the
+     * standard model file name and the query parameters.
+     *
+     * Default: `false`.
+     */
+    fromTFHub?: boolean;
+    /**
+     * An async function to convert weight file name to URL. The weight file
+     * names are stored in model.json's weightsManifest.paths field. By default we
+     * consider weight files are colocated with the model.json file. For example:
+     *     model.json URL: https://www.google.com/models/1/model.json
+     *     group1-shard1of1.bin url:
+     *        https://www.google.com/models/1/group1-shard1of1.bin
+     *
+     * With this func you can convert the weight file name to any URL.
+     */
+    weightUrlConverter?: (weightFileName: string) => Promise<string>;
+}
+
+/**
+ * Reads a weights manifest JSON configuration, fetches the weights and
+ * returns them as `Tensor`s.
+ *
+ * @param manifest The weights manifest JSON.
+ * @param filePathPrefix The path prefix for filenames given in the manifest.
+ *     Defaults to the empty string.
+ * @param weightNames The names of the weights to be fetched.
+ */
+declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestInit?: RequestInit): Promise<NamedTensorMap>;
+
+declare namespace match {
+    export {
+        distance,
+        similarity,
+        match_2 as match,
+        Descriptor,
+        MatchOptions
+    }
+}
+export { match }
+
+/** Matches given descriptor to a closest entry in array of descriptors
+ * @param descriptor - face descriptor
+ * @param descriptors - array of face descriptors to commpare given descriptor to
+ * @param options - see `similarity` method for options description
+ * Returns
+ * - `index` index array index where best match was found or -1 if no matches
+ * - `distance` calculated `distance` of given descriptor to the best match
+ * - `similarity` calculated normalized `similarity` of given descriptor to the best match
+ */
+declare function match_2(descriptor: Descriptor, descriptors: Array<Descriptor>, options?: MatchOptions): {
+    index: number;
+    distance: number;
+    similarity: number;
+};
+
+declare type MatchOptions = {
+    order?: number;
+    threshold?: number;
+    multiplier?: number;
+    min?: number;
+    max?: number;
+} | undefined;
+
+/**
+ * The serialized artifacts of a model, including topology and weights.
+ *
+ * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
+ * of this interface are optional, in order to support topology- or weights-only
+ * saving and loading.
+ *
+ * Note this interface is used internally in IOHandlers.  For the file format
+ * written to disk as `model.json`, see `ModelJSON`.
+ */
+declare interface ModelArtifacts {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology?: {} | ArrayBuffer;
+    /**
+     * Serialized configuration for the model's training.
+     */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weight specifications.
+     *
+     * This corresponds to the weightsData below.
+     */
+    weightSpecs?: WeightsManifestEntry[];
+    /**
+     * Binary buffer for all weight values concatenated in the order specified
+     * by `weightSpecs`.
+     */
+    weightData?: ArrayBuffer;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelArtifactsInfo {
+    /**
+     * Timestamp for when the model is saved.
+     */
+    dateSaved: Date;
+    /**
+     * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
+     * come in a JSON format and none of our IOHandlers support a non json
+     * format. We could conder replacing this with 'Binary' if we want to
+     * allow future handlers to save to non json formats (though they will
+     * probably want more information than 'Binary').
+     * Type of the model topology
+     *
+     * Type of the model topology
+     *
+     * Possible values:
+     *   - JSON: JSON config (human-readable, e.g., Keras JSON).
+     *   - GraphDef: TensorFlow
+     *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
+     *     protocol buffer (binary).
+     */
+    modelTopologyType: 'JSON' | 'GraphDef';
+    /**
+     * Size of model topology (Keras JSON or GraphDef), in bytes.
+     */
+    modelTopologyBytes?: number;
+    /**
+     * Size of weight specification or manifest, in bytes.
+     */
+    weightSpecsBytes?: number;
+    /**
+     * Size of weight value data, in bytes.
+     */
+    weightDataBytes?: number;
+}
+
+export declare type ModelInfo = {
+    name: string;
+    inCache: boolean;
+    sizeDesired: number;
+    sizeFromManifest: number;
+    sizeLoadedWeights: number;
+};
+
+/**
+ * The on-disk format of the `model.json` file.
+ *
+ * TF.js 1.0 always populates the optional fields when writing model.json.
+ * Prior versions did not provide those fields.
+ */
+declare interface ModelJSON {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology: {};
+    /** Model training configuration. */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weights manifest.
+     *
+     * The weights manifest consists of an ordered list of weight-manifest
+     * groups. Each weight-manifest group consists of a number of weight values
+     * stored in a number of paths. See the documentation of
+     * `WeightsManifestConfig` for more details.
+     */
+    weightsManifest: WeightsManifestConfig;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelPredictConfig {
+    /**
+     * Optional. Batch size (Integer). If unspecified, it will default to 32.
+     */
+    batchSize?: number;
+    /**
+     * Optional. Verbosity mode. Defaults to false.
+     */
+    verbose?: boolean;
+}
+
+/** Instances of all possible TFJS Graph Models used by Human
+ * - loaded as needed based on configuration
+ * - initialized explictly with `human.load()` method
+ * - initialized implicity on first call to `human.detect()`
+ * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
+ */
+export declare class Models {
+    ssrnetage: null | GraphModel | Promise<GraphModel>;
+    gear: null | GraphModel | Promise<GraphModel>;
+    blazeposedetect: null | GraphModel | Promise<GraphModel>;
+    blazepose: null | GraphModel | Promise<GraphModel>;
+    centernet: null | GraphModel | Promise<GraphModel>;
+    efficientpose: null | GraphModel | Promise<GraphModel>;
+    mobilefacenet: null | GraphModel | Promise<GraphModel>;
+    emotion: null | GraphModel | Promise<GraphModel>;
+    facedetect: null | GraphModel | Promise<GraphModel>;
+    faceiris: null | GraphModel | Promise<GraphModel>;
+    facemesh: null | GraphModel | Promise<GraphModel>;
+    faceres: null | GraphModel | Promise<GraphModel>;
+    ssrnetgender: null | GraphModel | Promise<GraphModel>;
+    handpose: null | GraphModel | Promise<GraphModel>;
+    handskeleton: null | GraphModel | Promise<GraphModel>;
+    handtrack: null | GraphModel | Promise<GraphModel>;
+    liveness: null | GraphModel | Promise<GraphModel>;
+    movenet: null | GraphModel | Promise<GraphModel>;
+    nanodet: null | GraphModel | Promise<GraphModel>;
+    posenet: null | GraphModel | Promise<GraphModel>;
+    segmentation: null | GraphModel | Promise<GraphModel>;
+    antispoof: null | GraphModel | Promise<GraphModel>;
+}
+
+declare namespace models {
+    export {
+        reset,
+        load,
+        validate,
+        Models,
+        ModelStats,
+        getModelStats
+    }
+}
+export { models }
+
+export declare type ModelStats = {
+    numLoadedModels: number;
+    numEnabledModels: undefined;
+    numDefinedModels: number;
+    percentageLoaded: number;
+    totalSizeFromManifest: number;
+    totalSizeWeights: number;
+    totalSizeLoading: number;
+    totalSizeEnabled: undefined;
+    modelStats: ModelInfo[];
+};
+
+/**
+ * An interface for the manager of a model store.
+ *
+ * A model store is defined as a storage medium on which multiple models can
+ * be stored. Each stored model has a unique `path` as its identifier.
+ * A `ModelStoreManager` for the store allows actions including
+ *
+ * - Listing the models stored in the store.
+ * - Deleting a model from the store.
+ */
+declare interface ModelStoreManager {
+    /**
+     * List all models in the model store.
+     *
+     * @returns A dictionary mapping paths of existing models to their
+     *   model artifacts info. Model artifacts info include type of the model's
+     *   topology, byte sizes of the topology, weights, etc.
+     */
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    /**
+     * Remove a model specified by `path`.
+     *
+     * @param path
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     */
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
+
+/**
+ * Interface for model input/output tensor info.
+ */
+declare interface ModelTensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+    tfDtype?: string;
+}
+
+/**
+ * Move a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Moving within a storage medium, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Moving between two storage mediums, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Move the model, from Local Storage to IndexedDB.
+ * await tf.io.moveModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove the moved model.
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of moving.
+ * @param destURL Destination URL of moving.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+declare interface NamedTensor {
+    name: string;
+    tensor: Tensor;
+}
+
+/** @docalias {[name: string]: Tensor} */
+declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+
+declare type NamedTensorsMap = {
+    [key: string]: Tensor[];
+};
+
+declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+
+/** draw detected objects */
+declare function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all object detection specific options */
+export declare interface ObjectConfig extends GenericConfig {
+    /** minimum confidence for a detected objects before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected objects before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected objects */
+    maxDetected: number;
+}
+
+/** Object results */
+export declare interface ObjectResult {
+    /** object id */
+    id: number;
+    /** object detection score */
+    score: number;
+    /** detected object class id */
+    class: number;
+    /** detected object class name */
+    label: ObjectType;
+    /** detected object box */
+    box: Box;
+    /** detected object box normalized to 0..1 */
+    boxRaw: Box;
+}
+
+export declare type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter' | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee' | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork' | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant' | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book' | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';
+
+/**
+ * Callback for the progress of a long-running action such as an HTTP
+ * request for a large binary object.
+ *
+ * `fraction` should be a number in the [0, 1] interval, indicating how
+ * much of the action has completed.
+ */
+declare type OnProgressCallback = (fraction: number) => void;
+
+/** currently set draw options {@link DrawOptions} */
+declare const options: DrawOptions;
+
+/** draw combined person results instead of individual detection result objects */
+declare function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Person getter
+ * - Triggers combining all individual results into a virtual person object
+ */
+export declare interface PersonResult {
+    /** person id */
+    id: number;
+    /** face result that belongs to this person */
+    face: FaceResult;
+    /** body result that belongs to this person */
+    body: BodyResult | null;
+    /** left and right hand results that belong to this person */
+    hands: {
+        left: HandResult | null;
+        right: HandResult | null;
+    };
+    /** detected gestures specific to this person */
+    gestures: Array<GestureResult>;
+    /** box that defines the person */
+    box: Box;
+    /** box that defines the person normalized to 0..1 */
+    boxRaw?: Box;
+}
+
+/** generic point as [x, y, z?] */
+export declare type Point = [number, number, number?];
+
+declare type PromiseFunction = (...args: unknown[]) => Promise<unknown>;
+
+export declare type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';
+
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
+declare const registerLoadRouter: (loudRouter: IORouter) => void;
+
+declare const registerSaveRouter: (loudRouter: IORouter) => void;
+
+/**
+ * Remove a model specified by URL from a reigstered storage medium.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @param url A URL to a stored model, with a scheme prefix, e.g.,
+ *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+ * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+ *   is successful).
+ * @throws Error if deletion fails, e.g., if no model exists at `path`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * Additional options for Platform.fetch
+ */
+declare interface RequestDetails {
+    /**
+     * Is this request for a binary file (as opposed to a json file)
+     */
+    isBinary?: boolean;
+}
+
+declare function reset(instance: Human): void;
+
+/**
+ * Result interface definition for **Human** library
+ *
+ * Contains all possible detection results
+ */
+export declare interface Result {
+    /** {@link FaceResult}: detection & analysis results */
+    face: Array<FaceResult>;
+    /** {@link BodyResult}: detection & analysis results */
+    body: Array<BodyResult>;
+    /** {@link HandResult}: detection & analysis results */
+    hand: Array<HandResult>;
+    /** {@link GestureResult}: detection & analysis results */
+    gesture: Array<GestureResult>;
+    /** {@link ObjectResult}: detection & analysis results */
+    object: Array<ObjectResult>;
+    /** global performance object with timing values for each operation */
+    performance: Record<string, number>;
+    /** optional processed canvas that can be used to draw input on screen */
+    canvas?: AnyCanvas | null;
+    /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
+    readonly timestamp: number;
+    /** getter property that returns unified persons object  */
+    persons: Array<PersonResult>;
+    /** Last known error message */
+    error: string | null;
+}
+
+/**
+ * Options for saving a model.
+ * @innamespace io
+ */
+declare interface SaveConfig {
+    /**
+     * Whether to save only the trainable weights of the model, ignoring the
+     * non-trainable ones.
+     */
+    trainableOnly?: boolean;
+    /**
+     * Whether the optimizer will be saved (if exists).
+     *
+     * Default: `false`.
+     */
+    includeOptimizer?: boolean;
+}
+
+/**
+ * Type definition for handlers of saving operations.
+ */
+declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+
+/**
+ * Result of a saving operation.
+ */
+declare interface SaveResult {
+    /**
+     * Information about the model artifacts saved.
+     */
+    modelArtifactsInfo: ModelArtifactsInfo;
+    /**
+     * HTTP responses from the server that handled the model-saving request (if
+     * any). This is applicable only to server-based saving routes.
+     */
+    responses?: Response[];
+    /**
+     * Error messages and related data (if any).
+     */
+    errors?: Array<{} | string>;
+}
+
+/** Configures all body segmentation module
+ * removes background from input containing person
+ * if segmentation is enabled it will run as preprocessing task before any other model
+ * alternatively leave it disabled and use it on-demand using human.segmentation method which can
+ * remove background or replace it with user-provided background
+ */
+export declare interface SegmentationConfig extends GenericConfig {
+    /** blur segmentation output by <number> pixels for more realistic image */
+    blur: number;
+}
+
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/// <amd-module name="@tensorflow/tfjs-core/dist/types" />
+/** @docalias number[] */
+declare interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+
+/** Calculates normalized similarity between two face descriptors based on their `distance`
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ * - min - normalize similarity result to a given range
+ * - max - normalzie similarity resutl to a given range
+ *   default is 0.2...0.8
+ * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
+ */
+declare function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+
+declare type Syncify<T extends PromiseFunction> = T extends (...args: infer Args) => Promise<infer R> ? (...args: Args) => R : never;
+
+export declare namespace Tensor { }
+
+/**
+ * A `tf.Tensor` object represents an immutable, multidimensional array of
+ * numbers that has a shape and a data type.
+ *
+ * For performance reasons, functions that create tensors do not necessarily
+ * perform a copy of the data passed to them (e.g. if the data is passed as a
+ * `Float32Array`), and changes to the data will change the tensor. This is not
+ * a feature and is not supported. To avoid this behavior, use the tensor before
+ * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+ *
+ * See `tf.tensor` for details on how to create a `tf.Tensor`.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+export declare class Tensor<R extends Rank = Rank> {
+    /** Unique id of this tensor. */
+    readonly id: number;
+    /**
+     * Id of the bucket holding the data for this tensor. Multiple arrays can
+     * point to the same bucket (e.g. when calling array.reshape()).
+     */
+    dataId: DataId;
+    /** The shape of the tensor. */
+    readonly shape: ShapeMap[R];
+    /** Number of elements in the tensor. */
+    readonly size: number;
+    /** The data type for the array. */
+    readonly dtype: DataType;
+    /** The rank type for the array (see `Rank` enum). */
+    readonly rankType: R;
+    /** Whether this tensor has been globally kept. */
+    kept: boolean;
+    /** The id of the scope this tensor is being tracked in. */
+    scopeId: number;
+    /**
+     * Number of elements to skip in each dimension when indexing. See
+     * https://docs.scipy.org/doc/numpy/reference/generated/\
+     * numpy.ndarray.strides.html
+     */
+    readonly strides: number[];
+    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
+    readonly rank: number;
+    /**
+     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
+    /**
+     * Returns a `tf.TensorBuffer` that holds the underlying data.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * asynchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    array(): Promise<ArrayMap[R]>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * synchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    arraySync(): ArrayMap[R];
+    /**
+     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+     * promise of `TypedArray` that resolves when the computation has finished.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    /**
+     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
+     * and `data()`, this method prevents data from being downloaded to CPU.
+     *
+     * For WebGL backend, the data will be stored on a densely packed texture.
+     * This means that the texture will use the RGBA channels to store value.
+     *
+     * For WebGPU backend, the data will be stored on a buffer. There is no
+     * parameter, so can not use an user defined size to create the buffer.
+     *
+     * @param options:
+     *     For WebGL,
+     *         - customTexShape: Optional. If set, will use the user defined
+     *     texture shape to create the texture.
+     *
+     * @returns For WebGL backend, a GPUData contains the new texture and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this texture,
+     *        texture: WebGLTexture,
+     *        texShape: [number, number] // [height, width]
+     *     }
+     *
+     *     For WebGPU backend, a GPUData contains the new buffer and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this buffer,
+     *        buffer: GPUBuffer,
+     *        bufSize: number
+     *     }
+     *
+     *     Remember to dispose the GPUData after it is used by
+     *     `res.tensorRef.dispose()`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataToGPU(options?: DataToGPUOptions): GPUData;
+    /**
+     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+     * UI thread until the values are ready, which can cause performance issues.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    /** Returns the underlying bytes of the tensor's data. */
+    bytes(): Promise<Uint8Array[] | Uint8Array>;
+    /**
+     * Disposes `tf.Tensor` from memory.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dispose(): void;
+    protected isDisposedInternal: boolean;
+    readonly isDisposed: boolean;
+    throwIfDisposed(): void;
+    /**
+     * Prints the `tf.Tensor`. See `tf.print` for details.
+     *
+     * @param verbose Whether to print verbose information about the tensor,
+     *    including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    print(verbose?: boolean): void;
+    /**
+     * Returns a copy of the tensor. See `tf.clone` for details.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    clone<T extends Tensor>(this: T): T;
+    /**
+     * Returns a human-readable description of the tensor. Useful for logging.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    toString(verbose?: boolean): string;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+}
+
+/**
+ * A mutable object, similar to `tf.Tensor`, that allows users to set values
+ * at locations before converting to an immutable `tf.Tensor`.
+ *
+ * See `tf.buffer` for creating a tensor buffer.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    /**
+     * Sets a value in the buffer at a given location.
+     *
+     * @param value The value to set.
+     * @param locs  The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    /**
+     * Returns the value in the buffer at the provided location.
+     *
+     * @param locs The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    /**
+     * Creates an immutable `tf.Tensor` object from the buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    toTensor(): Tensor<R>;
+}
+
+declare interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
+
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
+/** Model training configuration. */
+declare interface TrainingConfig {
+    /** Optimizer used for the model training. */
+    optimizer_config: {};
+    /** Loss function(s) for the model's output(s). */
+    loss: string | string[] | {
+        [key: string]: string;
+    };
+    /** Metric function(s) for the model's output(s). */
+    metrics?: string[] | {
+        [key: string]: string;
+    };
+    weighted_metrics?: string[];
+    sample_weight_mode?: string;
+    loss_weights?: number[] | {
+        [key: string]: number;
+    };
+}
+
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
+declare type Url = string | io.IOHandler | io.IOHandlerSync;
+
+declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
+
+declare function validate(instance: Human): Promise<void>;
+
+/**
+ * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
+    /**
+     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+     * the same shape and dtype as the old `tf.Tensor`.
+     *
+     * @param newValue New tensor to be assigned to this variable.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    assign(newValue: Tensor<R>): void;
+    dispose(): void;
+}
+
+/** Possible values for `human.warmup` */
+export declare type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];
+
+/**
+ * Group to which the weight belongs.
+ *
+ * - 'optimizer': Weight from a stateful optimizer.
+ */
+declare type WeightGroup = 'model' | 'optimizer';
+
+/**
+ * Creates a function, which reads a weights manifest JSON configuration,
+ * fetches the weight files using the specified function and returns them as
+ * `Tensor`s.
+ *
+ * ```js
+ * // example for creating a nodejs weight loader, which reads the weight files
+ * // from disk using fs.readFileSync
+ *
+ * import * as fs from 'fs'
+ *
+ * const fetchWeightsFromDisk = (filePaths: string[]) =>
+ *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+ *
+ * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+ *
+ * const manifest = JSON.parse(
+ *   fs.readFileSync('./my_model-weights_manifest').toString()
+ * )
+ * const weightMap = await loadWeights(manifest, './')
+ * ```
+ * @param fetchWeightsFunction The function used for fetching the weight files.
+ * @returns Weight loading function.
+ */
+declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
+
+/**
+ * A weight manifest.
+ *
+ * The weight manifest consists of an ordered list of weight-manifest groups.
+ * Each weight-manifest group ("group" for short hereafter) consists of a
+ * number of weight values stored in a number of paths.
+ * See the documentation of `WeightManifestGroupConfig` below for more details.
+ */
+declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+
+/**
+ * An entry in the weight manifest.
+ *
+ * The entry contains specification of a weight.
+ */
+declare interface WeightsManifestEntry {
+    /**
+     * Name of the weight, e.g., 'Dense_1/bias'
+     */
+    name: string;
+    /**
+     * Shape of the weight.
+     */
+    shape: number[];
+    /**
+     * Data type of the weight.
+     */
+    dtype: 'float32' | 'int32' | 'bool' | 'string' | 'complex64';
+    /**
+     * Type of the weight.
+     *
+     * Optional.
+     *
+     * The value 'optimizer' indicates the weight belongs to an optimizer
+     * (i.e., used only during model training and not during inference).
+     */
+    group?: WeightGroup;
+    /**
+     * Information for dequantization of the weight.
+     */
+    quantization?: {
+        scale?: number;
+        min?: number;
+        dtype: 'uint16' | 'uint8' | 'float16';
+    };
+}
+
+/**
+ * A weight-manifest group.
+ *
+ * Consists of an ordered list of weight values encoded in binary format,
+ * stored in an ordered list of paths.
+ */
+declare interface WeightsManifestGroupConfig {
+    /**
+     * An ordered list of paths.
+     *
+     * Paths are intentionally abstract in order to be general. For example, they
+     * can be relative URL paths or relative paths on the file system.
+     */
+    paths: string[];
+    /**
+     * Specifications of the weights stored in the paths.
+     */
+    weights: WeightsManifestEntry[];
+}
+
+/**
+ * Creates an IOHandler that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     promise that resolves to a `SaveResult`.
+ */
+declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
+
+/**
+ * Creates an IOHandlerSync that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     `SaveResult`.
+ */
+declare function withSaveHandlerSync(saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync;
+
+export { }
diff --git a/dist/human.esm-nobundle.d.ts b/dist/human.esm-nobundle.d.ts
new file mode 100644
index 00000000..fed34cea
--- /dev/null
+++ b/dist/human.esm-nobundle.d.ts
@@ -0,0 +1,2643 @@
+/// <reference types="@webgpu/types/dist" />
+/// <reference types="offscreencanvas" />
+
+/** meta-function that performs draw for: canvas, face, body, hand */
+declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
+
+/** Defines all possible canvas types */
+export declare type AnyCanvas = HTMLCanvasElement | OffscreenCanvas;
+
+/** Defines all possible image types */
+export declare type AnyImage = HTMLImageElement | typeof Image;
+
+/** Defines all possible video types */
+export declare type AnyVideo = HTMLMediaElement | HTMLVideoElement;
+
+/** @docalias number[] */
+declare interface ArrayMap {
+    R0: number;
+    R1: number[];
+    R2: number[][];
+    R3: number[][][];
+    R4: number[][][][];
+    R5: number[][][][][];
+    R6: number[][][][][][];
+}
+
+/** Possible TensorFlow backends */
+export declare type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];
+
+/** draw detected bodies */
+declare function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+export declare type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;
+
+export declare type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';
+
+export declare type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';
+
+/** Configures all body detection specific options */
+export declare interface BodyConfig extends GenericConfig {
+    /** maximum number of detected bodies */
+    maxDetected: number;
+    /** minimum confidence for a detected body before results are discarded */
+    minConfidence: number;
+}
+
+/** body gesture type */
+export declare type BodyGesture = `leaning ${'left' | 'right'}` | `raise ${'left' | 'right'} hand` | 'i give up';
+
+/** Body Result keypoints */
+export declare interface BodyKeypoint {
+    /** body part name */
+    part: BodyLandmark;
+    /** body part position */
+    position: Point;
+    /** body part position normalized to 0..1 */
+    positionRaw: Point;
+    /** body part position relative to body center in meters */
+    distance?: Point;
+    /** body part detection score */
+    score: number;
+}
+
+export declare type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;
+
+export declare type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle' | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';
+
+export declare type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';
+
+export declare type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+export declare type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+/** Body results */
+export declare interface BodyResult {
+    /** body id */
+    id: number;
+    /** body detection score */
+    score: number;
+    /** detected body box */
+    box: Box;
+    /** detected body box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected body keypoints */
+    keypoints: Array<BodyKeypoint>;
+    /** detected body keypoints combined into annotated parts */
+    annotations: Record<BodyAnnotation, Point[][]>;
+}
+
+/** generic box as [x, y, width, height] */
+export declare type Box = [number, number, number, number];
+
+/**
+ * Creates an IOHandler that loads model artifacts from user-selected files.
+ *
+ * This method can be used for loading from files such as user-selected files
+ * in the browser.
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * // Note: This code snippet won't run properly without the actual file input
+ * //   elements in the HTML DOM.
+ *
+ * // Suppose there are two HTML file input (`<input type="file" ...>`)
+ * // elements.
+ * const uploadJSONInput = document.getElementById('upload-json');
+ * const uploadWeightsInput = document.getElementById('upload-weights');
+ * const model = await tf.loadLayersModel(tf.io.browserFiles(
+ *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+ * ```
+ *
+ * @param files `File`s to load from. Currently, this function supports only
+ *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+ *   which an `Array` of `File`s is expected (in that order):
+ *   - A JSON file containing the model topology and weight manifest.
+ *   - Optionally, One or more binary files containing the binary weights.
+ *     These files must have names that match the paths in the `weightsManifest`
+ *     contained by the aforementioned JSON file, or errors will be thrown
+ *     during loading. These weights files have the same format as the ones
+ *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+ *     Python PIP package. If no weights files are provided, only the model
+ *     topology will be loaded from the JSON file above.
+ * @returns An instance of `Files` `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function browserFiles(files: File[]): IOHandler;
+
+/**
+ * Deprecated. Use `tf.io.http`.
+ * @param path
+ * @param loadOptions
+ */
+declare function browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** draw processed canvas */
+declare function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas): Promise<void>;
+
+/**
+ * Concatenate a number of ArrayBuffers into one.
+ *
+ * @param buffers A number of array buffers to concatenate.
+ * @returns Result of concatenating `buffers` in order.
+ */
+declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+
+/**
+ * Configuration interface definition for **Human** library
+ * Contains all configurable parameters
+ * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+ */
+export declare interface Config {
+    /** Backend used for TFJS operations
+     * valid build-in backends are:
+     * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`
+     * - NodeJS: `cpu`, `wasm`, `tensorflow`
+     * default: `humangl` for browser and `tensorflow` for nodejs
+     */
+    backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+    /** Path to *.wasm files if backend is set to `wasm`
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPath: string;
+    /** Force WASM loader to use platform fetch
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPlatformFetch: boolean;
+    /** Print debug statements to console
+     *
+     * default: `true`
+     */
+    debug: boolean;
+    /** Perform model loading and inference concurrently or sequentially
+     *
+     * default: `true`
+     */
+    async: boolean;
+    /** What to use for `human.warmup()`
+     * - warmup pre-initializes all models for faster inference but can take significant time on startup
+     * - used by `webgl`, `humangl` and `webgpu` backends
+     *
+     * default: `full`
+     */
+    warmup: '' | 'none' | 'face' | 'full' | 'body';
+    /** Base model path (typically starting with file://, http:// or https://) for all models
+     * - individual modelPath values are relative to this path
+     *
+     * default: `../models/` for browsers and `file://models/` for nodejs
+     */
+    modelBasePath: string;
+    /** Cache models in IndexDB on first sucessfull load
+     * default: true if indexdb is available (browsers), false if its not (nodejs)
+     */
+    cacheModels: boolean;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+     *
+     * default: 0.7
+     */
+    cacheSensitivity: number;
+    /** Perform immediate garbage collection on deallocated tensors instead of caching them */
+    deallocate: boolean;
+    /** Internal Variable */
+    skipAllowed: boolean;
+    /** Filter config {@link FilterConfig} */
+    filter: Partial<FilterConfig>;
+    /** Gesture config {@link GestureConfig} */
+    gesture: Partial<GestureConfig>;
+    /** Face config {@link FaceConfig} */
+    face: Partial<FaceConfig>;
+    /** Body config {@link BodyConfig} */
+    body: Partial<BodyConfig>;
+    /** Hand config {@link HandConfig} */
+    hand: Partial<HandConfig>;
+    /** Object config {@link ObjectConfig} */
+    object: Partial<ObjectConfig>;
+    /** Segmentation config {@link SegmentationConfig} */
+    segmentation: Partial<SegmentationConfig>;
+}
+
+/**
+ * Copy a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Copying within a storage medium, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Copying between two storage mediums, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Copy the model, from Local Storage to IndexedDB.
+ * await tf.io.copyModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove both models.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of copying.
+ * @param destURL Destination URL of copying.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * We wrap data id since we use weak map to avoid memory leaks.
+ * Since we have our own memory management, we have a reference counter
+ * mapping a tensor to its data, so there is always a pointer (even if that
+ * data is otherwise garbage collectable).
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
+ * Global_Objects/WeakMap
+ */
+declare type DataId = object;
+
+declare type DataToGPUOptions = DataToGPUWebGLOption;
+
+declare interface DataToGPUWebGLOption {
+    customTexShape?: [number, number];
+}
+
+/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
+declare type DataType = keyof DataTypeMap;
+
+declare interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+
+/**
+ * Decode flat ArrayBuffer as weights.
+ *
+ * This function does not handle sharding.
+ *
+ * This function is the reverse of `encodeWeights`.
+ *
+ * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+ *   concatenated in the order specified in `specs`.
+ * @param specs Specifications of the names, dtypes and shapes of the tensors
+ *   whose value are encoded by `buffer`.
+ * @return A map from tensor name to tensor value, with the names corresponding
+ *   to names in `specs`.
+ * @throws Error, if any of the tensors has unsupported dtype.
+ */
+declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+
+/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
+export declare const defaults: Config;
+
+/** Face descriptor type as number array */
+export declare type Descriptor = Array<number>;
+
+/** Calculates distance between two descriptors
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ */
+declare function distance(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare namespace draw {
+    export {
+        person,
+        canvas,
+        all,
+        options,
+        face,
+        body,
+        hand,
+        object,
+        gesture
+    }
+}
+export { draw }
+
+/** Draw Options
+ * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter
+ */
+export declare type DrawOptions = {
+    /** draw line color */
+    color: string;
+    /** alpha value used for lines */
+    alpha: number;
+    /** label color */
+    labelColor: string;
+    /** label shadow color */
+    shadowColor: string;
+    /** label font */
+    font: string;
+    /** line spacing between labels */
+    lineHeight: number;
+    /** line width for drawn lines */
+    lineWidth: number;
+    /** size of drawn points */
+    pointSize: number;
+    /** draw rounded boxes by n pixels */
+    roundRect: number;
+    /** should points be drawn? */
+    drawPoints: boolean;
+    /** should labels be drawn? */
+    drawLabels: boolean;
+    /** should face attention keypoints be highlighted */
+    drawAttention: boolean;
+    /** should detected gestures be drawn? */
+    drawGestures: boolean;
+    /** should draw boxes around detection results? */
+    drawBoxes: boolean;
+    /** should draw polygons from detection points? */
+    drawPolygons: boolean;
+    /** should draw gaze arrows? */
+    drawGaze: boolean;
+    /** should fill polygons? */
+    fillPolygons: boolean;
+    /** use z-coordinate when available */
+    useDepth: boolean;
+    /** should lines be curved? */
+    useCurves: boolean;
+};
+
+export declare type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';
+
+/**
+ * Encode a map from names to weight values as an ArrayBuffer, along with an
+ * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+ *
+ * This function does not perform sharding.
+ *
+ * This function is the reverse of `decodeWeights`.
+ *
+ * @param tensors A map ("dict") from names to tensors.
+ * @param group Group to which the weights belong (optional).
+ * @returns A `Promise` of
+ *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+ *     concatenated.
+ *   - An `Array` of `WeightManifestEntry`s, carrying information including
+ *     tensor names, `dtype`s and shapes.
+ * @throws Error: on unsupported tensor `dtype`.
+ */
+declare function encodeWeights(tensors: NamedTensorMap | NamedTensor[], group?: WeightGroup): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+
+/** Env class that holds detected capabilities */
+export declare class Env {
+    /** Running in Browser */
+    browser: boolean;
+    /** Running in NodeJS */
+    node: boolean;
+    /** Running in WebWorker thread */
+    worker: boolean;
+    /** Detected platform */
+    platform: string;
+    /** Detected agent */
+    agent: string;
+    /** List of supported backends */
+    backends: string[];
+    /** Has any work been performed so far */
+    initial: boolean;
+    /** Are image filters supported? */
+    filter: boolean | undefined;
+    /** TFJS instance details */
+    tfjs: {
+        version: undefined | string;
+    };
+    /** Is offscreenCanvas supported? */
+    offscreen: undefined | boolean;
+    /** Are performance counter instant values or additive */
+    perfadd: boolean;
+    /** WASM detected capabilities */
+    wasm: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        simd: undefined | boolean;
+        multithread: undefined | boolean;
+    };
+    /** WebGL detected capabilities */
+    webgl: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        version: undefined | string;
+        renderer: undefined | string;
+    };
+    /** WebGPU detected capabilities */
+    webgpu: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        adapter: undefined | string;
+    };
+    /** CPU info */
+    cpu: {
+        model: undefined | string;
+        flags: string[];
+    };
+    /** List of supported kernels for current backend */
+    kernels: string[];
+    /** MonkeyPatch for Canvas */
+    Canvas: undefined;
+    /** MonkeyPatch for Image */
+    Image: undefined;
+    /** MonkeyPatch for ImageData */
+    ImageData: undefined;
+    constructor();
+    /** update backend information */
+    updateBackend(): Promise<void>;
+    /** update cpu information */
+    updateCPU(): Promise<void>;
+}
+
+export declare const env: Env;
+
+/** Events dispatched by `human.events`
+ * - `create`: triggered when Human object is instantiated
+ * - `load`: triggered when models are loaded (explicitly or on-demand)
+ * - `image`: triggered when input image is processed
+ * - `result`: triggered when detection is complete
+ * - `warmup`: triggered when warmup is complete
+ */
+export declare type Events = 'create' | 'load' | 'image' | 'result' | 'warmup' | 'error';
+
+/** Defines possible externally defined canvas */
+export declare type ExternalCanvas = typeof env.Canvas;
+
+/** draw detected faces */
+declare function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Anti-spoofing part of face configuration */
+export declare interface FaceAntiSpoofConfig extends GenericConfig {
+}
+
+/** Attention part of face configuration */
+export declare interface FaceAttentionConfig extends GenericConfig {
+}
+
+/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */
+export declare interface FaceConfig extends GenericConfig {
+    detector: Partial<FaceDetectorConfig>;
+    mesh: Partial<FaceMeshConfig>;
+    attention: Partial<FaceAttentionConfig>;
+    iris: Partial<FaceIrisConfig>;
+    description: Partial<FaceDescriptionConfig>;
+    emotion: Partial<FaceEmotionConfig>;
+    antispoof: Partial<FaceAntiSpoofConfig>;
+    liveness: Partial<FaceLivenessConfig>;
+    gear: Partial<FaceGearConfig>;
+}
+
+/** Description or face embedding part of face configuration
+ * - also used by age and gender detection
+ */
+export declare interface FaceDescriptionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Detector part of face configuration */
+export declare interface FaceDetectorConfig extends GenericConfig {
+    /** is face rotation correction performed after detecting face?
+     * used to correctly analyze faces under high angles
+     */
+    rotation: boolean;
+    /** maximum number of detected faces */
+    maxDetected: number;
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected faces before one is discarded */
+    iouThreshold: number;
+    /** should child models perform on masked image of a face */
+    mask: boolean;
+    /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?
+     * if enabled it must be manually deallocated to avoid memory leak */
+    return: boolean;
+}
+
+/** Emotion part of face configuration */
+export declare interface FaceEmotionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Gear part of face configuration */
+export declare interface FaceGearConfig extends GenericConfig {
+    /** minimum confidence for a detected race before results are discarded */
+    minConfidence: number;
+}
+
+/** face gesture type */
+export declare type FaceGesture = `facing ${'left' | 'center' | 'right'}` | `blink ${'left' | 'right'} eye` | `mouth ${number}% open` | `head ${'up' | 'down'}`;
+
+/** Iris part of face configuration */
+export declare interface FaceIrisConfig extends GenericConfig {
+}
+
+export declare type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette' | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner' | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris' | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris' | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';
+
+/** Liveness part of face configuration */
+export declare interface FaceLivenessConfig extends GenericConfig {
+}
+
+/** Mesh part of face configuration */
+export declare interface FaceMeshConfig extends GenericConfig {
+    /** Keep detected faces that cannot be verified using facemesh */
+    keepInvalid: boolean;
+}
+
+/** Face results
+ * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
+ * - Some values may be null if specific model is not enabled
+ */
+export declare interface FaceResult {
+    /** face id */
+    id: number;
+    /** overall face score */
+    score: number;
+    /** detection score */
+    boxScore: number;
+    /** mesh score */
+    faceScore: number;
+    /** detected face box */
+    box: Box;
+    /** detected face box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected face mesh */
+    mesh: Array<Point>;
+    /** detected face mesh normalized to 0..1 */
+    meshRaw: Array<Point>;
+    /** face contours as array of 2d points normalized to 0..1 */
+    /** face contours as array of 2d points */
+    /** mesh keypoints combined into annotated results */
+    annotations: Record<FaceLandmark, Point[]>;
+    /** detected age */
+    age?: number;
+    /** detected gender */
+    gender?: Gender;
+    /** gender detection score */
+    genderScore?: number;
+    /** detected emotions */
+    emotion?: Array<{
+        score: number;
+        emotion: Emotion;
+    }>;
+    /** detected race */
+    race?: Array<{
+        score: number;
+        race: Race;
+    }>;
+    /** face descriptor */
+    embedding?: Array<number>;
+    /** face iris distance from camera */
+    iris?: number;
+    /** face anti-spoofing result confidence */
+    real?: number;
+    /** face liveness result confidence */
+    live?: number;
+    /** face rotation details */
+    rotation?: {
+        angle: {
+            roll: number;
+            yaw: number;
+            pitch: number;
+        };
+        matrix: [number, number, number, number, number, number, number, number, number];
+        gaze: {
+            bearing: number;
+            strength: number;
+        };
+    } | null;
+    /** detected face as tensor that can be used in further pipelines */
+    tensor?: Tensor;
+}
+
+/** Run input through image filters before inference
+ * - available only in Browser environments
+ * - image filters run with near-zero latency as they are executed on the GPU using WebGL
+ */
+export declare interface FilterConfig {
+    /** are image filters enabled? */
+    enabled: boolean;
+    /** perform image histogram equalization
+     * - equalization is performed on input as a whole and detected face before its passed for further analysis
+     */
+    equalization: boolean;
+    /** resize input width
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    width: number;
+    /** resize input height
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    height: number;
+    /** return processed canvas imagedata in result */
+    return: boolean;
+    /** flip input as mirror image */
+    flip: boolean;
+    /** range: -1 (darken) to 1 (lighten) */
+    brightness: number;
+    /** range: -1 (reduce contrast) to 1 (increase contrast) */
+    contrast: number;
+    /** range: 0 (no sharpening) to 1 (maximum sharpening) */
+    sharpness: number;
+    /** range: 0 (no blur) to N (blur radius in pixels) */
+    blur: number;
+    /** range: -1 (reduce saturation) to 1 (increase saturation) */
+    saturation: number;
+    /** range: 0 (no change) to 360 (hue rotation in degrees) */
+    hue: number;
+    /** image negative */
+    negative: boolean;
+    /** image sepia colors */
+    sepia: boolean;
+    /** image vintage colors */
+    vintage: boolean;
+    /** image kodachrome colors */
+    kodachrome: boolean;
+    /** image technicolor colors */
+    technicolor: boolean;
+    /** image polaroid camera effect */
+    polaroid: boolean;
+    /** range: 0 (no pixelate) to N (number of pixels to pixelate) */
+    pixelate: number;
+}
+
+export declare type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';
+
+export declare type FingerCurl = 'none' | 'half' | 'full';
+
+export declare type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandler` that simply loads the provided data.
+ */
+declare function fromMemory(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler;
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
+ */
+declare function fromMemorySync(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandlerSync;
+
+export declare type Gender = 'male' | 'female' | 'unknown';
+
+/** Generic config type inherited by all module types */
+export declare interface GenericConfig {
+    /** is module enabled? */
+    enabled: boolean;
+    /** path to model json file (relative to `modelBasePath` */
+    modelPath: string;
+    /** how many max frames to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipFrames: number;
+    /** how many max milliseconds to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipTime: number;
+}
+
+/** draw detected gestures */
+declare function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Controlls gesture detection */
+export declare interface GestureConfig {
+    /** is gesture detection enabled? */
+    enabled: boolean;
+}
+
+/** Gesture combined results
+ * Each result has:
+ * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`
+ * - gesture: gesture detected
+ */
+export declare type GestureResult = {
+    'face': number;
+    gesture: FaceGesture;
+} | {
+    'iris': number;
+    gesture: IrisGesture;
+} | {
+    'body': number;
+    gesture: BodyGesture;
+} | {
+    'hand': number;
+    gesture: HandGesture;
+};
+
+declare const getLoadHandlers: (url: string | string[], loadOptions?: LoadOptions) => IOHandler[];
+
+/**
+ * Create `ModelArtifacts` from a JSON file.
+ *
+ * @param modelJSON Object containing the parsed JSON of `model.json`
+ * @param loadWeights Function that takes the JSON file's weights manifest,
+ *     reads weights from the listed path(s), and returns a Promise of the
+ *     weight manifest entries along with the weights data.
+ * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+ */
+declare function getModelArtifactsForJSON(modelJSON: ModelJSON, loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[WeightsManifestEntry[], /* weightData */ ArrayBuffer]>): Promise<ModelArtifacts>;
+
+/**
+ * Populate ModelArtifactsInfo fields for a model with JSON topology.
+ * @param modelArtifacts
+ * @returns A ModelArtifactsInfo object.
+ */
+declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
+
+declare const getModelStats: (instance: Human) => ModelStats;
+
+declare const getSaveHandlers: (url: string | string[]) => IOHandler[];
+
+declare interface GPUData {
+    tensorRef: Tensor;
+    texture?: WebGLTexture;
+    buffer?: GPUBuffer;
+    texShape?: [number, number];
+    bufSize?: number;
+}
+
+/**
+ * A `tf.GraphModel` is a directed, acyclic graph built from a
+ * SavedModel GraphDef and allows inference execution.
+ *
+ * A `tf.GraphModel` can only be created by loading from a model converted from
+ * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+ * the command line converter tool and loaded via `tf.loadGraphModel`.
+ *
+ * @doc {heading: 'Models', subheading: 'Classes'}
+ */
+export declare class GraphModel<ModelURL extends Url = string | io.IOHandler> implements InferenceModel {
+    private modelUrl;
+    private loadOptions;
+    private executor;
+    private version;
+    private handler;
+    private artifacts;
+    private initializer;
+    private resourceManager;
+    private signature;
+    readonly modelVersion: string;
+    readonly inputNodes: string[];
+    readonly outputNodes: string[];
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
+    readonly metadata: {};
+    readonly modelSignature: {};
+    /**
+     * @param modelUrl url for the model, or an `io.IOHandler`.
+     * @param weightManifestUrl url for the weight file generated by
+     * scripts/convert.py script.
+     * @param requestOption options for Request, which allows to send credentials
+     * and custom headers.
+     * @param onProgress Optional, progress callback function, fired periodically
+     * before the load is completed.
+     */
+    constructor(modelUrl: ModelURL, loadOptions?: io.LoadOptions);
+    private findIOHandler;
+    /**
+     * Loads the model and weight files, construct the in memory weight map and
+     * compile the inference graph.
+     */
+    load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync ? boolean : Promise<boolean>;
+    /**
+     * Synchronously construct the in memory weight map and
+     * compile the inference graph. Also initialize hashtable if any.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    loadSync(artifacts: io.ModelArtifacts): boolean;
+    /**
+     * Save the configuration and/or weights of the GraphModel.
+     *
+     * An `IOHandler` is an object that has a `save` method of the proper
+     * signature defined. The `save` method manages the storing or
+     * transmission of serialized data ("artifacts") that represent the
+     * model's topology and weights onto or via a specific medium, such as
+     * file downloads, local storage, IndexedDB in the web browser and HTTP
+     * requests to a server. TensorFlow.js provides `IOHandler`
+     * implementations for a number of frequently used saving mediums, such as
+     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+     * for more details.
+     *
+     * This method also allows you to refer to certain types of `IOHandler`s
+     * as URL-like string shortcuts, such as 'localstorage://' and
+     * 'indexeddb://'.
+     *
+     * Example 1: Save `model`'s topology and weights to browser [local
+     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+     * then load it back.
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     *
+     * const saveResults = await model.save('localstorage://my-model-1');
+     *
+     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+     * console.log('Prediction from loaded model:');
+     * model.predict(zeros).print();
+     * ```
+     *
+     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+     * scheme-based string shortcut for `IOHandler`.
+     * @param config Options for saving the model.
+     * @returns A `Promise` of `SaveResult`, which summarizes the result of
+     * the saving, such as byte sizes of the saved artifacts for the model's
+     *   topology and weight values.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+     * inputs params should be in either `tf.Tensor`[] if the input order is
+     * fixed, or otherwise NamedTensorMap format.
+     *
+     * For model with multiple inputs, we recommend you use NamedTensorMap as the
+     * input type, if you use `tf.Tensor`[], the order of the array needs to
+     * follow the
+     * order of inputNodes array. @see {@link GraphModel.inputNodes}
+     *
+     * You can also feed any intermediate nodes using the NamedTensorMap as the
+     * input type. For example, given the graph
+     *    InputNode => Intermediate => OutputNode,
+     * you can execute the subgraph Intermediate => OutputNode by calling
+     *    model.execute('IntermediateNode' : tf.tensor(...));
+     *
+     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+     * state needs to be fed manually.
+     *
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size and
+     * output node names. Currently the batch size option is ignored for graph
+     * model.
+     *
+     * @returns Inference result tensors. The output would be single `tf.Tensor`
+     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+     * will be returned for model with multiple outputs.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    private normalizeInputs;
+    private normalizeOutputs;
+    /**
+     * Executes inference for the model for given input tensors.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no
+     * outputs are specified, the default outputs of the model would be used.
+     * You can inspect intermediate nodes of the model by adding them to the
+     * outputs array.
+     *
+     * @returns A single tensor if provided with a single output or no outputs
+     * are provided and there is only one default output, otherwise return a
+     * tensor array. The order of the tensor array is the same as the outputs
+     * if provided, otherwise the order of outputNodes attribute of the model.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
+    /**
+     * Executes inference for the model for given input tensors in async
+     * fashion, use this method when your model contains control flow ops.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no outputs
+     * are specified, the default outputs of the model would be used. You can
+     * inspect intermediate nodes of the model by adding them to the outputs
+     * array.
+     *
+     * @returns A Promise of single tensor if provided with a single output or
+     * no outputs are provided and there is only one default output, otherwise
+     * return a tensor map.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
+    /**
+     * Get intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    getIntermediateTensors(): NamedTensorsMap;
+    /**
+     * Dispose intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    disposeIntermediateTensors(): void;
+    private convertTensorMapToTensorsMap;
+    /**
+     * Releases the memory used by the weight tensors and resourceManager.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    dispose(): void;
+}
+
+/** draw detected hands */
+declare function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all hand detection specific options */
+export declare interface HandConfig extends GenericConfig {
+    /** should hand rotation correction be performed after hand detection? */
+    rotation: boolean;
+    /** minimum confidence for a detected hand before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected hands before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected hands */
+    maxDetected: number;
+    /** should hand landmarks be detected or just return detected hand box */
+    landmarks: boolean;
+    detector: {
+        /** path to hand detector model json */
+        modelPath?: string;
+    };
+    skeleton: {
+        /** path to hand skeleton model json */
+        modelPath?: string;
+    };
+}
+
+/** hand gesture type */
+export declare type HandGesture = `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward` | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up` | 'victory' | 'thumbs up';
+
+/** Hand results */
+export declare interface HandResult {
+    /** hand id */
+    id: number;
+    /** hand overal score */
+    score: number;
+    /** hand detection score */
+    boxScore: number;
+    /** hand skelton score */
+    fingerScore: number;
+    /** detected hand box */
+    box: Box;
+    /** detected hand box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected hand keypoints */
+    keypoints: Array<Point>;
+    /** detected hand class */
+    label: HandType;
+    /** detected hand keypoints combined into annotated parts */
+    annotations: Record<Finger, Array<Point>>;
+    /** detected hand parts annotated with part gestures */
+    landmarks: Record<Finger, {
+        curl: FingerCurl;
+        direction: FingerDirection;
+    }>;
+}
+
+export declare type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';
+
+/**
+ * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+ *
+ * An HTTP request of the `multipart/form-data` mime type will be sent to the
+ * `path` URL. The form data includes artifacts that represent the topology
+ * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+ * blobs (files) exist in form-data:
+ *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+ *   - A binary weights file consisting of the concatenated weight values.
+ * These files are in the same format as the one generated by
+ * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+ *
+ * The following code snippet exemplifies the client-side code that uses this
+ * function:
+ *
+ * ```js
+ * const model = tf.sequential();
+ * model.add(
+ *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+ *
+ * const saveResult = await model.save(tf.io.http(
+ *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+ * console.log(saveResult);
+ * ```
+ *
+ * If the default `POST` method is to be used, without any custom parameters
+ * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+ *
+ * ```js
+ * const saveResult = await model.save('http://model-server:5000/upload');
+ * ```
+ *
+ * The following GitHub Gist
+ * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+ * implements a server based on [flask](https://github.com/pallets/flask) that
+ * can receive the request. Upon receiving the model artifacts via the requst,
+ * this particular server reconsistutes instances of [Keras
+ * Models](https://keras.io/models/model/) in memory.
+ *
+ *
+ * @param path A URL path to the model.
+ *   Can be an absolute HTTP path (e.g.,
+ *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+ *   './model-upload').
+ * @param requestInit Request configurations to be used when sending
+ *    HTTP request to server using `fetch`. It can contain fields such as
+ *    `method`, `credentials`, `headers`, `mode`, etc. See
+ *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+ *    for more information. `requestInit` must not have a body, because the
+ * body will be set by TensorFlow.js. File blobs representing the model
+ * topology (filename: 'model.json') and the weights of the model (filename:
+ * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+ * `body`, an Error will be thrown.
+ * @param loadOptions Optional configuration for the loading. It includes the
+ *   following fields:
+ *   - weightPathPrefix Optional, this specifies the path prefix for weight
+ *     files, by default this is calculated from the path param.
+ *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+ *     the `fetch` from node-fetch can be used here.
+ *   - onProgress Optional, progress callback function, fired periodically
+ *     before the load is completed.
+ * @returns An instance of `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function http(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** **Human** library main class
+ *
+ * All methods and properties are available only as members of Human class
+ *
+ * - Configuration object definition: {@link Config}
+ * - Results object definition: {@link Result}
+ * - Possible inputs: {@link Input}
+ *
+ * @param userConfig - {@link Config}
+ * @returns instance of {@link Human}
+ */
+declare class Human {
+    #private;
+    /** Current version of Human library in *semver* format */
+    version: string;
+    /** Current configuration
+     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+     */
+    config: Config;
+    /** Last known result of detect run
+     * - Can be accessed anytime after initial detection
+     */
+    result: Result;
+    /** Current state of Human library
+     * - Can be polled to determine operations that are currently executed
+     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
+     */
+    state: string;
+    /** currenty processed image tensor and canvas */
+    process: {
+        tensor: Tensor | null;
+        canvas: AnyCanvas | null;
+    };
+    /** Instance of TensorFlow/JS used by Human
+     *  - Can be embedded or externally provided
+     * [TFJS API](https://js.tensorflow.org/api/latest/)
+     */
+    tf: any;
+    /** Object containing environment information used for diagnostics */
+    env: Env;
+    /** Draw helper classes that can draw detected objects on canvas using specified draw
+     * - canvas: draws input to canvas
+     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
+     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
+     */
+    draw: {
+        canvas: typeof draw.canvas;
+        face: typeof draw.face;
+        body: typeof draw.body;
+        hand: typeof draw.hand;
+        gesture: typeof draw.gesture;
+        object: typeof draw.object;
+        person: typeof draw.person;
+        all: typeof draw.all;
+        options: DrawOptions;
+    };
+    /** Currently loaded models
+     * @internal
+     * {@link Models}
+     */
+    models: models.Models;
+    /** Container for events dispatched by Human
+     * Possible events:
+     * - `create`: triggered when Human object is instantiated
+     * - `load`: triggered when models are loaded (explicitly or on-demand)
+     * - `image`: triggered when input image is processed
+     * - `result`: triggered when detection is complete
+     * - `warmup`: triggered when warmup is complete
+     * - `error`: triggered on some errors
+     */
+    events: EventTarget | undefined;
+    /** Reference face triangualtion array of 468 points, used for triangle references between points */
+    faceTriangulation: number[];
+    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
+    faceUVMap: [number, number][];
+    /** Performance object that contains values for all recently performed operations */
+    performance: Record<string, number>;
+    /** WebGL debug info */
+    gl: Record<string, unknown>;
+    /** Constructor for **Human** library that is futher used for all operations
+     * @param userConfig - user configuration object {@link Config}
+     */
+    constructor(userConfig?: Partial<Config>);
+    /** internal function to measure tensor leaks */
+    analyze: (...msg: string[]) => void;
+    /** Reset configuration to default values */
+    reset(): void;
+    /** Validate current configuration schema */
+    validate(userConfig?: Partial<Config>): {
+        reason: string;
+        where: string;
+        expected?: string;
+    }[];
+    /** Exports face matching methods {@link match#similarity} */
+    similarity: typeof match.similarity;
+    /** Exports face matching methods {@link match#distance} */
+    distance: typeof match.distance;
+    /** Exports face matching methods {@link match#match} */
+    match: typeof match.match;
+    /** Utility wrapper for performance.now() */
+    now(): number;
+    /** Process input as return canvas and tensor
+     *
+     * @param input - any input {@link Input}
+     * @param getTensor - should image processing also return tensor or just canvas
+     * Returns object with `tensor` and `canvas`
+     */
+    image(input: Input, getTensor?: boolean): Promise<{
+        tensor: Tensor<Rank> | null;
+        canvas: AnyCanvas | null;
+    }>;
+    /** Segmentation method takes any input and returns processed canvas with body segmentation
+     *  - Segmentation is not triggered as part of detect process
+     * @param input - {@link Input}
+     * @param background - {@link Input}
+     *  - Optional parameter background is used to fill the background with specific input
+     *  Returns:
+     *  - `data` as raw data array with per-pixel segmentation values
+     *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
+     *  - `alpha` as grayscale canvas that represents segmentation alpha values
+     */
+    segmentation(input: Input, background?: Input): Promise<{
+        data: number[] | Tensor;
+        canvas: AnyCanvas | null;
+        alpha: AnyCanvas | null;
+    }>;
+    /** Enhance method performs additional enhacements to face image previously detected for futher processing
+     *
+     * @param input - Tensor as provided in human.result.face[n].tensor
+     * @returns Tensor
+     */
+    enhance(input: Tensor): Tensor | null;
+    /** Compare two input tensors for pixel simmilarity
+     * - use `human.image` to process any valid input and get a tensor that can be used for compare
+     * - when passing manually generated tensors:
+     *  - both input tensors must be in format [1, height, width, 3]
+     *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
+     * - return value is pixel similarity score normalized by input resolution and rgb channels
+     */
+    compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
+    /** Explicit backend initialization
+     *  - Normally done implicitly during initial load phase
+     *  - Call to explictly register and initialize TFJS backend without any other operations
+     *  - Use when changing backend during runtime
+     */
+    init(): Promise<void>;
+    /** Load method preloads all configured models on-demand
+     * - Not explicitly required as any required model is load implicitly on it's first run
+     *
+     * @param userConfig - {@link Config}
+     */
+    load(userConfig?: Partial<Config>): Promise<void>;
+    /** emit event */
+    emit: (event: string) => void;
+    /** Runs interpolation using last known result and returns smoothened result
+     * Interpolation is based on time since last known result so can be called independently
+     *
+     * @param result - {@link Result} optional use specific result set to run interpolation on
+     * @returns result - {@link Result}
+     */
+    next(result?: Result): Result;
+    /** get model loading/loaded stats */
+    getModelStats(): ModelStats;
+    /** Warmup method pre-initializes all configured models for faster inference
+     * - can take significant time on startup
+     * - only used for `webgl` and `humangl` backends
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    warmup(userConfig?: Partial<Config>): Promise<Result | undefined>;
+    /** Run detect with tensorflow profiling
+     * - result object will contain total exeuction time information for top-20 kernels
+     * - actual detection object can be accessed via `human.result`
+     */
+    profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>>;
+    /** Main detection method
+     * - Analyze configuration: {@link Config}
+     * - Pre-process input: {@link Input}
+     * - Run inference for all configured models
+     * - Process and return result: {@link Result}
+     *
+     * @param input - {@link Input}
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    detect(input: Input, userConfig?: Partial<Config>): Promise<Result>;
+}
+export { Human }
+export default Human;
+
+/** Defines all possible image objects */
+export declare type ImageObjects = ImageData | ImageBitmap;
+
+/**
+ * Common interface for a machine learning model that can do inference.
+ */
+declare interface InferenceModel {
+    /**
+     * Return the array of input tensor info.
+     */
+    readonly inputs: ModelTensorInfo[];
+    /**
+     * Return the array of output tensor info.
+     */
+    readonly outputs: ModelTensorInfo[];
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size.
+     *
+     * @returns Inference result tensors. The output would be single Tensor if
+     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
+     * be returned for model with multiple outputs.
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    /**
+     * Single Execute the inference for the input tensors and return activation
+     * values for specified output node names without batching.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     *
+     * @param outputs string|string[]. List of output node names to retrieve
+     * activation from.
+     *
+     * @returns Activation values for the output nodes result tensors. The return
+     * type matches specified parameter outputs type. The output would be single
+     * Tensor if single output is specified, otherwise Tensor[] for multiple
+     * outputs.
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
+
+/** Defines all possible input types for **Human** detection */
+export declare type Input = Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas;
+
+declare namespace io {
+    export {
+        copyModel,
+        listModels,
+        moveModel,
+        removeModel,
+        browserFiles,
+        browserHTTPRequest,
+        concatenateArrayBuffers,
+        decodeWeights,
+        encodeWeights,
+        fromMemory,
+        fromMemorySync,
+        getLoadHandlers,
+        getModelArtifactsForJSON,
+        getModelArtifactsInfoForJSON,
+        getSaveHandlers,
+        http,
+        IOHandler,
+        IOHandlerSync,
+        isHTTPScheme,
+        LoadHandler,
+        LoadOptions,
+        loadWeights,
+        ModelArtifacts,
+        ModelArtifactsInfo,
+        ModelJSON,
+        ModelStoreManager,
+        OnProgressCallback,
+        registerLoadRouter,
+        registerSaveRouter,
+        RequestDetails,
+        SaveConfig,
+        SaveHandler,
+        SaveResult,
+        TrainingConfig,
+        WeightGroup,
+        weightsLoaderFactory,
+        WeightsManifestConfig,
+        WeightsManifestEntry,
+        withSaveHandler,
+        withSaveHandlerSync
+    }
+}
+
+/**
+ * Interface for a model import/export handler.
+ *
+ * The `save` and `load` handlers are both optional, in order to allow handlers
+ * that support only saving or loading.
+ */
+declare interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+
+declare type IOHandlerSync = {
+    [K in keyof IOHandler]: Syncify<IOHandler[K]>;
+};
+
+declare type IORouter = (url: string | string[], loadOptions?: LoadOptions) => IOHandler;
+
+/** iris gesture type */
+export declare type IrisGesture = 'facing center' | `looking ${'left' | 'right' | 'up' | 'down'}` | 'looking center';
+
+declare function isHTTPScheme(url: string): boolean;
+
+/**
+ * List all models stored in registered storage mediums.
+ *
+ * For a web browser environment, the registered mediums are Local Storage and
+ * IndexedDB.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @returns A `Promise` of a dictionary mapping URLs of existing models to
+ * their model artifacts info. URLs include medium-specific schemes, e.g.,
+ *   'indexeddb://my/model/1'. Model artifacts info include type of the
+ * model's topology, byte sizes of the topology, weights, etc.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+
+/** Load method preloads all instance.configured models on-demand */
+declare function load(instance: Human): Promise<void>;
+
+/**
+ * Type definition for handlers of loading operations.
+ */
+declare type LoadHandler = () => Promise<ModelArtifacts>;
+
+/** @innamespace io */
+declare interface LoadOptions {
+    /**
+     * RequestInit (options) for HTTP requests.
+     *
+     * For detailed information on the supported fields, see
+     * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
+     *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
+     */
+    requestInit?: RequestInit;
+    /**
+     * Progress callback.
+     */
+    onProgress?: OnProgressCallback;
+    /**
+     * A function used to override the `window.fetch` function.
+     */
+    fetchFunc?: Function;
+    /**
+     * Strict loading model: whether extraneous weights or missing
+     * weights should trigger an `Error`.
+     *
+     * If `true`, require that the provided weights exactly match those
+     * required by the layers. `false` means that both extra weights
+     * and missing weights will be silently ignored.
+     *
+     * Default: `true`.
+     */
+    strict?: boolean;
+    /**
+     * Path prefix for weight files, by default this is calculated from the
+     * path of the model JSON file.
+     *
+     * For instance, if the path to the model JSON file is
+     * `http://localhost/foo/model.json`, then the default path prefix will be
+     * `http://localhost/foo/`. If a weight file has the path value
+     * `group1-shard1of2` in the weight manifest, then the weight file will be
+     * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
+     * if you provide a `weightPathPrefix` value of
+     * `http://localhost/foo/alt-weights`, then the weight file will be loaded
+     * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
+     */
+    weightPathPrefix?: string;
+    /**
+     * Whether the module or model is to be loaded from TF Hub.
+     *
+     * Setting this to `true` allows passing a TF-Hub module URL, omitting the
+     * standard model file name and the query parameters.
+     *
+     * Default: `false`.
+     */
+    fromTFHub?: boolean;
+    /**
+     * An async function to convert weight file name to URL. The weight file
+     * names are stored in model.json's weightsManifest.paths field. By default we
+     * consider weight files are colocated with the model.json file. For example:
+     *     model.json URL: https://www.google.com/models/1/model.json
+     *     group1-shard1of1.bin url:
+     *        https://www.google.com/models/1/group1-shard1of1.bin
+     *
+     * With this func you can convert the weight file name to any URL.
+     */
+    weightUrlConverter?: (weightFileName: string) => Promise<string>;
+}
+
+/**
+ * Reads a weights manifest JSON configuration, fetches the weights and
+ * returns them as `Tensor`s.
+ *
+ * @param manifest The weights manifest JSON.
+ * @param filePathPrefix The path prefix for filenames given in the manifest.
+ *     Defaults to the empty string.
+ * @param weightNames The names of the weights to be fetched.
+ */
+declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestInit?: RequestInit): Promise<NamedTensorMap>;
+
+declare namespace match {
+    export {
+        distance,
+        similarity,
+        match_2 as match,
+        Descriptor,
+        MatchOptions
+    }
+}
+export { match }
+
+/** Matches given descriptor to a closest entry in array of descriptors
+ * @param descriptor - face descriptor
+ * @param descriptors - array of face descriptors to commpare given descriptor to
+ * @param options - see `similarity` method for options description
+ * Returns
+ * - `index` index array index where best match was found or -1 if no matches
+ * - `distance` calculated `distance` of given descriptor to the best match
+ * - `similarity` calculated normalized `similarity` of given descriptor to the best match
+ */
+declare function match_2(descriptor: Descriptor, descriptors: Array<Descriptor>, options?: MatchOptions): {
+    index: number;
+    distance: number;
+    similarity: number;
+};
+
+declare type MatchOptions = {
+    order?: number;
+    threshold?: number;
+    multiplier?: number;
+    min?: number;
+    max?: number;
+} | undefined;
+
+/**
+ * The serialized artifacts of a model, including topology and weights.
+ *
+ * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
+ * of this interface are optional, in order to support topology- or weights-only
+ * saving and loading.
+ *
+ * Note this interface is used internally in IOHandlers.  For the file format
+ * written to disk as `model.json`, see `ModelJSON`.
+ */
+declare interface ModelArtifacts {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology?: {} | ArrayBuffer;
+    /**
+     * Serialized configuration for the model's training.
+     */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weight specifications.
+     *
+     * This corresponds to the weightsData below.
+     */
+    weightSpecs?: WeightsManifestEntry[];
+    /**
+     * Binary buffer for all weight values concatenated in the order specified
+     * by `weightSpecs`.
+     */
+    weightData?: ArrayBuffer;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelArtifactsInfo {
+    /**
+     * Timestamp for when the model is saved.
+     */
+    dateSaved: Date;
+    /**
+     * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
+     * come in a JSON format and none of our IOHandlers support a non json
+     * format. We could conder replacing this with 'Binary' if we want to
+     * allow future handlers to save to non json formats (though they will
+     * probably want more information than 'Binary').
+     * Type of the model topology
+     *
+     * Type of the model topology
+     *
+     * Possible values:
+     *   - JSON: JSON config (human-readable, e.g., Keras JSON).
+     *   - GraphDef: TensorFlow
+     *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
+     *     protocol buffer (binary).
+     */
+    modelTopologyType: 'JSON' | 'GraphDef';
+    /**
+     * Size of model topology (Keras JSON or GraphDef), in bytes.
+     */
+    modelTopologyBytes?: number;
+    /**
+     * Size of weight specification or manifest, in bytes.
+     */
+    weightSpecsBytes?: number;
+    /**
+     * Size of weight value data, in bytes.
+     */
+    weightDataBytes?: number;
+}
+
+export declare type ModelInfo = {
+    name: string;
+    inCache: boolean;
+    sizeDesired: number;
+    sizeFromManifest: number;
+    sizeLoadedWeights: number;
+};
+
+/**
+ * The on-disk format of the `model.json` file.
+ *
+ * TF.js 1.0 always populates the optional fields when writing model.json.
+ * Prior versions did not provide those fields.
+ */
+declare interface ModelJSON {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology: {};
+    /** Model training configuration. */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weights manifest.
+     *
+     * The weights manifest consists of an ordered list of weight-manifest
+     * groups. Each weight-manifest group consists of a number of weight values
+     * stored in a number of paths. See the documentation of
+     * `WeightsManifestConfig` for more details.
+     */
+    weightsManifest: WeightsManifestConfig;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelPredictConfig {
+    /**
+     * Optional. Batch size (Integer). If unspecified, it will default to 32.
+     */
+    batchSize?: number;
+    /**
+     * Optional. Verbosity mode. Defaults to false.
+     */
+    verbose?: boolean;
+}
+
+/** Instances of all possible TFJS Graph Models used by Human
+ * - loaded as needed based on configuration
+ * - initialized explictly with `human.load()` method
+ * - initialized implicity on first call to `human.detect()`
+ * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
+ */
+export declare class Models {
+    ssrnetage: null | GraphModel | Promise<GraphModel>;
+    gear: null | GraphModel | Promise<GraphModel>;
+    blazeposedetect: null | GraphModel | Promise<GraphModel>;
+    blazepose: null | GraphModel | Promise<GraphModel>;
+    centernet: null | GraphModel | Promise<GraphModel>;
+    efficientpose: null | GraphModel | Promise<GraphModel>;
+    mobilefacenet: null | GraphModel | Promise<GraphModel>;
+    emotion: null | GraphModel | Promise<GraphModel>;
+    facedetect: null | GraphModel | Promise<GraphModel>;
+    faceiris: null | GraphModel | Promise<GraphModel>;
+    facemesh: null | GraphModel | Promise<GraphModel>;
+    faceres: null | GraphModel | Promise<GraphModel>;
+    ssrnetgender: null | GraphModel | Promise<GraphModel>;
+    handpose: null | GraphModel | Promise<GraphModel>;
+    handskeleton: null | GraphModel | Promise<GraphModel>;
+    handtrack: null | GraphModel | Promise<GraphModel>;
+    liveness: null | GraphModel | Promise<GraphModel>;
+    movenet: null | GraphModel | Promise<GraphModel>;
+    nanodet: null | GraphModel | Promise<GraphModel>;
+    posenet: null | GraphModel | Promise<GraphModel>;
+    segmentation: null | GraphModel | Promise<GraphModel>;
+    antispoof: null | GraphModel | Promise<GraphModel>;
+}
+
+declare namespace models {
+    export {
+        reset,
+        load,
+        validate,
+        Models,
+        ModelStats,
+        getModelStats
+    }
+}
+export { models }
+
+export declare type ModelStats = {
+    numLoadedModels: number;
+    numEnabledModels: undefined;
+    numDefinedModels: number;
+    percentageLoaded: number;
+    totalSizeFromManifest: number;
+    totalSizeWeights: number;
+    totalSizeLoading: number;
+    totalSizeEnabled: undefined;
+    modelStats: ModelInfo[];
+};
+
+/**
+ * An interface for the manager of a model store.
+ *
+ * A model store is defined as a storage medium on which multiple models can
+ * be stored. Each stored model has a unique `path` as its identifier.
+ * A `ModelStoreManager` for the store allows actions including
+ *
+ * - Listing the models stored in the store.
+ * - Deleting a model from the store.
+ */
+declare interface ModelStoreManager {
+    /**
+     * List all models in the model store.
+     *
+     * @returns A dictionary mapping paths of existing models to their
+     *   model artifacts info. Model artifacts info include type of the model's
+     *   topology, byte sizes of the topology, weights, etc.
+     */
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    /**
+     * Remove a model specified by `path`.
+     *
+     * @param path
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     */
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
+
+/**
+ * Interface for model input/output tensor info.
+ */
+declare interface ModelTensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+    tfDtype?: string;
+}
+
+/**
+ * Move a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Moving within a storage medium, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Moving between two storage mediums, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Move the model, from Local Storage to IndexedDB.
+ * await tf.io.moveModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove the moved model.
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of moving.
+ * @param destURL Destination URL of moving.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+declare interface NamedTensor {
+    name: string;
+    tensor: Tensor;
+}
+
+/** @docalias {[name: string]: Tensor} */
+declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+
+declare type NamedTensorsMap = {
+    [key: string]: Tensor[];
+};
+
+declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+
+/** draw detected objects */
+declare function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all object detection specific options */
+export declare interface ObjectConfig extends GenericConfig {
+    /** minimum confidence for a detected objects before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected objects before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected objects */
+    maxDetected: number;
+}
+
+/** Object results */
+export declare interface ObjectResult {
+    /** object id */
+    id: number;
+    /** object detection score */
+    score: number;
+    /** detected object class id */
+    class: number;
+    /** detected object class name */
+    label: ObjectType;
+    /** detected object box */
+    box: Box;
+    /** detected object box normalized to 0..1 */
+    boxRaw: Box;
+}
+
+export declare type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter' | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee' | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork' | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant' | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book' | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';
+
+/**
+ * Callback for the progress of a long-running action such as an HTTP
+ * request for a large binary object.
+ *
+ * `fraction` should be a number in the [0, 1] interval, indicating how
+ * much of the action has completed.
+ */
+declare type OnProgressCallback = (fraction: number) => void;
+
+/** currently set draw options {@link DrawOptions} */
+declare const options: DrawOptions;
+
+/** draw combined person results instead of individual detection result objects */
+declare function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Person getter
+ * - Triggers combining all individual results into a virtual person object
+ */
+export declare interface PersonResult {
+    /** person id */
+    id: number;
+    /** face result that belongs to this person */
+    face: FaceResult;
+    /** body result that belongs to this person */
+    body: BodyResult | null;
+    /** left and right hand results that belong to this person */
+    hands: {
+        left: HandResult | null;
+        right: HandResult | null;
+    };
+    /** detected gestures specific to this person */
+    gestures: Array<GestureResult>;
+    /** box that defines the person */
+    box: Box;
+    /** box that defines the person normalized to 0..1 */
+    boxRaw?: Box;
+}
+
+/** generic point as [x, y, z?] */
+export declare type Point = [number, number, number?];
+
+declare type PromiseFunction = (...args: unknown[]) => Promise<unknown>;
+
+export declare type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';
+
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
+declare const registerLoadRouter: (loudRouter: IORouter) => void;
+
+declare const registerSaveRouter: (loudRouter: IORouter) => void;
+
+/**
+ * Remove a model specified by URL from a reigstered storage medium.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @param url A URL to a stored model, with a scheme prefix, e.g.,
+ *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+ * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+ *   is successful).
+ * @throws Error if deletion fails, e.g., if no model exists at `path`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * Additional options for Platform.fetch
+ */
+declare interface RequestDetails {
+    /**
+     * Is this request for a binary file (as opposed to a json file)
+     */
+    isBinary?: boolean;
+}
+
+declare function reset(instance: Human): void;
+
+/**
+ * Result interface definition for **Human** library
+ *
+ * Contains all possible detection results
+ */
+export declare interface Result {
+    /** {@link FaceResult}: detection & analysis results */
+    face: Array<FaceResult>;
+    /** {@link BodyResult}: detection & analysis results */
+    body: Array<BodyResult>;
+    /** {@link HandResult}: detection & analysis results */
+    hand: Array<HandResult>;
+    /** {@link GestureResult}: detection & analysis results */
+    gesture: Array<GestureResult>;
+    /** {@link ObjectResult}: detection & analysis results */
+    object: Array<ObjectResult>;
+    /** global performance object with timing values for each operation */
+    performance: Record<string, number>;
+    /** optional processed canvas that can be used to draw input on screen */
+    canvas?: AnyCanvas | null;
+    /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
+    readonly timestamp: number;
+    /** getter property that returns unified persons object  */
+    persons: Array<PersonResult>;
+    /** Last known error message */
+    error: string | null;
+}
+
+/**
+ * Options for saving a model.
+ * @innamespace io
+ */
+declare interface SaveConfig {
+    /**
+     * Whether to save only the trainable weights of the model, ignoring the
+     * non-trainable ones.
+     */
+    trainableOnly?: boolean;
+    /**
+     * Whether the optimizer will be saved (if exists).
+     *
+     * Default: `false`.
+     */
+    includeOptimizer?: boolean;
+}
+
+/**
+ * Type definition for handlers of saving operations.
+ */
+declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+
+/**
+ * Result of a saving operation.
+ */
+declare interface SaveResult {
+    /**
+     * Information about the model artifacts saved.
+     */
+    modelArtifactsInfo: ModelArtifactsInfo;
+    /**
+     * HTTP responses from the server that handled the model-saving request (if
+     * any). This is applicable only to server-based saving routes.
+     */
+    responses?: Response[];
+    /**
+     * Error messages and related data (if any).
+     */
+    errors?: Array<{} | string>;
+}
+
+/** Configures all body segmentation module
+ * removes background from input containing person
+ * if segmentation is enabled it will run as preprocessing task before any other model
+ * alternatively leave it disabled and use it on-demand using human.segmentation method which can
+ * remove background or replace it with user-provided background
+ */
+export declare interface SegmentationConfig extends GenericConfig {
+    /** blur segmentation output by <number> pixels for more realistic image */
+    blur: number;
+}
+
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/// <amd-module name="@tensorflow/tfjs-core/dist/types" />
+/** @docalias number[] */
+declare interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+
+/** Calculates normalized similarity between two face descriptors based on their `distance`
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ * - min - normalize similarity result to a given range
+ * - max - normalzie similarity resutl to a given range
+ *   default is 0.2...0.8
+ * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
+ */
+declare function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+
+declare type Syncify<T extends PromiseFunction> = T extends (...args: infer Args) => Promise<infer R> ? (...args: Args) => R : never;
+
+export declare namespace Tensor { }
+
+/**
+ * A `tf.Tensor` object represents an immutable, multidimensional array of
+ * numbers that has a shape and a data type.
+ *
+ * For performance reasons, functions that create tensors do not necessarily
+ * perform a copy of the data passed to them (e.g. if the data is passed as a
+ * `Float32Array`), and changes to the data will change the tensor. This is not
+ * a feature and is not supported. To avoid this behavior, use the tensor before
+ * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+ *
+ * See `tf.tensor` for details on how to create a `tf.Tensor`.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+export declare class Tensor<R extends Rank = Rank> {
+    /** Unique id of this tensor. */
+    readonly id: number;
+    /**
+     * Id of the bucket holding the data for this tensor. Multiple arrays can
+     * point to the same bucket (e.g. when calling array.reshape()).
+     */
+    dataId: DataId;
+    /** The shape of the tensor. */
+    readonly shape: ShapeMap[R];
+    /** Number of elements in the tensor. */
+    readonly size: number;
+    /** The data type for the array. */
+    readonly dtype: DataType;
+    /** The rank type for the array (see `Rank` enum). */
+    readonly rankType: R;
+    /** Whether this tensor has been globally kept. */
+    kept: boolean;
+    /** The id of the scope this tensor is being tracked in. */
+    scopeId: number;
+    /**
+     * Number of elements to skip in each dimension when indexing. See
+     * https://docs.scipy.org/doc/numpy/reference/generated/\
+     * numpy.ndarray.strides.html
+     */
+    readonly strides: number[];
+    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
+    readonly rank: number;
+    /**
+     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
+    /**
+     * Returns a `tf.TensorBuffer` that holds the underlying data.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * asynchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    array(): Promise<ArrayMap[R]>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * synchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    arraySync(): ArrayMap[R];
+    /**
+     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+     * promise of `TypedArray` that resolves when the computation has finished.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    /**
+     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
+     * and `data()`, this method prevents data from being downloaded to CPU.
+     *
+     * For WebGL backend, the data will be stored on a densely packed texture.
+     * This means that the texture will use the RGBA channels to store value.
+     *
+     * For WebGPU backend, the data will be stored on a buffer. There is no
+     * parameter, so can not use an user defined size to create the buffer.
+     *
+     * @param options:
+     *     For WebGL,
+     *         - customTexShape: Optional. If set, will use the user defined
+     *     texture shape to create the texture.
+     *
+     * @returns For WebGL backend, a GPUData contains the new texture and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this texture,
+     *        texture: WebGLTexture,
+     *        texShape: [number, number] // [height, width]
+     *     }
+     *
+     *     For WebGPU backend, a GPUData contains the new buffer and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this buffer,
+     *        buffer: GPUBuffer,
+     *        bufSize: number
+     *     }
+     *
+     *     Remember to dispose the GPUData after it is used by
+     *     `res.tensorRef.dispose()`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataToGPU(options?: DataToGPUOptions): GPUData;
+    /**
+     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+     * UI thread until the values are ready, which can cause performance issues.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    /** Returns the underlying bytes of the tensor's data. */
+    bytes(): Promise<Uint8Array[] | Uint8Array>;
+    /**
+     * Disposes `tf.Tensor` from memory.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dispose(): void;
+    protected isDisposedInternal: boolean;
+    readonly isDisposed: boolean;
+    throwIfDisposed(): void;
+    /**
+     * Prints the `tf.Tensor`. See `tf.print` for details.
+     *
+     * @param verbose Whether to print verbose information about the tensor,
+     *    including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    print(verbose?: boolean): void;
+    /**
+     * Returns a copy of the tensor. See `tf.clone` for details.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    clone<T extends Tensor>(this: T): T;
+    /**
+     * Returns a human-readable description of the tensor. Useful for logging.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    toString(verbose?: boolean): string;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+}
+
+/**
+ * A mutable object, similar to `tf.Tensor`, that allows users to set values
+ * at locations before converting to an immutable `tf.Tensor`.
+ *
+ * See `tf.buffer` for creating a tensor buffer.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    /**
+     * Sets a value in the buffer at a given location.
+     *
+     * @param value The value to set.
+     * @param locs  The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    /**
+     * Returns the value in the buffer at the provided location.
+     *
+     * @param locs The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    /**
+     * Creates an immutable `tf.Tensor` object from the buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    toTensor(): Tensor<R>;
+}
+
+declare interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
+
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
+/** Model training configuration. */
+declare interface TrainingConfig {
+    /** Optimizer used for the model training. */
+    optimizer_config: {};
+    /** Loss function(s) for the model's output(s). */
+    loss: string | string[] | {
+        [key: string]: string;
+    };
+    /** Metric function(s) for the model's output(s). */
+    metrics?: string[] | {
+        [key: string]: string;
+    };
+    weighted_metrics?: string[];
+    sample_weight_mode?: string;
+    loss_weights?: number[] | {
+        [key: string]: number;
+    };
+}
+
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
+declare type Url = string | io.IOHandler | io.IOHandlerSync;
+
+declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
+
+declare function validate(instance: Human): Promise<void>;
+
+/**
+ * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
+    /**
+     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+     * the same shape and dtype as the old `tf.Tensor`.
+     *
+     * @param newValue New tensor to be assigned to this variable.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    assign(newValue: Tensor<R>): void;
+    dispose(): void;
+}
+
+/** Possible values for `human.warmup` */
+export declare type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];
+
+/**
+ * Group to which the weight belongs.
+ *
+ * - 'optimizer': Weight from a stateful optimizer.
+ */
+declare type WeightGroup = 'model' | 'optimizer';
+
+/**
+ * Creates a function, which reads a weights manifest JSON configuration,
+ * fetches the weight files using the specified function and returns them as
+ * `Tensor`s.
+ *
+ * ```js
+ * // example for creating a nodejs weight loader, which reads the weight files
+ * // from disk using fs.readFileSync
+ *
+ * import * as fs from 'fs'
+ *
+ * const fetchWeightsFromDisk = (filePaths: string[]) =>
+ *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+ *
+ * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+ *
+ * const manifest = JSON.parse(
+ *   fs.readFileSync('./my_model-weights_manifest').toString()
+ * )
+ * const weightMap = await loadWeights(manifest, './')
+ * ```
+ * @param fetchWeightsFunction The function used for fetching the weight files.
+ * @returns Weight loading function.
+ */
+declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
+
+/**
+ * A weight manifest.
+ *
+ * The weight manifest consists of an ordered list of weight-manifest groups.
+ * Each weight-manifest group ("group" for short hereafter) consists of a
+ * number of weight values stored in a number of paths.
+ * See the documentation of `WeightManifestGroupConfig` below for more details.
+ */
+declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+
+/**
+ * An entry in the weight manifest.
+ *
+ * The entry contains specification of a weight.
+ */
+declare interface WeightsManifestEntry {
+    /**
+     * Name of the weight, e.g., 'Dense_1/bias'
+     */
+    name: string;
+    /**
+     * Shape of the weight.
+     */
+    shape: number[];
+    /**
+     * Data type of the weight.
+     */
+    dtype: 'float32' | 'int32' | 'bool' | 'string' | 'complex64';
+    /**
+     * Type of the weight.
+     *
+     * Optional.
+     *
+     * The value 'optimizer' indicates the weight belongs to an optimizer
+     * (i.e., used only during model training and not during inference).
+     */
+    group?: WeightGroup;
+    /**
+     * Information for dequantization of the weight.
+     */
+    quantization?: {
+        scale?: number;
+        min?: number;
+        dtype: 'uint16' | 'uint8' | 'float16';
+    };
+}
+
+/**
+ * A weight-manifest group.
+ *
+ * Consists of an ordered list of weight values encoded in binary format,
+ * stored in an ordered list of paths.
+ */
+declare interface WeightsManifestGroupConfig {
+    /**
+     * An ordered list of paths.
+     *
+     * Paths are intentionally abstract in order to be general. For example, they
+     * can be relative URL paths or relative paths on the file system.
+     */
+    paths: string[];
+    /**
+     * Specifications of the weights stored in the paths.
+     */
+    weights: WeightsManifestEntry[];
+}
+
+/**
+ * Creates an IOHandler that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     promise that resolves to a `SaveResult`.
+ */
+declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
+
+/**
+ * Creates an IOHandlerSync that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     `SaveResult`.
+ */
+declare function withSaveHandlerSync(saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync;
+
+export { }
diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
new file mode 100644
index 00000000..0cce9f4e
--- /dev/null
+++ b/dist/human.esm-nobundle.js
@@ -0,0 +1,839 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var J2=Object.defineProperty;var Po=Object.getOwnPropertyDescriptor;var Ro=Object.getOwnPropertyNames;var vo=Object.prototype.hasOwnProperty;var To=(e,t,o)=>t in e?J2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var U0=(e,t)=>{for(var o in t)J2(e,o,{get:t[o],enumerable:!0})},I1=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Ro(t))!vo.call(e,r)&&r!==o&&J2(e,r,{get:()=>t[r],enumerable:!(n=Po(t,r))||n.enumerable});return e},q=(e,t,o)=>(I1(e,t,"default"),o&&I1(o,t,"default"));var w=(e,t,o)=>(To(e,typeof t!="symbol"?t+"":t,o),o),N1=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var Xe=(e,t,o)=>(N1(e,t,"read from private field"),o?o.call(e):t.get(e)),qe=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},Ue=(e,t,o,n)=>(N1(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);function u(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function O1(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var b=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Q2(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Q2(e[r],t[r],r,n);else{let s=e&&typeof e[r]!="undefined";s||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let a=e&&typeof e[r]==typeof t[r];s&&!a&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&u("invalid configuration",n),n}function J(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let s=o[r],a=n[r];Array.isArray(s)&&Array.isArray(a)?o[r]=s.concat(...a):t(s)&&t(a)?o[r]=J(s,a):o[r]=a}),o),{})}var se={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var A={};U0(A,{GraphModel:()=>_2,Tensor:()=>ge,version:()=>Ye});q(A,SA);q(A,CA);import*as SA from"@tensorflow/tfjs/dist/index.js";import*as CA from"@tensorflow/tfjs-backend-webgl/dist/index.js";import{Tensor as ge}from"@tensorflow/tfjs/dist/index.js";import{GraphModel as _2}from"@tensorflow/tfjs-converter/dist/index";var wo="3.18.0",ko="3.18.0",Eo="3.18.0",zo="3.18.0",So="3.18.0",Co="3.18.0",jo="3.18.0",Ye={tfjs:wo,"tfjs-core":ko,"tfjs-data":Eo,"tfjs-layers":zo,"tfjs-converter":So,"tfjs-backend-webgl":Co,"tfjs-backend-wasm":jo};var L1=`
+  precision highp float;
+  attribute vec2 pos;
+  attribute vec2 uv;
+  varying vec2 vUv;
+  uniform float flipY;
+  void main(void) {
+    vUv = uv;
+    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
+  }
+`;var W1=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
+    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
+  }
+`,F1=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
+    gl_FragColor.a = c.a;
+  }
+`,G1=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform vec2 size;
+  uniform sampler2D texture;
+  vec2 pixelate(vec2 coord, vec2 size) {
+    return floor( coord / size ) * size;
+  }
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    vec2 coord = pixelate(vUv, size);
+    gl_FragColor += texture2D(texture, coord);
+  }
+`,B1=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
+    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;
+    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
+  }
+`,H1=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  uniform float m[9];
+  void main(void) {
+    vec4 c11 = texture2D(texture, vUv - px); // top left
+    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
+    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
+    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
+    vec4 c22 = texture2D(texture, vUv); // mid center
+    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
+    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
+    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
+    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
+    gl_FragColor = 
+    c11 * m[0] + c12 * m[1] + c22 * m[2] +
+    c21 * m[3] + c22 * m[4] + c23 * m[5] +
+    c31 * m[6] + c32 * m[7] + c33 * m[8];
+    gl_FragColor.a = c22.a;
+  }
+`;var $2=(e,t,o)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(r,s)=>(o[s]=0,r))},e5=class{constructor(t,o,n){w(this,"uniform",{});w(this,"attribute",{});w(this,"gl");w(this,"id");w(this,"compile",(t,o)=>{let n=this.gl.createShader(o);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(u(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)}`),null)):(u("filter: could not create shader"),null)});this.gl=t;let r=this.compile(o,this.gl.VERTEX_SHADER),s=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!s)){if(!this.id){u("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){u(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),$2(o,"attribute",this.attribute);for(let a in 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o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var Et={initial:!0},m0={detector:null,landmarks:null},Te={detector:[224,224],landmarks:[256,256]},P5=Number.MAX_SAFE_INTEGER,er={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},R2=null,_e,J0=[[0,0],[0,0],[0,0],[0,0]],wt=0,kt=e=>1-1/(1+Math.exp(e));async function zt(e){if(Et.initial&&(m0.detector=null),!m0.detector&&e.body.detector&&e.body.detector.modelPath){m0.detector=await L(e.body.detector.modelPath);let t=Object.values(m0.detector.modelSignature.inputs);Te.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Te.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&m0.detector&&u("cached model:",m0.detector.modelUrl);return await vt(),m0.detector}async function St(e){if(Et.initial&&(m0.landmarks=null),m0.landmarks)e.debug&&u("cached model:",m0.landmarks.modelUrl);else{m0.landmarks=await 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o=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>b()-wt,r=P5<(t.body.skipFrames||0);if(t.skipAllowed&&n&&r&&R2!==null)P5++;else{let s={};s.landmarks=await tr(e,256),R2=await rr(s.landmarks,t,o),Object.keys(s).forEach(a=>A.dispose(s[a])),wt=b(),P5=0}return R2?[R2]:[]}var we=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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k5=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],E5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var s0,Ot=0,c0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},z5=Number.MAX_SAFE_INTEGER;async function Lt(e){return T.initial&&(s0=null),s0?e.debug&&u("cached model:",s0.modelUrl):s0=await L(e.body.modelPath),s0}async function sr(e,t){let[o,n]=e.shape,r=A.reshape(e,[n*o]),s=A.max(r,0),a=(await s.data())[0];if(A.dispose([r,s]),a>t){let i=A.argMax(r,0),l=A.mod(i,o),c=(await l.data())[0],d=A.div(i,A.scalar(o,"int32")),y=(await d.data())[0];return A.dispose([l,d]),[c,y,a]}return[0,0,a]}async function S5(e,t){let o=(t.body.skipTime||0)>b()-Ot,n=z5<(t.body.skipFrames||0);return t.skipAllowed&&o&&n&&Object.keys(c0.keypoints).length>0?(z5++,[c0]):(z5=0,new Promise(async r=>{var y;let s=A.tidy(()=>{if(!(s0!=null&&s0.inputs[0].shape))return null;let x=A.image.resizeBilinear(e,[s0.inputs[0].shape[2],s0.inputs[0].shape[1]],!1),m=A.mul(x,G.tf2);return A.sub(m,G.tf1)}),a;if(t.body.enabled&&(a=s0==null?void 0:s0.execute(s)),Ot=b(),A.dispose(s),a){c0.keypoints.length=0;let x=a.squeeze();A.dispose(a);let m=x.unstack(2);A.dispose(x);for(let h=0;h<m.length;h++){let[p,R,M]=await sr(m[h],t.body.minConfidence);M>(((y=t.body)==null?void 0:y.minConfidence)||0)&&c0.keypoints.push({score:Math.round(100*M)/100,part:k5[h],positionRaw:[p/s0.inputs[0].shape[2],R/s0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/s0.inputs[0].shape[2]),Math.round(e.shape[1]*R/s0.inputs[0].shape[1])]})}m.forEach(h=>A.dispose(h))}c0.score=c0.keypoints.reduce((x,m)=>m.score>x?m.score:x,0);let i=c0.keypoints.map(x=>x.position[0]),l=c0.keypoints.map(x=>x.position[1]);c0.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=c0.keypoints.map(x=>x.positionRaw[0]),d=c0.keypoints.map(x=>x.positionRaw[1]);c0.boxRaw=[Math.min(...c),Math.min(...d),Math.max(...c)-Math.min(...c),Math.max(...d)-Math.min(...d)];for(let[x,m]of Object.entries(E5)){let h=[];for(let p=0;p<m.length-1;p++){let R=c0.keypoints.find(g=>g.part===m[p]),M=c0.keypoints.find(g=>g.part===m[p+1]);R&&M&&R.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&h.push([R.position,M.position])}c0.annotations[x]=h}r([c0])}))}var ar=["angry","disgust","fear","happy","sad","surprise","neutral"],P0,T2=[],Ft=0,Gt=0,C5=Number.MAX_SAFE_INTEGER;async function Bt(e){var t;return T.initial&&(P0=null),P0?e.debug&&u("cached model:",P0.modelUrl):P0=await L((t=e.face.emotion)==null?void 0:t.modelPath),P0}async function j5(e,t,o,n){var a,i;if(!P0)return[];let r=C5<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),s=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>b()-Gt;return t.skipAllowed&&s&&r&&Ft===n&&T2[o]&&T2[o].length>0?(C5++,T2[o]):(C5=0,new Promise(async l=>{var d,y;let c=[];if((d=t.face.emotion)!=null&&d.enabled){let x={},m=P0!=null&&P0.inputs[0].shape?P0.inputs[0].shape[2]:0;x.resize=A.image.resizeBilinear(e,[m,m],!1),x.channels=A.mul(x.resize,G.rgb),x.grayscale=A.sum(x.channels,3,!0),x.grayscaleSub=A.sub(x.grayscale,G.tf05),x.grayscaleMul=A.mul(x.grayscaleSub,G.tf2),x.emotion=P0==null?void 0:P0.execute(x.grayscaleMul),Gt=b();let h=await x.emotion.data();for(let p=0;p<h.length;p++)h[p]>(((y=t.face.emotion)==null?void 0:y.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*h[p])/100),emotion:ar[p]});c.sort((p,R)=>R.score-p.score),Object.keys(x).forEach(p=>A.dispose(x[p]))}T2[o]=c,Ft=n,l(c)}))}var p0,I5=[],Vt=0,Dt=0,Zt=Number.MAX_SAFE_INTEGER;async function Xt(e){return T.initial&&(p0=null),p0?e.debug&&u("cached 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0:t.modelPath),Q0=H0.inputs[0].shape?H0.inputs[0].shape[2]:0,Q0===-1&&(Q0=64),H0}function w2(e,t,o,n){for(let r=0;r<m5.length;r++){let{key:s,indices:a}=m5[r],i=w0[`${o}${s}`];if(!n||n.includes(s))for(let l=0;l<a.length;l++){let c=a[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var lr=e=>{let t=e[ke.leftBounds[0]][2],o=e[ke.rightBounds[0]][2];return t-o},Ut=(e,t,o,n,r,s=!1)=>{let a=h2(u2(ct([e[o],e[n]]),ir)),i=Re(a),l=A.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[Q0,Q0]);if(s&&T.kernels.includes("flipleftright")){let c=A.image.flipLeftRight(l);A.dispose(l),l=c}return{box:a,boxSize:i,crop:l}},Yt=(e,t,o,n=!1)=>{let r=[];for(let s=0;s<Ee.numCoordinates;s++){let a=e[s*3],i=e[s*3+1],l=e[s*3+2];r.push([(n?1-a/Q0:a/Q0)*o[0]+t.startPoint[0],i/Q0*o[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(Ee.index)}},Kt=(e,t,o)=>{let n=e[w0[`${o}EyeUpper0`][Ee.upperCenter]][2],r=e[w0[`${o}EyeLower0`][Ee.lowerCenter]][2],s=(n+r)/2;return t.map((a,i)=>{let l=s;return i===2?l=n:i===4&&(l=r),[a[0],a[1],l]})};async function Qt(e,t,o,n){if(!H0)return o.debug&&u("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:s,crop:a}=Ut(e,t,ke.leftBounds[0],ke.leftBounds[1],n,!0),{box:i,boxSize:l,crop:c}=Ut(e,t,ke.rightBounds[0],ke.rightBounds[1],n,!0),d=A.concat([a,c]);A.dispose(a),A.dispose(c);let y=H0.execute(d);A.dispose(d);let x=await y.data();A.dispose(y);let m=x.slice(0,Ee.numCoordinates*3),{rawCoords:h,iris:p}=Yt(m,r,s,!0),R=x.slice(Ee.numCoordinates*3),{rawCoords:M,iris:g}=Yt(R,i,l,!1),f=lr(e);Math.abs(f)<30?(w2(e,h,"left",null),w2(e,M,"right",null)):f<1?w2(e,h,"left",["EyeUpper0","EyeLower0"]):w2(e,M,"right",["EyeUpper0","EyeLower0"]);let P=Kt(e,p,"left"),C=Kt(e,g,"right");return e.concat(P).concat(C)}var yr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],xr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],cr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],dr=[[474,475],[475,476],[476,477],[477,474]],fr=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],mr=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],pr=[[469,470],[470,471],[471,472],[472,469]],ur=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function _0(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var hr={lips:_0(yr),leftEye:_0(xr),leftEyebrow:_0(cr),leftIris:_0(dr),rightEye:_0(fr),rightEyebrow:_0(mr),rightIris:_0(pr),faceOval:_0(ur)},br=Object.entries(hr).map(([e,t])=>t.map(o=>[o,e])).flat(),C7=new 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t.filter(s=>s.size===142)[0].data(),irisR:await t.filter(s=>s.size===10)[1].data(),eyeR:await t.filter(s=>s.size===142)[1].data()},n=xe.reduce((s,a)=>s+=e[a][2],0)/xe.length;for(let s=0;s<o.irisL.length/2;s++)e.push([o.irisL[2*s+0],o.irisL[2*s+1],n]);let r=ce.reduce((s,a)=>s+=e[a][2],0)/ce.length;for(let s=0;s<o.irisR.length/2;s++)e.push([o.irisR[2*s+0],o.irisR[2*s+1],r]);for(let s=0;s<o.eyeL.length/2;s++)e[xe[s]]=[o.eyeL[2*s+0],o.eyeL[2*s+1],e[xe[s]][2]];for(let s=0;s<o.eyeR.length/2;s++)e[ce[s]]=[o.eyeR[2*s+0],o.eyeR[2*s+1],e[ce[s]][2]];for(let s=0;s<o.lips.length/2;s++)e[$e[s]]=[o.lips[2*s+0],o.lips[2*s+1],e[$e[s]][2]];return e}var I0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},a0=null,de=0;async function t3(e,t){var i,l,c,d,y,x,m,h,p,R,M;let o=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>b()-I0.timestamp,n=I0.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!o||!n||I0.boxes.length===0?(I0.boxes=await Mt(e,t),I0.timestamp=b(),I0.skipped=0):I0.skipped++;let r=[],s=[],a=0;for(let g=0;g<I0.boxes.length;g++){let f=I0.boxes[g],P=0,C,k={id:a++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,C,k.tensor]=pt((c=t.face.detector)==null?void 0:c.rotation,f,e,(d=t.face.mesh)!=null&&d.enabled?de:ve()),(y=t==null?void 0:t.filter)!=null&&y.equalization){let I=await i2(k.tensor);A.dispose(k.tensor),k.tensor=I}if(k.boxScore=Math.round(100*f.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!a0)t.debug&&u("face mesh detection requested, but model is not loaded");else{let I=a0.execute(k.tensor),O=await I.find(F=>F.shape[F.shape.length-1]===1).data();if(k.faceScore=Math.round(100*O[0])/100,k.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(f.confidence=k.faceScore,(h=t.face.mesh)!=null&&h.keepInvalid){k.box=m2(f,e),k.boxRaw=p2(f,e),k.score=k.boxScore,k.mesh=f.landmarks.map(F=>[(f.startPoint[0]+f.endPoint[0])/2+(f.endPoint[0]+f.startPoint[0])*F[0]/ve(),(f.startPoint[1]+f.endPoint[1])/2+(f.endPoint[1]+f.startPoint[1])*F[1]/ve()]),k.meshRaw=k.mesh.map(F=>[F[0]/(e.shape[2]||1),F[1]/(e.shape[1]||1),(F[2]||0)/de]);for(let F of Object.keys(ae))k.annotations[F]=[k.mesh[ae[F]]]}}else{let F=I.find(z=>z.shape[z.shape.length-1]===1404),V=A.reshape(F,[-1,3]),Z=await V.array();A.dispose(V),(p=t.face.attention)!=null&&p.enabled?Z=await e3(Z,I):(R=t.face.iris)!=null&&R.enabled&&(Z=await Qt(Z,k.tensor,t,de)),k.mesh=mt(Z,f,P,C,de),k.meshRaw=k.mesh.map(z=>[z[0]/(e.shape[2]||0),z[1]/(e.shape[1]||0),(z[2]||0)/de]);for(let z of Object.keys(w0))k.annotations[z]=w0[z].map(g0=>k.mesh[g0]);k.score=k.faceScore;let t0={...ut(k.mesh,f),confidence:f.confidence,landmarks:f.landmarks};k.box=m2(t0,e),k.boxRaw=p2(t0,e),s.push(t0)}A.dispose(I)}else{k.box=m2(f,e),k.boxRaw=p2(f,e),k.score=k.boxScore,k.mesh=f.landmarks.map(I=>[(f.startPoint[0]+f.endPoint[0])/2+(f.endPoint[0]+f.startPoint[0])*I[0]/ve(),(f.startPoint[1]+f.endPoint[1])/2+(f.endPoint[1]+f.startPoint[1])*I[1]/ve()]),k.meshRaw=k.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/de]);for(let I of Object.keys(ae))k.annotations[I]=[k.mesh[ae[I]]]}k.score>(((M=t.face.detector)==null?void 0:M.minConfidence)||1)?r.push(k):A.dispose(k.tensor)}return I0.boxes=s,r}async function o3(e){var t,o,n,r,s,a;return T.initial&&(a0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(a0==null?void 0:a0.signature)&&Object.keys(((n=a0==null?void 0:a0.signature)==null?void 0:n.outputs)||{}).length<6&&(a0=null),a0?e.debug&&u("cached model:",a0.modelUrl):(r=e.face.attention)!=null&&r.enabled?a0=await 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Object.keys(o).forEach(r=>A.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=A.reshape(t,[-1,7,2]),n.div=A.div(n.reshape,this.inputSizeTensor),n.landmarks=A.add(n.div,this.anchors[o]);let r=A.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>A.dispose(n[s])),r}async predict(t,o){let n={};n.resize=A.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=A.div(n.resize,G.tf127),n.image=A.sub(n.div,G.tf1),n.batched=this.model.execute(n.image),n.predictions=A.squeeze(n.batched),n.slice=A.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=A.sigmoid(n.slice),n.scores=A.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=A.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await A.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await n.nms.array(),a=[];for(let i of s){let l={};l.box=A.slice(n.norm,[i,0],[1,-1]),l.slice=A.slice(n.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=A.reshape(l.norm,[-1,2]);let c=await l.box.data(),d=c.slice(0,2),y=c.slice(2,4),x=await l.palmLandmarks.array(),m={startPoint:d,endPoint:y,palmLandmarks:x,confidence:r[i]},h=x3(m,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(l).forEach(p=>A.dispose(l[p]))}return Object.keys(n).forEach(i=>A.dispose(n[i])),a}};var Tr=5,p3=1.65,u3=[0,5,9,13,17,1,2],wr=0,kr=2,h3=0,j2=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),n=t.map(a=>a[1]),r=[Math.min(...o),Math.min(...n)],s=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,o){let n=t.map(s=>D5([...s,1],o)),r=this.calculateLandmarksBoundingBox(n);return z2(S2(r),Tr)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=z2(S2(o),p3);n.palmLandmarks=[];for(let r=0;r<u3.length;r++)n.palmLandmarks.push(t[u3[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let s=E2(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(m=>[a[0]*(m[0]-this.inputSize/2),a[1]*(m[1]-this.inputSize/2),a[2]*m[2]]),l=V5(n,[0,0]),c=i.map(m=>[...D5(m,l),m[2]]),d=d3(r),y=[...e2(o),1],x=[$0(y,d[0]),$0(y,d[1])];return c.map(m=>[Math.trunc(m[0]+x[0]),Math.trunc(m[1]+x[1]),Math.trunc(m[2])])}async estimateHands(t,o){let n=!1,r,s=(o.hand.skipTime||0)>b()-h3,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(r=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(n=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(!!c)if(o.hand.landmarks){let d=o.hand.rotation?c3(c.palmLandmarks[wr],c.palmLandmarks[kr]):0,y=e2(c),x=[y[0]/t.shape[2],y[1]/t.shape[1]],m=o.hand.rotation&&T.kernels.includes("rotatewithoffset")?A.image.rotateWithOffset(t,d,0,x):t.clone(),h=V5(-d,y),p=n?this.getBoxForPalmLandmarks(c.palmLandmarks,h):c,R=y3(p,m,[this.inputSize,this.inputSize]),M=A.div(R,G.tf255);A.dispose(R),A.dispose(m);let[g,f]=this.handPoseModel.execute(M);h3=b(),A.dispose(M);let P=(await g.data())[0];if(A.dispose(g),P>=o.hand.minConfidence/4){let C=A.reshape(f,[-1,3]),k=await C.array();A.dispose(f),A.dispose(C);let I=this.transformRawCoords(k,p,d,h),j=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...j,confidence:P};let O={landmarks:I,confidence:P,boxConfidence:c.confidence,fingerConfidence:P,box:{topLeft:j.startPoint,bottomRight:j.endPoint}};i.push(O)}else this.storedBoxes[l]=null;A.dispose(f)}else{let d=z2(S2(c),p3),y={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var d0={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>d0.nameMapping[e],getPoints:e=>d0.pointsMapping[e]},te={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>te.nameMapping[e]},K={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>K.nameMapping[e]},ee=class{constructor(t){w(this,"name");w(this,"curls");w(this,"directions");w(this,"weights");w(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,o,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([o,n])}direction(t,o,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([o,n])}weight(t,o){this.weights[t]=o;let n=this.weights.reduce((r,s)=>r+s,0);this.weightsRelative=this.weights.map(r=>r*5/n)}matchAgainst(t,o){let n=0;for(let r in t){let s=t[r],a=this.curls[r];if(typeof a=="undefined"){n+=this.weightsRelative[r];continue}for(let[i,l]of a)if(s===i){n+=l*this.weightsRelative[r];break}}for(let r in o){let s=o[r],a=this.directions[r];if(typeof a=="undefined"){n+=this.weightsRelative[r];continue}for(let[i,l]of a)if(s===i){n+=l*this.weightsRelative[r];break}}return n/10}};var{thumb:E0,index:V0,middle:D0,ring:fe,pinky:me}=d0,{none:z0,half:zr,full:S0}=te,{verticalUp:ze,verticalDown:q7,horizontalLeft:Z5,horizontalRight:Sr,diagonalUpRight:Cr,diagonalUpLeft:Se,diagonalDownRight:U7,diagonalDownLeft:Y7}=K,oe=new ee("thumbs 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palm");Ce.curl(E0,z0,.75);Ce.curl(V0,z0,.75);Ce.curl(D0,z0,.75);Ce.curl(fe,z0,.75);Ce.curl(me,z0,.75);var b3=[oe,e0,ne,re,Ce];var jr=.7,pe={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function g3(e,t,o,n){let r=(t-n)/(e-o),s=Math.atan(r)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function P3(e,t){if(!e||!t)return[0,0];let o=g3(e[0],e[1],t[0],t[1]);if(e.length===2)return o;let n=g3(e[1],e[2],t[1],t[2]);return[o,n]}function M3(e,t=1){let o=0,n=0,r=0;return e>=75&&e<=105?o=1*t:e>=25&&e<=155?n=1*t:r=1*t,[o,n,r]}function Ir(e,t,o){let n=e[0]-t[0],r=e[0]-o[0],s=t[0]-o[0],a=e[1]-t[1],i=e[1]-o[1],l=t[1]-o[1],c=e[2]-t[2],d=e[2]-o[2],y=t[2]-o[2],x=Math.sqrt(n*n+a*a+c*c),m=Math.sqrt(r*r+i*i+d*d),h=Math.sqrt(s*s+l*l+y*y),p=(h*h+x*x-m*m)/(2*h*x);p>1?p=1:p<-1&&(p=-1);let R=Math.acos(p);R=57.2958*R%180;let M;return R>pe.NO_CURL_START_LIMIT?M=te.none:R>pe.HALF_CURL_START_LIMIT?M=te.half:M=te.full,M}function R3(e,t,o,n){let r;return n===Math.abs(e)?e>0?r=K.horizontalLeft:r=K.horizontalRight:n===Math.abs(t)?t>0?r=K.horizontalLeft:r=K.horizontalRight:o>0?r=K.horizontalLeft:r=K.horizontalRight,r}function v3(e,t,o,n){let r;return n===Math.abs(e)?e<0?r=K.verticalDown:r=K.verticalUp:n===Math.abs(t)?t<0?r=K.verticalDown:r=K.verticalUp:o<0?r=K.verticalDown:r=K.verticalUp,r}function Nr(e,t,o,n,r,s,a,i){let l,c=v3(e,t,o,n),d=R3(r,s,a,i);return c===K.verticalUp?d===K.horizontalLeft?l=K.diagonalUpLeft:l=K.diagonalUpRight:d===K.horizontalLeft?l=K.diagonalDownLeft:l=K.diagonalDownRight,l}function Or(e,t,o,n){let r=e[0]-t[0],s=e[0]-o[0],a=t[0]-o[0],i=e[1]-t[1],l=e[1]-o[1],c=t[1]-o[1],d=Math.max(Math.abs(r),Math.abs(s),Math.abs(a)),y=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),x=0,m=0,h=0,p=y/(d+1e-5);p>1.5?x+=pe.DISTANCE_VOTE_POWER:p>.66?m+=pe.DISTANCE_VOTE_POWER:h+=pe.DISTANCE_VOTE_POWER;let R=Math.sqrt(r*r+i*i),M=Math.sqrt(s*s+l*l),g=Math.sqrt(a*a+c*c),f=Math.max(R,M,g),P=e[0],C=e[1],k=o[0],I=o[1];f===R?(k=o[0],I=o[1]):f===g&&(P=t[0],C=t[1]);let F=P3([P,C],[k,I]),V=M3(F,pe.TOTAL_ANGLE_VOTE_POWER);x+=V[0],m+=V[1],h+=V[2];for(let t0 of n){let z=M3(t0,pe.SINGLE_ANGLE_VOTE_POWER);x+=z[0],m+=z[1],h+=z[2]}let Z;return x===Math.max(x,m,h)?Z=v3(l,i,c,y):h===Math.max(m,h)?Z=R3(s,r,a,d):Z=Nr(l,i,c,y,s,r,a,d),Z}function T3(e){let t=[],o=[],n=[],r=[];if(!e)return{curls:n,directions:r};for(let s of d0.all){let a=d0.getPoints(s),i=[],l=[];for(let c of a){let d=e[c[0]],y=e[c[1]],x=P3(d,y),m=x[0],h=x[1];i.push(m),l.push(h)}t.push(i),o.push(l)}for(let s of d0.all){let a=s===d0.thumb?1:0,i=d0.getPoints(s),l=e[i[a][0]],c=e[i[a+1][1]],d=e[i[3][1]],y=Ir(l,c,d),x=Or(l,c,d,t[s].slice(a));n[s]=y,r[s]=x}return{curls:n,directions:r}}function I2(e){if(!e||e.length===0)return null;let t=T3(e),o={};for(let n of d0.all)o[d0.getName(n)]={curl:te.getName(t.curls[n]),direction:K.getName(t.directions[n])};return o}function w3(e){let t=[];if(!e||e.length===0)return t;let o=T3(e);for(let n of b3){let r=n.matchAgainst(o.curls,o.directions);r>=jr&&t.push({name:n.name,confidence:r})}return t}var k3={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},je,Ie,E3;async function q5(e,t){let o=await E3.estimateHands(e,t);if(!o)return[];let n=[];for(let r=0;r<o.length;r++){let s={};if(o[r].landmarks)for(let d of Object.keys(k3))s[d]=k3[d].map(y=>o[r].landmarks[y]);let a=o[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(a&&a.length>0){for(let d of a)d[0]<i[0]&&(i[0]=d[0]),d[1]<i[1]&&(i[1]=d[1]),d[0]>i[2]&&(i[2]=d[0]),d[1]>i[3]&&(i[3]=d[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],l=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let c=I2(a);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:a,annotations:s,landmarks:c})}return n}async function U5(e){var o,n;T.initial&&(je=null,Ie=null),!je||!Ie?[je,Ie]=await Promise.all([e.hand.enabled?L((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?L((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&u("cached model:",je.modelUrl),e.debug&&u("cached model:",Ie.modelUrl));let t=new C2(je);return E3=new j2(t,Ie),[je,Ie]}var n0=[null,null],Lr=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ae=[[0,0],[0,0]],Wr=["hand","fist","pinch","point","face","tip","pinchtip"],S3=4,C3=1.6,Fr=512,Gr=1.4,N2=Number.MAX_SAFE_INTEGER,Y5=0,Z0=[0,0],Q={boxes:[],hands:[]},j3={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function I3(e){var t;if(T.initial&&(n0[0]=null),n0[0])e.debug&&u("cached model:",n0[0].modelUrl);else{O2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),n0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(n0[0].modelSignature.inputs);Ae[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ae[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return n0[0]}async function N3(e){var t;if(T.initial&&(n0[1]=null),n0[1])e.debug&&u("cached model:",n0[1].modelUrl);else{n0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(n0[1].modelSignature.inputs);Ae[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ae[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return n0[1]}async function Br(e,t){let o=[];if(!e||!n0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,Fr),a=Math.round(s*r/8)*8;n.resize=A.image.resizeBilinear(e,[s,a]),n.cast=A.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await n0[0].executeAsync(n.cast,Lr),n.boxes=A.squeeze(n.rawBoxes,[0,2]),n.scores=A.squeeze(n.rawScores,[0]);let i=A.unstack(n.scores,1);A.dispose(i[S3]),i.splice(S3,1),n.filtered=A.stack(i,1),A.dispose(i),n.max=A.max(n.filtered,1),n.argmax=A.argMax(n.filtered,1);let l=0;n.nms=await A.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await n.nms.data(),d=await n.max.data(),y=await n.argmax.data();for(let x of Array.from(c)){let m=A.slice(n.boxes,x,1),h=await m.data();A.dispose(m);let p=[h[1],h[0],h[3]-h[1],h[2]-h[0]],R=M2(p,Gr),M=[Math.trunc(p[0]*Z0[0]),Math.trunc(p[1]*Z0[1]),Math.trunc(p[2]*Z0[0]),Math.trunc(p[3]*Z0[1])],g=d[x],f=Wr[y[x]],P={id:l++,score:g,box:M,boxRaw:R,label:f};o.push(P)}return Object.keys(n).forEach(x=>A.dispose(n[x])),o.sort((x,m)=>m.score-x.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function K5(e,t,o){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&n0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=A.image.cropAndResize(e,[s],[0],[Ae[1][0],Ae[1][1]],"bilinear"),r.div=A.div(r.crop,G.tf255),[r.score,r.keypoints]=n0[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){n.fingerScore=i,r.reshaped=A.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(y=>[y[0]/Ae[1][1],y[1]/Ae[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);n.keypoints=d.map(y=>[Z0[0]*(y[0]+t.boxRaw[0]),Z0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),n.landmarks=I2(n.keypoints);for(let y of Object.keys(j3))n.annotations[y]=j3[y].map(x=>n.landmarks&&n.keypoints[x]?n.keypoints[x]:null)}Object.keys(r).forEach(l=>A.dispose(r[l]))}return n}async function J5(e,t){var r,s;if(!n0[0]||!n0[1]||!((r=n0[0])!=null&&r.inputs[0].shape)||!((s=n0[1])!=null&&s.inputs[0].shape))return[];Z0=[e.shape[2]||0,e.shape[1]||0],N2++;let o=(t.hand.skipTime||0)>b()-Y5,n=N2<(t.hand.skipFrames||0);return t.skipAllowed&&o&&n?Q.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>b()-Y5,l=N2<3*(t.hand.skipFrames||0);t.skipAllowed&&Q.hands.length===t.hand.maxDetected?Q.hands=await Promise.all(Q.boxes.map(d=>K5(e,d,t))):t.skipAllowed&&i&&l&&Q.hands.length>0?Q.hands=await Promise.all(Q.boxes.map(d=>K5(e,d,t))):(Q.boxes=await Br(e,t),Y5=b(),Q.hands=await Promise.all(Q.boxes.map(d=>K5(e,d,t))),N2=0);let c=[...Q.boxes];if(Q.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Q.hands.length;d++){let y=Tt(Q.hands[d].keypoints,Z0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&Q.hands[d].fingerScore&&Q.hands[d].fingerScore>(t.hand.minConfidence||0)){let x=M2(y.box,C3),m=M2(y.boxRaw,C3);Q.boxes.push({...c[d],box:x,boxRaw:m})}}for(let d=0;d<Q.hands.length;d++){let y=G0(Q.hands[d].keypoints,Z0);Q.hands[d].box=y.box,Q.hands[d].boxRaw=y.boxRaw}a(Q.hands)})}var i0,L2=[],Q5=Number.MAX_SAFE_INTEGER,L3=0,W3=0;async function F3(e){var t;return T.initial&&(i0=null),i0?e.debug&&u("cached model:",i0.modelUrl):i0=await L((t=e.face.liveness)==null?void 0:t.modelPath),i0}async function _5(e,t,o,n){var a,i;if(!i0)return 0;let r=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>b()-W3,s=Q5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&s&&L3===n&&L2[o]?(Q5++,L2[o]):(Q5=0,new Promise(async l=>{let c=A.image.resizeBilinear(e,[i0!=null&&i0.inputs[0].shape?i0.inputs[0].shape[2]:0,i0!=null&&i0.inputs[0].shape?i0.inputs[0].shape[1]:0],!1),d=i0==null?void 0:i0.execute(c),y=(await d.data())[0];L2[o]=Math.round(100*y)/100,L3=n,W3=b(),A.dispose([c,d]),l(L2[o])}))}var t2={};U0(t2,{connected:()=>F2,horizontal:()=>$5,kpt:()=>W2,relative:()=>t1,vertical:()=>e1});var W2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],$5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],e1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],t1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],F2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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i=[];for(let y=0;y<17;y++){let x=s[3*y+2];if(x>t.body.minConfidence){let m=[s[3*y+1],s[3*y+0]];i.push({part:W2[y],score:Math.round(100*x)/100,positionRaw:m,position:[Math.round((o.shape[2]||0)*m[0]),Math.round((o.shape[1]||0)*m[1])]})}}let l=G0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),c={};for(let[y,x]of Object.entries(F2)){let m=[];for(let h=0;h<x.length-1;h++){let p=i.find(M=>M.part===x[h]),R=i.find(M=>M.part===x[h+1]);p&&R&&p.score>(t.body.minConfidence||0)&&R.score>(t.body.minConfidence||0)&&m.push([p.position,R.position])}c[y]=m}let d={id:r,score:a,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:c};o1(d),n.push(d)}}return n.sort((r,s)=>s.score-r.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function r1(e,t){if(!b0||!(b0!=null&&b0.inputs[0].shape))return[];t.skipAllowed||(ue.boxes.length=0),n1++;let o=(t.body.skipTime||0)>b()-ue.last,n=n1<(t.body.skipFrames||0);return t.skipAllowed&&o&&n?ue.bodies:new Promise(async r=>{let s={};n1=0,s.input=V3(e,G2),s.res=b0==null?void 0:b0.execute(s.input),ue.last=b();let a=await s.res.array();ue.bodies=s.res.shape[2]===17?await Vr(a,t,e):await Dr(a,t,e);for(let i of ue.bodies)D3(i,[e.shape[2]||1,e.shape[1]||1]),H3(i.keypoints);Object.keys(s).forEach(i=>A.dispose(s[i])),r(ue.bodies)})}var Ne,B2=[],q3=0,A1=Number.MAX_SAFE_INTEGER,V2=0,H2=2.5;async function U3(e){if(!Ne||T.initial){Ne=await L(e.object.modelPath);let t=Object.values(Ne.modelSignature.inputs);V2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&u("cached model:",Ne.modelUrl);return Ne}async function Zr(e,t,o){let n=0,r=[];for(let l of[1,2,4])A.tidy(async()=>{let c=l*13,d=A.squeeze(e.find(p=>p.shape[1]===c**2&&(p.shape[2]||0)===we.length)),y=A.squeeze(e.find(p=>p.shape[1]===c**2&&(p.shape[2]||0)<we.length)),m=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await d.array();for(let p=0;p<d.shape[0];p++)for(let R=0;R<d.shape[1];R++){let M=h[p][R];if(M>(o.object.minConfidence||0)&&R!==61){let g=(.5+Math.trunc(p%c))/c,f=(.5+Math.trunc(p/c))/c,P=m[p].map(Z=>Z*(c/l/V2)),[C,k]=[g-H2/l*P[0],f-H2/l*P[1]],[I,j]=[g+H2/l*P[2]-C,f+H2/l*P[3]-k],O=[C,k,I,j];O=O.map(Z=>Math.max(0,Math.min(Z,1)));let F=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],V={id:n++,score:Math.round(100*M)/100,class:R+1,label:we[R].label,box:F.map(Z=>Math.trunc(Z)),boxRaw:O};r.push(V)}}});e.forEach(l=>A.dispose(l));let s=r.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),a=r.map(l=>l.score),i=[];if(s&&s.length>0){let l=await A.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await l.data(),A.dispose(l)}return r=r.filter((l,c)=>i.includes(c)).sort((l,c)=>c.score-l.score),r}async function s1(e,t){let o=(t.object.skipTime||0)>b()-q3,n=A1<(t.object.skipFrames||0);return t.skipAllowed&&o&&n&&B2.length>0?(A1++,B2):(A1=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?B2:new Promise(async 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n2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Xr=n2.length,o2=n2.reduce((e,t,o)=>(e[t]=o,e),{}),qr=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],h4=qr.map(([e,t])=>[o2[e],o2[t]]),K3=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function J3(e){let t=e.reduce(({maxX:o,maxY:n,minX:r,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(n,i),minX:Math.min(r,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Q3(e,[t,o],[n,r]){let s=t/n,a=o/r,i=(c,d)=>({id:d,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/n,c.box[2]/r,c.box[3]/n],box:[Math.trunc(c.box[0]*a),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*a),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:y,part:x,position:m})=>({score:y,part:x,position:[Math.trunc(m.x*a),Math.trunc(m.y*s)],positionRaw:[m.x/n,m.y/n]})),annotations:{}});return e.map((c,d)=>i(c,d))}var D2=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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ao(e,t,o){if(!(t.length<2)){if(e.lineWidth=o.lineWidth,!o.useCurves||t.length<=2){h1(e,t,o);return}e.moveTo(t[0][0],t[0][1]);for(let n=0;n<t.length-2;n++){let r=(t[n][0]+t[n+1][0])/2,s=(t[n][1]+t[n+1][1])/2;e.quadraticCurveTo(t[n][0],t[n][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),o.fillPolygons&&(e.closePath(),e.fill())}}function b1(e,t,o,n=5){let r,s,a;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(o[0],o[1]),r=Math.atan2(o[1]-t[1],o[0]-t[0]),s=n*Math.cos(r)+o[0],a=n*Math.sin(r)+o[1],e.moveTo(s,a),r+=1/3*(2*Math.PI),s=n*Math.cos(r)+o[0],a=n*Math.sin(r)+o[1],e.lineTo(s,a),r+=1/3*(2*Math.PI),s=n*Math.cos(r)+o[0],a=n*Math.sin(r)+o[1],e.lineTo(s,a),e.closePath(),e.stroke(),e.fill()}var l0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe 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${he(e.rotation.gaze.bearing)}\xB0`)),o.length===0&&o.push("face"),t.fillStyle=H.color;for(let n=o.length-1;n>=0;n--){let r=Math.max(e.box[0],0),s=n*H.lineHeight+e.box[1];H.shadowColor&&H.shadowColor!==""&&(t.fillStyle=H.shadowColor,t.fillText(o[n],r+5,s+16)),t.fillStyle=H.labelColor,t.fillText(o[n],r+4,s+15)}}}function AA(e,t){if(e.annotations&&e.annotations.leftEyeIris&&e.annotations.leftEyeIris[0]){t.strokeStyle=H.useDepth?"rgba(255, 200, 255, 0.3)":H.color,t.beginPath();let o=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),H.fillPolygons&&(t.fillStyle=H.useDepth?"rgba(255, 255, 200, 0.3)":H.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=H.useDepth?"rgba(255, 200, 255, 0.3)":H.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),H.fillPolygons&&(t.fillStyle=H.useDepth?"rgba(255, 255, 200, 0.3)":H.color,t.fill())}}function sA(e,t){var o;if(H.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*he(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*he(e.rotation.angle.pitch)/90,s=new Path2D(`
+      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+      C
+        ${n} ${e.box[1]},
+        ${n} ${e.box[1]+e.box[3]},
+        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
+    `),a=new Path2D(`
+      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+      C 
+        ${e.box[0]} ${r},
+        ${e.box[0]+e.box[2]} ${r},
+        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
+    `);t.stroke(a),t.stroke(s)}}function aA(e,t){var o,n,r,s;if(H.drawGaze&&((n=(o=e.rotation)==null?void 0:o.gaze)==null?void 0:n.strength)&&((s=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:s.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let a=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];b1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[a[0],a[1]],4);let i=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];b1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[i[0],i[1]],4)}}function iA(e,t){if(H.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let o=0;o<ie.length/3;o++){let n=[ie[o*3+0],ie[o*3+1],ie[o*3+2]].map(r=>e.mesh[r]);h1(t,n,H)}AA(e,t)}}function lA(e,t){if(H.drawPoints&&e.mesh.length>=468)for(let o=0;o<e.mesh.length;o++)q0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2],H),H.drawAttention&&($e.includes(o)&&q0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]+127,H),xe.includes(o)&&q0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,H),ce.includes(o)&&q0(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,H))}function yA(e,t){H.drawBoxes&&O0(t,e.box[0],e.box[1],e.box[2],e.box[3],H)}async function Le(e,t,o){if(H=J(l0,o),!t||!e)return;let n=R0(e);if(!!n){n.font=H.font,n.strokeStyle=H.color,n.fillStyle=H.color;for(let r of t)yA(r,n),rA(r,n),r.mesh&&r.mesh.length>0&&(lA(r,n),iA(r,n),sA(r,n),aA(r,n))}}async function We(e,t,o){var s;let n=J(l0,o);if(!t||!e)return;let r=R0(e);if(!!r){r.lineJoin="round";for(let a=0;a<t.length;a++){if(r.strokeStyle=n.color,r.fillStyle=n.color,r.lineWidth=n.lineWidth,r.font=n.font,n.drawBoxes&&t[a].box&&((s=t[a].box)==null?void 0:s.length)===4&&(O0(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(r.fillStyle=n.shadowColor,r.fillText(`body ${100*t[a].score}%`,t[a].box[0]+3,1+t[a].box[1]+n.lineHeight,t[a].box[2])),r.fillStyle=n.labelColor,r.fillText(`body ${100*t[a].score}%`,t[a].box[0]+2,0+t[a].box[1]+n.lineHeight,t[a].box[2]))),n.drawPoints&&t[a].keypoints)for(let i=0;i<t[a].keypoints.length;i++)!t[a].keypoints[i].score||t[a].keypoints[i].score===0||(r.fillStyle=X0(t[a].keypoints[i].position[2],n),q0(r,t[a].keypoints[i].position[0],t[a].keypoints[i].position[1],0,n));if(n.drawLabels&&t[a].keypoints){r.font=n.font;for(let i of t[a].keypoints)!i.score||i.score===0||(r.fillStyle=X0(i.position[2],n),r.fillText(`${i.part} 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w0.silhouette)r.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});He&&He>0&&(r=r.map(a=>({x:a.x>.5?a.x+He:a.x-He,y:a.y>.5?a.y+He:a.y-He})));for(let a=0;a<t;a++)for(let i=0;i<o;i++)xA(a/t,i/t,r)||(n.set(v1*n.get(0,i,a,0),0,i,a,0),n.set(v1*n.get(0,i,a,1),0,i,a,1),n.set(v1*n.get(0,i,a,2),0,i,a,2));let s=n.toTensor();return A.dispose(n),s}var dA=e=>{let t=(y,x)=>Math.atan2(y[1]-x[1],y[0]-x[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let o=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=r?e.mesh[473]:e.mesh[468],a=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(a[0]-s[0])/i[0]-o[0],n*(s[1]-a[1])/i[1]-o[1]],c=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},yo=(e,t)=>{let o=p=>{let R=Math.sqrt(p[0]*p[0]+p[1]*p[1]+p[2]*p[2]);return p[0]/=R,p[1]/=R,p[2]/=R,p},n=(p,R)=>{let M=p[0]-R[0],g=p[1]-R[1],f=p[2]-R[2];return[M,g,f]},r=(p,R)=>{let M=p[1]*R[2]-p[2]*R[1],g=p[2]*R[0]-p[0]*R[2],f=p[0]*R[1]-p[1]*R[0];return[M,g,f]},s=p=>{let[R,M,g,f,P,C,k,I,j]=p,O,F,V;return f<1?f>-1?(V=Math.asin(f),F=Math.atan2(-k,R),O=Math.atan2(-C,P)):(V=-Math.PI/2,F=-Math.atan2(I,j),O=0):(V=Math.PI/2,F=Math.atan2(I,j),O=0),isNaN(O)&&(O=0),isNaN(F)&&(F=0),isNaN(V)&&(V=0),{pitch:2*-O,yaw:2*-F,roll:2*-V}},a=e.meshRaw;if(!a||a.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[a[10],a[152],a[234],a[454]].map(p=>[p[0]*t[0]/i,p[1]*t[1]/i,p[2]]),c=o(n(l[1],l[0])),d=o(n(l[3],l[2])),y=o(r(d,c));d=r(c,y);let x=[d[0],d[1],d[2],c[0],c[1],c[2],y[0],y[1],y[2]],m=s(x),h=a.length===478?dA(e):{bearing:0,strength:0};return{angle:m,matrix:x,gaze:h}};var T1=async(e,t)=>{var m,h,p,R,M,g,f,P,C,k,I,j,O,F,V,Z,t0,z,g0,M0,v,o0;let o=b(),n,r,s,a,i,l,c,d,y=[];e.state="run:face";let x=await t3(t,e.config);if(e.performance.face=T.perfadd?(e.performance.face||0)+Math.trunc(b()-o):Math.trunc(b()-o),!t.shape||t.shape.length!==4)return[];if(!x)return[];for(let E=0;E<x.length;E++){if(e.analyze("Get Face"),!x[E].tensor||x[E].tensor.isDisposedInternal){u("Face object is disposed:",x[E].tensor);continue}if((m=e.config.face.detector)!=null&&m.mask){let D=await lo(x[E]);A.dispose(x[E].tensor),x[E].tensor=D}let X=x[E].mesh&&x[E].mesh.length>200?yo(x[E],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?a=(h=e.config.face.emotion)!=null&&h.enabled?j5(x[E].tensor||A.tensor([]),e.config,E,x.length):[]:(e.state="run:emotion",o=b(),a=(p=e.config.face.emotion)!=null&&p.enabled?await j5(x[E].tensor||A.tensor([]),e.config,E,x.length):[],e.performance.emotion=T.perfadd?(e.performance.emotion||0)+Math.trunc(b()-o):Math.trunc(b()-o)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(R=e.config.face.antispoof)!=null&&R.enabled?d5(x[E].tensor||A.tensor([]),e.config,E,x.length):0:(e.state="run:antispoof",o=b(),l=(M=e.config.face.antispoof)!=null&&M.enabled?await d5(x[E].tensor||A.tensor([]),e.config,E,x.length):0,e.performance.antispoof=T.perfadd?(e.performance.antispoof||0)+Math.trunc(b()-o):Math.trunc(b()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=(g=e.config.face.liveness)!=null&&g.enabled?_5(x[E].tensor||A.tensor([]),e.config,E,x.length):0:(e.state="run:liveness",o=b(),c=(f=e.config.face.liveness)!=null&&f.enabled?await _5(x[E].tensor||A.tensor([]),e.config,E,x.length):0,e.performance.liveness=T.perfadd?(e.performance.antispoof||0)+Math.trunc(b()-o):Math.trunc(b()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(P=e.config.face.gear)!=null&&P.enabled?s5(x[E].tensor||A.tensor([]),e.config,E,x.length):null:(e.state="run:gear",o=b(),r=(C=e.config.face.gear)!=null&&C.enabled?await s5(x[E].tensor||A.tensor([]),e.config,E,x.length):null,e.performance.gear=Math.trunc(b()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(k=e.config.face.ssrnet)!=null&&k.enabled?i5(x[E].tensor||A.tensor([]),e.config,E,x.length):null,s=(I=e.config.face.ssrnet)!=null&&I.enabled?x5(x[E].tensor||A.tensor([]),e.config,E,x.length):null):(e.state="run:ssrnet",o=b(),n=(j=e.config.face.ssrnet)!=null&&j.enabled?await i5(x[E].tensor||A.tensor([]),e.config,E,x.length):null,s=(O=e.config.face.ssrnet)!=null&&O.enabled?await x5(x[E].tensor||A.tensor([]),e.config,E,x.length):null,e.performance.ssrnet=Math.trunc(b()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(F=e.config.face.mobilefacenet)!=null&&F.enabled?N5(x[E].tensor||A.tensor([]),e.config,E,x.length):null:(e.state="run:mobilefacenet",o=b(),i=(V=e.config.face.mobilefacenet)!=null&&V.enabled?await N5(x[E].tensor||A.tensor([]),e.config,E,x.length):null,e.performance.mobilefacenet=Math.trunc(b()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=(Z=e.config.face.description)!=null&&Z.enabled?B5(x[E].tensor||A.tensor([]),e.config,E,x.length):null:(e.state="run:description",o=b(),d=(t0=e.config.face.description)!=null&&t0.enabled?await B5(x[E].tensor||A.tensor([]),e.config,E,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(b()-o):Math.trunc(b()-o)),e.analyze("End Description:"),e.config.async&&([n,s,a,i,d,r,l,c]=await Promise.all([n,s,a,i,d,r,l,c])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((g0=e.config.face.gear)==null?void 0:g0.enabled)&&r&&(d={...d,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((M0=e.config.face.mobilefacenet)==null?void 0:M0.enabled)&&i&&(d.descriptor=i),(v=e.config.face.iris)!=null&&v.enabled;let _=x[E].annotations&&x[E].annotations.leftEyeIris&&x[E].annotations.leftEyeIris[0]&&x[E].annotations.rightEyeIris&&x[E].annotations.rightEyeIris[0]&&x[E].annotations.leftEyeIris.length>0&&x[E].annotations.rightEyeIris.length>0&&x[E].annotations.leftEyeIris[0]!==null&&x[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[E].annotations.leftEyeIris[3][0]-x[E].annotations.leftEyeIris[1][0]),Math.abs(x[E].annotations.rightEyeIris[4][1]-x[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,r0=(o0=e.config.face.detector)!=null&&o0.return?A.squeeze(x[E].tensor):null;A.dispose(x[E].tensor),x[E].tensor&&delete x[E].tensor;let N={...x[E],id:E};d!=null&&d.age&&(N.age=d.age),d!=null&&d.gender&&(N.gender=d.gender),d!=null&&d.genderScore&&(N.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(N.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(N.race=d==null?void 0:d.race),a&&(N.emotion=a),l&&(N.real=l),c&&(N.live=c),_&&_!==0&&(N.iris=Math.trunc(500/_/11.7)/100),X&&(N.rotation=X),r0&&(N.tensor=r0),y.push(N),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var xo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(l=>l.part==="leftWrist"),r=e[o].keypoints.find(l=>l.part==="rightWrist"),s=e[o].keypoints.find(l=>l.part==="nose");s&&n&&r&&n.position[1]<s.position[1]&&r.position[1]<s.position[1]?t.push({body:o,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:o,gesture:"raise left hand"}):s&&r&&r.position[1]<s.position[1]&&t.push({body:o,gesture:"raise right hand"});let a=e[o].keypoints.find(l=>l.part==="leftShoulder"),i=e[o].keypoints.find(l=>l.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},co=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[o].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:o,gesture:`head ${l<0?"up":"down"}`})}return t},fo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],s=Math.abs(n*r),a=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],i=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],l=Math.abs(a*i),c=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let y=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(y>.06||x>.06)&&(c=!1),y>x?y>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let m=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],h=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(h<.01||m<.01||h>.022||m>.022)&&(c=!1),(h<.01||m<.01)&&t.push({iris:o,gesture:"looking down"}),(h>.022||m>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},mo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,s]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:r.toLowerCase(),position:s[0]});if(n&&n.length>0){let r=n.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:o,gesture:`${r.name} forward`});let s=n.reduce((a,i)=>a.position[1]<i.position[1]?a:i);t.push({hand:o,gesture:`${s.name} up`})}if(e[o].keypoints){let r=w3(e[o].keypoints);for(let s of r)t.push({hand:o,gesture:s.name})}}return t};var S={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},w1=0;function po(e,t){var a,i,l,c,d,y,x,m,h,p,R,M,g,f,P,C,k,I,j,O,F,V,Z,t0,z,g0,M0;let o=b();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(S.canvas=e.canvas),e.error&&(S.error=e.error),!S.body||e.body.length!==S.body.length)S.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let o0=e.body[v].box.map((N,D)=>((r-1)*S.body[v].box[D]+N)/r),E=e.body[v].boxRaw.map((N,D)=>((r-1)*S.body[v].boxRaw[D]+N)/r),X=e.body[v].keypoints.map((N,D)=>{var L0,W0,De,Ze,be,z1,S1,C1,j1;return{score:N.score,part:N.part,position:[S.body[v].keypoints[D]?((r-1)*(S.body[v].keypoints[D].position[0]||0)+(N.position[0]||0))/r:N.position[0],S.body[v].keypoints[D]?((r-1)*(S.body[v].keypoints[D].position[1]||0)+(N.position[1]||0))/r:N.position[1],S.body[v].keypoints[D]?((r-1)*(S.body[v].keypoints[D].position[2]||0)+(N.position[2]||0))/r:N.position[2]],positionRaw:[S.body[v].keypoints[D]?((r-1)*(S.body[v].keypoints[D].positionRaw[0]||0)+(N.positionRaw[0]||0))/r:N.positionRaw[0],S.body[v].keypoints[D]?((r-1)*(S.body[v].keypoints[D].positionRaw[1]||0)+(N.positionRaw[1]||0))/r:N.positionRaw[1],S.body[v].keypoints[D]?((r-1)*(S.body[v].keypoints[D].positionRaw[2]||0)+(N.positionRaw[2]||0))/r:N.positionRaw[2]],distance:[S.body[v].keypoints[D]?((r-1)*(((L0=S.body[v].keypoints[D].distance)==null?void 0:L0[0])||0)+(((W0=N.distance)==null?void 0:W0[0])||0))/r:(De=N.distance)==null?void 0:De[0],S.body[v].keypoints[D]?((r-1)*(((Ze=S.body[v].keypoints[D].distance)==null?void 0:Ze[1])||0)+(((be=N.distance)==null?void 0:be[1])||0))/r:(z1=N.distance)==null?void 0:z1[1],S.body[v].keypoints[D]?((r-1)*(((S1=S.body[v].keypoints[D].distance)==null?void 0:S1[2])||0)+(((C1=N.distance)==null?void 0:C1[2])||0))/r:(j1=N.distance)==null?void 0:j1[2]]}}),_={},r0={connected:{}};(i=(a=t.body)==null?void 0:a.modelPath)!=null&&i.includes("efficientpose")?r0=v2:(c=(l=t.body)==null?void 0:l.modelPath)!=null&&c.includes("blazepose")?r0=b2:(y=(d=t.body)==null?void 0:d.modelPath)!=null&&y.includes("movenet")&&(r0=t2);for(let[N,D]of Object.entries(r0.connected)){let L0=[];for(let W0=0;W0<D.length-1;W0++){let De=X.find(be=>be.part===D[W0]),Ze=X.find(be=>be.part===D[W0+1]);De&&Ze&&L0.push([De.position,Ze.position])}_[N]=L0}S.body[v]={...e.body[v],box:o0,boxRaw:E,keypoints:X,annotations:_}}if(!S.hand||e.hand.length!==S.hand.length)S.hand=JSON.parse(JSON.stringify(e.hand));else for(let v=0;v<e.hand.length;v++){let o0=e.hand[v].box.map((r0,N)=>((r-1)*S.hand[v].box[N]+r0)/r),E=e.hand[v].boxRaw.map((r0,N)=>((r-1)*S.hand[v].boxRaw[N]+r0)/r);S.hand[v].keypoints.length!==e.hand[v].keypoints.length&&(S.hand[v].keypoints=e.hand[v].keypoints);let X=e.hand[v].keypoints&&e.hand[v].keypoints.length>0?e.hand[v].keypoints.map((r0,N)=>r0.map((D,L0)=>((r-1)*(S.hand[v].keypoints[N][L0]||1)+(D||0))/r)):[],_={};if(Object.keys(S.hand[v].annotations).length!==Object.keys(e.hand[v].annotations).length)S.hand[v].annotations=e.hand[v].annotations,_=S.hand[v].annotations;else if(e.hand[v].annotations)for(let r0 of Object.keys(e.hand[v].annotations))_[r0]=e.hand[v].annotations[r0]&&e.hand[v].annotations[r0][0]?e.hand[v].annotations[r0].map((N,D)=>N.map((L0,W0)=>((r-1)*S.hand[v].annotations[r0][D][W0]+L0)/r)):null;S.hand[v]={...e.hand[v],box:o0,boxRaw:E,keypoints:X,annotations:_}}if(!S.face||e.face.length!==S.face.length)S.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let o0=e.face[v].box.map((X,_)=>((r-1)*S.face[v].box[_]+X)/r),E=e.face[v].boxRaw.map((X,_)=>((r-1)*S.face[v].boxRaw[_]+X)/r);if(e.face[v].rotation){let X={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};X.matrix=(x=e.face[v].rotation)==null?void 0:x.matrix,X.angle={roll:((r-1)*(((h=(m=S.face[v].rotation)==null?void 0:m.angle)==null?void 0:h.roll)||0)+(((R=(p=e.face[v].rotation)==null?void 0:p.angle)==null?void 0:R.roll)||0))/r,yaw:((r-1)*(((g=(M=S.face[v].rotation)==null?void 0:M.angle)==null?void 0:g.yaw)||0)+(((P=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:P.yaw)||0))/r,pitch:((r-1)*(((k=(C=S.face[v].rotation)==null?void 0:C.angle)==null?void 0:k.pitch)||0)+(((j=(I=e.face[v].rotation)==null?void 0:I.angle)==null?void 0:j.pitch)||0))/r},X.gaze={bearing:((r-1)*(((F=(O=S.face[v].rotation)==null?void 0:O.gaze)==null?void 0:F.bearing)||0)+(((Z=(V=e.face[v].rotation)==null?void 0:V.gaze)==null?void 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r=A.node.decodeJpeg(o),s=r.expandDims(0);e.tf.dispose(r),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&u("Warmup tfjs-node not loaded");return n}async function PA(e){let t;return typeof createImageBitmap=="function"?t=await bA(e):typeof Image!="undefined"||T.Canvas!==void 0?t=await gA(e):t=await MA(e),t}async function RA(e){let t=A.getBackend(),o=A.backend();if(t!=="webgl"&&t!=="humangl"||!o||!o.checkCompileCompletion)return;A.env().set("ENGINE_COMPILE_ONLY",!0);let n=A.engine().state.numTensors,r=[];for(let[i,l]of Object.entries(e).filter(([c,d])=>c!==null&&d!==null)){let c=l.inputs&&l.inputs[0]&&l.inputs[0].shape?[...l.inputs[0].shape]:[1,64,64,3],d=l.inputs&&l.inputs[0]&&l.inputs[0].dtype?l.inputs[0].dtype:"float32";for(let x=0;x<c.length;x++)c[x]===-1&&(c[x]=x===0?1:64);let y=A.zeros(c,d);try{let x=l.execute(y);r.push(i),Array.isArray(x)?x.forEach(m=>A.dispose(m)):A.dispose(x)}catch(x){u("compile fail model:",i)}A.dispose(y)}let s=await o.checkCompileCompletionAsync();o.getUniformLocations(),u("compile pass models:",r),u("compile pass kernels:",s.length),A.env().set("ENGINE_COMPILE_ONLY",!1);let a=A.engine().state.numTensors;a-n>0&&u("tensor leak:",a-n)}async function go(e,t){let o=b();return e.state="warmup",t&&(e.config=J(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:b(),persons:[],error:null}:new Promise(async n=>{await RA(e.models);let r=await PA(e),s=b();e.config.debug&&u("warmup",e.config.warmup,Math.round(s-o),"ms"),e.emit("warmup"),n(r)})}var Ve,s2,a2,K2,Mo=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env");w(this,"draw");w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");qe(this,Ve,void 0);qe(this,s2,void 0);qe(this,a2,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!Xe(this,s2))return;let o=this.tf.engine().state.numTensors,n=Xe(this,Ve);Ue(this,Ve,o);let r=o-n;r!==0&&u(...t,r)});qe(this,K2,t=>{if(!Xe(this,a2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ge))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",k1);w(this,"distance",A2);w(this,"match",E1);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});var n;this.env=T;let o=(((n=Ye)==null?void 0:n.tfjs)||A.version_core).replace(/-(.*)/,"");se.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${o}/dist/`,se.modelBasePath=T.browser?"../models/":"file://models/",se.backend=T.browser?"humangl":"tensorflow",this.version=n5,Object.defineProperty(this,"version",{value:n5}),this.config=JSON.parse(JSON.stringify(se)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=J(this.config,t)),X1(this.config),this.tf=A,this.state="idle",Ue(this,Ve,0),Ue(this,s2,!1),Ue(this,a2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new r2,this.draw={options:l0,canvas:(r,s)=>P1(r,s),face:(r,s,a)=>Le(r,s,a),body:(r,s,a)=>We(r,s,a),hand:(r,s,a)=>Fe(r,s,a),gesture:(r,s,a)=>Be(r,s,a),object:(r,s,a)=>Ge(r,s,a),person:(r,s,a)=>M1(r,s,a),all:(r,s,a)=>R1(r,s,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=n3,this.faceUVMap=r3,this.gl=Y,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(se)),this.config.backend=t}validate(t){return Q2(se,t||this.config)}now(){return b()}image(t,o=!0){return Pe(t,this.config,o)}async segmentation(t,o){return no(t,o,this.config)}enhance(t){return G5(t)}compare(t,o){return Z1(this.config,t,o)}async init(){await q2(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=b(),n=Object.values(this.models).filter(a=>a).length;t&&(this.config=J(this.config,t)),this.env.initial&&(this.config.debug&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`),await q2(this)||u("error: backend check failed"),await A.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("environment:",this.env),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await m1(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(a=>a).length!==n&&(await p1(this),this.emit("load"));let s=Math.trunc(b()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return po(t,this.config)}getModelStats(){return f1(this)}async warmup(t){let o=b(),n=await go(this,t),r=b();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let i of n.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let s=[];Object.entries(r).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,l)=>l.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async n=>{var R,M,g,f,P,C,k,I,j,O,F,V,Z,t0,z,g0,M0,v,o0,E,X,_;this.state="config";let r;this.config=J(this.config,o),this.state="check";let s=Xe(this,K2).call(this,t);s&&(u(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:b(),persons:[],error:s}));let a=b();await q2(this),await this.load(),r=b(),this.state="image";let i=await Pe(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(b()-r):Math.trunc(b()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:b(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=b(),this.config.skipAllowed=await D1(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(b()-r):Math.trunc(b()-r),this.analyze("Check Changed:");let l=[],c=[],d=[],y=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?T1(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=b(),l=this.config.face.enabled?await T1(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?J(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((R=this.config.body.modelPath)!=null&&R.includes("posenet")?c=this.config.body.enabled?x1(i.tensor,x):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?R5(i.tensor,x):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?S5(i.tensor,x):[]:(f=this.config.body.modelPath)!=null&&f.includes("movenet")&&(c=this.config.body.enabled?r1(i.tensor,x):[]),this.performance.body&&delete this.performance.body):(r=b(),(P=this.config.body.modelPath)!=null&&P.includes("posenet")?c=this.config.body.enabled?await x1(i.tensor,x):[]:(C=this.config.body.modelPath)!=null&&C.includes("blazepose")?c=this.config.body.enabled?await R5(i.tensor,x):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?c=this.config.body.enabled?await S5(i.tensor,x):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await r1(i.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let m=this.config.hand.maxDetected===-1?J(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((O=(j=this.config.hand.detector)==null?void 0:j.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?q5(i.tensor,m):[]:(V=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&V.includes("handtrack")&&(d=this.config.hand.enabled?J5(i.tensor,m):[]),this.performance.hand&&delete this.performance.hand):(r=b(),(t0=(Z=this.config.hand.detector)==null?void 0:Z.modelPath)!=null&&t0.includes("handdetect")?d=this.config.hand.enabled?await q5(i.tensor,m):[]:(g0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&g0.includes("handtrack")&&(d=this.config.hand.enabled?await J5(i.tensor,m):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M0=this.config.object.modelPath)!=null&&M0.includes("nanodet")?y=this.config.object.enabled?s1(i.tensor,this.config):[]:(v=this.config.object.modelPath)!=null&&v.includes("centernet")&&(y=this.config.object.enabled?w5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=b(),(o0=this.config.object.modelPath)!=null&&o0.includes("nanodet")?y=this.config.object.enabled?await s1(i.tensor,this.config):[]:(E=this.config.object.modelPath)!=null&&E.includes("centernet")&&(y=this.config.object.enabled?await w5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,d,y]=await Promise.all([l,c,d,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(r=b(),h=[...co(l),...xo(c),...mo(d),...fo(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(b()-r):Math.trunc(b()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(b()-a):Math.trunc(b()-a);let p=((_=(X=this.process)==null?void 0:X.tensor)==null?void 0:_.shape)||[];this.result={face:l,body:c,hand:d,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return bo(l,c,d,h,p)}},A.dispose(i.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Ve=new WeakMap,s2=new WeakMap,a2=new WeakMap,K2=new WeakMap;export{Mo as Human,Mo as default,se as defaults,io as draw,T as env,ho as match,u1 as models};
+/**
+ * Human main module
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
+//# sourceMappingURL=human.esm-nobundle.js.map
diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map
new file mode 100644
index 00000000..cdd405dd
--- /dev/null
+++ b/dist/human.esm-nobundle.js.map
@@ -0,0 +1,7 @@
+{
+  "version": 3,
+  "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/constants.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** Keep detected faces that cannot be verified using facemesh */\n  keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n  /** minimum confidence for a detected race before results are discarded */\n  minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n  gear: Partial<FaceGearConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: false,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      keepInvalid: false,\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nexport*from\"@tensorflow/tfjs/dist/index.js\";export*from\"@tensorflow/tfjs-backend-webgl/dist/index.js\";var r=\"3.18.0\",e=\"3.18.0\",a=\"3.18.0\",o=\"3.18.0\",t=\"3.18.0\",s=\"3.18.0\",f=\"3.18.0\",v={tfjs:r,\"tfjs-core\":e,\"tfjs-data\":a,\"tfjs-layers\":o,\"tfjs-converter\":t,\"tfjs-backend-webgl\":s,\"tfjs-backend-wasm\":f};import{Tensor as d}from\"@tensorflow/tfjs/dist/index.js\";import{GraphModel as b}from\"@tensorflow/tfjs-converter/dist/index\";export{b as GraphModel,d as Tensor,v as version};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        env.webgl.supported = false;\n        config.filter.enabled = false;\n        copy(inCanvas, outCanvas); // filter failed to initialize\n        // return { tensor: null, canvas: inCanvas };\n      } else {\n        fx.reset();\n        if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n        if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n        if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n        if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n        if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n        if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n        if (config.filter.negative) fx.add('negative');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.vintage) fx.add('brownie');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.kodachrome) fx.add('kodachrome');\n        if (config.filter.technicolor) fx.add('technicolor');\n        if (config.filter.polaroid) fx.add('polaroid');\n        if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n        if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n        else outCanvas = fx.draw(inCanvas);\n      }\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n  cacheModels: true,\n  cacheSupported: true,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nexport type ModelInfo = {\n  name: string,\n  inCache: boolean,\n  sizeDesired: number,\n  sizeFromManifest: number,\n  sizeLoadedWeights: number,\n}\n\nexport const modelStats: Record<string, ModelInfo> = {};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n  const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n  modelStats[shortModelName] = {\n    name: shortModelName,\n    sizeFromManifest: 0,\n    sizeLoadedWeights: 0,\n    sizeDesired: modelsDefs[shortModelName],\n    inCache: false,\n  };\n  options.cacheSupported = (typeof window !== 'undefined') && (typeof window.localStorage !== 'undefined') && (typeof window.indexedDB !== 'undefined'); // check if running in browser and if indexedb is available\n  let cachedModels = {};\n  try {\n    cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n  } catch {\n    options.cacheSupported = false;\n  }\n  modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    if (options.debug) log('model load handler:', model['handler']);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n    model.loadSync(artifacts); // load weights\n    // @ts-ignore private property\n    modelStats[shortModelName].sizeLoadedWeights = model?.artifacts?.weightData?.byteLength || 0;\n    if (options.verbose) log('load model:', model['modelUrl'], { bytes: modelStats[shortModelName].sizeLoadedWeights }, options);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport type ModelStats = {\n  numLoadedModels: number,\n  numEnabledModels: undefined,\n  numDefinedModels: number,\n  percentageLoaded: number,\n  totalSizeFromManifest: number,\n  totalSizeWeights: number,\n  totalSizeLoading: number,\n  totalSizeEnabled: undefined,\n  modelStats: ModelInfo[],\n}\n\nexport const getModelStats = (instance: Human): ModelStats => {\n  let totalSizeFromManifest = 0;\n  let totalSizeWeights = 0;\n  let totalSizeLoading = 0;\n  for (const m of Object.values(modelStats)) {\n    totalSizeFromManifest += m.sizeFromManifest;\n    totalSizeWeights += m.sizeLoadedWeights;\n    totalSizeLoading += m.sizeDesired;\n  }\n  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n  return {\n    numLoadedModels: Object.values(modelStats).length,\n    numEnabledModels: undefined,\n    numDefinedModels: Object.keys(instance.models).length,\n    percentageLoaded,\n    totalSizeFromManifest,\n    totalSizeWeights,\n    totalSizeLoading,\n    totalSizeEnabled: undefined,\n    modelStats: Object.values(modelStats),\n  };\n};\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') {\n      instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n    }\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const irisIndices: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = Array<[number, number]>;\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const pairsLips: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n  [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n  [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n  [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n  [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n  lips: connectionsToIndices(pairsLips),\n  leftEye: connectionsToIndices(pairsLeftEye),\n  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n  leftIris: connectionsToIndices(pairsLeftIris),\n  rightEye: connectionsToIndices(pairsRightEye),\n  rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n  rightIris: connectionsToIndices(pairsRightIris),\n  faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n  [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n  [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n  [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n  [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n  [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n  [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n  [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n  [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n  [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n  [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n  [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n  [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n  [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n  [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n  [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n  [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n  [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n  [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n  [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n  [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n  [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n  [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n  [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n  [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n  [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n  [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n  [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n  [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n  [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n  [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n  [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n  [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n  [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n  [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n  [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n  [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n  [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n  [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n  [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n  [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n  [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n  [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n  [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n  [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n  [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n  [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n  [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n  [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n  [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n  [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n  [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n  [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n  [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n  [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n  [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n  [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n  [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n  [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n  [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n  [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n  [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n  [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n  [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n  [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n  [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n  [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n  [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n  [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n  [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n  [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n  [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n  [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n  [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n  [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n  [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n  [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n  [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n  [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n  [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n  [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n  [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n  [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n  [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n  [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n  [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n  [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n  [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n  [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n  [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n  [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n  [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n  [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n  [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n  [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n  [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n  [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n  [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n  [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n  [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n  [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n  [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n  [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n  [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n  [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n  [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n  [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n  [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n  [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n  [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n  [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n  [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n  [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n  [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n  [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n  [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n  [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n  [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n  [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n  [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n  [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n  [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n  [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n  [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n  [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n  [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n  [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n  [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n  [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n  [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n  [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n  [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n  [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n  [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n  [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n  [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n  [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n  [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n  [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n  [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n  [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n  [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n  [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n  [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n  [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n  [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n  [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n  [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n  [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n  [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n  [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n  [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n  [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n  [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n  [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n  [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n  [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n  [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n  [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n  [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n  [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n  [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n  [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n  [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n  [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n  [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n  [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n  [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n  [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n  [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n  [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n  [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n  [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n  [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n  [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n  [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n  [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n  [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n  [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n  [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n  [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n  [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n  [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n  [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n  [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n  [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n  [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n  [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n  [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n  [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n  [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n  [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n  [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n  [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n  [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n  [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n  [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n  [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n  [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n  [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n  [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n  [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n  [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n  [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n  [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n  [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n  [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n  [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n  [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n  [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n  [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n  [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n  [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n  [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n  [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n  [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n  [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n  [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n  [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n  [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n  [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n  [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n  [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n  [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n  [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n  [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n  [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n  [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n  [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n  [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n  [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n  [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n  [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n  [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n  [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n  [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n  [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n  [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n  [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n  [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n  [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n  [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n  [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n  [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n  [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n  [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n  [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n  [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n  [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n    t.batch = tf.squeeze(res[0]);\n  } else { // original blazeface tfhub model\n    t.batch = tf.squeeze(res);\n  }\n  tf.dispose(res);\n  t.boxes = decodeBoxes(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.irisIndices.length; i++) {\n    const { key, indices } = coords.irisIndices[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = Array<[number, number]>;\n\nconst LIPS_CONNECTIONS: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n  lips: connectionsToIndices(LIPS_CONNECTIONS),\n  leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n  leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n  leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n  rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n  rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n  rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n  faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: Array<[number, string]> = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n  .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n  .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n  indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n  zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n  61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n  185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n  78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n  191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n  76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n  184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n  62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n  183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n  130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n  247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n  226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n  113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n  35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n  143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n  156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n  359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n  467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n  446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n  342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n  265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n  372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n  383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n  468, // Center.\n  469, // Iris right edge.\n  470, // Iris top edge.\n  471, // Iris left edge.\n  472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n  473, // Center.\n  474, // Iris right edge.\n  475, // Iris top edge.\n  476, // Iris left edge.\n  477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n    // flag: results[1], // already processed in parent // conv_faceflag\n    lips: await results.filter((r) => r.size === 160)[0].data() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: await results.filter((r) => r.size === 10)[0].data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: await results.filter((r) => r.size === 142)[0].data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: await results.filter((r) => r.size === 10)[1].data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: await results.filter((r) => r.size === 142)[1].data() as Float32Array, // 71 x 2d = 142// output_left_eye\n  };\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n  const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n  return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      // contoursRaw: [],\n      // contours: [],\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n        if (config.face.mesh?.keepInvalid) {\n          face.box = util.clampBox(box, input);\n          face.boxRaw = util.getRawBox(box, input);\n          face.score = face.boxScore;\n          face.mesh = box.landmarks.map((pt) => [\n            ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n            ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n          ]);\n          face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / inputSize]);\n          for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n            face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n          }\n        }\n      } else {\n        const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n        const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n        let rawCoords = await coordsReshaped.array();\n        tf.dispose(coordsReshaped);\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // run iris model and augment results\n        }\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        /*\n        const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n        const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n        face.contoursRaw = [];\n        for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n        face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n        */\n        newCache.push(calculatedBox);\n      }\n      tf.dispose(results);\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  // @ts-ignore private property\n  if (config?.face?.attention?.enabled && model?.signature) {\n    // @ts-ignore private property\n    if (Object.keys(model?.signature?.outputs || {}).length < 6) model = null;\n  }\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention?.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.140625, y: 0.171875 },\n  { x: 0.140625, y: 0.171875 },\n  { x: 0.171875, y: 0.171875 },\n  { x: 0.171875, y: 0.171875 },\n  { x: 0.203125, y: 0.171875 },\n  { x: 0.203125, y: 0.171875 },\n  { x: 0.234375, y: 0.171875 },\n  { x: 0.234375, y: 0.171875 },\n  { x: 0.265625, y: 0.171875 },\n  { x: 0.265625, y: 0.171875 },\n  { x: 0.296875, y: 0.171875 },\n  { x: 0.296875, y: 0.171875 },\n  { x: 0.328125, y: 0.171875 },\n  { x: 0.328125, y: 0.171875 },\n  { x: 0.359375, y: 0.171875 },\n  { x: 0.359375, y: 0.171875 },\n  { x: 0.390625, y: 0.171875 },\n  { x: 0.390625, y: 0.171875 },\n  { x: 0.421875, y: 0.171875 },\n  { x: 0.421875, y: 0.171875 },\n  { x: 0.453125, y: 0.171875 },\n  { x: 0.453125, y: 0.171875 },\n  { x: 0.484375, y: 0.171875 },\n  { x: 0.484375, y: 0.171875 },\n  { x: 0.515625, y: 0.171875 },\n  { x: 0.515625, y: 0.171875 },\n  { x: 0.546875, y: 0.171875 },\n  { x: 0.546875, y: 0.171875 },\n  { x: 0.578125, y: 0.171875 },\n  { x: 0.578125, y: 0.171875 },\n  { x: 0.609375, 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x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'humangl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            // eslint-disable-next-line no-undef\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).requestAdapterInfo() : undefined;\n            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n            log('webgpu adapter info:', adapterInfo);\n          }\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        try {\n          tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        } catch { /**/ }\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n  return color;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  ctx.fillStyle = colorDepth(z, localOptions);\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should face attention keypoints be highlighted */\n  drawAttention: boolean;\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n    const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n    ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n    const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n    ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n    ctx.strokeStyle = 'pink';\n    const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n    const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n    const pathV = new Path2D(`\n      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n      C\n        ${valX} ${f.box[1]},\n        ${valX} ${f.box[1] + f.box[3]},\n        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n    `);\n    const pathH = new Path2D(`\n      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n      C \n        ${f.box[0]} ${valY},\n        ${f.box[0] + f.box[2]} ${valY},\n        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n    `);\n    ctx.stroke(pathH);\n    ctx.stroke(pathV);\n  }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = 'pink';\n    ctx.fillStyle = 'pink';\n    const leftGaze = [\n      f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n    const rightGaze = [\n      f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n  }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPolygons && f.mesh.length >= 468) {\n    ctx.lineWidth = 1;\n    for (let i = 0; i < triangulation.length / 3; i++) {\n      const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n      lines(ctx, points, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n  /*\n  if (opt.drawPolygons && f.contours.length > 1) {\n    ctx.lineWidth = 5;\n    lines(ctx, f.contours, opt);\n  }\n  ctx.lineWidth = 1;\n  */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && f.mesh.length >= 468) {\n    for (let i = 0; i < f.mesh.length; i++) {\n      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);\n      if (opt.drawAttention) {\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.color;\n  for (const f of result) {\n    drawFaceBoxes(f, ctx);\n    drawLabels(f, ctx);\n    if (f.mesh && f.mesh.length > 0) {\n      drawFacePoints(f, ctx);\n      drawFacePolygons(f, ctx);\n      drawGazeSpheres(f, ctx);\n      drawGazeArrows(f, ctx);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = colorDepth(i * z, localOptions);\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n    // log('compile pass: skip');\n    return;\n  }\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  const compiledModels: string[] = [];\n  for (const [modelName, model] of Object.entries(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    // const res = await model.executeAsync(tensor); // fails with current tfjs\n    try {\n      const res = model.execute(tensor);\n      compiledModels.push(modelName);\n      if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n      else tf.dispose(res);\n    } catch {\n      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass models:', compiledModels);\n  log('compile pass kernels:', kernels.length);\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await runCompile(instance.models);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas, ModelStats } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API](https://js.tensorflow.org/api/latest/)\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    /*\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    */\n    const tfVersion = (tf.version?.tfjs || tf.version_core).replace(/-(.*)/, '');\n    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** get model loading/loaded stats */\n  getModelStats(): ModelStats { return models.getModelStats(this); }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "names": []
+}
diff --git a/dist/human.esm.d.ts b/dist/human.esm.d.ts
new file mode 100644
index 00000000..fed34cea
--- /dev/null
+++ b/dist/human.esm.d.ts
@@ -0,0 +1,2643 @@
+/// <reference types="@webgpu/types/dist" />
+/// <reference types="offscreencanvas" />
+
+/** meta-function that performs draw for: canvas, face, body, hand */
+declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
+
+/** Defines all possible canvas types */
+export declare type AnyCanvas = HTMLCanvasElement | OffscreenCanvas;
+
+/** Defines all possible image types */
+export declare type AnyImage = HTMLImageElement | typeof Image;
+
+/** Defines all possible video types */
+export declare type AnyVideo = HTMLMediaElement | HTMLVideoElement;
+
+/** @docalias number[] */
+declare interface ArrayMap {
+    R0: number;
+    R1: number[];
+    R2: number[][];
+    R3: number[][][];
+    R4: number[][][][];
+    R5: number[][][][][];
+    R6: number[][][][][][];
+}
+
+/** Possible TensorFlow backends */
+export declare type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];
+
+/** draw detected bodies */
+declare function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+export declare type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;
+
+export declare type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';
+
+export declare type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';
+
+/** Configures all body detection specific options */
+export declare interface BodyConfig extends GenericConfig {
+    /** maximum number of detected bodies */
+    maxDetected: number;
+    /** minimum confidence for a detected body before results are discarded */
+    minConfidence: number;
+}
+
+/** body gesture type */
+export declare type BodyGesture = `leaning ${'left' | 'right'}` | `raise ${'left' | 'right'} hand` | 'i give up';
+
+/** Body Result keypoints */
+export declare interface BodyKeypoint {
+    /** body part name */
+    part: BodyLandmark;
+    /** body part position */
+    position: Point;
+    /** body part position normalized to 0..1 */
+    positionRaw: Point;
+    /** body part position relative to body center in meters */
+    distance?: Point;
+    /** body part detection score */
+    score: number;
+}
+
+export declare type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;
+
+export declare type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle' | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';
+
+export declare type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';
+
+export declare type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+export declare type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+/** Body results */
+export declare interface BodyResult {
+    /** body id */
+    id: number;
+    /** body detection score */
+    score: number;
+    /** detected body box */
+    box: Box;
+    /** detected body box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected body keypoints */
+    keypoints: Array<BodyKeypoint>;
+    /** detected body keypoints combined into annotated parts */
+    annotations: Record<BodyAnnotation, Point[][]>;
+}
+
+/** generic box as [x, y, width, height] */
+export declare type Box = [number, number, number, number];
+
+/**
+ * Creates an IOHandler that loads model artifacts from user-selected files.
+ *
+ * This method can be used for loading from files such as user-selected files
+ * in the browser.
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * // Note: This code snippet won't run properly without the actual file input
+ * //   elements in the HTML DOM.
+ *
+ * // Suppose there are two HTML file input (`<input type="file" ...>`)
+ * // elements.
+ * const uploadJSONInput = document.getElementById('upload-json');
+ * const uploadWeightsInput = document.getElementById('upload-weights');
+ * const model = await tf.loadLayersModel(tf.io.browserFiles(
+ *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+ * ```
+ *
+ * @param files `File`s to load from. Currently, this function supports only
+ *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+ *   which an `Array` of `File`s is expected (in that order):
+ *   - A JSON file containing the model topology and weight manifest.
+ *   - Optionally, One or more binary files containing the binary weights.
+ *     These files must have names that match the paths in the `weightsManifest`
+ *     contained by the aforementioned JSON file, or errors will be thrown
+ *     during loading. These weights files have the same format as the ones
+ *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+ *     Python PIP package. If no weights files are provided, only the model
+ *     topology will be loaded from the JSON file above.
+ * @returns An instance of `Files` `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function browserFiles(files: File[]): IOHandler;
+
+/**
+ * Deprecated. Use `tf.io.http`.
+ * @param path
+ * @param loadOptions
+ */
+declare function browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** draw processed canvas */
+declare function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas): Promise<void>;
+
+/**
+ * Concatenate a number of ArrayBuffers into one.
+ *
+ * @param buffers A number of array buffers to concatenate.
+ * @returns Result of concatenating `buffers` in order.
+ */
+declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+
+/**
+ * Configuration interface definition for **Human** library
+ * Contains all configurable parameters
+ * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+ */
+export declare interface Config {
+    /** Backend used for TFJS operations
+     * valid build-in backends are:
+     * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`
+     * - NodeJS: `cpu`, `wasm`, `tensorflow`
+     * default: `humangl` for browser and `tensorflow` for nodejs
+     */
+    backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+    /** Path to *.wasm files if backend is set to `wasm`
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPath: string;
+    /** Force WASM loader to use platform fetch
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPlatformFetch: boolean;
+    /** Print debug statements to console
+     *
+     * default: `true`
+     */
+    debug: boolean;
+    /** Perform model loading and inference concurrently or sequentially
+     *
+     * default: `true`
+     */
+    async: boolean;
+    /** What to use for `human.warmup()`
+     * - warmup pre-initializes all models for faster inference but can take significant time on startup
+     * - used by `webgl`, `humangl` and `webgpu` backends
+     *
+     * default: `full`
+     */
+    warmup: '' | 'none' | 'face' | 'full' | 'body';
+    /** Base model path (typically starting with file://, http:// or https://) for all models
+     * - individual modelPath values are relative to this path
+     *
+     * default: `../models/` for browsers and `file://models/` for nodejs
+     */
+    modelBasePath: string;
+    /** Cache models in IndexDB on first sucessfull load
+     * default: true if indexdb is available (browsers), false if its not (nodejs)
+     */
+    cacheModels: boolean;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+     *
+     * default: 0.7
+     */
+    cacheSensitivity: number;
+    /** Perform immediate garbage collection on deallocated tensors instead of caching them */
+    deallocate: boolean;
+    /** Internal Variable */
+    skipAllowed: boolean;
+    /** Filter config {@link FilterConfig} */
+    filter: Partial<FilterConfig>;
+    /** Gesture config {@link GestureConfig} */
+    gesture: Partial<GestureConfig>;
+    /** Face config {@link FaceConfig} */
+    face: Partial<FaceConfig>;
+    /** Body config {@link BodyConfig} */
+    body: Partial<BodyConfig>;
+    /** Hand config {@link HandConfig} */
+    hand: Partial<HandConfig>;
+    /** Object config {@link ObjectConfig} */
+    object: Partial<ObjectConfig>;
+    /** Segmentation config {@link SegmentationConfig} */
+    segmentation: Partial<SegmentationConfig>;
+}
+
+/**
+ * Copy a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Copying within a storage medium, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Copying between two storage mediums, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Copy the model, from Local Storage to IndexedDB.
+ * await tf.io.copyModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove both models.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of copying.
+ * @param destURL Destination URL of copying.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * We wrap data id since we use weak map to avoid memory leaks.
+ * Since we have our own memory management, we have a reference counter
+ * mapping a tensor to its data, so there is always a pointer (even if that
+ * data is otherwise garbage collectable).
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
+ * Global_Objects/WeakMap
+ */
+declare type DataId = object;
+
+declare type DataToGPUOptions = DataToGPUWebGLOption;
+
+declare interface DataToGPUWebGLOption {
+    customTexShape?: [number, number];
+}
+
+/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
+declare type DataType = keyof DataTypeMap;
+
+declare interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+
+/**
+ * Decode flat ArrayBuffer as weights.
+ *
+ * This function does not handle sharding.
+ *
+ * This function is the reverse of `encodeWeights`.
+ *
+ * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+ *   concatenated in the order specified in `specs`.
+ * @param specs Specifications of the names, dtypes and shapes of the tensors
+ *   whose value are encoded by `buffer`.
+ * @return A map from tensor name to tensor value, with the names corresponding
+ *   to names in `specs`.
+ * @throws Error, if any of the tensors has unsupported dtype.
+ */
+declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+
+/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
+export declare const defaults: Config;
+
+/** Face descriptor type as number array */
+export declare type Descriptor = Array<number>;
+
+/** Calculates distance between two descriptors
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ */
+declare function distance(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare namespace draw {
+    export {
+        person,
+        canvas,
+        all,
+        options,
+        face,
+        body,
+        hand,
+        object,
+        gesture
+    }
+}
+export { draw }
+
+/** Draw Options
+ * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter
+ */
+export declare type DrawOptions = {
+    /** draw line color */
+    color: string;
+    /** alpha value used for lines */
+    alpha: number;
+    /** label color */
+    labelColor: string;
+    /** label shadow color */
+    shadowColor: string;
+    /** label font */
+    font: string;
+    /** line spacing between labels */
+    lineHeight: number;
+    /** line width for drawn lines */
+    lineWidth: number;
+    /** size of drawn points */
+    pointSize: number;
+    /** draw rounded boxes by n pixels */
+    roundRect: number;
+    /** should points be drawn? */
+    drawPoints: boolean;
+    /** should labels be drawn? */
+    drawLabels: boolean;
+    /** should face attention keypoints be highlighted */
+    drawAttention: boolean;
+    /** should detected gestures be drawn? */
+    drawGestures: boolean;
+    /** should draw boxes around detection results? */
+    drawBoxes: boolean;
+    /** should draw polygons from detection points? */
+    drawPolygons: boolean;
+    /** should draw gaze arrows? */
+    drawGaze: boolean;
+    /** should fill polygons? */
+    fillPolygons: boolean;
+    /** use z-coordinate when available */
+    useDepth: boolean;
+    /** should lines be curved? */
+    useCurves: boolean;
+};
+
+export declare type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';
+
+/**
+ * Encode a map from names to weight values as an ArrayBuffer, along with an
+ * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+ *
+ * This function does not perform sharding.
+ *
+ * This function is the reverse of `decodeWeights`.
+ *
+ * @param tensors A map ("dict") from names to tensors.
+ * @param group Group to which the weights belong (optional).
+ * @returns A `Promise` of
+ *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+ *     concatenated.
+ *   - An `Array` of `WeightManifestEntry`s, carrying information including
+ *     tensor names, `dtype`s and shapes.
+ * @throws Error: on unsupported tensor `dtype`.
+ */
+declare function encodeWeights(tensors: NamedTensorMap | NamedTensor[], group?: WeightGroup): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+
+/** Env class that holds detected capabilities */
+export declare class Env {
+    /** Running in Browser */
+    browser: boolean;
+    /** Running in NodeJS */
+    node: boolean;
+    /** Running in WebWorker thread */
+    worker: boolean;
+    /** Detected platform */
+    platform: string;
+    /** Detected agent */
+    agent: string;
+    /** List of supported backends */
+    backends: string[];
+    /** Has any work been performed so far */
+    initial: boolean;
+    /** Are image filters supported? */
+    filter: boolean | undefined;
+    /** TFJS instance details */
+    tfjs: {
+        version: undefined | string;
+    };
+    /** Is offscreenCanvas supported? */
+    offscreen: undefined | boolean;
+    /** Are performance counter instant values or additive */
+    perfadd: boolean;
+    /** WASM detected capabilities */
+    wasm: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        simd: undefined | boolean;
+        multithread: undefined | boolean;
+    };
+    /** WebGL detected capabilities */
+    webgl: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        version: undefined | string;
+        renderer: undefined | string;
+    };
+    /** WebGPU detected capabilities */
+    webgpu: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        adapter: undefined | string;
+    };
+    /** CPU info */
+    cpu: {
+        model: undefined | string;
+        flags: string[];
+    };
+    /** List of supported kernels for current backend */
+    kernels: string[];
+    /** MonkeyPatch for Canvas */
+    Canvas: undefined;
+    /** MonkeyPatch for Image */
+    Image: undefined;
+    /** MonkeyPatch for ImageData */
+    ImageData: undefined;
+    constructor();
+    /** update backend information */
+    updateBackend(): Promise<void>;
+    /** update cpu information */
+    updateCPU(): Promise<void>;
+}
+
+export declare const env: Env;
+
+/** Events dispatched by `human.events`
+ * - `create`: triggered when Human object is instantiated
+ * - `load`: triggered when models are loaded (explicitly or on-demand)
+ * - `image`: triggered when input image is processed
+ * - `result`: triggered when detection is complete
+ * - `warmup`: triggered when warmup is complete
+ */
+export declare type Events = 'create' | 'load' | 'image' | 'result' | 'warmup' | 'error';
+
+/** Defines possible externally defined canvas */
+export declare type ExternalCanvas = typeof env.Canvas;
+
+/** draw detected faces */
+declare function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Anti-spoofing part of face configuration */
+export declare interface FaceAntiSpoofConfig extends GenericConfig {
+}
+
+/** Attention part of face configuration */
+export declare interface FaceAttentionConfig extends GenericConfig {
+}
+
+/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */
+export declare interface FaceConfig extends GenericConfig {
+    detector: Partial<FaceDetectorConfig>;
+    mesh: Partial<FaceMeshConfig>;
+    attention: Partial<FaceAttentionConfig>;
+    iris: Partial<FaceIrisConfig>;
+    description: Partial<FaceDescriptionConfig>;
+    emotion: Partial<FaceEmotionConfig>;
+    antispoof: Partial<FaceAntiSpoofConfig>;
+    liveness: Partial<FaceLivenessConfig>;
+    gear: Partial<FaceGearConfig>;
+}
+
+/** Description or face embedding part of face configuration
+ * - also used by age and gender detection
+ */
+export declare interface FaceDescriptionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Detector part of face configuration */
+export declare interface FaceDetectorConfig extends GenericConfig {
+    /** is face rotation correction performed after detecting face?
+     * used to correctly analyze faces under high angles
+     */
+    rotation: boolean;
+    /** maximum number of detected faces */
+    maxDetected: number;
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected faces before one is discarded */
+    iouThreshold: number;
+    /** should child models perform on masked image of a face */
+    mask: boolean;
+    /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?
+     * if enabled it must be manually deallocated to avoid memory leak */
+    return: boolean;
+}
+
+/** Emotion part of face configuration */
+export declare interface FaceEmotionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Gear part of face configuration */
+export declare interface FaceGearConfig extends GenericConfig {
+    /** minimum confidence for a detected race before results are discarded */
+    minConfidence: number;
+}
+
+/** face gesture type */
+export declare type FaceGesture = `facing ${'left' | 'center' | 'right'}` | `blink ${'left' | 'right'} eye` | `mouth ${number}% open` | `head ${'up' | 'down'}`;
+
+/** Iris part of face configuration */
+export declare interface FaceIrisConfig extends GenericConfig {
+}
+
+export declare type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette' | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner' | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris' | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris' | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';
+
+/** Liveness part of face configuration */
+export declare interface FaceLivenessConfig extends GenericConfig {
+}
+
+/** Mesh part of face configuration */
+export declare interface FaceMeshConfig extends GenericConfig {
+    /** Keep detected faces that cannot be verified using facemesh */
+    keepInvalid: boolean;
+}
+
+/** Face results
+ * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
+ * - Some values may be null if specific model is not enabled
+ */
+export declare interface FaceResult {
+    /** face id */
+    id: number;
+    /** overall face score */
+    score: number;
+    /** detection score */
+    boxScore: number;
+    /** mesh score */
+    faceScore: number;
+    /** detected face box */
+    box: Box;
+    /** detected face box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected face mesh */
+    mesh: Array<Point>;
+    /** detected face mesh normalized to 0..1 */
+    meshRaw: Array<Point>;
+    /** face contours as array of 2d points normalized to 0..1 */
+    /** face contours as array of 2d points */
+    /** mesh keypoints combined into annotated results */
+    annotations: Record<FaceLandmark, Point[]>;
+    /** detected age */
+    age?: number;
+    /** detected gender */
+    gender?: Gender;
+    /** gender detection score */
+    genderScore?: number;
+    /** detected emotions */
+    emotion?: Array<{
+        score: number;
+        emotion: Emotion;
+    }>;
+    /** detected race */
+    race?: Array<{
+        score: number;
+        race: Race;
+    }>;
+    /** face descriptor */
+    embedding?: Array<number>;
+    /** face iris distance from camera */
+    iris?: number;
+    /** face anti-spoofing result confidence */
+    real?: number;
+    /** face liveness result confidence */
+    live?: number;
+    /** face rotation details */
+    rotation?: {
+        angle: {
+            roll: number;
+            yaw: number;
+            pitch: number;
+        };
+        matrix: [number, number, number, number, number, number, number, number, number];
+        gaze: {
+            bearing: number;
+            strength: number;
+        };
+    } | null;
+    /** detected face as tensor that can be used in further pipelines */
+    tensor?: Tensor;
+}
+
+/** Run input through image filters before inference
+ * - available only in Browser environments
+ * - image filters run with near-zero latency as they are executed on the GPU using WebGL
+ */
+export declare interface FilterConfig {
+    /** are image filters enabled? */
+    enabled: boolean;
+    /** perform image histogram equalization
+     * - equalization is performed on input as a whole and detected face before its passed for further analysis
+     */
+    equalization: boolean;
+    /** resize input width
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    width: number;
+    /** resize input height
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    height: number;
+    /** return processed canvas imagedata in result */
+    return: boolean;
+    /** flip input as mirror image */
+    flip: boolean;
+    /** range: -1 (darken) to 1 (lighten) */
+    brightness: number;
+    /** range: -1 (reduce contrast) to 1 (increase contrast) */
+    contrast: number;
+    /** range: 0 (no sharpening) to 1 (maximum sharpening) */
+    sharpness: number;
+    /** range: 0 (no blur) to N (blur radius in pixels) */
+    blur: number;
+    /** range: -1 (reduce saturation) to 1 (increase saturation) */
+    saturation: number;
+    /** range: 0 (no change) to 360 (hue rotation in degrees) */
+    hue: number;
+    /** image negative */
+    negative: boolean;
+    /** image sepia colors */
+    sepia: boolean;
+    /** image vintage colors */
+    vintage: boolean;
+    /** image kodachrome colors */
+    kodachrome: boolean;
+    /** image technicolor colors */
+    technicolor: boolean;
+    /** image polaroid camera effect */
+    polaroid: boolean;
+    /** range: 0 (no pixelate) to N (number of pixels to pixelate) */
+    pixelate: number;
+}
+
+export declare type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';
+
+export declare type FingerCurl = 'none' | 'half' | 'full';
+
+export declare type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandler` that simply loads the provided data.
+ */
+declare function fromMemory(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler;
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
+ */
+declare function fromMemorySync(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandlerSync;
+
+export declare type Gender = 'male' | 'female' | 'unknown';
+
+/** Generic config type inherited by all module types */
+export declare interface GenericConfig {
+    /** is module enabled? */
+    enabled: boolean;
+    /** path to model json file (relative to `modelBasePath` */
+    modelPath: string;
+    /** how many max frames to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipFrames: number;
+    /** how many max milliseconds to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipTime: number;
+}
+
+/** draw detected gestures */
+declare function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Controlls gesture detection */
+export declare interface GestureConfig {
+    /** is gesture detection enabled? */
+    enabled: boolean;
+}
+
+/** Gesture combined results
+ * Each result has:
+ * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`
+ * - gesture: gesture detected
+ */
+export declare type GestureResult = {
+    'face': number;
+    gesture: FaceGesture;
+} | {
+    'iris': number;
+    gesture: IrisGesture;
+} | {
+    'body': number;
+    gesture: BodyGesture;
+} | {
+    'hand': number;
+    gesture: HandGesture;
+};
+
+declare const getLoadHandlers: (url: string | string[], loadOptions?: LoadOptions) => IOHandler[];
+
+/**
+ * Create `ModelArtifacts` from a JSON file.
+ *
+ * @param modelJSON Object containing the parsed JSON of `model.json`
+ * @param loadWeights Function that takes the JSON file's weights manifest,
+ *     reads weights from the listed path(s), and returns a Promise of the
+ *     weight manifest entries along with the weights data.
+ * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+ */
+declare function getModelArtifactsForJSON(modelJSON: ModelJSON, loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[WeightsManifestEntry[], /* weightData */ ArrayBuffer]>): Promise<ModelArtifacts>;
+
+/**
+ * Populate ModelArtifactsInfo fields for a model with JSON topology.
+ * @param modelArtifacts
+ * @returns A ModelArtifactsInfo object.
+ */
+declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
+
+declare const getModelStats: (instance: Human) => ModelStats;
+
+declare const getSaveHandlers: (url: string | string[]) => IOHandler[];
+
+declare interface GPUData {
+    tensorRef: Tensor;
+    texture?: WebGLTexture;
+    buffer?: GPUBuffer;
+    texShape?: [number, number];
+    bufSize?: number;
+}
+
+/**
+ * A `tf.GraphModel` is a directed, acyclic graph built from a
+ * SavedModel GraphDef and allows inference execution.
+ *
+ * A `tf.GraphModel` can only be created by loading from a model converted from
+ * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+ * the command line converter tool and loaded via `tf.loadGraphModel`.
+ *
+ * @doc {heading: 'Models', subheading: 'Classes'}
+ */
+export declare class GraphModel<ModelURL extends Url = string | io.IOHandler> implements InferenceModel {
+    private modelUrl;
+    private loadOptions;
+    private executor;
+    private version;
+    private handler;
+    private artifacts;
+    private initializer;
+    private resourceManager;
+    private signature;
+    readonly modelVersion: string;
+    readonly inputNodes: string[];
+    readonly outputNodes: string[];
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
+    readonly metadata: {};
+    readonly modelSignature: {};
+    /**
+     * @param modelUrl url for the model, or an `io.IOHandler`.
+     * @param weightManifestUrl url for the weight file generated by
+     * scripts/convert.py script.
+     * @param requestOption options for Request, which allows to send credentials
+     * and custom headers.
+     * @param onProgress Optional, progress callback function, fired periodically
+     * before the load is completed.
+     */
+    constructor(modelUrl: ModelURL, loadOptions?: io.LoadOptions);
+    private findIOHandler;
+    /**
+     * Loads the model and weight files, construct the in memory weight map and
+     * compile the inference graph.
+     */
+    load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync ? boolean : Promise<boolean>;
+    /**
+     * Synchronously construct the in memory weight map and
+     * compile the inference graph. Also initialize hashtable if any.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    loadSync(artifacts: io.ModelArtifacts): boolean;
+    /**
+     * Save the configuration and/or weights of the GraphModel.
+     *
+     * An `IOHandler` is an object that has a `save` method of the proper
+     * signature defined. The `save` method manages the storing or
+     * transmission of serialized data ("artifacts") that represent the
+     * model's topology and weights onto or via a specific medium, such as
+     * file downloads, local storage, IndexedDB in the web browser and HTTP
+     * requests to a server. TensorFlow.js provides `IOHandler`
+     * implementations for a number of frequently used saving mediums, such as
+     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+     * for more details.
+     *
+     * This method also allows you to refer to certain types of `IOHandler`s
+     * as URL-like string shortcuts, such as 'localstorage://' and
+     * 'indexeddb://'.
+     *
+     * Example 1: Save `model`'s topology and weights to browser [local
+     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+     * then load it back.
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     *
+     * const saveResults = await model.save('localstorage://my-model-1');
+     *
+     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+     * console.log('Prediction from loaded model:');
+     * model.predict(zeros).print();
+     * ```
+     *
+     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+     * scheme-based string shortcut for `IOHandler`.
+     * @param config Options for saving the model.
+     * @returns A `Promise` of `SaveResult`, which summarizes the result of
+     * the saving, such as byte sizes of the saved artifacts for the model's
+     *   topology and weight values.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+     * inputs params should be in either `tf.Tensor`[] if the input order is
+     * fixed, or otherwise NamedTensorMap format.
+     *
+     * For model with multiple inputs, we recommend you use NamedTensorMap as the
+     * input type, if you use `tf.Tensor`[], the order of the array needs to
+     * follow the
+     * order of inputNodes array. @see {@link GraphModel.inputNodes}
+     *
+     * You can also feed any intermediate nodes using the NamedTensorMap as the
+     * input type. For example, given the graph
+     *    InputNode => Intermediate => OutputNode,
+     * you can execute the subgraph Intermediate => OutputNode by calling
+     *    model.execute('IntermediateNode' : tf.tensor(...));
+     *
+     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+     * state needs to be fed manually.
+     *
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size and
+     * output node names. Currently the batch size option is ignored for graph
+     * model.
+     *
+     * @returns Inference result tensors. The output would be single `tf.Tensor`
+     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+     * will be returned for model with multiple outputs.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    private normalizeInputs;
+    private normalizeOutputs;
+    /**
+     * Executes inference for the model for given input tensors.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no
+     * outputs are specified, the default outputs of the model would be used.
+     * You can inspect intermediate nodes of the model by adding them to the
+     * outputs array.
+     *
+     * @returns A single tensor if provided with a single output or no outputs
+     * are provided and there is only one default output, otherwise return a
+     * tensor array. The order of the tensor array is the same as the outputs
+     * if provided, otherwise the order of outputNodes attribute of the model.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
+    /**
+     * Executes inference for the model for given input tensors in async
+     * fashion, use this method when your model contains control flow ops.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no outputs
+     * are specified, the default outputs of the model would be used. You can
+     * inspect intermediate nodes of the model by adding them to the outputs
+     * array.
+     *
+     * @returns A Promise of single tensor if provided with a single output or
+     * no outputs are provided and there is only one default output, otherwise
+     * return a tensor map.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
+    /**
+     * Get intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    getIntermediateTensors(): NamedTensorsMap;
+    /**
+     * Dispose intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    disposeIntermediateTensors(): void;
+    private convertTensorMapToTensorsMap;
+    /**
+     * Releases the memory used by the weight tensors and resourceManager.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    dispose(): void;
+}
+
+/** draw detected hands */
+declare function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all hand detection specific options */
+export declare interface HandConfig extends GenericConfig {
+    /** should hand rotation correction be performed after hand detection? */
+    rotation: boolean;
+    /** minimum confidence for a detected hand before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected hands before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected hands */
+    maxDetected: number;
+    /** should hand landmarks be detected or just return detected hand box */
+    landmarks: boolean;
+    detector: {
+        /** path to hand detector model json */
+        modelPath?: string;
+    };
+    skeleton: {
+        /** path to hand skeleton model json */
+        modelPath?: string;
+    };
+}
+
+/** hand gesture type */
+export declare type HandGesture = `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward` | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up` | 'victory' | 'thumbs up';
+
+/** Hand results */
+export declare interface HandResult {
+    /** hand id */
+    id: number;
+    /** hand overal score */
+    score: number;
+    /** hand detection score */
+    boxScore: number;
+    /** hand skelton score */
+    fingerScore: number;
+    /** detected hand box */
+    box: Box;
+    /** detected hand box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected hand keypoints */
+    keypoints: Array<Point>;
+    /** detected hand class */
+    label: HandType;
+    /** detected hand keypoints combined into annotated parts */
+    annotations: Record<Finger, Array<Point>>;
+    /** detected hand parts annotated with part gestures */
+    landmarks: Record<Finger, {
+        curl: FingerCurl;
+        direction: FingerDirection;
+    }>;
+}
+
+export declare type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';
+
+/**
+ * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+ *
+ * An HTTP request of the `multipart/form-data` mime type will be sent to the
+ * `path` URL. The form data includes artifacts that represent the topology
+ * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+ * blobs (files) exist in form-data:
+ *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+ *   - A binary weights file consisting of the concatenated weight values.
+ * These files are in the same format as the one generated by
+ * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+ *
+ * The following code snippet exemplifies the client-side code that uses this
+ * function:
+ *
+ * ```js
+ * const model = tf.sequential();
+ * model.add(
+ *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+ *
+ * const saveResult = await model.save(tf.io.http(
+ *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+ * console.log(saveResult);
+ * ```
+ *
+ * If the default `POST` method is to be used, without any custom parameters
+ * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+ *
+ * ```js
+ * const saveResult = await model.save('http://model-server:5000/upload');
+ * ```
+ *
+ * The following GitHub Gist
+ * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+ * implements a server based on [flask](https://github.com/pallets/flask) that
+ * can receive the request. Upon receiving the model artifacts via the requst,
+ * this particular server reconsistutes instances of [Keras
+ * Models](https://keras.io/models/model/) in memory.
+ *
+ *
+ * @param path A URL path to the model.
+ *   Can be an absolute HTTP path (e.g.,
+ *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+ *   './model-upload').
+ * @param requestInit Request configurations to be used when sending
+ *    HTTP request to server using `fetch`. It can contain fields such as
+ *    `method`, `credentials`, `headers`, `mode`, etc. See
+ *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+ *    for more information. `requestInit` must not have a body, because the
+ * body will be set by TensorFlow.js. File blobs representing the model
+ * topology (filename: 'model.json') and the weights of the model (filename:
+ * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+ * `body`, an Error will be thrown.
+ * @param loadOptions Optional configuration for the loading. It includes the
+ *   following fields:
+ *   - weightPathPrefix Optional, this specifies the path prefix for weight
+ *     files, by default this is calculated from the path param.
+ *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+ *     the `fetch` from node-fetch can be used here.
+ *   - onProgress Optional, progress callback function, fired periodically
+ *     before the load is completed.
+ * @returns An instance of `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function http(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** **Human** library main class
+ *
+ * All methods and properties are available only as members of Human class
+ *
+ * - Configuration object definition: {@link Config}
+ * - Results object definition: {@link Result}
+ * - Possible inputs: {@link Input}
+ *
+ * @param userConfig - {@link Config}
+ * @returns instance of {@link Human}
+ */
+declare class Human {
+    #private;
+    /** Current version of Human library in *semver* format */
+    version: string;
+    /** Current configuration
+     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+     */
+    config: Config;
+    /** Last known result of detect run
+     * - Can be accessed anytime after initial detection
+     */
+    result: Result;
+    /** Current state of Human library
+     * - Can be polled to determine operations that are currently executed
+     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
+     */
+    state: string;
+    /** currenty processed image tensor and canvas */
+    process: {
+        tensor: Tensor | null;
+        canvas: AnyCanvas | null;
+    };
+    /** Instance of TensorFlow/JS used by Human
+     *  - Can be embedded or externally provided
+     * [TFJS API](https://js.tensorflow.org/api/latest/)
+     */
+    tf: any;
+    /** Object containing environment information used for diagnostics */
+    env: Env;
+    /** Draw helper classes that can draw detected objects on canvas using specified draw
+     * - canvas: draws input to canvas
+     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
+     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
+     */
+    draw: {
+        canvas: typeof draw.canvas;
+        face: typeof draw.face;
+        body: typeof draw.body;
+        hand: typeof draw.hand;
+        gesture: typeof draw.gesture;
+        object: typeof draw.object;
+        person: typeof draw.person;
+        all: typeof draw.all;
+        options: DrawOptions;
+    };
+    /** Currently loaded models
+     * @internal
+     * {@link Models}
+     */
+    models: models.Models;
+    /** Container for events dispatched by Human
+     * Possible events:
+     * - `create`: triggered when Human object is instantiated
+     * - `load`: triggered when models are loaded (explicitly or on-demand)
+     * - `image`: triggered when input image is processed
+     * - `result`: triggered when detection is complete
+     * - `warmup`: triggered when warmup is complete
+     * - `error`: triggered on some errors
+     */
+    events: EventTarget | undefined;
+    /** Reference face triangualtion array of 468 points, used for triangle references between points */
+    faceTriangulation: number[];
+    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
+    faceUVMap: [number, number][];
+    /** Performance object that contains values for all recently performed operations */
+    performance: Record<string, number>;
+    /** WebGL debug info */
+    gl: Record<string, unknown>;
+    /** Constructor for **Human** library that is futher used for all operations
+     * @param userConfig - user configuration object {@link Config}
+     */
+    constructor(userConfig?: Partial<Config>);
+    /** internal function to measure tensor leaks */
+    analyze: (...msg: string[]) => void;
+    /** Reset configuration to default values */
+    reset(): void;
+    /** Validate current configuration schema */
+    validate(userConfig?: Partial<Config>): {
+        reason: string;
+        where: string;
+        expected?: string;
+    }[];
+    /** Exports face matching methods {@link match#similarity} */
+    similarity: typeof match.similarity;
+    /** Exports face matching methods {@link match#distance} */
+    distance: typeof match.distance;
+    /** Exports face matching methods {@link match#match} */
+    match: typeof match.match;
+    /** Utility wrapper for performance.now() */
+    now(): number;
+    /** Process input as return canvas and tensor
+     *
+     * @param input - any input {@link Input}
+     * @param getTensor - should image processing also return tensor or just canvas
+     * Returns object with `tensor` and `canvas`
+     */
+    image(input: Input, getTensor?: boolean): Promise<{
+        tensor: Tensor<Rank> | null;
+        canvas: AnyCanvas | null;
+    }>;
+    /** Segmentation method takes any input and returns processed canvas with body segmentation
+     *  - Segmentation is not triggered as part of detect process
+     * @param input - {@link Input}
+     * @param background - {@link Input}
+     *  - Optional parameter background is used to fill the background with specific input
+     *  Returns:
+     *  - `data` as raw data array with per-pixel segmentation values
+     *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
+     *  - `alpha` as grayscale canvas that represents segmentation alpha values
+     */
+    segmentation(input: Input, background?: Input): Promise<{
+        data: number[] | Tensor;
+        canvas: AnyCanvas | null;
+        alpha: AnyCanvas | null;
+    }>;
+    /** Enhance method performs additional enhacements to face image previously detected for futher processing
+     *
+     * @param input - Tensor as provided in human.result.face[n].tensor
+     * @returns Tensor
+     */
+    enhance(input: Tensor): Tensor | null;
+    /** Compare two input tensors for pixel simmilarity
+     * - use `human.image` to process any valid input and get a tensor that can be used for compare
+     * - when passing manually generated tensors:
+     *  - both input tensors must be in format [1, height, width, 3]
+     *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
+     * - return value is pixel similarity score normalized by input resolution and rgb channels
+     */
+    compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
+    /** Explicit backend initialization
+     *  - Normally done implicitly during initial load phase
+     *  - Call to explictly register and initialize TFJS backend without any other operations
+     *  - Use when changing backend during runtime
+     */
+    init(): Promise<void>;
+    /** Load method preloads all configured models on-demand
+     * - Not explicitly required as any required model is load implicitly on it's first run
+     *
+     * @param userConfig - {@link Config}
+     */
+    load(userConfig?: Partial<Config>): Promise<void>;
+    /** emit event */
+    emit: (event: string) => void;
+    /** Runs interpolation using last known result and returns smoothened result
+     * Interpolation is based on time since last known result so can be called independently
+     *
+     * @param result - {@link Result} optional use specific result set to run interpolation on
+     * @returns result - {@link Result}
+     */
+    next(result?: Result): Result;
+    /** get model loading/loaded stats */
+    getModelStats(): ModelStats;
+    /** Warmup method pre-initializes all configured models for faster inference
+     * - can take significant time on startup
+     * - only used for `webgl` and `humangl` backends
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    warmup(userConfig?: Partial<Config>): Promise<Result | undefined>;
+    /** Run detect with tensorflow profiling
+     * - result object will contain total exeuction time information for top-20 kernels
+     * - actual detection object can be accessed via `human.result`
+     */
+    profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>>;
+    /** Main detection method
+     * - Analyze configuration: {@link Config}
+     * - Pre-process input: {@link Input}
+     * - Run inference for all configured models
+     * - Process and return result: {@link Result}
+     *
+     * @param input - {@link Input}
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    detect(input: Input, userConfig?: Partial<Config>): Promise<Result>;
+}
+export { Human }
+export default Human;
+
+/** Defines all possible image objects */
+export declare type ImageObjects = ImageData | ImageBitmap;
+
+/**
+ * Common interface for a machine learning model that can do inference.
+ */
+declare interface InferenceModel {
+    /**
+     * Return the array of input tensor info.
+     */
+    readonly inputs: ModelTensorInfo[];
+    /**
+     * Return the array of output tensor info.
+     */
+    readonly outputs: ModelTensorInfo[];
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size.
+     *
+     * @returns Inference result tensors. The output would be single Tensor if
+     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
+     * be returned for model with multiple outputs.
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    /**
+     * Single Execute the inference for the input tensors and return activation
+     * values for specified output node names without batching.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     *
+     * @param outputs string|string[]. List of output node names to retrieve
+     * activation from.
+     *
+     * @returns Activation values for the output nodes result tensors. The return
+     * type matches specified parameter outputs type. The output would be single
+     * Tensor if single output is specified, otherwise Tensor[] for multiple
+     * outputs.
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
+
+/** Defines all possible input types for **Human** detection */
+export declare type Input = Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas;
+
+declare namespace io {
+    export {
+        copyModel,
+        listModels,
+        moveModel,
+        removeModel,
+        browserFiles,
+        browserHTTPRequest,
+        concatenateArrayBuffers,
+        decodeWeights,
+        encodeWeights,
+        fromMemory,
+        fromMemorySync,
+        getLoadHandlers,
+        getModelArtifactsForJSON,
+        getModelArtifactsInfoForJSON,
+        getSaveHandlers,
+        http,
+        IOHandler,
+        IOHandlerSync,
+        isHTTPScheme,
+        LoadHandler,
+        LoadOptions,
+        loadWeights,
+        ModelArtifacts,
+        ModelArtifactsInfo,
+        ModelJSON,
+        ModelStoreManager,
+        OnProgressCallback,
+        registerLoadRouter,
+        registerSaveRouter,
+        RequestDetails,
+        SaveConfig,
+        SaveHandler,
+        SaveResult,
+        TrainingConfig,
+        WeightGroup,
+        weightsLoaderFactory,
+        WeightsManifestConfig,
+        WeightsManifestEntry,
+        withSaveHandler,
+        withSaveHandlerSync
+    }
+}
+
+/**
+ * Interface for a model import/export handler.
+ *
+ * The `save` and `load` handlers are both optional, in order to allow handlers
+ * that support only saving or loading.
+ */
+declare interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+
+declare type IOHandlerSync = {
+    [K in keyof IOHandler]: Syncify<IOHandler[K]>;
+};
+
+declare type IORouter = (url: string | string[], loadOptions?: LoadOptions) => IOHandler;
+
+/** iris gesture type */
+export declare type IrisGesture = 'facing center' | `looking ${'left' | 'right' | 'up' | 'down'}` | 'looking center';
+
+declare function isHTTPScheme(url: string): boolean;
+
+/**
+ * List all models stored in registered storage mediums.
+ *
+ * For a web browser environment, the registered mediums are Local Storage and
+ * IndexedDB.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @returns A `Promise` of a dictionary mapping URLs of existing models to
+ * their model artifacts info. URLs include medium-specific schemes, e.g.,
+ *   'indexeddb://my/model/1'. Model artifacts info include type of the
+ * model's topology, byte sizes of the topology, weights, etc.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+
+/** Load method preloads all instance.configured models on-demand */
+declare function load(instance: Human): Promise<void>;
+
+/**
+ * Type definition for handlers of loading operations.
+ */
+declare type LoadHandler = () => Promise<ModelArtifacts>;
+
+/** @innamespace io */
+declare interface LoadOptions {
+    /**
+     * RequestInit (options) for HTTP requests.
+     *
+     * For detailed information on the supported fields, see
+     * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
+     *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
+     */
+    requestInit?: RequestInit;
+    /**
+     * Progress callback.
+     */
+    onProgress?: OnProgressCallback;
+    /**
+     * A function used to override the `window.fetch` function.
+     */
+    fetchFunc?: Function;
+    /**
+     * Strict loading model: whether extraneous weights or missing
+     * weights should trigger an `Error`.
+     *
+     * If `true`, require that the provided weights exactly match those
+     * required by the layers. `false` means that both extra weights
+     * and missing weights will be silently ignored.
+     *
+     * Default: `true`.
+     */
+    strict?: boolean;
+    /**
+     * Path prefix for weight files, by default this is calculated from the
+     * path of the model JSON file.
+     *
+     * For instance, if the path to the model JSON file is
+     * `http://localhost/foo/model.json`, then the default path prefix will be
+     * `http://localhost/foo/`. If a weight file has the path value
+     * `group1-shard1of2` in the weight manifest, then the weight file will be
+     * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
+     * if you provide a `weightPathPrefix` value of
+     * `http://localhost/foo/alt-weights`, then the weight file will be loaded
+     * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
+     */
+    weightPathPrefix?: string;
+    /**
+     * Whether the module or model is to be loaded from TF Hub.
+     *
+     * Setting this to `true` allows passing a TF-Hub module URL, omitting the
+     * standard model file name and the query parameters.
+     *
+     * Default: `false`.
+     */
+    fromTFHub?: boolean;
+    /**
+     * An async function to convert weight file name to URL. The weight file
+     * names are stored in model.json's weightsManifest.paths field. By default we
+     * consider weight files are colocated with the model.json file. For example:
+     *     model.json URL: https://www.google.com/models/1/model.json
+     *     group1-shard1of1.bin url:
+     *        https://www.google.com/models/1/group1-shard1of1.bin
+     *
+     * With this func you can convert the weight file name to any URL.
+     */
+    weightUrlConverter?: (weightFileName: string) => Promise<string>;
+}
+
+/**
+ * Reads a weights manifest JSON configuration, fetches the weights and
+ * returns them as `Tensor`s.
+ *
+ * @param manifest The weights manifest JSON.
+ * @param filePathPrefix The path prefix for filenames given in the manifest.
+ *     Defaults to the empty string.
+ * @param weightNames The names of the weights to be fetched.
+ */
+declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestInit?: RequestInit): Promise<NamedTensorMap>;
+
+declare namespace match {
+    export {
+        distance,
+        similarity,
+        match_2 as match,
+        Descriptor,
+        MatchOptions
+    }
+}
+export { match }
+
+/** Matches given descriptor to a closest entry in array of descriptors
+ * @param descriptor - face descriptor
+ * @param descriptors - array of face descriptors to commpare given descriptor to
+ * @param options - see `similarity` method for options description
+ * Returns
+ * - `index` index array index where best match was found or -1 if no matches
+ * - `distance` calculated `distance` of given descriptor to the best match
+ * - `similarity` calculated normalized `similarity` of given descriptor to the best match
+ */
+declare function match_2(descriptor: Descriptor, descriptors: Array<Descriptor>, options?: MatchOptions): {
+    index: number;
+    distance: number;
+    similarity: number;
+};
+
+declare type MatchOptions = {
+    order?: number;
+    threshold?: number;
+    multiplier?: number;
+    min?: number;
+    max?: number;
+} | undefined;
+
+/**
+ * The serialized artifacts of a model, including topology and weights.
+ *
+ * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
+ * of this interface are optional, in order to support topology- or weights-only
+ * saving and loading.
+ *
+ * Note this interface is used internally in IOHandlers.  For the file format
+ * written to disk as `model.json`, see `ModelJSON`.
+ */
+declare interface ModelArtifacts {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology?: {} | ArrayBuffer;
+    /**
+     * Serialized configuration for the model's training.
+     */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weight specifications.
+     *
+     * This corresponds to the weightsData below.
+     */
+    weightSpecs?: WeightsManifestEntry[];
+    /**
+     * Binary buffer for all weight values concatenated in the order specified
+     * by `weightSpecs`.
+     */
+    weightData?: ArrayBuffer;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelArtifactsInfo {
+    /**
+     * Timestamp for when the model is saved.
+     */
+    dateSaved: Date;
+    /**
+     * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
+     * come in a JSON format and none of our IOHandlers support a non json
+     * format. We could conder replacing this with 'Binary' if we want to
+     * allow future handlers to save to non json formats (though they will
+     * probably want more information than 'Binary').
+     * Type of the model topology
+     *
+     * Type of the model topology
+     *
+     * Possible values:
+     *   - JSON: JSON config (human-readable, e.g., Keras JSON).
+     *   - GraphDef: TensorFlow
+     *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
+     *     protocol buffer (binary).
+     */
+    modelTopologyType: 'JSON' | 'GraphDef';
+    /**
+     * Size of model topology (Keras JSON or GraphDef), in bytes.
+     */
+    modelTopologyBytes?: number;
+    /**
+     * Size of weight specification or manifest, in bytes.
+     */
+    weightSpecsBytes?: number;
+    /**
+     * Size of weight value data, in bytes.
+     */
+    weightDataBytes?: number;
+}
+
+export declare type ModelInfo = {
+    name: string;
+    inCache: boolean;
+    sizeDesired: number;
+    sizeFromManifest: number;
+    sizeLoadedWeights: number;
+};
+
+/**
+ * The on-disk format of the `model.json` file.
+ *
+ * TF.js 1.0 always populates the optional fields when writing model.json.
+ * Prior versions did not provide those fields.
+ */
+declare interface ModelJSON {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology: {};
+    /** Model training configuration. */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weights manifest.
+     *
+     * The weights manifest consists of an ordered list of weight-manifest
+     * groups. Each weight-manifest group consists of a number of weight values
+     * stored in a number of paths. See the documentation of
+     * `WeightsManifestConfig` for more details.
+     */
+    weightsManifest: WeightsManifestConfig;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelPredictConfig {
+    /**
+     * Optional. Batch size (Integer). If unspecified, it will default to 32.
+     */
+    batchSize?: number;
+    /**
+     * Optional. Verbosity mode. Defaults to false.
+     */
+    verbose?: boolean;
+}
+
+/** Instances of all possible TFJS Graph Models used by Human
+ * - loaded as needed based on configuration
+ * - initialized explictly with `human.load()` method
+ * - initialized implicity on first call to `human.detect()`
+ * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
+ */
+export declare class Models {
+    ssrnetage: null | GraphModel | Promise<GraphModel>;
+    gear: null | GraphModel | Promise<GraphModel>;
+    blazeposedetect: null | GraphModel | Promise<GraphModel>;
+    blazepose: null | GraphModel | Promise<GraphModel>;
+    centernet: null | GraphModel | Promise<GraphModel>;
+    efficientpose: null | GraphModel | Promise<GraphModel>;
+    mobilefacenet: null | GraphModel | Promise<GraphModel>;
+    emotion: null | GraphModel | Promise<GraphModel>;
+    facedetect: null | GraphModel | Promise<GraphModel>;
+    faceiris: null | GraphModel | Promise<GraphModel>;
+    facemesh: null | GraphModel | Promise<GraphModel>;
+    faceres: null | GraphModel | Promise<GraphModel>;
+    ssrnetgender: null | GraphModel | Promise<GraphModel>;
+    handpose: null | GraphModel | Promise<GraphModel>;
+    handskeleton: null | GraphModel | Promise<GraphModel>;
+    handtrack: null | GraphModel | Promise<GraphModel>;
+    liveness: null | GraphModel | Promise<GraphModel>;
+    movenet: null | GraphModel | Promise<GraphModel>;
+    nanodet: null | GraphModel | Promise<GraphModel>;
+    posenet: null | GraphModel | Promise<GraphModel>;
+    segmentation: null | GraphModel | Promise<GraphModel>;
+    antispoof: null | GraphModel | Promise<GraphModel>;
+}
+
+declare namespace models {
+    export {
+        reset,
+        load,
+        validate,
+        Models,
+        ModelStats,
+        getModelStats
+    }
+}
+export { models }
+
+export declare type ModelStats = {
+    numLoadedModels: number;
+    numEnabledModels: undefined;
+    numDefinedModels: number;
+    percentageLoaded: number;
+    totalSizeFromManifest: number;
+    totalSizeWeights: number;
+    totalSizeLoading: number;
+    totalSizeEnabled: undefined;
+    modelStats: ModelInfo[];
+};
+
+/**
+ * An interface for the manager of a model store.
+ *
+ * A model store is defined as a storage medium on which multiple models can
+ * be stored. Each stored model has a unique `path` as its identifier.
+ * A `ModelStoreManager` for the store allows actions including
+ *
+ * - Listing the models stored in the store.
+ * - Deleting a model from the store.
+ */
+declare interface ModelStoreManager {
+    /**
+     * List all models in the model store.
+     *
+     * @returns A dictionary mapping paths of existing models to their
+     *   model artifacts info. Model artifacts info include type of the model's
+     *   topology, byte sizes of the topology, weights, etc.
+     */
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    /**
+     * Remove a model specified by `path`.
+     *
+     * @param path
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     */
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
+
+/**
+ * Interface for model input/output tensor info.
+ */
+declare interface ModelTensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+    tfDtype?: string;
+}
+
+/**
+ * Move a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Moving within a storage medium, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Moving between two storage mediums, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Move the model, from Local Storage to IndexedDB.
+ * await tf.io.moveModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove the moved model.
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of moving.
+ * @param destURL Destination URL of moving.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+declare interface NamedTensor {
+    name: string;
+    tensor: Tensor;
+}
+
+/** @docalias {[name: string]: Tensor} */
+declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+
+declare type NamedTensorsMap = {
+    [key: string]: Tensor[];
+};
+
+declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+
+/** draw detected objects */
+declare function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all object detection specific options */
+export declare interface ObjectConfig extends GenericConfig {
+    /** minimum confidence for a detected objects before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected objects before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected objects */
+    maxDetected: number;
+}
+
+/** Object results */
+export declare interface ObjectResult {
+    /** object id */
+    id: number;
+    /** object detection score */
+    score: number;
+    /** detected object class id */
+    class: number;
+    /** detected object class name */
+    label: ObjectType;
+    /** detected object box */
+    box: Box;
+    /** detected object box normalized to 0..1 */
+    boxRaw: Box;
+}
+
+export declare type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter' | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee' | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork' | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant' | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book' | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';
+
+/**
+ * Callback for the progress of a long-running action such as an HTTP
+ * request for a large binary object.
+ *
+ * `fraction` should be a number in the [0, 1] interval, indicating how
+ * much of the action has completed.
+ */
+declare type OnProgressCallback = (fraction: number) => void;
+
+/** currently set draw options {@link DrawOptions} */
+declare const options: DrawOptions;
+
+/** draw combined person results instead of individual detection result objects */
+declare function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Person getter
+ * - Triggers combining all individual results into a virtual person object
+ */
+export declare interface PersonResult {
+    /** person id */
+    id: number;
+    /** face result that belongs to this person */
+    face: FaceResult;
+    /** body result that belongs to this person */
+    body: BodyResult | null;
+    /** left and right hand results that belong to this person */
+    hands: {
+        left: HandResult | null;
+        right: HandResult | null;
+    };
+    /** detected gestures specific to this person */
+    gestures: Array<GestureResult>;
+    /** box that defines the person */
+    box: Box;
+    /** box that defines the person normalized to 0..1 */
+    boxRaw?: Box;
+}
+
+/** generic point as [x, y, z?] */
+export declare type Point = [number, number, number?];
+
+declare type PromiseFunction = (...args: unknown[]) => Promise<unknown>;
+
+export declare type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';
+
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
+declare const registerLoadRouter: (loudRouter: IORouter) => void;
+
+declare const registerSaveRouter: (loudRouter: IORouter) => void;
+
+/**
+ * Remove a model specified by URL from a reigstered storage medium.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @param url A URL to a stored model, with a scheme prefix, e.g.,
+ *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+ * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+ *   is successful).
+ * @throws Error if deletion fails, e.g., if no model exists at `path`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * Additional options for Platform.fetch
+ */
+declare interface RequestDetails {
+    /**
+     * Is this request for a binary file (as opposed to a json file)
+     */
+    isBinary?: boolean;
+}
+
+declare function reset(instance: Human): void;
+
+/**
+ * Result interface definition for **Human** library
+ *
+ * Contains all possible detection results
+ */
+export declare interface Result {
+    /** {@link FaceResult}: detection & analysis results */
+    face: Array<FaceResult>;
+    /** {@link BodyResult}: detection & analysis results */
+    body: Array<BodyResult>;
+    /** {@link HandResult}: detection & analysis results */
+    hand: Array<HandResult>;
+    /** {@link GestureResult}: detection & analysis results */
+    gesture: Array<GestureResult>;
+    /** {@link ObjectResult}: detection & analysis results */
+    object: Array<ObjectResult>;
+    /** global performance object with timing values for each operation */
+    performance: Record<string, number>;
+    /** optional processed canvas that can be used to draw input on screen */
+    canvas?: AnyCanvas | null;
+    /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
+    readonly timestamp: number;
+    /** getter property that returns unified persons object  */
+    persons: Array<PersonResult>;
+    /** Last known error message */
+    error: string | null;
+}
+
+/**
+ * Options for saving a model.
+ * @innamespace io
+ */
+declare interface SaveConfig {
+    /**
+     * Whether to save only the trainable weights of the model, ignoring the
+     * non-trainable ones.
+     */
+    trainableOnly?: boolean;
+    /**
+     * Whether the optimizer will be saved (if exists).
+     *
+     * Default: `false`.
+     */
+    includeOptimizer?: boolean;
+}
+
+/**
+ * Type definition for handlers of saving operations.
+ */
+declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+
+/**
+ * Result of a saving operation.
+ */
+declare interface SaveResult {
+    /**
+     * Information about the model artifacts saved.
+     */
+    modelArtifactsInfo: ModelArtifactsInfo;
+    /**
+     * HTTP responses from the server that handled the model-saving request (if
+     * any). This is applicable only to server-based saving routes.
+     */
+    responses?: Response[];
+    /**
+     * Error messages and related data (if any).
+     */
+    errors?: Array<{} | string>;
+}
+
+/** Configures all body segmentation module
+ * removes background from input containing person
+ * if segmentation is enabled it will run as preprocessing task before any other model
+ * alternatively leave it disabled and use it on-demand using human.segmentation method which can
+ * remove background or replace it with user-provided background
+ */
+export declare interface SegmentationConfig extends GenericConfig {
+    /** blur segmentation output by <number> pixels for more realistic image */
+    blur: number;
+}
+
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/// <amd-module name="@tensorflow/tfjs-core/dist/types" />
+/** @docalias number[] */
+declare interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+
+/** Calculates normalized similarity between two face descriptors based on their `distance`
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ * - min - normalize similarity result to a given range
+ * - max - normalzie similarity resutl to a given range
+ *   default is 0.2...0.8
+ * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
+ */
+declare function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+
+declare type Syncify<T extends PromiseFunction> = T extends (...args: infer Args) => Promise<infer R> ? (...args: Args) => R : never;
+
+export declare namespace Tensor { }
+
+/**
+ * A `tf.Tensor` object represents an immutable, multidimensional array of
+ * numbers that has a shape and a data type.
+ *
+ * For performance reasons, functions that create tensors do not necessarily
+ * perform a copy of the data passed to them (e.g. if the data is passed as a
+ * `Float32Array`), and changes to the data will change the tensor. This is not
+ * a feature and is not supported. To avoid this behavior, use the tensor before
+ * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+ *
+ * See `tf.tensor` for details on how to create a `tf.Tensor`.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+export declare class Tensor<R extends Rank = Rank> {
+    /** Unique id of this tensor. */
+    readonly id: number;
+    /**
+     * Id of the bucket holding the data for this tensor. Multiple arrays can
+     * point to the same bucket (e.g. when calling array.reshape()).
+     */
+    dataId: DataId;
+    /** The shape of the tensor. */
+    readonly shape: ShapeMap[R];
+    /** Number of elements in the tensor. */
+    readonly size: number;
+    /** The data type for the array. */
+    readonly dtype: DataType;
+    /** The rank type for the array (see `Rank` enum). */
+    readonly rankType: R;
+    /** Whether this tensor has been globally kept. */
+    kept: boolean;
+    /** The id of the scope this tensor is being tracked in. */
+    scopeId: number;
+    /**
+     * Number of elements to skip in each dimension when indexing. See
+     * https://docs.scipy.org/doc/numpy/reference/generated/\
+     * numpy.ndarray.strides.html
+     */
+    readonly strides: number[];
+    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
+    readonly rank: number;
+    /**
+     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
+    /**
+     * Returns a `tf.TensorBuffer` that holds the underlying data.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * asynchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    array(): Promise<ArrayMap[R]>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * synchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    arraySync(): ArrayMap[R];
+    /**
+     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+     * promise of `TypedArray` that resolves when the computation has finished.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    /**
+     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
+     * and `data()`, this method prevents data from being downloaded to CPU.
+     *
+     * For WebGL backend, the data will be stored on a densely packed texture.
+     * This means that the texture will use the RGBA channels to store value.
+     *
+     * For WebGPU backend, the data will be stored on a buffer. There is no
+     * parameter, so can not use an user defined size to create the buffer.
+     *
+     * @param options:
+     *     For WebGL,
+     *         - customTexShape: Optional. If set, will use the user defined
+     *     texture shape to create the texture.
+     *
+     * @returns For WebGL backend, a GPUData contains the new texture and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this texture,
+     *        texture: WebGLTexture,
+     *        texShape: [number, number] // [height, width]
+     *     }
+     *
+     *     For WebGPU backend, a GPUData contains the new buffer and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this buffer,
+     *        buffer: GPUBuffer,
+     *        bufSize: number
+     *     }
+     *
+     *     Remember to dispose the GPUData after it is used by
+     *     `res.tensorRef.dispose()`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataToGPU(options?: DataToGPUOptions): GPUData;
+    /**
+     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+     * UI thread until the values are ready, which can cause performance issues.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    /** Returns the underlying bytes of the tensor's data. */
+    bytes(): Promise<Uint8Array[] | Uint8Array>;
+    /**
+     * Disposes `tf.Tensor` from memory.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dispose(): void;
+    protected isDisposedInternal: boolean;
+    readonly isDisposed: boolean;
+    throwIfDisposed(): void;
+    /**
+     * Prints the `tf.Tensor`. See `tf.print` for details.
+     *
+     * @param verbose Whether to print verbose information about the tensor,
+     *    including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    print(verbose?: boolean): void;
+    /**
+     * Returns a copy of the tensor. See `tf.clone` for details.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    clone<T extends Tensor>(this: T): T;
+    /**
+     * Returns a human-readable description of the tensor. Useful for logging.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    toString(verbose?: boolean): string;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+}
+
+/**
+ * A mutable object, similar to `tf.Tensor`, that allows users to set values
+ * at locations before converting to an immutable `tf.Tensor`.
+ *
+ * See `tf.buffer` for creating a tensor buffer.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    /**
+     * Sets a value in the buffer at a given location.
+     *
+     * @param value The value to set.
+     * @param locs  The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    /**
+     * Returns the value in the buffer at the provided location.
+     *
+     * @param locs The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    /**
+     * Creates an immutable `tf.Tensor` object from the buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    toTensor(): Tensor<R>;
+}
+
+declare interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
+
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
+/** Model training configuration. */
+declare interface TrainingConfig {
+    /** Optimizer used for the model training. */
+    optimizer_config: {};
+    /** Loss function(s) for the model's output(s). */
+    loss: string | string[] | {
+        [key: string]: string;
+    };
+    /** Metric function(s) for the model's output(s). */
+    metrics?: string[] | {
+        [key: string]: string;
+    };
+    weighted_metrics?: string[];
+    sample_weight_mode?: string;
+    loss_weights?: number[] | {
+        [key: string]: number;
+    };
+}
+
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
+declare type Url = string | io.IOHandler | io.IOHandlerSync;
+
+declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
+
+declare function validate(instance: Human): Promise<void>;
+
+/**
+ * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
+    /**
+     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+     * the same shape and dtype as the old `tf.Tensor`.
+     *
+     * @param newValue New tensor to be assigned to this variable.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    assign(newValue: Tensor<R>): void;
+    dispose(): void;
+}
+
+/** Possible values for `human.warmup` */
+export declare type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];
+
+/**
+ * Group to which the weight belongs.
+ *
+ * - 'optimizer': Weight from a stateful optimizer.
+ */
+declare type WeightGroup = 'model' | 'optimizer';
+
+/**
+ * Creates a function, which reads a weights manifest JSON configuration,
+ * fetches the weight files using the specified function and returns them as
+ * `Tensor`s.
+ *
+ * ```js
+ * // example for creating a nodejs weight loader, which reads the weight files
+ * // from disk using fs.readFileSync
+ *
+ * import * as fs from 'fs'
+ *
+ * const fetchWeightsFromDisk = (filePaths: string[]) =>
+ *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+ *
+ * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+ *
+ * const manifest = JSON.parse(
+ *   fs.readFileSync('./my_model-weights_manifest').toString()
+ * )
+ * const weightMap = await loadWeights(manifest, './')
+ * ```
+ * @param fetchWeightsFunction The function used for fetching the weight files.
+ * @returns Weight loading function.
+ */
+declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
+
+/**
+ * A weight manifest.
+ *
+ * The weight manifest consists of an ordered list of weight-manifest groups.
+ * Each weight-manifest group ("group" for short hereafter) consists of a
+ * number of weight values stored in a number of paths.
+ * See the documentation of `WeightManifestGroupConfig` below for more details.
+ */
+declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+
+/**
+ * An entry in the weight manifest.
+ *
+ * The entry contains specification of a weight.
+ */
+declare interface WeightsManifestEntry {
+    /**
+     * Name of the weight, e.g., 'Dense_1/bias'
+     */
+    name: string;
+    /**
+     * Shape of the weight.
+     */
+    shape: number[];
+    /**
+     * Data type of the weight.
+     */
+    dtype: 'float32' | 'int32' | 'bool' | 'string' | 'complex64';
+    /**
+     * Type of the weight.
+     *
+     * Optional.
+     *
+     * The value 'optimizer' indicates the weight belongs to an optimizer
+     * (i.e., used only during model training and not during inference).
+     */
+    group?: WeightGroup;
+    /**
+     * Information for dequantization of the weight.
+     */
+    quantization?: {
+        scale?: number;
+        min?: number;
+        dtype: 'uint16' | 'uint8' | 'float16';
+    };
+}
+
+/**
+ * A weight-manifest group.
+ *
+ * Consists of an ordered list of weight values encoded in binary format,
+ * stored in an ordered list of paths.
+ */
+declare interface WeightsManifestGroupConfig {
+    /**
+     * An ordered list of paths.
+     *
+     * Paths are intentionally abstract in order to be general. For example, they
+     * can be relative URL paths or relative paths on the file system.
+     */
+    paths: string[];
+    /**
+     * Specifications of the weights stored in the paths.
+     */
+    weights: WeightsManifestEntry[];
+}
+
+/**
+ * Creates an IOHandler that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     promise that resolves to a `SaveResult`.
+ */
+declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
+
+/**
+ * Creates an IOHandlerSync that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     `SaveResult`.
+ */
+declare function withSaveHandlerSync(saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync;
+
+export { }
diff --git a/dist/human.esm.js b/dist/human.esm.js
new file mode 100644
index 00000000..73d93c0b
--- /dev/null
+++ b/dist/human.esm.js
@@ -0,0 +1,87055 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var __defProp = Object.defineProperty;
+var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
+var __export = (target, all6) => {
+  for (var name in all6)
+    __defProp(target, name, { get: all6[name], enumerable: true });
+};
+var __publicField = (obj, key, value) => {
+  __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
+  return value;
+};
+var __accessCheck = (obj, member, msg) => {
+  if (!member.has(obj))
+    throw TypeError("Cannot " + msg);
+};
+var __privateGet = (obj, member, getter) => {
+  __accessCheck(obj, member, "read from private field");
+  return getter ? getter.call(obj) : member.get(obj);
+};
+var __privateAdd = (obj, member, value) => {
+  if (member.has(obj))
+    throw TypeError("Cannot add the same private member more than once");
+  member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
+};
+var __privateSet = (obj, member, value, setter) => {
+  __accessCheck(obj, member, "write to private field");
+  setter ? setter.call(obj, value) : member.set(obj, value);
+  return value;
+};
+
+// src/util/util.ts
+function log(...msg) {
+  const dt = new Date();
+  const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
+  if (msg)
+    console.log(ts, "Human:", ...msg);
+}
+function join(folder, file) {
+  const separator = folder.endsWith("/") ? "" : "/";
+  const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
+  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
+  if (!path.toLocaleLowerCase().includes(".json"))
+    throw new Error(`modelpath error: expecting json file: ${path}`);
+  return path;
+}
+var now = () => {
+  if (typeof performance !== "undefined")
+    return performance.now();
+  return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
+};
+function validate(defaults, config3, parent = "config", msgs = []) {
+  for (const key of Object.keys(config3)) {
+    if (typeof config3[key] === "object") {
+      validate(defaults[key], config3[key], key, msgs);
+    } else {
+      const defined = defaults && typeof defaults[key] !== "undefined";
+      if (!defined)
+        msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
+      const same = defaults && typeof defaults[key] === typeof config3[key];
+      if (defined && !same)
+        msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
+    }
+  }
+  if (config3.debug && parent === "config" && msgs.length > 0)
+    log("invalid configuration", msgs);
+  return msgs;
+}
+function mergeDeep(...objects) {
+  const isObject = (obj) => obj && typeof obj === "object";
+  return objects.reduce((prev, obj) => {
+    Object.keys(obj || {}).forEach((key) => {
+      const pVal = prev[key];
+      const oVal = obj[key];
+      if (Array.isArray(pVal) && Array.isArray(oVal))
+        prev[key] = pVal.concat(...oVal);
+      else if (isObject(pVal) && isObject(oVal))
+        prev[key] = mergeDeep(pVal, oVal);
+      else
+        prev[key] = oVal;
+    });
+    return prev;
+  }, {});
+}
+
+// src/config.ts
+var config = {
+  backend: "",
+  modelBasePath: "",
+  cacheModels: true,
+  wasmPath: "",
+  wasmPlatformFetch: false,
+  debug: false,
+  async: true,
+  warmup: "full",
+  cacheSensitivity: 0.7,
+  skipAllowed: false,
+  deallocate: false,
+  filter: {
+    enabled: true,
+    equalization: false,
+    width: 0,
+    height: 0,
+    flip: false,
+    return: true,
+    brightness: 0,
+    contrast: 0,
+    sharpness: 0,
+    blur: 0,
+    saturation: 0,
+    hue: 0,
+    negative: false,
+    sepia: false,
+    vintage: false,
+    kodachrome: false,
+    technicolor: false,
+    polaroid: false,
+    pixelate: 0
+  },
+  gesture: {
+    enabled: true
+  },
+  face: {
+    enabled: true,
+    detector: {
+      modelPath: "blazeface.json",
+      rotation: true,
+      maxDetected: 1,
+      skipFrames: 99,
+      skipTime: 2500,
+      minConfidence: 0.2,
+      iouThreshold: 0.1,
+      mask: false,
+      return: false
+    },
+    mesh: {
+      enabled: true,
+      modelPath: "facemesh.json",
+      keepInvalid: false
+    },
+    attention: {
+      enabled: false,
+      modelPath: "facemesh-attention.json"
+    },
+    iris: {
+      enabled: true,
+      modelPath: "iris.json"
+    },
+    emotion: {
+      enabled: true,
+      minConfidence: 0.1,
+      skipFrames: 99,
+      skipTime: 1500,
+      modelPath: "emotion.json"
+    },
+    description: {
+      enabled: true,
+      modelPath: "faceres.json",
+      skipFrames: 99,
+      skipTime: 3e3,
+      minConfidence: 0.1
+    },
+    antispoof: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "antispoof.json"
+    },
+    liveness: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "liveness.json"
+    }
+  },
+  body: {
+    enabled: true,
+    modelPath: "movenet-lightning.json",
+    maxDetected: -1,
+    minConfidence: 0.3,
+    skipFrames: 1,
+    skipTime: 200
+  },
+  hand: {
+    enabled: true,
+    rotation: true,
+    skipFrames: 99,
+    skipTime: 1e3,
+    minConfidence: 0.5,
+    iouThreshold: 0.2,
+    maxDetected: -1,
+    landmarks: true,
+    detector: {
+      modelPath: "handtrack.json"
+    },
+    skeleton: {
+      modelPath: "handlandmark-full.json"
+    }
+  },
+  object: {
+    enabled: false,
+    modelPath: "mb3-centernet.json",
+    minConfidence: 0.2,
+    iouThreshold: 0.4,
+    maxDetected: 10,
+    skipFrames: 99,
+    skipTime: 2e3
+  },
+  segmentation: {
+    enabled: false,
+    modelPath: "selfie.json",
+    blur: 8
+  }
+};
+
+// dist/tfjs.esm.js
+var tfjs_esm_exports = {};
+__export(tfjs_esm_exports, {
+  Abs: () => Abs,
+  Acos: () => Acos,
+  Acosh: () => Acosh,
+  AdadeltaOptimizer: () => AdadeltaOptimizer,
+  AdagradOptimizer: () => AdagradOptimizer,
+  AdamOptimizer: () => AdamOptimizer,
+  AdamaxOptimizer: () => AdamaxOptimizer,
+  Add: () => Add,
+  AddN: () => AddN,
+  All: () => All,
+  Any: () => Any,
+  ArgMax: () => ArgMax,
+  ArgMin: () => ArgMin,
+  Asin: () => Asin,
+  Asinh: () => Asinh,
+  Atan: () => Atan,
+  Atan2: () => Atan2,
+  Atanh: () => Atanh,
+  AvgPool: () => AvgPool,
+  AvgPool3D: () => AvgPool3D,
+  AvgPool3DGrad: () => AvgPool3DGrad,
+  AvgPoolGrad: () => AvgPoolGrad,
+  BackendWasm: () => BackendWasm,
+  BatchMatMul: () => BatchMatMul,
+  BatchToSpaceND: () => BatchToSpaceND,
+  Bincount: () => Bincount,
+  BroadcastArgs: () => BroadcastArgs,
+  BroadcastTo: () => BroadcastTo,
+  Callback: () => Callback,
+  CallbackList: () => CallbackList,
+  Cast: () => Cast,
+  Ceil: () => Ceil,
+  ClipByValue: () => ClipByValue,
+  Complex: () => Complex,
+  ComplexAbs: () => ComplexAbs,
+  Concat: () => Concat,
+  Conv2D: () => Conv2D,
+  Conv2DBackpropFilter: () => Conv2DBackpropFilter,
+  Conv2DBackpropInput: () => Conv2DBackpropInput,
+  Conv3D: () => Conv3D,
+  Conv3DBackpropFilterV2: () => Conv3DBackpropFilterV2,
+  Conv3DBackpropInputV2: () => Conv3DBackpropInputV2,
+  Cos: () => Cos,
+  Cosh: () => Cosh,
+  CropAndResize: () => CropAndResize,
+  Cumprod: () => Cumprod,
+  Cumsum: () => Cumsum,
+  CustomCallback: () => CustomCallback,
+  DataStorage: () => DataStorage,
+  DenseBincount: () => DenseBincount,
+  DepthToSpace: () => DepthToSpace,
+  DepthwiseConv2dNative: () => DepthwiseConv2dNative,
+  DepthwiseConv2dNativeBackpropFilter: () => DepthwiseConv2dNativeBackpropFilter,
+  DepthwiseConv2dNativeBackpropInput: () => DepthwiseConv2dNativeBackpropInput,
+  Diag: () => Diag,
+  Dilation2D: () => Dilation2D,
+  Dilation2DBackpropFilter: () => Dilation2DBackpropFilter,
+  Dilation2DBackpropInput: () => Dilation2DBackpropInput,
+  ENV: () => ENV,
+  EarlyStopping: () => EarlyStopping,
+  Einsum: () => Einsum,
+  Elu: () => Elu,
+  EluGrad: () => EluGrad,
+  Environment: () => Environment,
+  Equal: () => Equal,
+  Erf: () => Erf,
+  Exp: () => Exp,
+  ExpandDims: () => ExpandDims,
+  Expm1: () => Expm1,
+  FFT: () => FFT,
+  Fill: () => Fill,
+  FlipLeftRight: () => FlipLeftRight,
+  Floor: () => Floor,
+  FloorDiv: () => FloorDiv,
+  FromPixels: () => FromPixels,
+  FusedBatchNorm: () => FusedBatchNorm,
+  FusedConv2D: () => FusedConv2D,
+  FusedDepthwiseConv2D: () => FusedDepthwiseConv2D,
+  GPGPUContext: () => GPGPUContext,
+  GatherNd: () => GatherNd,
+  GatherV2: () => GatherV2,
+  GraphModel: () => GraphModel,
+  Greater: () => Greater,
+  GreaterEqual: () => GreaterEqual,
+  History: () => History,
+  IFFT: () => IFFT,
+  Identity: () => Identity,
+  Imag: () => Imag,
+  InputSpec: () => InputSpec,
+  IsFinite: () => IsFinite,
+  IsInf: () => IsInf,
+  IsNan: () => IsNan,
+  KernelBackend: () => KernelBackend,
+  LRN: () => LRN,
+  LRNGrad: () => LRNGrad,
+  LayerVariable: () => LayerVariable,
+  LayersModel: () => LayersModel,
+  LeakyRelu: () => LeakyRelu,
+  Less: () => Less,
+  LessEqual: () => LessEqual,
+  LinSpace: () => LinSpace,
+  Log: () => Log,
+  Log1p: () => Log1p,
+  LogSoftmax: () => LogSoftmax,
+  LogicalAnd: () => LogicalAnd,
+  LogicalNot: () => LogicalNot,
+  LogicalOr: () => LogicalOr,
+  LowerBound: () => LowerBound,
+  MathBackendWebGL: () => MathBackendWebGL,
+  Max: () => Max,
+  MaxPool: () => MaxPool,
+  MaxPool3D: () => MaxPool3D,
+  MaxPool3DGrad: () => MaxPool3DGrad,
+  MaxPoolGrad: () => MaxPoolGrad,
+  MaxPoolWithArgmax: () => MaxPoolWithArgmax,
+  Maximum: () => Maximum,
+  Mean: () => Mean,
+  Min: () => Min,
+  Minimum: () => Minimum,
+  MirrorPad: () => MirrorPad,
+  Mod: () => Mod,
+  MomentumOptimizer: () => MomentumOptimizer,
+  Multinomial: () => Multinomial,
+  Multiply: () => Multiply,
+  Neg: () => Neg,
+  NonMaxSuppressionV3: () => NonMaxSuppressionV3,
+  NonMaxSuppressionV4: () => NonMaxSuppressionV4,
+  NonMaxSuppressionV5: () => NonMaxSuppressionV5,
+  NotEqual: () => NotEqual,
+  OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,
+  OneHot: () => OneHot,
+  OnesLike: () => OnesLike,
+  Optimizer: () => Optimizer,
+  OptimizerConstructors: () => OptimizerConstructors,
+  Pack: () => Pack,
+  PadV2: () => PadV2,
+  Pool: () => Pool,
+  Pow: () => Pow,
+  Prelu: () => Prelu,
+  Prod: () => Prod,
+  RMSPropOptimizer: () => RMSPropOptimizer,
+  RNN: () => RNN,
+  Range: () => Range,
+  Rank: () => Rank,
+  Real: () => Real,
+  RealDiv: () => RealDiv,
+  Reciprocal: () => Reciprocal,
+  Reduction: () => Reduction,
+  Relu: () => Relu,
+  Relu6: () => Relu6,
+  Reshape: () => Reshape,
+  ResizeBilinear: () => ResizeBilinear,
+  ResizeBilinearGrad: () => ResizeBilinearGrad,
+  ResizeNearestNeighbor: () => ResizeNearestNeighbor,
+  ResizeNearestNeighborGrad: () => ResizeNearestNeighborGrad,
+  Reverse: () => Reverse,
+  RotateWithOffset: () => RotateWithOffset,
+  Round: () => Round,
+  Rsqrt: () => Rsqrt,
+  SGDOptimizer: () => SGDOptimizer,
+  ScatterNd: () => ScatterNd,
+  SearchSorted: () => SearchSorted,
+  Select: () => Select,
+  Selu: () => Selu,
+  Sequential: () => Sequential,
+  Sigmoid: () => Sigmoid,
+  Sign: () => Sign,
+  Sin: () => Sin,
+  Sinh: () => Sinh,
+  Slice: () => Slice,
+  Softmax: () => Softmax,
+  Softplus: () => Softplus,
+  SpaceToBatchND: () => SpaceToBatchND,
+  SparseFillEmptyRows: () => SparseFillEmptyRows,
+  SparseReshape: () => SparseReshape,
+  SparseSegmentMean: () => SparseSegmentMean,
+  SparseSegmentSum: () => SparseSegmentSum,
+  SparseToDense: () => SparseToDense,
+  SplitV: () => SplitV,
+  Sqrt: () => Sqrt,
+  Square: () => Square,
+  SquaredDifference: () => SquaredDifference,
+  Step: () => Step,
+  StridedSlice: () => StridedSlice,
+  StringNGrams: () => StringNGrams,
+  StringSplit: () => StringSplit,
+  StringToHashBucketFast: () => StringToHashBucketFast,
+  Sub: () => Sub,
+  Sum: () => Sum,
+  SymbolicTensor: () => SymbolicTensor,
+  Tan: () => Tan,
+  Tanh: () => Tanh,
+  Tensor: () => Tensor,
+  TensorBuffer: () => TensorBuffer,
+  Tile: () => Tile,
+  TopK: () => TopK,
+  Transform: () => Transform,
+  Transpose: () => Transpose,
+  Unique: () => Unique,
+  Unpack: () => Unpack,
+  UnsortedSegmentSum: () => UnsortedSegmentSum,
+  UpperBound: () => UpperBound,
+  Variable: () => Variable,
+  ZerosLike: () => ZerosLike,
+  _FusedMatMul: () => _FusedMatMul,
+  abs: () => abs,
+  acos: () => acos,
+  acosh: () => acosh,
+  add: () => add2,
+  addN: () => addN,
+  all: () => all,
+  any: () => any,
+  argMax: () => argMax,
+  argMin: () => argMin,
+  asin: () => asin,
+  asinh: () => asinh,
+  atan: () => atan,
+  atan2: () => atan2,
+  atanh: () => atanh,
+  avgPool: () => avgPool,
+  avgPool3d: () => avgPool3d,
+  backend: () => backend,
+  backend_util: () => backend_util_exports,
+  basicLSTMCell: () => basicLSTMCell,
+  batchNorm: () => batchNorm,
+  batchNorm2d: () => batchNorm2d,
+  batchNorm3d: () => batchNorm3d,
+  batchNorm4d: () => batchNorm4d,
+  batchToSpaceND: () => batchToSpaceND,
+  bincount: () => bincount,
+  booleanMaskAsync: () => booleanMaskAsync,
+  broadcastArgs: () => broadcastArgs,
+  broadcastTo: () => broadcastTo,
+  broadcast_util: () => broadcast_util_exports,
+  browser: () => browser_exports,
+  buffer: () => buffer,
+  callbacks: () => callbacks,
+  cast: () => cast,
+  ceil: () => ceil,
+  clipByValue: () => clipByValue,
+  clone: () => clone,
+  complex: () => complex,
+  concat: () => concat,
+  concat1d: () => concat1d,
+  concat2d: () => concat2d,
+  concat3d: () => concat3d,
+  concat4d: () => concat4d,
+  constraints: () => exports_constraints_exports,
+  conv1d: () => conv1d,
+  conv2d: () => conv2d,
+  conv2dTranspose: () => conv2dTranspose,
+  conv3d: () => conv3d,
+  conv3dTranspose: () => conv3dTranspose,
+  copyRegisteredKernels: () => copyRegisteredKernels,
+  cos: () => cos,
+  cosh: () => cosh,
+  cosineWindow: () => cosineWindow,
+  cumprod: () => cumprod,
+  cumsum: () => cumsum,
+  customGrad: () => customGrad,
+  data: () => dist_exports2,
+  denseBincount: () => denseBincount,
+  deprecationWarn: () => deprecationWarn,
+  depthToSpace: () => depthToSpace,
+  depthwiseConv2d: () => depthwiseConv2d,
+  deregisterOp: () => deregisterOp,
+  device_util: () => device_util_exports,
+  diag: () => diag,
+  dilation2d: () => dilation2d,
+  disableDeprecationWarnings: () => disableDeprecationWarnings,
+  dispose: () => dispose,
+  disposeVariables: () => disposeVariables,
+  div: () => div,
+  divNoNan: () => divNoNan,
+  dot: () => dot,
+  dropout: () => dropout,
+  einsum: () => einsum,
+  elu: () => elu,
+  enableDebugMode: () => enableDebugMode,
+  enableProdMode: () => enableProdMode,
+  enclosingPowerOfTwo: () => enclosingPowerOfTwo,
+  engine: () => engine,
+  env: () => env,
+  equal: () => equal,
+  erf: () => erf,
+  euclideanNorm: () => euclideanNorm,
+  exp: () => exp,
+  expandDims: () => expandDims,
+  expm1: () => expm1,
+  eye: () => eye,
+  fft: () => fft,
+  fill: () => fill,
+  findBackend: () => findBackend,
+  findBackendFactory: () => findBackendFactory,
+  floor: () => floor,
+  floorDiv: () => floorDiv,
+  forceHalfFloat: () => forceHalfFloat,
+  fused: () => fused_ops_exports,
+  gather: () => gather,
+  gatherND: () => gatherND,
+  gather_util: () => gather_nd_util_exports,
+  getBackend: () => getBackend,
+  getGradient: () => getGradient,
+  getKernel: () => getKernel,
+  getKernelsForBackend: () => getKernelsForBackend,
+  getThreadsCount: () => getThreadsCount,
+  gpgpu_util: () => gpgpu_util_exports,
+  grad: () => grad,
+  grads: () => grads,
+  greater: () => greater,
+  greaterEqual: () => greaterEqual,
+  ifft: () => ifft,
+  imag: () => imag,
+  image: () => image,
+  inTopKAsync: () => inTopKAsync,
+  initializers: () => exports_initializers_exports,
+  input: () => input,
+  io: () => io_exports,
+  irfft: () => irfft,
+  isFinite: () => isFinite2,
+  isInf: () => isInf,
+  isNaN: () => isNaN2,
+  keep: () => keep,
+  kernel_impls: () => kernel_impls_exports,
+  layers: () => exports_layers_exports,
+  leakyRelu: () => leakyRelu,
+  less: () => less,
+  lessEqual: () => lessEqual,
+  linalg: () => linalg,
+  linspace: () => linspace,
+  loadGraphModel: () => loadGraphModel,
+  loadGraphModelSync: () => loadGraphModelSync,
+  loadLayersModel: () => loadLayersModel,
+  localResponseNormalization: () => localResponseNormalization,
+  log: () => log22,
+  log1p: () => log1p,
+  logSigmoid: () => logSigmoid,
+  logSoftmax: () => logSoftmax,
+  logSumExp: () => logSumExp,
+  logicalAnd: () => logicalAnd,
+  logicalNot: () => logicalNot,
+  logicalOr: () => logicalOr,
+  logicalXor: () => logicalXor,
+  losses: () => losses,
+  lowerBound: () => lowerBound,
+  matMul: () => matMul,
+  math: () => math_exports,
+  max: () => max,
+  maxPool: () => maxPool,
+  maxPool3d: () => maxPool3d,
+  maxPoolWithArgmax: () => maxPoolWithArgmax,
+  maximum: () => maximum,
+  mean: () => mean,
+  memory: () => memory,
+  meshgrid: () => meshgrid,
+  metrics: () => exports_metrics_exports,
+  min: () => min,
+  minimum: () => minimum,
+  mirrorPad: () => mirrorPad,
+  mod: () => mod,
+  model: () => model,
+  models: () => exports_models_exports,
+  moments: () => moments,
+  movingAverage: () => movingAverage,
+  mul: () => mul,
+  multiRNNCell: () => multiRNNCell,
+  multinomial: () => multinomial,
+  neg: () => neg,
+  nextFrame: () => nextFrame,
+  norm: () => norm,
+  notEqual: () => notEqual,
+  oneHot: () => oneHot,
+  ones: () => ones2,
+  onesLike: () => onesLike,
+  op: () => op,
+  outerProduct: () => outerProduct,
+  pad: () => pad,
+  pad1d: () => pad1d,
+  pad2d: () => pad2d,
+  pad3d: () => pad3d,
+  pad4d: () => pad4d,
+  pool: () => pool,
+  pow: () => pow,
+  prelu: () => prelu,
+  print: () => print,
+  prod: () => prod,
+  profile: () => profile,
+  rand: () => rand,
+  randomGamma: () => randomGamma,
+  randomNormal: () => randomNormal,
+  randomUniform: () => randomUniform,
+  range: () => range,
+  ready: () => ready,
+  real: () => real,
+  reciprocal: () => reciprocal,
+  registerBackend: () => registerBackend,
+  registerCallbackConstructor: () => registerCallbackConstructor,
+  registerGradient: () => registerGradient,
+  registerKernel: () => registerKernel,
+  registerOp: () => registerOp,
+  regularizers: () => exports_regularizers_exports,
+  relu: () => relu,
+  relu6: () => relu6,
+  removeBackend: () => removeBackend,
+  reshape: () => reshape,
+  reverse: () => reverse,
+  reverse1d: () => reverse1d,
+  reverse2d: () => reverse2d,
+  reverse3d: () => reverse3d,
+  reverse4d: () => reverse4d,
+  rfft: () => rfft,
+  round: () => round2,
+  rsqrt: () => rsqrt,
+  scalar: () => scalar,
+  scatterND: () => scatterND,
+  scatter_util: () => scatter_nd_util_exports,
+  searchSorted: () => searchSorted,
+  selu: () => selu,
+  separableConv2d: () => separableConv2d,
+  sequential: () => sequential,
+  serialization: () => serialization_exports,
+  setBackend: () => setBackend,
+  setPlatform: () => setPlatform,
+  setThreadsCount: () => setThreadsCount,
+  setWasmPath: () => setWasmPath,
+  setWasmPaths: () => setWasmPaths,
+  setWebGLContext: () => setWebGLContext,
+  setdiff1dAsync: () => setdiff1dAsync,
+  sigmoid: () => sigmoid,
+  sign: () => sign,
+  signal: () => signal,
+  sin: () => sin,
+  sinh: () => sinh,
+  slice: () => slice,
+  slice1d: () => slice1d,
+  slice2d: () => slice2d,
+  slice3d: () => slice3d,
+  slice4d: () => slice4d,
+  slice_util: () => slice_util_exports,
+  softmax: () => softmax,
+  softplus: () => softplus,
+  spaceToBatchND: () => spaceToBatchND,
+  sparse: () => sparse,
+  sparseToDense: () => sparseToDense,
+  spectral: () => spectral,
+  split: () => split,
+  sqrt: () => sqrt,
+  square: () => square,
+  squaredDifference: () => squaredDifference,
+  squeeze: () => squeeze,
+  stack: () => stack,
+  step: () => step,
+  stridedSlice: () => stridedSlice,
+  string: () => string,
+  sub: () => sub,
+  sum: () => sum2,
+  sumOutType: () => sumOutType,
+  tan: () => tan,
+  tanh: () => tanh2,
+  tensor: () => tensor,
+  tensor1d: () => tensor1d,
+  tensor2d: () => tensor2d,
+  tensor3d: () => tensor3d,
+  tensor4d: () => tensor4d,
+  tensor5d: () => tensor5d,
+  tensor6d: () => tensor6d,
+  tensor_util: () => tensor_util_exports,
+  test_util: () => test_util_exports,
+  tidy: () => tidy,
+  tile: () => tile,
+  time: () => time,
+  topk: () => topk,
+  train: () => train,
+  transpose: () => transpose,
+  truncatedNormal: () => truncatedNormal,
+  unique: () => unique,
+  unregisterGradient: () => unregisterGradient,
+  unregisterKernel: () => unregisterKernel,
+  unsortedSegmentSum: () => unsortedSegmentSum,
+  unstack: () => unstack,
+  upcastType: () => upcastType,
+  upperBound: () => upperBound,
+  util: () => util_exports,
+  valueAndGrad: () => valueAndGrad,
+  valueAndGrads: () => valueAndGrads,
+  variable: () => variable,
+  variableGrads: () => variableGrads,
+  version: () => V,
+  version_converter: () => version3,
+  version_core: () => version,
+  version_layers: () => version2,
+  version_wasm: () => version8,
+  version_webgl: () => version6,
+  webgl: () => webgl,
+  webgl_util: () => webgl_util_exports,
+  webgpu: () => webgpu_exports,
+  where: () => where,
+  whereAsync: () => whereAsync,
+  zeros: () => zeros,
+  zerosLike: () => zerosLike
+});
+var __create = Object.create;
+var __defProp2 = Object.defineProperty;
+var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
+var __getOwnPropNames = Object.getOwnPropertyNames;
+var __getProtoOf = Object.getPrototypeOf;
+var __hasOwnProp = Object.prototype.hasOwnProperty;
+var __commonJS = (cb, mod4) => function __require() {
+  return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports;
+};
+var __export2 = (target, all52) => {
+  for (var name in all52)
+    __defProp2(target, name, { get: all52[name], enumerable: true });
+};
+var __copyProps = (to, from, except, desc) => {
+  if (from && typeof from === "object" || typeof from === "function") {
+    for (let key of __getOwnPropNames(from))
+      if (!__hasOwnProp.call(to, key) && key !== except)
+        __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
+  }
+  return to;
+};
+var __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps(isNodeMode || !mod4 || !mod4.__esModule ? __defProp2(target, "default", { value: mod4, enumerable: true }) : target, mod4));
+var require_long = __commonJS({
+  "node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(exports, module) {
+    module.exports = Long2;
+    var wasm = null;
+    try {
+      wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([
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+        132,
+        127,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        128,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        129,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        130,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11
+      ])), {}).exports;
+    } catch (e2) {
+    }
+    function Long2(low, high, unsigned) {
+      this.low = low | 0;
+      this.high = high | 0;
+      this.unsigned = !!unsigned;
+    }
+    Long2.prototype.__isLong__;
+    Object.defineProperty(Long2.prototype, "__isLong__", { value: true });
+    function isLong(obj) {
+      return (obj && obj["__isLong__"]) === true;
+    }
+    Long2.isLong = isLong;
+    var INT_CACHE = {};
+    var UINT_CACHE = {};
+    function fromInt(value, unsigned) {
+      var obj, cachedObj, cache7;
+      if (unsigned) {
+        value >>>= 0;
+        if (cache7 = 0 <= value && value < 256) {
+          cachedObj = UINT_CACHE[value];
+          if (cachedObj)
+            return cachedObj;
+        }
+        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);
+        if (cache7)
+          UINT_CACHE[value] = obj;
+        return obj;
+      } else {
+        value |= 0;
+        if (cache7 = -128 <= value && value < 128) {
+          cachedObj = INT_CACHE[value];
+          if (cachedObj)
+            return cachedObj;
+        }
+        obj = fromBits(value, value < 0 ? -1 : 0, false);
+        if (cache7)
+          INT_CACHE[value] = obj;
+        return obj;
+      }
+    }
+    Long2.fromInt = fromInt;
+    function fromNumber(value, unsigned) {
+      if (isNaN(value))
+        return unsigned ? UZERO : ZERO;
+      if (unsigned) {
+        if (value < 0)
+          return UZERO;
+        if (value >= TWO_PWR_64_DBL)
+          return MAX_UNSIGNED_VALUE;
+      } else {
+        if (value <= -TWO_PWR_63_DBL)
+          return MIN_VALUE;
+        if (value + 1 >= TWO_PWR_63_DBL)
+          return MAX_VALUE;
+      }
+      if (value < 0)
+        return fromNumber(-value, unsigned).neg();
+      return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);
+    }
+    Long2.fromNumber = fromNumber;
+    function fromBits(lowBits, highBits, unsigned) {
+      return new Long2(lowBits, highBits, unsigned);
+    }
+    Long2.fromBits = fromBits;
+    var pow_dbl = Math.pow;
+    function fromString(str, unsigned, radix) {
+      if (str.length === 0)
+        throw Error("empty string");
+      if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity")
+        return ZERO;
+      if (typeof unsigned === "number") {
+        radix = unsigned, unsigned = false;
+      } else {
+        unsigned = !!unsigned;
+      }
+      radix = radix || 10;
+      if (radix < 2 || 36 < radix)
+        throw RangeError("radix");
+      var p2;
+      if ((p2 = str.indexOf("-")) > 0)
+        throw Error("interior hyphen");
+      else if (p2 === 0) {
+        return fromString(str.substring(1), unsigned, radix).neg();
+      }
+      var radixToPower = fromNumber(pow_dbl(radix, 8));
+      var result = ZERO;
+      for (var i2 = 0; i2 < str.length; i2 += 8) {
+        var size2 = Math.min(8, str.length - i2), value = parseInt(str.substring(i2, i2 + size2), radix);
+        if (size2 < 8) {
+          var power = fromNumber(pow_dbl(radix, size2));
+          result = result.mul(power).add(fromNumber(value));
+        } else {
+          result = result.mul(radixToPower);
+          result = result.add(fromNumber(value));
+        }
+      }
+      result.unsigned = unsigned;
+      return result;
+    }
+    Long2.fromString = fromString;
+    function fromValue(val, unsigned) {
+      if (typeof val === "number")
+        return fromNumber(val, unsigned);
+      if (typeof val === "string")
+        return fromString(val, unsigned);
+      return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned);
+    }
+    Long2.fromValue = fromValue;
+    var TWO_PWR_16_DBL = 1 << 16;
+    var TWO_PWR_24_DBL = 1 << 24;
+    var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;
+    var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;
+    var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;
+    var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);
+    var ZERO = fromInt(0);
+    Long2.ZERO = ZERO;
+    var UZERO = fromInt(0, true);
+    Long2.UZERO = UZERO;
+    var ONE = fromInt(1);
+    Long2.ONE = ONE;
+    var UONE = fromInt(1, true);
+    Long2.UONE = UONE;
+    var NEG_ONE = fromInt(-1);
+    Long2.NEG_ONE = NEG_ONE;
+    var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);
+    Long2.MAX_VALUE = MAX_VALUE;
+    var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);
+    Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
+    var MIN_VALUE = fromBits(0, 2147483648 | 0, false);
+    Long2.MIN_VALUE = MIN_VALUE;
+    var LongPrototype = Long2.prototype;
+    LongPrototype.toInt = function toInt() {
+      return this.unsigned ? this.low >>> 0 : this.low;
+    };
+    LongPrototype.toNumber = function toNumber() {
+      if (this.unsigned)
+        return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);
+      return this.high * TWO_PWR_32_DBL + (this.low >>> 0);
+    };
+    LongPrototype.toString = function toString(radix) {
+      radix = radix || 10;
+      if (radix < 2 || 36 < radix)
+        throw RangeError("radix");
+      if (this.isZero())
+        return "0";
+      if (this.isNegative()) {
+        if (this.eq(MIN_VALUE)) {
+          var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);
+          return div3.toString(radix) + rem1.toInt().toString(radix);
+        } else
+          return "-" + this.neg().toString(radix);
+      }
+      var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;
+      var result = "";
+      while (true) {
+        var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);
+        rem = remDiv;
+        if (rem.isZero())
+          return digits + result;
+        else {
+          while (digits.length < 6)
+            digits = "0" + digits;
+          result = "" + digits + result;
+        }
+      }
+    };
+    LongPrototype.getHighBits = function getHighBits() {
+      return this.high;
+    };
+    LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {
+      return this.high >>> 0;
+    };
+    LongPrototype.getLowBits = function getLowBits() {
+      return this.low;
+    };
+    LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {
+      return this.low >>> 0;
+    };
+    LongPrototype.getNumBitsAbs = function getNumBitsAbs() {
+      if (this.isNegative())
+        return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();
+      var val = this.high != 0 ? this.high : this.low;
+      for (var bit = 31; bit > 0; bit--)
+        if ((val & 1 << bit) != 0)
+          break;
+      return this.high != 0 ? bit + 33 : bit + 1;
+    };
+    LongPrototype.isZero = function isZero() {
+      return this.high === 0 && this.low === 0;
+    };
+    LongPrototype.eqz = LongPrototype.isZero;
+    LongPrototype.isNegative = function isNegative() {
+      return !this.unsigned && this.high < 0;
+    };
+    LongPrototype.isPositive = function isPositive() {
+      return this.unsigned || this.high >= 0;
+    };
+    LongPrototype.isOdd = function isOdd() {
+      return (this.low & 1) === 1;
+    };
+    LongPrototype.isEven = function isEven2() {
+      return (this.low & 1) === 0;
+    };
+    LongPrototype.equals = function equals(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)
+        return false;
+      return this.high === other.high && this.low === other.low;
+    };
+    LongPrototype.eq = LongPrototype.equals;
+    LongPrototype.notEquals = function notEquals(other) {
+      return !this.eq(other);
+    };
+    LongPrototype.neq = LongPrototype.notEquals;
+    LongPrototype.ne = LongPrototype.notEquals;
+    LongPrototype.lessThan = function lessThan(other) {
+      return this.comp(other) < 0;
+    };
+    LongPrototype.lt = LongPrototype.lessThan;
+    LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {
+      return this.comp(other) <= 0;
+    };
+    LongPrototype.lte = LongPrototype.lessThanOrEqual;
+    LongPrototype.le = LongPrototype.lessThanOrEqual;
+    LongPrototype.greaterThan = function greaterThan(other) {
+      return this.comp(other) > 0;
+    };
+    LongPrototype.gt = LongPrototype.greaterThan;
+    LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {
+      return this.comp(other) >= 0;
+    };
+    LongPrototype.gte = LongPrototype.greaterThanOrEqual;
+    LongPrototype.ge = LongPrototype.greaterThanOrEqual;
+    LongPrototype.compare = function compare2(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      if (this.eq(other))
+        return 0;
+      var thisNeg = this.isNegative(), otherNeg = other.isNegative();
+      if (thisNeg && !otherNeg)
+        return -1;
+      if (!thisNeg && otherNeg)
+        return 1;
+      if (!this.unsigned)
+        return this.sub(other).isNegative() ? -1 : 1;
+      return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;
+    };
+    LongPrototype.comp = LongPrototype.compare;
+    LongPrototype.negate = function negate() {
+      if (!this.unsigned && this.eq(MIN_VALUE))
+        return MIN_VALUE;
+      return this.not().add(ONE);
+    };
+    LongPrototype.neg = LongPrototype.negate;
+    LongPrototype.add = function add5(addend) {
+      if (!isLong(addend))
+        addend = fromValue(addend);
+      var a48 = this.high >>> 16;
+      var a32 = this.high & 65535;
+      var a16 = this.low >>> 16;
+      var a00 = this.low & 65535;
+      var b48 = addend.high >>> 16;
+      var b32 = addend.high & 65535;
+      var b16 = addend.low >>> 16;
+      var b00 = addend.low & 65535;
+      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
+      c00 += a00 + b00;
+      c16 += c00 >>> 16;
+      c00 &= 65535;
+      c16 += a16 + b16;
+      c32 += c16 >>> 16;
+      c16 &= 65535;
+      c32 += a32 + b32;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c48 += a48 + b48;
+      c48 &= 65535;
+      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
+    };
+    LongPrototype.subtract = function subtract(subtrahend) {
+      if (!isLong(subtrahend))
+        subtrahend = fromValue(subtrahend);
+      return this.add(subtrahend.neg());
+    };
+    LongPrototype.sub = LongPrototype.subtract;
+    LongPrototype.multiply = function multiply4(multiplier) {
+      if (this.isZero())
+        return ZERO;
+      if (!isLong(multiplier))
+        multiplier = fromValue(multiplier);
+      if (wasm) {
+        var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high);
+        return fromBits(low, wasm.get_high(), this.unsigned);
+      }
+      if (multiplier.isZero())
+        return ZERO;
+      if (this.eq(MIN_VALUE))
+        return multiplier.isOdd() ? MIN_VALUE : ZERO;
+      if (multiplier.eq(MIN_VALUE))
+        return this.isOdd() ? MIN_VALUE : ZERO;
+      if (this.isNegative()) {
+        if (multiplier.isNegative())
+          return this.neg().mul(multiplier.neg());
+        else
+          return this.neg().mul(multiplier).neg();
+      } else if (multiplier.isNegative())
+        return this.mul(multiplier.neg()).neg();
+      if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))
+        return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);
+      var a48 = this.high >>> 16;
+      var a32 = this.high & 65535;
+      var a16 = this.low >>> 16;
+      var a00 = this.low & 65535;
+      var b48 = multiplier.high >>> 16;
+      var b32 = multiplier.high & 65535;
+      var b16 = multiplier.low >>> 16;
+      var b00 = multiplier.low & 65535;
+      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
+      c00 += a00 * b00;
+      c16 += c00 >>> 16;
+      c00 &= 65535;
+      c16 += a16 * b00;
+      c32 += c16 >>> 16;
+      c16 &= 65535;
+      c16 += a00 * b16;
+      c32 += c16 >>> 16;
+      c16 &= 65535;
+      c32 += a32 * b00;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c32 += a16 * b16;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c32 += a00 * b32;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;
+      c48 &= 65535;
+      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
+    };
+    LongPrototype.mul = LongPrototype.multiply;
+    LongPrototype.divide = function divide(divisor) {
+      if (!isLong(divisor))
+        divisor = fromValue(divisor);
+      if (divisor.isZero())
+        throw Error("division by zero");
+      if (wasm) {
+        if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {
+          return this;
+        }
+        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(this.low, this.high, divisor.low, divisor.high);
+        return fromBits(low, wasm.get_high(), this.unsigned);
+      }
+      if (this.isZero())
+        return this.unsigned ? UZERO : ZERO;
+      var approx, rem, res;
+      if (!this.unsigned) {
+        if (this.eq(MIN_VALUE)) {
+          if (divisor.eq(ONE) || divisor.eq(NEG_ONE))
+            return MIN_VALUE;
+          else if (divisor.eq(MIN_VALUE))
+            return ONE;
+          else {
+            var halfThis = this.shr(1);
+            approx = halfThis.div(divisor).shl(1);
+            if (approx.eq(ZERO)) {
+              return divisor.isNegative() ? ONE : NEG_ONE;
+            } else {
+              rem = this.sub(divisor.mul(approx));
+              res = approx.add(rem.div(divisor));
+              return res;
+            }
+          }
+        } else if (divisor.eq(MIN_VALUE))
+          return this.unsigned ? UZERO : ZERO;
+        if (this.isNegative()) {
+          if (divisor.isNegative())
+            return this.neg().div(divisor.neg());
+          return this.neg().div(divisor).neg();
+        } else if (divisor.isNegative())
+          return this.div(divisor.neg()).neg();
+        res = ZERO;
+      } else {
+        if (!divisor.unsigned)
+          divisor = divisor.toUnsigned();
+        if (divisor.gt(this))
+          return UZERO;
+        if (divisor.gt(this.shru(1)))
+          return UONE;
+        res = UZERO;
+      }
+      rem = this;
+      while (rem.gte(divisor)) {
+        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));
+        var log222 = Math.ceil(Math.log(approx) / Math.LN2), delta = log222 <= 48 ? 1 : pow_dbl(2, log222 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);
+        while (approxRem.isNegative() || approxRem.gt(rem)) {
+          approx -= delta;
+          approxRes = fromNumber(approx, this.unsigned);
+          approxRem = approxRes.mul(divisor);
+        }
+        if (approxRes.isZero())
+          approxRes = ONE;
+        res = res.add(approxRes);
+        rem = rem.sub(approxRem);
+      }
+      return res;
+    };
+    LongPrototype.div = LongPrototype.divide;
+    LongPrototype.modulo = function modulo(divisor) {
+      if (!isLong(divisor))
+        divisor = fromValue(divisor);
+      if (wasm) {
+        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(this.low, this.high, divisor.low, divisor.high);
+        return fromBits(low, wasm.get_high(), this.unsigned);
+      }
+      return this.sub(this.div(divisor).mul(divisor));
+    };
+    LongPrototype.mod = LongPrototype.modulo;
+    LongPrototype.rem = LongPrototype.modulo;
+    LongPrototype.not = function not() {
+      return fromBits(~this.low, ~this.high, this.unsigned);
+    };
+    LongPrototype.and = function and(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      return fromBits(this.low & other.low, this.high & other.high, this.unsigned);
+    };
+    LongPrototype.or = function or(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      return fromBits(this.low | other.low, this.high | other.high, this.unsigned);
+    };
+    LongPrototype.xor = function xor(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);
+    };
+    LongPrototype.shiftLeft = function shiftLeft(numBits) {
+      if (isLong(numBits))
+        numBits = numBits.toInt();
+      if ((numBits &= 63) === 0)
+        return this;
+      else if (numBits < 32)
+        return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);
+      else
+        return fromBits(0, this.low << numBits - 32, this.unsigned);
+    };
+    LongPrototype.shl = LongPrototype.shiftLeft;
+    LongPrototype.shiftRight = function shiftRight(numBits) {
+      if (isLong(numBits))
+        numBits = numBits.toInt();
+      if ((numBits &= 63) === 0)
+        return this;
+      else if (numBits < 32)
+        return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);
+      else
+        return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);
+    };
+    LongPrototype.shr = LongPrototype.shiftRight;
+    LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {
+      if (isLong(numBits))
+        numBits = numBits.toInt();
+      numBits &= 63;
+      if (numBits === 0)
+        return this;
+      else {
+        var high = this.high;
+        if (numBits < 32) {
+          var low = this.low;
+          return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);
+        } else if (numBits === 32)
+          return fromBits(high, 0, this.unsigned);
+        else
+          return fromBits(high >>> numBits - 32, 0, this.unsigned);
+      }
+    };
+    LongPrototype.shru = LongPrototype.shiftRightUnsigned;
+    LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;
+    LongPrototype.toSigned = function toSigned() {
+      if (!this.unsigned)
+        return this;
+      return fromBits(this.low, this.high, false);
+    };
+    LongPrototype.toUnsigned = function toUnsigned() {
+      if (this.unsigned)
+        return this;
+      return fromBits(this.low, this.high, true);
+    };
+    LongPrototype.toBytes = function toBytes(le) {
+      return le ? this.toBytesLE() : this.toBytesBE();
+    };
+    LongPrototype.toBytesLE = function toBytesLE() {
+      var hi = this.high, lo = this.low;
+      return [
+        lo & 255,
+        lo >>> 8 & 255,
+        lo >>> 16 & 255,
+        lo >>> 24,
+        hi & 255,
+        hi >>> 8 & 255,
+        hi >>> 16 & 255,
+        hi >>> 24
+      ];
+    };
+    LongPrototype.toBytesBE = function toBytesBE() {
+      var hi = this.high, lo = this.low;
+      return [
+        hi >>> 24,
+        hi >>> 16 & 255,
+        hi >>> 8 & 255,
+        hi & 255,
+        lo >>> 24,
+        lo >>> 16 & 255,
+        lo >>> 8 & 255,
+        lo & 255
+      ];
+    };
+    Long2.fromBytes = function fromBytes(bytes, unsigned, le) {
+      return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);
+    };
+    Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {
+      return new Long2(bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned);
+    };
+    Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {
+      return new Long2(bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned);
+    };
+  }
+});
+var require_browser = __commonJS({
+  "(disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js"() {
+  }
+});
+var require_util = __commonJS({
+  "(disabled):util"() {
+  }
+});
+var require_alea = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function Alea(seed) {
+        var me = this, mash = Mash();
+        me.next = function() {
+          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;
+          me.s0 = me.s1;
+          me.s1 = me.s2;
+          return me.s2 = t2 - (me.c = t2 | 0);
+        };
+        me.c = 1;
+        me.s0 = mash(" ");
+        me.s1 = mash(" ");
+        me.s2 = mash(" ");
+        me.s0 -= mash(seed);
+        if (me.s0 < 0) {
+          me.s0 += 1;
+        }
+        me.s1 -= mash(seed);
+        if (me.s1 < 0) {
+          me.s1 += 1;
+        }
+        me.s2 -= mash(seed);
+        if (me.s2 < 0) {
+          me.s2 += 1;
+        }
+        mash = null;
+      }
+      function copy2(f, t2) {
+        t2.c = f.c;
+        t2.s0 = f.s0;
+        t2.s1 = f.s1;
+        t2.s2 = f.s2;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
+        prng.int32 = function() {
+          return xg.next() * 4294967296 | 0;
+        };
+        prng.double = function() {
+          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
+        };
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      function Mash() {
+        var n = 4022871197;
+        var mash = function(data) {
+          data = data.toString();
+          for (var i2 = 0; i2 < data.length; i2++) {
+            n += data.charCodeAt(i2);
+            var h = 0.02519603282416938 * n;
+            n = h >>> 0;
+            h -= n;
+            h *= n;
+            n = h >>> 0;
+            h -= n;
+            n += h * 4294967296;
+          }
+          return (n >>> 0) * 23283064365386963e-26;
+        };
+        return mash;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.alea = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xor128 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.next = function() {
+          var t2 = me.x ^ me.x << 11;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;
+        };
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy2(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor128 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xorwow = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var t2 = me.x ^ me.x >>> 2;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          me.w = me.v;
+          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;
+        };
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.v = 0;
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          if (k == strseed.length) {
+            me.d = me.x << 10 ^ me.x >>> 4;
+          }
+          me.next();
+        }
+      }
+      function copy2(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        t2.v = f.v;
+        t2.d = f.d;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorwow = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xorshift7 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var X = me.x, i2 = me.i, t2, v, w;
+          t2 = X[i2];
+          t2 ^= t2 >>> 7;
+          v = t2 ^ t2 << 24;
+          t2 = X[i2 + 1 & 7];
+          v ^= t2 ^ t2 >>> 10;
+          t2 = X[i2 + 3 & 7];
+          v ^= t2 ^ t2 >>> 3;
+          t2 = X[i2 + 4 & 7];
+          v ^= t2 ^ t2 << 7;
+          t2 = X[i2 + 7 & 7];
+          t2 = t2 ^ t2 << 13;
+          v ^= t2 ^ t2 << 9;
+          X[i2] = v;
+          me.i = i2 + 1 & 7;
+          return v;
+        };
+        function init22(me2, seed2) {
+          var j, w, X = [];
+          if (seed2 === (seed2 | 0)) {
+            w = X[0] = seed2;
+          } else {
+            seed2 = "" + seed2;
+            for (j = 0; j < seed2.length; ++j) {
+              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
+            }
+          }
+          while (X.length < 8)
+            X.push(0);
+          for (j = 0; j < 8 && X[j] === 0; ++j)
+            ;
+          if (j == 8)
+            w = X[7] = -1;
+          else
+            w = X[j];
+          me2.x = X;
+          me2.i = 0;
+          for (j = 256; j > 0; --j) {
+            me2.next();
+          }
+        }
+        init22(me, seed);
+      }
+      function copy2(f, t2) {
+        t2.x = f.x.slice();
+        t2.i = f.i;
+        return t2;
+      }
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.x)
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorshift7 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xor4096 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var w = me.w, X = me.X, i2 = me.i, t2, v;
+          me.w = w = w + 1640531527 | 0;
+          v = X[i2 + 34 & 127];
+          t2 = X[i2 = i2 + 1 & 127];
+          v ^= v << 13;
+          t2 ^= t2 << 17;
+          v ^= v >>> 15;
+          t2 ^= t2 >>> 12;
+          v = X[i2] = v ^ t2;
+          me.i = i2;
+          return v + (w ^ w >>> 16) | 0;
+        };
+        function init22(me2, seed2) {
+          var t2, v, i2, j, w, X = [], limit = 128;
+          if (seed2 === (seed2 | 0)) {
+            v = seed2;
+            seed2 = null;
+          } else {
+            seed2 = seed2 + "\0";
+            v = 0;
+            limit = Math.max(limit, seed2.length);
+          }
+          for (i2 = 0, j = -32; j < limit; ++j) {
+            if (seed2)
+              v ^= seed2.charCodeAt((j + 32) % seed2.length);
+            if (j === 0)
+              w = v;
+            v ^= v << 10;
+            v ^= v >>> 15;
+            v ^= v << 4;
+            v ^= v >>> 13;
+            if (j >= 0) {
+              w = w + 1640531527 | 0;
+              t2 = X[j & 127] ^= v + w;
+              i2 = 0 == t2 ? i2 + 1 : 0;
+            }
+          }
+          if (i2 >= 128) {
+            X[(seed2 && seed2.length || 0) & 127] = -1;
+          }
+          i2 = 127;
+          for (j = 4 * 128; j > 0; --j) {
+            v = X[i2 + 34 & 127];
+            t2 = X[i2 = i2 + 1 & 127];
+            v ^= v << 13;
+            t2 ^= t2 << 17;
+            v ^= v >>> 15;
+            t2 ^= t2 >>> 12;
+            X[i2] = v ^ t2;
+          }
+          me2.w = w;
+          me2.X = X;
+          me2.i = i2;
+        }
+        init22(me, seed);
+      }
+      function copy2(f, t2) {
+        t2.i = f.i;
+        t2.w = f.w;
+        t2.X = f.X.slice();
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.X)
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor4096 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_tychei = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var b = me.b, c = me.c, d = me.d, a6 = me.a;
+          b = b << 25 ^ b >>> 7 ^ c;
+          c = c - d | 0;
+          d = d << 24 ^ d >>> 8 ^ a6;
+          a6 = a6 - b | 0;
+          me.b = b = b << 20 ^ b >>> 12 ^ c;
+          me.c = c = c - d | 0;
+          me.d = d << 16 ^ c >>> 16 ^ a6;
+          return me.a = a6 - b | 0;
+        };
+        me.a = 0;
+        me.b = 0;
+        me.c = 2654435769 | 0;
+        me.d = 1367130551;
+        if (seed === Math.floor(seed)) {
+          me.a = seed / 4294967296 | 0;
+          me.b = seed | 0;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 20; k++) {
+          me.b ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy2(f, t2) {
+        t2.a = f.a;
+        t2.b = f.b;
+        t2.c = f.c;
+        t2.d = f.d;
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.tychei = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_crypto = __commonJS({
+  "(disabled):crypto"() {
+  }
+});
+var require_seedrandom = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(exports, module) {
+    (function(pool3, math) {
+      var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask2 = width - 1, nodecrypto;
+      function seedrandom5(seed, options4, callback) {
+        var key = [];
+        options4 = options4 == true ? { entropy: true } : options4 || {};
+        var shortseed = mixkey(flatten4(options4.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
+        var arc4 = new ARC4(key);
+        var prng = function() {
+          var n = arc4.g(chunks), d = startdenom, x = 0;
+          while (n < significance) {
+            n = (n + x) * width;
+            d *= width;
+            x = arc4.g(1);
+          }
+          while (n >= overflow) {
+            n /= 2;
+            d /= 2;
+            x >>>= 1;
+          }
+          return (n + x) / d;
+        };
+        prng.int32 = function() {
+          return arc4.g(4) | 0;
+        };
+        prng.quick = function() {
+          return arc4.g(4) / 4294967296;
+        };
+        prng.double = prng;
+        mixkey(tostring(arc4.S), pool3);
+        return (options4.pass || callback || function(prng2, seed2, is_math_call, state) {
+          if (state) {
+            if (state.S) {
+              copy2(state, arc4);
+            }
+            prng2.state = function() {
+              return copy2(arc4, {});
+            };
+          }
+          if (is_math_call) {
+            math[rngname] = prng2;
+            return seed2;
+          } else
+            return prng2;
+        })(prng, shortseed, "global" in options4 ? options4.global : this == math, options4.state);
+      }
+      math["seed" + rngname] = seedrandom5;
+      function ARC4(key) {
+        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];
+        if (!keylen) {
+          key = [keylen++];
+        }
+        while (i2 < width) {
+          s2[i2] = i2++;
+        }
+        for (i2 = 0; i2 < width; i2++) {
+          s2[i2] = s2[j = mask2 & j + key[i2 % keylen] + (t2 = s2[i2])];
+          s2[j] = t2;
+        }
+        (me.g = function(count22) {
+          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;
+          while (count22--) {
+            t3 = s3[i3 = mask2 & i3 + 1];
+            r2 = r2 * width + s3[mask2 & (s3[i3] = s3[j2 = mask2 & j2 + t3]) + (s3[j2] = t3)];
+          }
+          me.i = i3;
+          me.j = j2;
+          return r2;
+        })(width);
+      }
+      function copy2(f, t2) {
+        t2.i = f.i;
+        t2.j = f.j;
+        t2.S = f.S.slice();
+        return t2;
+      }
+      ;
+      function flatten4(obj, depth) {
+        var result = [], typ = typeof obj, prop;
+        if (depth && typ == "object") {
+          for (prop in obj) {
+            try {
+              result.push(flatten4(obj[prop], depth - 1));
+            } catch (e2) {
+            }
+          }
+        }
+        return result.length ? result : typ == "string" ? obj : obj + "\0";
+      }
+      function mixkey(seed, key) {
+        var stringseed = seed + "", smear, j = 0;
+        while (j < stringseed.length) {
+          key[mask2 & j] = mask2 & (smear ^= key[mask2 & j] * 19) + stringseed.charCodeAt(j++);
+        }
+        return tostring(key);
+      }
+      function autoseed() {
+        try {
+          var out;
+          if (nodecrypto && (out = nodecrypto.randomBytes)) {
+            out = out(width);
+          } else {
+            out = new Uint8Array(width);
+            (global2.crypto || global2.msCrypto).getRandomValues(out);
+          }
+          return tostring(out);
+        } catch (e2) {
+          var browser = global2.navigator, plugins = browser && browser.plugins;
+          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
+        }
+      }
+      function tostring(a6) {
+        return String.fromCharCode.apply(0, a6);
+      }
+      mixkey(math.random(), pool3);
+      if (typeof module == "object" && module.exports) {
+        module.exports = seedrandom5;
+        try {
+          nodecrypto = require_crypto();
+        } catch (ex) {
+        }
+      } else if (typeof define == "function" && define.amd) {
+        define(function() {
+          return seedrandom5;
+        });
+      }
+    })([], Math);
+  }
+});
+var require_seedrandom2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(exports, module) {
+    var alea5 = require_alea();
+    var xor128 = require_xor128();
+    var xorwow = require_xorwow();
+    var xorshift7 = require_xorshift7();
+    var xor4096 = require_xor4096();
+    var tychei = require_tychei();
+    var sr = require_seedrandom();
+    sr.alea = alea5;
+    sr.xor128 = xor128;
+    sr.xorwow = xorwow;
+    sr.xorshift7 = xorshift7;
+    sr.xor4096 = xor4096;
+    sr.tychei = tychei;
+    module.exports = sr;
+  }
+});
+var require_alea2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function Alea(seed) {
+        var me = this, mash = Mash();
+        me.next = function() {
+          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;
+          me.s0 = me.s1;
+          me.s1 = me.s2;
+          return me.s2 = t2 - (me.c = t2 | 0);
+        };
+        me.c = 1;
+        me.s0 = mash(" ");
+        me.s1 = mash(" ");
+        me.s2 = mash(" ");
+        me.s0 -= mash(seed);
+        if (me.s0 < 0) {
+          me.s0 += 1;
+        }
+        me.s1 -= mash(seed);
+        if (me.s1 < 0) {
+          me.s1 += 1;
+        }
+        me.s2 -= mash(seed);
+        if (me.s2 < 0) {
+          me.s2 += 1;
+        }
+        mash = null;
+      }
+      function copy2(f, t2) {
+        t2.c = f.c;
+        t2.s0 = f.s0;
+        t2.s1 = f.s1;
+        t2.s2 = f.s2;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
+        prng.int32 = function() {
+          return xg.next() * 4294967296 | 0;
+        };
+        prng.double = function() {
+          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
+        };
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      function Mash() {
+        var n = 4022871197;
+        var mash = function(data) {
+          data = String(data);
+          for (var i2 = 0; i2 < data.length; i2++) {
+            n += data.charCodeAt(i2);
+            var h = 0.02519603282416938 * n;
+            n = h >>> 0;
+            h -= n;
+            h *= n;
+            n = h >>> 0;
+            h -= n;
+            n += h * 4294967296;
+          }
+          return (n >>> 0) * 23283064365386963e-26;
+        };
+        return mash;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.alea = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xor1282 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.next = function() {
+          var t2 = me.x ^ me.x << 11;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;
+        };
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy2(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor128 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xorwow2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var t2 = me.x ^ me.x >>> 2;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          me.w = me.v;
+          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;
+        };
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.v = 0;
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          if (k == strseed.length) {
+            me.d = me.x << 10 ^ me.x >>> 4;
+          }
+          me.next();
+        }
+      }
+      function copy2(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        t2.v = f.v;
+        t2.d = f.d;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorwow = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xorshift72 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var X = me.x, i2 = me.i, t2, v, w;
+          t2 = X[i2];
+          t2 ^= t2 >>> 7;
+          v = t2 ^ t2 << 24;
+          t2 = X[i2 + 1 & 7];
+          v ^= t2 ^ t2 >>> 10;
+          t2 = X[i2 + 3 & 7];
+          v ^= t2 ^ t2 >>> 3;
+          t2 = X[i2 + 4 & 7];
+          v ^= t2 ^ t2 << 7;
+          t2 = X[i2 + 7 & 7];
+          t2 = t2 ^ t2 << 13;
+          v ^= t2 ^ t2 << 9;
+          X[i2] = v;
+          me.i = i2 + 1 & 7;
+          return v;
+        };
+        function init22(me2, seed2) {
+          var j, w, X = [];
+          if (seed2 === (seed2 | 0)) {
+            w = X[0] = seed2;
+          } else {
+            seed2 = "" + seed2;
+            for (j = 0; j < seed2.length; ++j) {
+              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
+            }
+          }
+          while (X.length < 8)
+            X.push(0);
+          for (j = 0; j < 8 && X[j] === 0; ++j)
+            ;
+          if (j == 8)
+            w = X[7] = -1;
+          else
+            w = X[j];
+          me2.x = X;
+          me2.i = 0;
+          for (j = 256; j > 0; --j) {
+            me2.next();
+          }
+        }
+        init22(me, seed);
+      }
+      function copy2(f, t2) {
+        t2.x = f.x.slice();
+        t2.i = f.i;
+        return t2;
+      }
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.x)
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorshift7 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_xor40962 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var w = me.w, X = me.X, i2 = me.i, t2, v;
+          me.w = w = w + 1640531527 | 0;
+          v = X[i2 + 34 & 127];
+          t2 = X[i2 = i2 + 1 & 127];
+          v ^= v << 13;
+          t2 ^= t2 << 17;
+          v ^= v >>> 15;
+          t2 ^= t2 >>> 12;
+          v = X[i2] = v ^ t2;
+          me.i = i2;
+          return v + (w ^ w >>> 16) | 0;
+        };
+        function init22(me2, seed2) {
+          var t2, v, i2, j, w, X = [], limit = 128;
+          if (seed2 === (seed2 | 0)) {
+            v = seed2;
+            seed2 = null;
+          } else {
+            seed2 = seed2 + "\0";
+            v = 0;
+            limit = Math.max(limit, seed2.length);
+          }
+          for (i2 = 0, j = -32; j < limit; ++j) {
+            if (seed2)
+              v ^= seed2.charCodeAt((j + 32) % seed2.length);
+            if (j === 0)
+              w = v;
+            v ^= v << 10;
+            v ^= v >>> 15;
+            v ^= v << 4;
+            v ^= v >>> 13;
+            if (j >= 0) {
+              w = w + 1640531527 | 0;
+              t2 = X[j & 127] ^= v + w;
+              i2 = 0 == t2 ? i2 + 1 : 0;
+            }
+          }
+          if (i2 >= 128) {
+            X[(seed2 && seed2.length || 0) & 127] = -1;
+          }
+          i2 = 127;
+          for (j = 4 * 128; j > 0; --j) {
+            v = X[i2 + 34 & 127];
+            t2 = X[i2 = i2 + 1 & 127];
+            v ^= v << 13;
+            t2 ^= t2 << 17;
+            v ^= v >>> 15;
+            t2 ^= t2 >>> 12;
+            X[i2] = v ^ t2;
+          }
+          me2.w = w;
+          me2.X = X;
+          me2.i = i2;
+        }
+        init22(me, seed);
+      }
+      function copy2(f, t2) {
+        t2.i = f.i;
+        t2.w = f.w;
+        t2.X = f.X.slice();
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.X)
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor4096 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_tychei2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var b = me.b, c = me.c, d = me.d, a6 = me.a;
+          b = b << 25 ^ b >>> 7 ^ c;
+          c = c - d | 0;
+          d = d << 24 ^ d >>> 8 ^ a6;
+          a6 = a6 - b | 0;
+          me.b = b = b << 20 ^ b >>> 12 ^ c;
+          me.c = c = c - d | 0;
+          me.d = d << 16 ^ c >>> 16 ^ a6;
+          return me.a = a6 - b | 0;
+        };
+        me.a = 0;
+        me.b = 0;
+        me.c = 2654435769 | 0;
+        me.d = 1367130551;
+        if (seed === Math.floor(seed)) {
+          me.a = seed / 4294967296 | 0;
+          me.b = seed | 0;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 20; k++) {
+          me.b ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy2(f, t2) {
+        t2.a = f.a;
+        t2.b = f.b;
+        t2.c = f.c;
+        t2.d = f.d;
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy2(state, xg);
+          prng.state = function() {
+            return copy2(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.tychei = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+var require_seedrandom3 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(exports, module) {
+    (function(global2, pool3, math) {
+      var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask2 = width - 1, nodecrypto;
+      function seedrandom5(seed, options4, callback) {
+        var key = [];
+        options4 = options4 == true ? { entropy: true } : options4 || {};
+        var shortseed = mixkey(flatten4(options4.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
+        var arc4 = new ARC4(key);
+        var prng = function() {
+          var n = arc4.g(chunks), d = startdenom, x = 0;
+          while (n < significance) {
+            n = (n + x) * width;
+            d *= width;
+            x = arc4.g(1);
+          }
+          while (n >= overflow) {
+            n /= 2;
+            d /= 2;
+            x >>>= 1;
+          }
+          return (n + x) / d;
+        };
+        prng.int32 = function() {
+          return arc4.g(4) | 0;
+        };
+        prng.quick = function() {
+          return arc4.g(4) / 4294967296;
+        };
+        prng.double = prng;
+        mixkey(tostring(arc4.S), pool3);
+        return (options4.pass || callback || function(prng2, seed2, is_math_call, state) {
+          if (state) {
+            if (state.S) {
+              copy2(state, arc4);
+            }
+            prng2.state = function() {
+              return copy2(arc4, {});
+            };
+          }
+          if (is_math_call) {
+            math[rngname] = prng2;
+            return seed2;
+          } else
+            return prng2;
+        })(prng, shortseed, "global" in options4 ? options4.global : this == math, options4.state);
+      }
+      function ARC4(key) {
+        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];
+        if (!keylen) {
+          key = [keylen++];
+        }
+        while (i2 < width) {
+          s2[i2] = i2++;
+        }
+        for (i2 = 0; i2 < width; i2++) {
+          s2[i2] = s2[j = mask2 & j + key[i2 % keylen] + (t2 = s2[i2])];
+          s2[j] = t2;
+        }
+        (me.g = function(count22) {
+          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;
+          while (count22--) {
+            t3 = s3[i3 = mask2 & i3 + 1];
+            r2 = r2 * width + s3[mask2 & (s3[i3] = s3[j2 = mask2 & j2 + t3]) + (s3[j2] = t3)];
+          }
+          me.i = i3;
+          me.j = j2;
+          return r2;
+        })(width);
+      }
+      function copy2(f, t2) {
+        t2.i = f.i;
+        t2.j = f.j;
+        t2.S = f.S.slice();
+        return t2;
+      }
+      ;
+      function flatten4(obj, depth) {
+        var result = [], typ = typeof obj, prop;
+        if (depth && typ == "object") {
+          for (prop in obj) {
+            try {
+              result.push(flatten4(obj[prop], depth - 1));
+            } catch (e2) {
+            }
+          }
+        }
+        return result.length ? result : typ == "string" ? obj : obj + "\0";
+      }
+      function mixkey(seed, key) {
+        var stringseed = seed + "", smear, j = 0;
+        while (j < stringseed.length) {
+          key[mask2 & j] = mask2 & (smear ^= key[mask2 & j] * 19) + stringseed.charCodeAt(j++);
+        }
+        return tostring(key);
+      }
+      function autoseed() {
+        try {
+          var out;
+          if (nodecrypto && (out = nodecrypto.randomBytes)) {
+            out = out(width);
+          } else {
+            out = new Uint8Array(width);
+            (global2.crypto || global2.msCrypto).getRandomValues(out);
+          }
+          return tostring(out);
+        } catch (e2) {
+          var browser = global2.navigator, plugins = browser && browser.plugins;
+          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
+        }
+      }
+      function tostring(a6) {
+        return String.fromCharCode.apply(0, a6);
+      }
+      mixkey(math.random(), pool3);
+      if (typeof module == "object" && module.exports) {
+        module.exports = seedrandom5;
+        try {
+          nodecrypto = require_crypto();
+        } catch (ex) {
+        }
+      } else if (typeof define == "function" && define.amd) {
+        define(function() {
+          return seedrandom5;
+        });
+      } else {
+        math["seed" + rngname] = seedrandom5;
+      }
+    })(typeof self !== "undefined" ? self : exports, [], Math);
+  }
+});
+var require_seedrandom4 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(exports, module) {
+    var alea5 = require_alea2();
+    var xor128 = require_xor1282();
+    var xorwow = require_xorwow2();
+    var xorshift7 = require_xorshift72();
+    var xor4096 = require_xor40962();
+    var tychei = require_tychei2();
+    var sr = require_seedrandom3();
+    sr.alea = alea5;
+    sr.xor128 = xor128;
+    sr.xorwow = xorwow;
+    sr.xorshift7 = xorshift7;
+    sr.xor4096 = xor4096;
+    sr.tychei = tychei;
+    module.exports = sr;
+  }
+});
+var require_string_decoder = __commonJS({
+  "(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"() {
+  }
+});
+var require_fs = __commonJS({
+  "(disabled):fs"() {
+  }
+});
+var require_path = __commonJS({
+  "(disabled):path"() {
+  }
+});
+var require_worker_threads = __commonJS({
+  "(disabled):worker_threads"() {
+  }
+});
+var require_perf_hooks = __commonJS({
+  "(disabled):perf_hooks"() {
+  }
+});
+var require_os = __commonJS({
+  "(disabled):os"() {
+  }
+});
+var require_tfjs_backend_wasm_threaded_simd = __commonJS({
+  "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module) {
+    var WasmBackendModuleThreadedSimd2 = (() => {
+      var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
+      if (typeof __filename !== "undefined")
+        _scriptDir = _scriptDir || __filename;
+      return function(WasmBackendModuleThreadedSimd3) {
+        WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {};
+        function GROWABLE_HEAP_I8() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAP8;
+        }
+        function GROWABLE_HEAP_U8() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPU8;
+        }
+        function GROWABLE_HEAP_I16() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAP16;
+        }
+        function GROWABLE_HEAP_U16() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPU16;
+        }
+        function GROWABLE_HEAP_I32() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAP32;
+        }
+        function GROWABLE_HEAP_F32() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPF32;
+        }
+        function GROWABLE_HEAP_F64() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPF64;
+        }
+        var Module = typeof WasmBackendModuleThreadedSimd3 !== "undefined" ? WasmBackendModuleThreadedSimd3 : {};
+        var readyPromiseResolve, readyPromiseReject;
+        Module["ready"] = new Promise(function(resolve, reject) {
+          readyPromiseResolve = resolve;
+          readyPromiseReject = reject;
+        });
+        var beforeListeners;
+        if (typeof process !== "undefined" && process.listeners) {
+          beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") };
+        }
+        var moduleOverrides = Object.assign({}, Module);
+        var arguments_ = [];
+        var thisProgram = "./this.program";
+        var quit_ = (status, toThrow) => {
+          throw toThrow;
+        };
+        var ENVIRONMENT_IS_WEB = typeof window === "object";
+        var ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
+        var ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
+        var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false;
+        var scriptDirectory = "";
+        function locateFile(path) {
+          if (Module["locateFile"]) {
+            return Module["locateFile"](path, scriptDirectory);
+          }
+          return scriptDirectory + path;
+        }
+        var read_, readAsync, readBinary, setWindowTitle;
+        function logExceptionOnExit(e2) {
+          if (e2 instanceof ExitStatus)
+            return;
+          let toLog = e2;
+          err("exiting due to exception: " + toLog);
+        }
+        var fs;
+        var nodePath;
+        var requireNodeFS;
+        if (ENVIRONMENT_IS_NODE) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = require_path().dirname(scriptDirectory) + "/";
+          } else {
+            scriptDirectory = __dirname + "/";
+          }
+          requireNodeFS = () => {
+            if (!nodePath) {
+              fs = require_fs();
+              nodePath = require_path();
+            }
+          };
+          read_ = function shell_read(filename, binary) {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            return fs.readFileSync(filename, binary ? void 0 : "utf8");
+          };
+          readBinary = (filename) => {
+            var ret = read_(filename, true);
+            if (!ret.buffer) {
+              ret = new Uint8Array(ret);
+            }
+            return ret;
+          };
+          readAsync = (filename, onload, onerror) => {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            fs.readFile(filename, function(err2, data) {
+              if (err2)
+                onerror(err2);
+              else
+                onload(data.buffer);
+            });
+          };
+          if (process["argv"].length > 1) {
+            thisProgram = process["argv"][1].replace(/\\/g, "/");
+          }
+          arguments_ = process["argv"].slice(2);
+          process["on"]("uncaughtException", function(ex) {
+            if (!(ex instanceof ExitStatus)) {
+              throw ex;
+            }
+          });
+          process["on"]("unhandledRejection", function(reason) {
+            throw reason;
+          });
+          quit_ = (status, toThrow) => {
+            if (keepRuntimeAlive()) {
+              process["exitCode"] = status;
+              throw toThrow;
+            }
+            logExceptionOnExit(toThrow);
+            process["exit"](status);
+          };
+          Module["inspect"] = function() {
+            return "[Emscripten Module object]";
+          };
+          let nodeWorkerThreads;
+          try {
+            nodeWorkerThreads = require_worker_threads();
+          } catch (e2) {
+            console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');
+            throw e2;
+          }
+          global.Worker = nodeWorkerThreads.Worker;
+        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = self.location.href;
+          } else if (typeof document !== "undefined" && document.currentScript) {
+            scriptDirectory = document.currentScript.src;
+          }
+          if (typeof _scriptDir !== "undefined" && _scriptDir) {
+            scriptDirectory = _scriptDir;
+          }
+          if (scriptDirectory.indexOf("blob:") !== 0) {
+            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
+          } else {
+            scriptDirectory = "";
+          }
+          if (!ENVIRONMENT_IS_NODE) {
+            read_ = (url) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, false);
+              xhr.send(null);
+              return xhr.responseText;
+            };
+            if (ENVIRONMENT_IS_WORKER) {
+              readBinary = (url) => {
+                var xhr = new XMLHttpRequest();
+                xhr.open("GET", url, false);
+                xhr.responseType = "arraybuffer";
+                xhr.send(null);
+                return new Uint8Array(xhr.response);
+              };
+            }
+            readAsync = (url, onload, onerror) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, true);
+              xhr.responseType = "arraybuffer";
+              xhr.onload = () => {
+                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
+                  onload(xhr.response);
+                  return;
+                }
+                onerror();
+              };
+              xhr.onerror = onerror;
+              xhr.send(null);
+            };
+          }
+          setWindowTitle = (title) => document.title = title;
+        } else {
+        }
+        if (ENVIRONMENT_IS_NODE) {
+          if (typeof performance === "undefined") {
+            global.performance = require_perf_hooks().performance;
+          }
+        }
+        var defaultPrint = console.log.bind(console);
+        var defaultPrintErr = console.warn.bind(console);
+        if (ENVIRONMENT_IS_NODE) {
+          requireNodeFS();
+          defaultPrint = (str) => fs.writeSync(1, str + "\n");
+          defaultPrintErr = (str) => fs.writeSync(2, str + "\n");
+        }
+        var out = Module["print"] || defaultPrint;
+        var err = Module["printErr"] || defaultPrintErr;
+        Object.assign(Module, moduleOverrides);
+        moduleOverrides = null;
+        if (Module["arguments"])
+          arguments_ = Module["arguments"];
+        if (Module["thisProgram"])
+          thisProgram = Module["thisProgram"];
+        if (Module["quit"])
+          quit_ = Module["quit"];
+        var POINTER_SIZE = 4;
+        function warnOnce(text) {
+          if (!warnOnce.shown)
+            warnOnce.shown = {};
+          if (!warnOnce.shown[text]) {
+            warnOnce.shown[text] = 1;
+            err(text);
+          }
+        }
+        function convertJsFunctionToWasm(func2, sig) {
+          if (typeof WebAssembly.Function === "function") {
+            var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
+            var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
+            for (var i2 = 1; i2 < sig.length; ++i2) {
+              type.parameters.push(typeNames[sig[i2]]);
+            }
+            return new WebAssembly.Function(type, func2);
+          }
+          var typeSection = [1, 0, 1, 96];
+          var sigRet = sig.slice(0, 1);
+          var sigParam = sig.slice(1);
+          var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
+          typeSection.push(sigParam.length);
+          for (var i2 = 0; i2 < sigParam.length; ++i2) {
+            typeSection.push(typeCodes[sigParam[i2]]);
+          }
+          if (sigRet == "v") {
+            typeSection.push(0);
+          } else {
+            typeSection = typeSection.concat([1, typeCodes[sigRet]]);
+          }
+          typeSection[1] = typeSection.length - 2;
+          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));
+          var module2 = new WebAssembly.Module(bytes);
+          var instance = new WebAssembly.Instance(module2, { "e": { "f": func2 } });
+          var wrappedFunc = instance.exports["f"];
+          return wrappedFunc;
+        }
+        var freeTableIndexes = [];
+        var functionsInTableMap;
+        function getEmptyTableSlot() {
+          if (freeTableIndexes.length) {
+            return freeTableIndexes.pop();
+          }
+          try {
+            wasmTable.grow(1);
+          } catch (err2) {
+            if (!(err2 instanceof RangeError)) {
+              throw err2;
+            }
+            throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
+          }
+          return wasmTable.length - 1;
+        }
+        function updateTableMap(offset, count22) {
+          for (var i2 = offset; i2 < offset + count22; i2++) {
+            var item = getWasmTableEntry(i2);
+            if (item) {
+              functionsInTableMap.set(item, i2);
+            }
+          }
+        }
+        var tempRet0 = 0;
+        var setTempRet0 = (value) => {
+          tempRet0 = value;
+        };
+        var Atomics_load = Atomics.load;
+        var Atomics_store = Atomics.store;
+        var Atomics_compareExchange = Atomics.compareExchange;
+        var wasmBinary;
+        if (Module["wasmBinary"])
+          wasmBinary = Module["wasmBinary"];
+        var noExitRuntime = Module["noExitRuntime"] || true;
+        if (typeof WebAssembly !== "object") {
+          abort("no native wasm support detected");
+        }
+        var wasmMemory;
+        var wasmModule;
+        var ABORT = false;
+        var EXITSTATUS;
+        function assert3(condition, text) {
+          if (!condition) {
+            abort(text);
+          }
+        }
+        function getCFunc(ident) {
+          var func2 = Module["_" + ident];
+          return func2;
+        }
+        function ccall(ident, returnType, argTypes, args, opts) {
+          var toC = { "string": function(str) {
+            var ret2 = 0;
+            if (str !== null && str !== void 0 && str !== 0) {
+              var len = (str.length << 2) + 1;
+              ret2 = stackAlloc(len);
+              stringToUTF8(str, ret2, len);
+            }
+            return ret2;
+          }, "array": function(arr) {
+            var ret2 = stackAlloc(arr.length);
+            writeArrayToMemory(arr, ret2);
+            return ret2;
+          } };
+          function convertReturnValue(ret2) {
+            if (returnType === "string")
+              return UTF8ToString(ret2);
+            if (returnType === "boolean")
+              return Boolean(ret2);
+            return ret2;
+          }
+          var func2 = getCFunc(ident);
+          var cArgs = [];
+          var stack2 = 0;
+          if (args) {
+            for (var i2 = 0; i2 < args.length; i2++) {
+              var converter = toC[argTypes[i2]];
+              if (converter) {
+                if (stack2 === 0)
+                  stack2 = stackSave();
+                cArgs[i2] = converter(args[i2]);
+              } else {
+                cArgs[i2] = args[i2];
+              }
+            }
+          }
+          var ret = func2.apply(null, cArgs);
+          function onDone(ret2) {
+            if (stack2 !== 0)
+              stackRestore(stack2);
+            return convertReturnValue(ret2);
+          }
+          ret = onDone(ret);
+          return ret;
+        }
+        function cwrap(ident, returnType, argTypes, opts) {
+          argTypes = argTypes || [];
+          var numericArgs = argTypes.every(function(type) {
+            return type === "number";
+          });
+          var numericRet = returnType !== "string";
+          if (numericRet && numericArgs && !opts) {
+            return getCFunc(ident);
+          }
+          return function() {
+            return ccall(ident, returnType, argTypes, arguments, opts);
+          };
+        }
+        var ALLOC_STACK = 1;
+        function TextDecoderWrapper(encoding) {
+          var textDecoder = new TextDecoder(encoding);
+          this.decode = (data) => {
+            if (data.buffer instanceof SharedArrayBuffer) {
+              data = new Uint8Array(data);
+            }
+            return textDecoder.decode.call(textDecoder, data);
+          };
+        }
+        var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoderWrapper("utf8") : void 0;
+        function UTF8ArrayToString(heap, idx, maxBytesToRead) {
+          var endIdx = idx + maxBytesToRead;
+          var endPtr = idx;
+          while (heap[endPtr] && !(endPtr >= endIdx))
+            ++endPtr;
+          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {
+            return UTF8Decoder.decode(heap.subarray(idx, endPtr));
+          } else {
+            var str = "";
+            while (idx < endPtr) {
+              var u0 = heap[idx++];
+              if (!(u0 & 128)) {
+                str += String.fromCharCode(u0);
+                continue;
+              }
+              var u1 = heap[idx++] & 63;
+              if ((u0 & 224) == 192) {
+                str += String.fromCharCode((u0 & 31) << 6 | u1);
+                continue;
+              }
+              var u2 = heap[idx++] & 63;
+              if ((u0 & 240) == 224) {
+                u0 = (u0 & 15) << 12 | u1 << 6 | u2;
+              } else {
+                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
+              }
+              if (u0 < 65536) {
+                str += String.fromCharCode(u0);
+              } else {
+                var ch = u0 - 65536;
+                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
+              }
+            }
+          }
+          return str;
+        }
+        function UTF8ToString(ptr, maxBytesToRead) {
+          return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : "";
+        }
+        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
+          if (!(maxBytesToWrite > 0))
+            return 0;
+          var startIdx = outIdx;
+          var endIdx = outIdx + maxBytesToWrite - 1;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343) {
+              var u1 = str.charCodeAt(++i2);
+              u = 65536 + ((u & 1023) << 10) | u1 & 1023;
+            }
+            if (u <= 127) {
+              if (outIdx >= endIdx)
+                break;
+              heap[outIdx++] = u;
+            } else if (u <= 2047) {
+              if (outIdx + 1 >= endIdx)
+                break;
+              heap[outIdx++] = 192 | u >> 6;
+              heap[outIdx++] = 128 | u & 63;
+            } else if (u <= 65535) {
+              if (outIdx + 2 >= endIdx)
+                break;
+              heap[outIdx++] = 224 | u >> 12;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            } else {
+              if (outIdx + 3 >= endIdx)
+                break;
+              heap[outIdx++] = 240 | u >> 18;
+              heap[outIdx++] = 128 | u >> 12 & 63;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            }
+          }
+          heap[outIdx] = 0;
+          return outIdx - startIdx;
+        }
+        function stringToUTF8(str, outPtr, maxBytesToWrite) {
+          return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);
+        }
+        function lengthBytesUTF8(str) {
+          var len = 0;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343)
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+            if (u <= 127)
+              ++len;
+            else if (u <= 2047)
+              len += 2;
+            else if (u <= 65535)
+              len += 3;
+            else
+              len += 4;
+          }
+          return len;
+        }
+        var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoderWrapper("utf-16le") : void 0;
+        function writeArrayToMemory(array2, buffer3) {
+          GROWABLE_HEAP_I8().set(array2, buffer3);
+        }
+        function writeAsciiToMemory(str, buffer3, dontAddNull) {
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i2);
+          }
+          if (!dontAddNull)
+            GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;
+        }
+        function alignUp(x, multiple) {
+          if (x % multiple > 0) {
+            x += multiple - x % multiple;
+          }
+          return x;
+        }
+        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
+        if (ENVIRONMENT_IS_PTHREAD) {
+          buffer2 = Module["buffer"];
+        }
+        function updateGlobalBufferAndViews(buf) {
+          buffer2 = buf;
+          Module["HEAP8"] = HEAP8 = new Int8Array(buf);
+          Module["HEAP16"] = HEAP16 = new Int16Array(buf);
+          Module["HEAP32"] = HEAP32 = new Int32Array(buf);
+          Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
+          Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
+          Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
+          Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
+          Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
+        }
+        var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
+        if (ENVIRONMENT_IS_PTHREAD) {
+          wasmMemory = Module["wasmMemory"];
+          buffer2 = Module["buffer"];
+        } else {
+          if (Module["wasmMemory"]) {
+            wasmMemory = Module["wasmMemory"];
+          } else {
+            wasmMemory = new WebAssembly.Memory({ "initial": INITIAL_MEMORY / 65536, "maximum": 2147483648 / 65536, "shared": true });
+            if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {
+              err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag");
+              if (ENVIRONMENT_IS_NODE) {
+                console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)");
+              }
+              throw Error("bad memory");
+            }
+          }
+        }
+        if (wasmMemory) {
+          buffer2 = wasmMemory.buffer;
+        }
+        INITIAL_MEMORY = buffer2.byteLength;
+        updateGlobalBufferAndViews(buffer2);
+        var wasmTable;
+        var __ATPRERUN__ = [];
+        var __ATINIT__ = [];
+        var __ATEXIT__ = [];
+        var __ATPOSTRUN__ = [];
+        var runtimeInitialized = false;
+        var runtimeExited = false;
+        var runtimeKeepaliveCounter = 0;
+        function keepRuntimeAlive() {
+          return noExitRuntime || runtimeKeepaliveCounter > 0;
+        }
+        function preRun() {
+          if (Module["preRun"]) {
+            if (typeof Module["preRun"] == "function")
+              Module["preRun"] = [Module["preRun"]];
+            while (Module["preRun"].length) {
+              addOnPreRun(Module["preRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPRERUN__);
+        }
+        function initRuntime() {
+          runtimeInitialized = true;
+          if (ENVIRONMENT_IS_PTHREAD)
+            return;
+          callRuntimeCallbacks(__ATINIT__);
+        }
+        function exitRuntime() {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return;
+          PThread.terminateAllThreads();
+          runtimeExited = true;
+        }
+        function postRun() {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return;
+          if (Module["postRun"]) {
+            if (typeof Module["postRun"] == "function")
+              Module["postRun"] = [Module["postRun"]];
+            while (Module["postRun"].length) {
+              addOnPostRun(Module["postRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPOSTRUN__);
+        }
+        function addOnPreRun(cb) {
+          __ATPRERUN__.unshift(cb);
+        }
+        function addOnInit(cb) {
+          __ATINIT__.unshift(cb);
+        }
+        function addOnPostRun(cb) {
+          __ATPOSTRUN__.unshift(cb);
+        }
+        var runDependencies = 0;
+        var runDependencyWatcher = null;
+        var dependenciesFulfilled = null;
+        function addRunDependency(id) {
+          runDependencies++;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+        }
+        function removeRunDependency(id) {
+          runDependencies--;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+          if (runDependencies == 0) {
+            if (runDependencyWatcher !== null) {
+              clearInterval(runDependencyWatcher);
+              runDependencyWatcher = null;
+            }
+            if (dependenciesFulfilled) {
+              var callback = dependenciesFulfilled;
+              dependenciesFulfilled = null;
+              callback();
+            }
+          }
+        }
+        Module["preloadedImages"] = {};
+        Module["preloadedAudios"] = {};
+        function abort(what) {
+          if (ENVIRONMENT_IS_PTHREAD) {
+            postMessage({ "cmd": "onAbort", "arg": what });
+          } else {
+            if (Module["onAbort"]) {
+              Module["onAbort"](what);
+            }
+          }
+          what = "Aborted(" + what + ")";
+          err(what);
+          ABORT = true;
+          EXITSTATUS = 1;
+          what += ". Build with -s ASSERTIONS=1 for more info.";
+          var e2 = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e2);
+          throw e2;
+        }
+        var dataURIPrefix = "data:application/octet-stream;base64,";
+        function isDataURI(filename) {
+          return filename.startsWith(dataURIPrefix);
+        }
+        function isFileURI(filename) {
+          return filename.startsWith("file://");
+        }
+        var wasmBinaryFile;
+        wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm";
+        if (!isDataURI(wasmBinaryFile)) {
+          wasmBinaryFile = locateFile(wasmBinaryFile);
+        }
+        function getBinary(file) {
+          try {
+            if (file == wasmBinaryFile && wasmBinary) {
+              return new Uint8Array(wasmBinary);
+            }
+            if (readBinary) {
+              return readBinary(file);
+            } else {
+              throw "both async and sync fetching of the wasm failed";
+            }
+          } catch (err2) {
+            abort(err2);
+          }
+        }
+        function getBinaryPromise() {
+          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
+            if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                if (!response["ok"]) {
+                  throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
+                }
+                return response["arrayBuffer"]();
+              }).catch(function() {
+                return getBinary(wasmBinaryFile);
+              });
+            } else {
+              if (readAsync) {
+                return new Promise(function(resolve, reject) {
+                  readAsync(wasmBinaryFile, function(response) {
+                    resolve(new Uint8Array(response));
+                  }, reject);
+                });
+              }
+            }
+          }
+          return Promise.resolve().then(function() {
+            return getBinary(wasmBinaryFile);
+          });
+        }
+        function createWasm() {
+          var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg };
+          function receiveInstance(instance, module2) {
+            var exports3 = instance.exports;
+            Module["asm"] = exports3;
+            registerTlsInit(Module["asm"]["emscripten_tls_init"]);
+            wasmTable = Module["asm"]["__indirect_function_table"];
+            addOnInit(Module["asm"]["__wasm_call_ctors"]);
+            wasmModule = module2;
+            if (!ENVIRONMENT_IS_PTHREAD) {
+              var numWorkersToLoad = PThread.unusedWorkers.length;
+              PThread.unusedWorkers.forEach(function(w) {
+                PThread.loadWasmModuleToWorker(w, function() {
+                  if (!--numWorkersToLoad)
+                    removeRunDependency("wasm-instantiate");
+                });
+              });
+            }
+          }
+          if (!ENVIRONMENT_IS_PTHREAD) {
+            addRunDependency("wasm-instantiate");
+          }
+          function receiveInstantiationResult(result) {
+            receiveInstance(result["instance"], result["module"]);
+          }
+          function instantiateArrayBuffer(receiver) {
+            return getBinaryPromise().then(function(binary) {
+              return WebAssembly.instantiate(binary, info);
+            }).then(function(instance) {
+              return instance;
+            }).then(receiver, function(reason) {
+              err("failed to asynchronously prepare wasm: " + reason);
+              abort(reason);
+            });
+          }
+          function instantiateAsync() {
+            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                var result = WebAssembly.instantiateStreaming(response, info);
+                return result.then(receiveInstantiationResult, function(reason) {
+                  err("wasm streaming compile failed: " + reason);
+                  err("falling back to ArrayBuffer instantiation");
+                  return instantiateArrayBuffer(receiveInstantiationResult);
+                });
+              });
+            } else {
+              return instantiateArrayBuffer(receiveInstantiationResult);
+            }
+          }
+          if (Module["instantiateWasm"]) {
+            try {
+              var exports2 = Module["instantiateWasm"](info, receiveInstance);
+              return exports2;
+            } catch (e2) {
+              err("Module.instantiateWasm callback failed with error: " + e2);
+              return false;
+            }
+          }
+          instantiateAsync().catch(readyPromiseReject);
+          return {};
+        }
+        var tempDouble;
+        var tempI64;
+        var ASM_CONSTS = {};
+        function callRuntimeCallbacks(callbacks2) {
+          while (callbacks2.length > 0) {
+            var callback = callbacks2.shift();
+            if (typeof callback == "function") {
+              callback(Module);
+              continue;
+            }
+            var func2 = callback.func;
+            if (typeof func2 === "number") {
+              if (callback.arg === void 0) {
+                getWasmTableEntry(func2)();
+              } else {
+                getWasmTableEntry(func2)(callback.arg);
+              }
+            } else {
+              func2(callback.arg === void 0 ? null : callback.arg);
+            }
+          }
+        }
+        function withStackSave(f) {
+          var stack2 = stackSave();
+          var ret = f();
+          stackRestore(stack2);
+          return ret;
+        }
+        function demangle(func2) {
+          return func2;
+        }
+        function demangleAll(text) {
+          var regex = /\b_Z[\w\d_]+/g;
+          return text.replace(regex, function(x) {
+            var y = demangle(x);
+            return x === y ? x : y + " [" + x + "]";
+          });
+        }
+        function killThread(pthread_ptr) {
+          GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;
+          var pthread = PThread.pthreads[pthread_ptr];
+          delete PThread.pthreads[pthread_ptr];
+          pthread.worker.terminate();
+          __emscripten_thread_free_data(pthread_ptr);
+          PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);
+          pthread.worker.pthread = void 0;
+        }
+        function cancelThread(pthread_ptr) {
+          var pthread = PThread.pthreads[pthread_ptr];
+          pthread.worker.postMessage({ "cmd": "cancel" });
+        }
+        function cleanupThread(pthread_ptr) {
+          var pthread = PThread.pthreads[pthread_ptr];
+          if (pthread) {
+            GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;
+            var worker = pthread.worker;
+            PThread.returnWorkerToPool(worker);
+          }
+        }
+        function _exit(status) {
+          exit(status);
+        }
+        function handleException(e2) {
+          if (e2 instanceof ExitStatus || e2 == "unwind") {
+            return EXITSTATUS;
+          }
+          quit_(1, e2);
+        }
+        var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
+          if (ENVIRONMENT_IS_PTHREAD) {
+            PThread.initWorker();
+          } else {
+            PThread.initMainThread();
+          }
+        }, initMainThread: function() {
+          var pthreadPoolSize = 8;
+          for (var i2 = 0; i2 < pthreadPoolSize; ++i2) {
+            PThread.allocateUnusedWorker();
+          }
+        }, initWorker: function() {
+          noExitRuntime = false;
+        }, pthreads: {}, setExitStatus: function(status) {
+          EXITSTATUS = status;
+        }, terminateAllThreads: function() {
+          for (var t2 in PThread.pthreads) {
+            var pthread = PThread.pthreads[t2];
+            if (pthread && pthread.worker) {
+              PThread.returnWorkerToPool(pthread.worker);
+            }
+          }
+          for (var i2 = 0; i2 < PThread.unusedWorkers.length; ++i2) {
+            var worker = PThread.unusedWorkers[i2];
+            worker.terminate();
+          }
+          PThread.unusedWorkers = [];
+        }, returnWorkerToPool: function(worker) {
+          PThread.runWithoutMainThreadQueuedCalls(function() {
+            delete PThread.pthreads[worker.pthread.threadInfoStruct];
+            PThread.unusedWorkers.push(worker);
+            PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);
+            __emscripten_thread_free_data(worker.pthread.threadInfoStruct);
+            worker.pthread = void 0;
+          });
+        }, runWithoutMainThreadQueuedCalls: function(func2) {
+          GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0;
+          try {
+            func2();
+          } finally {
+            GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1;
+          }
+        }, receiveObjectTransfer: function(data) {
+        }, threadInit: function() {
+          for (var i2 in PThread.tlsInitFunctions) {
+            PThread.tlsInitFunctions[i2]();
+          }
+        }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {
+          worker.onmessage = (e2) => {
+            var d = e2["data"];
+            var cmd = d["cmd"];
+            if (worker.pthread)
+              PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;
+            if (d["targetThread"] && d["targetThread"] != _pthread_self()) {
+              var thread = PThread.pthreads[d.targetThread];
+              if (thread) {
+                thread.worker.postMessage(d, d["transferList"]);
+              } else {
+                err('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!");
+              }
+              PThread.currentProxiedOperationCallerThread = void 0;
+              return;
+            }
+            if (cmd === "processQueuedMainThreadWork") {
+              _emscripten_main_thread_process_queued_calls();
+            } else if (cmd === "spawnThread") {
+              spawnThread(d);
+            } else if (cmd === "cleanupThread") {
+              cleanupThread(d["thread"]);
+            } else if (cmd === "killThread") {
+              killThread(d["thread"]);
+            } else if (cmd === "cancelThread") {
+              cancelThread(d["thread"]);
+            } else if (cmd === "loaded") {
+              worker.loaded = true;
+              if (onFinishedLoading)
+                onFinishedLoading(worker);
+              if (worker.runPthread) {
+                worker.runPthread();
+                delete worker.runPthread;
+              }
+            } else if (cmd === "print") {
+              out("Thread " + d["threadId"] + ": " + d["text"]);
+            } else if (cmd === "printErr") {
+              err("Thread " + d["threadId"] + ": " + d["text"]);
+            } else if (cmd === "alert") {
+              alert("Thread " + d["threadId"] + ": " + d["text"]);
+            } else if (d.target === "setimmediate") {
+              worker.postMessage(d);
+            } else if (cmd === "onAbort") {
+              if (Module["onAbort"]) {
+                Module["onAbort"](d["arg"]);
+              }
+            } else {
+              err("worker sent an unknown command " + cmd);
+            }
+            PThread.currentProxiedOperationCallerThread = void 0;
+          };
+          worker.onerror = (e2) => {
+            var message = "worker sent an error!";
+            err(message + " " + e2.filename + ":" + e2.lineno + ": " + e2.message);
+            throw e2;
+          };
+          if (ENVIRONMENT_IS_NODE) {
+            worker.on("message", function(data) {
+              worker.onmessage({ data });
+            });
+            worker.on("error", function(e2) {
+              worker.onerror(e2);
+            });
+            worker.on("detachedExit", function() {
+            });
+          }
+          worker.postMessage({ "cmd": "load", "urlOrBlob": Module["mainScriptUrlOrBlob"] || _scriptDir, "wasmMemory": wasmMemory, "wasmModule": wasmModule });
+        }, allocateUnusedWorker: function() {
+          var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js");
+          PThread.unusedWorkers.push(new Worker(pthreadMainJs));
+        }, getNewWorker: function() {
+          if (PThread.unusedWorkers.length == 0) {
+            PThread.allocateUnusedWorker();
+            PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);
+          }
+          return PThread.unusedWorkers.pop();
+        } };
+        function establishStackSpace() {
+          var pthread_ptr = _pthread_self();
+          var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2];
+          var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2];
+          var stackMax = stackTop - stackSize;
+          _emscripten_stack_set_limits(stackTop, stackMax);
+          stackRestore(stackTop);
+        }
+        Module["establishStackSpace"] = establishStackSpace;
+        function exitOnMainThread(returnCode) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);
+          try {
+            _exit(returnCode);
+          } catch (e2) {
+            handleException(e2);
+          }
+        }
+        var wasmTableMirror = [];
+        function getWasmTableEntry(funcPtr) {
+          var func2 = wasmTableMirror[funcPtr];
+          if (!func2) {
+            if (funcPtr >= wasmTableMirror.length)
+              wasmTableMirror.length = funcPtr + 1;
+            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);
+          }
+          return func2;
+        }
+        function invokeEntryPoint(ptr, arg) {
+          return getWasmTableEntry(ptr)(arg);
+        }
+        Module["invokeEntryPoint"] = invokeEntryPoint;
+        function jsStackTrace() {
+          var error = new Error();
+          if (!error.stack) {
+            try {
+              throw new Error();
+            } catch (e2) {
+              error = e2;
+            }
+            if (!error.stack) {
+              return "(no stack trace available)";
+            }
+          }
+          return error.stack.toString();
+        }
+        function registerTlsInit(tlsInitFunc, moduleExports, metadata) {
+          PThread.tlsInitFunctions.push(tlsInitFunc);
+        }
+        function setWasmTableEntry(idx, func2) {
+          wasmTable.set(idx, func2);
+          wasmTableMirror[idx] = func2;
+        }
+        var _emscripten_get_now;
+        if (ENVIRONMENT_IS_NODE) {
+          _emscripten_get_now = () => {
+            var t2 = process["hrtime"]();
+            return t2[0] * 1e3 + t2[1] / 1e6;
+          };
+        } else if (ENVIRONMENT_IS_PTHREAD) {
+          _emscripten_get_now = () => performance.now() - Module["__performance_now_clock_drift"];
+        } else
+          _emscripten_get_now = () => performance.now();
+        var _emscripten_get_now_is_monotonic = true;
+        function setErrNo(value) {
+          GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;
+          return value;
+        }
+        function _clock_gettime(clk_id, tp) {
+          var now22;
+          if (clk_id === 0) {
+            now22 = Date.now();
+          } else if ((clk_id === 1 || clk_id === 4) && _emscripten_get_now_is_monotonic) {
+            now22 = _emscripten_get_now();
+          } else {
+            setErrNo(28);
+            return -1;
+          }
+          GROWABLE_HEAP_I32()[tp >> 2] = now22 / 1e3 | 0;
+          GROWABLE_HEAP_I32()[tp + 4 >> 2] = now22 % 1e3 * 1e3 * 1e3 | 0;
+          return 0;
+        }
+        function ___clock_gettime(a0, a12) {
+          return _clock_gettime(a0, a12);
+        }
+        function ___emscripten_init_main_thread_js(tb) {
+          __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);
+          PThread.threadInit();
+        }
+        function ___emscripten_thread_cleanup(thread) {
+          if (!ENVIRONMENT_IS_PTHREAD)
+            cleanupThread(thread);
+          else
+            postMessage({ "cmd": "cleanupThread", "thread": thread });
+        }
+        function spawnThread(threadParams) {
+          var worker = PThread.getNewWorker();
+          if (!worker) {
+            return 6;
+          }
+          PThread.runningWorkers.push(worker);
+          var pthread = PThread.pthreads[threadParams.pthread_ptr] = { worker, threadInfoStruct: threadParams.pthread_ptr };
+          worker.pthread = pthread;
+          var msg = { "cmd": "run", "start_routine": threadParams.startRoutine, "arg": threadParams.arg, "threadInfoStruct": threadParams.pthread_ptr };
+          worker.runPthread = () => {
+            msg.time = performance.now();
+            worker.postMessage(msg, threadParams.transferList);
+          };
+          if (worker.loaded) {
+            worker.runPthread();
+            delete worker.runPthread;
+          }
+          return 0;
+        }
+        function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) {
+          if (typeof SharedArrayBuffer === "undefined") {
+            err("Current environment does not support SharedArrayBuffer, pthreads are not available!");
+            return 6;
+          }
+          var transferList = [];
+          var error = 0;
+          if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {
+            return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);
+          }
+          if (error)
+            return error;
+          var threadParams = { startRoutine: start_routine, pthread_ptr, arg, transferList };
+          if (ENVIRONMENT_IS_PTHREAD) {
+            threadParams.cmd = "spawnThread";
+            postMessage(threadParams, transferList);
+            return 0;
+          }
+          return spawnThread(threadParams);
+        }
+        function __emscripten_default_pthread_stack_size() {
+          return 2097152;
+        }
+        function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {
+          if (targetThreadId == mainThreadId) {
+            postMessage({ "cmd": "processQueuedMainThreadWork" });
+          } else if (ENVIRONMENT_IS_PTHREAD) {
+            postMessage({ "targetThread": targetThreadId, "cmd": "processThreadQueue" });
+          } else {
+            var pthread = PThread.pthreads[targetThreadId];
+            var worker = pthread && pthread.worker;
+            if (!worker) {
+              return;
+            }
+            worker.postMessage({ "cmd": "processThreadQueue" });
+          }
+          return 1;
+        }
+        function _abort() {
+          abort("");
+        }
+        function _emscripten_check_blocking_allowed() {
+          if (ENVIRONMENT_IS_NODE)
+            return;
+          if (ENVIRONMENT_IS_WORKER)
+            return;
+          warnOnce("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
+        }
+        function _emscripten_get_heap_max() {
+          return 2147483648;
+        }
+        function _emscripten_memcpy_big(dest, src, num) {
+          GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);
+        }
+        function _emscripten_num_logical_cores() {
+          if (ENVIRONMENT_IS_NODE)
+            return require_os().cpus().length;
+          return navigator["hardwareConcurrency"];
+        }
+        function _emscripten_proxy_to_main_thread_js(index2, sync) {
+          var numCallArgs = arguments.length - 2;
+          var outerArgs = arguments;
+          return withStackSave(function() {
+            var serializedNumCallArgs = numCallArgs;
+            var args = stackAlloc(serializedNumCallArgs * 8);
+            var b = args >> 3;
+            for (var i2 = 0; i2 < numCallArgs; i2++) {
+              var arg = outerArgs[2 + i2];
+              GROWABLE_HEAP_F64()[b + i2] = arg;
+            }
+            return _emscripten_run_in_main_runtime_thread_js(index2, serializedNumCallArgs, args, sync);
+          });
+        }
+        var _emscripten_receive_on_main_thread_js_callArgs = [];
+        function _emscripten_receive_on_main_thread_js(index2, numCallArgs, args) {
+          _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
+          var b = args >> 3;
+          for (var i2 = 0; i2 < numCallArgs; i2++) {
+            _emscripten_receive_on_main_thread_js_callArgs[i2] = GROWABLE_HEAP_F64()[b + i2];
+          }
+          var isEmAsmConst = index2 < 0;
+          var func2 = !isEmAsmConst ? proxiedFunctionTable[index2] : ASM_CONSTS[-index2 - 1];
+          return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);
+        }
+        function emscripten_realloc_buffer(size2) {
+          try {
+            wasmMemory.grow(size2 - buffer2.byteLength + 65535 >>> 16);
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+            return 1;
+          } catch (e2) {
+          }
+        }
+        function _emscripten_resize_heap(requestedSize) {
+          var oldSize = GROWABLE_HEAP_U8().length;
+          requestedSize = requestedSize >>> 0;
+          if (requestedSize <= oldSize) {
+            return false;
+          }
+          var maxHeapSize = _emscripten_get_heap_max();
+          if (requestedSize > maxHeapSize) {
+            return false;
+          }
+          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
+            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
+            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
+            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
+            var replacement = emscripten_realloc_buffer(newSize);
+            if (replacement) {
+              return true;
+            }
+          }
+          return false;
+        }
+        var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {
+          for (var i2 = JSEvents.eventHandlers.length - 1; i2 >= 0; --i2) {
+            JSEvents._removeHandler(i2);
+          }
+          JSEvents.eventHandlers = [];
+          JSEvents.deferredCalls = [];
+        }, registerRemoveEventListeners: function() {
+          if (!JSEvents.removeEventListenersRegistered) {
+            __ATEXIT__.push(JSEvents.removeAllEventListeners);
+            JSEvents.removeEventListenersRegistered = true;
+          }
+        }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {
+          function arraysHaveEqualContent(arrA, arrB) {
+            if (arrA.length != arrB.length)
+              return false;
+            for (var i3 in arrA) {
+              if (arrA[i3] != arrB[i3])
+                return false;
+            }
+            return true;
+          }
+          for (var i2 in JSEvents.deferredCalls) {
+            var call = JSEvents.deferredCalls[i2];
+            if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
+              return;
+            }
+          }
+          JSEvents.deferredCalls.push({ targetFunction, precedence, argsList });
+          JSEvents.deferredCalls.sort(function(x, y) {
+            return x.precedence < y.precedence;
+          });
+        }, removeDeferredCalls: function(targetFunction) {
+          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
+            if (JSEvents.deferredCalls[i2].targetFunction == targetFunction) {
+              JSEvents.deferredCalls.splice(i2, 1);
+              --i2;
+            }
+          }
+        }, canPerformEventHandlerRequests: function() {
+          return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;
+        }, runDeferredCalls: function() {
+          if (!JSEvents.canPerformEventHandlerRequests()) {
+            return;
+          }
+          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
+            var call = JSEvents.deferredCalls[i2];
+            JSEvents.deferredCalls.splice(i2, 1);
+            --i2;
+            call.targetFunction.apply(null, call.argsList);
+          }
+        }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {
+          for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
+            if (JSEvents.eventHandlers[i2].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i2].eventTypeString)) {
+              JSEvents._removeHandler(i2--);
+            }
+          }
+        }, _removeHandler: function(i2) {
+          var h = JSEvents.eventHandlers[i2];
+          h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
+          JSEvents.eventHandlers.splice(i2, 1);
+        }, registerOrRemoveHandler: function(eventHandler) {
+          var jsEventHandler = function jsEventHandler2(event) {
+            ++JSEvents.inEventHandler;
+            JSEvents.currentEventHandler = eventHandler;
+            JSEvents.runDeferredCalls();
+            eventHandler.handlerFunc(event);
+            JSEvents.runDeferredCalls();
+            --JSEvents.inEventHandler;
+          };
+          if (eventHandler.callbackfunc) {
+            eventHandler.eventListenerFunc = jsEventHandler;
+            eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);
+            JSEvents.eventHandlers.push(eventHandler);
+            JSEvents.registerRemoveEventListeners();
+          } else {
+            for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
+              if (JSEvents.eventHandlers[i2].target == eventHandler.target && JSEvents.eventHandlers[i2].eventTypeString == eventHandler.eventTypeString) {
+                JSEvents._removeHandler(i2--);
+              }
+            }
+          }
+        }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {
+          withStackSave(function() {
+            var varargs = stackAlloc(12);
+            GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;
+            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;
+            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;
+            _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);
+          });
+        }, getTargetThreadForEventCallback: function(targetThread) {
+          switch (targetThread) {
+            case 1:
+              return 0;
+            case 2:
+              return PThread.currentProxiedOperationCallerThread;
+            default:
+              return targetThread;
+          }
+        }, getNodeNameForTarget: function(target) {
+          if (!target)
+            return "";
+          if (target == window)
+            return "#window";
+          if (target == screen)
+            return "#screen";
+          return target && target.nodeName ? target.nodeName : "";
+        }, fullscreenEnabled: function() {
+          return document.fullscreenEnabled || document.webkitFullscreenEnabled;
+        } };
+        function stringToNewUTF8(jsString) {
+          var length = lengthBytesUTF8(jsString) + 1;
+          var cString = _malloc(length);
+          stringToUTF8(jsString, cString, length);
+          return cString;
+        }
+        function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {
+          withStackSave(function() {
+            var varargs = stackAlloc(12);
+            var targetCanvasPtr = 0;
+            if (targetCanvas) {
+              targetCanvasPtr = stringToNewUTF8(targetCanvas);
+            }
+            GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;
+            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;
+            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;
+            _emscripten_dispatch_to_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs);
+          });
+        }
+        function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {
+          targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : "";
+          _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);
+        }
+        function maybeCStringToJsString(cString) {
+          return cString > 2 ? UTF8ToString(cString) : cString;
+        }
+        var specialHTMLTargets = [0, typeof document !== "undefined" ? document : 0, typeof window !== "undefined" ? window : 0];
+        function findEventTarget(target) {
+          target = maybeCStringToJsString(target);
+          var domElement = specialHTMLTargets[target] || (typeof document !== "undefined" ? document.querySelector(target) : void 0);
+          return domElement;
+        }
+        function findCanvasEventTarget(target) {
+          return findEventTarget(target);
+        }
+        function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {
+          var canvas3 = findCanvasEventTarget(target);
+          if (!canvas3)
+            return -4;
+          if (canvas3.canvasSharedPtr) {
+            GROWABLE_HEAP_I32()[canvas3.canvasSharedPtr >> 2] = width;
+            GROWABLE_HEAP_I32()[canvas3.canvasSharedPtr + 4 >> 2] = height;
+          }
+          if (canvas3.offscreenCanvas || !canvas3.controlTransferredOffscreen) {
+            if (canvas3.offscreenCanvas)
+              canvas3 = canvas3.offscreenCanvas;
+            var autoResizeViewport = false;
+            if (canvas3.GLctxObject && canvas3.GLctxObject.GLctx) {
+              var prevViewport = canvas3.GLctxObject.GLctx.getParameter(2978);
+              autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas3.width && prevViewport[3] === canvas3.height;
+            }
+            canvas3.width = width;
+            canvas3.height = height;
+            if (autoResizeViewport) {
+              canvas3.GLctxObject.GLctx.viewport(0, 0, width, height);
+            }
+          } else if (canvas3.canvasSharedPtr) {
+            var targetThread = GROWABLE_HEAP_I32()[canvas3.canvasSharedPtr + 8 >> 2];
+            _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);
+            return 1;
+          } else {
+            return -4;
+          }
+          return 0;
+        }
+        function _emscripten_set_canvas_element_size_main_thread(target, width, height) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);
+          return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
+        }
+        function _emscripten_set_canvas_element_size(target, width, height) {
+          var canvas3 = findCanvasEventTarget(target);
+          if (canvas3) {
+            return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
+          } else {
+            return _emscripten_set_canvas_element_size_main_thread(target, width, height);
+          }
+        }
+        function _emscripten_unwind_to_js_event_loop() {
+          throw "unwind";
+        }
+        function __webgl_enable_ANGLE_instanced_arrays(ctx) {
+          var ext = ctx.getExtension("ANGLE_instanced_arrays");
+          if (ext) {
+            ctx["vertexAttribDivisor"] = function(index2, divisor) {
+              ext["vertexAttribDivisorANGLE"](index2, divisor);
+            };
+            ctx["drawArraysInstanced"] = function(mode, first, count22, primcount) {
+              ext["drawArraysInstancedANGLE"](mode, first, count22, primcount);
+            };
+            ctx["drawElementsInstanced"] = function(mode, count22, type, indices, primcount) {
+              ext["drawElementsInstancedANGLE"](mode, count22, type, indices, primcount);
+            };
+            return 1;
+          }
+        }
+        function __webgl_enable_OES_vertex_array_object(ctx) {
+          var ext = ctx.getExtension("OES_vertex_array_object");
+          if (ext) {
+            ctx["createVertexArray"] = function() {
+              return ext["createVertexArrayOES"]();
+            };
+            ctx["deleteVertexArray"] = function(vao) {
+              ext["deleteVertexArrayOES"](vao);
+            };
+            ctx["bindVertexArray"] = function(vao) {
+              ext["bindVertexArrayOES"](vao);
+            };
+            ctx["isVertexArray"] = function(vao) {
+              return ext["isVertexArrayOES"](vao);
+            };
+            return 1;
+          }
+        }
+        function __webgl_enable_WEBGL_draw_buffers(ctx) {
+          var ext = ctx.getExtension("WEBGL_draw_buffers");
+          if (ext) {
+            ctx["drawBuffers"] = function(n, bufs) {
+              ext["drawBuffersWEBGL"](n, bufs);
+            };
+            return 1;
+          }
+        }
+        function __webgl_enable_WEBGL_multi_draw(ctx) {
+          return !!(ctx.multiDrawWebgl = ctx.getExtension("WEBGL_multi_draw"));
+        }
+        var GL = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) {
+          if (!GL.lastError) {
+            GL.lastError = errorCode;
+          }
+        }, getNewId: function(table) {
+          var ret = GL.counter++;
+          for (var i2 = table.length; i2 < ret; i2++) {
+            table[i2] = null;
+          }
+          return ret;
+        }, getSource: function(shader, count22, string2, length) {
+          var source = "";
+          for (var i2 = 0; i2 < count22; ++i2) {
+            var len = length ? GROWABLE_HEAP_I32()[length + i2 * 4 >> 2] : -1;
+            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i2 * 4 >> 2], len < 0 ? void 0 : len);
+          }
+          return source;
+        }, createContext: function(canvas3, webGLContextAttributes) {
+          if (!canvas3.getContextSafariWebGL2Fixed) {
+            canvas3.getContextSafariWebGL2Fixed = canvas3.getContext;
+            canvas3.getContext = function(ver, attrs) {
+              var gl = canvas3.getContextSafariWebGL2Fixed(ver, attrs);
+              return ver == "webgl" == gl instanceof WebGLRenderingContext ? gl : null;
+            };
+          }
+          var ctx = canvas3.getContext("webgl", webGLContextAttributes);
+          if (!ctx)
+            return 0;
+          var handle = GL.registerContext(ctx, webGLContextAttributes);
+          return handle;
+        }, registerContext: function(ctx, webGLContextAttributes) {
+          var handle = _malloc(8);
+          GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();
+          var context = { handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx };
+          if (ctx.canvas)
+            ctx.canvas.GLctxObject = context;
+          GL.contexts[handle] = context;
+          if (typeof webGLContextAttributes.enableExtensionsByDefault === "undefined" || webGLContextAttributes.enableExtensionsByDefault) {
+            GL.initExtensions(context);
+          }
+          return handle;
+        }, makeContextCurrent: function(contextHandle) {
+          GL.currentContext = GL.contexts[contextHandle];
+          Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;
+          return !(contextHandle && !GLctx);
+        }, getContext: function(contextHandle) {
+          return GL.contexts[contextHandle];
+        }, deleteContext: function(contextHandle) {
+          if (GL.currentContext === GL.contexts[contextHandle])
+            GL.currentContext = null;
+          if (typeof JSEvents === "object")
+            JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);
+          if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)
+            GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;
+          _free(GL.contexts[contextHandle].handle);
+          GL.contexts[contextHandle] = null;
+        }, initExtensions: function(context) {
+          if (!context)
+            context = GL.currentContext;
+          if (context.initExtensionsDone)
+            return;
+          context.initExtensionsDone = true;
+          var GLctx2 = context.GLctx;
+          __webgl_enable_ANGLE_instanced_arrays(GLctx2);
+          __webgl_enable_OES_vertex_array_object(GLctx2);
+          __webgl_enable_WEBGL_draw_buffers(GLctx2);
+          {
+            GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query");
+          }
+          __webgl_enable_WEBGL_multi_draw(GLctx2);
+          var exts = GLctx2.getSupportedExtensions() || [];
+          exts.forEach(function(ext) {
+            if (!ext.includes("lose_context") && !ext.includes("debug")) {
+              GLctx2.getExtension(ext);
+            }
+          });
+        } };
+        var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"];
+        function _emscripten_webgl_do_create_context(target, attributes) {
+          var a6 = attributes >> 2;
+          var powerPreference = GROWABLE_HEAP_I32()[a6 + (24 >> 2)];
+          var contextAttributes = { "alpha": !!GROWABLE_HEAP_I32()[a6 + (0 >> 2)], "depth": !!GROWABLE_HEAP_I32()[a6 + (4 >> 2)], "stencil": !!GROWABLE_HEAP_I32()[a6 + (8 >> 2)], "antialias": !!GROWABLE_HEAP_I32()[a6 + (12 >> 2)], "premultipliedAlpha": !!GROWABLE_HEAP_I32()[a6 + (16 >> 2)], "preserveDrawingBuffer": !!GROWABLE_HEAP_I32()[a6 + (20 >> 2)], "powerPreference": __emscripten_webgl_power_preferences[powerPreference], "failIfMajorPerformanceCaveat": !!GROWABLE_HEAP_I32()[a6 + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a6 + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a6 + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a6 + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a6 + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a6 + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a6 + (52 >> 2)] };
+          var canvas3 = findCanvasEventTarget(target);
+          if (!canvas3) {
+            return 0;
+          }
+          if (contextAttributes.explicitSwapControl) {
+            return 0;
+          }
+          var contextHandle = GL.createContext(canvas3, contextAttributes);
+          return contextHandle;
+        }
+        function _emscripten_webgl_create_context(a0, a12) {
+          return _emscripten_webgl_do_create_context(a0, a12);
+        }
+        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
+          var buffer3 = SYSCALLS.buffers[stream];
+          if (curr === 0 || curr === 10) {
+            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));
+            buffer3.length = 0;
+          } else {
+            buffer3.push(curr);
+          }
+        }, varargs: void 0, get: function() {
+          SYSCALLS.varargs += 4;
+          var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];
+          return ret;
+        }, getStr: function(ptr) {
+          var ret = UTF8ToString(ptr);
+          return ret;
+        }, get64: function(low, high) {
+          return low;
+        } };
+        function _fd_close(fd) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(3, 1, fd);
+          return 0;
+        }
+        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);
+        }
+        function _fd_write(fd, iov, iovcnt, pnum) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);
+          var num = 0;
+          for (var i2 = 0; i2 < iovcnt; i2++) {
+            var ptr = GROWABLE_HEAP_I32()[iov >> 2];
+            var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];
+            iov += 8;
+            for (var j = 0; j < len; j++) {
+              SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);
+            }
+            num += len;
+          }
+          GROWABLE_HEAP_I32()[pnum >> 2] = num;
+          return 0;
+        }
+        function _setTempRet0(val) {
+          setTempRet0(val);
+        }
+        PThread.init();
+        var GLctx;
+        var proxiedFunctionTable = [null, exitOnMainThread, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write];
+        var ASSERTIONS = false;
+        var asmLibraryArg = { "__clock_gettime": ___clock_gettime, "__emscripten_init_main_thread_js": ___emscripten_init_main_thread_js, "__emscripten_thread_cleanup": ___emscripten_thread_cleanup, "__pthread_create_js": ___pthread_create_js, "_emscripten_default_pthread_stack_size": __emscripten_default_pthread_stack_size, "_emscripten_notify_thread_queue": __emscripten_notify_thread_queue, "abort": _abort, "emscripten_check_blocking_allowed": _emscripten_check_blocking_allowed, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_get_now": _emscripten_get_now, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_num_logical_cores": _emscripten_num_logical_cores, "emscripten_receive_on_main_thread_js": _emscripten_receive_on_main_thread_js, "emscripten_resize_heap": _emscripten_resize_heap, "emscripten_set_canvas_element_size": _emscripten_set_canvas_element_size, "emscripten_unwind_to_js_event_loop": _emscripten_unwind_to_js_event_loop, "emscripten_webgl_create_context": _emscripten_webgl_create_context, "exit": _exit, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "memory": wasmMemory || Module["wasmMemory"], "setTempRet0": _setTempRet0 };
+        var asm = createWasm();
+        var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
+          return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments);
+        };
+        var _init = Module["_init"] = function() {
+          return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments);
+        };
+        var _init_with_threads_count = Module["_init_with_threads_count"] = function() {
+          return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments);
+        };
+        var _get_threads_count = Module["_get_threads_count"] = function() {
+          return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments);
+        };
+        var _register_tensor = Module["_register_tensor"] = function() {
+          return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments);
+        };
+        var _dispose_data = Module["_dispose_data"] = function() {
+          return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments);
+        };
+        var _dispose = Module["_dispose"] = function() {
+          return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments);
+        };
+        var _Abs = Module["_Abs"] = function() {
+          return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments);
+        };
+        var _Add = Module["_Add"] = function() {
+          return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments);
+        };
+        var _AddN = Module["_AddN"] = function() {
+          return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments);
+        };
+        var _All = Module["_All"] = function() {
+          return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments);
+        };
+        var _Any = Module["_Any"] = function() {
+          return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments);
+        };
+        var _ArgMax = Module["_ArgMax"] = function() {
+          return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments);
+        };
+        var _AvgPool = Module["_AvgPool"] = function() {
+          return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments);
+        };
+        var _BatchMatMul = Module["_BatchMatMul"] = function() {
+          return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments);
+        };
+        var _Ceil = Module["_Ceil"] = function() {
+          return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments);
+        };
+        var _ClipByValue = Module["_ClipByValue"] = function() {
+          return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments);
+        };
+        var _Conv2D = Module["_Conv2D"] = function() {
+          return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments);
+        };
+        var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
+          return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments);
+        };
+        var _Cos = Module["_Cos"] = function() {
+          return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments);
+        };
+        var _Cosh = Module["_Cosh"] = function() {
+          return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments);
+        };
+        var _CropAndResize = Module["_CropAndResize"] = function() {
+          return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments);
+        };
+        var _Cumprod = Module["_Cumprod"] = function() {
+          return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments);
+        };
+        var _Cumsum = Module["_Cumsum"] = function() {
+          return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments);
+        };
+        var _DepthToSpace = Module["_DepthToSpace"] = function() {
+          return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments);
+        };
+        var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
+          return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments);
+        };
+        var _Elu = Module["_Elu"] = function() {
+          return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments);
+        };
+        var _Equal = Module["_Equal"] = function() {
+          return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments);
+        };
+        var _Exp = Module["_Exp"] = function() {
+          return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments);
+        };
+        var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
+          return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments);
+        };
+        var _Floor = Module["_Floor"] = function() {
+          return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments);
+        };
+        var _FloorDiv = Module["_FloorDiv"] = function() {
+          return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments);
+        };
+        var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
+          return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments);
+        };
+        var _FusedConv2D = Module["_FusedConv2D"] = function() {
+          return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments);
+        };
+        var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
+          return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments);
+        };
+        var _Gather = Module["_Gather"] = function() {
+          return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments);
+        };
+        var _GatherNd = Module["_GatherNd"] = function() {
+          return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments);
+        };
+        var _Greater = Module["_Greater"] = function() {
+          return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments);
+        };
+        var _GreaterEqual = Module["_GreaterEqual"] = function() {
+          return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments);
+        };
+        var _LeakyRelu = Module["_LeakyRelu"] = function() {
+          return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments);
+        };
+        var _Less = Module["_Less"] = function() {
+          return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments);
+        };
+        var _LessEqual = Module["_LessEqual"] = function() {
+          return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments);
+        };
+        var _Log = Module["_Log"] = function() {
+          return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments);
+        };
+        var _LogicalAnd = Module["_LogicalAnd"] = function() {
+          return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments);
+        };
+        var _Max = Module["_Max"] = function() {
+          return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments);
+        };
+        var _MaxPool = Module["_MaxPool"] = function() {
+          return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments);
+        };
+        var _Maximum = Module["_Maximum"] = function() {
+          return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments);
+        };
+        var _Mean = Module["_Mean"] = function() {
+          return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments);
+        };
+        var _Min = Module["_Min"] = function() {
+          return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments);
+        };
+        var _Minimum = Module["_Minimum"] = function() {
+          return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments);
+        };
+        var _MirrorPad = Module["_MirrorPad"] = function() {
+          return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments);
+        };
+        var _Multiply = Module["_Multiply"] = function() {
+          return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments);
+        };
+        var _Neg = Module["_Neg"] = function() {
+          return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
+          return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
+          return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
+          return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments);
+        };
+        var _NotEqual = Module["_NotEqual"] = function() {
+          return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments);
+        };
+        var _OneHot = Module["_OneHot"] = function() {
+          return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments);
+        };
+        var _PadV2 = Module["_PadV2"] = function() {
+          return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments);
+        };
+        var _Pow = Module["_Pow"] = function() {
+          return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments);
+        };
+        var _Prelu = Module["_Prelu"] = function() {
+          return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments);
+        };
+        var _Prod = Module["_Prod"] = function() {
+          return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments);
+        };
+        var _RealDiv = Module["_RealDiv"] = function() {
+          return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments);
+        };
+        var _Relu = Module["_Relu"] = function() {
+          return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments);
+        };
+        var _Relu6 = Module["_Relu6"] = function() {
+          return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments);
+        };
+        var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
+          return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments);
+        };
+        var _Reverse = Module["_Reverse"] = function() {
+          return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments);
+        };
+        var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
+          return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments);
+        };
+        var _Round = Module["_Round"] = function() {
+          return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments);
+        };
+        var _Rsqrt = Module["_Rsqrt"] = function() {
+          return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments);
+        };
+        var _ScatterNd = Module["_ScatterNd"] = function() {
+          return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments);
+        };
+        var _SelectV2 = Module["_SelectV2"] = function() {
+          return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments);
+        };
+        var _Sigmoid = Module["_Sigmoid"] = function() {
+          return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments);
+        };
+        var _Sin = Module["_Sin"] = function() {
+          return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments);
+        };
+        var _Softmax = Module["_Softmax"] = function() {
+          return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments);
+        };
+        var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() {
+          return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments);
+        };
+        var _SparseReshape = Module["_SparseReshape"] = function() {
+          return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments);
+        };
+        var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() {
+          return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments);
+        };
+        var _Sqrt = Module["_Sqrt"] = function() {
+          return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments);
+        };
+        var _Square = Module["_Square"] = function() {
+          return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments);
+        };
+        var _SquaredDifference = Module["_SquaredDifference"] = function() {
+          return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments);
+        };
+        var _Step = Module["_Step"] = function() {
+          return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments);
+        };
+        var _StridedSlice = Module["_StridedSlice"] = function() {
+          return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments);
+        };
+        var _Sub = Module["_Sub"] = function() {
+          return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments);
+        };
+        var _Sum = Module["_Sum"] = function() {
+          return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments);
+        };
+        var _Tan = Module["_Tan"] = function() {
+          return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments);
+        };
+        var _Tanh = Module["_Tanh"] = function() {
+          return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments);
+        };
+        var _Tile = Module["_Tile"] = function() {
+          return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments);
+        };
+        var _TopK = Module["_TopK"] = function() {
+          return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments);
+        };
+        var _Transform = Module["_Transform"] = function() {
+          return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments);
+        };
+        var _Transpose = Module["_Transpose"] = function() {
+          return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments);
+        };
+        var __FusedMatMul = Module["__FusedMatMul"] = function() {
+          return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments);
+        };
+        var _malloc = Module["_malloc"] = function() {
+          return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments);
+        };
+        var _free = Module["_free"] = function() {
+          return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments);
+        };
+        var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() {
+          return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["emscripten_tls_init"]).apply(null, arguments);
+        };
+        var ___errno_location = Module["___errno_location"] = function() {
+          return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments);
+        };
+        var _pthread_self = Module["_pthread_self"] = function() {
+          return (_pthread_self = Module["_pthread_self"] = Module["asm"]["pthread_self"]).apply(null, arguments);
+        };
+        var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() {
+          return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments);
+        };
+        var __emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = function() {
+          return (__emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = Module["asm"]["_emscripten_thread_crashed"]).apply(null, arguments);
+        };
+        var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() {
+          return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["_emscripten_thread_init"]).apply(null, arguments);
+        };
+        var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() {
+          return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["emscripten_current_thread_process_queued_calls"]).apply(null, arguments);
+        };
+        var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() {
+          return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["emscripten_main_browser_thread_id"]).apply(null, arguments);
+        };
+        var _emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = function() {
+          return (_emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = Module["asm"]["emscripten_sync_run_in_main_thread_2"]).apply(null, arguments);
+        };
+        var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() {
+          return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["emscripten_sync_run_in_main_thread_4"]).apply(null, arguments);
+        };
+        var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() {
+          return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["emscripten_run_in_main_runtime_thread_js"]).apply(null, arguments);
+        };
+        var _emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = function() {
+          return (_emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = Module["asm"]["emscripten_dispatch_to_thread_"]).apply(null, arguments);
+        };
+        var __emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = function() {
+          return (__emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = Module["asm"]["_emscripten_thread_free_data"]).apply(null, arguments);
+        };
+        var __emscripten_thread_exit = Module["__emscripten_thread_exit"] = function() {
+          return (__emscripten_thread_exit = Module["__emscripten_thread_exit"] = Module["asm"]["_emscripten_thread_exit"]).apply(null, arguments);
+        };
+        var _memalign = Module["_memalign"] = function() {
+          return (_memalign = Module["_memalign"] = Module["asm"]["memalign"]).apply(null, arguments);
+        };
+        var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() {
+          return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["emscripten_stack_set_limits"]).apply(null, arguments);
+        };
+        var stackSave = Module["stackSave"] = function() {
+          return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments);
+        };
+        var stackRestore = Module["stackRestore"] = function() {
+          return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments);
+        };
+        var stackAlloc = Module["stackAlloc"] = function() {
+          return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments);
+        };
+        var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() {
+          return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments);
+        };
+        var dynCall_jiji = Module["dynCall_jiji"] = function() {
+          return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments);
+        };
+        var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 21464;
+        Module["cwrap"] = cwrap;
+        Module["keepRuntimeAlive"] = keepRuntimeAlive;
+        Module["PThread"] = PThread;
+        Module["PThread"] = PThread;
+        Module["wasmMemory"] = wasmMemory;
+        Module["ExitStatus"] = ExitStatus;
+        var calledRun;
+        function ExitStatus(status) {
+          this.name = "ExitStatus";
+          this.message = "Program terminated with exit(" + status + ")";
+          this.status = status;
+        }
+        dependenciesFulfilled = function runCaller() {
+          if (!calledRun)
+            run();
+          if (!calledRun)
+            dependenciesFulfilled = runCaller;
+        };
+        function run(args) {
+          args = args || arguments_;
+          if (runDependencies > 0) {
+            return;
+          }
+          if (ENVIRONMENT_IS_PTHREAD) {
+            readyPromiseResolve(Module);
+            initRuntime();
+            postMessage({ "cmd": "loaded" });
+            return;
+          }
+          preRun();
+          if (runDependencies > 0) {
+            return;
+          }
+          function doRun() {
+            if (calledRun)
+              return;
+            calledRun = true;
+            Module["calledRun"] = true;
+            if (ABORT)
+              return;
+            initRuntime();
+            readyPromiseResolve(Module);
+            if (Module["onRuntimeInitialized"])
+              Module["onRuntimeInitialized"]();
+            postRun();
+          }
+          if (Module["setStatus"]) {
+            Module["setStatus"]("Running...");
+            setTimeout(function() {
+              setTimeout(function() {
+                Module["setStatus"]("");
+              }, 1);
+              doRun();
+            }, 1);
+          } else {
+            doRun();
+          }
+        }
+        Module["run"] = run;
+        function exit(status, implicit) {
+          EXITSTATUS = status;
+          if (!implicit) {
+            if (ENVIRONMENT_IS_PTHREAD) {
+              exitOnMainThread(status);
+              throw "unwind";
+            } else {
+            }
+          }
+          if (keepRuntimeAlive()) {
+          } else {
+            exitRuntime();
+          }
+          procExit(status);
+        }
+        function procExit(code) {
+          EXITSTATUS = code;
+          if (!keepRuntimeAlive()) {
+            PThread.terminateAllThreads();
+            if (Module["onExit"])
+              Module["onExit"](code);
+            ABORT = true;
+          }
+          quit_(code, new ExitStatus(code));
+        }
+        if (Module["preInit"]) {
+          if (typeof Module["preInit"] == "function")
+            Module["preInit"] = [Module["preInit"]];
+          while (Module["preInit"].length > 0) {
+            Module["preInit"].pop()();
+          }
+        }
+        run();
+        var listenersAdded;
+        if (beforeListeners) {
+          listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) {
+            return !beforeListeners.uncaughtException.indexOf(listener) > -1;
+          }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) {
+            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;
+          }) };
+        }
+        var actualModule;
+        if (typeof WasmBackendModule !== "undefined") {
+          actualModule = WasmBackendModule;
+        } else if (typeof WasmBackendModuleThreadedSimd3 !== "undefined") {
+          actualModule = WasmBackendModuleThreadedSimd3;
+        } else {
+          throw new Error("Could not find wasm module in post.js");
+        }
+        if (listenersAdded) {
+          var tmpDispose = actualModule["_dispose"];
+          actualModule["_dispose"] = function() {
+            tmpDispose();
+            listenersAdded.uncaughtException.forEach(function(listener) {
+              process.removeListener("uncaughtException", listener);
+            });
+            listenersAdded.unhandledRejection.forEach(function(listener) {
+              process.removeListener("unhandledRejection", listener);
+            });
+          };
+        }
+        return WasmBackendModuleThreadedSimd3.ready;
+      };
+    })();
+    if (typeof exports === "object" && typeof module === "object")
+      module.exports = WasmBackendModuleThreadedSimd2;
+    else if (typeof define === "function" && define["amd"])
+      define([], function() {
+        return WasmBackendModuleThreadedSimd2;
+      });
+    else if (typeof exports === "object")
+      exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd2;
+  }
+});
+var require_tfjs_backend_wasm = __commonJS({
+  "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module) {
+    var WasmBackendModule2 = (() => {
+      var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
+      if (typeof __filename !== "undefined")
+        _scriptDir = _scriptDir || __filename;
+      return function(WasmBackendModule3) {
+        WasmBackendModule3 = WasmBackendModule3 || {};
+        var Module = typeof WasmBackendModule3 !== "undefined" ? WasmBackendModule3 : {};
+        var readyPromiseResolve, readyPromiseReject;
+        Module["ready"] = new Promise(function(resolve, reject) {
+          readyPromiseResolve = resolve;
+          readyPromiseReject = reject;
+        });
+        var beforeListeners;
+        if (typeof process !== "undefined" && process.listeners) {
+          beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") };
+        }
+        var moduleOverrides = Object.assign({}, Module);
+        var arguments_ = [];
+        var thisProgram = "./this.program";
+        var quit_ = (status, toThrow) => {
+          throw toThrow;
+        };
+        var ENVIRONMENT_IS_WEB = typeof window === "object";
+        var ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
+        var ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
+        var scriptDirectory = "";
+        function locateFile(path) {
+          if (Module["locateFile"]) {
+            return Module["locateFile"](path, scriptDirectory);
+          }
+          return scriptDirectory + path;
+        }
+        var read_, readAsync, readBinary, setWindowTitle;
+        function logExceptionOnExit(e2) {
+          if (e2 instanceof ExitStatus)
+            return;
+          let toLog = e2;
+          err("exiting due to exception: " + toLog);
+        }
+        var fs;
+        var nodePath;
+        var requireNodeFS;
+        if (ENVIRONMENT_IS_NODE) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = require_path().dirname(scriptDirectory) + "/";
+          } else {
+            scriptDirectory = __dirname + "/";
+          }
+          requireNodeFS = () => {
+            if (!nodePath) {
+              fs = require_fs();
+              nodePath = require_path();
+            }
+          };
+          read_ = function shell_read(filename, binary) {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            return fs.readFileSync(filename, binary ? void 0 : "utf8");
+          };
+          readBinary = (filename) => {
+            var ret = read_(filename, true);
+            if (!ret.buffer) {
+              ret = new Uint8Array(ret);
+            }
+            return ret;
+          };
+          readAsync = (filename, onload, onerror) => {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            fs.readFile(filename, function(err2, data) {
+              if (err2)
+                onerror(err2);
+              else
+                onload(data.buffer);
+            });
+          };
+          if (process["argv"].length > 1) {
+            thisProgram = process["argv"][1].replace(/\\/g, "/");
+          }
+          arguments_ = process["argv"].slice(2);
+          process["on"]("uncaughtException", function(ex) {
+            if (!(ex instanceof ExitStatus)) {
+              throw ex;
+            }
+          });
+          process["on"]("unhandledRejection", function(reason) {
+            throw reason;
+          });
+          quit_ = (status, toThrow) => {
+            if (keepRuntimeAlive()) {
+              process["exitCode"] = status;
+              throw toThrow;
+            }
+            logExceptionOnExit(toThrow);
+            process["exit"](status);
+          };
+          Module["inspect"] = function() {
+            return "[Emscripten Module object]";
+          };
+        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = self.location.href;
+          } else if (typeof document !== "undefined" && document.currentScript) {
+            scriptDirectory = document.currentScript.src;
+          }
+          if (_scriptDir) {
+            scriptDirectory = _scriptDir;
+          }
+          if (scriptDirectory.indexOf("blob:") !== 0) {
+            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
+          } else {
+            scriptDirectory = "";
+          }
+          {
+            read_ = (url) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, false);
+              xhr.send(null);
+              return xhr.responseText;
+            };
+            if (ENVIRONMENT_IS_WORKER) {
+              readBinary = (url) => {
+                var xhr = new XMLHttpRequest();
+                xhr.open("GET", url, false);
+                xhr.responseType = "arraybuffer";
+                xhr.send(null);
+                return new Uint8Array(xhr.response);
+              };
+            }
+            readAsync = (url, onload, onerror) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, true);
+              xhr.responseType = "arraybuffer";
+              xhr.onload = () => {
+                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
+                  onload(xhr.response);
+                  return;
+                }
+                onerror();
+              };
+              xhr.onerror = onerror;
+              xhr.send(null);
+            };
+          }
+          setWindowTitle = (title) => document.title = title;
+        } else {
+        }
+        var out = Module["print"] || console.log.bind(console);
+        var err = Module["printErr"] || console.warn.bind(console);
+        Object.assign(Module, moduleOverrides);
+        moduleOverrides = null;
+        if (Module["arguments"])
+          arguments_ = Module["arguments"];
+        if (Module["thisProgram"])
+          thisProgram = Module["thisProgram"];
+        if (Module["quit"])
+          quit_ = Module["quit"];
+        var POINTER_SIZE = 4;
+        function warnOnce(text) {
+          if (!warnOnce.shown)
+            warnOnce.shown = {};
+          if (!warnOnce.shown[text]) {
+            warnOnce.shown[text] = 1;
+            err(text);
+          }
+        }
+        function convertJsFunctionToWasm(func2, sig) {
+          if (typeof WebAssembly.Function === "function") {
+            var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
+            var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
+            for (var i2 = 1; i2 < sig.length; ++i2) {
+              type.parameters.push(typeNames[sig[i2]]);
+            }
+            return new WebAssembly.Function(type, func2);
+          }
+          var typeSection = [1, 0, 1, 96];
+          var sigRet = sig.slice(0, 1);
+          var sigParam = sig.slice(1);
+          var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
+          typeSection.push(sigParam.length);
+          for (var i2 = 0; i2 < sigParam.length; ++i2) {
+            typeSection.push(typeCodes[sigParam[i2]]);
+          }
+          if (sigRet == "v") {
+            typeSection.push(0);
+          } else {
+            typeSection = typeSection.concat([1, typeCodes[sigRet]]);
+          }
+          typeSection[1] = typeSection.length - 2;
+          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));
+          var module2 = new WebAssembly.Module(bytes);
+          var instance = new WebAssembly.Instance(module2, { "e": { "f": func2 } });
+          var wrappedFunc = instance.exports["f"];
+          return wrappedFunc;
+        }
+        var freeTableIndexes = [];
+        var functionsInTableMap;
+        function getEmptyTableSlot() {
+          if (freeTableIndexes.length) {
+            return freeTableIndexes.pop();
+          }
+          try {
+            wasmTable.grow(1);
+          } catch (err2) {
+            if (!(err2 instanceof RangeError)) {
+              throw err2;
+            }
+            throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
+          }
+          return wasmTable.length - 1;
+        }
+        function updateTableMap(offset, count22) {
+          for (var i2 = offset; i2 < offset + count22; i2++) {
+            var item = getWasmTableEntry(i2);
+            if (item) {
+              functionsInTableMap.set(item, i2);
+            }
+          }
+        }
+        var tempRet0 = 0;
+        var setTempRet0 = (value) => {
+          tempRet0 = value;
+        };
+        var wasmBinary;
+        if (Module["wasmBinary"])
+          wasmBinary = Module["wasmBinary"];
+        var noExitRuntime = Module["noExitRuntime"] || true;
+        if (typeof WebAssembly !== "object") {
+          abort("no native wasm support detected");
+        }
+        var wasmMemory;
+        var ABORT = false;
+        var EXITSTATUS;
+        function assert3(condition, text) {
+          if (!condition) {
+            abort(text);
+          }
+        }
+        function getCFunc(ident) {
+          var func2 = Module["_" + ident];
+          return func2;
+        }
+        function ccall(ident, returnType, argTypes, args, opts) {
+          var toC = { "string": function(str) {
+            var ret2 = 0;
+            if (str !== null && str !== void 0 && str !== 0) {
+              var len = (str.length << 2) + 1;
+              ret2 = stackAlloc(len);
+              stringToUTF8(str, ret2, len);
+            }
+            return ret2;
+          }, "array": function(arr) {
+            var ret2 = stackAlloc(arr.length);
+            writeArrayToMemory(arr, ret2);
+            return ret2;
+          } };
+          function convertReturnValue(ret2) {
+            if (returnType === "string")
+              return UTF8ToString(ret2);
+            if (returnType === "boolean")
+              return Boolean(ret2);
+            return ret2;
+          }
+          var func2 = getCFunc(ident);
+          var cArgs = [];
+          var stack2 = 0;
+          if (args) {
+            for (var i2 = 0; i2 < args.length; i2++) {
+              var converter = toC[argTypes[i2]];
+              if (converter) {
+                if (stack2 === 0)
+                  stack2 = stackSave();
+                cArgs[i2] = converter(args[i2]);
+              } else {
+                cArgs[i2] = args[i2];
+              }
+            }
+          }
+          var ret = func2.apply(null, cArgs);
+          function onDone(ret2) {
+            if (stack2 !== 0)
+              stackRestore(stack2);
+            return convertReturnValue(ret2);
+          }
+          ret = onDone(ret);
+          return ret;
+        }
+        function cwrap(ident, returnType, argTypes, opts) {
+          argTypes = argTypes || [];
+          var numericArgs = argTypes.every(function(type) {
+            return type === "number";
+          });
+          var numericRet = returnType !== "string";
+          if (numericRet && numericArgs && !opts) {
+            return getCFunc(ident);
+          }
+          return function() {
+            return ccall(ident, returnType, argTypes, arguments, opts);
+          };
+        }
+        var ALLOC_STACK = 1;
+        var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0;
+        function UTF8ArrayToString(heap, idx, maxBytesToRead) {
+          var endIdx = idx + maxBytesToRead;
+          var endPtr = idx;
+          while (heap[endPtr] && !(endPtr >= endIdx))
+            ++endPtr;
+          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {
+            return UTF8Decoder.decode(heap.subarray(idx, endPtr));
+          } else {
+            var str = "";
+            while (idx < endPtr) {
+              var u0 = heap[idx++];
+              if (!(u0 & 128)) {
+                str += String.fromCharCode(u0);
+                continue;
+              }
+              var u1 = heap[idx++] & 63;
+              if ((u0 & 224) == 192) {
+                str += String.fromCharCode((u0 & 31) << 6 | u1);
+                continue;
+              }
+              var u2 = heap[idx++] & 63;
+              if ((u0 & 240) == 224) {
+                u0 = (u0 & 15) << 12 | u1 << 6 | u2;
+              } else {
+                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
+              }
+              if (u0 < 65536) {
+                str += String.fromCharCode(u0);
+              } else {
+                var ch = u0 - 65536;
+                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
+              }
+            }
+          }
+          return str;
+        }
+        function UTF8ToString(ptr, maxBytesToRead) {
+          return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "";
+        }
+        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
+          if (!(maxBytesToWrite > 0))
+            return 0;
+          var startIdx = outIdx;
+          var endIdx = outIdx + maxBytesToWrite - 1;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343) {
+              var u1 = str.charCodeAt(++i2);
+              u = 65536 + ((u & 1023) << 10) | u1 & 1023;
+            }
+            if (u <= 127) {
+              if (outIdx >= endIdx)
+                break;
+              heap[outIdx++] = u;
+            } else if (u <= 2047) {
+              if (outIdx + 1 >= endIdx)
+                break;
+              heap[outIdx++] = 192 | u >> 6;
+              heap[outIdx++] = 128 | u & 63;
+            } else if (u <= 65535) {
+              if (outIdx + 2 >= endIdx)
+                break;
+              heap[outIdx++] = 224 | u >> 12;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            } else {
+              if (outIdx + 3 >= endIdx)
+                break;
+              heap[outIdx++] = 240 | u >> 18;
+              heap[outIdx++] = 128 | u >> 12 & 63;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            }
+          }
+          heap[outIdx] = 0;
+          return outIdx - startIdx;
+        }
+        function stringToUTF8(str, outPtr, maxBytesToWrite) {
+          return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);
+        }
+        function lengthBytesUTF8(str) {
+          var len = 0;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343)
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+            if (u <= 127)
+              ++len;
+            else if (u <= 2047)
+              len += 2;
+            else if (u <= 65535)
+              len += 3;
+            else
+              len += 4;
+          }
+          return len;
+        }
+        var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf-16le") : void 0;
+        function writeArrayToMemory(array2, buffer3) {
+          HEAP8.set(array2, buffer3);
+        }
+        function writeAsciiToMemory(str, buffer3, dontAddNull) {
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            HEAP8[buffer3++ >> 0] = str.charCodeAt(i2);
+          }
+          if (!dontAddNull)
+            HEAP8[buffer3 >> 0] = 0;
+        }
+        function alignUp(x, multiple) {
+          if (x % multiple > 0) {
+            x += multiple - x % multiple;
+          }
+          return x;
+        }
+        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
+        function updateGlobalBufferAndViews(buf) {
+          buffer2 = buf;
+          Module["HEAP8"] = HEAP8 = new Int8Array(buf);
+          Module["HEAP16"] = HEAP16 = new Int16Array(buf);
+          Module["HEAP32"] = HEAP32 = new Int32Array(buf);
+          Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
+          Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
+          Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
+          Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
+          Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
+        }
+        var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
+        var wasmTable;
+        var __ATPRERUN__ = [];
+        var __ATINIT__ = [];
+        var __ATPOSTRUN__ = [];
+        var runtimeInitialized = false;
+        var runtimeExited = false;
+        var runtimeKeepaliveCounter = 0;
+        function keepRuntimeAlive() {
+          return noExitRuntime || runtimeKeepaliveCounter > 0;
+        }
+        function preRun() {
+          if (Module["preRun"]) {
+            if (typeof Module["preRun"] == "function")
+              Module["preRun"] = [Module["preRun"]];
+            while (Module["preRun"].length) {
+              addOnPreRun(Module["preRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPRERUN__);
+        }
+        function initRuntime() {
+          runtimeInitialized = true;
+          callRuntimeCallbacks(__ATINIT__);
+        }
+        function exitRuntime() {
+          runtimeExited = true;
+        }
+        function postRun() {
+          if (Module["postRun"]) {
+            if (typeof Module["postRun"] == "function")
+              Module["postRun"] = [Module["postRun"]];
+            while (Module["postRun"].length) {
+              addOnPostRun(Module["postRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPOSTRUN__);
+        }
+        function addOnPreRun(cb) {
+          __ATPRERUN__.unshift(cb);
+        }
+        function addOnInit(cb) {
+          __ATINIT__.unshift(cb);
+        }
+        function addOnPostRun(cb) {
+          __ATPOSTRUN__.unshift(cb);
+        }
+        var runDependencies = 0;
+        var runDependencyWatcher = null;
+        var dependenciesFulfilled = null;
+        function addRunDependency(id) {
+          runDependencies++;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+        }
+        function removeRunDependency(id) {
+          runDependencies--;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+          if (runDependencies == 0) {
+            if (runDependencyWatcher !== null) {
+              clearInterval(runDependencyWatcher);
+              runDependencyWatcher = null;
+            }
+            if (dependenciesFulfilled) {
+              var callback = dependenciesFulfilled;
+              dependenciesFulfilled = null;
+              callback();
+            }
+          }
+        }
+        Module["preloadedImages"] = {};
+        Module["preloadedAudios"] = {};
+        function abort(what) {
+          {
+            if (Module["onAbort"]) {
+              Module["onAbort"](what);
+            }
+          }
+          what = "Aborted(" + what + ")";
+          err(what);
+          ABORT = true;
+          EXITSTATUS = 1;
+          what += ". Build with -s ASSERTIONS=1 for more info.";
+          var e2 = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e2);
+          throw e2;
+        }
+        var dataURIPrefix = "data:application/octet-stream;base64,";
+        function isDataURI(filename) {
+          return filename.startsWith(dataURIPrefix);
+        }
+        function isFileURI(filename) {
+          return filename.startsWith("file://");
+        }
+        var wasmBinaryFile;
+        wasmBinaryFile = "tfjs-backend-wasm.wasm";
+        if (!isDataURI(wasmBinaryFile)) {
+          wasmBinaryFile = locateFile(wasmBinaryFile);
+        }
+        function getBinary(file) {
+          try {
+            if (file == wasmBinaryFile && wasmBinary) {
+              return new Uint8Array(wasmBinary);
+            }
+            if (readBinary) {
+              return readBinary(file);
+            } else {
+              throw "both async and sync fetching of the wasm failed";
+            }
+          } catch (err2) {
+            abort(err2);
+          }
+        }
+        function getBinaryPromise() {
+          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
+            if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                if (!response["ok"]) {
+                  throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
+                }
+                return response["arrayBuffer"]();
+              }).catch(function() {
+                return getBinary(wasmBinaryFile);
+              });
+            } else {
+              if (readAsync) {
+                return new Promise(function(resolve, reject) {
+                  readAsync(wasmBinaryFile, function(response) {
+                    resolve(new Uint8Array(response));
+                  }, reject);
+                });
+              }
+            }
+          }
+          return Promise.resolve().then(function() {
+            return getBinary(wasmBinaryFile);
+          });
+        }
+        function createWasm() {
+          var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg };
+          function receiveInstance(instance, module2) {
+            var exports3 = instance.exports;
+            Module["asm"] = exports3;
+            wasmMemory = Module["asm"]["memory"];
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+            wasmTable = Module["asm"]["__indirect_function_table"];
+            addOnInit(Module["asm"]["__wasm_call_ctors"]);
+            removeRunDependency("wasm-instantiate");
+          }
+          addRunDependency("wasm-instantiate");
+          function receiveInstantiationResult(result) {
+            receiveInstance(result["instance"]);
+          }
+          function instantiateArrayBuffer(receiver) {
+            return getBinaryPromise().then(function(binary) {
+              return WebAssembly.instantiate(binary, info);
+            }).then(function(instance) {
+              return instance;
+            }).then(receiver, function(reason) {
+              err("failed to asynchronously prepare wasm: " + reason);
+              abort(reason);
+            });
+          }
+          function instantiateAsync() {
+            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                var result = WebAssembly.instantiateStreaming(response, info);
+                return result.then(receiveInstantiationResult, function(reason) {
+                  err("wasm streaming compile failed: " + reason);
+                  err("falling back to ArrayBuffer instantiation");
+                  return instantiateArrayBuffer(receiveInstantiationResult);
+                });
+              });
+            } else {
+              return instantiateArrayBuffer(receiveInstantiationResult);
+            }
+          }
+          if (Module["instantiateWasm"]) {
+            try {
+              var exports2 = Module["instantiateWasm"](info, receiveInstance);
+              return exports2;
+            } catch (e2) {
+              err("Module.instantiateWasm callback failed with error: " + e2);
+              return false;
+            }
+          }
+          instantiateAsync().catch(readyPromiseReject);
+          return {};
+        }
+        var tempDouble;
+        var tempI64;
+        function callRuntimeCallbacks(callbacks2) {
+          while (callbacks2.length > 0) {
+            var callback = callbacks2.shift();
+            if (typeof callback == "function") {
+              callback(Module);
+              continue;
+            }
+            var func2 = callback.func;
+            if (typeof func2 === "number") {
+              if (callback.arg === void 0) {
+                getWasmTableEntry(func2)();
+              } else {
+                getWasmTableEntry(func2)(callback.arg);
+              }
+            } else {
+              func2(callback.arg === void 0 ? null : callback.arg);
+            }
+          }
+        }
+        function demangle(func2) {
+          return func2;
+        }
+        function demangleAll(text) {
+          var regex = /\b_Z[\w\d_]+/g;
+          return text.replace(regex, function(x) {
+            var y = demangle(x);
+            return x === y ? x : y + " [" + x + "]";
+          });
+        }
+        var wasmTableMirror = [];
+        function getWasmTableEntry(funcPtr) {
+          var func2 = wasmTableMirror[funcPtr];
+          if (!func2) {
+            if (funcPtr >= wasmTableMirror.length)
+              wasmTableMirror.length = funcPtr + 1;
+            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);
+          }
+          return func2;
+        }
+        function jsStackTrace() {
+          var error = new Error();
+          if (!error.stack) {
+            try {
+              throw new Error();
+            } catch (e2) {
+              error = e2;
+            }
+            if (!error.stack) {
+              return "(no stack trace available)";
+            }
+          }
+          return error.stack.toString();
+        }
+        function setWasmTableEntry(idx, func2) {
+          wasmTable.set(idx, func2);
+          wasmTableMirror[idx] = func2;
+        }
+        function _abort() {
+          abort("");
+        }
+        function _emscripten_get_heap_max() {
+          return 2147483648;
+        }
+        function _emscripten_memcpy_big(dest, src, num) {
+          HEAPU8.copyWithin(dest, src, src + num);
+        }
+        function emscripten_realloc_buffer(size2) {
+          try {
+            wasmMemory.grow(size2 - buffer2.byteLength + 65535 >>> 16);
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+            return 1;
+          } catch (e2) {
+          }
+        }
+        function _emscripten_resize_heap(requestedSize) {
+          var oldSize = HEAPU8.length;
+          requestedSize = requestedSize >>> 0;
+          var maxHeapSize = _emscripten_get_heap_max();
+          if (requestedSize > maxHeapSize) {
+            return false;
+          }
+          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
+            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
+            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
+            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
+            var replacement = emscripten_realloc_buffer(newSize);
+            if (replacement) {
+              return true;
+            }
+          }
+          return false;
+        }
+        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
+          var buffer3 = SYSCALLS.buffers[stream];
+          if (curr === 0 || curr === 10) {
+            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));
+            buffer3.length = 0;
+          } else {
+            buffer3.push(curr);
+          }
+        }, varargs: void 0, get: function() {
+          SYSCALLS.varargs += 4;
+          var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];
+          return ret;
+        }, getStr: function(ptr) {
+          var ret = UTF8ToString(ptr);
+          return ret;
+        }, get64: function(low, high) {
+          return low;
+        } };
+        function _fd_close(fd) {
+          return 0;
+        }
+        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
+        }
+        function _fd_write(fd, iov, iovcnt, pnum) {
+          var num = 0;
+          for (var i2 = 0; i2 < iovcnt; i2++) {
+            var ptr = HEAP32[iov >> 2];
+            var len = HEAP32[iov + 4 >> 2];
+            iov += 8;
+            for (var j = 0; j < len; j++) {
+              SYSCALLS.printChar(fd, HEAPU8[ptr + j]);
+            }
+            num += len;
+          }
+          HEAP32[pnum >> 2] = num;
+          return 0;
+        }
+        function _setTempRet0(val) {
+          setTempRet0(val);
+        }
+        var ASSERTIONS = false;
+        var asmLibraryArg = { "abort": _abort, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_resize_heap": _emscripten_resize_heap, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "setTempRet0": _setTempRet0 };
+        var asm = createWasm();
+        var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
+          return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments);
+        };
+        var _init = Module["_init"] = function() {
+          return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments);
+        };
+        var _init_with_threads_count = Module["_init_with_threads_count"] = function() {
+          return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments);
+        };
+        var _get_threads_count = Module["_get_threads_count"] = function() {
+          return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments);
+        };
+        var _register_tensor = Module["_register_tensor"] = function() {
+          return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments);
+        };
+        var _dispose_data = Module["_dispose_data"] = function() {
+          return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments);
+        };
+        var _dispose = Module["_dispose"] = function() {
+          return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments);
+        };
+        var _Abs = Module["_Abs"] = function() {
+          return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments);
+        };
+        var _Add = Module["_Add"] = function() {
+          return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments);
+        };
+        var _AddN = Module["_AddN"] = function() {
+          return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments);
+        };
+        var _All = Module["_All"] = function() {
+          return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments);
+        };
+        var _Any = Module["_Any"] = function() {
+          return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments);
+        };
+        var _ArgMax = Module["_ArgMax"] = function() {
+          return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments);
+        };
+        var _AvgPool = Module["_AvgPool"] = function() {
+          return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments);
+        };
+        var _BatchMatMul = Module["_BatchMatMul"] = function() {
+          return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments);
+        };
+        var _Ceil = Module["_Ceil"] = function() {
+          return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments);
+        };
+        var _ClipByValue = Module["_ClipByValue"] = function() {
+          return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments);
+        };
+        var _Conv2D = Module["_Conv2D"] = function() {
+          return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments);
+        };
+        var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
+          return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments);
+        };
+        var _Cos = Module["_Cos"] = function() {
+          return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments);
+        };
+        var _Cosh = Module["_Cosh"] = function() {
+          return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments);
+        };
+        var _CropAndResize = Module["_CropAndResize"] = function() {
+          return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments);
+        };
+        var _Cumprod = Module["_Cumprod"] = function() {
+          return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments);
+        };
+        var _Cumsum = Module["_Cumsum"] = function() {
+          return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments);
+        };
+        var _DepthToSpace = Module["_DepthToSpace"] = function() {
+          return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments);
+        };
+        var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
+          return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments);
+        };
+        var _Elu = Module["_Elu"] = function() {
+          return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments);
+        };
+        var _Equal = Module["_Equal"] = function() {
+          return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments);
+        };
+        var _Exp = Module["_Exp"] = function() {
+          return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments);
+        };
+        var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
+          return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments);
+        };
+        var _Floor = Module["_Floor"] = function() {
+          return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments);
+        };
+        var _FloorDiv = Module["_FloorDiv"] = function() {
+          return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments);
+        };
+        var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
+          return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments);
+        };
+        var _FusedConv2D = Module["_FusedConv2D"] = function() {
+          return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments);
+        };
+        var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
+          return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments);
+        };
+        var _Gather = Module["_Gather"] = function() {
+          return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments);
+        };
+        var _GatherNd = Module["_GatherNd"] = function() {
+          return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments);
+        };
+        var _Greater = Module["_Greater"] = function() {
+          return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments);
+        };
+        var _GreaterEqual = Module["_GreaterEqual"] = function() {
+          return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments);
+        };
+        var _LeakyRelu = Module["_LeakyRelu"] = function() {
+          return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments);
+        };
+        var _Less = Module["_Less"] = function() {
+          return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments);
+        };
+        var _LessEqual = Module["_LessEqual"] = function() {
+          return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments);
+        };
+        var _Log = Module["_Log"] = function() {
+          return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments);
+        };
+        var _LogicalAnd = Module["_LogicalAnd"] = function() {
+          return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments);
+        };
+        var _Max = Module["_Max"] = function() {
+          return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments);
+        };
+        var _MaxPool = Module["_MaxPool"] = function() {
+          return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments);
+        };
+        var _Maximum = Module["_Maximum"] = function() {
+          return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments);
+        };
+        var _Mean = Module["_Mean"] = function() {
+          return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments);
+        };
+        var _Min = Module["_Min"] = function() {
+          return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments);
+        };
+        var _Minimum = Module["_Minimum"] = function() {
+          return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments);
+        };
+        var _MirrorPad = Module["_MirrorPad"] = function() {
+          return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments);
+        };
+        var _Multiply = Module["_Multiply"] = function() {
+          return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments);
+        };
+        var _Neg = Module["_Neg"] = function() {
+          return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
+          return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
+          return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
+          return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments);
+        };
+        var _NotEqual = Module["_NotEqual"] = function() {
+          return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments);
+        };
+        var _OneHot = Module["_OneHot"] = function() {
+          return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments);
+        };
+        var _PadV2 = Module["_PadV2"] = function() {
+          return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments);
+        };
+        var _Pow = Module["_Pow"] = function() {
+          return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments);
+        };
+        var _Prelu = Module["_Prelu"] = function() {
+          return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments);
+        };
+        var _Prod = Module["_Prod"] = function() {
+          return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments);
+        };
+        var _RealDiv = Module["_RealDiv"] = function() {
+          return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments);
+        };
+        var _Relu = Module["_Relu"] = function() {
+          return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments);
+        };
+        var _Relu6 = Module["_Relu6"] = function() {
+          return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments);
+        };
+        var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
+          return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments);
+        };
+        var _Reverse = Module["_Reverse"] = function() {
+          return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments);
+        };
+        var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
+          return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments);
+        };
+        var _Round = Module["_Round"] = function() {
+          return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments);
+        };
+        var _Rsqrt = Module["_Rsqrt"] = function() {
+          return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments);
+        };
+        var _ScatterNd = Module["_ScatterNd"] = function() {
+          return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments);
+        };
+        var _SelectV2 = Module["_SelectV2"] = function() {
+          return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments);
+        };
+        var _Sigmoid = Module["_Sigmoid"] = function() {
+          return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments);
+        };
+        var _Sin = Module["_Sin"] = function() {
+          return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments);
+        };
+        var _Softmax = Module["_Softmax"] = function() {
+          return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments);
+        };
+        var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() {
+          return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments);
+        };
+        var _SparseReshape = Module["_SparseReshape"] = function() {
+          return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments);
+        };
+        var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() {
+          return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments);
+        };
+        var _Sqrt = Module["_Sqrt"] = function() {
+          return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments);
+        };
+        var _Square = Module["_Square"] = function() {
+          return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments);
+        };
+        var _SquaredDifference = Module["_SquaredDifference"] = function() {
+          return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments);
+        };
+        var _Step = Module["_Step"] = function() {
+          return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments);
+        };
+        var _StridedSlice = Module["_StridedSlice"] = function() {
+          return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments);
+        };
+        var _Sub = Module["_Sub"] = function() {
+          return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments);
+        };
+        var _Sum = Module["_Sum"] = function() {
+          return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments);
+        };
+        var _Tan = Module["_Tan"] = function() {
+          return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments);
+        };
+        var _Tanh = Module["_Tanh"] = function() {
+          return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments);
+        };
+        var _Tile = Module["_Tile"] = function() {
+          return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments);
+        };
+        var _TopK = Module["_TopK"] = function() {
+          return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments);
+        };
+        var _Transform = Module["_Transform"] = function() {
+          return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments);
+        };
+        var _Transpose = Module["_Transpose"] = function() {
+          return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments);
+        };
+        var __FusedMatMul = Module["__FusedMatMul"] = function() {
+          return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments);
+        };
+        var _malloc = Module["_malloc"] = function() {
+          return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments);
+        };
+        var _free = Module["_free"] = function() {
+          return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments);
+        };
+        var ___errno_location = Module["___errno_location"] = function() {
+          return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments);
+        };
+        var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() {
+          return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments);
+        };
+        var stackSave = Module["stackSave"] = function() {
+          return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments);
+        };
+        var stackRestore = Module["stackRestore"] = function() {
+          return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments);
+        };
+        var stackAlloc = Module["stackAlloc"] = function() {
+          return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments);
+        };
+        var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() {
+          return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments);
+        };
+        var dynCall_jiji = Module["dynCall_jiji"] = function() {
+          return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments);
+        };
+        Module["cwrap"] = cwrap;
+        var calledRun;
+        function ExitStatus(status) {
+          this.name = "ExitStatus";
+          this.message = "Program terminated with exit(" + status + ")";
+          this.status = status;
+        }
+        dependenciesFulfilled = function runCaller() {
+          if (!calledRun)
+            run();
+          if (!calledRun)
+            dependenciesFulfilled = runCaller;
+        };
+        function run(args) {
+          args = args || arguments_;
+          if (runDependencies > 0) {
+            return;
+          }
+          preRun();
+          if (runDependencies > 0) {
+            return;
+          }
+          function doRun() {
+            if (calledRun)
+              return;
+            calledRun = true;
+            Module["calledRun"] = true;
+            if (ABORT)
+              return;
+            initRuntime();
+            readyPromiseResolve(Module);
+            if (Module["onRuntimeInitialized"])
+              Module["onRuntimeInitialized"]();
+            postRun();
+          }
+          if (Module["setStatus"]) {
+            Module["setStatus"]("Running...");
+            setTimeout(function() {
+              setTimeout(function() {
+                Module["setStatus"]("");
+              }, 1);
+              doRun();
+            }, 1);
+          } else {
+            doRun();
+          }
+        }
+        Module["run"] = run;
+        function procExit(code) {
+          EXITSTATUS = code;
+          if (!keepRuntimeAlive()) {
+            if (Module["onExit"])
+              Module["onExit"](code);
+            ABORT = true;
+          }
+          quit_(code, new ExitStatus(code));
+        }
+        if (Module["preInit"]) {
+          if (typeof Module["preInit"] == "function")
+            Module["preInit"] = [Module["preInit"]];
+          while (Module["preInit"].length > 0) {
+            Module["preInit"].pop()();
+          }
+        }
+        run();
+        var listenersAdded;
+        if (beforeListeners) {
+          listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) {
+            return !beforeListeners.uncaughtException.indexOf(listener) > -1;
+          }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) {
+            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;
+          }) };
+        }
+        var actualModule;
+        if (typeof WasmBackendModule3 !== "undefined") {
+          actualModule = WasmBackendModule3;
+        } else if (typeof WasmBackendModuleThreadedSimd !== "undefined") {
+          actualModule = WasmBackendModuleThreadedSimd;
+        } else {
+          throw new Error("Could not find wasm module in post.js");
+        }
+        if (listenersAdded) {
+          var tmpDispose = actualModule["_dispose"];
+          actualModule["_dispose"] = function() {
+            tmpDispose();
+            listenersAdded.uncaughtException.forEach(function(listener) {
+              process.removeListener("uncaughtException", listener);
+            });
+            listenersAdded.unhandledRejection.forEach(function(listener) {
+              process.removeListener("unhandledRejection", listener);
+            });
+          };
+        }
+        return WasmBackendModule3.ready;
+      };
+    })();
+    if (typeof exports === "object" && typeof module === "object")
+      module.exports = WasmBackendModule2;
+    else if (typeof define === "function" && define["amd"])
+      define([], function() {
+        return WasmBackendModule2;
+      });
+    else if (typeof exports === "object")
+      exports["WasmBackendModule"] = WasmBackendModule2;
+  }
+});
+var EPSILON_FLOAT32 = 1e-7;
+var EPSILON_FLOAT16 = 1e-4;
+var DataStorage = class {
+  constructor(backend2, dataMover) {
+    this.backend = backend2;
+    this.dataMover = dataMover;
+    this.data = /* @__PURE__ */ new WeakMap();
+    this.dataIdsCount = 0;
+  }
+  get(dataId) {
+    if (!this.data.has(dataId)) {
+      this.dataMover.moveData(this.backend, dataId);
+    }
+    return this.data.get(dataId);
+  }
+  set(dataId, value) {
+    this.dataIdsCount++;
+    this.data.set(dataId, value);
+  }
+  has(dataId) {
+    return this.data.has(dataId);
+  }
+  delete(dataId) {
+    this.dataIdsCount--;
+    return this.data.delete(dataId);
+  }
+  numDataIds() {
+    return this.dataIdsCount;
+  }
+};
+var KernelBackend = class {
+  refCount(dataId) {
+    return notYetImplemented("refCount");
+  }
+  incRef(dataId) {
+    return notYetImplemented("incRef");
+  }
+  timerAvailable() {
+    return true;
+  }
+  time(f) {
+    return notYetImplemented("time");
+  }
+  read(dataId) {
+    return notYetImplemented("read");
+  }
+  readSync(dataId) {
+    return notYetImplemented("readSync");
+  }
+  readToGPU(dataId, options4) {
+    return notYetImplemented("readToGPU");
+  }
+  numDataIds() {
+    return notYetImplemented("numDataIds");
+  }
+  disposeData(dataId, force) {
+    return notYetImplemented("disposeData");
+  }
+  write(values, shape, dtype) {
+    return notYetImplemented("write");
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    return notYetImplemented("move");
+  }
+  memory() {
+    return notYetImplemented("memory");
+  }
+  floatPrecision() {
+    return notYetImplemented("floatPrecision");
+  }
+  epsilon() {
+    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
+  }
+  dispose() {
+    return notYetImplemented("dispose");
+  }
+};
+function notYetImplemented(kernelName) {
+  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
+}
+function shuffle(array2) {
+  let counter = array2.length;
+  let index2 = 0;
+  while (counter > 0) {
+    index2 = Math.random() * counter | 0;
+    counter--;
+    swap(array2, counter, index2);
+  }
+}
+function shuffleCombo(array2, array22) {
+  if (array2.length !== array22.length) {
+    throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);
+  }
+  let counter = array2.length;
+  let index2 = 0;
+  while (counter > 0) {
+    index2 = Math.random() * counter | 0;
+    counter--;
+    swap(array2, counter, index2);
+    swap(array22, counter, index2);
+  }
+}
+function clamp(min7, x, max7) {
+  return Math.max(min7, Math.min(x, max7));
+}
+function nearestLargerEven(val) {
+  return val % 2 === 0 ? val : val + 1;
+}
+function swap(object2, left, right) {
+  const temp = object2[left];
+  object2[left] = object2[right];
+  object2[right] = temp;
+}
+function sum(arr) {
+  let sum7 = 0;
+  for (let i2 = 0; i2 < arr.length; i2++) {
+    sum7 += arr[i2];
+  }
+  return sum7;
+}
+function randUniform(a6, b) {
+  const r2 = Math.random();
+  return b * r2 + (1 - r2) * a6;
+}
+function distSquared(a6, b) {
+  let result = 0;
+  for (let i2 = 0; i2 < a6.length; i2++) {
+    const diff = Number(a6[i2]) - Number(b[i2]);
+    result += diff * diff;
+  }
+  return result;
+}
+function assert(expr, msg) {
+  if (!expr) {
+    throw new Error(typeof msg === "string" ? msg : msg());
+  }
+}
+function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
+  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+}
+function assertNonNull(a6) {
+  assert(a6 != null, () => `The input to the tensor constructor must be a non-null value.`);
+}
+function flatten(arr, result = [], skipTypedArray = false) {
+  if (result == null) {
+    result = [];
+  }
+  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
+    for (let i2 = 0; i2 < arr.length; ++i2) {
+      flatten(arr[i2], result, skipTypedArray);
+    }
+  } else {
+    result.push(arr);
+  }
+  return result;
+}
+function sizeFromShape(shape) {
+  if (shape.length === 0) {
+    return 1;
+  }
+  let size2 = shape[0];
+  for (let i2 = 1; i2 < shape.length; i2++) {
+    size2 *= shape[i2];
+  }
+  return size2;
+}
+function isScalarShape(shape) {
+  return shape.length === 0;
+}
+function arraysEqual(n1, n2) {
+  if (n1 === n2) {
+    return true;
+  }
+  if (n1 == null || n2 == null) {
+    return false;
+  }
+  if (n1.length !== n2.length) {
+    return false;
+  }
+  for (let i2 = 0; i2 < n1.length; i2++) {
+    if (n1[i2] !== n2[i2]) {
+      return false;
+    }
+  }
+  return true;
+}
+function isInt(a6) {
+  return a6 % 1 === 0;
+}
+function tanh(x) {
+  if (Math.tanh != null) {
+    return Math.tanh(x);
+  }
+  if (x === Infinity) {
+    return 1;
+  } else if (x === -Infinity) {
+    return -1;
+  } else {
+    const e2x = Math.exp(2 * x);
+    return (e2x - 1) / (e2x + 1);
+  }
+}
+function sizeToSquarishShape(size2) {
+  const width = Math.ceil(Math.sqrt(size2));
+  return [width, Math.ceil(size2 / width)];
+}
+function createShuffledIndices(n) {
+  const shuffledIndices = new Uint32Array(n);
+  for (let i2 = 0; i2 < n; ++i2) {
+    shuffledIndices[i2] = i2;
+  }
+  shuffle(shuffledIndices);
+  return shuffledIndices;
+}
+function rightPad(a6, size2) {
+  if (size2 <= a6.length) {
+    return a6;
+  }
+  return a6 + " ".repeat(size2 - a6.length);
+}
+function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
+  return new Promise((resolve, reject) => {
+    let tryCount = 0;
+    const tryFn = () => {
+      if (checkFn()) {
+        resolve();
+        return;
+      }
+      tryCount++;
+      const nextBackoff = delayFn(tryCount);
+      if (maxCounter != null && tryCount >= maxCounter) {
+        reject();
+        return;
+      }
+      setTimeout(tryFn, nextBackoff);
+    };
+    tryFn();
+  });
+}
+function inferFromImplicitShape(shape, size2) {
+  let shapeProd = 1;
+  let implicitIdx = -1;
+  for (let i2 = 0; i2 < shape.length; ++i2) {
+    if (shape[i2] >= 0) {
+      shapeProd *= shape[i2];
+    } else if (shape[i2] === -1) {
+      if (implicitIdx !== -1) {
+        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i2}`);
+      }
+      implicitIdx = i2;
+    } else if (shape[i2] < 0) {
+      throw Error(`Shapes can not be < 0. Found ${shape[i2]} at dim ${i2}`);
+    }
+  }
+  if (implicitIdx === -1) {
+    if (size2 > 0 && size2 !== shapeProd) {
+      throw Error(`Size(${size2}) must match the product of shape ${shape}`);
+    }
+    return shape;
+  }
+  if (shapeProd === 0) {
+    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
+  }
+  if (size2 % shapeProd !== 0) {
+    throw Error(`The implicit shape can't be a fractional number. Got ${size2} / ${shapeProd}`);
+  }
+  const newShape = shape.slice();
+  newShape[implicitIdx] = size2 / shapeProd;
+  return newShape;
+}
+function parseAxisParam(axis, shape) {
+  const rank = shape.length;
+  axis = axis == null ? shape.map((s2, i2) => i2) : [].concat(axis);
+  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
+  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
+  return axis.map((a6) => a6 < 0 ? rank + a6 : a6);
+}
+function squeezeShape(shape, axis) {
+  const newShape = [];
+  const keptDims = [];
+  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
+  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
+  let j = 0;
+  for (let i2 = 0; i2 < shape.length; ++i2) {
+    if (axes != null) {
+      if (axes[j] === i2 && shape[i2] !== 1) {
+        throw new Error(`Can't squeeze axis ${i2} since its dim '${shape[i2]}' is not 1`);
+      }
+      if ((axes[j] == null || axes[j] > i2) && shape[i2] === 1) {
+        newShape.push(shape[i2]);
+        keptDims.push(i2);
+      }
+      if (axes[j] <= i2) {
+        j++;
+      }
+    }
+    if (shape[i2] !== 1) {
+      newShape.push(shape[i2]);
+      keptDims.push(i2);
+    }
+  }
+  return { newShape, keptDims };
+}
+function getTypedArrayFromDType(dtype, size2) {
+  let values = null;
+  if (dtype == null || dtype === "float32") {
+    values = new Float32Array(size2);
+  } else if (dtype === "int32") {
+    values = new Int32Array(size2);
+  } else if (dtype === "bool") {
+    values = new Uint8Array(size2);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+  return values;
+}
+function getArrayFromDType(dtype, size2) {
+  let values = null;
+  if (dtype == null || dtype === "float32") {
+    values = new Float32Array(size2);
+  } else if (dtype === "int32") {
+    values = new Int32Array(size2);
+  } else if (dtype === "bool") {
+    values = new Uint8Array(size2);
+  } else if (dtype === "string") {
+    values = new Array(size2);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+  return values;
+}
+function checkConversionForErrors(vals, dtype) {
+  for (let i2 = 0; i2 < vals.length; i2++) {
+    const num = vals[i2];
+    if (isNaN(num) || !isFinite(num)) {
+      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
+    }
+  }
+}
+function isValidDtype(dtype) {
+  return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
+}
+function hasEncodingLoss(oldType, newType) {
+  if (newType === "complex64") {
+    return false;
+  }
+  if (newType === "float32" && oldType !== "complex64") {
+    return false;
+  }
+  if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
+    return false;
+  }
+  if (newType === "bool" && oldType === "bool") {
+    return false;
+  }
+  return true;
+}
+function isTypedArray(a6) {
+  return a6 instanceof Float32Array || a6 instanceof Int32Array || a6 instanceof Uint8Array || a6 instanceof Uint8ClampedArray;
+}
+function bytesPerElement(dtype) {
+  if (dtype === "float32" || dtype === "int32") {
+    return 4;
+  } else if (dtype === "complex64") {
+    return 8;
+  } else if (dtype === "bool") {
+    return 1;
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+function bytesFromStringArray(arr) {
+  if (arr == null) {
+    return 0;
+  }
+  let bytes = 0;
+  arr.forEach((x) => bytes += x.length);
+  return bytes;
+}
+function isString(value) {
+  return typeof value === "string" || value instanceof String;
+}
+function isBoolean(value) {
+  return typeof value === "boolean";
+}
+function isNumber(value) {
+  return typeof value === "number";
+}
+function inferDtype(values) {
+  if (Array.isArray(values)) {
+    return inferDtype(values[0]);
+  }
+  if (values instanceof Float32Array) {
+    return "float32";
+  } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {
+    return "int32";
+  } else if (isNumber(values)) {
+    return "float32";
+  } else if (isString(values)) {
+    return "string";
+  } else if (isBoolean(values)) {
+    return "bool";
+  }
+  return "float32";
+}
+function isFunction(f) {
+  return !!(f && f.constructor && f.call && f.apply);
+}
+function nearestDivisor(size2, start) {
+  for (let i2 = start; i2 < size2; ++i2) {
+    if (size2 % i2 === 0) {
+      return i2;
+    }
+  }
+  return size2;
+}
+function computeStrides(shape) {
+  const rank = shape.length;
+  if (rank < 2) {
+    return [];
+  }
+  const strides2 = new Array(rank - 1);
+  strides2[rank - 2] = shape[rank - 1];
+  for (let i2 = rank - 3; i2 >= 0; --i2) {
+    strides2[i2] = strides2[i2 + 1] * shape[i2 + 1];
+  }
+  return strides2;
+}
+function createNestedArray(offset, shape, a6, isComplex = false) {
+  const ret = new Array();
+  if (shape.length === 1) {
+    const d = shape[0] * (isComplex ? 2 : 1);
+    for (let i2 = 0; i2 < d; i2++) {
+      ret[i2] = a6[offset + i2];
+    }
+  } else {
+    const d = shape[0];
+    const rest = shape.slice(1);
+    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
+    for (let i2 = 0; i2 < d; i2++) {
+      ret[i2] = createNestedArray(offset + i2 * len, rest, a6, isComplex);
+    }
+  }
+  return ret;
+}
+function toNestedArray(shape, a6, isComplex = false) {
+  if (shape.length === 0) {
+    return a6[0];
+  }
+  const size2 = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
+  if (size2 === 0) {
+    return [];
+  }
+  if (size2 !== a6.length) {
+    throw new Error(`[${shape}] does not match the input size ${a6.length}${isComplex ? " for a complex tensor" : ""}.`);
+  }
+  return createNestedArray(0, shape, a6, isComplex);
+}
+function makeOnesTypedArray(size2, dtype) {
+  const array2 = makeZerosTypedArray(size2, dtype);
+  for (let i2 = 0; i2 < array2.length; i2++) {
+    array2[i2] = 1;
+  }
+  return array2;
+}
+function makeZerosTypedArray(size2, dtype) {
+  if (dtype == null || dtype === "float32" || dtype === "complex64") {
+    return new Float32Array(size2);
+  } else if (dtype === "int32") {
+    return new Int32Array(size2);
+  } else if (dtype === "bool") {
+    return new Uint8Array(size2);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+}
+function makeZerosNestedTypedArray(shape, dtype) {
+  const size2 = shape.reduce((prev, curr) => prev * curr, 1);
+  if (dtype == null || dtype === "float32") {
+    return toNestedArray(shape, new Float32Array(size2));
+  } else if (dtype === "int32") {
+    return toNestedArray(shape, new Int32Array(size2));
+  } else if (dtype === "bool") {
+    return toNestedArray(shape, new Uint8Array(size2));
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+}
+function assertNonNegativeIntegerDimensions(shape) {
+  shape.forEach((dimSize) => {
+    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
+  });
+}
+function locToIndex(locs, rank, strides2) {
+  if (rank === 0) {
+    return 0;
+  } else if (rank === 1) {
+    return locs[0];
+  }
+  let index2 = locs[locs.length - 1];
+  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+    index2 += strides2[i2] * locs[i2];
+  }
+  return index2;
+}
+function indexToLoc(index2, rank, strides2) {
+  if (rank === 0) {
+    return [];
+  } else if (rank === 1) {
+    return [index2];
+  }
+  const locs = new Array(rank);
+  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+    locs[i2] = Math.floor(index2 / strides2[i2]);
+    index2 -= locs[i2] * strides2[i2];
+  }
+  locs[locs.length - 1] = index2;
+  return locs;
+}
+function isPromise(object2) {
+  return object2 && object2.then && typeof object2.then === "function";
+}
+var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
+var Environment = class {
+  constructor(global2) {
+    this.global = global2;
+    this.flags = {};
+    this.flagRegistry = {};
+    this.urlFlags = {};
+    this.getQueryParams = getQueryParams;
+    this.populateURLFlags();
+  }
+  setPlatform(platformName, platform) {
+    if (this.platform != null) {
+      if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);
+      }
+    }
+    this.platformName = platformName;
+    this.platform = platform;
+  }
+  registerFlag(flagName, evaluationFn, setHook) {
+    this.flagRegistry[flagName] = { evaluationFn, setHook };
+    if (this.urlFlags[flagName] != null) {
+      const flagValue = this.urlFlags[flagName];
+      if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
+      }
+      this.set(flagName, flagValue);
+    }
+  }
+  async getAsync(flagName) {
+    if (flagName in this.flags) {
+      return this.flags[flagName];
+    }
+    this.flags[flagName] = await this.evaluateFlag(flagName);
+    return this.flags[flagName];
+  }
+  get(flagName) {
+    if (flagName in this.flags) {
+      return this.flags[flagName];
+    }
+    const flagValue = this.evaluateFlag(flagName);
+    if (isPromise(flagValue)) {
+      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
+    }
+    this.flags[flagName] = flagValue;
+    return this.flags[flagName];
+  }
+  getNumber(flagName) {
+    return this.get(flagName);
+  }
+  getBool(flagName) {
+    return this.get(flagName);
+  }
+  getFlags() {
+    return this.flags;
+  }
+  get features() {
+    return this.flags;
+  }
+  set(flagName, value) {
+    if (this.flagRegistry[flagName] == null) {
+      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
+    }
+    this.flags[flagName] = value;
+    if (this.flagRegistry[flagName].setHook != null) {
+      this.flagRegistry[flagName].setHook(value);
+    }
+  }
+  evaluateFlag(flagName) {
+    if (this.flagRegistry[flagName] == null) {
+      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
+    }
+    return this.flagRegistry[flagName].evaluationFn();
+  }
+  setFlags(flags) {
+    this.flags = Object.assign({}, flags);
+  }
+  reset() {
+    this.flags = {};
+    this.urlFlags = {};
+    this.populateURLFlags();
+  }
+  populateURLFlags() {
+    if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
+      return;
+    }
+    const urlParams = this.getQueryParams(this.global.location.search);
+    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
+      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
+      keyValues.forEach((keyValue) => {
+        const [key, value] = keyValue.split(":");
+        this.urlFlags[key] = parseValue(key, value);
+      });
+    }
+  }
+};
+function getQueryParams(queryString) {
+  const params = {};
+  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s2, ...t2) => {
+    decodeParam(params, t2[0], t2[1]);
+    return t2.join("=");
+  });
+  return params;
+}
+function decodeParam(params, name, value) {
+  params[decodeURIComponent(name)] = decodeURIComponent(value || "");
+}
+function parseValue(flagName, value) {
+  value = value.toLowerCase();
+  if (value === "true" || value === "false") {
+    return value === "true";
+  } else if (`${+value}` === value) {
+    return +value;
+  }
+  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
+}
+function env() {
+  return ENV;
+}
+var ENV = null;
+function setEnvironmentGlobal(environment) {
+  ENV = environment;
+}
+var globalNameSpace;
+function getGlobalNamespace() {
+  if (globalNameSpace == null) {
+    let ns;
+    if (typeof window !== "undefined") {
+      ns = window;
+    } else if (typeof global !== "undefined") {
+      ns = global;
+    } else if (typeof process !== "undefined") {
+      ns = process;
+    } else if (typeof self !== "undefined") {
+      ns = self;
+    } else {
+      throw new Error("Could not find a global object");
+    }
+    globalNameSpace = ns;
+  }
+  return globalNameSpace;
+}
+function getGlobalMap() {
+  const ns = getGlobalNamespace();
+  if (ns._tfGlobals == null) {
+    ns._tfGlobals = /* @__PURE__ */ new Map();
+  }
+  return ns._tfGlobals;
+}
+function getGlobal(key, init22) {
+  const globalMap = getGlobalMap();
+  if (globalMap.has(key)) {
+    return globalMap.get(key);
+  } else {
+    const singleton = init22();
+    globalMap.set(key, singleton);
+    return globalMap.get(key);
+  }
+}
+var Abs = "Abs";
+var Acos = "Acos";
+var Acosh = "Acosh";
+var Add = "Add";
+var AddN = "AddN";
+var All = "All";
+var Any = "Any";
+var ArgMax = "ArgMax";
+var ArgMin = "ArgMin";
+var Asin = "Asin";
+var Asinh = "Asinh";
+var Atan = "Atan";
+var Atanh = "Atanh";
+var Atan2 = "Atan2";
+var AvgPool = "AvgPool";
+var AvgPoolGrad = "AvgPoolGrad";
+var AvgPool3D = "AvgPool3D";
+var AvgPool3DGrad = "AvgPool3DGrad";
+var BatchMatMul = "BatchMatMul";
+var BatchToSpaceND = "BatchToSpaceND";
+var Bincount = "Bincount";
+var BroadcastTo = "BroadcastTo";
+var BroadcastArgs = "BroadcastArgs";
+var Cast = "Cast";
+var Ceil = "Ceil";
+var ClipByValue = "ClipByValue";
+var Complex = "Complex";
+var ComplexAbs = "ComplexAbs";
+var Concat = "Concat";
+var Conv2D = "Conv2D";
+var Conv2DBackpropFilter = "Conv2DBackpropFilter";
+var Conv2DBackpropInput = "Conv2DBackpropInput";
+var Conv3D = "Conv3D";
+var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
+var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
+var Cos = "Cos";
+var Cosh = "Cosh";
+var Cumprod = "Cumprod";
+var Cumsum = "Cumsum";
+var CropAndResize = "CropAndResize";
+var DenseBincount = "DenseBincount";
+var DepthToSpace = "DepthToSpace";
+var DepthwiseConv2dNative = "DepthwiseConv2dNative";
+var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
+var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
+var Diag = "Diag";
+var Dilation2D = "Dilation2D";
+var Dilation2DBackpropInput = "Dilation2DBackpropInput";
+var Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
+var RealDiv = "RealDiv";
+var Einsum = "Einsum";
+var Elu = "Elu";
+var EluGrad = "EluGrad";
+var Erf = "Erf";
+var Equal = "Equal";
+var Exp = "Exp";
+var ExpandDims = "ExpandDims";
+var Expm1 = "Expm1";
+var FFT = "FFT";
+var Fill = "Fill";
+var FlipLeftRight = "FlipLeftRight";
+var Floor = "Floor";
+var FloorDiv = "FloorDiv";
+var FusedBatchNorm = "FusedBatchNorm";
+var GatherV2 = "GatherV2";
+var GatherNd = "GatherNd";
+var Greater = "Greater";
+var GreaterEqual = "GreaterEqual";
+var Identity = "Identity";
+var IFFT = "IFFT";
+var Imag = "Imag";
+var IsFinite = "IsFinite";
+var IsInf = "IsInf";
+var IsNan = "IsNan";
+var LeakyRelu = "LeakyRelu";
+var Less = "Less";
+var LessEqual = "LessEqual";
+var LinSpace = "LinSpace";
+var Log = "Log";
+var Log1p = "Log1p";
+var LogicalAnd = "LogicalAnd";
+var LogicalNot = "LogicalNot";
+var LogicalOr = "LogicalOr";
+var LogSoftmax = "LogSoftmax";
+var LowerBound = "LowerBound";
+var LRN = "LRN";
+var LRNGrad = "LRNGrad";
+var Max = "Max";
+var Maximum = "Maximum";
+var MaxPool = "MaxPool";
+var MaxPoolGrad = "MaxPoolGrad";
+var MaxPool3D = "MaxPool3D";
+var MaxPool3DGrad = "MaxPool3DGrad";
+var MaxPoolWithArgmax = "MaxPoolWithArgmax";
+var Mean = "Mean";
+var Min = "Min";
+var Minimum = "Minimum";
+var MirrorPad = "MirrorPad";
+var Mod = "Mod";
+var Multinomial = "Multinomial";
+var Multiply = "Multiply";
+var Neg = "Neg";
+var NotEqual = "NotEqual";
+var NonMaxSuppressionV3 = "NonMaxSuppressionV3";
+var NonMaxSuppressionV4 = "NonMaxSuppressionV4";
+var NonMaxSuppressionV5 = "NonMaxSuppressionV5";
+var OnesLike = "OnesLike";
+var OneHot = "OneHot";
+var Pack = "Pack";
+var PadV2 = "PadV2";
+var Pool = "Pool";
+var Pow = "Pow";
+var Prelu = "Prelu";
+var Prod = "Prod";
+var Range = "Range";
+var Real = "Real";
+var Reciprocal = "Reciprocal";
+var Relu = "Relu";
+var Reshape = "Reshape";
+var ResizeNearestNeighbor = "ResizeNearestNeighbor";
+var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
+var ResizeBilinear = "ResizeBilinear";
+var ResizeBilinearGrad = "ResizeBilinearGrad";
+var Relu6 = "Relu6";
+var Reverse = "Reverse";
+var Round = "Round";
+var Rsqrt = "Rsqrt";
+var ScatterNd = "ScatterNd";
+var SearchSorted = "SearchSorted";
+var Select = "Select";
+var Selu = "Selu";
+var Slice = "Slice";
+var Sin = "Sin";
+var Sinh = "Sinh";
+var Sign = "Sign";
+var Sigmoid = "Sigmoid";
+var Softplus = "Softplus";
+var Sqrt = "Sqrt";
+var Sum = "Sum";
+var SpaceToBatchND = "SpaceToBatchND";
+var SplitV = "SplitV";
+var Softmax = "Softmax";
+var SparseFillEmptyRows = "SparseFillEmptyRows";
+var SparseReshape = "SparseReshape";
+var SparseSegmentMean = "SparseSegmentMean";
+var SparseSegmentSum = "SparseSegmentSum";
+var SparseToDense = "SparseToDense";
+var SquaredDifference = "SquaredDifference";
+var Square = "Square";
+var StridedSlice = "StridedSlice";
+var StringNGrams = "StringNGrams";
+var StringSplit = "StringSplit";
+var StringToHashBucketFast = "StringToHashBucketFast";
+var Sub = "Sub";
+var Tan = "Tan";
+var Tanh = "Tanh";
+var Tile = "Tile";
+var TopK = "TopK";
+var Transform = "Transform";
+var Transpose = "Transpose";
+var Unique = "Unique";
+var Unpack = "Unpack";
+var UnsortedSegmentSum = "UnsortedSegmentSum";
+var UpperBound = "UpperBound";
+var ZerosLike = "ZerosLike";
+var Step = "Step";
+var FromPixels = "FromPixels";
+var RotateWithOffset = "RotateWithOffset";
+var _FusedMatMul = "_FusedMatMul";
+var FusedConv2D = "FusedConv2D";
+var FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
+function warn(...msg) {
+  if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+    console.warn(...msg);
+  }
+}
+function log2(...msg) {
+  if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+    console.log(...msg);
+  }
+}
+var kernelRegistry = getGlobal("kernelRegistry", () => /* @__PURE__ */ new Map());
+var gradRegistry = getGlobal("gradRegistry", () => /* @__PURE__ */ new Map());
+function getKernel(kernelName, backendName) {
+  const key = makeKey(kernelName, backendName);
+  return kernelRegistry.get(key);
+}
+function getGradient(kernelName) {
+  return gradRegistry.get(kernelName);
+}
+function getKernelsForBackend(backendName) {
+  const it = kernelRegistry.entries();
+  const result = [];
+  while (true) {
+    const { done, value } = it.next();
+    if (done) {
+      break;
+    }
+    const [key, config3] = value;
+    const [backend2] = key.split("_");
+    if (backend2 === backendName) {
+      result.push(config3);
+    }
+  }
+  return result;
+}
+function registerKernel(config3) {
+  const { kernelName, backendName } = config3;
+  const key = makeKey(kernelName, backendName);
+  if (kernelRegistry.has(key)) {
+    warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
+  }
+  kernelRegistry.set(key, config3);
+}
+function registerGradient(config3) {
+  const { kernelName } = config3;
+  if (gradRegistry.has(kernelName)) {
+    if (env().getBool("DEBUG")) {
+      warn(`Overriding the gradient for '${kernelName}'`);
+    }
+  }
+  gradRegistry.set(kernelName, config3);
+}
+function unregisterKernel(kernelName, backendName) {
+  const key = makeKey(kernelName, backendName);
+  if (!kernelRegistry.has(key)) {
+    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
+  }
+  kernelRegistry.delete(key);
+}
+function unregisterGradient(kernelName) {
+  if (!gradRegistry.has(kernelName)) {
+    throw new Error(`The gradient '${kernelName}' for backend is not registered`);
+  }
+  gradRegistry.delete(kernelName);
+}
+function copyRegisteredKernels(registeredBackendName, newBackendName) {
+  const kernels = getKernelsForBackend(registeredBackendName);
+  kernels.forEach((kernelConfig) => {
+    const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });
+    registerKernel(newKernelConfig);
+  });
+}
+function makeKey(kernelName, backendName) {
+  return `${backendName}_${kernelName}`;
+}
+var util_exports = {};
+__export2(util_exports, {
+  arraysEqual: () => arraysEqual,
+  assert: () => assert,
+  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
+  assertNonNull: () => assertNonNull,
+  assertShapesMatch: () => assertShapesMatch,
+  bytesFromStringArray: () => bytesFromStringArray,
+  bytesPerElement: () => bytesPerElement,
+  checkConversionForErrors: () => checkConversionForErrors,
+  clamp: () => clamp,
+  computeStrides: () => computeStrides,
+  createScalarValue: () => createScalarValue,
+  createShuffledIndices: () => createShuffledIndices,
+  decodeString: () => decodeString,
+  distSquared: () => distSquared,
+  encodeString: () => encodeString,
+  fetch: () => fetch3,
+  fingerPrint64: () => fingerPrint64,
+  flatten: () => flatten,
+  getArrayFromDType: () => getArrayFromDType,
+  getTypedArrayFromDType: () => getTypedArrayFromDType,
+  hasEncodingLoss: () => hasEncodingLoss,
+  hexToLong: () => hexToLong,
+  indexToLoc: () => indexToLoc,
+  inferDtype: () => inferDtype,
+  inferFromImplicitShape: () => inferFromImplicitShape,
+  isBoolean: () => isBoolean,
+  isFunction: () => isFunction,
+  isInt: () => isInt,
+  isNumber: () => isNumber,
+  isPromise: () => isPromise,
+  isScalarShape: () => isScalarShape,
+  isString: () => isString,
+  isTypedArray: () => isTypedArray,
+  isValidDtype: () => isValidDtype,
+  locToIndex: () => locToIndex,
+  makeOnesTypedArray: () => makeOnesTypedArray,
+  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
+  makeZerosTypedArray: () => makeZerosTypedArray,
+  nearestDivisor: () => nearestDivisor,
+  nearestLargerEven: () => nearestLargerEven,
+  now: () => now2,
+  parseAxisParam: () => parseAxisParam,
+  randUniform: () => randUniform,
+  repeatedTry: () => repeatedTry,
+  rightPad: () => rightPad,
+  shuffle: () => shuffle,
+  shuffleCombo: () => shuffleCombo,
+  sizeFromShape: () => sizeFromShape,
+  sizeToSquarishShape: () => sizeToSquarishShape,
+  squeezeShape: () => squeezeShape,
+  sum: () => sum,
+  swap: () => swap,
+  tanh: () => tanh,
+  toNestedArray: () => toNestedArray,
+  toTypedArray: () => toTypedArray
+});
+var LongExports = __toESM(require_long());
+var Long = LongExports.default || LongExports;
+function hexToLong(hex) {
+  return Long.fromString(hex, true, 16);
+}
+var k0 = hexToLong("c3a5c85c97cb3127");
+var k1 = hexToLong("b492b66fbe98f273");
+var k2 = hexToLong("9ae16a3b2f90404f");
+function shiftMix(val) {
+  return val.xor(val.shru(47));
+}
+function fetch2(s2, offset, numBytes) {
+  const bytes = s2.slice(offset, offset + numBytes);
+  return Long.fromBytes(Array.from(bytes), true, true);
+}
+function fetch64(s2, offset) {
+  return fetch2(s2, offset, 8);
+}
+function fetch32(s2, offset) {
+  return fetch2(s2, offset, 4);
+}
+function rotate64(val, shift) {
+  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
+}
+function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) {
+  let a6 = u.xor(v).mul(mul2);
+  a6 = a6.xor(a6.shru(47));
+  let b = v.xor(a6).mul(mul2);
+  b = b.xor(b.shru(47));
+  b = b.mul(mul2);
+  return b;
+}
+function weakHashLen32WithSeeds(w, x, y, z, a6, b) {
+  a6 = a6.add(w);
+  b = rotate64(b.add(a6).add(z), 21);
+  const c = a6;
+  a6 = a6.add(x);
+  a6 = a6.add(y);
+  b = b.add(rotate64(a6, 44));
+  return [a6.add(z), b.add(c)];
+}
+function weakHashLen32WithSeedsStr(s2, offset, a6, b) {
+  return weakHashLen32WithSeeds(fetch64(s2, offset), fetch64(s2, offset + 8), fetch64(s2, offset + 16), fetch64(s2, offset + 24), a6, b);
+}
+function hashLen0to16(s2, len = s2.length) {
+  if (len >= 8) {
+    const mul2 = k2.add(len * 2);
+    const a6 = fetch64(s2, 0).add(k2);
+    const b = fetch64(s2, len - 8);
+    const c = rotate64(b, 37).mul(mul2).add(a6);
+    const d = rotate64(a6, 25).add(b).mul(mul2);
+    return hashLen16(c, d, mul2);
+  }
+  if (len >= 4) {
+    const mul2 = k2.add(len * 2);
+    const a6 = fetch32(s2, 0);
+    return hashLen16(a6.shl(3).add(len), fetch32(s2, len - 4), mul2);
+  }
+  if (len > 0) {
+    const a6 = s2[0];
+    const b = s2[len >> 1];
+    const c = s2[len - 1];
+    const y = a6 + (b << 8);
+    const z = len + (c << 2);
+    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
+  }
+  return k2;
+}
+function hashLen17to32(s2, len = s2.length) {
+  const mul2 = k2.add(len * 2);
+  const a6 = fetch64(s2, 0).mul(k1);
+  const b = fetch64(s2, 8);
+  const c = fetch64(s2, len - 8).mul(mul2);
+  const d = fetch64(s2, len - 16).mul(k2);
+  return hashLen16(rotate64(a6.add(b), 43).add(rotate64(c, 30)).add(d), a6.add(rotate64(b.add(k2), 18)).add(c), mul2);
+}
+function hashLen33to64(s2, len = s2.length) {
+  const mul2 = k2.add(len * 2);
+  const a6 = fetch64(s2, 0).mul(k2);
+  const b = fetch64(s2, 8);
+  const c = fetch64(s2, len - 8).mul(mul2);
+  const d = fetch64(s2, len - 16).mul(k2);
+  const y = rotate64(a6.add(b), 43).add(rotate64(c, 30)).add(d);
+  const z = hashLen16(y, a6.add(rotate64(b.add(k2), 18)).add(c), mul2);
+  const e2 = fetch64(s2, 16).mul(mul2);
+  const f = fetch64(s2, 24);
+  const g = y.add(fetch64(s2, len - 32)).mul(mul2);
+  const h = z.add(fetch64(s2, len - 24)).mul(mul2);
+  return hashLen16(rotate64(e2.add(f), 43).add(rotate64(g, 30)).add(h), e2.add(rotate64(f.add(a6), 18)).add(g), mul2);
+}
+function fingerPrint64(s2, len = s2.length) {
+  const seed = Long.fromNumber(81, true);
+  if (len <= 32) {
+    if (len <= 16) {
+      return hashLen0to16(s2, len);
+    } else {
+      return hashLen17to32(s2, len);
+    }
+  } else if (len <= 64) {
+    return hashLen33to64(s2, len);
+  }
+  let x = seed;
+  let y = seed.mul(k1).add(113);
+  let z = shiftMix(y.mul(k2).add(113)).mul(k2);
+  let v = [Long.UZERO, Long.UZERO];
+  let w = [Long.UZERO, Long.UZERO];
+  x = x.mul(k2).add(fetch64(s2, 0));
+  let offset = 0;
+  const end = (len - 1 >> 6) * 64;
+  const last64 = end + (len - 1 & 63) - 63;
+  do {
+    x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(k1);
+    y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(k1);
+    x = x.xor(w[1]);
+    y = y.add(v[0]).add(fetch64(s2, offset + 40));
+    z = rotate64(z.add(w[0]), 33).mul(k1);
+    v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(k1), x.add(w[0]));
+    w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+    [z, x] = [x, z];
+    offset += 64;
+  } while (offset !== end);
+  const mul2 = k1.add(z.and(255).shl(1));
+  offset = last64;
+  w[0] = w[0].add(len - 1 & 63);
+  v[0] = v[0].add(w[0]);
+  w[0] = w[0].add(v[0]);
+  x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(mul2);
+  y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(mul2);
+  x = x.xor(w[1].mul(9));
+  y = y.add(v[0].mul(9).add(fetch64(s2, offset + 40)));
+  z = rotate64(z.add(w[0]), 33).mul(mul2);
+  v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(mul2), x.add(w[0]));
+  w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+  [z, x] = [x, z];
+  return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);
+}
+function createScalarValue(value, dtype) {
+  if (dtype === "string") {
+    return encodeString(value);
+  }
+  return toTypedArray([value], dtype);
+}
+function noConversionNeeded(a6, dtype) {
+  return a6 instanceof Float32Array && dtype === "float32" || a6 instanceof Int32Array && dtype === "int32" || a6 instanceof Uint8Array && dtype === "bool";
+}
+function toTypedArray(a6, dtype) {
+  if (dtype === "string") {
+    throw new Error("Cannot convert a string[] to a TypedArray");
+  }
+  if (Array.isArray(a6)) {
+    a6 = flatten(a6);
+  }
+  if (env().getBool("DEBUG")) {
+    checkConversionForErrors(a6, dtype);
+  }
+  if (noConversionNeeded(a6, dtype)) {
+    return a6;
+  }
+  if (dtype == null || dtype === "float32" || dtype === "complex64") {
+    return new Float32Array(a6);
+  } else if (dtype === "int32") {
+    return new Int32Array(a6);
+  } else if (dtype === "bool") {
+    const bool = new Uint8Array(a6.length);
+    for (let i2 = 0; i2 < bool.length; ++i2) {
+      if (Math.round(a6[i2]) !== 0) {
+        bool[i2] = 1;
+      }
+    }
+    return bool;
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+}
+function now2() {
+  return env().platform.now();
+}
+function fetch3(path, requestInits) {
+  return env().platform.fetch(path, requestInits);
+}
+function encodeString(s2, encoding = "utf-8") {
+  encoding = encoding || "utf-8";
+  return env().platform.encode(s2, encoding);
+}
+function decodeString(bytes, encoding = "utf-8") {
+  encoding = encoding || "utf-8";
+  return env().platform.decode(bytes, encoding);
+}
+var Profiler = class {
+  constructor(backendTimer, logger) {
+    this.backendTimer = backendTimer;
+    this.logger = logger;
+    if (logger == null) {
+      this.logger = new Logger();
+    }
+  }
+  profileKernel(kernelName, inputs, f) {
+    let outputs;
+    const holdResultWrapperFn = () => {
+      outputs = f();
+    };
+    let timer;
+    const start = now2();
+    if (this.backendTimer.timerAvailable()) {
+      timer = this.backendTimer.time(holdResultWrapperFn);
+    } else {
+      holdResultWrapperFn();
+      for (const output of outputs) {
+        output.dataSync();
+      }
+      timer = Promise.resolve({ kernelMs: now2() - start });
+    }
+    if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) {
+      for (let i2 = 0; i2 < outputs.length; i2++) {
+        const output = outputs[i2];
+        output.data().then((tensorVals) => {
+          checkComputationForErrors(tensorVals, output.dtype, kernelName);
+        });
+      }
+    }
+    const kernelProfile = {
+      kernelName,
+      outputs,
+      inputs,
+      timeMs: timer.then((timing) => timing.kernelMs),
+      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
+    };
+    return kernelProfile;
+  }
+  logKernelProfile(kernelProfile) {
+    const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;
+    outputs.forEach((result) => {
+      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
+        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
+      });
+    });
+  }
+};
+function checkComputationForErrors(vals, dtype, kernelName) {
+  if (dtype !== "float32") {
+    return false;
+  }
+  for (let i2 = 0; i2 < vals.length; i2++) {
+    const num = vals[i2];
+    if (isNaN(num) || !isFinite(num)) {
+      console.warn(`Found ${num} in the result of '${kernelName}'`);
+      return true;
+    }
+  }
+  return false;
+}
+var Logger = class {
+  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
+    const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
+    const paddedName = rightPad(name, 25);
+    const rank = result.rank;
+    const size2 = result.size;
+    const shape = rightPad(result.shape.toString(), 14);
+    let inputShapesDescription = "";
+    for (const name2 in inputs) {
+      const input2 = inputs[name2];
+      if (input2 != null) {
+        const inputShape = input2.shape || result.shape;
+        const inputRank = inputShape.length;
+        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
+      }
+    }
+    console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size2}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
+  }
+};
+function getFilteredNodesXToY(tape, xs, y) {
+  const tensorsFromX = {};
+  const nodesFromX = {};
+  for (let i2 = 0; i2 < xs.length; i2++) {
+    tensorsFromX[xs[i2].id] = true;
+  }
+  for (let i2 = 0; i2 < tape.length; i2++) {
+    const node2 = tape[i2];
+    const nodeInputs = node2.inputs;
+    for (const inputName in nodeInputs) {
+      const input2 = nodeInputs[inputName];
+      let anyInputFromX = false;
+      for (let j = 0; j < xs.length; j++) {
+        if (tensorsFromX[input2.id]) {
+          node2.outputs.forEach((output) => tensorsFromX[output.id] = true);
+          anyInputFromX = true;
+          nodesFromX[node2.id] = true;
+          break;
+        }
+      }
+      if (anyInputFromX) {
+        break;
+      }
+    }
+  }
+  const tensorsLeadToY = {};
+  tensorsLeadToY[y.id] = true;
+  const nodesToY = {};
+  for (let i2 = tape.length - 1; i2 >= 0; i2--) {
+    const node2 = tape[i2];
+    const nodeInputs = node2.inputs;
+    for (let j = 0; j < node2.outputs.length; j++) {
+      if (tensorsLeadToY[node2.outputs[j].id]) {
+        for (const inputName in nodeInputs) {
+          tensorsLeadToY[nodeInputs[inputName].id] = true;
+          nodesToY[node2.id] = true;
+        }
+        break;
+      }
+    }
+  }
+  const filteredTape = [];
+  for (let i2 = 0; i2 < tape.length; i2++) {
+    const node2 = tape[i2];
+    if (nodesFromX[node2.id] && nodesToY[node2.id]) {
+      const prunedInputs = {};
+      for (const inputName in node2.inputs) {
+        const nodeInput = node2.inputs[inputName];
+        if (tensorsFromX[nodeInput.id]) {
+          prunedInputs[inputName] = nodeInput;
+        }
+      }
+      const prunedNode = Object.assign({}, node2);
+      prunedNode.inputs = prunedInputs;
+      prunedNode.outputs = node2.outputs;
+      filteredTape.push(prunedNode);
+    }
+  }
+  return filteredTape;
+}
+function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {
+  for (let i2 = filteredTape.length - 1; i2 >= 0; i2--) {
+    const node2 = filteredTape[i2];
+    const dys = [];
+    node2.outputs.forEach((o) => {
+      const gradTensor = tensorAccumulatedGradientMap[o.id];
+      if (gradTensor != null) {
+        dys.push(gradTensor);
+      } else {
+        dys.push(null);
+      }
+    });
+    if (node2.gradient == null) {
+      throw new Error(`Cannot compute gradient: gradient function not found for ${node2.kernelName}.`);
+    }
+    const inputGradients = node2.gradient(dys);
+    for (const inputName in node2.inputs) {
+      if (!(inputName in inputGradients)) {
+        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
+      }
+      const dx = tidy2(() => inputGradients[inputName]());
+      if (dx.dtype !== "float32") {
+        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
+      }
+      const x = node2.inputs[inputName];
+      if (!arraysEqual(dx.shape, x.shape)) {
+        throw new Error(`Error in gradient for op ${node2.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
+      }
+      if (tensorAccumulatedGradientMap[x.id] == null) {
+        tensorAccumulatedGradientMap[x.id] = dx;
+      } else {
+        const curGradient = tensorAccumulatedGradientMap[x.id];
+        tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);
+        curGradient.dispose();
+      }
+    }
+  }
+}
+var FORMAT_LIMIT_NUM_VALS = 20;
+var FORMAT_NUM_FIRST_LAST_VALS = 3;
+var FORMAT_NUM_SIG_DIGITS = 7;
+function tensorToString(vals, shape, dtype, verbose) {
+  const strides2 = computeStrides(shape);
+  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides2);
+  const rank = shape.length;
+  const valsLines = subTensorToString(vals, shape, dtype, strides2, padPerCol);
+  const lines2 = ["Tensor"];
+  if (verbose) {
+    lines2.push(`  dtype: ${dtype}`);
+    lines2.push(`  rank: ${rank}`);
+    lines2.push(`  shape: [${shape}]`);
+    lines2.push(`  values:`);
+  }
+  lines2.push(valsLines.map((l3) => "    " + l3).join("\n"));
+  return lines2.join("\n");
+}
+function computeMaxSizePerColumn(vals, shape, dtype, strides2) {
+  const n = sizeFromShape(shape);
+  const numCols = strides2[strides2.length - 1];
+  const padPerCol = new Array(numCols).fill(0);
+  const rank = shape.length;
+  const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
+  if (rank > 1) {
+    for (let row = 0; row < n / numCols; row++) {
+      const offset = row * numCols;
+      for (let j = 0; j < numCols; j++) {
+        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
+      }
+    }
+  }
+  return padPerCol;
+}
+function valToString(val, pad3, dtype) {
+  let valStr;
+  if (Array.isArray(val)) {
+    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
+  } else if (isString(val)) {
+    valStr = `'${val}'`;
+  } else if (dtype === "bool") {
+    valStr = boolNumToString(val);
+  } else {
+    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
+  }
+  return rightPad(valStr, pad3);
+}
+function boolNumToString(v) {
+  return v === 0 ? "false" : "true";
+}
+function subTensorToString(vals, shape, dtype, strides2, padPerCol, isLast = true) {
+  const storagePerElement = dtype === "complex64" ? 2 : 1;
+  const size2 = shape[0];
+  const rank = shape.length;
+  if (rank === 0) {
+    if (dtype === "complex64") {
+      const complexTuple = createComplexTuples(vals);
+      return [valToString(complexTuple[0], 0, dtype)];
+    }
+    if (dtype === "bool") {
+      return [boolNumToString(vals[0])];
+    }
+    return [vals[0].toString()];
+  }
+  if (rank === 1) {
+    if (size2 > FORMAT_LIMIT_NUM_VALS) {
+      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
+      let firstVals = Array.from(vals.slice(0, firstValsSize));
+      let lastVals = Array.from(vals.slice((size2 - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size2 * storagePerElement));
+      if (dtype === "complex64") {
+        firstVals = createComplexTuples(firstVals);
+        lastVals = createComplexTuples(lastVals);
+      }
+      return [
+        "[" + firstVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + ", ..., " + lastVals.map((x, i2) => valToString(x, padPerCol[size2 - FORMAT_NUM_FIRST_LAST_VALS + i2], dtype)).join(", ") + "]"
+      ];
+    }
+    const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
+    return [
+      "[" + displayVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + "]"
+    ];
+  }
+  const subshape = shape.slice(1);
+  const substrides = strides2.slice(1);
+  const stride = strides2[0] * storagePerElement;
+  const lines2 = [];
+  if (size2 > FORMAT_LIMIT_NUM_VALS) {
+    for (let i2 = 0; i2 < FORMAT_NUM_FIRST_LAST_VALS; i2++) {
+      const start = i2 * stride;
+      const end = start + stride;
+      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
+    }
+    lines2.push("...");
+    for (let i2 = size2 - FORMAT_NUM_FIRST_LAST_VALS; i2 < size2; i2++) {
+      const start = i2 * stride;
+      const end = start + stride;
+      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size2 - 1));
+    }
+  } else {
+    for (let i2 = 0; i2 < size2; i2++) {
+      const start = i2 * stride;
+      const end = start + stride;
+      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size2 - 1));
+    }
+  }
+  const sep = rank === 2 ? "," : "";
+  lines2[0] = "[" + lines2[0] + sep;
+  for (let i2 = 1; i2 < lines2.length - 1; i2++) {
+    lines2[i2] = " " + lines2[i2] + sep;
+  }
+  let newLineSep = ",\n";
+  for (let i2 = 2; i2 < rank; i2++) {
+    newLineSep += "\n";
+  }
+  lines2[lines2.length - 1] = " " + lines2[lines2.length - 1] + "]" + (isLast ? "" : newLineSep);
+  return lines2;
+}
+function createComplexTuples(vals) {
+  const complexTuples = [];
+  for (let i2 = 0; i2 < vals.length; i2 += 2) {
+    complexTuples.push([vals[i2], vals[i2 + 1]]);
+  }
+  return complexTuples;
+}
+var TensorBuffer = class {
+  constructor(shape, dtype, values) {
+    this.dtype = dtype;
+    this.shape = shape.slice();
+    this.size = sizeFromShape(shape);
+    if (values != null) {
+      const n = values.length;
+      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
+    }
+    if (dtype === "complex64") {
+      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
+    }
+    this.values = values || getArrayFromDType(dtype, this.size);
+    this.strides = computeStrides(shape);
+  }
+  set(value, ...locs) {
+    if (locs.length === 0) {
+      locs = [0];
+    }
+    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
+    const index2 = this.locToIndex(locs);
+    this.values[index2] = value;
+  }
+  get(...locs) {
+    if (locs.length === 0) {
+      locs = [0];
+    }
+    let i2 = 0;
+    for (const loc of locs) {
+      if (loc < 0 || loc >= this.shape[i2]) {
+        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
+        throw new Error(msg);
+      }
+      i2++;
+    }
+    let index2 = locs[locs.length - 1];
+    for (let i3 = 0; i3 < locs.length - 1; ++i3) {
+      index2 += this.strides[i3] * locs[i3];
+    }
+    return this.values[index2];
+  }
+  locToIndex(locs) {
+    if (this.rank === 0) {
+      return 0;
+    } else if (this.rank === 1) {
+      return locs[0];
+    }
+    let index2 = locs[locs.length - 1];
+    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+      index2 += this.strides[i2] * locs[i2];
+    }
+    return index2;
+  }
+  indexToLoc(index2) {
+    if (this.rank === 0) {
+      return [];
+    } else if (this.rank === 1) {
+      return [index2];
+    }
+    const locs = new Array(this.shape.length);
+    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+      locs[i2] = Math.floor(index2 / this.strides[i2]);
+      index2 -= locs[i2] * this.strides[i2];
+    }
+    locs[locs.length - 1] = index2;
+    return locs;
+  }
+  get rank() {
+    return this.shape.length;
+  }
+  toTensor() {
+    return trackerFn().makeTensor(this.values, this.shape, this.dtype);
+  }
+};
+var trackerFn = null;
+var opHandler = null;
+var deprecationWarningFn = null;
+function setTensorTracker(fn) {
+  trackerFn = fn;
+}
+function setOpHandler(handler) {
+  opHandler = handler;
+}
+function setDeprecationWarningFn(fn) {
+  deprecationWarningFn = fn;
+}
+var Tensor = class {
+  constructor(shape, dtype, dataId, id) {
+    this.kept = false;
+    this.isDisposedInternal = false;
+    this.shape = shape.slice();
+    this.dtype = dtype || "float32";
+    this.size = sizeFromShape(shape);
+    this.strides = computeStrides(shape);
+    this.dataId = dataId;
+    this.id = id;
+    this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
+  }
+  get rank() {
+    return this.shape.length;
+  }
+  async buffer() {
+    const vals = await this.data();
+    return opHandler.buffer(this.shape, this.dtype, vals);
+  }
+  bufferSync() {
+    return opHandler.buffer(this.shape, this.dtype, this.dataSync());
+  }
+  async array() {
+    const vals = await this.data();
+    return toNestedArray(this.shape, vals, this.dtype === "complex64");
+  }
+  arraySync() {
+    return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64");
+  }
+  async data() {
+    this.throwIfDisposed();
+    const data = trackerFn().read(this.dataId);
+    if (this.dtype === "string") {
+      const bytes = await data;
+      try {
+        return bytes.map((b) => decodeString(b));
+      } catch (_a) {
+        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
+      }
+    }
+    return data;
+  }
+  dataToGPU(options4) {
+    this.throwIfDisposed();
+    return trackerFn().readToGPU(this.dataId, options4);
+  }
+  dataSync() {
+    this.throwIfDisposed();
+    const data = trackerFn().readSync(this.dataId);
+    if (this.dtype === "string") {
+      try {
+        return data.map((b) => decodeString(b));
+      } catch (_a) {
+        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
+      }
+    }
+    return data;
+  }
+  async bytes() {
+    this.throwIfDisposed();
+    const data = await trackerFn().read(this.dataId);
+    if (this.dtype === "string") {
+      return data;
+    } else {
+      return new Uint8Array(data.buffer);
+    }
+  }
+  dispose() {
+    if (this.isDisposed) {
+      return;
+    }
+    trackerFn().disposeTensor(this);
+    this.isDisposedInternal = true;
+  }
+  get isDisposed() {
+    return this.isDisposedInternal;
+  }
+  throwIfDisposed() {
+    if (this.isDisposed) {
+      throw new Error(`Tensor is disposed.`);
+    }
+  }
+  print(verbose = false) {
+    return opHandler.print(this, verbose);
+  }
+  clone() {
+    this.throwIfDisposed();
+    return opHandler.clone(this);
+  }
+  toString(verbose = false) {
+    const vals = this.dataSync();
+    return tensorToString(vals, this.shape, this.dtype, verbose);
+  }
+  cast(dtype) {
+    this.throwIfDisposed();
+    return opHandler.cast(this, dtype);
+  }
+  variable(trainable = true, name, dtype) {
+    this.throwIfDisposed();
+    return trackerFn().makeVariable(this, trainable, name, dtype);
+  }
+};
+Object.defineProperty(Tensor, Symbol.hasInstance, {
+  value: (instance) => {
+    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
+  }
+});
+function getGlobalTensorClass() {
+  return getGlobal("Tensor", () => {
+    return Tensor;
+  });
+}
+getGlobalTensorClass();
+var Variable = class extends Tensor {
+  constructor(initialValue, trainable, name, tensorId) {
+    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
+    this.trainable = trainable;
+    this.name = name;
+  }
+  assign(newValue) {
+    if (newValue.dtype !== this.dtype) {
+      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
+    }
+    if (!arraysEqual(newValue.shape, this.shape)) {
+      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
+    }
+    trackerFn().disposeTensor(this);
+    this.dataId = newValue.dataId;
+    trackerFn().incRef(this, null);
+  }
+  dispose() {
+    trackerFn().disposeVariable(this);
+    this.isDisposedInternal = true;
+  }
+};
+Object.defineProperty(Variable, Symbol.hasInstance, {
+  value: (instance) => {
+    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
+  }
+});
+var tensor_util_exports = {};
+__export2(tensor_util_exports, {
+  assertTypesMatch: () => assertTypesMatch,
+  getTensorsInContainer: () => getTensorsInContainer,
+  isTensorInList: () => isTensorInList,
+  makeTypesMatch: () => makeTypesMatch
+});
+var Rank;
+(function(Rank2) {
+  Rank2["R0"] = "R0";
+  Rank2["R1"] = "R1";
+  Rank2["R2"] = "R2";
+  Rank2["R3"] = "R3";
+  Rank2["R4"] = "R4";
+  Rank2["R5"] = "R5";
+  Rank2["R6"] = "R6";
+})(Rank || (Rank = {}));
+var UpcastInt32AndMap;
+(function(UpcastInt32AndMap2) {
+  UpcastInt32AndMap2["float32"] = "float32";
+  UpcastInt32AndMap2["int32"] = "int32";
+  UpcastInt32AndMap2["bool"] = "int32";
+  UpcastInt32AndMap2["complex64"] = "complex64";
+})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
+var UpcastBoolAndMap;
+(function(UpcastBoolAndMap2) {
+  UpcastBoolAndMap2["float32"] = "float32";
+  UpcastBoolAndMap2["int32"] = "int32";
+  UpcastBoolAndMap2["bool"] = "bool";
+  UpcastBoolAndMap2["complex64"] = "complex64";
+})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
+var UpcastFloat32AndMap;
+(function(UpcastFloat32AndMap2) {
+  UpcastFloat32AndMap2["float32"] = "float32";
+  UpcastFloat32AndMap2["int32"] = "float32";
+  UpcastFloat32AndMap2["bool"] = "float32";
+  UpcastFloat32AndMap2["complex64"] = "complex64";
+})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
+var UpcastComplex64AndMap;
+(function(UpcastComplex64AndMap2) {
+  UpcastComplex64AndMap2["float32"] = "complex64";
+  UpcastComplex64AndMap2["int32"] = "complex64";
+  UpcastComplex64AndMap2["bool"] = "complex64";
+  UpcastComplex64AndMap2["complex64"] = "complex64";
+})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
+var upcastTypeMap = {
+  "float32": UpcastFloat32AndMap,
+  "int32": UpcastInt32AndMap,
+  "bool": UpcastBoolAndMap,
+  "complex64": UpcastComplex64AndMap
+};
+function upcastType(typeA, typeB) {
+  if (typeA === "string" || typeB === "string") {
+    if (typeA === "string" && typeB === "string") {
+      return "string";
+    }
+    throw new Error(`Can not upcast ${typeA} with ${typeB}`);
+  }
+  return upcastTypeMap[typeA][typeB];
+}
+function sumOutType(type) {
+  return upcastType(type, "int32");
+}
+function makeTypesMatch(a6, b) {
+  if (a6.dtype === b.dtype) {
+    return [a6, b];
+  }
+  const dtype = upcastType(a6.dtype, b.dtype);
+  return [a6.cast(dtype), b.cast(dtype)];
+}
+function assertTypesMatch(a6, b) {
+  assert(a6.dtype === b.dtype, () => `The dtypes of the first(${a6.dtype}) and second(${b.dtype}) input must match`);
+}
+function isTensorInList(tensor2, tensorList) {
+  return tensorList.some((x) => x.id === tensor2.id);
+}
+function getTensorsInContainer(result) {
+  const list = [];
+  const seen = /* @__PURE__ */ new Set();
+  walkTensorContainer(result, list, seen);
+  return list;
+}
+function walkTensorContainer(container, list, seen) {
+  if (container == null) {
+    return;
+  }
+  if (container instanceof Tensor) {
+    list.push(container);
+    return;
+  }
+  if (!isIterable(container)) {
+    return;
+  }
+  const iterable = container;
+  for (const k in iterable) {
+    const val = iterable[k];
+    if (!seen.has(val)) {
+      seen.add(val);
+      walkTensorContainer(val, list, seen);
+    }
+  }
+}
+function isIterable(obj) {
+  return Array.isArray(obj) || typeof obj === "object";
+}
+function isRegisteredKernelInvocation(kernelInvocation) {
+  return kernelInvocation.kernelName != null;
+}
+var EngineState = class {
+  constructor() {
+    this.registeredVariables = {};
+    this.nextTapeNodeId = 0;
+    this.numBytes = 0;
+    this.numTensors = 0;
+    this.numStringTensors = 0;
+    this.numDataBuffers = 0;
+    this.gradientDepth = 0;
+    this.kernelDepth = 0;
+    this.scopeStack = [];
+    this.numDataMovesStack = [];
+    this.nextScopeId = 0;
+    this.tensorInfo = /* @__PURE__ */ new WeakMap();
+    this.profiling = false;
+    this.activeProfile = {
+      newBytes: 0,
+      newTensors: 0,
+      peakBytes: 0,
+      kernels: [],
+      result: null,
+      get kernelNames() {
+        return Array.from(new Set(this.kernels.map((k) => k.name)));
+      }
+    };
+  }
+  dispose() {
+    for (const variableName in this.registeredVariables) {
+      this.registeredVariables[variableName].dispose();
+    }
+  }
+};
+var Engine = class {
+  constructor(ENV8) {
+    this.ENV = ENV8;
+    this.registry = {};
+    this.registryFactory = {};
+    this.pendingBackendInitId = 0;
+    this.state = new EngineState();
+  }
+  async ready() {
+    if (this.pendingBackendInit != null) {
+      return this.pendingBackendInit.then(() => {
+      });
+    }
+    if (this.backendInstance != null) {
+      return;
+    }
+    const sortedBackends = this.getSortedBackends();
+    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
+      const backendName = sortedBackends[i2];
+      const success = await this.initializeBackend(backendName).success;
+      if (success) {
+        await this.setBackend(backendName);
+        return;
+      }
+    }
+    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
+  }
+  get backend() {
+    if (this.pendingBackendInit != null) {
+      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
+    }
+    if (this.backendInstance == null) {
+      const { name, asyncInit } = this.initializeBackendsAndReturnBest();
+      if (asyncInit) {
+        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
+      }
+      this.setBackend(name);
+    }
+    return this.backendInstance;
+  }
+  backendNames() {
+    return Object.keys(this.registryFactory);
+  }
+  findBackend(backendName) {
+    if (!(backendName in this.registry)) {
+      if (backendName in this.registryFactory) {
+        const { asyncInit } = this.initializeBackend(backendName);
+        if (asyncInit) {
+          return null;
+        }
+      } else {
+        return null;
+      }
+    }
+    return this.registry[backendName];
+  }
+  findBackendFactory(backendName) {
+    if (!(backendName in this.registryFactory)) {
+      return null;
+    }
+    return this.registryFactory[backendName].factory;
+  }
+  registerBackend(backendName, factory, priority = 1) {
+    if (backendName in this.registryFactory) {
+      warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
+      return false;
+    }
+    this.registryFactory[backendName] = { factory, priority };
+    return true;
+  }
+  async setBackend(backendName) {
+    if (this.registryFactory[backendName] == null) {
+      throw new Error(`Backend name '${backendName}' not found in registry`);
+    }
+    this.backendName = backendName;
+    if (this.registry[backendName] == null) {
+      this.backendInstance = null;
+      const { success, asyncInit } = this.initializeBackend(backendName);
+      const result = asyncInit ? await success : success;
+      if (!result) {
+        return false;
+      }
+    }
+    this.backendInstance = this.registry[backendName];
+    this.setupRegisteredKernels();
+    this.profiler = new Profiler(this.backendInstance);
+    return true;
+  }
+  setupRegisteredKernels() {
+    const kernels = getKernelsForBackend(this.backendName);
+    kernels.forEach((kernel) => {
+      if (kernel.setupFunc != null) {
+        kernel.setupFunc(this.backendInstance);
+      }
+    });
+  }
+  disposeRegisteredKernels(backendName) {
+    const kernels = getKernelsForBackend(backendName);
+    kernels.forEach((kernel) => {
+      if (kernel.disposeFunc != null) {
+        kernel.disposeFunc(this.registry[backendName]);
+      }
+    });
+  }
+  initializeBackend(backendName) {
+    const registryFactoryEntry = this.registryFactory[backendName];
+    if (registryFactoryEntry == null) {
+      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
+    }
+    try {
+      const backend2 = registryFactoryEntry.factory();
+      if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") {
+        const promiseId = ++this.pendingBackendInitId;
+        const success = backend2.then((backendInstance) => {
+          if (promiseId < this.pendingBackendInitId) {
+            return false;
+          }
+          this.registry[backendName] = backendInstance;
+          this.pendingBackendInit = null;
+          return true;
+        }).catch((err) => {
+          if (promiseId < this.pendingBackendInitId) {
+            return false;
+          }
+          this.pendingBackendInit = null;
+          warn(`Initialization of backend ${backendName} failed`);
+          warn(err.stack || err.message);
+          return false;
+        });
+        this.pendingBackendInit = success;
+        return { success, asyncInit: true };
+      } else {
+        this.registry[backendName] = backend2;
+        return { success: true, asyncInit: false };
+      }
+    } catch (err) {
+      warn(`Initialization of backend ${backendName} failed`);
+      warn(err.stack || err.message);
+      return { success: false, asyncInit: false };
+    }
+  }
+  removeBackend(backendName) {
+    if (!(backendName in this.registryFactory)) {
+      throw new Error(`${backendName} backend not found in registry`);
+    }
+    if (this.backendName === backendName && this.pendingBackendInit != null) {
+      this.pendingBackendInitId++;
+    }
+    if (backendName in this.registry) {
+      this.disposeRegisteredKernels(backendName);
+      this.registry[backendName].dispose();
+      delete this.registry[backendName];
+    }
+    delete this.registryFactory[backendName];
+    if (this.backendName === backendName) {
+      this.pendingBackendInit = null;
+      this.backendName = null;
+      this.backendInstance = null;
+    }
+  }
+  getSortedBackends() {
+    if (Object.keys(this.registryFactory).length === 0) {
+      throw new Error("No backend found in registry.");
+    }
+    return Object.keys(this.registryFactory).sort((a6, b) => {
+      return this.registryFactory[b].priority - this.registryFactory[a6].priority;
+    });
+  }
+  initializeBackendsAndReturnBest() {
+    const sortedBackends = this.getSortedBackends();
+    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
+      const backendName = sortedBackends[i2];
+      const { success, asyncInit } = this.initializeBackend(backendName);
+      if (asyncInit || success) {
+        return { name: backendName, asyncInit };
+      }
+    }
+    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
+  }
+  moveData(backend2, dataId) {
+    const info = this.state.tensorInfo.get(dataId);
+    const srcBackend = info.backend;
+    const values = this.readSync(dataId);
+    const refCount = srcBackend.refCount(dataId);
+    srcBackend.disposeData(dataId, true);
+    info.backend = backend2;
+    backend2.move(dataId, values, info.shape, info.dtype, refCount);
+    if (this.shouldCheckForMemLeaks()) {
+      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
+    }
+  }
+  tidy(nameOrFn, fn) {
+    let name = null;
+    if (fn == null) {
+      if (typeof nameOrFn !== "function") {
+        throw new Error("Please provide a function to tidy()");
+      }
+      fn = nameOrFn;
+    } else {
+      if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
+        throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
+      }
+      if (typeof fn !== "function") {
+        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
+      }
+      name = nameOrFn;
+    }
+    let result;
+    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
+      result = fn();
+      if (result instanceof Promise) {
+        console.error("Cannot return a Promise inside of tidy.");
+      }
+      return result;
+    });
+  }
+  scopedRun(start, end, f) {
+    start();
+    try {
+      const res = f();
+      end();
+      return res;
+    } catch (ex) {
+      end();
+      throw ex;
+    }
+  }
+  nextTensorId() {
+    return Engine.nextTensorId++;
+  }
+  nextVariableId() {
+    return Engine.nextVariableId++;
+  }
+  clone(x) {
+    const y = ENGINE.runKernel(Identity, { x });
+    const inputs = { x };
+    const grad2 = (dy) => ({
+      x: () => {
+        const dtype = "float32";
+        const gradInputs = { x: dy };
+        const attrs = { dtype };
+        return ENGINE.runKernel(Cast, gradInputs, attrs);
+      }
+    });
+    const saved = [];
+    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
+    return y;
+  }
+  runKernel(kernelName, inputs, attrs) {
+    if (this.backendName == null) {
+      this.backend;
+    }
+    const hasKernel = getKernel(kernelName, this.backendName) != null;
+    if (!hasKernel) {
+      throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);
+    }
+    return this.runKernelFunc({ kernelName, inputs, attrs });
+  }
+  shouldCheckForMemLeaks() {
+    return this.ENV.getBool("IS_TEST");
+  }
+  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
+    const numDataIdsAfter = this.backend.numDataIds();
+    let numOutputDataIds = 0;
+    outInfos.forEach((info) => {
+      numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
+    });
+    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
+    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
+    if (dataIdsLeaked > 0) {
+      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
+    }
+  }
+  runKernelFunc(kernelParams) {
+    let outputs;
+    let saved = [];
+    const isTapeOn = this.isTapeOn();
+    const startingBytecount = this.state.numBytes;
+    const startingNumTensors = this.state.numTensors;
+    if (this.shouldCheckForMemLeaks()) {
+      this.state.numDataMovesStack.push(0);
+    }
+    let kernelFunc3;
+    if (this.backendName == null) {
+      this.backend;
+    }
+    let out;
+    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
+    if (isRegisteredKernelInvocation(kernelParams)) {
+      const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;
+      if (this.backendName == null) {
+        this.backend;
+      }
+      const kernel = getKernel(kernelName, this.backendName);
+      assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);
+      kernelFunc3 = () => {
+        const numDataIdsBefore = this.backend.numDataIds();
+        out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });
+        const outInfos = Array.isArray(out) ? out : [out];
+        if (this.shouldCheckForMemLeaks()) {
+          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
+        }
+        const outTensors = outInfos.map((outInfo) => {
+          if (outInfo.rank != null) {
+            return outInfo;
+          }
+          return this.makeTensorFromTensorInfo(outInfo);
+        });
+        if (isTapeOn) {
+          const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);
+          saved = this.saveTensorsForBackwardMode(tensorsToSave);
+        }
+        return outTensors;
+      };
+    } else {
+      const { forwardFunc } = kernelParams;
+      const saveFunc = (tensors) => {
+        if (!isTapeOn) {
+          return;
+        }
+        saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));
+      };
+      kernelFunc3 = () => {
+        const numDataIdsBefore = this.backend.numDataIds();
+        out = this.tidy(() => forwardFunc(this.backend, saveFunc));
+        const outs = Array.isArray(out) ? out : [out];
+        if (this.shouldCheckForMemLeaks()) {
+          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
+        }
+        return outs;
+      };
+    }
+    const { inputs, attrs } = kernelParams;
+    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;
+    let kernelProfile;
+    this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
+      if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
+        outputs = kernelFunc3();
+      } else {
+        kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());
+        if (this.ENV.getBool("DEBUG")) {
+          this.profiler.logKernelProfile(kernelProfile);
+        }
+        outputs = kernelProfile.outputs;
+      }
+    });
+    if (isTapeOn) {
+      this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
+    }
+    if (this.state.profiling) {
+      this.state.activeProfile.kernels.push({
+        name: kernelOrScopeName,
+        bytesAdded: this.state.numBytes - startingBytecount,
+        totalBytesSnapshot: this.state.numBytes,
+        tensorsAdded: this.state.numTensors - startingNumTensors,
+        totalTensorsSnapshot: this.state.numTensors,
+        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
+        outputShapes: outputs.map((item) => item.shape),
+        kernelTimeMs: kernelProfile.timeMs,
+        extraInfo: kernelProfile.extraInfo
+      });
+    }
+    return Array.isArray(out) ? outputs : outputs[0];
+  }
+  saveTensorsForBackwardMode(tensors) {
+    const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));
+    return saved;
+  }
+  getTensorsForGradient(kernelName, inputs, outputs) {
+    const gradConfig = getGradient(kernelName);
+    if (gradConfig != null) {
+      const inputsToSave = gradConfig.inputsToSave || [];
+      const outputsToSave = gradConfig.outputsToSave || [];
+      let inputTensorsToSave;
+      if (gradConfig.saveAllInputs) {
+        assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
+        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
+      } else {
+        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
+      }
+      const outputTensorsToSave = outputs.filter((_, i2) => outputsToSave[i2]);
+      return inputTensorsToSave.concat(outputTensorsToSave);
+    }
+    return [];
+  }
+  makeTensor(values, shape, dtype, backend2) {
+    if (values == null) {
+      throw new Error("Values passed to engine.makeTensor() are null");
+    }
+    dtype = dtype || "float32";
+    backend2 = backend2 || this.backend;
+    let backendVals = values;
+    if (dtype === "string" && isString(values[0])) {
+      backendVals = values.map((d) => encodeString(d));
+    }
+    const dataId = backend2.write(backendVals, shape, dtype);
+    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());
+    this.trackTensor(t2, backend2);
+    if (dtype === "string") {
+      const info = this.state.tensorInfo.get(dataId);
+      const newBytes = bytesFromStringArray(backendVals);
+      this.state.numBytes += newBytes - info.bytes;
+      info.bytes = newBytes;
+    }
+    return t2;
+  }
+  makeTensorFromDataId(dataId, shape, dtype, backend2) {
+    dtype = dtype || "float32";
+    const tensorInfo = { dataId, shape, dtype };
+    return this.makeTensorFromTensorInfo(tensorInfo, backend2);
+  }
+  makeTensorFromTensorInfo(tensorInfo, backend2) {
+    const { dataId, shape, dtype } = tensorInfo;
+    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());
+    this.trackTensor(t2, backend2);
+    return t2;
+  }
+  makeVariable(initialValue, trainable = true, name, dtype) {
+    name = name || this.nextVariableId().toString();
+    if (dtype != null && dtype !== initialValue.dtype) {
+      initialValue = initialValue.cast(dtype);
+    }
+    const v = new Variable(initialValue, trainable, name, this.nextTensorId());
+    if (this.state.registeredVariables[v.name] != null) {
+      throw new Error(`Variable with name ${v.name} was already registered`);
+    }
+    this.state.registeredVariables[v.name] = v;
+    this.incRef(v, this.backend);
+    return v;
+  }
+  trackTensor(a6, backend2) {
+    this.state.numTensors++;
+    if (a6.dtype === "string") {
+      this.state.numStringTensors++;
+    }
+    let bytes = 0;
+    if (a6.dtype !== "complex64" && a6.dtype !== "string") {
+      bytes = a6.size * bytesPerElement(a6.dtype);
+    }
+    this.state.numBytes += bytes;
+    if (!this.state.tensorInfo.has(a6.dataId)) {
+      this.state.numDataBuffers++;
+      this.state.tensorInfo.set(a6.dataId, {
+        backend: backend2 || this.backend,
+        dtype: a6.dtype,
+        shape: a6.shape,
+        bytes
+      });
+    }
+    if (!(a6 instanceof Variable)) {
+      this.track(a6);
+    }
+  }
+  incRef(a6, backend2) {
+    this.trackTensor(a6, backend2);
+    this.backend.incRef(a6.dataId);
+  }
+  removeDataId(dataId, backend2) {
+    if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {
+      this.state.tensorInfo.delete(dataId);
+      this.state.numDataBuffers--;
+    }
+  }
+  disposeTensor(a6) {
+    if (!this.state.tensorInfo.has(a6.dataId)) {
+      return;
+    }
+    const info = this.state.tensorInfo.get(a6.dataId);
+    this.state.numTensors--;
+    if (a6.dtype === "string") {
+      this.state.numStringTensors--;
+      this.state.numBytes -= info.bytes;
+    }
+    if (a6.dtype !== "complex64" && a6.dtype !== "string") {
+      const bytes = a6.size * bytesPerElement(a6.dtype);
+      this.state.numBytes -= bytes;
+    }
+    if (info.backend.disposeData(a6.dataId)) {
+      this.removeDataId(a6.dataId, info.backend);
+    }
+  }
+  disposeVariables() {
+    for (const varName in this.state.registeredVariables) {
+      const v = this.state.registeredVariables[varName];
+      this.disposeVariable(v);
+    }
+  }
+  disposeVariable(v) {
+    this.disposeTensor(v);
+    if (this.state.registeredVariables[v.name] != null) {
+      delete this.state.registeredVariables[v.name];
+    }
+  }
+  memory() {
+    const info = this.backend.memory();
+    info.numTensors = this.state.numTensors;
+    info.numDataBuffers = this.state.numDataBuffers;
+    info.numBytes = this.state.numBytes;
+    if (this.state.numStringTensors > 0) {
+      info.unreliable = true;
+      if (info.reasons == null) {
+        info.reasons = [];
+      }
+      info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
+    }
+    return info;
+  }
+  async profile(query) {
+    this.state.profiling = true;
+    const startBytes = this.state.numBytes;
+    const startNumTensors = this.state.numTensors;
+    this.state.activeProfile.kernels = [];
+    this.state.activeProfile.result = await query();
+    this.state.profiling = false;
+    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
+    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
+    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
+    for (const kernel of this.state.activeProfile.kernels) {
+      kernel.kernelTimeMs = await kernel.kernelTimeMs;
+      kernel.extraInfo = await kernel.extraInfo;
+    }
+    return this.state.activeProfile;
+  }
+  isTapeOn() {
+    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
+  }
+  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
+    const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };
+    const gradConfig = getGradient(kernelName);
+    if (gradConfig != null) {
+      gradientsFunc = gradConfig.gradFunc;
+    }
+    if (gradientsFunc != null) {
+      tapeNode.gradient = (dys) => {
+        dys = dys.map((dy, i2) => {
+          if (dy == null) {
+            const output = outputs[i2];
+            const vals = makeZerosTypedArray(output.size, output.dtype);
+            return this.makeTensor(vals, output.shape, output.dtype);
+          }
+          return dy;
+        });
+        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
+      };
+    }
+    this.state.activeTape.push(tapeNode);
+  }
+  keep(result) {
+    result.kept = true;
+    return result;
+  }
+  startTape() {
+    if (this.state.gradientDepth === 0) {
+      this.state.activeTape = [];
+    }
+    this.state.gradientDepth++;
+  }
+  endTape() {
+    this.state.gradientDepth--;
+  }
+  startScope(name) {
+    const scopeInfo = {
+      track: [],
+      name: "unnamed scope",
+      id: this.state.nextScopeId++
+    };
+    if (name) {
+      scopeInfo.name = name;
+    }
+    this.state.scopeStack.push(scopeInfo);
+    this.state.activeScope = scopeInfo;
+  }
+  endScope(result) {
+    const tensorsToTrackInParent = getTensorsInContainer(result);
+    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t2) => t2.id));
+    for (let i2 = 0; i2 < this.state.activeScope.track.length; i2++) {
+      const tensor2 = this.state.activeScope.track[i2];
+      if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {
+        tensor2.dispose();
+      }
+    }
+    const oldScope = this.state.scopeStack.pop();
+    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
+    tensorsToTrackInParent.forEach((tensor2) => {
+      if (!tensor2.kept && tensor2.scopeId === oldScope.id) {
+        this.track(tensor2);
+      }
+    });
+  }
+  gradients(f, xs, dy, allowNoGradients = false) {
+    assert(xs.length > 0, () => "gradients() received an empty list of xs.");
+    if (dy != null && dy.dtype !== "float32") {
+      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
+    }
+    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
+    assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
+    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
+    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
+      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
+    }
+    return this.tidy("backward", () => {
+      const accumulatedGradientMap = {};
+      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
+      backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
+      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
+      if (this.state.gradientDepth === 0) {
+        this.state.activeTape.forEach((node2) => {
+          for (const tensor2 of node2.saved) {
+            tensor2.dispose();
+          }
+        });
+        this.state.activeTape = null;
+      }
+      return { value: y, grads: grads2 };
+    });
+  }
+  customGrad(f) {
+    assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
+    return (...inputs) => {
+      assert(inputs.every((t2) => t2 instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
+      let res;
+      const inputMap = {};
+      inputs.forEach((input2, i2) => {
+        inputMap[i2] = input2;
+      });
+      const forwardFunc = (_, save) => {
+        res = f(...[...inputs, save]);
+        assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
+        assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
+        return res.value;
+      };
+      const backwardsFunc = (dy, saved) => {
+        const gradRes = res.gradFunc(dy, saved);
+        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
+        assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
+        assert(grads2.every((t2) => t2 instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
+        const gradMap = {};
+        grads2.forEach((grad2, i2) => {
+          gradMap[i2] = () => grad2;
+        });
+        return gradMap;
+      };
+      return this.runKernelFunc({
+        forwardFunc,
+        backwardsFunc,
+        inputs: inputMap
+      });
+    };
+  }
+  readSync(dataId) {
+    const info = this.state.tensorInfo.get(dataId);
+    return info.backend.readSync(dataId);
+  }
+  read(dataId) {
+    const info = this.state.tensorInfo.get(dataId);
+    return info.backend.read(dataId);
+  }
+  readToGPU(dataId, options4) {
+    const info = this.state.tensorInfo.get(dataId);
+    return info.backend.readToGPU(dataId, options4);
+  }
+  async time(query) {
+    const start = now2();
+    const timingInfo = await this.backend.time(query);
+    timingInfo.wallMs = now2() - start;
+    return timingInfo;
+  }
+  track(result) {
+    if (this.state.activeScope != null) {
+      result.scopeId = this.state.activeScope.id;
+      this.state.activeScope.track.push(result);
+    }
+    return result;
+  }
+  get registeredVariables() {
+    return this.state.registeredVariables;
+  }
+  reset() {
+    this.pendingBackendInitId++;
+    this.state.dispose();
+    this.ENV.reset();
+    this.state = new EngineState();
+    for (const backendName in this.registry) {
+      this.disposeRegisteredKernels(backendName);
+      this.registry[backendName].dispose();
+      delete this.registry[backendName];
+    }
+    this.backendName = null;
+    this.backendInstance = null;
+    this.pendingBackendInit = null;
+  }
+};
+Engine.nextTensorId = 0;
+Engine.nextVariableId = 0;
+function ones(shape) {
+  const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
+  return ENGINE.makeTensor(values, shape, "float32");
+}
+function getOrMakeEngine() {
+  const ns = getGlobalNamespace();
+  if (ns._tfengine == null) {
+    const environment = new Environment(ns);
+    ns._tfengine = new Engine(environment);
+  }
+  setEnvironmentGlobal(ns._tfengine.ENV);
+  setTensorTracker(() => ns._tfengine);
+  return ns._tfengine;
+}
+var ENGINE = getOrMakeEngine();
+function add(a6, b) {
+  const inputs = { a: a6, b };
+  return ENGINE.runKernel(Add, inputs);
+}
+var device_util_exports = {};
+__export2(device_util_exports, {
+  isBrowser: () => isBrowser,
+  isMobile: () => isMobile,
+  mockIsMobile: () => mockIsMobile
+});
+function _isNavigatorDefined() {
+  return typeof navigator !== "undefined" && navigator != null;
+}
+var isMobileMockValue;
+function mockIsMobile(value) {
+  isMobileMockValue = value;
+}
+function isMobile(nav) {
+  if (isMobileMockValue !== void 0) {
+    return isMobileMockValue;
+  }
+  if (nav || _isNavigatorDefined()) {
+    if (!nav) {
+      nav = navigator;
+    }
+    if (nav.product === "ReactNative") {
+      return true;
+    }
+    const a6 = nav.userAgent || nav.vendor || (typeof window !== "undefined" ? window.opera : "");
+    if (!a6) {
+      const navAny = nav;
+      return navAny.userAgentData && navAny.userAgentData.mobile;
+    }
+    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a6) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a6.substr(0, 4));
+  }
+  return false;
+}
+function isBrowser() {
+  return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
+}
+var ENV2 = env();
+ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
+  if (debugValue) {
+    console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
+  }
+});
+ENV2.registerFlag("IS_BROWSER", () => isBrowser());
+ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
+ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
+ENV2.registerFlag("PROD", () => false);
+ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
+ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
+ENV2.registerFlag("IS_TEST", () => false);
+ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
+ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
+ENV2.registerFlag("ENGINE_COMPILE_ONLY", () => false);
+function inferShape(val, dtype) {
+  let firstElem = val;
+  if (isTypedArray(val)) {
+    return dtype === "string" ? [] : [val.length];
+  }
+  if (!Array.isArray(val)) {
+    return [];
+  }
+  const shape = [];
+  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
+    shape.push(firstElem.length);
+    firstElem = firstElem[0];
+  }
+  if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
+    deepAssertShapeConsistency(val, shape, []);
+  }
+  return shape;
+}
+function deepAssertShapeConsistency(val, shape, indices) {
+  indices = indices || [];
+  if (!Array.isArray(val) && !isTypedArray(val)) {
+    assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
+    return;
+  }
+  assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
+  assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
+  const subShape = shape.slice(1);
+  for (let i2 = 0; i2 < val.length; ++i2) {
+    deepAssertShapeConsistency(val[i2], subShape, indices.concat(i2));
+  }
+}
+function assertDtype(expectedDtype, actualDType, argName, functionName) {
+  if (expectedDtype === "string_or_numeric") {
+    return;
+  }
+  if (expectedDtype == null) {
+    throw new Error(`Expected dtype cannot be null.`);
+  }
+  if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
+    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
+  }
+}
+function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
+  if (x instanceof Tensor) {
+    assertDtype(parseAsDtype, x.dtype, argName, functionName);
+    return x;
+  }
+  let inferredDtype = inferDtype(x);
+  if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
+    inferredDtype = parseAsDtype;
+  }
+  assertDtype(parseAsDtype, inferredDtype, argName, functionName);
+  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
+    const type = x == null ? "null" : x.constructor.name;
+    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
+  }
+  const inferredShape = inferShape(x, inferredDtype);
+  if (!isTypedArray(x) && !Array.isArray(x)) {
+    x = [x];
+  }
+  const skipTypedArray = true;
+  const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
+  return ENGINE.makeTensor(values, inferredShape, inferredDtype);
+}
+function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
+  if (!Array.isArray(arg)) {
+    throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
+  }
+  const tensors = arg;
+  return tensors.map((t2, i2) => convertToTensor(t2, `${argName}[${i2}]`, functionName, parseAsDtype));
+}
+var OP_SCOPE_SUFFIX = "__op";
+function op(f) {
+  const keys = Object.keys(f);
+  if (keys.length !== 1) {
+    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
+  }
+  let opName = keys[0];
+  const fn = f[opName];
+  if (opName.endsWith("_")) {
+    opName = opName.substring(0, opName.length - 1);
+  }
+  opName = opName + OP_SCOPE_SUFFIX;
+  const f2 = (...args) => {
+    ENGINE.startScope(opName);
+    try {
+      const result = fn(...args);
+      if (isPromise(result)) {
+        console.error("Cannot return a Promise inside of tidy.");
+      }
+      ENGINE.endScope(result);
+      return result;
+    } catch (ex) {
+      ENGINE.endScope(null);
+      throw ex;
+    }
+  };
+  Object.defineProperty(f2, "name", { value: opName, configurable: true });
+  return f2;
+}
+function complex_(real5, imag5) {
+  const $real = convertToTensor(real5, "real", "complex");
+  const $imag = convertToTensor(imag5, "imag", "complex");
+  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
+  const inputs = { real: $real, imag: $imag };
+  return ENGINE.runKernel(Complex, inputs);
+}
+var complex = op({ complex_ });
+function makeTensor(values, shape, inferredShape, dtype) {
+  if (dtype == null) {
+    dtype = inferDtype(values);
+  }
+  if (dtype === "complex64") {
+    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
+  }
+  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
+    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
+  }
+  if (shape != null) {
+    assertNonNegativeIntegerDimensions(shape);
+    const providedSize = sizeFromShape(shape);
+    const inferredSize = sizeFromShape(inferredShape);
+    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
+    for (let i2 = 0; i2 < inferredShape.length; ++i2) {
+      const inferred = inferredShape[i2];
+      const flatDimsDontMatch = i2 === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i2)) : true;
+      assert(inferredShape[i2] === shape[i2] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
+    }
+  }
+  if (!isTypedArray(values) && !Array.isArray(values)) {
+    values = [values];
+  }
+  shape = shape || inferredShape;
+  values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+function tensor(values, shape, dtype) {
+  const inferredShape = inferShape(values, dtype);
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+var DTYPE_VALUE_SIZE_MAP = {
+  "float32": 4,
+  "float16": 2,
+  "int32": 4,
+  "uint16": 2,
+  "uint8": 1,
+  "bool": 1,
+  "complex64": 8
+};
+var NUM_BYTES_STRING_LENGTH = 4;
+async function encodeWeights(tensors, group) {
+  const specs = [];
+  const dataPromises = [];
+  const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);
+  for (let i2 = 0; i2 < names.length; ++i2) {
+    const name = names[i2];
+    const t2 = Array.isArray(tensors) ? tensors[i2].tensor : tensors[name];
+    if (t2.dtype !== "float32" && t2.dtype !== "int32" && t2.dtype !== "bool" && t2.dtype !== "string" && t2.dtype !== "complex64") {
+      throw new Error(`Unsupported dtype in weight '${name}': ${t2.dtype}`);
+    }
+    const spec = { name, shape: t2.shape, dtype: t2.dtype };
+    if (t2.dtype === "string") {
+      const utf8bytes = new Promise(async (resolve) => {
+        const vals = await t2.bytes();
+        const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
+        const bytes = new Uint8Array(totalNumBytes);
+        let offset = 0;
+        for (let i3 = 0; i3 < vals.length; i3++) {
+          const val = vals[i3];
+          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
+          bytes.set(bytesOfLength, offset);
+          offset += NUM_BYTES_STRING_LENGTH;
+          bytes.set(val, offset);
+          offset += val.length;
+        }
+        resolve(bytes);
+      });
+      dataPromises.push(utf8bytes);
+    } else {
+      dataPromises.push(t2.data());
+    }
+    if (group != null) {
+      spec.group = group;
+    }
+    specs.push(spec);
+  }
+  const tensorValues = await Promise.all(dataPromises);
+  return { data: concatenateTypedArrays(tensorValues), specs };
+}
+function decodeWeights(buffer2, specs) {
+  const out = {};
+  let float16Decode;
+  let offset = 0;
+  for (const spec of specs) {
+    const name = spec.name;
+    const dtype = spec.dtype;
+    const shape = spec.shape;
+    const size2 = sizeFromShape(shape);
+    let values;
+    if ("quantization" in spec) {
+      const quantization = spec.quantization;
+      if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
+        if (!("min" in quantization && "scale" in quantization)) {
+          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
+        }
+      } else if (quantization.dtype === "float16") {
+        if (dtype !== "float32") {
+          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
+        }
+      } else {
+        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
+      }
+      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
+      const byteBuffer = buffer2.slice(offset, offset + size2 * quantizationSizeFactor);
+      const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
+      if (dtype === "float32") {
+        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
+          values = new Float32Array(quantizedArray.length);
+          for (let i2 = 0; i2 < quantizedArray.length; i2++) {
+            const v = quantizedArray[i2];
+            values[i2] = v * quantization.scale + quantization.min;
+          }
+        } else if (quantization.dtype === "float16") {
+          if (float16Decode === void 0) {
+            float16Decode = getFloat16Decoder();
+          }
+          values = float16Decode(quantizedArray);
+        } else {
+          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
+        }
+      } else if (dtype === "int32") {
+        if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
+          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
+        }
+        values = new Int32Array(quantizedArray.length);
+        for (let i2 = 0; i2 < quantizedArray.length; i2++) {
+          const v = quantizedArray[i2];
+          values[i2] = Math.round(v * quantization.scale + quantization.min);
+        }
+      } else {
+        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
+      }
+      offset += size2 * quantizationSizeFactor;
+    } else if (dtype === "string") {
+      const size22 = sizeFromShape(spec.shape);
+      values = [];
+      for (let i2 = 0; i2 < size22; i2++) {
+        const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
+        offset += NUM_BYTES_STRING_LENGTH;
+        const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));
+        values.push(bytes);
+        offset += byteLength;
+      }
+    } else {
+      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
+      const byteBuffer = buffer2.slice(offset, offset + size2 * dtypeFactor);
+      if (dtype === "float32") {
+        values = new Float32Array(byteBuffer);
+      } else if (dtype === "int32") {
+        values = new Int32Array(byteBuffer);
+      } else if (dtype === "bool") {
+        values = new Uint8Array(byteBuffer);
+      } else if (dtype === "complex64") {
+        values = new Float32Array(byteBuffer);
+        const real5 = new Float32Array(values.length / 2);
+        const image2 = new Float32Array(values.length / 2);
+        for (let i2 = 0; i2 < real5.length; i2++) {
+          real5[i2] = values[i2 * 2];
+          image2[i2] = values[i2 * 2 + 1];
+        }
+        const realTensor = tensor(real5, shape, "float32");
+        const imageTensor = tensor(image2, shape, "float32");
+        out[name] = complex(realTensor, imageTensor);
+        realTensor.dispose();
+        imageTensor.dispose();
+      } else {
+        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
+      }
+      offset += size2 * dtypeFactor;
+    }
+    if (dtype !== "complex64") {
+      out[name] = tensor(values, shape, dtype);
+    }
+  }
+  return out;
+}
+function concatenateTypedArrays(xs) {
+  if (xs === null) {
+    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
+  }
+  let totalByteLength = 0;
+  const normalizedXs = [];
+  xs.forEach((x) => {
+    totalByteLength += x.byteLength;
+    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
+    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
+      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
+    }
+  });
+  const y = new Uint8Array(totalByteLength);
+  let offset = 0;
+  normalizedXs.forEach((x) => {
+    y.set(new Uint8Array(x.buffer), offset);
+    offset += x.byteLength;
+  });
+  return y.buffer;
+}
+var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
+function stringByteLength(str) {
+  if (useNodeBuffer) {
+    return Buffer.byteLength(str);
+  }
+  return new Blob([str]).size;
+}
+function arrayBufferToBase64String(buffer2) {
+  if (useNodeBuffer) {
+    return Buffer.from(buffer2).toString("base64");
+  }
+  const buf = new Uint8Array(buffer2);
+  let s2 = "";
+  for (let i2 = 0, l3 = buf.length; i2 < l3; i2++) {
+    s2 += String.fromCharCode(buf[i2]);
+  }
+  return btoa(s2);
+}
+function base64StringToArrayBuffer(str) {
+  if (useNodeBuffer) {
+    const buf = Buffer.from(str, "base64");
+    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
+  }
+  const s2 = atob(str);
+  const buffer2 = new Uint8Array(s2.length);
+  for (let i2 = 0; i2 < s2.length; ++i2) {
+    buffer2.set([s2.charCodeAt(i2)], i2);
+  }
+  return buffer2.buffer;
+}
+function concatenateArrayBuffers(buffers) {
+  if (buffers.length === 1) {
+    return buffers[0];
+  }
+  let totalByteLength = 0;
+  buffers.forEach((buffer2) => {
+    totalByteLength += buffer2.byteLength;
+  });
+  const temp = new Uint8Array(totalByteLength);
+  let offset = 0;
+  buffers.forEach((buffer2) => {
+    temp.set(new Uint8Array(buffer2), offset);
+    offset += buffer2.byteLength;
+  });
+  return temp.buffer;
+}
+function basename(path) {
+  const SEPARATOR = "/";
+  path = path.trim();
+  while (path.endsWith(SEPARATOR)) {
+    path = path.slice(0, path.length - 1);
+  }
+  const items = path.split(SEPARATOR);
+  return items[items.length - 1];
+}
+function getModelJSONForModelArtifacts(artifacts, manifest) {
+  const result = {
+    modelTopology: artifacts.modelTopology,
+    format: artifacts.format,
+    generatedBy: artifacts.generatedBy,
+    convertedBy: artifacts.convertedBy,
+    weightsManifest: manifest
+  };
+  if (artifacts.signature != null) {
+    result.signature = artifacts.signature;
+  }
+  if (artifacts.userDefinedMetadata != null) {
+    result.userDefinedMetadata = artifacts.userDefinedMetadata;
+  }
+  if (artifacts.modelInitializer != null) {
+    result.modelInitializer = artifacts.modelInitializer;
+  }
+  if (artifacts.trainingConfig != null) {
+    result.trainingConfig = artifacts.trainingConfig;
+  }
+  return result;
+}
+async function getModelArtifactsForJSON(modelJSON, loadWeights2) {
+  const modelArtifacts = {
+    modelTopology: modelJSON.modelTopology,
+    format: modelJSON.format,
+    generatedBy: modelJSON.generatedBy,
+    convertedBy: modelJSON.convertedBy
+  };
+  if (modelJSON.trainingConfig != null) {
+    modelArtifacts.trainingConfig = modelJSON.trainingConfig;
+  }
+  if (modelJSON.weightsManifest != null) {
+    const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);
+    modelArtifacts.weightSpecs = weightSpecs;
+    modelArtifacts.weightData = weightData;
+  }
+  if (modelJSON.signature != null) {
+    modelArtifacts.signature = modelJSON.signature;
+  }
+  if (modelJSON.userDefinedMetadata != null) {
+    modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
+  }
+  if (modelJSON.modelInitializer != null) {
+    modelArtifacts.modelInitializer = modelJSON.modelInitializer;
+  }
+  return modelArtifacts;
+}
+function getModelArtifactsInfoForJSON(modelArtifacts) {
+  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+    throw new Error("Expected JSON model topology, received ArrayBuffer.");
+  }
+  return {
+    dateSaved: new Date(),
+    modelTopologyType: "JSON",
+    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
+    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
+    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
+  };
+}
+function computeFloat16MantisaTable() {
+  const convertMantissa = (i2) => {
+    let m = i2 << 13;
+    let e2 = 0;
+    while ((m & 8388608) === 0) {
+      e2 -= 8388608;
+      m <<= 1;
+    }
+    m &= ~8388608;
+    e2 += 947912704;
+    return m | e2;
+  };
+  const mantisaTable = new Uint32Array(2048);
+  mantisaTable[0] = 0;
+  for (let i2 = 1; i2 < 1024; i2++) {
+    mantisaTable[i2] = convertMantissa(i2);
+  }
+  for (let i2 = 1024; i2 < 2048; i2++) {
+    mantisaTable[i2] = 939524096 + (i2 - 1024 << 13);
+  }
+  return mantisaTable;
+}
+function computeFloat16ExponentTable() {
+  const exponentTable = new Uint32Array(64);
+  exponentTable[0] = 0;
+  exponentTable[31] = 1199570944;
+  exponentTable[32] = 2147483648;
+  exponentTable[63] = 3347054592;
+  for (let i2 = 1; i2 < 31; i2++) {
+    exponentTable[i2] = i2 << 23;
+  }
+  for (let i2 = 33; i2 < 63; i2++) {
+    exponentTable[i2] = 2147483648 + (i2 - 32 << 23);
+  }
+  return exponentTable;
+}
+function computeFloat16OffsetTable() {
+  const offsetTable = new Uint32Array(64);
+  for (let i2 = 0; i2 < 64; i2++) {
+    offsetTable[i2] = 1024;
+  }
+  offsetTable[0] = offsetTable[32] = 0;
+  return offsetTable;
+}
+function getFloat16Decoder() {
+  const mantisaTable = computeFloat16MantisaTable();
+  const exponentTable = computeFloat16ExponentTable();
+  const offsetTable = computeFloat16OffsetTable();
+  return (quantizedArray) => {
+    const buffer2 = new ArrayBuffer(4 * quantizedArray.length);
+    const bufferUint32View = new Uint32Array(buffer2);
+    for (let index2 = 0; index2 < quantizedArray.length; index2++) {
+      const float16Bits = quantizedArray[index2];
+      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
+      bufferUint32View[index2] = float32Bits;
+    }
+    return new Float32Array(buffer2);
+  };
+}
+var IORouterRegistry = class {
+  constructor() {
+    this.saveRouters = [];
+    this.loadRouters = [];
+  }
+  static getInstance() {
+    if (IORouterRegistry.instance == null) {
+      IORouterRegistry.instance = new IORouterRegistry();
+    }
+    return IORouterRegistry.instance;
+  }
+  static registerSaveRouter(saveRouter) {
+    IORouterRegistry.getInstance().saveRouters.push(saveRouter);
+  }
+  static registerLoadRouter(loadRouter) {
+    IORouterRegistry.getInstance().loadRouters.push(loadRouter);
+  }
+  static getSaveHandlers(url) {
+    return IORouterRegistry.getHandlers(url, "save");
+  }
+  static getLoadHandlers(url, loadOptions) {
+    return IORouterRegistry.getHandlers(url, "load", loadOptions);
+  }
+  static getHandlers(url, handlerType, loadOptions) {
+    const validHandlers = [];
+    const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
+    routers.forEach((router) => {
+      const handler = router(url, loadOptions);
+      if (handler !== null) {
+        validHandlers.push(handler);
+      }
+    });
+    return validHandlers;
+  }
+};
+var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
+var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
+var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
+var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
+var DATABASE_NAME = "tensorflowjs";
+var DATABASE_VERSION = 1;
+var MODEL_STORE_NAME = "models_store";
+var INFO_STORE_NAME = "model_info_store";
+function getIndexedDBFactory() {
+  if (!env().getBool("IS_BROWSER")) {
+    throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
+  }
+  const theWindow = typeof window === "undefined" ? self : window;
+  const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;
+  if (factory == null) {
+    throw new Error("The current browser does not appear to support IndexedDB.");
+  }
+  return factory;
+}
+function setUpDatabase(openRequest) {
+  const db = openRequest.result;
+  db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" });
+  db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" });
+}
+var BrowserIndexedDB = class {
+  constructor(modelPath) {
+    this.indexedDB = getIndexedDBFactory();
+    if (modelPath == null || !modelPath) {
+      throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
+    }
+    this.modelPath = modelPath;
+  }
+  async save(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
+    }
+    return this.databaseAction(this.modelPath, modelArtifacts);
+  }
+  async load() {
+    return this.databaseAction(this.modelPath);
+  }
+  databaseAction(modelPath, modelArtifacts) {
+    return new Promise((resolve, reject) => {
+      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
+      openRequest.onsuccess = () => {
+        const db = openRequest.result;
+        if (modelArtifacts == null) {
+          const modelTx = db.transaction(MODEL_STORE_NAME, "readonly");
+          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+          const getRequest = modelStore.get(this.modelPath);
+          getRequest.onsuccess = () => {
+            if (getRequest.result == null) {
+              db.close();
+              return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
+            } else {
+              resolve(getRequest.result.modelArtifacts);
+            }
+          };
+          getRequest.onerror = (error) => {
+            db.close();
+            return reject(getRequest.error);
+          };
+          modelTx.oncomplete = () => db.close();
+        } else {
+          const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
+          const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
+          let infoStore = infoTx.objectStore(INFO_STORE_NAME);
+          const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });
+          let modelTx;
+          putInfoRequest.onsuccess = () => {
+            modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
+            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+            const putModelRequest = modelStore.put({
+              modelPath: this.modelPath,
+              modelArtifacts,
+              modelArtifactsInfo
+            });
+            putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });
+            putModelRequest.onerror = (error) => {
+              infoStore = infoTx.objectStore(INFO_STORE_NAME);
+              const deleteInfoRequest = infoStore.delete(this.modelPath);
+              deleteInfoRequest.onsuccess = () => {
+                db.close();
+                return reject(putModelRequest.error);
+              };
+              deleteInfoRequest.onerror = (error2) => {
+                db.close();
+                return reject(putModelRequest.error);
+              };
+            };
+          };
+          putInfoRequest.onerror = (error) => {
+            db.close();
+            return reject(putInfoRequest.error);
+          };
+          infoTx.oncomplete = () => {
+            if (modelTx == null) {
+              db.close();
+            } else {
+              modelTx.oncomplete = () => db.close();
+            }
+          };
+        }
+      };
+      openRequest.onerror = (error) => reject(openRequest.error);
+    });
+  }
+};
+BrowserIndexedDB.URL_SCHEME = "indexeddb://";
+var indexedDBRouter = (url) => {
+  if (!env().getBool("IS_BROWSER")) {
+    return null;
+  } else {
+    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
+      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
+    } else {
+      return null;
+    }
+  }
+};
+IORouterRegistry.registerSaveRouter(indexedDBRouter);
+IORouterRegistry.registerLoadRouter(indexedDBRouter);
+function browserIndexedDB(modelPath) {
+  return new BrowserIndexedDB(modelPath);
+}
+function maybeStripScheme(key) {
+  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;
+}
+var BrowserIndexedDBManager = class {
+  constructor() {
+    this.indexedDB = getIndexedDBFactory();
+  }
+  async listModels() {
+    return new Promise((resolve, reject) => {
+      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
+      openRequest.onsuccess = () => {
+        const db = openRequest.result;
+        const tx = db.transaction(INFO_STORE_NAME, "readonly");
+        const store = tx.objectStore(INFO_STORE_NAME);
+        const getAllInfoRequest = store.getAll();
+        getAllInfoRequest.onsuccess = () => {
+          const out = {};
+          for (const item of getAllInfoRequest.result) {
+            out[item.modelPath] = item.modelArtifactsInfo;
+          }
+          resolve(out);
+        };
+        getAllInfoRequest.onerror = (error) => {
+          db.close();
+          return reject(getAllInfoRequest.error);
+        };
+        tx.oncomplete = () => db.close();
+      };
+      openRequest.onerror = (error) => reject(openRequest.error);
+    });
+  }
+  async removeModel(path) {
+    path = maybeStripScheme(path);
+    return new Promise((resolve, reject) => {
+      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
+      openRequest.onsuccess = () => {
+        const db = openRequest.result;
+        const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
+        const infoStore = infoTx.objectStore(INFO_STORE_NAME);
+        const getInfoRequest = infoStore.get(path);
+        let modelTx;
+        getInfoRequest.onsuccess = () => {
+          if (getInfoRequest.result == null) {
+            db.close();
+            return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));
+          } else {
+            const deleteInfoRequest = infoStore.delete(path);
+            const deleteModelData = () => {
+              modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
+              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+              const deleteModelRequest = modelStore.delete(path);
+              deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);
+              deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);
+            };
+            deleteInfoRequest.onsuccess = deleteModelData;
+            deleteInfoRequest.onerror = (error) => {
+              deleteModelData();
+              db.close();
+              return reject(getInfoRequest.error);
+            };
+          }
+        };
+        getInfoRequest.onerror = (error) => {
+          db.close();
+          return reject(getInfoRequest.error);
+        };
+        infoTx.oncomplete = () => {
+          if (modelTx == null) {
+            db.close();
+          } else {
+            modelTx.oncomplete = () => db.close();
+          }
+        };
+      };
+      openRequest.onerror = (error) => reject(openRequest.error);
+    });
+  }
+};
+var PATH_SEPARATOR = "/";
+var PATH_PREFIX = "tensorflowjs_models";
+var INFO_SUFFIX = "info";
+var MODEL_TOPOLOGY_SUFFIX = "model_topology";
+var WEIGHT_SPECS_SUFFIX = "weight_specs";
+var WEIGHT_DATA_SUFFIX = "weight_data";
+var MODEL_METADATA_SUFFIX = "model_metadata";
+function getModelKeys(path) {
+  return {
+    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
+    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
+    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
+    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
+    modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
+  };
+}
+function removeItems(keys) {
+  for (const key of Object.values(keys)) {
+    window.localStorage.removeItem(key);
+  }
+}
+function getModelPathFromKey(key) {
+  const items = key.split(PATH_SEPARATOR);
+  if (items.length < 3) {
+    throw new Error(`Invalid key format: ${key}`);
+  }
+  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
+}
+function maybeStripScheme2(key) {
+  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;
+}
+var BrowserLocalStorage = class {
+  constructor(modelPath) {
+    if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") {
+      throw new Error("The current environment does not support local storage.");
+    }
+    this.LS = window.localStorage;
+    if (modelPath == null || !modelPath) {
+      throw new Error("For local storage, modelPath must not be null, undefined or empty.");
+    }
+    this.modelPath = modelPath;
+    this.keys = getModelKeys(this.modelPath);
+  }
+  async save(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
+    } else {
+      const topology = JSON.stringify(modelArtifacts.modelTopology);
+      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
+      const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
+      try {
+        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
+        this.LS.setItem(this.keys.topology, topology);
+        this.LS.setItem(this.keys.weightSpecs, weightSpecs);
+        this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));
+        const metadata = {
+          format: modelArtifacts.format,
+          generatedBy: modelArtifacts.generatedBy,
+          convertedBy: modelArtifacts.convertedBy,
+          signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,
+          userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,
+          modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,
+          trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0
+        };
+        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));
+        return { modelArtifactsInfo };
+      } catch (err) {
+        removeItems(this.keys);
+        throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);
+      }
+    }
+  }
+  async load() {
+    const info = JSON.parse(this.LS.getItem(this.keys.info));
+    if (info == null) {
+      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
+    }
+    if (info.modelTopologyType !== "JSON") {
+      throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
+    }
+    const out = {};
+    const topology = JSON.parse(this.LS.getItem(this.keys.topology));
+    if (topology == null) {
+      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
+    }
+    out.modelTopology = topology;
+    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
+    if (weightSpecs == null) {
+      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
+    }
+    out.weightSpecs = weightSpecs;
+    const metadataString = this.LS.getItem(this.keys.modelMetadata);
+    if (metadataString != null) {
+      const metadata = JSON.parse(metadataString);
+      out.format = metadata.format;
+      out.generatedBy = metadata.generatedBy;
+      out.convertedBy = metadata.convertedBy;
+      if (metadata.signature != null) {
+        out.signature = metadata.signature;
+      }
+      if (metadata.userDefinedMetadata != null) {
+        out.userDefinedMetadata = metadata.userDefinedMetadata;
+      }
+      if (metadata.modelInitializer != null) {
+        out.modelInitializer = metadata.modelInitializer;
+      }
+      if (metadata.trainingConfig != null) {
+        out.trainingConfig = metadata.trainingConfig;
+      }
+    }
+    const weightDataBase64 = this.LS.getItem(this.keys.weightData);
+    if (weightDataBase64 == null) {
+      throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
+    }
+    out.weightData = base64StringToArrayBuffer(weightDataBase64);
+    return out;
+  }
+};
+BrowserLocalStorage.URL_SCHEME = "localstorage://";
+var localStorageRouter = (url) => {
+  if (!env().getBool("IS_BROWSER")) {
+    return null;
+  } else {
+    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
+      return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
+    } else {
+      return null;
+    }
+  }
+};
+IORouterRegistry.registerSaveRouter(localStorageRouter);
+IORouterRegistry.registerLoadRouter(localStorageRouter);
+function browserLocalStorage(modelPath) {
+  return new BrowserLocalStorage(modelPath);
+}
+var BrowserLocalStorageManager = class {
+  constructor() {
+    assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser");
+    assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage");
+    this.LS = window.localStorage;
+  }
+  async listModels() {
+    const out = {};
+    const prefix = PATH_PREFIX + PATH_SEPARATOR;
+    const suffix = PATH_SEPARATOR + INFO_SUFFIX;
+    for (let i2 = 0; i2 < this.LS.length; ++i2) {
+      const key = this.LS.key(i2);
+      if (key.startsWith(prefix) && key.endsWith(suffix)) {
+        const modelPath = getModelPathFromKey(key);
+        out[modelPath] = JSON.parse(this.LS.getItem(key));
+      }
+    }
+    return out;
+  }
+  async removeModel(path) {
+    path = maybeStripScheme2(path);
+    const keys = getModelKeys(path);
+    if (this.LS.getItem(keys.info) == null) {
+      throw new Error(`Cannot find model at path '${path}'`);
+    }
+    const info = JSON.parse(this.LS.getItem(keys.info));
+    removeItems(keys);
+    return info;
+  }
+};
+var URL_SCHEME_SUFFIX = "://";
+var ModelStoreManagerRegistry = class {
+  constructor() {
+    this.managers = {};
+  }
+  static getInstance() {
+    if (ModelStoreManagerRegistry.instance == null) {
+      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
+    }
+    return ModelStoreManagerRegistry.instance;
+  }
+  static registerManager(scheme, manager) {
+    assert(scheme != null, () => "scheme must not be undefined or null.");
+    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
+      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
+    }
+    assert(scheme.length > 0, () => "scheme must not be an empty string.");
+    const registry = ModelStoreManagerRegistry.getInstance();
+    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
+    registry.managers[scheme] = manager;
+  }
+  static getManager(scheme) {
+    const manager = this.getInstance().managers[scheme];
+    if (manager == null) {
+      throw new Error(`Cannot find model manager for scheme '${scheme}'`);
+    }
+    return manager;
+  }
+  static getSchemes() {
+    return Object.keys(this.getInstance().managers);
+  }
+};
+function parseURL(url) {
+  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
+    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
+  }
+  return {
+    scheme: url.split(URL_SCHEME_SUFFIX)[0],
+    path: url.split(URL_SCHEME_SUFFIX)[1]
+  };
+}
+async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
+  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
+  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
+  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
+  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
+  const loadHandler = loadHandlers[0];
+  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
+  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
+  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
+  const saveHandler = saveHandlers[0];
+  const sourceScheme = parseURL(sourceURL).scheme;
+  const sourcePath = parseURL(sourceURL).path;
+  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
+  const modelArtifacts = await loadHandler.load();
+  if (deleteSource && sameMedium) {
+    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
+  }
+  const saveResult = await saveHandler.save(modelArtifacts);
+  if (deleteSource && !sameMedium) {
+    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
+  }
+  return saveResult.modelArtifactsInfo;
+}
+async function listModels() {
+  const schemes = ModelStoreManagerRegistry.getSchemes();
+  const out = {};
+  for (const scheme of schemes) {
+    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
+    for (const path in schemeOut) {
+      const url = scheme + URL_SCHEME_SUFFIX + path;
+      out[url] = schemeOut[path];
+    }
+  }
+  return out;
+}
+async function removeModel(url) {
+  const schemeAndPath = parseURL(url);
+  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
+  return manager.removeModel(schemeAndPath.path);
+}
+async function copyModel(sourceURL, destURL) {
+  const deleteSource = false;
+  return cloneModelInternal(sourceURL, destURL, deleteSource);
+}
+async function moveModel(sourceURL, destURL) {
+  const deleteSource = true;
+  return cloneModelInternal(sourceURL, destURL, deleteSource);
+}
+var PlatformBrowser = class {
+  fetch(path, init22) {
+    return fetch(path, init22);
+  }
+  now() {
+    return performance.now();
+  }
+  encode(text, encoding) {
+    if (encoding !== "utf-8" && encoding !== "utf8") {
+      throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
+    }
+    if (this.textEncoder == null) {
+      this.textEncoder = new TextEncoder();
+    }
+    return this.textEncoder.encode(text);
+  }
+  decode(bytes, encoding) {
+    return new TextDecoder(encoding).decode(bytes);
+  }
+};
+if (env().get("IS_BROWSER")) {
+  env().setPlatform("browser", new PlatformBrowser());
+  try {
+    ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
+  } catch (err) {
+  }
+  try {
+    ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
+  } catch (err) {
+  }
+}
+var getNodeFetch = {
+  importFetch: () => require_browser()
+};
+var systemFetch;
+var PlatformNode = class {
+  constructor() {
+    this.util = require_util();
+    this.textEncoder = new this.util.TextEncoder();
+  }
+  fetch(path, requestInits) {
+    if (env().global.fetch != null) {
+      return env().global.fetch(path, requestInits);
+    }
+    if (systemFetch == null) {
+      systemFetch = getNodeFetch.importFetch();
+    }
+    return systemFetch(path, requestInits);
+  }
+  now() {
+    const time2 = process.hrtime();
+    return time2[0] * 1e3 + time2[1] / 1e6;
+  }
+  encode(text, encoding) {
+    if (encoding !== "utf-8" && encoding !== "utf8") {
+      throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);
+    }
+    return this.textEncoder.encode(text);
+  }
+  decode(bytes, encoding) {
+    if (bytes.length === 0) {
+      return "";
+    }
+    return new this.util.TextDecoder(encoding).decode(bytes);
+  }
+};
+if (env().get("IS_NODE") && !env().get("IS_BROWSER")) {
+  env().setPlatform("node", new PlatformNode());
+}
+function buffer(shape, dtype = "float32", values) {
+  dtype = dtype || "float32";
+  assertNonNegativeIntegerDimensions(shape);
+  return new TensorBuffer(shape, dtype, values);
+}
+function cast_(x, dtype) {
+  const $x = convertToTensor(x, "x", "cast");
+  if (!isValidDtype(dtype)) {
+    throw new Error(`Failed to cast to unknown dtype ${dtype}`);
+  }
+  if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
+    throw new Error("Only strings can be casted to strings");
+  }
+  const inputs = { x: $x };
+  const attrs = { dtype };
+  return ENGINE.runKernel(Cast, inputs, attrs);
+}
+var cast = op({ cast_ });
+function clone_(x) {
+  const $x = convertToTensor(x, "x", "clone", "string_or_numeric");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Identity, inputs);
+}
+var clone = op({ clone_ });
+function print(x, verbose = false) {
+  console.log(x.toString(verbose));
+}
+getOrMakeEngine();
+var opHandler2 = {
+  buffer,
+  cast,
+  clone,
+  print
+};
+setOpHandler(opHandler2);
+var io_exports = {};
+__export2(io_exports, {
+  browserFiles: () => browserFiles,
+  browserHTTPRequest: () => browserHTTPRequest,
+  concatenateArrayBuffers: () => concatenateArrayBuffers,
+  copyModel: () => copyModel,
+  decodeWeights: () => decodeWeights,
+  encodeWeights: () => encodeWeights,
+  fromMemory: () => fromMemory,
+  fromMemorySync: () => fromMemorySync,
+  getLoadHandlers: () => getLoadHandlers,
+  getModelArtifactsForJSON: () => getModelArtifactsForJSON,
+  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
+  getSaveHandlers: () => getSaveHandlers,
+  http: () => http,
+  isHTTPScheme: () => isHTTPScheme,
+  listModels: () => listModels,
+  loadWeights: () => loadWeights,
+  moveModel: () => moveModel,
+  registerLoadRouter: () => registerLoadRouter,
+  registerSaveRouter: () => registerSaveRouter,
+  removeModel: () => removeModel,
+  weightsLoaderFactory: () => weightsLoaderFactory,
+  withSaveHandler: () => withSaveHandler,
+  withSaveHandlerSync: () => withSaveHandlerSync
+});
+var DEFAULT_FILE_NAME_PREFIX = "model";
+var DEFAULT_JSON_EXTENSION_NAME = ".json";
+var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
+function defer(f) {
+  return new Promise((resolve) => setTimeout(resolve)).then(f);
+}
+var BrowserDownloads = class {
+  constructor(fileNamePrefix) {
+    if (!env().getBool("IS_BROWSER")) {
+      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
+    }
+    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
+      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
+    }
+    if (fileNamePrefix == null || fileNamePrefix.length === 0) {
+      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
+    }
+    this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
+    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
+  }
+  async save(modelArtifacts) {
+    if (typeof document === "undefined") {
+      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
+    }
+    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: "application/octet-stream" }));
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
+    } else {
+      const weightsManifest = [{
+        paths: ["./" + this.weightDataFileName],
+        weights: modelArtifacts.weightSpecs
+      }];
+      const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
+      const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" }));
+      const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
+      jsonAnchor.download = this.modelJsonFileName;
+      jsonAnchor.href = modelJsonURL;
+      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
+      if (modelArtifacts.weightData != null) {
+        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
+        weightDataAnchor.download = this.weightDataFileName;
+        weightDataAnchor.href = weightsURL;
+        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
+      }
+      return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };
+    }
+  }
+};
+BrowserDownloads.URL_SCHEME = "downloads://";
+var BrowserFiles = class {
+  constructor(files) {
+    if (files == null || files.length < 1) {
+      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
+    }
+    this.jsonFile = files[0];
+    this.weightsFiles = files.slice(1);
+  }
+  async load() {
+    return new Promise((resolve, reject) => {
+      const jsonReader = new FileReader();
+      jsonReader.onload = (event) => {
+        const modelJSON = JSON.parse(event.target.result);
+        const modelTopology = modelJSON.modelTopology;
+        if (modelTopology == null) {
+          reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
+          return;
+        }
+        const weightsManifest = modelJSON.weightsManifest;
+        if (weightsManifest == null) {
+          reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
+          return;
+        }
+        if (this.weightsFiles.length === 0) {
+          resolve({ modelTopology });
+          return;
+        }
+        const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));
+        resolve(modelArtifactsPromise);
+      };
+      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
+      jsonReader.readAsText(this.jsonFile);
+    });
+  }
+  loadWeights(weightsManifest) {
+    const weightSpecs = [];
+    const paths = [];
+    for (const entry of weightsManifest) {
+      weightSpecs.push(...entry.weights);
+      paths.push(...entry.paths);
+    }
+    const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);
+    const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));
+    return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]);
+  }
+  loadWeightsFile(path, file) {
+    return new Promise((resolve, reject) => {
+      const weightFileReader = new FileReader();
+      weightFileReader.onload = (event) => {
+        const weightData = event.target.result;
+        resolve(weightData);
+      };
+      weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
+      weightFileReader.readAsArrayBuffer(file);
+    });
+  }
+  checkManifestAndWeightFiles(manifest) {
+    const basenames = [];
+    const fileNames = this.weightsFiles.map((file) => basename(file.name));
+    const pathToFile = {};
+    for (const group of manifest) {
+      group.paths.forEach((path) => {
+        const pathBasename = basename(path);
+        if (basenames.indexOf(pathBasename) !== -1) {
+          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
+        }
+        basenames.push(pathBasename);
+        if (fileNames.indexOf(pathBasename) === -1) {
+          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
+        } else {
+          pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];
+        }
+      });
+    }
+    if (basenames.length !== this.weightsFiles.length) {
+      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
+    }
+    return pathToFile;
+  }
+};
+var browserDownloadsRouter = (url) => {
+  if (!env().getBool("IS_BROWSER")) {
+    return null;
+  } else {
+    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
+      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
+    } else {
+      return null;
+    }
+  }
+};
+IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
+function browserDownloads(fileNamePrefix = "model") {
+  return new BrowserDownloads(fileNamePrefix);
+}
+function browserFiles(files) {
+  return new BrowserFiles(files);
+}
+function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
+  checkPromises(promises);
+  startFraction = startFraction == null ? 0 : startFraction;
+  endFraction = endFraction == null ? 1 : endFraction;
+  checkFraction(startFraction, endFraction);
+  let resolvedPromise = 0;
+  const registerMonitor = (promise) => {
+    promise.then((value) => {
+      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
+      onProgress(fraction);
+      return value;
+    });
+    return promise;
+  };
+  function checkPromises(promises2) {
+    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
+  }
+  function checkFraction(startFraction2, endFraction2) {
+    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
+    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
+    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
+  }
+  return Promise.all(promises.map(registerMonitor));
+}
+async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
+  if (loadOptions == null) {
+    loadOptions = {};
+  }
+  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
+  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));
+  const fetchStartFraction = 0;
+  const fetchEndFraction = 0.5;
+  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
+  const bufferPromises = responses.map((response) => response.arrayBuffer());
+  const bufferStartFraction = 0.5;
+  const bufferEndFraction = 1;
+  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
+  return buffers;
+}
+async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
+  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });
+  const loadWeights2 = weightsLoaderFactory(fetchWeights);
+  return loadWeights2(manifest, filePathPrefix, weightNames);
+}
+function weightsLoaderFactory(fetchWeightsFunction) {
+  return async (manifest, filePathPrefix = "", weightNames) => {
+    const groupIndicesToFetchMap = manifest.map(() => false);
+    const groupWeightsToFetch = {};
+    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
+    const allManifestWeightNames = [];
+    manifest.forEach((manifestGroupConfig, groupIndex) => {
+      let groupOffset = 0;
+      manifestGroupConfig.weights.forEach((weightsEntry) => {
+        const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
+        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
+        const enqueueWeightsForFetchingFn = () => {
+          groupIndicesToFetchMap[groupIndex] = true;
+          if (groupWeightsToFetch[groupIndex] == null) {
+            groupWeightsToFetch[groupIndex] = [];
+          }
+          groupWeightsToFetch[groupIndex].push({
+            manifestEntry: weightsEntry,
+            groupOffset,
+            sizeBytes: weightsBytes
+          });
+        };
+        if (weightNames != null) {
+          weightNames.forEach((weightName, weightIndex) => {
+            if (weightName === weightsEntry.name) {
+              enqueueWeightsForFetchingFn();
+              weightsFound[weightIndex] = true;
+            }
+          });
+        } else {
+          enqueueWeightsForFetchingFn();
+        }
+        allManifestWeightNames.push(weightsEntry.name);
+        groupOffset += weightsBytes;
+      });
+    });
+    if (!weightsFound.every((found) => found)) {
+      const weightsNotFound = weightNames.filter((_, i2) => !weightsFound[i2]);
+      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
+    }
+    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i2) => {
+      if (shouldFetch) {
+        accumulator.push(i2);
+      }
+      return accumulator;
+    }, []);
+    const fetchUrls = [];
+    groupIndicesToFetch.forEach((i2) => {
+      manifest[i2].paths.forEach((filepath) => {
+        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
+        fetchUrls.push(fetchUrl);
+      });
+    });
+    const buffers = await fetchWeightsFunction(fetchUrls);
+    const weightsTensorMap = {};
+    let bufferIndexOffset = 0;
+    groupIndicesToFetch.forEach((i2) => {
+      const numBuffers = manifest[i2].paths.length;
+      let groupBytes = 0;
+      for (let i3 = 0; i3 < numBuffers; i3++) {
+        groupBytes += buffers[bufferIndexOffset + i3].byteLength;
+      }
+      const groupBuffer = new ArrayBuffer(groupBytes);
+      const groupByteBuffer = new Uint8Array(groupBuffer);
+      let groupBufferOffset = 0;
+      for (let i3 = 0; i3 < numBuffers; i3++) {
+        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i3]);
+        groupByteBuffer.set(buffer2, groupBufferOffset);
+        groupBufferOffset += buffer2.byteLength;
+      }
+      const weightsEntries = groupWeightsToFetch[i2];
+      weightsEntries.forEach((weightsEntry) => {
+        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
+        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
+        for (const name in nameToTensorMap) {
+          weightsTensorMap[name] = nameToTensorMap[name];
+        }
+      });
+      bufferIndexOffset += numBuffers;
+    });
+    return weightsTensorMap;
+  };
+}
+var OCTET_STREAM_MIME_TYPE = "application/octet-stream";
+var JSON_TYPE = "application/json";
+var HTTPRequest = class {
+  constructor(path, loadOptions) {
+    this.DEFAULT_METHOD = "POST";
+    if (loadOptions == null) {
+      loadOptions = {};
+    }
+    this.weightPathPrefix = loadOptions.weightPathPrefix;
+    this.onProgress = loadOptions.onProgress;
+    this.weightUrlConverter = loadOptions.weightUrlConverter;
+    if (loadOptions.fetchFunc != null) {
+      assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
+      this.fetch = loadOptions.fetchFunc;
+    } else {
+      this.fetch = env().platform.fetch;
+    }
+    assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
+    if (Array.isArray(path)) {
+      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
+    }
+    this.path = path;
+    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
+      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
+    }
+    this.requestInit = loadOptions.requestInit || {};
+  }
+  async save(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
+    }
+    const init22 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
+    init22.body = new FormData();
+    const weightsManifest = [{
+      paths: ["./model.weights.bin"],
+      weights: modelArtifacts.weightSpecs
+    }];
+    const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
+    init22.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json");
+    if (modelArtifacts.weightData != null) {
+      init22.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin");
+    }
+    const response = await this.fetch(this.path, init22);
+    if (response.ok) {
+      return {
+        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
+        responses: [response]
+      };
+    } else {
+      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
+    }
+  }
+  async load() {
+    const modelConfigRequest = await this.fetch(this.path, this.requestInit);
+    if (!modelConfigRequest.ok) {
+      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
+    }
+    let modelJSON;
+    try {
+      modelJSON = await modelConfigRequest.json();
+    } catch (e2) {
+      let message = `Failed to parse model JSON of response from ${this.path}.`;
+      if (this.path.endsWith(".pb")) {
+        message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
+      } else {
+        message += " Please make sure the server is serving valid JSON for this request.";
+      }
+      throw new Error(message);
+    }
+    const modelTopology = modelJSON.modelTopology;
+    const weightsManifest = modelJSON.weightsManifest;
+    if (modelTopology == null && weightsManifest == null) {
+      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
+    }
+    return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));
+  }
+  async loadWeights(weightsManifest) {
+    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
+    const [prefix, suffix] = parseUrl(weightPath);
+    const pathPrefix = this.weightPathPrefix || prefix;
+    const weightSpecs = [];
+    for (const entry of weightsManifest) {
+      weightSpecs.push(...entry.weights);
+    }
+    const fetchURLs = [];
+    const urlPromises = [];
+    for (const weightsGroup of weightsManifest) {
+      for (const path of weightsGroup.paths) {
+        if (this.weightUrlConverter != null) {
+          urlPromises.push(this.weightUrlConverter(path));
+        } else {
+          fetchURLs.push(pathPrefix + path + suffix);
+        }
+      }
+    }
+    if (this.weightUrlConverter) {
+      fetchURLs.push(...await Promise.all(urlPromises));
+    }
+    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
+      requestInit: this.requestInit,
+      fetchFunc: this.fetch,
+      onProgress: this.onProgress
+    });
+    return [weightSpecs, concatenateArrayBuffers(buffers)];
+  }
+};
+HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
+function parseUrl(url) {
+  const lastSlash = url.lastIndexOf("/");
+  const lastSearchParam = url.lastIndexOf("?");
+  const prefix = url.substring(0, lastSlash);
+  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
+  return [prefix + "/", suffix];
+}
+function isHTTPScheme(url) {
+  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
+}
+var httpRouter = (url, loadOptions) => {
+  if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
+    return null;
+  } else {
+    let isHTTP = true;
+    if (Array.isArray(url)) {
+      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
+    } else {
+      isHTTP = isHTTPScheme(url);
+    }
+    if (isHTTP) {
+      return http(url, loadOptions);
+    }
+  }
+  return null;
+};
+IORouterRegistry.registerSaveRouter(httpRouter);
+IORouterRegistry.registerLoadRouter(httpRouter);
+function http(path, loadOptions) {
+  return new HTTPRequest(path, loadOptions);
+}
+function browserHTTPRequest(path, loadOptions) {
+  return http(path, loadOptions);
+}
+var PassthroughLoader = class {
+  constructor(modelArtifacts) {
+    this.modelArtifacts = modelArtifacts;
+  }
+  load() {
+    return this.modelArtifacts;
+  }
+};
+var PassthroughSaver = class {
+  constructor(saveHandler) {
+    this.saveHandler = saveHandler;
+  }
+  save(modelArtifacts) {
+    return this.saveHandler(modelArtifacts);
+  }
+};
+var PassthroughAsync = class {
+  constructor(handler) {
+    if (handler.load) {
+      this.load = () => Promise.resolve(handler.load());
+    }
+    if (handler.save) {
+      this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));
+    }
+  }
+};
+function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
+  const args = arguments;
+  return new PassthroughAsync(fromMemorySync(...args));
+}
+function fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {
+  if (arguments.length === 1) {
+    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
+    if (isModelArtifacts) {
+      return new PassthroughLoader(modelArtifacts);
+    } else {
+      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
+      return new PassthroughLoader({ modelTopology: modelArtifacts });
+    }
+  } else {
+    console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
+    return new PassthroughLoader({
+      modelTopology: modelArtifacts,
+      weightSpecs,
+      weightData,
+      trainingConfig
+    });
+  }
+}
+function withSaveHandler(saveHandler) {
+  return new PassthroughSaver(saveHandler);
+}
+function withSaveHandlerSync(saveHandler) {
+  return new PassthroughSaver(saveHandler);
+}
+var math_exports = {};
+__export2(math_exports, {
+  confusionMatrix: () => confusionMatrix
+});
+function matMul_(a6, b, transposeA = false, transposeB = false) {
+  let $a = convertToTensor(a6, "a", "matMul");
+  let $b = convertToTensor(b, "b", "matMul");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  const attrs = { transposeA, transposeB };
+  return ENGINE.runKernel(BatchMatMul, inputs, attrs);
+}
+var matMul = op({ matMul_ });
+function oneHot_(indices, depth, onValue = 1, offValue = 0) {
+  if (depth < 2) {
+    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
+  }
+  const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
+  const inputs = { indices: $indices };
+  const attrs = { depth, onValue, offValue };
+  return ENGINE.runKernel(OneHot, inputs, attrs);
+}
+var oneHot = op({ oneHot_ });
+function enableProdMode() {
+  env().set("PROD", true);
+}
+function enableDebugMode() {
+  env().set("DEBUG", true);
+}
+function disableDeprecationWarnings() {
+  env().set("DEPRECATION_WARNINGS_ENABLED", false);
+  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
+}
+function deprecationWarn(msg) {
+  if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
+    console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
+  }
+}
+setDeprecationWarningFn(deprecationWarn);
+function disposeVariables() {
+  ENGINE.disposeVariables();
+}
+function engine() {
+  return ENGINE;
+}
+function memory() {
+  return ENGINE.memory();
+}
+function profile(f) {
+  return ENGINE.profile(f);
+}
+function tidy(nameOrFn, fn) {
+  return ENGINE.tidy(nameOrFn, fn);
+}
+function dispose(container) {
+  const tensors = getTensorsInContainer(container);
+  tensors.forEach((tensor2) => tensor2.dispose());
+}
+function keep(result) {
+  return ENGINE.keep(result);
+}
+function time(f) {
+  return ENGINE.time(f);
+}
+function setBackend(backendName) {
+  return ENGINE.setBackend(backendName);
+}
+function ready() {
+  return ENGINE.ready();
+}
+function getBackend() {
+  return ENGINE.backendName;
+}
+function removeBackend(name) {
+  ENGINE.removeBackend(name);
+}
+function findBackend(name) {
+  return ENGINE.findBackend(name);
+}
+function findBackendFactory(name) {
+  return ENGINE.findBackendFactory(name);
+}
+function registerBackend(name, factory, priority = 1) {
+  return ENGINE.registerBackend(name, factory, priority);
+}
+function backend() {
+  return ENGINE.backend;
+}
+function setPlatform(platformName, platform) {
+  env().setPlatform(platformName, platform);
+}
+function imag_(input2) {
+  const $input = convertToTensor(input2, "input", "imag");
+  const inputs = { input: $input };
+  return ENGINE.runKernel(Imag, inputs);
+}
+var imag = op({ imag_ });
+function neg_(x) {
+  const $x = convertToTensor(x, "x", "neg");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Neg, inputs);
+}
+var neg = op({ neg_ });
+function real_(input2) {
+  const $input = convertToTensor(input2, "input", "real");
+  const inputs = { input: $input };
+  return ENGINE.runKernel(Real, inputs);
+}
+var real = op({ real_ });
+function transpose_(x, perm, conjugate) {
+  const $x = convertToTensor(x, "x", "transpose");
+  if (perm == null) {
+    perm = $x.shape.map((s2, i2) => i2).reverse();
+  }
+  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
+  perm.forEach((axis) => {
+    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
+  });
+  if ($x.rank <= 1) {
+    return $x.clone();
+  }
+  const inputs = { x: $x };
+  const attrs = { perm };
+  if ($x.dtype === "complex64") {
+    return tidy(() => {
+      let $real = real($x);
+      let $imag = imag($x);
+      $real = ENGINE.runKernel(Transpose, { x: $real }, attrs);
+      $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);
+      if (conjugate) {
+        $imag = neg($imag);
+      }
+      return complex($real, $imag);
+    });
+  }
+  return ENGINE.runKernel(Transpose, inputs, attrs);
+}
+var transpose = op({ transpose_ });
+function confusionMatrix_(labels2, predictions, numClasses) {
+  const $labels = convertToTensor(labels2, "labels", "confusionMatrix");
+  const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
+  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
+  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
+  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
+  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
+  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
+  const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
+  const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
+  const oneHotLabelsT = transpose(oneHotLabels);
+  const product = matMul(oneHotLabelsT, oneHotPredictions);
+  return cast(product, "int32");
+}
+var confusionMatrix = op({ confusionMatrix_ });
+var broadcast_util_exports = {};
+__export2(broadcast_util_exports, {
+  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
+  getBroadcastDims: () => getBroadcastDims,
+  getReductionAxes: () => getReductionAxes
+});
+function getBroadcastDims(inShape, outShape) {
+  const inRank = inShape.length;
+  const dims = [];
+  for (let i2 = 0; i2 < inRank; i2++) {
+    const dim = inRank - 1 - i2;
+    const a6 = inShape[dim] || 1;
+    const b = outShape[outShape.length - 1 - i2] || 1;
+    if (b > 1 && a6 === 1) {
+      dims.unshift(dim);
+    }
+  }
+  return dims;
+}
+function getReductionAxes(inShape, outShape) {
+  const result = [];
+  for (let i2 = 0; i2 < outShape.length; i2++) {
+    const inDim = inShape[inShape.length - i2 - 1];
+    const outAxis = outShape.length - i2 - 1;
+    const outDim = outShape[outAxis];
+    if (inDim == null || inDim === 1 && outDim > 1) {
+      result.unshift(outAxis);
+    }
+  }
+  return result;
+}
+function assertAndGetBroadcastShape(shapeA, shapeB) {
+  const result = [];
+  const l3 = Math.max(shapeA.length, shapeB.length);
+  for (let i2 = 0; i2 < l3; i2++) {
+    let a6 = shapeA[shapeA.length - i2 - 1];
+    if (a6 == null) {
+      a6 = 1;
+    }
+    let b = shapeB[shapeB.length - i2 - 1];
+    if (b == null) {
+      b = 1;
+    }
+    if (a6 === 1) {
+      result.unshift(b);
+    } else if (b === 1) {
+      result.unshift(a6);
+    } else if (a6 !== b) {
+      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
+      throw Error(errMsg);
+    } else {
+      result.unshift(a6);
+    }
+  }
+  return result;
+}
+var browser_exports = {};
+__export2(browser_exports, {
+  fromPixels: () => fromPixels,
+  fromPixelsAsync: () => fromPixelsAsync,
+  toPixels: () => toPixels
+});
+function tensor3d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 3) {
+    throw new Error("tensor3d() requires shape to have three numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 3 && inferredShape.length !== 1) {
+    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+var fromPixels2DContext;
+function fromPixels_(pixels, numChannels = 3) {
+  if (numChannels > 4) {
+    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
+  }
+  if (pixels == null) {
+    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
+  }
+  let isPixelData2 = false;
+  let isImageData = false;
+  let isVideo = false;
+  let isImage = false;
+  let isCanvasLike = false;
+  let isImageBitmap = false;
+  if (pixels.data instanceof Uint8Array) {
+    isPixelData2 = true;
+  } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
+    isImageData = true;
+  } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
+    isVideo = true;
+  } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
+    isImage = true;
+  } else if (pixels.getContext != null) {
+    isCanvasLike = true;
+  } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) {
+    isImageBitmap = true;
+  } else {
+    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
+  }
+  if (isVideo) {
+    const HAVE_CURRENT_DATA_READY_STATE = 2;
+    if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
+      throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
+    }
+  }
+  const kernel = getKernel(FromPixels, ENGINE.backendName);
+  if (kernel != null) {
+    const inputs = { pixels };
+    const attrs = { numChannels };
+    return ENGINE.runKernel(FromPixels, inputs, attrs);
+  }
+  const [width, height] = isVideo ? [
+    pixels.videoWidth,
+    pixels.videoHeight
+  ] : [pixels.width, pixels.height];
+  let vals;
+  if (isCanvasLike) {
+    vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
+  } else if (isImageData || isPixelData2) {
+    vals = pixels.data;
+  } else if (isImage || isVideo || isImageBitmap) {
+    if (fromPixels2DContext == null) {
+      if (typeof document === "undefined") {
+        if (typeof OffscreenCanvas !== "undefined" && typeof OffscreenCanvasRenderingContext2D !== "undefined") {
+          fromPixels2DContext = new OffscreenCanvas(1, 1).getContext("2d");
+        } else {
+          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
+        }
+      } else {
+        fromPixels2DContext = document.createElement("canvas").getContext("2d");
+      }
+    }
+    fromPixels2DContext.canvas.width = width;
+    fromPixels2DContext.canvas.height = height;
+    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
+    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
+  }
+  let values;
+  if (numChannels === 4) {
+    values = new Int32Array(vals);
+  } else {
+    const numPixels = width * height;
+    values = new Int32Array(numPixels * numChannels);
+    for (let i2 = 0; i2 < numPixels; i2++) {
+      for (let channel = 0; channel < numChannels; ++channel) {
+        values[i2 * numChannels + channel] = vals[i2 * 4 + channel];
+      }
+    }
+  }
+  const outShape = [height, width, numChannels];
+  return tensor3d(values, outShape, "int32");
+}
+function isPixelData(pixels) {
+  return pixels != null && pixels.data instanceof Uint8Array;
+}
+function isImageBitmapFullySupported() {
+  return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap");
+}
+function isNonEmptyPixels(pixels) {
+  return pixels != null && pixels.width !== 0 && pixels.height !== 0;
+}
+function canWrapPixelsToImageBitmap(pixels) {
+  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);
+}
+async function fromPixelsAsync(pixels, numChannels = 3) {
+  let inputs = null;
+  if (env().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) {
+    let imageBitmap;
+    try {
+      imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" });
+    } catch (e2) {
+      imageBitmap = null;
+    }
+    if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {
+      inputs = imageBitmap;
+    } else {
+      inputs = pixels;
+    }
+  } else {
+    inputs = pixels;
+  }
+  return fromPixels_(inputs, numChannels);
+}
+async function toPixels(img, canvas3) {
+  let $img = convertToTensor(img, "img", "toPixels");
+  if (!(img instanceof Tensor)) {
+    const originalImgTensor = $img;
+    $img = cast(originalImgTensor, "int32");
+    originalImgTensor.dispose();
+  }
+  if ($img.rank !== 2 && $img.rank !== 3) {
+    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
+  }
+  const [height, width] = $img.shape.slice(0, 2);
+  const depth = $img.rank === 2 ? 1 : $img.shape[2];
+  if (depth > 4 || depth === 2) {
+    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
+  }
+  if ($img.dtype !== "float32" && $img.dtype !== "int32") {
+    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
+  }
+  const data = await $img.data();
+  const multiplier = $img.dtype === "float32" ? 255 : 1;
+  const bytes = new Uint8ClampedArray(width * height * 4);
+  for (let i2 = 0; i2 < height * width; ++i2) {
+    const rgba = [0, 0, 0, 255];
+    for (let d = 0; d < depth; d++) {
+      const value = data[i2 * depth + d];
+      if ($img.dtype === "float32") {
+        if (value < 0 || value > 1) {
+          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
+        }
+      } else if ($img.dtype === "int32") {
+        if (value < 0 || value > 255) {
+          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
+        }
+      }
+      if (depth === 1) {
+        rgba[0] = value * multiplier;
+        rgba[1] = value * multiplier;
+        rgba[2] = value * multiplier;
+      } else {
+        rgba[d] = value * multiplier;
+      }
+    }
+    const j = i2 * 4;
+    bytes[j + 0] = Math.round(rgba[0]);
+    bytes[j + 1] = Math.round(rgba[1]);
+    bytes[j + 2] = Math.round(rgba[2]);
+    bytes[j + 3] = Math.round(rgba[3]);
+  }
+  if (canvas3 != null) {
+    canvas3.width = width;
+    canvas3.height = height;
+    const ctx = canvas3.getContext("2d");
+    const imageData = new ImageData(bytes, width, height);
+    ctx.putImageData(imageData, 0, 0);
+  }
+  if ($img !== img) {
+    $img.dispose();
+  }
+  return bytes;
+}
+var fromPixels = op({ fromPixels_ });
+var gather_nd_util_exports = {};
+__export2(gather_nd_util_exports, {
+  prepareAndValidate: () => prepareAndValidate
+});
+function prepareAndValidate(tensor2, indices) {
+  const tensorRank = tensor2.shape.length;
+  const indicesRank = indices.shape.length;
+  if (tensorRank < 1) {
+    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);
+  }
+  if (indicesRank < 1) {
+    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);
+  }
+  if (indices.dtype !== "int32") {
+    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
+  }
+  if (indices.shape[indicesRank - 1] > tensorRank) {
+    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);
+  }
+  if (sizeFromShape(tensor2.shape) === 0) {
+    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);
+  }
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  let nResult = 1;
+  for (let i2 = 0; i2 < indicesShape.length - 1; ++i2) {
+    nResult *= indicesShape[i2];
+  }
+  const inputShape = tensor2.shape;
+  const resultShape = indicesShape.slice();
+  resultShape.pop();
+  let sliceSize = 1;
+  for (let i2 = sliceRank; i2 < tensorRank; ++i2) {
+    sliceSize *= inputShape[i2];
+    resultShape.push(inputShape[i2]);
+  }
+  const strides2 = [
+    ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),
+    1
+  ].slice(0, sliceRank);
+  return [resultShape, nResult, sliceSize, strides2];
+}
+var scatter_nd_util_exports = {};
+__export2(scatter_nd_util_exports, {
+  calculateShapes: () => calculateShapes,
+  validateInput: () => validateInput,
+  validateUpdateShape: () => validateUpdateShape
+});
+function validateUpdateShape(shape, indices, updates) {
+  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
+  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
+  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
+  if (updates.rank < batchDim) {
+    throw new Error(shapeError + ` update.rank < ${batchDim}. `);
+  }
+  if (shape.length < sliceDim + (updates.rank - batchDim)) {
+    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
+  }
+  if (updates.rank !== batchDim + shape.length - sliceDim) {
+    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
+  }
+  for (let d = 0; d < batchDim; ++d) {
+    if (updates.shape[d] !== indices.shape[d]) {
+      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
+    }
+  }
+  for (let d = 0; d < updates.rank - batchDim; ++d) {
+    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
+      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
+    }
+  }
+}
+function validateInput(updates, indices, shape) {
+  if (indices.rank < 1) {
+    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
+  }
+  if (updates.rank < 1) {
+    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
+  }
+  if (indices.dtype !== "int32") {
+    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
+  }
+  if (shape.length < 1) {
+    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
+  }
+  if (shape.length === 0) {
+    if (indices.size === 0) {
+      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
+    }
+    if (updates.size === 0) {
+      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
+    }
+  }
+  validateUpdateShape(shape, indices, updates);
+}
+function calculateShapes(updates, indices, shape) {
+  const indicesRank = indices.shape.length;
+  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
+  const totalNd = shape.length;
+  let sliceSize = 1;
+  for (let i2 = sliceRank; i2 < totalNd; ++i2) {
+    sliceSize *= shape[i2];
+  }
+  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
+  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
+  const strides2 = [...computeStrides(shape.slice(0, sliceRank)), 1];
+  const outputSize2 = sizeFromShape(shape);
+  return { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 };
+}
+var slice_util_exports = {};
+__export2(slice_util_exports, {
+  assertParamsValid: () => assertParamsValid,
+  computeFlatOffset: () => computeFlatOffset,
+  computeOutShape: () => computeOutShape,
+  getNormalizedAxes: () => getNormalizedAxes,
+  isSliceContinous: () => isSliceContinous,
+  maskToAxes: () => maskToAxes,
+  parseSliceParams: () => parseSliceParams,
+  sliceInfo: () => sliceInfo,
+  startForAxis: () => startForAxis,
+  startIndicesWithElidedDims: () => startIndicesWithElidedDims,
+  stopForAxis: () => stopForAxis,
+  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
+  stridesForAxis: () => stridesForAxis,
+  stridesWithElidedDims: () => stridesWithElidedDims
+});
+var NEW_AXIS = -2;
+var SHRINK_AXIS = -1;
+function assertParamsValid(input2, begin, size2) {
+  const inputRank = input2.shape.length;
+  assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);
+  assert(inputRank === size2.length, () => `Error in slice${inputRank}D: Length of size ${size2} must match the rank of the array (${inputRank}).`);
+  for (let i2 = 0; i2 < inputRank; ++i2) {
+    assert(begin[i2] + size2[i2] <= input2.shape[i2], () => `Error in slice${inputRank}D: begin[${i2}] + size[${i2}] (${begin[i2] + size2[i2]}) would overflow input.shape[${i2}] (${input2.shape[i2]})`);
+  }
+}
+function maskToAxes(mask2) {
+  const axes = [];
+  let axis = 0;
+  while (mask2 > 0) {
+    if (mask2 & 1) {
+      axes.push(axis);
+    }
+    mask2 /= 2;
+    axis++;
+  }
+  return axes;
+}
+function computeOutShape(begin, end, strides2) {
+  const size2 = [];
+  for (let axis = 0; axis < begin.length; axis++) {
+    size2[axis] = Math.ceil((end[axis] - begin[axis]) / strides2[axis]);
+  }
+  return size2;
+}
+function stridesWithElidedDims(strides2, ellipsisInsertionIndex, numElidedAxes, inputShape) {
+  const newStrides = [...strides2];
+  for (let i2 = newStrides.length; i2 < inputShape.length; i2++) {
+    newStrides.push(1);
+  }
+  for (let i2 = 0; i2 < numElidedAxes; i2++) {
+    if (i2 === 0) {
+      newStrides[ellipsisInsertionIndex] = 1;
+    } else {
+      newStrides.splice(ellipsisInsertionIndex, 0, 1);
+      newStrides.pop();
+    }
+  }
+  return newStrides;
+}
+function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
+  if (normalizedAxis <= ellipsisInsertionIndex) {
+    return normalizedAxis;
+  }
+  return normalizedAxis - (numElidedAxes - 1);
+}
+function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
+  const elidedAxes = [];
+  for (let i2 = 0; i2 < numElidedAxes; i2++) {
+    elidedAxes.push(ellipsisInsertionIndex + i2);
+  }
+  return elidedAxes;
+}
+function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides2, beginMask, endMask, ellipsisMask) {
+  const inputRank = inputShape.length;
+  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
+  if (ellipsisAxes.length && numInterpolatedAxes > 0) {
+    const fullIndex = ellipsisAxes[0];
+    const numElidedAxes = numInterpolatedAxes + 1;
+    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
+    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
+    normalizedStrides = stridesWithElidedDims(strides2, fullIndex, numElidedAxes, inputShape);
+  } else {
+    for (let axis = 0; axis < inputRank; axis++) {
+      normalizedBegin[axis] = startForAxis(beginMask, begin, strides2, inputShape, axis, ellipsisMask);
+      normalizedEnd[axis] = stopForAxis(endMask, end, strides2, inputShape, axis, ellipsisMask);
+      normalizedStrides[axis] = stridesForAxis(strides2, axis, ellipsisMask);
+    }
+  }
+  return {
+    begin: normalizedBegin,
+    end: normalizedEnd,
+    strides: normalizedStrides
+  };
+}
+function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
+  const newIndices = [...inputShape];
+  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
+  for (let axis = 0; axis < newIndices.length; axis++) {
+    if (elidedAxes.indexOf(axis) > -1) {
+      newIndices[axis] = 0;
+    } else {
+      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
+      let originalValue = originalBegin[originalAxis];
+      if (beginMask & 1 << originalAxis) {
+        originalValue = 0;
+      }
+      newIndices[axis] = originalValue;
+    }
+  }
+  return newIndices;
+}
+function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
+  const newIndices = [...inputShape];
+  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
+  for (let axis = 0; axis < newIndices.length; axis++) {
+    if (elidedAxes.indexOf(axis) > -1) {
+      newIndices[axis] = Number.MAX_SAFE_INTEGER;
+    } else {
+      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
+      let originalValue = originalEnd[originalAxis];
+      if (endMask & 1 << originalAxis) {
+        originalValue = Number.MAX_SAFE_INTEGER;
+      }
+      newIndices[axis] = originalValue;
+    }
+  }
+  for (let i2 = 0; i2 < newIndices.length; i2++) {
+    const axisSize = inputShape[i2];
+    if (newIndices[i2] < 0) {
+      newIndices[i2] += axisSize;
+    }
+    newIndices[i2] = clamp(0, newIndices[i2], inputShape[i2]);
+  }
+  return newIndices;
+}
+function stridesForAxis(strides2, axis, ellipsisMask) {
+  let stride = strides2[axis];
+  if (ellipsisMask & 1 << axis || stride == null) {
+    stride = 1;
+  }
+  return stride;
+}
+function startForAxis(beginMask, startIndices, strides2, inputShape, axis, ellipsisMask) {
+  let start = startIndices[axis];
+  const stride = strides2[axis] || 1;
+  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
+    if (stride > 0) {
+      start = Number.MIN_SAFE_INTEGER;
+    } else {
+      start = Number.MAX_SAFE_INTEGER;
+    }
+  }
+  const axisSize = inputShape[axis];
+  if (start < 0) {
+    start += axisSize;
+  }
+  start = clamp(0, start, axisSize - 1);
+  return start;
+}
+function stopForAxis(endMask, stopIndices, strides2, inputShape, axis, ellipsisMask) {
+  let stop = stopIndices[axis];
+  const stride = strides2[axis] || 1;
+  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
+    if (stride > 0) {
+      stop = Number.MAX_SAFE_INTEGER;
+    } else {
+      stop = Number.MIN_SAFE_INTEGER;
+    }
+  }
+  const axisSize = inputShape[axis];
+  if (stop < 0) {
+    stop += axisSize;
+  }
+  if (stride > 0) {
+    stop = clamp(0, stop, axisSize);
+  } else {
+    stop = clamp(-1, stop, axisSize - 1);
+  }
+  return stop;
+}
+function isSliceContinous(shape, begin, size2) {
+  let firstNonOneAxis = size2.length;
+  for (let i2 = 0; i2 < size2.length; i2++) {
+    if (size2[i2] > 1) {
+      firstNonOneAxis = i2;
+      break;
+    }
+  }
+  for (let i2 = firstNonOneAxis + 1; i2 < size2.length; i2++) {
+    if (begin[i2] > 0 || size2[i2] !== shape[i2]) {
+      return false;
+    }
+  }
+  return true;
+}
+function computeFlatOffset(begin, strides2) {
+  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
+  for (let i2 = 0; i2 < begin.length - 1; i2++) {
+    flatOffset += begin[i2] * strides2[i2];
+  }
+  return flatOffset;
+}
+function parseSliceParams(x, begin, size2) {
+  let begin_;
+  const xRank = x.shape.length;
+  if (typeof begin === "number") {
+    begin_ = [begin, ...new Array(xRank - 1).fill(0)];
+  } else if (begin.length < xRank) {
+    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
+  } else {
+    begin_ = begin.slice();
+  }
+  begin_.forEach((d) => {
+    assert(d !== -1, () => "slice() does not support negative begin indexing.");
+  });
+  let size_;
+  if (size2 == null) {
+    size_ = new Array(xRank).fill(-1);
+  } else if (typeof size2 === "number") {
+    size_ = [size2, ...new Array(xRank - 1).fill(-1)];
+  } else if (size2.length < xRank) {
+    size_ = size2.concat(new Array(xRank - size2.length).fill(-1));
+  } else {
+    size_ = size2;
+  }
+  size_ = size_.map((d, i2) => {
+    if (d >= 0) {
+      return d;
+    } else {
+      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i2}.`);
+      return x.shape[i2] - begin_[i2];
+    }
+  });
+  return [begin_, size_];
+}
+function sliceInfo(xShape, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+  let stridesNonNull;
+  if (strides2 == null) {
+    stridesNonNull = new Array(begin.length);
+    stridesNonNull.fill(1);
+  } else {
+    stridesNonNull = strides2;
+  }
+  if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {
+    throw new Error("Multiple ellipses in slice is not allowed.");
+  }
+  let ellipsisSeen = false;
+  const sparseSpec = {
+    dims: stridesNonNull.length,
+    numAddAxisAfterEllipsis: 0,
+    begin: begin.slice(),
+    end: end.slice(),
+    strides: stridesNonNull.slice(),
+    beginMask,
+    endMask,
+    ellipsisMask,
+    newAxisMask,
+    shrinkAxisMask
+  };
+  for (let i2 = 0; i2 < sparseSpec.dims; i2++) {
+    if (ellipsisSeen && (1 << i2 & newAxisMask) !== 0) {
+      sparseSpec.numAddAxisAfterEllipsis++;
+    }
+    if (1 << i2 & ellipsisMask) {
+      ellipsisSeen = true;
+    }
+  }
+  if (!ellipsisSeen) {
+    sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;
+    sparseSpec.dims++;
+  }
+  const denseSpec = {
+    dims: xShape.length,
+    beginMask: 0,
+    endMask: 0,
+    beginValid: false,
+    endValid: false
+  };
+  buildDenseSpec(sparseSpec, denseSpec);
+  let isIdentity = true;
+  let sliceDim0 = true;
+  let isSimpleSlice = true;
+  const processingShape = [];
+  const finalShape = [];
+  for (let i2 = 0; i2 < xShape.length; ++i2) {
+    if (denseSpec.strides[i2] === 0) {
+      throw Error(`strides[${i2}] must be non-zero`);
+    }
+    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i2);
+    const dimI = xShape[i2];
+    if (dimI === -1) {
+      processingShape.push(shrinkI ? 1 : -1);
+      continue;
+    }
+    const masks = [denseSpec.beginMask & 1 << i2, denseSpec.endMask & 1 << i2];
+    const validRange = [
+      denseSpec.strides[i2] > 0 ? 0 : -1,
+      denseSpec.strides[i2] > 0 ? dimI : dimI - 1
+    ];
+    if (shrinkI && denseSpec.strides[i2] <= 0) {
+      throw Error("only stride 1 allowed on non-range indexing.");
+    }
+    isSimpleSlice = isSimpleSlice && denseSpec.strides[i2] === 1;
+    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i2 && denseSpec.endMask & 1 << i2);
+    if (denseSpec.beginValid && denseSpec.endValid) {
+      if (shrinkI) {
+        const xFwd = denseSpec.begin[i2] < 0 ? dimI + denseSpec.begin[i2] : denseSpec.begin[i2];
+        denseSpec.begin[i2] = xFwd;
+        denseSpec.end[i2] = denseSpec.begin[i2] + 1;
+        if (xFwd < 0 || xFwd >= dimI) {
+          throw Error(`slice index ${denseSpec.begin[i2]} of dimension ${i2} out of bounds.`);
+        }
+      } else {
+        denseSpec.begin[i2] = canonical(denseSpec.begin[i2], 0, denseSpec.strides[i2], dimI, masks, validRange);
+        denseSpec.end[i2] = canonical(denseSpec.end[i2], 1, denseSpec.strides[i2], dimI, masks, validRange);
+      }
+      const takeAllInDimension = denseSpec.strides[i2] === 1 && denseSpec.begin[i2] === 0 && denseSpec.end[i2] === dimI;
+      isIdentity = isIdentity && takeAllInDimension;
+      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || takeAllInDimension);
+    } else {
+      isIdentity = isIdentity && (denseSpec.strides[i2] === 1 && beginAndEndMasked);
+      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || beginAndEndMasked);
+    }
+    let intervalLength;
+    let knownInterval = false;
+    if (denseSpec.beginValid && denseSpec.endValid) {
+      intervalLength = denseSpec.end[i2] - denseSpec.begin[i2];
+      knownInterval = true;
+    } else if (shrinkI) {
+      intervalLength = 1;
+      knownInterval = true;
+    } else if (beginAndEndMasked) {
+      if (dimI >= 0) {
+        if (denseSpec.strides[i2] < 0) {
+          intervalLength = -dimI;
+        } else {
+          intervalLength = dimI;
+        }
+        knownInterval = true;
+      }
+    }
+    if (knownInterval) {
+      let sizeI;
+      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i2] < 0) {
+        sizeI = 0;
+      } else {
+        sizeI = Math.trunc(intervalLength / denseSpec.strides[i2]) + (intervalLength % denseSpec.strides[i2] !== 0 ? 1 : 0);
+      }
+      processingShape.push(sizeI);
+    } else {
+      processingShape.push(-1);
+    }
+  }
+  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {
+    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];
+    if (gatherIndex >= 0) {
+      finalShape.push(processingShape[gatherIndex]);
+    } else if (gatherIndex === NEW_AXIS) {
+      finalShape.push(1);
+    }
+  }
+  const finalShapeSparse = finalShape.filter((dim, i2) => denseSpec.finalShapeGatherIndices[i2] !== NEW_AXIS);
+  return {
+    finalShapeSparse,
+    finalShape,
+    isIdentity,
+    sliceDim0,
+    isSimpleSlice,
+    begin: denseSpec.begin,
+    end: denseSpec.end,
+    strides: denseSpec.strides
+  };
+}
+function buildDenseSpec(sparse2, dense2) {
+  dense2.beginMask = 0;
+  dense2.endMask = 0;
+  dense2.shrinkAxisMask = 0;
+  let fullIndex = 0;
+  dense2.beginValid = sparse2.begin != null;
+  dense2.endValid = sparse2.end != null;
+  dense2.begin = new Array(dense2.dims);
+  dense2.end = new Array(dense2.dims);
+  dense2.strides = new Array(dense2.dims);
+  dense2.finalShapeGatherIndices = [];
+  dense2.finalShapeGatherIndicesSparse = [];
+  dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);
+  for (let i2 = 0; i2 < sparse2.dims; i2++) {
+    if (1 << i2 & sparse2.ellipsisMask) {
+      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i2) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);
+      for (; fullIndex < nextIndex; fullIndex++) {
+        dense2.begin[fullIndex] = 0;
+        dense2.end[fullIndex] = 0;
+        dense2.strides[fullIndex] = 1;
+        dense2.beginMask |= 1 << fullIndex;
+        dense2.endMask |= 1 << fullIndex;
+        dense2.finalShapeGatherIndices.push(fullIndex);
+        dense2.finalShapeGatherIndicesSparse.push(-1);
+        dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+      }
+    } else if (1 << i2 & sparse2.newAxisMask) {
+      dense2.finalShapeGatherIndices.push(NEW_AXIS);
+      dense2.finalShapeGatherIndicesSparse.push(-1);
+    } else {
+      if (fullIndex === dense2.begin.length) {
+        throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);
+      }
+      if (sparse2.begin != null) {
+        dense2.begin[fullIndex] = sparse2.begin[i2];
+      }
+      if (sparse2.end != null) {
+        dense2.end[fullIndex] = sparse2.end[i2];
+      }
+      dense2.strides[fullIndex] = sparse2.strides[i2];
+      if (sparse2.beginMask & 1 << i2) {
+        dense2.beginMask |= 1 << fullIndex;
+      }
+      if (sparse2.endMask & 1 << i2) {
+        dense2.endMask |= 1 << fullIndex;
+      }
+      if (sparse2.shrinkAxisMask & 1 << i2) {
+        dense2.finalShapeGatherIndices.push(SHRINK_AXIS);
+        dense2.finalShapeGatherIndicesSparse.push(-1);
+        dense2.shrinkAxisMask |= 1 << fullIndex;
+      } else {
+        dense2.finalShapeGatherIndices.push(fullIndex);
+        dense2.finalShapeGatherIndicesSparse.push(i2);
+      }
+      dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+      fullIndex++;
+    }
+  }
+}
+function canonical(x, c, strideI, dimI, masks, validRange) {
+  if (masks[c]) {
+    return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];
+  } else {
+    const xFwd = x < 0 ? dimI + x : x;
+    return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;
+  }
+}
+var serialization_exports = {};
+__export2(serialization_exports, {
+  Serializable: () => Serializable,
+  SerializationMap: () => SerializationMap,
+  registerClass: () => registerClass
+});
+var Serializable = class {
+  getClassName() {
+    return this.constructor.className;
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3);
+  }
+};
+var SerializationMap = class {
+  constructor() {
+    this.classNameMap = {};
+  }
+  static getMap() {
+    if (SerializationMap.instance == null) {
+      SerializationMap.instance = new SerializationMap();
+    }
+    return SerializationMap.instance;
+  }
+  static register(cls) {
+    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
+  }
+};
+function registerClass(cls) {
+  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
+  assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
+  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
+  SerializationMap.register(cls);
+}
+var test_util_exports = {};
+__export2(test_util_exports, {
+  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
+  encodeStrings: () => encodeStrings,
+  expectArrayBuffersEqual: () => expectArrayBuffersEqual,
+  expectArraysClose: () => expectArraysClose,
+  expectArraysEqual: () => expectArraysEqual,
+  expectNumbersClose: () => expectNumbersClose,
+  expectPromiseToFail: () => expectPromiseToFail,
+  expectValuesInRange: () => expectValuesInRange,
+  testEpsilon: () => testEpsilon
+});
+var TEST_EPSILON_FLOAT32 = 1e-3;
+var TEST_EPSILON_FLOAT16 = 0.1;
+function expectArraysClose(actual, expected, epsilon3) {
+  if (epsilon3 == null) {
+    epsilon3 = testEpsilon();
+  }
+  return expectArraysPredicate(actual, expected, (a6, b) => areClose(a6, b, epsilon3));
+}
+function testEpsilon() {
+  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
+}
+function expectArraysPredicate(actual, expected, predicate) {
+  let checkClassType = true;
+  if (isTypedArray(actual) || isTypedArray(expected)) {
+    checkClassType = false;
+  }
+  if (isTypedArray(actual) && isTypedArray(expected)) {
+    checkClassType = true;
+  }
+  if (checkClassType) {
+    const aType = actual.constructor.name;
+    const bType = expected.constructor.name;
+    if (aType !== bType) {
+      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
+    }
+  }
+  if (Array.isArray(actual) && Array.isArray(expected)) {
+    const actualShape = inferShape(actual);
+    const expectedShape = inferShape(expected);
+    if (!arraysEqual(actualShape, expectedShape)) {
+      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
+    }
+  }
+  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
+  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
+  if (actualFlat.length !== expectedFlat.length) {
+    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);
+  }
+  for (let i2 = 0; i2 < expectedFlat.length; ++i2) {
+    const a6 = actualFlat[i2];
+    const e2 = expectedFlat[i2];
+    if (!predicate(a6, e2)) {
+      throw new Error(`Arrays differ: actual[${i2}] = ${a6}, expected[${i2}] = ${e2}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);
+    }
+  }
+}
+function expectPromiseToFail(fn, done) {
+  fn().then(() => done.fail(), () => done());
+}
+function expectArraysEqual(actual, expected) {
+  const exp5 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
+  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
+    return expectArraysPredicate(actual, exp5, (a6, b) => a6 == b);
+  }
+  return expectArraysPredicate(actual, expected, (a6, b) => areClose(a6, b, 0));
+}
+function expectNumbersClose(a6, e2, epsilon3) {
+  if (epsilon3 == null) {
+    epsilon3 = testEpsilon();
+  }
+  if (!areClose(a6, e2, epsilon3)) {
+    throw new Error(`Numbers differ: actual === ${a6}, expected === ${e2}`);
+  }
+}
+function areClose(a6, e2, epsilon3) {
+  if (!isFinite(a6) && !isFinite(e2)) {
+    return true;
+  }
+  if (isNaN(a6) || isNaN(e2) || Math.abs(a6 - e2) > epsilon3) {
+    return false;
+  }
+  return true;
+}
+function expectValuesInRange(actual, low, high) {
+  for (let i2 = 0; i2 < actual.length; i2++) {
+    if (actual[i2] < low || actual[i2] > high) {
+      throw new Error(`Value out of range:${actual[i2]} low: ${low}, high: ${high}`);
+    }
+  }
+}
+function expectArrayBuffersEqual(actual, expected) {
+  const actualArray = new Float32Array(actual);
+  const expectedArray = new Float32Array(expected);
+  if (actualArray.length !== expectedArray.length) {
+    throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);
+  }
+  for (let i2 = 0; i2 < expectedArray.length; i2++) {
+    if (actualArray[i2] !== expectedArray[i2]) {
+      throw new Error(`Expected ArrayBuffer value at ${i2} to be ${expectedArray[i2]} but got ${actualArray[i2]} instead`);
+    }
+  }
+}
+function encodeStrings(a6) {
+  for (let i2 = 0; i2 < a6.length; i2++) {
+    const val = a6[i2];
+    if (Array.isArray(val)) {
+      encodeStrings(val);
+    } else {
+      a6[i2] = encodeString(val);
+    }
+  }
+  return a6;
+}
+var version = "3.18.0";
+function add_(a6, b) {
+  let $a = convertToTensor(a6, "a", "add");
+  let $b = convertToTensor(b, "b", "add");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Add, inputs);
+}
+var add2 = op({ add_ });
+function floorDiv_(a6, b) {
+  let $a = convertToTensor(a6, "a", "floorDiv");
+  let $b = convertToTensor(b, "b", "floorDiv");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(FloorDiv, inputs);
+}
+var floorDiv = op({ floorDiv_ });
+function div_(a6, b) {
+  let $a = convertToTensor(a6, "a", "div");
+  let $b = convertToTensor(b, "b", "div");
+  [$a, $b] = makeTypesMatch($a, $b);
+  if ($a.dtype === "int32" && $b.dtype === "int32") {
+    return floorDiv($a, $b);
+  }
+  const inputs = { a: $a, b: $b };
+  const attrs = {};
+  return ENGINE.runKernel(RealDiv, inputs, attrs);
+}
+var div = op({ div_ });
+function mul_(a6, b) {
+  let $a = convertToTensor(a6, "a", "mul");
+  let $b = convertToTensor(b, "b", "mul");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Multiply, inputs);
+}
+var mul = op({ mul_ });
+function abs_(x) {
+  const $x = convertToTensor(x, "x", "abs");
+  if ($x.dtype === "complex64") {
+    const inputs = { x: $x };
+    return ENGINE.runKernel(ComplexAbs, inputs);
+  } else {
+    const inputs = { x: $x };
+    return ENGINE.runKernel(Abs, inputs);
+  }
+}
+var abs = op({ abs_ });
+function acos_(x) {
+  const $x = convertToTensor(x, "x", "acos");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Acos, inputs);
+}
+var acos = op({ acos_ });
+function acosh_(x) {
+  const $x = convertToTensor(x, "x", "acosh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Acosh, inputs);
+}
+var acosh = op({ acosh_ });
+function addN_(tensors) {
+  assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
+  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
+  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "addN"));
+  const firstTensor = $tensors[0];
+  $tensors.forEach((t2) => {
+    if (t2.dtype !== firstTensor.dtype) {
+      throw new Error("All tensors passed to tf.addN() must have the same dtype");
+    }
+  });
+  $tensors.forEach((t2) => {
+    if (!arraysEqual(t2.shape, firstTensor.shape)) {
+      throw new Error("All tensors passed to tf.addN() must have the same shape");
+    }
+  });
+  const inputs = $tensors;
+  return ENGINE.runKernel(AddN, inputs);
+}
+var addN = op({ addN_ });
+function all_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "all", "bool");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(All, inputs, attrs);
+}
+var all = op({ all_ });
+function any_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "any", "bool");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Any, inputs, attrs);
+}
+var any = op({ any_ });
+function argMax_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "argMax");
+  const inputs = { x: $x };
+  const attrs = { axis };
+  return ENGINE.runKernel(ArgMax, inputs, attrs);
+}
+var argMax = op({ argMax_ });
+function argMin_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "argMin");
+  const inputs = { x: $x };
+  const attrs = { axis };
+  return ENGINE.runKernel(ArgMin, inputs, attrs);
+}
+var argMin = op({ argMin_ });
+function asin_(x) {
+  const $x = convertToTensor(x, "x", "asin");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Asin, inputs);
+}
+var asin = op({ asin_ });
+function asinh_(x) {
+  const $x = convertToTensor(x, "x", "asinh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Asinh, inputs);
+}
+var asinh = op({ asinh_ });
+function atan_(x) {
+  const $x = convertToTensor(x, "x", "atan");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Atan, inputs);
+}
+var atan = op({ atan_ });
+function atan2_(a6, b) {
+  let $a = convertToTensor(a6, "a", "atan2");
+  let $b = convertToTensor(b, "b", "atan2");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Atan2, inputs);
+}
+var atan2 = op({ atan2_ });
+function atanh_(x) {
+  const $x = convertToTensor(x, "x", "atanh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Atanh, inputs);
+}
+var atanh = op({ atanh_ });
+function computeDilation2DInfo(inputShape, filterShape, strides2, pad3, dataFormat = "NHWC", dilations) {
+  const inputChannels = inputShape[3];
+  const $filterShape = [...filterShape, inputChannels];
+  const $dataFormat = convertConv2DDataFormat(dataFormat);
+  return computeConv2DInfo(inputShape, $filterShape, strides2, dilations, pad3, null, null, $dataFormat);
+}
+function computePool2DInfo(inShape, filterSize, strides2, dilations, pad3, roundingMode, dataFormat = "channelsLast") {
+  const [filterHeight, filterWidth] = parseTupleParam(filterSize);
+  let filterShape;
+  if (dataFormat === "channelsLast") {
+    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
+  } else if (dataFormat === "channelsFirst") {
+    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  return computeConv2DInfo(inShape, filterShape, strides2, dilations, pad3, roundingMode, false, dataFormat);
+}
+function computePool3DInfo(inShape, filterSize, strides2, dilations, pad3, roundingMode, dataFormat = "NDHWC") {
+  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
+  let filterShape;
+  let $dataFormat;
+  if (dataFormat === "NDHWC") {
+    $dataFormat = "channelsLast";
+    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
+  } else if (dataFormat === "NCDHW") {
+    $dataFormat = "channelsFirst";
+    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  return computeConv3DInfo(inShape, filterShape, strides2, dilations, pad3, false, $dataFormat, roundingMode);
+}
+function computeConv2DInfo(inShape, filterShape, strides2, dilations, pad3, roundingMode, depthwise = false, dataFormat = "channelsLast") {
+  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
+  if (dataFormat === "channelsLast") {
+    [batchSize, inHeight, inWidth, inChannels] = inShape;
+  } else if (dataFormat === "channelsFirst") {
+    [batchSize, inChannels, inHeight, inWidth] = inShape;
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  const [filterHeight, filterWidth, , filterChannels] = filterShape;
+  const [strideHeight, strideWidth] = parseTupleParam(strides2);
+  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
+  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+  const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
+  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+  let outShape;
+  if (dataFormat === "channelsFirst") {
+    outShape = [batchSize, outChannels, outHeight, outWidth];
+  } else if (dataFormat === "channelsLast") {
+    outShape = [batchSize, outHeight, outWidth, outChannels];
+  }
+  return {
+    batchSize,
+    dataFormat,
+    inHeight,
+    inWidth,
+    inChannels,
+    outHeight,
+    outWidth,
+    outChannels,
+    padInfo,
+    strideHeight,
+    strideWidth,
+    filterHeight,
+    filterWidth,
+    effectiveFilterHeight,
+    effectiveFilterWidth,
+    dilationHeight,
+    dilationWidth,
+    inShape,
+    outShape,
+    filterShape
+  };
+}
+function computeConv3DInfo(inShape, filterShape, strides2, dilations, pad3, depthwise = false, dataFormat = "channelsLast", roundingMode) {
+  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
+  if (dataFormat === "channelsLast") {
+    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
+  } else if (dataFormat === "channelsFirst") {
+    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
+  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides2);
+  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
+  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
+  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+  const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
+  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+  let outShape;
+  if (dataFormat === "channelsFirst") {
+    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
+  } else if (dataFormat === "channelsLast") {
+    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
+  }
+  return {
+    batchSize,
+    dataFormat,
+    inDepth,
+    inHeight,
+    inWidth,
+    inChannels,
+    outDepth,
+    outHeight,
+    outWidth,
+    outChannels,
+    padInfo,
+    strideDepth,
+    strideHeight,
+    strideWidth,
+    filterDepth,
+    filterHeight,
+    filterWidth,
+    effectiveFilterDepth,
+    effectiveFilterHeight,
+    effectiveFilterWidth,
+    dilationDepth,
+    dilationHeight,
+    dilationWidth,
+    inShape,
+    outShape,
+    filterShape
+  };
+}
+function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
+  if (zeroPad == null) {
+    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+  }
+  const inputRows = inShape[0];
+  const inputCols = inShape[1];
+  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  return [outputRows, outputCols];
+}
+function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
+  if (zeroPad == null) {
+    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+  }
+  const inputDepth = inShape[0];
+  const inputRows = inShape[1];
+  const inputCols = inShape[2];
+  const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  return [outputDepths, outputRows, outputCols, outChannels];
+}
+function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
+  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
+  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
+}
+function parseTupleParam(param) {
+  if (typeof param === "number") {
+    return [param, param, param];
+  }
+  if (param.length === 2) {
+    return [param[0], param[1], 1];
+  }
+  return param;
+}
+function parse3TupleParam(param) {
+  return typeof param === "number" ? [param, param, param] : param;
+}
+function getEffectiveFilterSize(filterSize, dilation) {
+  if (dilation <= 1) {
+    return filterSize;
+  }
+  return filterSize + (filterSize - 1) * (dilation - 1);
+}
+function getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
+  let padInfo;
+  let outHeight;
+  let outWidth;
+  if (typeof pad3 === "number") {
+    const padType = pad3 === 0 ? "VALID" : "NUMBER";
+    padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType };
+    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);
+    outHeight = outShape[0];
+    outWidth = outShape[1];
+  } else if (pad3 === "same") {
+    outHeight = Math.ceil(inHeight / strideHeight);
+    outWidth = Math.ceil(inWidth / strideWidth);
+    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
+    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
+    const top = Math.floor(padAlongHeight / 2);
+    const bottom = padAlongHeight - top;
+    const left = Math.floor(padAlongWidth / 2);
+    const right = padAlongWidth - left;
+    padInfo = { top, bottom, left, right, type: "SAME" };
+  } else if (pad3 === "valid") {
+    padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" };
+    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+  } else if (typeof pad3 === "object") {
+    const top = dataFormat === "channelsLast" ? pad3[1][0] : pad3[2][0];
+    const bottom = dataFormat === "channelsLast" ? pad3[1][1] : pad3[2][1];
+    const left = dataFormat === "channelsLast" ? pad3[2][0] : pad3[3][0];
+    const right = dataFormat === "channelsLast" ? pad3[2][1] : pad3[3][1];
+    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
+    padInfo = { top, bottom, left, right, type: padType };
+    outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
+    outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
+  } else {
+    throw Error(`Unknown padding parameter: ${pad3}`);
+  }
+  return { padInfo, outHeight, outWidth };
+}
+function get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
+  let padInfo;
+  let outDepth;
+  let outHeight;
+  let outWidth;
+  if (typeof pad3 === "number") {
+    const padType = pad3 === 0 ? "VALID" : "NUMBER";
+    padInfo = {
+      top: pad3,
+      bottom: pad3,
+      left: pad3,
+      right: pad3,
+      front: pad3,
+      back: pad3,
+      type: padType
+    };
+    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad3, roundingMode);
+    outDepth = outShape[0];
+    outHeight = outShape[1];
+    outWidth = outShape[2];
+  } else if (pad3 === "same") {
+    outDepth = Math.ceil(inDepth / strideDepth);
+    outHeight = Math.ceil(inHeight / strideHeight);
+    outWidth = Math.ceil(inWidth / strideWidth);
+    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
+    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+    const front = Math.floor(padAlongDepth / 2);
+    const back = padAlongDepth - front;
+    const top = Math.floor(padAlongHeight / 2);
+    const bottom = padAlongHeight - top;
+    const left = Math.floor(padAlongWidth / 2);
+    const right = padAlongWidth - left;
+    padInfo = { top, bottom, left, right, front, back, type: "SAME" };
+  } else if (pad3 === "valid") {
+    padInfo = {
+      top: 0,
+      bottom: 0,
+      left: 0,
+      right: 0,
+      front: 0,
+      back: 0,
+      type: "VALID"
+    };
+    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
+    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+  } else {
+    throw Error(`Unknown padding parameter: ${pad3}`);
+  }
+  return { padInfo, outDepth, outHeight, outWidth };
+}
+function round(value, roundingMode) {
+  if (!roundingMode) {
+    return Math.trunc(value);
+  }
+  switch (roundingMode) {
+    case "round":
+      return Math.round(value);
+    case "ceil":
+      return Math.ceil(value);
+    case "floor":
+      return Math.floor(value);
+    default:
+      throw new Error(`Unknown roundingMode ${roundingMode}`);
+  }
+}
+function tupleValuesAreOne(param) {
+  const [dimA, dimB, dimC] = parseTupleParam(param);
+  return dimA === 1 && dimB === 1 && dimC === 1;
+}
+function eitherStridesOrDilationsAreOne(strides2, dilations) {
+  return tupleValuesAreOne(strides2) || tupleValuesAreOne(dilations);
+}
+function convertConv2DDataFormat(dataFormat) {
+  if (dataFormat === "NHWC") {
+    return "channelsLast";
+  } else if (dataFormat === "NCHW") {
+    return "channelsFirst";
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+}
+function checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) {
+  if (dimRoundingMode != null) {
+    if (typeof pad3 === "string") {
+      throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
+    } else if (typeof pad3 === "number") {
+      assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
+    } else if (typeof pad3 === "object") {
+      pad3.forEach((p2) => {
+        p2.forEach((v) => {
+          assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);
+        });
+      });
+    } else {
+      throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`);
+    }
+  }
+}
+function reshape_(x, shape) {
+  const $x = convertToTensor(x, "x", "reshape", "string_or_numeric");
+  const inputs = { x: $x };
+  const attrs = { shape };
+  return ENGINE.runKernel(Reshape, inputs, attrs);
+}
+var reshape = op({ reshape_ });
+function avgPool_(x, filterSize, strides2, pad3, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "avgPool", "float32");
+  const dilations = 1;
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
+  checkPadOnDimRoundingMode("avgPool", pad3, dimRoundingMode);
+  const inputs = { x: x4D };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode };
+  let res = ENGINE.runKernel(AvgPool, inputs, attrs);
+  res = cast(res, $x.dtype);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var avgPool = op({ avgPool_ });
+function avgPool3d_(x, filterSize, strides2, pad3, dimRoundingMode, dataFormat = "NDHWC") {
+  const $x = convertToTensor(x, "x", "avgPool3d", "float32");
+  let x5D = $x;
+  let reshapedTo5D = false;
+  if ($x.rank === 4) {
+    reshapedTo5D = true;
+    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
+  assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
+  checkPadOnDimRoundingMode("avgPool3d", pad3, dimRoundingMode);
+  const inputs = { x: x5D };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode, dataFormat };
+  let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);
+  res = cast(res, x5D.dtype);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var avgPool3d = op({ avgPool3d_ });
+function concat_(tensors, axis = 0) {
+  assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
+  const $tensors = convertToTensorArray(tensors, "tensors", "concat", "string_or_numeric");
+  if ($tensors[0].dtype === "complex64") {
+    $tensors.forEach((tensor2) => {
+      if (tensor2.dtype !== "complex64") {
+        throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor2.dtype}. `);
+      }
+    });
+  }
+  if ($tensors.length === 1) {
+    return clone($tensors[0]);
+  }
+  const inputs = $tensors;
+  const attr = { axis };
+  return ENGINE.runKernel(Concat, inputs, attr);
+}
+var concat = op({ concat_ });
+function sigmoid_(x) {
+  const $x = convertToTensor(x, "x", "sigmoid", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sigmoid, inputs);
+}
+var sigmoid = op({ sigmoid_ });
+function slice_(x, begin, size2) {
+  const $x = convertToTensor(x, "x", "slice", "string_or_numeric");
+  if ($x.rank === 0) {
+    throw new Error("Slicing scalar is not possible");
+  }
+  const inputs = { x: $x };
+  const attrs = { begin, size: size2 };
+  return ENGINE.runKernel(Slice, inputs, attrs);
+}
+var slice = op({ slice_ });
+function tanh_(x) {
+  const $x = convertToTensor(x, "x", "tanh", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Tanh, inputs);
+}
+var tanh2 = op({ tanh_ });
+function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
+  const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
+  const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
+  const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
+  const $data = convertToTensor(data, "data", "basicLSTMCell");
+  const $c = convertToTensor(c, "c", "basicLSTMCell");
+  const $h = convertToTensor(h, "h", "basicLSTMCell");
+  const combined = concat([$data, $h], 1);
+  const weighted = matMul(combined, $lstmKernel);
+  const res = add2(weighted, $lstmBias);
+  const batchSize = res.shape[0];
+  const sliceCols = res.shape[1] / 4;
+  const sliceSize = [batchSize, sliceCols];
+  const i2 = slice(res, [0, 0], sliceSize);
+  const j = slice(res, [0, sliceCols], sliceSize);
+  const f = slice(res, [0, sliceCols * 2], sliceSize);
+  const o = slice(res, [0, sliceCols * 3], sliceSize);
+  const newC = add2(mul(sigmoid(i2), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
+  const newH = mul(tanh2(newC), sigmoid(o));
+  return [newC, newH];
+}
+var basicLSTMCell = op({ basicLSTMCell_ });
+function batchToSpaceND_(x, blockShape, crops) {
+  const $x = convertToTensor(x, "x", "batchToSpaceND");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
+  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
+  assert($x.shape[0] % prod6 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod6}`);
+  const inputs = { x: $x };
+  const attrs = { blockShape, crops };
+  return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);
+}
+var batchToSpaceND = op({ batchToSpaceND_ });
+function xAs4D(x) {
+  let x4D;
+  if (x.rank === 0 || x.rank === 1) {
+    x4D = reshape(x, [1, 1, 1, x.size]);
+  } else if (x.rank === 2) {
+    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
+  } else if (x.rank === 3) {
+    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+  } else {
+    x4D = x;
+  }
+  return x4D;
+}
+function batchNorm_(x, mean5, variance, offset, scale22, varianceEpsilon) {
+  if (varianceEpsilon == null) {
+    varianceEpsilon = 1e-3;
+  }
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale22 != null) {
+    $scale = convertToTensor(scale22, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
+  assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
+  assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  const x4D = xAs4D($x);
+  const inputs = {
+    x: x4D,
+    scale: $scale,
+    offset: $offset,
+    mean: $mean,
+    variance: $variance
+  };
+  const attrs = { varianceEpsilon };
+  const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);
+  return reshape(res, $x.shape);
+}
+var batchNorm = op({ batchNorm_ });
+function batchNorm2d_(x, mean5, variance, offset, scale22, varianceEpsilon) {
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale22 != null) {
+    $scale = convertToTensor(scale22, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
+  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
+  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
+  if ($scale != null) {
+    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
+  }
+  if ($offset != null) {
+    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
+  }
+  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+}
+var batchNorm2d = op({ batchNorm2d_ });
+function batchNorm3d_(x, mean5, variance, offset, scale22, varianceEpsilon) {
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale22 != null) {
+    $scale = convertToTensor(scale22, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
+  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
+  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
+  if ($scale != null) {
+    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
+  }
+  if ($offset != null) {
+    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
+  }
+  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+}
+var batchNorm3d = op({ batchNorm3d_ });
+function batchNorm4d_(x, mean5, variance, offset, scale22, varianceEpsilon) {
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale22 != null) {
+    $scale = convertToTensor(scale22, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
+  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
+  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
+  if ($scale != null) {
+    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
+  }
+  if ($offset != null) {
+    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
+  }
+  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+}
+var batchNorm4d = op({ batchNorm4d_ });
+function bincount_(x, weights, size2) {
+  const $x = convertToTensor(x, "x", "bincount");
+  const $weights = convertToTensor(weights, "weights", "bincount");
+  assert($x.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);
+  assert(size2 >= 0, () => `size must be non-negative, but got ${size2}.`);
+  assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
+  const inputs = { x: $x, weights: $weights };
+  const attrs = { size: size2 };
+  return ENGINE.runKernel(Bincount, inputs, attrs);
+}
+var bincount = op({ bincount_ });
+function broadcastArgs_(s0, s1) {
+  const shape1Input = convertToTensor(s0, "s0", "broadcastArgs", "int32");
+  const shape2Input = convertToTensor(s1, "s1", "broadcastArgs", "int32");
+  if (shape1Input.rank !== 1) {
+    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`);
+  }
+  if (shape2Input.rank !== 1) {
+    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`);
+  }
+  const inputs = { s0: shape1Input, s1: shape2Input };
+  return ENGINE.runKernel(BroadcastArgs, inputs);
+}
+var broadcastArgs = op({ broadcastArgs_ });
+function broadcastTo_(x, shape) {
+  let input2 = convertToTensor(x, "broadcastTo", "x");
+  const xShape = input2.shape;
+  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
+    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
+  }
+  if (shape.length < input2.rank) {
+    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);
+  }
+  if (shape.length > input2.rank) {
+    const newShape = input2.shape.slice();
+    while (newShape.length < shape.length) {
+      newShape.unshift(1);
+    }
+    input2 = reshape(input2, newShape);
+  }
+  const inputShape = input2.shape;
+  const reps = Array.from(shape);
+  for (let i2 = shape.length - 1; i2 >= 0; i2--) {
+    if (inputShape[i2] === shape[i2]) {
+      reps[i2] = 1;
+    } else if (input2.shape[i2] !== 1) {
+      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
+    }
+  }
+  const axes = reps.map((n, i2) => n > 1 ? i2 : -1).filter((i2) => i2 >= 0);
+  if (axes.length === 0) {
+    return clone(input2);
+  }
+  const inputs = { x: input2 };
+  const attrs = { reps };
+  return ENGINE.runKernel(Tile, inputs, attrs);
+}
+var broadcastTo = op({ broadcastTo_ });
+function ceil_(x) {
+  const $x = convertToTensor(x, "x", "ceil", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Ceil, inputs);
+}
+var ceil = op({ ceil_ });
+function clipByValue_(x, clipValueMin, clipValueMax) {
+  const $x = convertToTensor(x, "x", "clipByValue");
+  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
+  const inputs = { x: $x };
+  const attrs = { clipValueMin, clipValueMax };
+  return ENGINE.runKernel(ClipByValue, inputs, attrs);
+}
+var clipByValue = op({ clipByValue_ });
+function concat1d_(tensors) {
+  return concat(tensors, 0);
+}
+var concat1d = op({ concat1d_ });
+function concat2d_(tensors, axis) {
+  return concat(tensors, axis);
+}
+var concat2d = op({ concat2d_ });
+function concat3d_(tensors, axis) {
+  return concat(tensors, axis);
+}
+var concat3d = op({ concat3d_ });
+function concat4d_(tensors, axis) {
+  return concat(tensors, axis);
+}
+var concat4d = op({ concat4d_ });
+function conv2d_(x, filter, strides2, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "conv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "conv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  checkPadOnDimRoundingMode("conv2d", pad3, dimRoundingMode);
+  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
+  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const inputs = { x: x4D, filter: $filter };
+  const attrs = { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode };
+  const res = ENGINE.runKernel(Conv2D, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var conv2d = op({ conv2d_ });
+function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "conv1d");
+  const $filter = convertToTensor(filter, "filter", "conv1d");
+  let x3D = $x;
+  let reshapedTo3D = false;
+  if ($x.rank === 2) {
+    reshapedTo3D = true;
+    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
+  }
+  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
+  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
+  checkPadOnDimRoundingMode("conv1d", pad3, dimRoundingMode);
+  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
+  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
+  assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
+  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
+  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
+  const strides2 = [1, stride];
+  const dilations = [1, dilation];
+  const conv2dDataFormat = "NHWC";
+  const res = conv2d(input4D, filter4D, strides2, pad3, conv2dDataFormat, dilations, dimRoundingMode);
+  if (reshapedTo3D) {
+    return reshape(res, [res.shape[2], res.shape[3]]);
+  }
+  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
+}
+var conv1d = op({ conv1d_ });
+function conv2DBackpropInput_(xShape, dy, filter, strides2, pad3, dataFormat = "NHWC", dimRoundingMode) {
+  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
+  let xShape4D = xShape;
+  let dy4D = dy;
+  let reshapedTo4D = false;
+  if (dy.rank === 3) {
+    reshapedTo4D = true;
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+    xShape4D = [1, xShape[0], xShape[1], xShape[2]];
+  }
+  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
+  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
+  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
+  const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
+  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
+  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
+  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
+  checkPadOnDimRoundingMode("conv2dDerInput", pad3, dimRoundingMode);
+  const inputs = { dy: dy4D, filter };
+  const attrs = { strides: strides2, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D };
+  const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var conv2DBackpropInput = op({ conv2DBackpropInput_ });
+function conv2dTranspose_(x, filter, outputShape, strides2, pad3, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "conv2dTranspose");
+  const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
+  return conv2DBackpropInput(outputShape, $x, $filter, strides2, pad3, "NHWC", dimRoundingMode);
+}
+var conv2dTranspose = op({ conv2dTranspose_ });
+function conv3d_(x, filter, strides2, pad3, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
+  const $x = convertToTensor(x, "x", "conv3d");
+  const $filter = convertToTensor(filter, "filter", "conv3d");
+  let x5D = $x;
+  let reshapedTo5D = false;
+  if ($x.rank === 4) {
+    reshapedTo5D = true;
+    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
+  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
+  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
+  const inputs = { x: x5D, filter: $filter };
+  const attrs = { strides: strides2, pad: pad3, dataFormat, dilations };
+  const res = ENGINE.runKernel(Conv3D, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var conv3d = op({ conv3d_ });
+function conv3DBackpropInput_(xShape, dy, filter, strides2, pad3) {
+  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
+  let xShape5D = xShape;
+  let dy5D = dy;
+  let reshapedTo5D = false;
+  if (dy.rank === 4) {
+    reshapedTo5D = true;
+    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
+    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
+  }
+  const inDepth = xShape5D[4];
+  const outDepth = dy5D.shape[4];
+  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
+  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
+  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
+  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
+  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
+  const inputs = { dy: dy5D, filter };
+  const attrs = { pad: pad3, strides: strides2, inputShape: xShape5D };
+  const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var conv3DBackpropInput = op({ conv3DBackpropInput_ });
+function conv3dTranspose_(x, filter, outputShape, strides2, pad3) {
+  const $x = convertToTensor(x, "x", "conv3dTranspose");
+  const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
+  return conv3DBackpropInput(outputShape, $x, $filter, strides2, pad3);
+}
+var conv3dTranspose = op({ conv3dTranspose_ });
+function cos_(x) {
+  const $x = convertToTensor(x, "x", "cos", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Cos, inputs);
+}
+var cos = op({ cos_ });
+function cosh_(x) {
+  const $x = convertToTensor(x, "x", "cosh", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Cosh, inputs);
+}
+var cosh = op({ cosh_ });
+function cumprod_(x, axis = 0, exclusive = false, reverse5 = false) {
+  const $x = convertToTensor(x, "x", "cumprod");
+  const inputs = { x: $x };
+  const attrs = { axis, exclusive, reverse: reverse5 };
+  return ENGINE.runKernel(Cumprod, inputs, attrs);
+}
+var cumprod = op({ cumprod_ });
+function cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {
+  const $x = convertToTensor(x, "x", "cumsum");
+  const inputs = { x: $x };
+  const attrs = { axis, exclusive, reverse: reverse5 };
+  return ENGINE.runKernel(Cumsum, inputs, attrs);
+}
+var cumsum = op({ cumsum_ });
+function denseBincount_(x, weights, size2, binaryOutput = false) {
+  const $x = convertToTensor(x, "x", "denseBincount");
+  const $weights = convertToTensor(weights, "weights", "denseBincount");
+  assert($x.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);
+  assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);
+  assert(size2 >= 0, () => `size must be non-negative, but got ${size2}.`);
+  assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
+  const inputs = { x: $x, weights: $weights };
+  const attrs = { size: size2, binaryOutput };
+  return ENGINE.runKernel(DenseBincount, inputs, attrs);
+}
+var denseBincount = op({ denseBincount_ });
+function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
+  const $x = convertToTensor(x, "x", "depthToSpace", "float32");
+  const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
+  assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
+  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`);
+  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`);
+  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
+  const inputs = { x: $x };
+  const attrs = { blockSize, dataFormat };
+  return ENGINE.runKernel(DepthToSpace, inputs, attrs);
+}
+var depthToSpace = op({ depthToSpace_ });
+function depthwiseConv2d_(x, filter, strides2, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
+  checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode);
+  const inputs = { x: x4D, filter: $filter };
+  const attrs = { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode };
+  const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var depthwiseConv2d = op({ depthwiseConv2d_ });
+function diag_(x) {
+  const $x = convertToTensor(x, "x", "diag");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Diag, inputs);
+}
+var diag = op({ diag_ });
+function dilation2d_(x, filter, strides2, pad3, dilations = [1, 1], dataFormat = "NHWC") {
+  const $x = convertToTensor(x, "x", "dilation2d");
+  const $filter = convertToTensor(filter, "filter", "dilation2d");
+  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
+  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
+  assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+    reshapedTo4D = true;
+  }
+  const inputs = { x: x4D, filter: $filter };
+  const attrs = { strides: strides2, pad: pad3, dilations };
+  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var dilation2d = op({ dilation2d_ });
+function equal_(a6, b) {
+  let $a = convertToTensor(a6, "a", "equal", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "equal", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Equal, inputs);
+}
+var equal = op({ equal_ });
+function where_(condition, a6, b) {
+  const $a = convertToTensor(a6, "a", "where");
+  const $b = convertToTensor(b, "b", "where");
+  const $condition = convertToTensor(condition, "condition", "where", "bool");
+  const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);
+  const $broadcastedCondition = broadcastTo($condition, broadcastShape);
+  const $broadcastedA = broadcastTo($a, broadcastShape);
+  const $broadcastedB = broadcastTo($b, broadcastShape);
+  const inputs = {
+    condition: $broadcastedCondition,
+    t: $broadcastedA,
+    e: $broadcastedB
+  };
+  return ENGINE.runKernel(Select, inputs);
+}
+var where = op({ where_ });
+function zerosLike_(x) {
+  const $x = convertToTensor(x, "x", "zerosLike");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(ZerosLike, inputs);
+}
+var zerosLike = op({ zerosLike_ });
+function divNoNan_(a6, b) {
+  let $a = convertToTensor(a6, "a", "div");
+  let $b = convertToTensor(b, "b", "div");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const divResult = div($a, $b);
+  const zeros4 = zerosLike(divResult);
+  const bEqualsZero = equal($b, zeros4);
+  return where(bEqualsZero, zeros4, divResult);
+}
+var divNoNan = op({ divNoNan_ });
+function dot_(t1, t2) {
+  const $t1 = convertToTensor(t1, "t1", "dot");
+  const $t2 = convertToTensor(t2, "t2", "dot");
+  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
+  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
+  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
+  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
+  if ($t1.rank === 1 && $t2.rank === 1) {
+    const t12D = reshape($t1, [1, -1]);
+    const t22D = reshape($t2, [-1, 1]);
+    const t1t2 = matMul(t12D, t22D);
+    return reshape(t1t2, []);
+  } else if ($t1.rank === 1 && $t2.rank === 2) {
+    const t12D = reshape($t1, [1, -1]);
+    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+    const t1t2 = matMul(t12D, t22D);
+    return reshape(t1t2, [t1t2.size]);
+  } else if ($t1.rank === 2 && $t2.rank === 1) {
+    const t22D = reshape($t2, [-1, 1]);
+    const t1t2 = matMul($t1, t22D);
+    return reshape(t1t2, [t1t2.size]);
+  } else {
+    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+    const t1t2 = matMul($t1, t22D);
+    return t1t2;
+  }
+}
+var dot = op({ dot_ });
+function einsum_(equation, ...tensors) {
+  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "einsum"));
+  const attrs = { equation };
+  return ENGINE.runKernel(Einsum, $tensors, attrs);
+}
+var einsum = op({ einsum_ });
+function elu_(x) {
+  const $x = convertToTensor(x, "x", "elu", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Elu, inputs);
+}
+var elu = op({ elu_ });
+function erf_(x) {
+  let $x = convertToTensor(x, "x", "erf");
+  assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
+  if ($x.dtype === "int32") {
+    $x = cast($x, "float32");
+  }
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Erf, inputs);
+}
+var erf = op({ erf_ });
+function axesAreInnerMostDims(axes, rank) {
+  for (let i2 = 0; i2 < axes.length; ++i2) {
+    if (axes[axes.length - i2 - 1] !== rank - 1 - i2) {
+      return false;
+    }
+  }
+  return true;
+}
+function combineLocations(outputLoc, reduceLoc, axes) {
+  const rank = outputLoc.length + reduceLoc.length;
+  const loc = [];
+  let outIdx = 0;
+  let reduceIdx = 0;
+  for (let dim = 0; dim < rank; dim++) {
+    if (axes.indexOf(dim) === -1) {
+      loc.push(outputLoc[outIdx++]);
+    } else {
+      loc.push(reduceLoc[reduceIdx++]);
+    }
+  }
+  return loc;
+}
+function computeOutAndReduceShapes(aShape, axes) {
+  const outShape = [];
+  const rank = aShape.length;
+  for (let dim = 0; dim < rank; dim++) {
+    if (axes.indexOf(dim) === -1) {
+      outShape.push(aShape[dim]);
+    }
+  }
+  const reduceShape = axes.map((dim) => aShape[dim]);
+  return [outShape, reduceShape];
+}
+function expandShapeToKeepDim(shape, axes) {
+  const reduceSubShape = axes.map((x) => 1);
+  return combineLocations(shape, reduceSubShape, axes);
+}
+function assertAxesAreInnerMostDims(msg, axes, rank) {
+  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
+}
+function getAxesPermutation(axes, rank) {
+  if (axesAreInnerMostDims(axes, rank)) {
+    return null;
+  }
+  const result = [];
+  for (let i2 = 0; i2 < rank; ++i2) {
+    if (axes.indexOf(i2) === -1) {
+      result.push(i2);
+    }
+  }
+  axes.forEach((axis) => result.push(axis));
+  return result;
+}
+function getUndoAxesPermutation(axes) {
+  return axes.map((axis, i2) => [i2, axis]).sort((a6, b) => a6[1] - b[1]).map((x) => x[0]);
+}
+function getInnerMostAxes(numAxes, rank) {
+  const res = [];
+  for (let i2 = rank - numAxes; i2 < rank; ++i2) {
+    res.push(i2);
+  }
+  return res;
+}
+function max_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "max");
+  const inputs = { x: $x };
+  const attrs = { reductionIndices: axis, keepDims };
+  return ENGINE.runKernel(Max, inputs, attrs);
+}
+var max = op({ max_ });
+function min_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "min");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Min, inputs, attrs);
+}
+var min = op({ min_ });
+function pow_(base, exp5) {
+  let $base = convertToTensor(base, "base", "pow");
+  let $exp = convertToTensor(exp5, "exp", "pow");
+  [$base, $exp] = makeTypesMatch($base, $exp);
+  const inputs = { a: $base, b: $exp };
+  return ENGINE.runKernel(Pow, inputs);
+}
+var pow = op({ pow_ });
+function scalar(value, dtype) {
+  if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
+    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
+  }
+  if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
+    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
+  }
+  const shape = [];
+  const inferredShape = [];
+  return makeTensor(value, shape, inferredShape, dtype);
+}
+function sqrt_(x) {
+  const $x = convertToTensor(x, "x", "sqrt", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sqrt, inputs);
+}
+var sqrt = op({ sqrt_ });
+function square_(x) {
+  const $x = convertToTensor(x, "x", "square");
+  const attrs = {};
+  return ENGINE.runKernel("Square", { x: $x }, attrs);
+}
+var square = op({ square_ });
+function sum_(x, axis = null, keepDims = false) {
+  let $x = convertToTensor(x, "x", "sum");
+  if ($x.dtype === "bool") {
+    $x = cast($x, "int32");
+  }
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Sum, inputs, attrs);
+}
+var sum2 = op({ sum_ });
+function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
+  x = convertToTensor(x, "x", "norm");
+  const norm2 = normImpl(x, ord, axis);
+  let keepDimsShape = norm2.shape;
+  if (keepDims) {
+    const axes = parseAxisParam(axis, x.shape);
+    keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);
+  }
+  return reshape(norm2, keepDimsShape);
+}
+function normImpl(x, p2, axis = null) {
+  if (x.rank === 0) {
+    return abs(x);
+  }
+  if (x.rank !== 1 && axis === null) {
+    return normImpl(reshape(x, [-1]), p2, axis);
+  }
+  if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
+    if (p2 === 1) {
+      return sum2(abs(x), axis);
+    }
+    if (p2 === Infinity) {
+      return max(abs(x), axis);
+    }
+    if (p2 === -Infinity) {
+      return min(abs(x), axis);
+    }
+    if (p2 === "euclidean" || p2 === 2) {
+      return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
+    }
+    throw new Error(`Error in norm: invalid ord value: ${p2}`);
+  }
+  if (Array.isArray(axis) && axis.length === 2) {
+    if (p2 === 1) {
+      return max(sum2(abs(x), axis[0]), axis[1] - 1);
+    }
+    if (p2 === Infinity) {
+      return max(sum2(abs(x), axis[1]), axis[0]);
+    }
+    if (p2 === -Infinity) {
+      return min(sum2(abs(x), axis[1]), axis[0]);
+    }
+    if (p2 === "fro" || p2 === "euclidean") {
+      return sqrt(sum2(square(x), axis));
+    }
+    throw new Error(`Error in norm: invalid ord value: ${p2}`);
+  }
+  throw new Error(`Error in norm: invalid axis: ${axis}`);
+}
+var norm = op({ norm_ });
+function euclideanNorm_(x, axis = null, keepDims = false) {
+  return norm(x, "euclidean", axis, keepDims);
+}
+var euclideanNorm = op({ euclideanNorm_ });
+function exp_(x) {
+  const $x = convertToTensor(x, "x", "exp");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Exp, inputs);
+}
+var exp = op({ exp_ });
+function expandDims_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "expandDims", "string_or_numeric");
+  assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
+  const inputs = { input: $x };
+  const attrs = { dim: axis };
+  return ENGINE.runKernel(ExpandDims, inputs, attrs);
+}
+var expandDims = op({ expandDims_ });
+function expm1_(x) {
+  const $x = convertToTensor(x, "x", "expm1");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Expm1, inputs);
+}
+var expm1 = op({ expm1_ });
+function tile_(x, reps) {
+  const $x = convertToTensor(x, "x", "tile", "string_or_numeric");
+  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
+  const inputs = { x: $x };
+  const attrs = { reps };
+  return ENGINE.runKernel(Tile, inputs, attrs);
+}
+var tile = op({ tile_ });
+function eye_(numRows, numColumns, batchShape, dtype = "float32") {
+  if (numColumns == null) {
+    numColumns = numRows;
+  }
+  const buff = buffer([numRows, numColumns], dtype);
+  const n = numRows <= numColumns ? numRows : numColumns;
+  for (let i2 = 0; i2 < n; ++i2) {
+    buff.set(1, i2, i2);
+  }
+  const out = reshape(buff.toTensor(), [numRows, numColumns]);
+  if (batchShape == null) {
+    return out;
+  } else {
+    if (batchShape.length === 1) {
+      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
+    } else if (batchShape.length === 2) {
+      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
+    } else if (batchShape.length === 3) {
+      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
+        batchShape[0],
+        batchShape[1],
+        batchShape[2],
+        1,
+        1
+      ]);
+    } else {
+      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
+    }
+  }
+}
+var eye = op({ eye_ });
+function fill(shape, value, dtype) {
+  const attrs = { shape, value, dtype };
+  return ENGINE.runKernel(Fill, {}, attrs);
+}
+function floor_(x) {
+  const $x = convertToTensor(x, "x", "floor", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Floor, inputs);
+}
+var floor = op({ floor_ });
+function gather_(x, indices, axis = 0, batchDims = 0) {
+  const $x = convertToTensor(x, "x", "gather");
+  const $indices = convertToTensor(indices, "indices", "gather", "int32");
+  const inputs = { x: $x, indices: $indices };
+  const attrs = { axis, batchDims };
+  return ENGINE.runKernel(GatherV2, inputs, attrs);
+}
+var gather = op({ gather_ });
+function greater_(a6, b) {
+  let $a = convertToTensor(a6, "a", "greater", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "greater", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Greater, inputs);
+}
+var greater = op({ greater_ });
+function greaterEqual_(a6, b) {
+  let $a = convertToTensor(a6, "a", "greaterEqual", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "greaterEqual", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(GreaterEqual, inputs);
+}
+var greaterEqual = op({ greaterEqual_ });
+function isFinite_(x) {
+  const $x = convertToTensor(x, "x", "isFinite");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(IsFinite, inputs);
+}
+var isFinite2 = op({ isFinite_ });
+function isInf_(x) {
+  const $x = convertToTensor(x, "x", "isInf");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(IsInf, inputs);
+}
+var isInf = op({ isInf_ });
+function isNaN_(x) {
+  const $x = convertToTensor(x, "x", "isNaN");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(IsNan, inputs);
+}
+var isNaN2 = op({ isNaN_ });
+function leakyRelu_(x, alpha2 = 0.2) {
+  const $x = convertToTensor(x, "x", "leakyRelu");
+  const inputs = { x: $x };
+  const attrs = { alpha: alpha2 };
+  return ENGINE.runKernel(LeakyRelu, inputs, attrs);
+}
+var leakyRelu = op({ leakyRelu_ });
+function less_(a6, b) {
+  let $a = convertToTensor(a6, "a", "less", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "less", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Less, inputs);
+}
+var less = op({ less_ });
+function lessEqual_(a6, b) {
+  let $a = convertToTensor(a6, "a", "lessEqual", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "lessEqual", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(LessEqual, inputs);
+}
+var lessEqual = op({ lessEqual_ });
+function linspace(start, stop, num) {
+  if (num <= 0) {
+    throw new Error("The number of values should be positive.");
+  }
+  const attrs = { start, stop, num };
+  return ENGINE.runKernel(LinSpace, {}, attrs);
+}
+function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha2 = 1, beta = 0.5) {
+  const $x = convertToTensor(x, "x", "localResponseNormalization");
+  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`);
+  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  const inputs = { x: x4D };
+  const attrs = { depthRadius, bias, alpha: alpha2, beta };
+  const res = ENGINE.runKernel(LRN, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  } else {
+    return res;
+  }
+}
+var localResponseNormalization = op({ localResponseNormalization_ });
+function log_(x) {
+  const $x = convertToTensor(x, "x", "log", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Log, inputs);
+}
+var log22 = op({ log_ });
+function log1p_(x) {
+  const $x = convertToTensor(x, "x", "log1p");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Log1p, inputs);
+}
+var log1p = op({ log1p_ });
+function grad(f) {
+  assert(isFunction(f), () => "The f passed in grad(f) must be a function");
+  return (x, dy) => {
+    const $x = convertToTensor(x, "x", "tf.grad", "string_or_numeric");
+    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
+    return ENGINE.tidy(() => {
+      const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy);
+      if ($dy != null) {
+        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
+      }
+      checkGrads(grads2);
+      return grads2[0];
+    });
+  };
+}
+function grads(f) {
+  assert(isFunction(f), () => "The f passed in grads(f) must be a function");
+  return (args, dy) => {
+    assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
+    const $args = convertToTensorArray(args, "args", "tf.grads", "string_or_numeric");
+    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
+    return ENGINE.tidy(() => {
+      const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy);
+      if ($dy != null) {
+        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
+      }
+      checkGrads(grads2);
+      return grads2;
+    });
+  };
+}
+function valueAndGrad(f) {
+  assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
+  return (x, dy) => {
+    assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
+    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
+    const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy);
+    checkGrads(grads2);
+    return { grad: grads2[0], value };
+  };
+}
+function valueAndGrads(f) {
+  assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
+  return (args, dy) => {
+    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
+    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
+    const res = ENGINE.gradients(() => f(...args), args, dy);
+    if (dy != null) {
+      assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
+    }
+    checkGrads(res.grads);
+    return res;
+  };
+}
+function variableGrads(f, varList) {
+  assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
+  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
+  const specifiedVarList = varList != null;
+  if (!specifiedVarList) {
+    varList = [];
+    for (const varName in ENGINE.registeredVariables) {
+      varList.push(ENGINE.registeredVariables[varName]);
+    }
+  }
+  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;
+  const originalVarCount = varList.length;
+  varList = varList.filter((variable2) => variable2.trainable);
+  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
+  const allowNoGradients = true;
+  const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients);
+  assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
+  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
+  const namedGrads = {};
+  varList.forEach((v, i2) => {
+    if (grads2[i2] != null) {
+      namedGrads[v.name] = grads2[i2];
+    }
+  });
+  if (specifiedNonTrainable != null) {
+    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
+  }
+  return { value, grads: namedGrads };
+}
+function customGrad(f) {
+  return ENGINE.customGrad(f);
+}
+function checkGrads(grads2) {
+  const numNullGradients = grads2.filter((g) => g == null).length;
+  if (numNullGradients > 0) {
+    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
+    the f you passed encloses all operations that lead from x to y.`);
+  }
+}
+function softplus_(x) {
+  const $x = convertToTensor(x, "x", "softplus");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Softplus, inputs);
+}
+var softplus = op({ softplus_ });
+function logSigmoid_(x) {
+  const $x = convertToTensor(x, "x", "logSigmoid");
+  const customOp = customGrad((x2) => {
+    const value = neg(softplus(neg(x2)));
+    const gradFunc = (dy) => {
+      const derX = mul(dy, sigmoid(neg(x2)));
+      return derX;
+    };
+    return { value, gradFunc };
+  });
+  return customOp($x);
+}
+var logSigmoid = op({ logSigmoid_ });
+function sub_(a6, b) {
+  let $a = convertToTensor(a6, "a", "sub");
+  let $b = convertToTensor(b, "b", "sub");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Sub, inputs);
+}
+var sub = op({ sub_ });
+function logSoftmax_(logits, axis = -1) {
+  const $logits = convertToTensor(logits, "logits", "logSoftmax");
+  if (axis === -1) {
+    axis = $logits.rank - 1;
+  }
+  if (axis !== $logits.rank - 1) {
+    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
+  }
+  const customOp = customGrad((logits2, save) => {
+    const keepDims = true;
+    const xMax = max(logits2, axis, true);
+    const shifted = sub(logits2, xMax);
+    const value = sub(cast(shifted, "float32"), log22(sum2(exp(shifted), axis, keepDims)));
+    save([value]);
+    const gradFunc = (dy, saved) => {
+      const [value2] = saved;
+      const keepDims2 = true;
+      const softmax7 = exp(value2);
+      return sub(dy, mul(sum2(dy, axis, keepDims2), softmax7));
+    };
+    return { value, gradFunc };
+  });
+  return customOp($logits);
+}
+var logSoftmax = op({ logSoftmax_ });
+function logSumExp_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "logSumExp");
+  const axes = parseAxisParam(axis, $x.shape);
+  const xMax = max($x, axes, true);
+  const a6 = sub($x, xMax);
+  const b = exp(a6);
+  const c = sum2(b, axes);
+  const d = log22(c);
+  const res = add2(reshape(xMax, d.shape), d);
+  if (keepDims) {
+    const newShape = expandShapeToKeepDim(res.shape, axes);
+    return reshape(res, newShape);
+  }
+  return res;
+}
+var logSumExp = op({ logSumExp_ });
+function logicalAnd_(a6, b) {
+  const $a = convertToTensor(a6, "a", "logicalAnd", "bool");
+  const $b = convertToTensor(b, "b", "logicalAnd", "bool");
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(LogicalAnd, inputs);
+}
+var logicalAnd = op({ logicalAnd_ });
+function logicalNot_(x) {
+  const $x = convertToTensor(x, "x", "logicalNot", "bool");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(LogicalNot, inputs);
+}
+var logicalNot = op({ logicalNot_ });
+function logicalOr_(a6, b) {
+  const $a = convertToTensor(a6, "a", "logicalOr", "bool");
+  const $b = convertToTensor(b, "b", "logicalOr", "bool");
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(LogicalOr, inputs);
+}
+var logicalOr = op({ logicalOr_ });
+function logicalXor_(a6, b) {
+  const $a = convertToTensor(a6, "a", "logicalXor", "bool");
+  const $b = convertToTensor(b, "b", "logicalXor", "bool");
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  return logicalAnd(logicalOr(a6, b), logicalNot(logicalAnd(a6, b)));
+}
+var logicalXor = op({ logicalXor_ });
+var INT32_MAX = 2147483648;
+function searchSorted_(sortedSequence, values, side = "left") {
+  const $sortedSequence = convertToTensor(sortedSequence, "sortedSequence", "searchSorted");
+  const $values = convertToTensor(values, "values", "searchSorted");
+  const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];
+  const valuesSize = $values.shape[$values.shape.length - 1];
+  const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);
+  const $values2D = reshape($values, [-1, valuesSize]);
+  if ($sortedSequence2D.rank < 2) {
+    throw new Error(`Sorted input argument must be at least 2-dimensional`);
+  }
+  if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {
+    throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);
+  }
+  if (sizeFromShape($values2D.shape) >= INT32_MAX) {
+    throw new Error(`values tensor size must less than ${INT32_MAX}`);
+  }
+  if ($sortedSequence2D.shape[1] >= INT32_MAX) {
+    throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);
+  }
+  const inputs = {
+    sortedSequence: $sortedSequence2D,
+    values: $values2D
+  };
+  const attrs = { side };
+  return ENGINE.runKernel(SearchSorted, inputs, attrs);
+}
+var searchSorted = op({ searchSorted_ });
+function lowerBound(sortedSequence, values) {
+  return searchSorted(sortedSequence, values, "left");
+}
+function maxPool_(x, filterSize, strides2, pad3, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "maxPool");
+  const dilations = 1;
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  checkPadOnDimRoundingMode("maxPool", pad3, dimRoundingMode);
+  const inputs = { x: x4D };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode };
+  const res = ENGINE.runKernel(MaxPool, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var maxPool = op({ maxPool_ });
+function maxPool3d_(x, filterSize = [1, 1, 1], strides2, pad3, dimRoundingMode, dataFormat = "NDHWC") {
+  const $x = convertToTensor(x, "x", "maxPool3d");
+  let x5D = $x;
+  let reshapedTo5D = false;
+  if ($x.rank === 4) {
+    reshapedTo5D = true;
+    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
+  assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
+  checkPadOnDimRoundingMode("maxPool3d", pad3, dimRoundingMode);
+  const inputs = { x: x5D };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode, dataFormat };
+  const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var maxPool3d = op({ maxPool3d_ });
+function maxPoolWithArgmax_(x, filterSize, strides2, pad3, includeBatchInIndex = false) {
+  const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
+  const inputs = { x: $x };
+  const attrs = { filterSize, strides: strides2, pad: pad3, includeBatchInIndex };
+  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
+  return { result: result[0], indexes: result[1] };
+}
+var maxPoolWithArgmax = op({ maxPoolWithArgmax_ });
+function maximum_(a6, b) {
+  let $a = convertToTensor(a6, "a", "maximum");
+  let $b = convertToTensor(b, "b", "maximum");
+  [$a, $b] = makeTypesMatch($a, $b);
+  if ($a.dtype === "bool") {
+    $a = cast($a, "int32");
+    $b = cast($b, "int32");
+  }
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Maximum, inputs);
+}
+var maximum = op({ maximum_ });
+function mean_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "mean");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Mean, inputs, attrs);
+}
+var mean = op({ mean_ });
+function zeros(shape, dtype = "float32") {
+  if (dtype === "complex64") {
+    const real5 = zeros(shape, "float32");
+    const imag5 = zeros(shape, "float32");
+    return complex(real5, imag5);
+  }
+  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+function ones2(shape, dtype = "float32") {
+  if (dtype === "complex64") {
+    const real5 = ones2(shape, "float32");
+    const imag5 = zeros(shape, "float32");
+    return complex(real5, imag5);
+  }
+  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+function meshgrid(x, y, { indexing = "xy" } = {}) {
+  if (indexing !== "xy" && indexing !== "ij") {
+    throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);
+  }
+  if (x === void 0) {
+    return [];
+  }
+  let $x = convertToTensor(x, "x", "meshgrid", x instanceof Tensor ? x.dtype : "float32");
+  if (y === void 0) {
+    return [$x];
+  }
+  let $y = convertToTensor(y, "y", "meshgrid", y instanceof Tensor ? y.dtype : "float32");
+  const w = sizeFromShape($x.shape);
+  const h = sizeFromShape($y.shape);
+  if (indexing === "xy") {
+    $x = reshape($x, [1, -1]);
+    $y = reshape($y, [-1, 1]);
+    return [
+      matMul(ones2([h, 1], $x.dtype), $x),
+      matMul($y, ones2([1, w], $y.dtype))
+    ];
+  }
+  $x = reshape($x, [-1, 1]);
+  $y = reshape($y, [1, -1]);
+  return [
+    matMul($x, ones2([1, h], $x.dtype)),
+    matMul(ones2([w, 1], $y.dtype), $y)
+  ];
+}
+function minimum_(a6, b) {
+  let $a = convertToTensor(a6, "a", "minimum");
+  let $b = convertToTensor(b, "b", "minimum");
+  [$a, $b] = makeTypesMatch($a, $b);
+  if ($a.dtype === "bool") {
+    $a = cast($a, "int32");
+    $b = cast($b, "int32");
+  }
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Minimum, inputs);
+}
+var minimum = op({ minimum_ });
+function mirrorPad_(x, paddings, mode) {
+  assert(mode === "reflect" || mode === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);
+  const $x = convertToTensor(x, "x", "mirrorPad");
+  if ($x.rank === 0) {
+    throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
+  }
+  assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);
+  const shapeOffset = mode === "reflect" ? 1 : 0;
+  for (let i2 = 0; i2 < $x.rank; i2++) {
+    assert(paddings[i2].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
+    assert(paddings[i2][0] >= 0 && paddings[i2][0] <= $x.shape[i2] - shapeOffset && paddings[i2][1] >= 0 && paddings[i2][1] <= $x.shape[i2] - shapeOffset, () => `Padding in dimension ${i2} cannot be greater than or equal to ${$x.shape[i2] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
+  }
+  const attrs = { paddings, mode };
+  const inputs = { x: $x };
+  return ENGINE.runKernel(MirrorPad, inputs, attrs);
+}
+var mirrorPad = op({ mirrorPad_ });
+function mod_(a6, b) {
+  let $a = convertToTensor(a6, "a", "mod");
+  let $b = convertToTensor(b, "b", "mod");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Mod, inputs);
+}
+var mod = op({ mod_ });
+function moments_(x, axis = null, keepDims = false) {
+  x = convertToTensor(x, "x", "moments");
+  const axes = parseAxisParam(axis, x.shape);
+  const xMean = mean(x, axes, keepDims);
+  let keepDimsShape = xMean.shape;
+  if (!keepDims) {
+    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
+  }
+  const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
+  const variance = mean(devSquared, axes, keepDims);
+  return { mean: xMean, variance };
+}
+var moments = op({ moments_ });
+function multiRNNCell_(lstmCells, data, c, h) {
+  const $data = convertToTensor(data, "data", "multiRNNCell");
+  const $c = convertToTensorArray(c, "c", "multiRNNCell");
+  const $h = convertToTensorArray(h, "h", "multiRNNCell");
+  let input2 = $data;
+  const newStates = [];
+  for (let i2 = 0; i2 < lstmCells.length; i2++) {
+    const output = lstmCells[i2](input2, $c[i2], $h[i2]);
+    newStates.push(output[0]);
+    newStates.push(output[1]);
+    input2 = output[1];
+  }
+  const newC = [];
+  const newH = [];
+  for (let i2 = 0; i2 < newStates.length; i2 += 2) {
+    newC.push(newStates[i2]);
+    newH.push(newStates[i2 + 1]);
+  }
+  return [newC, newH];
+}
+var multiRNNCell = op({ multiRNNCell_ });
+function multinomial_(logits, numSamples, seed, normalized = false) {
+  const $logits = convertToTensor(logits, "logits", "multinomial");
+  const numOutcomes = $logits.size;
+  const origRank = $logits.rank;
+  if (numOutcomes < 2) {
+    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
+  }
+  if (origRank > 2) {
+    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
+  }
+  seed = seed || Math.random();
+  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
+  const inputs = { logits: logits2D };
+  const attrs = { numSamples, seed, normalized };
+  const res = ENGINE.runKernel(Multinomial, inputs, attrs);
+  return origRank === 1 ? reshape(res, [res.size]) : res;
+}
+var multinomial = op({ multinomial_ });
+function notEqual_(a6, b) {
+  let $a = convertToTensor(a6, "a", "notEqual", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "notEqual", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(NotEqual, inputs);
+}
+var notEqual = op({ notEqual_ });
+function onesLike_(x) {
+  const $x = convertToTensor(x, "x", "onesLike");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(OnesLike, inputs);
+}
+var onesLike = op({ onesLike_ });
+function outerProduct_(v1, v2) {
+  const $v1 = convertToTensor(v1, "v1", "outerProduct");
+  const $v2 = convertToTensor(v2, "v2", "outerProduct");
+  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
+  const v12D = reshape($v1, [-1, 1]);
+  const v22D = reshape($v2, [1, -1]);
+  return matMul(v12D, v22D);
+}
+var outerProduct = op({ outerProduct_ });
+function pad_(x, paddings, constantValue = 0) {
+  const $x = convertToTensor(x, "x", "pad");
+  if ($x.rank === 0) {
+    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
+  }
+  const attrs = { paddings, constantValue };
+  const inputs = { x: $x };
+  return ENGINE.runKernel(PadV2, inputs, attrs);
+}
+var pad = op({ pad_ });
+function pad1d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
+  return pad(x, [paddings], constantValue);
+}
+var pad1d = op({ pad1d_ });
+function pad2d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
+  return pad(x, paddings, constantValue);
+}
+var pad2d = op({ pad2d_ });
+function pad3d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
+  return pad(x, paddings, constantValue);
+}
+var pad3d = op({ pad3d_ });
+function pad4d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
+  return pad(x, paddings, constantValue);
+}
+var pad4d = op({ pad4d_ });
+function spaceToBatchND_(x, blockShape, paddings) {
+  const $x = convertToTensor(x, "x", "spaceToBatchND");
+  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
+  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
+  assert($x.shape.reduce((a6, b, i2) => {
+    if (i2 > 0 && i2 <= blockShape.length) {
+      return a6 && (b + paddings[i2 - 1][0] + paddings[i2 - 1][1]) % blockShape[i2 - 1] === 0;
+    }
+    return a6;
+  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
+  const inputs = { x: $x };
+  const attrs = { blockShape, paddings };
+  return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);
+}
+var spaceToBatchND = op({ spaceToBatchND_ });
+function pool_(input2, windowShape, poolingType, pad3, dilations, strides2, dimRoundingMode) {
+  if (dilations == null) {
+    dilations = [1, 1];
+  }
+  if (strides2 == null) {
+    strides2 = 1;
+  }
+  if (pad3 === 0) {
+    pad3 = "valid";
+  }
+  const $x = convertToTensor(input2, "x", "maxPool");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides2, dilations, pad3);
+  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
+  let basePadding;
+  if (pad3 === "same") {
+    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
+  } else {
+    basePadding = [[0, 0], [0, 0]];
+  }
+  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
+  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
+  const convertedPad = isDilationOne ? pad3 : "valid";
+  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
+  const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides2, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides2, convertedPad, dimRoundingMode);
+  const y = forwardOp();
+  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
+  const padStart = basePadding.map((b) => b[0]);
+  const origPadEnd = basePadding.map((b) => b[1]);
+  const fullInputShape = inputShape.concat(padStart, origPadEnd);
+  const padEndExtra = blockShape.map((b, i2) => (b - fullInputShape[i2] % b) % b);
+  const padEnd = origPadEnd.map((s2, i2) => s2 + padEndExtra[i2]);
+  const paddings = blockShape.map((_, i2) => [padStart[i2], padEnd[i2]]);
+  const crops = blockShape.map((_, i2) => [0, padEndExtra[i2]]);
+  return [paddings, crops];
+}
+function withSpaceToBatchBasePaddings(filterShape, dilation) {
+  const dilatedFilterShape = filterShape.map((s2, i2) => {
+    return s2 + (s2 - 1) * (dilation[i2] - 1);
+  });
+  const padExtraShape = dilatedFilterShape.map((s2) => s2 - 1);
+  const padExtraStart = padExtraShape.map((s2) => Math.floor(s2 / 2));
+  const padExtraEnd = padExtraShape.map((s2, i2) => s2 - padExtraStart[i2]);
+  return padExtraShape.map((_, i2) => {
+    return [padExtraStart[i2], padExtraEnd[i2]];
+  });
+}
+var pool = op({ pool_ });
+function prelu_(x, alpha2) {
+  const $x = convertToTensor(x, "x", "prelu");
+  const $alpha = convertToTensor(alpha2, "alpha", "prelu");
+  const inputs = { x: $x, alpha: $alpha };
+  return ENGINE.runKernel(Prelu, inputs);
+}
+var prelu = op({ prelu_ });
+function prod_(x, axis = null, keepDims = false) {
+  let $x = convertToTensor(x, "x", "prod");
+  if ($x.dtype === "bool") {
+    $x = cast($x, "int32");
+  }
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Prod, inputs, attrs);
+}
+var prod = op({ prod_ });
+function rand_(shape, randFunction, dtype) {
+  const size2 = sizeFromShape(shape);
+  let values = null;
+  if (dtype == null || dtype === "float32") {
+    values = new Float32Array(size2);
+  } else if (dtype === "int32") {
+    values = new Int32Array(size2);
+  } else if (dtype === "bool") {
+    values = new Uint8Array(size2);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+  for (let i2 = 0; i2 < size2; i2++) {
+    values[i2] = randFunction();
+  }
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+var rand = op({ rand_ });
+var seedrandom = __toESM(require_seedrandom2());
+var MPRandGauss = class {
+  constructor(mean5, stdDeviation, dtype, truncated, seed) {
+    this.mean = mean5;
+    this.stdDev = stdDeviation;
+    this.dtype = dtype;
+    this.nextVal = NaN;
+    this.truncated = truncated;
+    if (this.truncated) {
+      this.upper = this.mean + this.stdDev * 2;
+      this.lower = this.mean - this.stdDev * 2;
+    }
+    const seedValue = seed ? seed : Math.random();
+    this.random = seedrandom.alea(seedValue.toString());
+  }
+  nextValue() {
+    if (!isNaN(this.nextVal)) {
+      const value = this.nextVal;
+      this.nextVal = NaN;
+      return value;
+    }
+    let resultX, resultY;
+    let isValid = false;
+    while (!isValid) {
+      let v1, v2, s2;
+      do {
+        v1 = 2 * this.random() - 1;
+        v2 = 2 * this.random() - 1;
+        s2 = v1 * v1 + v2 * v2;
+      } while (s2 >= 1 || s2 === 0);
+      const mul2 = Math.sqrt(-2 * Math.log(s2) / s2);
+      resultX = this.mean + this.stdDev * v1 * mul2;
+      resultY = this.mean + this.stdDev * v2 * mul2;
+      if (!this.truncated || this.isValidTruncated(resultX)) {
+        isValid = true;
+      }
+    }
+    if (!this.truncated || this.isValidTruncated(resultY)) {
+      this.nextVal = this.convertValue(resultY);
+    }
+    return this.convertValue(resultX);
+  }
+  convertValue(value) {
+    if (this.dtype == null || this.dtype === "float32") {
+      return value;
+    }
+    return Math.round(value);
+  }
+  isValidTruncated(value) {
+    return value <= this.upper && value >= this.lower;
+  }
+};
+var RandGamma = class {
+  constructor(alpha2, beta, dtype, seed) {
+    this.alpha = alpha2;
+    this.beta = 1 / beta;
+    this.dtype = dtype;
+    const seedValue = seed ? seed : Math.random();
+    this.randu = seedrandom.alea(seedValue.toString());
+    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
+    if (alpha2 < 1) {
+      this.d = alpha2 + 2 / 3;
+    } else {
+      this.d = alpha2 - 1 / 3;
+    }
+    this.c = 1 / Math.sqrt(9 * this.d);
+  }
+  nextValue() {
+    let x2, v0, v1, x, u, v;
+    while (true) {
+      do {
+        x = this.randn.nextValue();
+        v = 1 + this.c * x;
+      } while (v <= 0);
+      v *= v * v;
+      x2 = x * x;
+      v0 = 1 - 0.331 * x2 * x2;
+      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
+      u = this.randu();
+      if (u < v0 || Math.log(u) < v1) {
+        break;
+      }
+    }
+    v = 1 / this.beta * this.d * v;
+    if (this.alpha < 1) {
+      v *= Math.pow(this.randu(), 1 / this.alpha);
+    }
+    return this.convertValue(v);
+  }
+  convertValue(value) {
+    if (this.dtype === "float32") {
+      return value;
+    }
+    return Math.round(value);
+  }
+};
+var UniformRandom = class {
+  constructor(min7 = 0, max7 = 1, dtype, seed) {
+    this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
+    this.min = min7;
+    this.range = max7 - min7;
+    this.dtype = dtype;
+    if (seed == null) {
+      seed = Math.random();
+    }
+    if (typeof seed === "number") {
+      seed = seed.toString();
+    }
+    if (!this.canReturnFloat() && this.range <= 1) {
+      throw new Error(`The difference between ${min7} - ${max7} <= 1 and dtype is not float`);
+    }
+    this.random = seedrandom.alea(seed);
+  }
+  convertValue(value) {
+    if (this.canReturnFloat()) {
+      return value;
+    }
+    return Math.round(value);
+  }
+  nextValue() {
+    return this.convertValue(this.min + this.range * this.random());
+  }
+};
+function randomGamma_(shape, alpha2, beta = 1, dtype = "float32", seed) {
+  if (beta == null) {
+    beta = 1;
+  }
+  if (dtype == null) {
+    dtype = "float32";
+  }
+  if (dtype !== "float32" && dtype !== "int32") {
+    throw new Error(`Unsupported data type ${dtype}`);
+  }
+  const rgamma = new RandGamma(alpha2, beta, dtype, seed);
+  const res = buffer(shape, dtype);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = rgamma.nextValue();
+  }
+  return res.toTensor();
+}
+var randomGamma = op({ randomGamma_ });
+function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
+  if (dtype != null && dtype === "bool") {
+    throw new Error(`Unsupported data type ${dtype}`);
+  }
+  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
+  const res = buffer(shape, dtype);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = randGauss.nextValue();
+  }
+  return res.toTensor();
+}
+var randomNormal = op({ randomNormal_ });
+function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
+  const res = buffer(shape, dtype);
+  const random = new UniformRandom(minval, maxval, null, seed);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = random.nextValue();
+  }
+  return res.toTensor();
+}
+var randomUniform = op({ randomUniform_ });
+function range(start, stop, step5 = 1, dtype = "float32") {
+  if (step5 === 0) {
+    throw new Error("Cannot have a step of zero");
+  }
+  const attrs = { start, stop, step: step5, dtype };
+  return ENGINE.runKernel(Range, {}, attrs);
+}
+function reciprocal_(x) {
+  const $x = convertToTensor(x, "x", "reciprocal");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Reciprocal, inputs);
+}
+var reciprocal = op({ reciprocal_ });
+function relu_(x) {
+  const $x = convertToTensor(x, "x", "relu");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Relu, inputs);
+}
+var relu = op({ relu_ });
+function relu6_(x) {
+  const $x = convertToTensor(x, "x", "relu6");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Relu6, inputs);
+}
+var relu6 = op({ relu6_ });
+function reverse_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  const inputs = { x: $x };
+  const attrs = { dims: axis };
+  return ENGINE.runKernel(Reverse, inputs, attrs);
+}
+var reverse = op({ reverse_ });
+function reverse1d_(x) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
+  return reverse($x, 0);
+}
+var reverse1d = op({ reverse1d_ });
+function reverse2d_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
+  return reverse($x, axis);
+}
+var reverse2d = op({ reverse2d_ });
+function reverse3d_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
+  return reverse($x, axis);
+}
+var reverse3d = op({ reverse3d_ });
+function reverse4d_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
+  return reverse($x, axis);
+}
+var reverse4d = op({ reverse4d_ });
+function round_(x) {
+  const $x = convertToTensor(x, "x", "round");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Round, inputs);
+}
+var round2 = op({ round_ });
+function rsqrt_(x) {
+  const $x = convertToTensor(x, "x", "rsqrt", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Rsqrt, inputs);
+}
+var rsqrt = op({ rsqrt_ });
+function selu_(x) {
+  const $x = convertToTensor(x, "x", "selu");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Selu, inputs);
+}
+var selu = op({ selu_ });
+function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides2, pad3, dilation = [1, 1], dataFormat = "NHWC") {
+  const $x = convertToTensor(x, "x", "separableConv2d");
+  const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
+  const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  if (dataFormat === "NCHW") {
+    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
+  }
+  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
+  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
+  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
+  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
+  const inChannels = $depthwiseFilter.shape[2];
+  const channelMultiplier = $depthwiseFilter.shape[3];
+  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
+  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides2, pad3, dataFormat, dilation);
+  const pointwiseStride = 1;
+  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var separableConv2d = op({ separableConv2d_ });
+async function setdiff1dAsync_(x, y) {
+  const $x = convertToTensor(x, "x", "setdiff1d");
+  const $y = convertToTensor(y, "y", "setdiff1d");
+  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
+  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
+  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
+  const xVals = await $x.data();
+  const yVals = await $y.data();
+  const ySet = new Set(yVals);
+  let outputSize2 = 0;
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    if (!ySet.has(xVals[i2])) {
+      outputSize2++;
+    }
+  }
+  const buffer2 = new TensorBuffer([outputSize2], $x.dtype);
+  const indices = new TensorBuffer([outputSize2], "int32");
+  for (let i2 = 0, p2 = 0; i2 < xVals.length; i2++) {
+    if (!ySet.has(xVals[i2])) {
+      buffer2.values[p2] = xVals[i2];
+      indices.values[p2] = i2;
+      p2++;
+    }
+  }
+  return [buffer2.toTensor(), indices.toTensor()];
+}
+var setdiff1dAsync = setdiff1dAsync_;
+function sign_(x) {
+  const $x = convertToTensor(x, "x", "sign");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sign, inputs);
+}
+var sign = op({ sign_ });
+function sin_(x) {
+  const $x = convertToTensor(x, "x", "sin", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sin, inputs);
+}
+var sin = op({ sin_ });
+function sinh_(x) {
+  const $x = convertToTensor(x, "x", "sinh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sinh, inputs);
+}
+var sinh = op({ sinh_ });
+function slice1d_(x, begin, size2) {
+  const $x = convertToTensor(x, "x", "slice1d");
+  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, [begin], [size2]);
+}
+var slice1d = op({ slice1d_ });
+function slice2d_(x, begin, size2) {
+  const $x = convertToTensor(x, "x", "slice2d");
+  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, begin, size2);
+}
+var slice2d = op({ slice2d_ });
+function slice3d_(x, begin, size2) {
+  const $x = convertToTensor(x, "x", "slice3d");
+  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, begin, size2);
+}
+var slice3d = op({ slice3d_ });
+function slice4d_(x, begin, size2) {
+  const $x = convertToTensor(x, "x", "slice4d");
+  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, begin, size2);
+}
+var slice4d = op({ slice4d_ });
+function softmax_(logits, dim = -1) {
+  const $logits = convertToTensor(logits, "logits", "softmax", "float32");
+  if (dim === -1) {
+    dim = $logits.rank - 1;
+  }
+  if (dim !== $logits.rank - 1) {
+    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
+  }
+  const inputs = { logits: $logits };
+  const attrs = { dim };
+  return ENGINE.runKernel(Softmax, inputs, attrs);
+}
+var softmax = op({ softmax_ });
+function fft_(input2) {
+  assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);
+  const inputs = { input: input2 };
+  return ENGINE.runKernel(FFT, inputs);
+}
+var fft = op({ fft_ });
+function ifft_(input2) {
+  assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);
+  const inputs = { input: input2 };
+  return ENGINE.runKernel(IFFT, inputs);
+}
+var ifft = op({ ifft_ });
+function irfft_(input2) {
+  const innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = input2.size / innerDimensionSize;
+  let ret;
+  if (innerDimensionSize <= 2) {
+    const complexInput = reshape(input2, [batch, innerDimensionSize]);
+    ret = ifft(complexInput);
+  } else {
+    const outputShape = [batch, 2 * (innerDimensionSize - 1)];
+    const realInput = reshape(real(input2), [batch, innerDimensionSize]);
+    const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);
+    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
+    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
+    const r2 = concat([realInput, realConjugate], 1);
+    const i2 = concat([imagInput, imagConjugate], 1);
+    const complexInput = reshape(complex(r2, i2), [outputShape[0], outputShape[1]]);
+    ret = ifft(complexInput);
+  }
+  ret = real(ret);
+  if (input2.rank === 3 && input2.shape[0] !== 0) {
+    const temp = ret;
+    const batch2 = input2.shape[0];
+    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
+    temp.dispose();
+  }
+  return ret;
+}
+var irfft = op({ irfft_ });
+function split_(x, numOrSizeSplits, axis = 0) {
+  const $x = convertToTensor(x, "x", "split");
+  const inputs = { x: $x };
+  const attr = { numOrSizeSplits, axis };
+  return ENGINE.runKernel(SplitV, inputs, attr);
+}
+var split = op({ split_ });
+function rfft_(input2, fftLength) {
+  assert(input2.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);
+  let innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = input2.size / innerDimensionSize;
+  let adjustedInput;
+  if (fftLength != null && fftLength < innerDimensionSize) {
+    const begin = input2.shape.map((v) => 0);
+    const size2 = input2.shape.map((v) => v);
+    size2[input2.shape.length - 1] = fftLength;
+    adjustedInput = slice(input2, begin, size2);
+    innerDimensionSize = fftLength;
+  } else if (fftLength != null && fftLength > innerDimensionSize) {
+    const zerosShape = input2.shape.map((v) => v);
+    zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;
+    adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);
+    innerDimensionSize = fftLength;
+  } else {
+    adjustedInput = input2;
+  }
+  const zerosInput = zerosLike(adjustedInput);
+  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
+  const ret = fft(complexInput);
+  const half2 = Math.floor(innerDimensionSize / 2) + 1;
+  const realValues = real(ret);
+  const imagValues = imag(ret);
+  const realComplexConjugate = split(realValues, [half2, innerDimensionSize - half2], realValues.shape.length - 1);
+  const imagComplexConjugate = split(imagValues, [half2, innerDimensionSize - half2], imagValues.shape.length - 1);
+  const outputShape = adjustedInput.shape.slice();
+  outputShape[adjustedInput.shape.length - 1] = half2;
+  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
+}
+var rfft = op({ rfft_ });
+function squaredDifference_(a6, b) {
+  let $a = convertToTensor(a6, "a", "squaredDifference");
+  let $b = convertToTensor(b, "b", "squaredDifference");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  const attrs = {};
+  return ENGINE.runKernel(SquaredDifference, inputs, attrs);
+}
+var squaredDifference = op({ squaredDifference_ });
+function squeeze_(x, axis) {
+  const $x = convertToTensor(x, "x", "squeeze");
+  return reshape($x, squeezeShape($x.shape, axis).newShape);
+}
+var squeeze = op({ squeeze_ });
+function stack_(tensors, axis = 0) {
+  const $tensors = convertToTensorArray(tensors, "tensors", "stack", "string_or_numeric");
+  assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
+  if ($tensors.length > 0) {
+    assert(axis <= $tensors[0].rank, () => "Axis must be <= rank of the tensor");
+  }
+  const inputs = $tensors;
+  const attrs = { axis };
+  return ENGINE.runKernel(Pack, inputs, attrs);
+}
+var stack = op({ stack_ });
+function step_(x, alpha2 = 0) {
+  const $x = convertToTensor(x, "x", "step");
+  const inputs = { x: $x };
+  const attrs = { alpha: alpha2 };
+  return ENGINE.runKernel(Step, inputs, attrs);
+}
+var step = op({ step_ });
+function stridedSlice_(x, begin, end, strides2, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
+  const $x = convertToTensor(x, "x", "stridedSlice", "string_or_numeric");
+  const inputs = { x: $x };
+  const attrs = {
+    begin,
+    end,
+    strides: strides2,
+    beginMask,
+    endMask,
+    ellipsisMask,
+    newAxisMask,
+    shrinkAxisMask
+  };
+  return ENGINE.runKernel(StridedSlice, inputs, attrs);
+}
+var stridedSlice = op({ stridedSlice_ });
+function tan_(x) {
+  const $x = convertToTensor(x, "x", "tan", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Tan, inputs);
+}
+var tan = op({ tan_ });
+function tensor1d(values, dtype) {
+  assertNonNull(values);
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 1) {
+    throw new Error("tensor1d() requires values to be a flat/TypedArray");
+  }
+  const shape = null;
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+function tensor2d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 2) {
+    throw new Error("tensor2d() requires shape to have two numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 2 && inferredShape.length !== 1) {
+    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+function tensor4d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 4) {
+    throw new Error("tensor4d() requires shape to have four numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 4 && inferredShape.length !== 1) {
+    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+function tensor5d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 5) {
+    throw new Error("tensor5d() requires shape to have five numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 5 && inferredShape.length !== 1) {
+    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+function tensor6d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 6) {
+    throw new Error("tensor6d() requires shape to have six numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 6 && inferredShape.length !== 1) {
+    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
+  }
+  shape = shape || inferredShape;
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+function topk_(x, k = 1, sorted = true) {
+  const $x = convertToTensor(x, "x", "topk");
+  if ($x.rank === 0) {
+    throw new Error("topk() expects the input to be of rank 1 or higher");
+  }
+  const lastDim = $x.shape[$x.shape.length - 1];
+  if (k < 0) {
+    throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);
+  }
+  if (k > lastDim) {
+    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
+  }
+  const inputs = { x: $x };
+  const attrs = { k, sorted };
+  const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);
+  return { values, indices };
+}
+var topk = op({ topk_ });
+function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
+  if (dtype != null && dtype === "bool") {
+    throw new Error(`Unsupported data type $ { dtype }`);
+  }
+  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
+  const res = buffer(shape, dtype);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = randGauss.nextValue();
+  }
+  return res.toTensor();
+}
+var truncatedNormal = op({ truncatedNormal_ });
+function unique_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "unique", "string_or_numeric");
+  assert($x.rank > 0, () => "The input tensor must be at least 1D");
+  const inputs = { x: $x };
+  const attrs = { axis };
+  const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);
+  return { values, indices };
+}
+var unique = op({ unique_ });
+function unsortedSegmentSum_(x, segmentIds, numSegments) {
+  const $x = convertToTensor(x, "x", "unsortedSegmentSum");
+  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
+  assert(isInt(numSegments), () => "numSegments must be of dtype int");
+  const inputs = { x: $x, segmentIds: $segmentIds };
+  const attrs = { numSegments };
+  return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);
+}
+var unsortedSegmentSum = op({ unsortedSegmentSum_ });
+function unstack_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "unstack", "string_or_numeric");
+  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
+  const inputs = { value: $x };
+  const attrs = { axis };
+  return ENGINE.runKernel(Unpack, inputs, attrs);
+}
+var unstack = op({ unstack_ });
+function upperBound(sortedSequence, values) {
+  return searchSorted(sortedSequence, values, "right");
+}
+function variable(initialValue, trainable = true, name, dtype) {
+  return ENGINE.makeVariable(initialValue, trainable, name, dtype);
+}
+function whereImpl(condShape, condVals) {
+  const indices = [];
+  for (let i2 = 0; i2 < condVals.length; i2++) {
+    if (condVals[i2]) {
+      indices.push(i2);
+    }
+  }
+  const inBuffer = buffer(condShape, "int32");
+  const out = buffer([indices.length, condShape.length], "int32");
+  for (let i2 = 0; i2 < indices.length; i2++) {
+    const loc = inBuffer.indexToLoc(indices[i2]);
+    const offset = i2 * condShape.length;
+    out.values.set(loc, offset);
+  }
+  return out.toTensor();
+}
+async function whereAsync_(condition) {
+  const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
+  const vals = await $condition.data();
+  const res = whereImpl($condition.shape, vals);
+  if (condition !== $condition) {
+    $condition.dispose();
+  }
+  return res;
+}
+var whereAsync = whereAsync_;
+async function booleanMaskAsync_(tensor2, mask2, axis) {
+  const $tensor = convertToTensor(tensor2, "tensor", "boolMask");
+  const $mask = convertToTensor(mask2, "mask", "boolMask", "bool");
+  const axisFrom = axis == null ? 0 : axis;
+  const maskDim = $mask.rank;
+  const tensorShape = $tensor.shape;
+  assert(maskDim > 0, () => "mask cannot be scalar");
+  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
+  let leadingSize = 1;
+  for (let i2 = axisFrom; i2 < axisFrom + maskDim; i2++) {
+    leadingSize *= tensorShape[i2];
+  }
+  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
+  const reshapedTensor = reshape($tensor, targetTensorShape);
+  const reshapedMask = reshape($mask, [-1]);
+  const positivePositions = await whereAsync(reshapedMask);
+  const indices = squeeze(positivePositions, [1]);
+  const res = gather(reshapedTensor, indices, axisFrom);
+  if (tensor2 !== $tensor) {
+    $tensor.dispose();
+  }
+  if (mask2 !== $mask) {
+    $mask.dispose();
+  }
+  indices.dispose();
+  reshapedTensor.dispose();
+  reshapedMask.dispose();
+  positivePositions.dispose();
+  return res;
+}
+var booleanMaskAsync = booleanMaskAsync_;
+function movingAverage_(v, x, decay, step5, zeroDebias = true) {
+  const $v = convertToTensor(v, "v", "movingAverage");
+  const $x = convertToTensor(x, "x", "movingAverage");
+  const $decay = convertToTensor(decay, "decay", "movingAverage");
+  assertTypesMatch($v, $x);
+  assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
+  const one = scalar(1);
+  const oneMinusDecay = sub(one, $decay);
+  let update = mul(sub($x, $v), oneMinusDecay);
+  if (zeroDebias) {
+    assert(step5 != null, () => "When using zeroDebias: true, step is required.");
+    const $step = convertToTensor(step5, "step", "movingAverage");
+    update = div(update, sub(one, pow($decay, $step)));
+  }
+  return add2($v, update);
+}
+var movingAverage = op({ movingAverage_ });
+function scatterND_(indices, updates, shape) {
+  const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
+  const $updates = convertToTensor(updates, "updates", "scatterND");
+  validateInput($updates, $indices, shape);
+  const inputs = { indices: $indices, updates: $updates };
+  const attrs = { shape };
+  return ENGINE.runKernel(ScatterNd, inputs, attrs);
+}
+var scatterND = op({ scatterND_ });
+function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
+  if (sparseIndices.dtype !== "int32") {
+    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
+  }
+  if (sparseIndices.rank > 2) {
+    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
+  }
+  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
+  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
+  if (outputShape.length !== numDims) {
+    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
+  }
+  const numValues = sparseValues.size;
+  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
+    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
+  }
+  if (sparseValues.dtype !== defaultValues.dtype) {
+    throw new Error("sparseValues.dtype must match defaultValues.dtype");
+  }
+}
+function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
+  const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
+  const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense", "string_or_numeric");
+  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
+  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
+  const inputs = {
+    sparseIndices: $sparseIndices,
+    sparseValues: $sparseValues,
+    defaultValue: $defaultValue
+  };
+  const attrs = { outputShape };
+  return ENGINE.runKernel(SparseToDense, inputs, attrs);
+}
+var sparseToDense = op({ sparseToDense_ });
+function gatherND_(x, indices) {
+  const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
+  const $x = convertToTensor(x, "x", "gatherND", "string_or_numeric");
+  const inputs = { params: $x, indices: $indices };
+  return ENGINE.runKernel(GatherNd, inputs);
+}
+var gatherND = op({ gatherND_ });
+function getNoiseShape(x, noiseShape) {
+  if (noiseShape == null) {
+    return x.shape.slice();
+  }
+  if (arraysEqual(x.shape, noiseShape)) {
+    return noiseShape;
+  }
+  if (x.shape.length === noiseShape.length) {
+    const newDimension = [];
+    for (let i2 = 0; i2 < x.shape.length; i2++) {
+      if (noiseShape[i2] == null && x.shape[i2] != null) {
+        newDimension.push(x.shape[i2]);
+      } else {
+        newDimension.push(noiseShape[i2]);
+      }
+    }
+    return newDimension;
+  }
+  return noiseShape;
+}
+function dropout_(x, rate, noiseShape, seed) {
+  const $x = convertToTensor(x, "x", "dropout");
+  assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
+  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
+  if (rate === 0) {
+    return x instanceof Tensor ? $x.clone() : $x;
+  }
+  const $noiseShape = getNoiseShape($x, noiseShape);
+  const keepProb = 1 - rate;
+  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
+  return mul($x, multiplier);
+}
+var dropout = op({ dropout_ });
+function enclosingPowerOfTwo(value) {
+  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
+}
+function cosineWindow(windowLength, a6, b) {
+  const even = 1 - windowLength % 2;
+  const newValues = new Float32Array(windowLength);
+  for (let i2 = 0; i2 < windowLength; ++i2) {
+    const cosArg = 2 * Math.PI * i2 / (windowLength + even - 1);
+    newValues[i2] = a6 - b * Math.cos(cosArg);
+  }
+  return tensor1d(newValues, "float32");
+}
+async function inTopKAsync_(predictions, targets, k = 1) {
+  const $predictions = convertToTensor(predictions, "predictions", "inTopK");
+  const $targets = convertToTensor(targets, "targets", "inTopK");
+  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
+  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
+  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
+  const lastDim = $predictions.shape[$predictions.shape.length - 1];
+  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
+  const predictionsVals = await $predictions.data();
+  const targetsVals = await $targets.data();
+  const [batch, size2] = [predictionsVals.length / lastDim, lastDim];
+  const precision3 = getTypedArrayFromDType("bool", batch);
+  for (let b = 0; b < batch; b++) {
+    const offset = b * size2;
+    const vals = predictionsVals.subarray(offset, offset + size2);
+    const valAndInd = [];
+    for (let i2 = 0; i2 < vals.length; i2++) {
+      valAndInd.push({ value: vals[i2], index: i2 });
+    }
+    valAndInd.sort((a6, b2) => b2.value - a6.value);
+    precision3[b] = 0;
+    for (let i2 = 0; i2 < k; i2++) {
+      if (valAndInd[i2].index === targetsVals[b]) {
+        precision3[b] = 1;
+        break;
+      }
+    }
+  }
+  if (predictions !== $predictions) {
+    $predictions.dispose();
+  }
+  if (targets !== $targets) {
+    $targets.dispose();
+  }
+  return tensor(precision3, $targets.shape, "bool");
+}
+var inTopKAsync = inTopKAsync_;
+var fused_ops_exports = {};
+__export2(fused_ops_exports, {
+  conv2d: () => conv2d2,
+  depthwiseConv2d: () => depthwiseConv2d2,
+  matMul: () => matMul2
+});
+function conv2DBackpropFilter_(x, dy, filterShape, strides2, pad3, dataFormat = "NHWC", dimRoundingMode) {
+  let x4D = x;
+  if (x.rank === 3) {
+    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+  }
+  let dy4D = dy;
+  if (dy4D.rank === 3) {
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
+  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
+  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
+  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
+  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
+  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
+  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
+  checkPadOnDimRoundingMode("conv2dDerFilter", pad3, dimRoundingMode);
+  const inputs = { x: x4D, dy: dy4D };
+  const attrs = { strides: strides2, pad: pad3, dataFormat, dimRoundingMode, filterShape };
+  return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);
+}
+var conv2DBackpropFilter = op({ conv2DBackpropFilter_ });
+function getFusedDyActivation(dy, y, activation2) {
+  if (activation2 == null || activation2 === "linear") {
+    return dy;
+  }
+  if (activation2 === "relu") {
+    return mul(dy, step(y));
+  }
+  throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);
+}
+function getFusedBiasGradient(bias, dyActivation) {
+  let res = dyActivation;
+  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
+  if (reduceAxes.length > 0) {
+    res = sum2(res, reduceAxes);
+  }
+  return reshape(res, bias.shape);
+}
+function applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {
+  if (activation2 === "linear") {
+    return x;
+  } else if (activation2 === "relu") {
+    return relu(x);
+  } else if (activation2 === "elu") {
+    return elu(x);
+  } else if (activation2 === "relu6") {
+    return relu6(x);
+  } else if (activation2 === "prelu") {
+    return prelu(x, preluActivationWeights);
+  } else if (activation2 === "leakyrelu") {
+    return leakyRelu(x, leakyreluAlpha);
+  } else if (activation2 === "sigmoid") {
+    return sigmoid(x);
+  }
+  throw new Error(`Unknown fused activation ${activation2}.`);
+}
+var shouldFuse = (gradientDepth, activation2) => {
+  const gradientMode = gradientDepth > 0;
+  return !gradientMode || activation2 === "linear";
+};
+function fusedConv2d_({ x, filter, strides: strides2, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) {
+  activation2 = activation2 || "linear";
+  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
+    assert(dataFormat === "NHWC", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
+    let result = conv2d(x, filter, strides2, pad3, dataFormat, dilations, dimRoundingMode);
+    if (bias != null) {
+      result = add2(result, bias);
+    }
+    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
+  }
+  const $x = convertToTensor(x, "x", "conv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "conv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  checkPadOnDimRoundingMode("fused conv2d", pad3, dimRoundingMode);
+  const inputChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
+  assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`);
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides2, dilations, pad3, dimRoundingMode);
+  let $bias;
+  if (bias != null) {
+    $bias = convertToTensor(bias, "bias", "fused conv2d");
+    [$bias] = makeTypesMatch($bias, $x);
+    if (dataFormat === "NHWC") {
+      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
+    } else {
+      assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);
+      assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);
+    }
+  }
+  let $preluActivationWeights;
+  if (preluActivationWeights != null) {
+    const alphaShape = preluActivationWeights.shape;
+    assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);
+    if (alphaShape.length === 1) {
+      assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);
+    } else if (alphaShape.length === 3) {
+      try {
+        assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
+      } catch (e2) {
+        const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;
+        throw Error(errMsg);
+      }
+    }
+    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
+  }
+  const grad2 = (dy, saved) => {
+    assert(dataFormat === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
+    const [$filter2, x4D2, y, $bias2] = saved;
+    const dyActivation = getFusedDyActivation(dy, y, activation2);
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
+    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides2, pad3);
+    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides2, pad3);
+    const der = [xDer, filterDer];
+    if ($bias2 != null) {
+      const biasDer = getFusedBiasGradient($bias2, dyActivation);
+      der.push(biasDer);
+    }
+    return der;
+  };
+  const inputs = {
+    x: x4D,
+    filter: $filter,
+    bias: $bias,
+    preluActivationWeights: $preluActivationWeights
+  };
+  const attrs = {
+    strides: strides2,
+    pad: pad3,
+    dataFormat,
+    dilations,
+    dimRoundingMode,
+    activation: activation2,
+    leakyreluAlpha
+  };
+  if (bias == null) {
+    const customOp = customGrad((x4D2, filter2, save) => {
+      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
+      save([filter2, x4D2, res]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOp(x4D, $filter);
+  } else {
+    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
+      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
+      save([filter2, x4D2, res, bias2]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOpWithBias(x4D, $filter, $bias);
+  }
+}
+var conv2d2 = op({ fusedConv2d_ });
+function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides2, pad3, dilations = [1, 1], dimRoundingMode) {
+  let x4D = x;
+  if (x.rank === 3) {
+    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+  }
+  let dy4D = dy;
+  if (dy4D.rank === 3) {
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+  }
+  const inputs = { x: x4D, dy: dy4D };
+  const attrs = { strides: strides2, pad: pad3, dimRoundingMode, dilations, filterShape };
+  return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);
+}
+var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });
+function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides2, pad3, dilations = [1, 1], dimRoundingMode) {
+  let dy4D = dy;
+  let reshapedTo4D = false;
+  if (dy.rank === 3) {
+    reshapedTo4D = true;
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+  }
+  const inputs = { dy: dy4D, filter };
+  const attrs = { strides: strides2, pad: pad3, dimRoundingMode, dilations, inputShape: xShape };
+  const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });
+function fusedDepthwiseConv2d_({ x, filter, strides: strides2, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) {
+  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
+    let result = depthwiseConv2d(x, filter, strides2, pad3, dataFormat, dilations, dimRoundingMode);
+    if (bias != null) {
+      result = add2(result, bias);
+    }
+    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
+  }
+  const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
+  if (dilations == null) {
+    dilations = [1, 1];
+  }
+  assert(eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  checkPadOnDimRoundingMode("fused depthwiseConv2d", pad3, dimRoundingMode);
+  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides2, dilations, pad3, dimRoundingMode, true);
+  let $bias;
+  if (bias != null) {
+    $bias = convertToTensor(bias, "bias", "fused conv2d");
+    [$bias] = makeTypesMatch($bias, $x);
+    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
+  }
+  let $preluActivationWeights;
+  if (preluActivationWeights != null) {
+    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
+  }
+  const grad2 = (dy, saved) => {
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
+    const [$filter2, x4D2, y, bias2] = saved;
+    const dyActivation = getFusedDyActivation(dy, y, activation2);
+    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides2, pad3, dilations, dimRoundingMode);
+    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides2, pad3, dilations, dimRoundingMode);
+    if (bias2 != null) {
+      const biasDer = getFusedBiasGradient($bias, dyActivation);
+      return [xDer, filterDer, biasDer];
+    }
+    return [xDer, filterDer];
+  };
+  const inputs = {
+    x: x4D,
+    filter: $filter,
+    bias: $bias,
+    preluActivationWeights: $preluActivationWeights
+  };
+  const attrs = {
+    strides: strides2,
+    pad: pad3,
+    dataFormat,
+    dilations,
+    dimRoundingMode,
+    activation: activation2,
+    leakyreluAlpha
+  };
+  if (bias == null) {
+    const customOp = customGrad((x4D2, filter2, save) => {
+      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
+      save([filter2, x4D2, res]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOp(x4D, $filter);
+  } else {
+    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
+      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
+      save([filter2, x4D2, res, bias2]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOpWithBias(x4D, $filter, $bias);
+  }
+}
+var depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });
+function fusedMatMul_({ a: a6, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) {
+  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
+    let result = matMul(a6, b, transposeA, transposeB);
+    if (bias != null) {
+      result = add2(result, bias);
+    }
+    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
+  }
+  let $a = convertToTensor(a6, "a", "fused matMul");
+  let $b = convertToTensor(b, "b", "fused matMul");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
+  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
+  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
+  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
+  const outerDimsA = $a.shape.slice(0, -2);
+  const outerDimsB = $b.shape.slice(0, -2);
+  const batchDimA = sizeFromShape(outerDimsA);
+  const batchDimB = sizeFromShape(outerDimsB);
+  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
+  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
+  let $bias;
+  if (bias != null) {
+    $bias = convertToTensor(bias, "bias", "fused matMul");
+    [$bias] = makeTypesMatch($bias, $a);
+    assertAndGetBroadcastShape(outShape, $bias.shape);
+  }
+  let $preluActivationWeights;
+  if (preluActivationWeights != null) {
+    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
+  }
+  const grad2 = (dy, saved) => {
+    const [a3D2, b3D2, y, $bias2] = saved;
+    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);
+    let aDer;
+    let bDer;
+    if (!transposeA && !transposeB) {
+      aDer = matMul(dyActivation, b3D2, false, true);
+      bDer = matMul(a3D2, dyActivation, true, false);
+    } else if (!transposeA && transposeB) {
+      aDer = matMul(dyActivation, b3D2, false, false);
+      bDer = matMul(dyActivation, a3D2, true, false);
+    } else if (transposeA && !transposeB) {
+      aDer = matMul(b3D2, dyActivation, false, true);
+      bDer = matMul(a3D2, dyActivation, false, false);
+    } else {
+      aDer = matMul(b3D2, dyActivation, true, true);
+      bDer = matMul(dyActivation, a3D2, true, true);
+    }
+    if (bias != null) {
+      const biasDer = getFusedBiasGradient($bias2, dyActivation);
+      return [aDer, bDer, biasDer];
+    } else {
+      return [aDer, bDer];
+    }
+  };
+  const inputs = {
+    a: a3D,
+    b: b3D,
+    bias: $bias,
+    preluActivationWeights: $preluActivationWeights
+  };
+  const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha };
+  if (bias == null) {
+    const customOp = customGrad((a3D2, b3D2, save) => {
+      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
+      save([a3D2, b3D2, res]);
+      return { value: reshape(res, outShape), gradFunc: grad2 };
+    });
+    return customOp(a3D, b3D);
+  } else {
+    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
+      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
+      save([a3D2, b3D2, res, $bias2]);
+      return { value: reshape(res, outShape), gradFunc: grad2 };
+    });
+    return customOpWithBias(a3D, b3D, $bias);
+  }
+}
+var matMul2 = op({ fusedMatMul_ });
+function hammingWindow_(windowLength) {
+  return cosineWindow(windowLength, 0.54, 0.46);
+}
+var hammingWindow = op({ hammingWindow_ });
+function hannWindow_(windowLength) {
+  return cosineWindow(windowLength, 0.5, 0.5);
+}
+var hannWindow = op({ hannWindow_ });
+function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
+  let start = 0;
+  const output = [];
+  while (start + frameLength <= signal2.size) {
+    output.push(slice(signal2, start, frameLength));
+    start += frameStep;
+  }
+  if (padEnd) {
+    while (start < signal2.size) {
+      const padLen = start + frameLength - signal2.size;
+      const pad3 = concat([
+        slice(signal2, start, frameLength - padLen),
+        fill([padLen], padValue)
+      ]);
+      output.push(pad3);
+      start += frameStep;
+    }
+  }
+  if (output.length === 0) {
+    return tensor2d([], [0, frameLength]);
+  }
+  return reshape(concat(output), [output.length, frameLength]);
+}
+var frame = op({ frame_ });
+function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
+  if (fftLength == null) {
+    fftLength = enclosingPowerOfTwo(frameLength);
+  }
+  const framedSignal = frame(signal2, frameLength, frameStep);
+  const windowedSignal = mul(framedSignal, windowFn(frameLength));
+  return rfft(windowedSignal, fftLength);
+}
+var stft = op({ stft_ });
+function cropAndResize_(image2, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) {
+  const $image = convertToTensor(image2, "image", "cropAndResize");
+  const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
+  const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
+  const numBoxes = $boxes.shape[0];
+  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
+  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
+  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
+  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
+  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
+  assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
+  const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };
+  const attrs = { method, extrapolationValue, cropSize };
+  const res = ENGINE.runKernel(CropAndResize, inputs, attrs);
+  return res;
+}
+var cropAndResize = op({ cropAndResize_ });
+function flipLeftRight_(image2) {
+  const $image = convertToTensor(image2, "image", "flipLeftRight", "float32");
+  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
+  const inputs = { image: $image };
+  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
+  return res;
+}
+var flipLeftRight = op({ flipLeftRight_ });
+function grayscaleToRGB_(image2) {
+  const $image = convertToTensor(image2, "image", "grayscaleToRGB");
+  const lastDimsIdx = $image.rank - 1;
+  const lastDims = $image.shape[lastDimsIdx];
+  assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);
+  assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);
+  const reps = new Array($image.rank);
+  reps.fill(1, 0, lastDimsIdx);
+  reps[lastDimsIdx] = 3;
+  return tile($image, reps);
+}
+var grayscaleToRGB = op({ grayscaleToRGB_ });
+function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {
+  const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32");
+  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
+  const inputs = { image: $image };
+  const attrs = { radians, fillValue, center };
+  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
+  return res;
+}
+var rotateWithOffset = op({ rotateWithOffset_ });
+function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+  if (iouThreshold == null) {
+    iouThreshold = 0.5;
+  }
+  if (scoreThreshold == null) {
+    scoreThreshold = Number.NEGATIVE_INFINITY;
+  }
+  if (softNmsSigma == null) {
+    softNmsSigma = 0;
+  }
+  const numBoxes = boxes.shape[0];
+  maxOutputSize = Math.min(maxOutputSize, numBoxes);
+  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
+  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
+  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
+  assert(scores.rank === 1, () => "scores must be a 1D tensor");
+  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
+  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
+  return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };
+}
+function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression", "float32");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression", "float32");
+  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+  maxOutputSize = inputs.maxOutputSize;
+  iouThreshold = inputs.iouThreshold;
+  scoreThreshold = inputs.scoreThreshold;
+  const attrs = { maxOutputSize, iouThreshold, scoreThreshold };
+  return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);
+}
+var nonMaxSuppression = op({ nonMaxSuppression_ });
+function binaryInsert(arr, element, comparator) {
+  const index2 = binarySearch(arr, element, comparator);
+  const insertionPoint = index2 < 0 ? -(index2 + 1) : index2;
+  arr.splice(insertionPoint, 0, element);
+}
+function binarySearch(arr, target, comparator) {
+  return binarySearch_(arr, target, comparator || defaultComparator);
+}
+function defaultComparator(a6, b) {
+  return a6 > b ? 1 : a6 < b ? -1 : 0;
+}
+function binarySearch_(arr, target, comparator) {
+  let left = 0;
+  let right = arr.length;
+  let middle2 = 0;
+  let found = false;
+  while (left < right) {
+    middle2 = left + (right - left >>> 1);
+    const compareResult = comparator(target, arr[middle2]);
+    if (compareResult > 0) {
+      left = middle2 + 1;
+    } else {
+      right = middle2;
+      found = !compareResult;
+    }
+  }
+  return found ? left : -left - 1;
+}
+function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);
+}
+function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
+  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
+}
+function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
+}
+function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
+  const candidates = [];
+  for (let i2 = 0; i2 < scores.length; i2++) {
+    if (scores[i2] > scoreThreshold) {
+      candidates.push({ score: scores[i2], boxIndex: i2, suppressBeginIndex: 0 });
+    }
+  }
+  candidates.sort(ascendingComparator);
+  const scale22 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
+  const selectedIndices = [];
+  const selectedScores = [];
+  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
+    const candidate = candidates.pop();
+    const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;
+    if (originalScore < scoreThreshold) {
+      break;
+    }
+    let ignoreCandidate = false;
+    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
+      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
+      if (iou >= iouThreshold) {
+        ignoreCandidate = true;
+        break;
+      }
+      candidate.score = candidate.score * suppressWeight(iouThreshold, scale22, iou);
+      if (candidate.score <= scoreThreshold) {
+        break;
+      }
+    }
+    candidate.suppressBeginIndex = selectedIndices.length;
+    if (!ignoreCandidate) {
+      if (candidate.score === originalScore) {
+        selectedIndices.push(boxIndex);
+        selectedScores.push(candidate.score);
+      } else if (candidate.score > scoreThreshold) {
+        binaryInsert(candidates, candidate, ascendingComparator);
+      }
+    }
+  }
+  const validOutputs = selectedIndices.length;
+  const elemsToPad = maxOutputSize - validOutputs;
+  if (padToMaxOutputSize && elemsToPad > 0) {
+    selectedIndices.push(...new Array(elemsToPad).fill(0));
+    selectedScores.push(...new Array(elemsToPad).fill(0));
+  }
+  const result = { selectedIndices };
+  if (returnScoresTensor) {
+    result["selectedScores"] = selectedScores;
+  }
+  if (returnValidOutputs) {
+    result["validOutputs"] = validOutputs;
+  }
+  return result;
+}
+function intersectionOverUnion(boxes, i2, j) {
+  const iCoord = boxes.subarray(i2 * 4, i2 * 4 + 4);
+  const jCoord = boxes.subarray(j * 4, j * 4 + 4);
+  const yminI = Math.min(iCoord[0], iCoord[2]);
+  const xminI = Math.min(iCoord[1], iCoord[3]);
+  const ymaxI = Math.max(iCoord[0], iCoord[2]);
+  const xmaxI = Math.max(iCoord[1], iCoord[3]);
+  const yminJ = Math.min(jCoord[0], jCoord[2]);
+  const xminJ = Math.min(jCoord[1], jCoord[3]);
+  const ymaxJ = Math.max(jCoord[0], jCoord[2]);
+  const xmaxJ = Math.max(jCoord[1], jCoord[3]);
+  const areaI = (ymaxI - yminI) * (xmaxI - xminI);
+  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+  if (areaI <= 0 || areaJ <= 0) {
+    return 0;
+  }
+  const intersectionYmin = Math.max(yminI, yminJ);
+  const intersectionXmin = Math.max(xminI, xminJ);
+  const intersectionYmax = Math.min(ymaxI, ymaxJ);
+  const intersectionXmax = Math.min(xmaxI, xmaxJ);
+  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
+  return intersectionArea / (areaI + areaJ - intersectionArea);
+}
+function suppressWeight(iouThreshold, scale22, iou) {
+  const weight = Math.exp(scale22 * iou * iou);
+  return iou <= iouThreshold ? weight : 0;
+}
+function ascendingComparator(c1, c2) {
+  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
+}
+async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
+  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+  maxOutputSize = inputs.maxOutputSize;
+  iouThreshold = inputs.iouThreshold;
+  scoreThreshold = inputs.scoreThreshold;
+  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
+  const boxesVals = boxesAndScores[0];
+  const scoresVals = boxesAndScores[1];
+  const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  if ($boxes !== boxes) {
+    $boxes.dispose();
+  }
+  if ($scores !== scores) {
+    $scores.dispose();
+  }
+  return tensor1d(selectedIndices, "int32");
+}
+var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
+function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  maxOutputSize = params.maxOutputSize;
+  iouThreshold = params.iouThreshold;
+  scoreThreshold = params.scoreThreshold;
+  softNmsSigma = params.softNmsSigma;
+  const inputs = { boxes: $boxes, scores: $scores };
+  const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };
+  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
+  return { selectedIndices: result[0], selectedScores: result[1] };
+}
+var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });
+async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  maxOutputSize = params.maxOutputSize;
+  iouThreshold = params.iouThreshold;
+  scoreThreshold = params.scoreThreshold;
+  softNmsSigma = params.softNmsSigma;
+  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
+  const boxesVals = boxesAndScores[0];
+  const scoresVals = boxesAndScores[1];
+  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  if ($boxes !== boxes) {
+    $boxes.dispose();
+  }
+  if ($scores !== scores) {
+    $scores.dispose();
+  }
+  return {
+    selectedIndices: tensor1d(selectedIndices, "int32"),
+    selectedScores: tensor1d(selectedScores)
+  };
+}
+var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
+function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
+  const $maxOutputSize = params.maxOutputSize;
+  const $iouThreshold = params.iouThreshold;
+  const $scoreThreshold = params.scoreThreshold;
+  const inputs = { boxes: $boxes, scores: $scores };
+  const attrs = {
+    maxOutputSize: $maxOutputSize,
+    iouThreshold: $iouThreshold,
+    scoreThreshold: $scoreThreshold,
+    padToMaxOutputSize
+  };
+  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
+  return { selectedIndices: result[0], validOutputs: result[1] };
+}
+var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });
+async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
+  const $maxOutputSize = params.maxOutputSize;
+  const $iouThreshold = params.iouThreshold;
+  const $scoreThreshold = params.scoreThreshold;
+  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
+  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
+  if ($boxes !== boxes) {
+    $boxes.dispose();
+  }
+  if ($scores !== scores) {
+    $scores.dispose();
+  }
+  return {
+    selectedIndices: tensor1d(selectedIndices, "int32"),
+    validOutputs: scalar(validOutputs, "int32")
+  };
+}
+var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
+function resizeBilinear_(images, size2, alignCorners = false, halfPixelCenters = false) {
+  const $images = convertToTensor(images, "images", "resizeBilinear");
+  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
+  assert(size2.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size2}.`);
+  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);
+  let batchImages = $images;
+  let reshapedTo4D = false;
+  if ($images.rank === 3) {
+    reshapedTo4D = true;
+    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
+  }
+  const [] = size2;
+  const inputs = { images: batchImages };
+  const attrs = { alignCorners, halfPixelCenters, size: size2 };
+  const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var resizeBilinear = op({ resizeBilinear_ });
+function resizeNearestNeighbor_(images, size2, alignCorners = false, halfPixelCenters = false) {
+  const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
+  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
+  assert(size2.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size2}.`);
+  assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
+  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);
+  let batchImages = $images;
+  let reshapedTo4D = false;
+  if ($images.rank === 3) {
+    reshapedTo4D = true;
+    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
+  }
+  const [] = size2;
+  const inputs = { images: batchImages };
+  const attrs = { alignCorners, halfPixelCenters, size: size2 };
+  const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var resizeNearestNeighbor = op({ resizeNearestNeighbor_ });
+function threshold_(image2, method = "binary", inverted = false, threshValue = 0.5) {
+  const $image = convertToTensor(image2, "image", "threshold");
+  const RED_INTENCITY_COEF = 0.2989;
+  const GREEN_INTENCITY_COEF = 0.587;
+  const BLUE_INTENCITY_COEF = 0.114;
+  const totalPixelsInImage = $image.shape[0] * $image.shape[1];
+  let $threshold = mul(tensor1d([threshValue]), 255);
+  let r2, g, b, grayscale;
+  assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);
+  assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);
+  assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);
+  assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`);
+  if ($image.shape[2] === 3) {
+    [r2, g, b] = split($image, [1, 1, 1], -1);
+    const $r = mul(r2, RED_INTENCITY_COEF);
+    const $g = mul(g, GREEN_INTENCITY_COEF);
+    const $b = mul(b, BLUE_INTENCITY_COEF);
+    grayscale = add2(add2($r, $g), $b);
+  } else {
+    grayscale = image2;
+  }
+  if (method === "otsu") {
+    const $histogram = bincount(cast(round2(grayscale), "int32"), tensor([]), 256);
+    $threshold = otsu($histogram, totalPixelsInImage);
+  }
+  const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);
+  const result = cast(mul(invCondition, 255), "int32");
+  return result;
+}
+function otsu(histogram, total) {
+  let bestThresh = tensor1d([-1]);
+  let bestInBetVar = tensor1d([0]);
+  let cInBetVar = tensor1d([0]);
+  let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;
+  for (let index2 = 0; index2 < histogram.size - 1; index2++) {
+    classFirst = slice(histogram, 0, index2 + 1);
+    classSecond = slice(histogram, index2 + 1);
+    weightForeground = div(sum2(classFirst), total);
+    weightBack = div(sum2(classSecond), total);
+    const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));
+    meanFirst = div(meanFirstDivA, sum2(classFirst));
+    const meanSecFill = fill(classSecond.shape, classFirst.size);
+    const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);
+    const meanSecMul = mul(classSecond, meanSecAdd);
+    meanSec = div(sum2(meanSecMul), sum2(classSecond));
+    const cInBetVarSubA = sub(meanFirst, meanSec);
+    const cInBetVarSubB = sub(meanFirst, meanSec);
+    const cInBetVarMul = mul(weightForeground, weightBack);
+    cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);
+    const condition = greater(cInBetVar, bestInBetVar);
+    bestInBetVar = where(condition, cInBetVar, bestInBetVar);
+    bestThresh = where(condition, tensor1d([index2]), bestThresh);
+  }
+  return bestThresh;
+}
+var threshold = op({ threshold_ });
+function transform_(image2, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) {
+  const $image = convertToTensor(image2, "image", "transform", "float32");
+  const $transforms = convertToTensor(transforms, "transforms", "transform", "float32");
+  assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);
+  assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);
+  assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);
+  const inputs = { image: $image, transforms: $transforms };
+  const attrs = { interpolation, fillMode, fillValue, outputShape };
+  return ENGINE.runKernel(Transform, inputs, attrs);
+}
+var transform = op({ transform_ });
+function bandPart_(a6, numLower, numUpper) {
+  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
+  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
+  const $a = convertToTensor(a6, "a", "bandPart");
+  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
+  const shape = $a.shape;
+  const [M, N] = $a.shape.slice(-2);
+  if (!(numLower <= M)) {
+    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
+  }
+  if (!(numUpper <= N)) {
+    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
+  }
+  if (numLower < 0) {
+    numLower = M;
+  }
+  if (numUpper < 0) {
+    numUpper = N;
+  }
+  const i2 = reshape(range(0, M, 1, "int32"), [-1, 1]);
+  const j = range(0, N, 1, "int32");
+  const ij = sub(i2, j);
+  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
+  const zero = zeros([M, N], $a.dtype);
+  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
+}
+var bandPart = op({ bandPart_ });
+function gramSchmidt_(xs) {
+  let inputIsTensor2D;
+  if (Array.isArray(xs)) {
+    inputIsTensor2D = false;
+    assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
+    const dim = xs[0].shape[0];
+    for (let i2 = 1; i2 < xs.length; ++i2) {
+      assert(xs[i2].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i2].shape[0]} vs. ${dim})`);
+    }
+  } else {
+    inputIsTensor2D = true;
+    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
+  }
+  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
+  const ys = [];
+  const xs1d = xs;
+  for (let i2 = 0; i2 < xs.length; ++i2) {
+    ys.push(ENGINE.tidy(() => {
+      let x = xs1d[i2];
+      if (i2 > 0) {
+        for (let j = 0; j < i2; ++j) {
+          const proj = mul(sum2(mul(ys[j], x)), ys[j]);
+          x = sub(x, proj);
+        }
+      }
+      return div(x, norm(x, "euclidean"));
+    }));
+  }
+  if (inputIsTensor2D) {
+    return stack(ys, 0);
+  } else {
+    return ys;
+  }
+}
+var gramSchmidt = op({ gramSchmidt_ });
+function qr_(x, fullMatrices = false) {
+  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
+  if (x.rank === 2) {
+    return qr2d(x, fullMatrices);
+  } else {
+    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
+    const x2ds = unstack(reshape(x, [
+      outerDimsProd,
+      x.shape[x.shape.length - 2],
+      x.shape[x.shape.length - 1]
+    ]), 0);
+    const q2ds = [];
+    const r2ds = [];
+    x2ds.forEach((x2d) => {
+      const [q2d, r2d] = qr2d(x2d, fullMatrices);
+      q2ds.push(q2d);
+      r2ds.push(r2d);
+    });
+    const q = reshape(stack(q2ds, 0), x.shape);
+    const r2 = reshape(stack(r2ds, 0), x.shape);
+    return [q, r2];
+  }
+}
+function qr2d(x, fullMatrices = false) {
+  return ENGINE.tidy(() => {
+    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
+    const m = x.shape[0];
+    const n = x.shape[1];
+    let q = eye(m);
+    let r2 = clone(x);
+    const one2D = tensor2d([[1]], [1, 1]);
+    let w = clone(one2D);
+    const iters = m >= n ? n : m;
+    for (let j = 0; j < iters; ++j) {
+      const rTemp = r2;
+      const wTemp = w;
+      const qTemp = q;
+      [w, r2, q] = ENGINE.tidy(() => {
+        const rjEnd1 = slice(r2, [j, j], [m - j, 1]);
+        const normX = norm(rjEnd1);
+        const rjj = slice(r2, [j, j], [1, 1]);
+        const s2 = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
+        const u1 = sub(rjj, mul(s2, normX));
+        const wPre = div(rjEnd1, u1);
+        if (wPre.shape[0] === 1) {
+          w = clone(one2D);
+        } else {
+          w = concat([
+            one2D,
+            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
+          ], 0);
+        }
+        const tau = neg(div(matMul(s2, u1), normX));
+        const rjEndAll = slice(r2, [j, 0], [m - j, n]);
+        const tauTimesW = mul(tau, w);
+        const wT = transpose(w);
+        if (j === 0) {
+          r2 = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+        } else {
+          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+          r2 = concat([slice(r2, [0, 0], [j, n]), rTimesTau], 0);
+        }
+        const tawTimesWT = transpose(tauTimesW);
+        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
+        if (j === 0) {
+          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
+        } else {
+          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
+          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
+        }
+        return [w, r2, q];
+      });
+      dispose([rTemp, wTemp, qTemp]);
+    }
+    if (!fullMatrices && m > n) {
+      q = slice(q, [0, 0], [m, n]);
+      r2 = slice(r2, [0, 0], [n, n]);
+    }
+    return [q, r2];
+  });
+}
+var qr = op({ qr_ });
+var Reduction;
+(function(Reduction2) {
+  Reduction2[Reduction2["NONE"] = 0] = "NONE";
+  Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
+  Reduction2[Reduction2["SUM"] = 2] = "SUM";
+  Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
+})(Reduction || (Reduction = {}));
+function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
+  }
+  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
+  if (reduction === Reduction.NONE) {
+    return weightedLoss;
+  }
+  if (reduction === Reduction.SUM) {
+    return sum2(weightedLoss);
+  }
+  if (reduction === Reduction.MEAN) {
+    if ($weights == null) {
+      return mean(weightedLoss);
+    } else {
+      const broadcastFactor = $losses.size / $weights.size;
+      const result = div(sum2(weightedLoss), sum2($weights));
+      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
+    }
+  }
+  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
+    if ($weights == null) {
+      return div(sum2(weightedLoss), scalar($losses.size));
+    } else {
+      const broadcastedWeights = mul($weights, ones2($losses.shape));
+      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
+      return div(sum2(weightedLoss), numNonZeros);
+    }
+  }
+  throw Error(`Unknown reduction: ${reduction}`);
+}
+var computeWeightedLoss = op({ computeWeightedLoss_ });
+function absoluteDifference_(labels2, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels2, "labels", "absoluteDifference");
+  const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "absoluteDifference");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
+  const losses2 = abs(sub($labels, $predictions));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var absoluteDifference = op({ absoluteDifference_ });
+function cosineDistance_(labels2, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels2, "labels", "cosineDistance");
+  const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "cosineDistance");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
+  const one = scalar(1);
+  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var cosineDistance = op({ cosineDistance_ });
+function hingeLoss_(labels2, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  let $labels = convertToTensor(labels2, "labels", "hingeLoss");
+  const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "hingeLoss");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
+  const one = scalar(1);
+  $labels = sub(mul(scalar(2), $labels), one);
+  const losses2 = relu(sub(one, mul($labels, $predictions)));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var hingeLoss = op({ hingeLoss_ });
+function huberLoss_(labels2, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels2, "labels", "huberLoss");
+  const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "huberLoss");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
+  const deltaScalar = scalar(delta);
+  const error = abs(sub($predictions, $labels));
+  const quadratic = minimum(error, deltaScalar);
+  const linear = sub(error, quadratic);
+  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var huberLoss = op({ huberLoss_ });
+function logLoss_(labels2, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels2, "labels", "logLoss");
+  const $predictions = convertToTensor(predictions, "predictions", "logLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "logLoss");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
+  const one = scalar(1);
+  const epsilonScalar = scalar(epsilon3);
+  const l13 = neg(mul($labels, log22(add2($predictions, epsilonScalar))));
+  const l23 = mul(sub(one, $labels), log22(add2(sub(one, $predictions), epsilonScalar)));
+  const losses2 = sub(l13, l23);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var logLoss = op({ logLoss_ });
+function meanSquaredError_(labels2, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels2, "labels", "meanSquaredError");
+  const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "meanSquaredError");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
+  const losses2 = squaredDifference($labels, $predictions);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var meanSquaredError = op({ meanSquaredError_ });
+function sigmoidCrossEntropyWithLogits_(labels2, logits) {
+  const $labels = convertToTensor(labels2, "labels", "sigmoidCrossEntropyWithLogits");
+  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
+  assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
+  const maxOutput = relu($logits);
+  const outputXTarget = mul($logits, $labels);
+  const sigmoidOutput = log1p(exp(neg(abs($logits))));
+  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
+}
+function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
+  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
+  }
+  assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
+  if (labelSmoothing > 0) {
+    const labelSmoothingScalar = scalar(labelSmoothing);
+    const one = scalar(1);
+    const half2 = scalar(0.5);
+    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half2, labelSmoothingScalar));
+  }
+  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });
+function softmaxCrossEntropyWithLogits_(labels2, logits, dim = -1) {
+  if (dim === -1) {
+    dim = logits.rank - 1;
+  }
+  if (dim !== logits.rank - 1) {
+    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
+  }
+  const customOp = customGrad((labels22, logits2, save) => {
+    const keepDims = true;
+    const lse = logSumExp(logits2, [dim], keepDims);
+    const logResult = sub(cast(logits2, "float32"), lse);
+    save([labels22, logResult]);
+    const costVector = neg(mul(logResult, labels22));
+    const value = sum2(costVector, [dim]);
+    const gradFunc = (dy, saved) => {
+      const [labels3, logResult2] = saved;
+      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
+      return [
+        mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
+        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
+      ];
+    };
+    return { value, gradFunc };
+  });
+  return customOp(labels2, logits);
+}
+function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
+  const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
+  }
+  assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
+  if (labelSmoothing > 0) {
+    const labelSmoothingScalar = scalar(labelSmoothing);
+    const one = scalar(1);
+    const numClasses = scalar($onehotLabels.shape[1]);
+    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
+  }
+  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var softmaxCrossEntropy = op({ softmaxCrossEntropy_ });
+function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {
+  const $indices = convertToTensor(indices, "indices", "sparseFillEmptyRows", "int32");
+  const $values = convertToTensor(values, "values", "sparseFillEmptyRows");
+  const $denseShape = convertToTensor(denseShape, "denseShape", "sparseFillEmptyRows", "int32");
+  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseFillEmptyRows", $values.dtype);
+  if ($indices.rank !== 2) {
+    throw new Error(`Indices should be Tensor2D but received shape
+        ${$indices.shape}`);
+  }
+  if ($values.rank !== 1) {
+    throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);
+  }
+  if ($denseShape.rank !== 1) {
+    throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);
+  }
+  if ($defaultValue.rank !== 0) {
+    throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);
+  }
+  const inputs = {
+    indices: $indices,
+    values: $values,
+    denseShape: $denseShape,
+    defaultValue: $defaultValue
+  };
+  const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);
+  return {
+    outputIndices: result[0],
+    outputValues: result[1],
+    emptyRowIndicator: result[2],
+    reverseIndexMap: result[3]
+  };
+}
+var sparseFillEmptyRows = op({ sparseFillEmptyRows_ });
+function sparseReshape_(inputIndices, inputShape, newShape) {
+  const $inputIndices = convertToTensor(inputIndices, "inputIndices", "sparseReshape", "int32");
+  const $inputShape = convertToTensor(inputShape, "inputShape", "sparseReshape", "int32");
+  const $newShape = convertToTensor(newShape, "newShape", "sparseReshape", "int32");
+  if ($inputIndices.rank !== 2) {
+    throw new Error(`Input indices should be Tensor2D but received shape
+        ${$inputIndices.shape}`);
+  }
+  if ($inputShape.rank !== 1) {
+    throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);
+  }
+  if ($newShape.rank !== 1) {
+    throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);
+  }
+  const inputs = {
+    inputIndices: $inputIndices,
+    inputShape: $inputShape,
+    newShape: $newShape
+  };
+  const result = ENGINE.runKernel(SparseReshape, inputs);
+  return { outputIndices: result[0], outputShape: result[1] };
+}
+var sparseReshape = op({ sparseReshape_ });
+function sparseSegmentMean_(data, indices, segmentIds) {
+  const $data = convertToTensor(data, "data", "sparseSegmentMean");
+  const $indices = convertToTensor(indices, "indices", "sparseSegmentMean", "int32");
+  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentMean", "int32");
+  if ($data.rank < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if ($indices.rank !== 1) {
+    throw new Error(`Indices should be Tensor1D but received shape
+          ${$indices.shape}`);
+  }
+  if ($segmentIds.rank !== 1) {
+    throw new Error(`Segment ids should be Tensor1D but received shape
+          ${$segmentIds.shape}`);
+  }
+  const inputs = {
+    data: $data,
+    indices: $indices,
+    segmentIds: $segmentIds
+  };
+  return ENGINE.runKernel(SparseSegmentMean, inputs);
+}
+var sparseSegmentMean = op({ sparseSegmentMean_ });
+function sparseSegmentSum_(data, indices, segmentIds) {
+  const $data = convertToTensor(data, "data", "sparseSegmentSum");
+  const $indices = convertToTensor(indices, "indices", "sparseSegmentSum", "int32");
+  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentSum", "int32");
+  if ($data.rank < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if ($indices.rank !== 1) {
+    throw new Error(`Indices should be Tensor1D but received shape
+         ${$indices.shape}`);
+  }
+  if ($segmentIds.rank !== 1) {
+    throw new Error(`Segment ids should be Tensor1D but received shape
+         ${$segmentIds.shape}`);
+  }
+  const inputs = {
+    data: $data,
+    indices: $indices,
+    segmentIds: $segmentIds
+  };
+  return ENGINE.runKernel(SparseSegmentSum, inputs);
+}
+var sparseSegmentSum = op({ sparseSegmentSum_ });
+function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
+  const $data = convertToTensor(data, "data", "stringNGrams", "string");
+  if ($data.dtype !== "string") {
+    throw new Error("Data must be of datatype string");
+  }
+  if ($data.shape.length !== 1) {
+    throw new Error(`Data must be a vector, saw: ${$data.shape}`);
+  }
+  const $dataSplits = convertToTensor(dataSplits, "dataSplits", "stringNGrams");
+  if ($dataSplits.dtype !== "int32") {
+    throw new Error("Data splits must be of datatype int32");
+  }
+  const attrs = {
+    separator,
+    nGramWidths,
+    leftPad,
+    rightPad: rightPad2,
+    padWidth,
+    preserveShortSequences
+  };
+  const inputs = { data: $data, dataSplits: $dataSplits };
+  const result = ENGINE.runKernel(StringNGrams, inputs, attrs);
+  return { nGrams: result[0], nGramsSplits: result[1] };
+}
+var stringNGrams = op({ stringNGrams_ });
+function stringSplit_(input2, delimiter, skipEmpty = true) {
+  const $input = convertToTensor(input2, "input", "stringSplit", "string");
+  const $delimiter = convertToTensor(delimiter, "delimiter", "stringSplit", "string");
+  if ($input.rank !== 1) {
+    throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);
+  }
+  if ($delimiter.rank !== 0) {
+    throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);
+  }
+  const attrs = { skipEmpty };
+  const inputs = { input: $input, delimiter: $delimiter };
+  const result = ENGINE.runKernel(StringSplit, inputs, attrs);
+  return { indices: result[0], values: result[1], shape: result[2] };
+}
+var stringSplit = op({ stringSplit_ });
+function stringToHashBucketFast_(input2, numBuckets) {
+  const $input = convertToTensor(input2, "input", "stringToHashBucketFast", "string");
+  const attrs = { numBuckets };
+  if (numBuckets <= 0) {
+    throw new Error(`Number of buckets must be at least 1`);
+  }
+  const inputs = { input: $input };
+  return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);
+}
+var stringToHashBucketFast = op({ stringToHashBucketFast_ });
+var spectral = {
+  fft,
+  ifft,
+  rfft,
+  irfft
+};
+var signal = {
+  hammingWindow,
+  hannWindow,
+  frame,
+  stft
+};
+var image = {
+  flipLeftRight,
+  grayscaleToRGB,
+  resizeNearestNeighbor,
+  resizeBilinear,
+  rotateWithOffset,
+  cropAndResize,
+  nonMaxSuppression,
+  nonMaxSuppressionAsync,
+  nonMaxSuppressionWithScore,
+  nonMaxSuppressionWithScoreAsync,
+  nonMaxSuppressionPadded,
+  nonMaxSuppressionPaddedAsync,
+  threshold,
+  transform
+};
+var linalg = {
+  bandPart,
+  gramSchmidt,
+  qr
+};
+var losses = {
+  absoluteDifference,
+  computeWeightedLoss,
+  cosineDistance,
+  hingeLoss,
+  huberLoss,
+  logLoss,
+  meanSquaredError,
+  sigmoidCrossEntropy,
+  softmaxCrossEntropy
+};
+var sparse = {
+  sparseFillEmptyRows,
+  sparseReshape,
+  sparseSegmentMean,
+  sparseSegmentSum
+};
+var string = {
+  stringNGrams,
+  stringSplit,
+  stringToHashBucketFast
+};
+var Optimizer = class extends Serializable {
+  minimize(f, returnCost = false, varList) {
+    const { value, grads: grads2 } = this.computeGradients(f, varList);
+    if (varList != null) {
+      const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] }));
+      this.applyGradients(gradArray);
+    } else {
+      this.applyGradients(grads2);
+    }
+    dispose(grads2);
+    if (returnCost) {
+      return value;
+    } else {
+      value.dispose();
+      return null;
+    }
+  }
+  get iterations() {
+    if (this.iterations_ == null) {
+      this.iterations_ = 0;
+    }
+    return this.iterations_;
+  }
+  incrementIterations() {
+    this.iterations_ = this.iterations + 1;
+  }
+  computeGradients(f, varList) {
+    return variableGrads(f, varList);
+  }
+  dispose() {
+    if (this.iterations_ != null) {
+      dispose(this.iterations_);
+    }
+  }
+  async saveIterations() {
+    if (this.iterations_ == null) {
+      this.iterations_ = 0;
+    }
+    return {
+      name: "iter",
+      tensor: scalar(this.iterations_, "int32")
+    };
+  }
+  async getWeights() {
+    throw new Error("getWeights() is not implemented for this optimizer yet.");
+  }
+  async setWeights(weightValues) {
+    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
+  }
+  async extractIterations(weightValues) {
+    this.iterations_ = (await weightValues[0].tensor.data())[0];
+    return weightValues.slice(1);
+  }
+};
+Object.defineProperty(Optimizer, Symbol.hasInstance, {
+  value: (instance) => {
+    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
+  }
+});
+var AdadeltaOptimizer = class extends Optimizer {
+  constructor(learningRate, rho, epsilon3 = null) {
+    super();
+    this.learningRate = learningRate;
+    this.rho = rho;
+    this.epsilon = epsilon3;
+    this.accumulatedGrads = [];
+    this.accumulatedUpdates = [];
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      const trainable = false;
+      if (this.accumulatedGrads[i2] == null) {
+        this.accumulatedGrads[i2] = {
+          originalName: `${name}/accum_grad`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      if (this.accumulatedUpdates[i2] == null) {
+        this.accumulatedUpdates[i2] = {
+          originalName: `${name}/accum_var`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const accumulatedGrad = this.accumulatedGrads[i2].variable;
+      const accumulatedUpdate = this.accumulatedUpdates[i2].variable;
+      tidy(() => {
+        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
+        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
+        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
+        accumulatedGrad.assign(newAccumulatedGrad);
+        accumulatedUpdate.assign(newAccumulatedUpdate);
+        const newValue = add2(mul(updates, -this.learningRate), value);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    if (this.accumulatedUpdates != null) {
+      dispose(this.accumulatedGrads.map((v) => v.variable));
+      dispose(this.accumulatedUpdates.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
+    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const variableCount = weightValues.length / 2;
+    const trainable = false;
+    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "rho": this.rho,
+      "epsilon": this.epsilon
+    };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"], config3["rho"], config3["epsilon"]);
+  }
+};
+AdadeltaOptimizer.className = "Adadelta";
+registerClass(AdadeltaOptimizer);
+var AdagradOptimizer = class extends Optimizer {
+  constructor(learningRate, initialAccumulatorValue = 0.1) {
+    super();
+    this.learningRate = learningRate;
+    this.initialAccumulatorValue = initialAccumulatorValue;
+    this.accumulatedGrads = [];
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      if (this.accumulatedGrads[i2] == null) {
+        const trainable = false;
+        this.accumulatedGrads[i2] = {
+          originalName: `${name}/accumulator`,
+          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
+        };
+      }
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const accumulatedGrad = this.accumulatedGrads[i2].variable;
+      tidy(() => {
+        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
+        accumulatedGrad.assign(newAccumulatedGrad);
+        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    if (this.accumulatedGrads != null) {
+      dispose(this.accumulatedGrads.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const trainable = false;
+    this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "initialAccumulatorValue": this.initialAccumulatorValue
+    };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"], config3["initialAccumulatorValue"]);
+  }
+};
+AdagradOptimizer.className = "Adagrad";
+registerClass(AdagradOptimizer);
+var AdamOptimizer = class extends Optimizer {
+  constructor(learningRate, beta1, beta2, epsilon3 = null) {
+    super();
+    this.learningRate = learningRate;
+    this.beta1 = beta1;
+    this.beta2 = beta2;
+    this.epsilon = epsilon3;
+    this.accumulatedFirstMoment = [];
+    this.accumulatedSecondMoment = [];
+    tidy(() => {
+      this.accBeta1 = scalar(beta1).variable();
+      this.accBeta2 = scalar(beta2).variable();
+    });
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+  }
+  applyGradients(variableGradients) {
+    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
+    tidy(() => {
+      const oneMinusAccBeta1 = sub(1, this.accBeta1);
+      const oneMinusAccBeta2 = sub(1, this.accBeta2);
+      varNames.forEach((name, i2) => {
+        const value = ENGINE.registeredVariables[name];
+        const trainable = false;
+        if (this.accumulatedFirstMoment[i2] == null) {
+          this.accumulatedFirstMoment[i2] = {
+            originalName: `${name}/m`,
+            variable: tidy(() => zerosLike(value).variable(trainable))
+          };
+        }
+        if (this.accumulatedSecondMoment[i2] == null) {
+          this.accumulatedSecondMoment[i2] = {
+            originalName: `${name}/v`,
+            variable: tidy(() => zerosLike(value).variable(trainable))
+          };
+        }
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        if (gradient == null) {
+          return;
+        }
+        const firstMoment = this.accumulatedFirstMoment[i2].variable;
+        const secondMoment = this.accumulatedSecondMoment[i2].variable;
+        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
+        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
+        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
+        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
+        firstMoment.assign(newFirstMoment);
+        secondMoment.assign(newSecondMoment);
+        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
+        value.assign(newValue);
+      });
+      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
+      this.accBeta2.assign(mul(this.accBeta2, this.beta2));
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    this.accBeta1.dispose();
+    this.accBeta2.dispose();
+    if (this.accumulatedFirstMoment != null) {
+      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
+    }
+    if (this.accumulatedSecondMoment != null) {
+      dispose(this.accumulatedSecondMoment.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
+    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    tidy(() => {
+      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
+      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
+    });
+    const variableCount = weightValues.length / 2;
+    const trainable = false;
+    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "beta1": this.beta1,
+      "beta2": this.beta2,
+      "epsilon": this.epsilon
+    };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"], config3["beta1"], config3["beta2"], config3["epsilon"]);
+  }
+};
+AdamOptimizer.className = "Adam";
+registerClass(AdamOptimizer);
+var AdamaxOptimizer = class extends Optimizer {
+  constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {
+    super();
+    this.learningRate = learningRate;
+    this.beta1 = beta1;
+    this.beta2 = beta2;
+    this.epsilon = epsilon3;
+    this.decay = decay;
+    this.accumulatedFirstMoment = [];
+    this.accumulatedWeightedInfNorm = [];
+    tidy(() => {
+      this.iteration = scalar(0).variable();
+      this.accBeta1 = scalar(beta1).variable();
+    });
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    tidy(() => {
+      const oneMinusAccBeta1 = sub(1, this.accBeta1);
+      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
+      variableNames.forEach((name, i2) => {
+        const value = ENGINE.registeredVariables[name];
+        const trainable = false;
+        if (this.accumulatedFirstMoment[i2] == null) {
+          this.accumulatedFirstMoment[i2] = {
+            originalName: `${name}/m`,
+            variable: zerosLike(value).variable(trainable)
+          };
+        }
+        if (this.accumulatedWeightedInfNorm[i2] == null) {
+          this.accumulatedWeightedInfNorm[i2] = {
+            originalName: `${name}/v`,
+            variable: zerosLike(value).variable(trainable)
+          };
+        }
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        if (gradient == null) {
+          return;
+        }
+        const firstMoment = this.accumulatedFirstMoment[i2].variable;
+        const weightedInfNorm = this.accumulatedWeightedInfNorm[i2].variable;
+        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
+        const ut0 = mul(weightedInfNorm, this.beta2);
+        const ut1 = abs(gradient);
+        const newWeightedInfNorm = maximum(ut0, ut1);
+        firstMoment.assign(newFirstMoment);
+        weightedInfNorm.assign(newWeightedInfNorm);
+        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
+        value.assign(newValue);
+      });
+      this.iteration.assign(add2(this.iteration, 1));
+      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    this.accBeta1.dispose();
+    this.iteration.dispose();
+    if (this.accumulatedFirstMoment != null) {
+      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
+    }
+    if (this.accumulatedWeightedInfNorm != null) {
+      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    throw new Error("getWeights() is not implemented for Adamax yet.");
+  }
+  async setWeights(weightValues) {
+    throw new Error("setWeights() is not implemented for Adamax yet.");
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "beta1": this.beta1,
+      "beta2": this.beta2,
+      "epsilon": this.epsilon,
+      "decay": this.decay
+    };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"], config3["beta1"], config3["beta2"], config3["epsilon"], config3["decay"]);
+  }
+};
+AdamaxOptimizer.className = "Adamax";
+registerClass(AdamaxOptimizer);
+var SGDOptimizer = class extends Optimizer {
+  constructor(learningRate) {
+    super();
+    this.learningRate = learningRate;
+    this.setLearningRate(learningRate);
+  }
+  applyGradients(variableGradients) {
+    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
+    varNames.forEach((name, i2) => {
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const value = ENGINE.registeredVariables[name];
+      tidy(() => {
+        const newValue = add2(mul(this.c, gradient), value);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  setLearningRate(learningRate) {
+    this.learningRate = learningRate;
+    if (this.c != null) {
+      this.c.dispose();
+    }
+    this.c = keep(scalar(-learningRate));
+  }
+  dispose() {
+    this.c.dispose();
+  }
+  async getWeights() {
+    return [await this.saveIterations()];
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    if (weightValues.length !== 0) {
+      throw new Error("SGD optimizer does not have settable weights.");
+    }
+  }
+  getConfig() {
+    return { "learningRate": this.learningRate };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"]);
+  }
+};
+SGDOptimizer.className = "SGD";
+registerClass(SGDOptimizer);
+var MomentumOptimizer = class extends SGDOptimizer {
+  constructor(learningRate, momentum, useNesterov = false) {
+    super(learningRate);
+    this.learningRate = learningRate;
+    this.momentum = momentum;
+    this.useNesterov = useNesterov;
+    this.accumulations = [];
+    this.m = scalar(this.momentum);
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      if (this.accumulations[i2] == null) {
+        const trainable = false;
+        this.accumulations[i2] = {
+          originalName: `${name}/momentum`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      const accumulation = this.accumulations[i2].variable;
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      tidy(() => {
+        let newValue;
+        const newAccumulation = add2(mul(this.m, accumulation), gradient);
+        if (this.useNesterov) {
+          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
+        } else {
+          newValue = add2(mul(this.c, newAccumulation), value);
+        }
+        accumulation.assign(newAccumulation);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    this.m.dispose();
+    if (this.accumulations != null) {
+      dispose(this.accumulations.map((v) => v.variable));
+    }
+  }
+  setMomentum(momentum) {
+    this.momentum = momentum;
+  }
+  async getWeights() {
+    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const trainable = false;
+    this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "momentum": this.momentum,
+      "useNesterov": this.useNesterov
+    };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"], config3["momentum"], config3["useNesterov"]);
+  }
+};
+MomentumOptimizer.className = "Momentum";
+registerClass(MomentumOptimizer);
+var RMSPropOptimizer = class extends Optimizer {
+  constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {
+    super();
+    this.learningRate = learningRate;
+    this.decay = decay;
+    this.momentum = momentum;
+    this.epsilon = epsilon3;
+    this.accumulatedMeanSquares = [];
+    this.accumulatedMoments = [];
+    this.accumulatedMeanGrads = [];
+    this.centered = centered;
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+    if (learningRate == null) {
+      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
+    }
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      const trainable = false;
+      if (this.accumulatedMeanSquares[i2] == null) {
+        this.accumulatedMeanSquares[i2] = {
+          originalName: `${name}/rms`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      if (this.accumulatedMoments[i2] == null) {
+        this.accumulatedMoments[i2] = {
+          originalName: `${name}/momentum`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      if (this.accumulatedMeanGrads[i2] == null && this.centered) {
+        this.accumulatedMeanGrads[i2] = {
+          originalName: `${name}/mg`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const accumulatedMeanSquare = this.accumulatedMeanSquares[i2].variable;
+      const accumulatedMoments = this.accumulatedMoments[i2].variable;
+      tidy(() => {
+        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
+        if (this.centered) {
+          const accumulatedMeanGrad = this.accumulatedMeanGrads[i2].variable;
+          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
+          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
+          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
+          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
+          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
+          accumulatedMoments.assign(newAccumulatedMoments);
+          const newValue = sub(value, newAccumulatedMoments);
+          value.assign(newValue);
+        } else {
+          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
+          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
+          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
+          accumulatedMoments.assign(newAccumulatedMoments);
+          const newValue = sub(value, newAccumulatedMoments);
+          value.assign(newValue);
+        }
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    if (this.accumulatedMeanSquares != null) {
+      dispose(this.accumulatedMeanSquares.map((v) => v.variable));
+    }
+    if (this.accumulatedMeanGrads != null && this.centered) {
+      dispose(this.accumulatedMeanGrads.map((v) => v.variable));
+    }
+    if (this.accumulatedMoments != null) {
+      dispose(this.accumulatedMoments.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
+    if (this.centered) {
+      variables.push(...this.accumulatedMeanGrads);
+    }
+    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
+    const trainable = false;
+    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    if (this.centered) {
+      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
+        originalName: v.name,
+        variable: v.tensor.variable(trainable)
+      }));
+    }
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "decay": this.decay,
+      "momentum": this.momentum,
+      "epsilon": this.epsilon,
+      "centered": this.centered
+    };
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3["learningRate"], config3["decay"], config3["momentum"], config3["epsilon"], config3["centered"]);
+  }
+};
+RMSPropOptimizer.className = "RMSProp";
+registerClass(RMSPropOptimizer);
+var OptimizerConstructors = class {
+  static sgd(learningRate) {
+    return new SGDOptimizer(learningRate);
+  }
+  static momentum(learningRate, momentum, useNesterov = false) {
+    return new MomentumOptimizer(learningRate, momentum, useNesterov);
+  }
+  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {
+    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);
+  }
+  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {
+    return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);
+  }
+  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {
+    return new AdadeltaOptimizer(learningRate, rho, epsilon3);
+  }
+  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {
+    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);
+  }
+  static adagrad(learningRate, initialAccumulatorValue = 0.1) {
+    return new AdagradOptimizer(learningRate, initialAccumulatorValue);
+  }
+};
+var train = {
+  sgd: OptimizerConstructors.sgd,
+  momentum: OptimizerConstructors.momentum,
+  adadelta: OptimizerConstructors.adadelta,
+  adagrad: OptimizerConstructors.adagrad,
+  rmsprop: OptimizerConstructors.rmsprop,
+  adamax: OptimizerConstructors.adamax,
+  adam: OptimizerConstructors.adam
+};
+var delayCallback = (() => {
+  if (typeof requestAnimationFrame !== "undefined") {
+    return requestAnimationFrame;
+  } else if (typeof setImmediate !== "undefined") {
+    return setImmediate;
+  }
+  return (f) => f();
+})();
+function nextFrame() {
+  return new Promise((resolve) => delayCallback(() => resolve()));
+}
+var backend_util_exports = {};
+__export2(backend_util_exports, {
+  ERF_A1: () => ERF_A1,
+  ERF_A2: () => ERF_A2,
+  ERF_A3: () => ERF_A3,
+  ERF_A4: () => ERF_A4,
+  ERF_A5: () => ERF_A5,
+  ERF_P: () => ERF_P,
+  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
+  SELU_SCALE: () => SELU_SCALE,
+  SELU_SCALEALPHA: () => SELU_SCALEALPHA,
+  applyActivation: () => applyActivation,
+  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
+  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
+  assertParamsConsistent: () => assertParamsConsistent,
+  assignToTypedArray: () => assignToTypedArray,
+  axesAreInnerMostDims: () => axesAreInnerMostDims,
+  calculateShapes: () => calculateShapes,
+  checkEinsumDimSizes: () => checkEinsumDimSizes,
+  checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,
+  combineLocations: () => combineLocations,
+  complexWithEvenIndex: () => complexWithEvenIndex,
+  complexWithOddIndex: () => complexWithOddIndex,
+  computeConv2DInfo: () => computeConv2DInfo,
+  computeConv3DInfo: () => computeConv3DInfo,
+  computeDefaultPad: () => computeDefaultPad,
+  computeDilation2DInfo: () => computeDilation2DInfo,
+  computeOptimalWindowSize: () => computeOptimalWindowSize,
+  computeOutAndReduceShapes: () => computeOutAndReduceShapes,
+  computeOutShape: () => computeOutShape2,
+  computePool2DInfo: () => computePool2DInfo,
+  computePool3DInfo: () => computePool3DInfo,
+  convertConv2DDataFormat: () => convertConv2DDataFormat,
+  decodeEinsumEquation: () => decodeEinsumEquation,
+  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
+  expandShapeToKeepDim: () => expandShapeToKeepDim,
+  exponent: () => exponent,
+  exponents: () => exponents,
+  fromStringArrayToUint8: () => fromStringArrayToUint8,
+  fromUint8ToStringArray: () => fromUint8ToStringArray,
+  getAxesPermutation: () => getAxesPermutation,
+  getBroadcastDims: () => getBroadcastDims,
+  getComplexWithIndex: () => getComplexWithIndex,
+  getEinsumComputePath: () => getEinsumComputePath,
+  getEinsumPermutation: () => getEinsumPermutation,
+  getFusedBiasGradient: () => getFusedBiasGradient,
+  getFusedDyActivation: () => getFusedDyActivation,
+  getImageCenter: () => getImageCenter,
+  getInnerMostAxes: () => getInnerMostAxes,
+  getPermuted: () => getPermuted,
+  getReductionAxes: () => getReductionAxes,
+  getReshaped: () => getReshaped,
+  getReshapedPermuted: () => getReshapedPermuted,
+  getSliceBeginCoords: () => getSliceBeginCoords,
+  getSliceSize: () => getSliceSize,
+  getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,
+  getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,
+  getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,
+  getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,
+  getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,
+  getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,
+  getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,
+  getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,
+  getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,
+  getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,
+  getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,
+  getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,
+  getUndoAxesPermutation: () => getUndoAxesPermutation,
+  isIdentityPermutation: () => isIdentityPermutation,
+  log: () => log2,
+  mergeRealAndImagArrays: () => mergeRealAndImagArrays,
+  prepareAndValidate: () => prepareAndValidate,
+  prepareSplitSize: () => prepareSplitSize,
+  segment_util: () => segment_util_exports,
+  shouldFuse: () => shouldFuse,
+  slice_util: () => slice_util_exports,
+  splitRealAndImagArrays: () => splitRealAndImagArrays,
+  tupleValuesAreOne: () => tupleValuesAreOne,
+  upcastType: () => upcastType,
+  validateInput: () => validateInput,
+  validateUpdateShape: () => validateUpdateShape,
+  warn: () => warn
+});
+function assertParamsConsistent(shapes, axis) {
+  const rank = shapes[0].length;
+  shapes.forEach((shape, i2) => {
+    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i2}] must be the same as the rank of the rest (${rank})`);
+  });
+  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
+  const firstShape = shapes[0];
+  shapes.forEach((shape, i2) => {
+    for (let r2 = 0; r2 < rank; r2++) {
+      assert(r2 === axis || shape[r2] === firstShape[r2], () => `Error in concat${rank}D: Shape of tensors[${i2}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i2}.`);
+    }
+  });
+}
+function computeOutShape2(shapes, axis) {
+  const outputShape = shapes[0].slice();
+  for (let i2 = 1; i2 < shapes.length; i2++) {
+    outputShape[axis] += shapes[i2][axis];
+  }
+  return outputShape;
+}
+var PARALLELIZE_THRESHOLD = 30;
+function computeOptimalWindowSize(inSize) {
+  if (inSize <= PARALLELIZE_THRESHOLD) {
+    return inSize;
+  }
+  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+}
+function getImageCenter(center, imageHeight, imageWidth) {
+  const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
+  const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
+  return [centerX, centerY];
+}
+function getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {
+  let reshaped = [];
+  if (batchToSpace) {
+    reshaped = reshaped.concat(blockShape.slice(0));
+    reshaped.push(inputShape[0] / prod6);
+    reshaped = reshaped.concat(inputShape.slice(1));
+  } else {
+    reshaped = reshaped.concat(inputShape[0]);
+    const spatialLength = blockShape.length;
+    for (let i2 = 0; i2 < spatialLength; ++i2) {
+      reshaped = reshaped.concat([inputShape[i2 + 1] / blockShape[i2], blockShape[i2]]);
+    }
+    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
+  }
+  return reshaped;
+}
+function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
+  const permuted = [];
+  if (batchToSpace) {
+    permuted.push(blockShapeRank);
+    for (let i2 = blockShapeRank + 1; i2 < reshapedRank; ++i2) {
+      if (i2 <= 2 * blockShapeRank) {
+        permuted.push(i2);
+        permuted.push(i2 - (blockShapeRank + 1));
+      } else {
+        permuted.push(i2);
+      }
+    }
+  } else {
+    const permutedBeforeBatch = [];
+    const permutedAfterBatch = [];
+    for (let i2 = 1; i2 < reshapedRank; ++i2) {
+      if (i2 >= blockShapeRank * 2 + 1 || i2 % 2 === 1) {
+        permutedAfterBatch.push(i2);
+      } else {
+        permutedBeforeBatch.push(i2);
+      }
+    }
+    permuted.push(...permutedBeforeBatch);
+    permuted.push(0);
+    permuted.push(...permutedAfterBatch);
+  }
+  return permuted;
+}
+function getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true) {
+  const reshapedPermuted = [];
+  if (batchToSpace) {
+    reshapedPermuted.push(inputShape[0] / prod6);
+  } else {
+    reshapedPermuted.push(inputShape[0] * prod6);
+  }
+  for (let i2 = 1; i2 < inputShape.length; ++i2) {
+    if (i2 <= blockShape.length) {
+      if (batchToSpace) {
+        reshapedPermuted.push(blockShape[i2 - 1] * inputShape[i2]);
+      } else {
+        reshapedPermuted.push(inputShape[i2] / blockShape[i2 - 1]);
+      }
+    } else {
+      reshapedPermuted.push(inputShape[i2]);
+    }
+  }
+  return reshapedPermuted;
+}
+function getSliceBeginCoords(crops, blockShape) {
+  const sliceBeginCoords = [0];
+  for (let i2 = 0; i2 < blockShape; ++i2) {
+    sliceBeginCoords.push(crops[i2][0]);
+  }
+  return sliceBeginCoords;
+}
+function getSliceSize(uncroppedShape, crops, blockShape) {
+  const sliceSize = uncroppedShape.slice(0, 1);
+  for (let i2 = 0; i2 < blockShape; ++i2) {
+    sliceSize.push(uncroppedShape[i2 + 1] - crops[i2][0] - crops[i2][1]);
+  }
+  return sliceSize;
+}
+var SELU_SCALEALPHA = 1.7580993408473768;
+var SELU_SCALE = 1.0507009873554805;
+var ERF_P = 0.3275911;
+var ERF_A1 = 0.254829592;
+var ERF_A2 = -0.284496736;
+var ERF_A3 = 1.421413741;
+var ERF_A4 = -1.453152027;
+var ERF_A5 = 1.061405429;
+function mergeRealAndImagArrays(real5, imag5) {
+  if (real5.length !== imag5.length) {
+    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);
+  }
+  const result = new Float32Array(real5.length * 2);
+  for (let i2 = 0; i2 < result.length; i2 += 2) {
+    result[i2] = real5[i2 / 2];
+    result[i2 + 1] = imag5[i2 / 2];
+  }
+  return result;
+}
+function splitRealAndImagArrays(complex5) {
+  const real5 = new Float32Array(complex5.length / 2);
+  const imag5 = new Float32Array(complex5.length / 2);
+  for (let i2 = 0; i2 < complex5.length; i2 += 2) {
+    real5[i2 / 2] = complex5[i2];
+    imag5[i2 / 2] = complex5[i2 + 1];
+  }
+  return { real: real5, imag: imag5 };
+}
+function complexWithEvenIndex(complex5) {
+  const len = Math.ceil(complex5.length / 4);
+  const real5 = new Float32Array(len);
+  const imag5 = new Float32Array(len);
+  for (let i2 = 0; i2 < complex5.length; i2 += 4) {
+    real5[Math.floor(i2 / 4)] = complex5[i2];
+    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  }
+  return { real: real5, imag: imag5 };
+}
+function complexWithOddIndex(complex5) {
+  const len = Math.floor(complex5.length / 4);
+  const real5 = new Float32Array(len);
+  const imag5 = new Float32Array(len);
+  for (let i2 = 2; i2 < complex5.length; i2 += 4) {
+    real5[Math.floor(i2 / 4)] = complex5[i2];
+    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  }
+  return { real: real5, imag: imag5 };
+}
+function getComplexWithIndex(complex5, index2) {
+  const real5 = complex5[index2 * 2];
+  const imag5 = complex5[index2 * 2 + 1];
+  return { real: real5, imag: imag5 };
+}
+function assignToTypedArray(data, real5, imag5, index2) {
+  data[index2 * 2] = real5;
+  data[index2 * 2 + 1] = imag5;
+}
+function exponents(n, inverse) {
+  const real5 = new Float32Array(n / 2);
+  const imag5 = new Float32Array(n / 2);
+  for (let i2 = 0; i2 < Math.ceil(n / 2); i2++) {
+    const x = (inverse ? 2 : -2) * Math.PI * (i2 / n);
+    real5[i2] = Math.cos(x);
+    imag5[i2] = Math.sin(x);
+  }
+  return { real: real5, imag: imag5 };
+}
+function exponent(k, n, inverse) {
+  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
+  const real5 = Math.cos(x);
+  const imag5 = Math.sin(x);
+  return { real: real5, imag: imag5 };
+}
+var ARROW = "->";
+var ARROW_REGEX = /->/g;
+var COMMA = ",";
+var ELLIPSIS = "...";
+function decodeEinsumEquation(equation, numTensors) {
+  equation = equation.replace(/\s/g, "");
+  const numArrows = (equation.length - equation.replace(ARROW_REGEX, "").length) / ARROW.length;
+  if (numArrows < 1) {
+    throw new Error("Equations without an arrow are not supported.");
+  } else if (numArrows > 1) {
+    throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`);
+  }
+  const [inputString, outputString] = equation.split(ARROW);
+  assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`);
+  const inputTerms = inputString.split(COMMA);
+  const numInputs = inputTerms.length;
+  if (numTensors !== numInputs) {
+    throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);
+  }
+  if (numInputs > 2) {
+    throw new Error("Support for more than 2 input tensors is not implemented yet.");
+  }
+  const allDims = [];
+  for (let i2 = 0; i2 < outputString.length; ++i2) {
+    const dimName = outputString[i2];
+    if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {
+      throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);
+    }
+    if (allDims.indexOf(dimName) === -1) {
+      allDims.push(dimName);
+    }
+  }
+  for (let i2 = 0; i2 < inputString.length; ++i2) {
+    const dimName = inputString[i2];
+    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
+      allDims.push(dimName);
+    }
+  }
+  const idDims = new Array(inputTerms.length);
+  for (let i2 = 0; i2 < numInputs; ++i2) {
+    if (new Set(inputTerms[i2].split("")).size !== inputTerms[i2].length) {
+      throw new Error(`Found duplicate axes in input component ${inputTerms[i2]}. Support for duplicate axes in input is not implemented yet.`);
+    }
+    idDims[i2] = [];
+    for (let j = 0; j < inputTerms[i2].length; ++j) {
+      idDims[i2].push(allDims.indexOf(inputTerms[i2][j]));
+    }
+  }
+  const numDims = allDims.length;
+  const numOutDims = outputString.length;
+  const summedDims = [];
+  for (let i2 = numOutDims; i2 < numDims; ++i2) {
+    summedDims.push(i2);
+  }
+  return { allDims, summedDims, idDims };
+}
+function getEinsumPermutation(nDims, idDims) {
+  let permutationIndices = new Array(nDims);
+  permutationIndices.fill(-1);
+  for (let i2 = 0; i2 < idDims.length; ++i2) {
+    permutationIndices[idDims[i2]] = i2;
+  }
+  const expandDims7 = [];
+  for (let i2 = 0; i2 < nDims; ++i2) {
+    if (permutationIndices[i2] === -1) {
+      expandDims7.push(i2);
+    }
+  }
+  permutationIndices = permutationIndices.filter((d) => d !== -1);
+  return { permutationIndices, expandDims: expandDims7 };
+}
+function checkEinsumDimSizes(nDims, idDims, tensors) {
+  const dimSizes = new Array(nDims);
+  for (let i2 = 0; i2 < tensors.length; ++i2) {
+    const shape = tensors[i2].shape;
+    for (let j = 0; j < idDims[i2].length; ++j) {
+      if (dimSizes[idDims[i2][j]] === void 0) {
+        dimSizes[idDims[i2][j]] = shape[j];
+      } else {
+        assert(dimSizes[idDims[i2][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i2][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
+      }
+    }
+  }
+}
+function getEinsumComputePath(summedDims, idDims) {
+  const path = summedDims;
+  const steps = [];
+  let nSteps = 0;
+  if (summedDims.length === 0) {
+    path.push(-1);
+  }
+  nSteps = summedDims.length + 1;
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    steps.push([]);
+  }
+  const computedTermIndices = [];
+  for (let i2 = 0; i2 < path.length; ++i2) {
+    const summedDim = path[i2];
+    const termIndices = findTermsWithDim(idDims, summedDim);
+    for (const termIndex of termIndices) {
+      if (computedTermIndices.indexOf(termIndex) === -1) {
+        steps[i2].push(termIndex);
+        computedTermIndices.push(termIndex);
+      }
+    }
+  }
+  return { path, steps };
+}
+function isIdentityPermutation(perm) {
+  return perm.every((dim, index2) => dim === index2);
+}
+function findTermsWithDim(idDims, dim) {
+  const termIndices = [];
+  for (let i2 = 0; i2 < idDims.length; ++i2) {
+    if (idDims[i2].length === 0 || idDims[i2].indexOf(dim) !== -1 || dim === -1) {
+      termIndices.push(i2);
+    }
+  }
+  return termIndices;
+}
+function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
+  let splitSizes = [];
+  if (typeof numOrSizeSplits === "number") {
+    assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
+    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
+  } else {
+    const numOfNegs = numOrSizeSplits.reduce((count22, value) => {
+      if (value === -1) {
+        count22 += 1;
+      }
+      return count22;
+    }, 0);
+    assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
+    const negIndex = numOrSizeSplits.indexOf(-1);
+    if (negIndex !== -1) {
+      const total = numOrSizeSplits.reduce((a6, b) => b > 0 ? a6 + b : a6);
+      numOrSizeSplits[negIndex] = x.shape[axis] - total;
+    }
+    assert(x.shape[axis] === numOrSizeSplits.reduce((a6, b) => a6 + b), () => "The sum of sizes must match the size of the axis dimension.");
+    splitSizes = numOrSizeSplits;
+  }
+  return splitSizes;
+}
+function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {
+  return `Received SparseTensor with denseShape[0] = 0 but
+  indices.shape[0] = ${indicesLength}`;
+}
+function getSparseFillEmptyRowsNegativeIndexErrorMessage(index2, value) {
+  return `indices(${index2}, 0) is invalid: ${value} < 0`;
+}
+function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index2, value, limit) {
+  return `indices(${index2}, 0) is invalid: ${value} >= ${limit}`;
+}
+function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {
+  return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;
+}
+function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {
+  return `size ${dim} must be non-negative, not ${value}`;
+}
+function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {
+  return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
+}
+function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {
+  const inputSize10 = sizeFromShape(inputShape);
+  const outputSize2 = sizeFromShape(outputShape);
+  return `Input to reshape is a SparseTensor with ${inputSize10}
+  dense values, but the requested shape requires a multiple of ${outputSize2}. inputShape=${inputShape} outputShape= ${outputShape}`;
+}
+function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {
+  const inputSize10 = sizeFromShape(inputShape);
+  const outputSize2 = sizeFromShape(outputShape);
+  return `Input to reshape is a tensor with ${inputSize10} dense values, but the requested shape has ${outputSize2}. inputShape=${inputShape} outputShape=${outputShape}`;
+}
+function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {
+  return `segment ids must be >= 0`;
+}
+function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {
+  return `segment ids are not increasing`;
+}
+function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {
+  return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;
+}
+function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index2, indexValue, inputRows) {
+  return `Bad: indices[${index2}] == ${indexValue} out of range [0, ${inputRows})`;
+}
+var segment_util_exports = {};
+__export2(segment_util_exports, {
+  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
+  computeOutShape: () => computeOutShape3,
+  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
+});
+function segOpComputeOptimalWindowSize(inSize, numSegments) {
+  let done = false;
+  let res;
+  if (inSize <= PARALLELIZE_THRESHOLD) {
+    res = inSize;
+    done = true;
+  } else {
+    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+  }
+  while (!done) {
+    if (res > numSegments || res === inSize) {
+      done = true;
+    } else {
+      res = nearestDivisor(inSize, res + 1);
+    }
+  }
+  return res;
+}
+function computeOutShape3(aShape, axis, numSegments) {
+  const outShape = [];
+  const rank = aShape.length;
+  for (let dim = 0; dim < rank; dim++) {
+    if (dim !== axis) {
+      outShape.push(aShape[dim]);
+    } else {
+      outShape.push(numSegments);
+    }
+  }
+  return outShape;
+}
+function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
+  const indicesRank = indices.shape.length;
+  const xRank = x.shape.length;
+  if (batchDims !== 0) {
+    if (batchDims < -indicesRank || batchDims > indicesRank) {
+      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);
+    }
+  }
+  if (batchDims < 0) {
+    batchDims += indicesRank;
+  }
+  if (batchDims > xRank) {
+    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
+    ${xRank}).`);
+  }
+  if (axis < batchDims) {
+    throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
+  }
+  for (let i2 = 0; i2 < batchDims; ++i2) {
+    if (x.shape[i2] !== indices.shape[i2]) {
+      throw new Error(`x.shape[${i2}]: ${x.shape[i2]} should be equal to indices.shape[${i2}]: ${indices.shape[i2]}.`);
+    }
+  }
+  const dimSize = x.shape[axis];
+  const outputShape = [];
+  let batchSize = 1;
+  let outerSize = 1;
+  let sliceSize = 1;
+  for (let i2 = 0; i2 < batchDims; ++i2) {
+    outputShape.push(x.shape[i2]);
+    batchSize *= x.shape[i2];
+  }
+  for (let i2 = batchDims; i2 < axis; i2++) {
+    outputShape.push(x.shape[i2]);
+    outerSize *= x.shape[i2];
+  }
+  for (let i2 = batchDims; i2 < indicesRank; i2++) {
+    outputShape.push(indices.shape[i2]);
+  }
+  for (let i2 = axis + 1; i2 < xRank; i2++) {
+    outputShape.push(x.shape[i2]);
+    sliceSize *= x.shape[i2];
+  }
+  return { batchSize, sliceSize, outerSize, dimSize, outputShape };
+}
+function fromUint8ToStringArray(vals) {
+  try {
+    return vals.map((val) => decodeString(val));
+  } catch (err) {
+    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);
+  }
+}
+function fromStringArrayToUint8(strings) {
+  return strings.map((s2) => encodeString(s2));
+}
+var kernel_impls_exports = {};
+__export2(kernel_impls_exports, {
+  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
+  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
+  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
+  whereImpl: () => whereImpl
+});
+var absGradConfig = {
+  kernelName: Abs,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, step(cast(x, "float32"), -1)) };
+  }
+};
+var acosGradConfig = {
+  kernelName: Acos,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const a6 = square(cast(x, "float32"));
+        const b = sqrt(sub(scalar(1), a6));
+        return neg(div(dy, b));
+      }
+    };
+  }
+};
+var acoshGradConfig = {
+  kernelName: Acosh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const a6 = sqrt(sub(square(cast(x, "float32")), 1));
+        return div(dy, a6);
+      }
+    };
+  }
+};
+var addGradConfig = {
+  kernelName: Add,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, a6.shape);
+    };
+    const derB = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, b.shape);
+    };
+    return { a: derA, b: derB };
+  }
+};
+var addNGradConfig = {
+  kernelName: AddN,
+  saveAllInputs: true,
+  gradFunc: (dy, saved) => {
+    const ders = {};
+    saved.forEach((_, i2) => {
+      ders[i2] = () => dy.clone();
+    });
+    return ders;
+  }
+};
+var argMaxGradConfig = {
+  kernelName: ArgMax,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => zerosLike(x) };
+  }
+};
+var argMinGradConfig = {
+  kernelName: ArgMin,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => zerosLike(x) };
+  }
+};
+var asinGradConfig = {
+  kernelName: Asin,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32"))))) };
+  }
+};
+var asinhGradConfig = {
+  kernelName: Asinh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const a6 = sqrt(add2(scalar(1), square(cast(x, "float32"))));
+        return div(dy, a6);
+      }
+    };
+  }
+};
+var atan2GradConfig = {
+  kernelName: Atan2,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const d = add2(square(a6), square(b));
+      let res = mul(dy, div(b, d));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, a6.shape);
+    };
+    const derB = () => {
+      const d = add2(square(a6), square(b));
+      let res = neg(mul(dy, div(a6, d)));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, b.shape);
+    };
+    return { a: derA, b: derB };
+  }
+};
+var atanGradConfig = {
+  kernelName: Atan,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, add2(square(cast(x, "float32")), 1)) };
+  }
+};
+var atanhGradConfig = {
+  kernelName: Atanh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, sub(scalar(1), square(cast(x, "float32")))) };
+  }
+};
+function avgPool3dGrad_(dy, input2, filterSize, strides2, pad3, dimRoundingMode) {
+  const $dy = convertToTensor(dy, "dy", "avgPool3dGrad");
+  const $input = convertToTensor(input2, "input", "avgPool3dGrad");
+  let dy5D = $dy;
+  let input5D = $input;
+  let reshapedTo5D = false;
+  if ($input.rank === 4) {
+    reshapedTo5D = true;
+    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
+    input5D = reshape($input, [
+      1,
+      $input.shape[0],
+      $input.shape[1],
+      $input.shape[2],
+      $input.shape[3]
+    ]);
+  }
+  assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
+  assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
+  checkPadOnDimRoundingMode("avgPool3dGrad", pad3, dimRoundingMode);
+  const inputs = { dy: dy5D, input: input5D };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode };
+  const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var avgPool3dGrad = op({ avgPool3dGrad_ });
+var avgPool3DGradConfig = {
+  kernelName: AvgPool3D,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+    return {
+      x: () => avgPool3dGrad(dy, x, filterSize, strides2, pad3, dimRoundingMode)
+    };
+  }
+};
+function avgPoolGrad_(dy, input2, filterSize, strides2, pad3) {
+  const $dy = convertToTensor(dy, "dy", "avgPoolGrad");
+  const $input = convertToTensor(input2, "input", "avgPoolGrad");
+  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
+  let input4D = $input;
+  let dy4D = $dy;
+  let reshapedTo4D = false;
+  if ($input.rank === 3) {
+    reshapedTo4D = true;
+    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
+    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
+  }
+  assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);
+  assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);
+  const inputs = { dy: dy4D, input: input4D };
+  const attrs = { filterSize, strides: strides2, pad: pad3 };
+  const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var avgPoolGrad = op({ avgPoolGrad_ });
+var avgPoolGradConfig = {
+  kernelName: AvgPool,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { filterSize, strides: strides2, pad: pad3 } = attrs;
+    return { x: () => avgPoolGrad(dy, x, filterSize, strides2, pad3) };
+  }
+};
+var batchMatMulGradConfig = {
+  kernelName: BatchMatMul,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved, attrs) => {
+    const [a6, b] = saved;
+    const { transposeA, transposeB } = attrs;
+    if (!transposeA && !transposeB) {
+      return {
+        a: () => matMul(dy, b, false, true),
+        b: () => matMul(a6, dy, true, false)
+      };
+    } else if (!transposeA && transposeB) {
+      return {
+        a: () => matMul(dy, b, false, false),
+        b: () => matMul(dy, a6, true, false)
+      };
+    } else if (transposeA && !transposeB) {
+      return {
+        a: () => matMul(b, dy, false, true),
+        b: () => matMul(a6, dy, false, false)
+      };
+    } else {
+      return {
+        a: () => matMul(b, dy, true, true),
+        b: () => matMul(dy, a6, true, true)
+      };
+    }
+  }
+};
+var batchToSpaceNDGradConfig = {
+  kernelName: BatchToSpaceND,
+  gradFunc: (dy, saved, attrs) => {
+    const { blockShape, crops } = attrs;
+    return { x: () => spaceToBatchND(dy, blockShape, crops) };
+  }
+};
+var broadcastToGradConfig = {
+  kernelName: BroadcastTo,
+  gradFunc: (dy, saved, attrs) => {
+    const broadCastToAttrs = attrs;
+    const inputShape = broadCastToAttrs.inputShape;
+    const outputShape = broadCastToAttrs.shape;
+    const reps = Array.from(outputShape);
+    for (let i2 = inputShape.length - 1; i2 >= 0; i2--) {
+      if (inputShape[i2] === outputShape[i2]) {
+        reps[i2] = 1;
+      } else if (inputShape[i2] !== 1) {
+        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
+      }
+    }
+    const axes = [];
+    for (let i2 = 0; i2 < reps.length; i2++) {
+      if (reps[i2] > 1) {
+        axes.push(i2);
+      }
+    }
+    return { x: () => sum2(dy, axes, true) };
+  }
+};
+var castGradConfig = {
+  kernelName: Cast,
+  gradFunc: (dy) => {
+    return { x: () => dy.clone() };
+  }
+};
+var ceilGradConfig = {
+  kernelName: Ceil,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var clipByValueGradConfig = {
+  kernelName: ClipByValue,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { clipValueMin, clipValueMax } = attrs;
+    return {
+      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
+    };
+  }
+};
+var complexAbsGradConfig = {
+  kernelName: ComplexAbs,
+  inputsToSave: ["x"],
+  gradFunc: absGradConfig.gradFunc
+};
+var concatGradConfig = {
+  kernelName: Concat,
+  saveAllInputs: true,
+  gradFunc: (dy, saved, attrs) => {
+    const shapes = saved.map((t2) => t2.shape);
+    const { axis } = attrs;
+    const $axis = parseAxisParam(axis, saved[0].shape)[0];
+    const sizeSplits = shapes.map((s2) => s2[$axis]);
+    const derTensors = split(dy, sizeSplits, $axis);
+    return derTensors.map((t2) => () => t2);
+  }
+};
+var conv2DGradConfig = {
+  kernelName: Conv2D,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x4D, $filter] = saved;
+    const { dilations, strides: strides2, pad: pad3, dataFormat } = attrs;
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
+    return {
+      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides2, pad3, dataFormat),
+      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides2, pad3, dataFormat)
+    };
+  }
+};
+var conv2DBackpropInputGradConfig = {
+  kernelName: Conv2DBackpropInput,
+  inputsToSave: ["dy", "filter"],
+  gradFunc: (ddx, saved, attrs) => {
+    const [dy, filter] = saved;
+    const { strides: strides2, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+    return {
+      dy: () => conv2d(ddx, filter, strides2, pad3, dataFormat, 1, dimRoundingMode),
+      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides2, pad3, dataFormat, dimRoundingMode)
+    };
+  }
+};
+function conv3DBackpropFilter_(x, dy, filterShape, strides2, pad3) {
+  let x5D = x;
+  if (x.rank === 4) {
+    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
+  }
+  let dy5D = dy;
+  if (dy5D.rank === 4) {
+    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
+  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
+  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
+  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
+  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
+  const inputs = { x: x5D, dy: dy5D };
+  const attrs = { strides: strides2, pad: pad3, filterShape };
+  return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);
+}
+var conv3DBackpropFilter = op({ conv3DBackpropFilter_ });
+var conv3DGradConfig = {
+  kernelName: Conv3D,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const { dilations, strides: strides2, pad: pad3 } = attrs;
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
+    const [x5D, $filter] = saved;
+    return {
+      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides2, pad3),
+      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides2, pad3)
+    };
+  }
+};
+var cosGradConfig = {
+  kernelName: Cos,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(neg(sin(cast(x, "float32"))), dy) };
+  }
+};
+var coshGradConfig = {
+  kernelName: Cosh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(sinh(cast(x, "float32")), dy) };
+  }
+};
+var cumsumGradConfig = {
+  kernelName: Cumsum,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { axis, exclusive, reverse: reverse5 } = attrs;
+    return {
+      x: () => {
+        const permutation = getAxesPermutation([axis], x.rank);
+        let out = cumsum(dy, axis, exclusive, !reverse5);
+        if (permutation != null) {
+          out = transpose(out, permutation);
+        }
+        return out;
+      }
+    };
+  }
+};
+var depthwiseConv2dNativeGradConfig = {
+  kernelName: DepthwiseConv2dNative,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const { dilations, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+    const $dilations = dilations == null ? [1, 1] : dilations;
+    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
+    const [x, filter] = saved;
+    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
+    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
+    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
+    assert(eitherStridesOrDilationsAreOne(strides2, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides2} and dilations '${$dilations}'.`);
+    checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode);
+    return {
+      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides2, pad3, $dilations, dimRoundingMode),
+      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides2, pad3, $dilations, dimRoundingMode)
+    };
+  }
+};
+var dilation2dGradConfig = {
+  kernelName: Dilation2D,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, filter] = saved;
+    const inputInputs = { x, filter, dy };
+    const filterInputs = { x, filter, dy };
+    return {
+      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
+      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
+    };
+  }
+};
+var eluGradConfig = {
+  kernelName: Elu,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    const inputs = { dy, y };
+    return { x: () => ENGINE.runKernel(EluGrad, inputs) };
+  }
+};
+var erfGradConfig = {
+  kernelName: Erf,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    const a6 = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
+    return { x: () => mul(dy, a6) };
+  }
+};
+var expGradConfig = {
+  kernelName: Exp,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    return { x: () => mul(dy, y) };
+  }
+};
+var expandDimsGradConfig = {
+  kernelName: ExpandDims,
+  inputsToSave: ["input"],
+  gradFunc: (dy, saved) => {
+    const [input2] = saved;
+    return { input: () => reshape(dy, input2.shape) };
+  }
+};
+var expm1GradConfig = {
+  kernelName: Expm1,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, exp(x)) };
+  }
+};
+var floorGradConfig = {
+  kernelName: Floor,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var floorDivGradConfig = {
+  kernelName: FloorDiv,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const res = div(dy, cast(b, "float32"));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), a6.shape);
+      }
+      return res;
+    };
+    const derB = () => {
+      let res = mul(dy, cast(a6, "float32"));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = reshape(sum2(res, reduceAxes), b.shape);
+      }
+      const tmp = square(b);
+      return neg(div(res, cast(tmp, "float32")));
+    };
+    return { a: derA, b: derB };
+  }
+};
+var fusedBatchNormGradConfig = {
+  kernelName: FusedBatchNorm,
+  inputsToSave: ["x", "mean", "variance", "scale"],
+  gradFunc: (dy, saved, attrs) => {
+    const { varianceEpsilon } = attrs;
+    const [x, mean5, variance, scale22] = saved;
+    const scaleValue = scale22 == null ? scalar(1) : scale22;
+    const reductionAxes = getReductionAxes(mean5.shape, x.shape);
+    const tileShape = [];
+    if (mean5.rank === 1) {
+      for (let i2 = 0; i2 < x.shape.length - 1; ++i2) {
+        tileShape.push(x.shape[i2]);
+      }
+      tileShape.push(1);
+    }
+    const xMinusMean = sub(x, mean5);
+    const dyTimesScaleValue = mul(dy, scaleValue);
+    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
+    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
+    const derX = () => {
+      if (mean5.rank === 1) {
+        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);
+      } else {
+        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
+      }
+    };
+    const derMean = () => {
+      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
+      if (mean5.rank === 1) {
+        meanDer = sum2(meanDer, reductionAxes);
+      }
+      return reshape(meanDer, mean5.shape);
+    };
+    const derVariance = () => {
+      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
+      if (mean5.rank === 1) {
+        varianceDer = sum2(varianceDer, reductionAxes);
+      }
+      return reshape(varianceDer, mean5.shape);
+    };
+    const derScale = () => {
+      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
+      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
+      if (mean5.rank === 1) {
+        scaleDer = sum2(scaleDer, reductionAxes);
+      }
+      return reshape(scaleDer, mean5.shape);
+    };
+    const derOffset = () => {
+      let offsetDer = dy;
+      if (mean5.rank === 1) {
+        offsetDer = sum2(offsetDer, reductionAxes);
+      }
+      return reshape(offsetDer, mean5.shape);
+    };
+    return {
+      x: derX,
+      mean: derMean,
+      variance: derVariance,
+      scale: derScale,
+      offset: derOffset
+    };
+  }
+};
+var gatherGradConfig = {
+  kernelName: GatherV2,
+  inputsToSave: ["x", "indices"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, indices] = saved;
+    const { axis } = attrs;
+    const parsedAxis = parseAxisParam(axis, x.shape)[0];
+    const derX = () => {
+      const paramsShape = x.shape;
+      const indicesSize = indices.size;
+      const outerShape = paramsShape.slice(0, parsedAxis);
+      const outerDims = outerShape.length;
+      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
+      const innerDims = innerShape.length;
+      const outerAxesIndices = arrayRange(0, outerDims);
+      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
+      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
+      const values = reshape(dy, valuesShape);
+      const reshapedIndices = reshape(indices, [indicesSize]);
+      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
+      const valuesTranspose = transpose(values, transposeDims);
+      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
+      const invertTransposeDims = getUndoAxesPermutation(transposeDims);
+      paramsGrad = transpose(paramsGrad, invertTransposeDims);
+      return paramsGrad;
+    };
+    return { x: derX, indices: () => indices };
+  }
+};
+function arrayRange(start, stop) {
+  const result = [];
+  for (let i2 = start; i2 < stop; ++i2) {
+    result.push(i2);
+  }
+  return result;
+}
+function arrayConcat(arrays) {
+  const result = [];
+  for (let i2 = 0; i2 < arrays.length; ++i2) {
+    for (let j = 0; j < arrays[i2].length; ++j) {
+      result.push(arrays[i2][j]);
+    }
+  }
+  return result;
+}
+var greaterEqualGradConfig = {
+  kernelName: GreaterEqual,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    return { a: () => zerosLike(a6), b: () => zerosLike(b) };
+  }
+};
+var identityGradConfig = {
+  kernelName: Identity,
+  gradFunc: (dy) => {
+    return { x: () => cast(dy, "float32") };
+  }
+};
+var isFiniteGradConfig = {
+  kernelName: IsFinite,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var isInfGradConfig = {
+  kernelName: IsInf,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var isNanGradConfig = {
+  kernelName: IsNan,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var leakyReluGradConfig = {
+  kernelName: LeakyRelu,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { alpha: alpha2 } = attrs;
+    const mask2 = greater(x, 0);
+    return { x: () => where(mask2, dy, mul(dy, alpha2)) };
+  }
+};
+var log1pGradConfig = {
+  kernelName: Log1p,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, add2(x, 1)) };
+  }
+};
+var logGradConfig = {
+  kernelName: Log,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, cast(x, "float32")) };
+  }
+};
+var logSoftmaxGradConfig = {
+  kernelName: LogSoftmax,
+  inputsToSave: [],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [value] = saved;
+    const { axis } = attrs;
+    return {
+      logits: () => {
+        const keepDims = true;
+        const softmax7 = exp(value);
+        return sub(dy, mul(sum2(dy, axis, keepDims), softmax7));
+      }
+    };
+  }
+};
+function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha2 = 1, beta = 0.5) {
+  const inputs = { x, y, dy };
+  const attrs = { depthRadius, bias, alpha: alpha2, beta };
+  return ENGINE.runKernel(LRNGrad, inputs, attrs);
+}
+var localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });
+var lrnGradConfig = {
+  kernelName: LRN,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, y] = saved;
+    const { depthRadius, bias, alpha: alpha2, beta } = attrs;
+    return {
+      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha2, beta)
+    };
+  }
+};
+function gradForMinAndMax(dy, y, xOrig, origAxes) {
+  if (y.rank < xOrig.rank) {
+    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
+  }
+  if (dy.rank < xOrig.rank) {
+    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
+  }
+  return {
+    x: () => {
+      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
+      return dx;
+    }
+  };
+}
+var maxGradConfig = {
+  kernelName: Max,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const maxAttrs = attrs;
+    const { reductionIndices } = maxAttrs;
+    const x = saved[0];
+    const y = saved[1];
+    const origAxes = parseAxisParam(reductionIndices, x.shape);
+    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);
+    return {
+      x: () => {
+        return maxGrad["x"]();
+      }
+    };
+  }
+};
+var maximumGradConfig = {
+  kernelName: Maximum,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const derA = () => mul(dy, cast(greaterEqual(a6, b), "float32"));
+    const derB = () => mul(dy, cast(less(a6, b), "float32"));
+    return { a: derA, b: derB };
+  }
+};
+function maxPool3dGrad_(dy, input2, output, filterSize, strides2, pad3, dimRoundingMode) {
+  const $dy = convertToTensor(dy, "dy", "maxPool3dGrad");
+  const $input = convertToTensor(input2, "input", "maxPool3dGrad");
+  const $output = convertToTensor(output, "output", "maxPool3dGrad");
+  let dy5D = $dy;
+  let input5D = $input;
+  let output5D = $output;
+  let reshapedTo5D = false;
+  if ($input.rank === 4) {
+    reshapedTo5D = true;
+    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
+    input5D = reshape($input, [
+      1,
+      $input.shape[0],
+      $input.shape[1],
+      $input.shape[2],
+      $input.shape[3]
+    ]);
+    output5D = reshape($output, [
+      1,
+      $output.shape[0],
+      $output.shape[1],
+      $output.shape[2],
+      $output.shape[3]
+    ]);
+  }
+  assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
+  assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
+  assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);
+  checkPadOnDimRoundingMode("maxPool3dGrad", pad3, dimRoundingMode);
+  const inputs = { dy: dy5D, input: input5D, output: output5D };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode };
+  const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var maxPool3dGrad = op({ maxPool3dGrad_ });
+var maxPool3DGradConfig = {
+  kernelName: MaxPool3D,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, y] = saved;
+    const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+    return {
+      x: () => maxPool3dGrad(dy, x, y, filterSize, strides2, pad3, dimRoundingMode)
+    };
+  }
+};
+function maxPoolGrad_(dy, input2, output, filterSize, strides2, pad3, dimRoundingMode) {
+  const $dy = convertToTensor(dy, "dy", "maxPoolGrad");
+  const $input = convertToTensor(input2, "input", "maxPoolGrad");
+  const $output = convertToTensor(output, "output", "maxPoolGrad");
+  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
+  assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);
+  assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);
+  checkPadOnDimRoundingMode("maxPoolGrad", pad3, dimRoundingMode);
+  const inputs = { dy: $dy, input: $input, output: $output };
+  const attrs = { filterSize, strides: strides2, pad: pad3, dimRoundingMode };
+  return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);
+}
+var maxPoolGrad = op({ maxPoolGrad_ });
+var maxPoolGradConfig = {
+  kernelName: MaxPool,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, y] = saved;
+    const { filterSize, strides: strides2, pad: pad3 } = attrs;
+    return {
+      x: () => maxPoolGrad(dy, x, y, filterSize, strides2, pad3)
+    };
+  }
+};
+var meanGradConfig = {
+  kernelName: Mean,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { axis } = attrs;
+    const axes = parseAxisParam(axis, x.shape);
+    const shapes = computeOutAndReduceShapes(x.shape, axes);
+    const reduceShape = shapes[1];
+    const reduceSize = sizeFromShape(reduceShape);
+    const derX = () => {
+      const expandedDyShape = x.shape.slice();
+      axes.forEach((axis2) => {
+        expandedDyShape[axis2] = 1;
+      });
+      const expandedDy = reshape(dy, expandedDyShape);
+      const res = div(mul(expandedDy, ones2(x.shape, "float32")), reduceSize);
+      return res;
+    };
+    return { x: derX };
+  }
+};
+var minGradConfig = {
+  kernelName: Min,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const minAttrs = attrs;
+    const { axis } = minAttrs;
+    const [x, y] = saved;
+    const origAxes = parseAxisParam(axis, x.shape);
+    const minGrad = gradForMinAndMax(dy, y, x, origAxes);
+    return {
+      x: () => {
+        return minGrad["x"]();
+      }
+    };
+  }
+};
+var minimumGradConfig = {
+  kernelName: Minimum,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const derA = () => mul(dy, cast(lessEqual(a6, b), "float32"));
+    const derB = () => mul(dy, cast(greater(a6, b), "float32"));
+    return { a: derA, b: derB };
+  }
+};
+var mirrorPadGradConfig = {
+  kernelName: MirrorPad,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const x = saved[0];
+    const { paddings } = attrs;
+    const begin = paddings.map((p2) => p2[0]);
+    return { x: () => slice(dy, begin, x.shape) };
+  }
+};
+var modGradConfig = {
+  kernelName: Mod,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(dy, reduceAxes), a6.shape);
+      }
+      return dy;
+    };
+    const derB = () => {
+      const res = mul(dy, neg(floor(div(a6, b))));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), b.shape);
+      }
+      return res;
+    };
+    return { a: derA, b: derB };
+  }
+};
+var multiplyGradConfig = {
+  kernelName: Multiply,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const res = mul(dy, cast(b, "float32"));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), a6.shape);
+      }
+      return res;
+    };
+    const derB = () => {
+      const res = mul(dy, cast(a6, "float32"));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), b.shape);
+      }
+      return res;
+    };
+    return { a: derA, b: derB };
+  }
+};
+var negGradConfig = {
+  kernelName: Neg,
+  gradFunc: (dy) => {
+    return { x: () => neg(dy) };
+  }
+};
+var oneHotGradConfig = {
+  kernelName: OneHot,
+  inputsToSave: ["indices"],
+  gradFunc: (dy, saved) => {
+    const indices = saved[0];
+    return { indices: () => zeros(indices.shape, "float32") };
+  }
+};
+var onesLikeGradConfig = {
+  kernelName: OnesLike,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var packGradConfig = {
+  kernelName: Pack,
+  saveAllInputs: true,
+  gradFunc: (dy, saved, attrs) => {
+    const { axis } = attrs;
+    const derTensors = unstack(dy, axis);
+    return derTensors.map((t2) => () => t2);
+  }
+};
+var padV2GradConfig = {
+  kernelName: PadV2,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const x = saved[0];
+    const { paddings } = attrs;
+    const begin = paddings.map((p2) => p2[0]);
+    return { x: () => slice(dy, begin, x.shape) };
+  }
+};
+var powGradConfig = {
+  kernelName: Pow,
+  inputsToSave: ["a", "b"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [a6, b, y] = saved;
+    const base = a6;
+    const exp5 = b;
+    const outShape = assertAndGetBroadcastShape(base.shape, exp5.shape);
+    const derBase = () => {
+      const expFloat = cast(exp5, "float32");
+      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
+      const reduceAxes = getReductionAxes(base.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, base.shape);
+    };
+    const derExp = () => {
+      const condition = greater(base, 0);
+      const logBase = where(condition, log22(base), zerosLike(base));
+      let res = mul(dy, mul(y, logBase));
+      const reduceAxes = getReductionAxes(exp5.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, exp5.shape);
+    };
+    return { a: derBase, b: derExp };
+  }
+};
+var preluGradConfig = {
+  kernelName: Prelu,
+  inputsToSave: ["x", "alpha"],
+  gradFunc: (dy, saved) => {
+    const [x, alpha2] = saved;
+    const mask2 = greater(x, 0);
+    return {
+      x: () => where(mask2, dy, mul(dy, alpha2)),
+      alpha: () => {
+        let res = where(mask2, zerosLike(dy), mul(dy, x));
+        const reduceAxes = getReductionAxes(alpha2.shape, dy.shape);
+        if (reduceAxes.length > 0) {
+          res = sum2(res, reduceAxes);
+        }
+        return reshape(res, alpha2.shape);
+      }
+    };
+  }
+};
+function prodGradFn_(x, dy, axis) {
+  const expandedYShape = x.shape.slice();
+  expandedYShape[axis] = 1;
+  const expandedDy = reshape(dy, expandedYShape);
+  const xCumProd = cumprod(x, axis, true, false);
+  const xCumRevProd = cumprod(x, axis, true, true);
+  const dx = mul(xCumProd, xCumRevProd);
+  return mul(expandedDy, dx);
+}
+function prodsGradFn_(x, dy, axis) {
+  const xRank = x.shape.length;
+  const finalProdAxis = xRank - axis.length;
+  const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);
+  let permutedX = x;
+  if (xPermutation != null) {
+    permutedX = transpose(x, xPermutation);
+  }
+  const newShape = permutedX.shape.slice();
+  const removedShape = newShape.splice(xRank - axis.length, axis.length);
+  const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);
+  newShape.push(endPartShape);
+  const reshapedPermutedX = permutedX.reshape(newShape);
+  let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);
+  prodGrad = prodGrad.reshape(permutedX.shape);
+  if (xPermutation != null) {
+    const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);
+    prodGrad = transpose(prodGrad, undoPermutation);
+  }
+  return prodGrad;
+}
+var prodGradConfig = {
+  kernelName: Prod,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { axis } = attrs;
+    let axisArr = [];
+    if (axis === void 0 || axis === null) {
+      axisArr = x.shape.map((_, i2) => i2);
+    } else if (typeof axis === "number") {
+      axisArr = [axis];
+    } else {
+      axisArr = axis;
+    }
+    return { x: () => prodsGradFn_(x, dy, axisArr) };
+  }
+};
+var divGradConfig = {
+  kernelName: RealDiv,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const res = div(dy, cast(b, "float32"));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), a6.shape);
+      }
+      return res;
+    };
+    const derB = () => {
+      let res = mul(dy, cast(a6, "float32"));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = reshape(sum2(res, reduceAxes), b.shape);
+      }
+      const tmp = square(b);
+      return neg(div(res, cast(tmp, "float32")));
+    };
+    return { a: derA, b: derB };
+  }
+};
+var reciprocalGradConfig = {
+  kernelName: Reciprocal,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, neg(square(x))) };
+  }
+};
+var relu6GradConfig = {
+  kernelName: Relu6,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    const mask2 = mul(lessEqual(x, 6), step(x));
+    return { x: () => mul(dy, cast(mask2, "float32")) };
+  }
+};
+var reluGradConfig = {
+  kernelName: Relu,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, cast(step(x), "float32")) };
+  }
+};
+var reshapeGradConfig = {
+  kernelName: Reshape,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => reshape(dy, x.shape) };
+  }
+};
+var resizeBilinearGradConfig = {
+  kernelName: ResizeBilinear,
+  inputsToSave: ["images"],
+  gradFunc: (dy, saved, attrs) => {
+    const [images] = saved;
+    const inputs = { dy, images };
+    const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);
+    return { images: imagesDer };
+  }
+};
+var resizeNearestNeighborGradConfig = {
+  kernelName: ResizeNearestNeighbor,
+  inputsToSave: ["images"],
+  gradFunc: (dy, saved, attrs) => {
+    const [images] = saved;
+    const inputs = { dy, images };
+    const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);
+    return { images: imagesDer };
+  }
+};
+var reverseGradConfig = {
+  kernelName: Reverse,
+  gradFunc: (dy, saved, attrs) => {
+    const { dims } = attrs;
+    const axes = parseAxisParam(dims, dy.shape);
+    return { x: () => reverse(dy, axes) };
+  }
+};
+var roundGradConfig = {
+  kernelName: Round,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var rsqrtGradConfig = {
+  kernelName: Rsqrt,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };
+  }
+};
+var selectGradConfig = {
+  kernelName: Select,
+  inputsToSave: ["condition"],
+  gradFunc: (dy, saved) => {
+    const [condition] = saved;
+    return {
+      condition: () => cast(zerosLike(condition), "float32"),
+      t: () => mul(dy, cast(condition, dy.dtype)),
+      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
+    };
+  }
+};
+var seluGradConfig = {
+  kernelName: Selu,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const mask2 = greater(x, scalar(0));
+        const scaleAlpha2 = scalar(SELU_SCALEALPHA);
+        const scale22 = scalar(SELU_SCALE);
+        const greaterThanZeroDer = mul(dy, scale22);
+        const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, "float32")));
+        return where(mask2, greaterThanZeroDer, lessEqualZeroDer);
+      }
+    };
+  }
+};
+var sigmoidGradConfig = {
+  kernelName: Sigmoid,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };
+  }
+};
+var signGradConfig = {
+  kernelName: Sign,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var sinGradConfig = {
+  kernelName: Sin,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(cos(cast(x, "float32")), dy) };
+  }
+};
+var sinhGradConfig = {
+  kernelName: Sinh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(cosh(cast(x, "float32")), dy) };
+  }
+};
+var sliceGradConfig = {
+  kernelName: Slice,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { begin, size: size2 } = attrs;
+    const inputShape = x.shape;
+    const [begin_, size_] = parseSliceParams(x, begin, size2);
+    const paddings = [];
+    for (let i2 = 0; i2 < dy.rank; i2++) {
+      paddings.push([begin_[i2], inputShape[i2] - begin_[i2] - size_[i2]]);
+    }
+    return { x: () => pad(dy, paddings) };
+  }
+};
+var softmaxGradConfig = {
+  kernelName: Softmax,
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [y] = saved;
+    const { dim } = attrs;
+    const keepDims = true;
+    const dyTimesY = mul(dy, y);
+    return {
+      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
+    };
+  }
+};
+var softplusGradConfig = {
+  kernelName: Softplus,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, sigmoid(x)) };
+  }
+};
+var spaceToBatchNDGradConfig = {
+  kernelName: SpaceToBatchND,
+  gradFunc: (dy, saved, attrs) => {
+    const { blockShape, paddings } = attrs;
+    return { x: () => batchToSpaceND(dy, blockShape, paddings) };
+  }
+};
+var splitVGradConfig = {
+  kernelName: SplitV,
+  gradFunc: (dy, saved, attrs) => {
+    const { axis } = attrs;
+    return { x: () => concat(dy, axis) };
+  }
+};
+var sqrtGradConfig = {
+  kernelName: Sqrt,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, mul(sqrt(cast(x, "float32")), 2)) };
+  }
+};
+var squareGradConfig = {
+  kernelName: Square,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, mul(cast(x, "float32"), 2)) };
+  }
+};
+var squaredDifferenceGradConfig = {
+  kernelName: SquaredDifference,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const two = scalar(2);
+    const derA = () => mul(dy, mul(two, sub(a6, b)));
+    const derB = () => mul(dy, mul(two, sub(b, a6)));
+    return { a: derA, b: derB };
+  }
+};
+var stepGradConfig = {
+  kernelName: Step,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var subGradConfig = {
+  kernelName: Sub,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, a6.shape);
+    };
+    const derB = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(neg(res), b.shape);
+    };
+    return { a: derA, b: derB };
+  }
+};
+var sumGradConfig = {
+  kernelName: Sum,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const expandedDyShape = x.shape.slice();
+    const { axis } = attrs;
+    const axes = parseAxisParam(axis, x.shape);
+    axes.forEach((axis2) => {
+      expandedDyShape[axis2] = 1;
+    });
+    const expandedDy = reshape(dy, expandedDyShape);
+    const derX = mul(expandedDy, ones2(x.shape, "float32"));
+    return { x: () => derX };
+  }
+};
+var tanGradConfig = {
+  kernelName: Tan,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, square(cos(x))) };
+  }
+};
+var tanhGradConfig = {
+  kernelName: Tanh,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    return { x: () => mul(sub(scalar(1), square(y)), dy) };
+  }
+};
+var tileGradConfig = {
+  kernelName: Tile,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { reps } = attrs;
+    const derX = () => {
+      let xGrad = zerosLike(x);
+      if (x.rank === 1) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0]], [x.shape[0]]));
+        }
+      } else if (x.rank === 2) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          for (let j = 0; j < reps[1]; ++j) {
+            xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1]], [
+              x.shape[0],
+              x.shape[1]
+            ]));
+          }
+        }
+      } else if (x.rank === 3) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          for (let j = 0; j < reps[1]; ++j) {
+            for (let k = 0; k < reps[2]; ++k) {
+              xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
+            }
+          }
+        }
+      } else if (x.rank === 4) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          for (let j = 0; j < reps[1]; ++j) {
+            for (let k = 0; k < reps[2]; ++k) {
+              for (let l3 = 0; l3 < reps[3]; ++l3) {
+                xGrad = add2(xGrad, slice(dy, [
+                  i2 * x.shape[0],
+                  j * x.shape[1],
+                  k * x.shape[2],
+                  l3 * x.shape[3]
+                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
+              }
+            }
+          }
+        }
+      } else {
+        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
+      }
+      return xGrad;
+    };
+    return { x: derX };
+  }
+};
+var transposeGradConfig = {
+  kernelName: Transpose,
+  gradFunc: (dy, saved, attrs) => {
+    const transposeAttrs = attrs;
+    const { perm } = transposeAttrs;
+    const undoPerm = getUndoAxesPermutation(perm);
+    return { x: () => transpose(dy, undoPerm) };
+  }
+};
+var unpackGradConfig = {
+  kernelName: Unpack,
+  gradFunc: (dy, saved, attrs) => {
+    const unpackAttrs = attrs;
+    const { axis } = unpackAttrs;
+    return { value: () => stack(dy, axis) };
+  }
+};
+var unsortedSegmentSumGradConfig = {
+  kernelName: UnsortedSegmentSum,
+  inputsToSave: ["segmentIds"],
+  gradFunc: (dy, saved) => {
+    const [segmentIds] = saved;
+    const derX = () => {
+      return gatherDropNegatives(dy, segmentIds);
+    };
+    return { x: derX };
+  }
+};
+function gatherDropNegatives(x, indices) {
+  const zeroClippedIndices = maximum(indices, zerosLike(indices));
+  const gathered = gather(x, zeroClippedIndices);
+  let isPositive = greaterEqual(indices, scalar(0, "int32"));
+  const numIters = gathered.rank - isPositive.rank;
+  for (let i2 = 0; i2 < numIters; ++i2) {
+    isPositive = expandDims(isPositive, i2 + 1);
+  }
+  isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
+  const zeroSlice = zerosLike(gathered);
+  return where(isPositive, gathered, zeroSlice);
+}
+var zerosLikeGradConfig = {
+  kernelName: ZerosLike,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+var gradConfigs = [
+  absGradConfig,
+  acosGradConfig,
+  acoshGradConfig,
+  addGradConfig,
+  addNGradConfig,
+  argMaxGradConfig,
+  argMinGradConfig,
+  asinGradConfig,
+  asinhGradConfig,
+  atan2GradConfig,
+  atanGradConfig,
+  atanhGradConfig,
+  avgPool3DGradConfig,
+  avgPoolGradConfig,
+  batchMatMulGradConfig,
+  batchToSpaceNDGradConfig,
+  broadcastToGradConfig,
+  castGradConfig,
+  ceilGradConfig,
+  clipByValueGradConfig,
+  complexAbsGradConfig,
+  concatGradConfig,
+  conv2DBackpropInputGradConfig,
+  conv2DGradConfig,
+  conv3DGradConfig,
+  cosGradConfig,
+  coshGradConfig,
+  cumsumGradConfig,
+  depthwiseConv2dNativeGradConfig,
+  dilation2dGradConfig,
+  divGradConfig,
+  eluGradConfig,
+  erfGradConfig,
+  expGradConfig,
+  expandDimsGradConfig,
+  expm1GradConfig,
+  floorDivGradConfig,
+  floorGradConfig,
+  fusedBatchNormGradConfig,
+  gatherGradConfig,
+  greaterEqualGradConfig,
+  identityGradConfig,
+  isFiniteGradConfig,
+  isInfGradConfig,
+  isNanGradConfig,
+  leakyReluGradConfig,
+  log1pGradConfig,
+  logGradConfig,
+  logSoftmaxGradConfig,
+  lrnGradConfig,
+  maxGradConfig,
+  maxGradConfig,
+  maximumGradConfig,
+  maxPool3DGradConfig,
+  maxPoolGradConfig,
+  meanGradConfig,
+  minGradConfig,
+  minimumGradConfig,
+  mirrorPadGradConfig,
+  modGradConfig,
+  multiplyGradConfig,
+  negGradConfig,
+  oneHotGradConfig,
+  onesLikeGradConfig,
+  packGradConfig,
+  padV2GradConfig,
+  padV2GradConfig,
+  powGradConfig,
+  preluGradConfig,
+  prodGradConfig,
+  reciprocalGradConfig,
+  relu6GradConfig,
+  reluGradConfig,
+  reshapeGradConfig,
+  resizeBilinearGradConfig,
+  resizeNearestNeighborGradConfig,
+  reverseGradConfig,
+  roundGradConfig,
+  rsqrtGradConfig,
+  selectGradConfig,
+  seluGradConfig,
+  sigmoidGradConfig,
+  signGradConfig,
+  sinGradConfig,
+  sinhGradConfig,
+  sliceGradConfig,
+  softmaxGradConfig,
+  softplusGradConfig,
+  spaceToBatchNDGradConfig,
+  spaceToBatchNDGradConfig,
+  splitVGradConfig,
+  splitVGradConfig,
+  sqrtGradConfig,
+  squaredDifferenceGradConfig,
+  squareGradConfig,
+  stepGradConfig,
+  subGradConfig,
+  sumGradConfig,
+  tanGradConfig,
+  tanhGradConfig,
+  tileGradConfig,
+  transposeGradConfig,
+  unpackGradConfig,
+  unsortedSegmentSumGradConfig,
+  zerosLikeGradConfig
+];
+for (const gradientConfig of gradConfigs) {
+  registerGradient(gradientConfig);
+}
+getGlobalTensorClass().prototype.abs = function() {
+  this.throwIfDisposed();
+  return abs(this);
+};
+getGlobalTensorClass().prototype.acos = function() {
+  this.throwIfDisposed();
+  return acos(this);
+};
+getGlobalTensorClass().prototype.acosh = function() {
+  this.throwIfDisposed();
+  return acosh(this);
+};
+getGlobalTensorClass().prototype.add = function(b) {
+  this.throwIfDisposed();
+  return add2(this, b);
+};
+getGlobalTensorClass().prototype.all = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return all(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.any = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return any(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.argMax = function(axis) {
+  this.throwIfDisposed();
+  return argMax(this, axis);
+};
+getGlobalTensorClass().prototype.argMin = function(axis) {
+  this.throwIfDisposed();
+  return argMin(this, axis);
+};
+getGlobalTensorClass().prototype.asScalar = function() {
+  this.throwIfDisposed();
+  assert(this.size === 1, () => "The array must have only 1 element.");
+  return reshape(this, []);
+};
+getGlobalTensorClass().prototype.asType = function(dtype) {
+  this.throwIfDisposed();
+  return cast(this, dtype);
+};
+getGlobalTensorClass().prototype.as1D = function() {
+  this.throwIfDisposed();
+  return reshape(this, [this.size]);
+};
+getGlobalTensorClass().prototype.as2D = function(rows, columns) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns]);
+};
+getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns, depth]);
+};
+getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns, depth, depth2]);
+};
+getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns, depth, depth2, depth3]);
+};
+getGlobalTensorClass().prototype.asin = function() {
+  this.throwIfDisposed();
+  return asin(this);
+};
+getGlobalTensorClass().prototype.asinh = function() {
+  this.throwIfDisposed();
+  return asinh(this);
+};
+getGlobalTensorClass().prototype.atan = function() {
+  this.throwIfDisposed();
+  return atan(this);
+};
+getGlobalTensorClass().prototype.atan2 = function(b) {
+  this.throwIfDisposed();
+  return atan2(this, b);
+};
+getGlobalTensorClass().prototype.atanh = function() {
+  this.throwIfDisposed();
+  return atanh(this);
+};
+getGlobalTensorClass().prototype.avgPool = function(filterSize, strides2, pad3, dimRoundingMode) {
+  this.throwIfDisposed();
+  return avgPool(this, filterSize, strides2, pad3, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {
+  this.throwIfDisposed();
+  return batchToSpaceND(this, blockShape, crops);
+};
+getGlobalTensorClass().prototype.batchNorm = function(mean5, variance, offset, scale22, varianceEpsilon) {
+  this.throwIfDisposed();
+  return batchNorm(this, mean5, variance, offset, scale22, varianceEpsilon);
+};
+getGlobalTensorClass().prototype.broadcastTo = function(shape) {
+  this.throwIfDisposed();
+  return broadcastTo(this, shape);
+};
+getGlobalTensorClass().prototype.cast = function(dtype) {
+  this.throwIfDisposed();
+  return cast(this, dtype);
+};
+getGlobalTensorClass().prototype.ceil = function() {
+  this.throwIfDisposed();
+  return ceil(this);
+};
+getGlobalTensorClass().prototype.clipByValue = function(min7, max7) {
+  this.throwIfDisposed();
+  return clipByValue(this, min7, max7);
+};
+getGlobalTensorClass().prototype.concat = function(x, axis) {
+  this.throwIfDisposed();
+  if (x instanceof Tensor) {
+    x = [x];
+  }
+  return concat([this, ...x], axis);
+};
+getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {
+  this.throwIfDisposed();
+  return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides2, pad3, dimRoundingMode) {
+  this.throwIfDisposed();
+  return conv2dTranspose(this, filter, outputShape, strides2, pad3, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.conv2d = function(filter, strides2, pad3, dataFormat, dilations, dimRoundingMode) {
+  this.throwIfDisposed();
+  return conv2d(this, filter, strides2, pad3, dataFormat, dilations, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.cos = function() {
+  this.throwIfDisposed();
+  return cos(this);
+};
+getGlobalTensorClass().prototype.cosh = function() {
+  this.throwIfDisposed();
+  return cosh(this);
+};
+getGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) {
+  this.throwIfDisposed();
+  return cumprod(this, axis, exclusive, reverse5);
+};
+getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {
+  this.throwIfDisposed();
+  return cumsum(this, axis, exclusive, reverse5);
+};
+getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {
+  this.throwIfDisposed();
+  return depthToSpace(this, blockSize, dataFormat);
+};
+getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides2, pad3, dataFormat, dilations, dimRoundingMode) {
+  this.throwIfDisposed();
+  return depthwiseConv2d(this, filter, strides2, pad3, dataFormat, dilations, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.dilation2d = function(filter, strides2, pad3, dilations, dataFormat) {
+  this.throwIfDisposed();
+  return dilation2d(this, filter, strides2, pad3, dilations, dataFormat);
+};
+getGlobalTensorClass().prototype.divNoNan = function(b) {
+  this.throwIfDisposed();
+  return divNoNan(this, b);
+};
+getGlobalTensorClass().prototype.div = function(b) {
+  this.throwIfDisposed();
+  return div(this, b);
+};
+getGlobalTensorClass().prototype.dot = function(b) {
+  this.throwIfDisposed();
+  return dot(this, b);
+};
+getGlobalTensorClass().prototype.elu = function() {
+  this.throwIfDisposed();
+  return elu(this);
+};
+getGlobalTensorClass().prototype.equal = function(b) {
+  this.throwIfDisposed();
+  return equal(this, b);
+};
+getGlobalTensorClass().prototype.erf = function() {
+  this.throwIfDisposed();
+  return erf(this);
+};
+getGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return euclideanNorm(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.exp = function() {
+  this.throwIfDisposed();
+  return exp(this);
+};
+getGlobalTensorClass().prototype.expandDims = function(axis) {
+  this.throwIfDisposed();
+  return expandDims(this, axis);
+};
+getGlobalTensorClass().prototype.expm1 = function() {
+  this.throwIfDisposed();
+  return expm1(this);
+};
+getGlobalTensorClass().prototype.fft = function() {
+  this.throwIfDisposed();
+  return fft(this);
+};
+getGlobalTensorClass().prototype.flatten = function() {
+  this.throwIfDisposed();
+  return reshape(this, [this.size]);
+};
+getGlobalTensorClass().prototype.floor = function() {
+  this.throwIfDisposed();
+  return floor(this);
+};
+getGlobalTensorClass().prototype.floorDiv = function(b) {
+  this.throwIfDisposed();
+  return floorDiv(this, b);
+};
+getGlobalTensorClass().prototype.gather = function(indices, axis) {
+  this.throwIfDisposed();
+  return gather(this, indices, axis);
+};
+getGlobalTensorClass().prototype.greaterEqual = function(b) {
+  this.throwIfDisposed();
+  return greaterEqual(this, b);
+};
+getGlobalTensorClass().prototype.greater = function(b) {
+  this.throwIfDisposed();
+  return greater(this, b);
+};
+getGlobalTensorClass().prototype.ifft = function() {
+  this.throwIfDisposed();
+  return ifft(this);
+};
+getGlobalTensorClass().prototype.irfft = function() {
+  this.throwIfDisposed();
+  return irfft(this);
+};
+getGlobalTensorClass().prototype.isFinite = function() {
+  this.throwIfDisposed();
+  return isFinite2(this);
+};
+getGlobalTensorClass().prototype.isInf = function() {
+  this.throwIfDisposed();
+  return isInf(this);
+};
+getGlobalTensorClass().prototype.isNaN = function() {
+  this.throwIfDisposed();
+  return isNaN2(this);
+};
+getGlobalTensorClass().prototype.leakyRelu = function(alpha2) {
+  this.throwIfDisposed();
+  return leakyRelu(this, alpha2);
+};
+getGlobalTensorClass().prototype.lessEqual = function(b) {
+  this.throwIfDisposed();
+  return lessEqual(this, b);
+};
+getGlobalTensorClass().prototype.less = function(b) {
+  this.throwIfDisposed();
+  return less(this, b);
+};
+getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha2, beta) {
+  this.throwIfDisposed();
+  return localResponseNormalization(this, depthRadius, bias, alpha2, beta);
+};
+getGlobalTensorClass().prototype.logSigmoid = function() {
+  this.throwIfDisposed();
+  return logSigmoid(this);
+};
+getGlobalTensorClass().prototype.logSoftmax = function(axis) {
+  this.throwIfDisposed();
+  return logSoftmax(this, axis);
+};
+getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return logSumExp(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.log = function() {
+  this.throwIfDisposed();
+  return log22(this);
+};
+getGlobalTensorClass().prototype.log1p = function() {
+  this.throwIfDisposed();
+  return log1p(this);
+};
+getGlobalTensorClass().prototype.logicalAnd = function(b) {
+  this.throwIfDisposed();
+  return logicalAnd(this, b);
+};
+getGlobalTensorClass().prototype.logicalNot = function() {
+  this.throwIfDisposed();
+  return logicalNot(this);
+};
+getGlobalTensorClass().prototype.logicalOr = function(b) {
+  this.throwIfDisposed();
+  return logicalOr(this, b);
+};
+getGlobalTensorClass().prototype.logicalXor = function(b) {
+  this.throwIfDisposed();
+  return logicalXor(this, b);
+};
+getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {
+  this.throwIfDisposed();
+  return matMul(this, b, transposeA, transposeB);
+};
+getGlobalTensorClass().prototype.maxPool = function(filterSize, strides2, pad3, dimRoundingMode) {
+  this.throwIfDisposed();
+  return maxPool(this, filterSize, strides2, pad3, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.max = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return max(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.maximum = function(b) {
+  this.throwIfDisposed();
+  return maximum(this, b);
+};
+getGlobalTensorClass().prototype.mean = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return mean(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.min = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return min(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.minimum = function(b) {
+  this.throwIfDisposed();
+  return minimum(this, b);
+};
+getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {
+  this.throwIfDisposed();
+  return mirrorPad(this, paddings, mode);
+};
+getGlobalTensorClass().prototype.mod = function(b) {
+  this.throwIfDisposed();
+  return mod(this, b);
+};
+getGlobalTensorClass().prototype.mul = function(b) {
+  this.throwIfDisposed();
+  return mul(this, b);
+};
+getGlobalTensorClass().prototype.neg = function() {
+  this.throwIfDisposed();
+  return neg(this);
+};
+getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {
+  this.throwIfDisposed();
+  return norm(this, ord, axis, keepDims);
+};
+getGlobalTensorClass().prototype.notEqual = function(b) {
+  this.throwIfDisposed();
+  return notEqual(this, b);
+};
+getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {
+  this.throwIfDisposed();
+  return oneHot(this, depth, onValue, offValue);
+};
+getGlobalTensorClass().prototype.onesLike = function() {
+  this.throwIfDisposed();
+  return onesLike(this);
+};
+getGlobalTensorClass().prototype.pad = function(paddings, constantValue) {
+  this.throwIfDisposed();
+  return pad(this, paddings, constantValue);
+};
+getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding2, dilationRate, strides2, dimRoundingMode) {
+  this.throwIfDisposed();
+  return pool(this, windowShape, poolingType, padding2, dilationRate, strides2, dimRoundingMode);
+};
+getGlobalTensorClass().prototype.pow = function(exp5) {
+  this.throwIfDisposed();
+  return pow(this, exp5);
+};
+getGlobalTensorClass().prototype.prelu = function(alpha2) {
+  this.throwIfDisposed();
+  return prelu(this, alpha2);
+};
+getGlobalTensorClass().prototype.prod = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return prod(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.reciprocal = function() {
+  this.throwIfDisposed();
+  return reciprocal(this);
+};
+getGlobalTensorClass().prototype.relu = function() {
+  this.throwIfDisposed();
+  return relu(this);
+};
+getGlobalTensorClass().prototype.relu6 = function() {
+  this.throwIfDisposed();
+  return relu6(this);
+};
+getGlobalTensorClass().prototype.reshapeAs = function(x) {
+  this.throwIfDisposed();
+  return reshape(this, x.shape);
+};
+getGlobalTensorClass().prototype.reshape = function(shape) {
+  this.throwIfDisposed();
+  return reshape(this, shape);
+};
+getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {
+  this.throwIfDisposed();
+  return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);
+};
+getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {
+  this.throwIfDisposed();
+  return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);
+};
+getGlobalTensorClass().prototype.reverse = function(axis) {
+  this.throwIfDisposed();
+  return reverse(this, axis);
+};
+getGlobalTensorClass().prototype.rfft = function() {
+  this.throwIfDisposed();
+  return rfft(this);
+};
+getGlobalTensorClass().prototype.round = function() {
+  this.throwIfDisposed();
+  return round2(this);
+};
+getGlobalTensorClass().prototype.rsqrt = function() {
+  this.throwIfDisposed();
+  return rsqrt(this);
+};
+getGlobalTensorClass().prototype.selu = function() {
+  this.throwIfDisposed();
+  return selu(this);
+};
+getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides2, pad3, dilation, dataFormat) {
+  this.throwIfDisposed();
+  return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides2, pad3, dilation, dataFormat);
+};
+getGlobalTensorClass().prototype.sigmoid = function() {
+  this.throwIfDisposed();
+  return sigmoid(this);
+};
+getGlobalTensorClass().prototype.sign = function() {
+  this.throwIfDisposed();
+  return sign(this);
+};
+getGlobalTensorClass().prototype.sin = function() {
+  this.throwIfDisposed();
+  return sin(this);
+};
+getGlobalTensorClass().prototype.sinh = function() {
+  this.throwIfDisposed();
+  return sinh(this);
+};
+getGlobalTensorClass().prototype.slice = function(begin, size2) {
+  this.throwIfDisposed();
+  return slice(this, begin, size2);
+};
+getGlobalTensorClass().prototype.softmax = function(dim) {
+  this.throwIfDisposed();
+  return softmax(this, dim);
+};
+getGlobalTensorClass().prototype.softplus = function() {
+  this.throwIfDisposed();
+  return softplus(this);
+};
+getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {
+  this.throwIfDisposed();
+  return spaceToBatchND(this, blockShape, paddings);
+};
+getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {
+  this.throwIfDisposed();
+  return split(this, numOrSizeSplits, axis);
+};
+getGlobalTensorClass().prototype.sqrt = function() {
+  this.throwIfDisposed();
+  return sqrt(this);
+};
+getGlobalTensorClass().prototype.square = function() {
+  this.throwIfDisposed();
+  return square(this);
+};
+getGlobalTensorClass().prototype.squaredDifference = function(b) {
+  this.throwIfDisposed();
+  return squaredDifference(this, b);
+};
+getGlobalTensorClass().prototype.squeeze = function(axis) {
+  this.throwIfDisposed();
+  return squeeze(this, axis);
+};
+getGlobalTensorClass().prototype.stack = function(x, axis) {
+  this.throwIfDisposed();
+  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];
+  return stack(tensorsToBeStacked, axis);
+};
+getGlobalTensorClass().prototype.step = function(alpha2) {
+  this.throwIfDisposed();
+  return step(this, alpha2);
+};
+getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+  this.throwIfDisposed();
+  return stridedSlice(this, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+};
+getGlobalTensorClass().prototype.sub = function(b) {
+  this.throwIfDisposed();
+  return sub(this, b);
+};
+getGlobalTensorClass().prototype.sum = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return sum2(this, axis, keepDims);
+};
+getGlobalTensorClass().prototype.tan = function() {
+  this.throwIfDisposed();
+  return tan(this);
+};
+getGlobalTensorClass().prototype.tanh = function() {
+  this.throwIfDisposed();
+  return tanh2(this);
+};
+getGlobalTensorClass().prototype.tile = function(reps) {
+  this.throwIfDisposed();
+  return tile(this, reps);
+};
+getGlobalTensorClass().prototype.toBool = function() {
+  this.throwIfDisposed();
+  return cast(this, "bool");
+};
+getGlobalTensorClass().prototype.toFloat = function() {
+  this.throwIfDisposed();
+  return cast(this, "float32");
+};
+getGlobalTensorClass().prototype.toInt = function() {
+  this.throwIfDisposed();
+  return cast(this, "int32");
+};
+getGlobalTensorClass().prototype.topk = function(k, sorted) {
+  this.throwIfDisposed();
+  return topk(this, k, sorted);
+};
+getGlobalTensorClass().prototype.transpose = function(perm) {
+  this.throwIfDisposed();
+  return transpose(this, perm);
+};
+getGlobalTensorClass().prototype.unique = function(axis) {
+  this.throwIfDisposed();
+  return unique(this, axis);
+};
+getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {
+  this.throwIfDisposed();
+  return unsortedSegmentSum(this, segmentIds, numSegments);
+};
+getGlobalTensorClass().prototype.unstack = function(axis) {
+  this.throwIfDisposed();
+  return unstack(this, axis);
+};
+getGlobalTensorClass().prototype.where = function(condition, x) {
+  this.throwIfDisposed();
+  return where(condition, this, x);
+};
+getGlobalTensorClass().prototype.zerosLike = function() {
+  this.throwIfDisposed();
+  return zerosLike(this);
+};
+var AttributeError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, AttributeError.prototype);
+  }
+};
+var RuntimeError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, RuntimeError.prototype);
+  }
+};
+var ValueError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, ValueError.prototype);
+  }
+};
+var NotImplementedError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, NotImplementedError.prototype);
+  }
+};
+var AssertionError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, AssertionError.prototype);
+  }
+};
+var LruCache = class {
+  constructor(maxEntries) {
+    this.maxEntries = maxEntries || 100;
+    this.cache = /* @__PURE__ */ new Map();
+  }
+  get(key) {
+    let entry;
+    if (this.cache.has(key)) {
+      entry = this.cache.get(key);
+      this.cache.delete(key);
+      this.cache.set(key, entry);
+    }
+    return entry;
+  }
+  put(key, value) {
+    if (this.cache.has(key)) {
+      this.cache.delete(key);
+    } else if (this.cache.size >= this.maxEntries) {
+      const keyToDelete = this.cache.keys().next().value;
+      this.cache.delete(keyToDelete);
+    }
+    this.cache.set(key, value);
+  }
+  getMaxEntries() {
+    return this.maxEntries;
+  }
+  setMaxEntries(maxEntries) {
+    if (maxEntries < 0) {
+      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);
+    }
+    if (this.maxEntries > maxEntries) {
+      for (let i2 = 0; i2 < this.maxEntries - maxEntries; i2++) {
+        const keyToDelete = this.cache.keys().next().value;
+        this.cache.delete(keyToDelete);
+      }
+    }
+    this.maxEntries = maxEntries;
+  }
+};
+function pyListRepeat(value, numValues) {
+  if (Array.isArray(value)) {
+    let newArray = [];
+    for (let i2 = 0; i2 < numValues; i2++) {
+      newArray = newArray.concat(value);
+    }
+    return newArray;
+  } else {
+    const newArray = new Array(numValues);
+    newArray.fill(value);
+    return newArray;
+  }
+}
+function assert2(val, message) {
+  if (!val) {
+    throw new AssertionError(message);
+  }
+}
+function count(array2, refernce) {
+  let counter = 0;
+  for (const item of array2) {
+    if (item === refernce) {
+      counter++;
+    }
+  }
+  return counter;
+}
+function singletonOrArray(xs) {
+  if (xs.length === 1) {
+    return xs[0];
+  }
+  return xs;
+}
+function toList(x) {
+  if (Array.isArray(x)) {
+    return x;
+  }
+  return [x];
+}
+function toSnakeCase(name) {
+  const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2");
+  const insecure = intermediate.replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase();
+  if (insecure[0] !== "_") {
+    return insecure;
+  }
+  return "private" + insecure;
+}
+function toCamelCase(identifier) {
+  if (identifier.length <= 1) {
+    return identifier;
+  }
+  if (identifier.indexOf("_") === -1) {
+    return identifier;
+  }
+  return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase());
+}
+var _GLOBAL_CUSTOM_OBJECTS = {};
+function serializeKerasObject(instance) {
+  if (instance === null || instance === void 0) {
+    return null;
+  }
+  const dict = {};
+  dict["className"] = instance.getClassName();
+  dict["config"] = instance.getConfig();
+  return dict;
+}
+function convertNDArrayScalarsInConfig(config3) {
+  if (config3 == null || typeof config3 !== "object") {
+    return;
+  } else if (Array.isArray(config3)) {
+    config3.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));
+  } else {
+    const fields = Object.keys(config3);
+    for (const field of fields) {
+      const value = config3[field];
+      if (value != null && typeof value === "object") {
+        if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") {
+          config3[field] = value["value"];
+        } else {
+          convertNDArrayScalarsInConfig(value);
+        }
+      }
+    }
+  }
+}
+function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = "object", fastWeightInit = false) {
+  if (typeof identifier === "string") {
+    const functionName = identifier;
+    let fn;
+    if (functionName in customObjects) {
+      fn = customObjects[functionName];
+    } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {
+      fn = _GLOBAL_CUSTOM_OBJECTS[functionName];
+    } else {
+      fn = moduleObjects[functionName];
+      if (fn == null) {
+        throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
+      }
+    }
+    return fn;
+  } else {
+    const config3 = identifier;
+    if (config3["className"] == null || config3["config"] == null) {
+      throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config3)}.
+'className' and 'config' must set.`);
+    }
+    const className = config3["className"];
+    let cls, fromConfig;
+    if (className in customObjects) {
+      [cls, fromConfig] = customObjects[className];
+    } else if (className in _GLOBAL_CUSTOM_OBJECTS) {
+      [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS["className"];
+    } else if (className in moduleObjects) {
+      [cls, fromConfig] = moduleObjects[className];
+    }
+    if (cls == null) {
+      throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
+    }
+    if (fromConfig != null) {
+      const customObjectsCombined = {};
+      for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {
+        customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];
+      }
+      for (const key of Object.keys(customObjects)) {
+        customObjectsCombined[key] = customObjects[key];
+      }
+      const nestedConfig = config3["config"];
+      nestedConfig["customObjects"] = customObjectsCombined;
+      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
+      for (const key of Object.keys(customObjects)) {
+        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
+      }
+      convertNDArrayScalarsInConfig(config3["config"]);
+      const returnObj = fromConfig(cls, config3["config"], customObjects, fastWeightInit);
+      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
+      return returnObj;
+    } else {
+      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
+      for (const key of Object.keys(customObjects)) {
+        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
+      }
+      const returnObj = new cls(config3["config"]);
+      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
+      return returnObj;
+    }
+  }
+}
+function numberCompare(a6, b) {
+  return a6 < b ? -1 : a6 > b ? 1 : 0;
+}
+function reverseNumberCompare(a6, b) {
+  return -1 * numberCompare(a6, b);
+}
+function unique2(xs) {
+  if (xs == null) {
+    return xs;
+  }
+  const out = [];
+  for (const x of xs) {
+    if (out.indexOf(x) === -1) {
+      out.push(x);
+    }
+  }
+  return out;
+}
+function isObjectEmpty(obj) {
+  if (obj == null) {
+    throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);
+  }
+  for (const key in obj) {
+    if (obj.hasOwnProperty(key)) {
+      return false;
+    }
+  }
+  return true;
+}
+function checkStringTypeUnionValue(values, label, value) {
+  if (value == null) {
+    return;
+  }
+  if (values.indexOf(value) < 0) {
+    throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);
+  }
+}
+function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {
+  assert2(minLength >= 0);
+  assert2(maxLength >= minLength);
+  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e2) => typeof e2 === expectedType);
+}
+function assertPositiveInteger(value, name) {
+  if (Array.isArray(value)) {
+    util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);
+    value.forEach((v, i2) => assertPositiveInteger(v, `element ${i2 + 1} of ${name}`));
+  } else {
+    util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);
+  }
+}
+function formatAsFriendlyString(value) {
+  if (value === null) {
+    return "null";
+  } else if (Array.isArray(value)) {
+    return "[" + value.map((v) => formatAsFriendlyString(v)).join(",") + "]";
+  } else if (typeof value === "string") {
+    return `"${value}"`;
+  } else {
+    return `${value}`;
+  }
+}
+function debounce(f, waitMs, nowFunc) {
+  let lastTime15 = nowFunc != null ? nowFunc() : util_exports.now();
+  let lastResult;
+  const f2 = (...args) => {
+    const now22 = nowFunc != null ? nowFunc() : util_exports.now();
+    if (now22 - lastTime15 < waitMs) {
+      return lastResult;
+    }
+    lastTime15 = now22;
+    lastResult = f(...args);
+    return lastResult;
+  };
+  return f2;
+}
+function mapActivationToFusedKernel(activationName) {
+  if (activationName === "relu") {
+    return "relu";
+  }
+  if (activationName === "linear") {
+    return "linear";
+  }
+  if (activationName === "elu") {
+    return "elu";
+  }
+  return null;
+}
+var _nextUniqueTensorId = 0;
+function getNextUniqueTensorId() {
+  return _nextUniqueTensorId++;
+}
+var _uidPrefixes = {};
+function getUid(prefix = "") {
+  if (!(prefix in _uidPrefixes)) {
+    _uidPrefixes[prefix] = 0;
+  }
+  _uidPrefixes[prefix] += 1;
+  return prefix + _uidPrefixes[prefix].toString();
+}
+var VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"];
+var VALID_INTERPOLATION_FORMAT_VALUES = ["nearest", "bilinear"];
+var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"];
+var VALID_POOL_MODE_VALUES = ["max", "avg"];
+var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"];
+var nameMap = /* @__PURE__ */ new Map();
+function checkDataFormat(value) {
+  checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value);
+}
+function checkInterpolationFormat(value) {
+  checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, "InterpolationFormat", value);
+}
+function checkPaddingMode(value) {
+  checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value);
+}
+function checkPoolMode(value) {
+  checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value);
+}
+var _nameScopeStack = [];
+var _nameScopeDivider = "/";
+function nameScope(name, fn) {
+  _nameScopeStack.push(name);
+  try {
+    const val = fn();
+    _nameScopeStack.pop();
+    return val;
+  } catch (e2) {
+    _nameScopeStack.pop();
+    throw e2;
+  }
+}
+function currentNameScopePrefix() {
+  if (_nameScopeStack.length === 0) {
+    return "";
+  } else {
+    return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;
+  }
+}
+function getScopedTensorName(tensorName) {
+  if (!isValidTensorName(tensorName)) {
+    throw new Error("Not a valid tensor name: '" + tensorName + "'");
+  }
+  return currentNameScopePrefix() + tensorName;
+}
+function getUniqueTensorName(scopedName) {
+  if (!isValidTensorName(scopedName)) {
+    throw new Error("Not a valid tensor name: '" + scopedName + "'");
+  }
+  if (!nameMap.has(scopedName)) {
+    nameMap.set(scopedName, 0);
+  }
+  const index2 = nameMap.get(scopedName);
+  nameMap.set(scopedName, nameMap.get(scopedName) + 1);
+  if (index2 > 0) {
+    const result = `${scopedName}_${index2}`;
+    nameMap.set(result, 1);
+    return result;
+  } else {
+    return scopedName;
+  }
+}
+var tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
+function isValidTensorName(name) {
+  return !!name.match(tensorNameRegex);
+}
+function isInteger(x) {
+  return x === parseInt(x.toString(), 10);
+}
+function arrayProd(array2, begin, end) {
+  if (begin == null) {
+    begin = 0;
+  }
+  if (end == null) {
+    end = array2.length;
+  }
+  let prod6 = 1;
+  for (let i2 = begin; i2 < end; ++i2) {
+    prod6 *= array2[i2];
+  }
+  return prod6;
+}
+function min2(array2) {
+  if (array2.length === 0) {
+    return Number.NaN;
+  }
+  let min7 = Number.POSITIVE_INFINITY;
+  for (let i2 = 0; i2 < array2.length; i2++) {
+    const value = array2[i2];
+    if (value < min7) {
+      min7 = value;
+    }
+  }
+  return min7;
+}
+function max2(array2) {
+  if (array2.length === 0) {
+    return Number.NaN;
+  }
+  let max7 = Number.NEGATIVE_INFINITY;
+  for (let i2 = 0; i2 < array2.length; i2++) {
+    const value = array2[i2];
+    if (value > max7) {
+      max7 = value;
+    }
+  }
+  return max7;
+}
+function range2(begin, end) {
+  if (end < begin) {
+    throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
+  }
+  const out = [];
+  for (let i2 = begin; i2 < end; ++i2) {
+    out.push(i2);
+  }
+  return out;
+}
+var _epsilon;
+function epsilon() {
+  if (_epsilon == null) {
+    _epsilon = backend().epsilon();
+  }
+  return _epsilon;
+}
+function imageDataFormat() {
+  return "channelsLast";
+}
+function cast2(x, dtype) {
+  return cast(x, dtype);
+}
+function expandDims2(x, axis = -1) {
+  const outShape = x.shape.slice();
+  if (axis < 0) {
+    axis = outShape.length + axis + 1;
+  }
+  outShape.splice(axis, 0, 1);
+  return reshape(x, outShape);
+}
+function repeat(x, n) {
+  return tidy(() => {
+    if (x.shape.length !== 2) {
+      throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);
+    }
+    const y = expandDims2(x, 1);
+    return tile2(y, [1, n, 1]);
+  });
+}
+function flatten2(x) {
+  const newShape = [arrayProd(x.shape)];
+  return reshape(x, newShape);
+}
+function batchFlatten(x) {
+  if (x.rank <= 1) {
+    throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);
+  }
+  const newShape = [x.shape[0], arrayProd(x.shape, 1)];
+  return reshape(x, newShape);
+}
+function sliceAlongFirstAxis(array2, start, size2) {
+  return tidy(() => {
+    switch (array2.rank) {
+      case 1:
+        return slice1d(array2, start, size2);
+      case 2:
+        return slice2d(array2, [start, 0], [size2, array2.shape[1]]);
+      case 3:
+        return slice3d(array2, [start, 0, 0], [size2, array2.shape[1], array2.shape[2]]);
+      case 4:
+        return slice4d(array2, [start, 0, 0, 0], [size2, array2.shape[1], array2.shape[2], array2.shape[3]]);
+      case 5:
+        return slice(array2, [start, 0, 0, 0, 0], [
+          size2,
+          array2.shape[1],
+          array2.shape[2],
+          array2.shape[3],
+          array2.shape[4]
+        ]);
+      case 6:
+        return slice(array2, [start, 0, 0, 0, 0, 0], [
+          size2,
+          array2.shape[1],
+          array2.shape[2],
+          array2.shape[3],
+          array2.shape[4],
+          array2.shape[5]
+        ]);
+      default:
+        throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);
+    }
+  });
+}
+function sliceAlongLastAxis(array2, start, size2) {
+  return tidy(() => {
+    switch (array2.rank) {
+      case 1:
+        return slice1d(array2, start, size2);
+      case 2:
+        return slice2d(array2, [0, start], [array2.shape[0], size2]);
+      case 3:
+        return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size2]);
+      case 4:
+        return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size2]);
+      default:
+        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
+    }
+  });
+}
+function sliceAlongAxis(array2, start, size2, axis) {
+  return tidy(() => {
+    switch (array2.rank) {
+      case 1:
+        return slice1d(array2, start, size2);
+      case 2:
+        switch (axis) {
+          case 1:
+            return sliceAlongFirstAxis(array2, start, size2);
+          case 2:
+            return sliceAlongLastAxis(array2, start, size2);
+          default:
+            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
+        }
+      case 3:
+        switch (axis) {
+          case 1:
+            return sliceAlongFirstAxis(array2, start, size2);
+          case 2:
+            return slice3d(array2, [0, start, 0], [array2.shape[0], size2, array2.shape[2]]);
+          case 3:
+            return sliceAlongLastAxis(array2, start, size2);
+          default:
+            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
+        }
+      case 4:
+        switch (axis) {
+          case 1:
+            return sliceAlongFirstAxis(array2, start, size2);
+          case 2:
+            return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size2, array2.shape[2], array2.shape[3]]);
+          case 3:
+            return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size2, array2.shape[3]]);
+          case 4:
+            return sliceAlongLastAxis(array2, start, size2);
+          default:
+            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
+        }
+      default:
+        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
+    }
+  });
+}
+function concatenate(tensors, axis = -1) {
+  let rank;
+  if (axis < 0) {
+    rank = tensors[0].rank;
+    if (rank !== 0) {
+      axis = rank;
+    } else {
+      axis = 0;
+    }
+  }
+  if (axis === tensors[0].rank) {
+    axis = -1;
+  }
+  return concat(tensors, axis);
+}
+function concatAlongFirstAxis(a6, b) {
+  switch (a6.rank) {
+    case 1:
+      return concat1d([a6, b]);
+    case 2:
+      return concat2d([a6, b], 0);
+    case 3:
+      return concat3d([a6, b], 0);
+    case 4:
+      return concat4d([a6, b], 0);
+    default:
+      throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a6.rank}`);
+  }
+}
+function tile2(x, n) {
+  if (!Array.isArray(n)) {
+    n = [n];
+  }
+  if (x.rank !== n.length) {
+    throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
+  }
+  return tile(x, n);
+}
+function randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {
+  return randomNormal(shape, mean5, stddev, dtype, seed);
+}
+function dot2(a6, b, activation2, bias) {
+  if (a6.rank < 2 || b.rank < 2) {
+    throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a6.shape} and y shape = ${b.shape}`);
+  }
+  if (b.rank >= 3) {
+    const xLastDim = a6.shape.slice(-1)[0];
+    const ySecondLastDim = b.shape.slice(-2)[0];
+    if (xLastDim !== ySecondLastDim) {
+      throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a6.shape} and  y shape = ${b.shape}`);
+    }
+  }
+  if (a6.rank === 2 && b.rank === 2) {
+    const transposeA = false;
+    const transposeB = false;
+    return fused_ops_exports.matMul({
+      a: a6,
+      b,
+      transposeA,
+      transposeB,
+      bias: bias ? reshapeBias(a6.rank, bias, imageDataFormat()) : null,
+      activation: activation2
+    });
+  } else {
+    const aFirstDims = a6.shape.slice();
+    const aLastDim = aFirstDims.pop();
+    a6 = reshape(a6, [-1, aLastDim]);
+    const bShape = b.shape.slice();
+    const bLastDim = bShape.pop();
+    const ySecondLastDim = bShape.pop();
+    const yOtherDims = [...bShape, bLastDim];
+    const perm = Array.from({ length: b.rank }, (_, i2) => {
+      if (i2 === 0) {
+        return b.rank - 2;
+      } else if (i2 <= b.rank - 2) {
+        return i2 - 1;
+      }
+      return i2;
+    });
+    b = reshape(transpose(b, perm), [ySecondLastDim, -1]);
+    const outputShape = [...aFirstDims, ...yOtherDims];
+    const transposeA = false;
+    const transposeB = false;
+    return reshape(fused_ops_exports.matMul({
+      a: a6,
+      b,
+      transposeA,
+      transposeB,
+      bias: bias ? reshapeBias(a6.rank, bias, imageDataFormat()) : null,
+      activation: activation2
+    }), outputShape);
+  }
+}
+function gather2(reference, indices, axis) {
+  return tidy(() => {
+    if (Array.isArray(indices)) {
+      indices = tensor1d(indices, "int32");
+    } else {
+      indices = cast(indices, "int32");
+    }
+    return gather(reference, indices, axis);
+  });
+}
+function square2(x) {
+  return mul(x, x);
+}
+function reshapeBias(xRank, bias, dataFormat) {
+  const biasShape = bias.shape;
+  if (bias.rank !== 1 && bias.rank !== xRank) {
+    throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);
+  }
+  if (xRank === 5) {
+    if (dataFormat === "channelsFirst") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, biasShape[0], 1, 1, 1]);
+      } else {
+        return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
+      }
+    } else if (dataFormat === "channelsLast") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, 1, 1, 1, biasShape[0]]);
+      } else {
+        return reshape(bias, [1].concat(biasShape));
+      }
+    }
+  } else if (xRank === 4) {
+    if (dataFormat === "channelsFirst") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, biasShape[0], 1, 1]);
+      } else {
+        return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);
+      }
+    } else if (dataFormat === "channelsLast") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, 1, 1, biasShape[0]]);
+      } else {
+        return reshape(bias, [1].concat(biasShape));
+      }
+    }
+  } else if (xRank === 3) {
+    if (dataFormat === "channelsFirst") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, biasShape[0], 1]);
+      } else {
+        return reshape(bias, [1, biasShape[1], biasShape[0]]);
+      }
+    } else if (dataFormat === "channelsLast") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, 1, biasShape[0]]);
+      } else {
+        return reshape(bias, [1].concat(biasShape));
+      }
+    }
+  } else if (xRank < 3) {
+    return bias;
+  }
+  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
+}
+function biasAdd(x, bias, dataFormat) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    return add2(x, reshapeBias(x.rank, bias, dataFormat));
+  });
+}
+function elu2(x, alpha2 = 1) {
+  if (alpha2 !== 1) {
+    throw new NotImplementedError(`Support for alpha values other than 1 (${alpha2}) is not implemented yet.`);
+  }
+  return elu(x);
+}
+function softsign(x) {
+  return tidy(() => div(x, add2(abs(x), 1)));
+}
+function dropout2(x, level, noiseShape, seed) {
+  return tidy(() => dropout(x, level, noiseShape, seed));
+}
+function hardSigmoid(x) {
+  return tidy(() => {
+    const y = add2(0.5, mul(0.2, x));
+    return clipByValue(y, 0, 1);
+  });
+}
+function inTrainPhase(x, alt, training = false) {
+  return training ? x() : alt();
+}
+var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"];
+var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"];
+function checkFanMode(value) {
+  checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value);
+}
+function checkDistribution(value) {
+  checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value);
+}
+var Initializer = class extends serialization_exports.Serializable {
+  fromConfigUsesCustomObjects() {
+    return false;
+  }
+  getConfig() {
+    return {};
+  }
+};
+var Zeros = class extends Initializer {
+  apply(shape, dtype) {
+    return zeros(shape, dtype);
+  }
+};
+Zeros.className = "Zeros";
+serialization_exports.registerClass(Zeros);
+var Ones = class extends Initializer {
+  apply(shape, dtype) {
+    return ones2(shape, dtype);
+  }
+};
+Ones.className = "Ones";
+serialization_exports.registerClass(Ones);
+var Constant = class extends Initializer {
+  constructor(args) {
+    super();
+    if (typeof args !== "object") {
+      throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);
+    }
+    if (args.value === void 0) {
+      throw new ValueError(`config must have value set but got ${args}`);
+    }
+    this.value = args.value;
+  }
+  apply(shape, dtype) {
+    return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));
+  }
+  getConfig() {
+    return {
+      value: this.value
+    };
+  }
+};
+Constant.className = "Constant";
+serialization_exports.registerClass(Constant);
+var RandomUniform = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_MINVAL = -0.05;
+    this.DEFAULT_MAXVAL = 0.05;
+    this.minval = args.minval || this.DEFAULT_MINVAL;
+    this.maxval = args.maxval || this.DEFAULT_MAXVAL;
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    return randomUniform(shape, this.minval, this.maxval, dtype);
+  }
+  getConfig() {
+    return { minval: this.minval, maxval: this.maxval, seed: this.seed };
+  }
+};
+RandomUniform.className = "RandomUniform";
+serialization_exports.registerClass(RandomUniform);
+var RandomNormal = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_MEAN = 0;
+    this.DEFAULT_STDDEV = 0.05;
+    this.mean = args.mean || this.DEFAULT_MEAN;
+    this.stddev = args.stddev || this.DEFAULT_STDDEV;
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    dtype = dtype || "float32";
+    if (dtype !== "float32" && dtype !== "int32") {
+      throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);
+    }
+    return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);
+  }
+  getConfig() {
+    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
+  }
+};
+RandomNormal.className = "RandomNormal";
+serialization_exports.registerClass(RandomNormal);
+var TruncatedNormal = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_MEAN = 0;
+    this.DEFAULT_STDDEV = 0.05;
+    this.mean = args.mean || this.DEFAULT_MEAN;
+    this.stddev = args.stddev || this.DEFAULT_STDDEV;
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    dtype = dtype || "float32";
+    if (dtype !== "float32" && dtype !== "int32") {
+      throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);
+    }
+    return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);
+  }
+  getConfig() {
+    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
+  }
+};
+TruncatedNormal.className = "TruncatedNormal";
+serialization_exports.registerClass(TruncatedNormal);
+var Identity2 = class extends Initializer {
+  constructor(args) {
+    super();
+    this.gain = args.gain != null ? args.gain : 1;
+  }
+  apply(shape, dtype) {
+    return tidy(() => {
+      if (shape.length !== 2 || shape[0] !== shape[1]) {
+        throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");
+      } else {
+        return mul(this.gain, eye(shape[0]));
+      }
+    });
+  }
+  getConfig() {
+    return { gain: this.gain };
+  }
+};
+Identity2.className = "Identity";
+serialization_exports.registerClass(Identity2);
+function computeFans(shape, dataFormat = "channelsLast") {
+  let fanIn;
+  let fanOut;
+  checkDataFormat(dataFormat);
+  if (shape.length === 2) {
+    fanIn = shape[0];
+    fanOut = shape[1];
+  } else if ([3, 4, 5].indexOf(shape.length) !== -1) {
+    if (dataFormat === "channelsFirst") {
+      const receptiveFieldSize = arrayProd(shape, 2);
+      fanIn = shape[1] * receptiveFieldSize;
+      fanOut = shape[0] * receptiveFieldSize;
+    } else if (dataFormat === "channelsLast") {
+      const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);
+      fanIn = shape[shape.length - 2] * receptiveFieldSize;
+      fanOut = shape[shape.length - 1] * receptiveFieldSize;
+    }
+  } else {
+    const shapeProd = arrayProd(shape);
+    fanIn = Math.sqrt(shapeProd);
+    fanOut = Math.sqrt(shapeProd);
+  }
+  return [fanIn, fanOut];
+}
+var VarianceScaling = class extends Initializer {
+  constructor(args) {
+    super();
+    if (args.scale < 0) {
+      throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);
+    }
+    this.scale = args.scale == null ? 1 : args.scale;
+    this.mode = args.mode == null ? "fanIn" : args.mode;
+    checkFanMode(this.mode);
+    this.distribution = args.distribution == null ? "normal" : args.distribution;
+    checkDistribution(this.distribution);
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    const fans = computeFans(shape);
+    const fanIn = fans[0];
+    const fanOut = fans[1];
+    let scale22 = this.scale;
+    if (this.mode === "fanIn") {
+      scale22 /= Math.max(1, fanIn);
+    } else if (this.mode === "fanOut") {
+      scale22 /= Math.max(1, fanOut);
+    } else {
+      scale22 /= Math.max(1, (fanIn + fanOut) / 2);
+    }
+    if (this.distribution === "normal") {
+      const stddev = Math.sqrt(scale22);
+      dtype = dtype || "float32";
+      if (dtype !== "float32" && dtype !== "int32") {
+        throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);
+      }
+      return truncatedNormal(shape, 0, stddev, dtype, this.seed);
+    } else {
+      const limit = Math.sqrt(3 * scale22);
+      return randomUniform(shape, -limit, limit, dtype);
+    }
+  }
+  getConfig() {
+    return {
+      scale: this.scale,
+      mode: this.mode,
+      distribution: this.distribution,
+      seed: this.seed
+    };
+  }
+};
+VarianceScaling.className = "VarianceScaling";
+serialization_exports.registerClass(VarianceScaling);
+var GlorotUniform = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanAvg",
+      distribution: "uniform",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+GlorotUniform.className = "GlorotUniform";
+serialization_exports.registerClass(GlorotUniform);
+var GlorotNormal = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanAvg",
+      distribution: "normal",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+GlorotNormal.className = "GlorotNormal";
+serialization_exports.registerClass(GlorotNormal);
+var HeNormal = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 2,
+      mode: "fanIn",
+      distribution: "normal",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+HeNormal.className = "HeNormal";
+serialization_exports.registerClass(HeNormal);
+var HeUniform = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 2,
+      mode: "fanIn",
+      distribution: "uniform",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+HeUniform.className = "HeUniform";
+serialization_exports.registerClass(HeUniform);
+var LeCunNormal = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanIn",
+      distribution: "normal",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+LeCunNormal.className = "LeCunNormal";
+serialization_exports.registerClass(LeCunNormal);
+var LeCunUniform = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanIn",
+      distribution: "uniform",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+LeCunUniform.className = "LeCunNormal";
+serialization_exports.registerClass(LeCunUniform);
+var Orthogonal = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_GAIN = 1;
+    this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;
+    this.seed = args.seed;
+    if (this.seed != null) {
+      throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.");
+    }
+  }
+  apply(shape, dtype) {
+    return tidy(() => {
+      if (shape.length < 2) {
+        throw new NotImplementedError("Shape must be at least 2D.");
+      }
+      if (shape[0] * shape[1] > 2e3) {
+        console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);
+      }
+      const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;
+      const a6 = randomNormal2(normalizedShape, 0, 1, "float32");
+      let q = linalg.gramSchmidt(a6);
+      if (shape[0] > shape[1]) {
+        q = transpose(q);
+      }
+      return mul(this.gain, q);
+    });
+  }
+  getConfig() {
+    return {
+      gain: this.gain,
+      seed: this.seed
+    };
+  }
+};
+Orthogonal.className = "Orthogonal";
+serialization_exports.registerClass(Orthogonal);
+var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
+  "constant": "Constant",
+  "glorotNormal": "GlorotNormal",
+  "glorotUniform": "GlorotUniform",
+  "heNormal": "HeNormal",
+  "heUniform": "HeUniform",
+  "identity": "Identity",
+  "leCunNormal": "LeCunNormal",
+  "leCunUniform": "LeCunUniform",
+  "ones": "Ones",
+  "orthogonal": "Orthogonal",
+  "randomNormal": "RandomNormal",
+  "randomUniform": "RandomUniform",
+  "truncatedNormal": "TruncatedNormal",
+  "varianceScaling": "VarianceScaling",
+  "zeros": "Zeros"
+};
+function deserializeInitializer(config3, customObjects = {}) {
+  return deserializeKerasObject(config3, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "initializer");
+}
+function serializeInitializer(initializer) {
+  return serializeKerasObject(initializer);
+}
+function getInitializer(identifier) {
+  if (typeof identifier === "string") {
+    const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
+    if (className === "GlorotNormal") {
+      return new GlorotNormal();
+    } else if (className === "GlorotUniform") {
+      return new GlorotUniform();
+    } else if (className === "HeNormal") {
+      return new HeNormal();
+    } else if (className === "HeUniform") {
+      return new HeUniform();
+    } else if (className === "LeCunNormal") {
+      return new LeCunNormal();
+    } else if (className === "LeCunUniform") {
+      return new LeCunUniform();
+    } else {
+      const config3 = {};
+      config3["className"] = className;
+      config3["config"] = {};
+      return deserializeInitializer(config3);
+    }
+  } else if (identifier instanceof Initializer) {
+    return identifier;
+  } else {
+    return deserializeInitializer(identifier);
+  }
+}
+function isArrayOfShapes(x) {
+  return Array.isArray(x) && Array.isArray(x[0]);
+}
+function normalizeShapeList(x) {
+  if (x.length === 0) {
+    return [];
+  }
+  if (!Array.isArray(x[0])) {
+    return [x];
+  }
+  return x;
+}
+function getExactlyOneTensor(xs) {
+  let x;
+  if (Array.isArray(xs)) {
+    if (xs.length !== 1) {
+      throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);
+    }
+    x = xs[0];
+  } else {
+    x = xs;
+  }
+  return x;
+}
+function getExactlyOneShape(shapes) {
+  if (Array.isArray(shapes) && Array.isArray(shapes[0])) {
+    if (shapes.length === 1) {
+      shapes = shapes;
+      return shapes[0];
+    } else {
+      throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);
+    }
+  } else {
+    return shapes;
+  }
+}
+function countParamsInWeights(weights) {
+  let count22 = 0;
+  for (const weight of weights) {
+    if (weight.shape.length === 0) {
+      count22 += 1;
+    } else {
+      count22 += weight.shape.reduce((a6, b) => a6 * b);
+    }
+  }
+  return count22;
+}
+var DEFAULT_VARIABLE_NAME_PREFIX = "Variable";
+var LayerVariable = class {
+  constructor(val, dtype = "float32", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {
+    this.dtype = dtype == null ? "float32" : dtype;
+    this.shape = val.shape;
+    this.id = getNextUniqueTensorId();
+    name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;
+    this.originalName = getScopedTensorName(name);
+    this.name = getUniqueTensorName(this.originalName);
+    this.trainable_ = trainable;
+    this.constraint = constraint;
+    this.val = variable(val, this.trainable_, this.name, this.dtype);
+  }
+  read() {
+    this.assertNotDisposed();
+    return this.val;
+  }
+  write(newVal) {
+    this.assertNotDisposed();
+    checkShapesMatch(this.val, newVal);
+    if (this.val.id !== newVal.id) {
+      this.val.assign(newVal);
+      if (this.constraint != null) {
+        this.val.assign(this.constraint.apply(this.val));
+      }
+    }
+    return this;
+  }
+  dispose() {
+    this.assertNotDisposed();
+    this.val.dispose();
+  }
+  assertNotDisposed() {
+    if (this.val.isDisposed) {
+      throw new Error(`LayersVariable ${this.name} is already disposed.`);
+    }
+  }
+  get trainable() {
+    return this.trainable_;
+  }
+  set trainable(trainable) {
+    this.trainable_ = trainable;
+    this.val.trainable = trainable;
+  }
+};
+function checkShapesMatch(x, y) {
+  if (x.shape.toString() !== y.shape.toString()) {
+    throw new Error("Shape mismatch: " + JSON.stringify(x.shape) + " vs. " + JSON.stringify(y.shape));
+  }
+}
+function batchGetValue(xs) {
+  return xs.map((x) => x.read());
+}
+function batchSetValue(variablesAndValues) {
+  variablesAndValues.forEach((variableAndValue) => {
+    const variable2 = variableAndValue[0];
+    variable2.write(variableAndValue[1]);
+  });
+}
+var InputSpec = class {
+  constructor(args) {
+    this.dtype = args.dtype;
+    this.shape = args.shape;
+    if (args.shape != null) {
+      this.ndim = args.shape.length;
+    } else {
+      this.ndim = args.ndim;
+    }
+    this.maxNDim = args.maxNDim;
+    this.minNDim = args.minNDim;
+    this.axes = args.axes || {};
+  }
+};
+var SymbolicTensor = class {
+  constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {
+    this.dtype = dtype;
+    this.shape = shape;
+    this.sourceLayer = sourceLayer;
+    this.inputs = inputs;
+    this.callArgs = callArgs;
+    this.outputTensorIndex = outputTensorIndex;
+    this.id = getNextUniqueTensorId();
+    if (name != null) {
+      this.originalName = getScopedTensorName(name);
+      this.name = getUniqueTensorName(this.originalName);
+    }
+    this.rank = shape.length;
+  }
+};
+var _nextNodeID = 0;
+var Node = class {
+  constructor(args, callArgs) {
+    this.callArgs = callArgs;
+    this.id = _nextNodeID++;
+    this.outboundLayer = args.outboundLayer;
+    this.inboundLayers = args.inboundLayers;
+    this.nodeIndices = args.nodeIndices;
+    this.tensorIndices = args.tensorIndices;
+    this.inputTensors = args.inputTensors;
+    this.outputTensors = args.outputTensors;
+    this.inputMasks = args.inputMasks;
+    this.outputMasks = args.outputMasks;
+    this.inputShapes = args.inputShapes;
+    this.outputShapes = args.outputShapes;
+    for (const layer of args.inboundLayers) {
+      if (layer != null) {
+        layer.outboundNodes.push(this);
+      }
+    }
+    args.outboundLayer.inboundNodes.push(this);
+  }
+  getConfig() {
+    const inboundNames = [];
+    for (const layer of this.inboundLayers) {
+      if (layer != null) {
+        inboundNames.push(layer.name);
+      } else {
+        inboundNames.push(null);
+      }
+    }
+    return {
+      outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,
+      inboundLayers: inboundNames,
+      nodeIndices: this.nodeIndices,
+      tensorIndices: this.tensorIndices
+    };
+  }
+};
+var _nextLayerID = 0;
+var Layer = class extends serialization_exports.Serializable {
+  constructor(args = {}) {
+    super();
+    this._callHook = null;
+    this._addedWeightNames = [];
+    this._stateful = false;
+    this.id = _nextLayerID++;
+    this.activityRegularizer = null;
+    this.inputSpec = null;
+    this.supportsMasking = false;
+    this._trainableWeights = [];
+    this._nonTrainableWeights = [];
+    this._losses = [];
+    this._updates = [];
+    this._built = false;
+    this.inboundNodes = [];
+    this.outboundNodes = [];
+    let name = args.name;
+    if (!name) {
+      const prefix = this.getClassName();
+      name = toSnakeCase(prefix) + "_" + getUid(prefix);
+    }
+    this.name = name;
+    this.trainable_ = args.trainable == null ? true : args.trainable;
+    if (args.inputShape != null || args.batchInputShape != null) {
+      let batchInputShape;
+      if (args.batchInputShape != null) {
+        batchInputShape = args.batchInputShape;
+      } else if (args.inputShape != null) {
+        let batchSize = null;
+        if (args.batchSize != null) {
+          batchSize = args.batchSize;
+        }
+        batchInputShape = [batchSize].concat(args.inputShape);
+      }
+      this.batchInputShape = batchInputShape;
+      let dtype = args.dtype;
+      if (dtype == null) {
+        dtype = args.inputDType;
+      }
+      if (dtype == null) {
+        dtype = "float32";
+      }
+      this.dtype = dtype;
+    }
+    if (args.weights != null) {
+      this.initialWeights = args.weights;
+    } else {
+      this.initialWeights = null;
+    }
+    this._refCount = null;
+    this.fastWeightInitDuringBuild = false;
+  }
+  static nodeKey(layer, nodeIndex) {
+    return layer.name + "_ib-" + nodeIndex.toString();
+  }
+  getNodeAtIndex(nodeIndex, attrName) {
+    if (this.inboundNodes.length === 0) {
+      throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);
+    }
+    if (this.inboundNodes.length <= nodeIndex) {
+      throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);
+    }
+    return this.inboundNodes[nodeIndex];
+  }
+  getInputAt(nodeIndex) {
+    return singletonOrArray(this.getNodeAtIndex(nodeIndex, "input").inputTensors);
+  }
+  getOutputAt(nodeIndex) {
+    return singletonOrArray(this.getNodeAtIndex(nodeIndex, "output").outputTensors);
+  }
+  get input() {
+    if (this.inboundNodes.length > 1) {
+      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);
+    } else if (this.inboundNodes.length === 0) {
+      throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);
+    }
+    return singletonOrArray(this.getNodeAtIndex(0, "input").inputTensors);
+  }
+  get output() {
+    if (this.inboundNodes.length === 0) {
+      throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);
+    }
+    if (this.inboundNodes.length > 1) {
+      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);
+    }
+    return singletonOrArray(this.getNodeAtIndex(0, "output").outputTensors);
+  }
+  get losses() {
+    return this._losses;
+  }
+  calculateLosses() {
+    return this.losses.map((lossFn) => lossFn());
+  }
+  get updates() {
+    return this._updates;
+  }
+  get built() {
+    return this._built;
+  }
+  set built(built) {
+    this._built = built;
+  }
+  get trainable() {
+    return this.trainable_;
+  }
+  set trainable(trainable) {
+    this._trainableWeights.forEach((w) => w.trainable = trainable);
+    this.trainable_ = trainable;
+  }
+  get trainableWeights() {
+    if (this.trainable_) {
+      return this._trainableWeights.filter((w) => w.trainable);
+    } else {
+      return [];
+    }
+  }
+  set trainableWeights(weights) {
+    this._trainableWeights = weights;
+  }
+  get nonTrainableWeights() {
+    if (this.trainable) {
+      return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);
+    } else {
+      return this._trainableWeights.concat(this._nonTrainableWeights);
+    }
+  }
+  set nonTrainableWeights(weights) {
+    this._nonTrainableWeights = weights;
+  }
+  get weights() {
+    return this.trainableWeights.concat(this.nonTrainableWeights);
+  }
+  get stateful() {
+    return this._stateful;
+  }
+  resetStates() {
+    if (!this.stateful) {
+      throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.");
+    }
+  }
+  assertInputCompatibility(inputs) {
+    inputs = toList(inputs);
+    if (this.inputSpec == null || this.inputSpec.length === 0) {
+      return;
+    }
+    const inputSpec = toList(this.inputSpec);
+    if (inputs.length !== inputSpec.length) {
+      throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);
+    }
+    for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {
+      const x = inputs[inputIndex];
+      const spec = inputSpec[inputIndex];
+      if (spec == null) {
+        continue;
+      }
+      const ndim = x.rank;
+      if (spec.ndim != null) {
+        if (ndim !== spec.ndim) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);
+        }
+      }
+      if (spec.maxNDim != null) {
+        if (ndim > spec.maxNDim) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);
+        }
+      }
+      if (spec.minNDim != null) {
+        if (ndim < spec.minNDim) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);
+        }
+      }
+      if (spec.dtype != null) {
+        if (x.dtype !== spec.dtype) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);
+        }
+      }
+      if (spec.axes) {
+        const xShape = x.shape;
+        for (const key in spec.axes) {
+          const axis = Number(key);
+          const value = spec.axes[key];
+          const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];
+          if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {
+            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);
+          }
+        }
+      }
+      if (spec.shape != null) {
+        for (let i2 = 0; i2 < spec.shape.length; ++i2) {
+          const specDim = spec.shape[i2];
+          const dim = x.shape[i2];
+          if (specDim != null && dim != null) {
+            if (specDim !== dim) {
+              throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);
+            }
+          }
+        }
+      }
+    }
+  }
+  call(inputs, kwargs) {
+    return inputs;
+  }
+  invokeCallHook(inputs, kwargs) {
+    if (this._callHook != null) {
+      this._callHook(inputs, kwargs);
+    }
+  }
+  setCallHook(callHook) {
+    this._callHook = callHook;
+  }
+  clearCallHook() {
+    this._callHook = null;
+  }
+  apply(inputs, kwargs) {
+    kwargs = kwargs || {};
+    this.assertNotDisposed();
+    const inputsList = toList(inputs);
+    let allAreSymbolic = true;
+    for (const input2 of inputsList) {
+      if (!(input2 instanceof SymbolicTensor)) {
+        allAreSymbolic = false;
+        break;
+      }
+    }
+    let noneAreSymbolic = true;
+    for (const input2 of inputsList) {
+      if (input2 instanceof SymbolicTensor) {
+        noneAreSymbolic = false;
+        break;
+      }
+    }
+    if (allAreSymbolic === noneAreSymbolic) {
+      throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");
+    }
+    return nameScope(this.name, () => {
+      if (!this.built) {
+        this.assertInputCompatibility(inputs);
+        const inputShapes = [];
+        for (const xElem of toList(inputs)) {
+          inputShapes.push(xElem.shape);
+        }
+        this.build(singletonOrArray(inputShapes));
+        this.built = true;
+        if (this.initialWeights) {
+          this.setWeights(this.initialWeights);
+        }
+        if (this._refCount === null && noneAreSymbolic) {
+          this._refCount = 1;
+        }
+      }
+      this.assertInputCompatibility(inputs);
+      if (noneAreSymbolic) {
+        let output = this.call(inputs, kwargs);
+        const outputList = toList(output);
+        const outputListCopy = [];
+        for (let x of outputList) {
+          if (inputsList.indexOf(x) !== -1) {
+            x = x.clone();
+          }
+          outputListCopy.push(x);
+        }
+        output = singletonOrArray(outputListCopy);
+        if (this.activityRegularizer != null) {
+          throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
+        }
+        return output;
+      } else {
+        const inputShape = collectInputShape(inputs);
+        const outputShape = this.computeOutputShape(inputShape);
+        let output;
+        const outputDType = guessOutputDType(inputs);
+        this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);
+        if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {
+          output = outputShape.map((shape, index2) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index2));
+        } else {
+          output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);
+        }
+        this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);
+        this._refCount++;
+        if (this.activityRegularizer != null) {
+          throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
+        }
+        return output;
+      }
+    });
+  }
+  warnOnIncompatibleInputShape(inputShape) {
+    if (this.batchInputShape == null) {
+      return;
+    } else if (inputShape.length !== this.batchInputShape.length) {
+      console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
+    } else {
+      let dimMismatch = false;
+      this.batchInputShape.forEach((dimension, i2) => {
+        if (dimension != null && inputShape[i2] != null && inputShape[i2] !== dimension) {
+          dimMismatch = true;
+        }
+      });
+      if (dimMismatch) {
+        console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);
+      }
+    }
+  }
+  get outputShape() {
+    if (this.inboundNodes == null || this.inboundNodes.length === 0) {
+      throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);
+    }
+    const allOutputShapes = [];
+    for (const node2 of this.inboundNodes) {
+      const shapeString = JSON.stringify(node2.outputShapes);
+      if (allOutputShapes.indexOf(shapeString) === -1) {
+        allOutputShapes.push(shapeString);
+      }
+    }
+    if (allOutputShapes.length === 1) {
+      const outputShapes = this.inboundNodes[0].outputShapes;
+      if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {
+        return outputShapes[0];
+      } else {
+        return outputShapes;
+      }
+    } else {
+      throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`);
+    }
+  }
+  countParams() {
+    if (!this.built) {
+      throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
+    }
+    return countParamsInWeights(this.weights);
+  }
+  build(inputShape) {
+    this.built = true;
+  }
+  getWeights(trainableOnly = false) {
+    return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);
+  }
+  setWeights(weights) {
+    tidy(() => {
+      const params = this.weights;
+      if (params.length !== weights.length) {
+        throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);
+      }
+      if (params.length === 0) {
+        return;
+      }
+      const weightValueTuples = [];
+      const paramValues = batchGetValue(params);
+      for (let i2 = 0; i2 < paramValues.length; ++i2) {
+        const pv = paramValues[i2];
+        const p2 = params[i2];
+        const w = weights[i2];
+        if (!util_exports.arraysEqual(pv.shape, w.shape)) {
+          throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);
+        }
+        weightValueTuples.push([p2, w]);
+      }
+      batchSetValue(weightValueTuples);
+    });
+  }
+  addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {
+    if (this._addedWeightNames.indexOf(name) !== -1) {
+      throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);
+    }
+    this._addedWeightNames.push(name);
+    if (dtype == null) {
+      dtype = "float32";
+    }
+    if (this.fastWeightInitDuringBuild) {
+      initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer("zeros");
+    }
+    const initValue = initializer.apply(shape, dtype);
+    const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);
+    initValue.dispose();
+    if (regularizer != null) {
+      this.addLoss(() => regularizer.apply(weight.read()));
+    }
+    if (trainable == null) {
+      trainable = true;
+    }
+    if (trainable) {
+      this._trainableWeights.push(weight);
+    } else {
+      this._nonTrainableWeights.push(weight);
+    }
+    return weight;
+  }
+  setFastWeightInitDuringBuild(value) {
+    this.fastWeightInitDuringBuild = value;
+  }
+  addLoss(losses2) {
+    if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) {
+      return;
+    }
+    losses2 = toList(losses2);
+    if (this._losses !== void 0 && this._losses !== null) {
+      this.losses.push(...losses2);
+    }
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  computeMask(inputs, mask2) {
+    if (!this.supportsMasking) {
+      if (mask2 != null) {
+        if (Array.isArray(mask2)) {
+          mask2.forEach((maskElement) => {
+            if (maskElement != null) {
+              throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
+            }
+          });
+        } else {
+          throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
+        }
+      }
+      return null;
+    }
+    return mask2;
+  }
+  addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {
+    const inputTensorList = toList(inputTensors);
+    outputTensors = toList(outputTensors);
+    inputMasks = toList(inputMasks);
+    outputMasks = toList(outputMasks);
+    inputShapes = normalizeShapeList(inputShapes);
+    outputShapes = normalizeShapeList(outputShapes);
+    const inboundLayers = [];
+    const nodeIndices = [];
+    const tensorIndices = [];
+    for (const x of inputTensorList) {
+      inboundLayers.push(x.sourceLayer);
+      nodeIndices.push(x.nodeIndex);
+      tensorIndices.push(x.tensorIndex);
+    }
+    new Node({
+      outboundLayer: this,
+      inboundLayers,
+      nodeIndices,
+      tensorIndices,
+      inputTensors: inputTensorList,
+      outputTensors,
+      inputMasks,
+      outputMasks,
+      inputShapes,
+      outputShapes
+    }, kwargs);
+    for (let i2 = 0; i2 < outputTensors.length; i2++) {
+      outputTensors[i2].sourceLayer = this;
+      outputTensors[i2].nodeIndex = this.inboundNodes.length - 1;
+      outputTensors[i2].tensorIndex = i2;
+    }
+  }
+  getConfig() {
+    const config3 = { name: this.name, trainable: this.trainable };
+    if (this.batchInputShape != null) {
+      config3["batchInputShape"] = this.batchInputShape;
+    }
+    if (this.dtype != null) {
+      config3["dtype"] = this.dtype;
+    }
+    return config3;
+  }
+  disposeWeights() {
+    this.weights.forEach((weight) => weight.dispose());
+    return this.weights.length;
+  }
+  assertNotDisposed() {
+    if (this._refCount === 0) {
+      throw new Error(`Layer '${this.name}' is already disposed.`);
+    }
+  }
+  dispose() {
+    if (!this.built) {
+      throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);
+    }
+    if (this._refCount === null) {
+      throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);
+    }
+    this.assertNotDisposed();
+    let numDisposedVariables = 0;
+    if (--this._refCount === 0) {
+      numDisposedVariables = this.disposeWeights();
+    }
+    return { refCountAfterDispose: this._refCount, numDisposedVariables };
+  }
+};
+function collectInputShape(inputTensors) {
+  inputTensors = toList(inputTensors);
+  const shapes = [];
+  for (const x of inputTensors) {
+    shapes.push(x.shape);
+  }
+  return singletonOrArray(shapes);
+}
+function guessOutputDType(inputTensors) {
+  return "float32";
+}
+function getSourceInputs(tensor2, layer, nodeIndex) {
+  if (layer == null || nodeIndex != null && nodeIndex > 0) {
+    layer = tensor2.sourceLayer;
+    nodeIndex = tensor2.nodeIndex;
+  }
+  if (layer.inboundNodes.length === 0) {
+    return [tensor2];
+  } else {
+    const node2 = layer.inboundNodes[nodeIndex];
+    if (node2.inboundLayers.length === 0) {
+      return node2.inputTensors;
+    } else {
+      const sourceTensors = [];
+      for (let i2 = 0; i2 < node2.inboundLayers.length; i2++) {
+        const x = node2.inputTensors[i2];
+        const layer2 = node2.inboundLayers[i2];
+        const nodeIndex2 = node2.nodeIndices[i2];
+        const previousSources = getSourceInputs(x, layer2, nodeIndex2);
+        for (const x2 of previousSources) {
+          if (sourceTensors.indexOf(x2) === -1) {
+            sourceTensors.push(x2);
+          }
+        }
+      }
+      return sourceTensors;
+    }
+  }
+}
+var InputLayer = class extends Layer {
+  constructor(args) {
+    super({
+      dtype: args.dtype,
+      name: args.name != null ? args.name : getUid("input").toString()
+    });
+    if (args.batchSize == null) {
+      args.batchSize = null;
+    }
+    if (args.sparse == null) {
+      args.sparse = false;
+    }
+    this.trainable = false;
+    this.built = true;
+    this.sparse = args.sparse;
+    if (args.inputShape != null && args.batchInputShape != null) {
+      throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
+    }
+    let batchInputShape = args.batchInputShape;
+    if (batchInputShape == null) {
+      if (args.inputShape == null) {
+        throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
+      } else {
+        batchInputShape = [args.batchSize].concat(args.inputShape);
+      }
+    } else {
+      if (args.batchSize != null) {
+        throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");
+      }
+    }
+    const dtype = args.dtype || "float32";
+    this.batchInputShape = batchInputShape;
+    this.dtype = dtype;
+    this.inputSpec = [{ shape: batchInputShape }];
+    const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);
+    inputTensor.nodeIndex = 0;
+    inputTensor.tensorIndex = 0;
+    new Node({
+      outboundLayer: this,
+      inboundLayers: [],
+      nodeIndices: [],
+      tensorIndices: [],
+      inputTensors: [inputTensor],
+      outputTensors: [inputTensor],
+      inputMasks: [null],
+      outputMasks: [null],
+      inputShapes: [batchInputShape],
+      outputShapes: [batchInputShape]
+    });
+  }
+  apply(inputs, kwargs) {
+    throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
+  }
+  dispose() {
+    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };
+  }
+  getConfig() {
+    return {
+      batchInputShape: this.batchInputShape,
+      dtype: this.dtype,
+      sparse: this.sparse,
+      name: this.name
+    };
+  }
+};
+InputLayer.className = "InputLayer";
+serialization_exports.registerClass(InputLayer);
+function Input(config3) {
+  if (config3.batchShape == null && config3.shape == null) {
+    throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
+  }
+  if (config3.batchShape != null && config3.shape != null) {
+    throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
+  }
+  let batchShape = config3.batchShape;
+  if (config3.shape != null && batchShape == null) {
+    batchShape = [null].concat(config3.shape);
+  }
+  let dtype = config3.dtype;
+  if (dtype == null) {
+    dtype = "float32";
+  }
+  const inputLayer2 = new InputLayer({
+    batchInputShape: batchShape,
+    name: config3.name,
+    dtype,
+    sparse: config3.sparse
+  });
+  const outputs = inputLayer2.inboundNodes[0].outputTensors;
+  return outputs[0];
+}
+function assertFeedCompatibility(key, val) {
+  if (key.dtype == null || key.dtype === val.dtype) {
+    return val;
+  }
+  try {
+    return cast(val, key.dtype);
+  } catch (err) {
+    throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);
+  }
+}
+var FeedDict = class {
+  constructor(feeds) {
+    this.id2Value = {};
+    this.id2Mask = {};
+    this.name2Id = {};
+    if (feeds instanceof FeedDict) {
+      for (const id in feeds.id2Value) {
+        this.id2Value[id] = feeds.id2Value[id];
+        if (id in feeds.id2Mask) {
+          this.id2Mask[id] = feeds.id2Mask[id];
+        }
+      }
+    } else {
+      if (feeds == null) {
+        return;
+      }
+      for (const feed of feeds) {
+        this.add(feed.key, feed.value);
+      }
+    }
+  }
+  add(key, value, mask2) {
+    if (this.id2Value[key.id] == null) {
+      this.id2Value[key.id] = assertFeedCompatibility(key, value);
+      this.name2Id[key.name] = key.id;
+      if (mask2 != null) {
+        this.id2Mask[key.id] = mask2;
+      }
+    } else {
+      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);
+    }
+    return this;
+  }
+  addFeed(feed) {
+    this.add(feed.key, feed.value);
+  }
+  hasKey(key) {
+    return this.id2Value[key.id] != null;
+  }
+  names() {
+    return Object.keys(this.name2Id);
+  }
+  getValue(key) {
+    if (key instanceof SymbolicTensor) {
+      if (this.id2Value[key.id] == null) {
+        throw new ValueError(`Nonexistent key: ${key.name}`);
+      } else {
+        return this.id2Value[key.id];
+      }
+    } else {
+      const id = this.name2Id[key];
+      if (id == null) {
+        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
+      }
+      return this.id2Value[id];
+    }
+  }
+  getMask(key) {
+    if (key instanceof SymbolicTensor) {
+      if (this.id2Value[key.id] == null) {
+        throw new ValueError(`Nonexistent key: ${key.name}`);
+      } else {
+        return this.id2Mask[key.id];
+      }
+    } else {
+      const id = this.name2Id[key];
+      if (id == null) {
+        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
+      }
+      return this.id2Mask[id];
+    }
+  }
+  disposeMasks() {
+    if (this.id2Mask != null) {
+      dispose(this.id2Mask);
+    }
+  }
+};
+var cachedSorted = new LruCache();
+var cachedRecipientCounts = new LruCache();
+function updateCacheMaxEntries(maxEntries) {
+  if (cachedSorted != null) {
+    cachedSorted.setMaxEntries(maxEntries);
+  }
+  if (cachedRecipientCounts != null) {
+    cachedRecipientCounts.setMaxEntries(maxEntries);
+  }
+}
+function execute(fetches, feedDict, kwargs, probe) {
+  const training = kwargs == null ? false : kwargs["training"];
+  const arrayFetches = Array.isArray(fetches);
+  const fetchArray = arrayFetches ? fetches : [fetches];
+  const outputNames = fetchArray.map((t2) => t2.name);
+  const finalOutputs = [];
+  const feedNames = feedDict.names();
+  for (const outputName of outputNames) {
+    if (feedNames.indexOf(outputName) !== -1) {
+      finalOutputs.push(feedDict.getValue(outputName));
+    } else {
+      finalOutputs.push(null);
+    }
+  }
+  if (probe != null) {
+    probe.maxNumTensors = -Infinity;
+    probe.minNumTensors = Infinity;
+  }
+  const fetchAndFeedKey = outputNames.join(",") + "|" + feedDict.names().sort().join(",");
+  let sorted = cachedSorted.get(fetchAndFeedKey);
+  let recipientCounts;
+  if (sorted == null) {
+    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
+    sorted = out.sorted;
+    recipientCounts = out.recipientCounts;
+    cachedSorted.put(fetchAndFeedKey, sorted);
+    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);
+  }
+  recipientCounts = {};
+  if (!training) {
+    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));
+  }
+  const internalFeedDict = new FeedDict(feedDict);
+  for (let i2 = 0; i2 < sorted.length; ++i2) {
+    if (probe != null) {
+      const numTensors = memory().numTensors;
+      if (numTensors > probe.maxNumTensors) {
+        probe.maxNumTensors = numTensors;
+      }
+      if (numTensors < probe.minNumTensors) {
+        probe.minNumTensors = numTensors;
+      }
+    }
+    const symbolic = sorted[i2];
+    const srcLayer = symbolic.sourceLayer;
+    if (srcLayer instanceof InputLayer) {
+      continue;
+    }
+    const inputValues = [];
+    const inputMasks = [];
+    const tensorsToDispose = [];
+    let maskExists = false;
+    for (const input2 of symbolic.inputs) {
+      const value = internalFeedDict.getValue(input2);
+      const mask2 = internalFeedDict.getMask(input2);
+      inputValues.push(value);
+      inputMasks.push(mask2);
+      if (mask2 != null) {
+        maskExists = true;
+      }
+      if (!training) {
+        recipientCounts[input2.name]--;
+        if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {
+          tensorsToDispose.push(value);
+        }
+      }
+    }
+    if (maskExists) {
+      kwargs = kwargs || {};
+      kwargs["mask"] = inputMasks[0];
+    }
+    const outputTensors = toList(srcLayer.apply(inputValues, kwargs));
+    let outputMask = null;
+    if (srcLayer.supportsMasking) {
+      outputMask = srcLayer.computeMask(inputValues, inputMasks);
+    }
+    const layerOutputs = getNodeOutputs(symbolic);
+    const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
+    for (let i3 = 0; i3 < outputSymbolicTensors.length; ++i3) {
+      if (!internalFeedDict.hasKey(outputSymbolicTensors[i3])) {
+        internalFeedDict.add(outputSymbolicTensors[i3], outputTensors[i3], Array.isArray(outputMask) ? outputMask[0] : outputMask);
+      }
+      const index2 = outputNames.indexOf(outputSymbolicTensors[i3].name);
+      if (index2 !== -1) {
+        finalOutputs[index2] = outputTensors[i3];
+      }
+    }
+    if (!training) {
+      dispose(tensorsToDispose);
+    }
+  }
+  internalFeedDict.disposeMasks();
+  return arrayFetches ? finalOutputs : finalOutputs[0];
+}
+function getTopologicalSortAndRecipientCounts(fetches, feedDict) {
+  util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);
+  let finalSorted = [];
+  let finalRecipientMap = {};
+  if (fetches.length === 1) {
+    const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);
+    finalSorted = out.sorted;
+    finalRecipientMap = out.recipientMap;
+  } else {
+    const visited = /* @__PURE__ */ new Set();
+    for (const fetch4 of fetches) {
+      const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict);
+      for (const symbolicTensor of sorted) {
+        if (!visited.has(symbolicTensor.name)) {
+          finalSorted.push(symbolicTensor);
+          visited.add(symbolicTensor.name);
+        }
+      }
+      for (const name in recipientMap) {
+        if (finalRecipientMap[name] == null) {
+          finalRecipientMap[name] = /* @__PURE__ */ new Set();
+        }
+        recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));
+      }
+    }
+  }
+  return {
+    sorted: finalSorted,
+    recipientCounts: recipientMap2Counts(finalRecipientMap)
+  };
+}
+function recipientMap2Counts(recipientMap) {
+  const recipientCounts = {};
+  for (const name in recipientMap) {
+    recipientCounts[name] = recipientMap[name].size;
+  }
+  return recipientCounts;
+}
+function getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) {
+  const visited = /* @__PURE__ */ new Set();
+  const sorted = [];
+  const recipientMap = {};
+  for (const key of feedDict.names()) {
+    visited.add(key);
+  }
+  const stack2 = [];
+  const marks = [];
+  stack2.push(fetch4);
+  while (stack2.length > 0) {
+    const top = stack2[stack2.length - 1];
+    if (visited.has(top.name)) {
+      stack2.pop();
+      continue;
+    }
+    const topIsMarked = marks[marks.length - 1] === stack2.length - 1;
+    if (top.inputs.length === 0 || topIsMarked) {
+      stack2.pop();
+      sorted.push(top);
+      visited.add(top.name);
+      if (topIsMarked) {
+        marks.pop();
+      }
+    } else {
+      marks.push(stack2.length - 1);
+      for (const input2 of top.inputs) {
+        if (recipientMap[input2.name] == null) {
+          recipientMap[input2.name] = /* @__PURE__ */ new Set();
+        }
+        recipientMap[input2.name].add(top.name);
+        if (visited.has(input2.name)) {
+          continue;
+        }
+        stack2.push(input2);
+      }
+    }
+  }
+  return { sorted, recipientMap };
+}
+function getNodeOutputs(fetch4) {
+  let layerOutputs;
+  if (fetch4.sourceLayer.inboundNodes.length === 1) {
+    layerOutputs = fetch4.sourceLayer.output;
+  } else {
+    let nodeIndex = null;
+    for (let i2 = 0; i2 < fetch4.sourceLayer.inboundNodes.length; ++i2) {
+      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i2].outputTensors) {
+        if (outputTensor.id === fetch4.id) {
+          nodeIndex = i2;
+          break;
+        }
+      }
+    }
+    layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);
+  }
+  return layerOutputs;
+}
+var ENV3 = env();
+ENV3.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, updateCacheMaxEntries);
+var exports_constraints_exports = {};
+__export2(exports_constraints_exports, {
+  maxNorm: () => maxNorm,
+  minMaxNorm: () => minMaxNorm,
+  nonNeg: () => nonNeg,
+  unitNorm: () => unitNorm
+});
+function calcL2Norms(w, axis) {
+  return tidy(() => sqrt(sum2(mul(w, w), axis, true)));
+}
+var Constraint = class extends serialization_exports.Serializable {
+  getConfig() {
+    return {};
+  }
+};
+var MaxNorm = class extends Constraint {
+  constructor(args) {
+    super();
+    this.defaultMaxValue = 2;
+    this.defaultAxis = 0;
+    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
+    this.axis = args.axis != null ? args.axis : this.defaultAxis;
+  }
+  apply(w) {
+    return tidy(() => {
+      const norms = calcL2Norms(w, this.axis);
+      const desired = clipByValue(norms, 0, this.maxValue);
+      return mul(w, div(desired, add2(epsilon(), norms)));
+    });
+  }
+  getConfig() {
+    return { maxValue: this.maxValue, axis: this.axis };
+  }
+};
+MaxNorm.className = "MaxNorm";
+serialization_exports.registerClass(MaxNorm);
+var UnitNorm = class extends Constraint {
+  constructor(args) {
+    super();
+    this.defaultAxis = 0;
+    this.axis = args.axis != null ? args.axis : this.defaultAxis;
+  }
+  apply(w) {
+    return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));
+  }
+  getConfig() {
+    return { axis: this.axis };
+  }
+};
+UnitNorm.className = "UnitNorm";
+serialization_exports.registerClass(UnitNorm);
+var NonNeg = class extends Constraint {
+  apply(w) {
+    return relu(w);
+  }
+};
+NonNeg.className = "NonNeg";
+serialization_exports.registerClass(NonNeg);
+var MinMaxNorm = class extends Constraint {
+  constructor(args) {
+    super();
+    this.defaultMinValue = 0;
+    this.defaultMaxValue = 1;
+    this.defaultRate = 1;
+    this.defaultAxis = 0;
+    this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;
+    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
+    this.rate = args.rate != null ? args.rate : this.defaultRate;
+    this.axis = args.axis != null ? args.axis : this.defaultAxis;
+  }
+  apply(w) {
+    return tidy(() => {
+      const norms = calcL2Norms(w, this.axis);
+      const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));
+      return mul(w, div(desired, add2(epsilon(), norms)));
+    });
+  }
+  getConfig() {
+    return {
+      minValue: this.minValue,
+      maxValue: this.maxValue,
+      rate: this.rate,
+      axis: this.axis
+    };
+  }
+};
+MinMaxNorm.className = "MinMaxNorm";
+serialization_exports.registerClass(MinMaxNorm);
+var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
+  "maxNorm": "MaxNorm",
+  "minMaxNorm": "MinMaxNorm",
+  "nonNeg": "NonNeg",
+  "unitNorm": "UnitNorm"
+};
+function serializeConstraint(constraint) {
+  return serializeKerasObject(constraint);
+}
+function deserializeConstraint(config3, customObjects = {}) {
+  return deserializeKerasObject(config3, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "constraint");
+}
+function getConstraint(identifier) {
+  if (identifier == null) {
+    return null;
+  }
+  if (typeof identifier === "string") {
+    const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
+    const config3 = { className, config: {} };
+    return deserializeConstraint(config3);
+  } else if (identifier instanceof Constraint) {
+    return identifier;
+  } else {
+    return deserializeConstraint(identifier);
+  }
+}
+function maxNorm(args) {
+  return new MaxNorm(args);
+}
+function unitNorm(args) {
+  return new UnitNorm(args);
+}
+function nonNeg() {
+  return new NonNeg();
+}
+function minMaxNorm(config3) {
+  return new MinMaxNorm(config3);
+}
+var exports_initializers_exports = {};
+__export2(exports_initializers_exports, {
+  constant: () => constant,
+  glorotNormal: () => glorotNormal,
+  glorotUniform: () => glorotUniform,
+  heNormal: () => heNormal,
+  heUniform: () => heUniform,
+  identity: () => identity,
+  leCunNormal: () => leCunNormal,
+  leCunUniform: () => leCunUniform,
+  ones: () => ones3,
+  orthogonal: () => orthogonal,
+  randomNormal: () => randomNormal3,
+  randomUniform: () => randomUniform2,
+  truncatedNormal: () => truncatedNormal2,
+  varianceScaling: () => varianceScaling,
+  zeros: () => zeros2
+});
+function zeros2() {
+  return new Zeros();
+}
+function ones3() {
+  return new Ones();
+}
+function constant(args) {
+  return new Constant(args);
+}
+function randomUniform2(args) {
+  return new RandomUniform(args);
+}
+function randomNormal3(args) {
+  return new RandomNormal(args);
+}
+function truncatedNormal2(args) {
+  return new TruncatedNormal(args);
+}
+function identity(args) {
+  return new Identity2(args);
+}
+function varianceScaling(config3) {
+  return new VarianceScaling(config3);
+}
+function glorotUniform(args) {
+  return new GlorotUniform(args);
+}
+function glorotNormal(args) {
+  return new GlorotNormal(args);
+}
+function heNormal(args) {
+  return new HeNormal(args);
+}
+function heUniform(args) {
+  return new HeUniform(args);
+}
+function leCunNormal(args) {
+  return new LeCunNormal(args);
+}
+function leCunUniform(args) {
+  return new LeCunUniform(args);
+}
+function orthogonal(args) {
+  return new Orthogonal(args);
+}
+var exports_layers_exports = {};
+__export2(exports_layers_exports, {
+  Layer: () => Layer,
+  RNN: () => RNN,
+  RNNCell: () => RNNCell,
+  activation: () => activation,
+  add: () => add3,
+  alphaDropout: () => alphaDropout,
+  average: () => average,
+  averagePooling1d: () => averagePooling1d,
+  averagePooling2d: () => averagePooling2d,
+  averagePooling3d: () => averagePooling3d,
+  avgPool1d: () => avgPool1d,
+  avgPool2d: () => avgPool2d,
+  avgPool3d: () => avgPool3d2,
+  avgPooling1d: () => avgPooling1d,
+  avgPooling2d: () => avgPooling2d,
+  avgPooling3d: () => avgPooling3d,
+  batchNormalization: () => batchNormalization2,
+  bidirectional: () => bidirectional,
+  concatenate: () => concatenate2,
+  conv1d: () => conv1d2,
+  conv2d: () => conv2d3,
+  conv2dTranspose: () => conv2dTranspose2,
+  conv3d: () => conv3d2,
+  conv3dTranspose: () => conv3dTranspose2,
+  convLstm2d: () => convLstm2d,
+  convLstm2dCell: () => convLstm2dCell,
+  cropping2D: () => cropping2D,
+  dense: () => dense,
+  depthwiseConv2d: () => depthwiseConv2d4,
+  dot: () => dot3,
+  dropout: () => dropout3,
+  elu: () => elu3,
+  embedding: () => embedding,
+  flatten: () => flatten3,
+  gaussianDropout: () => gaussianDropout,
+  gaussianNoise: () => gaussianNoise,
+  globalAveragePooling1d: () => globalAveragePooling1d,
+  globalAveragePooling2d: () => globalAveragePooling2d,
+  globalMaxPool1d: () => globalMaxPool1d,
+  globalMaxPool2d: () => globalMaxPool2d,
+  globalMaxPooling1d: () => globalMaxPooling1d,
+  globalMaxPooling2d: () => globalMaxPooling2d,
+  gru: () => gru,
+  gruCell: () => gruCell,
+  input: () => input,
+  inputLayer: () => inputLayer,
+  layerNormalization: () => layerNormalization,
+  leakyReLU: () => leakyReLU,
+  lstm: () => lstm,
+  lstmCell: () => lstmCell,
+  masking: () => masking,
+  maxPool1d: () => maxPool1d,
+  maxPool2d: () => maxPool2d,
+  maxPooling1d: () => maxPooling1d,
+  maxPooling2d: () => maxPooling2d,
+  maxPooling3d: () => maxPooling3d,
+  maximum: () => maximum2,
+  minimum: () => minimum2,
+  multiply: () => multiply,
+  permute: () => permute,
+  prelu: () => prelu2,
+  reLU: () => reLU,
+  repeatVector: () => repeatVector,
+  reshape: () => reshape2,
+  rnn: () => rnn2,
+  separableConv2d: () => separableConv2d2,
+  simpleRNN: () => simpleRNN,
+  simpleRNNCell: () => simpleRNNCell,
+  softmax: () => softmax2,
+  spatialDropout1d: () => spatialDropout1d,
+  stackedRNNCells: () => stackedRNNCells,
+  thresholdedReLU: () => thresholdedReLU,
+  timeDistributed: () => timeDistributed,
+  upSampling2d: () => upSampling2d,
+  zeroPadding2d: () => zeroPadding2d
+});
+async function resolveScalarsInLogs(logs) {
+  if (logs == null) {
+    return;
+  }
+  const promises = [];
+  const keys = [];
+  const scalarsToDispose = [];
+  for (const key in logs) {
+    const value = logs[key];
+    if (typeof value !== "number") {
+      const valueScalar = value;
+      promises.push(valueScalar.data());
+      keys.push(key);
+      scalarsToDispose.push(valueScalar);
+    }
+  }
+  if (promises.length > 0) {
+    const values = await Promise.all(promises);
+    for (let i2 = 0; i2 < values.length; ++i2) {
+      logs[keys[i2]] = values[i2][0];
+    }
+    dispose(scalarsToDispose);
+  }
+}
+function disposeTensorsInLogs(logs) {
+  if (logs == null) {
+    return;
+  }
+  for (const key in logs) {
+    const value = logs[key];
+    if (typeof value !== "number") {
+      value.dispose();
+    }
+  }
+}
+var ModelLoggingVerbosity;
+(function(ModelLoggingVerbosity2) {
+  ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT";
+  ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE";
+})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));
+var DEFAULT_YIELD_EVERY_MS = 125;
+var BaseCallback = class {
+  constructor() {
+    this.validationData = null;
+  }
+  setParams(params) {
+    this.params = params;
+  }
+  async onEpochBegin(epoch, logs) {
+  }
+  async onEpochEnd(epoch, logs) {
+  }
+  async onBatchBegin(batch, logs) {
+  }
+  async onBatchEnd(batch, logs) {
+  }
+  async onTrainBegin(logs) {
+  }
+  async onTrainEnd(logs) {
+  }
+  setModel(model22) {
+  }
+};
+var CallbackList = class {
+  constructor(callbacks2, queueLength = 10) {
+    if (callbacks2 == null) {
+      callbacks2 = [];
+    }
+    this.callbacks = callbacks2;
+    this.queueLength = queueLength;
+  }
+  append(callback) {
+    this.callbacks.push(callback);
+  }
+  setParams(params) {
+    for (const callback of this.callbacks) {
+      callback.setParams(params);
+    }
+  }
+  setModel(model22) {
+    for (const callback of this.callbacks) {
+      callback.setModel(model22);
+    }
+  }
+  async onEpochBegin(epoch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onEpochBegin(epoch, logs);
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onEpochEnd(epoch, logs);
+    }
+  }
+  async onBatchBegin(batch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onBatchBegin(batch, logs);
+    }
+  }
+  async onBatchEnd(batch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onBatchEnd(batch, logs);
+    }
+  }
+  async onTrainBegin(logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onTrainBegin(logs);
+    }
+  }
+  async onTrainEnd(logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onTrainEnd(logs);
+    }
+  }
+};
+var BaseLogger = class extends BaseCallback {
+  constructor() {
+    super();
+  }
+  async onEpochBegin(epoch) {
+    this.seen = 0;
+    this.totals = {};
+  }
+  async onBatchEnd(batch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    const batchSize = logs["size"] == null ? 0 : logs["size"];
+    this.seen += batchSize;
+    for (const key in logs) {
+      const value = logs[key];
+      if (typeof value === "number") {
+        if (!this.totals.hasOwnProperty(key)) {
+          this.totals[key] = 0;
+        }
+        this.totals[key] = this.totals[key] + value * batchSize;
+      } else {
+        let oldTotalsToDispose;
+        if (key in this.totals) {
+          oldTotalsToDispose = this.totals[key];
+        } else {
+          this.totals[key] = 0;
+        }
+        const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));
+        this.totals[key] = total;
+        if (oldTotalsToDispose != null) {
+          oldTotalsToDispose.dispose();
+        }
+      }
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    if (logs != null) {
+      for (const key of this.params["metrics"]) {
+        if (this.totals[key] == null) {
+          continue;
+        }
+        if (typeof this.totals[key] === "number") {
+          logs[key] = this.totals[key] / this.seen;
+        } else {
+          tidy(() => {
+            const log6 = mul(div(1, this.seen), this.totals[key]);
+            logs[key] = log6;
+            this.totals[key].dispose();
+            keep(logs[key]);
+          });
+        }
+      }
+    }
+  }
+};
+var History = class extends BaseCallback {
+  async onTrainBegin(logs) {
+    this.epoch = [];
+    this.history = {};
+  }
+  async onEpochEnd(epoch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    this.epoch.push(epoch);
+    for (const key in logs) {
+      if (this.history[key] == null) {
+        this.history[key] = [];
+      }
+      this.history[key].push(logs[key]);
+    }
+  }
+  async syncData() {
+    const promises = [];
+    const keys = [];
+    const indices = [];
+    for (const key in this.history) {
+      const valueArray = this.history[key];
+      for (let i2 = 0; i2 < valueArray.length; ++i2) {
+        if (typeof valueArray[i2] !== "number") {
+          const valueScalar = valueArray[i2];
+          promises.push(valueScalar.data());
+          keys.push(key);
+          indices.push(i2);
+        }
+      }
+    }
+    const values = await Promise.all(promises);
+    for (let n = 0; n < values.length; ++n) {
+      const tensorToDispose = this.history[keys[n]][indices[n]];
+      tensorToDispose.dispose();
+      this.history[keys[n]][indices[n]] = values[n][0];
+    }
+  }
+};
+var CustomCallback = class extends BaseCallback {
+  constructor(args, yieldEvery) {
+    super();
+    this.currentEpoch = 0;
+    this.nowFunc = args.nowFunc;
+    this.nextFrameFunc = args.nextFrameFunc || nextFrame;
+    this.yieldEvery = yieldEvery || "auto";
+    if (this.yieldEvery === "auto") {
+      this.yieldEvery = DEFAULT_YIELD_EVERY_MS;
+    }
+    if (this.yieldEvery === "never" && args.onYield != null) {
+      throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
+    }
+    if (util_exports.isNumber(this.yieldEvery)) {
+      this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc);
+    }
+    this.trainBegin = args.onTrainBegin;
+    this.trainEnd = args.onTrainEnd;
+    this.epochBegin = args.onEpochBegin;
+    this.epochEnd = args.onEpochEnd;
+    this.batchBegin = args.onBatchBegin;
+    this.batchEnd = args.onBatchEnd;
+    this.yield = args.onYield;
+  }
+  async maybeWait(epoch, batch, logs) {
+    const ps = [];
+    if (this.yield != null) {
+      await resolveScalarsInLogs(logs);
+      ps.push(this.yield(epoch, batch, logs));
+    }
+    ps.push(this.nextFrameFunc());
+    await Promise.all(ps);
+  }
+  async onEpochBegin(epoch, logs) {
+    this.currentEpoch = epoch;
+    if (this.epochBegin != null) {
+      await resolveScalarsInLogs(logs);
+      await this.epochBegin(epoch, logs);
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    const ps = [];
+    if (this.epochEnd != null) {
+      await resolveScalarsInLogs(logs);
+      ps.push(this.epochEnd(epoch, logs));
+    }
+    if (this.yieldEvery === "epoch") {
+      ps.push(this.nextFrameFunc());
+    }
+    await Promise.all(ps);
+  }
+  async onBatchBegin(batch, logs) {
+    if (this.batchBegin != null) {
+      await resolveScalarsInLogs(logs);
+      await this.batchBegin(batch, logs);
+    }
+  }
+  async onBatchEnd(batch, logs) {
+    const ps = [];
+    if (this.batchEnd != null) {
+      await resolveScalarsInLogs(logs);
+      ps.push(this.batchEnd(batch, logs));
+    }
+    if (this.yieldEvery === "batch") {
+      ps.push(this.nextFrameFunc());
+    } else if (util_exports.isNumber(this.yieldEvery)) {
+      ps.push(this.maybeWait(this.currentEpoch, batch, logs));
+    }
+    await Promise.all(ps);
+  }
+  async onTrainBegin(logs) {
+    if (this.trainBegin != null) {
+      await resolveScalarsInLogs(logs);
+      await this.trainBegin(logs);
+    }
+  }
+  async onTrainEnd(logs) {
+    if (this.trainEnd != null) {
+      await resolveScalarsInLogs(logs);
+      await this.trainEnd(logs);
+    }
+  }
+};
+function standardizeCallbacks(callbacks2, yieldEvery) {
+  if (callbacks2 == null) {
+    callbacks2 = {};
+  }
+  if (callbacks2 instanceof BaseCallback) {
+    return [callbacks2];
+  }
+  if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {
+    return callbacks2;
+  }
+  const callbackConfigs = toList(callbacks2);
+  return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));
+}
+var CallbackConstructorRegistry = class {
+  constructor() {
+  }
+  static registerCallbackConstructor(verbosityLevel, callbackConstructor) {
+    util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);
+    CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);
+    if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {
+      CallbackConstructorRegistry.constructors[verbosityLevel] = [];
+    }
+    CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);
+  }
+  static checkForDuplicate(callbackConstructor) {
+    for (const levelName in CallbackConstructorRegistry.constructors) {
+      const constructors = CallbackConstructorRegistry.constructors[+levelName];
+      constructors.forEach((ctor) => {
+        if (ctor === callbackConstructor) {
+          throw new ValueError("Duplicate callback constructor.");
+        }
+      });
+    }
+  }
+  static clear() {
+    CallbackConstructorRegistry.constructors = {};
+  }
+  static createCallbacks(verbosityLevel) {
+    const constructors = [];
+    for (const levelName in CallbackConstructorRegistry.constructors) {
+      const level = +levelName;
+      if (verbosityLevel >= level) {
+        constructors.push(...CallbackConstructorRegistry.constructors[level]);
+      }
+    }
+    return constructors.map((ctor) => new ctor());
+  }
+};
+CallbackConstructorRegistry.constructors = {};
+function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {
+  const history = new History();
+  const actualCallbacks = [
+    new BaseLogger(),
+    ...CallbackConstructorRegistry.createCallbacks(verbose)
+  ];
+  if (callbacks2 != null) {
+    actualCallbacks.push(...callbacks2);
+  }
+  actualCallbacks.push(history);
+  const callbackList = new CallbackList(actualCallbacks);
+  callbackList.setParams({
+    epochs,
+    initialEpoch,
+    samples: numTrainSamples,
+    steps: stepsPerEpoch,
+    batchSize,
+    verbose,
+    doValidation,
+    metrics: callbackMetrics
+  });
+  return { callbackList, history };
+}
+function deserialize(config3, customObjects = {}, fastWeightInit = false) {
+  return deserializeKerasObject(config3, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit);
+}
+function l2Normalize(x, axis) {
+  return tidy(() => {
+    if (x.dtype !== "float32") {
+      x = cast(x, "float32");
+    }
+    const squareSum = sum2(square2(x), axis, true);
+    const epsilonTensor = fill(squareSum.shape, epsilon());
+    const norm2 = sqrt(maximum(squareSum, epsilonTensor));
+    return div(x, norm2);
+  });
+}
+function meanSquaredError2(yTrue, yPred) {
+  return tidy(() => mean(square2(sub(yPred, yTrue)), -1));
+}
+function meanAbsoluteError(yTrue, yPred) {
+  return tidy(() => mean(abs(sub(yPred, yTrue)), -1));
+}
+function meanAbsolutePercentageError(yTrue, yPred) {
+  return tidy(() => {
+    const diff = sub(yTrue, yPred);
+    const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);
+    const absResult = abs(div(diff, clippedTrue));
+    return mul(100, mean(absResult, -1));
+  });
+}
+function meanSquaredLogarithmicError(yTrue, yPred) {
+  return tidy(() => {
+    const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);
+    const firstLog = log22(add2(1, clippedPred));
+    const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
+    const secondLog = log22(add2(1, clippedTrue));
+    return mean(square2(sub(firstLog, secondLog)), -1);
+  });
+}
+function squaredHinge(yTrue, yPred) {
+  return tidy(() => {
+    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
+    return mean(square2(maxResult), -1);
+  });
+}
+function hinge(yTrue, yPred) {
+  return tidy(() => {
+    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
+    return mean(maxResult, -1);
+  });
+}
+function categoricalHinge(yTrue, yPred) {
+  return tidy(() => {
+    const pos = sum2(mul(yTrue, yPred), -1);
+    const neg5 = max(mul(sub(1, yTrue), yPred), -1);
+    return maximum(0, add2(1, sub(neg5, pos)));
+  });
+}
+function logcosh(yTrue, yPred) {
+  return tidy(() => {
+    const log222 = Math.log(2);
+    const predictionDiff = sub(yPred, yTrue);
+    const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log222);
+    return mean(logcoshResult, -1);
+  });
+}
+function categoricalCrossentropy(target, output, fromLogits = false) {
+  return tidy(() => {
+    if (fromLogits) {
+      output = softmax(output);
+    } else {
+      const outputSum = sum2(output, output.shape.length - 1, true);
+      output = div(output, outputSum);
+    }
+    output = clipByValue(output, epsilon(), 1 - epsilon());
+    return neg(sum2(mul(cast(target, "float32"), log22(output)), output.shape.length - 1));
+  });
+}
+function sparseCategoricalCrossentropy(target, output, fromLogits = false) {
+  return tidy(() => {
+    const flatTarget = cast(floor(flatten2(target)), "int32");
+    output = clipByValue(output, epsilon(), 1 - epsilon());
+    const outputShape = output.shape;
+    const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);
+    return categoricalCrossentropy(oneHotTarget, output, fromLogits);
+  });
+}
+function sigmoidCrossEntropyWithLogits(labels2, logits) {
+  if (!util_exports.arraysEqual(labels2.shape, logits.shape)) {
+    throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels2.shape)} and ${JSON.stringify(logits.shape)}`);
+  }
+  return tidy(() => {
+    const reluLogits = relu(logits);
+    const negAbsLogits = neg(abs(logits));
+    return add2(sub(reluLogits, mul(logits, labels2)), log1p(exp(negAbsLogits)));
+  });
+}
+function binaryCrossentropy(yTrue, yPred) {
+  return tidy(() => {
+    let y;
+    y = clipByValue(yPred, epsilon(), 1 - epsilon());
+    y = log22(div(y, sub(1, y)));
+    return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);
+  });
+}
+function kullbackLeiblerDivergence(yTrue, yPred) {
+  return tidy(() => {
+    const clippedTrue = clipByValue(yTrue, epsilon(), 1);
+    const clippedPred = clipByValue(yPred, epsilon(), 1);
+    return sum2(mul(yTrue, log22(div(clippedTrue, clippedPred))), -1);
+  });
+}
+function poisson(yTrue, yPred) {
+  return tidy(() => {
+    const logPred = log22(add2(epsilon(), yPred));
+    return mean(sub(yPred, mul(yTrue, logPred)), -1);
+  });
+}
+function cosineProximity(yTrue, yPred) {
+  return tidy(() => {
+    const trueNormalized = l2Normalize(yTrue, -1);
+    const predNormalized = l2Normalize(yPred, -1);
+    const trueXPred = mul(trueNormalized, predNormalized);
+    return neg(sum2(trueXPred, -1));
+  });
+}
+var lossesMap = {
+  meanSquaredError: meanSquaredError2,
+  meanAbsoluteError,
+  meanAbsolutePercentageError,
+  meanSquaredLogarithmicError,
+  squaredHinge,
+  hinge,
+  categoricalHinge,
+  logcosh,
+  categoricalCrossentropy,
+  sparseCategoricalCrossentropy,
+  binaryCrossentropy,
+  kullbackLeiblerDivergence,
+  poisson,
+  cosineProximity
+};
+function get(identifierOrFn) {
+  if (typeof identifierOrFn === "string") {
+    if (identifierOrFn in lossesMap) {
+      return lossesMap[identifierOrFn];
+    }
+    let errMsg = `Unknown loss ${identifierOrFn}`;
+    if (identifierOrFn.toLowerCase().includes("softmaxcrossentropy")) {
+      errMsg = `Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`;
+    }
+    throw new ValueError(errMsg);
+  } else {
+    return identifierOrFn;
+  }
+}
+function binaryAccuracy(yTrue, yPred) {
+  return tidy(() => {
+    const threshold3 = mul(0.5, onesLike(yPred));
+    const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);
+    return mean(equal(yTrue, yPredThresholded), -1);
+  });
+}
+function categoricalAccuracy(yTrue, yPred) {
+  return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32"));
+}
+function truePositives(yTrue, yPred) {
+  return tidy(() => {
+    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), "float32");
+  });
+}
+function falseNegatives(yTrue, yPred) {
+  return tidy(() => {
+    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), "float32");
+  });
+}
+function falsePositives(yTrue, yPred) {
+  return tidy(() => {
+    return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), "float32");
+  });
+}
+function precision(yTrue, yPred) {
+  return tidy(() => {
+    const tp = truePositives(yTrue, yPred);
+    const fp = falsePositives(yTrue, yPred);
+    const denominator = add2(tp, fp);
+    return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32");
+  });
+}
+function recall(yTrue, yPred) {
+  return tidy(() => {
+    const tp = truePositives(yTrue, yPred);
+    const fn = falseNegatives(yTrue, yPred);
+    const denominator = add2(tp, fn);
+    return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32");
+  });
+}
+function binaryCrossentropy2(yTrue, yPred) {
+  return binaryCrossentropy(yTrue, yPred);
+}
+function sparseCategoricalAccuracy(yTrue, yPred) {
+  if (yTrue.rank === yPred.rank) {
+    yTrue = squeeze(yTrue, [yTrue.rank - 1]);
+  }
+  yPred = argMax(yPred, -1);
+  if (yPred.dtype !== yTrue.dtype) {
+    yPred = cast(yPred, yTrue.dtype);
+  }
+  return cast(equal(yTrue, yPred), "float32");
+}
+var mse = meanSquaredError2;
+var MSE = meanSquaredError2;
+var mae = meanAbsoluteError;
+var MAE = meanAbsoluteError;
+var mape = meanAbsolutePercentageError;
+var MAPE = meanAbsolutePercentageError;
+var categoricalCrossentropy2 = categoricalCrossentropy;
+var cosine = cosineProximity;
+var sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;
+var metricsMap = {
+  binaryAccuracy,
+  categoricalAccuracy,
+  precision,
+  categoricalCrossentropy: categoricalCrossentropy2,
+  sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,
+  mse,
+  MSE,
+  mae,
+  MAE,
+  mape,
+  MAPE,
+  cosine
+};
+function get2(identifier) {
+  if (typeof identifier === "string" && identifier in metricsMap) {
+    return metricsMap[identifier];
+  } else if (typeof identifier !== "string" && identifier != null) {
+    return identifier;
+  } else {
+    throw new ValueError(`Unknown metric ${identifier}`);
+  }
+}
+function getLossOrMetricName(fn) {
+  assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);
+  if (typeof fn === "string") {
+    return fn;
+  } else {
+    let fnName;
+    for (const key of Object.keys(lossesMap)) {
+      if (lossesMap[key] === fn) {
+        fnName = key;
+        break;
+      }
+    }
+    if (fnName !== void 0) {
+      return fnName;
+    }
+    for (const key of Object.keys(metricsMap)) {
+      if (metricsMap[key] === fn) {
+        fnName = key;
+        break;
+      }
+    }
+    if (fnName !== void 0) {
+      return fnName;
+    }
+    return fn.name;
+  }
+}
+function getOptimizer(identifier) {
+  const optimizerMap = {
+    "Adagrad": () => train.adagrad(0.01),
+    "Adadelta": () => train.adadelta(1, 0.95, epsilon()),
+    "Adam": () => train.adam(1e-3, 0.9, 0.999, epsilon()),
+    "Adamax": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),
+    "RMSProp": () => train.rmsprop(1e-3, 0.9, 0, epsilon()),
+    "SGD": () => train.sgd(0.01)
+  };
+  optimizerMap["adagrad"] = optimizerMap["Adagrad"];
+  optimizerMap["adadelta"] = optimizerMap["Adadelta"];
+  optimizerMap["adam"] = optimizerMap["Adam"];
+  optimizerMap["adamax"] = optimizerMap["Adamax"];
+  optimizerMap["rmsprop"] = optimizerMap["RMSProp"];
+  optimizerMap["sgd"] = optimizerMap["SGD"];
+  if (identifier in optimizerMap) {
+    return optimizerMap[identifier]();
+  }
+  throw new ValueError(`Unknown Optimizer ${identifier}`);
+}
+var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;
+function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {
+  if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {
+    throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
+  }
+  if (checkSize) {
+    const out = JSON.stringify(userDefinedMetadata);
+    if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {
+      console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);
+    }
+  }
+}
+function plainObjectCheck(x) {
+  if (x === null) {
+    return true;
+  } else if (typeof x === "object") {
+    if (Object.getPrototypeOf(x) === Object.prototype) {
+      const keys = Object.keys(x);
+      for (const key of keys) {
+        if (typeof key !== "string") {
+          return false;
+        }
+        if (!plainObjectCheck(x[key])) {
+          return false;
+        }
+      }
+      return true;
+    } else {
+      if (Array.isArray(x)) {
+        for (const item of x) {
+          if (!plainObjectCheck(item)) {
+            return false;
+          }
+        }
+        return true;
+      } else {
+        return false;
+      }
+    }
+  } else {
+    const xType = typeof x;
+    return xType === "string" || xType === "number" || xType === "boolean";
+  }
+}
+function printSummary(model22, lineLength, positions, printFn = console.log) {
+  const sequentialLike = isModelSequentialLike(model22);
+  const toDisplay = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
+  if (sequentialLike) {
+    lineLength = lineLength || 90;
+    positions = positions || [0.32, 0.61, 0.89, 1];
+  } else {
+    lineLength = lineLength || 115;
+    positions = positions || [0.24, 0.48, 0.7, 0.8, 1];
+  }
+  if (positions[positions.length - 1] <= 1) {
+    positions = positions.map((p2) => Math.floor(lineLength * p2));
+  }
+  let relevantNodes;
+  if (!sequentialLike) {
+    toDisplay.push("Receives inputs");
+    relevantNodes = [];
+    for (const depth in model22.nodesByDepth) {
+      relevantNodes.push(...model22.nodesByDepth[depth]);
+    }
+  }
+  printFn("_".repeat(lineLength));
+  printRow(toDisplay, positions, printFn);
+  printFn("=".repeat(lineLength));
+  const layers = model22.layers;
+  for (let i2 = 0; i2 < layers.length; ++i2) {
+    if (sequentialLike) {
+      printLayerSummary(layers[i2], positions, printFn);
+    } else {
+      printLayerSummaryWithConnections(layers[i2], positions, relevantNodes, printFn);
+    }
+    printFn((i2 === layers.length - 1 ? "=" : "_").repeat(lineLength));
+  }
+  model22.checkTrainableWeightsConsistency();
+  const trainableCount = countTrainableParams(model22);
+  const nonTrainableCount = countParamsInWeights(model22.nonTrainableWeights);
+  printFn(`Total params: ${trainableCount + nonTrainableCount}`);
+  printFn(`Trainable params: ${trainableCount}`);
+  printFn(`Non-trainable params: ${nonTrainableCount}`);
+  printFn("_".repeat(lineLength));
+}
+function countTrainableParams(model22) {
+  let trainableCount;
+  if (model22.collectedTrainableWeights != null) {
+    trainableCount = countParamsInWeights(model22.collectedTrainableWeights);
+  } else {
+    trainableCount = countParamsInWeights(model22.trainableWeights);
+  }
+  return trainableCount;
+}
+function isModelSequentialLike(model22) {
+  let sequentialLike = true;
+  const nodesByDepth = [];
+  const nodes = [];
+  for (const depth in model22.nodesByDepth) {
+    nodesByDepth.push(model22.nodesByDepth[depth]);
+  }
+  for (const depthNodes of nodesByDepth) {
+    if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {
+      sequentialLike = false;
+      break;
+    }
+    nodes.push(...depthNodes);
+  }
+  if (sequentialLike) {
+    for (const layer of model22.layers) {
+      let flag = false;
+      for (const node2 of layer.inboundNodes) {
+        if (nodes.indexOf(node2) !== -1) {
+          if (flag) {
+            sequentialLike = false;
+            break;
+          } else {
+            flag = true;
+          }
+        }
+      }
+      if (!sequentialLike) {
+        break;
+      }
+    }
+  }
+  return sequentialLike;
+}
+function printRow(fields, positions, printFn = console.log) {
+  let line = "";
+  for (let i2 = 0; i2 < fields.length; ++i2) {
+    if (i2 > 0) {
+      line = line.slice(0, line.length - 1) + " ";
+    }
+    line += fields[i2];
+    line = line.slice(0, positions[i2]);
+    line += " ".repeat(positions[i2] - line.length);
+  }
+  printFn(line);
+}
+function printLayerSummary(layer, positions, printFn) {
+  let outputShape;
+  let inputShape;
+  try {
+    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(",");
+  } catch (err) {
+    inputShape = "multiple";
+  }
+  try {
+    outputShape = JSON.stringify(layer.outputShape);
+  } catch (err) {
+    outputShape = "multiple";
+  }
+  const name = layer.name;
+  const className = layer.getClassName();
+  const fields = [
+    `${name} (${className})`,
+    inputShape,
+    outputShape,
+    layer.countParams().toString()
+  ];
+  printRow(fields, positions, printFn);
+}
+function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {
+  let outputShape;
+  let inputShape;
+  try {
+    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(",");
+  } catch (err) {
+    inputShape = "multiple";
+  }
+  try {
+    outputShape = JSON.stringify(layer.outputShape);
+  } catch (err) {
+    outputShape = "multiple";
+  }
+  const connections = [];
+  for (const node2 of layer.inboundNodes) {
+    if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node2) === -1) {
+      continue;
+    }
+    for (let i2 = 0; i2 < node2.inboundLayers.length; ++i2) {
+      const inboundLayer = node2.inboundLayers[i2].name;
+      const inboundLayerIndex = node2.nodeIndices[i2];
+      const inboundTensorIndex = node2.tensorIndices[i2];
+      connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
+    }
+  }
+  const name = layer.name;
+  const className = layer.getClassName();
+  const firstConnection = connections.length === 0 ? "" : connections[0];
+  const fields = [
+    `${name} (${className})`,
+    inputShape,
+    outputShape,
+    layer.countParams().toString(),
+    firstConnection
+  ];
+  printRow(fields, positions, printFn);
+  for (let i2 = 1; i2 < connections.length; ++i2) {
+    printRow(["", "", "", "", connections[i2]], positions, printFn);
+  }
+}
+function isArrayItemInputOrOutputName(key, index2, value) {
+  return (key === "inboundNodes" || key === "outputLayers" || key === "inputLayers") && index2 === 0 && typeof value === "string";
+}
+function convertPythonicToTs(pythonicConfig, key) {
+  if (pythonicConfig === null) {
+    return null;
+  } else if (typeof pythonicConfig === "string") {
+    return toCamelCase(pythonicConfig);
+  } else if (typeof pythonicConfig === "number" || typeof pythonicConfig === "boolean") {
+    return pythonicConfig;
+  } else if (pythonicConfig instanceof Array) {
+    const tsArray = [];
+    const arrayLength = pythonicConfig.length;
+    for (let i2 = 0; i2 < arrayLength; ++i2) {
+      const item = pythonicConfig[i2];
+      if (isArrayItemInputOrOutputName(key, i2, item)) {
+        tsArray.push(item);
+      } else {
+        tsArray.push(convertPythonicToTs(item, key));
+      }
+    }
+    return tsArray;
+  } else {
+    const tsDict = {};
+    for (const pythonicKey of Object.keys(pythonicConfig)) {
+      const pythonicValue = pythonicConfig[pythonicKey];
+      if (pythonicKey === "name" && typeof pythonicValue === "string") {
+        tsDict[pythonicKey] = pythonicValue;
+      } else {
+        const tsKey = toCamelCase(pythonicKey);
+        tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);
+      }
+    }
+    return tsDict;
+  }
+}
+function convertTsToPythonic(tsConfig, key) {
+  if (tsConfig === null || tsConfig === void 0) {
+    return null;
+  } else if (typeof tsConfig === "string") {
+    return toSnakeCase(tsConfig);
+  } else if (typeof tsConfig === "number" || typeof tsConfig === "boolean") {
+    return tsConfig;
+  } else if (tsConfig instanceof Array) {
+    const pyArray = [];
+    const arrayLength = tsConfig.length;
+    for (let i2 = 0; i2 < arrayLength; ++i2) {
+      const item = tsConfig[i2];
+      if (isArrayItemInputOrOutputName(key, i2, item)) {
+        pyArray.push(item);
+      } else {
+        pyArray.push(convertTsToPythonic(item, key));
+      }
+    }
+    return pyArray;
+  } else {
+    const pyDict = {};
+    for (const tsKey of Object.keys(tsConfig)) {
+      const tsValue = tsConfig[tsKey];
+      const pyKey = toSnakeCase(tsKey);
+      if ((tsKey === "name" || tsKey === "className") && typeof tsValue === "string") {
+        pyDict[pyKey] = tsValue;
+      } else {
+        pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);
+      }
+    }
+    return pyDict;
+  }
+}
+var version2 = "3.18.0";
+var Container = class extends Layer {
+  constructor(args) {
+    super({});
+    this.containerNodes = /* @__PURE__ */ new Set();
+    this.name = args.name;
+    if (this.name == null) {
+      const prefix = this.getClassName().toLowerCase();
+      this.name = getUid(prefix);
+    }
+    this.supportsMasking = false;
+    this.trainable_ = true;
+    if (Array.isArray(args.inputs)) {
+      this.inputs = args.inputs.slice();
+    } else {
+      this.inputs = [args.inputs];
+    }
+    if (Array.isArray(args.outputs)) {
+      this.outputs = args.outputs.slice();
+    } else {
+      this.outputs = [args.outputs];
+    }
+    if (unique2(this.inputs).length !== this.inputs.length) {
+      throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);
+    }
+    if (unique2(this.outputs).length !== this.outputs.length) {
+      console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);
+    }
+    this.inputLayers = [];
+    this.inputLayersNodeIndices = [];
+    this.inputLayersTensorIndices = [];
+    this.outputLayers = [];
+    this.outputLayersNodeIndices = [];
+    this.outputLayersTensorIndices = [];
+    this.layers = [];
+    this.internalContainerRefs = [];
+    for (const x of this.outputs) {
+      const layer = x.sourceLayer;
+      const nodeIndex = x.nodeIndex;
+      const tensorIndex = x.tensorIndex;
+      this.outputLayers.push(layer);
+      this.outputLayersNodeIndices.push(nodeIndex);
+      this.outputLayersTensorIndices.push(tensorIndex);
+    }
+    for (const x of this.inputs) {
+      const layer = x.sourceLayer;
+      const nodeIndex = x.nodeIndex;
+      const tensorIndex = x.tensorIndex;
+      assert2(nodeIndex === 0, "input layer has >1 nodes");
+      assert2(tensorIndex === 0, "input layer has >1 tensors");
+      this.inputLayers.push(layer);
+      this.inputLayersNodeIndices.push(nodeIndex);
+      this.inputLayersTensorIndices.push(tensorIndex);
+    }
+    this.inputNames = [];
+    this.outputNames = [];
+    this.feedInputShapes = [];
+    this.feedInputNames = [];
+    this.feedOutputNames = [];
+    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
+      const layer = this.inputLayers[i2];
+      if (!(layer instanceof InputLayer)) {
+        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i2} (0-based) originates from layer type ${layer.getClassName()}.`);
+      }
+      this.inputNames.push(layer.name);
+      this.feedInputShapes.push(layer.batchInputShape);
+      this.feedInputNames.push(layer.name);
+    }
+    for (const layer of this.outputLayers) {
+      this.outputNames.push(layer.name);
+    }
+    this.internalInputShapes = this.inputs.map((x) => x.shape);
+    this.internalOutputShapes = this.outputs.map((x) => x.shape);
+    const nodesDepths = {};
+    const nodeIDToNode = {};
+    const layersDepths = {};
+    const layerIDToLayer = {};
+    const layerIndices = {};
+    const nodesInDecreasingDepth = [];
+    const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {
+      if (layer == null || nodeIndex == null || tensorIndex == null) {
+        layer = tensor2.sourceLayer;
+        nodeIndex = tensor2.nodeIndex;
+        tensorIndex = tensor2.tensorIndex;
+      }
+      const node2 = layer.inboundNodes[nodeIndex];
+      if (nodesInProgress2.indexOf(node2) !== -1) {
+        throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);
+      }
+      if (finishedNodes2.indexOf(node2) !== -1) {
+        return;
+      }
+      this.containerNodes.add(Container.nodeKey(layer, nodeIndex));
+      if (!(layer.id in layerIndices)) {
+        layerIndices[layer.id] = Object.keys(layerIndices).length;
+      }
+      if (nodesInProgress2.indexOf(node2) === -1) {
+        nodesInProgress2.push(node2);
+      }
+      const numInboundLayers = node2.inboundLayers.length;
+      for (let i2 = 0; i2 < numInboundLayers; i2++) {
+        const x = node2.inputTensors[i2];
+        const layer2 = node2.inboundLayers[i2];
+        const nodeIndex2 = node2.nodeIndices[i2];
+        const tensorIndex2 = node2.tensorIndices[i2];
+        buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);
+      }
+      finishedNodes2.push(node2);
+      while (nodesInProgress2.indexOf(node2) >= 0) {
+        nodesInProgress2.splice(nodesInProgress2.indexOf(node2), 1);
+      }
+      nodesInDecreasingDepth.push(node2);
+    };
+    const finishedNodes = [];
+    const nodesInProgress = [];
+    for (const x of this.outputs) {
+      buildMapOfGraph(x, finishedNodes, nodesInProgress);
+    }
+    const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();
+    for (const node2 of reversedNodesInDecreasingDepth) {
+      nodeIDToNode[node2.id] = node2;
+      if (!(node2.id in nodesDepths)) {
+        nodesDepths[node2.id] = 0;
+      }
+      let depth = nodesDepths[node2.id];
+      const previousDepth = layersDepths[node2.outboundLayer.id] == null ? 0 : layersDepths[node2.outboundLayer.id];
+      depth = Math.max(depth, previousDepth);
+      layersDepths[node2.outboundLayer.id] = depth;
+      layerIDToLayer[node2.outboundLayer.id] = node2.outboundLayer;
+      nodesDepths[node2.id] = depth;
+      for (let i2 = 0; i2 < node2.inboundLayers.length; i2++) {
+        const inboundLayer = node2.inboundLayers[i2];
+        const nodeIndex = node2.nodeIndices[i2];
+        const inboundNode = inboundLayer.inboundNodes[nodeIndex];
+        const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];
+        nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);
+        nodeIDToNode[inboundNode.id] = inboundNode;
+      }
+    }
+    const nodesByDepth = {};
+    for (const nodeID in nodesDepths) {
+      const depth = nodesDepths[nodeID];
+      if (!(depth in nodesByDepth)) {
+        nodesByDepth[depth] = [];
+      }
+      nodesByDepth[depth].push(nodeIDToNode[nodeID]);
+    }
+    const layersByDepth = {};
+    for (const layerID in layersDepths) {
+      const depth = layersDepths[layerID];
+      if (!(depth in layersByDepth)) {
+        layersByDepth[depth] = [];
+      }
+      layersByDepth[depth].push(layerIDToLayer[layerID]);
+    }
+    let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    this.layers = [];
+    for (const depth of depthKeys) {
+      const layersForDepth = layersByDepth[depth];
+      layersForDepth.sort((a6, b) => {
+        const aIndex = layerIndices[a6.id];
+        const bIndex = layerIndices[b.id];
+        if (aIndex < bIndex) {
+          return -1;
+        }
+        if (aIndex > bIndex) {
+          return 1;
+        }
+        return 0;
+      });
+      for (const layer of layersForDepth) {
+        if (layer instanceof Container) {
+          this.internalContainerRefs.push(layer);
+        }
+        this.layers.push(layer);
+      }
+    }
+    this.layersByDepth = layersByDepth;
+    depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    const computableTensors = this.inputs.slice();
+    const layersWithCompleteInput = [];
+    for (const depth of depthKeys) {
+      for (const node2 of nodesByDepth[depth]) {
+        const layer = node2.outboundLayer;
+        if (layer != null) {
+          for (const x of node2.inputTensors) {
+            if (computableTensors.indexOf(x) === -1) {
+              throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);
+            }
+          }
+          for (const x of node2.outputTensors) {
+            computableTensors.push(x);
+          }
+          layersWithCompleteInput.push(layer.name);
+        }
+      }
+    }
+    this.nodesByDepth = nodesByDepth;
+    const allNames = this.layers.map((x) => x.name);
+    for (const name of allNames) {
+      const numOccurrences = allNames.filter((x) => x === name).length;
+      if (numOccurrences !== 1) {
+        throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));
+      }
+    }
+    this.outboundNodes = [];
+    this.inboundNodes = [];
+    new Node({
+      outboundLayer: this,
+      inboundLayers: [],
+      nodeIndices: [],
+      tensorIndices: [],
+      inputTensors: this.inputs,
+      outputTensors: this.outputs,
+      inputMasks: this.inputs.map((x) => null),
+      outputMasks: this.outputs.map((x) => null),
+      inputShapes: this.inputs.map((x) => x.shape),
+      outputShapes: this.outputs.map((x) => x.shape)
+    });
+    this.built = true;
+    this._refCount = 1;
+  }
+  assertNotDisposed() {
+    if (this._refCount === 0) {
+      throw new Error(`Container '${this.name}' is already disposed.`);
+    }
+  }
+  dispose() {
+    this.assertNotDisposed();
+    const result = { refCountAfterDispose: null, numDisposedVariables: 0 };
+    if (--this._refCount === 0) {
+      for (const layer of this.layers) {
+        result.numDisposedVariables += layer.dispose().numDisposedVariables;
+      }
+      for (const container of this.internalContainerRefs) {
+        result.numDisposedVariables += container.dispose().numDisposedVariables;
+      }
+    }
+    result.refCountAfterDispose = this._refCount;
+    return result;
+  }
+  get trainable() {
+    return this.trainable_;
+  }
+  set trainable(trainable) {
+    this.layers.forEach((layer) => {
+      layer._trainableWeights.forEach((w) => w.trainable = trainable);
+    });
+    this.trainable_ = trainable;
+  }
+  get trainableWeights() {
+    if (this._trainableWeights.length > 0) {
+      throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");
+    }
+    if (!this.trainable) {
+      return [];
+    }
+    let weights = [];
+    for (const layer of this.layers) {
+      weights = weights.concat(layer.trainableWeights);
+    }
+    return weights;
+  }
+  get nonTrainableWeights() {
+    const weights = [];
+    for (const layer of this.layers) {
+      weights.push(...layer.nonTrainableWeights);
+    }
+    if (!this.trainable) {
+      const trainableWeights = [];
+      for (const layer of this.layers) {
+        trainableWeights.push(...layer.trainableWeights);
+      }
+      return trainableWeights.concat(weights);
+    }
+    return weights;
+  }
+  get weights() {
+    return this.trainableWeights.concat(this.nonTrainableWeights);
+  }
+  loadWeights(weights, strict = true) {
+    const nameToWeight = {};
+    let totalWeightsCount = 0;
+    for (const layer of this.layers) {
+      for (const weight of layer.weights) {
+        if (nameToWeight[weight.originalName] != null) {
+          throw new ValueError(`Duplicate weight name: ${weight.originalName}`);
+        }
+        nameToWeight[weight.originalName] = weight;
+        totalWeightsCount++;
+      }
+    }
+    const weightValueTuples = [];
+    for (const name in weights) {
+      let validatedName = name;
+      if (nameToWeight[name] == null) {
+        const tokens = name.split("/");
+        const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);
+        validatedName = shortenNameArray.join("/");
+      }
+      if (nameToWeight[validatedName] != null) {
+        weightValueTuples.push([nameToWeight[validatedName], weights[name]]);
+      } else if (strict) {
+        throw new ValueError(`Provided weight data has no target variable: ${name}`);
+      }
+      delete nameToWeight[validatedName];
+    }
+    if (strict) {
+      const unsetNames = [];
+      for (const name in nameToWeight) {
+        unsetNames.push(name);
+      }
+      if (unsetNames.length > 0) {
+        throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);
+      }
+    }
+    batchSetValue(weightValueTuples);
+  }
+  updatedConfig() {
+    const theConfig = this.getConfig();
+    const modelConfig = {};
+    modelConfig["className"] = this.getClassName();
+    modelConfig["config"] = theConfig;
+    modelConfig["kerasVersion"] = `tfjs-layers ${version2}`;
+    modelConfig["backend"] = "TensorFlow.js";
+    return modelConfig;
+  }
+  toJSON(unused, returnString = true) {
+    const modelConfig = convertTsToPythonic(this.updatedConfig());
+    return returnString ? JSON.stringify(modelConfig) : modelConfig;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = toList(inputs);
+      const feedDict = new FeedDict();
+      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+        feedDict.add(this.inputs[i2], inputs[i2]);
+      }
+      return execute(this.outputs, feedDict, kwargs);
+    });
+  }
+  computeMask(inputs, mask2) {
+    return tidy(() => {
+      inputs = toList(inputs);
+      let masks;
+      if (mask2 == null) {
+        masks = pyListRepeat(null, inputs.length);
+      } else {
+        masks = toList(mask2);
+      }
+      return this.runInternalGraph(inputs, masks)[1];
+    });
+  }
+  computeOutputShape(inputShape) {
+    const inputShapes = normalizeShapeList(inputShape);
+    if (inputShapes.length !== this.inputLayers.length) {
+      throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);
+    }
+    const layersToOutputShapes = {};
+    for (let i2 = 0; i2 < inputShapes.length; i2++) {
+      const layer = this.inputLayers[i2];
+      const inputShape2 = inputShapes[i2];
+      const shapeKey = layer.name + "_0_0";
+      layersToOutputShapes[shapeKey] = inputShape2;
+    }
+    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    if (depthKeys.length > 1) {
+      for (const depth of depthKeys) {
+        const nodes = this.nodesByDepth[depth];
+        for (const node2 of nodes) {
+          const layer = node2.outboundLayer;
+          if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {
+            continue;
+          }
+          const inputShapes2 = [];
+          for (let j = 0; j < node2.inboundLayers.length; j++) {
+            const inboundLayer = node2.inboundLayers[j];
+            const nodeIndex2 = node2.nodeIndices[j];
+            const tensorIndex = node2.tensorIndices[j];
+            const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;
+            const inputShape2 = layersToOutputShapes[shapeKey];
+            inputShapes2.push(inputShape2);
+          }
+          const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));
+          const outputShapes2 = normalizeShapeList(outputShape);
+          const nodeIndex = layer.inboundNodes.indexOf(node2);
+          for (let j = 0; j < outputShapes2.length; j++) {
+            const shapeKey = `${layer.name}_${nodeIndex}_${j}`;
+            layersToOutputShapes[shapeKey] = outputShapes2[j];
+          }
+        }
+      }
+    }
+    const outputShapes = [];
+    const outputShapeKeys = [];
+    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
+      const layer = this.outputLayers[i2];
+      const nodeIndex = this.outputLayersNodeIndices[i2];
+      const tensorIndex = this.outputLayersTensorIndices[i2];
+      const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
+      outputShapeKeys.push(shapeKey);
+    }
+    for (let i2 = 0; i2 < outputShapeKeys.length; i2++) {
+      const key = outputShapeKeys[i2];
+      assert2(key in layersToOutputShapes);
+      outputShapes.push(layersToOutputShapes[key]);
+    }
+    return singletonOrArray(outputShapes);
+  }
+  runInternalGraph(inputs, masks) {
+    if (masks == null) {
+      masks = pyListRepeat(null, inputs.length);
+    }
+    const tensorMap = {};
+    for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+      const x = this.inputs[i2];
+      const y = inputs[i2];
+      const mask2 = masks[i2];
+      tensorMap[x.id] = [y, mask2];
+    }
+    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    for (const depth of depthKeys) {
+      const nodes = this.nodesByDepth[depth];
+      for (const node2 of nodes) {
+        const layer = node2.outboundLayer;
+        const referenceInputTensors = node2.inputTensors;
+        const referenceOutputTensors = node2.outputTensors;
+        const computedData = new Array();
+        for (const x of referenceInputTensors) {
+          if (x.id in tensorMap) {
+            computedData.push(tensorMap[x.id]);
+          }
+        }
+        if (computedData.length === referenceInputTensors.length) {
+          let kwargs = {};
+          let computedTensors;
+          let computedMasks;
+          let outputTensors2;
+          let outputMasks2;
+          if (node2.callArgs != null) {
+            kwargs = node2.callArgs;
+          }
+          if (computedData.length === 1) {
+            const [computedTensor, computedMask] = computedData[0];
+            if (kwargs["mask"] == null) {
+              kwargs["mask"] = computedMask;
+            }
+            outputTensors2 = toList(layer.call(computedTensor, kwargs));
+            outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));
+            computedTensors = [computedTensor];
+            computedMasks = [computedMask];
+          } else {
+            computedTensors = computedData.map((x) => x[0]);
+            computedMasks = computedData.map((x) => x[1]);
+            if (kwargs["mask"] == null) {
+              kwargs["mask"] = computedMasks;
+            }
+            outputTensors2 = toList(layer.call(computedTensors, kwargs));
+            outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));
+          }
+          if (layer.activityRegularizer) {
+            throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
+          }
+          for (let i2 = 0; i2 < referenceOutputTensors.length; ++i2) {
+            const x = referenceOutputTensors[i2];
+            const y = outputTensors2[i2];
+            const mask2 = outputMasks2[i2];
+            tensorMap[x.id] = [y, mask2];
+          }
+        }
+      }
+    }
+    const outputTensors = [];
+    const outputMasks = [];
+    const outputShapes = [];
+    for (const x of this.outputs) {
+      assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);
+      const [tensor2, mask2] = tensorMap[x.id];
+      outputShapes.push(tensor2.shape);
+      outputTensors.push(tensor2);
+      outputMasks.push(mask2);
+    }
+    return [outputTensors, outputMasks, outputShapes];
+  }
+  buildNodeConversionMap(layers) {
+    const nodeConversionMap = {};
+    let keptNodes;
+    for (const layer of this.layers) {
+      keptNodes = layer instanceof Container ? 1 : 0;
+      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
+        const nodeKey = Container.nodeKey(layer, originalNodeIndex);
+        if (this.containerNodes.has(nodeKey)) {
+          nodeConversionMap[nodeKey] = keptNodes;
+          keptNodes += 1;
+        }
+      }
+    }
+    return nodeConversionMap;
+  }
+  getLayer(name, index2) {
+    if (index2 != null) {
+      if (this.layers.length <= index2) {
+        throw new ValueError(`Was asked to retrieve layer at index ${index2}, but model only has ${this.layers.length} layer(s).`);
+      } else {
+        return this.layers[index2];
+      }
+    } else {
+      if (name == null) {
+        throw new ValueError("Provide either a layer name or layer index");
+      }
+    }
+    for (const layer of this.layers) {
+      if (layer.name === name) {
+        return layer;
+      }
+    }
+    throw new ValueError(`No such layer: ${name}`);
+  }
+  calculateLosses() {
+    return tidy(() => {
+      const losses2 = [];
+      for (const layer of this.layers) {
+        for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {
+          const nodeKey = Container.nodeKey(layer, nodeIndex);
+          if (this.containerNodes.has(nodeKey)) {
+            losses2.push(...layer.calculateLosses());
+          }
+        }
+      }
+      return losses2;
+    });
+  }
+  getConfig() {
+    const config3 = { name: this.name };
+    const nodeConversionMap = this.buildNodeConversionMap(this.layers);
+    const layerConfigs = [];
+    for (const layer of this.layers) {
+      const layerClassName = layer.getClassName();
+      const layerConfig = layer.getConfig();
+      const filteredInboundNodes = [];
+      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
+        const node2 = layer.inboundNodes[originalNodeIndex];
+        const nodeKey = Container.nodeKey(layer, originalNodeIndex);
+        let kwargs = {};
+        if (this.containerNodes.has(nodeKey)) {
+          if (node2.callArgs) {
+            try {
+              JSON.stringify(node2.callArgs);
+              kwargs = node2.callArgs;
+            } catch (err) {
+              console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node2.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);
+              kwargs = {};
+            }
+          }
+          if (node2.inboundLayers.length > 0) {
+            const nodeData = [];
+            for (let i2 = 0; i2 < node2.inboundLayers.length; i2++) {
+              const inboundLayer = node2.inboundLayers[i2];
+              const nodeIndex = node2.nodeIndices[i2];
+              const tensorIndex = node2.tensorIndices[i2];
+              const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);
+              let newNodeIndex = nodeConversionMap[nodeKey2];
+              if (newNodeIndex == null) {
+                newNodeIndex = 0;
+              }
+              nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);
+            }
+            filteredInboundNodes.push(nodeData);
+          }
+        }
+      }
+      const dict = {};
+      dict["name"] = layer.name;
+      dict["className"] = layerClassName;
+      dict["config"] = layerConfig;
+      dict["inboundNodes"] = filteredInboundNodes;
+      layerConfigs.push(dict);
+    }
+    config3["layers"] = layerConfigs;
+    const modelInputs = [];
+    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
+      const layer = this.inputLayers[i2];
+      const nodeIndex = this.inputLayersNodeIndices[i2];
+      const nodeKey = Container.nodeKey(layer, nodeIndex);
+      if (!this.containerNodes.has(nodeKey)) {
+        continue;
+      }
+      let newNodeIndex = nodeConversionMap[nodeKey];
+      if (newNodeIndex === null || newNodeIndex === void 0) {
+        newNodeIndex = 0;
+      }
+      const tensorIndex = this.inputLayersTensorIndices[i2];
+      modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
+    }
+    config3["inputLayers"] = modelInputs;
+    const modelOutputs = [];
+    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
+      const layer = this.outputLayers[i2];
+      const nodeIndex = this.outputLayersNodeIndices[i2];
+      const nodeKey = Container.nodeKey(layer, nodeIndex);
+      if (!this.containerNodes.has(nodeKey)) {
+        continue;
+      }
+      let newNodeIndex = nodeConversionMap[nodeKey];
+      if (newNodeIndex === null || newNodeIndex === void 0) {
+        newNodeIndex = 0;
+      }
+      const tensorIndex = this.outputLayersTensorIndices[i2];
+      modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
+    }
+    config3["outputLayers"] = modelOutputs;
+    return config3;
+  }
+  static fromConfig(cls, config3, customObjects = {}, fastWeightInit = false) {
+    const createdLayers = {};
+    const unprocessedNodes = {};
+    function addUnprocessedNode(layer, nodeData) {
+      if (!(layer.name in unprocessedNodes)) {
+        unprocessedNodes[layer.name] = [nodeData];
+      } else {
+        unprocessedNodes[layer.name].push(nodeData);
+      }
+    }
+    function processNode(layer, nodeData) {
+      const inputTensors2 = [];
+      let kwargs;
+      for (const inputData of nodeData) {
+        const inboundLayerName = inputData[0];
+        const inboundNodeIndex = inputData[1];
+        const inboundTensorIndex = inputData[2];
+        kwargs = inputData[3] == null ? {} : inputData[3];
+        if (!(inboundLayerName in createdLayers)) {
+          addUnprocessedNode(layer, nodeData);
+          return;
+        }
+        const inboundLayer = createdLayers[inboundLayerName];
+        if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {
+          addUnprocessedNode(layer, nodeData);
+          return;
+        }
+        const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];
+        inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);
+      }
+      if (inputTensors2.length > 0) {
+        layer.apply(singletonOrArray(inputTensors2), kwargs);
+      }
+    }
+    function processLayer(layerData) {
+      const layerName = layerData["name"];
+      const layer = deserialize(layerData, config3["customObjects"] != null ? config3["customObjects"] : {});
+      layer.setFastWeightInitDuringBuild(fastWeightInit);
+      createdLayers[layerName] = layer;
+      const inboundNodesData = layerData["inboundNodes"];
+      inboundNodesData.forEach((nodeData) => {
+        if (!(nodeData instanceof Array)) {
+          throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);
+        }
+        addUnprocessedNode(layer, nodeData);
+      });
+    }
+    const name = config3["name"];
+    const layersFromConfig = config3["layers"];
+    for (const layerData of layersFromConfig) {
+      processLayer(layerData);
+    }
+    while (!isObjectEmpty(unprocessedNodes)) {
+      for (const layerData of layersFromConfig) {
+        const layer = createdLayers[layerData["name"]];
+        if (layer.name in unprocessedNodes) {
+          const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];
+          delete unprocessedNodes[layer.name];
+          for (const nodeData of currentUnprocessedNodesForLayer) {
+            processNode(layer, nodeData);
+          }
+        }
+      }
+    }
+    const inputTensors = [];
+    const outputTensors = [];
+    const inputLayersFromConfig = config3["inputLayers"];
+    for (const layerData of inputLayersFromConfig) {
+      const layerName = layerData[0];
+      const nodeIndex = layerData[1];
+      const tensorIndex = layerData[2];
+      assert2(layerName in createdLayers);
+      const layer = createdLayers[layerName];
+      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
+      inputTensors.push(layerOutputTensors[tensorIndex]);
+    }
+    const outputLayersFromConfig = config3["outputLayers"];
+    for (const layerData of outputLayersFromConfig) {
+      const layerName = layerData[0];
+      const nodeIndex = layerData[1];
+      const tensorIndex = layerData[2];
+      assert2(layerName in createdLayers);
+      const layer = createdLayers[layerName];
+      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
+      outputTensors.push(layerOutputTensors[tensorIndex]);
+    }
+    return new cls({ inputs: inputTensors, outputs: outputTensors, name });
+  }
+  get stateful() {
+    if (this._stateful) {
+      throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");
+    }
+    for (const layer of this.layers) {
+      if (layer.stateful) {
+        return true;
+      }
+    }
+    return false;
+  }
+  resetStates() {
+    tidy(() => {
+      this.layers.forEach((layer) => {
+        if (layer.stateful) {
+          layer.resetStates();
+        }
+      });
+    });
+  }
+};
+function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {
+  const numOutputs = outputNames.length;
+  if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {
+    return outputNames.map((name) => null);
+  }
+  if (numOutputs === 1) {
+    if (Array.isArray(xWeight) && xWeight.length === 1) {
+      return xWeight;
+    } else if (typeof xWeight === "object" && outputNames[0] in xWeight) {
+      return [xWeight[outputNames[0]]];
+    } else {
+      return [xWeight];
+    }
+  }
+  if (Array.isArray(xWeight)) {
+    if (xWeight.length !== numOutputs) {
+      throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);
+    }
+    return xWeight;
+  } else if (typeof xWeight === "object" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === "object") {
+    const output = [];
+    outputNames.forEach((outputName) => {
+      if (outputName in xWeight) {
+        output.push(xWeight[outputName]);
+      } else {
+        output.push(null);
+      }
+    });
+    return output;
+  } else {
+    throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);
+  }
+}
+function standardizeClassWeights(classWeight, outputNames) {
+  return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight");
+}
+async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {
+  if (sampleWeight != null || sampleWeightMode != null) {
+    throw new Error("Support sampleWeight is not implemented yet");
+  }
+  if (classWeight != null) {
+    const yClasses = tidy(() => {
+      if (y.shape.length === 1) {
+        return clone(y);
+      } else if (y.shape.length === 2) {
+        if (y.shape[1] > 1) {
+          const axis = 1;
+          return argMax(y, axis);
+        } else if (y.shape[1] === 1) {
+          return reshape(y, [y.shape[0]]);
+        } else {
+          throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
+        }
+      } else {
+        throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
+      }
+    });
+    const yClassIndices = Array.from(await yClasses.data());
+    dispose(yClasses);
+    const classSampleWeight = [];
+    yClassIndices.forEach((classIndex) => {
+      if (classWeight[classIndex] == null) {
+        throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);
+      } else {
+        classSampleWeight.push(classWeight[classIndex]);
+      }
+    });
+    return tensor1d(classSampleWeight, "float32");
+  } else {
+    return null;
+  }
+}
+function computeWeightedLoss2(losses2, sampleWeights) {
+  return mul(losses2, sampleWeights);
+}
+var DEFAULT_VALIDATION_BATCH_SIZE = 32;
+function standardizeDataIteratorOutput(model22, iteratorOut) {
+  let xs;
+  let ys;
+  const iteratorOutObj = iteratorOut;
+  xs = iteratorOutObj["xs"];
+  ys = iteratorOutObj["ys"];
+  util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);
+  const flattenedXs = flattenTensorOrArrayOrMap("input", model22.inputNames, xs);
+  const flattenedYs = flattenTensorOrArrayOrMap("output", model22.outputNames, ys);
+  const batchSize = flattenedXs[0].shape[0];
+  util_exports.assert(flattenedXs.length === model22.inputs.length, () => `LayersModel has ${model22.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model22.inputNames)})`);
+  util_exports.assert(flattenedYs.length === model22.outputs.length, () => `LayersModel has ${model22.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model22.outputNames)})`);
+  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
+    util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model22.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model22.inputNames[0]}.`);
+  }
+  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
+    util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model22.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model22.inputNames[0]}.`);
+  }
+  return { xs: flattenedXs, ys: flattenedYs };
+}
+function flattenTensorOrArrayOrMap(inputOrOutput, names, values) {
+  if (values instanceof Tensor) {
+    return [values];
+  } else if (Array.isArray(values)) {
+    util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);
+    return values;
+  } else {
+    const result = [];
+    for (const name of names) {
+      if (values[name] == null) {
+        throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);
+      }
+      result.push(values[name]);
+    }
+    return result;
+  }
+}
+function standardizeTensorValidationData(data) {
+  if (data.length === 3) {
+    throw new NotImplementedError("Validation with sample weights is not implemented yet.");
+  }
+  return { xs: data[0], ys: data[1] };
+}
+async function fitDataset(model22, dataset, args) {
+  const hasBatchesPerEpoch = args.batchesPerEpoch != null;
+  util_exports.assert(model22.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).");
+  util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);
+  util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);
+  util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);
+  util_exports.assert(args["validationSplit"] == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead.");
+  if (model22.isTraining) {
+    throw new Error("Cannot start training because another fit() call is ongoing.");
+  }
+  model22.isTraining = true;
+  try {
+    const doValidation = args.validationData != null;
+    let valXs;
+    let valYs;
+    if (doValidation) {
+      if (isDatasetObject(args.validationData)) {
+        util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);
+      } else {
+        const validationData = standardizeTensorValidationData(args.validationData);
+        valXs = validationData.xs;
+        valYs = validationData.ys;
+      }
+    }
+    const trainFunction = model22.makeTrainFunction();
+    const outLabels = model22.getDedupedMetricsNames();
+    let callbackMetrics;
+    if (doValidation) {
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
+    } else {
+      callbackMetrics = outLabels.slice();
+    }
+    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
+    const verbose = args.verbose == null ? 1 : args.verbose;
+    const { callbackList, history } = configureCallbacks(callbacks2, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, doValidation, callbackMetrics);
+    callbackList.setModel(model22);
+    model22.history = history;
+    await callbackList.onTrainBegin();
+    model22.stopTraining_ = false;
+    let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;
+    let dataIterator = await dataset.iterator();
+    while (epoch < args.epochs) {
+      const epochLogs = {};
+      await callbackList.onEpochBegin(epoch);
+      let stepsDone = 0;
+      let batchIndex = 0;
+      if (!hasBatchesPerEpoch) {
+        dataIterator = await dataset.iterator();
+      }
+      while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {
+        const iteratorOut = await dataIterator.next();
+        if (hasBatchesPerEpoch && iteratorOut.done) {
+          console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);
+          break;
+        }
+        if (iteratorOut.value != null) {
+          const { xs, ys } = standardizeDataIteratorOutput(model22, iteratorOut.value);
+          const batchLogs = {};
+          batchLogs["batch"] = batchIndex;
+          batchLogs["size"] = xs[0].shape[0];
+          await callbackList.onBatchBegin(batchIndex, batchLogs);
+          const sampleWeights = [];
+          if (args.classWeight != null) {
+            const standardClassWeights = standardizeClassWeights(args.classWeight, model22.outputNames);
+            for (let i2 = 0; i2 < standardClassWeights.length; ++i2) {
+              sampleWeights.push(await standardizeWeights(ys[i2], null, standardClassWeights[i2]));
+            }
+          }
+          const ins = xs.concat(ys).concat(sampleWeights);
+          const outs = trainFunction(ins);
+          dispose(ins);
+          for (let i2 = 0; i2 < outLabels.length; ++i2) {
+            const label = outLabels[i2];
+            const out = outs[i2];
+            batchLogs[label] = out;
+            keep(out);
+          }
+          await callbackList.onBatchEnd(batchIndex, batchLogs);
+          disposeTensorsInLogs(batchLogs);
+          batchIndex++;
+          stepsDone++;
+        }
+        if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {
+          if (doValidation) {
+            let valOuts;
+            if (isDatasetObject(args.validationData)) {
+              valOuts = toList(await model22.evaluateDataset(args.validationData, { batches: args.validationBatches }));
+            } else {
+              valOuts = toList(model22.evaluate(valXs, valYs, {
+                batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,
+                verbose: 0
+              }));
+            }
+            for (let i2 = 0; i2 < model22.metricsNames.length; ++i2) {
+              epochLogs[`val_${model22.metricsNames[i2]}`] = valOuts[i2];
+            }
+          }
+          break;
+        }
+        if (model22.stopTraining_) {
+          break;
+        }
+      }
+      await callbackList.onEpochEnd(epoch, epochLogs);
+      epoch++;
+      if (model22.stopTraining_) {
+        break;
+      }
+    }
+    await callbackList.onTrainEnd();
+    await model22.history.syncData();
+    return model22.history;
+  } finally {
+    model22.isTraining = false;
+  }
+}
+function getStepsPerEpoch(dataset, args) {
+  let stepsPerEpoch = null;
+  if (args.batchesPerEpoch != null) {
+    stepsPerEpoch = args.batchesPerEpoch;
+  } else if (Number.isFinite(dataset.size)) {
+    stepsPerEpoch = dataset.size;
+  }
+  return stepsPerEpoch;
+}
+function isDatasetObject(dataset) {
+  return typeof dataset.iterator === "function";
+}
+function isLazyIteratorObject(iterator) {
+  return typeof iterator.next === "function";
+}
+async function evaluateDataset(model22, dataset, args) {
+  args = args || {};
+  const hasBatches = args.batches != null;
+  const f = model22.testFunction;
+  let outs = [];
+  if (args.verbose > 0) {
+    throw new NotImplementedError("Verbose mode is not implemented yet.");
+  }
+  util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);
+  const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();
+  let numExamples = 0;
+  let batch = 0;
+  while (hasBatches ? batch < args.batches : true) {
+    const iteratorOut = await dataIterator.next();
+    outs = tidy(() => {
+      if (iteratorOut.value) {
+        const { xs, ys } = standardizeDataIteratorOutput(model22, iteratorOut.value);
+        const xsAndYs = xs.concat(ys);
+        const batchOuts = tidy(() => f(xsAndYs));
+        dispose(xsAndYs);
+        if (batch === 0) {
+          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+            outs.push(scalar(0));
+          }
+        }
+        const batchSize = xsAndYs[0].shape[0];
+        for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+          const batchOut = batchOuts[i2];
+          const oldScalar = outs[i2];
+          outs[i2] = tidy(() => add2(outs[i2], mul(batchSize, batchOut)));
+          if (batch > 0) {
+            dispose(oldScalar);
+          }
+        }
+        dispose(batchOuts);
+        numExamples += batchSize;
+        ++batch;
+      }
+      return outs;
+    });
+    if (iteratorOut.done) {
+      if (hasBatches) {
+        console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);
+      }
+      break;
+    }
+  }
+  for (let i2 = 0; i2 < outs.length; ++i2) {
+    const oldScalar = outs[i2];
+    outs[i2] = div(outs[i2], numExamples);
+    dispose(oldScalar);
+  }
+  return singletonOrArray(outs);
+}
+function checkBatchSize(batchSize) {
+  util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);
+}
+function sliceArrays(arrays, start, stop) {
+  if (arrays == null) {
+    return [null];
+  } else if (Array.isArray(arrays)) {
+    return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));
+  } else {
+    return sliceAlongFirstAxis(arrays, start, stop - start);
+  }
+}
+function sliceArraysByIndices(arrays, indices) {
+  return tidy(() => {
+    if (arrays == null) {
+      return null;
+    } else if (Array.isArray(arrays)) {
+      return arrays.map((array2) => sliceArraysByIndices(array2, indices));
+    } else {
+      return gather2(arrays, indices.dtype === "int32" ? indices : cast(indices, "int32"));
+    }
+  });
+}
+function makeBatches(size2, batchSize) {
+  const output = [];
+  let batchStart = 0;
+  let batchEnd = null;
+  while (batchStart < size2) {
+    batchEnd = batchStart + batchSize;
+    if (batchEnd >= size2) {
+      batchEnd = size2;
+    }
+    output.push([batchStart, batchEnd]);
+    batchStart = batchEnd;
+  }
+  return output;
+}
+async function fitLoop(model22, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {
+  if (batchSize == null) {
+    batchSize = 32;
+  }
+  if (epochs == null) {
+    epochs = 1;
+  }
+  if (shuffle2 == null) {
+    shuffle2 = true;
+  }
+  if (initialEpoch == null) {
+    initialEpoch = 0;
+  }
+  let doValidation = false;
+  if (valF != null && valIns != null) {
+    doValidation = true;
+  }
+  if (validationSteps != null) {
+    doValidation = true;
+    if (stepsPerEpoch == null) {
+      throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
+    }
+  }
+  const numTrainSamples = model22.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch");
+  let indexArray;
+  if (numTrainSamples != null) {
+    indexArray = range2(0, numTrainSamples);
+  }
+  if (verbose == null) {
+    verbose = 1;
+  }
+  const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);
+  callbackList.setModel(model22);
+  model22.history = history;
+  await callbackList.onTrainBegin();
+  model22.stopTraining_ = false;
+  for (let epoch = initialEpoch; epoch < epochs; ++epoch) {
+    await callbackList.onEpochBegin(epoch);
+    const epochLogs = {};
+    if (stepsPerEpoch != null) {
+      throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");
+    } else {
+      if (shuffle2 === "batch") {
+        throw new NotImplementedError("batch shuffling is not implemneted yet");
+      } else if (shuffle2) {
+        util_exports.shuffle(indexArray);
+      }
+      const epochIndexArray1D = tensor1d(indexArray);
+      const batches = makeBatches(numTrainSamples, batchSize);
+      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
+        const batchLogs = {};
+        await callbackList.onBatchBegin(batchIndex, batchLogs);
+        tidy(() => {
+          const batchStart = batches[batchIndex][0];
+          const batchEnd = batches[batchIndex][1];
+          const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);
+          batchLogs["batch"] = batchIndex;
+          batchLogs["size"] = batchEnd - batchStart;
+          const insBatch = sliceArraysByIndices(ins, batchIds);
+          const outs = f(insBatch);
+          for (let i2 = 0; i2 < outLabels.length; ++i2) {
+            const label = outLabels[i2];
+            const out = outs[i2];
+            batchLogs[label] = out;
+            keep(out);
+          }
+          if (batchIndex === batches.length - 1) {
+            if (doValidation) {
+              const valOuts = model22.testLoop(valF, valIns, batchSize);
+              for (let i2 = 0; i2 < outLabels.length; ++i2) {
+                const label = outLabels[i2];
+                const out = valOuts[i2];
+                keep(out);
+                epochLogs["val_" + label] = out;
+              }
+            }
+          }
+        });
+        await callbackList.onBatchEnd(batchIndex, batchLogs);
+        disposeTensorsInLogs(batchLogs);
+        if (model22.stopTraining_) {
+          break;
+        }
+      }
+      epochIndexArray1D.dispose();
+    }
+    await callbackList.onEpochEnd(epoch, epochLogs);
+    if (model22.stopTraining_) {
+      break;
+    }
+  }
+  await callbackList.onTrainEnd();
+  await model22.history.syncData();
+  return model22.history;
+}
+async function fitTensors(model22, x, y, args = {}) {
+  if (model22.isTraining) {
+    throw new Error("Cannot start training because another fit() call is ongoing.");
+  }
+  model22.isTraining = true;
+  let inputs;
+  let targets;
+  let originalInputs;
+  let originalTargets;
+  let inputValX;
+  let inputValY;
+  let valX;
+  let valY;
+  let sampleWeights;
+  try {
+    const batchSize = args.batchSize == null ? 32 : args.batchSize;
+    checkBatchSize(batchSize);
+    const checkBatchAxis = false;
+    const standardizedOuts = await model22.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);
+    inputs = standardizedOuts[0];
+    targets = standardizedOuts[1];
+    sampleWeights = standardizedOuts[2];
+    let doValidation = false;
+    let valIns;
+    if (args.validationData != null && args.validationData.length > 0) {
+      doValidation = true;
+      if (args.validationData.length === 2) {
+        inputValX = args.validationData[0];
+        inputValY = args.validationData[1];
+      } else if (args.validationData.length === 3) {
+        throw new NotImplementedError("validationData including sample weights is not supported yet.");
+      } else {
+        throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);
+      }
+      const checkBatchAxis2 = true;
+      const valStandardized = await model22.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);
+      valX = valStandardized[0];
+      valY = valStandardized[1];
+      valIns = valX.concat(valY);
+    } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {
+      doValidation = true;
+      const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));
+      const originalBatchSize = inputs[0].shape[0];
+      valX = sliceArrays(inputs, splitAt, originalBatchSize);
+      originalInputs = inputs;
+      inputs = sliceArrays(inputs, 0, splitAt);
+      valY = sliceArrays(targets, splitAt, originalBatchSize);
+      originalTargets = targets;
+      targets = sliceArrays(targets, 0, splitAt);
+      valIns = valX.concat(valY);
+    } else if (args.validationSteps != null) {
+      doValidation = true;
+    }
+    const ins = inputs.concat(targets).concat(sampleWeights);
+    model22.checkTrainableWeightsConsistency();
+    const trainFunction = model22.makeTrainFunction();
+    const outLabels = model22.getDedupedMetricsNames();
+    let valFunction;
+    let callbackMetrics;
+    if (doValidation) {
+      model22.makeTestFunction();
+      valFunction = model22.testFunction;
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
+    } else {
+      valFunction = null;
+      valIns = [];
+      callbackMetrics = outLabels.slice();
+    }
+    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
+    const out = await fitLoop(model22, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);
+    return out;
+  } finally {
+    model22.isTraining = false;
+    disposeNewTensors(inputs, x);
+    disposeNewTensors(targets, y);
+    disposeNewTensors(originalInputs, x);
+    disposeNewTensors(originalTargets, y);
+    disposeNewTensors(valX, inputValX);
+    disposeNewTensors(valY, inputValY);
+    if (sampleWeights != null) {
+      dispose(sampleWeights);
+    }
+  }
+}
+function ensureTensorsRank2OrHigher(tensors) {
+  const outs = [];
+  if (tensors instanceof Tensor) {
+    tensors = [tensors];
+  }
+  for (let i2 = 0; i2 < tensors.length; ++i2) {
+    const tensor2 = tensors[i2];
+    if (tensor2.rank === 1) {
+      outs.push(expandDims2(tensor2, 1));
+    } else if (tensor2.rank === 0) {
+      throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
+    } else {
+      outs.push(tensor2);
+    }
+  }
+  return outs;
+}
+function disposeNewTensors(tensors, refTensors) {
+  if (tensors == null) {
+    return;
+  }
+  const oldTensorIds = [];
+  if (refTensors instanceof Tensor) {
+    oldTensorIds.push(refTensors.id);
+  } else if (Array.isArray(refTensors)) {
+    refTensors.forEach((t2) => oldTensorIds.push(t2.id));
+  } else if (refTensors != null) {
+    for (const name in refTensors) {
+      const oldTensor = refTensors[name];
+      oldTensorIds.push(oldTensor.id);
+    }
+  }
+  const tensorsToDispose = [];
+  if (tensors instanceof Tensor) {
+    if (oldTensorIds.indexOf(tensors.id) === -1) {
+      tensorsToDispose.push(tensors);
+    }
+  } else if (Array.isArray(tensors)) {
+    tensors.forEach((t2) => {
+      if (oldTensorIds.indexOf(t2.id) === -1) {
+        tensorsToDispose.push(t2);
+      }
+    });
+  } else if (tensors != null) {
+    for (const name in tensors) {
+      const tensor2 = tensors[name];
+      if (oldTensorIds.indexOf(tensor2.id) === -1) {
+        tensorsToDispose.push(tensor2);
+      }
+    }
+  }
+  tensorsToDispose.forEach((t2) => {
+    if (!t2.isDisposed) {
+      t2.dispose();
+    }
+  });
+}
+function isDataTensor(x) {
+  return x instanceof Tensor;
+}
+function isDataArray(x) {
+  return Array.isArray(x);
+}
+function isDataDict(x) {
+  return !isDataTensor(x) && !isDataArray(x);
+}
+function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
+  if (names == null || names.length === 0) {
+    if (data != null) {
+      let gotUnexpectedData = false;
+      if (isDataArray(data) && data.length > 0) {
+        gotUnexpectedData = true;
+      } else if (isDataDict(data)) {
+        for (const key in data) {
+          if (data.hasOwnProperty(key)) {
+            gotUnexpectedData = true;
+            break;
+          }
+        }
+      } else {
+        gotUnexpectedData = true;
+      }
+      if (gotUnexpectedData) {
+        throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);
+      }
+    }
+    return [];
+  }
+  if (data == null) {
+    return names.map((name) => null);
+  }
+  let arrays;
+  if (isDataDict(data)) {
+    data = data;
+    arrays = [];
+    for (const name of names) {
+      if (data[name] == null) {
+        throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);
+      }
+      arrays.push(data[name]);
+    }
+  } else if (isDataArray(data)) {
+    data = data;
+    if (data.length !== names.length) {
+      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);
+    }
+    arrays = data;
+  } else {
+    data = data;
+    if (names.length > 1) {
+      throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);
+    }
+    arrays = [data];
+  }
+  arrays = ensureTensorsRank2OrHigher(arrays);
+  if (shapes != null) {
+    for (let i2 = 0; i2 < names.length; ++i2) {
+      if (shapes[i2] == null) {
+        continue;
+      }
+      const array2 = arrays[i2];
+      if (array2.shape.length !== shapes[i2].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s). but got array with shape ${array2.shape}`);
+      }
+      for (let j = 0; j < shapes[i2].length; ++j) {
+        if (j === 0 && !checkBatchAxis) {
+          continue;
+        }
+        const dim = array2.shape[j];
+        const refDim = shapes[i2][j];
+        if (refDim != null && refDim >= 0 && dim !== refDim) {
+          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i2].slice(1, shapes[i2].length)}] (i.e.,tensor shape [*,${shapes[i2].slice(1, shapes[i2].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
+        }
+      }
+    }
+  }
+  return arrays;
+}
+function checkArrayLengths(inputs, targets, weights) {
+  const setX = unique2(inputs.map((input2) => input2.shape[0]));
+  setX.sort();
+  const setY = unique2(targets.map((target) => target.shape[0]));
+  setY.sort();
+  if (setX.length > 1) {
+    throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);
+  }
+  if (setY.length > 1) {
+    throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);
+  }
+  if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {
+    throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);
+  }
+}
+function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {
+  const keyLosses = [
+    meanSquaredError2,
+    binaryCrossentropy,
+    categoricalCrossentropy
+  ];
+  for (let i2 = 0; i2 < targets.length; ++i2) {
+    const y = targets[i2];
+    const loss = lossFns[i2];
+    const shape = outputShapes[i2];
+    if (loss == null) {
+      continue;
+    }
+    if (loss === categoricalCrossentropy) {
+      if (y.shape[y.shape.length - 1] === 1) {
+        throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
+      }
+    }
+    if (keyLosses.indexOf(loss) !== -1) {
+      const slicedYShape = y.shape.slice(1);
+      const slicedShape = shape.slice(1);
+      for (let j = 0; j < slicedYShape.length; ++j) {
+        const targetDim = slicedYShape[j];
+        const outDim = slicedShape[j];
+        if (outDim != null && targetDim !== outDim) {
+          throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);
+        }
+      }
+    }
+  }
+}
+function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
+  let arrays;
+  if (Array.isArray(data)) {
+    if (data.length !== names.length) {
+      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);
+    }
+    arrays = data;
+  } else {
+    if (names.length > 1) {
+      throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);
+    }
+    arrays = [data];
+  }
+  if (shapes != null) {
+    for (let i2 = 0; i2 < names.length; ++i2) {
+      if (shapes[i2] == null) {
+        continue;
+      }
+      const array2 = arrays[i2];
+      if (array2.shape.length !== shapes[i2].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
+      }
+      for (let j = 0; j < shapes[i2].length; ++j) {
+        if (j === 0 && !checkBatchAxis) {
+          continue;
+        }
+        const dim = array2.shape[j];
+        const refDim = shapes[i2][j];
+        if (refDim != null) {
+          if (refDim !== dim) {
+            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have shape ${JSON.stringify(shapes[i2])} but got array with shape ${JSON.stringify(array2.shape)}.`);
+          }
+        }
+      }
+    }
+  }
+}
+function collectMetrics(metrics, outputNames) {
+  if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {
+    return outputNames.map((name) => []);
+  }
+  let wrappedMetrics;
+  if (typeof metrics === "string" || typeof metrics === "function") {
+    wrappedMetrics = [metrics];
+  } else if (Array.isArray(metrics) || typeof metrics === "object") {
+    wrappedMetrics = metrics;
+  } else {
+    throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);
+  }
+  if (Array.isArray(wrappedMetrics)) {
+    return outputNames.map((name) => wrappedMetrics);
+  } else {
+    const nestedMetrics = [];
+    for (const name of outputNames) {
+      let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];
+      if (!Array.isArray(outputMetrics)) {
+        outputMetrics = [outputMetrics];
+      }
+      nestedMetrics.push(outputMetrics);
+    }
+    return nestedMetrics;
+  }
+}
+var LAYERS_MODEL_FORMAT_NAME = "layers-model";
+var LayersModel = class extends Container {
+  constructor(args) {
+    super(args);
+    this.isTraining = false;
+  }
+  summary(lineLength, positions, printFn = console.log) {
+    if (!this.built) {
+      throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);
+    }
+    printSummary(this, lineLength, positions, printFn);
+  }
+  compile(args) {
+    if (args.loss == null) {
+      args.loss = [];
+    }
+    this.loss = args.loss;
+    if (typeof args.optimizer === "string") {
+      this.optimizer_ = getOptimizer(args.optimizer);
+      this.isOptimizerOwned = true;
+    } else {
+      if (!(args.optimizer instanceof Optimizer)) {
+        throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);
+      }
+      this.optimizer_ = args.optimizer;
+      this.isOptimizerOwned = false;
+    }
+    let lossFunctions = [];
+    if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") {
+      args.loss = args.loss;
+      for (const name in args.loss) {
+        if (this.outputNames.indexOf(name) === -1) {
+          throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);
+        }
+      }
+      for (const name of this.outputNames) {
+        if (args.loss[name] == null) {
+          console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);
+        }
+        lossFunctions.push(get(args.loss[name]));
+      }
+    } else if (Array.isArray(args.loss)) {
+      if (args.loss.length !== this.outputs.length) {
+        throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);
+      }
+      const theLosses = args.loss;
+      lossFunctions = theLosses.map((l3) => get(l3));
+    } else {
+      const lossFunction = get(args.loss);
+      this.outputs.forEach((_) => {
+        lossFunctions.push(lossFunction);
+      });
+    }
+    this.lossFunctions = lossFunctions;
+    this.feedOutputNames = [];
+    this.feedOutputShapes = [];
+    this.feedLossFns = [];
+    for (let i2 = 0; i2 < this.outputs.length; ++i2) {
+      const shape = this.internalOutputShapes[i2];
+      const name = this.outputNames[i2];
+      this.feedOutputNames.push(name);
+      this.feedOutputShapes.push(shape);
+      this.feedLossFns.push(this.lossFunctions[i2]);
+    }
+    const skipTargetIndices = [];
+    this.metrics = args.metrics;
+    this.metricsNames = ["loss"];
+    this.metricsTensors = [];
+    nameScope("loss", () => {
+      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
+        if (skipTargetIndices.indexOf(i2) !== -1) {
+          continue;
+        }
+        const weightedLoss = this.lossFunctions[i2];
+        if (this.outputs.length > 1) {
+          this.metricsTensors.push([weightedLoss, i2]);
+          this.metricsNames.push(this.outputNames[i2] + "_loss");
+        }
+      }
+    });
+    const nestedMetrics = collectMetrics(args.metrics, this.outputNames);
+    const appendMetric = (outputIndex, metricName, metricTensor) => {
+      if (this.outputNames.length > 1) {
+        metricName = this.outputNames[outputIndex] + "_" + metricName;
+      }
+      this.metricsNames.push(metricName);
+      this.metricsTensors.push([metricTensor, outputIndex]);
+    };
+    nameScope("metric", () => {
+      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
+        if (skipTargetIndices.indexOf(i2) !== -1) {
+          continue;
+        }
+        const outputMetrics = nestedMetrics[i2];
+        const handleMetrics = (metrics) => {
+          const metricNamePrefix = "";
+          let metricName;
+          let accFn;
+          let weightedMetricFn;
+          for (const metric of metrics) {
+            if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) {
+              const outputShape = this.internalOutputShapes[i2];
+              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i2] === binaryCrossentropy) {
+                if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                  accFn = binaryAccuracy;
+                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                  accFn = binaryCrossentropy2;
+                }
+              } else if (this.lossFunctions[i2] === sparseCategoricalCrossentropy) {
+                if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                  accFn = sparseCategoricalAccuracy;
+                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                  accFn = sparseCategoricalCrossentropy2;
+                }
+              } else {
+                if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                  accFn = categoricalAccuracy;
+                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                  accFn = categoricalCrossentropy2;
+                }
+              }
+              let suffix;
+              if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                suffix = "acc";
+              } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                suffix = "ce";
+              }
+              weightedMetricFn = accFn;
+              metricName = metricNamePrefix + suffix;
+            } else {
+              const metricFn = get2(metric);
+              weightedMetricFn = metricFn;
+              metricName = metricNamePrefix + getLossOrMetricName(metric);
+            }
+            let metricResult;
+            nameScope(metricName, () => {
+              metricResult = weightedMetricFn;
+            });
+            appendMetric(i2, metricName, metricResult);
+          }
+        };
+        handleMetrics(outputMetrics);
+      }
+    });
+    this.collectedTrainableWeights = this.trainableWeights;
+  }
+  checkTrainableWeightsConsistency() {
+    if (this.collectedTrainableWeights == null) {
+      return;
+    }
+    if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {
+      console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?");
+    }
+  }
+  evaluate(x, y, args = {}) {
+    const batchSize = args.batchSize == null ? 32 : args.batchSize;
+    checkBatchSize(batchSize);
+    const checkBatchAxis = true;
+    const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
+    try {
+      const ins = standardizedOuts[0].concat(standardizedOuts[1]);
+      this.makeTestFunction();
+      const f = this.testFunction;
+      const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);
+      return singletonOrArray(testOuts);
+    } finally {
+      disposeNewTensors(standardizedOuts[0], x);
+      disposeNewTensors(standardizedOuts[1], y);
+    }
+  }
+  async evaluateDataset(dataset, args) {
+    this.makeTestFunction();
+    return evaluateDataset(this, dataset, args);
+  }
+  checkNumSamples(ins, batchSize, steps, stepsName = "steps") {
+    let numSamples;
+    if (steps != null) {
+      numSamples = null;
+      if (batchSize != null) {
+        throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);
+      }
+    } else if (ins != null) {
+      if (Array.isArray(ins)) {
+        numSamples = ins[0].shape[0];
+      } else {
+        numSamples = ins.shape[0];
+      }
+    } else {
+      throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);
+    }
+    return numSamples;
+  }
+  execute(inputs, outputs) {
+    if (Array.isArray(outputs) && outputs.length === 0) {
+      throw new ValueError("`outputs` is an empty Array, which is not allowed.");
+    }
+    const outputsIsArray = Array.isArray(outputs);
+    const outputNames = outputsIsArray ? outputs : [outputs];
+    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);
+    const feedDict = new FeedDict();
+    if (inputs instanceof Tensor) {
+      inputs = [inputs];
+    }
+    if (Array.isArray(inputs)) {
+      if (inputs.length !== this.inputs.length) {
+        throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
+      }
+      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+        feedDict.add(this.inputs[i2], inputs[i2]);
+      }
+    } else {
+      for (const input2 of this.inputs) {
+        const tensorValue = inputs[input2.name];
+        if (tensorValue == null) {
+          throw new ValueError(`No value is provided for the model's input ${input2.name}`);
+        }
+        feedDict.add(input2, tensorValue);
+      }
+    }
+    const executeOutputs = execute(outputSymbolicTensors, feedDict);
+    return outputsIsArray ? executeOutputs : executeOutputs[0];
+  }
+  retrieveSymbolicTensors(symbolicTensorNames) {
+    const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);
+    let outputsRemaining = symbolicTensorNames.length;
+    for (const layer of this.layers) {
+      const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];
+      const layerOutputNames = layerOutputs.map((output) => output.name);
+      for (let i2 = 0; i2 < symbolicTensorNames.length; ++i2) {
+        const index2 = layerOutputNames.indexOf(symbolicTensorNames[i2]);
+        if (index2 !== -1) {
+          outputSymbolicTensors[i2] = layerOutputs[index2];
+          outputsRemaining--;
+        }
+        if (outputsRemaining === 0) {
+          break;
+        }
+      }
+      if (outputsRemaining === 0) {
+        break;
+      }
+    }
+    if (outputsRemaining > 0) {
+      const remainingNames = [];
+      outputSymbolicTensors.forEach((tensor2, i2) => {
+        if (tensor2 == null) {
+          remainingNames.push(symbolicTensorNames[i2]);
+        }
+      });
+      throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);
+    }
+    return outputSymbolicTensors;
+  }
+  predictLoop(ins, batchSize = 32, verbose = false) {
+    return tidy(() => {
+      const numSamples = this.checkNumSamples(ins);
+      if (verbose) {
+        throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");
+      }
+      const batches = makeBatches(numSamples, batchSize);
+      const outsBatches = this.outputs.map((output) => []);
+      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
+        const batchOuts = tidy(() => {
+          const batchStart = batches[batchIndex][0];
+          const batchEnd = batches[batchIndex][1];
+          const insBatch = sliceArrays(ins, batchStart, batchEnd);
+          const feeds = [];
+          if (Array.isArray(insBatch)) {
+            for (let i2 = 0; i2 < insBatch.length; ++i2) {
+              feeds.push({ key: this.inputs[i2], value: insBatch[i2] });
+            }
+          } else {
+            feeds.push({ key: this.inputs[0], value: insBatch });
+          }
+          const feedDict = new FeedDict(feeds);
+          return execute(this.outputs, feedDict);
+        });
+        batchOuts.forEach((batchOut, i2) => outsBatches[i2].push(batchOut));
+      }
+      return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));
+    });
+  }
+  predict(x, args = {}) {
+    const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);
+    checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);
+    try {
+      const batchSize = args.batchSize == null ? 32 : args.batchSize;
+      checkBatchSize(batchSize);
+      return this.predictLoop(xsRank2OrHigher, batchSize);
+    } finally {
+      disposeNewTensors(xsRank2OrHigher, x);
+    }
+  }
+  predictOnBatch(x) {
+    checkInputData(x, this.inputNames, this.feedInputShapes, true);
+    const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];
+    return this.predictLoop(x, batchSize);
+  }
+  standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {
+    if (this.optimizer_ == null) {
+      throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
+    }
+    const outputShapes = [];
+    for (let i2 = 0; i2 < this.feedOutputShapes.length; ++i2) {
+      const outputShape = this.feedOutputShapes[i2];
+      const lossFn = this.feedLossFns[i2];
+      if (lossFn === sparseCategoricalCrossentropy) {
+        outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));
+      } else {
+        outputShapes.push(outputShape);
+      }
+    }
+    x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, "input");
+    y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, "target");
+    checkArrayLengths(x, y, null);
+    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);
+    if (this.stateful && batchSize != null && batchSize > 0) {
+      if (x[0].shape[0] % batchSize !== 0) {
+        throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);
+      }
+    }
+    return [x, y];
+  }
+  async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {
+    const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
+    if (sampleWeight != null) {
+      throw new Error("sample weight is not supported yet.");
+    }
+    let standardSampleWeights = null;
+    if (classWeight != null) {
+      const classWeights = standardizeClassWeights(classWeight, this.outputNames);
+      standardSampleWeights = [];
+      for (let i2 = 0; i2 < classWeights.length; ++i2) {
+        standardSampleWeights.push(await standardizeWeights(standardYs[i2], null, classWeights[i2]));
+      }
+    }
+    return [standardXs, standardYs, standardSampleWeights];
+  }
+  testLoop(f, ins, batchSize, verbose = 0, steps) {
+    return tidy(() => {
+      const numSamples = this.checkNumSamples(ins, batchSize, steps, "steps");
+      const outs = [];
+      if (verbose > 0) {
+        throw new NotImplementedError("Verbose mode is not implemented yet.");
+      }
+      if (steps != null) {
+        throw new NotImplementedError("steps mode in testLoop() is not implemented yet");
+      } else {
+        const batches = makeBatches(numSamples, batchSize);
+        const indexArray = tensor1d(range2(0, numSamples));
+        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
+          const batchStart = batches[batchIndex][0];
+          const batchEnd = batches[batchIndex][1];
+          const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);
+          const insBatch = sliceArraysByIndices(ins, batchIds);
+          const batchOuts = f(insBatch);
+          if (batchIndex === 0) {
+            for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+              outs.push(scalar(0));
+            }
+          }
+          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+            const batchOut = batchOuts[i2];
+            outs[i2] = add2(outs[i2], mul(batchEnd - batchStart, batchOut));
+          }
+        }
+        for (let i2 = 0; i2 < outs.length; ++i2) {
+          outs[i2] = div(outs[i2], numSamples);
+        }
+      }
+      return outs;
+    });
+  }
+  getDedupedMetricsNames() {
+    const outLabels = this.metricsNames;
+    const dedupedOutLabels = [];
+    for (let i2 = 0; i2 < outLabels.length; ++i2) {
+      const label = outLabels[i2];
+      let newLabel = label;
+      if (count(outLabels, label) > 1) {
+        const dupIndex = count(outLabels.slice(0, i2), label);
+        newLabel += `_${dupIndex}`;
+      }
+      dedupedOutLabels.push(newLabel);
+    }
+    return dedupedOutLabels;
+  }
+  makeTrainFunction() {
+    return (data) => {
+      const lossValues = [];
+      const inputs = data.slice(0, this.inputs.length);
+      const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
+      const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);
+      const metricsValues = [];
+      const totalLossFunction = () => {
+        const feeds = [];
+        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        }
+        const feedDict = new FeedDict(feeds);
+        const outputs = execute(this.outputs, feedDict, { "training": true });
+        let totalLoss;
+        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
+          const lossFunction = this.lossFunctions[i2];
+          let loss = lossFunction(targets[i2], outputs[i2]);
+          if (sampleWeights[i2] != null) {
+            loss = computeWeightedLoss2(loss, sampleWeights[i2]);
+          }
+          const meanLoss = mean(loss);
+          lossValues.push(meanLoss);
+          if (i2 === 0) {
+            totalLoss = loss;
+          } else {
+            totalLoss = add2(totalLoss, loss);
+          }
+        }
+        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
+          let weightedMetric;
+          if (this.outputs.length > 1 && i2 < this.outputs.length) {
+            weightedMetric = lossValues[i2];
+          } else {
+            const metric = this.metricsTensors[i2][0];
+            const outputIndex = this.metricsTensors[i2][1];
+            weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
+          }
+          keep(weightedMetric);
+          metricsValues.push(weightedMetric);
+        }
+        totalLoss = mean(totalLoss);
+        this.calculateLosses().forEach((regularizerLoss) => {
+          totalLoss = add2(totalLoss, regularizerLoss);
+        });
+        return totalLoss;
+      };
+      const variables = this.collectedTrainableWeights.map((param) => param.read());
+      const returnCost = true;
+      const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);
+      return [totalLossValue].concat(metricsValues);
+    };
+  }
+  makeTestFunction() {
+    this.testFunction = (data) => {
+      return tidy(() => {
+        const valOutputs = [];
+        let totalLoss;
+        const inputs = data.slice(0, this.inputs.length);
+        const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
+        const feeds = [];
+        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        }
+        const feedDict = new FeedDict(feeds);
+        const outputs = execute(this.outputs, feedDict);
+        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
+          const lossFunction = this.lossFunctions[i2];
+          const loss = mean(lossFunction(targets[i2], outputs[i2]));
+          if (i2 === 0) {
+            totalLoss = loss;
+          } else {
+            totalLoss = add2(totalLoss, loss);
+          }
+          valOutputs.push(totalLoss);
+        }
+        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
+          const metric = this.metricsTensors[i2][0];
+          const outputIndex = this.metricsTensors[i2][1];
+          const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
+          valOutputs.push(meanMetric);
+        }
+        return valOutputs;
+      });
+    };
+  }
+  async fit(x, y, args = {}) {
+    return fitTensors(this, x, y, args);
+  }
+  async fitDataset(dataset, args) {
+    return fitDataset(this, dataset, args);
+  }
+  async trainOnBatch(x, y) {
+    const standardizeOut = await this.standardizeUserData(x, y);
+    const inputs = standardizeOut[0];
+    const targets = standardizeOut[1];
+    const trainFunction = this.makeTrainFunction();
+    const losses2 = trainFunction(inputs.concat(targets));
+    const lossValues = [];
+    for (const loss of losses2) {
+      const v = await loss.data();
+      lossValues.push(v[0]);
+    }
+    dispose(losses2);
+    disposeNewTensors(standardizeOut[0], x);
+    disposeNewTensors(standardizeOut[1], y);
+    return singletonOrArray(lossValues);
+  }
+  getNamedWeights(config3) {
+    const namedWeights = [];
+    const trainableOnly = config3 != null && config3.trainableOnly;
+    const weights = trainableOnly ? this.trainableWeights : this.weights;
+    const weightValues = this.getWeights(trainableOnly);
+    for (let i2 = 0; i2 < weights.length; ++i2) {
+      if (trainableOnly && !weights[i2].trainable) {
+        continue;
+      }
+      namedWeights.push({ name: weights[i2].originalName, tensor: weightValues[i2] });
+    }
+    return namedWeights;
+  }
+  set stopTraining(stop) {
+    this.stopTraining_ = stop;
+  }
+  get stopTraining() {
+    return this.stopTraining_;
+  }
+  get optimizer() {
+    return this.optimizer_;
+  }
+  set optimizer(optimizer) {
+    if (this.optimizer_ !== optimizer) {
+      this.optimizer_ = optimizer;
+      this.isOptimizerOwned = false;
+    }
+  }
+  dispose() {
+    const result = super.dispose();
+    if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
+      const numTensorsBeforeOptmizerDisposal = memory().numTensors;
+      this.optimizer_.dispose();
+      result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;
+    }
+    return result;
+  }
+  getLossIdentifiers() {
+    let lossNames;
+    if (typeof this.loss === "string") {
+      lossNames = toSnakeCase(this.loss);
+    } else if (Array.isArray(this.loss)) {
+      for (const loss of this.loss) {
+        if (typeof loss !== "string") {
+          throw new Error("Serialization of non-string loss is not supported.");
+        }
+      }
+      lossNames = this.loss.map((name) => toSnakeCase(name));
+    } else {
+      const outputNames = Object.keys(this.loss);
+      lossNames = {};
+      const losses2 = this.loss;
+      for (const outputName of outputNames) {
+        if (typeof losses2[outputName] === "string") {
+          lossNames[outputName] = toSnakeCase(losses2[outputName]);
+        } else {
+          throw new Error("Serialization of non-string loss is not supported.");
+        }
+      }
+    }
+    return lossNames;
+  }
+  getMetricIdentifiers() {
+    if (typeof this.metrics === "string" || typeof this.metrics === "function") {
+      return [toSnakeCase(getLossOrMetricName(this.metrics))];
+    } else if (Array.isArray(this.metrics)) {
+      return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));
+    } else {
+      const metricsIdentifiers = {};
+      for (const key in this.metrics) {
+        metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));
+      }
+      return metricsIdentifiers;
+    }
+  }
+  getTrainingConfig() {
+    return {
+      loss: this.getLossIdentifiers(),
+      metrics: this.getMetricIdentifiers(),
+      optimizer_config: {
+        class_name: this.optimizer.getClassName(),
+        config: this.optimizer.getConfig()
+      }
+    };
+  }
+  loadTrainingConfig(trainingConfig) {
+    if (trainingConfig.weighted_metrics != null) {
+      throw new Error("Loading weight_metrics is not supported yet.");
+    }
+    if (trainingConfig.loss_weights != null) {
+      throw new Error("Loading loss_weights is not supported yet.");
+    }
+    if (trainingConfig.sample_weight_mode != null) {
+      throw new Error("Loading sample_weight_mode is not supported yet.");
+    }
+    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);
+    const optimizer = deserialize(tsConfig);
+    let loss;
+    if (typeof trainingConfig.loss === "string") {
+      loss = toCamelCase(trainingConfig.loss);
+    } else if (Array.isArray(trainingConfig.loss)) {
+      loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));
+    } else if (trainingConfig.loss != null) {
+      loss = {};
+      for (const key in trainingConfig.loss) {
+        loss[key] = toCamelCase(trainingConfig.loss[key]);
+      }
+    }
+    let metrics;
+    if (Array.isArray(trainingConfig.metrics)) {
+      metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));
+    } else if (trainingConfig.metrics != null) {
+      metrics = {};
+      for (const key in trainingConfig.metrics) {
+        metrics[key] = toCamelCase(trainingConfig.metrics[key]);
+      }
+    }
+    this.compile({ loss, metrics, optimizer });
+  }
+  async save(handlerOrURL, config3) {
+    if (typeof handlerOrURL === "string") {
+      const handlers = io_exports.getSaveHandlers(handlerOrURL);
+      if (handlers.length === 0) {
+        throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);
+      } else if (handlers.length > 1) {
+        throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
+      }
+      handlerOrURL = handlers[0];
+    }
+    if (handlerOrURL.save == null) {
+      throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
+    }
+    const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config3));
+    const returnString = false;
+    const unusedArg = null;
+    const modelConfig = this.toJSON(unusedArg, returnString);
+    const modelArtifacts = {
+      modelTopology: modelConfig,
+      format: LAYERS_MODEL_FORMAT_NAME,
+      generatedBy: `TensorFlow.js tfjs-layers v${version2}`,
+      convertedBy: null
+    };
+    const includeOptimizer = config3 == null ? false : config3.includeOptimizer;
+    if (includeOptimizer && this.optimizer != null) {
+      modelArtifacts.trainingConfig = this.getTrainingConfig();
+      const weightType = "optimizer";
+      const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);
+      weightDataAndSpecs.specs.push(...optimizerWeightSpecs);
+      weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);
+    }
+    if (this.userDefinedMetadata != null) {
+      const checkSize = true;
+      checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);
+      modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;
+    }
+    modelArtifacts.weightData = weightDataAndSpecs.data;
+    modelArtifacts.weightSpecs = weightDataAndSpecs.specs;
+    return handlerOrURL.save(modelArtifacts);
+  }
+  setUserDefinedMetadata(userDefinedMetadata) {
+    checkUserDefinedMetadata(userDefinedMetadata, this.name);
+    this.userDefinedMetadata = userDefinedMetadata;
+  }
+  getUserDefinedMetadata() {
+    return this.userDefinedMetadata;
+  }
+};
+LayersModel.className = "Model";
+serialization_exports.registerClass(LayersModel);
+var Functional = class extends LayersModel {
+};
+Functional.className = "Functional";
+serialization_exports.registerClass(Functional);
+async function modelFromJSON(modelAndWeightsConfig, customObjects) {
+  if (!("modelTopology" in modelAndWeightsConfig)) {
+    modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };
+  }
+  modelAndWeightsConfig = modelAndWeightsConfig;
+  let modelTopology = modelAndWeightsConfig.modelTopology;
+  if (modelTopology["model_config"] != null) {
+    modelTopology = modelTopology["model_config"];
+  }
+  const tsConfig = convertPythonicToTs(modelTopology);
+  const model22 = deserialize(tsConfig, customObjects);
+  if (modelAndWeightsConfig.weightsManifest != null) {
+    const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model22.weights.map((weight) => weight.originalName));
+    const uniqueWeightValues = {};
+    for (const weight of model22.weights) {
+      uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];
+    }
+    model22.loadWeights(uniqueWeightValues);
+    dispose(weightValues);
+  }
+  return model22;
+}
+async function loadLayersModelInternal(pathOrIOHandler, options4) {
+  if (options4 == null) {
+    options4 = {};
+  }
+  if (typeof pathOrIOHandler === "string") {
+    const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options4);
+    if (handlers.length === 0) {
+      handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options4));
+    } else if (handlers.length > 1) {
+      throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);
+    }
+    pathOrIOHandler = handlers[0];
+  }
+  return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options4);
+}
+async function loadLayersModelFromIOHandler(handler, customObjects, options4) {
+  if (options4 == null) {
+    options4 = {};
+  }
+  if (handler.load == null) {
+    throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
+  }
+  const artifacts = await handler.load();
+  let modelTopology = artifacts.modelTopology;
+  if (modelTopology["model_config"] != null) {
+    modelTopology = modelTopology["model_config"];
+  }
+  const strict = options4.strict == null ? true : options4.strict;
+  const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;
+  const model22 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);
+  const trainingConfig = artifacts.trainingConfig;
+  if (trainingConfig != null) {
+    model22.loadTrainingConfig(trainingConfig);
+  }
+  if (artifacts.userDefinedMetadata != null) {
+    model22.setUserDefinedMetadata(artifacts.userDefinedMetadata);
+  }
+  if (artifacts.weightData != null) {
+    if (artifacts.weightSpecs == null) {
+      throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
+    }
+    const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);
+    model22.loadWeights(modelWeights, strict);
+    if (model22.optimizer != null && optimizerWeights.length > 0) {
+      await model22.optimizer.setWeights(optimizerWeights);
+    }
+    dispose(modelWeights);
+    dispose(optimizerWeights.map((w) => w.tensor));
+  }
+  return model22;
+}
+function decodeModelAndOptimizerWeights(buffer2, specs) {
+  const name2Tensor = io_exports.decodeWeights(buffer2, specs);
+  const modelWeights = {};
+  const optimizerWeights = [];
+  specs.forEach((spec) => {
+    if (spec.group === "optimizer") {
+      optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });
+    } else {
+      modelWeights[spec.name] = name2Tensor[spec.name];
+    }
+  });
+  return { modelWeights, optimizerWeights };
+}
+var Sequential = class extends LayersModel {
+  constructor(args) {
+    super({ inputs: [], outputs: [] });
+    args = args || {};
+    this.trainable = true;
+    this.built = false;
+    this.name = args.name != null ? args.name : getUid("sequential_");
+    if (args.layers != null) {
+      for (const layer of args.layers) {
+        this.add(layer);
+      }
+    }
+  }
+  checkShape(layer) {
+    const shape = layer.inboundNodes[0].outputTensors[0].shape;
+    if (shape.some((x) => x < 0)) {
+      throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);
+    }
+  }
+  add(layer) {
+    const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;
+    let modelLayer;
+    if (isLayerModelInstance) {
+      modelLayer = layer;
+      if (modelLayer.outputs.length !== 1) {
+        throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
+      }
+      if (modelLayer.inputs.length !== 1) {
+        throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.");
+      }
+    }
+    if (this.outputs.length === 0) {
+      if (layer.inboundNodes.length === 0) {
+        if (layer.batchInputShape == null) {
+          throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
+        }
+        const x = Input({
+          batchShape: layer.batchInputShape,
+          dtype: layer.dtype,
+          name: layer.name + "_input"
+        });
+        layer.apply(x);
+      }
+      if (isLayerModelInstance) {
+        this.outputs = modelLayer.outputs;
+        this.inputs = modelLayer.inputs;
+      } else {
+        if (layer.inboundNodes.length !== 1) {
+          throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);
+        }
+        if (layer.inboundNodes[0].outputTensors.length !== 1) {
+          throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
+        }
+        this.checkShape(layer);
+        this.outputs = [layer.inboundNodes[0].outputTensors[0]];
+        this.inputs = getSourceInputs(this.outputs[0]);
+      }
+      this.inboundNodes = [];
+      new Node({
+        outboundLayer: this,
+        inboundLayers: [],
+        nodeIndices: [],
+        tensorIndices: [],
+        inputTensors: this.inputs,
+        outputTensors: this.outputs,
+        inputMasks: pyListRepeat(null, this.inputs.length),
+        outputMasks: [null],
+        inputShapes: this.inputs.map((x) => x.shape),
+        outputShapes: this.outputs[0].shape
+      });
+    } else {
+      const outputTensor = layer.apply(this.outputs[0]);
+      if (Array.isArray(outputTensor)) {
+        throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
+      }
+      this.checkShape(layer);
+      this.outputs = [outputTensor];
+      this.inboundNodes[0].outputTensors = this.outputs;
+      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
+    }
+    this.layers.push(layer);
+    this.built = false;
+  }
+  pop() {
+    if (this.layers.length === 0) {
+      throw new TypeError("There are no layers in the model.");
+    }
+    this.layers.pop();
+    if (this.layers.length === 0) {
+      this.outputs = [];
+      this.inboundNodes = [];
+      this.outboundNodes = [];
+    } else {
+      const lastLayerIndex = this.layers.length - 1;
+      this.layers[lastLayerIndex].outboundNodes = [];
+      this.outputs = [this.layers[lastLayerIndex].output];
+      this.inboundNodes[0].outputTensors = this.outputs;
+      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
+    }
+  }
+  call(inputs, kwargs) {
+    if (this.model == null) {
+      this.build();
+    }
+    return this.model.call(inputs, kwargs);
+  }
+  build(inputShape) {
+    getExactlyOneShape(inputShape);
+    if (this.inputs.length === 0 || this.outputs.length === 0) {
+      throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
+    }
+    this.model = new LayersModel({
+      inputs: this.inputs,
+      outputs: this.outputs[0],
+      name: this.name + "_model"
+    });
+    this.model.trainable = this.trainable;
+    this.supportsMasking = this.model.supportsMasking;
+    this.inputLayers = this.model.inputLayers;
+    this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;
+    this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;
+    this.outputLayers = this.model.outputLayers;
+    this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;
+    this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;
+    this.nodesByDepth = this.model.nodesByDepth;
+    this.containerNodes = this.model.containerNodes;
+    this.outputNames = this.model.outputNames;
+    this.inputNames = this.model.inputNames;
+    this.built = true;
+  }
+  countParams() {
+    if (!this.built) {
+      this.build();
+    }
+    return super.countParams();
+  }
+  summary(lineLength, positions, printFn = console.log) {
+    if (!this.built) {
+      this.build();
+    }
+    super.summary(lineLength, positions, printFn);
+  }
+  setWeights(weights) {
+    if (this.model == null) {
+      this.build();
+    }
+    this.model.setWeights(weights);
+  }
+  evaluate(x, y, args = {}) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.evaluate(x, y, args);
+  }
+  async evaluateDataset(dataset, args) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.evaluateDataset(dataset, args);
+  }
+  predict(x, args = {}) {
+    if (this.model == null) {
+      this.build();
+    }
+    return this.model.predict(x, args);
+  }
+  predictOnBatch(x) {
+    if (this.model == null) {
+      this.build();
+    }
+    return this.model.predictOnBatch(x);
+  }
+  compile(args) {
+    this.build();
+    this.model.compile(args);
+    this.optimizer_ = this.model.optimizer;
+    this.isOptimizerOwned = this.model.isOptimizerOwned;
+    this.loss = this.model.loss;
+    this.metrics = this.model.metrics;
+    this.metricsTensors = this.model.metricsTensors;
+    this.metricsNames = this.model.metricsNames;
+  }
+  get optimizer() {
+    return this.model == null ? void 0 : this.model.optimizer;
+  }
+  set optimizer(optimizer) {
+    this.model.optimizer = optimizer;
+  }
+  async fit(x, y, args = {}) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.fit(x, y, args);
+  }
+  async fitDataset(dataset, args) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.fitDataset(dataset, args);
+  }
+  async trainOnBatch(x, y) {
+    return this.model.trainOnBatch(x, y);
+  }
+  static fromConfig(cls, config3, customObjects = {}, fastWeightInit = false) {
+    let configArray;
+    let extraModelConfig = {};
+    if (config3 instanceof Array) {
+      if (!(config3[0].className != null) || config3[0]["className"] === "Merge") {
+        throw new ValueError("Legacy serialization format not supported yet.");
+      }
+      configArray = config3;
+    } else {
+      util_exports.assert(config3["layers"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);
+      configArray = config3["layers"];
+      delete config3["layers"];
+      extraModelConfig = config3;
+    }
+    const model22 = new cls(extraModelConfig);
+    if (!(model22 instanceof Sequential)) {
+      throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model22}`);
+    }
+    for (const conf of configArray) {
+      const customObjects2 = void 0;
+      const layer = deserialize(conf, customObjects2, fastWeightInit);
+      if (fastWeightInit) {
+        layer.setFastWeightInitDuringBuild(true);
+      }
+      model22.add(layer);
+    }
+    return model22;
+  }
+  set stopTraining(stop) {
+    if (this.model == null) {
+      throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");
+    }
+    this.model.stopTraining = stop;
+  }
+  get stopTraining() {
+    if (this.model == null) {
+      throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");
+    }
+    return this.model.stopTraining;
+  }
+  getConfig() {
+    const layers = [];
+    for (const layer of this.layers) {
+      const dict = {};
+      dict["className"] = layer.getClassName();
+      dict["config"] = layer.getConfig();
+      layers.push(dict);
+    }
+    return { name: this.name, layers };
+  }
+};
+Sequential.className = "Sequential";
+serialization_exports.registerClass(Sequential);
+function model(args) {
+  return new LayersModel(args);
+}
+function sequential(config3) {
+  return new Sequential(config3);
+}
+function loadLayersModel(pathOrIOHandler, options4) {
+  if (options4 == null) {
+    options4 = {};
+  }
+  return loadLayersModelInternal(pathOrIOHandler, options4);
+}
+function input(config3) {
+  return Input(config3);
+}
+function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
+  CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
+}
+var Activation = class extends serialization_exports.Serializable {
+  getConfig() {
+    return {};
+  }
+};
+var Elu2 = class extends Activation {
+  apply(x, alpha2 = 1) {
+    return elu2(x, alpha2);
+  }
+};
+Elu2.className = "elu";
+serialization_exports.registerClass(Elu2);
+var Selu2 = class extends Activation {
+  apply(x) {
+    return selu(x);
+  }
+};
+Selu2.className = "selu";
+serialization_exports.registerClass(Selu2);
+var Relu2 = class extends Activation {
+  apply(x) {
+    return relu(x);
+  }
+};
+Relu2.className = "relu";
+serialization_exports.registerClass(Relu2);
+var Relu62 = class extends Activation {
+  apply(x) {
+    return tidy(() => minimum(6, relu(x)));
+  }
+};
+Relu62.className = "relu6";
+serialization_exports.registerClass(Relu62);
+var Linear = class extends Activation {
+  apply(x) {
+    return x;
+  }
+};
+Linear.className = "linear";
+serialization_exports.registerClass(Linear);
+var Sigmoid2 = class extends Activation {
+  apply(x) {
+    return sigmoid(x);
+  }
+};
+Sigmoid2.className = "sigmoid";
+serialization_exports.registerClass(Sigmoid2);
+var HardSigmoid = class extends Activation {
+  apply(x) {
+    return hardSigmoid(x);
+  }
+};
+HardSigmoid.className = "hardSigmoid";
+serialization_exports.registerClass(HardSigmoid);
+var Softplus2 = class extends Activation {
+  apply(x) {
+    return softplus(x);
+  }
+};
+Softplus2.className = "softplus";
+serialization_exports.registerClass(Softplus2);
+var Softsign = class extends Activation {
+  apply(x) {
+    return softsign(x);
+  }
+};
+Softsign.className = "softsign";
+serialization_exports.registerClass(Softsign);
+var Tanh2 = class extends Activation {
+  apply(x) {
+    return tanh2(x);
+  }
+};
+Tanh2.className = "tanh";
+serialization_exports.registerClass(Tanh2);
+var Softmax2 = class extends Activation {
+  apply(x, axis = -1) {
+    return softmax(x, axis);
+  }
+};
+Softmax2.className = "softmax";
+serialization_exports.registerClass(Softmax2);
+var LogSoftmax2 = class extends Activation {
+  apply(x, axis = -1) {
+    return logSoftmax(x, axis);
+  }
+};
+LogSoftmax2.className = "logSoftmax";
+serialization_exports.registerClass(LogSoftmax2);
+var Swish = class extends Activation {
+  apply(x, alpha2 = 1) {
+    return tidy(() => mul(sigmoid(mul(x, alpha2)), x));
+  }
+};
+Swish.className = "swish";
+serialization_exports.registerClass(Swish);
+var Mish = class extends Activation {
+  apply(x) {
+    return tidy(() => mul(x, tanh2(softplus(x))));
+  }
+};
+Mish.className = "mish";
+serialization_exports.registerClass(Mish);
+function serializeActivation(activation2) {
+  return activation2.getClassName();
+}
+function deserializeActivation(config3, customObjects = {}) {
+  return deserializeKerasObject(config3, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation");
+}
+function getActivation(identifier) {
+  if (identifier == null) {
+    const config3 = {};
+    config3["className"] = "linear";
+    config3["config"] = {};
+    return deserializeActivation(config3);
+  }
+  if (typeof identifier === "string") {
+    const config3 = {};
+    config3["className"] = identifier;
+    config3["config"] = {};
+    return deserializeActivation(config3);
+  } else if (identifier instanceof Activation) {
+    return identifier;
+  } else {
+    return deserializeActivation(identifier);
+  }
+}
+function assertObjectArgs(args) {
+  if (args != null && typeof args !== "object") {
+    throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);
+  }
+}
+var Regularizer = class extends serialization_exports.Serializable {
+};
+var L1L2 = class extends Regularizer {
+  constructor(args) {
+    super();
+    assertObjectArgs(args);
+    this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;
+    this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;
+    this.hasL1 = this.l1 !== 0;
+    this.hasL2 = this.l2 !== 0;
+  }
+  apply(x) {
+    return tidy(() => {
+      let regularization = zeros([1]);
+      if (this.hasL1) {
+        regularization = add2(regularization, sum2(mul(this.l1, abs(x))));
+      }
+      if (this.hasL2) {
+        regularization = add2(regularization, sum2(mul(this.l2, square2(x))));
+      }
+      return reshape(regularization, []);
+    });
+  }
+  getConfig() {
+    return { "l1": this.l1, "l2": this.l2 };
+  }
+  static fromConfig(cls, config3) {
+    return new cls({ l1: config3["l1"], l2: config3["l2"] });
+  }
+};
+L1L2.className = "L1L2";
+serialization_exports.registerClass(L1L2);
+function l1(args) {
+  assertObjectArgs(args);
+  return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });
+}
+function l2(args) {
+  assertObjectArgs(args);
+  return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });
+}
+var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
+  "l1l2": "L1L2"
+};
+function serializeRegularizer(constraint) {
+  return serializeKerasObject(constraint);
+}
+function deserializeRegularizer(config3, customObjects = {}) {
+  return deserializeKerasObject(config3, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "regularizer");
+}
+function getRegularizer(identifier) {
+  if (identifier == null) {
+    return null;
+  }
+  if (typeof identifier === "string") {
+    const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
+    const config3 = { className, config: {} };
+    return deserializeRegularizer(config3);
+  } else if (identifier instanceof Regularizer) {
+    return identifier;
+  } else {
+    return deserializeRegularizer(identifier);
+  }
+}
+var ReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.supportsMasking = true;
+    if (args != null) {
+      this.maxValue = args.maxValue;
+    }
+  }
+  call(inputs, kwargs) {
+    inputs = getExactlyOneTensor(inputs);
+    let output = relu(inputs);
+    if (this.maxValue != null) {
+      output = clipByValue(output, 0, this.maxValue);
+    }
+    return output;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config3 = { maxValue: this.maxValue };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+ReLU.className = "ReLU";
+serialization_exports.registerClass(ReLU);
+var LeakyReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_ALPHA = 0.3;
+    if (args == null) {
+      args = {};
+    }
+    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return leakyRelu(x, this.alpha);
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config3 = { alpha: this.alpha };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+LeakyReLU.className = "LeakyReLU";
+serialization_exports.registerClass(LeakyReLU);
+var PReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_ALPHA_INITIALIZER = "zeros";
+    if (args == null) {
+      args = {};
+    }
+    this.supportsMasking = true;
+    this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);
+    this.alphaRegularizer = getRegularizer(args.alphaRegularizer);
+    this.alphaConstraint = getConstraint(args.alphaConstraint);
+    if (args.sharedAxes == null) {
+      this.sharedAxes = null;
+    } else if (Array.isArray(args.sharedAxes)) {
+      this.sharedAxes = args.sharedAxes;
+    } else if (typeof args.sharedAxes === "number") {
+      this.sharedAxes = [args.sharedAxes];
+    } else {
+      throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);
+    }
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const paramShape = inputShape.slice(1);
+    if (this.sharedAxes != null) {
+      for (const i2 of this.sharedAxes) {
+        paramShape[i2 - 1] = 1;
+      }
+    }
+    this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
+    const axes = {};
+    if (this.sharedAxes != null) {
+      for (let i2 = 1; i2 < inputShape.length; ++i2) {
+        axes[i2] = inputShape[i2];
+      }
+    }
+    this.inputSpec = [new InputSpec({
+      ndim: inputShape.length,
+      axes
+    })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    inputs = getExactlyOneTensor(inputs);
+    return prelu(inputs, this.alpha.read());
+  }
+  getConfig() {
+    const config3 = {
+      alphaInitializer: serializeInitializer(this.alphaInitializer),
+      alphaRegularizer: serializeRegularizer(this.alphaRegularizer),
+      alphaConstraint: serializeConstraint(this.alphaConstraint),
+      sharedAxes: this.sharedAxes
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+PReLU.className = "PReLU";
+serialization_exports.registerClass(PReLU);
+var ELU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_ALPHA = 1;
+    if (args == null) {
+      args = {};
+    }
+    if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {
+      throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);
+    }
+    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return elu(x);
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config3 = { alpha: this.alpha };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+ELU.className = "ELU";
+serialization_exports.registerClass(ELU);
+var ThresholdedReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_THETA = 1;
+    if (args == null) {
+      args = {};
+    }
+    this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return mul(x, cast(greater(x, this.theta), "float32"));
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config3 = { theta: this.theta };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+ThresholdedReLU.className = "ThresholdedReLU";
+serialization_exports.registerClass(ThresholdedReLU);
+var Softmax3 = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_AXIS = 1;
+    if (args == null) {
+      args = {};
+    }
+    this.softmax = new Softmax2().apply;
+    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return this.softmax(x, this.axis);
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config3 = { axis: this.axis };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Softmax3.className = "Softmax";
+serialization_exports.registerClass(Softmax3);
+function normalizeArray(value, n, name) {
+  if (typeof value === "number") {
+    return pyListRepeat(value, n);
+  } else {
+    if (value.length !== n) {
+      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);
+    }
+    for (let i2 = 0; i2 < n; ++i2) {
+      const singleValue = value[i2];
+      if (!isInteger(singleValue)) {
+        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
+      }
+    }
+    return value;
+  }
+}
+function convOutputLength(inputLength, filterSize, padding2, stride, dilation = 1) {
+  if (inputLength == null) {
+    return inputLength;
+  }
+  const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);
+  let outputLength;
+  if (padding2 === "same") {
+    outputLength = inputLength;
+  } else {
+    outputLength = inputLength - dilatedFilterSize + 1;
+  }
+  return Math.floor((outputLength + stride - 1) / stride);
+}
+function deconvLength(dimSize, strideSize, kernelSize, padding2) {
+  if (dimSize == null) {
+    return null;
+  }
+  if (padding2 === "valid") {
+    dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);
+  } else if (padding2 === "same") {
+    dimSize = dimSize * strideSize;
+  } else {
+    throw new ValueError(`Unsupport padding mode: ${padding2}.`);
+  }
+  return dimSize;
+}
+function preprocessConv2DInput(x, dataFormat) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    if (dataFormat === "channelsFirst") {
+      return transpose(x, [0, 2, 3, 1]);
+    } else {
+      return x;
+    }
+  });
+}
+function preprocessConv3DInput(x, dataFormat) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    if (dataFormat === "channelsFirst") {
+      return transpose(x, [0, 2, 3, 4, 1]);
+    } else {
+      return x;
+    }
+  });
+}
+function conv1dWithBias(x, kernel, bias, strides2 = 1, padding2 = "valid", dataFormat, dilationRate = 1) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    if (x.shape.length !== 3) {
+      throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);
+    }
+    if (kernel.shape.length !== 3) {
+      throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);
+    }
+    if (bias != null && bias.shape.length !== 1) {
+      throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);
+    }
+    if (dataFormat === "channelsFirst") {
+      x = transpose(x, [0, 2, 1]);
+    }
+    if (padding2 === "causal") {
+      throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
+    }
+    let y = conv1d(x, kernel, strides2, padding2 === "same" ? "same" : "valid", "NWC", dilationRate);
+    if (bias != null) {
+      y = biasAdd(y, bias);
+    }
+    return y;
+  });
+}
+function conv2dWithBiasActivation(x, kernel, bias, strides2 = [1, 1], padding2 = "valid", dataFormat, dilationRate, activation2 = null) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    if (x.rank !== 3 && x.rank !== 4) {
+      throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);
+    }
+    if (kernel.rank !== 3 && kernel.rank !== 4) {
+      throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);
+    }
+    let y = preprocessConv2DInput(x, dataFormat);
+    if (padding2 === "causal") {
+      throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
+    }
+    y = fused_ops_exports.conv2d({
+      x: y,
+      filter: kernel,
+      strides: strides2,
+      pad: padding2 === "same" ? "same" : "valid",
+      dilations: dilationRate,
+      dataFormat: "NHWC",
+      bias,
+      activation: activation2
+    });
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 3, 1, 2]);
+    }
+    return y;
+  });
+}
+function conv3dWithBias(x, kernel, bias, strides2 = [1, 1, 1], padding2 = "valid", dataFormat, dilationRate) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    if (x.rank !== 4 && x.rank !== 5) {
+      throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);
+    }
+    if (kernel.rank !== 4 && kernel.rank !== 5) {
+      throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);
+    }
+    let y = preprocessConv3DInput(x, dataFormat);
+    if (padding2 === "causal") {
+      throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
+    }
+    y = conv3d(y, kernel, strides2, padding2 === "same" ? "same" : "valid", "NDHWC", dilationRate);
+    if (bias != null) {
+      y = biasAdd(y, bias);
+    }
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 4, 1, 2, 3]);
+    }
+    return y;
+  });
+}
+var BaseConv = class extends Layer {
+  constructor(rank, args) {
+    super(args);
+    this.bias = null;
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    BaseConv.verifyArgs(args);
+    this.rank = rank;
+    assertPositiveInteger(this.rank, "rank");
+    if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {
+      throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
+    }
+    this.kernelSize = normalizeArray(args.kernelSize, rank, "kernelSize");
+    this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    checkPaddingMode(this.padding);
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    this.activation = getActivation(args.activation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.activityRegularizer = getRegularizer(args.activityRegularizer);
+    this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, "dilationRate");
+    if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {
+      throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
+    } else if (this.rank === 2) {
+      if (typeof this.dilationRate === "number") {
+        this.dilationRate = [this.dilationRate, this.dilationRate];
+      } else if (this.dilationRate.length !== 2) {
+        throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);
+      }
+    } else if (this.rank === 3) {
+      if (typeof this.dilationRate === "number") {
+        this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];
+      } else if (this.dilationRate.length !== 3) {
+        throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);
+      }
+    }
+  }
+  static verifyArgs(args) {
+    assert2("kernelSize" in args, `required key 'kernelSize' not in config`);
+    if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 3)) {
+      throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);
+    }
+  }
+  getConfig() {
+    const config3 = {
+      kernelSize: this.kernelSize,
+      strides: this.strides,
+      padding: this.padding,
+      dataFormat: this.dataFormat,
+      dilationRate: this.dilationRate,
+      activation: serializeActivation(this.activation),
+      useBias: this.useBias,
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      biasConstraint: serializeConstraint(this.biasConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+var Conv = class extends BaseConv {
+  constructor(rank, args) {
+    super(rank, args);
+    this.kernel = null;
+    Conv.verifyArgs(args);
+    this.filters = args.filters;
+    assertPositiveInteger(this.filters, "filters");
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const kernelShape = this.kernelSize.concat([inputDim, this.filters]);
+    this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      let outputs;
+      const biasValue = this.bias == null ? null : this.bias.read();
+      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
+      if (fusedActivationName != null && this.rank === 2) {
+        outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);
+      } else {
+        if (this.rank === 1) {
+          outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
+        } else if (this.rank === 2) {
+          outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
+        } else if (this.rank === 3) {
+          outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
+        } else {
+          throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");
+        }
+        if (this.activation != null) {
+          outputs = this.activation.apply(outputs);
+        }
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const newSpace = [];
+    const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);
+    for (let i2 = 0; i2 < space.length; ++i2) {
+      const newDim = convOutputLength(space[i2], this.kernelSize[i2], this.padding, this.strides[i2], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i2]);
+      newSpace.push(newDim);
+    }
+    let outputShape = [inputShape[0]];
+    if (this.dataFormat === "channelsLast") {
+      outputShape = outputShape.concat(newSpace);
+      outputShape.push(this.filters);
+    } else {
+      outputShape.push(this.filters);
+      outputShape = outputShape.concat(newSpace);
+    }
+    return outputShape;
+  }
+  getConfig() {
+    const config3 = {
+      filters: this.filters,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  static verifyArgs(args) {
+    if (!("filters" in args) || typeof args.filters !== "number" || args.filters < 1) {
+      throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);
+    }
+  }
+};
+var Conv2D2 = class extends Conv {
+  constructor(args) {
+    super(2, args);
+    Conv2D2.verifyArgs(args);
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    delete config3["rank"];
+    return config3;
+  }
+  static verifyArgs(args) {
+    if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 2)) {
+      throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);
+    }
+  }
+};
+Conv2D2.className = "Conv2D";
+serialization_exports.registerClass(Conv2D2);
+var Conv3D2 = class extends Conv {
+  constructor(args) {
+    super(3, args);
+    Conv3D2.verifyArgs(args);
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    delete config3["rank"];
+    return config3;
+  }
+  static verifyArgs(args) {
+    if (typeof args.kernelSize !== "number") {
+      if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {
+        throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);
+      }
+    }
+  }
+};
+Conv3D2.className = "Conv3D";
+serialization_exports.registerClass(Conv3D2);
+var Conv2DTranspose = class extends Conv2D2 {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+    if (this.padding !== "same" && this.padding !== "valid") {
+      throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
+    }
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length !== 4) {
+      throw new ValueError("Input should have rank 4; Received input shape: " + JSON.stringify(inputShape));
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
+    }
+    const inputDim = inputShape[channelAxis];
+    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
+    this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      let input2 = getExactlyOneTensor(inputs);
+      if (input2.shape.length !== 4) {
+        throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
+      }
+      const inputShape = input2.shape;
+      const batchSize = inputShape[0];
+      let hAxis;
+      let wAxis;
+      if (this.dataFormat === "channelsFirst") {
+        hAxis = 2;
+        wAxis = 3;
+      } else {
+        hAxis = 1;
+        wAxis = 2;
+      }
+      const height = inputShape[hAxis];
+      const width = inputShape[wAxis];
+      const kernelH = this.kernelSize[0];
+      const kernelW = this.kernelSize[1];
+      const strideH = this.strides[0];
+      const strideW = this.strides[1];
+      const outHeight = deconvLength(height, strideH, kernelH, this.padding);
+      const outWidth = deconvLength(width, strideW, kernelW, this.padding);
+      const outputShape = [batchSize, outHeight, outWidth, this.filters];
+      if (this.dataFormat !== "channelsLast") {
+        input2 = transpose(input2, [0, 2, 3, 1]);
+      }
+      let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
+      if (this.dataFormat !== "channelsLast") {
+        outputs = transpose(outputs, [0, 3, 1, 2]);
+      }
+      if (this.bias != null) {
+        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation != null) {
+        outputs = this.activation.apply(outputs);
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    let channelAxis;
+    let heightAxis;
+    let widthAxis;
+    if (this.dataFormat === "channelsFirst") {
+      channelAxis = 1;
+      heightAxis = 2;
+      widthAxis = 3;
+    } else {
+      channelAxis = 3;
+      heightAxis = 1;
+      widthAxis = 2;
+    }
+    const kernelH = this.kernelSize[0];
+    const kernelW = this.kernelSize[1];
+    const strideH = this.strides[0];
+    const strideW = this.strides[1];
+    outputShape[channelAxis] = this.filters;
+    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
+    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
+    return outputShape;
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    delete config3["dilationRate"];
+    return config3;
+  }
+};
+Conv2DTranspose.className = "Conv2DTranspose";
+serialization_exports.registerClass(Conv2DTranspose);
+var Conv3DTranspose = class extends Conv3D2 {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 5 })];
+    if (this.padding !== "same" && this.padding !== "valid") {
+      throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
+    }
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length !== 5) {
+      throw new ValueError("Input should have rank 5; Received input shape: " + JSON.stringify(inputShape));
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
+    }
+    const inputDim = inputShape[channelAxis];
+    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
+    this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      let input2 = getExactlyOneTensor(inputs);
+      if (input2.shape.length !== 5) {
+        throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
+      }
+      const inputShape = input2.shape;
+      const batchSize = inputShape[0];
+      let hAxis;
+      let wAxis;
+      let dAxis;
+      if (this.dataFormat === "channelsFirst") {
+        dAxis = 2;
+        hAxis = 3;
+        wAxis = 4;
+      } else {
+        dAxis = 1;
+        hAxis = 2;
+        wAxis = 3;
+      }
+      const depth = inputShape[dAxis];
+      const height = inputShape[hAxis];
+      const width = inputShape[wAxis];
+      const kernelD = this.kernelSize[0];
+      const kernelH = this.kernelSize[1];
+      const kernelW = this.kernelSize[2];
+      const strideD = this.strides[0];
+      const strideH = this.strides[1];
+      const strideW = this.strides[2];
+      const outDepth = deconvLength(depth, strideD, kernelD, this.padding);
+      const outHeight = deconvLength(height, strideH, kernelH, this.padding);
+      const outWidth = deconvLength(width, strideW, kernelW, this.padding);
+      const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];
+      if (this.dataFormat !== "channelsLast") {
+        input2 = transpose(input2, [0, 2, 3, 4, 1]);
+      }
+      let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
+      if (this.dataFormat !== "channelsLast") {
+        outputs = transpose(outputs, [0, 4, 1, 2, 3]);
+      }
+      if (this.bias !== null) {
+        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation !== null) {
+        outputs = this.activation.apply(outputs);
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    let channelAxis;
+    let depthAxis;
+    let heightAxis;
+    let widthAxis;
+    if (this.dataFormat === "channelsFirst") {
+      channelAxis = 1;
+      depthAxis = 2;
+      heightAxis = 3;
+      widthAxis = 4;
+    } else {
+      channelAxis = 4;
+      depthAxis = 1;
+      heightAxis = 2;
+      widthAxis = 3;
+    }
+    const kernelD = this.kernelSize[0];
+    const kernelH = this.kernelSize[1];
+    const kernelW = this.kernelSize[2];
+    const strideD = this.strides[0];
+    const strideH = this.strides[1];
+    const strideW = this.strides[2];
+    outputShape[channelAxis] = this.filters;
+    outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);
+    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
+    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
+    return outputShape;
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    delete config3["dilationRate"];
+    return config3;
+  }
+};
+Conv3DTranspose.className = "Conv3DTranspose";
+serialization_exports.registerClass(Conv3DTranspose);
+var SeparableConv = class extends Conv {
+  constructor(rank, config3) {
+    super(rank, config3);
+    this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform";
+    this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform";
+    this.depthwiseKernel = null;
+    this.pointwiseKernel = null;
+    if (config3.filters == null) {
+      throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");
+    }
+    if (config3.kernelInitializer != null || config3.kernelRegularizer != null || config3.kernelConstraint != null) {
+      throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
+    }
+    if (config3.padding != null && config3.padding !== "same" && config3.padding !== "valid") {
+      throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config3.padding)}`);
+    }
+    this.depthMultiplier = config3.depthMultiplier == null ? 1 : config3.depthMultiplier;
+    this.depthwiseInitializer = getInitializer(config3.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);
+    this.depthwiseRegularizer = getRegularizer(config3.depthwiseRegularizer);
+    this.depthwiseConstraint = getConstraint(config3.depthwiseConstraint);
+    this.pointwiseInitializer = getInitializer(config3.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);
+    this.pointwiseRegularizer = getRegularizer(config3.pointwiseRegularizer);
+    this.pointwiseConstraint = getConstraint(config3.pointwiseConstraint);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length < this.rank + 2) {
+      throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
+      throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);
+    const pointwiseKernelShape = [];
+    for (let i2 = 0; i2 < this.rank; ++i2) {
+      pointwiseKernelShape.push(1);
+    }
+    pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
+    const trainable = true;
+    this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);
+    this.pointwiseKernel = this.addWeight("pointwise_kernel", pointwiseKernelShape, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      let output;
+      if (this.rank === 1) {
+        throw new NotImplementedError("1D separable convolution is not implemented yet.");
+      } else if (this.rank === 2) {
+        if (this.dataFormat === "channelsFirst") {
+          inputs = transpose(inputs, [0, 2, 3, 1]);
+        }
+        output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC");
+      }
+      if (this.useBias) {
+        output = biasAdd(output, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation != null) {
+        output = this.activation.apply(output);
+      }
+      if (this.dataFormat === "channelsFirst") {
+        output = transpose(output, [0, 3, 1, 2]);
+      }
+      return output;
+    });
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    delete config3["rank"];
+    delete config3["kernelInitializer"];
+    delete config3["kernelRegularizer"];
+    delete config3["kernelConstraint"];
+    config3["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
+    config3["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer);
+    config3["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
+    config3["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer);
+    config3["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint);
+    config3["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint);
+    return config3;
+  }
+};
+SeparableConv.className = "SeparableConv";
+var SeparableConv2D = class extends SeparableConv {
+  constructor(args) {
+    super(2, args);
+  }
+};
+SeparableConv2D.className = "SeparableConv2D";
+serialization_exports.registerClass(SeparableConv2D);
+var Conv1D = class extends Conv {
+  constructor(args) {
+    super(1, args);
+    Conv1D.verifyArgs(args);
+    this.inputSpec = [{ ndim: 3 }];
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    delete config3["rank"];
+    delete config3["dataFormat"];
+    return config3;
+  }
+  static verifyArgs(args) {
+    if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 1)) {
+      throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);
+    }
+  }
+};
+Conv1D.className = "Conv1D";
+serialization_exports.registerClass(Conv1D);
+var Cropping2D = class extends Layer {
+  constructor(args) {
+    super(args);
+    if (typeof args.cropping === "number") {
+      this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];
+    } else if (typeof args.cropping[0] === "number") {
+      this.cropping = [
+        [args.cropping[0], args.cropping[0]],
+        [args.cropping[1], args.cropping[1]]
+      ];
+    } else {
+      this.cropping = args.cropping;
+    }
+    this.dataFormat = args.dataFormat === void 0 ? "channelsLast" : args.dataFormat;
+    this.inputSpec = [{ ndim: 4 }];
+  }
+  computeOutputShape(inputShape) {
+    if (this.dataFormat === "channelsFirst") {
+      return [
+        inputShape[0],
+        inputShape[1],
+        inputShape[2] - this.cropping[0][0] - this.cropping[0][1],
+        inputShape[3] - this.cropping[1][0] - this.cropping[1][1]
+      ];
+    } else {
+      return [
+        inputShape[0],
+        inputShape[1] - this.cropping[0][0] - this.cropping[0][1],
+        inputShape[2] - this.cropping[1][0] - this.cropping[1][1],
+        inputShape[3]
+      ];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsLast") {
+        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
+        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
+      } else {
+        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
+        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
+      }
+    });
+  }
+  getConfig() {
+    const config3 = { cropping: this.cropping, dataFormat: this.dataFormat };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Cropping2D.className = "Cropping2D";
+serialization_exports.registerClass(Cropping2D);
+var UpSampling2D = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_SIZE = [2, 2];
+    this.inputSpec = [{ ndim: 4 }];
+    this.size = args.size == null ? this.DEFAULT_SIZE : args.size;
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    this.interpolation = args.interpolation == null ? "nearest" : args.interpolation;
+    checkInterpolationFormat(this.interpolation);
+  }
+  computeOutputShape(inputShape) {
+    if (this.dataFormat === "channelsFirst") {
+      const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];
+      const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];
+      return [inputShape[0], inputShape[1], height, width];
+    } else {
+      const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];
+      const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];
+      return [inputShape[0], height, width, inputShape[3]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      let input2 = getExactlyOneTensor(inputs);
+      const inputShape = input2.shape;
+      if (this.dataFormat === "channelsFirst") {
+        input2 = transpose(input2, [0, 2, 3, 1]);
+        const height = this.size[0] * inputShape[2];
+        const width = this.size[1] * inputShape[3];
+        const resized = this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);
+        return transpose(resized, [0, 3, 1, 2]);
+      } else {
+        const height = this.size[0] * inputShape[1];
+        const width = this.size[1] * inputShape[2];
+        return this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);
+      }
+    });
+  }
+  getConfig() {
+    const config3 = {
+      size: this.size,
+      dataFormat: this.dataFormat,
+      interpolation: this.interpolation
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+UpSampling2D.className = "UpSampling2D";
+serialization_exports.registerClass(UpSampling2D);
+function depthwiseConv2d3(x, depthwiseKernel, strides2 = [1, 1], padding2 = "valid", dataFormat, dilationRate) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    let y = preprocessConv2DInput(x, dataFormat);
+    if (x.rank !== 4) {
+      throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);
+    }
+    if (depthwiseKernel.rank !== 4) {
+      throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);
+    }
+    y = depthwiseConv2d(y, depthwiseKernel, strides2, padding2 === "same" ? "same" : "valid", "NHWC", dilationRate);
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 3, 1, 2]);
+    }
+    return y;
+  });
+}
+var DepthwiseConv2D = class extends BaseConv {
+  constructor(args) {
+    super(2, args);
+    this.depthwiseKernel = null;
+    this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;
+    this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);
+    this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length < 4) {
+      throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : 3;
+    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
+      throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const depthwiseKernelShape = [
+      this.kernelSize[0],
+      this.kernelSize[1],
+      inputDim,
+      this.depthMultiplier
+    ];
+    this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
+      if (this.useBias) {
+        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation != null) {
+        outputs = this.activation.apply(outputs);
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
+    const cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
+    const outFilters = this.dataFormat === "channelsFirst" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;
+    const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);
+    const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);
+    if (this.dataFormat === "channelsFirst") {
+      return [inputShape[0], outFilters, outRows, outCols];
+    } else {
+      return [inputShape[0], outRows, outCols, outFilters];
+    }
+  }
+  getConfig() {
+    const config3 = super.getConfig();
+    config3["depthMultiplier"] = this.depthMultiplier;
+    config3["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
+    config3["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
+    config3["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer);
+    return config3;
+  }
+};
+DepthwiseConv2D.className = "DepthwiseConv2D";
+serialization_exports.registerClass(DepthwiseConv2D);
+function standardizeArgs(inputs, initialState, constants2, numConstants) {
+  if (Array.isArray(inputs)) {
+    if (initialState != null || constants2 != null) {
+      throw new ValueError("When inputs is an array, neither initialState or constants should be provided");
+    }
+    if (numConstants != null) {
+      constants2 = inputs.slice(inputs.length - numConstants, inputs.length);
+      inputs = inputs.slice(0, inputs.length - numConstants);
+    }
+    if (inputs.length > 1) {
+      initialState = inputs.slice(1, inputs.length);
+    }
+    inputs = inputs[0];
+  }
+  function toListOrNull(x) {
+    if (x == null || Array.isArray(x)) {
+      return x;
+    } else {
+      return [x];
+    }
+  }
+  initialState = toListOrNull(initialState);
+  constants2 = toListOrNull(constants2);
+  return { inputs, initialState, constants: constants2 };
+}
+function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask2, constants2, unroll = false, needPerStepOutputs = false) {
+  return tidy(() => {
+    const ndim = inputs.shape.length;
+    if (ndim < 3) {
+      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);
+    }
+    const axes = [1, 0].concat(range2(2, ndim));
+    inputs = transpose(inputs, axes);
+    if (constants2 != null) {
+      throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
+    }
+    if (unroll) {
+      console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.");
+    }
+    if (mask2 != null) {
+      mask2 = cast(cast(mask2, "bool"), "float32");
+      if (mask2.rank === ndim - 1) {
+        mask2 = expandDims(mask2, -1);
+      }
+      mask2 = transpose(mask2, axes);
+    }
+    if (goBackwards) {
+      inputs = reverse(inputs, 0);
+      if (mask2 != null) {
+        mask2 = reverse(mask2, 0);
+      }
+    }
+    const perStepOutputs = [];
+    let lastOutput;
+    let states = initialStates;
+    const timeSteps = inputs.shape[0];
+    const perStepInputs = unstack(inputs);
+    let perStepMasks;
+    if (mask2 != null) {
+      perStepMasks = unstack(mask2);
+    }
+    for (let t2 = 0; t2 < timeSteps; ++t2) {
+      const currentInput = perStepInputs[t2];
+      const stepOutputs = tidy(() => stepFunction(currentInput, states));
+      if (mask2 == null) {
+        lastOutput = stepOutputs[0];
+        states = stepOutputs[1];
+      } else {
+        const maskedOutputs = tidy(() => {
+          const stepMask = perStepMasks[t2];
+          const negStepMask = sub(onesLike(stepMask), stepMask);
+          const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));
+          const newStates = states.map((state, i2) => {
+            return add2(mul(stepOutputs[1][i2], stepMask), mul(state, negStepMask));
+          });
+          return { output, newStates };
+        });
+        lastOutput = maskedOutputs.output;
+        states = maskedOutputs.newStates;
+      }
+      if (needPerStepOutputs) {
+        perStepOutputs.push(lastOutput);
+      }
+    }
+    let outputs;
+    if (needPerStepOutputs) {
+      const axis = 1;
+      outputs = stack(perStepOutputs, axis);
+    }
+    return [lastOutput, outputs, states];
+  });
+}
+var RNN = class extends Layer {
+  constructor(args) {
+    super(args);
+    let cell;
+    if (args.cell == null) {
+      throw new ValueError("cell property is missing for the constructor of RNN.");
+    } else if (Array.isArray(args.cell)) {
+      cell = new StackedRNNCells({ cells: args.cell });
+    } else {
+      cell = args.cell;
+    }
+    if (cell.stateSize == null) {
+      throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
+    }
+    this.cell = cell;
+    this.returnSequences = args.returnSequences == null ? false : args.returnSequences;
+    this.returnState = args.returnState == null ? false : args.returnState;
+    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;
+    this._stateful = args.stateful == null ? false : args.stateful;
+    this.unroll = args.unroll == null ? false : args.unroll;
+    this.supportsMasking = true;
+    this.inputSpec = [new InputSpec({ ndim: 3 })];
+    this.stateSpec = null;
+    this.states_ = null;
+    this.numConstants = null;
+    this.keptStates = [];
+  }
+  getStates() {
+    if (this.states_ == null) {
+      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
+      return range2(0, numStates).map((x) => null);
+    } else {
+      return this.states_;
+    }
+  }
+  setStates(states) {
+    this.states_ = states;
+  }
+  computeOutputShape(inputShape) {
+    if (isArrayOfShapes(inputShape)) {
+      inputShape = inputShape[0];
+    }
+    inputShape = inputShape;
+    let stateSize = this.cell.stateSize;
+    if (!Array.isArray(stateSize)) {
+      stateSize = [stateSize];
+    }
+    const outputDim = stateSize[0];
+    let outputShape;
+    if (this.returnSequences) {
+      outputShape = [inputShape[0], inputShape[1], outputDim];
+    } else {
+      outputShape = [inputShape[0], outputDim];
+    }
+    if (this.returnState) {
+      const stateShape = [];
+      for (const dim of stateSize) {
+        stateShape.push([inputShape[0], dim]);
+      }
+      return [outputShape].concat(stateShape);
+    } else {
+      return outputShape;
+    }
+  }
+  computeMask(inputs, mask2) {
+    return tidy(() => {
+      if (Array.isArray(mask2)) {
+        mask2 = mask2[0];
+      }
+      const outputMask = this.returnSequences ? mask2 : null;
+      if (this.returnState) {
+        const stateMask = this.states.map((s2) => null);
+        return [outputMask].concat(stateMask);
+      } else {
+        return outputMask;
+      }
+    });
+  }
+  get states() {
+    if (this.states_ == null) {
+      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
+      const output = [];
+      for (let i2 = 0; i2 < numStates; ++i2) {
+        output.push(null);
+      }
+      return output;
+    } else {
+      return this.states_;
+    }
+  }
+  set states(s2) {
+    this.states_ = s2;
+  }
+  build(inputShape) {
+    const constantShape = null;
+    if (this.numConstants != null) {
+      throw new NotImplementedError("Constants support is not implemented in RNN yet.");
+    }
+    if (isArrayOfShapes(inputShape)) {
+      inputShape = inputShape[0];
+    }
+    inputShape = inputShape;
+    const batchSize = this.stateful ? inputShape[0] : null;
+    const inputDim = inputShape.slice(2);
+    this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });
+    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));
+    if (constantShape != null) {
+      throw new NotImplementedError("Constants support is not implemented in RNN yet.");
+    } else {
+      this.cell.build(stepInputShape);
+    }
+    let stateSize;
+    if (Array.isArray(this.cell.stateSize)) {
+      stateSize = this.cell.stateSize;
+    } else {
+      stateSize = [this.cell.stateSize];
+    }
+    if (this.stateSpec != null) {
+      if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {
+        throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
+      }
+    } else {
+      this.stateSpec = stateSize.map((dim) => new InputSpec({ shape: [null, dim] }));
+    }
+    if (this.stateful) {
+      this.resetStates();
+    }
+  }
+  resetStates(states, training = false) {
+    tidy(() => {
+      if (!this.stateful) {
+        throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
+      }
+      const batchSize = this.inputSpec[0].shape[0];
+      if (batchSize == null) {
+        throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
+      }
+      if (this.states_ == null) {
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
+        } else {
+          this.states_ = [zeros([batchSize, this.cell.stateSize])];
+        }
+      } else if (states == null) {
+        dispose(this.states_);
+        if (this.keptStates != null) {
+          dispose(this.keptStates);
+          this.keptStates = [];
+        }
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
+        } else {
+          this.states_[0] = zeros([batchSize, this.cell.stateSize]);
+        }
+      } else {
+        if (!Array.isArray(states)) {
+          states = [states];
+        }
+        if (states.length !== this.states_.length) {
+          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
+        }
+        if (training === true) {
+          this.keptStates.push(this.states_.slice());
+        } else {
+          dispose(this.states_);
+        }
+        for (let index2 = 0; index2 < this.states_.length; ++index2) {
+          const value = states[index2];
+          const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index2] : this.cell.stateSize;
+          const expectedShape = [batchSize, dim];
+          if (!util_exports.arraysEqual(value.shape, expectedShape)) {
+            throw new ValueError(`State ${index2} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
+          }
+          this.states_[index2] = value;
+        }
+      }
+      this.states_ = this.states_.map((state) => keep(state.clone()));
+    });
+  }
+  apply(inputs, kwargs) {
+    let initialState = kwargs == null ? null : kwargs["initialState"];
+    let constants2 = kwargs == null ? null : kwargs["constants"];
+    if (kwargs == null) {
+      kwargs = {};
+    }
+    const standardized = standardizeArgs(inputs, initialState, constants2, this.numConstants);
+    inputs = standardized.inputs;
+    initialState = standardized.initialState;
+    constants2 = standardized.constants;
+    let additionalInputs = [];
+    let additionalSpecs = [];
+    if (initialState != null) {
+      kwargs["initialState"] = initialState;
+      additionalInputs = additionalInputs.concat(initialState);
+      this.stateSpec = [];
+      for (const state of initialState) {
+        this.stateSpec.push(new InputSpec({ shape: state.shape }));
+      }
+      additionalSpecs = additionalSpecs.concat(this.stateSpec);
+    }
+    if (constants2 != null) {
+      kwargs["constants"] = constants2;
+      additionalInputs = additionalInputs.concat(constants2);
+      this.numConstants = constants2.length;
+    }
+    const isTensor = additionalInputs[0] instanceof SymbolicTensor;
+    if (isTensor) {
+      const fullInput = [inputs].concat(additionalInputs);
+      const fullInputSpec = this.inputSpec.concat(additionalSpecs);
+      const originalInputSpec = this.inputSpec;
+      this.inputSpec = fullInputSpec;
+      const output = super.apply(fullInput, kwargs);
+      this.inputSpec = originalInputSpec;
+      return output;
+    } else {
+      return super.apply(inputs, kwargs);
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const mask2 = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      let initialState = kwargs == null ? null : kwargs["initialState"];
+      inputs = getExactlyOneTensor(inputs);
+      if (initialState == null) {
+        if (this.stateful) {
+          initialState = this.states_;
+        } else {
+          initialState = this.getInitialState(inputs);
+        }
+      }
+      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
+      if (initialState.length !== numStates) {
+        throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);
+      }
+      if (this.unroll) {
+        console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
+      }
+      const cellCallKwargs = { training };
+      const step5 = (inputs2, states2) => {
+        const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);
+        return [outputs2[0], outputs2.slice(1)];
+      };
+      const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask2, null, this.unroll, this.returnSequences);
+      const lastOutput = rnnOutputs[0];
+      const outputs = rnnOutputs[1];
+      const states = rnnOutputs[2];
+      if (this.stateful) {
+        this.resetStates(states, training);
+      }
+      const output = this.returnSequences ? outputs : lastOutput;
+      if (this.returnState) {
+        return [output].concat(states);
+      } else {
+        return output;
+      }
+    });
+  }
+  getInitialState(inputs) {
+    return tidy(() => {
+      let initialState = zeros(inputs.shape);
+      initialState = sum2(initialState, [1, 2]);
+      initialState = expandDims2(initialState);
+      if (Array.isArray(this.cell.stateSize)) {
+        return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);
+      } else {
+        return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];
+      }
+    });
+  }
+  get trainableWeights() {
+    if (!this.trainable) {
+      return [];
+    }
+    return this.cell.trainableWeights;
+  }
+  get nonTrainableWeights() {
+    if (!this.trainable) {
+      return this.cell.weights;
+    }
+    return this.cell.nonTrainableWeights;
+  }
+  setFastWeightInitDuringBuild(value) {
+    super.setFastWeightInitDuringBuild(value);
+    if (this.cell != null) {
+      this.cell.setFastWeightInitDuringBuild(value);
+    }
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = {
+      returnSequences: this.returnSequences,
+      returnState: this.returnState,
+      goBackwards: this.goBackwards,
+      stateful: this.stateful,
+      unroll: this.unroll
+    };
+    if (this.numConstants != null) {
+      config3["numConstants"] = this.numConstants;
+    }
+    const cellConfig = this.cell.getConfig();
+    if (this.getClassName() === RNN.className) {
+      config3["cell"] = {
+        "className": this.cell.getClassName(),
+        "config": cellConfig
+      };
+    }
+    return Object.assign({}, cellConfig, baseConfig, config3);
+  }
+  static fromConfig(cls, config3, customObjects = {}) {
+    const cellConfig = config3["cell"];
+    const cell = deserialize(cellConfig, customObjects);
+    return new cls(Object.assign(config3, { cell }));
+  }
+};
+RNN.className = "RNN";
+serialization_exports.registerClass(RNN);
+var RNNCell = class extends Layer {
+};
+var SimpleRNNCell = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_ACTIVATION = "tanh";
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    this.units = args.units;
+    assertPositiveInteger(this.units, `units`);
+    this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
+    this.recurrentDropout = min2([
+      1,
+      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
+    ]);
+    this.dropoutFunc = args.dropoutFunc;
+    this.stateSize = this.units;
+    this.dropoutMask = null;
+    this.recurrentDropoutMask = null;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    this.kernel = this.addWeight("kernel", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      if (inputs.length !== 2) {
+        throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);
+      }
+      let prevOutput = inputs[1];
+      inputs = inputs[0];
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(inputs),
+          rate: this.dropout,
+          training,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(prevOutput),
+          rate: this.recurrentDropout,
+          training,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      let h;
+      const dpMask = this.dropoutMask;
+      const recDpMask = this.recurrentDropoutMask;
+      if (dpMask != null) {
+        h = dot2(mul(inputs, dpMask), this.kernel.read());
+      } else {
+        h = dot2(inputs, this.kernel.read());
+      }
+      if (this.bias != null) {
+        h = biasAdd(h, this.bias.read());
+      }
+      if (recDpMask != null) {
+        prevOutput = mul(prevOutput, recDpMask);
+      }
+      let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));
+      if (this.activation != null) {
+        output = this.activation.apply(output);
+      }
+      return [output, output];
+    });
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint),
+      dropout: this.dropout,
+      recurrentDropout: this.recurrentDropout
+    };
+    return Object.assign({}, baseConfig, config3);
+  }
+};
+SimpleRNNCell.className = "SimpleRNNCell";
+serialization_exports.registerClass(SimpleRNNCell);
+var SimpleRNN = class extends RNN {
+  constructor(args) {
+    args.cell = new SimpleRNNCell(args);
+    super(args);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      const mask2 = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask: mask2, training, initialState });
+    });
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3);
+  }
+};
+SimpleRNN.className = "SimpleRNN";
+serialization_exports.registerClass(SimpleRNN);
+var GRUCell = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_ACTIVATION = "tanh";
+    this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    if (args.resetAfter) {
+      throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);
+    }
+    this.units = args.units;
+    assertPositiveInteger(this.units, "units");
+    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
+    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
+    this.recurrentDropout = min2([
+      1,
+      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
+    ]);
+    this.dropoutFunc = args.dropoutFunc;
+    this.implementation = args.implementation;
+    this.stateSize = this.units;
+    this.dropoutMask = null;
+    this.recurrentDropoutMask = null;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const inputDim = inputShape[inputShape.length - 1];
+    this.kernel = this.addWeight("kernel", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      if (inputs.length !== 2) {
+        throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);
+      }
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      let hTMinus1 = inputs[1];
+      inputs = inputs[0];
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(inputs),
+          rate: this.dropout,
+          training,
+          count: 3,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(hTMinus1),
+          rate: this.recurrentDropout,
+          training,
+          count: 3,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const dpMask = this.dropoutMask;
+      const recDpMask = this.recurrentDropoutMask;
+      let z;
+      let r2;
+      let hh;
+      if (0 < this.dropout && this.dropout < 1) {
+        inputs = mul(inputs, dpMask[0]);
+      }
+      let matrixX = dot2(inputs, this.kernel.read());
+      if (this.useBias) {
+        matrixX = biasAdd(matrixX, this.bias.read());
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
+        hTMinus1 = mul(hTMinus1, recDpMask[0]);
+      }
+      const recurrentKernelValue = this.recurrentKernel.read();
+      const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);
+      const matrixInner = dot2(hTMinus1, rk1);
+      const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);
+      const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);
+      z = this.recurrentActivation.apply(add2(xZ, recurrentZ));
+      r2 = this.recurrentActivation.apply(add2(xR, recurrentR));
+      const recurrentH = dot2(mul(r2, hTMinus1), rk2);
+      hh = this.activation.apply(add2(xH, recurrentH));
+      const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));
+      return [h, h];
+    });
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      recurrentActivation: serializeActivation(this.recurrentActivation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint),
+      dropout: this.dropout,
+      recurrentDropout: this.recurrentDropout,
+      implementation: this.implementation,
+      resetAfter: false
+    };
+    return Object.assign({}, baseConfig, config3);
+  }
+};
+GRUCell.className = "GRUCell";
+serialization_exports.registerClass(GRUCell);
+var GRU = class extends RNN {
+  constructor(args) {
+    if (args.implementation === 0) {
+      console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
+    }
+    args.cell = new GRUCell(args);
+    super(args);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      const mask2 = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask: mask2, training, initialState });
+    });
+  }
+  static fromConfig(cls, config3) {
+    if (config3["implmentation"] === 0) {
+      config3["implementation"] = 1;
+    }
+    return new cls(config3);
+  }
+};
+GRU.className = "GRU";
+serialization_exports.registerClass(GRU);
+var LSTMCell = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_ACTIVATION = "tanh";
+    this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    this.units = args.units;
+    assertPositiveInteger(this.units, "units");
+    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
+    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.unitForgetBias = args.unitForgetBias;
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
+    this.recurrentDropout = min2([
+      1,
+      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
+    ]);
+    this.dropoutFunc = args.dropoutFunc;
+    this.implementation = args.implementation;
+    this.stateSize = [this.units, this.units];
+    this.dropoutMask = null;
+    this.recurrentDropoutMask = null;
+  }
+  build(inputShape) {
+    var _a;
+    inputShape = getExactlyOneShape(inputShape);
+    const inputDim = inputShape[inputShape.length - 1];
+    this.kernel = this.addWeight("kernel", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    let biasInitializer;
+    if (this.useBias) {
+      if (this.unitForgetBias) {
+        const capturedBiasInit = this.biasInitializer;
+        const capturedUnits = this.units;
+        biasInitializer = new (_a = class CustomInit extends Initializer {
+          apply(shape, dtype) {
+            const bI = capturedBiasInit.apply([capturedUnits]);
+            const bF = new Ones().apply([capturedUnits]);
+            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);
+            return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);
+          }
+        }, _a.className = "CustomInit", _a)();
+      } else {
+        biasInitializer = this.biasInitializer;
+      }
+      this.bias = this.addWeight("bias", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      inputs = inputs;
+      if (inputs.length !== 3) {
+        throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
+      }
+      let hTMinus1 = inputs[1];
+      const cTMinus1 = inputs[2];
+      inputs = inputs[0];
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(inputs),
+          rate: this.dropout,
+          training,
+          count: 4,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(hTMinus1),
+          rate: this.recurrentDropout,
+          training,
+          count: 4,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const dpMask = this.dropoutMask;
+      const recDpMask = this.recurrentDropoutMask;
+      let i2;
+      let f;
+      let c;
+      let o;
+      if (0 < this.dropout && this.dropout < 1) {
+        inputs = mul(inputs, dpMask[0]);
+      }
+      let z = dot2(inputs, this.kernel.read());
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
+        hTMinus1 = mul(hTMinus1, recDpMask[0]);
+      }
+      z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));
+      if (this.useBias) {
+        z = biasAdd(z, this.bias.read());
+      }
+      const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);
+      i2 = this.recurrentActivation.apply(z0);
+      f = this.recurrentActivation.apply(z1);
+      c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(z2)));
+      o = this.recurrentActivation.apply(z3);
+      const h = mul(o, this.activation.apply(c));
+      return [h, h, c];
+    });
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      recurrentActivation: serializeActivation(this.recurrentActivation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      unitForgetBias: this.unitForgetBias,
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint),
+      dropout: this.dropout,
+      recurrentDropout: this.recurrentDropout,
+      implementation: this.implementation
+    };
+    return Object.assign({}, baseConfig, config3);
+  }
+};
+LSTMCell.className = "LSTMCell";
+serialization_exports.registerClass(LSTMCell);
+var LSTM = class extends RNN {
+  constructor(args) {
+    if (args.implementation === 0) {
+      console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
+    }
+    args.cell = new LSTMCell(args);
+    super(args);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      const mask2 = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask: mask2, training, initialState });
+    });
+  }
+  static fromConfig(cls, config3) {
+    if (config3["implmentation"] === 0) {
+      config3["implementation"] = 1;
+    }
+    return new cls(config3);
+  }
+};
+LSTM.className = "LSTM";
+serialization_exports.registerClass(LSTM);
+var StackedRNNCells = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.cells = args.cells;
+  }
+  get stateSize() {
+    const stateSize = [];
+    for (const cell of this.cells.slice().reverse()) {
+      if (Array.isArray(cell.stateSize)) {
+        stateSize.push(...cell.stateSize);
+      } else {
+        stateSize.push(cell.stateSize);
+      }
+    }
+    return stateSize;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      let states = inputs.slice(1);
+      const nestedStates = [];
+      for (const cell of this.cells.slice().reverse()) {
+        if (Array.isArray(cell.stateSize)) {
+          nestedStates.push(states.splice(0, cell.stateSize.length));
+        } else {
+          nestedStates.push(states.splice(0, 1));
+        }
+      }
+      nestedStates.reverse();
+      const newNestedStates = [];
+      let callInputs;
+      for (let i2 = 0; i2 < this.cells.length; ++i2) {
+        const cell = this.cells[i2];
+        states = nestedStates[i2];
+        if (i2 === 0) {
+          callInputs = [inputs[0]].concat(states);
+        } else {
+          callInputs = [callInputs[0]].concat(states);
+        }
+        callInputs = cell.call(callInputs, kwargs);
+        newNestedStates.push(callInputs.slice(1));
+      }
+      states = [];
+      for (const cellStates of newNestedStates.slice().reverse()) {
+        states.push(...cellStates);
+      }
+      return [callInputs[0]].concat(states);
+    });
+  }
+  build(inputShape) {
+    if (isArrayOfShapes(inputShape)) {
+      inputShape = inputShape[0];
+    }
+    inputShape = inputShape;
+    let outputDim;
+    this.cells.forEach((cell, i2) => {
+      nameScope(`RNNCell_${i2}`, () => {
+        cell.build(inputShape);
+        if (Array.isArray(cell.stateSize)) {
+          outputDim = cell.stateSize[0];
+        } else {
+          outputDim = cell.stateSize;
+        }
+        inputShape = [inputShape[0], outputDim];
+      });
+    });
+    this.built = true;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const getCellConfig = (cell) => {
+      return {
+        "className": cell.getClassName(),
+        "config": cell.getConfig()
+      };
+    };
+    const cellConfigs = this.cells.map(getCellConfig);
+    const config3 = { "cells": cellConfigs };
+    return Object.assign({}, baseConfig, config3);
+  }
+  static fromConfig(cls, config3, customObjects = {}) {
+    const cells = [];
+    for (const cellConfig of config3["cells"]) {
+      cells.push(deserialize(cellConfig, customObjects));
+    }
+    return new cls({ cells });
+  }
+  get trainableWeights() {
+    if (!this.trainable) {
+      return [];
+    }
+    const weights = [];
+    for (const cell of this.cells) {
+      weights.push(...cell.trainableWeights);
+    }
+    return weights;
+  }
+  get nonTrainableWeights() {
+    const weights = [];
+    for (const cell of this.cells) {
+      weights.push(...cell.nonTrainableWeights);
+    }
+    if (!this.trainable) {
+      const trainableWeights = [];
+      for (const cell of this.cells) {
+        trainableWeights.push(...cell.trainableWeights);
+      }
+      return trainableWeights.concat(weights);
+    }
+    return weights;
+  }
+  getWeights() {
+    const weights = [];
+    for (const cell of this.cells) {
+      weights.push(...cell.weights);
+    }
+    return batchGetValue(weights);
+  }
+  setWeights(weights) {
+    const tuples = [];
+    for (const cell of this.cells) {
+      const numParams = cell.weights.length;
+      const inputWeights = weights.splice(numParams);
+      for (let i2 = 0; i2 < cell.weights.length; ++i2) {
+        tuples.push([cell.weights[i2], inputWeights[i2]]);
+      }
+    }
+    batchSetValue(tuples);
+  }
+};
+StackedRNNCells.className = "StackedRNNCells";
+serialization_exports.registerClass(StackedRNNCells);
+function generateDropoutMask(args) {
+  const { ones: ones4, rate, training = false, count: count22 = 1, dropoutFunc } = args;
+  const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate);
+  const createMask = () => inTrainPhase(droppedInputs, ones4, training);
+  if (!count22 || count22 <= 1) {
+    return keep(createMask().clone());
+  }
+  const masks = Array(count22).fill(void 0).map(createMask);
+  return masks.map((m) => keep(m.clone()));
+}
+var __rest = function(s2, e2) {
+  var t2 = {};
+  for (var p2 in s2)
+    if (Object.prototype.hasOwnProperty.call(s2, p2) && e2.indexOf(p2) < 0)
+      t2[p2] = s2[p2];
+  if (s2 != null && typeof Object.getOwnPropertySymbols === "function")
+    for (var i2 = 0, p2 = Object.getOwnPropertySymbols(s2); i2 < p2.length; i2++) {
+      if (e2.indexOf(p2[i2]) < 0 && Object.prototype.propertyIsEnumerable.call(s2, p2[i2]))
+        t2[p2[i2]] = s2[p2[i2]];
+    }
+  return t2;
+};
+var ConvRNN2D = class extends RNN {
+  constructor(args) {
+    if (args.unroll) {
+      throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");
+    }
+    if (Array.isArray(args.cell)) {
+      throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");
+    }
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 5 })];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      if (kwargs && kwargs["constants"]) {
+        throw new ValueError("ConvRNN2D cell does not support constants");
+      }
+      const mask2 = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask: mask2, training, initialState });
+    });
+  }
+  computeOutputShape(inputShape) {
+    let outShape = this.computeSingleOutputShape(inputShape);
+    if (!this.returnSequences) {
+      outShape = [outShape[0], ...outShape.slice(2)];
+    }
+    if (this.returnState) {
+      outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];
+    }
+    return outShape;
+  }
+  getInitialState(inputs) {
+    return tidy(() => {
+      const { stateSize } = this.cell;
+      const inputShape = inputs.shape;
+      const outputShape = this.computeSingleOutputShape(inputShape);
+      const stateShape = [outputShape[0], ...outputShape.slice(2)];
+      const initialState = zeros(stateShape);
+      if (Array.isArray(stateSize)) {
+        return Array(stateSize.length).fill(initialState);
+      }
+      return [initialState];
+    });
+  }
+  resetStates(states, training = false) {
+    tidy(() => {
+      if (!this.stateful) {
+        throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
+      }
+      const inputShape = this.inputSpec[0].shape;
+      const outputShape = this.computeSingleOutputShape(inputShape);
+      const stateShape = [outputShape[0], ...outputShape.slice(2)];
+      const batchSize = inputShape[0];
+      if (batchSize == null) {
+        throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
+      }
+      if (this.getStates() == null) {
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
+        } else {
+          this.states_ = [zeros(stateShape)];
+        }
+      } else if (states == null) {
+        dispose(this.states_);
+        if (this.keptStates != null) {
+          dispose(this.keptStates);
+          this.keptStates = [];
+        }
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
+        } else {
+          this.states_[0] = zeros(stateShape);
+        }
+      } else {
+        if (!Array.isArray(states)) {
+          states = [states];
+        }
+        if (states.length !== this.states_.length) {
+          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
+        }
+        if (training) {
+          this.keptStates.push(this.states_.slice());
+        } else {
+          dispose(this.states_);
+        }
+        for (let index2 = 0; index2 < this.states_.length; ++index2) {
+          const value = states[index2];
+          const expectedShape = stateShape;
+          if (!util_exports.arraysEqual(value.shape, expectedShape)) {
+            throw new ValueError(`State ${index2} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
+          }
+          this.states_[index2] = value;
+        }
+      }
+      this.states_ = this.states_.map((state) => keep(state.clone()));
+    });
+  }
+  computeSingleOutputShape(inputShape) {
+    const { dataFormat, filters, kernelSize, padding: padding2, strides: strides2, dilationRate } = this.cell;
+    const isChannelsFirst = dataFormat === "channelsFirst";
+    const h = inputShape[isChannelsFirst ? 3 : 2];
+    const w = inputShape[isChannelsFirst ? 4 : 3];
+    const hOut = convOutputLength(h, kernelSize[0], padding2, strides2[0], dilationRate[0]);
+    const wOut = convOutputLength(w, kernelSize[1], padding2, strides2[1], dilationRate[1]);
+    const outShape = [
+      ...inputShape.slice(0, 2),
+      ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]
+    ];
+    return outShape;
+  }
+};
+ConvRNN2D.className = "ConvRNN2D";
+var ConvLSTM2DCell = class extends LSTMCell {
+  constructor(args) {
+    const { filters, kernelSize, strides: strides2, padding: padding2, dataFormat, dilationRate } = args;
+    super(Object.assign({}, args, { units: filters }));
+    this.filters = filters;
+    assertPositiveInteger(this.filters, "filters");
+    this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize");
+    this.kernelSize.forEach((size2) => assertPositiveInteger(size2, "kernelSize"));
+    this.strides = normalizeArray(strides2 || 1, 2, "strides");
+    this.strides.forEach((stride) => assertPositiveInteger(stride, "strides"));
+    this.padding = padding2 || "valid";
+    checkPaddingMode(this.padding);
+    this.dataFormat = dataFormat || "channelsLast";
+    checkDataFormat(this.dataFormat);
+    this.dilationRate = normalizeArray(dilationRate || 1, 2, "dilationRate");
+    this.dilationRate.forEach((rate) => assertPositiveInteger(rate, "dilationRate"));
+  }
+  build(inputShape) {
+    var _a;
+    inputShape = getExactlyOneShape(inputShape);
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const numOfKernels = 4;
+    const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);
+    this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    if (this.useBias) {
+      let biasInitializer;
+      if (this.unitForgetBias) {
+        const init22 = this.biasInitializer;
+        const filters = this.filters;
+        biasInitializer = new (_a = class CustomInit extends Initializer {
+          apply(shape, dtype) {
+            const biasI = init22.apply([filters]);
+            const biasF = ones2([filters]);
+            const biasCAndO = init22.apply([filters * 2]);
+            return concatenate([biasI, biasF, biasCAndO]);
+          }
+        }, _a.className = "CustomInit", _a)();
+      } else {
+        biasInitializer = this.biasInitializer;
+      }
+      this.bias = this.addWeight("bias", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (inputs.length !== 3) {
+        throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
+      }
+      const training = kwargs["training"] || false;
+      const x = inputs[0];
+      const hTMinus1 = inputs[1];
+      const cTMinus1 = inputs[2];
+      const numOfKernels = 4;
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(x),
+          rate: this.dropout,
+          training,
+          count: numOfKernels,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const dropoutMask = this.dropoutMask;
+      const applyDropout = (x2, mask2, index2) => {
+        if (!mask2 || !mask2[index2]) {
+          return x2;
+        }
+        return mul(mask2[index2], x2);
+      };
+      let xI = applyDropout(x, dropoutMask, 0);
+      let xF = applyDropout(x, dropoutMask, 1);
+      let xC = applyDropout(x, dropoutMask, 2);
+      let xO = applyDropout(x, dropoutMask, 3);
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(hTMinus1),
+          rate: this.recurrentDropout,
+          training,
+          count: numOfKernels,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const recDropoutMask = this.recurrentDropoutMask;
+      let hI = applyDropout(hTMinus1, recDropoutMask, 0);
+      let hF = applyDropout(hTMinus1, recDropoutMask, 1);
+      let hC = applyDropout(hTMinus1, recDropoutMask, 2);
+      let hO = applyDropout(hTMinus1, recDropoutMask, 3);
+      const kernelChannelAxis = 3;
+      const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);
+      const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];
+      xI = this.inputConv(xI, kernelI, biasI, this.padding);
+      xF = this.inputConv(xF, kernelF, biasF, this.padding);
+      xC = this.inputConv(xC, kernelC, biasC, this.padding);
+      xO = this.inputConv(xO, kernelO, biasO, this.padding);
+      const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);
+      hI = this.recurrentConv(hI, recKernelI);
+      hF = this.recurrentConv(hF, recKernelF);
+      hC = this.recurrentConv(hC, recKernelC);
+      hO = this.recurrentConv(hO, recKernelO);
+      const i2 = this.recurrentActivation.apply(add2(xI, hI));
+      const f = this.recurrentActivation.apply(add2(xF, hF));
+      const c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(add2(xC, hC))));
+      const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));
+      return [h, h, c];
+    });
+  }
+  getConfig() {
+    const _a = super.getConfig(), { "units": _ } = _a, baseConfig = __rest(_a, ["units"]);
+    const config3 = {
+      filters: this.filters,
+      kernelSize: this.kernelSize,
+      padding: this.padding,
+      dataFormat: this.dataFormat,
+      dilationRate: this.dilationRate,
+      strides: this.strides
+    };
+    return Object.assign({}, baseConfig, config3);
+  }
+  inputConv(x, w, b, padding2) {
+    const out = conv2d(x, w, this.strides, padding2 || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
+    if (b) {
+      return biasAdd(out, b, this.dataFormat);
+    }
+    return out;
+  }
+  recurrentConv(x, w) {
+    const strides2 = 1;
+    return conv2d(x, w, strides2, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
+  }
+};
+ConvLSTM2DCell.className = "ConvLSTM2DCell";
+serialization_exports.registerClass(ConvLSTM2DCell);
+var ConvLSTM2D = class extends ConvRNN2D {
+  constructor(args) {
+    const cell = new ConvLSTM2DCell(args);
+    super(Object.assign({}, args, { cell }));
+  }
+  static fromConfig(cls, config3) {
+    return new cls(config3);
+  }
+};
+ConvLSTM2D.className = "ConvLSTM2D";
+serialization_exports.registerClass(ConvLSTM2D);
+var Dropout = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.rate = Math.max(Math.min(args.rate, 1), 0);
+    this.noiseShape = args.noiseShape;
+    this.seed = args.seed;
+    this.supportsMasking = true;
+  }
+  getNoiseShape(input2) {
+    if (this.noiseShape == null) {
+      return this.noiseShape;
+    }
+    const inputShape = input2.shape;
+    const noiseShape = [];
+    for (let i2 = 0; i2 < this.noiseShape.length; ++i2) {
+      noiseShape.push(this.noiseShape[i2] == null ? inputShape[i2] : this.noiseShape[i2]);
+    }
+    return noiseShape;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      if (0 < this.rate && this.rate < 1) {
+        const training = kwargs["training"] == null ? false : kwargs["training"];
+        const noiseShape = this.getNoiseShape(input2);
+        const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);
+        return output;
+      }
+      return inputs;
+    });
+  }
+  getConfig() {
+    const config3 = {
+      rate: this.rate,
+      noiseShape: this.noiseShape,
+      seed: this.seed
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  dispose() {
+    return super.dispose();
+  }
+};
+Dropout.className = "Dropout";
+serialization_exports.registerClass(Dropout);
+var SpatialDropout1D = class extends Dropout {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [{ ndim: 3 }];
+  }
+  getNoiseShape(input2) {
+    const inputShape = input2.shape;
+    return [inputShape[0], 1, inputShape[2]];
+  }
+};
+SpatialDropout1D.className = "SpatialDropout1D";
+serialization_exports.registerClass(SpatialDropout1D);
+var Dense = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.activation = null;
+    this.useBias = true;
+    this.kernel = null;
+    this.bias = null;
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {
+      let batchSize = null;
+      if (args.batchSize != null) {
+        batchSize = args.batchSize;
+      }
+      this.batchInputShape = [batchSize, args.inputDim];
+    }
+    this.units = args.units;
+    assertPositiveInteger(this.units, "units");
+    this.activation = getActivation(args.activation);
+    if (args.useBias != null) {
+      this.useBias = args.useBias;
+    }
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.activityRegularizer = getRegularizer(args.activityRegularizer);
+    this.supportsMasking = true;
+    this.inputSpec = [{ minNDim: 2 }];
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const inputLastDim = inputShape[inputShape.length - 1];
+    if (this.kernel == null) {
+      this.kernel = this.addWeight("kernel", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+      if (this.useBias) {
+        this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+      }
+    }
+    this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];
+    this.built = true;
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    outputShape[outputShape.length - 1] = this.units;
+    return outputShape;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
+      let output;
+      if (fusedActivationName != null) {
+        output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);
+      } else {
+        output = dot2(input2, this.kernel.read());
+        if (this.bias != null) {
+          output = biasAdd(output, this.bias.read());
+        }
+        if (this.activation != null) {
+          output = this.activation.apply(output);
+        }
+      }
+      return output;
+    });
+  }
+  getConfig() {
+    const config3 = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Dense.className = "Dense";
+serialization_exports.registerClass(Dense);
+var Flatten = class extends Layer {
+  constructor(args) {
+    args = args || {};
+    super(args);
+    this.inputSpec = [{ minNDim: 3 }];
+    this.dataFormat = args.dataFormat;
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    for (const dim of inputShape.slice(1)) {
+      if (dim == null) {
+        throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
+      }
+    }
+    return [inputShape[0], arrayProd(inputShape, 1)];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      let input2 = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsFirst" && input2.rank > 1) {
+        const permutation = [0];
+        for (let i2 = 2; i2 < input2.rank; ++i2) {
+          permutation.push(i2);
+        }
+        permutation.push(1);
+        input2 = transpose(input2, permutation);
+      }
+      return batchFlatten(input2);
+    });
+  }
+  getConfig() {
+    const config3 = {};
+    if (this.dataFormat != null) {
+      config3["dataFormat"] = this.dataFormat;
+    }
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Flatten.className = "Flatten";
+serialization_exports.registerClass(Flatten);
+var Activation2 = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.activation = getActivation(args.activation);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      return this.activation.apply(input2);
+    });
+  }
+  getConfig() {
+    const config3 = { activation: serializeActivation(this.activation) };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Activation2.className = "Activation";
+serialization_exports.registerClass(Activation2);
+var RepeatVector = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.n = args.n;
+    this.inputSpec = [{ ndim: 2 }];
+  }
+  computeOutputShape(inputShape) {
+    return [inputShape[0], this.n, inputShape[1]];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      return repeat(inputs, this.n);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      n: this.n
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+RepeatVector.className = "RepeatVector";
+serialization_exports.registerClass(RepeatVector);
+var Reshape2 = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.targetShape = args.targetShape;
+    for (let i2 = 0; i2 < this.targetShape.length; ++i2) {
+      if (this.isUnknown(this.targetShape[i2])) {
+        this.targetShape[i2] = null;
+      }
+    }
+  }
+  isUnknown(dim) {
+    return dim < 0 || dim == null;
+  }
+  fixUnknownDimension(inputShape, outputShape) {
+    const errorMsg = "Total size of new array must be unchanged.";
+    const finalShape = outputShape.slice();
+    let known = 1;
+    let unknown = null;
+    for (let i2 = 0; i2 < finalShape.length; ++i2) {
+      const dim = finalShape[i2];
+      if (this.isUnknown(dim)) {
+        if (unknown === null) {
+          unknown = i2;
+        } else {
+          throw new ValueError("Can only specifiy one unknown dimension.");
+        }
+      } else {
+        known *= dim;
+      }
+    }
+    const originalSize = arrayProd(inputShape);
+    if (unknown !== null) {
+      if (known === 0 || originalSize % known !== 0) {
+        throw new ValueError(errorMsg);
+      }
+      finalShape[unknown] = originalSize / known;
+    } else if (originalSize !== known) {
+      throw new ValueError(errorMsg);
+    }
+    return finalShape;
+  }
+  computeOutputShape(inputShape) {
+    let anyUnknownDims = false;
+    for (let i2 = 0; i2 < inputShape.length; ++i2) {
+      if (this.isUnknown(inputShape[i2])) {
+        anyUnknownDims = true;
+        break;
+      }
+    }
+    if (anyUnknownDims) {
+      return inputShape.slice(0, 1).concat(this.targetShape);
+    } else {
+      return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const inputShape = input2.shape;
+      const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
+      return reshape(input2, outputShape);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      targetShape: this.targetShape
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Reshape2.className = "Reshape";
+serialization_exports.registerClass(Reshape2);
+var Permute = class extends Layer {
+  constructor(args) {
+    super(args);
+    if (args.dims == null) {
+      throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
+    }
+    if (!Array.isArray(args.dims)) {
+      throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);
+    }
+    const expectedSortedIndices = range2(1, args.dims.length + 1);
+    if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {
+      throw new Error("Invalid permutation `dims`: " + JSON.stringify(args.dims) + " `dims` must contain consecutive integers starting from 1.");
+    }
+    this.dims = args.dims;
+    this.dimsIncludingBatch = [0].concat(this.dims);
+    this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    this.dims.forEach((dim, i2) => {
+      outputShape[i2 + 1] = inputShape[dim];
+    });
+    return outputShape;
+  }
+  call(inputs, kwargs) {
+    return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);
+  }
+  getConfig() {
+    const config3 = {
+      dims: this.dims
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Permute.className = "Permute";
+serialization_exports.registerClass(Permute);
+var Masking = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.supportsMasking = true;
+    if (args != null) {
+      this.maskValue = args.maskValue == null ? 0 : args.maskValue;
+    } else {
+      this.maskValue = 0;
+    }
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = { maskValue: this.maskValue };
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  computeMask(inputs, mask2) {
+    const input2 = getExactlyOneTensor(inputs);
+    const axis = -1;
+    return any(notEqual(input2, this.maskValue), axis);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const axis = -1;
+      const keepDims = true;
+      const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);
+      const output = mul(input2, cast(booleanMask, input2.dtype));
+      return output;
+    });
+  }
+};
+Masking.className = "Masking";
+serialization_exports.registerClass(Masking);
+var Embedding = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.embeddings = null;
+    this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform";
+    if (args.batchInputShape == null && args.inputShape == null) {
+      let batchSize = null;
+      if (args.batchSize != null) {
+        batchSize = args.batchSize;
+      }
+      if (args.inputLength == null) {
+        this.batchInputShape = [batchSize, null];
+      } else {
+        this.batchInputShape = [batchSize].concat(toList(args.inputLength));
+      }
+    }
+    this.inputDim = args.inputDim;
+    assertPositiveInteger(this.inputDim, "inputDim");
+    this.outputDim = args.outputDim;
+    assertPositiveInteger(this.outputDim, "outputDim");
+    this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);
+    this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);
+    this.activityRegularizer = getRegularizer(args.activityRegularizer);
+    this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);
+    this.maskZero = args.maskZero;
+    this.supportsMasking = args.maskZero;
+    this.inputLength = args.inputLength;
+  }
+  build(inputShape) {
+    this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);
+    this.built = true;
+  }
+  warnOnIncompatibleInputShape(inputShape) {
+  }
+  computeMask(inputs, mask2) {
+    return tidy(() => {
+      if (!this.maskZero) {
+        return null;
+      } else {
+        inputs = getExactlyOneTensor(inputs);
+        return notEqual(inputs, zerosLike(inputs));
+      }
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (this.inputLength == null) {
+      return [...inputShape, this.outputDim];
+    }
+    const inLens = toList(this.inputLength);
+    if (inLens.length !== inputShape.length - 1) {
+      throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
+    } else {
+      let i2 = 0;
+      for (let k = 0; k < inLens.length; ++k) {
+        const s1 = inLens[k];
+        const s2 = inputShape[k + 1];
+        if (s1 != null && s2 != null && s1 !== s2) {
+          throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
+        } else if (s1 == null) {
+          inLens[i2] = s2;
+        }
+        i2++;
+      }
+    }
+    return [inputShape[0], ...inLens, this.outputDim];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      let input2 = getExactlyOneTensor(inputs);
+      if (input2.dtype !== "int32") {
+        input2 = cast2(input2, "int32");
+      }
+      const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));
+      return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));
+    });
+  }
+  getConfig() {
+    const config3 = {
+      inputDim: this.inputDim,
+      outputDim: this.outputDim,
+      embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),
+      embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),
+      maskZero: this.maskZero,
+      inputLength: this.inputLength
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Embedding.className = "Embedding";
+serialization_exports.registerClass(Embedding);
+var Merge = class extends Layer {
+  constructor(args) {
+    super(args || {});
+    this.supportsMasking = true;
+  }
+  mergeFunction(inputs) {
+    throw new NotImplementedError();
+  }
+  computeElementwiseOpOutputShape(shape1, shape2) {
+    if (shape1 == null || shape2 == null) {
+      return null;
+    } else if (shape1.length < shape2.length) {
+      return this.computeElementwiseOpOutputShape(shape2, shape1);
+    } else if (shape2.length === 0) {
+      return shape1;
+    }
+    const outputShape = shape1.slice(0, shape1.length - shape2.length);
+    for (let k = 0; k < shape2.length; ++k) {
+      const i2 = shape1[shape1.length - shape2.length + k];
+      const j = shape2[k];
+      if (i2 == null || j == null || i2 < 0 || j < 0) {
+        outputShape.push(null);
+      } else if (i2 === 1) {
+        outputShape.push(j);
+      } else if (j === 1) {
+        outputShape.push(i2);
+      } else {
+        if (i2 !== j) {
+          throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2));
+        }
+        outputShape.push(i2);
+      }
+    }
+    return outputShape;
+  }
+  build(inputShape) {
+    if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {
+      inputShape = [getExactlyOneShape(inputShape)];
+    }
+    inputShape = inputShape;
+    if (inputShape.length < 2) {
+      throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);
+    }
+    let batchSizes = [];
+    for (const shape of inputShape) {
+      if (shape != null && shape[0] !== null) {
+        batchSizes.push(shape[0]);
+      }
+    }
+    batchSizes = unique2(batchSizes);
+    if (batchSizes.length > 1) {
+      throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
+    }
+    let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);
+    for (let i2 = 1; i2 < inputShape.length; ++i2) {
+      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
+    }
+    const allRanks = inputShape.map((shape) => shape.length);
+    if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {
+      this.reshapeRequired = false;
+    } else {
+      this.reshapeRequired = true;
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      if (this.reshapeRequired) {
+        const reshapedInputs = [];
+        const inputDims = inputs.map((input2) => input2.rank);
+        if (inputDims.indexOf(null) === -1) {
+          const maxNDim = max2(inputDims);
+          for (let x of inputs) {
+            const xNDim = x.rank;
+            for (let k = 0; k < maxNDim - xNDim; ++k) {
+              x = expandDims2(x, 1);
+            }
+            reshapedInputs.push(x);
+          }
+          return this.mergeFunction(reshapedInputs);
+        } else {
+          let transposed = false;
+          for (const x of inputs) {
+            const xNDim = x.rank;
+            if (xNDim == null) {
+              const xShape = x.shape;
+              const batchSize = xShape[0];
+              const newShape = xShape.slice(1).concat([batchSize]);
+              let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));
+              xTransposed = transpose(xTransposed, [1, 0]);
+              xTransposed = reshape(xTransposed, newShape);
+              reshapedInputs.push(xTransposed);
+              transposed = true;
+            } else if (xNDim > 1) {
+              const dims = range2(1, xNDim).concat([0]);
+              reshapedInputs.push(transpose(x, dims));
+              transposed = true;
+            } else {
+              reshapedInputs.push(x);
+            }
+          }
+          let y = this.mergeFunction(reshapedInputs);
+          const yNDim = y.rank;
+          if (transposed) {
+            if (yNDim == null) {
+              const yShape = y.shape;
+              const yNDim2 = yShape.length;
+              const batchSize = yShape[yNDim2 - 1];
+              const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));
+              y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);
+            } else if (yNDim > 1) {
+              const dims = [yNDim - 1].concat(range2(0, yNDim - 1));
+              y = transpose(y, dims);
+            }
+          }
+          return y;
+        }
+      } else {
+        return this.mergeFunction(inputs);
+      }
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = inputShape;
+    let outputShape;
+    if (inputShape[0] == null) {
+      outputShape = null;
+    } else {
+      outputShape = inputShape[0].slice(1);
+    }
+    for (let i2 = 1; i2 < inputShape.length; ++i2) {
+      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
+    }
+    let batchSizes = [];
+    for (const shape of inputShape) {
+      if (shape != null && shape[0] !== null) {
+        batchSizes.push(shape[0]);
+      }
+    }
+    batchSizes = unique2(batchSizes);
+    if (batchSizes.length === 1) {
+      outputShape = batchSizes.concat(outputShape);
+    } else {
+      outputShape = [null].concat(outputShape);
+    }
+    return outputShape;
+  }
+  computeMask(inputs, mask2) {
+    return tidy(() => {
+      if (mask2 == null) {
+        return null;
+      }
+      if (!Array.isArray(mask2)) {
+        throw new ValueError("`mask` should be an Array");
+      }
+      if (!Array.isArray(inputs)) {
+        throw new ValueError("`inputs` should be an Array");
+      }
+      if (mask2.length !== inputs.length) {
+        throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask2.length})`);
+      }
+      if (mask2.every((m) => m == null)) {
+        return null;
+      }
+      mask2 = mask2.map((m) => m == null ? m : expandDims(m, 0));
+      let output = mask2[0];
+      for (let i2 = 1; i2 < mask2.length - 1; ++i2) {
+        output = logicalAnd(output, mask2[i2]);
+      }
+      return output;
+    });
+  }
+};
+var Add2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0].clone();
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = add2(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Add2.className = "Add";
+serialization_exports.registerClass(Add2);
+var Multiply2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0].clone();
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = mul(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Multiply2.className = "Multiply";
+serialization_exports.registerClass(Multiply2);
+var Average = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0].clone();
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = add2(output, inputs[i2]);
+      }
+      return mul(1 / inputs.length, output);
+    });
+  }
+};
+Average.className = "Average";
+serialization_exports.registerClass(Average);
+var Maximum2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0];
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = maximum(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Maximum2.className = "Maximum";
+serialization_exports.registerClass(Maximum2);
+var Minimum2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0];
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = minimum(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Minimum2.className = "Minimum";
+serialization_exports.registerClass(Minimum2);
+var Concatenate = class extends Merge {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_AXIS = -1;
+    if (args == null) {
+      args = {};
+    }
+    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
+    this.supportsMasking = true;
+    this.reshapeRequired = false;
+  }
+  build(inputShape) {
+    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {
+      throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");
+    }
+    inputShape = inputShape;
+    let allNoneShape = true;
+    for (const shape of inputShape) {
+      if (shape != null) {
+        allNoneShape = false;
+        break;
+      }
+    }
+    if (allNoneShape) {
+      return;
+    }
+    const shapeSet = [];
+    for (let i2 = 0; i2 < inputShape.length; ++i2) {
+      const shapeWithoutConcatAxis = inputShape[i2].slice();
+      shapeWithoutConcatAxis.splice(this.axis, 1);
+      let exists = false;
+      for (const shape of shapeSet) {
+        if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {
+          exists = true;
+          break;
+        }
+      }
+      if (!exists) {
+        shapeSet.push(shapeWithoutConcatAxis);
+      }
+    }
+    if (shapeSet.length > 1) {
+      throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(inputShape));
+    }
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      return concatenate(inputs, this.axis);
+    });
+  }
+  computeOutputShape(inputShape) {
+    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {
+      throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");
+    }
+    const inputShapes = inputShape;
+    const outputShape = inputShapes[0].slice();
+    const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;
+    for (const shape of inputShapes.slice(1)) {
+      if (outputShape[axis] == null || shape[axis] == null) {
+        outputShape[axis] = null;
+        break;
+      }
+      outputShape[axis] += shape[axis];
+    }
+    return outputShape;
+  }
+  computeMask(inputs, mask2) {
+    if (mask2 == null) {
+      return null;
+    }
+    if (!Array.isArray(mask2)) {
+      throw new ValueError("`mask` should be an array for Concatenate");
+    }
+    if (!Array.isArray(inputs)) {
+      throw new ValueError("`inputs` should be an array for Concatenate");
+    }
+    if (mask2.length !== inputs.length) {
+      throw new ValueError(`Mismatch in the length of mask (${mask2.length}) and the legnth of inputs (${inputs.length})`);
+    }
+    return tidy(() => {
+      let allNullMasks = true;
+      mask2.forEach((m) => {
+        if (m != null) {
+          allNullMasks = false;
+          return;
+        }
+      });
+      if (allNullMasks) {
+        return null;
+      }
+      const outputMasks = [];
+      for (let i2 = 0; i2 < inputs.length; ++i2) {
+        if (mask2[i2] == null) {
+          outputMasks.push(cast(onesLike(inputs[i2]), "bool"));
+        } else if (mask2[i2].rank < inputs[i2].rank) {
+          outputMasks.push(expandDims(mask2[i2], -1));
+        } else {
+          outputMasks.push(mask2[i2]);
+        }
+      }
+      const concatenatedMasks = concat(outputMasks, this.axis);
+      return all(concatenatedMasks, -1, false);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      "axis": this.axis
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Concatenate.className = "Concatenate";
+serialization_exports.registerClass(Concatenate);
+function interpretAxis(axis, dim) {
+  while (axis < 0) {
+    axis += dim;
+  }
+  return axis;
+}
+function batchDot(x, y, axes) {
+  if (x.shape.length > 3 || y.shape.length > 3) {
+    throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");
+  }
+  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);
+  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);
+  if (typeof axes === "number") {
+    axes = [axes, axes];
+  }
+  if (x.dtype === "complex64" || y.dtype === "complex64") {
+    throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");
+  }
+  const xNDim = x.shape.length;
+  const yNDim = y.shape.length;
+  if (axes == null) {
+    axes = [xNDim - 1, yNDim - 2];
+  }
+  const axesArray = axes;
+  return tidy(() => {
+    let diff;
+    if (xNDim > yNDim) {
+      diff = xNDim - yNDim;
+      const diffShape = [];
+      for (let i2 = 0; i2 < diff; ++i2) {
+        diffShape.push(1);
+      }
+      y = reshape(y, y.shape.concat(diffShape));
+    } else if (yNDim > xNDim) {
+      diff = yNDim - xNDim;
+      const diffShape = [];
+      for (let i2 = 0; i2 < diff; ++i2) {
+        diffShape.push(1);
+      }
+      x = reshape(x, x.shape.concat(diffShape));
+    } else {
+      diff = 0;
+    }
+    let out;
+    if (x.shape.length === 2 && y.shape.length === 2) {
+      if (axesArray[0] === axesArray[1]) {
+        out = sum2(mul(x, y), axesArray[0]);
+      } else {
+        out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);
+      }
+    } else {
+      const adjX = axesArray[0] !== x.shape.length - 1;
+      const adjY = axesArray[1] === y.shape.length - 1;
+      out = matMul(x, y, adjX, adjY);
+    }
+    if (diff > 0) {
+      let idx;
+      if (xNDim > yNDim) {
+        idx = xNDim + yNDim - 3;
+      } else {
+        idx = xNDim - 1;
+      }
+      const squeezeAxes = [];
+      for (let i2 = idx; i2 < idx + diff; ++i2) {
+        squeezeAxes.push(i2);
+      }
+      out = squeeze(out, squeezeAxes);
+    }
+    if (out.shape.length === 1) {
+      out = expandDims(out, 1);
+    }
+    return out;
+  });
+}
+var Dot = class extends Merge {
+  constructor(args) {
+    super(args);
+    this.axes = args.axes;
+    this.normalize = args.normalize == null ? false : args.normalize;
+    this.supportsMasking = true;
+    this.reshapeRequired = false;
+  }
+  build(inputShape) {
+    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
+    const shape1 = inputShape[0];
+    const shape2 = inputShape[1];
+    if (shape1.length > 3 || shape2.length > 3) {
+      throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
+    }
+    const axes = this.interpretAxes(shape1, shape2);
+    if (shape1[axes[0]] !== shape2[axes[1]]) {
+      throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);
+    }
+  }
+  mergeFunction(inputs) {
+    if (inputs.length !== 2) {
+      throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);
+    }
+    let x1 = inputs[0];
+    let x2 = inputs[1];
+    let axes;
+    if (!Array.isArray(this.axes)) {
+      axes = [
+        interpretAxis(this.axes, x1.shape.length),
+        interpretAxis(this.axes, x2.shape.length)
+      ];
+    } else {
+      axes = this.axes.map((axis, i2) => interpretAxis(axis, inputs[i2].shape.length));
+    }
+    if (this.normalize) {
+      x1 = l2Normalize(x1, axes[0]);
+      x2 = l2Normalize(x2, axes[1]);
+    }
+    return batchDot(x1, x2, axes);
+  }
+  interpretAxes(shape1, shape2) {
+    let axes;
+    if (!Array.isArray(this.axes)) {
+      axes = [
+        interpretAxis(this.axes, shape1.length),
+        interpretAxis(this.axes, shape2.length)
+      ];
+    } else {
+      axes = this.axes;
+    }
+    return axes;
+  }
+  computeOutputShape(inputShape) {
+    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
+    const shape1 = inputShape[0].slice();
+    const shape2 = inputShape[1].slice();
+    if (shape1.length > 3 || shape2.length > 3) {
+      throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
+    }
+    const axes = this.interpretAxes(shape1, shape2);
+    shape1.splice(axes[0], 1);
+    shape2.splice(axes[1], 1);
+    shape2.splice(0, 1);
+    const outputShape = shape1.concat(shape2);
+    if (outputShape.length === 1) {
+      outputShape.push(1);
+    }
+    return outputShape;
+  }
+  computeMask(inputs, mask2) {
+    return null;
+  }
+  getConfig() {
+    const config3 = {
+      "axes": this.axes,
+      "normalize": this.normalize
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+Dot.className = "Dot";
+serialization_exports.registerClass(Dot);
+var GaussianNoise = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.stddev = args.stddev;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = { stddev: this.stddev };
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);
+      const output = inTrainPhase(noised, () => input2, kwargs["training"] || false);
+      return output;
+    });
+  }
+};
+GaussianNoise.className = "GaussianNoise";
+serialization_exports.registerClass(GaussianNoise);
+var GaussianDropout = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.rate = args.rate;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = { rate: this.rate };
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      if (this.rate > 0 && this.rate < 1) {
+        const noised = () => {
+          const stddev = Math.sqrt(this.rate / (1 - this.rate));
+          return mul(input2, randomNormal2(input2.shape, 1, stddev));
+        };
+        return inTrainPhase(noised, () => input2, kwargs["training"] || false);
+      }
+      return input2;
+    });
+  }
+};
+GaussianDropout.className = "GaussianDropout";
+serialization_exports.registerClass(GaussianDropout);
+var AlphaDropout = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.rate = args.rate;
+    this.noiseShape = args.noiseShape;
+  }
+  _getNoiseShape(inputs) {
+    return this.noiseShape || getExactlyOneTensor(inputs).shape;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config3 = { rate: this.rate };
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.rate < 1 && this.rate > 0) {
+        const noiseShape = this._getNoiseShape(inputs);
+        const droppedInputs = () => {
+          const input2 = getExactlyOneTensor(inputs);
+          const alpha2 = 1.6732632423543772;
+          const scale22 = 1.0507009873554805;
+          const alphaP = -alpha2 * scale22;
+          let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);
+          keptIdx = cast2(keptIdx, "float32");
+          const a6 = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;
+          const b = -a6 * alphaP * this.rate;
+          const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));
+          return add2(mul(x, a6), b);
+        };
+        return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false);
+      }
+      return inputs;
+    });
+  }
+};
+AlphaDropout.className = "AlphaDropout";
+serialization_exports.registerClass(AlphaDropout);
+function batchNormalization(x, mean5, variance, beta, gamma, epsilon3 = 1e-3) {
+  let out;
+  if (x.rank === 2) {
+    out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon3);
+  } else if (x.rank === 3) {
+    out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon3);
+  } else if (x.rank === 4) {
+    out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon3);
+  } else {
+    throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);
+  }
+  return out;
+}
+function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
+  return tidy(() => {
+    const meanAndVariance = moments(x, reductionAxes);
+    const mean5 = meanAndVariance.mean;
+    const variance = meanAndVariance.variance;
+    const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon3);
+    return [normed, mean5, variance];
+  });
+}
+function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
+  return tidy(() => {
+    const meanAndVariance = moments(x, reductionAxes);
+    const mean5 = meanAndVariance.mean;
+    const variance = meanAndVariance.variance;
+    const targetShape = [];
+    for (const axis of range2(0, x.rank)) {
+      if (reductionAxes.indexOf(axis) !== -1) {
+        targetShape.push(1);
+      } else {
+        targetShape.push(x.shape[axis]);
+      }
+    }
+    const broadcastMean = reshape(mean5, targetShape);
+    const broadcastVariance = reshape(variance, targetShape);
+    const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);
+    const broadcastBeta = beta == null ? null : reshape(beta, targetShape);
+    const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);
+    return [normed, mean5, variance];
+  });
+}
+function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
+  if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {
+    return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);
+  } else {
+    return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);
+  }
+}
+var BatchNormalization = class extends Layer {
+  constructor(args) {
+    if (args == null) {
+      args = {};
+    }
+    super(args);
+    this.supportsMasking = true;
+    this.axis = args.axis == null ? -1 : args.axis;
+    this.momentum = args.momentum == null ? 0.99 : args.momentum;
+    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
+    this.center = args.center == null ? true : args.center;
+    this.scale = args.scale == null ? true : args.scale;
+    this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
+    this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
+    this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || "zeros");
+    this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || "ones");
+    this.betaConstraint = getConstraint(args.betaConstraint);
+    this.gammaConstraint = getConstraint(args.gammaConstraint);
+    this.betaRegularizer = getRegularizer(args.betaRegularizer);
+    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;
+    const dim = inputShape[axis];
+    if (dim == null) {
+      throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);
+    }
+    this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];
+    const shape = [dim];
+    if (this.scale) {
+      this.gamma = this.addWeight("gamma", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);
+    }
+    if (this.center) {
+      this.beta = this.addWeight("beta", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);
+    }
+    this.movingMean = this.addWeight("moving_mean", shape, null, this.movingMeanInitializer, null, false);
+    this.movingVariance = this.addWeight("moving_variance", shape, null, this.movingVarianceInitializer, null, false);
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      const input2 = getExactlyOneTensor(inputs);
+      const inputShape = input2.shape;
+      const ndim = inputShape.length;
+      const reductionAxes = range2(0, ndim);
+      const axis = this.axis >= 0 ? this.axis : this.axis + ndim;
+      reductionAxes.splice(axis, 1);
+      const broadcastShape = pyListRepeat(1, ndim);
+      broadcastShape[axis] = inputShape[axis];
+      const sortedReductionAxes = reductionAxes.slice();
+      sortedReductionAxes.sort();
+      const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));
+      const normalizeInference = () => {
+        if (needsBroadcasting) {
+          const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);
+          const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);
+          const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;
+          const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;
+          return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);
+        } else {
+          return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
+        }
+      };
+      if (!training) {
+        return normalizeInference();
+      }
+      const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);
+      const doMovingAverage = (variable2, value, momentum) => {
+        tidy(() => {
+          const decay = 1 - momentum;
+          const origValue = variable2.read();
+          const updateDelta = mul(sub(origValue, value), decay);
+          variable2.write(sub(origValue, updateDelta));
+        });
+      };
+      const updateMovingMeanAndVariance = () => {
+        doMovingAverage(this.movingMean, mean5, this.momentum);
+        doMovingAverage(this.movingVariance, variance, this.momentum);
+      };
+      updateMovingMeanAndVariance();
+      return normedTraining;
+    });
+  }
+  getConfig() {
+    const config3 = {
+      axis: this.axis,
+      momentum: this.momentum,
+      epsilon: this.epsilon,
+      center: this.center,
+      scale: this.scale,
+      betaInitializer: serializeInitializer(this.betaInitializer),
+      gammaInitializer: serializeInitializer(this.gammaInitializer),
+      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),
+      movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),
+      betaRegularizer: serializeRegularizer(this.betaRegularizer),
+      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),
+      betaConstraint: serializeConstraint(this.betaConstraint),
+      gammaConstraint: serializeConstraint(this.gammaConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+BatchNormalization.className = "BatchNormalization";
+serialization_exports.registerClass(BatchNormalization);
+var LayerNormalization = class extends Layer {
+  constructor(args) {
+    if (args == null) {
+      args = {};
+    }
+    super(args);
+    this.axis = args.axis == null ? -1 : args.axis;
+    if (typeof this.axis === "number") {
+      if (!Number.isInteger(this.axis)) {
+        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
+      }
+    } else if (Array.isArray(this.axis)) {
+      for (const axis of this.axis) {
+        if (!Number.isInteger(axis)) {
+          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);
+        }
+      }
+    } else {
+      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);
+    }
+    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
+    this.center = args.center == null ? true : args.center;
+    this.scale = args.scale == null ? true : args.scale;
+    this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
+    this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
+    this.betaRegularizer = getRegularizer(args.betaRegularizer);
+    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
+    this.supportsMasking = true;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const nDims = inputShape.length;
+    if (typeof this.axis === "number") {
+      this.axis = [this.axis];
+    }
+    for (let i2 = 0; i2 < this.axis.length; ++i2) {
+      if (this.axis[i2] < 0) {
+        this.axis[i2] += nDims;
+      }
+    }
+    for (const axis of this.axis) {
+      if (axis < 0 || axis >= nDims) {
+        throw new Error(`Invalid axis: ${axis}`);
+      }
+    }
+    if (this.axis.length !== unique2(this.axis).length) {
+      throw new Error(`Found duplicate axes in: ${this.axis}`);
+    }
+    const paramShape = this.axis.map((axis) => inputShape[axis]);
+    const trainable = true;
+    if (this.scale) {
+      this.gamma = this.addWeight("gamma", paramShape, "float32", this.gammaInitializer, this.gammaRegularizer, trainable);
+    } else {
+      this.gamma = null;
+    }
+    if (this.center) {
+      this.beta = this.addWeight("beta", paramShape, "float32", this.betaInitializer, this.betaRegularizer, trainable);
+    } else {
+      this.beta = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    const input2 = getExactlyOneTensor(inputs);
+    const inputShape = input2.shape;
+    const nDims = inputShape.length;
+    return tidy(() => {
+      const keepDims = true;
+      let { mean: mean5, variance } = moments(input2, this.axis, keepDims);
+      const broadcastShape = pyListRepeat(1, nDims);
+      for (const dim of this.axis) {
+        broadcastShape[dim] = inputShape[dim];
+      }
+      const broadcast = (v) => {
+        if (v != null && v.shape.length !== nDims) {
+          return reshape(v, broadcastShape);
+        } else {
+          return v;
+        }
+      };
+      let scale22 = this.scale ? broadcast(this.gamma.read()) : null;
+      let offset = this.center ? broadcast(this.beta.read()) : null;
+      const momentsTiling = [];
+      const scaleOffsetTiling = [];
+      for (let i2 = 0; i2 < nDims; ++i2) {
+        if (this.axis.indexOf(i2) !== -1) {
+          momentsTiling.push(inputShape[i2]);
+          scaleOffsetTiling.push(1);
+        } else {
+          momentsTiling.push(1);
+          scaleOffsetTiling.push(inputShape[i2]);
+        }
+      }
+      mean5 = tile(mean5, momentsTiling);
+      variance = tile(variance, momentsTiling);
+      if (scale22 != null) {
+        scale22 = tile(scale22, scaleOffsetTiling);
+      }
+      if (offset != null) {
+        offset = tile(offset, scaleOffsetTiling);
+      }
+      return batchNormalization(input2, mean5, variance, offset, scale22, this.epsilon);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      axis: this.axis,
+      epsilon: this.epsilon,
+      center: this.center,
+      scale: this.scale,
+      betaInitializer: serializeInitializer(this.betaInitializer),
+      gammaInitializer: serializeInitializer(this.gammaInitializer),
+      betaRegularizer: serializeRegularizer(this.betaRegularizer),
+      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+LayerNormalization.className = "LayerNormalization";
+serialization_exports.registerClass(LayerNormalization);
+function spatial2dPadding(x, padding2, dataFormat) {
+  return tidy(() => {
+    if (x.rank !== 4) {
+      throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);
+    }
+    if (padding2 == null) {
+      padding2 = [[1, 1], [1, 1]];
+    }
+    if (padding2.length !== 2 || padding2[0].length !== 2 || padding2[1].length !== 2) {
+      throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
+    }
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    if (dataFormat !== "channelsLast" && dataFormat !== "channelsFirst") {
+      throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
+    }
+    let pattern;
+    if (dataFormat === "channelsFirst") {
+      pattern = [[0, 0], [0, 0], padding2[0], padding2[1]];
+    } else {
+      pattern = [[0, 0], padding2[0], padding2[1], [0, 0]];
+    }
+    return pad(x, pattern);
+  });
+}
+var ZeroPadding2D = class extends Layer {
+  constructor(args) {
+    if (args == null) {
+      args = {};
+    }
+    super(args);
+    this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;
+    if (args.padding == null) {
+      this.padding = [[1, 1], [1, 1]];
+    } else if (typeof args.padding === "number") {
+      this.padding = [[args.padding, args.padding], [args.padding, args.padding]];
+    } else {
+      args.padding = args.padding;
+      if (args.padding.length !== 2) {
+        throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);
+      }
+      let heightPadding;
+      let widthPadding;
+      if (typeof args.padding[0] === "number") {
+        heightPadding = [args.padding[0], args.padding[0]];
+        widthPadding = [args.padding[1], args.padding[1]];
+      } else {
+        args.padding = args.padding;
+        if (args.padding[0].length !== 2) {
+          throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);
+        }
+        heightPadding = args.padding[0];
+        if (args.padding[1].length !== 2) {
+          throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);
+        }
+        widthPadding = args.padding[1];
+      }
+      this.padding = [heightPadding, widthPadding];
+    }
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    let rows;
+    let cols;
+    if (this.dataFormat === "channelsFirst") {
+      if (inputShape[2] != null && inputShape[2] >= 0) {
+        rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];
+      } else {
+        rows = null;
+      }
+      if (inputShape[3] != null && inputShape[3] >= 0) {
+        cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];
+      } else {
+        cols = null;
+      }
+      return [inputShape[0], inputShape[1], rows, cols];
+    } else {
+      if (inputShape[1] != null && inputShape[1] >= 0) {
+        rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];
+      } else {
+        rows = null;
+      }
+      if (inputShape[2] != null && inputShape[2] >= 0) {
+        cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];
+      } else {
+        cols = null;
+      }
+      return [inputShape[0], rows, cols, inputShape[3]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));
+  }
+  getConfig() {
+    const config3 = {
+      padding: this.padding,
+      dataFormat: this.dataFormat
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+ZeroPadding2D.className = "ZeroPadding2D";
+serialization_exports.registerClass(ZeroPadding2D);
+function pool2d(x, poolSize, strides2, padding2, dataFormat, poolMode) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    checkPoolMode(poolMode);
+    checkPaddingMode(padding2);
+    if (strides2 == null) {
+      strides2 = [1, 1];
+    }
+    if (padding2 == null) {
+      padding2 = "valid";
+    }
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    if (poolMode == null) {
+      poolMode = "max";
+    }
+    x = preprocessConv2DInput(x, dataFormat);
+    let y;
+    const paddingString = padding2 === "same" ? "same" : "valid";
+    if (poolMode === "max") {
+      y = maxPool(x, poolSize, strides2, paddingString);
+    } else {
+      y = avgPool(x, poolSize, strides2, paddingString);
+    }
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 3, 1, 2]);
+    }
+    return y;
+  });
+}
+function pool3d(x, poolSize, strides2, padding2, dataFormat, poolMode) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    checkPoolMode(poolMode);
+    checkPaddingMode(padding2);
+    if (strides2 == null) {
+      strides2 = [1, 1, 1];
+    }
+    if (padding2 == null) {
+      padding2 = "valid";
+    }
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    if (poolMode == null) {
+      poolMode = "max";
+    }
+    x = preprocessConv3DInput(x, dataFormat);
+    let y;
+    const paddingString = padding2 === "same" ? "same" : "valid";
+    if (poolMode === "max") {
+      y = maxPool3d(x, poolSize, strides2, paddingString);
+    } else {
+      y = avgPool3d(x, poolSize, strides2, paddingString);
+    }
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 4, 1, 2, 3]);
+    }
+    return y;
+  });
+}
+var Pooling1D = class extends Layer {
+  constructor(args) {
+    if (args.poolSize == null) {
+      args.poolSize = 2;
+    }
+    super(args);
+    if (typeof args.poolSize === "number") {
+      this.poolSize = [args.poolSize];
+    } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === "number") {
+      this.poolSize = args.poolSize;
+    } else {
+      throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);
+    }
+    assertPositiveInteger(this.poolSize, "poolSize");
+    if (args.strides == null) {
+      this.strides = this.poolSize;
+    } else {
+      if (typeof args.strides === "number") {
+        this.strides = [args.strides];
+      } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === "number") {
+        this.strides = args.strides;
+      } else {
+        throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);
+      }
+    }
+    assertPositiveInteger(this.strides, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    checkPaddingMode(this.padding);
+    this.inputSpec = [new InputSpec({ ndim: 3 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);
+    return [inputShape[0], length, inputShape[2]];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      inputs = expandDims2(getExactlyOneTensor(inputs), 2);
+      const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
+      return squeeze(output, [2]);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      poolSize: this.poolSize,
+      padding: this.padding,
+      strides: this.strides
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+var MaxPooling1D = class extends Pooling1D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides2, padding2, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding2);
+    return pool2d(inputs, poolSize, strides2, padding2, dataFormat, "max");
+  }
+};
+MaxPooling1D.className = "MaxPooling1D";
+serialization_exports.registerClass(MaxPooling1D);
+var AveragePooling1D = class extends Pooling1D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides2, padding2, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding2);
+    return pool2d(inputs, poolSize, strides2, padding2, dataFormat, "avg");
+  }
+};
+AveragePooling1D.className = "AveragePooling1D";
+serialization_exports.registerClass(AveragePooling1D);
+var Pooling2D = class extends Layer {
+  constructor(args) {
+    if (args.poolSize == null) {
+      args.poolSize = [2, 2];
+    }
+    super(args);
+    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];
+    if (args.strides == null) {
+      this.strides = this.poolSize;
+    } else if (Array.isArray(args.strides)) {
+      if (args.strides.length !== 2) {
+        throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);
+      }
+      this.strides = args.strides;
+    } else {
+      this.strides = [args.strides, args.strides];
+    }
+    assertPositiveInteger(this.poolSize, "poolSize");
+    assertPositiveInteger(this.strides, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    checkPaddingMode(this.padding);
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    let rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
+    let cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
+    rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);
+    cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);
+    if (this.dataFormat === "channelsFirst") {
+      return [inputShape[0], inputShape[1], rows, cols];
+    } else {
+      return [inputShape[0], rows, cols, inputShape[3]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      poolSize: this.poolSize,
+      padding: this.padding,
+      strides: this.strides,
+      dataFormat: this.dataFormat
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+var MaxPooling2D = class extends Pooling2D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides2, padding2, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding2);
+    return pool2d(inputs, poolSize, strides2, padding2, dataFormat, "max");
+  }
+};
+MaxPooling2D.className = "MaxPooling2D";
+serialization_exports.registerClass(MaxPooling2D);
+var AveragePooling2D = class extends Pooling2D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides2, padding2, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding2);
+    return pool2d(inputs, poolSize, strides2, padding2, dataFormat, "avg");
+  }
+};
+AveragePooling2D.className = "AveragePooling2D";
+serialization_exports.registerClass(AveragePooling2D);
+var Pooling3D = class extends Layer {
+  constructor(args) {
+    if (args.poolSize == null) {
+      args.poolSize = [2, 2, 2];
+    }
+    super(args);
+    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];
+    if (args.strides == null) {
+      this.strides = this.poolSize;
+    } else if (Array.isArray(args.strides)) {
+      if (args.strides.length !== 3) {
+        throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);
+      }
+      this.strides = args.strides;
+    } else {
+      this.strides = [args.strides, args.strides, args.strides];
+    }
+    assertPositiveInteger(this.poolSize, "poolSize");
+    assertPositiveInteger(this.strides, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    checkPaddingMode(this.padding);
+    this.inputSpec = [new InputSpec({ ndim: 5 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    let depths = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
+    let rows = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
+    let cols = this.dataFormat === "channelsFirst" ? inputShape[4] : inputShape[3];
+    depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);
+    rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);
+    cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);
+    if (this.dataFormat === "channelsFirst") {
+      return [inputShape[0], inputShape[1], depths, rows, cols];
+    } else {
+      return [inputShape[0], depths, rows, cols, inputShape[4]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
+    });
+  }
+  getConfig() {
+    const config3 = {
+      poolSize: this.poolSize,
+      padding: this.padding,
+      strides: this.strides,
+      dataFormat: this.dataFormat
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+var MaxPooling3D = class extends Pooling3D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides2, padding2, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding2);
+    return pool3d(inputs, poolSize, strides2, padding2, dataFormat, "max");
+  }
+};
+MaxPooling3D.className = "MaxPooling3D";
+serialization_exports.registerClass(MaxPooling3D);
+var AveragePooling3D = class extends Pooling3D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides2, padding2, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding2);
+    return pool3d(inputs, poolSize, strides2, padding2, dataFormat, "avg");
+  }
+};
+AveragePooling3D.className = "AveragePooling3D";
+serialization_exports.registerClass(AveragePooling3D);
+var GlobalPooling1D = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 3 })];
+  }
+  computeOutputShape(inputShape) {
+    return [inputShape[0], inputShape[2]];
+  }
+  call(inputs, kwargs) {
+    throw new NotImplementedError();
+  }
+};
+var GlobalAveragePooling1D = class extends GlobalPooling1D {
+  constructor(args) {
+    super(args || {});
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      return mean(input2, 1);
+    });
+  }
+};
+GlobalAveragePooling1D.className = "GlobalAveragePooling1D";
+serialization_exports.registerClass(GlobalAveragePooling1D);
+var GlobalMaxPooling1D = class extends GlobalPooling1D {
+  constructor(args) {
+    super(args || {});
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      return max(input2, 1);
+    });
+  }
+};
+GlobalMaxPooling1D.className = "GlobalMaxPooling1D";
+serialization_exports.registerClass(GlobalMaxPooling1D);
+var GlobalPooling2D = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = inputShape;
+    if (this.dataFormat === "channelsLast") {
+      return [inputShape[0], inputShape[3]];
+    } else {
+      return [inputShape[0], inputShape[1]];
+    }
+  }
+  call(inputs, kwargs) {
+    throw new NotImplementedError();
+  }
+  getConfig() {
+    const config3 = { dataFormat: this.dataFormat };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+};
+var GlobalAveragePooling2D = class extends GlobalPooling2D {
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsLast") {
+        return mean(input2, [1, 2]);
+      } else {
+        return mean(input2, [2, 3]);
+      }
+    });
+  }
+};
+GlobalAveragePooling2D.className = "GlobalAveragePooling2D";
+serialization_exports.registerClass(GlobalAveragePooling2D);
+var GlobalMaxPooling2D = class extends GlobalPooling2D {
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsLast") {
+        return max(input2, [1, 2]);
+      } else {
+        return max(input2, [2, 3]);
+      }
+    });
+  }
+};
+GlobalMaxPooling2D.className = "GlobalMaxPooling2D";
+serialization_exports.registerClass(GlobalMaxPooling2D);
+var Wrapper = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.layer = args.layer;
+  }
+  build(inputShape) {
+    this.built = true;
+  }
+  get trainable() {
+    if (this.layer != null) {
+      return this.layer.trainable;
+    } else {
+      return false;
+    }
+  }
+  set trainable(value) {
+    if (this.layer != null) {
+      this.layer.trainable = value;
+    }
+  }
+  get trainableWeights() {
+    return this.layer.trainableWeights;
+  }
+  get nonTrainableWeights() {
+    return this.layer.nonTrainableWeights;
+  }
+  get updates() {
+    return this.layer._updates;
+  }
+  get losses() {
+    return this.layer.losses;
+  }
+  getWeights() {
+    return this.layer.getWeights();
+  }
+  setWeights(weights) {
+    this.layer.setWeights(weights);
+  }
+  getConfig() {
+    const config3 = {
+      "layer": {
+        "className": this.layer.getClassName(),
+        "config": this.layer.getConfig()
+      }
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  setFastWeightInitDuringBuild(value) {
+    super.setFastWeightInitDuringBuild(value);
+    if (this.layer != null) {
+      this.layer.setFastWeightInitDuringBuild(value);
+    }
+  }
+  static fromConfig(cls, config3, customObjects = {}) {
+    const layerConfig = config3["layer"];
+    const layer = deserialize(layerConfig, customObjects);
+    delete config3["layer"];
+    const newConfig = { layer };
+    Object.assign(newConfig, config3);
+    return new cls(newConfig);
+  }
+};
+var TimeDistributed = class extends Wrapper {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length < 3) {
+      throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);
+    }
+    this.inputSpec = [{ shape: inputShape }];
+    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
+    if (!this.layer.built) {
+      this.layer.build(childInputShape);
+      this.layer.built = true;
+    }
+    super.build(inputShape);
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
+    const childOutputShape = this.layer.computeOutputShape(childInputShape);
+    const timesteps = inputShape[1];
+    return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      const step5 = (inputs2, states) => {
+        const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));
+        return [output, []];
+      };
+      const rnnOutputs = rnn(step5, inputs, [], false, null, null, false, true);
+      const y = rnnOutputs[1];
+      return y;
+    });
+  }
+};
+TimeDistributed.className = "TimeDistributed";
+serialization_exports.registerClass(TimeDistributed);
+function checkBidirectionalMergeMode(value) {
+  checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", value);
+}
+var DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat";
+var Bidirectional = class extends Wrapper {
+  constructor(args) {
+    super(args);
+    const layerConfig = args.layer.getConfig();
+    const forwDict = {};
+    forwDict["className"] = args.layer.getClassName();
+    forwDict["config"] = layerConfig;
+    this.forwardLayer = deserialize(forwDict);
+    layerConfig["goBackwards"] = layerConfig["goBackwards"] === true ? false : true;
+    const backDict = {};
+    backDict["className"] = args.layer.getClassName();
+    backDict["config"] = layerConfig;
+    this.backwardLayer = deserialize(backDict);
+    this.forwardLayer.name = "forward_" + this.forwardLayer.name;
+    this.backwardLayer.name = "backward_" + this.backwardLayer.name;
+    this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;
+    checkBidirectionalMergeMode(this.mergeMode);
+    if (args.weights) {
+      throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");
+    }
+    this._stateful = args.layer.stateful;
+    this.returnSequences = args.layer.returnSequences;
+    this.returnState = args.layer.returnState;
+    this.supportsMasking = true;
+    this._trainable = true;
+    this.inputSpec = args.layer.inputSpec;
+    this.numConstants = null;
+  }
+  get trainable() {
+    return this._trainable;
+  }
+  set trainable(value) {
+    this._trainable = value;
+    if (this.forwardLayer != null) {
+      this.forwardLayer.trainable = value;
+    }
+    if (this.backwardLayer != null) {
+      this.backwardLayer.trainable = value;
+    }
+  }
+  getWeights() {
+    return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());
+  }
+  setWeights(weights) {
+    const numWeights = weights.length;
+    const numeightsOver2 = Math.floor(numWeights / 2);
+    this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));
+    this.backwardLayer.setWeights(weights.slice(numeightsOver2));
+  }
+  computeOutputShape(inputShape) {
+    let layerShapes = this.forwardLayer.computeOutputShape(inputShape);
+    if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {
+      layerShapes = [layerShapes];
+    }
+    layerShapes = layerShapes;
+    let outputShape;
+    let outputShapes;
+    let stateShape;
+    if (this.returnState) {
+      stateShape = layerShapes.slice(1);
+      outputShape = layerShapes[0];
+    } else {
+      outputShape = layerShapes[0];
+    }
+    outputShape = outputShape;
+    if (this.mergeMode === "concat") {
+      outputShape[outputShape.length - 1] *= 2;
+      outputShapes = [outputShape];
+    } else if (this.mergeMode == null) {
+      outputShapes = [outputShape, outputShape.slice()];
+    } else {
+      outputShapes = [outputShape];
+    }
+    if (this.returnState) {
+      if (this.mergeMode == null) {
+        return outputShapes.concat(stateShape).concat(stateShape.slice());
+      }
+      return [outputShape].concat(stateShape).concat(stateShape.slice());
+    }
+    return singletonOrArray(outputShapes);
+  }
+  apply(inputs, kwargs) {
+    let initialState = kwargs == null ? null : kwargs["initialState"];
+    let constants2 = kwargs == null ? null : kwargs["constants"];
+    if (kwargs == null) {
+      kwargs = {};
+    }
+    const standardized = standardizeArgs(inputs, initialState, constants2, this.numConstants);
+    inputs = standardized.inputs;
+    initialState = standardized.initialState;
+    constants2 = standardized.constants;
+    if (Array.isArray(inputs)) {
+      initialState = inputs.slice(1);
+      inputs = inputs[0];
+    }
+    if ((initialState == null || initialState.length === 0) && constants2 == null) {
+      return super.apply(inputs, kwargs);
+    }
+    const additionalInputs = [];
+    const additionalSpecs = [];
+    if (initialState != null) {
+      const numStates = initialState.length;
+      if (numStates % 2 > 0) {
+        throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
+      }
+      kwargs["initialState"] = initialState;
+      additionalInputs.push(...initialState);
+      const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape }));
+      this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);
+      this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);
+      additionalSpecs.push(...stateSpecs);
+    }
+    if (constants2 != null) {
+      throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");
+    }
+    const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;
+    for (const tensor2 of additionalInputs) {
+      if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {
+        throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");
+      }
+    }
+    if (isSymbolicTensor) {
+      const fullInput = [inputs].concat(additionalInputs);
+      const fullInputSpec = this.inputSpec.concat(additionalSpecs);
+      const originalInputSpec = this.inputSpec;
+      this.inputSpec = fullInputSpec;
+      const output = super.apply(fullInput, kwargs);
+      this.inputSpec = originalInputSpec;
+      return output;
+    } else {
+      return super.apply(inputs, kwargs);
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const initialState = kwargs["initialState"];
+      let y;
+      let yRev;
+      if (initialState == null) {
+        y = this.forwardLayer.call(inputs, kwargs);
+        yRev = this.backwardLayer.call(inputs, kwargs);
+      } else {
+        const forwardState = initialState.slice(0, initialState.length / 2);
+        const backwardState = initialState.slice(initialState.length / 2);
+        y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));
+        yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));
+      }
+      let states;
+      if (this.returnState) {
+        if (Array.isArray(y)) {
+          states = y.slice(1).concat(yRev.slice(1));
+        } else {
+        }
+        y = y[0];
+        yRev = yRev[0];
+      }
+      if (this.returnSequences) {
+        yRev = reverse(yRev, 1);
+      }
+      let output;
+      if (this.mergeMode === "concat") {
+        output = concatenate([y, yRev]);
+      } else if (this.mergeMode === "sum") {
+        output = add2(y, yRev);
+      } else if (this.mergeMode === "ave") {
+        output = mul(0.5, add2(y, yRev));
+      } else if (this.mergeMode === "mul") {
+        output = mul(y, yRev);
+      } else if (this.mergeMode == null) {
+        output = [y, yRev];
+      }
+      if (this.returnState) {
+        if (this.mergeMode == null) {
+          return output.concat(states);
+        }
+        return [output].concat(states);
+      }
+      return output;
+    });
+  }
+  resetStates(states) {
+    this.forwardLayer.resetStates();
+    this.backwardLayer.resetStates();
+  }
+  build(inputShape) {
+    nameScope(this.forwardLayer.name, () => {
+      this.forwardLayer.build(inputShape);
+    });
+    nameScope(this.backwardLayer.name, () => {
+      this.backwardLayer.build(inputShape);
+    });
+    this.built = true;
+  }
+  computeMask(inputs, mask2) {
+    if (Array.isArray(mask2)) {
+      mask2 = mask2[0];
+    }
+    let outputMask;
+    if (this.returnSequences) {
+      if (this.mergeMode == null) {
+        outputMask = [mask2, mask2];
+      } else {
+        outputMask = mask2;
+      }
+    } else {
+      if (this.mergeMode == null) {
+        outputMask = [null, null];
+      } else {
+        outputMask = null;
+      }
+    }
+    if (this.returnState) {
+      const states = this.forwardLayer.states;
+      const stateMask = states.map((state) => null);
+      if (Array.isArray(outputMask)) {
+        return outputMask.concat(stateMask).concat(stateMask);
+      } else {
+        return [outputMask].concat(stateMask).concat(stateMask);
+      }
+    } else {
+      return outputMask;
+    }
+  }
+  get trainableWeights() {
+    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);
+  }
+  get nonTrainableWeights() {
+    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);
+  }
+  setFastWeightInitDuringBuild(value) {
+    super.setFastWeightInitDuringBuild(value);
+    if (this.forwardLayer != null) {
+      this.forwardLayer.setFastWeightInitDuringBuild(value);
+    }
+    if (this.backwardLayer != null) {
+      this.backwardLayer.setFastWeightInitDuringBuild(value);
+    }
+  }
+  getConfig() {
+    const config3 = {
+      "mergeMode": this.mergeMode
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config3, baseConfig);
+    return config3;
+  }
+  static fromConfig(cls, config3) {
+    const rnnLayer = deserialize(config3["layer"]);
+    delete config3["layer"];
+    if (config3["numConstants"] != null) {
+      throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);
+    }
+    const newConfig = config3;
+    newConfig["layer"] = rnnLayer;
+    return new cls(newConfig);
+  }
+};
+Bidirectional.className = "Bidirectional";
+serialization_exports.registerClass(Bidirectional);
+function inputLayer(args) {
+  return new InputLayer(args);
+}
+function elu3(args) {
+  return new ELU(args);
+}
+function reLU(args) {
+  return new ReLU(args);
+}
+function leakyReLU(args) {
+  return new LeakyReLU(args);
+}
+function prelu2(args) {
+  return new PReLU(args);
+}
+function softmax2(args) {
+  return new Softmax3(args);
+}
+function thresholdedReLU(args) {
+  return new ThresholdedReLU(args);
+}
+function conv1d2(args) {
+  return new Conv1D(args);
+}
+function conv2d3(args) {
+  return new Conv2D2(args);
+}
+function conv2dTranspose2(args) {
+  return new Conv2DTranspose(args);
+}
+function conv3d2(args) {
+  return new Conv3D2(args);
+}
+function conv3dTranspose2(args) {
+  return new Conv3DTranspose(args);
+}
+function separableConv2d2(args) {
+  return new SeparableConv2D(args);
+}
+function cropping2D(args) {
+  return new Cropping2D(args);
+}
+function upSampling2d(args) {
+  return new UpSampling2D(args);
+}
+function depthwiseConv2d4(args) {
+  return new DepthwiseConv2D(args);
+}
+function activation(args) {
+  return new Activation2(args);
+}
+function dense(args) {
+  return new Dense(args);
+}
+function dropout3(args) {
+  return new Dropout(args);
+}
+function spatialDropout1d(args) {
+  return new SpatialDropout1D(args);
+}
+function flatten3(args) {
+  return new Flatten(args);
+}
+function repeatVector(args) {
+  return new RepeatVector(args);
+}
+function reshape2(args) {
+  return new Reshape2(args);
+}
+function permute(args) {
+  return new Permute(args);
+}
+function embedding(args) {
+  return new Embedding(args);
+}
+function add3(args) {
+  return new Add2(args);
+}
+function average(args) {
+  return new Average(args);
+}
+function concatenate2(args) {
+  return new Concatenate(args);
+}
+function maximum2(args) {
+  return new Maximum2(args);
+}
+function minimum2(args) {
+  return new Minimum2(args);
+}
+function multiply(args) {
+  return new Multiply2(args);
+}
+function dot3(args) {
+  return new Dot(args);
+}
+function batchNormalization2(args) {
+  return new BatchNormalization(args);
+}
+function layerNormalization(args) {
+  return new LayerNormalization(args);
+}
+function zeroPadding2d(args) {
+  return new ZeroPadding2D(args);
+}
+function averagePooling1d(args) {
+  return new AveragePooling1D(args);
+}
+function avgPool1d(args) {
+  return averagePooling1d(args);
+}
+function avgPooling1d(args) {
+  return averagePooling1d(args);
+}
+function averagePooling2d(args) {
+  return new AveragePooling2D(args);
+}
+function avgPool2d(args) {
+  return averagePooling2d(args);
+}
+function avgPooling2d(args) {
+  return averagePooling2d(args);
+}
+function averagePooling3d(args) {
+  return new AveragePooling3D(args);
+}
+function avgPool3d2(args) {
+  return averagePooling3d(args);
+}
+function avgPooling3d(args) {
+  return averagePooling3d(args);
+}
+function globalAveragePooling1d(args) {
+  return new GlobalAveragePooling1D(args);
+}
+function globalAveragePooling2d(args) {
+  return new GlobalAveragePooling2D(args);
+}
+function globalMaxPooling1d(args) {
+  return new GlobalMaxPooling1D(args);
+}
+function globalMaxPooling2d(args) {
+  return new GlobalMaxPooling2D(args);
+}
+function maxPooling1d(args) {
+  return new MaxPooling1D(args);
+}
+function maxPooling2d(args) {
+  return new MaxPooling2D(args);
+}
+function maxPooling3d(args) {
+  return new MaxPooling3D(args);
+}
+function gru(args) {
+  return new GRU(args);
+}
+function gruCell(args) {
+  return new GRUCell(args);
+}
+function lstm(args) {
+  return new LSTM(args);
+}
+function lstmCell(args) {
+  return new LSTMCell(args);
+}
+function simpleRNN(args) {
+  return new SimpleRNN(args);
+}
+function simpleRNNCell(args) {
+  return new SimpleRNNCell(args);
+}
+function convLstm2d(args) {
+  return new ConvLSTM2D(args);
+}
+function convLstm2dCell(args) {
+  return new ConvLSTM2DCell(args);
+}
+function rnn2(args) {
+  return new RNN(args);
+}
+function stackedRNNCells(args) {
+  return new StackedRNNCells(args);
+}
+function bidirectional(args) {
+  return new Bidirectional(args);
+}
+function timeDistributed(args) {
+  return new TimeDistributed(args);
+}
+var globalMaxPool1d = globalMaxPooling1d;
+var globalMaxPool2d = globalMaxPooling2d;
+var maxPool1d = maxPooling1d;
+var maxPool2d = maxPooling2d;
+function gaussianNoise(args) {
+  return new GaussianNoise(args);
+}
+function gaussianDropout(args) {
+  return new GaussianDropout(args);
+}
+function alphaDropout(args) {
+  return new AlphaDropout(args);
+}
+function masking(args) {
+  return new Masking(args);
+}
+var exports_metrics_exports = {};
+__export2(exports_metrics_exports, {
+  MAPE: () => MAPE2,
+  MSE: () => MSE2,
+  binaryAccuracy: () => binaryAccuracy2,
+  binaryCrossentropy: () => binaryCrossentropy3,
+  categoricalAccuracy: () => categoricalAccuracy2,
+  categoricalCrossentropy: () => categoricalCrossentropy3,
+  cosineProximity: () => cosineProximity2,
+  mape: () => mape2,
+  meanAbsoluteError: () => meanAbsoluteError2,
+  meanAbsolutePercentageError: () => meanAbsolutePercentageError2,
+  meanSquaredError: () => meanSquaredError3,
+  mse: () => mse2,
+  precision: () => precision2,
+  recall: () => recall2,
+  sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2
+});
+function binaryAccuracy2(yTrue, yPred) {
+  return binaryAccuracy(yTrue, yPred);
+}
+function binaryCrossentropy3(yTrue, yPred) {
+  return binaryCrossentropy2(yTrue, yPred);
+}
+function sparseCategoricalAccuracy2(yTrue, yPred) {
+  return sparseCategoricalAccuracy(yTrue, yPred);
+}
+function categoricalAccuracy2(yTrue, yPred) {
+  return categoricalAccuracy(yTrue, yPred);
+}
+function categoricalCrossentropy3(yTrue, yPred) {
+  return categoricalCrossentropy2(yTrue, yPred);
+}
+function precision2(yTrue, yPred) {
+  return precision(yTrue, yPred);
+}
+function recall2(yTrue, yPred) {
+  return recall(yTrue, yPred);
+}
+function cosineProximity2(yTrue, yPred) {
+  return cosineProximity(yTrue, yPred);
+}
+function meanAbsoluteError2(yTrue, yPred) {
+  return meanAbsoluteError(yTrue, yPred);
+}
+function meanAbsolutePercentageError2(yTrue, yPred) {
+  return meanAbsolutePercentageError(yTrue, yPred);
+}
+function MAPE2(yTrue, yPred) {
+  return meanAbsolutePercentageError(yTrue, yPred);
+}
+function mape2(yTrue, yPred) {
+  return meanAbsolutePercentageError(yTrue, yPred);
+}
+function meanSquaredError3(yTrue, yPred) {
+  return meanSquaredError2(yTrue, yPred);
+}
+function MSE2(yTrue, yPred) {
+  return meanSquaredError2(yTrue, yPred);
+}
+function mse2(yTrue, yPred) {
+  return meanSquaredError2(yTrue, yPred);
+}
+var exports_models_exports = {};
+__export2(exports_models_exports, {
+  modelFromJSON: () => modelFromJSON
+});
+var exports_regularizers_exports = {};
+__export2(exports_regularizers_exports, {
+  l1: () => l12,
+  l1l2: () => l1l2,
+  l2: () => l22
+});
+function l1l2(config3) {
+  return new L1L2(config3);
+}
+function l12(config3) {
+  return l1(config3);
+}
+function l22(config3) {
+  return l2(config3);
+}
+var Callback = class extends BaseCallback {
+  constructor() {
+    super(...arguments);
+    this.model = null;
+  }
+  setModel(model22) {
+    if (!(model22 instanceof LayersModel)) {
+      throw new Error("model must be a LayersModel, not some other Container");
+    }
+    this.model = model22;
+  }
+};
+function less2(currVal, prevVal) {
+  return currVal < prevVal;
+}
+function greater2(currVal, prevVal) {
+  return currVal > prevVal;
+}
+var EarlyStopping = class extends Callback {
+  constructor(args) {
+    super();
+    if (args == null) {
+      args = {};
+    }
+    if (args.restoreBestWeights) {
+      throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");
+    }
+    this.monitor = args.monitor || "val_loss";
+    this.minDelta = Math.abs(args.minDelta || 0);
+    this.patience = args.patience || 0;
+    this.verbose = args.verbose || 0;
+    this.mode = args.mode || "auto";
+    this.baseline = args.baseline;
+    if (["auto", "min", "max"].indexOf(this.mode) === -1) {
+      console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);
+      this.mode = "auto";
+    }
+    if (this.mode === "min") {
+      this.monitorFunc = less2;
+    } else if (this.mode === "max") {
+      this.monitorFunc = greater2;
+    } else {
+      if (this.monitor.indexOf("acc") !== -1) {
+        this.monitorFunc = greater2;
+      } else {
+        this.monitorFunc = less2;
+      }
+    }
+    if (this.monitorFunc === less2) {
+      this.minDelta *= -1;
+    }
+  }
+  async onTrainBegin(logs) {
+    this.wait = 0;
+    this.stoppedEpoch = 0;
+    if (this.baseline != null) {
+      this.best = this.baseline;
+    } else {
+      this.best = this.monitorFunc === less2 ? Infinity : -Infinity;
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    await resolveScalarsInLogs(logs);
+    const current = this.getMonitorValue(logs);
+    if (current == null) {
+      return;
+    }
+    if (this.monitorFunc(current - this.minDelta, this.best)) {
+      this.best = current;
+      this.wait = 0;
+    } else {
+      this.wait++;
+      if (this.wait >= this.patience) {
+        this.stoppedEpoch = epoch;
+        this.model.stopTraining = true;
+      }
+    }
+  }
+  async onTrainEnd(logs) {
+    if (this.stoppedEpoch > 0 && this.verbose) {
+      console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);
+    }
+  }
+  getMonitorValue(logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    const monitorValue = logs[this.monitor];
+    if (monitorValue == null) {
+      console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);
+    }
+    return monitorValue;
+  }
+};
+function earlyStopping(args) {
+  return new EarlyStopping(args);
+}
+var callbacks = { earlyStopping };
+var ENV4 = env();
+ENV4.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (debugValue) => {
+  if (debugValue) {
+    console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
+  }
+});
+var DataType;
+(function(DataType2) {
+  DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID";
+  DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT";
+  DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE";
+  DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32";
+  DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8";
+  DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16";
+  DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8";
+  DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING";
+  DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64";
+  DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64";
+  DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL";
+  DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8";
+  DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8";
+  DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32";
+  DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16";
+  DataType2[DataType2["DT_QINT16"] = 15] = "DT_QINT16";
+  DataType2[DataType2["DT_QUINT16"] = 16] = "DT_QUINT16";
+  DataType2[DataType2["DT_UINT16"] = 17] = "DT_UINT16";
+  DataType2[DataType2["DT_COMPLEX128"] = 18] = "DT_COMPLEX128";
+  DataType2[DataType2["DT_HALF"] = 19] = "DT_HALF";
+  DataType2[DataType2["DT_RESOURCE"] = 20] = "DT_RESOURCE";
+  DataType2[DataType2["DT_VARIANT"] = 21] = "DT_VARIANT";
+  DataType2[DataType2["DT_UINT32"] = 22] = "DT_UINT32";
+  DataType2[DataType2["DT_UINT64"] = 23] = "DT_UINT64";
+  DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF";
+  DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF";
+  DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF";
+  DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF";
+  DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF";
+  DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF";
+  DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF";
+  DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF";
+  DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF";
+  DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF";
+  DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF";
+  DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF";
+  DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF";
+  DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF";
+  DataType2[DataType2["DT_QINT16_REF"] = 115] = "DT_QINT16_REF";
+  DataType2[DataType2["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF";
+  DataType2[DataType2["DT_UINT16_REF"] = 117] = "DT_UINT16_REF";
+  DataType2[DataType2["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF";
+  DataType2[DataType2["DT_HALF_REF"] = 119] = "DT_HALF_REF";
+  DataType2[DataType2["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF";
+  DataType2[DataType2["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF";
+  DataType2[DataType2["DT_UINT32_REF"] = 122] = "DT_UINT32_REF";
+  DataType2[DataType2["DT_UINT64_REF"] = 123] = "DT_UINT64_REF";
+})(DataType || (DataType = {}));
+var SaverDef;
+(function(SaverDef2) {
+  let CheckpointFormatVersion;
+  (function(CheckpointFormatVersion2) {
+    CheckpointFormatVersion2[CheckpointFormatVersion2["LEGACY"] = 0] = "LEGACY";
+    CheckpointFormatVersion2[CheckpointFormatVersion2["V1"] = 1] = "V1";
+    CheckpointFormatVersion2[CheckpointFormatVersion2["V2"] = 2] = "V2";
+  })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));
+})(SaverDef || (SaverDef = {}));
+var CUSTOM_OPS = {};
+function registerOp(name, opFunc) {
+  const opMapper = {
+    tfOpName: name,
+    category: "custom",
+    inputs: [],
+    attrs: [],
+    customExecutor: opFunc
+  };
+  CUSTOM_OPS[name] = opMapper;
+}
+function getRegisteredOp(name) {
+  return CUSTOM_OPS[name];
+}
+function deregisterOp(name) {
+  delete CUSTOM_OPS[name];
+}
+function getParamValue(paramName, node2, tensorMap, context, resourceManager) {
+  const inputParam = node2.inputParams[paramName];
+  if (inputParam && inputParam.inputIndexStart !== void 0) {
+    const start = inputParam.inputIndexStart;
+    const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;
+    if (inputParam.type === "tensor") {
+      return getTensor(node2.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);
+    }
+    if (inputParam.type === "tensors") {
+      const inputs = node2.inputNames.slice(start, end);
+      return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));
+    }
+    const tensor2 = getTensor(node2.inputNames.slice(start)[0], tensorMap, context, resourceManager);
+    const data = tensor2.dataSync();
+    return inputParam.type === "number" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);
+  }
+  const attrParam = node2.attrParams[paramName];
+  return attrParam && attrParam.value;
+}
+function getTensor(name, tensorsMap, context, resourceManager) {
+  const [nodeName, index2] = parseNodeName(name);
+  if (resourceManager != null) {
+    const tensor2 = resourceManager.getHashTableHandleByName(nodeName);
+    if (tensor2 != null) {
+      return tensor2;
+    }
+  }
+  const contextId = context.currentContextIds.find((contextId2) => {
+    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];
+  });
+  return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index2] : void 0;
+}
+function getTensorsForCurrentContenxt(name, tensorsMap, context) {
+  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
+}
+function getNodeNameAndIndex(inputName, context) {
+  const [nodeName, index2, outputName] = parseNodeName(inputName);
+  return [
+    getNodeNameWithContextId(nodeName, context && context.currentContextId),
+    index2,
+    outputName
+  ];
+}
+function getNodeNameWithContextId(name, contextId) {
+  return !!contextId ? `${name}-${contextId}` : name;
+}
+function parseNodeName(name) {
+  const parts = name.split(":");
+  if (parts.length === 1) {
+    return [name, 0, void 0];
+  }
+  const nodeName = parts[0];
+  const outputName = parts.length === 3 ? parts[1] : void 0;
+  const index2 = Number(parts[parts.length - 1]);
+  return [nodeName, index2, outputName];
+}
+function getPadding(node2, tensorMap, context) {
+  let pad3 = getParamValue("pad", node2, tensorMap, context);
+  if (pad3 === "explicit") {
+    pad3 = getParamValue("explicitPaddings", node2, tensorMap, context);
+    const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+    for (let i2 = 0; i2 < 4; i2++) {
+      explicitPadding[i2][0] = pad3[i2 * 2];
+      explicitPadding[i2][1] = pad3[i2 * 2 + 1];
+    }
+    return explicitPadding;
+  }
+  return pad3;
+}
+function cloneTensor(tensor2) {
+  return tensor2.kept ? tensor2 : clone(tensor2);
+}
+var arithmetic_exports = {};
+__export2(arithmetic_exports, {
+  json: () => json
+});
+var json = [
+  {
+    "tfOpName": "Add",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "AddV2",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "AddN",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "BiasAdd",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sub",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "RealDiv",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Div",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "DivNoNan",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FloorDiv",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Mul",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Maximum",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Minimum",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Pow",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "SquaredDifference",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Mod",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FloorMod",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+var basic_math_exports = {};
+__export2(basic_math_exports, {
+  json: () => json2
+});
+var json2 = [
+  {
+    "tfOpName": "Abs",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Acos",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Asin",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Atan",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Atan2",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "y",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
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+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
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+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ClipByValue",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "clipValueMin",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "clipValueMax",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
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+    "category": "basic_math",
+    "inputs": [
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+        "name": "real",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "imag",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ComplexAbs",
+    "category": "basic_math",
+    "inputs": [
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+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cos",
+    "category": "basic_math",
+    "inputs": [
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+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cosh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Elu",
+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
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+        "name": "dtype",
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+      }
+    ]
+  },
+  {
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+    "category": "basic_math",
+    "inputs": [
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+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Floor",
+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
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+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
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+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Imag",
+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "outputType",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Neg",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Real",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "outputType",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Prelu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "alpha",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Relu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Relu6",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Selu",
+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sigmoid",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sin",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sinh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sqrt",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Rsqrt",
+    "category": "basic_math",
+    "inputs": [
+      {
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Square",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Tan",
+    "category": "basic_math",
+    "inputs": [
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+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
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+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Tanh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sign",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Round",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Expm1",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Log1p",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Reciprocal",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Softplus",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Asinh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Acosh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Atanh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Erf",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Prod",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axes",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool",
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LeakyRelu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "alpha",
+        "name": "alpha",
+        "type": "number",
+        "defaultValue": 0.2
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "IsNan",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+var control_exports = {};
+__export2(control_exports, {
+  json: () => json3
+});
+var json3 = [
+  {
+    "tfOpName": "EmptyTensorList",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 1,
+        "name": "maxNumElements",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LoopCond",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "pred",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Switch",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "pred",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Merge",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Enter",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "frame_name",
+        "name": "frameName",
+        "type": "string"
+      },
+      {
+        "tfName": "is_constant",
+        "name": "isConstant",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Exit",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "NextIteration",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "size",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfName": "dynamic_size",
+        "name": "dynamicSize",
+        "type": "bool"
+      },
+      {
+        "tfName": "clear_after_read",
+        "name": "clearAfterRead",
+        "type": "bool"
+      },
+      {
+        "tfName": "identical_element_shapes",
+        "name": "identicalElementShapes",
+        "type": "bool"
+      },
+      {
+        "tfName": "tensor_array_name",
+        "name": "name",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayWriteV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayReadV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayGatherV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayScatterV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayConcatV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "element_shape_except0",
+        "name": "elementShapeExcept0",
+        "type": "shape",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArraySplitV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "lengths",
+        "type": "number[]"
+      },
+      {
+        "start": 3,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArraySizeV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayCloseV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "StatelessIf",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "cond",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "then_branch",
+        "name": "thenBranch",
+        "type": "func"
+      },
+      {
+        "tfName": "else_branch",
+        "name": "elseBranch",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "If",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "cond",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "then_branch",
+        "name": "thenBranch",
+        "type": "func"
+      },
+      {
+        "tfName": "else_branch",
+        "name": "elseBranch",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "StatelessWhile",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "cond",
+        "name": "cond",
+        "type": "func"
+      },
+      {
+        "tfName": "body",
+        "name": "body",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "While",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "cond",
+        "name": "cond",
+        "type": "func"
+      },
+      {
+        "tfName": "body",
+        "name": "body",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListScatter",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListScatterV2",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 3,
+        "name": "numElements",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListGather",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListGetItem",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListSetItem",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListReserve",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 1,
+        "name": "numElements",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListFromTensor",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListStack",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      },
+      {
+        "tfName": "num_elements",
+        "name": "numElements",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListSplit",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 2,
+        "name": "lengths",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListConcat",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListConcatV2",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListPopBack",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListPushBack",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListLength",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListResize",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number"
+      }
+    ]
+  }
+];
+var convolution_exports = {};
+__export2(convolution_exports, {
+  json: () => json4
+});
+var json4 = [
+  {
+    "tfOpName": "AvgPool",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MaxPool",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": [],
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MaxPoolWithArgmax",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "include_batch_in_index",
+        "name": "includeBatchInIndex",
+        "type": "bool"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "AvgPool3D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MaxPool3D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv1D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "stride",
+        "name": "stride",
+        "type": "number"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NWC"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "dilation",
+        "name": "dilation",
+        "type": "number",
+        "defaultValue": 1
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv2D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "useCudnnOnGpu",
+        "name": "useCudnnOnGpu",
+        "type": "bool"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "_FusedConv2D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_args",
+        "name": "numArgs",
+        "type": "number"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "use_cudnn_on_gpu",
+        "name": "useCudnnOnGpu",
+        "type": "bool",
+        "defaultValue": true
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]",
+        "defaultValue": [
+          1,
+          1,
+          1,
+          1
+        ]
+      },
+      {
+        "tfName": "fused_ops",
+        "name": "fusedOps",
+        "type": "string[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-4
+      },
+      {
+        "tfName": "leakyrelu_alpha",
+        "name": "leakyreluAlpha",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv2DBackpropInput",
+    "category": "convolution",
+    "inputs": [
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+        "name": "x",
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+      },
+      {
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+        "name": "filter",
+        "type": "tensor"
+      },
+      {
+        "start": 0,
+        "name": "outputShape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "DepthwiseConv2d",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "DepthwiseConv2dNative",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedDepthwiseConv2dNative",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_args",
+        "name": "numArgs",
+        "type": "number"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]",
+        "defaultValue": [
+          1,
+          1,
+          1,
+          1
+        ]
+      },
+      {
+        "tfName": "fused_ops",
+        "name": "fusedOps",
+        "type": "string[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv3D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Dilation2D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "rates",
+        "name": "dilations",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      }
+    ]
+  }
+];
+var creation_exports = {};
+__export2(creation_exports, {
+  json: () => json5
+});
+var json5 = [
+  {
+    "tfOpName": "Fill",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      },
+      {
+        "start": 1,
+        "name": "value",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LinSpace",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "start",
+        "type": "number"
+      },
+      {
+        "start": 1,
+        "name": "stop",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "num",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "OneHot",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "depth",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "onValue",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "start": 3,
+        "name": "offValue",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "name": "axis",
+        "type": "number",
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Ones",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "OnesLike",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "RandomUniform",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "minval",
+        "name": "minval",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "maxval",
+        "name": "maxval",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "seed",
+        "name": "seed",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "seed2",
+        "name": "seed2",
+        "type": "number",
+        "defaultValue": 0,
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "T",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Range",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "start",
+        "type": "number"
+      },
+      {
+        "start": 1,
+        "name": "stop",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "step",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tidx",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TruncatedNormal",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "means",
+        "name": "mean",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "stddev",
+        "name": "stdDev",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "seed",
+        "name": "seed",
+        "type": "number"
+      },
+      {
+        "tfName": "seed2",
+        "name": "seed2",
+        "type": "number",
+        "defaultValue": 0,
+        "notSupported": true
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "T",
+        "name": "T",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Zeros",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ZerosLike",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Multinomial",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "logits",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "numSamples",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "seed",
+        "name": "seed",
+        "type": "number"
+      },
+      {
+        "tfName": "seed2",
+        "name": "seed2",
+        "type": "number"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "output_dtype",
+        "name": "output_dtype",
+        "type": "dtype"
+      }
+    ]
+  }
+];
+var dynamic_exports = {};
+__export2(dynamic_exports, {
+  json: () => json6
+});
+var json6 = [
+  {
+    "tfOpName": "NonMaxSuppressionV2",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "NonMaxSuppressionV3",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      },
+      {
+        "start": 4,
+        "name": "scoreThreshold",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "NonMaxSuppressionV4",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      },
+      {
+        "start": 4,
+        "name": "scoreThreshold",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "T_threshold",
+        "name": "threshold",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "pad_to_max_output_size",
+        "name": "padToMaxOutputSize",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "NonMaxSuppressionV5",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      },
+      {
+        "start": 4,
+        "name": "scoreThreshold",
+        "type": "number"
+      },
+      {
+        "start": 5,
+        "name": "softNmsSigma",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Where",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "condition",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ListDiff",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "y",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+var evaluation_exports = {};
+__export2(evaluation_exports, {
+  json: () => json7
+});
+var json7 = [
+  {
+    "tfOpName": "LowerBound",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sortedSequence",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TopKV2",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "k",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "sorted",
+        "name": "sorted",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "UpperBound",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sortedSequence",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Unique",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "UniqueV2",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  }
+];
+var graph_exports = {};
+__export2(graph_exports, {
+  json: () => json8
+});
+var json8 = [
+  {
+    "tfOpName": "PlaceholderWithDefault",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "default",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "shape",
+        "name": "shape",
+        "type": "shape"
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Placeholder",
+    "category": "graph",
+    "attrs": [
+      {
+        "tfName": "shape",
+        "name": "shape",
+        "type": "shape"
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Const",
+    "category": "graph"
+  },
+  {
+    "tfOpName": "Identity",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "IdentityN",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "x",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Snapshot",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Rank",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Size",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Shape",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ShapeN",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "x",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Print",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "data",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "message",
+        "name": "message",
+        "type": "string"
+      },
+      {
+        "tfName": "first_n",
+        "name": "firstN",
+        "type": "number",
+        "notSupported": true
+      },
+      {
+        "tfName": "summarize",
+        "name": "summarize",
+        "type": "number",
+        "defaultValue": 3
+      }
+    ]
+  },
+  {
+    "tfOpName": "NoOp",
+    "category": "graph",
+    "inputs": []
+  },
+  {
+    "tfOpName": "StopGradient",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "FakeQuantWithMinMaxVars",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "min",
+        "name": "min",
+        "type": "number"
+      },
+      {
+        "tfName": "max",
+        "name": "max",
+        "type": "number"
+      }
+    ]
+  }
+];
+var hash_table_exports = {};
+__export2(hash_table_exports, {
+  json: () => json9
+});
+var json9 = [
+  {
+    "tfOpName": "HashTable",
+    "category": "hash_table",
+    "inputs": [],
+    "attrs": [
+      {
+        "tfName": "shared_name",
+        "name": "sharedName",
+        "type": "string"
+      },
+      {
+        "tfName": "use_node_name_sharing",
+        "name": "useNodeNameSharing",
+        "type": "bool"
+      },
+      {
+        "tfName": "key_dtype",
+        "name": "keyDType",
+        "type": "dtype"
+      },
+      {
+        "tfName": "value_dtype",
+        "name": "valueDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "HashTableV2",
+    "category": "hash_table",
+    "inputs": [],
+    "attrs": [
+      {
+        "tfName": "shared_name",
+        "name": "sharedName",
+        "type": "string"
+      },
+      {
+        "tfName": "use_node_name_sharing",
+        "name": "useNodeNameSharing",
+        "type": "bool"
+      },
+      {
+        "tfName": "key_dtype",
+        "name": "keyDType",
+        "type": "dtype"
+      },
+      {
+        "tfName": "value_dtype",
+        "name": "valueDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableImport",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "values",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableImportV2",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "values",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableFind",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableFindV2",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableSize",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableSizeV2",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      }
+    ]
+  }
+];
+var image_exports = {};
+__export2(image_exports, {
+  json: () => json10
+});
+var json10 = [
+  {
+    "tfOpName": "ResizeBilinear",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "images",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "align_corners",
+        "name": "alignCorners",
+        "type": "bool"
+      },
+      {
+        "tfName": "half_pixel_centers",
+        "name": "halfPixelCenters",
+        "type": "bool"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ResizeNearestNeighbor",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "images",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "align_corners",
+        "name": "alignCorners",
+        "type": "bool"
+      },
+      {
+        "tfName": "half_pixel_centers",
+        "name": "halfPixelCenters",
+        "type": "bool"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "CropAndResize",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "image",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "boxInd",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "cropSize",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "method",
+        "name": "method",
+        "type": "string"
+      },
+      {
+        "tfName": "extrapolation_value",
+        "name": "extrapolationValue",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ImageProjectiveTransformV3",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "images",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "transforms",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "outputShape",
+        "type": "number[]"
+      },
+      {
+        "start": 3,
+        "name": "fillValue",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "interpolation",
+        "name": "interpolation",
+        "type": "string"
+      },
+      {
+        "tfName": "fill_mode",
+        "name": "fillMode",
+        "type": "string"
+      }
+    ]
+  }
+];
+var logical_exports = {};
+__export2(logical_exports, {
+  json: () => json11
+});
+var json11 = [
+  {
+    "tfOpName": "Equal",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "NotEqual",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Greater",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "GreaterEqual",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Less",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LessEqual",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogicalAnd",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogicalNot",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogicalOr",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Select",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "condition",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "SelectV2",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "condition",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+var matrices_exports = {};
+__export2(matrices_exports, {
+  json: () => json12
+});
+var json12 = [
+  {
+    "tfOpName": "_FusedMatMul",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_args",
+        "name": "numArgs",
+        "type": "number"
+      },
+      {
+        "tfName": "fused_ops",
+        "name": "fusedOps",
+        "type": "string[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-4
+      },
+      {
+        "tfName": "transpose_a",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "transpose_b",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MatMul",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "transpose_a",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "transpose_b",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "BatchMatMul",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "adj_x",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "adj_y",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "BatchMatMulV2",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "adj_x",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "adj_y",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Transpose",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "perm",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Einsum",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "equation",
+        "name": "equation",
+        "type": "string"
+      },
+      {
+        "tfName": "N",
+        "name": "n",
+        "type": "number",
+        "defaultValue": 2
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  }
+];
+var normalization_exports = {};
+__export2(normalization_exports, {
+  json: () => json13
+});
+var json13 = [
+  {
+    "tfOpName": "EuclideanNorm",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool",
+        "defaultValue": false
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedBatchNorm",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scale",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "offset",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "mean",
+        "type": "tensor"
+      },
+      {
+        "start": 4,
+        "name": "variance",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-3
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedBatchNormV2",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scale",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "offset",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "mean",
+        "type": "tensor"
+      },
+      {
+        "start": 4,
+        "name": "variance",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-3
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedBatchNormV3",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scale",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "offset",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "mean",
+        "type": "tensor"
+      },
+      {
+        "start": 4,
+        "name": "variance",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-3
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LRN",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "depth_radius",
+        "name": "radius",
+        "type": "number",
+        "defaultValue": 5
+      },
+      {
+        "tfName": "bias",
+        "name": "bias",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "alpha",
+        "name": "alpha",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "beta",
+        "name": "beta",
+        "type": "number",
+        "defaultValue": 0.5
+      }
+    ]
+  },
+  {
+    "tfOpName": "Softmax",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogSoftmax",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseToDense",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sparseIndices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "outputShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "sparseValues",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "validate_indices",
+        "name": "validateIndices",
+        "type": "bool",
+        "defaultValue": true,
+        "notSupported": true
+      }
+    ]
+  }
+];
+var reduction_exports = {};
+__export2(reduction_exports, {
+  json: () => json14
+});
+var json14 = [
+  {
+    "tfOpName": "Bincount",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "weights",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "DenseBincount",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "weights",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "binary_output",
+        "name": "binaryOutput",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Max",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Mean",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Min",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sum",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "All",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Any",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ArgMax",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ArgMin",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Prod",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cumprod",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "exclusive",
+        "name": "exclusive",
+        "type": "bool"
+      },
+      {
+        "tfName": "reverse",
+        "name": "reverse",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cumsum",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "exclusive",
+        "name": "exclusive",
+        "type": "bool"
+      },
+      {
+        "tfName": "reverse",
+        "name": "reverse",
+        "type": "bool"
+      }
+    ]
+  }
+];
+var slice_join_exports = {};
+__export2(slice_join_exports, {
+  json: () => json15
+});
+var json15 = [
+  {
+    "tfOpName": "ConcatV2",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "end": -1,
+        "name": "tensors",
+        "type": "tensors"
+      },
+      {
+        "start": -1,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "N",
+        "name": "n",
+        "type": "number",
+        "defaultValue": 2
+      }
+    ]
+  },
+  {
+    "tfOpName": "Concat",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 1,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      },
+      {
+        "start": 0,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "N",
+        "name": "n",
+        "type": "number",
+        "defaultValue": 2
+      }
+    ]
+  },
+  {
+    "tfOpName": "GatherV2",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "batch_dims",
+        "name": "batchDims",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Gather",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "validate_indices",
+        "name": "validateIndices",
+        "type": "bool",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Reverse",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "dims",
+        "type": "bool[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ReverseV2",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Slice",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "begin",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "size",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "StridedSlice",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "begin",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "end",
+        "type": "number[]"
+      },
+      {
+        "start": 3,
+        "name": "strides",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "begin_mask",
+        "name": "beginMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "end_mask",
+        "name": "endMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "new_axis_mask",
+        "name": "newAxisMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "ellipsis_mask",
+        "name": "ellipsisMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "shrink_axis_mask",
+        "name": "shrinkAxisMask",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Pack",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Unpack",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "num",
+        "name": "num",
+        "type": "number",
+        "defaultValue": 0,
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Tile",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "reps",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Split",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "start": 1,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_split",
+        "name": "numOrSizeSplits",
+        "type": "number",
+        "defaultValue": 1
+      }
+    ]
+  },
+  {
+    "tfOpName": "SplitV",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "numOrSizeSplits",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "ScatterNd",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "GatherNd",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseToDense",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sparseIndices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "outputShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "sparseValues",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "validate_indices",
+        "name": "validateIndices",
+        "type": "bool",
+        "defaultValue": false,
+        "notSupported": true
+      }
+    ]
+  }
+];
+var sparse_exports = {};
+__export2(sparse_exports, {
+  json: () => json16
+});
+var json16 = [
+  {
+    "tfOpName": "SparseFillEmptyRows",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "denseShape",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseReshape",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "inputIndices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "inputShape",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "newShape",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseSegmentMean",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "segmentIds",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseSegmentSum",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "segmentIds",
+        "type": "tensor"
+      }
+    ]
+  }
+];
+var spectral_exports = {};
+__export2(spectral_exports, {
+  json: () => json17
+});
+var json17 = [
+  {
+    "tfOpName": "FFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "IFFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "RFFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "fft_length",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "IRFFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "fft_length",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  }
+];
+var string_exports = {};
+__export2(string_exports, {
+  json: () => json18
+});
+var json18 = [
+  {
+    "tfOpName": "StringNGrams",
+    "category": "string",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "dataSplits",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "separator",
+        "name": "separator",
+        "type": "string"
+      },
+      {
+        "tfName": "ngram_widths",
+        "name": "nGramWidths",
+        "type": "number[]"
+      },
+      {
+        "tfName": "left_pad",
+        "name": "leftPad",
+        "type": "string"
+      },
+      {
+        "tfName": "right_pad",
+        "name": "rightPad",
+        "type": "string"
+      },
+      {
+        "tfName": "pad_width",
+        "name": "padWidth",
+        "type": "number"
+      },
+      {
+        "tfName": "preserve_short_sequences",
+        "name": "preserveShortSequences",
+        "type": "bool"
+      }
+    ],
+    "outputs": [
+      "ngrams",
+      "ngrams_splits"
+    ]
+  },
+  {
+    "tfOpName": "StringSplit",
+    "category": "string",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "delimiter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "skip_empty",
+        "name": "skipEmpty",
+        "type": "bool"
+      }
+    ],
+    "outputs": [
+      "indices",
+      "values",
+      "shape"
+    ]
+  },
+  {
+    "tfOpName": "StringToHashBucketFast",
+    "category": "string",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_buckets",
+        "name": "numBuckets",
+        "type": "number"
+      }
+    ]
+  }
+];
+var transformation_exports = {};
+__export2(transformation_exports, {
+  json: () => json19
+});
+var json19 = [
+  {
+    "tfOpName": "Cast",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "SrcT",
+        "name": "sdtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "DstT",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ExpandDims",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "MirrorPad",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "padding",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "mode",
+        "name": "mode",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Pad",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "padding",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "constant_value",
+        "name": "constantValue",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "PadV2",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "padding",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "constantValue",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Reshape",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Squeeze",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "tfDeprecatedName": "squeeze_dims",
+        "name": "axis",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SpaceToBatchND",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "blockShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "paddings",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "BatchToSpaceND",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "blockShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "crops",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "DepthToSpace",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "block_size",
+        "name": "blockSize",
+        "type": "number"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "BroadcastTo",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": []
+  },
+  {
+    "tfOpName": "BroadcastArgs",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "s0",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "s1",
+        "type": "tensor"
+      }
+    ],
+    "attrs": []
+  }
+];
+var OperationMapper = class {
+  static get Instance() {
+    return this._instance || (this._instance = new this());
+  }
+  constructor() {
+    const ops = [
+      arithmetic_exports,
+      basic_math_exports,
+      control_exports,
+      convolution_exports,
+      creation_exports,
+      dynamic_exports,
+      evaluation_exports,
+      graph_exports,
+      hash_table_exports,
+      image_exports,
+      logical_exports,
+      matrices_exports,
+      normalization_exports,
+      reduction_exports,
+      slice_join_exports,
+      sparse_exports,
+      spectral_exports,
+      string_exports,
+      transformation_exports
+    ];
+    const mappersJson = [].concat(...ops.map((op2) => op2.json));
+    this.opMappers = mappersJson.reduce((map, mapper) => {
+      map[mapper.tfOpName] = mapper;
+      return map;
+    }, {});
+  }
+  transformGraph(graph, signature = {}) {
+    const tfNodes = graph.node;
+    const placeholders = [];
+    const weights = [];
+    const initNodes = [];
+    const nodes = tfNodes.reduce((map, node2) => {
+      map[node2.name] = this.mapNode(node2);
+      if (node2.op.startsWith("Placeholder")) {
+        placeholders.push(map[node2.name]);
+      } else if (node2.op === "Const") {
+        weights.push(map[node2.name]);
+      } else if (node2.input == null || node2.input.length === 0) {
+        initNodes.push(map[node2.name]);
+      }
+      return map;
+    }, {});
+    let inputs = [];
+    const outputs = [];
+    let inputNodeNameToKey = {};
+    let outputNodeNameToKey = {};
+    if (signature != null) {
+      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
+      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
+    }
+    const allNodes = Object.keys(nodes);
+    allNodes.forEach((key) => {
+      const node2 = nodes[key];
+      node2.inputNames.forEach((name, index2) => {
+        const [nodeName, , outputName] = getNodeNameAndIndex(name);
+        const inputNode = nodes[nodeName];
+        if (inputNode.outputs != null) {
+          const outputIndex = inputNode.outputs.indexOf(outputName);
+          if (outputIndex !== -1) {
+            const inputName = `${nodeName}:${outputIndex}`;
+            node2.inputNames[index2] = inputName;
+          }
+        }
+        node2.inputs.push(inputNode);
+        inputNode.children.push(node2);
+      });
+    });
+    if (Object.keys(outputNodeNameToKey).length === 0) {
+      allNodes.forEach((key) => {
+        const node2 = nodes[key];
+        if (node2.children.length === 0) {
+          outputs.push(node2);
+        }
+      });
+    } else {
+      Object.keys(outputNodeNameToKey).forEach((name) => {
+        const [nodeName] = getNodeNameAndIndex(name);
+        const node2 = nodes[nodeName];
+        if (node2 != null) {
+          node2.signatureKey = outputNodeNameToKey[name];
+          outputs.push(node2);
+        }
+      });
+    }
+    if (Object.keys(inputNodeNameToKey).length > 0) {
+      Object.keys(inputNodeNameToKey).forEach((name) => {
+        const [nodeName] = getNodeNameAndIndex(name);
+        const node2 = nodes[nodeName];
+        if (node2) {
+          node2.signatureKey = inputNodeNameToKey[name];
+          inputs.push(node2);
+        }
+      });
+    } else {
+      inputs = placeholders;
+    }
+    let functions = {};
+    if (graph.library != null && graph.library.function != null) {
+      functions = graph.library.function.reduce((functions2, func2) => {
+        functions2[func2.signature.name] = this.mapFunction(func2);
+        return functions2;
+      }, {});
+    }
+    const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };
+    if (initNodes.length > 0) {
+      result.initNodes = initNodes;
+    }
+    return result;
+  }
+  mapSignatureEntries(entries) {
+    return Object.keys(entries || {}).reduce((prev, curr) => {
+      prev[entries[curr].name] = curr;
+      return prev;
+    }, {});
+  }
+  mapNode(node2) {
+    const mapper = getRegisteredOp(node2.op) || this.opMappers[node2.op] || {};
+    if (node2.attr == null) {
+      node2.attr = {};
+    }
+    const newNode = {
+      name: node2.name,
+      op: node2.op,
+      category: mapper.category,
+      inputNames: (node2.input || []).map((input2) => input2.startsWith("^") ? input2.slice(1) : input2),
+      inputs: [],
+      children: [],
+      inputParams: {},
+      attrParams: {},
+      rawAttrs: node2.attr,
+      outputs: mapper.outputs
+    };
+    if (mapper.inputs != null) {
+      newNode.inputParams = mapper.inputs.reduce((map, param) => {
+        map[param.name] = {
+          type: param.type,
+          inputIndexStart: param.start,
+          inputIndexEnd: param.end
+        };
+        return map;
+      }, {});
+    }
+    if (mapper.attrs != null) {
+      newNode.attrParams = mapper.attrs.reduce((map, param) => {
+        const type = param.type;
+        let value = void 0;
+        switch (param.type) {
+          case "string":
+            value = getStringParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getStringParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "string[]":
+            value = getStringArrayParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getStringArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "number":
+            value = getNumberParam(node2.attr, param.tfName, param.defaultValue || 0);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getNumberParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "number[]":
+            value = getNumericArrayParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getNumericArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "bool":
+            value = getBoolParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getBoolParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "bool[]":
+            value = getBoolArrayParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getBoolArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "shape":
+            value = getTensorShapeParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getTensorShapeParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "shape[]":
+            value = getTensorShapeArrayParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getTensorShapeArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "dtype":
+            value = getDtypeParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getDtypeParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "dtype[]":
+            value = getDtypeArrayParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getDtypeArrayParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "func":
+            value = getFuncParam(node2.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getFuncParam(node2.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "tensor":
+          case "tensors":
+            break;
+          default:
+            throw new Error(`Unsupported param type: ${param.type} for op: ${node2.op}`);
+        }
+        map[param.name] = { value, type };
+        return map;
+      }, {});
+    }
+    return newNode;
+  }
+  mapFunction(functionDef) {
+    const tfNodes = functionDef.nodeDef;
+    const placeholders = [];
+    const weights = [];
+    let nodes = {};
+    if (tfNodes != null) {
+      nodes = tfNodes.reduce((map, node2) => {
+        map[node2.name] = this.mapNode(node2);
+        if (node2.op === "Const") {
+          weights.push(map[node2.name]);
+        }
+        return map;
+      }, {});
+    }
+    const inputs = [];
+    const outputs = [];
+    functionDef.signature.inputArg.forEach((arg) => {
+      const [nodeName] = getNodeNameAndIndex(arg.name);
+      const node2 = {
+        name: nodeName,
+        op: "Placeholder",
+        inputs: [],
+        inputNames: [],
+        category: "graph",
+        inputParams: {},
+        attrParams: { dtype: { value: parseDtypeParam(arg.type), type: "dtype" } },
+        children: []
+      };
+      node2.signatureKey = arg.name;
+      inputs.push(node2);
+      nodes[nodeName] = node2;
+    });
+    const allNodes = Object.keys(nodes);
+    allNodes.forEach((key) => {
+      const node2 = nodes[key];
+      node2.inputNames.forEach((name, index2) => {
+        const [nodeName, , outputName] = getNodeNameAndIndex(name);
+        const inputNode = nodes[nodeName];
+        if (inputNode.outputs != null) {
+          const outputIndex = inputNode.outputs.indexOf(outputName);
+          if (outputIndex !== -1) {
+            const inputName = `${nodeName}:${outputIndex}`;
+            node2.inputNames[index2] = inputName;
+          }
+        }
+        node2.inputs.push(inputNode);
+        inputNode.children.push(node2);
+      });
+    });
+    const returnNodeMap = functionDef.ret;
+    functionDef.signature.outputArg.forEach((output) => {
+      const [nodeName, index2] = getNodeNameAndIndex(returnNodeMap[output.name]);
+      const node2 = nodes[nodeName];
+      if (node2 != null) {
+        node2.defaultOutput = index2;
+        outputs.push(node2);
+      }
+    });
+    const signature = this.mapArgsToSignature(functionDef);
+    return { nodes, inputs, outputs, weights, placeholders, signature };
+  }
+  mapArgsToSignature(functionDef) {
+    return {
+      methodName: functionDef.signature.name,
+      inputs: functionDef.signature.inputArg.reduce((map, arg) => {
+        map[arg.name] = this.mapArgToTensorInfo(arg);
+        return map;
+      }, {}),
+      outputs: functionDef.signature.outputArg.reduce((map, arg) => {
+        map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);
+        return map;
+      }, {})
+    };
+  }
+  mapArgToTensorInfo(arg, nameMap2) {
+    let name = arg.name;
+    if (nameMap2 != null) {
+      name = nameMap2[name];
+    }
+    return { name, dtype: arg.type };
+  }
+};
+function decodeBase64(text) {
+  const global2 = env().global;
+  if (typeof global2.atob !== "undefined") {
+    return global2.atob(text);
+  } else if (typeof Buffer !== "undefined") {
+    return new Buffer(text, "base64").toString();
+  } else {
+    throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
+  }
+}
+function parseStringParam(s2, keepCase) {
+  const value = Array.isArray(s2) ? String.fromCharCode.apply(null, s2) : decodeBase64(s2);
+  return keepCase ? value : value.toLowerCase();
+}
+function getStringParam(attrs, name, def, keepCase = false) {
+  const param = attrs[name];
+  if (param != null) {
+    return parseStringParam(param.s, keepCase);
+  }
+  return def;
+}
+function getBoolParam(attrs, name, def) {
+  const param = attrs[name];
+  return param ? param.b : def;
+}
+function getNumberParam(attrs, name, def) {
+  const param = attrs[name] || {};
+  const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def;
+  return typeof value === "number" ? value : parseInt(value, 10);
+}
+function parseDtypeParam(value) {
+  if (typeof value === "string") {
+    value = DataType[value];
+  }
+  switch (value) {
+    case DataType.DT_FLOAT:
+    case DataType.DT_HALF:
+      return "float32";
+    case DataType.DT_INT32:
+    case DataType.DT_INT64:
+    case DataType.DT_INT8:
+    case DataType.DT_UINT8:
+      return "int32";
+    case DataType.DT_BOOL:
+      return "bool";
+    case DataType.DT_DOUBLE:
+      return "float32";
+    case DataType.DT_STRING:
+      return "string";
+    default:
+      return null;
+  }
+}
+function getFuncParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.func) {
+    return param.func.name;
+  }
+  return def;
+}
+function getDtypeParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.type) {
+    return parseDtypeParam(param.type);
+  }
+  return def;
+}
+function getDtypeArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.list && param.list.type) {
+    return param.list.type.map((v) => parseDtypeParam(v));
+  }
+  return def;
+}
+function parseTensorShapeParam(shape) {
+  if (shape.unknownRank) {
+    return void 0;
+  }
+  if (shape.dim != null) {
+    return shape.dim.map((dim) => typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10));
+  }
+  return [];
+}
+function getTensorShapeParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.shape) {
+    return parseTensorShapeParam(param.shape);
+  }
+  return def;
+}
+function getNumericArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param) {
+    return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === "number" ? v : parseInt(v, 10));
+  }
+  return def;
+}
+function getStringArrayParam(attrs, name, def, keepCase = false) {
+  const param = attrs[name];
+  if (param && param.list && param.list.s) {
+    return param.list.s.map((v) => {
+      return parseStringParam(v, keepCase);
+    });
+  }
+  return def;
+}
+function getTensorShapeArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.list && param.list.shape) {
+    return param.list.shape.map((v) => {
+      return parseTensorShapeParam(v);
+    });
+  }
+  return def;
+}
+function getBoolArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.list && param.list.b) {
+    return param.list.b;
+  }
+  return def;
+}
+var NodeValueImpl = class {
+  constructor(node2, tensorMap, context) {
+    this.node = node2;
+    this.tensorMap = tensorMap;
+    this.context = context;
+    this.inputs = [];
+    this.attrs = {};
+    this.inputs = node2.inputNames.map((name) => this.getInput(name));
+    if (node2.rawAttrs != null) {
+      this.attrs = Object.keys(node2.rawAttrs).reduce((attrs, key) => {
+        attrs[key] = this.getAttr(key);
+        return attrs;
+      }, {});
+    }
+  }
+  getInput(name) {
+    return getTensor(name, this.tensorMap, this.context);
+  }
+  getAttr(name, defaultValue) {
+    const value = this.node.rawAttrs[name];
+    if (value.tensor != null) {
+      return getTensor(name, this.tensorMap, this.context);
+    }
+    if (value.i != null || value.f != null) {
+      return getNumberParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.s != null) {
+      return getStringParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.b != null) {
+      return getBoolParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.shape != null) {
+      return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.type != null) {
+      return getDtypeParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.list != null) {
+      if (value.list.i != null || value.list.f != null) {
+        return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.s != null) {
+        return getStringArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.shape != null) {
+        return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.b != null) {
+        return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.type != null) {
+        return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+    }
+    return defaultValue;
+  }
+};
+var executeOp = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "BiasAdd":
+    case "AddV2":
+    case "Add": {
+      return [add2(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "AddN": {
+      return [addN(getParamValue("tensors", node2, tensorMap, context))];
+    }
+    case "FloorMod":
+    case "Mod":
+      return [mod(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    case "Mul":
+      return [mul(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    case "RealDiv":
+    case "Div": {
+      return [div(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "DivNoNan": {
+      return [divNoNan(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "FloorDiv": {
+      return [floorDiv(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Sub": {
+      return [sub(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Minimum": {
+      return [minimum(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Maximum": {
+      return [maximum(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Pow": {
+      return [pow(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "SquaredDifference": {
+      return [squaredDifference(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp2 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Abs":
+    case "ComplexAbs":
+      return [abs(getParamValue("x", node2, tensorMap, context))];
+    case "Acos":
+      return [acos(getParamValue("x", node2, tensorMap, context))];
+    case "Acosh":
+      return [acosh(getParamValue("x", node2, tensorMap, context))];
+    case "Asin":
+      return [asin(getParamValue("x", node2, tensorMap, context))];
+    case "Asinh":
+      return [asinh(getParamValue("x", node2, tensorMap, context))];
+    case "Atan":
+      return [atan(getParamValue("x", node2, tensorMap, context))];
+    case "Atan2":
+      return [atan2(getParamValue("x", node2, tensorMap, context), getParamValue("y", node2, tensorMap, context))];
+    case "Atanh":
+      return [atanh(getParamValue("x", node2, tensorMap, context))];
+    case "Ceil":
+      return [ceil(getParamValue("x", node2, tensorMap, context))];
+    case "Complex":
+      return [complex(getParamValue("real", node2, tensorMap, context), getParamValue("imag", node2, tensorMap, context))];
+    case "Cos":
+      return [cos(getParamValue("x", node2, tensorMap, context))];
+    case "Cosh":
+      return [cosh(getParamValue("x", node2, tensorMap, context))];
+    case "Elu":
+      return [elu(getParamValue("x", node2, tensorMap, context))];
+    case "Erf":
+      return [erf(getParamValue("x", node2, tensorMap, context))];
+    case "Exp":
+      return [exp(getParamValue("x", node2, tensorMap, context))];
+    case "Expm1": {
+      return [expm1(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Floor":
+      return [floor(getParamValue("x", node2, tensorMap, context))];
+    case "Log":
+      return [log22(getParamValue("x", node2, tensorMap, context))];
+    case "Log1p": {
+      return [log1p(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Imag":
+      return [imag(getParamValue("x", node2, tensorMap, context))];
+    case "Neg":
+      return [neg(getParamValue("x", node2, tensorMap, context))];
+    case "Reciprocal": {
+      return [reciprocal(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Real":
+      return [real(getParamValue("x", node2, tensorMap, context))];
+    case "Relu":
+      return [relu(getParamValue("x", node2, tensorMap, context))];
+    case "Round": {
+      return [round2(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Selu":
+      return [selu(getParamValue("x", node2, tensorMap, context))];
+    case "Sigmoid":
+      return [sigmoid(getParamValue("x", node2, tensorMap, context))];
+    case "Sin":
+      return [sin(getParamValue("x", node2, tensorMap, context))];
+    case "Sign": {
+      return [sign(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Sinh": {
+      return [sinh(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Softplus": {
+      return [softplus(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Sqrt": {
+      return [sqrt(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Square": {
+      return [square(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Tanh": {
+      return [tanh2(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "Tan":
+      return [tan(getParamValue("x", node2, tensorMap, context))];
+    case "ClipByValue":
+      return [clipByValue(getParamValue("x", node2, tensorMap, context), getParamValue("clipValueMin", node2, tensorMap, context), getParamValue("clipValueMax", node2, tensorMap, context))];
+    case "Relu6":
+      return [relu6(getParamValue("x", node2, tensorMap, context))];
+    case "Rsqrt":
+      return [rsqrt(getTensor(node2.inputNames[0], tensorMap, context))];
+    case "Prod":
+      return [prod(getParamValue("x", node2, tensorMap, context), getParamValue("axes", node2, tensorMap, context))];
+    case "LeakyRelu":
+      return [leakyRelu(getParamValue("x", node2, tensorMap, context), getParamValue("alpha", node2, tensorMap, context))];
+    case "Prelu":
+      return [prelu(getParamValue("x", node2, tensorMap, context), getParamValue("alpha", node2, tensorMap, context))];
+    case "IsNan":
+      return [isNaN2(getTensor(node2.inputNames[0], tensorMap, context))];
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") {
+  if (typeof shapeA === "number" || typeof shapeB === "number") {
+    return;
+  }
+  util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+  for (let i2 = 0; i2 < shapeA.length; i2++) {
+    const dim0 = shapeA[i2];
+    const dim1 = shapeB[i2];
+    util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+  }
+}
+function fullDefinedShape(elementShape) {
+  if (typeof elementShape === "number" || elementShape.some((dim) => dim < 0)) {
+    return false;
+  }
+  return true;
+}
+function inferElementShape(listElementShape, tensors, elementShape) {
+  let partialShape = mergeElementShape(listElementShape, elementShape);
+  const notfullDefinedShape = !fullDefinedShape(partialShape);
+  if (notfullDefinedShape && tensors.length === 0) {
+    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);
+  }
+  if (notfullDefinedShape) {
+    tensors.forEach((tensor2) => {
+      partialShape = mergeElementShape(tensor2.shape, partialShape);
+    });
+  }
+  if (!fullDefinedShape(partialShape)) {
+    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);
+  }
+  return partialShape;
+}
+function mergeElementShape(elementShapeA, elementShapeB) {
+  if (typeof elementShapeA === "number") {
+    return elementShapeB;
+  }
+  if (typeof elementShapeB === "number") {
+    return elementShapeA;
+  }
+  if (elementShapeA.length !== elementShapeB.length) {
+    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);
+  }
+  const result = [];
+  for (let i2 = 0; i2 < elementShapeA.length; ++i2) {
+    const dim0 = elementShapeA[i2];
+    const dim1 = elementShapeB[i2];
+    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
+      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);
+    }
+    result[i2] = dim0 >= 0 ? dim0 : dim1;
+  }
+  return result;
+}
+var TensorArray = class {
+  constructor(name, dtype, maxSize2, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {
+    this.name = name;
+    this.dtype = dtype;
+    this.maxSize = maxSize2;
+    this.elementShape = elementShape;
+    this.identicalElementShapes = identicalElementShapes;
+    this.dynamicSize = dynamicSize;
+    this.clearAfterRead = clearAfterRead;
+    this.tensors = [];
+    this.closed_ = false;
+    this.idTensor = scalar(0);
+    keep(this.idTensor);
+  }
+  get id() {
+    return this.idTensor.id;
+  }
+  get closed() {
+    return this.closed_;
+  }
+  clearAndClose(keepIds) {
+    this.tensors.forEach((tensor2) => {
+      if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {
+        tensor2.tensor.dispose();
+      }
+    });
+    this.tensors = [];
+    this.closed_ = true;
+    this.idTensor.dispose();
+  }
+  size() {
+    return this.tensors.length;
+  }
+  read(index2) {
+    if (this.closed_) {
+      throw new Error(`TensorArray ${this.name} has already been closed.`);
+    }
+    if (index2 < 0 || index2 >= this.size()) {
+      throw new Error(`Tried to read from index ${index2}, but array size is: ${this.size()}`);
+    }
+    const tensorWithState = this.tensors[index2];
+    if (tensorWithState.cleared) {
+      throw new Error(`TensorArray ${this.name}: Could not read index ${index2} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);
+    }
+    if (this.clearAfterRead) {
+      tensorWithState.cleared = true;
+    }
+    tensorWithState.read = true;
+    return tensorWithState.tensor;
+  }
+  readMany(indices) {
+    return indices.map((index2) => this.read(index2));
+  }
+  write(index2, tensor2) {
+    if (this.closed_) {
+      throw new Error(`TensorArray ${this.name} has already been closed.`);
+    }
+    if (index2 < 0 || !this.dynamicSize && index2 >= this.maxSize) {
+      throw new Error(`Tried to write to index ${index2}, but array is not resizeable and size is: ${this.maxSize}`);
+    }
+    const t2 = this.tensors[index2] || {};
+    if (tensor2.dtype !== this.dtype) {
+      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index2},
+          because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);
+    }
+    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {
+      this.elementShape = tensor2.shape;
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index2}.`);
+    if (t2.read) {
+      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index2}, because it has already been read.`);
+    }
+    if (t2.written) {
+      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index2}, because it has already been written.`);
+    }
+    t2.tensor = tensor2;
+    keep(tensor2);
+    t2.written = true;
+    this.tensors[index2] = t2;
+  }
+  writeMany(indices, tensors) {
+    if (indices.length !== tensors.length) {
+      throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
+    }
+    indices.forEach((i2, index2) => this.write(i2, tensors[index2]));
+  }
+  gather(indices, dtype) {
+    if (!!dtype && dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);
+    }
+    if (!indices) {
+      indices = [];
+      for (let i2 = 0; i2 < this.size(); i2++) {
+        indices.push(i2);
+      }
+    } else {
+      indices = indices.slice(0, this.size());
+    }
+    if (indices.length === 0) {
+      return tensor([], [0].concat(this.elementShape));
+    }
+    const tensors = this.readMany(indices);
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: ");
+    return stack(tensors, 0);
+  }
+  concat(dtype) {
+    if (!!dtype && dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);
+    }
+    if (this.size() === 0) {
+      return tensor([], [0].concat(this.elementShape));
+    }
+    const indices = [];
+    for (let i2 = 0; i2 < this.size(); i2++) {
+      indices.push(i2);
+    }
+    const tensors = this.readMany(indices);
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
+    return concat(tensors, 0);
+  }
+  scatter(indices, tensor2) {
+    if (tensor2.dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);
+    }
+    if (indices.length !== tensor2.shape[0]) {
+      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);
+    }
+    const maxIndex = Math.max(...indices);
+    if (!this.dynamicSize && maxIndex >= this.maxSize) {
+      throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);
+    }
+    this.writeMany(indices, unstack(tensor2, 0));
+  }
+  split(length, tensor2) {
+    if (tensor2.dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);
+    }
+    let totalLength = 0;
+    const cumulativeLengths = length.map((len) => {
+      totalLength += len;
+      return totalLength;
+    });
+    if (totalLength !== tensor2.shape[0]) {
+      throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);
+    }
+    if (!this.dynamicSize && length.length !== this.maxSize) {
+      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);
+    }
+    const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;
+    const tensors = [];
+    tidy(() => {
+      tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
+      for (let i2 = 0; i2 < length.length; ++i2) {
+        const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+        const indices2 = [0, previousLength, 0];
+        const sizes = [1, length[i2], elementPerRow];
+        tensors[i2] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
+      }
+      return tensors;
+    });
+    const indices = [];
+    for (let i2 = 0; i2 < length.length; i2++) {
+      indices[i2] = i2;
+    }
+    this.writeMany(indices, tensors);
+  }
+};
+var TensorList = class {
+  constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {
+    this.tensors = tensors;
+    this.elementShape = elementShape;
+    this.elementDtype = elementDtype;
+    if (tensors != null) {
+      tensors.forEach((tensor2) => {
+        if (elementDtype !== tensor2.dtype) {
+          throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);
+        }
+        assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, "TensorList shape mismatch: ");
+        keep(tensor2);
+      });
+    }
+    this.idTensor = scalar(0);
+    this.maxNumElements = maxNumElements;
+    keep(this.idTensor);
+  }
+  get id() {
+    return this.idTensor.id;
+  }
+  copy() {
+    return new TensorList([...this.tensors], this.elementShape, this.elementDtype);
+  }
+  clearAndClose(keepIds) {
+    this.tensors.forEach((tensor2) => {
+      if (keepIds == null || !keepIds.has(tensor2.id)) {
+        tensor2.dispose();
+      }
+    });
+    this.tensors.length = 0;
+    this.idTensor.dispose();
+  }
+  size() {
+    return this.tensors.length;
+  }
+  stack(elementShape, elementDtype, numElements = -1) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    if (numElements !== -1 && this.tensors.length !== numElements) {
+      throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);
+    }
+    assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, "TensorList shape mismatch: ");
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    return tidy(() => {
+      const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));
+      return stack(reshapedTensors, 0);
+    });
+  }
+  popBack(elementShape, elementDtype) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    if (this.size() === 0) {
+      throw new Error("Trying to pop from an empty list.");
+    }
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    const tensor2 = this.tensors.pop();
+    assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, "TensorList shape mismatch: ");
+    return reshape(tensor2, outputElementShape);
+  }
+  pushBack(tensor2) {
+    if (tensor2.dtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);
+    }
+    assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, "TensorList shape mismatch: ");
+    if (this.maxNumElements === this.size()) {
+      throw new Error(`Trying to push element into a full list.`);
+    }
+    keep(tensor2);
+    this.tensors.push(tensor2);
+  }
+  resize(size2) {
+    if (size2 < 0) {
+      throw new Error(`TensorListResize expects size to be non-negative. Got: ${size2}`);
+    }
+    if (this.maxNumElements !== -1 && size2 > this.maxNumElements) {
+      throw new Error(`TensorListResize input size ${size2} is greater maxNumElement ${this.maxNumElements}.`);
+    }
+    const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);
+    destTensorList.tensors.length = size2;
+    for (let i2 = 0; i2 < Math.min(this.tensors.length, size2); ++i2) {
+      destTensorList.tensors[i2] = this.tensors[i2];
+    }
+    return destTensorList;
+  }
+  getItem(elementIndex, elementShape, elementDtype) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    if (elementIndex < 0 || elementIndex > this.tensors.length) {
+      throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);
+    }
+    if (this.tensors[elementIndex] == null) {
+      throw new Error(`element at index ${elementIndex} is null.`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: ");
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    return reshape(this.tensors[elementIndex], outputElementShape);
+  }
+  setItem(elementIndex, tensor2) {
+    if (tensor2.dtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);
+    }
+    if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {
+      throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, "TensorList shape mismatch: ");
+    keep(tensor2);
+    this.tensors[elementIndex] = tensor2;
+  }
+  gather(indices, elementDtype, elementShape) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
+    indices = indices.slice(0, this.size());
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    if (indices.length === 0) {
+      return tensor([], [0].concat(outputElementShape));
+    }
+    return tidy(() => {
+      const tensors = indices.map((i2) => reshape(this.tensors[i2], outputElementShape));
+      return stack(tensors, 0);
+    });
+  }
+  concat(elementDtype, elementShape) {
+    if (!!elementDtype && elementDtype !== this.elementDtype) {
+      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    if (this.size() === 0) {
+      return tensor([], [0].concat(outputElementShape));
+    }
+    return tidy(() => {
+      const tensors = this.tensors.map((t2) => reshape(t2, outputElementShape));
+      return concat(tensors, 0);
+    });
+  }
+};
+function fromTensor(tensor2, elementShape, elementDtype) {
+  const dtype = tensor2.dtype;
+  if (tensor2.shape.length < 1) {
+    throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);
+  }
+  if (tensor2.dtype !== elementDtype) {
+    throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);
+  }
+  const tensorElementShape = tensor2.shape.slice(1);
+  assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, "TensorList shape mismatch: ");
+  const tensorList = unstack(tensor2);
+  return new TensorList(tensorList, elementShape, dtype);
+}
+function reserve(elementShape, elementDtype, numElements) {
+  return new TensorList([], elementShape, elementDtype, numElements);
+}
+function scatter(tensor2, indices, elementShape, numElements) {
+  if (indices.length !== tensor2.shape[0]) {
+    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);
+  }
+  const maxIndex = Math.max(...indices);
+  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {
+    throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);
+  }
+  const list = new TensorList([], elementShape, tensor2.dtype, numElements);
+  const tensors = unstack(tensor2, 0);
+  indices.forEach((value, index2) => {
+    list.setItem(value, tensors[index2]);
+  });
+  return list;
+}
+function split2(tensor2, length, elementShape) {
+  let totalLength = 0;
+  const cumulativeLengths = length.map((len) => {
+    totalLength += len;
+    return totalLength;
+  });
+  if (totalLength !== tensor2.shape[0]) {
+    throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);
+  }
+  const shapeWithoutFirstDim = tensor2.shape.slice(1);
+  const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);
+  const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;
+  const tensors = tidy(() => {
+    const tensors2 = [];
+    tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
+    for (let i2 = 0; i2 < length.length; ++i2) {
+      const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+      const indices = [0, previousLength, 0];
+      const sizes = [1, length[i2], elementPerRow];
+      tensors2[i2] = reshape(slice(tensor2, indices, sizes), outputElementShape);
+    }
+    tensor2.dispose();
+    return tensors2;
+  });
+  const list = new TensorList([], elementShape, tensor2.dtype, length.length);
+  for (let i2 = 0; i2 < tensors.length; i2++) {
+    list.setItem(i2, tensors[i2]);
+  }
+  return list;
+}
+var executeOp3 = async (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "If":
+    case "StatelessIf": {
+      const thenFunc = getParamValue("thenBranch", node2, tensorMap, context);
+      const elseFunc = getParamValue("elseBranch", node2, tensorMap, context);
+      const cond = getParamValue("cond", node2, tensorMap, context);
+      const args = getParamValue("args", node2, tensorMap, context);
+      const condValue = await cond.data();
+      if (condValue[0]) {
+        return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
+      } else {
+        return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
+      }
+    }
+    case "While":
+    case "StatelessWhile": {
+      const bodyFunc = getParamValue("body", node2, tensorMap, context);
+      const condFunc = getParamValue("cond", node2, tensorMap, context);
+      const args = getParamValue("args", node2, tensorMap, context);
+      const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
+      const argIds = args.map((tensor2) => tensor2.id);
+      let condValue = await condResult[0].data();
+      condResult.forEach((tensor2) => {
+        if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {
+          tensor2.dispose();
+        }
+      });
+      let result = args;
+      while (condValue[0]) {
+        const origResult = result;
+        result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
+        const resultIds = result.map((tensor2) => tensor2.id);
+        origResult.forEach((tensor2) => {
+          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {
+            tensor2.dispose();
+          }
+        });
+        const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
+        condValue = await condResult2[0].data();
+        condResult2.forEach((tensor2) => {
+          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {
+            tensor2.dispose();
+          }
+        });
+      }
+      return result;
+    }
+    case "LoopCond": {
+      const pred = getParamValue("pred", node2, tensorMap, context);
+      return [cloneTensor(pred)];
+    }
+    case "Switch": {
+      const pred = getParamValue("pred", node2, tensorMap, context);
+      let data = getParamValue("data", node2, tensorMap, context);
+      if (!data.kept) {
+        data = cloneTensor(data);
+      }
+      return (await pred.data())[0] ? [void 0, data] : [data, void 0];
+    }
+    case "Merge": {
+      const inputName = node2.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);
+      if (inputName) {
+        const data = getTensor(inputName, tensorMap, context);
+        return [cloneTensor(data)];
+      }
+      return void 0;
+    }
+    case "Enter": {
+      const frameId = getParamValue("frameName", node2, tensorMap, context);
+      const data = getParamValue("tensor", node2, tensorMap, context);
+      context.enterFrame(frameId);
+      return [cloneTensor(data)];
+    }
+    case "Exit": {
+      const data = getParamValue("tensor", node2, tensorMap, context);
+      context.exitFrame();
+      return [cloneTensor(data)];
+    }
+    case "NextIteration": {
+      const data = getParamValue("tensor", node2, tensorMap, context);
+      context.nextIteration();
+      return [cloneTensor(data)];
+    }
+    case "TensorArrayV3": {
+      const size2 = getParamValue("size", node2, tensorMap, context);
+      const dtype = getParamValue("dtype", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const dynamicSize = getParamValue("dynamicSize", node2, tensorMap, context);
+      const clearAfterRead = getParamValue("clearAfterRead", node2, tensorMap, context);
+      const identicalElementShapes = getParamValue("identicalElementShapes", node2, tensorMap, context);
+      const name = getParamValue("name", node2, tensorMap, context);
+      const tensorArray = new TensorArray(name, dtype, size2, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
+      context.addTensorArray(tensorArray);
+      return [tensorArray.idTensor, scalar(1)];
+    }
+    case "TensorArrayWriteV3": {
+      const id = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const index2 = getParamValue("index", node2, tensorMap, context);
+      const writeTensor = getParamValue("tensor", node2, tensorMap, context);
+      const writeTensorArray = context.getTensorArray(id.id);
+      writeTensorArray.write(index2, writeTensor);
+      return [writeTensorArray.idTensor];
+    }
+    case "TensorArrayReadV3": {
+      const readId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const readIndex = getParamValue("index", node2, tensorMap, context);
+      const readTensorArray = context.getTensorArray(readId.id);
+      return [readTensorArray.read(readIndex)];
+    }
+    case "TensorArrayGatherV3": {
+      const gatherId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const gatherIndices = getParamValue("indices", node2, tensorMap, context);
+      const gatherDtype = getParamValue("dtype", node2, tensorMap, context);
+      const gatherTensorArray = context.getTensorArray(gatherId.id);
+      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];
+    }
+    case "TensorArrayScatterV3": {
+      const scatterId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const scatterIndices = getParamValue("indices", node2, tensorMap, context);
+      const scatterTensor = getParamValue("tensor", node2, tensorMap, context);
+      const scatterTensorArray = context.getTensorArray(scatterId.id);
+      scatterTensorArray.scatter(scatterIndices, scatterTensor);
+      return [scatterTensorArray.idTensor];
+    }
+    case "TensorArrayConcatV3": {
+      const concatId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const concatTensorArray = context.getTensorArray(concatId.id);
+      const concatDtype = getParamValue("dtype", node2, tensorMap, context);
+      return [concatTensorArray.concat(concatDtype)];
+    }
+    case "TensorArraySplitV3": {
+      const splitId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const splitTensor = getParamValue("tensor", node2, tensorMap, context);
+      const lengths = getParamValue("lengths", node2, tensorMap, context);
+      const splitTensorArray = context.getTensorArray(splitId.id);
+      splitTensorArray.split(lengths, splitTensor);
+      return [splitTensorArray.idTensor];
+    }
+    case "TensorArraySizeV3": {
+      const sizeId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const sizeTensorArray = context.getTensorArray(sizeId.id);
+      return [scalar(sizeTensorArray.size(), "int32")];
+    }
+    case "TensorArrayCloseV3": {
+      const closeId = getParamValue("tensorArrayId", node2, tensorMap, context);
+      const closeTensorArray = context.getTensorArray(closeId.id);
+      closeTensorArray.clearAndClose();
+      return [closeTensorArray.idTensor];
+    }
+    case "TensorListSetItem": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const index2 = getParamValue("index", node2, tensorMap, context);
+      const writeTensor = getParamValue("tensor", node2, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      tensorList.setItem(index2, writeTensor);
+      return [tensorList.idTensor];
+    }
+    case "TensorListGetItem": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const readIndex = getParamValue("index", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const elementDType = getParamValue("elementDType", node2, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [tensorList.getItem(readIndex, elementShape, elementDType)];
+    }
+    case "TensorListScatterV2":
+    case "TensorListScatter": {
+      const scatterIndices = getParamValue("indices", node2, tensorMap, context);
+      const scatterTensor = getParamValue("tensor", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const numElements = getParamValue("numElements", node2, tensorMap, context);
+      const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListReserve":
+    case "EmptyTensorList": {
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
+      let numElementsParam;
+      if (node2.op === "TensorListReserve") {
+        numElementsParam = "numElements";
+      } else {
+        numElementsParam = "maxNumElements";
+      }
+      const numElements = getParamValue(numElementsParam, node2, tensorMap, context);
+      const tensorList = reserve(elementShape, elementDtype, numElements);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListGather": {
+      const gatherId = getParamValue("tensorListId", node2, tensorMap, context);
+      const gatherIndices = getParamValue("indices", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
+      const tensorList = context.getTensorList(gatherId.id);
+      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];
+    }
+    case "TensorListStack": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
+      const numElements = getParamValue("numElements", node2, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [tensorList.stack(elementShape, elementDtype, numElements)];
+    }
+    case "TensorListFromTensor": {
+      const tensor2 = getParamValue("tensor", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node2, tensorMap, context);
+      const tensorList = fromTensor(tensor2, elementShape, elementDtype);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListConcat":
+    case "TensorListConcatV2": {
+      const concatId = getParamValue("tensorListId", node2, tensorMap, context);
+      const tensorList = context.getTensorList(concatId.id);
+      const concatDtype = getParamValue("dtype", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      return [tensorList.concat(concatDtype, elementShape)];
+    }
+    case "TensorListPushBack": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const writeTensor = getParamValue("tensor", node2, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      tensorList.pushBack(writeTensor);
+      return [tensorList.idTensor];
+    }
+    case "TensorListPopBack": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const elementDType = getParamValue("elementDType", node2, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [tensorList.popBack(elementShape, elementDType)];
+    }
+    case "TensorListSplit": {
+      const splitTensor = getParamValue("tensor", node2, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node2, tensorMap, context);
+      const lengths = getParamValue("lengths", node2, tensorMap, context);
+      const tensorList = split2(splitTensor, lengths, elementShape);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListLength": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [scalar(tensorList.size(), "int32")];
+    }
+    case "TensorListResize": {
+      const idTensor = getParamValue("tensorListId", node2, tensorMap, context);
+      const size2 = getParamValue("size", node2, tensorMap, context);
+      const srcTensorList = context.getTensorList(idTensor.id);
+      const destTensorList = srcTensorList.resize(size2);
+      context.addTensorList(destTensorList);
+      return [destTensorList.idTensor];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+function fusedConvAndDepthWiseParams(node2, tensorMap, context) {
+  const [extraOp, activationFunc] = getParamValue("fusedOps", node2, tensorMap, context);
+  const isBiasAdd = extraOp === "biasadd";
+  const noBiasAdd = !isBiasAdd;
+  const isPrelu = activationFunc === "prelu";
+  const isBatchNorm = extraOp === "fusedbatchnorm";
+  const numArgs = getParamValue("numArgs", node2, tensorMap, context);
+  if (isBiasAdd) {
+    if (isPrelu && numArgs !== 2) {
+      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
+    }
+    if (!isPrelu && isBiasAdd && numArgs !== 1) {
+      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
+    }
+  }
+  if (isBatchNorm) {
+    throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
+  }
+  const stride = getParamValue("strides", node2, tensorMap, context);
+  const pad3 = getPadding(node2, tensorMap, context);
+  const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
+  const dilations = getParamValue("dilations", node2, tensorMap, context);
+  let [biasArg, preluArg] = getParamValue("args", node2, tensorMap, context);
+  if (noBiasAdd) {
+    preluArg = biasArg;
+    biasArg = void 0;
+  }
+  const leakyreluAlpha = getParamValue("leakyreluAlpha", node2, tensorMap, context);
+  return {
+    stride,
+    pad: pad3,
+    dataFormat,
+    dilations,
+    biasArg,
+    preluArg,
+    activationFunc,
+    leakyreluAlpha
+  };
+}
+var executeOp4 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Conv1D": {
+      const stride = getParamValue("stride", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
+      const dilation = getParamValue("dilation", node2, tensorMap, context);
+      return [conv1d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), stride, pad3, dataFormat, dilation)];
+    }
+    case "Conv2D": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getPadding(node2, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
+      const dilations = getParamValue("dilations", node2, tensorMap, context);
+      return [conv2d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];
+    }
+    case "_FusedConv2D": {
+      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node2, tensorMap, context);
+      return [fused_ops_exports.conv2d({
+        x: getParamValue("x", node2, tensorMap, context),
+        filter: getParamValue("filter", node2, tensorMap, context),
+        strides: [stride[1], stride[2]],
+        pad: pad3,
+        dataFormat,
+        dilations: [dilations[1], dilations[2]],
+        bias: biasArg,
+        activation: activationFunc,
+        preluActivationWeights: preluArg,
+        leakyreluAlpha
+      })];
+    }
+    case "FusedDepthwiseConv2dNative": {
+      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node2, tensorMap, context);
+      return [fused_ops_exports.depthwiseConv2d({
+        x: getParamValue("x", node2, tensorMap, context),
+        filter: getParamValue("filter", node2, tensorMap, context),
+        strides: [stride[1], stride[2]],
+        pad: pad3,
+        dataFormat,
+        dilations: [dilations[1], dilations[2]],
+        bias: biasArg,
+        activation: activationFunc,
+        preluActivationWeights: preluArg,
+        leakyreluAlpha
+      })];
+    }
+    case "Conv2DBackpropInput":
+    case "Conv2dTranspose": {
+      const shape = getParamValue("outputShape", node2, tensorMap, context);
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getPadding(node2, tensorMap, context);
+      return [conv2dTranspose(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), shape, [stride[1], stride[2]], pad3)];
+    }
+    case "DepthwiseConv2dNative":
+    case "DepthwiseConv2d": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getPadding(node2, tensorMap, context);
+      const dilations = getParamValue("dilations", node2, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
+      return [depthwiseConv2d(getParamValue("input", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];
+    }
+    case "Conv3D": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
+      const dilations = getParamValue("dilations", node2, tensorMap, context);
+      return [conv3d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];
+    }
+    case "AvgPool": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
+      return [avgPool(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];
+    }
+    case "MaxPool": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
+      return [maxPool(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];
+    }
+    case "MaxPoolWithArgmax": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
+      const includeBatchInIndex = getParamValue("includeBatchInIndex", node2, tensorMap, context);
+      const { result, indexes } = maxPoolWithArgmax(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);
+      return [result, indexes];
+    }
+    case "AvgPool3D": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
+      return [avgPool3d(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];
+    }
+    case "MaxPool3D": {
+      const stride = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node2, tensorMap, context);
+      return [maxPool3d(getParamValue("x", node2, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];
+    }
+    case "Dilation2D": {
+      const strides2 = getParamValue("strides", node2, tensorMap, context);
+      const pad3 = getParamValue("pad", node2, tensorMap, context);
+      const dilations = getParamValue("dilations", node2, tensorMap, context);
+      const strideHeight = strides2[1];
+      const strideWidth = strides2[2];
+      const dilationHeight = dilations[1];
+      const dilationWidth = dilations[2];
+      return [dilation2d(getParamValue("x", node2, tensorMap, context), getParamValue("filter", node2, tensorMap, context), [strideHeight, strideWidth], pad3, [dilationHeight, dilationWidth], "NHWC")];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp5 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Fill": {
+      const shape = getParamValue("shape", node2, tensorMap, context);
+      const dtype = getParamValue("dtype", node2, tensorMap, context);
+      const value = getParamValue("value", node2, tensorMap, context);
+      return [fill(shape, value, dtype)];
+    }
+    case "LinSpace": {
+      const start = getParamValue("start", node2, tensorMap, context);
+      const stop = getParamValue("stop", node2, tensorMap, context);
+      const num = getParamValue("num", node2, tensorMap, context);
+      return [linspace(start, stop, num)];
+    }
+    case "Multinomial": {
+      const logits = getParamValue("logits", node2, tensorMap, context);
+      const numSamples = getParamValue("numSamples", node2, tensorMap, context);
+      const seed = getParamValue("seed", node2, tensorMap, context);
+      return [multinomial(logits, numSamples, seed)];
+    }
+    case "OneHot": {
+      const indices = getParamValue("indices", node2, tensorMap, context);
+      const depth = getParamValue("depth", node2, tensorMap, context);
+      const onValue = getParamValue("onValue", node2, tensorMap, context);
+      const offValue = getParamValue("offValue", node2, tensorMap, context);
+      return [oneHot(indices, depth, onValue, offValue)];
+    }
+    case "Ones": {
+      return [ones2(getParamValue("shape", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
+    }
+    case "OnesLike": {
+      return [onesLike(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "RandomUniform": {
+      return [randomUniform(getParamValue("shape", node2, tensorMap, context), getParamValue("minval", node2, tensorMap, context), getParamValue("maxval", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
+    }
+    case "Range": {
+      const start = getParamValue("start", node2, tensorMap, context);
+      const stop = getParamValue("stop", node2, tensorMap, context);
+      const step5 = getParamValue("step", node2, tensorMap, context);
+      return [range(start, stop, step5, getParamValue("dtype", node2, tensorMap, context))];
+    }
+    case "TruncatedNormal": {
+      const shape = getParamValue("shape", node2, tensorMap, context);
+      const mean5 = getParamValue("mean", node2, tensorMap, context);
+      const stdDev = getParamValue("stdDev", node2, tensorMap, context);
+      const seed = getParamValue("seed", node2, tensorMap, context);
+      return [truncatedNormal(shape, mean5, stdDev, getParamValue("dtype", node2, tensorMap, context), seed)];
+    }
+    case "Zeros": {
+      return [zeros(getParamValue("shape", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
+    }
+    case "ZerosLike": {
+      return [zerosLike(getParamValue("x", node2, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+function nmsParams(node2, tensorMap, context) {
+  const boxes = getParamValue("boxes", node2, tensorMap, context);
+  const scores = getParamValue("scores", node2, tensorMap, context);
+  const maxOutputSize = getParamValue("maxOutputSize", node2, tensorMap, context);
+  const iouThreshold = getParamValue("iouThreshold", node2, tensorMap, context);
+  const scoreThreshold = getParamValue("scoreThreshold", node2, tensorMap, context);
+  const softNmsSigma = getParamValue("softNmsSigma", node2, tensorMap, context);
+  return {
+    boxes,
+    scores,
+    maxOutputSize,
+    iouThreshold,
+    scoreThreshold,
+    softNmsSigma
+  };
+}
+var executeOp6 = async (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "NonMaxSuppressionV5": {
+      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node2, tensorMap, context);
+      const result = await image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+      return [result.selectedIndices, result.selectedScores];
+    }
+    case "NonMaxSuppressionV4": {
+      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node2, tensorMap, context);
+      const padToMaxOutputSize = getParamValue("padToMaxOutputSize", node2, tensorMap, context);
+      const result = await image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+      return [result.selectedIndices, result.validOutputs];
+    }
+    case "NonMaxSuppressionV3":
+    case "NonMaxSuppressionV2": {
+      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node2, tensorMap, context);
+      return [await image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
+    }
+    case "Where": {
+      const condition = cast(getParamValue("condition", node2, tensorMap, context), "bool");
+      const result = [await whereAsync(condition)];
+      condition.dispose();
+      return result;
+    }
+    case "ListDiff": {
+      return setdiff1dAsync(getParamValue("x", node2, tensorMap, context), getParamValue("y", node2, tensorMap, context));
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp7 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "LowerBound": {
+      const sortedSequence = getParamValue("sortedSequence", node2, tensorMap, context);
+      const values = getParamValue("values", node2, tensorMap, context);
+      return [lowerBound(sortedSequence, values)];
+    }
+    case "TopKV2": {
+      const x = getParamValue("x", node2, tensorMap, context);
+      const k = getParamValue("k", node2, tensorMap, context);
+      const sorted = getParamValue("sorted", node2, tensorMap, context);
+      const result = topk(x, k, sorted);
+      return [result.values, result.indices];
+    }
+    case "UpperBound": {
+      const sortedSequence = getParamValue("sortedSequence", node2, tensorMap, context);
+      const values = getParamValue("values", node2, tensorMap, context);
+      return [upperBound(sortedSequence, values)];
+    }
+    case "Unique": {
+      const x = getParamValue("x", node2, tensorMap, context);
+      const result = unique(x);
+      return [result.values, result.indices];
+    }
+    case "UniqueV2": {
+      const x = getParamValue("x", node2, tensorMap, context);
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const result = unique(x, axis);
+      return [result.values, result.indices];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp8 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Const": {
+      return tensorMap[node2.name];
+    }
+    case "PlaceholderWithDefault":
+      const def = getParamValue("default", node2, tensorMap, context);
+      return [getTensor(node2.name, tensorMap, context) || def];
+    case "Placeholder":
+      return [getTensor(node2.name, tensorMap, context)];
+    case "Identity":
+    case "StopGradient":
+    case "FakeQuantWithMinMaxVars": {
+      const data2 = getParamValue("x", node2, tensorMap, context);
+      return [cloneTensor(data2)];
+    }
+    case "IdentityN":
+      return getParamValue("x", node2, tensorMap, context).map((t2) => cloneTensor(t2));
+    case "Snapshot":
+      const snapshot = getParamValue("x", node2, tensorMap, context);
+      return [cloneTensor(snapshot)];
+    case "Shape":
+      return [tensor1d(getParamValue("x", node2, tensorMap, context).shape, "int32")];
+    case "ShapeN":
+      return getParamValue("x", node2, tensorMap, context).map((t2) => tensor1d(t2.shape));
+    case "Size":
+      return [scalar(getParamValue("x", node2, tensorMap, context).size, "int32")];
+    case "Rank":
+      return [scalar(getParamValue("x", node2, tensorMap, context).rank, "int32")];
+    case "NoOp":
+      return [scalar(1)];
+    case "Print":
+      const input2 = getParamValue("x", node2, tensorMap, context);
+      const data = getParamValue("data", node2, tensorMap, context);
+      const message = getParamValue("message", node2, tensorMap, context);
+      const summarize = getParamValue("summarize", node2, tensorMap, context);
+      console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
+      console.log(message);
+      for (let i2 = 0; i2 < data.length; i2++) {
+        console.log(Array.prototype.slice.call(data[i2].dataSync()).slice(0, summarize));
+      }
+      return [input2];
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var HashTable = class {
+  constructor(keyDType, valueDType) {
+    this.keyDType = keyDType;
+    this.valueDType = valueDType;
+    this.handle = scalar(0);
+    this.tensorMap = /* @__PURE__ */ new Map();
+    keep(this.handle);
+  }
+  get id() {
+    return this.handle.id;
+  }
+  clearAndClose() {
+    this.tensorMap.forEach((value) => value.dispose());
+    this.tensorMap.clear();
+    this.handle.dispose();
+  }
+  size() {
+    return this.tensorMap.size;
+  }
+  tensorSize() {
+    return scalar(this.size(), "int32");
+  }
+  async import(keys, values) {
+    this.checkKeyAndValueTensor(keys, values);
+    const $keys = await keys.data();
+    this.tensorMap.forEach((value) => value.dispose());
+    this.tensorMap.clear();
+    return tidy(() => {
+      const $values = unstack(values);
+      const keysLength = $keys.length;
+      const valuesLength = $values.length;
+      util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);
+      for (let i2 = 0; i2 < keysLength; i2++) {
+        const key = $keys[i2];
+        const value = $values[i2];
+        keep(value);
+        this.tensorMap.set(key, value);
+      }
+      return this.handle;
+    });
+  }
+  async find(keys, defaultValue) {
+    this.checkKeyAndValueTensor(keys, defaultValue);
+    const $keys = await keys.data();
+    return tidy(() => {
+      const result = [];
+      for (let i2 = 0; i2 < $keys.length; i2++) {
+        const key = $keys[i2];
+        const value = this.findWithDefault(key, defaultValue);
+        result.push(value);
+      }
+      return stack(result);
+    });
+  }
+  findWithDefault(key, defaultValue) {
+    const result = this.tensorMap.get(key);
+    return result != null ? result : defaultValue;
+  }
+  checkKeyAndValueTensor(key, value) {
+    if (key.dtype !== this.keyDType) {
+      throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);
+    }
+    if (value.dtype !== this.valueDType) {
+      throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);
+    }
+  }
+};
+var executeOp9 = async (node2, tensorMap, context, resourceManager) => {
+  switch (node2.op) {
+    case "HashTable":
+    case "HashTableV2": {
+      const keyDType = getParamValue("keyDType", node2, tensorMap, context);
+      const valueDType = getParamValue("valueDType", node2, tensorMap, context);
+      const hashTable = new HashTable(keyDType, valueDType);
+      resourceManager.addHashTable(node2.name, hashTable);
+      return [hashTable.handle];
+    }
+    case "LookupTableImport":
+    case "LookupTableImportV2": {
+      const handle = getParamValue("tableHandle", node2, tensorMap, context, resourceManager);
+      const keys = getParamValue("keys", node2, tensorMap, context);
+      const values = getParamValue("values", node2, tensorMap, context);
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [await hashTable.import(keys, values)];
+    }
+    case "LookupTableFind":
+    case "LookupTableFindV2": {
+      const handle = getParamValue("tableHandle", node2, tensorMap, context, resourceManager);
+      const keys = getParamValue("keys", node2, tensorMap, context);
+      const defaultValue = getParamValue("defaultValue", node2, tensorMap, context);
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [await hashTable.find(keys, defaultValue)];
+    }
+    case "LookupTableSize":
+    case "LookupTableSizeV2": {
+      const handle = getParamValue("tableHandle", node2, tensorMap, context, resourceManager);
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [hashTable.tensorSize()];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp10 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "ResizeBilinear": {
+      const images = getParamValue("images", node2, tensorMap, context);
+      const size2 = getParamValue("size", node2, tensorMap, context);
+      const alignCorners = getParamValue("alignCorners", node2, tensorMap, context);
+      const halfPixelCenters = getParamValue("halfPixelCenters", node2, tensorMap, context);
+      return [image.resizeBilinear(images, [size2[0], size2[1]], alignCorners, halfPixelCenters)];
+    }
+    case "ResizeNearestNeighbor": {
+      const images = getParamValue("images", node2, tensorMap, context);
+      const size2 = getParamValue("size", node2, tensorMap, context);
+      const alignCorners = getParamValue("alignCorners", node2, tensorMap, context);
+      const halfPixelCenters = getParamValue("halfPixelCenters", node2, tensorMap, context);
+      return [image.resizeNearestNeighbor(images, [size2[0], size2[1]], alignCorners, halfPixelCenters)];
+    }
+    case "CropAndResize": {
+      const image2 = getParamValue("image", node2, tensorMap, context);
+      const boxes = getParamValue("boxes", node2, tensorMap, context);
+      const boxInd = getParamValue("boxInd", node2, tensorMap, context);
+      const cropSize = getParamValue("cropSize", node2, tensorMap, context);
+      const method = getParamValue("method", node2, tensorMap, context);
+      const extrapolationValue = getParamValue("extrapolationValue", node2, tensorMap, context);
+      return [image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)];
+    }
+    case "ImageProjectiveTransformV3": {
+      const images = getParamValue("images", node2, tensorMap, context);
+      const transforms = getParamValue("transforms", node2, tensorMap, context);
+      const outputShape = getParamValue("outputShape", node2, tensorMap, context);
+      const fillValue = getParamValue("fillValue", node2, tensorMap, context);
+      const interpolation = getParamValue("interpolation", node2, tensorMap, context);
+      const fillMode = getParamValue("fillMode", node2, tensorMap, context);
+      return [image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp11 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Equal": {
+      return [equal(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "NotEqual": {
+      return [notEqual(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Greater": {
+      return [greater(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "GreaterEqual": {
+      return [greaterEqual(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Less": {
+      return [less(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "LessEqual": {
+      return [lessEqual(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "LogicalAnd": {
+      return [logicalAnd(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "LogicalNot": {
+      return [logicalNot(getParamValue("a", node2, tensorMap, context))];
+    }
+    case "LogicalOr": {
+      return [logicalOr(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    case "Select":
+    case "SelectV2": {
+      return [where(getParamValue("condition", node2, tensorMap, context), getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp12 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "BatchMatMul":
+    case "BatchMatMulV2":
+    case "MatMul":
+      return [matMul(getParamValue("a", node2, tensorMap, context), getParamValue("b", node2, tensorMap, context), getParamValue("transposeA", node2, tensorMap, context), getParamValue("transposeB", node2, tensorMap, context))];
+    case "Einsum":
+      return [einsum(getParamValue("equation", node2, tensorMap, context), ...getParamValue("tensors", node2, tensorMap, context))];
+    case "Transpose":
+      return [transpose(getParamValue("x", node2, tensorMap, context), getParamValue("perm", node2, tensorMap, context))];
+    case "_FusedMatMul":
+      const [extraOp, activationFunc] = getParamValue("fusedOps", node2, tensorMap, context);
+      const isBiasAdd = extraOp === "biasadd";
+      const isPrelu = activationFunc === "prelu";
+      const numArgs = getParamValue("numArgs", node2, tensorMap, context);
+      const leakyreluAlpha = getParamValue("leakyreluAlpha", node2, tensorMap, context);
+      if (isBiasAdd) {
+        if (isPrelu && numArgs !== 2) {
+          throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
+        }
+        if (!isPrelu && numArgs !== 1) {
+          throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
+        }
+      }
+      const [biasArg, preluArg] = getParamValue("args", node2, tensorMap, context);
+      return [fused_ops_exports.matMul({
+        a: getParamValue("a", node2, tensorMap, context),
+        b: getParamValue("b", node2, tensorMap, context),
+        transposeA: getParamValue("transposeA", node2, tensorMap, context),
+        transposeB: getParamValue("transposeB", node2, tensorMap, context),
+        bias: biasArg,
+        activation: activationFunc,
+        preluActivationWeights: preluArg,
+        leakyreluAlpha
+      })];
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp13 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "EuclideanNorm":
+      return [euclideanNorm(getParamValue("x", node2, tensorMap, context), getParamValue("axis", node2, tensorMap, context), getParamValue("keepDims", node2, tensorMap, context))];
+    case "FusedBatchNorm":
+    case "FusedBatchNormV2": {
+      return [batchNorm(getParamValue("x", node2, tensorMap, context), getParamValue("mean", node2, tensorMap, context), getParamValue("variance", node2, tensorMap, context), getParamValue("offset", node2, tensorMap, context), getParamValue("scale", node2, tensorMap, context), getParamValue("epsilon", node2, tensorMap, context))];
+    }
+    case "FusedBatchNormV3": {
+      return [batchNorm(getParamValue("x", node2, tensorMap, context), getParamValue("mean", node2, tensorMap, context), getParamValue("variance", node2, tensorMap, context), getParamValue("offset", node2, tensorMap, context), getParamValue("scale", node2, tensorMap, context), getParamValue("epsilon", node2, tensorMap, context))];
+    }
+    case "LRN": {
+      return [localResponseNormalization(getParamValue("x", node2, tensorMap, context), getParamValue("radius", node2, tensorMap, context), getParamValue("bias", node2, tensorMap, context), getParamValue("alpha", node2, tensorMap, context), getParamValue("beta", node2, tensorMap, context))];
+    }
+    case "Softmax": {
+      return [softmax(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "LogSoftmax": {
+      return [logSoftmax(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "SparseToDense": {
+      return [sparseToDense(getParamValue("sparseIndices", node2, tensorMap, context), getParamValue("outputShape", node2, tensorMap, context), getParamValue("sparseValues", node2, tensorMap, context), getParamValue("defaultValue", node2, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp14 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Max": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [max(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "Mean": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [mean(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "Min": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [min(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "Sum": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [sum2(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "All": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [all(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "Any": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [any(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "ArgMax": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      return [argMax(getParamValue("x", node2, tensorMap, context), axis)];
+    }
+    case "ArgMin": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      return [argMin(getParamValue("x", node2, tensorMap, context), axis)];
+    }
+    case "Prod": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node2, tensorMap, context);
+      return [prod(getParamValue("x", node2, tensorMap, context), axis, keepDims)];
+    }
+    case "Cumprod": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const exclusive = getParamValue("exclusive", node2, tensorMap, context);
+      const reverse5 = getParamValue("reverse", node2, tensorMap, context);
+      return [cumprod(getParamValue("x", node2, tensorMap, context), axis, exclusive, reverse5)];
+    }
+    case "Cumsum": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const exclusive = getParamValue("exclusive", node2, tensorMap, context);
+      const reverse5 = getParamValue("reverse", node2, tensorMap, context);
+      return [cumsum(getParamValue("x", node2, tensorMap, context), axis, exclusive, reverse5)];
+    }
+    case "Bincount":
+      const x = getParamValue("x", node2, tensorMap, context);
+      const weights = getParamValue("weights", node2, tensorMap, context);
+      const size2 = getParamValue("size", node2, tensorMap, context);
+      return [bincount(x, weights, size2)];
+    case "DenseBincount": {
+      const x2 = getParamValue("x", node2, tensorMap, context);
+      const weights2 = getParamValue("weights", node2, tensorMap, context);
+      const size22 = getParamValue("size", node2, tensorMap, context);
+      const binaryOutput = getParamValue("binaryOutput", node2, tensorMap, context);
+      return [denseBincount(x2, weights2, size22, binaryOutput)];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp15 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "ConcatV2":
+    case "Concat": {
+      const n = getParamValue("n", node2, tensorMap, context);
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      let inputs = getParamValue("tensors", node2, tensorMap, context);
+      inputs = inputs.slice(0, n);
+      return [concat(inputs, axis)];
+    }
+    case "Gather": {
+      const input2 = getParamValue("x", node2, tensorMap, context);
+      const indices = getParamValue("indices", node2, tensorMap, context);
+      return [gather(input2, cast(indices, "int32"), 0)];
+    }
+    case "GatherV2": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const batchDims = getParamValue("batchDims", node2, tensorMap, context);
+      const input2 = getParamValue("x", node2, tensorMap, context);
+      const indices = getParamValue("indices", node2, tensorMap, context);
+      return [gather(input2, cast(indices, "int32"), axis, batchDims)];
+    }
+    case "Reverse": {
+      const dims = getParamValue("dims", node2, tensorMap, context);
+      const axis = [];
+      for (let i2 = 0; i2 < dims.length; i2++) {
+        if (dims[i2]) {
+          axis.push(i2);
+        }
+      }
+      const input2 = getParamValue("x", node2, tensorMap, context);
+      return [reverse(input2, axis)];
+    }
+    case "ReverseV2": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const input2 = getParamValue("x", node2, tensorMap, context);
+      return [reverse(input2, axis)];
+    }
+    case "Slice": {
+      const begin = getParamValue("begin", node2, tensorMap, context);
+      const size2 = getParamValue("size", node2, tensorMap, context);
+      return [slice(getParamValue("x", node2, tensorMap, context), begin, size2)];
+    }
+    case "StridedSlice": {
+      const begin = getParamValue("begin", node2, tensorMap, context);
+      const end = getParamValue("end", node2, tensorMap, context);
+      const strides2 = getParamValue("strides", node2, tensorMap, context);
+      const beginMask = getParamValue("beginMask", node2, tensorMap, context);
+      const endMask = getParamValue("endMask", node2, tensorMap, context);
+      const ellipsisMask = getParamValue("ellipsisMask", node2, tensorMap, context);
+      const newAxisMask = getParamValue("newAxisMask", node2, tensorMap, context);
+      const shrinkAxisMask = getParamValue("shrinkAxisMask", node2, tensorMap, context);
+      const tensor2 = getParamValue("x", node2, tensorMap, context);
+      return [stridedSlice(tensor2, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];
+    }
+    case "Pack": {
+      return tidy(() => {
+        const axis = getParamValue("axis", node2, tensorMap, context);
+        const tensors = getParamValue("tensors", node2, tensorMap, context);
+        const shape = tensors[0].shape;
+        const squeezedShape = squeeze(tensors[0]).shape;
+        const mapped = tensors.map((tensor2) => {
+          const sameShape = util_exports.arraysEqual(tensor2.shape, shape);
+          if (!sameShape && !util_exports.arraysEqual(squeeze(tensor2).shape, squeezedShape)) {
+            throw new Error("the input tensors shape does not match");
+          }
+          return sameShape ? tensor2 : reshape(tensor2, shape);
+        });
+        return [stack(mapped, axis)];
+      });
+    }
+    case "Unpack": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const tensor2 = getParamValue("tensor", node2, tensorMap, context);
+      return unstack(tensor2, axis);
+    }
+    case "Tile": {
+      const reps = getParamValue("reps", node2, tensorMap, context);
+      return [tile(getParamValue("x", node2, tensorMap, context), reps)];
+    }
+    case "Split":
+    case "SplitV": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      const numOrSizeSplits = getParamValue("numOrSizeSplits", node2, tensorMap, context);
+      const tensor2 = getParamValue("x", node2, tensorMap, context);
+      return split(tensor2, numOrSizeSplits, axis);
+    }
+    case "ScatterNd": {
+      const indices = getParamValue("indices", node2, tensorMap, context);
+      const values = getParamValue("values", node2, tensorMap, context);
+      const shape = getParamValue("shape", node2, tensorMap, context);
+      return [scatterND(indices, values, shape)];
+    }
+    case "GatherNd": {
+      const x = getParamValue("x", node2, tensorMap, context);
+      const indices = getParamValue("indices", node2, tensorMap, context);
+      return [gatherND(x, indices)];
+    }
+    case "SparseToDense": {
+      const indices = getParamValue("sparseIndices", node2, tensorMap, context);
+      const shape = getParamValue("outputShape", node2, tensorMap, context);
+      const sparseValues = getParamValue("sparseValues", node2, tensorMap, context);
+      const defaultValue = getParamValue("defaultValue", node2, tensorMap, context);
+      return [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : cast(defaultValue, sparseValues.dtype))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp16 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "SparseFillEmptyRows": {
+      const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = sparse.sparseFillEmptyRows(getParamValue("indices", node2, tensorMap, context), getParamValue("values", node2, tensorMap, context), getParamValue("denseShape", node2, tensorMap, context), getParamValue("defaultValue", node2, tensorMap, context));
+      return [
+        outputIndices,
+        outputValues,
+        emptyRowIndicator,
+        reverseIndexMap
+      ];
+    }
+    case "SparseReshape": {
+      const { outputIndices, outputShape } = sparse.sparseReshape(getParamValue("inputIndices", node2, tensorMap, context), getParamValue("inputShape", node2, tensorMap, context), getParamValue("newShape", node2, tensorMap, context));
+      return [outputIndices, outputShape];
+    }
+    case "SparseSegmentMean": {
+      const outputData = sparse.sparseSegmentMean(getParamValue("data", node2, tensorMap, context), getParamValue("indices", node2, tensorMap, context), getParamValue("segmentIds", node2, tensorMap, context));
+      return [outputData];
+    }
+    case "SparseSegmentSum": {
+      const outputData = sparse.sparseSegmentSum(getParamValue("data", node2, tensorMap, context), getParamValue("indices", node2, tensorMap, context), getParamValue("segmentIds", node2, tensorMap, context));
+      return [outputData];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp17 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "FFT": {
+      return [fft(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "IFFT": {
+      return [ifft(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "RFFT": {
+      return [rfft(getParamValue("x", node2, tensorMap, context))];
+    }
+    case "IRFFT": {
+      return [irfft(getParamValue("x", node2, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp18 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "StringNGrams": {
+      const { nGrams, nGramsSplits } = string.stringNGrams(getParamValue("data", node2, tensorMap, context), getParamValue("dataSplits", node2, tensorMap, context), getParamValue("separator", node2, tensorMap, context), getParamValue("nGramWidths", node2, tensorMap, context), getParamValue("leftPad", node2, tensorMap, context), getParamValue("rightPad", node2, tensorMap, context), getParamValue("padWidth", node2, tensorMap, context), getParamValue("preserveShortSequences", node2, tensorMap, context));
+      return [nGrams, nGramsSplits];
+    }
+    case "StringSplit": {
+      const { indices, values, shape } = string.stringSplit(getParamValue("input", node2, tensorMap, context), getParamValue("delimiter", node2, tensorMap, context), getParamValue("skipEmpty", node2, tensorMap, context));
+      return [indices, values, shape];
+    }
+    case "StringToHashBucketFast": {
+      const output = string.stringToHashBucketFast(getParamValue("input", node2, tensorMap, context), getParamValue("numBuckets", node2, tensorMap, context));
+      return [output];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+var executeOp19 = (node2, tensorMap, context) => {
+  switch (node2.op) {
+    case "Cast": {
+      return [cast(getParamValue("x", node2, tensorMap, context), getParamValue("dtype", node2, tensorMap, context))];
+    }
+    case "ExpandDims": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      return [expandDims(getParamValue("x", node2, tensorMap, context), axis)];
+    }
+    case "Squeeze": {
+      const axis = getParamValue("axis", node2, tensorMap, context);
+      return [squeeze(getParamValue("x", node2, tensorMap, context), axis)];
+    }
+    case "Reshape": {
+      return [reshape(getParamValue("x", node2, tensorMap, context), getParamValue("shape", node2, tensorMap, context))];
+    }
+    case "MirrorPad": {
+      return [mirrorPad(getParamValue("x", node2, tensorMap, context), getParamValue("padding", node2, tensorMap, context), getParamValue("mode", node2, tensorMap, context))];
+    }
+    case "PadV2":
+    case "Pad": {
+      return [pad(getParamValue("x", node2, tensorMap, context), getParamValue("padding", node2, tensorMap, context), getParamValue("constantValue", node2, tensorMap, context))];
+    }
+    case "SpaceToBatchND": {
+      const blockShape = getParamValue("blockShape", node2, tensorMap, context);
+      const paddings = getParamValue("paddings", node2, tensorMap, context);
+      return [spaceToBatchND(getParamValue("x", node2, tensorMap, context), blockShape, paddings)];
+    }
+    case "BatchToSpaceND": {
+      const blockShape = getParamValue("blockShape", node2, tensorMap, context);
+      const crops = getParamValue("crops", node2, tensorMap, context);
+      return [batchToSpaceND(getParamValue("x", node2, tensorMap, context), blockShape, crops)];
+    }
+    case "DepthToSpace": {
+      const blockSize = getParamValue("blockSize", node2, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node2, tensorMap, context).toUpperCase();
+      return [depthToSpace(getParamValue("x", node2, tensorMap, context), blockSize, dataFormat)];
+    }
+    case "BroadcastTo": {
+      return [broadcastTo(getParamValue("x", node2, tensorMap, context), getParamValue("shape", node2, tensorMap, context))];
+    }
+    case "BroadcastArgs": {
+      return [broadcastArgs(getParamValue("s0", node2, tensorMap, context), getParamValue("s1", node2, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node2.op} is not implemented`);
+  }
+};
+function executeOp20(node2, tensorMap, context, resourceManager) {
+  const value = ((node22, tensorMap2, context2) => {
+    switch (node22.category) {
+      case "arithmetic":
+        return tidy(() => executeOp(node22, tensorMap2, context2));
+      case "basic_math":
+        return tidy(() => executeOp2(node22, tensorMap2, context2));
+      case "control":
+        return executeOp3(node22, tensorMap2, context2);
+      case "convolution":
+        return tidy(() => executeOp4(node22, tensorMap2, context2));
+      case "creation":
+        return tidy(() => executeOp5(node22, tensorMap2, context2));
+      case "dynamic":
+        return executeOp6(node22, tensorMap2, context2);
+      case "evaluation":
+        return tidy(() => executeOp7(node22, tensorMap2, context2));
+      case "image":
+        return tidy(() => executeOp10(node22, tensorMap2, context2));
+      case "graph":
+        return tidy(() => executeOp8(node22, tensorMap2, context2));
+      case "logical":
+        return tidy(() => executeOp11(node22, tensorMap2, context2));
+      case "matrices":
+        return tidy(() => executeOp12(node22, tensorMap2, context2));
+      case "normalization":
+        return tidy(() => executeOp13(node22, tensorMap2, context2));
+      case "reduction":
+        return tidy(() => executeOp14(node22, tensorMap2, context2));
+      case "slice_join":
+        return tidy(() => executeOp15(node22, tensorMap2, context2));
+      case "sparse":
+        return tidy(() => executeOp16(node22, tensorMap2, context2));
+      case "spectral":
+        return tidy(() => executeOp17(node22, tensorMap2, context2));
+      case "string":
+        return tidy(() => executeOp18(node22, tensorMap2, context2));
+      case "transformation":
+        return tidy(() => executeOp19(node22, tensorMap2, context2));
+      case "hash_table":
+        return executeOp9(node22, tensorMap2, context2, resourceManager);
+      case "custom":
+        const opMapper = getRegisteredOp(node22.op);
+        if (opMapper && opMapper.customExecutor) {
+          return opMapper.customExecutor(new NodeValueImpl(node22, tensorMap2, context2));
+        } else {
+          throw TypeError(`Custom op ${node22.op} is not registered.`);
+        }
+      default:
+        throw TypeError(`Unknown op '${node22.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
+    }
+  })(node2, tensorMap, context);
+  if (util_exports.isPromise(value)) {
+    return value.then((data) => [].concat(data));
+  }
+  return [].concat(value);
+}
+var ExecutionContext = class {
+  constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {
+    this.weightMap = weightMap;
+    this.tensorArrayMap = tensorArrayMap;
+    this.tensorListMap = tensorListMap;
+    this.functionMap = functionMap;
+    this.rootContext = { id: 0, frameName: "", iterationId: 0 };
+    this.contexts = [this.rootContext];
+    this.lastId = 0;
+    this.generateCurrentContextIds();
+  }
+  newFrame(id, frameName) {
+    return { id, frameName, iterationId: 0 };
+  }
+  set currentContext(contexts2) {
+    if (this.contexts !== contexts2) {
+      this.contexts = contexts2;
+      this.generateCurrentContextIds();
+    }
+  }
+  get currentContext() {
+    return this.contexts;
+  }
+  get currentContextId() {
+    return this._currentContextIds[0];
+  }
+  get currentContextIds() {
+    return this._currentContextIds;
+  }
+  generateCurrentContextIds() {
+    const names = [];
+    for (let i2 = 0; i2 < this.contexts.length - 1; i2++) {
+      const contexts2 = this.contexts.slice(0, this.contexts.length - i2);
+      names.push(this.contextIdforContexts(contexts2));
+    }
+    names.push("");
+    this._currentContextIds = names;
+  }
+  contextIdforContexts(contexts2) {
+    return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : "";
+  }
+  enterFrame(frameId) {
+    if (this.contexts) {
+      this.lastId++;
+      this.contexts = this.contexts.slice();
+      this.contexts.push(this.newFrame(this.lastId, frameId));
+      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
+    }
+  }
+  exitFrame() {
+    if (this.contexts && this.contexts.length > 1) {
+      this.contexts = this.contexts.slice();
+      this.contexts.splice(-1);
+      this.currentContextIds.shift();
+    } else {
+      throw new Error("Cannot exit frame, the context is empty");
+    }
+  }
+  nextIteration() {
+    if (this.contexts && this.contexts.length > 0) {
+      this.contexts = this.contexts.slice();
+      this.lastId++;
+      const context = Object.assign({}, this.contexts[this.contexts.length - 1]);
+      context.iterationId += 1;
+      context.id = this.lastId;
+      this.contexts.splice(-1, 1, context);
+      this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
+    } else {
+      throw new Error("Cannot increase frame iteration, the context is empty");
+    }
+  }
+  getWeight(name) {
+    return this.weightMap[name];
+  }
+  addTensorArray(tensorArray) {
+    this.tensorArrayMap[tensorArray.id] = tensorArray;
+  }
+  getTensorArray(id) {
+    return this.tensorArrayMap[id];
+  }
+  addTensorList(tensorList) {
+    this.tensorListMap[tensorList.id] = tensorList;
+  }
+  getTensorList(id) {
+    return this.tensorListMap[id];
+  }
+  dispose(keepIds) {
+    for (const key in this.tensorArrayMap) {
+      this.tensorArrayMap[key].clearAndClose(keepIds);
+    }
+    for (const key in this.tensorListMap) {
+      this.tensorListMap[key].clearAndClose(keepIds);
+    }
+  }
+};
+function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {
+  const usedNodes = /* @__PURE__ */ new Set();
+  const missingInputs = [];
+  let dynamicNode = null;
+  let syncInputs = null;
+  const seen = /* @__PURE__ */ new Set();
+  const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);
+  let initNodeNames = [];
+  if (initNodes != null) {
+    initNodeNames = initNodes.map((node2) => parseNodeName(node2.name)[0]);
+  }
+  const frontier = [...outputs];
+  while (frontier.length > 0) {
+    const node2 = frontier.pop();
+    if (isControlFlow(node2) || isDynamicShape(node2) || isHashTable(node2)) {
+      if (dynamicNode == null) {
+        dynamicNode = node2;
+        syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));
+      }
+    }
+    usedNodes.add(node2.name);
+    if (weightMap[node2.name] != null) {
+      continue;
+    }
+    if (inputNodeNames.indexOf(node2.name) !== -1) {
+      continue;
+    }
+    if (initNodeNames.indexOf(node2.name) !== -1) {
+      continue;
+    }
+    if (node2.inputs.length === 0) {
+      missingInputs.push(node2.name);
+      continue;
+    }
+    node2.inputs.forEach((input2) => {
+      if (seen.has(input2.name)) {
+        return;
+      }
+      seen.add(input2.name);
+      frontier.push(input2);
+    });
+  }
+  return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };
+}
+function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {
+  const { usedNodes, inputs } = executionInfo;
+  const frontier = [];
+  const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]);
+  const initNodes = graph.initNodes;
+  inputNodes.forEach((input2) => {
+    if (usedNodes.has(input2.name)) {
+      frontier.push(input2);
+    }
+  });
+  graph.weights.forEach((weight) => {
+    if (usedNodes.has(weight.name)) {
+      frontier.push(weight);
+    }
+  });
+  if (initNodes != null) {
+    initNodes.forEach((node2) => {
+      if (usedNodes.has(node2.name)) {
+        frontier.push(node2);
+      }
+    });
+  }
+  const seen = /* @__PURE__ */ new Set();
+  const orderedNodes = [];
+  while (frontier.length > 0) {
+    const node2 = frontier.pop();
+    seen.add(node2.name);
+    if (!weightMap[node2.name]) {
+      orderedNodes.push(node2);
+    }
+    node2.children.forEach((child) => {
+      if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {
+        frontier.push(child);
+      }
+    });
+  }
+  return orderedNodes;
+}
+var CONTROL_FLOW_OPS = [
+  "Switch",
+  "Merge",
+  "Enter",
+  "Exit",
+  "NextIteration",
+  "StatelessIf",
+  "StatelessWhile",
+  "if",
+  "While"
+];
+var DYNAMIC_SHAPE_OPS = [
+  "NonMaxSuppressionV2",
+  "NonMaxSuppressionV3",
+  "NonMaxSuppressionV5",
+  "Where"
+];
+var HASH_TABLE_OPS = [
+  "HashTable",
+  "HashTableV2",
+  "LookupTableImport",
+  "LookupTableImportV2",
+  "LookupTableFind",
+  "LookupTableFindV2",
+  "LookupTableSize",
+  "LookupTableSizeV2"
+];
+function isControlFlow(node2) {
+  return CONTROL_FLOW_OPS.indexOf(node2.op) >= 0;
+}
+function isDynamicShape(node2) {
+  return DYNAMIC_SHAPE_OPS.indexOf(node2.op) >= 0;
+}
+function isHashTable(node2) {
+  return HASH_TABLE_OPS.indexOf(node2.op) >= 0;
+}
+var GraphExecutor = class {
+  constructor(graph, parent) {
+    this.graph = graph;
+    this.parent = parent;
+    this.compiledMap = /* @__PURE__ */ new Map();
+    this._weightMap = {};
+    this.SEPERATOR = ",";
+    this._functions = {};
+    this._functionExecutorMap = {};
+    this.intermediateTensors = {};
+    this.keepTensorForDebug = false;
+    this._outputs = graph.outputs;
+    this._inputs = graph.inputs;
+    this._initNodes = graph.initNodes;
+    this._signature = graph.signature;
+    this._functions = graph.functions;
+    if (graph.functions != null) {
+      Object.keys(graph.functions).forEach((name) => {
+        this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this);
+      });
+    }
+  }
+  get weightIds() {
+    return this.parent ? this.parent.weightIds : this._weightIds;
+  }
+  get functionExecutorMap() {
+    return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;
+  }
+  get weightMap() {
+    return this.parent ? this.parent.weightMap : this._weightMap;
+  }
+  set weightMap(weightMap) {
+    const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));
+    this._weightIds = [].concat(...weightIds);
+    this._weightMap = weightMap;
+  }
+  set resourceManager(resourceManager) {
+    this._resourceManager = resourceManager;
+  }
+  get inputs() {
+    return this._inputs.map((node2) => {
+      return {
+        name: node2.name,
+        shape: node2.attrParams["shape"] ? node2.attrParams["shape"].value : void 0,
+        dtype: node2.attrParams["dtype"] ? node2.attrParams["dtype"].value : void 0
+      };
+    });
+  }
+  get outputs() {
+    return this._outputs.map((node2) => {
+      return {
+        name: node2.name,
+        shape: node2.attrParams["shape"] ? node2.attrParams["shape"].value : void 0,
+        dtype: node2.attrParams["dtype"] ? node2.attrParams["dtype"].value : void 0
+      };
+    });
+  }
+  get inputNodes() {
+    return this._inputs.map((node2) => node2.signatureKey || node2.name);
+  }
+  get outputNodes() {
+    return this._outputs.map((node2) => {
+      const name = node2.signatureKey || node2.name;
+      return node2.defaultOutput ? `${name}:${node2.defaultOutput}` : name;
+    });
+  }
+  get functions() {
+    return Object.keys(this._functions).reduce((map, key) => {
+      map[key] = this._functions[key].signature;
+      return map;
+    }, {});
+  }
+  getCompilationKey(inputs, outputs) {
+    const sortedInputs = inputs.map((node2) => node2.name).sort();
+    const sortedOutputs = outputs.map((node2) => node2.name).sort();
+    return sortedInputs.join(this.SEPERATOR) + "--" + sortedOutputs.join(this.SEPERATOR);
+  }
+  compile(inputs, outputs) {
+    const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
+    const { missingInputs, dynamicNode, syncInputs } = executionInfo;
+    if (dynamicNode != null) {
+      throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);
+    }
+    if (missingInputs.length > 0) {
+      const outNames = outputs.map((n) => n.name);
+      const inNames = Object.keys(inputs);
+      throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);
+    }
+    return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);
+  }
+  execute(inputs, outputs) {
+    inputs = this.mapInputs(inputs);
+    const names = Object.keys(inputs).sort();
+    this.checkInputs(inputs);
+    this.checkInputShapeAndType(inputs);
+    outputs = this.mapOutputs(outputs);
+    this.checkOutputs(outputs);
+    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
+    const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);
+    let outputNodes2 = outputNodeNames.map((name) => this.graph.nodes[name]);
+    this.resetIntermediateTensors();
+    if (outputNodes2.length === 0) {
+      outputNodes2 = this._outputs;
+    }
+    const compilationKey = this.getCompilationKey(inputNodes, outputNodes2);
+    let orderedNodes = this.compiledMap.get(compilationKey);
+    if (orderedNodes == null) {
+      orderedNodes = this.compile(inputs, outputNodes2);
+      this.compiledMap.set(compilationKey, orderedNodes);
+    }
+    const tensorArrayMap = {};
+    const tensorListMap = {};
+    return tidy(() => {
+      const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
+      const tensorsMap = Object.assign({}, this.weightMap);
+      Object.keys(inputs).forEach((name) => {
+        const [nodeName, index2] = parseNodeName(name);
+        const tensors = [];
+        tensors[index2] = inputs[name];
+        tensorsMap[nodeName] = tensors;
+      });
+      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
+      const intermediateTensorConsumerCount = {};
+      for (let i2 = 0; i2 < orderedNodes.length; i2++) {
+        const node2 = orderedNodes[i2];
+        if (!tensorsMap[node2.name]) {
+          const tensors = executeOp20(node2, tensorsMap, context, this._resourceManager);
+          if (util_exports.isPromise(tensors)) {
+            throw new Error(`The execution of the op '${node2.op}' returned a promise. Please use model.executeAsync() instead.`);
+          }
+          tensorsMap[node2.name] = tensors;
+          this.checkTensorForDisposal(node2.name, node2, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);
+        }
+      }
+      if (this.parent == null) {
+        context.dispose(tensorsToKeep);
+      }
+      return outputs.map((name) => getTensor(name, tensorsMap, context));
+    });
+  }
+  getFrozenTensorIds(tensorMap) {
+    const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));
+    return new Set(ids);
+  }
+  checkTensorForDisposal(nodeName, node2, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {
+    if (node2.category === "control" || outputNames.indexOf(nodeName) !== -1) {
+      return;
+    }
+    tensorMap[nodeName].forEach((tensor2) => {
+      if (tensor2 != null) {
+        intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node2.children.length;
+      }
+    });
+    node2.inputs.forEach((input2) => {
+      if (input2.category !== "control") {
+        const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);
+        if (tensors != null) {
+          tensors.forEach((tensor2) => {
+            if (tensor2 && !tensor2.kept && !tensorsToKeep.has(tensor2.id)) {
+              const count22 = intermediateTensorConsumerCount[tensor2.id];
+              if (count22 === 1) {
+                if (!this.keepTensorForDebug) {
+                  tensor2.dispose();
+                } else {
+                  const [nodeName2, index2] = getNodeNameAndIndex(node2.name, context);
+                  if (this.intermediateTensors[nodeName2]) {
+                    this.intermediateTensors[nodeName2][index2] = tensor2;
+                  } else {
+                    this.intermediateTensors[nodeName2] = [];
+                    this.intermediateTensors[nodeName2][index2] = tensor2;
+                  }
+                }
+                delete intermediateTensorConsumerCount[tensor2.id];
+              } else if (count22 != null) {
+                intermediateTensorConsumerCount[tensor2.id]--;
+              }
+            }
+          });
+        }
+      }
+    });
+  }
+  async executeAsync(inputs, outputs) {
+    return this._executeAsync(inputs, outputs);
+  }
+  disposeIntermediateTensors() {
+    if (!this.intermediateTensors) {
+      return;
+    }
+    Object.keys(this.intermediateTensors).forEach((key) => this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose()));
+    this.disposeTensorsMap();
+  }
+  disposeTensorsMap() {
+    if (!this.tensorsMap) {
+      return;
+    }
+    Object.keys(this.tensorsMap).forEach((key) => {
+      const tensorArray = this.tensorsMap[key];
+      tensorArray.forEach((tensor2) => {
+        if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !this.keepIds.has(tensor2.id)) {
+          tensor2.dispose();
+        }
+      });
+    });
+  }
+  getIntermediateTensors() {
+    return this.tensorsMap;
+  }
+  resetIntermediateTensors() {
+    for (const key in this.intermediateTensors) {
+      this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose());
+      delete this.intermediateTensors[key];
+    }
+  }
+  async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {
+    if (!isFunctionExecution) {
+      inputs = this.mapInputs(inputs);
+      this.checkInputs(inputs);
+      this.checkInputShapeAndType(inputs);
+      outputs = this.mapOutputs(outputs);
+      this.checkOutputs(outputs);
+    }
+    try {
+      this.keepTensorForDebug = env().getBool("KEEP_INTERMEDIATE_TENSORS");
+    } catch (e2) {
+      console.warn(e2.message);
+    }
+    this.resetIntermediateTensors();
+    const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
+    this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
+    const results = outputs.map((name) => getTensor(name, this.tensorsMap, context));
+    const outputIds = results.map((t2) => t2.id);
+    const inputIds = Object.keys(inputs).map((name) => inputs[name].id);
+    this.keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);
+    if (!this.keepTensorForDebug) {
+      this.disposeTensorsMap();
+    }
+    if (this.parent == null) {
+      context.dispose(this.keepIds);
+    }
+    return results;
+  }
+  async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {
+    const mappedInputs = inputs.reduce((map, tensor2, index2) => {
+      map[this.inputs[index2].name] = tensor2;
+      return map;
+    }, {});
+    return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
+  }
+  async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {
+    const names = Object.keys(inputs);
+    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
+    const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);
+    let outputNodes2 = outputNodeNames.map((name) => this.graph.nodes[name]);
+    if (outputNodes2.length === 0) {
+      outputNodes2 = this._outputs;
+    }
+    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes2, this.weightMap, this._initNodes);
+    const stack2 = [
+      ...inputNodes,
+      ...this.graph.weights,
+      ...this._initNodes || []
+    ].map((node2) => {
+      return { node: node2, contexts: context.currentContext };
+    });
+    const tensorsMap = Object.assign({}, this.weightMap);
+    Object.keys(inputs).forEach((name) => {
+      const [nodeName, index2] = parseNodeName(name);
+      const tensors = [];
+      tensors[index2] = inputs[name];
+      tensorsMap[nodeName] = tensors;
+    });
+    const intermediateTensorConsumerCount = {};
+    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
+    const added = {};
+    while (stack2.length > 0) {
+      const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);
+      await Promise.all(promises);
+    }
+    if (dynamicNode == null && !isFunctionExecution) {
+      console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);
+    }
+    const missingOutputs = outputNodes2.filter((node2) => !isControlFlow(node2) && !getTensor(node2.name, tensorsMap, context)).map((node2) => node2.name);
+    if (missingOutputs.length > 0) {
+      let alternativeMsg = "";
+      if (dynamicNode != null) {
+        alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;
+      }
+      throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);
+    }
+    return tensorsMap;
+  }
+  processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {
+    const promises = [];
+    while (stack2.length > 0) {
+      const item = stack2.pop();
+      context.currentContext = item.contexts;
+      let nodeName = "";
+      if (item.node.op === "Enter" && getParamValue("isConstant", item.node, tensorMap, context)) {
+        [nodeName] = getNodeNameAndIndex(item.node.name, context);
+      }
+      if (tensorMap[item.node.name] == null) {
+        const tensors = executeOp20(item.node, tensorMap, context, this._resourceManager);
+        if (!nodeName) {
+          [nodeName] = getNodeNameAndIndex(item.node.name, context);
+        }
+        const currentContext = context.currentContext;
+        if (util_exports.isPromise(tensors)) {
+          promises.push(tensors.then((t2) => {
+            tensorMap[nodeName] = t2;
+            context.currentContext = currentContext;
+            this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
+            this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
+            return t2;
+          }));
+        } else {
+          tensorMap[nodeName] = tensors;
+          this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
+          this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
+        }
+      } else {
+        this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
+      }
+    }
+    return promises;
+  }
+  processChildNodes(node2, stack2, context, tensorMap, added, usedNodes) {
+    node2.children.forEach((childNode) => {
+      const [nodeName] = getNodeNameAndIndex(childNode.name, context);
+      if (added[nodeName] || !usedNodes.has(childNode.name)) {
+        return;
+      }
+      if (childNode.op === "Merge") {
+        if (childNode.inputNames.some((name) => {
+          return !!getTensor(name, tensorMap, context);
+        })) {
+          added[nodeName] = true;
+          stack2.push({ contexts: context.currentContext, node: childNode });
+        }
+      } else if (childNode.inputNames.every((name) => {
+        return !!getTensor(name, tensorMap, context);
+      })) {
+        added[nodeName] = true;
+        stack2.push({ contexts: context.currentContext, node: childNode });
+      }
+    });
+  }
+  dispose() {
+    Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));
+  }
+  checkInputShapeAndType(inputs) {
+    Object.keys(inputs).forEach((name) => {
+      const input2 = inputs[name];
+      const [nodeName] = parseNodeName(name);
+      const node2 = this.graph.nodes[nodeName];
+      if (node2.attrParams["shape"] && node2.attrParams["shape"].value) {
+        const shape = node2.attrParams["shape"].value;
+        const match3 = shape.length === input2.shape.length && input2.shape.every((dim, index2) => shape[index2] === -1 || shape[index2] === dim);
+        util_exports.assert(match3, () => `The shape of dict['${node2.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);
+      }
+      if (node2.attrParams["dtype"] && node2.attrParams["dtype"].value) {
+        util_exports.assert(input2.dtype === node2.attrParams["dtype"].value, () => `The dtype of dict['${node2.name}'] provided in model.execute(dict) must be ${node2.attrParams["dtype"].value}, but was ${input2.dtype}`);
+      }
+    });
+  }
+  mapInputs(inputs) {
+    const result = {};
+    for (const inputName in inputs) {
+      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {
+        const tensor2 = this._signature.inputs[inputName];
+        result[tensor2.name] = inputs[inputName];
+      } else {
+        result[inputName] = inputs[inputName];
+      }
+    }
+    return result;
+  }
+  checkInputs(inputs) {
+    const notInGraph = Object.keys(inputs).filter((name) => {
+      const [nodeName] = parseNodeName(name);
+      return this.graph.nodes[nodeName] == null;
+    });
+    if (notInGraph.length > 0) {
+      throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);
+    }
+  }
+  mapOutputs(outputs) {
+    return outputs.map((name) => {
+      if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {
+        const tensor2 = this._signature.outputs[name];
+        return tensor2.name;
+      }
+      return name;
+    }, {});
+  }
+  checkOutputs(outputs) {
+    outputs.forEach((name) => {
+      const [normalizedName] = parseNodeName(name);
+      if (!this.graph.nodes[normalizedName]) {
+        throw new Error(`The output '${name}' is not found in the graph`);
+      }
+    });
+  }
+};
+var ResourceManager = class {
+  constructor(hashTableNameToHandle = {}, hashTableMap = {}) {
+    this.hashTableNameToHandle = hashTableNameToHandle;
+    this.hashTableMap = hashTableMap;
+  }
+  addHashTable(name, hashTable) {
+    this.hashTableNameToHandle[name] = hashTable.handle;
+    this.hashTableMap[hashTable.id] = hashTable;
+  }
+  getHashTableHandleByName(name) {
+    return this.hashTableNameToHandle[name];
+  }
+  getHashTableById(id) {
+    return this.hashTableMap[id];
+  }
+  dispose() {
+    for (const key in this.hashTableMap) {
+      this.hashTableMap[key].clearAndClose();
+      delete this.hashTableMap[key];
+    }
+    for (const name in this.hashTableNameToHandle) {
+      this.hashTableNameToHandle[name].dispose();
+      delete this.hashTableNameToHandle[name];
+    }
+  }
+};
+var TFHUB_SEARCH_PARAM = "?tfjs-format=file";
+var DEFAULT_MODEL_NAME = "model.json";
+var GraphModel = class {
+  constructor(modelUrl, loadOptions = {}) {
+    this.modelUrl = modelUrl;
+    this.loadOptions = loadOptions;
+    this.version = "n/a";
+    if (loadOptions == null) {
+      this.loadOptions = {};
+    }
+    this.resourceManager = new ResourceManager();
+  }
+  get modelVersion() {
+    return this.version;
+  }
+  get inputNodes() {
+    return this.executor.inputNodes;
+  }
+  get outputNodes() {
+    return this.executor.outputNodes;
+  }
+  get inputs() {
+    return this.executor.inputs;
+  }
+  get outputs() {
+    return this.executor.outputs;
+  }
+  get weights() {
+    return this.executor.weightMap;
+  }
+  get metadata() {
+    return this.artifacts.userDefinedMetadata;
+  }
+  get modelSignature() {
+    return this.signature;
+  }
+  findIOHandler() {
+    const path = this.modelUrl;
+    if (path.load != null) {
+      this.handler = path;
+    } else if (this.loadOptions.requestInit != null) {
+      this.handler = io_exports.browserHTTPRequest(path, this.loadOptions);
+    } else {
+      const handlers = io_exports.getLoadHandlers(path, this.loadOptions);
+      if (handlers.length === 0) {
+        handlers.push(io_exports.browserHTTPRequest(path, this.loadOptions));
+      } else if (handlers.length > 1) {
+        throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);
+      }
+      this.handler = handlers[0];
+    }
+  }
+  load() {
+    this.findIOHandler();
+    if (this.handler.load == null) {
+      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
+    }
+    const loadResult = this.handler.load();
+    if (util_exports.isPromise(loadResult)) {
+      return loadResult.then((artifacts) => this.loadSync(artifacts));
+    }
+    return this.loadSync(loadResult);
+  }
+  loadSync(artifacts) {
+    this.artifacts = artifacts;
+    const graph = this.artifacts.modelTopology;
+    let signature;
+    if (this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null) {
+      signature = this.artifacts.userDefinedMetadata.signature;
+    } else {
+      signature = this.artifacts.signature;
+    }
+    this.signature = signature;
+    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;
+    const weightMap = io_exports.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
+    this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));
+    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
+    this.executor.resourceManager = this.resourceManager;
+    if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {
+      const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);
+      this.initializer = new GraphExecutor(initializer);
+      this.initializer.weightMap = this.executor.weightMap;
+      this.initializer.resourceManager = this.resourceManager;
+      this.initializer.executeAsync({}, []);
+    }
+    return true;
+  }
+  async save(handlerOrURL, config3) {
+    if (typeof handlerOrURL === "string") {
+      const handlers = io_exports.getSaveHandlers(handlerOrURL);
+      if (handlers.length === 0) {
+        throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);
+      } else if (handlers.length > 1) {
+        throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
+      }
+      handlerOrURL = handlers[0];
+    }
+    if (handlerOrURL.save == null) {
+      throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
+    }
+    return handlerOrURL.save(this.artifacts);
+  }
+  predict(inputs, config3) {
+    return this.execute(inputs, this.outputNodes);
+  }
+  normalizeInputs(inputs) {
+    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {
+      return inputs;
+    }
+    inputs = Array.isArray(inputs) ? inputs : [inputs];
+    if (inputs.length !== this.inputNodes.length) {
+      throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);
+    }
+    return this.inputNodes.reduce((map, inputName, i2) => {
+      map[inputName] = inputs[i2];
+      return map;
+    }, {});
+  }
+  normalizeOutputs(outputs) {
+    outputs = outputs || this.outputNodes;
+    return !Array.isArray(outputs) ? [outputs] : outputs;
+  }
+  execute(inputs, outputs) {
+    inputs = this.normalizeInputs(inputs);
+    outputs = this.normalizeOutputs(outputs);
+    const result = this.executor.execute(inputs, outputs);
+    return result.length > 1 ? result : result[0];
+  }
+  async executeAsync(inputs, outputs) {
+    inputs = this.normalizeInputs(inputs);
+    outputs = this.normalizeOutputs(outputs);
+    const result = await this.executor.executeAsync(inputs, outputs);
+    return result.length > 1 ? result : result[0];
+  }
+  getIntermediateTensors() {
+    return this.executor.getIntermediateTensors();
+  }
+  disposeIntermediateTensors() {
+    this.executor.disposeIntermediateTensors();
+  }
+  convertTensorMapToTensorsMap(map) {
+    return Object.keys(map).reduce((newMap, key) => {
+      newMap[key] = [map[key]];
+      return newMap;
+    }, {});
+  }
+  dispose() {
+    this.executor.dispose();
+    if (this.initializer) {
+      this.initializer.dispose();
+    }
+    this.resourceManager.dispose();
+  }
+};
+async function loadGraphModel(modelUrl, options4 = {}) {
+  if (modelUrl == null) {
+    throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
+  }
+  if (options4 == null) {
+    options4 = {};
+  }
+  if (options4.fromTFHub && typeof modelUrl === "string") {
+    modelUrl = getTFHubUrl(modelUrl);
+  }
+  const model22 = new GraphModel(modelUrl, options4);
+  await model22.load();
+  return model22;
+}
+function loadGraphModelSync(modelSource) {
+  if (modelSource == null) {
+    throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model");
+  }
+  if (!modelSource.load) {
+    throw new Error(`modelUrl IO Handler ${modelSource} has no load function`);
+  }
+  const model22 = new GraphModel(modelSource);
+  model22.load();
+  return model22;
+}
+function getTFHubUrl(modelUrl) {
+  if (!modelUrl.endsWith("/")) {
+    modelUrl = modelUrl + "/";
+  }
+  return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
+}
+var version3 = "3.18.0";
+var dist_exports2 = {};
+__export2(dist_exports2, {
+  CSVDataset: () => CSVDataset,
+  Dataset: () => Dataset,
+  FileDataSource: () => FileDataSource,
+  TextLineDataset: () => TextLineDataset,
+  URLDataSource: () => URLDataSource,
+  array: () => array,
+  csv: () => csv,
+  func: () => func,
+  generator: () => generator,
+  microphone: () => microphone,
+  version_data: () => version4,
+  webcam: () => webcam,
+  zip: () => zip
+});
+var seedrandom3 = __toESM(require_seedrandom4());
+var seedrandom2 = __toESM(require_seedrandom4());
+function deepMap(input2, mapFn) {
+  return deepMapInternal(input2, mapFn);
+}
+function deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {
+  if (input2 == null) {
+    return null;
+  }
+  if (typeof Blob === "function" && input2 instanceof Blob) {
+    return input2.slice();
+  }
+  if (containedIn.has(input2)) {
+    throw new Error("Circular references are not supported.");
+  }
+  if (seen.has(input2)) {
+    return seen.get(input2);
+  }
+  const result = mapFn(input2);
+  if (result.recurse && result.value !== null) {
+    throw new Error("A deep map function may not return both a value and recurse=true.");
+  }
+  if (!result.recurse) {
+    seen.set(input2, result.value);
+    return result.value;
+  } else if (isIterable2(input2)) {
+    const mappedIterable = Array.isArray(input2) ? [] : {};
+    containedIn.add(input2);
+    for (const k in input2) {
+      const child = input2[k];
+      const childResult = deepMapInternal(child, mapFn, seen, containedIn);
+      mappedIterable[k] = childResult;
+    }
+    containedIn.delete(input2);
+    if (input2.__proto__) {
+      mappedIterable.__proto__ = input2.__proto__;
+    }
+    return mappedIterable;
+  } else {
+    throw new Error(`Can't recurse into non-iterable type: ${input2}`);
+  }
+}
+function deepZip(inputs, zipFn = zipToList) {
+  return deepZipInternal(inputs, zipFn);
+}
+function deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) {
+  const input2 = inputs[0];
+  if (containedIn.has(input2)) {
+    throw new Error("Circular references are not supported.");
+  }
+  const result = zipFn(inputs);
+  if (result.recurse && result.value !== null) {
+    throw new Error("A deep zip function may not return both a value and recurse=true.");
+  }
+  if (!result.recurse) {
+    return result.value;
+  } else if (isIterable2(input2)) {
+    const mappedIterable = Array.isArray(input2) ? [] : {};
+    containedIn.add(input2);
+    for (const k in input2) {
+      const children = inputs.map((x) => x[k]);
+      const childResult = deepZipInternal(children, zipFn, containedIn);
+      mappedIterable[k] = childResult;
+    }
+    containedIn.delete(input2);
+    return mappedIterable;
+  } else {
+    throw new Error(`Can't recurse into non-iterable type: ${input2}`);
+  }
+}
+function zipToList(x) {
+  if (x === null) {
+    return null;
+  }
+  if (isIterable2(x[0])) {
+    return { value: null, recurse: true };
+  } else {
+    return { value: x, recurse: false };
+  }
+}
+async function deepMapAndAwaitAll(input2, mapFn) {
+  const seen = /* @__PURE__ */ new Map();
+  deepMapInternal(input2, mapFn, seen);
+  for (const key of Array.from(seen.keys())) {
+    const value = seen.get(key);
+    if (util_exports.isPromise(value)) {
+      const mappedValue = await value;
+      seen.set(key, mappedValue);
+    }
+  }
+  const result = deepMapInternal(input2, mapFn, seen);
+  return result;
+}
+function isIterable2(obj) {
+  let isTextDecoder = false;
+  if (env().get("IS_BROWSER")) {
+    isTextDecoder = obj instanceof TextDecoder;
+  } else {
+    const { StringDecoder } = require_string_decoder();
+    isTextDecoder = obj instanceof StringDecoder;
+  }
+  return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);
+}
+function canTensorify(obj) {
+  return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === "object" && obj instanceof Tensor || util_exports.isTypedArray(obj);
+}
+function isPrimitive(value) {
+  return value === null || typeof value !== "object" && typeof value !== "function";
+}
+function deepClone(container) {
+  return deepMap(container, cloneIfTensor);
+}
+function cloneIfTensor(item) {
+  if (item instanceof Tensor) {
+    return { value: item.clone(), recurse: false };
+  } else if (isIterable2(item)) {
+    return { value: null, recurse: true };
+  } else {
+    return { value: item, recurse: false };
+  }
+}
+var RingBuffer = class {
+  constructor(capacity) {
+    this.capacity = capacity;
+    this.begin = 0;
+    this.end = 0;
+    if (capacity == null) {
+      throw new RangeError("Can't create a ring buffer of unknown capacity.");
+    }
+    if (capacity < 1) {
+      throw new RangeError("Can't create ring buffer of capacity < 1.");
+    }
+    this.data = new Array(capacity);
+    this.doubledCapacity = 2 * capacity;
+  }
+  wrap(index2) {
+    while (index2 < 0) {
+      index2 += this.doubledCapacity;
+    }
+    return index2 % this.doubledCapacity;
+  }
+  get(index2) {
+    if (index2 < 0) {
+      throw new RangeError("Can't get item at a negative index.");
+    }
+    return this.data[index2 % this.capacity];
+  }
+  set(index2, value) {
+    if (index2 < 0) {
+      throw new RangeError("Can't set item at a negative index.");
+    }
+    this.data[index2 % this.capacity] = value;
+  }
+  length() {
+    let length = this.end - this.begin;
+    if (length < 0) {
+      length = this.doubledCapacity + length;
+    }
+    return length;
+  }
+  isFull() {
+    return this.length() === this.capacity;
+  }
+  isEmpty() {
+    return this.length() === 0;
+  }
+  push(value) {
+    if (this.isFull()) {
+      throw new RangeError("Ring buffer is full.");
+    }
+    this.set(this.end, value);
+    this.end = this.wrap(this.end + 1);
+  }
+  pushAll(values) {
+    for (const value of values) {
+      this.push(value);
+    }
+  }
+  pop() {
+    if (this.isEmpty()) {
+      throw new RangeError("Ring buffer is empty.");
+    }
+    this.end = this.wrap(this.end - 1);
+    const result = this.get(this.end);
+    this.set(this.end, void 0);
+    return result;
+  }
+  unshift(value) {
+    if (this.isFull()) {
+      throw new RangeError("Ring buffer is full.");
+    }
+    this.begin = this.wrap(this.begin - 1);
+    this.set(this.begin, value);
+  }
+  shift() {
+    if (this.isEmpty()) {
+      throw new RangeError("Ring buffer is empty.");
+    }
+    const result = this.get(this.begin);
+    this.set(this.begin, void 0);
+    this.begin = this.wrap(this.begin + 1);
+    return result;
+  }
+  shuffleExcise(relativeIndex) {
+    if (this.isEmpty()) {
+      throw new RangeError("Ring buffer is empty.");
+    }
+    const index2 = this.wrap(this.begin + relativeIndex);
+    const result = this.get(index2);
+    this.set(index2, this.pop());
+    return result;
+  }
+};
+var GrowingRingBuffer = class extends RingBuffer {
+  constructor() {
+    super(GrowingRingBuffer.INITIAL_CAPACITY);
+  }
+  isFull() {
+    return false;
+  }
+  push(value) {
+    if (super.isFull()) {
+      this.expand();
+    }
+    super.push(value);
+  }
+  unshift(value) {
+    if (super.isFull()) {
+      this.expand();
+    }
+    super.unshift(value);
+  }
+  expand() {
+    const newCapacity = this.capacity * 2;
+    const newData = new Array(newCapacity);
+    const len = this.length();
+    for (let i2 = 0; i2 < len; i2++) {
+      newData[i2] = this.get(this.wrap(this.begin + i2));
+    }
+    this.data = newData;
+    this.capacity = newCapacity;
+    this.doubledCapacity = 2 * this.capacity;
+    this.begin = 0;
+    this.end = len;
+  }
+};
+GrowingRingBuffer.INITIAL_CAPACITY = 32;
+function iteratorFromItems(items) {
+  return new ArrayIterator(items);
+}
+function iteratorFromFunction(func2) {
+  return new FunctionCallIterator(func2);
+}
+function iteratorFromConcatenated(baseIterators, baseErrorHandler) {
+  return new ChainedIterator(baseIterators, baseErrorHandler);
+}
+function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {
+  return new ZipIterator(iterators, mismatchMode);
+}
+var LazyIterator = class {
+  async toArray() {
+    const result = [];
+    let x = await this.next();
+    while (!x.done) {
+      result.push(x.value);
+      x = await this.next();
+    }
+    return result;
+  }
+  async toArrayForTest() {
+    const stream = this.prefetch(100);
+    const result = [];
+    let x = await stream.next();
+    while (!x.done) {
+      result.push(x.value);
+      x = await stream.next();
+    }
+    return result;
+  }
+  async resolveFully() {
+    let x = await this.next();
+    while (!x.done) {
+      x = await this.next();
+    }
+  }
+  async resolveWhile(predicate) {
+    let x = await this.next();
+    let shouldContinue = predicate(x.value);
+    while (!x.done && shouldContinue) {
+      x = await this.next();
+      shouldContinue = predicate(x.value);
+    }
+  }
+  handleErrors(handler) {
+    return new ErrorHandlingLazyIterator(this, handler);
+  }
+  filter(predicate) {
+    return new FilterIterator(this, predicate);
+  }
+  map(transform6) {
+    return new MapIterator(this, transform6);
+  }
+  mapAsync(transform6) {
+    return new AsyncMapIterator(this, transform6);
+  }
+  serialMapAsync(transform6) {
+    return new AsyncMapIterator(this, transform6).serial();
+  }
+  flatmap(transform6) {
+    return new FlatmapIterator(this, transform6);
+  }
+  async forEachAsync(f) {
+    return this.map(f).resolveFully();
+  }
+  async serialForEach(f) {
+    return this.serialMapAsync(f).resolveWhile((x) => x === true);
+  }
+  rowMajorBatch(batchSize, smallLastBatch = true) {
+    return new RowMajorBatchIterator(this, batchSize, smallLastBatch);
+  }
+  columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {
+    const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);
+    return rowBatches.map((x) => deepZip(x, zipFn));
+  }
+  concatenate(iterator, baseErrorHandler) {
+    return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);
+  }
+  take(count22) {
+    if (count22 < 0 || count22 == null) {
+      return this;
+    }
+    return new TakeIterator(this, count22);
+  }
+  skip(count22) {
+    if (count22 < 0 || count22 == null) {
+      return this;
+    }
+    return new SkipIterator(this, count22);
+  }
+  prefetch(bufferSize) {
+    return new PrefetchIterator(this, bufferSize);
+  }
+  shuffle(windowSize, seed) {
+    return new ShuffleIterator(this, windowSize, seed);
+  }
+  serial() {
+    return new SerialIterator(this);
+  }
+};
+var ArrayIterator = class extends LazyIterator {
+  constructor(items) {
+    super();
+    this.items = items;
+    this.trav = 0;
+  }
+  summary() {
+    return `Array of ${this.items.length} items`;
+  }
+  async next() {
+    if (this.trav >= this.items.length) {
+      return { value: null, done: true };
+    }
+    const item = this.items[this.trav];
+    this.trav++;
+    return { value: deepClone(item), done: false };
+  }
+};
+var FunctionCallIterator = class extends LazyIterator {
+  constructor(nextFn) {
+    super();
+    this.nextFn = nextFn;
+  }
+  summary() {
+    return `Function call`;
+  }
+  async next() {
+    try {
+      return this.nextFn();
+    } catch (e2) {
+      e2.message = `Error thrown while iterating through a dataset: ${e2.message}`;
+      throw e2;
+    }
+  }
+};
+var SerialIterator = class extends LazyIterator {
+  constructor(upstream) {
+    super();
+    this.upstream = upstream;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Serial`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    return this.upstream.next();
+  }
+};
+var SkipIterator = class extends LazyIterator {
+  constructor(upstream, maxCount) {
+    super();
+    this.upstream = upstream;
+    this.maxCount = maxCount;
+    this.count = 0;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Skip`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (this.count++ < this.maxCount) {
+      const skipped15 = await this.upstream.next();
+      if (skipped15.done) {
+        return skipped15;
+      }
+      dispose(skipped15.value);
+    }
+    return this.upstream.next();
+  }
+};
+var TakeIterator = class extends LazyIterator {
+  constructor(upstream, maxCount) {
+    super();
+    this.upstream = upstream;
+    this.maxCount = maxCount;
+    this.count = 0;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Take`;
+  }
+  async next() {
+    if (this.count++ >= this.maxCount) {
+      return { value: null, done: true };
+    }
+    return this.upstream.next();
+  }
+};
+var RowMajorBatchIterator = class extends LazyIterator {
+  constructor(upstream, batchSize, enableSmallLastBatch = true) {
+    super();
+    this.upstream = upstream;
+    this.batchSize = batchSize;
+    this.enableSmallLastBatch = enableSmallLastBatch;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> RowMajorBatch`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    const batch = [];
+    while (batch.length < this.batchSize) {
+      const item = await this.upstream.next();
+      if (item.done) {
+        if (this.enableSmallLastBatch && batch.length > 0) {
+          return { value: batch, done: false };
+        }
+        return { value: null, done: true };
+      }
+      batch.push(item.value);
+    }
+    return { value: batch, done: false };
+  }
+};
+var FilterIterator = class extends LazyIterator {
+  constructor(upstream, predicate) {
+    super();
+    this.upstream = upstream;
+    this.predicate = predicate;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Filter`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (true) {
+      const item = await this.upstream.next();
+      if (item.done || this.predicate(item.value)) {
+        return item;
+      }
+      dispose(item.value);
+    }
+  }
+};
+var MapIterator = class extends LazyIterator {
+  constructor(upstream, transform6) {
+    super();
+    this.upstream = upstream;
+    this.transform = transform6;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Map`;
+  }
+  async next() {
+    const item = await this.upstream.next();
+    if (item.done) {
+      return { value: null, done: true };
+    }
+    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
+    const mapped = this.transform(item.value);
+    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
+    for (const t2 of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
+        t2.dispose();
+      }
+    }
+    return { value: mapped, done: false };
+  }
+};
+var ErrorHandlingLazyIterator = class extends LazyIterator {
+  constructor(upstream, handler) {
+    super();
+    this.upstream = upstream;
+    this.handler = handler;
+    this.count = 0;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> handleErrors`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (true) {
+      try {
+        return await this.upstream.next();
+      } catch (e2) {
+        if (!this.handler(e2)) {
+          return { value: null, done: true };
+        }
+      }
+    }
+  }
+};
+var AsyncMapIterator = class extends LazyIterator {
+  constructor(upstream, transform6) {
+    super();
+    this.upstream = upstream;
+    this.transform = transform6;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> AsyncMap`;
+  }
+  async next() {
+    const item = await this.upstream.next();
+    if (item.done) {
+      return { value: null, done: true };
+    }
+    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
+    const mapped = await this.transform(item.value);
+    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
+    for (const t2 of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
+        t2.dispose();
+      }
+    }
+    return { value: mapped, done: false };
+  }
+};
+var OneToManyIterator = class extends LazyIterator {
+  constructor() {
+    super();
+    this.outputQueue = new GrowingRingBuffer();
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (this.outputQueue.length() === 0) {
+      if (!await this.pump()) {
+        return { value: null, done: true };
+      }
+    }
+    return { value: this.outputQueue.shift(), done: false };
+  }
+};
+var FlatmapIterator = class extends OneToManyIterator {
+  constructor(upstream, transform6) {
+    super();
+    this.upstream = upstream;
+    this.transform = transform6;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Flatmap`;
+  }
+  async pump() {
+    const item = await this.upstream.next();
+    if (item.done) {
+      return false;
+    }
+    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
+    const mappedArray = this.transform(item.value);
+    const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);
+    this.outputQueue.pushAll(mappedArray);
+    for (const t2 of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
+        t2.dispose();
+      }
+    }
+    return true;
+  }
+};
+var ChainedIterator = class extends LazyIterator {
+  constructor(iterators, baseErrorHandler) {
+    super();
+    this.baseErrorHandler = baseErrorHandler;
+    this.lastRead = null;
+    this.iterator = null;
+    this.moreIterators = iterators;
+  }
+  summary() {
+    const upstreamSummaries = "TODO: fill in upstream of chained summaries";
+    return `${upstreamSummaries} -> Chained`;
+  }
+  async next() {
+    this.lastRead = this.readFromChain(this.lastRead);
+    return this.lastRead;
+  }
+  async readFromChain(lastRead) {
+    await lastRead;
+    if (this.iterator == null) {
+      const iteratorResult = await this.moreIterators.next();
+      if (iteratorResult.done) {
+        return { value: null, done: true };
+      }
+      this.iterator = iteratorResult.value;
+      if (this.baseErrorHandler != null) {
+        this.iterator = this.iterator.handleErrors(this.baseErrorHandler);
+      }
+    }
+    const itemResult = await this.iterator.next();
+    if (itemResult.done) {
+      this.iterator = null;
+      return this.readFromChain(lastRead);
+    }
+    return itemResult;
+  }
+};
+var ZipMismatchMode;
+(function(ZipMismatchMode2) {
+  ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL";
+  ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST";
+  ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST";
+})(ZipMismatchMode || (ZipMismatchMode = {}));
+var ZipIterator = class extends LazyIterator {
+  constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {
+    super();
+    this.iterators = iterators;
+    this.mismatchMode = mismatchMode;
+    this.count = 0;
+    this.currentPromise = null;
+  }
+  summary() {
+    const upstreamSummaries = "TODO: fill in upstream of zip summaries";
+    return `{${upstreamSummaries}} -> Zip`;
+  }
+  async nextState(afterState) {
+    await afterState;
+    let numIterators = 0;
+    let iteratorsDone = 0;
+    function getNext(container) {
+      if (container instanceof LazyIterator) {
+        const result = container.next();
+        return {
+          value: result.then((x) => {
+            numIterators++;
+            if (x.done) {
+              iteratorsDone++;
+            }
+            return x.value;
+          }),
+          recurse: false
+        };
+      } else {
+        return { value: null, recurse: true };
+      }
+    }
+    const mapped = await deepMapAndAwaitAll(this.iterators, getNext);
+    if (numIterators === iteratorsDone) {
+      return { value: null, done: true };
+    }
+    if (iteratorsDone > 0) {
+      switch (this.mismatchMode) {
+        case ZipMismatchMode.FAIL:
+          throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);
+        case ZipMismatchMode.SHORTEST:
+          return { value: null, done: true };
+        case ZipMismatchMode.LONGEST:
+        default:
+      }
+    }
+    this.count++;
+    return { value: mapped, done: false };
+  }
+  async next() {
+    this.currentPromise = this.nextState(this.currentPromise);
+    return this.currentPromise;
+  }
+};
+var PrefetchIterator = class extends LazyIterator {
+  constructor(upstream, bufferSize) {
+    super();
+    this.upstream = upstream;
+    this.bufferSize = bufferSize;
+    this.buffer = new RingBuffer(bufferSize);
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Prefetch`;
+  }
+  refill() {
+    while (!this.buffer.isFull()) {
+      const v = this.upstream.next();
+      this.buffer.push(v);
+    }
+  }
+  next() {
+    this.refill();
+    return this.buffer.shift();
+  }
+};
+var ShuffleIterator = class extends PrefetchIterator {
+  constructor(upstream, windowSize, seed) {
+    super(upstream, windowSize);
+    this.upstream = upstream;
+    this.windowSize = windowSize;
+    this.upstreamExhausted = false;
+    this.random = seedrandom2.alea(seed || util_exports.now().toString());
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  randomInt(max7) {
+    return Math.floor(this.random() * max7);
+  }
+  chooseIndex() {
+    return this.randomInt(this.buffer.length());
+  }
+  async serialNext() {
+    if (!this.upstreamExhausted) {
+      this.refill();
+    }
+    while (!this.buffer.isEmpty()) {
+      const chosenIndex = this.chooseIndex();
+      const result = await this.buffer.shuffleExcise(chosenIndex);
+      if (result.done) {
+        this.upstreamExhausted = true;
+      } else {
+        this.refill();
+        return result;
+      }
+    }
+    return { value: null, done: true };
+  }
+};
+var Dataset = class {
+  constructor() {
+    this.size = null;
+  }
+  batch(batchSize, smallLastBatch = true) {
+    const base = this;
+    util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is
+      ${batchSize}`);
+    let size2;
+    if (this.size === Infinity || this.size == null) {
+      size2 = this.size;
+    } else if (smallLastBatch) {
+      size2 = Math.ceil(this.size / batchSize);
+    } else {
+      size2 = Math.floor(this.size / batchSize);
+    }
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);
+    }, size2);
+  }
+  concatenate(dataset) {
+    const base = this;
+    let size2;
+    if (this.size === Infinity || dataset.size === Infinity) {
+      size2 = Infinity;
+    } else if (this.size != null && dataset.size != null) {
+      size2 = this.size + dataset.size;
+    } else {
+      size2 = null;
+    }
+    return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size2);
+  }
+  filter(predicate) {
+    const base = this;
+    let size2;
+    if (this.size === Infinity) {
+      size2 = Infinity;
+    } else {
+      size2 = null;
+    }
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).filter((x) => tidy(() => predicate(x)));
+    }, size2);
+  }
+  async forEachAsync(f) {
+    return (await this.iterator()).forEachAsync(f);
+  }
+  map(transform6) {
+    const base = this;
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).map((x) => tidy(() => transform6(x)));
+    }, this.size);
+  }
+  mapAsync(transform6) {
+    const base = this;
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).mapAsync(transform6);
+    }, this.size);
+  }
+  prefetch(bufferSize) {
+    if (bufferSize == null) {
+      throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");
+    }
+    const base = this;
+    return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);
+  }
+  repeat(count22) {
+    const base = this;
+    let size2;
+    if (this.size != null && count22 > 0) {
+      size2 = this.size * count22;
+    } else if (count22 === 0) {
+      size2 = 0;
+    } else if (this.size != null && (count22 === void 0 || count22 < 0)) {
+      size2 = Infinity;
+    } else {
+      size2 = null;
+    }
+    return datasetFromIteratorFn(async () => {
+      const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));
+      return iteratorFromConcatenated(iteratorIterator.take(count22));
+    }, size2);
+  }
+  skip(count22) {
+    const base = this;
+    let size2;
+    if (this.size != null && count22 >= 0 && this.size >= count22) {
+      size2 = this.size - count22;
+    } else if (this.size != null && (this.size < count22 || count22 === void 0 || count22 < 0)) {
+      size2 = 0;
+    } else {
+      size2 = null;
+    }
+    return datasetFromIteratorFn(async () => (await base.iterator()).skip(count22), size2);
+  }
+  shuffle(bufferSize, seed, reshuffleEachIteration = true) {
+    if (bufferSize == null || bufferSize < 0) {
+      if (this.size == null) {
+        throw new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.");
+      } else {
+        throw new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
+      }
+    }
+    const base = this;
+    const random = seedrandom3.alea(seed || util_exports.now().toString());
+    return datasetFromIteratorFn(async () => {
+      let seed2 = random.int32();
+      if (reshuffleEachIteration) {
+        seed2 += random.int32();
+      }
+      return (await base.iterator()).shuffle(bufferSize, seed2.toString());
+    }, this.size);
+  }
+  take(count22) {
+    const base = this;
+    let size2;
+    if (this.size != null && this.size > count22) {
+      size2 = count22;
+    } else if (this.size != null && this.size <= count22) {
+      size2 = this.size;
+    } else {
+      size2 = null;
+    }
+    return datasetFromIteratorFn(async () => (await base.iterator()).take(count22), size2);
+  }
+  async toArray() {
+    if (this.size === Infinity) {
+      throw new Error("Can not convert infinite data stream to array.");
+    }
+    return (await this.iterator()).toArray();
+  }
+  async toArrayForTest() {
+    if (this.size === Infinity) {
+      throw new Error("Can not convert infinite data stream to array.");
+    }
+    return (await this.iterator()).toArrayForTest();
+  }
+};
+Dataset.MAX_BUFFER_SIZE = 1e4;
+function datasetFromIteratorFn(iteratorFn, size2 = null) {
+  return new class extends Dataset {
+    constructor() {
+      super(...arguments);
+      this.size = size2;
+    }
+    async iterator() {
+      return iteratorFn();
+    }
+  }();
+}
+function array(items) {
+  return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);
+}
+function zip(datasets) {
+  if (!isIterable2(datasets)) {
+    throw new Error("The argument to zip() must be an object or array.");
+  }
+  let size2;
+  if (Array.isArray(datasets)) {
+    for (let i2 = 0; i2 < datasets.length; i2++) {
+      size2 = size2 == null ? datasets[i2].size : Math.min(size2, datasets[i2].size);
+    }
+  } else if (datasets instanceof Object) {
+    for (const ds in datasets) {
+      size2 = size2 == null ? datasets[ds].size : Math.min(size2, datasets[ds].size);
+    }
+  }
+  return datasetFromIteratorFn(async () => {
+    const streams = await deepMapAndAwaitAll(datasets, (d) => {
+      if (d instanceof Dataset) {
+        return { value: d.iterator(), recurse: false };
+      } else if (isIterable2(d)) {
+        return { value: null, recurse: true };
+      } else {
+        throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
+      }
+    });
+    return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);
+  }, size2);
+}
+function deepBatchConcat(rows) {
+  if (rows === null) {
+    return null;
+  }
+  const exampleRow = rows[0];
+  if (canTensorify(exampleRow)) {
+    const value = batchConcat(rows);
+    return { value, recurse: false };
+  }
+  return { value: null, recurse: true };
+}
+function batchConcat(arrays) {
+  if (arrays.length === 0) {
+    throw new Error("Can't make a batch of zero elements.");
+  }
+  if (arrays[0] instanceof Tensor) {
+    return stack(arrays);
+  } else {
+    return tensor(arrays);
+  }
+}
+var TextLineDataset = class extends Dataset {
+  constructor(input2) {
+    super();
+    this.input = input2;
+  }
+  async iterator() {
+    const inputIterator = await this.input.iterator();
+    const utf8Iterator = inputIterator.decodeUTF8();
+    const lineIterator = utf8Iterator.split("\n").map((line) => {
+      if (line.endsWith("\r")) {
+        line = line.slice(0, -1);
+      }
+      return line;
+    });
+    return lineIterator;
+  }
+};
+var CODE_QUOTE = '"';
+var STATE_OUT = Symbol("out");
+var STATE_FIELD = Symbol("field");
+var STATE_QUOTE = Symbol("quote");
+var STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote");
+var STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote");
+var CSVDataset = class extends Dataset {
+  constructor(input2, csvConfig) {
+    super();
+    this.input = input2;
+    this.hasHeader = true;
+    this.fullColumnNames = null;
+    this.columnNamesValidated = false;
+    this.columnConfigs = null;
+    this.configuredColumnsOnly = false;
+    this.delimiter = ",";
+    this.delimWhitespace = false;
+    this.base = new TextLineDataset(input2);
+    if (!csvConfig) {
+      csvConfig = {};
+    }
+    this.hasHeader = csvConfig.hasHeader === false ? false : true;
+    this.fullColumnNames = csvConfig.columnNames;
+    this.columnConfigs = csvConfig.columnConfigs;
+    this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;
+    if (csvConfig.delimWhitespace) {
+      util_exports.assert(csvConfig.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true.");
+      this.delimWhitespace = true;
+      this.delimiter = " ";
+    } else {
+      this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ",";
+    }
+  }
+  async columnNames() {
+    if (!this.columnNamesValidated) {
+      await this.setColumnNames();
+    }
+    return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
+  }
+  async setColumnNames() {
+    const columnNamesFromFile = await this.maybeReadHeaderLine();
+    if (!this.fullColumnNames && !columnNamesFromFile) {
+      throw new Error("Column names must be provided if there is no header line.");
+    } else if (this.fullColumnNames && columnNamesFromFile) {
+      util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => "The length of provided columnNames (" + this.fullColumnNames.length.toString() + ") does not match the length of the header line read from file (" + columnNamesFromFile.length.toString() + ").");
+    }
+    if (!this.fullColumnNames) {
+      this.fullColumnNames = columnNamesFromFile;
+    }
+    const counts = this.fullColumnNames.reduce((countAcc, name) => {
+      countAcc[name] = countAcc[name] + 1 || 1;
+      return countAcc;
+    }, {});
+    const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);
+    util_exports.assert(duplicateNames.length === 0, () => "Duplicate column names found: " + duplicateNames.toString());
+    if (this.columnConfigs) {
+      for (const key of Object.keys(this.columnConfigs)) {
+        const index2 = this.fullColumnNames.indexOf(key);
+        if (index2 === -1) {
+          throw new Error('The key "' + key + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ").");
+        }
+      }
+    }
+    this.columnNamesValidated = true;
+  }
+  async maybeReadHeaderLine() {
+    if (this.hasHeader) {
+      const iter = await this.base.iterator();
+      const firstElement = await iter.next();
+      if (firstElement.done) {
+        throw new Error("No data was found for CSV parsing.");
+      }
+      const firstLine = firstElement.value;
+      const headers = this.parseRow(firstLine, false);
+      return headers;
+    } else {
+      return null;
+    }
+  }
+  async iterator() {
+    if (!this.columnNamesValidated) {
+      await this.setColumnNames();
+    }
+    let lines2 = await this.base.iterator();
+    if (this.hasHeader) {
+      lines2 = lines2.skip(1);
+    }
+    return lines2.map((x) => this.makeDataElement(x));
+  }
+  makeDataElement(line) {
+    const values = this.parseRow(line);
+    const features = {};
+    const labels2 = {};
+    for (let i2 = 0; i2 < this.fullColumnNames.length; i2++) {
+      const key = this.fullColumnNames[i2];
+      const config3 = this.columnConfigs ? this.columnConfigs[key] : null;
+      if (this.configuredColumnsOnly && !config3) {
+        continue;
+      } else {
+        const value = values[i2];
+        let parsedValue = null;
+        if (value === "") {
+          if (config3 && config3.default !== void 0) {
+            parsedValue = config3.default;
+          } else if (config3 && (config3.required || config3.isLabel)) {
+            throw new Error(`Required column ${key} is empty in this line: ${line}`);
+          } else {
+            parsedValue = void 0;
+          }
+        } else {
+          const valueAsNum = Number(value);
+          if (isNaN(valueAsNum)) {
+            if (config3 && config3.dtype === "bool") {
+              parsedValue = this.getBoolean(value);
+            } else {
+              parsedValue = value;
+            }
+          } else if (!config3 || !config3.dtype) {
+            parsedValue = valueAsNum;
+          } else {
+            switch (config3.dtype) {
+              case "float32":
+                parsedValue = valueAsNum;
+                break;
+              case "int32":
+                parsedValue = Math.floor(valueAsNum);
+                break;
+              case "bool":
+                parsedValue = this.getBoolean(value);
+                break;
+              default:
+                parsedValue = valueAsNum;
+            }
+          }
+        }
+        config3 && config3.isLabel ? labels2[key] = parsedValue : features[key] = parsedValue;
+      }
+    }
+    if (Object.keys(labels2).length === 0) {
+      return features;
+    } else {
+      return { xs: features, ys: labels2 };
+    }
+  }
+  getBoolean(value) {
+    if (value === "1" || value.toLowerCase() === "true") {
+      return 1;
+    } else {
+      return 0;
+    }
+  }
+  parseRow(line, validateElementCount = true) {
+    const result = [];
+    let readOffset = 0;
+    const readLength = line.length;
+    let currentState = STATE_OUT;
+    for (let i2 = 0; i2 < readLength; i2++) {
+      switch (currentState) {
+        case STATE_OUT:
+          switch (line.charAt(i2)) {
+            case CODE_QUOTE:
+              readOffset = i2 + 1;
+              currentState = STATE_QUOTE;
+              break;
+            case this.delimiter:
+              readOffset = i2 + 1;
+              if (this.delimiter === " " && this.delimWhitespace) {
+                break;
+              }
+              result.push("");
+              currentState = STATE_OUT;
+              break;
+            default:
+              currentState = STATE_FIELD;
+              readOffset = i2;
+              break;
+          }
+          break;
+        case STATE_FIELD:
+          switch (line.charAt(i2)) {
+            case this.delimiter:
+              result.push(line.substring(readOffset, i2));
+              currentState = STATE_OUT;
+              readOffset = i2 + 1;
+              break;
+            default:
+          }
+          break;
+        case STATE_QUOTE:
+          switch (line.charAt(i2)) {
+            case CODE_QUOTE:
+              currentState = STATE_QUOTE_AFTER_QUOTE;
+              break;
+            default:
+          }
+          break;
+        case STATE_QUOTE_AFTER_QUOTE:
+          switch (line.charAt(i2)) {
+            case this.delimiter:
+              result.push(line.substring(readOffset, i2 - 1));
+              currentState = STATE_OUT;
+              readOffset = i2 + 1;
+              break;
+            case CODE_QUOTE:
+              currentState = STATE_QUOTE;
+              break;
+            default:
+              currentState = STATE_WITHIN_QUOTE_IN_QUOTE;
+              break;
+          }
+          break;
+        case STATE_WITHIN_QUOTE_IN_QUOTE:
+          switch (line.charAt(i2)) {
+            case CODE_QUOTE:
+              currentState = STATE_QUOTE;
+              break;
+            default:
+          }
+          break;
+        default:
+      }
+    }
+    if (currentState === STATE_QUOTE_AFTER_QUOTE) {
+      result.push(line.substring(readOffset, readLength - 1));
+    } else {
+      result.push(line.substring(readOffset));
+    }
+    if (validateElementCount && result.length !== this.fullColumnNames.length) {
+      throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);
+    }
+    return result;
+  }
+};
+var MicrophoneIterator = class extends LazyIterator {
+  constructor(microphoneConfig) {
+    super();
+    this.microphoneConfig = microphoneConfig;
+    this.isClosed = false;
+    this.fftSize = microphoneConfig.fftSize || 1024;
+    const fftSizeLog2 = Math.log2(this.fftSize);
+    if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {
+      throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);
+    }
+    this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;
+    this.sampleRateHz = microphoneConfig.sampleRateHz;
+    this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;
+    this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;
+    this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;
+    this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;
+    this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;
+    if (!this.includeSpectrogram && !this.includeWaveform) {
+      throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.");
+    }
+  }
+  summary() {
+    return `microphone`;
+  }
+  static async create(microphoneConfig = {}) {
+    if (!env().get("IS_BROWSER")) {
+      throw new Error("microphone API is only supported in browser environment.");
+    }
+    const microphoneIterator = new MicrophoneIterator(microphoneConfig);
+    await microphoneIterator.start();
+    return microphoneIterator;
+  }
+  async start() {
+    try {
+      this.stream = await navigator.mediaDevices.getUserMedia({
+        audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,
+        video: false
+      });
+    } catch (e2) {
+      throw new Error(`Error thrown while initializing video stream: ${e2.message}`);
+    }
+    if (!this.stream) {
+      throw new Error("Could not obtain audio from microphone.");
+    }
+    const ctxConstructor = window.AudioContext || window.webkitAudioContext;
+    this.audioContext = new ctxConstructor();
+    if (!this.sampleRateHz) {
+      this.sampleRateHz = this.audioContext.sampleRate;
+    } else if (this.audioContext.sampleRate !== this.sampleRateHz) {
+      throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);
+    }
+    const streamSource = this.audioContext.createMediaStreamSource(this.stream);
+    this.analyser = this.audioContext.createAnalyser();
+    this.analyser.fftSize = this.fftSize * 2;
+    this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;
+    streamSource.connect(this.analyser);
+    this.freqData = new Float32Array(this.fftSize);
+    this.timeData = new Float32Array(this.fftSize);
+    return;
+  }
+  async next() {
+    if (this.isClosed) {
+      return { value: null, done: true };
+    }
+    let spectrogramTensor;
+    let waveformTensor;
+    const audioDataQueue = await this.getAudioData();
+    if (this.includeSpectrogram) {
+      const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);
+      spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);
+    }
+    if (this.includeWaveform) {
+      const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);
+      waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);
+    }
+    return {
+      value: { "spectrogram": spectrogramTensor, "waveform": waveformTensor },
+      done: false
+    };
+  }
+  async capture() {
+    return (await this.next()).value;
+  }
+  async getAudioData() {
+    const freqDataQueue = [];
+    const timeDataQueue = [];
+    let currentFrames = 0;
+    return new Promise((resolve) => {
+      const intervalID = setInterval(() => {
+        if (this.includeSpectrogram) {
+          this.analyser.getFloatFrequencyData(this.freqData);
+          if (this.freqData[0] === -Infinity) {
+            resolve({ freqDataQueue, timeDataQueue });
+          }
+          freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));
+        }
+        if (this.includeWaveform) {
+          this.analyser.getFloatTimeDomainData(this.timeData);
+          timeDataQueue.push(this.timeData.slice());
+        }
+        if (++currentFrames === this.numFrames) {
+          clearInterval(intervalID);
+          resolve({ freqDataQueue, timeDataQueue });
+        }
+      }, this.fftSize / this.sampleRateHz * 1e3);
+    });
+  }
+  stop() {
+    if (!this.isClosed) {
+      this.isClosed = true;
+      this.analyser.disconnect();
+      this.audioContext.close();
+      if (this.stream != null && this.stream.getTracks().length > 0) {
+        this.stream.getTracks()[0].stop();
+      }
+    }
+  }
+  toArray() {
+    throw new Error("Can not convert infinite audio stream to array.");
+  }
+  getSampleRate() {
+    return this.sampleRateHz;
+  }
+  flattenQueue(queue) {
+    const frameSize = queue[0].length;
+    const freqData = new Float32Array(queue.length * frameSize);
+    queue.forEach((data, i2) => freqData.set(data, i2 * frameSize));
+    return freqData;
+  }
+  getTensorFromAudioDataArray(freqData, shape) {
+    const vals = new Float32Array(util_exports.sizeFromShape(shape));
+    vals.set(freqData, vals.length - freqData.length);
+    return tensor(vals, shape);
+  }
+};
+var WebcamIterator = class extends LazyIterator {
+  constructor(webcamVideoElement, webcamConfig) {
+    super();
+    this.webcamVideoElement = webcamVideoElement;
+    this.webcamConfig = webcamConfig;
+    this.isClosed = true;
+    this.resize = false;
+    if (this.needToResize()) {
+      this.resize = true;
+      this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];
+      this.cropBoxInd = tensor1d([0], "int32");
+      if (this.webcamConfig.centerCrop) {
+        const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;
+        const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;
+        const widthCropStart = (1 - widthCroppingRatio) / 2;
+        const heightCropStart = (1 - heightCroppingRatio) / 2;
+        const widthCropEnd = widthCropStart + widthCroppingRatio;
+        const heightCropEnd = heightCroppingRatio + heightCropStart;
+        this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);
+      } else {
+        this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);
+      }
+    }
+  }
+  summary() {
+    return `webcam`;
+  }
+  static async create(webcamVideoElement, webcamConfig = {}) {
+    if (!env().get("IS_BROWSER")) {
+      throw new Error("tf.data.webcam is only supported in browser environment.");
+    }
+    if (!webcamVideoElement) {
+      webcamVideoElement = document.createElement("video");
+      if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {
+        throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
+      }
+      webcamVideoElement.width = webcamConfig.resizeWidth;
+      webcamVideoElement.height = webcamConfig.resizeHeight;
+    }
+    const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);
+    await webcamIterator.start();
+    return webcamIterator;
+  }
+  async start() {
+    if (this.webcamConfig.facingMode) {
+      util_exports.assert(this.webcamConfig.facingMode === "user" || this.webcamConfig.facingMode === "environment", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);
+    }
+    try {
+      this.stream = await navigator.mediaDevices.getUserMedia({
+        video: {
+          deviceId: this.webcamConfig.deviceId,
+          facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : "user",
+          width: this.webcamVideoElement.width,
+          height: this.webcamVideoElement.height
+        }
+      });
+    } catch (e2) {
+      e2.message = `Error thrown while initializing video stream: ${e2.message}`;
+      throw e2;
+    }
+    if (!this.stream) {
+      throw new Error("Could not obtain video from webcam.");
+    }
+    try {
+      this.webcamVideoElement.srcObject = this.stream;
+    } catch (error) {
+      console.log(error);
+      this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);
+    }
+    this.webcamVideoElement.play();
+    this.isClosed = false;
+    return new Promise((resolve) => {
+      this.webcamVideoElement.onloadedmetadata = () => {
+        resolve();
+      };
+    });
+  }
+  async next() {
+    if (this.isClosed) {
+      return { value: null, done: true };
+    }
+    let img;
+    try {
+      img = browser_exports.fromPixels(this.webcamVideoElement);
+    } catch (e2) {
+      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e2)}`);
+    }
+    if (this.resize) {
+      try {
+        return { value: this.cropAndResizeFrame(img), done: false };
+      } catch (e2) {
+        throw new Error(`Error thrown cropping the video: ${e2.message}`);
+      } finally {
+        img.dispose();
+      }
+    } else {
+      return { value: img, done: false };
+    }
+  }
+  needToResize() {
+    if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {
+      return true;
+    }
+    return false;
+  }
+  cropAndResizeFrame(img) {
+    return tidy(() => {
+      const expandedImage = expandDims(cast(img, "float32"), 0);
+      let resizedImage;
+      resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
+      const shape = resizedImage.shape;
+      return reshape(resizedImage, shape.slice(1));
+    });
+  }
+  async capture() {
+    return (await this.next()).value;
+  }
+  stop() {
+    const tracks = this.stream.getTracks();
+    tracks.forEach((track) => track.stop());
+    try {
+      this.webcamVideoElement.srcObject = null;
+    } catch (error) {
+      console.log(error);
+      this.webcamVideoElement.src = null;
+    }
+    this.isClosed = true;
+  }
+  toArray() {
+    throw new Error("Can not convert infinite video stream to array.");
+  }
+};
+var DataSource = class {
+};
+var StringIterator = class extends LazyIterator {
+  split(separator) {
+    return new SplitIterator(this, separator);
+  }
+};
+var SplitIterator = class extends StringIterator {
+  constructor(upstream, separator) {
+    super();
+    this.upstream = upstream;
+    this.impl = new SplitIteratorImpl(upstream, separator);
+  }
+  summary() {
+    return this.impl.summary();
+  }
+  async next() {
+    return this.impl.next();
+  }
+};
+var SplitIteratorImpl = class extends OneToManyIterator {
+  constructor(upstream, separator) {
+    super();
+    this.upstream = upstream;
+    this.separator = separator;
+    this.carryover = "";
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Split('${this.separator}')`;
+  }
+  async pump() {
+    const chunkResult = await this.upstream.next();
+    if (chunkResult.done) {
+      if (this.carryover === "") {
+        return false;
+      }
+      this.outputQueue.push(this.carryover);
+      this.carryover = "";
+      return true;
+    }
+    const lines2 = chunkResult.value.split(this.separator);
+    lines2[0] = this.carryover + lines2[0];
+    for (const line of lines2.slice(0, -1)) {
+      this.outputQueue.push(line);
+    }
+    this.carryover = lines2[lines2.length - 1];
+    return true;
+  }
+};
+var ByteChunkIterator = class extends LazyIterator {
+  decodeUTF8() {
+    return new Utf8Iterator(this);
+  }
+};
+var Utf8Iterator = class extends StringIterator {
+  constructor(upstream) {
+    super();
+    this.upstream = upstream;
+    this.impl = new Utf8IteratorImpl(upstream);
+  }
+  summary() {
+    return this.impl.summary();
+  }
+  async next() {
+    return this.impl.next();
+  }
+};
+var Utf8IteratorImpl = class extends OneToManyIterator {
+  constructor(upstream) {
+    super();
+    this.upstream = upstream;
+    if (env().get("IS_BROWSER")) {
+      this.decoder = new TextDecoder("utf-8");
+    } else {
+      const { StringDecoder } = require_string_decoder();
+      this.decoder = new StringDecoder("utf8");
+    }
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Utf8`;
+  }
+  async pump() {
+    const chunkResult = await this.upstream.next();
+    let chunk;
+    if (chunkResult.done) {
+      return false;
+    } else {
+      chunk = chunkResult.value;
+    }
+    let text;
+    if (env().get("IS_BROWSER")) {
+      text = this.decoder.decode(chunk, { stream: true });
+    } else {
+      text = this.decoder.write(Buffer.from(chunk.buffer));
+    }
+    this.outputQueue.push(text);
+    return true;
+  }
+};
+var FileChunkIterator = class extends ByteChunkIterator {
+  constructor(file, options4 = {}) {
+    super();
+    this.file = file;
+    this.options = options4;
+    util_exports.assert(file instanceof Uint8Array || (env().get("IS_BROWSER") ? file instanceof File || file instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now.");
+    this.offset = options4.offset || 0;
+    this.chunkSize = options4.chunkSize || 1024 * 1024;
+  }
+  summary() {
+    return `FileChunks ${this.file}`;
+  }
+  async next() {
+    if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {
+      return { value: null, done: true };
+    }
+    const chunk = new Promise((resolve, reject) => {
+      const end = this.offset + this.chunkSize;
+      if (this.file instanceof Uint8Array) {
+        resolve(new Uint8Array(this.file.slice(this.offset, end)));
+      } else {
+        const fileReader = new FileReader();
+        fileReader.onload = (event) => {
+          let data = fileReader.result;
+          if (data instanceof ArrayBuffer) {
+            data = new Uint8Array(data);
+          }
+          if (!(data instanceof Uint8Array)) {
+            return reject(new TypeError("FileReader returned unknown type."));
+          }
+          resolve(data);
+        };
+        fileReader.onabort = (event) => {
+          return reject(new Error("Aborted"));
+        };
+        fileReader.onerror = (event) => {
+          return reject(new Error(event.type));
+        };
+        const slice6 = this.file.slice(this.offset, end);
+        fileReader.readAsArrayBuffer(slice6);
+      }
+      this.offset = end;
+    });
+    return { value: await chunk, done: false };
+  }
+};
+async function urlChunkIterator(url, options4 = {}, fetchFunc) {
+  let urlString;
+  let requestInit;
+  if (typeof url === "string") {
+    urlString = url;
+  } else {
+    urlString = url.url;
+    requestInit = getRequestInitFromRequest(url);
+  }
+  const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit);
+  if (response.ok) {
+    const uint8Array = new Uint8Array(await response.arrayBuffer());
+    return new FileChunkIterator(uint8Array, options4);
+  } else {
+    throw new Error(response.statusText);
+  }
+}
+var getRequestInitFromRequest = (request) => {
+  const init22 = {
+    method: request.method,
+    headers: request.headers,
+    body: request.body,
+    mode: request.mode,
+    credentials: request.credentials,
+    cache: request.cache,
+    redirect: request.redirect,
+    referrer: request.referrer,
+    integrity: request.integrity
+  };
+  return init22;
+};
+function isLocalPath(source) {
+  return typeof source === "string" && source.slice(0, 7) === "file://";
+}
+var FileDataSource = class extends DataSource {
+  constructor(input2, options4 = {}) {
+    super();
+    this.input = input2;
+    this.options = options4;
+  }
+  async iterator() {
+    if (isLocalPath(this.input) && env().get("IS_NODE")) {
+      const fs = require_fs();
+      this.input = fs.readFileSync(this.input.slice(7));
+    }
+    return new FileChunkIterator(this.input, this.options);
+  }
+};
+var URLDataSource = class extends DataSource {
+  constructor(url, fileOptions = {}) {
+    super();
+    this.url = url;
+    this.fileOptions = fileOptions;
+  }
+  async iterator() {
+    if (isLocalPath(this.url)) {
+      return new FileDataSource(this.url, this.fileOptions).iterator();
+    } else {
+      return urlChunkIterator(this.url, this.fileOptions);
+    }
+  }
+};
+function csv(source, csvConfig = {}) {
+  return new CSVDataset(new URLDataSource(source), csvConfig);
+}
+function func(f) {
+  const iter = iteratorFromFunction(f);
+  return datasetFromIteratorFn(async () => iter);
+}
+function generator(generator2) {
+  return datasetFromIteratorFn(async () => {
+    const gen = await generator2();
+    return iteratorFromFunction(() => gen.next());
+  });
+}
+async function webcam(webcamVideoElement, webcamConfig) {
+  return WebcamIterator.create(webcamVideoElement, webcamConfig);
+}
+async function microphone(microphoneConfig) {
+  return MicrophoneIterator.create(microphoneConfig);
+}
+var version4 = "3.18.0";
+function assertNotComplex(tensor2, opName) {
+  if (!Array.isArray(tensor2)) {
+    tensor2 = [tensor2];
+  }
+  tensor2.forEach((t2) => {
+    if (t2 != null) {
+      util_exports.assert(t2.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`);
+    }
+  });
+}
+var whereImpl2 = kernel_impls_exports.whereImpl;
+var MathBackendCPU = class extends KernelBackend {
+  constructor() {
+    super();
+    this.blockSize = 48;
+    this.firstUse = true;
+    this.data = new DataStorage(this, engine());
+  }
+  nextDataId() {
+    return MathBackendCPU.nextDataId++;
+  }
+  write(values, shape, dtype) {
+    if (this.firstUse) {
+      this.firstUse = false;
+      if (env().get("IS_NODE")) {
+        backend_util_exports.warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================");
+      }
+    }
+    const dataId = { id: this.nextDataId() };
+    this.data.set(dataId, { values, dtype, refCount: 1 });
+    return dataId;
+  }
+  makeTensorInfo(shape, dtype, values) {
+    let outId;
+    if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
+      const encodedValues = values.map((d) => util_exports.encodeString(d));
+      outId = this.write(encodedValues, shape, dtype);
+    } else {
+      outId = this.write(values, shape, dtype);
+    }
+    return { dataId: outId, shape, dtype };
+  }
+  refCount(dataId) {
+    if (this.data.has(dataId)) {
+      const tensorData = this.data.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const tensorData = this.data.get(dataId);
+    tensorData.refCount++;
+  }
+  decRef(dataId) {
+    if (this.data.has(dataId)) {
+      const tensorData = this.data.get(dataId);
+      tensorData.refCount--;
+    }
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    this.data.set(dataId, { values, dtype, refCount });
+  }
+  numDataIds() {
+    return this.data.numDataIds();
+  }
+  async read(dataId) {
+    return this.readSync(dataId);
+  }
+  readSync(dataId) {
+    const { dtype, complexTensorInfos } = this.data.get(dataId);
+    if (dtype === "complex64") {
+      const realValues = this.readSync(complexTensorInfos.real.dataId);
+      const imagValues = this.readSync(complexTensorInfos.imag.dataId);
+      return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    }
+    return this.data.get(dataId).values;
+  }
+  bufferSync(t2) {
+    const data = this.readSync(t2.dataId);
+    if (t2.dtype === "string") {
+      try {
+        const strings = data.map((d) => util_exports.decodeString(d));
+        return buffer(t2.shape, t2.dtype, strings);
+      } catch (_a) {
+        throw new Error("Failed to decode encoded string bytes into utf-8");
+      }
+    }
+    return buffer(t2.shape, t2.dtype, data);
+  }
+  makeOutput(values, shape, dtype) {
+    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
+  }
+  disposeData(dataId, force = false) {
+    if (this.data.has(dataId)) {
+      this.data.get(dataId).refCount--;
+      if (!force && this.data.get(dataId).refCount > 0) {
+        return false;
+      }
+      const { complexTensorInfos } = this.data.get(dataId);
+      if (complexTensorInfos != null) {
+        this.disposeData(complexTensorInfos.real.dataId, true);
+        this.disposeData(complexTensorInfos.imag.dataId, true);
+      }
+      this.data.delete(dataId);
+    }
+    return true;
+  }
+  disposeIntermediateTensorInfo(tensorInfo) {
+    this.disposeData(tensorInfo.dataId);
+  }
+  async time(f) {
+    const start = util_exports.now();
+    f();
+    const kernelMs = util_exports.now() - start;
+    return { kernelMs };
+  }
+  memory() {
+    return {
+      unreliable: true,
+      reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]
+    };
+  }
+  where(condition) {
+    assertNotComplex([condition], "where");
+    const condVals = this.readSync(condition.dataId);
+    return whereImpl2(condition.shape, condVals);
+  }
+  dispose() {
+  }
+  floatPrecision() {
+    return 32;
+  }
+  epsilon() {
+    return super.epsilon();
+  }
+};
+MathBackendCPU.nextDataId = 0;
+var shared_exports = {};
+__export2(shared_exports, {
+  addImpl: () => addImpl,
+  bincountImpl: () => bincountImpl,
+  bincountReduceImpl: () => bincountReduceImpl,
+  ceilImpl: () => ceilImpl,
+  concatImpl: () => concatImpl,
+  equalImpl: () => equalImpl,
+  expImpl: () => expImpl,
+  expm1Impl: () => expm1Impl,
+  floorImpl: () => floorImpl,
+  gatherNdImpl: () => gatherNdImpl,
+  gatherV2Impl: () => gatherV2Impl,
+  greaterEqualImpl: () => greaterEqualImpl,
+  greaterImpl: () => greaterImpl,
+  lessEqualImpl: () => lessEqualImpl,
+  lessImpl: () => lessImpl,
+  linSpaceImpl: () => linSpaceImpl,
+  logImpl: () => logImpl,
+  maxImpl: () => maxImpl,
+  maximumImpl: () => maximumImpl,
+  minimumImpl: () => minimumImpl,
+  multiplyImpl: () => multiplyImpl,
+  negImpl: () => negImpl,
+  notEqualImpl: () => notEqualImpl,
+  prodImpl: () => prodImpl,
+  rangeImpl: () => rangeImpl,
+  rsqrtImpl: () => rsqrtImpl,
+  scatterImpl: () => scatterImpl,
+  sigmoidImpl: () => sigmoidImpl,
+  simpleAbsImpl: () => simpleAbsImpl,
+  sliceImpl: () => sliceImpl,
+  sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,
+  sparseReshapeImpl: () => sparseReshapeImpl,
+  sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,
+  sqrtImpl: () => sqrtImpl,
+  squaredDifferenceImpl: () => squaredDifferenceImpl,
+  stridedSliceImpl: () => stridedSliceImpl,
+  stringNGramsImpl: () => stringNGramsImpl,
+  stringSplitImpl: () => stringSplitImpl,
+  stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,
+  subImpl: () => subImpl,
+  tileImpl: () => tileImpl,
+  topKImpl: () => topKImpl,
+  transposeImpl: () => transposeImpl,
+  uniqueImpl: () => uniqueImpl
+});
+function simpleAbsImpl(vals) {
+  const resultValues = new Float32Array(vals.length);
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    resultValues[i2] = Math.abs(vals[i2]);
+  }
+  return resultValues;
+}
+var abs2 = (args) => {
+  const { x } = args.inputs;
+  const cpuBackend = args.backend;
+  assertNotComplex(x, "abs");
+  let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
+  const values = cpuBackend.data.get(x.dataId).values;
+  resultValues = simpleAbsImpl(values);
+  return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);
+};
+var absConfig = {
+  kernelName: Abs,
+  backendName: "cpu",
+  kernelFunc: abs2
+};
+function createSimpleBinaryKernelImpl(op2) {
+  return (aShape, bShape, aVals, bVals, dtype) => {
+    const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    const resultRank = newShape.length;
+    const resultStrides = util_exports.computeStrides(newShape);
+    const resultSize = util_exports.sizeFromShape(newShape);
+    const result = util_exports.getTypedArrayFromDType(dtype, resultSize);
+    const aRank = aShape.length;
+    const bRank = bShape.length;
+    const aStrides = util_exports.computeStrides(aShape);
+    const bStrides = util_exports.computeStrides(bShape);
+    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);
+    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);
+    if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+      for (let i2 = 0; i2 < result.length; ++i2) {
+        result[i2] = op2(aVals[i2 % aVals.length], bVals[i2 % bVals.length]);
+      }
+    } else {
+      for (let i2 = 0; i2 < result.length; ++i2) {
+        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+        const aLoc = loc.slice(-aRank);
+        aBroadcastDims.forEach((d) => aLoc[d] = 0);
+        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
+        const bLoc = loc.slice(-bRank);
+        bBroadcastDims.forEach((d) => bLoc[d] = 0);
+        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
+        result[i2] = op2(aVals[aIndex], bVals[bIndex]);
+      }
+    }
+    return [result, newShape];
+  };
+}
+function complex2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { real: real5, imag: imag5 } = inputs;
+  const realVals = backend2.data.get(real5.dataId).values;
+  const imagVals = backend2.data.get(imag5.dataId).values;
+  const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64");
+  const complex5 = backend2.data.get(complexInfo.dataId);
+  complex5.complexTensorInfos = {
+    real: backend2.makeTensorInfo(real5.shape, "float32", realVals),
+    imag: backend2.makeTensorInfo(imag5.shape, "float32", imagVals)
+  };
+  return complexInfo;
+}
+var complexConfig = {
+  kernelName: Complex,
+  backendName: "cpu",
+  kernelFunc: complex2
+};
+function zeros3(backend2, shape, dtype = "float32") {
+  if (dtype === "complex64") {
+    const real5 = zeros3(backend2, shape, "float32");
+    const imag5 = zeros3(backend2, shape, "float32");
+    return complex2({ inputs: { real: real5, imag: imag5 }, backend: backend2 });
+  }
+  const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);
+  return backend2.makeTensorInfo(shape, dtype, values);
+}
+function identity2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  backend2.incRef(x.dataId);
+  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+}
+var identityConfig = {
+  kernelName: Identity,
+  backendName: "cpu",
+  kernelFunc: identity2
+};
+function real2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const real5 = backend2.data.get(input2.dataId).complexTensorInfos.real;
+  const realVal = backend2.data.get(real5.dataId).values;
+  return backend2.makeTensorInfo(real5.shape, real5.dtype, realVal);
+}
+var realConfig = {
+  kernelName: Real,
+  backendName: "cpu",
+  kernelFunc: real2
+};
+function cast3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dtype } = attrs;
+  if (dtype === "complex64") {
+    if (x.dtype === "complex64") {
+      return identity2({ inputs: { x }, backend: backend2 });
+    }
+    const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);
+    const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+    const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
+    backend2.disposeIntermediateTensorInfo(floatX);
+    return result;
+  }
+  if (x.dtype === "complex64") {
+    const realPart = real2({ inputs: { input: x }, backend: backend2 });
+    const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    return result;
+  }
+  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
+    const result = identity2({ inputs: { x }, backend: backend2 });
+    return { dataId: result.dataId, shape: result.shape, dtype };
+  }
+  if (dtype === "int32") {
+    const values = backend2.data.get(x.dataId).values;
+    const resultValues = Int32Array.from(values);
+    return backend2.makeTensorInfo(x.shape, "int32", resultValues);
+  }
+  if (dtype === "bool") {
+    const xVals = backend2.data.get(x.dataId).values;
+    const zero = util_exports.toTypedArray([0], x.dtype);
+    const [resultData, resultShape] = createSimpleBinaryKernelImpl((a6, b) => a6 !== b ? 1 : 0)(x.shape, [], xVals, zero, "bool");
+    return backend2.makeTensorInfo(resultShape, "bool", resultData);
+  }
+  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
+}
+var castConfig = {
+  kernelName: Cast,
+  backendName: "cpu",
+  kernelFunc: cast3
+};
+function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {
+  if (complexImpl == null) {
+    return ({ inputs, backend: backend2 }) => {
+      const { a: a6, b } = inputs;
+      const cpuBackend = backend2;
+      assertNotComplex([a6, b], name);
+      const aVals = cpuBackend.data.get(a6.dataId).values;
+      const bVals = cpuBackend.data.get(b.dataId).values;
+      const decodedAVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;
+      const decodedBVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;
+      const $dtype = dtype || a6.dtype;
+      const [resultData, resultShape] = simpleImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);
+      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
+    };
+  }
+  return ({ inputs, backend: backend2 }) => {
+    const { a: a6, b } = inputs;
+    const cpuBackend = backend2;
+    if (a6.dtype === "complex64" || b.dtype === "complex64") {
+      const $aComplex = cast3({ inputs: { x: a6 }, backend: cpuBackend, attrs: { dtype: "complex64" } });
+      const $aComplexVals = cpuBackend.data.get($aComplex.dataId);
+      const aReal = $aComplexVals.complexTensorInfos.real;
+      const aImag = $aComplexVals.complexTensorInfos.imag;
+      const aRealVals = cpuBackend.data.get(aReal.dataId).values;
+      const aImagVals = cpuBackend.data.get(aImag.dataId).values;
+      const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: "complex64" } });
+      const $bComplexVals = cpuBackend.data.get($bComplex.dataId);
+      const bReal = $bComplexVals.complexTensorInfos.real;
+      const bImag = $bComplexVals.complexTensorInfos.imag;
+      const bRealVals = cpuBackend.data.get(bReal.dataId).values;
+      const bImagVals = cpuBackend.data.get(bImag.dataId).values;
+      const [resultRealData, resultImagData, resultShape] = complexImpl(a6.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);
+      const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData);
+      const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData);
+      const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });
+      cpuBackend.disposeIntermediateTensorInfo($aComplex);
+      cpuBackend.disposeIntermediateTensorInfo($bComplex);
+      cpuBackend.disposeIntermediateTensorInfo(resultReal);
+      cpuBackend.disposeIntermediateTensorInfo(resultImag);
+      return result;
+    } else {
+      const aVals = cpuBackend.data.get(a6.dataId).values;
+      const bVals = cpuBackend.data.get(b.dataId).values;
+      const $dtype = dtype || a6.dtype;
+      const [resultData, resultShape] = simpleImpl(a6.shape, b.shape, aVals, bVals, $dtype);
+      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
+    }
+  };
+}
+function createComplexBinaryKernelImpl(op2) {
+  return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {
+    const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    const resultSize = util_exports.sizeFromShape(resultShape);
+    const resultRank = resultShape.length;
+    const resultStrides = util_exports.computeStrides(resultShape);
+    const resultRealVals = util_exports.getTypedArrayFromDType("float32", resultSize);
+    const resultImagVals = util_exports.getTypedArrayFromDType("float32", resultSize);
+    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);
+    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);
+    const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);
+    const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);
+    const aRank = aShape.length;
+    const aStrides = util_exports.computeStrides(aShape);
+    const bRank = bShape.length;
+    const bStrides = util_exports.computeStrides(bShape);
+    if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
+        const aIdx = i2 % aVals.length;
+        const bIdx = i2 % bVals.length;
+        const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
+        resultRealVals[i2] = result.real;
+        resultImagVals[i2] = result.imag;
+      }
+    } else {
+      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
+        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+        const aLoc = loc.slice(-aRank);
+        aBroadcastDims.forEach((d) => aLoc[d] = 0);
+        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
+        const bLoc = loc.slice(-bRank);
+        bBroadcastDims.forEach((d) => bLoc[d] = 0);
+        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
+        const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
+        resultRealVals[i2] = opResult.real;
+        resultImagVals[i2] = opResult.imag;
+      }
+    }
+    return [resultRealVals, resultImagVals, resultShape];
+  };
+}
+var addImpl = createSimpleBinaryKernelImpl((a6, b) => a6 + b);
+var addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
+  return { real: aReal + bReal, imag: aImag + bImag };
+});
+var add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);
+var addConfig = {
+  kernelName: Add,
+  backendName: "cpu",
+  kernelFunc: add4
+};
+function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size2) {
+  const weightsSize = util_exports.sizeFromShape(weightsShape);
+  const outVals = util_exports.makeZerosTypedArray(size2, weightsDtype);
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    const value = xVals[i2];
+    if (value < 0) {
+      throw new Error("Input x must be non-negative!");
+    }
+    if (value >= size2) {
+      continue;
+    }
+    if (weightsSize > 0) {
+      outVals[value] += weightsVals[i2];
+    } else {
+      outVals[value] += 1;
+    }
+  }
+  return outVals;
+}
+function bincountReduceImpl(xBuf, weightsBuf, size2, binaryOutput = false) {
+  const numRows = xBuf.shape[0];
+  const numCols = xBuf.shape[1];
+  const outBuf = buffer([numRows, size2], weightsBuf.dtype);
+  for (let i2 = 0; i2 < numRows; i2++) {
+    for (let j = 0; j < numCols; j++) {
+      const value = xBuf.get(i2, j);
+      if (value < 0) {
+        throw new Error("Input x must be non-negative!");
+      }
+      if (value >= size2) {
+        continue;
+      }
+      if (binaryOutput) {
+        outBuf.set(1, i2, value);
+      } else {
+        if (weightsBuf.size > 0) {
+          outBuf.set(outBuf.get(i2, value) + weightsBuf.get(i2, j), i2, value);
+        } else {
+          outBuf.set(outBuf.get(i2, value) + 1, i2, value);
+        }
+      }
+    }
+  }
+  return outBuf;
+}
+function createSimpleUnaryImpl(op2) {
+  return (values, dtype, attrs) => {
+    const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);
+    for (let i2 = 0; i2 < values.length; ++i2) {
+      newValues[i2] = op2(values[i2], attrs);
+    }
+    return newValues;
+  };
+}
+function unaryKernelFunc(name, op2, dtype) {
+  return ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    assertNotComplex(x, name);
+    if (x.dtype === "string" || dtype === "string") {
+      throw new Error("unaryKernelFunc does not support string input/output");
+    }
+    const cpuBackend = backend2;
+    const values = cpuBackend.data.get(x.dataId).values;
+    const xSize = util_exports.sizeFromShape(x.shape);
+    const $dtype = dtype || x.dtype;
+    const newValues = util_exports.getArrayFromDType($dtype, xSize);
+    for (let i2 = 0; i2 < xSize; ++i2) {
+      newValues[i2] = op2(values[i2], attrs);
+    }
+    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
+  };
+}
+function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
+  return ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    assertNotComplex(x, name);
+    if (x.dtype === "string" || dtype === "string") {
+      throw new Error("unaryKernelFunc does not support string input/output");
+    }
+    const cpuBackend = backend2;
+    const values = cpuBackend.data.get(x.dataId).values;
+    const $dtype = dtype || x.dtype;
+    const newValues = unaryImpl(values, $dtype, attrs);
+    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
+  };
+}
+var ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));
+var ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);
+var ceilConfig = {
+  kernelName: Ceil,
+  backendName: "cpu",
+  kernelFunc: ceil2
+};
+function concatImpl(inputs, outShape, dtype, simplyConcat) {
+  const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
+  if (simplyConcat && dtype !== "string") {
+    let offset = 0;
+    inputs.forEach((input2) => {
+      const size2 = util_exports.sizeFromShape(input2.shape);
+      outVals.set(input2.vals, offset);
+      offset += size2;
+    });
+  } else {
+    let colOffset = 0;
+    inputs.forEach((input2) => {
+      const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;
+      let tIdx = 0;
+      for (let row = 0; row < input2.shape[0]; ++row) {
+        const resIdx = row * outShape[1] + colOffset;
+        for (let col = 0; col < input2.shape[1]; ++col) {
+          outVals[resIdx + col] = decodedData[tIdx++];
+        }
+      }
+      colOffset += input2.shape[1];
+    });
+  }
+  return outVals;
+}
+var equalImpl = createSimpleBinaryKernelImpl((a6, b) => a6 === b ? 1 : 0);
+var equal2 = binaryKernelFunc(Equal, equalImpl, null, "bool");
+var equalConfig = {
+  kernelName: Equal,
+  backendName: "cpu",
+  kernelFunc: equal2
+};
+var expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));
+var exp2 = unaryKernelFuncFromImpl(Exp, expImpl, "float32");
+var expConfig = {
+  kernelName: Exp,
+  backendName: "cpu",
+  kernelFunc: exp2
+};
+var expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));
+var expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);
+var expm1Config = {
+  kernelName: Expm1,
+  backendName: "cpu",
+  kernelFunc: expm12
+};
+var floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));
+var floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);
+var floorConfig = {
+  kernelName: Floor,
+  backendName: "cpu",
+  kernelFunc: floor2
+};
+function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides2, paramsShape, paramsSize) {
+  const outBuf = buffer([numSlices, sliceSize], dtype);
+  for (let i2 = 0; i2 < numSlices; i2++) {
+    const index2 = [];
+    let flattenIndex = 0;
+    for (let j = 0; j < sliceRank; j++) {
+      const dim = indicesData[i2 * sliceRank + j];
+      flattenIndex += dim * strides2[j];
+      index2.push(dim);
+    }
+    if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {
+      throw new Error(`Invalid indices: ${index2} does not index into ${paramsShape}`);
+    }
+    for (let k = 0; k < sliceSize; k++) {
+      outBuf.values[i2 * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
+    }
+  }
+  return outBuf;
+}
+function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
+  const outBuf = buffer(flattenOutputShape, xBuf.dtype);
+  for (let i2 = 0; i2 < outBuf.size; ++i2) {
+    const newLoc = outBuf.indexToLoc(i2);
+    const originalLoc = newLoc.slice();
+    const batchIdx = originalLoc[0];
+    const indicesIdx = originalLoc[2];
+    const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
+    originalLoc[2] = indicesBuf.values[indicesIndex];
+    const originalIndex = xBuf.locToIndex(originalLoc);
+    if (0 <= originalIndex && originalIndex < xBuf.values.length) {
+      outBuf.values[i2] = xBuf.values[originalIndex];
+    }
+  }
+  return outBuf;
+}
+var greaterImpl = createSimpleBinaryKernelImpl((a6, b) => a6 > b ? 1 : 0);
+var greater3 = binaryKernelFunc(Greater, greaterImpl, null, "bool");
+var greaterConfig = {
+  kernelName: Greater,
+  backendName: "cpu",
+  kernelFunc: greater3
+};
+var greaterEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 >= b ? 1 : 0);
+var greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, "bool");
+var greaterEqualConfig = {
+  kernelName: GreaterEqual,
+  backendName: "cpu",
+  kernelFunc: greaterEqual2
+};
+var lessImpl = createSimpleBinaryKernelImpl((a6, b) => a6 < b ? 1 : 0);
+var less3 = binaryKernelFunc(Less, lessImpl, null, "bool");
+var lessConfig = {
+  kernelName: Less,
+  backendName: "cpu",
+  kernelFunc: less3
+};
+var lessEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 <= b ? 1 : 0);
+var lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, "bool");
+var lessEqualConfig = {
+  kernelName: LessEqual,
+  backendName: "cpu",
+  kernelFunc: lessEqual2
+};
+function linSpaceImpl(start, stop, num) {
+  const step5 = (stop - start) / (num - 1);
+  const values = util_exports.makeZerosTypedArray(num, "float32");
+  values[0] = start;
+  for (let i2 = 1; i2 < values.length; i2++) {
+    values[i2] = values[i2 - 1] + step5;
+  }
+  return values;
+}
+var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));
+var log3 = unaryKernelFuncFromImpl(Log, logImpl);
+var logConfig = {
+  kernelName: Log,
+  backendName: "cpu",
+  kernelFunc: log3
+};
+function maxImpl(aVals, reduceSize, outShape, dtype) {
+  const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let max7 = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (Number.isNaN(value) || value > max7) {
+        max7 = value;
+      }
+    }
+    vals[i2] = max7;
+  }
+  return vals;
+}
+var maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));
+var maximum3 = binaryKernelFunc(Maximum, maximumImpl);
+var maximumConfig = {
+  kernelName: Maximum,
+  backendName: "cpu",
+  kernelFunc: maximum3
+};
+var minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));
+var minimum3 = binaryKernelFunc(Minimum, minimumImpl);
+var minimumConfig = {
+  kernelName: Minimum,
+  backendName: "cpu",
+  kernelFunc: minimum3
+};
+var multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);
+var multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
+  return {
+    real: aReal * bReal - aImag * bImag,
+    imag: aReal * bImag + aImag * bReal
+  };
+});
+var multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);
+var multiplyConfig = {
+  kernelName: Multiply,
+  backendName: "cpu",
+  kernelFunc: multiply2
+};
+function negImpl(xVals, xShape, xDtype) {
+  const minusOne = util_exports.createScalarValue(-1, xDtype);
+  return multiplyImpl([], xShape, minusOne, xVals, xDtype);
+}
+function neg2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  assertNotComplex(x, "neg");
+  const xVals = backend2.data.get(x.dataId).values;
+  const [res, newShape] = negImpl(xVals, x.shape, x.dtype);
+  return backend2.makeTensorInfo(newShape, x.dtype, res);
+}
+var negConfig = {
+  kernelName: Neg,
+  backendName: "cpu",
+  kernelFunc: neg2
+};
+var notEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 !== b ? 1 : 0);
+var notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, "bool");
+var notEqualConfig = {
+  kernelName: NotEqual,
+  backendName: "cpu",
+  kernelFunc: notEqual2
+};
+function transposeImpl(xVals, xShape, dtype, perm, newShape) {
+  const xRank = xShape.length;
+  const xSize = util_exports.sizeFromShape(xShape);
+  const xStrides = util_exports.computeStrides(xShape);
+  const newStrides = util_exports.computeStrides(newShape);
+  const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));
+  for (let i2 = 0; i2 < xSize; ++i2) {
+    const loc = util_exports.indexToLoc(i2, xRank, xStrides);
+    const newLoc = new Array(loc.length);
+    for (let i3 = 0; i3 < newLoc.length; i3++) {
+      newLoc[i3] = loc[perm[i3]];
+    }
+    const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);
+    result[newIndex] = xVals[i2];
+  }
+  return result;
+}
+function transpose2(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x } = inputs;
+  const { perm } = attrs;
+  assertNotComplex(x, "transpose");
+  const xRank = x.shape.length;
+  const newShape = new Array(xRank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[perm[i2]];
+  }
+  const values = backend2.data.get(x.dataId).values;
+  const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
+  const dataId = backend2.write(result, newShape, x.dtype);
+  return { dataId, shape: newShape, dtype: x.dtype };
+}
+var transposeConfig = {
+  kernelName: Transpose,
+  backendName: "cpu",
+  kernelFunc: transpose2
+};
+function prodImpl(xShape, xDtype, xVals, reductionAxes) {
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);
+  const outDtype = upcastType(xDtype, "int32");
+  const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  for (let i2 = 0; i2 < outVals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let prod6 = 1;
+    for (let j = 0; j < reduceSize; ++j) {
+      prod6 *= xVals[offset + j];
+    }
+    outVals[i2] = prod6;
+  }
+  return { outVals, outShape, outDtype };
+}
+function prod2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "prod");
+  const xRank = x.shape.length;
+  const axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
+  let reductionAxes = axes;
+  let permutedX = x;
+  const intermediateTensorInfos = [];
+  if (permutation != null) {
+    permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+    intermediateTensorInfos.push(permutedX);
+    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
+  }
+  const xVals = backend2.data.get(permutedX.dataId).values;
+  const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);
+  let resultShape = outShape;
+  if (keepDims) {
+    resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);
+  }
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return backend2.makeTensorInfo(resultShape, outDtype, outVals);
+}
+var prodConfig = {
+  kernelName: Prod,
+  backendName: "cpu",
+  kernelFunc: prod2
+};
+function rangeImpl(start, stop, step5, dtype) {
+  const sameStartStop = start === stop;
+  const increasingRangeNegativeStep = start < stop && step5 < 0;
+  const decreasingRangePositiveStep = stop < start && step5 > 1;
+  if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
+    return util_exports.makeZerosTypedArray(0, dtype);
+  }
+  const numElements = Math.abs(Math.ceil((stop - start) / step5));
+  const values = util_exports.makeZerosTypedArray(numElements, dtype);
+  if (stop < start && step5 === 1) {
+    step5 = -1;
+  }
+  values[0] = start;
+  for (let i2 = 1; i2 < values.length; i2++) {
+    values[i2] = values[i2 - 1] + step5;
+  }
+  return values;
+}
+var rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));
+var rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);
+var rsqrtConfig = {
+  kernelName: Rsqrt,
+  backendName: "cpu",
+  kernelFunc: rsqrt2
+};
+function scatterImpl(indices, updates, shape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, defaultValue, sumDupeIndices) {
+  const flattenShape = [outputSize2 / sliceSize, sliceSize];
+  const indicesData = indices.values;
+  const updatesData = updates.values;
+  if (outputSize2 === 0) {
+    return buffer(shape, updates.dtype);
+  }
+  const outBuf = buffer(flattenShape, updates.dtype);
+  if (typeof defaultValue === "string") {
+    outBuf.values.fill(defaultValue);
+  } else if (typeof defaultValue === "number") {
+    outBuf.values.fill(defaultValue);
+  } else if (typeof defaultValue === "boolean") {
+    outBuf.values.fill(+defaultValue);
+  }
+  for (let i2 = 0; i2 < numUpdates; i2++) {
+    const index2 = [];
+    let flattenIndex = 0;
+    for (let j = 0; j < sliceRank; j++) {
+      const dim = indicesData[i2 * sliceRank + j];
+      index2.push(dim);
+      flattenIndex += dim * strides2[j];
+    }
+    if (flattenIndex < 0 || flattenIndex >= outputSize2 / sliceSize) {
+      throw new Error(`Invalid indices: ${index2} does not index into ${shape}`);
+    }
+    for (let k = 0; k < sliceSize; k++) {
+      if (sumDupeIndices) {
+        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i2 * sliceSize + k];
+      } else {
+        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i2 * sliceSize + k];
+      }
+    }
+  }
+  return outBuf;
+}
+var sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));
+var sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));
+var sigmoidConfig = {
+  kernelName: Sigmoid,
+  backendName: "cpu",
+  kernelFunc: sigmoid2
+};
+function sliceImpl(vals, begin, size2, shape, dtype) {
+  const isContinous = slice_util_exports.isSliceContinous(shape, begin, size2);
+  const length = util_exports.sizeFromShape(size2);
+  const xStrides = util_exports.computeStrides(shape);
+  if (isContinous) {
+    const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);
+    if (dtype === "string") {
+      return vals.slice(flatOffset, flatOffset + length);
+    }
+    return vals.subarray(flatOffset, flatOffset + length);
+  }
+  const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
+  const inBuf = buffer(shape, dtype, decodedData);
+  const outBuf = buffer(size2, dtype);
+  for (let i2 = 0; i2 < outBuf.size; ++i2) {
+    const outLoc = outBuf.indexToLoc(i2);
+    const inLoc = outLoc.map((idx, j) => idx + begin[j]);
+    outBuf.set(inBuf.get(...inLoc), ...outLoc);
+  }
+  if (dtype === "string") {
+    return backend_util_exports.fromStringArrayToUint8(outBuf.values);
+  }
+  return outBuf.values;
+}
+function slice2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, size: size2 } = attrs;
+  assertNotComplex(x, "slice");
+  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size2);
+  slice_util_exports.assertParamsValid(x, $begin, $size);
+  const vals = backend2.data.get(x.dataId).values;
+  const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
+  return backend2.makeTensorInfo($size, x.dtype, outVals);
+}
+var sliceConfig = {
+  kernelName: Slice,
+  backendName: "cpu",
+  kernelFunc: slice2
+};
+function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {
+  const indicesCount = indicesShape[0];
+  const denseRows = denseShape[0];
+  const emptyRowIndicator = new Array(denseRows);
+  const reverseIndexMap = new Array(indicesCount);
+  const rank = indicesShape[1];
+  if (denseRows === 0) {
+    if (indicesCount !== 0) {
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));
+    }
+    const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);
+    const outputValues = util_exports.getArrayFromDType(valuesDType, 0);
+    return [
+      outputIndices,
+      [0, rank],
+      outputValues,
+      emptyRowIndicator,
+      reverseIndexMap
+    ];
+  }
+  let rowsAreOrdered = true;
+  let lastIndicesRow = 0;
+  const csrOffset = new Array(denseRows).fill(0);
+  for (let i2 = 0; i2 < indicesCount; ++i2) {
+    const row = indices[i2 * rank];
+    if (row < 0) {
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i2, row));
+    }
+    if (row >= denseRows) {
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i2, row, denseRows));
+    }
+    ++csrOffset[row];
+    rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;
+    lastIndicesRow = row;
+  }
+  let allRowsFull = true;
+  for (let row = 0; row < denseRows; ++row) {
+    const rowEmpty = csrOffset[row] === 0;
+    emptyRowIndicator[row] = rowEmpty;
+    allRowsFull = allRowsFull && !rowEmpty;
+    csrOffset[row] = Math.max(csrOffset[row], 1);
+    if (row > 0) {
+      csrOffset[row] += csrOffset[row - 1];
+    }
+  }
+  if (allRowsFull && rowsAreOrdered) {
+    const outputIndices = indices;
+    const outputValues = values;
+    for (let i2 = 0; i2 < indicesCount; ++i2) {
+      reverseIndexMap[i2] = i2;
+    }
+    return [
+      outputIndices,
+      [indicesCount, rank],
+      outputValues,
+      emptyRowIndicator,
+      reverseIndexMap
+    ];
+  } else {
+    const fullIndicesCount = csrOffset[denseRows - 1];
+    const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);
+    const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);
+    const filledCount = new Array(denseRows).fill(0);
+    for (let i2 = 0; i2 < indicesCount; ++i2) {
+      const row = indices[i2 * rank];
+      const offset = filledCount[row];
+      const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;
+      filledCount[row]++;
+      for (let j = 0; j < rank; ++j) {
+        outputIndices[outputI * rank + j] = indices[i2 * rank + j];
+      }
+      outputValues[outputI] = values[i2];
+      reverseIndexMap[i2] = outputI;
+    }
+    for (let row = 0; row < denseRows; ++row) {
+      const rowCount = filledCount[row];
+      if (rowCount === 0) {
+        const startingIndex = row === 0 ? 0 : csrOffset[row - 1];
+        outputIndices[startingIndex * rank + 0] = row;
+        for (let col = 1; col < rank; ++col) {
+          outputIndices[startingIndex * rank + col] = 0;
+        }
+        outputValues[startingIndex] = defaultValue;
+      }
+    }
+    return [
+      outputIndices,
+      [fullIndicesCount, rank],
+      outputValues,
+      emptyRowIndicator,
+      reverseIndexMap
+    ];
+  }
+}
+function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {
+  const denseSize = util_exports.sizeFromShape(inputShape);
+  const nnz = inputIndicesShape[0];
+  const outputRank = targetShape.length;
+  const outputShape = [];
+  let product = 1;
+  let unknownIndex = -1;
+  for (let d = 0; d < outputRank; ++d) {
+    const size2 = targetShape[d];
+    if (size2 === -1) {
+      if (unknownIndex !== -1) {
+        throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));
+      }
+      unknownIndex = d;
+      outputShape.push(1);
+    } else {
+      if (size2 < 0) {
+        throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size2));
+      }
+      product *= size2;
+      outputShape.push(size2);
+    }
+  }
+  if (unknownIndex !== -1) {
+    if (product <= 0) {
+      throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
+    }
+    const missing = Math.trunc(denseSize / product);
+    if (product * missing !== denseSize) {
+      throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));
+    }
+    outputShape[unknownIndex] = missing;
+  }
+  const outputSize2 = util_exports.sizeFromShape(outputShape);
+  if (outputSize2 !== denseSize) {
+    throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));
+  }
+  const inputRank = inputShape.length;
+  const inputStrides = [];
+  if (inputRank > 0) {
+    inputStrides[inputRank - 1] = 1;
+    for (let d = inputRank - 2; d >= 0; --d) {
+      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
+    }
+  }
+  const outputStrides = [];
+  if (outputRank > 0) {
+    outputStrides[outputRank - 1] = 1;
+    for (let d = outputRank - 2; d >= 0; --d) {
+      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
+    }
+  }
+  const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);
+  for (let i2 = 0; i2 < nnz; ++i2) {
+    let id = 0;
+    for (let j = 0; j < inputRank; ++j) {
+      id += inputIndices[i2 * inputRank + j] * inputStrides[j];
+    }
+    for (let j = 0; j < outputRank; ++j) {
+      newIndices[i2 * outputRank + j] = Math.trunc(id / outputStrides[j]);
+      id %= outputStrides[j];
+    }
+  }
+  return [newIndices, [nnz, outputRank], outputShape];
+}
+function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {
+  const numIndices = indices.length;
+  const inputFlat = [inputShape[0], input2.length / inputShape[0]];
+  const numCol = inputFlat[1];
+  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
+  const outputRows = lastSegmentIdPlusOne;
+  if (outputRows < 0) {
+    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+  }
+  const outputShape = inputShape.slice();
+  outputShape[0] = outputRows;
+  const outputLength = outputShape.reduce((product, value) => product * value, 1);
+  const output = util_exports.getArrayFromDType(inputDType, outputLength);
+  if (numIndices === 0) {
+    if (outputRows > 0) {
+      output.fill(defaultValue);
+    }
+    return [output, outputShape];
+  }
+  if (outputRows <= 0) {
+    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+  }
+  let start = 0, end = 1;
+  let uninitializedIndex = 0;
+  let outIndex = segmentIds[start];
+  while (true) {
+    let nextIndex = 0;
+    if (end < numIndices) {
+      nextIndex = segmentIds[end];
+      if (outIndex === nextIndex) {
+        ++end;
+        continue;
+      }
+      if (outIndex >= nextIndex) {
+        throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
+      }
+    }
+    if (outIndex < 0 || outIndex >= outputRows) {
+      throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));
+    }
+    if (outIndex > uninitializedIndex) {
+      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
+    }
+    for (let i2 = start; i2 < end; ++i2) {
+      const index2 = indices[i2];
+      if (index2 < 0 || index2 >= inputFlat[0]) {
+        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i2, indices[i2], inputFlat[0]));
+      }
+      for (let j = 0; j < numCol; j++) {
+        output[outIndex * numCol + j] += input2[index2 * numCol + j];
+      }
+    }
+    if (isMean) {
+      for (let j = 0; j < numCol; j++) {
+        output[outIndex * numCol + j] /= end - start;
+      }
+    }
+    start = end;
+    ++end;
+    uninitializedIndex = outIndex + 1;
+    outIndex = nextIndex;
+    if (end > numIndices) {
+      break;
+    }
+  }
+  if (uninitializedIndex < outputRows) {
+    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);
+  }
+  return [output, outputShape];
+}
+var sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));
+var sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));
+var sqrtConfig = {
+  kernelName: Sqrt,
+  backendName: "cpu",
+  kernelFunc: sqrt2
+};
+var squaredDifferenceImpl = createSimpleBinaryKernelImpl((a6, b) => {
+  const diff = a6 - b;
+  return diff * diff;
+});
+var squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);
+var squaredDifferenceConfig = {
+  kernelName: SquaredDifference,
+  backendName: "cpu",
+  kernelFunc: squaredDifference2
+};
+function stridedSliceImpl(outShape, xBuf, strides2, begin) {
+  const outBuf = buffer(outShape, xBuf.dtype);
+  for (let i2 = 0; i2 < outBuf.size; i2++) {
+    const loc = outBuf.indexToLoc(i2);
+    const newLoc = new Array(loc.length);
+    for (let j = 0; j < newLoc.length; j++) {
+      newLoc[j] = loc[j] * strides2[j] + begin[j];
+    }
+    outBuf.set(xBuf.get(...newLoc), ...loc);
+  }
+  return outBuf;
+}
+var StringNGramsOp = class {
+  constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
+    this.separator = util_exports.encodeString(separator);
+    this.nGramWidths = nGramWidths;
+    this.leftPad = util_exports.encodeString(leftPad);
+    this.rightPad = util_exports.encodeString(rightPad2);
+    this.padWidth = padWidth;
+    this.preserveShort = preserveShortSequences;
+  }
+  getPadWidth(nGramWidth) {
+    return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);
+  }
+  getNumNGrams(length, nGramWidth) {
+    const padWidth = this.getPadWidth(nGramWidth);
+    return Math.max(0, length + 2 * padWidth - nGramWidth + 1);
+  }
+  createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {
+    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {
+      const padWidth = this.getPadWidth(nGramWidth);
+      const leftPadding = Math.max(0, padWidth - nGramIndex);
+      const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
+      const numTokens = nGramWidth - (leftPadding + rightPadding);
+      const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
+      let nGramSize = 0;
+      nGramSize += leftPadding * this.leftPad.length;
+      for (let n = 0; n < numTokens; ++n) {
+        nGramSize += data[dataStartIndex + n].length;
+      }
+      nGramSize += rightPadding * this.rightPad.length;
+      const numSeparators = leftPadding + rightPadding + numTokens - 1;
+      nGramSize += numSeparators * this.separator.length;
+      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
+      const nGram = output[outputStartIndex + nGramIndex];
+      let nextNGramIndex = 0;
+      const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);
+      for (let n = 0; n < leftPadding; ++n) {
+        appendToNGram(this.leftPad);
+        appendToNGram(this.separator);
+      }
+      for (let n = 0; n < numTokens - 1; ++n) {
+        appendToNGram(data[dataStartIndex + n]);
+        appendToNGram(this.separator);
+      }
+      if (numTokens > 0) {
+        appendToNGram(data[dataStartIndex + numTokens - 1]);
+        for (let n = 0; n < rightPadding; ++n) {
+          appendToNGram(this.separator);
+          appendToNGram(this.rightPad);
+        }
+      } else {
+        for (let n = 0; n < rightPadding - 1; ++n) {
+          appendToNGram(this.rightPad);
+          appendToNGram(this.separator);
+        }
+        appendToNGram(this.rightPad);
+      }
+    }
+  }
+  compute(data, splits) {
+    const inputDataSize = data.length;
+    const splitsSize = splits.length;
+    if (splitsSize > 0) {
+      let prevSplit = splits[0];
+      if (prevSplit !== 0) {
+        throw new Error(`First split value must be 0, got ${prevSplit}`);
+      }
+      for (let i2 = 1; i2 < splitsSize; ++i2) {
+        let validSplits = splits[i2] >= prevSplit;
+        validSplits = validSplits && splits[i2] <= inputDataSize;
+        if (!validSplits) {
+          throw new Error(`Invalid split value ${splits[i2]}, must be in [${prevSplit}, ${inputDataSize}]`);
+        }
+        prevSplit = splits[i2];
+      }
+      if (prevSplit !== inputDataSize) {
+        throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
+      }
+    }
+    const numBatchItems = splitsSize - 1;
+    const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize);
+    if (inputDataSize === 0 || splitsSize === 0) {
+      const empty = new Array(inputDataSize);
+      for (let i2 = 0; i2 <= numBatchItems; ++i2) {
+        nGramsSplits[i2] = 0;
+      }
+      return [empty, nGramsSplits];
+    }
+    nGramsSplits[0] = 0;
+    for (let i2 = 1; i2 <= numBatchItems; ++i2) {
+      const length = splits[i2] - splits[i2 - 1];
+      let numNGrams = 0;
+      this.nGramWidths.forEach((nGramWidth) => {
+        numNGrams += this.getNumNGrams(length, nGramWidth);
+      });
+      if (this.preserveShort && length > 0 && numNGrams === 0) {
+        numNGrams = 1;
+      }
+      nGramsSplits[i2] = nGramsSplits[i2 - 1] + numNGrams;
+    }
+    const nGrams = new Array(nGramsSplits[numBatchItems]);
+    for (let i2 = 0; i2 < numBatchItems; ++i2) {
+      const splitIndex = splits[i2];
+      let outputStartIdx = nGramsSplits[i2];
+      this.nGramWidths.forEach((nGramWidth) => {
+        const length = splits[i2 + 1] - splits[i2];
+        const numNGrams = this.getNumNGrams(length, nGramWidth);
+        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+        outputStartIdx += numNGrams;
+      });
+      if (this.preserveShort && outputStartIdx === nGramsSplits[i2]) {
+        const dataLength = splits[i2 + 1] - splits[i2];
+        if (dataLength === 0) {
+          continue;
+        }
+        const nGramWidth = dataLength + 2 * this.padWidth;
+        const numNGrams = 1;
+        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+      }
+    }
+    return [nGrams, nGramsSplits];
+  }
+};
+function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
+  return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);
+}
+function split3(str, delimiters, skipEmpty, result) {
+  if (!str.length) {
+    return;
+  }
+  if (delimiters.length === 0) {
+    for (let i2 = 0; i2 < str.length; ++i2) {
+      result.push(str.subarray(i2, i2 + 1));
+    }
+    return;
+  }
+  if (delimiters.length === 1) {
+    const delimiter = delimiters[0];
+    let f = str.indexOf(delimiter);
+    while (f !== -1) {
+      const token = str.subarray(0, f);
+      if (!skipEmpty || token.length !== 0) {
+        result.push(token);
+      }
+      str = str.subarray(f + 1);
+      f = str.indexOf(delimiter);
+    }
+    if (!skipEmpty || str.length !== 0) {
+      result.push(str);
+    }
+    return;
+  }
+  let tokenStart = 0;
+  for (let i2 = 0; i2 < str.length + 1; i2++) {
+    if (i2 === str.length || delimiters.indexOf(str[i2]) !== -1) {
+      const token = str.subarray(tokenStart, i2);
+      if (!skipEmpty || token.length !== 0) {
+        result.push(token);
+      }
+      tokenStart = i2 + 1;
+    }
+  }
+}
+function stringSplitImpl(input2, delimiter, skipEmpty) {
+  const batchSize = input2.length;
+  const tokens = [];
+  let outputSize2 = 0;
+  let maxNumEntries = 0;
+  const numIndices = new Array(batchSize);
+  for (let i2 = 0; i2 < batchSize; ++i2) {
+    const prevTokensLength = tokens.length;
+    split3(input2[i2], delimiter, skipEmpty, tokens);
+    const nEntries = tokens.length - prevTokensLength;
+    numIndices[i2] = nEntries;
+    outputSize2 += nEntries;
+    maxNumEntries = Math.max(maxNumEntries, nEntries);
+  }
+  const indices = util_exports.getArrayFromDType("int32", outputSize2 * 2);
+  const values = new Array(outputSize2);
+  const shape = [batchSize, maxNumEntries];
+  let c = 0;
+  for (let i2 = 0; i2 < batchSize; ++i2) {
+    for (let j = 0; j < numIndices[i2]; ++j) {
+      indices[c * 2] = i2;
+      indices[c * 2 + 1] = j;
+      values[c] = tokens[c];
+      ++c;
+    }
+  }
+  return [indices, values, shape];
+}
+function stringToHashBucketFastImpl(input2, numBuckets) {
+  const output = util_exports.getArrayFromDType("int32", input2.length);
+  for (let i2 = 0; i2 < input2.length; ++i2) {
+    output[i2] = util_exports.fingerPrint64(input2[i2]).modulo(numBuckets).getLowBitsUnsigned();
+  }
+  return output;
+}
+var subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);
+var subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
+  return { real: aReal - bReal, imag: aImag - bImag };
+});
+var sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);
+var subConfig = {
+  kernelName: Sub,
+  backendName: "cpu",
+  kernelFunc: sub2
+};
+function tileImpl(xBuf, reps) {
+  const newShape = new Array(xBuf.rank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = xBuf.shape[i2] * reps[i2];
+  }
+  const result = buffer(newShape, xBuf.dtype);
+  for (let i2 = 0; i2 < result.values.length; ++i2) {
+    const newLoc = result.indexToLoc(i2);
+    const originalLoc = new Array(xBuf.rank);
+    for (let j = 0; j < originalLoc.length; j++) {
+      originalLoc[j] = newLoc[j] % xBuf.shape[j];
+    }
+    const originalIndex = xBuf.locToIndex(originalLoc);
+    result.values[i2] = xBuf.values[originalIndex];
+  }
+  return result;
+}
+var comparePair = (a6, b) => {
+  const valueDiff = b.value - a6.value;
+  return valueDiff === 0 ? a6.index - b.index : valueDiff;
+};
+function select(array2, k, left = 0, right = array2.length - 1) {
+  while (right > left) {
+    if (right - left > 600) {
+      const n = right - left + 1;
+      const i3 = k - left + 1;
+      const z = Math.log(n);
+      const s2 = 0.5 * Math.exp(2 * z / 3);
+      const sd = 0.5 * Math.sqrt(z * s2 * (n - s2) / n) * Math.sign(i3 - n / 2);
+      const newLeft = Math.max(left, Math.floor(k - i3 * s2 / n + sd));
+      const newRight = Math.min(right, Math.floor(k + (n - i3) * s2 / n + sd));
+      select(array2, k, newLeft, newRight);
+    }
+    const t2 = array2[k];
+    let i2 = left;
+    let j = right;
+    util_exports.swap(array2, left, k);
+    if (comparePair(array2[right], t2) > 0) {
+      util_exports.swap(array2, left, right);
+    }
+    while (i2 < j) {
+      util_exports.swap(array2, i2, j);
+      i2++;
+      j--;
+      while (comparePair(array2[i2], t2) < 0) {
+        i2 = i2 + 1;
+      }
+      while (comparePair(array2[j], t2) > 0) {
+        j = j - 1;
+      }
+    }
+    if (comparePair(array2[left], t2) === 0) {
+      util_exports.swap(array2, left, j);
+    } else {
+      j = j + 1;
+      util_exports.swap(array2, j, right);
+    }
+    if (j <= k) {
+      left = j + 1;
+    }
+    if (k <= j) {
+      right = j - 1;
+    }
+  }
+}
+function topKImpl(x, xShape, xDtype, k, sorted) {
+  const lastDim = xShape[xShape.length - 1];
+  const [batch, size2] = [x.length / lastDim, lastDim];
+  const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);
+  const allTopKIndices = util_exports.getTypedArrayFromDType("int32", batch * k);
+  for (let b = 0; b < batch; b++) {
+    const offset = b * size2;
+    const vals = x.subarray(offset, offset + size2);
+    let valAndInd = new Array(vals.length);
+    vals.forEach((value, index2) => valAndInd[index2] = { value, index: index2 });
+    if (k < valAndInd.length) {
+      select(valAndInd, k);
+      valAndInd = valAndInd.slice(0, k);
+    }
+    if (sorted) {
+      valAndInd.sort(comparePair);
+    }
+    const outOffset = b * k;
+    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
+    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
+    for (let i2 = 0; i2 < k; i2++) {
+      topKVals[i2] = valAndInd[i2].value;
+      topKIndices[i2] = valAndInd[i2].index;
+    }
+  }
+  const outputShape = xShape.slice();
+  outputShape[outputShape.length - 1] = k;
+  return [
+    buffer(outputShape, xDtype, allTopKVals),
+    buffer(outputShape, "int32", allTopKIndices)
+  ];
+}
+function uniqueImpl(values, axis, shape, dtype) {
+  const $axis = util_exports.parseAxisParam(axis, shape)[0];
+  const newShape = [1, shape[0], 1];
+  for (let i2 = 0; i2 < $axis; i2++) {
+    newShape[0] *= shape[i2];
+  }
+  newShape[1] = shape[$axis];
+  for (let i2 = $axis + 1; i2 < shape.length; i2++) {
+    newShape[2] *= shape[i2];
+  }
+  const uniqueElements = {};
+  const indices = new Int32Array(shape[$axis]);
+  const inputBuffer = new TensorBuffer(newShape, dtype, values);
+  const uniqueIndices = [];
+  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
+  for (let i2 = 0; i2 < shape[$axis]; i2++) {
+    let element;
+    if (is1DTensor) {
+      element = values[i2].toString();
+    } else {
+      const axisValues = [];
+      for (let m = 0; m < newShape[0]; m++) {
+        for (let n = 0; n < newShape[2]; n++) {
+          axisValues.push(inputBuffer.get(m, i2, n));
+        }
+      }
+      element = axisValues.join(",");
+    }
+    if (uniqueElements[element] !== void 0) {
+      indices[i2] = uniqueElements[element];
+    } else {
+      const uniqueIndex = Object.keys(uniqueElements).length;
+      uniqueElements[element] = uniqueIndex;
+      indices[i2] = uniqueIndex;
+      uniqueIndices.push(i2);
+    }
+  }
+  const outputTmpShape = newShape.slice();
+  outputTmpShape[1] = Object.keys(uniqueElements).length;
+  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
+  uniqueIndices.forEach((uniqueElementIndex, i2) => {
+    for (let m = 0; m < newShape[0]; m++) {
+      for (let n = 0; n < newShape[2]; n++) {
+        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i2, n);
+      }
+    }
+  });
+  const outputShape = shape.slice();
+  outputShape[$axis] = outputTmpShape[1];
+  return {
+    outputValues: outputBuffer.values,
+    outputShape,
+    indices
+  };
+}
+registerBackend("cpu", () => new MathBackendCPU(), 1);
+var elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);
+var eluConfig = {
+  kernelName: Elu,
+  backendName: "cpu",
+  kernelFunc: elu4
+};
+function leakyRelu2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { alpha: alpha2 } = attrs;
+  assertNotComplex([x], "leakyRelu");
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const xVals = backend2.data.get(x.dataId).values;
+  const outVals = util_exports.getTypedArrayFromDType("float32", xSize);
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    outVals[i2] = xVals[i2] < 0 ? alpha2 * xVals[i2] : xVals[i2];
+  }
+  return backend2.makeTensorInfo(x.shape, "float32", outVals);
+}
+var leakyReluConfig = {
+  kernelName: LeakyRelu,
+  backendName: "cpu",
+  kernelFunc: leakyRelu2
+};
+var preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);
+function prelu3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha: alpha2 } = inputs;
+  assertNotComplex([x, alpha2], "prelu");
+  const aVals = backend2.data.get(x.dataId).values;
+  const bVals = backend2.data.get(alpha2.dataId).values;
+  const [resultData, resultShape] = preluImpl(x.shape, alpha2.shape, aVals, bVals, "float32");
+  return backend2.makeTensorInfo(resultShape, "float32", resultData);
+}
+var preluConfig = {
+  kernelName: Prelu,
+  backendName: "cpu",
+  kernelFunc: prelu3
+};
+var relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));
+var reluConfig = {
+  kernelName: Relu,
+  backendName: "cpu",
+  kernelFunc: relu2
+};
+var relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));
+var relu6Config = {
+  kernelName: Relu6,
+  backendName: "cpu",
+  kernelFunc: relu62
+};
+function applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {
+  if (activation2 === "linear") {
+    return identity2({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "relu") {
+    return relu2({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "elu") {
+    return elu4({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "relu6") {
+    return relu62({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "prelu") {
+    return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });
+  } else if (activation2 === "leakyrelu") {
+    return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });
+  } else if (activation2 === "sigmoid") {
+    return sigmoid2({ inputs: { x }, backend: backend2 });
+  }
+  throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);
+}
+function reshape3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  const $xSize = util_exports.sizeFromShape($shape);
+  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
+  backend2.incRef(x.dataId);
+  const xData = backend2.data.get(x.dataId);
+  if (xData.complexTensorInfos != null) {
+    const real5 = xData.complexTensorInfos.real;
+    const imag5 = xData.complexTensorInfos.imag;
+    real5.shape = $shape;
+    imag5.shape = $shape;
+  }
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig = {
+  kernelName: Reshape,
+  backendName: "cpu",
+  kernelFunc: reshape3
+};
+function batchMatMul(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  assertNotComplex([a6, b], "matMul");
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape3({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
+  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
+  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const a3dValues = backend2.data.get(a3d.dataId).values;
+  const b3dValues = backend2.data.get(b3d.dataId).values;
+  const a3dStrides = util_exports.computeStrides(a3d.shape);
+  const b3dStrides = util_exports.computeStrides(b3d.shape);
+  const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];
+  const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];
+  const size2 = leftDim * rightDim;
+  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);
+  const resVals = result.values;
+  const blockSize = backend2.blockSize;
+  for (let bi = 0; bi < batchDim; bi++) {
+    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {
+      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {
+        for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {
+          const iBlock = Math.min(i0 + blockSize, leftDim);
+          const jBlock = Math.min(j0 + blockSize, rightDim);
+          const kBlock = Math.min(k02 + blockSize, sharedDim);
+          for (let i2 = i0; i2 < iBlock; i2++) {
+            for (let j = j0; j < jBlock; j++) {
+              let sum7 = 0;
+              for (let k = k02; k < kBlock; k++) {
+                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
+                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
+                const aVal = a3dValues[batchOffsetA + i2 * aOuterStep + k * aInnerStep];
+                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
+                sum7 += aVal * bVal;
+              }
+              resVals[bi * size2 + (i2 * rightDim + j)] += sum7;
+            }
+          }
+        }
+      }
+    }
+  }
+  backend2.disposeIntermediateTensorInfo(a3d);
+  backend2.disposeIntermediateTensorInfo(b3d);
+  return backend2.makeTensorInfo(outShape, result.dtype, result.values);
+}
+var batchMatMulConfig = {
+  kernelName: BatchMatMul,
+  backendName: "cpu",
+  kernelFunc: batchMatMul
+};
+function _fusedMatMul(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  let current;
+  let addRes;
+  let activationRes;
+  const intermediates = [];
+  const matMulRes = batchMatMul({ inputs: { a: a6, b }, attrs: { transposeA, transposeB }, backend: backend2 });
+  current = matMulRes;
+  if (bias) {
+    addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 });
+    intermediates.push(current);
+    current = addRes;
+  }
+  if (activation2) {
+    activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);
+    intermediates.push(current);
+    current = activationRes;
+  }
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return current;
+}
+var _fusedMatMulConfig = {
+  kernelName: _FusedMatMul,
+  backendName: "cpu",
+  kernelFunc: _fusedMatMul
+};
+var acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));
+var acosConfig = {
+  kernelName: Acos,
+  backendName: "cpu",
+  kernelFunc: acos2
+};
+var acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));
+var acoshConfig = {
+  kernelName: Acosh,
+  backendName: "cpu",
+  kernelFunc: acosh2
+};
+function addN2(args) {
+  const { inputs, backend: backend2 } = args;
+  const tensors = inputs;
+  assertNotComplex(inputs, "addN");
+  const vals = tensors.map((t2) => backend2.data.get(t2.dataId).values);
+  const outBuf = buffer(tensors[0].shape, tensors[0].dtype);
+  const outVals = outBuf.values;
+  for (let i2 = 0; i2 < tensors.length; i2++) {
+    const currVals = vals[i2];
+    for (let j = 0; j < outVals.length; j++) {
+      outVals[j] += currVals[j];
+    }
+  }
+  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+}
+var addNConfig = {
+  kernelName: AddN,
+  backendName: "cpu",
+  kernelFunc: addN2
+};
+function all2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "all");
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("all", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let all52 = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      all52 = all52 && value;
+    }
+    vals[i2] = all52;
+  }
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo($x);
+  }
+  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
+  if (keepDims) {
+    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
+    backend2.disposeIntermediateTensorInfo(result);
+    return reshapedResult;
+  }
+  return result;
+}
+var allConfig = {
+  kernelName: All,
+  backendName: "cpu",
+  kernelFunc: all2
+};
+function any2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "any");
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("any", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let anyVal = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      anyVal = anyVal || value;
+    }
+    vals[i2] = anyVal;
+  }
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo($x);
+  }
+  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
+  if (keepDims) {
+    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
+    backend2.disposeIntermediateTensorInfo(result);
+    return reshapedResult;
+  }
+  return result;
+}
+var anyConfig = {
+  kernelName: Any,
+  backendName: "cpu",
+  kernelFunc: any2
+};
+function argMax2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  assertNotComplex(x, "argMax");
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  axes = [axes[0]];
+  backend_util_exports.assertAxesAreInnerMostDims("argMax", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const outSize = util_exports.sizeFromShape(outShape);
+  const vals = util_exports.makeZerosTypedArray(outSize, "int32");
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let max7 = aVals[offset];
+    let maxIndex = 0;
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (value > max7) {
+        max7 = value;
+        maxIndex = j;
+      }
+    }
+    vals[i2] = maxIndex;
+  }
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return backend2.makeTensorInfo(outShape, "int32", vals);
+}
+var argMaxConfig = {
+  kernelName: ArgMax,
+  backendName: "cpu",
+  kernelFunc: argMax2
+};
+function argMin2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  assertNotComplex(x, "argMin");
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  axes = [axes[0]];
+  backend_util_exports.assertAxesAreInnerMostDims("argMin", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const outSize = util_exports.sizeFromShape(outShape);
+  const vals = util_exports.makeZerosTypedArray(outSize, "int32");
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let min7 = aVals[offset];
+    let minIndex = 0;
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (value < min7) {
+        min7 = value;
+        minIndex = j;
+      }
+    }
+    vals[i2] = minIndex;
+  }
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return backend2.makeTensorInfo(outShape, "int32", vals);
+}
+var argMinConfig = {
+  kernelName: ArgMin,
+  backendName: "cpu",
+  kernelFunc: argMin2
+};
+var asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));
+var asinConfig = {
+  kernelName: Asin,
+  backendName: "cpu",
+  kernelFunc: asin2
+};
+var asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));
+var asinhConfig = {
+  kernelName: Asinh,
+  backendName: "cpu",
+  kernelFunc: asinh2
+};
+var atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));
+var atanConfig = {
+  kernelName: Atan,
+  backendName: "cpu",
+  kernelFunc: atan3
+};
+var atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));
+var atan22 = binaryKernelFunc(Atan2, atan2Impl);
+var atan2Config = {
+  kernelName: Atan2,
+  backendName: "cpu",
+  kernelFunc: atan22
+};
+var atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));
+var atanhConfig = {
+  kernelName: Atanh,
+  backendName: "cpu",
+  kernelFunc: atanh2
+};
+function pool2(xValues, xShape, dtype, strides2, convInfo, poolType) {
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
+  const output = buffer(convInfo.outShape, dtype);
+  const outputVals = output.values;
+  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
+  const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
+  const outputColStrides = convInfo.outShape[3];
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const outputBatchOffset = b * outputBatchStrides;
+    const inputBatchOffset = b * strides2[0];
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+        const xRCorner = yR * strideHeight - padTop;
+        const xRMin = Math.max(0, xRCorner);
+        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+        const outputRowOffset = outputBatchOffset + yR * outputRowStrides;
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const xCCorner = yC * strideWidth - padLeft;
+          const xCMin = Math.max(0, xCCorner);
+          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+          let minMaxValue = initialValue;
+          let avgValue = 0;
+          let count22 = 0;
+          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
+            const xROffset = inputBatchOffset + xR * strides2[1];
+            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
+              const xCOffset = xROffset + xC * strides2[2];
+              const pixel = xValues[xCOffset + d];
+              if (poolType === "max" && pixel > minMaxValue) {
+                minMaxValue = pixel;
+              } else if (poolType === "avg") {
+                avgValue += pixel;
+                count22++;
+              }
+            }
+            if (isNaN(minMaxValue)) {
+              break;
+            }
+          }
+          const outputOffset = outputRowOffset + yC * outputColStrides + d;
+          outputVals[outputOffset] = poolType === "avg" ? avgValue / count22 : minMaxValue;
+        }
+      }
+    }
+  }
+  return output;
+}
+function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {
+  const maxPositions = buffer(convInfo.outShape, "int32");
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  const xBuf = buffer(xShape, dtype, xValues);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+        const xRCorner = yR * strideHeight - padTop;
+        let xRMin = xRCorner;
+        while (xRMin < 0) {
+          xRMin += dilationHeight;
+        }
+        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const xCCorner = yC * strideWidth - padLeft;
+          let xCMin = xCCorner;
+          while (xCMin < 0) {
+            xCMin += dilationWidth;
+          }
+          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+          let maxValue = Number.NEGATIVE_INFINITY;
+          let maxPosition = -1;
+          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
+            const wR = xR - xRCorner;
+            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
+              const wC = xC - xCCorner;
+              const pixel = xBuf.get(b, xR, xC, d);
+              if (pixel > maxValue) {
+                maxValue = pixel;
+                if (flattenPositions) {
+                  maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
+                } else {
+                  maxPosition = wR * effectiveFilterWidth + wC;
+                }
+              }
+            }
+          }
+          maxPositions.set(maxPosition, b, yR, yC, d);
+        }
+      }
+    }
+  }
+  return maxPositions;
+}
+function pool3d2(xValues, xShape, dtype, strides2, convInfo, poolType) {
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = convInfo.padInfo.front;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
+  const output = buffer(convInfo.outShape, dtype);
+  const outputVals = output.values;
+  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
+  const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
+  const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
+  const outputColStrides = convInfo.outShape[4];
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    const outputBatchOffset = batch * outputBatchStrides;
+    const inputBatchOffset = batch * strides2[0];
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
+        const xDepthCorner = yDepth * strideDepth - padFront;
+        let xDepthMin = xDepthCorner;
+        while (xDepthMin < 0) {
+          xDepthMin += dilationDepth;
+        }
+        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
+        const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;
+        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
+          const xRowCorner = yRow * strideHeight - padTop;
+          let xRowMin = xRowCorner;
+          while (xRowMin < 0) {
+            xRowMin += dilationHeight;
+          }
+          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
+          const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
+          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
+            const xColCorner = yCol * strideWidth - padLeft;
+            let xColMin = xColCorner;
+            while (xColMin < 0) {
+              xColMin += dilationWidth;
+            }
+            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
+            const outputColOffset = outputRowOffset + yCol * outputColStrides;
+            let minMaxValue = initialValue;
+            let avgValue = 0;
+            let count22 = 0;
+            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
+              const xDepthOffset = inputBatchOffset + xDepth * strides2[1];
+              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
+                const xRowOffset = xDepthOffset + xRow * strides2[2];
+                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
+                  const xColOffset = xRowOffset + xCol * strides2[3];
+                  const pixel = xValues[xColOffset + channel];
+                  if (poolType === "max" && pixel > minMaxValue) {
+                    minMaxValue = pixel;
+                  } else if (poolType === "avg") {
+                    avgValue += pixel;
+                    count22++;
+                  }
+                  if (isNaN(minMaxValue)) {
+                    break;
+                  }
+                }
+                if (isNaN(minMaxValue)) {
+                  break;
+                }
+              }
+              if (isNaN(minMaxValue)) {
+                break;
+              }
+            }
+            const outputOffset = outputColOffset + channel;
+            outputVals[outputOffset] = poolType === "avg" ? avgValue / count22 : minMaxValue;
+          }
+        }
+      }
+    }
+  }
+  return output;
+}
+function maxPool3dPositions(xBuf, convInfo) {
+  const maxPositions = buffer(convInfo.outShape, "int32");
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = convInfo.padInfo.front;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
+        const xDepthCorner = yDepth * strideDepth - padFront;
+        let xDepthMin = xDepthCorner;
+        while (xDepthMin < 0) {
+          xDepthMin += dilationDepth;
+        }
+        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
+        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
+          const xRowCorner = yRow * strideHeight - padTop;
+          let xRowMin = xRowCorner;
+          while (xRowMin < 0) {
+            xRowMin += dilationHeight;
+          }
+          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
+          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
+            const xColCorner = yCol * strideWidth - padLeft;
+            let xColMin = xColCorner;
+            while (xColMin < 0) {
+              xColMin += dilationWidth;
+            }
+            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
+            let maxValue = Number.NEGATIVE_INFINITY;
+            let maxPosition = -1;
+            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
+              const wDepth = xDepth - xDepthCorner;
+              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
+                const wRow = xRow - xRowCorner;
+                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
+                  const wCol = xCol - xColCorner;
+                  const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);
+                  if (pixel >= maxValue) {
+                    maxValue = pixel;
+                    maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;
+                  }
+                }
+              }
+            }
+            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
+          }
+        }
+      }
+    }
+  }
+  return maxPositions;
+}
+function avgPool2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex(x, "avgPool");
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  let res;
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    res = identity2({ inputs: { x }, backend: backend2 });
+  } else {
+    const xValues = backend2.data.get(x.dataId).values;
+    const strides22 = util_exports.computeStrides(x.shape);
+    const buffer2 = pool2(xValues, x.shape, x.dtype, strides22, convInfo, "avg");
+    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
+  }
+  return res;
+}
+var avgPoolConfig = {
+  kernelName: AvgPool,
+  backendName: "cpu",
+  kernelFunc: avgPool2
+};
+function avgPool3D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  assertNotComplex(x, "avgPool3d");
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides2, 1, pad3, dimRoundingMode, dataFormat);
+  const xValues = backend2.data.get(x.dataId).values;
+  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "avg");
+  return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
+}
+var avgPool3DConfig = {
+  kernelName: AvgPool3D,
+  backendName: "cpu",
+  kernelFunc: avgPool3D
+};
+function avgPool3DGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  assertNotComplex([dy, input2], "avgPool3DGrad");
+  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides2, 1, pad3, dimRoundingMode);
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const filterDepth = convInfo.filterDepth;
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(input2.shape, "float32");
+  const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
+  const dyBuf = backend2.bufferSync(dy);
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
+        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
+          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
+            const dyDepthCorner = dxDepth - padFront;
+            const dyRowCorner = dxRow - padTop;
+            const dyColCorner = dxCol - padLeft;
+            let dotProd = 0;
+            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
+              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
+              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
+                continue;
+              }
+              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
+                const dyRow = (dyRowCorner + wRow) / strideHeight;
+                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
+                  continue;
+                }
+                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
+                  const dyCol = (dyColCorner + wCol) / strideWidth;
+                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
+                    continue;
+                  }
+                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                  dotProd += pixel;
+                }
+              }
+            }
+            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var avgPool3DGradConfig2 = {
+  kernelName: AvgPool3DGrad,
+  backendName: "cpu",
+  kernelFunc: avgPool3DGrad
+};
+function avgPoolGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  assertNotComplex([dy, input2], "avgPoolGrad");
+  const { filterSize, strides: strides2, pad: pad3 } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, 1, pad3);
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(x.shape, "float32");
+  const avgMultiplier = 1 / (filterHeight * filterWidth);
+  const dyData = backend2.data.get(dy.dataId).values;
+  const dyBuf = buffer(dy.shape, "float32", dyData);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+          const dyRCorner = dxR - padTop;
+          const dyCCorner = dxC - padLeft;
+          let dotProd = 0;
+          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+            const dyR = (dyRCorner + wR) / strideHeight;
+            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
+              continue;
+            }
+            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+              const dyC = (dyCCorner + wC) / strideWidth;
+              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
+                continue;
+              }
+              const pixel = dyBuf.get(b, dyR, dyC, d);
+              dotProd += pixel;
+            }
+          }
+          dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var avgPoolGradConfig2 = {
+  kernelName: AvgPoolGrad,
+  backendName: "cpu",
+  kernelFunc: avgPoolGrad2
+};
+function batchNorm2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, scale: scale22, offset, mean: mean5, variance } = inputs;
+  util_exports.assert(mean5.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
+  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
+  util_exports.assert(scale22 == null || mean5.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  assertNotComplex([x, mean5, variance, scale22, offset], "batchNorm");
+  let { varianceEpsilon } = attrs;
+  if (varianceEpsilon == null) {
+    varianceEpsilon = 1e-3;
+  }
+  const xVals = backend2.data.get(x.dataId).values;
+  const mVals = backend2.data.get(mean5.dataId).values;
+  const varVals = backend2.data.get(variance.dataId).values;
+  const sVals = scale22 ? backend2.data.get(scale22.dataId).values : new Float32Array([1]);
+  const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);
+  const outVals = new Float32Array(xVals.length);
+  const offValsLength = offVals.length;
+  const sValsLength = sVals.length;
+  const varValsLength = varVals.length;
+  const mValsLength = mVals.length;
+  let offi = 0;
+  let mi = 0;
+  let si = 0;
+  let vi = 0;
+  for (let i2 = 0; i2 < xVals.length; ++i2) {
+    outVals[i2] = offVals[offi++] + (xVals[i2] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
+    if (offi >= offValsLength) {
+      offi = 0;
+    }
+    if (mi >= mValsLength) {
+      mi = 0;
+    }
+    if (si >= sValsLength) {
+      si = 0;
+    }
+    if (vi >= varValsLength) {
+      vi = 0;
+    }
+  }
+  return backend2.makeTensorInfo(x.shape, x.dtype, outVals);
+}
+var batchNormConfig = {
+  kernelName: FusedBatchNorm,
+  backendName: "cpu",
+  kernelFunc: batchNorm2
+};
+function batchToSpaceND2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  assertNotComplex([x], "batchToSpaceND");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });
+  const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });
+  const result = slice2({
+    inputs: { x: xTransposedReshaped },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  backend2.disposeIntermediateTensorInfo(xReshaped);
+  backend2.disposeIntermediateTensorInfo(xTransposed);
+  backend2.disposeIntermediateTensorInfo(xTransposedReshaped);
+  return result;
+}
+var batchToSpaceNDConfig = {
+  kernelName: BatchToSpaceND,
+  backendName: "cpu",
+  kernelFunc: batchToSpaceND2
+};
+function bincount2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size: size2 } = attrs;
+  const xVals = backend2.data.get(x.dataId).values;
+  const weightsVals = backend2.data.get(weights.dataId).values;
+  const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size2);
+  return backend2.makeTensorInfo([size2], weights.dtype, outVals);
+}
+var bincountConfig = {
+  kernelName: Bincount,
+  backendName: "cpu",
+  kernelFunc: bincount2
+};
+function broadcastArgs2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { s0, s1 } = inputs;
+  const s0Vals = backend2.data.get(s0.dataId).values;
+  const s1Vals = backend2.data.get(s1.dataId).values;
+  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
+  return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape));
+}
+var broadcastArgsConfig = {
+  kernelName: BroadcastArgs,
+  backendName: "cpu",
+  kernelFunc: broadcastArgs2
+};
+var clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {
+  const clipAttrs = attrs;
+  if (xi > clipAttrs.clipValueMax) {
+    return clipAttrs.clipValueMax;
+  }
+  return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
+});
+var clipByValueConfig = {
+  kernelName: ClipByValue,
+  backendName: "cpu",
+  kernelFunc: clipByValue2
+};
+var complexAbs = (args) => {
+  const { x } = args.inputs;
+  const cpuBackend = args.backend;
+  const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
+  const complexVals = cpuBackend.data.get(x.dataId);
+  const real5 = complexVals.complexTensorInfos.real;
+  const imag5 = complexVals.complexTensorInfos.imag;
+  const realVals = cpuBackend.data.get(real5.dataId).values;
+  const imagVals = cpuBackend.data.get(imag5.dataId).values;
+  for (let i2 = 0; i2 < realVals.length; i2++) {
+    const real6 = realVals[i2];
+    const imag6 = imagVals[i2];
+    resultValues[i2] = Math.hypot(real6, imag6);
+  }
+  return cpuBackend.makeOutput(resultValues, x.shape, "float32");
+};
+var complexAbsConfig = {
+  kernelName: ComplexAbs,
+  backendName: "cpu",
+  kernelFunc: complexAbs
+};
+function imag2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const imag5 = backend2.data.get(input2.dataId).complexTensorInfos.imag;
+  const imagVal = backend2.data.get(imag5.dataId).values;
+  return backend2.makeTensorInfo(imag5.shape, imag5.dtype, imagVal);
+}
+var imagConfig = {
+  kernelName: Imag,
+  backendName: "cpu",
+  kernelFunc: imag2
+};
+function concat2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
+  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
+  }
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, $axis);
+  if ($inputs[0].dtype === "complex64") {
+    const reals = $inputs.map((t2) => real2({ inputs: { input: t2 }, backend: backend2 }));
+    const imags = $inputs.map((t2) => imag2({ inputs: { input: t2 }, backend: backend2 }));
+    const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });
+    const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });
+    const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
+    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    backend2.disposeIntermediateTensorInfo(realConcated);
+    backend2.disposeIntermediateTensorInfo(imagConcated);
+    return result;
+  }
+  const inputs2D = $inputs.map((t2) => {
+    const innerSize = util_exports.sizeFromShape(t2.shape.slice($axis));
+    const shape = [-1, innerSize];
+    return reshape3({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+  });
+  const inputsValShapes = inputs2D.map((t2) => {
+    return { vals: backend2.data.get(t2.dataId).values, shape: t2.shape };
+  });
+  outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);
+  const simplyConcat = inputs2D[0].shape[0] === 1;
+  const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
+  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), $axis);
+  const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
+  inputs2D.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return outInfo;
+}
+var concatConfig = {
+  kernelName: Concat,
+  backendName: "cpu",
+  kernelFunc: concat2
+};
+function conv2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;
+  assertNotComplex([x, filter], "conv2d");
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const padLeft = convInfo.padInfo.left;
+  const padTop = convInfo.padInfo.top;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const y = new TensorBuffer(convInfo.outShape, x.dtype);
+  const xStrides = util_exports.computeStrides(x.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const xBatchStride = xStrides[0];
+  const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
+  const xColStride = isChannelsLast ? xStrides[2] : 1;
+  const xChannelStride = isChannelsLast ? 1 : xStrides[1];
+  const yBatchStride = y.strides[0];
+  const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
+  const yColStride = isChannelsLast ? y.strides[2] : 1;
+  const yChannelStride = isChannelsLast ? 1 : y.strides[1];
+  const xVals = backend2.data.get(x.dataId).values;
+  const wVals = backend2.data.get(filter.dataId).values;
+  const yVals = y.values;
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const xOffset1 = b * xBatchStride;
+    const yOffset1 = b * yBatchStride;
+    for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+      const yOffset2 = yOffset1 + yR * yRowStride;
+      const xRCorner = yR * convInfo.strideHeight - padTop;
+      for (let wR = 0; wR < filterHeight; ++wR) {
+        const xR = xRCorner + wR * dilationHeight;
+        if (xR < 0 || xR >= convInfo.inHeight) {
+          continue;
+        }
+        const wOffset1 = wR * filterStrides[0];
+        const xOffset2 = xOffset1 + xR * xRowStride;
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const yOffset3 = yOffset2 + yC * yColStride;
+          const xCCorner = yC * convInfo.strideWidth - padLeft;
+          for (let wC = 0; wC < filterWidth; ++wC) {
+            const xC = xCCorner + wC * dilationWidth;
+            if (xC < 0 || xC >= convInfo.inWidth) {
+              continue;
+            }
+            const wOffset2 = wOffset1 + wC * filterStrides[1];
+            const xOffset3 = xOffset2 + xC * xColStride;
+            let wOffset3 = wOffset2;
+            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+              const xVal = xVals[xOffset3 + d1 * xChannelStride];
+              for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];
+              }
+              wOffset3 += convInfo.outChannels;
+            }
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, y.dtype, yVals);
+}
+var conv2DConfig = {
+  kernelName: Conv2D,
+  backendName: "cpu",
+  kernelFunc: conv2D
+};
+function conv2DBackpropFilter2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;
+  assertNotComplex([x, dy], "conv2dBackpropFilter");
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides2, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const dW = new TensorBuffer(convInfo.filterShape, "float32");
+  const leftPad = convInfo.padInfo.left;
+  const topPad = convInfo.padInfo.top;
+  const xVals = backend2.data.get(x.dataId).values;
+  const dyVals = backend2.data.get(dy.dataId).values;
+  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
+  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
+  for (let wR = 0; wR < filterHeight; ++wR) {
+    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+    for (let wC = 0; wC < filterWidth; ++wC) {
+      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+      for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+          let dotProd = 0;
+          for (let b = 0; b < convInfo.batchSize; ++b) {
+            for (let yR = yRMin; yR < yRMax; ++yR) {
+              const xR = wR + yR * strideHeight - topPad;
+              for (let yC = yCMin; yC < yCMax; ++yC) {
+                const xC = wC + yC * strideWidth - leftPad;
+                if (isChannelsLast) {
+                  dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
+                } else {
+                  dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);
+                }
+              }
+            }
+          }
+          dW.set(dotProd, wR, wC, d1, d2);
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
+}
+var conv2DBackpropFilterConfig = {
+  kernelName: Conv2DBackpropFilter,
+  backendName: "cpu",
+  kernelFunc: conv2DBackpropFilter2
+};
+function conv2DBackpropInput2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { inputShape, strides: strides2, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  assertNotComplex([dy, filter], "conv2dBackpropInput");
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides2, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const dx = new TensorBuffer(convInfo.inShape, "float32");
+  const dxValues = dx.values;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const fltValues = backend2.data.get(filter.dataId).values;
+  const [fltS0, fltS1, fltS2] = filterStrides;
+  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
+  $dataFormat = convInfo.dataFormat;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  const isChannelsLast = $dataFormat === "channelsLast";
+  const xBatchStride = dx.strides[0];
+  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
+  const xColStride = isChannelsLast ? dx.strides[2] : 1;
+  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
+  const yBatchStride = dyStrides[0];
+  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
+  const yColStride = isChannelsLast ? dyStrides[2] : 1;
+  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
+  for (let b = 0; b < batchSize; ++b) {
+    for (let d1 = 0; d1 < inChannels; ++d1) {
+      for (let xR = 0; xR < inHeight; ++xR) {
+        const xRCorner = xR - topPad;
+        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+        for (let xC = 0; xC < inWidth; ++xC) {
+          const xCCorner = xC - leftPad;
+          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+          let dotProd = 0;
+          for (let yR = xRMin; yR < yRMax; ++yR) {
+            const wR = yR * strideHeight - xRCorner;
+            for (let yC = xCMin; yC < yCMax; ++yC) {
+              const wC = yC * strideWidth - xCCorner;
+              const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
+              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+              for (let d2 = 0; d2 < outChannels; ++d2) {
+                const pixel = dyValues[dyOffset + yChannelStride * d2];
+                const weight = fltValues[fltOffset + d2];
+                dotProd += pixel * weight;
+              }
+            }
+          }
+          const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;
+          dxValues[dxOffset] = dotProd;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var conv2DBackpropInputConfig = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "cpu",
+  kernelFunc: conv2DBackpropInput2
+};
+function conv3D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dilations } = attrs;
+  assertNotComplex([x, filter], "conv3d");
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides2, dilations, pad3);
+  const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;
+  const padFront = padInfo.front;
+  const padLeft = padInfo.left;
+  const padTop = padInfo.top;
+  const y = new TensorBuffer(convInfo.outShape, x.dtype);
+  const xVals = backend2.data.get(x.dataId).values;
+  const wVals = backend2.data.get(filter.dataId).values;
+  const yVals = y.values;
+  const xStrides = util_exports.computeStrides(x.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const xOffset1 = b * xStrides[0];
+    const yOffset1 = b * y.strides[0];
+    for (let yF = 0; yF < convInfo.outDepth; ++yF) {
+      const yOffset2 = yOffset1 + yF * y.strides[1];
+      const xFCorner = yF * convInfo.strideDepth - padFront;
+      for (let wF = 0; wF < filterDepth; ++wF) {
+        const xF = xFCorner + wF * dilationDepth;
+        if (xF < 0 || xF >= convInfo.inDepth) {
+          continue;
+        }
+        const wOffset1 = wF * filterStrides[0];
+        const xOffset2 = xOffset1 + xF * xStrides[1];
+        for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+          const yOffset3 = yOffset2 + yR * y.strides[2];
+          const xRCorner = yR * convInfo.strideHeight - padTop;
+          for (let wR = 0; wR < filterHeight; ++wR) {
+            const xR = xRCorner + wR * dilationHeight;
+            if (xR < 0 || xR >= convInfo.inHeight) {
+              continue;
+            }
+            const wOffset2 = wOffset1 + wR * filterStrides[1];
+            const xOffset3 = xOffset2 + xR * xStrides[2];
+            for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+              const yOffset4 = yOffset3 + yC * convInfo.outChannels;
+              const xCCorner = yC * convInfo.strideWidth - padLeft;
+              for (let wC = 0; wC < filterWidth; ++wC) {
+                const xC = xCCorner + wC * dilationWidth;
+                if (xC < 0 || xC >= convInfo.inWidth) {
+                  continue;
+                }
+                const wOffset3 = wOffset2 + wC * filterStrides[2];
+                const xOffset4 = xOffset3 + xC * convInfo.inChannels;
+                let wOffset4 = wOffset3;
+                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                  const xVal = xVals[xOffset4 + d1];
+                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
+                  }
+                  wOffset4 += convInfo.outChannels;
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
+}
+var conv3DConfig = {
+  kernelName: Conv3D,
+  backendName: "cpu",
+  kernelFunc: conv3D
+};
+function conv3DBackpropFilterV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides: strides2, pad: pad3, filterShape } = attrs;
+  assertNotComplex([x, dy], "conv3dBackpropFilterV2");
+  const xStrides = util_exports.computeStrides(x.shape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides2, 1, pad3);
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const filterDepth = convInfo.filterDepth;
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dw = new TensorBuffer(convInfo.filterShape, "float32");
+  const dwValues = dw.values;
+  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
+  const xValues = backend2.data.get(x.dataId).values;
+  const [xS0, xS1, xS2, xS3] = xStrides;
+  const frontPad = convInfo.padInfo.front;
+  const leftPad = convInfo.padInfo.left;
+  const topPad = convInfo.padInfo.top;
+  for (let wF = 0; wF < filterDepth; ++wF) {
+    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
+    const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
+    const wOffset1 = wF * dwS0;
+    for (let wR = 0; wR < filterHeight; ++wR) {
+      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+      const wOffset2 = wR * dwS1 + wOffset1;
+      for (let wC = 0; wC < filterWidth; ++wC) {
+        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+        const wOffset3 = wC * dwS2 + wOffset2;
+        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+          const wOffset4 = d1 * dwS3 + wOffset3;
+          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+            let dotProd = 0;
+            for (let b = 0; b < convInfo.batchSize; ++b) {
+              const xOffset1 = b * xS0;
+              const yOffset1 = b * dyS0;
+              for (let yF = yFMin; yF < yFMax; ++yF) {
+                const xF = wF + yF * strideDepth - frontPad;
+                const xOffset2 = xF * xS1 + xOffset1;
+                const yOffset2 = yF * dyS1 + yOffset1;
+                for (let yR = yRMin; yR < yRMax; ++yR) {
+                  const xR = wR + yR * strideHeight - topPad;
+                  const xOffset3 = xR * xS2 + xOffset2;
+                  const yOffset3 = yR * dyS2 + yOffset2;
+                  for (let yC = yCMin; yC < yCMax; ++yC) {
+                    const xC = wC + yC * strideWidth - leftPad;
+                    const xOffset4 = xC * xS3 + xOffset3;
+                    const yOffset4 = yC * dyS3 + yOffset3;
+                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
+                  }
+                }
+              }
+            }
+            dwValues[wOffset4 + d2] = dotProd;
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);
+}
+var conv3DBackpropFilterV2Config = {
+  kernelName: Conv3DBackpropFilterV2,
+  backendName: "cpu",
+  kernelFunc: conv3DBackpropFilterV2
+};
+function conv3DBackpropInputV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { pad: pad3, strides: strides2, inputShape } = attrs;
+  assertNotComplex([dy], "conv3dBackpropInputV2");
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides2, 1, pad3);
+  const dx = new TensorBuffer(convInfo.inShape, "float32");
+  const dxValues = dx.values;
+  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
+  const fltValues = backend2.data.get(filter.dataId).values;
+  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
+  const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;
+  const frontPad = filterDepth - 1 - convInfo.padInfo.front;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  for (let b = 0; b < batchSize; ++b) {
+    for (let d1 = 0; d1 < inChannels; ++d1) {
+      for (let xF = 0; xF < inDepth; ++xF) {
+        const xFCorner = xF - frontPad;
+        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
+        const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
+        for (let xR = 0; xR < inHeight; ++xR) {
+          const xRCorner = xR - topPad;
+          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+          for (let xC = 0; xC < inWidth; ++xC) {
+            const xCCorner = xC - leftPad;
+            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+            let dotProd = 0;
+            for (let yF = xFMin; yF < yFMax; ++yF) {
+              const wF = yF * strideDepth - xFCorner;
+              for (let yR = xRMin; yR < yRMax; ++yR) {
+                const wR = yR * strideHeight - xRCorner;
+                for (let yC = xCMin; yC < yCMax; ++yC) {
+                  const wC = yC * strideWidth - xCCorner;
+                  const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
+                  const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
+                  for (let d2 = 0; d2 < outChannels; ++d2) {
+                    const pixel = dyValues[dyOffset + d2];
+                    const weight = fltValues[fltOffset + d2];
+                    dotProd += pixel * weight;
+                  }
+                }
+              }
+            }
+            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var conv3DBackpropInputV2Config = {
+  kernelName: Conv3DBackpropInputV2,
+  backendName: "cpu",
+  kernelFunc: conv3DBackpropInputV2
+};
+var cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));
+var cosConfig = {
+  kernelName: Cos,
+  backendName: "cpu",
+  kernelFunc: cos2
+};
+var cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));
+var coshConfig = {
+  kernelName: Cosh,
+  backendName: "cpu",
+  kernelFunc: cosh2
+};
+function cropAndResize2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, boxes, boxInd } = inputs;
+  const { cropSize, method, extrapolationValue } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const numBoxes = boxes.shape[0];
+  const [cropHeight, cropWidth] = cropSize;
+  const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], "float32");
+  const boxVals = backend2.data.get(boxes.dataId).values;
+  const boxIndVals = backend2.data.get(boxInd.dataId).values;
+  const imageVals = backend2.data.get(image2.dataId).values;
+  const inStride = util_exports.computeStrides(image2.shape);
+  const outStride = util_exports.computeStrides(output.shape);
+  for (let b = 0; b < numBoxes; b++) {
+    const startInd = b * 4;
+    const y1 = boxVals[startInd];
+    const x1 = boxVals[startInd + 1];
+    const y2 = boxVals[startInd + 2];
+    const x2 = boxVals[startInd + 3];
+    const bInd = boxIndVals[b];
+    if (bInd >= batch) {
+      continue;
+    }
+    const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
+    const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
+    for (let y = 0; y < cropHeight; y++) {
+      const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);
+      if (yInd < 0 || yInd > imageHeight - 1) {
+        for (let x = 0; x < cropWidth; x++) {
+          for (let c = 0; c < numChannels; c++) {
+            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+            output.values[ind] = extrapolationValue;
+          }
+        }
+        continue;
+      }
+      if (method === "bilinear") {
+        const topInd = Math.floor(yInd);
+        const bottomInd = Math.ceil(yInd);
+        const yLerp = yInd - topInd;
+        for (let x = 0; x < cropWidth; x++) {
+          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
+          if (xInd < 0 || xInd > imageWidth - 1) {
+            for (let c = 0; c < numChannels; c++) {
+              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+              output.values[ind] = extrapolationValue;
+            }
+            continue;
+          }
+          const leftInd = Math.floor(xInd);
+          const rightInd = Math.ceil(xInd);
+          const xLerp = xInd - leftInd;
+          for (let c = 0; c < numChannels; c++) {
+            let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
+            const topLeft = imageVals[ind];
+            ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
+            const topRight = imageVals[ind];
+            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
+            const bottomLeft = imageVals[ind];
+            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
+            const bottomRight = imageVals[ind];
+            const top = topLeft + (topRight - topLeft) * xLerp;
+            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
+            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+            output.values[ind] = top + (bottom - top) * yLerp;
+          }
+        }
+      } else {
+        for (let x = 0; x < cropWidth; ++x) {
+          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
+          if (xInd < 0 || xInd > imageWidth - 1) {
+            for (let c = 0; c < numChannels; c++) {
+              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+              output.values[ind] = extrapolationValue;
+            }
+            continue;
+          }
+          const closestX = Math.round(xInd);
+          const closestY = Math.round(yInd);
+          for (let c = 0; c < numChannels; c++) {
+            const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];
+            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+            output.values[outInd] = imageVals[inInd];
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(output.shape, output.dtype, output.values);
+}
+var cropAndResizeConfig = {
+  kernelName: CropAndResize,
+  backendName: "cpu",
+  kernelFunc: cropAndResize2
+};
+function cumprod2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  assertNotComplex(x, "cumprod");
+  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
+  let $x = x;
+  if (permutation != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
+  if (permutedAxis !== $x.shape.length - 1) {
+    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
+  }
+  const resultDtype = upcastType($x.dtype, "int32");
+  const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  const finalDim = $x.shape[$x.shape.length - 1];
+  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
+  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+    for (let j = 0; j < finalDim; j++) {
+      const idx = indexAdjuster(i2, j);
+      if (j === 0) {
+        vals[idx] = exclusive ? 1 : aVals[idx];
+      } else {
+        const prevIdx = indexAdjuster(i2, j - 1);
+        vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];
+      }
+    }
+  }
+  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeIntermediateTensorInfo(result);
+    backend2.disposeIntermediateTensorInfo($x);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+var cumprodConfig = {
+  kernelName: Cumprod,
+  backendName: "cpu",
+  kernelFunc: cumprod2
+};
+function cumsum2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  assertNotComplex(x, "cumsum");
+  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
+  let $x = x;
+  if (permutation != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
+  if (permutedAxis !== $x.shape.length - 1) {
+    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
+  }
+  const resultDtype = upcastType($x.dtype, "int32");
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  const finalDim = $x.shape[$x.shape.length - 1];
+  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
+  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+    for (let j = 0; j < finalDim; j++) {
+      const idx = indexAdjuster(i2, j);
+      if (j === 0) {
+        vals[idx] = exclusive ? 0 : aVals[idx];
+      } else {
+        const prevIdx = indexAdjuster(i2, j - 1);
+        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
+      }
+    }
+  }
+  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeIntermediateTensorInfo(result);
+    backend2.disposeIntermediateTensorInfo($x);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+var cumsumConfig = {
+  kernelName: Cumsum,
+  backendName: "cpu",
+  kernelFunc: cumsum2
+};
+function denseBincount2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size: size2, binaryOutput } = attrs;
+  if (x.shape.length === 1) {
+    const xVals = backend2.data.get(x.dataId).values;
+    const weightsVals = backend2.data.get(weights.dataId).values;
+    const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size2);
+    return backend2.makeTensorInfo([size2], weights.dtype, outVals);
+  } else if (x.shape.length === 2) {
+    const xBuf = backend2.bufferSync(x);
+    const weightsBuf = backend2.bufferSync(weights);
+    const outBuf = bincountReduceImpl(xBuf, weightsBuf, size2, binaryOutput);
+    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
+  }
+  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
+}
+var denseBincountConfig = {
+  kernelName: DenseBincount,
+  backendName: "cpu",
+  kernelFunc: denseBincount2
+};
+function depthToSpace2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  util_exports.assert(dataFormat === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);
+  const batchSize = x.shape[0];
+  const inputHeight = x.shape[1];
+  const inputWidth = x.shape[2];
+  const inputDepth = x.shape[3];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const xValues = backend2.data.get(x.dataId).values;
+  const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
+  let outputIdx = 0;
+  for (let b = 0; b < batchSize; ++b) {
+    for (let h = 0; h < outputHeight; ++h) {
+      const inH = Math.floor(h / blockSize);
+      const offsetH = h % blockSize;
+      for (let w = 0; w < outputWidth; ++w) {
+        const inW = Math.floor(w / blockSize);
+        const offsetW = w % blockSize;
+        const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
+        for (let d = 0; d < outputDepth; ++d) {
+          const inD = d + offsetD;
+          const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
+          result[outputIdx++] = xValues[inputIdx];
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
+}
+var depthToSpaceConfig = {
+  kernelName: DepthToSpace,
+  backendName: "cpu",
+  kernelFunc: depthToSpace2
+};
+function depthwiseConv2dNative(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dimRoundingMode } = attrs;
+  assertNotComplex([x, filter], "depthwiseConv2DNative");
+  const xStrides = util_exports.computeStrides(x.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, $dilations, pad3, dimRoundingMode, true);
+  const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;
+  const padLeft = padInfo.left;
+  const padTop = padInfo.top;
+  const chMul = convInfo.outChannels / convInfo.inChannels;
+  const y = new TensorBuffer(convInfo.outShape, x.dtype);
+  const xVals = backend2.data.get(x.dataId).values;
+  const wVals = backend2.data.get(filter.dataId).values;
+  const yVals = y.values;
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const xOffset1 = b * xStrides[0];
+    const yOffset1 = b * y.strides[0];
+    for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+      const yOffset2 = yOffset1 + yR * y.strides[1];
+      const xRCorner = yR * convInfo.strideHeight - padTop;
+      for (let wR = 0; wR < filterHeight; ++wR) {
+        const xR = xRCorner + wR * dilationHeight;
+        if (xR < 0 || xR >= convInfo.inHeight) {
+          continue;
+        }
+        const wOffset1 = wR * filterStrides[0];
+        const xOffset2 = xOffset1 + xR * xStrides[1];
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const yOffset3 = yOffset2 + yC * y.strides[2];
+          const xCCorner = yC * convInfo.strideWidth - padLeft;
+          for (let wC = 0; wC < filterWidth; ++wC) {
+            const xC = xCCorner + wC * dilationWidth;
+            if (xC < 0 || xC >= convInfo.inWidth) {
+              continue;
+            }
+            const wOffset2 = wOffset1 + wC * filterStrides[1];
+            const xOffset3 = xOffset2 + xC * convInfo.inChannels;
+            let yOffset4 = yOffset3;
+            let wOffset3 = wOffset2;
+            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+              const xVal = xVals[xOffset3 + d1];
+              for (let q = 0; q < chMul; ++q) {
+                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
+              }
+              yOffset4 += chMul;
+              wOffset3 += chMul;
+            }
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
+}
+var depthwiseConv2dNativeConfig = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "cpu",
+  kernelFunc: depthwiseConv2dNative
+};
+function depthwiseConv2dNativeBackpropFilter2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides: strides2, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;
+  assertNotComplex([x, dy], "depthwiseConv2dNativeBackpropFilter");
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides2, dilations, pad3, dimRoundingMode, true);
+  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
+  const dW = new TensorBuffer(convInfo.filterShape, "float32");
+  const leftPad = convInfo.padInfo.left;
+  const topPad = convInfo.padInfo.top;
+  const chMul = convInfo.outChannels / convInfo.inChannels;
+  const xVals = backend2.data.get(x.dataId).values;
+  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
+  const dyVals = backend2.data.get(dy.dataId).values;
+  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
+  for (let wR = 0; wR < filterHeight; ++wR) {
+    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+    for (let wC = 0; wC < filterWidth; ++wC) {
+      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+      for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+        const d1 = Math.trunc(d2 / chMul);
+        const dm = d2 % chMul;
+        let dotProd = 0;
+        for (let b = 0; b < convInfo.batchSize; ++b) {
+          for (let yR = yRMin; yR < yRMax; ++yR) {
+            const xR = wR + yR * strideHeight - topPad;
+            for (let yC = yCMin; yC < yCMax; ++yC) {
+              const xC = wC + yC * strideWidth - leftPad;
+              dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
+            }
+          }
+        }
+        dW.set(dotProd, wR, wC, d1, dm);
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
+}
+var depthwiseConv2dNativeBackpropFilterConfig = {
+  kernelName: DepthwiseConv2dNativeBackpropFilter,
+  backendName: "cpu",
+  kernelFunc: depthwiseConv2dNativeBackpropFilter2
+};
+function depthwiseConv2dNativeBackpropInput2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { strides: strides2, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;
+  assertNotComplex([dy, filter], "depthwiseConv2DNativeBackpropInput");
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides2, dilations, pad3, dimRoundingMode, true);
+  const dx = new TensorBuffer(convInfo.inShape, "float32");
+  const dxValues = dx.values;
+  const [dxS0, dxS1, dxS2] = dx.strides;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const [dyS0, dyS1, dyS2] = dyStrides;
+  const fltValues = backend2.data.get(filter.dataId).values;
+  const [fltS0, fltS1, fltS2] = filterStrides;
+  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  const chMul = outChannels / inChannels;
+  for (let b = 0; b < batchSize; ++b) {
+    for (let d1 = 0; d1 < inChannels; ++d1) {
+      for (let xR = 0; xR < inHeight; ++xR) {
+        const xRCorner = xR - topPad;
+        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+        for (let xC = 0; xC < inWidth; ++xC) {
+          const xCCorner = xC - leftPad;
+          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+          let dotProd = 0;
+          for (let yR = xRMin; yR < yRMax; ++yR) {
+            const wR = yR * strideHeight - xRCorner;
+            for (let yC = xCMin; yC < yCMax; ++yC) {
+              const wC = yC * strideWidth - xCCorner;
+              const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+              for (let dm = 0; dm < chMul; ++dm) {
+                const d2 = d1 * chMul + dm;
+                const pixel = dyValues[dyOffset + d2];
+                const weight = fltValues[fltOffset + dm];
+                dotProd += pixel * weight;
+              }
+            }
+          }
+          dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var depthwiseConv2dNativeBackpropInputConfig = {
+  kernelName: DepthwiseConv2dNativeBackpropInput,
+  backendName: "cpu",
+  kernelFunc: depthwiseConv2dNativeBackpropInput2
+};
+function diag2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const xVals = backend2.data.get(x.dataId).values;
+  const outBuf = buffer([xSize, xSize], x.dtype);
+  const vals = outBuf.values;
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    vals[i2 * xSize + i2] = xVals[i2];
+  }
+  const outShape = [...x.shape, ...x.shape];
+  return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
+}
+var diagConfig = {
+  kernelName: Diag,
+  backendName: "cpu",
+  kernelFunc: diag2
+};
+var dilation2DConfig = {
+  kernelName: Dilation2D,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2, attrs }) => {
+    const { x, filter } = inputs;
+    const { strides: strides2, pad: pad3, dilations } = attrs;
+    const cpuBackend = backend2;
+    const xVals = cpuBackend.data.get(x.dataId).values;
+    const xRank = x.shape.length;
+    const filterVals = cpuBackend.data.get(filter.dataId).values;
+    const filterRank = filter.shape.length;
+    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides2, pad3, "NHWC", dilations);
+    const outSize = util_exports.sizeFromShape(outShape);
+    const outRank = outShape.length;
+    const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);
+    for (let b = 0; b < batchSize; ++b) {
+      for (let hOut = 0; hOut < outHeight; ++hOut) {
+        const hBeg = hOut * strideHeight - padInfo.top;
+        for (let wOut = 0; wOut < outWidth; ++wOut) {
+          const wBeg = wOut * strideWidth - padInfo.left;
+          for (let d = 0; d < inChannels; ++d) {
+            let curVal = Number.MIN_SAFE_INTEGER;
+            for (let h = 0; h < filterHeight; ++h) {
+              const hIn = hBeg + h * dilationHeight;
+              if (hIn >= 0 && hIn < inHeight) {
+                for (let w = 0; w < filterWidth; ++w) {
+                  const wIn = wBeg + w * dilationWidth;
+                  if (wIn >= 0 && wIn < inWidth) {
+                    const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));
+                    const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));
+                    const val = xVals[xIndex] + filterVals[filterIndex];
+                    if (val > curVal) {
+                      curVal = val;
+                    }
+                  }
+                }
+              }
+            }
+            const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));
+            outputVals[outputIndex] = curVal;
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
+    return { dataId, shape: outShape, dtype: x.dtype };
+  }
+};
+var dilation2DBackpropFilterConfig = {
+  kernelName: Dilation2DBackpropFilter,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2, attrs }) => {
+    const { x, filter, dy } = inputs;
+    const { strides: strides2, pad: pad3, dilations } = attrs;
+    const cpuBackend = backend2;
+    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
+    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
+    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides2, pad3, "NHWC", dilations);
+    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
+    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
+    const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);
+    for (let b = 0; b < batchSize; ++b) {
+      for (let hOut = 0; hOut < outHeight; ++hOut) {
+        const hBeg = hOut * strideHeight - padInfo.top;
+        for (let wOut = 0; wOut < outWidth; ++wOut) {
+          const wBeg = wOut * strideWidth - padInfo.left;
+          for (let d = 0; d < inChannels; ++d) {
+            let curVal = Number.MIN_SAFE_INTEGER;
+            let hMax = 0;
+            let wMax = 0;
+            for (let h = 0; h < filterHeight; ++h) {
+              const hIn = hBeg + h * dilationHeight;
+              if (hIn >= 0 && hIn < inHeight) {
+                for (let w = 0; w < filterWidth; ++w) {
+                  const wIn = wBeg + w * dilationWidth;
+                  if (wIn >= 0 && wIn < inWidth) {
+                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
+                    if (val > curVal) {
+                      curVal = val;
+                      hMax = h;
+                      wMax = w;
+                    }
+                  }
+                }
+              }
+            }
+            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
+    return { dataId, shape: filter.shape, dtype: filter.dtype };
+  }
+};
+var dilation2DBackpropInputConfig = {
+  kernelName: Dilation2DBackpropInput,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2, attrs }) => {
+    const { x, filter, dy } = inputs;
+    const { strides: strides2, pad: pad3, dilations } = attrs;
+    const cpuBackend = backend2;
+    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
+    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
+    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides2, pad3, "NHWC", dilations);
+    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
+    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
+    const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);
+    for (let b = 0; b < batchSize; ++b) {
+      for (let hOut = 0; hOut < outHeight; ++hOut) {
+        const hBeg = hOut * strideHeight - padInfo.top;
+        for (let wOut = 0; wOut < outWidth; ++wOut) {
+          const wBeg = wOut * strideWidth - padInfo.left;
+          for (let d = 0; d < inChannels; ++d) {
+            let curVal = Number.MIN_SAFE_INTEGER;
+            let hInMax = hBeg < 0 ? 0 : hBeg;
+            let wInMax = wBeg < 0 ? 0 : wBeg;
+            for (let h = 0; h < filterHeight; ++h) {
+              const hIn = hBeg + h * dilationHeight;
+              if (hIn >= 0 && hIn < inHeight) {
+                for (let w = 0; w < filterWidth; ++w) {
+                  const wIn = wBeg + w * dilationWidth;
+                  if (wIn >= 0 && wIn < inWidth) {
+                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
+                    if (val > curVal) {
+                      curVal = val;
+                      hInMax = hIn;
+                      wInMax = wIn;
+                    }
+                  }
+                }
+              }
+            }
+            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
+    return { dataId, shape: x.shape, dtype: x.dtype };
+  }
+};
+function sum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "sum");
+  let $x;
+  if (x.dtype === "bool") {
+    $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "int32" } });
+  } else {
+    $x = identity2({ inputs: { x }, backend: backend2 });
+  }
+  const xRank = $x.shape.length;
+  const axes = util_exports.parseAxisParam(axis, $x.shape);
+  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
+  let reductionAxes = axes;
+  let permutedX = $x;
+  if (permutation != null) {
+    permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });
+    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);
+  const resultDtype = backend_util_exports.upcastType(permutedX.dtype, "int32");
+  let result = zeros3(backend2, outShape, resultDtype);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = backend2.data.get(result.dataId).values;
+  const aVals = backend2.data.get(permutedX.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let sum7 = 0;
+    for (let j = 0; j < reduceSize; ++j) {
+      sum7 += aVals[offset + j];
+    }
+    vals[i2] = sum7;
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);
+    const oldResult = result;
+    result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });
+    backend2.disposeIntermediateTensorInfo(oldResult);
+  }
+  backend2.disposeIntermediateTensorInfo($x);
+  if (permutation != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return result;
+}
+var sumConfig = {
+  kernelName: Sum,
+  backendName: "cpu",
+  kernelFunc: sum3
+};
+function einsum2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { equation } = attrs;
+  const tensors = inputs;
+  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
+  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
+  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
+  const nSteps = steps.length;
+  let out = null;
+  let numDimsRemaining = allDims.length;
+  const tensorsToDispose = [];
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    for (const idTerm of steps[i2]) {
+      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
+      let x;
+      if (backend_util_exports.isIdentityPermutation(perm)) {
+        x = tensors[idTerm];
+      } else {
+        x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
+        tensorsToDispose.push(x);
+      }
+      const targetShape = x.shape.slice();
+      for (let k = 0; k < dimsToExpand.length; ++k) {
+        targetShape.splice(dimsToExpand[k], 0, 1);
+      }
+      if (!util_exports.arraysEqual(x.shape, targetShape)) {
+        x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
+        tensorsToDispose.push(x);
+      }
+      if (out === null) {
+        out = x;
+      } else {
+        out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });
+        tensorsToDispose.push(out);
+      }
+    }
+    if (i2 < nSteps - 1) {
+      if (path[i2] >= 0) {
+        out = sum3({
+          inputs: { x: out },
+          backend: backend2,
+          attrs: {
+            axis: path[i2] - (allDims.length - numDimsRemaining),
+            keepDims: false
+          }
+        });
+        tensorsToDispose.push(out);
+      }
+      numDimsRemaining--;
+    }
+  }
+  for (const tensorInfo of tensorsToDispose) {
+    if (tensorInfo === out) {
+      continue;
+    }
+    backend2.disposeIntermediateTensorInfo(tensorInfo);
+  }
+  return out;
+}
+var einsumConfig = {
+  kernelName: Einsum,
+  backendName: "cpu",
+  kernelFunc: einsum2
+};
+function eluGrad(args) {
+  const { inputs, backend: backend2 } = args;
+  const { dy, y } = inputs;
+  assertNotComplex([dy, y], "eluGrad");
+  const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));
+  const values = backend2.data.get(y.dataId).values;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  for (let i2 = 0; i2 < values.length; ++i2) {
+    const v = values[i2];
+    if (v >= 1) {
+      resultValues[i2] = dyValues[i2];
+    } else {
+      resultValues[i2] = dyValues[i2] * (v + 1);
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, "float32", resultValues);
+}
+var eluGradConfig2 = {
+  kernelName: EluGrad,
+  backendName: "cpu",
+  kernelFunc: eluGrad
+};
+var p = backend_util_exports.ERF_P;
+var a1 = backend_util_exports.ERF_A1;
+var a2 = backend_util_exports.ERF_A2;
+var a3 = backend_util_exports.ERF_A3;
+var a4 = backend_util_exports.ERF_A4;
+var a5 = backend_util_exports.ERF_A5;
+var erf2 = unaryKernelFunc(Erf, (xi) => {
+  const sign4 = Math.sign(xi);
+  const v = Math.abs(xi);
+  const t2 = 1 / (1 + p * v);
+  return sign4 * (1 - ((((a5 * t2 + a4) * t2 + a3) * t2 + a2) * t2 + a1) * t2 * Math.exp(-v * v));
+});
+var erfConfig = {
+  kernelName: Erf,
+  backendName: "cpu",
+  kernelFunc: erf2
+};
+function expandDims3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { input: input2 } = inputs;
+  const { dim } = attrs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig = {
+  kernelName: ExpandDims,
+  backendName: "cpu",
+  kernelFunc: expandDims3
+};
+var realDivImpl = createSimpleBinaryKernelImpl((a6, b) => a6 / b);
+var div2 = binaryKernelFunc(RealDiv, realDivImpl);
+var realDivConfig = {
+  kernelName: RealDiv,
+  backendName: "cpu",
+  kernelFunc: div2
+};
+function fftBatch(input2, inverse, cpuBackend) {
+  const inputShape = input2.shape;
+  const batch = inputShape[0];
+  const innerDim = inputShape[1];
+  const inputVals = cpuBackend.data.get(input2.dataId);
+  const real2D = inputVals.complexTensorInfos.real;
+  const imag2D = inputVals.complexTensorInfos.imag;
+  const resultShape = [batch, innerDim];
+  const resultSize = util_exports.sizeFromShape(resultShape);
+  const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize);
+  const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize);
+  for (let b = 0; b < batch; b++) {
+    const r2 = slice2({
+      inputs: { x: real2D },
+      backend: cpuBackend,
+      attrs: { begin: [b, 0], size: [1, innerDim] }
+    });
+    const i2 = slice2({
+      inputs: { x: imag2D },
+      backend: cpuBackend,
+      attrs: { begin: [b, 0], size: [1, innerDim] }
+    });
+    const input3 = complex2({ inputs: { real: r2, imag: i2 }, backend: cpuBackend });
+    const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);
+    const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);
+    for (let d = 0; d < innerDim; d++) {
+      const c = backend_util_exports.getComplexWithIndex(res, d);
+      resultReal[b * innerDim + d] = c.real;
+      resultImag[b * innerDim + d] = c.imag;
+    }
+    cpuBackend.disposeIntermediateTensorInfo(r2);
+    cpuBackend.disposeIntermediateTensorInfo(i2);
+    cpuBackend.disposeIntermediateTensorInfo(input3);
+  }
+  const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
+  const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag);
+  const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });
+  cpuBackend.disposeIntermediateTensorInfo($realInfo);
+  cpuBackend.disposeIntermediateTensorInfo($imagInfo);
+  return result;
+}
+function fftImpl(input2, inverse, cpuBackend) {
+  const inputSize10 = util_exports.sizeFromShape(input2.shape);
+  const inputVals = cpuBackend.data.get(input2.dataId);
+  const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;
+  const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;
+  if (isExponentOf2(inputSize10)) {
+    const result = fftRadix2(realVals, imagVals, inputSize10, inverse, cpuBackend);
+    const resultShape = [input2.shape[0], input2.shape[1]];
+    if (inverse) {
+      const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real);
+      const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag);
+      const sizeInfo = cpuBackend.makeTensorInfo([], "float32", util_exports.createScalarValue(inputSize10, "float32"));
+      const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend });
+      const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });
+      const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });
+      const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;
+      const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;
+      cpuBackend.disposeIntermediateTensorInfo(realInfo);
+      cpuBackend.disposeIntermediateTensorInfo(imagInfo);
+      cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
+      cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
+      cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
+      cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
+      return { real: divRealVals, imag: divImagVals };
+    }
+    return result;
+  } else {
+    const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
+    const rawOutput = fourierTransformByMatmul(data, inputSize10, inverse);
+    return backend_util_exports.splitRealAndImagArrays(rawOutput);
+  }
+}
+function isExponentOf2(size2) {
+  return (size2 & size2 - 1) === 0;
+}
+function fftRadix2(realVals, imagVals, size2, inverse, cpuBackend) {
+  if (size2 === 1) {
+    return { real: realVals, imag: imagVals };
+  }
+  const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
+  const half2 = size2 / 2;
+  const evenComplex = backend_util_exports.complexWithEvenIndex(data);
+  const evenRealVals = evenComplex.real;
+  const evenImagVals = evenComplex.imag;
+  const evenShape = [evenRealVals.length];
+  const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals);
+  const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals);
+  const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });
+  const oddComplex = backend_util_exports.complexWithOddIndex(data);
+  const oddRealVals = oddComplex.real;
+  const oddImagVals = oddComplex.imag;
+  const oddShape = [oddRealVals.length];
+  const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals);
+  const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals);
+  const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });
+  const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half2, inverse, cpuBackend);
+  const $evenRealVals = $evenComplex.real;
+  const $evenImagVals = $evenComplex.imag;
+  const $evenShape = [$evenRealVals.length];
+  const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals);
+  const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals);
+  const $evenTensorInfo = complex2({
+    inputs: { real: $evenRealInfo, imag: $evenImagInfo },
+    backend: cpuBackend
+  });
+  const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half2, inverse, cpuBackend);
+  const $oddRealVals = $oddComplex.real;
+  const $oddImagVals = $oddComplex.imag;
+  const $oddShape = [$oddRealVals.length];
+  const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
+  const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
+  const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });
+  const e2 = backend_util_exports.exponents(size2, inverse);
+  const eShape = [e2.real.length];
+  const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.real);
+  const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.imag);
+  const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });
+  const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });
+  const addPart = add4({
+    inputs: { a: $evenTensorInfo, b: exponentInfo },
+    backend: cpuBackend
+  });
+  const subPart = sub2({
+    inputs: { a: $evenTensorInfo, b: exponentInfo },
+    backend: cpuBackend
+  });
+  const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });
+  const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });
+  const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });
+  const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });
+  const $real = concat2({
+    inputs: [addPartReal, subPartReal],
+    backend: cpuBackend,
+    attrs: { axis: 0 }
+  });
+  const $imag = concat2({
+    inputs: [addPartImag, subPartImag],
+    backend: cpuBackend,
+    attrs: { axis: 0 }
+  });
+  const $realVals = cpuBackend.data.get($real.dataId).values;
+  const $imagVals = cpuBackend.data.get($imag.dataId).values;
+  cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo(complexInfo);
+  cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
+  cpuBackend.disposeIntermediateTensorInfo(addPart);
+  cpuBackend.disposeIntermediateTensorInfo(subPart);
+  cpuBackend.disposeIntermediateTensorInfo(addPartReal);
+  cpuBackend.disposeIntermediateTensorInfo(addPartImag);
+  cpuBackend.disposeIntermediateTensorInfo(subPartReal);
+  cpuBackend.disposeIntermediateTensorInfo(subPartImag);
+  cpuBackend.disposeIntermediateTensorInfo($real);
+  cpuBackend.disposeIntermediateTensorInfo($imag);
+  return { real: $realVals, imag: $imagVals };
+}
+function fourierTransformByMatmul(data, size2, inverse) {
+  const ret = new Float32Array(size2 * 2);
+  for (let r2 = 0; r2 < size2; r2++) {
+    let real5 = 0;
+    let imag5 = 0;
+    for (let c = 0; c < size2; c++) {
+      const e2 = backend_util_exports.exponent(r2 * c, size2, inverse);
+      const term = backend_util_exports.getComplexWithIndex(data, c);
+      real5 += term.real * e2.real - term.imag * e2.imag;
+      imag5 += term.real * e2.imag + term.imag * e2.real;
+    }
+    if (inverse) {
+      real5 /= size2;
+      imag5 /= size2;
+    }
+    backend_util_exports.assignToTypedArray(ret, real5, imag5, r2);
+  }
+  return ret;
+}
+function fft2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputSize10 = util_exports.sizeFromShape(input2.shape);
+  const innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = inputSize10 / innerDimensionSize;
+  const input2D = reshape3({
+    inputs: { x: input2 },
+    backend: backend2,
+    attrs: { shape: [batch, innerDimensionSize] }
+  });
+  const result = fftBatch(input2D, false, backend2);
+  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });
+  backend2.disposeIntermediateTensorInfo(input2D);
+  backend2.disposeIntermediateTensorInfo(result);
+  return resultReshaped;
+}
+var fftConfig = {
+  kernelName: FFT,
+  backendName: "cpu",
+  kernelFunc: fft2
+};
+function fill2(args) {
+  const { backend: backend2, attrs } = args;
+  const { shape, value, dtype } = attrs;
+  const $dtype = dtype || util_exports.inferDtype(value);
+  const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));
+  fillValues(values, value, $dtype);
+  return backend2.makeTensorInfo(shape, $dtype, values);
+}
+var fillConfig = {
+  kernelName: Fill,
+  backendName: "cpu",
+  kernelFunc: fill2
+};
+function fillValues(values, value, dtype) {
+  if (dtype === "string") {
+    values.fill(value);
+  } else {
+    values.fill(value);
+  }
+}
+var flipLeftRightConfig = {
+  kernelName: FlipLeftRight,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const cpuBackend = backend2;
+    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));
+    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+    const imageVals = cpuBackend.data.get(image2.dataId).values;
+    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
+      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
+      for (let row = 0; row < imageHeight; row++) {
+        const rowOffset = row * (imageWidth * numChannels);
+        for (let col = 0; col < imageWidth; col++) {
+          const colOffset = col * numChannels;
+          for (let channel = 0; channel < numChannels; channel++) {
+            const coordX = Math.round(imageWidth - col - 1);
+            const outIdx = batchOffset + rowOffset + colOffset + channel;
+            let outputValue = imageVals[outIdx];
+            if (coordX >= 0 && coordX < imageWidth) {
+              const rotatedColOffset = coordX * numChannels;
+              const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;
+              outputValue = imageVals[imageIdx];
+            }
+            output[outIdx] = outputValue;
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);
+    return { dataId, shape: image2.shape, dtype: image2.dtype };
+  }
+};
+var floorDivImpl = createSimpleBinaryKernelImpl((a6, b) => Math.floor(a6 / b));
+var floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, "int32");
+var floorDivConfig = {
+  kernelName: FloorDiv,
+  backendName: "cpu",
+  kernelFunc: floorDiv2
+};
+function fusedConv2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  let result = conv2D({
+    inputs: { x, filter },
+    backend: backend2,
+    attrs: { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode }
+  });
+  if (bias) {
+    const resultOld = result;
+    if (dataFormat === "NCHW" && bias.shape.length === 1 && bias.shape[0] !== 1) {
+      const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });
+      result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 });
+      backend2.disposeIntermediateTensorInfo(reshapedBias);
+    } else {
+      result = add4({ inputs: { a: result, b: bias }, backend: backend2 });
+    }
+    backend2.disposeIntermediateTensorInfo(resultOld);
+  }
+  if (activation2) {
+    const resultOld = result;
+    if (dataFormat === "NCHW" && activation2 === "prelu" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {
+      const reshapedAlpha = reshape3({
+        inputs: { x: preluActivationWeights },
+        backend: backend2,
+        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }
+      });
+      result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha);
+      backend2.disposeIntermediateTensorInfo(reshapedAlpha);
+    } else {
+      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);
+    }
+    backend2.disposeIntermediateTensorInfo(resultOld);
+  }
+  return result;
+}
+var fusedConv2DConfig = {
+  kernelName: FusedConv2D,
+  backendName: "cpu",
+  kernelFunc: fusedConv2D
+};
+function fusedDepthwiseConv2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  let result = depthwiseConv2dNative({
+    inputs: { x, filter },
+    backend: backend2,
+    attrs: { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode }
+  });
+  if (bias) {
+    const oldResult = result;
+    result = add4({ inputs: { a: result, b: bias }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(oldResult);
+  }
+  if (activation2) {
+    const oldResult = result;
+    result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);
+    backend2.disposeIntermediateTensorInfo(oldResult);
+  }
+  return result;
+}
+var fusedDepthwiseConv2DConfig = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "cpu",
+  kernelFunc: fusedDepthwiseConv2D
+};
+function gatherNd(args) {
+  const { inputs, backend: backend2 } = args;
+  const { params, indices } = inputs;
+  const paramsSize = util_exports.sizeFromShape(params.shape);
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const [resultShape, numSlices, sliceSize, strides2] = backend_util_exports.prepareAndValidate(params, indices);
+  if (numSlices === 0) {
+    return backend2.makeTensorInfo(resultShape, params.dtype, []);
+  }
+  const indicesData = backend2.data.get(indices.dataId).values;
+  const paramsBuf = backend2.bufferSync(params);
+  const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides2, params.shape, paramsSize);
+  return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);
+}
+var gatherNdConfig = {
+  kernelName: GatherNd,
+  backendName: "cpu",
+  kernelFunc: gatherNd
+};
+function gatherV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  assertNotComplex([x, indices], "gatherV2");
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const indicesVals = backend2.data.get(indices.dataId).values;
+  const axisDim = x.shape[parsedAxis];
+  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
+    const index2 = indicesVals[i2];
+    util_exports.assert(index2 <= axisDim - 1 && index2 >= 0, () => `GatherV2: the index value ${index2} is not in [0, ${axisDim - 1}]`);
+  }
+  let $batchDims = batchDims;
+  if (batchDims == null) {
+    $batchDims = 0;
+  }
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);
+  const flattenX = reshape3({
+    inputs: { x },
+    backend: backend2,
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    }
+  });
+  const flattenIndex = reshape3({
+    inputs: { x: indices },
+    backend: backend2,
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+  });
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  const indicesBuf = backend2.bufferSync(flattenIndex);
+  const xBuf = backend2.bufferSync(flattenX);
+  const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);
+  backend2.disposeIntermediateTensorInfo(flattenX);
+  backend2.disposeIntermediateTensorInfo(flattenIndex);
+  return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+}
+var gatherV2Config = {
+  kernelName: GatherV2,
+  backendName: "cpu",
+  kernelFunc: gatherV2
+};
+function ifft2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputSize10 = util_exports.sizeFromShape(input2.shape);
+  const innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = inputSize10 / innerDimensionSize;
+  const input2D = reshape3({
+    inputs: { x: input2 },
+    backend: backend2,
+    attrs: { shape: [batch, innerDimensionSize] }
+  });
+  const result = fftBatch(input2D, true, backend2);
+  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });
+  backend2.disposeIntermediateTensorInfo(input2D);
+  backend2.disposeIntermediateTensorInfo(result);
+  return resultReshaped;
+}
+var ifftConfig = {
+  kernelName: IFFT,
+  backendName: "cpu",
+  kernelFunc: ifft2
+};
+var isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, "bool");
+var isFiniteConfig = {
+  kernelName: IsFinite,
+  backendName: "cpu",
+  kernelFunc: isFinite3
+};
+var isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, "bool");
+var isInfConfig = {
+  kernelName: IsInf,
+  backendName: "cpu",
+  kernelFunc: isInf2
+};
+var isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, "bool");
+var isNaNConfig = {
+  kernelName: IsNan,
+  backendName: "cpu",
+  kernelFunc: isNaN3
+};
+function linSpace(args) {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, num } = attrs;
+  const outVals = linSpaceImpl(start, stop, num);
+  return backend2.makeTensorInfo([outVals.length], "float32", outVals);
+}
+var linSpaceConfig = {
+  kernelName: LinSpace,
+  backendName: "cpu",
+  kernelFunc: linSpace
+};
+var log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));
+var log1pConfig = {
+  kernelName: Log1p,
+  backendName: "cpu",
+  kernelFunc: log1p2
+};
+var logicalAndImpl = createSimpleBinaryKernelImpl((a6, b) => a6 && b);
+var logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, "bool");
+var logicalAndConfig = {
+  kernelName: LogicalAnd,
+  backendName: "cpu",
+  kernelFunc: logicalAnd2
+};
+var logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, "bool");
+var logicalNotConfig = {
+  kernelName: LogicalNot,
+  backendName: "cpu",
+  kernelFunc: logicalNot2
+};
+var logicalOrImpl = createSimpleBinaryKernelImpl((a6, b) => a6 || b);
+var logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, "bool");
+var logicalOrConfig = {
+  kernelName: LogicalOr,
+  backendName: "cpu",
+  kernelFunc: logicalOr2
+};
+function lRN(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { depthRadius, bias, alpha: alpha2, beta } = attrs;
+  assertNotComplex(x, "LRN");
+  const channels = x.shape[3];
+  const maxD = channels - 1;
+  const xValues = backend2.data.get(x.dataId).values;
+  const size2 = util_exports.sizeFromShape(x.shape);
+  const result = new Float32Array(size2);
+  function sumAcrossChannels(offset) {
+    const currentChannel = offset % channels;
+    let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+    const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
+    let sum7 = 0;
+    for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
+      const z = xValues[beginSumOffset];
+      sum7 += z * z;
+    }
+    return sum7;
+  }
+  for (let offset = 0; offset < size2; offset++) {
+    const sum7 = sumAcrossChannels(offset);
+    const val = xValues[offset] * Math.pow(bias + alpha2 * sum7, -beta);
+    result[offset] = val;
+  }
+  return backend2.makeTensorInfo(x.shape, x.dtype, result);
+}
+var LRNConfig = {
+  kernelName: LRN,
+  backendName: "cpu",
+  kernelFunc: lRN
+};
+function lRNGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, y, dy } = inputs;
+  const { depthRadius, bias, alpha: alpha2, beta } = attrs;
+  assertNotComplex(dy, "LRNGrad");
+  const dySize = util_exports.sizeFromShape(dy.shape);
+  const channels = dy.shape[3];
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const xValues = backend2.data.get(x.dataId).values;
+  const yValues = backend2.data.get(y.dataId).values;
+  const result = new Float32Array(dySize);
+  const size2 = dySize;
+  for (let offset = 0; offset < size2; offset++) {
+    const currentChannel = offset % channels;
+    const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+    const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);
+    let norm2 = 0;
+    for (let k = depthBegin; k < depthEnd; k++) {
+      norm2 += Math.pow(xValues[k], 2);
+    }
+    norm2 = alpha2 * norm2 + bias;
+    for (let k = depthBegin; k < depthEnd; k++) {
+      let dyi = -2 * alpha2 * beta * xValues[k] * yValues[offset] / norm2;
+      if (offset === k) {
+        dyi += Math.pow(norm2, -beta);
+      }
+      dyi *= dyValues[offset];
+      result[k] += dyi;
+    }
+  }
+  return backend2.makeTensorInfo(dy.shape, x.dtype, result);
+}
+var LRNGradConfig = {
+  kernelName: LRNGrad,
+  backendName: "cpu",
+  kernelFunc: lRNGrad
+};
+function max3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reductionIndices, keepDims } = attrs;
+  const cpuBackend = backend2;
+  let xShape = x.shape;
+  const xRank = xShape.length;
+  const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let xVals = cpuBackend.data.get(x.dataId).values;
+  if (permutedAxes != null) {
+    const newShape = new Array(xRank);
+    for (let i2 = 0; i2 < newShape.length; i2++) {
+      newShape[i2] = xShape[permutedAxes[i2]];
+    }
+    xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    xShape = newShape;
+  }
+  assertNotComplex(x, "max");
+  backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
+  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);
+  const dataId = cpuBackend.write(result, maxOutShape, x.dtype);
+  let outShape = maxOutShape;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
+    outShape = newShape;
+  }
+  return { dataId, shape: outShape, dtype: x.dtype };
+}
+var maxConfig = {
+  kernelName: Max,
+  backendName: "cpu",
+  kernelFunc: max3
+};
+function maxPool2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex(x, "maxPool");
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  let res;
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    res = identity2({ inputs: { x }, backend: backend2 });
+  } else {
+    const xValues = backend2.data.get(x.dataId).values;
+    const strides22 = util_exports.computeStrides(x.shape);
+    const buffer2 = pool2(xValues, x.shape, x.dtype, strides22, convInfo, "max");
+    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
+  }
+  return res;
+}
+var maxPoolConfig = {
+  kernelName: MaxPool,
+  backendName: "cpu",
+  kernelFunc: maxPool2
+};
+function maxPool3D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  assertNotComplex(x, "maxPool3d");
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides2, 1, pad3, dimRoundingMode, dataFormat);
+  const xValues = backend2.data.get(x.dataId).values;
+  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "max");
+  return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
+}
+var maxPool3DConfig = {
+  kernelName: MaxPool3D,
+  backendName: "cpu",
+  kernelFunc: maxPool3D
+};
+function maxPool3DGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  assertNotComplex([dy, input2], "maxPool3DGrad");
+  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides2, 1, pad3, dimRoundingMode);
+  const inputBuf = backend2.bufferSync(input2);
+  const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(input2.shape, "float32");
+  const dyBuf = backend2.bufferSync(dy);
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
+        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
+          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
+            const dyDepthCorner = dxDepth - padFront;
+            const dyRowCorner = dxRow - padTop;
+            const dyColCorner = dxCol - padLeft;
+            let dotProd = 0;
+            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
+              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
+              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
+                continue;
+              }
+              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
+                const dyRow = (dyRowCorner + wRow) / strideHeight;
+                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
+                  continue;
+                }
+                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
+                  const dyCol = (dyColCorner + wCol) / strideWidth;
+                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
+                    continue;
+                  }
+                  const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                  const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;
+                  const mask2 = maxPos === curPos ? 1 : 0;
+                  if (mask2 === 0) {
+                    continue;
+                  }
+                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                  dotProd += pixel * mask2;
+                }
+              }
+            }
+            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var maxPool3DGradConfig2 = {
+  kernelName: MaxPool3DGrad,
+  backendName: "cpu",
+  kernelFunc: maxPool3DGrad
+};
+function maxPoolGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2, output } = inputs;
+  const x = input2;
+  assertNotComplex([input2, output], "maxPoolGrad");
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, 1, pad3, dimRoundingMode);
+  const xValues = backend2.data.get(x.dataId).values;
+  const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(x.shape, "float32");
+  const dyData = backend2.data.get(dy.dataId).values;
+  const dyBuf = buffer(dy.shape, "float32", dyData);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+          const dyRCorner = dxR - padTop;
+          const dyCCorner = dxC - padLeft;
+          let dotProd = 0;
+          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+            const dyR = (dyRCorner + wR) / strideHeight;
+            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
+              continue;
+            }
+            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+              const dyC = (dyCCorner + wC) / strideWidth;
+              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
+                continue;
+              }
+              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);
+              const curPos = wR * effectiveFilterWidth + wC;
+              const mask2 = maxPos === curPos ? 1 : 0;
+              if (mask2 === 0) {
+                continue;
+              }
+              const pixel = dyBuf.get(b, dyR, dyC, d);
+              dotProd += pixel * mask2;
+            }
+          }
+          dx.set(dotProd, b, dxR, dxC, d);
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var maxPoolGradConfig2 = {
+  kernelName: MaxPoolGrad,
+  backendName: "cpu",
+  kernelFunc: maxPoolGrad2
+};
+function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {
+  const strides2 = util_exports.computeStrides(xShape);
+  const maxPools = pool2(xValues, xShape, dtype, strides2, convInfo, "max");
+  const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
+  return [maxPools.values, maxPositions.values];
+}
+var maxPoolWithArgmaxConfig = {
+  kernelName: MaxPoolWithArgmax,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { filterSize, strides: strides2, pad: pad3, includeBatchInIndex } = attrs;
+    const cpuBackend = backend2;
+    assertNotComplex(x, "MaxPoolWithArgmax");
+    const values = cpuBackend.data.get(x.dataId).values;
+    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, [1, 1], pad3);
+    const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);
+    const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);
+    const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);
+    return [
+      { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },
+      { dataId: indexesDataId, shape: convInfo.outShape, dtype: "int32" }
+    ];
+  }
+};
+function mean2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const axes = util_exports.parseAxisParam(axis, x.shape);
+  const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);
+  const reduceShape = shapes[1];
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const toDispose = [];
+  const reduceSizeScalar = backend2.makeTensorInfo([], "float32", new Float32Array([reduceSize]));
+  toDispose.push(reduceSizeScalar);
+  const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+  toDispose.push($x);
+  const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });
+  toDispose.push(res);
+  const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var meanConfig = {
+  kernelName: Mean,
+  backendName: "cpu",
+  kernelFunc: mean2
+};
+function min3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "min");
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("min", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let min7 = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (Number.isNaN(value) || value < min7) {
+        min7 = value;
+      }
+    }
+    vals[i2] = min7;
+  }
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo($x);
+  }
+  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
+  if (keepDims) {
+    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
+    backend2.disposeIntermediateTensorInfo(result);
+    return reshapedResult;
+  }
+  return result;
+}
+var minConfig = {
+  kernelName: Min,
+  backendName: "cpu",
+  kernelFunc: min3
+};
+function mirrorPad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, mode } = attrs;
+  assertNotComplex(x, "mirrorPad");
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const start = paddings.map((p2) => p2[0]);
+  const end = paddings.map((p2, i2) => p2[0] + x.shape[i2]);
+  const offset = mode === "reflect" ? 0 : 1;
+  const xVals = backend2.data.get(x.dataId).values;
+  const xRank = x.shape.length;
+  const xStrides = util_exports.computeStrides(x.shape);
+  const resultSize = util_exports.sizeFromShape(outShape);
+  const resultRank = outShape.length;
+  const resultStrides = util_exports.computeStrides(outShape);
+  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
+  for (let i2 = 0; i2 < resultSize; i2++) {
+    let coords3 = util_exports.indexToLoc(i2, resultRank, resultStrides);
+    for (let i3 = 0; i3 < resultRank; i3++) {
+      if (coords3[i3] < start[i3]) {
+        coords3[i3] = start[i3] * 2 - coords3[i3] - offset;
+      } else if (coords3[i3] >= end[i3]) {
+        coords3[i3] = (end[i3] - 1) * 2 - coords3[i3] + offset;
+      }
+    }
+    coords3 = coords3.map((c, i3) => c - start[i3]);
+    const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);
+    resVals[i2] = xVals[inIndex];
+  }
+  const outId = backend2.write(resVals, outShape, x.dtype);
+  return { dataId: outId, shape: outShape, dtype: x.dtype };
+}
+var mirrorPadConfig = {
+  kernelName: MirrorPad,
+  backendName: "cpu",
+  kernelFunc: mirrorPad2
+};
+var modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {
+  const rem = aValue % bValue;
+  if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {
+    return rem;
+  } else {
+    return (rem + bValue) % bValue;
+  }
+});
+var mod2 = binaryKernelFunc(Mod, modImpl);
+var modConfig = {
+  kernelName: Mod,
+  backendName: "cpu",
+  kernelFunc: mod2
+};
+var seedrandom4 = __toESM(require_seedrandom2());
+function softmax3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { dim } = attrs;
+  const logitsRank = logits.shape.length;
+  let $dim = dim;
+  if ($dim === -1) {
+    $dim = logitsRank - 1;
+  }
+  if ($dim !== logitsRank - 1) {
+    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);
+  }
+  const axes = util_exports.parseAxisParam([$dim], logits.shape);
+  const maxLogit = max3({
+    inputs: { x: logits },
+    backend: backend2,
+    attrs: { reductionIndices: axes, keepDims: false }
+  });
+  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
+  const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
+  const a6 = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });
+  const b = exp2({ inputs: { x: a6 }, backend: backend2 });
+  const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
+  const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
+  const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(maxLogit);
+  backend2.disposeIntermediateTensorInfo(maxLogitReshaped);
+  backend2.disposeIntermediateTensorInfo(a6);
+  backend2.disposeIntermediateTensorInfo(b);
+  backend2.disposeIntermediateTensorInfo(sumExp);
+  backend2.disposeIntermediateTensorInfo(sumReshaped);
+  return result;
+}
+var softmaxConfig = {
+  kernelName: Softmax,
+  backendName: "cpu",
+  kernelFunc: softmax3
+};
+function multinomial2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { numSamples, seed, normalized } = attrs;
+  assertNotComplex(logits, "multinomial");
+  const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });
+  const batchSize = probabilities.shape[0];
+  const numEvents = probabilities.shape[1];
+  const probVals = backend2.data.get(probabilities.dataId).values;
+  const resShape = [batchSize, numSamples];
+  const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), "int32");
+  for (let b = 0; b < batchSize; ++b) {
+    const offset = b * numEvents;
+    const cdf = new Float32Array(numEvents - 1);
+    cdf[0] = probVals[offset];
+    for (let event = 1; event < cdf.length; ++event) {
+      cdf[event] = cdf[event - 1] + probVals[offset + event];
+    }
+    const random = seedrandom4.alea(seed.toString());
+    const outOffset = b * numSamples;
+    for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
+      const r2 = random();
+      resVals[outOffset + sampleId] = cdf.length;
+      for (let event = 0; event < cdf.length; event++) {
+        if (r2 < cdf[event]) {
+          resVals[outOffset + sampleId] = event;
+          break;
+        }
+      }
+    }
+  }
+  if (!normalized) {
+    backend2.disposeIntermediateTensorInfo(probabilities);
+  }
+  return backend2.makeTensorInfo(resShape, "int32", resVals);
+}
+var multinomialConfig = {
+  kernelName: Multinomial,
+  backendName: "cpu",
+  kernelFunc: multinomial2
+};
+var nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;
+function nonMaxSuppressionV3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
+  assertNotComplex(boxes, "NonMaxSuppression");
+  const boxesVals = backend2.data.get(boxes.dataId).values;
+  const scoresVals = backend2.data.get(scores.dataId).values;
+  const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
+}
+var nonMaxSuppressionV3Config = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "cpu",
+  kernelFunc: nonMaxSuppressionV3
+};
+var nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;
+function nonMaxSuppressionV4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
+  assertNotComplex(boxes, "NonMaxSuppressionPadded");
+  const boxesVals = backend2.data.get(boxes.dataId).values;
+  const scoresVals = backend2.data.get(scores.dataId).values;
+  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
+  ];
+}
+var nonMaxSuppressionV4Config = {
+  kernelName: NonMaxSuppressionV4,
+  backendName: "cpu",
+  kernelFunc: nonMaxSuppressionV4
+};
+var nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;
+function nonMaxSuppressionV5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
+  assertNotComplex(boxes, "NonMaxSuppressionWithScore");
+  const boxesVals = backend2.data.get(boxes.dataId).values;
+  const scoresVals = backend2.data.get(scores.dataId).values;
+  const maxOutputSizeVal = maxOutputSize;
+  const iouThresholdVal = iouThreshold;
+  const scoreThresholdVal = scoreThreshold;
+  const softNmsSigmaVal = softNmsSigma;
+  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
+  ];
+}
+var nonMaxSuppressionV5Config = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "cpu",
+  kernelFunc: nonMaxSuppressionV5
+};
+function oneHot2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices } = inputs;
+  const { depth, onValue, offValue } = attrs;
+  assertNotComplex(indices, "oneHot");
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const res = new Float32Array(indicesSize * depth);
+  res.fill(offValue);
+  const indicesVal = backend2.data.get(indices.dataId).values;
+  for (let event = 0; event < indicesSize; ++event) {
+    if (indicesVal[event] >= 0 && indicesVal[event] < depth) {
+      res[event * depth + indicesVal[event]] = onValue;
+    }
+  }
+  return backend2.makeTensorInfo([...indices.shape, depth], "int32", res);
+}
+var oneHotConfig = {
+  kernelName: OneHot,
+  backendName: "cpu",
+  kernelFunc: oneHot2
+};
+function zerosLike2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("zerosLike is not supported for string tensors");
+  } else if (x.dtype === "complex64") {
+    const realPart = real2({ inputs: { input: x }, backend: backend2 });
+    const r2 = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });
+  }
+}
+var zerosLikeConfig = {
+  kernelName: ZerosLike,
+  backendName: "cpu",
+  kernelFunc: zerosLike2
+};
+function onesLike2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("onesLike is not supported for string tensors");
+  } else if (x.dtype === "complex64") {
+    const realPart = real2({ inputs: { input: x }, backend: backend2 });
+    const r2 = onesLike2({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });
+  }
+}
+var onesLikeConfig = {
+  kernelName: OnesLike,
+  backendName: "cpu",
+  kernelFunc: onesLike2
+};
+function pack(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims3({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var packConfig = {
+  kernelName: Pack,
+  backendName: "cpu",
+  kernelFunc: pack
+};
+function padV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, constantValue } = attrs;
+  assertNotComplex(x, "pad");
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const start = paddings.map((p2) => p2[0]);
+  const xVals = backend2.data.get(x.dataId).values;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const xRank = x.shape.length;
+  const xStrides = util_exports.computeStrides(x.shape);
+  const resultSize = util_exports.sizeFromShape(outShape);
+  const resultRank = outShape.length;
+  const resultStrides = util_exports.computeStrides(outShape);
+  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
+  if (constantValue !== 0) {
+    resVals.fill(constantValue);
+  }
+  for (let i2 = 0; i2 < xSize; i2++) {
+    const coords3 = util_exports.indexToLoc(i2, xRank, xStrides);
+    const outCoords = coords3.map((c, i3) => c + start[i3]);
+    const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);
+    resVals[outIndex] = xVals[i2];
+  }
+  const outId = backend2.write(resVals, outShape, x.dtype);
+  return { dataId: outId, shape: outShape, dtype: x.dtype };
+}
+var padV2Config = {
+  kernelName: PadV2,
+  backendName: "cpu",
+  kernelFunc: padV2
+};
+var powImpl = createSimpleBinaryKernelImpl((a6, b) => Math.pow(a6, b));
+var pow2 = binaryKernelFunc(Pow, powImpl);
+var powConfig = {
+  kernelName: Pow,
+  backendName: "cpu",
+  kernelFunc: pow2
+};
+function range3(args) {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, dtype, step: step5 } = attrs;
+  const values = rangeImpl(start, stop, step5, dtype);
+  return backend2.makeTensorInfo([values.length], dtype, values);
+}
+var rangeConfig = {
+  kernelName: Range,
+  backendName: "cpu",
+  kernelFunc: range3
+};
+var reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);
+var reciprocalConfig = {
+  kernelName: Reciprocal,
+  backendName: "cpu",
+  kernelFunc: reciprocal2
+};
+function resizeBilinear2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size: size2 } = attrs;
+  assertNotComplex(images, "resizeBilinear");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const [newHeight, newWidth] = size2;
+  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
+  const xValues = backend2.data.get(images.dataId).values;
+  const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));
+  const effectiveInputSize = [
+    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+  ];
+  const effectiveOutputSize = [
+    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+  ];
+  let outputIdx = 0;
+  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+  for (let b = 0; b < batch; b++) {
+    for (let r2 = 0; r2 < newHeight; r2++) {
+      let sourceFracRow;
+      if (halfPixelCenters) {
+        sourceFracRow = effectiveRowSizeRatio * (r2 + 0.5) - 0.5;
+      } else {
+        sourceFracRow = effectiveRowSizeRatio * r2;
+      }
+      const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
+      const rowFrac = sourceFracRow - sourceRowFloor;
+      const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
+      const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
+      const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
+      for (let c = 0; c < newWidth; c++) {
+        let sourceFracCol;
+        if (halfPixelCenters) {
+          sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
+        } else {
+          sourceFracCol = effectiveColSizeRatio * c;
+        }
+        const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
+        const colFrac = sourceFracCol - sourceColFloor;
+        const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
+        const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
+        const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
+        const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
+        const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
+        for (let d = 0; d < numChannels; d++) {
+          const topLeft = xValues[topLeftOffest + d];
+          const bottomLeft = xValues[botLeftOffset + d];
+          const topRight = xValues[topRightOffset + d];
+          const bottomRight = xValues[botRightOffest + d];
+          const top = topLeft + (topRight - topLeft) * colFrac;
+          const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
+          const newValue = top + (bottom - top) * rowFrac;
+          result[outputIdx++] = newValue;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], "float32", result);
+}
+var resizeBilinearConfig = {
+  kernelName: ResizeBilinear,
+  backendName: "cpu",
+  kernelFunc: resizeBilinear2
+};
+function resizeBilinearGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  assertNotComplex([dy, images], "resizeBilinearGrad");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const [batch, xHeight, xWidth, depth] = images.shape;
+  const [, yHeight, yWidth] = dy.shape;
+  const output = new Float32Array(batch * xHeight * xWidth * depth);
+  const effectiveXSize = [
+    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+  ];
+  const effectiveYSize = [
+    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+  ];
+  const heightScale = effectiveXSize[0] / effectiveYSize[0];
+  const widthScale = effectiveXSize[1] / effectiveYSize[1];
+  const dyValues = backend2.data.get(dy.dataId).values;
+  let offset = 0;
+  for (let b = 0; b < batch; b++) {
+    const bOffset = b * imagesStrides[0];
+    for (let r2 = 0; r2 < yHeight; r2++) {
+      const dxR = r2 * heightScale;
+      const topDxRIndex = Math.floor(dxR);
+      const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
+      const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
+      const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
+      const dxRLerp = dxR - topDxRIndex;
+      const inverseDxRLerp = 1 - dxRLerp;
+      for (let c = 0; c < yWidth; c++) {
+        const dxC = c * widthScale;
+        const leftDxCIndex = Math.floor(dxC);
+        const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
+        const dxCLerp = dxC - leftDxCIndex;
+        const inverseDxCLerp = 1 - dxCLerp;
+        const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
+        const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];
+        const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];
+        const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];
+        const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
+        const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
+        const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
+        const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
+        for (let d = 0; d < depth; d++) {
+          const dyVal = dyValues[offset++];
+          output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;
+          output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
+          output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
+          output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], "float32", output);
+}
+var resizeBilinearGradConfig2 = {
+  kernelName: ResizeBilinearGrad,
+  backendName: "cpu",
+  kernelFunc: resizeBilinearGrad
+};
+function resizeNearestNeighbor2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size: size2 } = attrs;
+  assertNotComplex(images, "resizeNearestNeighbor");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const [newHeight, newWidth] = size2;
+  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
+  const xValues = backend2.data.get(images.dataId).values;
+  const output = new Float32Array(batch * newHeight * newWidth * numChannels);
+  const effectiveInputSize = [
+    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+  ];
+  const effectiveOutputSize = [
+    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+  ];
+  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+  let outputOffset = 0;
+  for (let b = 0; b < batch; b++) {
+    const batchOffset = b * imagesStrides[0];
+    for (let r2 = 0; r2 < newHeight; r2++) {
+      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r2 + 0.5) : effectiveRowSizeRatio * r2;
+      let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
+      if (halfPixelCenters) {
+        sourceNearestRow = Math.max(0, sourceNearestRow);
+      }
+      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
+      for (let c = 0; c < newWidth; c++) {
+        const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;
+        let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
+        if (halfPixelCenters) {
+          sourceNearestCol = Math.max(0, sourceNearestCol);
+        }
+        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
+        for (let d = 0; d < numChannels; d++) {
+          const newVal = xValues[colOffset + d];
+          output[outputOffset++] = newVal;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);
+}
+var resizeNearestNeighborConfig = {
+  kernelName: ResizeNearestNeighbor,
+  backendName: "cpu",
+  kernelFunc: resizeNearestNeighbor2
+};
+function resizeNearestNeighborGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  assertNotComplex([dy, images], "resizeNearestNeighborGrad");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const [batch, xHeight, xWidth, depth] = images.shape;
+  const [, yHeight, yWidth] = dy.shape;
+  const output = new Float32Array(batch * xHeight * xWidth * depth);
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const effectiveXSize = [
+    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+  ];
+  const effectiveYSize = [
+    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+  ];
+  const heightScale = effectiveXSize[0] / effectiveYSize[0];
+  const widthScale = effectiveXSize[1] / effectiveYSize[1];
+  const invHeightScale = 1 / heightScale;
+  const invWidthScale = 1 / widthScale;
+  const winHeight = Math.ceil(invHeightScale) * 2 + 2;
+  const winWidth = Math.ceil(invWidthScale) * 2 + 2;
+  for (let b = 0; b < batch; b++) {
+    const batchOffset = b * imagesStrides[0];
+    for (let r2 = 0; r2 < xHeight; r2++) {
+      const rowOffset = batchOffset + r2 * imagesStrides[1];
+      const startRLerp = Math.floor(r2 * invHeightScale);
+      const startDyR = Math.floor(startRLerp - winHeight / 2);
+      for (let c = 0; c < xWidth; c++) {
+        const colOffset = rowOffset + c * imagesStrides[2];
+        const startCLerp = Math.floor(c * invWidthScale);
+        const startDyC = Math.floor(startCLerp - winWidth / 2);
+        for (let d = 0; d < depth; d++) {
+          let accum = 0;
+          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
+            const dyR = dyRIndex + startDyR;
+            if (dyR < 0 || dyR >= yHeight) {
+              continue;
+            }
+            const dyROffset = batchOffset + dyR * dyStrides[1];
+            const sourceFracRow = dyR * heightScale;
+            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
+            if (r2 !== sourceNearestRow) {
+              continue;
+            }
+            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
+              const dyC = dyCIndex + startDyC;
+              if (dyC < 0 || dyC >= yWidth) {
+                continue;
+              }
+              const dyCOffset = dyROffset + dyC * dyStrides[2];
+              const sourceFracCol = dyC * widthScale;
+              const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
+              if (c === sourceNearestCol) {
+                accum += dyValues[dyCOffset + d];
+              }
+            }
+          }
+          output[colOffset + d] = accum;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(images.shape, images.dtype, output);
+}
+var resizeNearestNeighborGradConfig2 = {
+  kernelName: ResizeNearestNeighborGrad,
+  backendName: "cpu",
+  kernelFunc: resizeNearestNeighborGrad
+};
+function reverse2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dims } = attrs;
+  assertNotComplex(x, "reverse");
+  const xRank = x.shape.length;
+  const $dims = util_exports.parseAxisParam(dims, x.shape);
+  if (xRank === 0) {
+    return identity2({ inputs: { x }, backend: backend2 });
+  }
+  const outBuf = new TensorBuffer(x.shape, x.dtype);
+  const xBuf = backend2.bufferSync(x);
+  for (let i2 = 0; i2 < outBuf.size; i2++) {
+    const outLoc = outBuf.indexToLoc(i2);
+    const inLoc = outLoc.slice();
+    $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
+    outBuf.set(xBuf.get(...inLoc), ...outLoc);
+  }
+  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+}
+var reverseConfig = {
+  kernelName: Reverse,
+  backendName: "cpu",
+  kernelFunc: reverse2
+};
+var rotateWithOffsetConfig = {
+  kernelName: RotateWithOffset,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const { radians, fillValue, center } = attrs;
+    const cpuBackend = backend2;
+    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));
+    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+    const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
+    const fullOpacityValue = 255;
+    const sinFactor = Math.sin(radians);
+    const cosFactor = Math.cos(radians);
+    const imageVals = cpuBackend.data.get(image2.dataId).values;
+    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
+      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
+      for (let row = 0; row < imageHeight; row++) {
+        const rowOffset = row * (imageWidth * numChannels);
+        for (let col = 0; col < imageWidth; col++) {
+          const colOffset = col * numChannels;
+          for (let channel = 0; channel < numChannels; channel++) {
+            const coords3 = [batch, row, col, channel];
+            const x = coords3[2];
+            const y = coords3[1];
+            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
+            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
+            coordX = Math.round(coordX + centerX);
+            coordY = Math.round(coordY + centerY);
+            let outputValue = fillValue;
+            if (typeof fillValue !== "number") {
+              if (channel === 3) {
+                outputValue = fullOpacityValue;
+              } else {
+                outputValue = fillValue[channel];
+              }
+            }
+            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {
+              const rotatedRowOffset = coordY * (imageWidth * numChannels);
+              const rotatedColOffset = coordX * numChannels;
+              const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
+              outputValue = imageVals[imageIdx];
+            }
+            const outIdx = batchOffset + rowOffset + colOffset + channel;
+            output[outIdx] = outputValue;
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);
+    return { dataId, shape: image2.shape, dtype: image2.dtype };
+  }
+};
+var round3 = unaryKernelFunc(Round, (xi) => {
+  const base = Math.floor(xi);
+  if (xi - base < 0.5) {
+    return Math.floor(xi);
+  } else if (xi - base > 0.5) {
+    return Math.ceil(xi);
+  } else {
+    if (base % 2 === 0) {
+      return base;
+    } else {
+      return base + 1;
+    }
+  }
+});
+var roundConfig = {
+  kernelName: Round,
+  backendName: "cpu",
+  kernelFunc: round3
+};
+function scatterNd(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = backend_util_exports.calculateShapes(updates, indices, shape);
+  const sumDupeIndices = true;
+  const indicesBuf = backend2.bufferSync(indices);
+  const updatesBuf = backend2.bufferSync(updates);
+  const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, 0, sumDupeIndices);
+  return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
+}
+var scatterNdConfig = {
+  kernelName: ScatterNd,
+  backendName: "cpu",
+  kernelFunc: scatterNd
+};
+function lowerBound2(array2, value) {
+  let left = 0;
+  let right = array2.length;
+  let mid = 0;
+  while (left < right) {
+    mid = Math.floor((left + right) / 2);
+    if (array2[mid] < value) {
+      left = mid + 1;
+    } else {
+      right = mid;
+    }
+  }
+  return right;
+}
+function upperBound2(array2, value) {
+  let left = 0;
+  let right = array2.length;
+  let mid = 0;
+  while (left < right) {
+    mid = Math.floor((left + right) / 2);
+    if (array2[mid] <= value) {
+      left = mid + 1;
+    } else {
+      right = mid;
+    }
+  }
+  return right;
+}
+function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {
+  const output = util_exports.getArrayFromDType("int32", batchSize * numValues);
+  for (let b = 0; b < batchSize; ++b) {
+    const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
+    const outputOffset = b * numValues;
+    for (let i2 = 0; i2 < numValues; ++i2) {
+      output[outputOffset + i2] = side === "left" ? lowerBound2(sortedInputsSlice, values[i2 + outputOffset]) : upperBound2(sortedInputsSlice, values[i2 + outputOffset]);
+    }
+  }
+  return output;
+}
+function searchSorted2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sortedSequence, values } = inputs;
+  const { side } = attrs;
+  const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;
+  const $values = backend2.data.get(values.dataId).values;
+  const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
+  return backend2.makeTensorInfo(values.shape, "int32", output);
+}
+var searchSortedConfig = {
+  kernelName: SearchSorted,
+  backendName: "cpu",
+  kernelFunc: searchSorted2
+};
+function select2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  assertNotComplex([condition, t2, e2], "select");
+  const conditionRank = condition.shape.length;
+  const values = backend2.data.get(condition.dataId).values;
+  const tValues = backend2.data.get(t2.dataId).values;
+  const eValues = backend2.data.get(e2.dataId).values;
+  const resultDtype = upcastType(t2.dtype, e2.dtype);
+  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t2.shape), resultDtype);
+  let index2 = 0;
+  const offset = conditionRank === 0 || conditionRank > 1 || t2.shape.length === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
+  for (let i2 = 0; i2 < values.length; i2++) {
+    for (let j = 0; j < offset; j++) {
+      if (values[i2] === 1) {
+        newValues[index2++] = tValues[i2];
+      } else {
+        newValues[index2++] = eValues[i2];
+      }
+    }
+  }
+  return backend2.makeTensorInfo(t2.shape, resultDtype, newValues);
+}
+var selectConfig = {
+  kernelName: Select,
+  backendName: "cpu",
+  kernelFunc: select2
+};
+var scaleAlpha = backend_util_exports.SELU_SCALEALPHA;
+var scale = backend_util_exports.SELU_SCALE;
+var selu2 = unaryKernelFunc(Selu, (xi) => {
+  if (xi >= 0) {
+    return scale * xi;
+  } else {
+    return scaleAlpha * (Math.exp(xi) - 1);
+  }
+});
+var seluConfig = {
+  kernelName: Selu,
+  backendName: "cpu",
+  kernelFunc: selu2
+};
+var sign2 = unaryKernelFunc(Sign, (xi) => {
+  if (xi < 0) {
+    return -1;
+  } else if (xi > 0) {
+    return 1;
+  } else {
+    return 0;
+  }
+});
+var signConfig = {
+  kernelName: Sign,
+  backendName: "cpu",
+  kernelFunc: sign2
+};
+var sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));
+var sinConfig = {
+  kernelName: Sin,
+  backendName: "cpu",
+  kernelFunc: sin2
+};
+var sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));
+var sinhConfig = {
+  kernelName: Sinh,
+  backendName: "cpu",
+  kernelFunc: sinh2
+};
+var epsilon2 = 11920928955078125e-23;
+var threshold2 = Math.log(epsilon2) + 2;
+var softplus2 = unaryKernelFunc(Softplus, (xi) => {
+  const tooLarge = xi > -threshold2;
+  const tooSmall = xi < threshold2;
+  const expX = Math.exp(xi);
+  let result;
+  if (tooSmall) {
+    result = expX;
+  } else if (tooLarge) {
+    result = xi;
+  } else {
+    result = Math.log(1 + expX);
+  }
+  return result;
+});
+var softplusConfig = {
+  kernelName: Softplus,
+  backendName: "cpu",
+  kernelFunc: softplus2
+};
+function spaceToBatchND2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  assertNotComplex([x], "spaceToBatchND");
+  const prod6 = util_exports.sizeFromShape(blockShape);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const paddedX = padV2Config.kernelFunc({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapeInputs = { x: paddedX };
+  const reshapeAttrs = { shape: reshapedPaddedShape };
+  const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });
+  const transposeInputs = { x: paddedXReshaped };
+  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };
+  const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });
+  const resultReshapeInputs = { x: paddedXT };
+  const resultReshapeAttrs = { shape: flattenShape };
+  const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });
+  backend2.disposeIntermediateTensorInfo(paddedX);
+  backend2.disposeIntermediateTensorInfo(paddedXReshaped);
+  backend2.disposeIntermediateTensorInfo(paddedXT);
+  return result;
+}
+var spaceToBatchNDConfig = {
+  kernelName: SpaceToBatchND,
+  backendName: "cpu",
+  kernelFunc: spaceToBatchND2
+};
+function sparseFillEmptyRows2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { indices, values, denseShape, defaultValue } = inputs;
+  if (denseShape.shape.length !== 1) {
+    throw new Error(`Dense shape must be a vector, saw:
+        ${denseShape.shape}`);
+  }
+  if (indices.shape.length !== 2) {
+    throw new Error(`Indices must be a matrix, saw:
+        ${indices.shape}`);
+  }
+  if (values.shape.length !== 1) {
+    throw new Error(`Values must be a vector, saw:
+        ${values.shape}`);
+  }
+  if (defaultValue.shape.length !== 0) {
+    throw new Error(`Default value must be a scalar, saw:
+        ${defaultValue.shape}`);
+  }
+  const $indices = backend2.data.get(indices.dataId).values;
+  const $values = backend2.data.get(values.dataId).values;
+  const $denseShape = backend2.data.get(denseShape.dataId).values;
+  const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
+  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
+  return [
+    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
+    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
+    backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),
+    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))
+  ];
+}
+var sparseFillEmptyRowsConfig = {
+  kernelName: SparseFillEmptyRows,
+  backendName: "cpu",
+  kernelFunc: sparseFillEmptyRows2
+};
+function sparseReshape2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { inputIndices, inputShape, newShape } = inputs;
+  if (inputIndices.shape.length !== 2) {
+    throw new Error(`Input indices should be a matrix but received shape
+        ${inputIndices.shape}`);
+  }
+  if (inputShape.shape.length !== 1) {
+    throw new Error(`Input shape should be a vector but received shape
+        ${inputShape.shape}`);
+  }
+  if (newShape.shape.length !== 1) {
+    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
+  }
+  const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);
+  const $inputIndices = backend2.data.get(inputIndices.dataId).values;
+  const targetShape = Array.from(backend2.data.get(newShape.dataId).values);
+  const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
+  return [
+    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
+    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))
+  ];
+}
+var sparseReshapeConfig = {
+  kernelName: SparseReshape,
+  backendName: "cpu",
+  kernelFunc: sparseReshape2
+};
+function sparseSegmentMean2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+          ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+          ${segmentIds.shape}`);
+  }
+  if (indices.shape[0] !== segmentIds.shape[0]) {
+    throw new Error(`segmentIds and indices should have same size.`);
+  }
+  const $data = backend2.data.get(data.dataId).values;
+  const $indices = backend2.data.get(indices.dataId).values;
+  const $segmentIds = backend2.data.get(segmentIds.dataId).values;
+  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentMeanConfig = {
+  kernelName: SparseSegmentMean,
+  backendName: "cpu",
+  kernelFunc: sparseSegmentMean2
+};
+function sparseSegmentSum2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+         ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+         ${segmentIds.shape}`);
+  }
+  if (indices.shape[0] !== segmentIds.shape[0]) {
+    throw new Error(`segmentIds and indices should have same size.`);
+  }
+  const $data = backend2.data.get(data.dataId).values;
+  const $indices = backend2.data.get(indices.dataId).values;
+  const $segmentIds = backend2.data.get(segmentIds.dataId).values;
+  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentSumConfig = {
+  kernelName: SparseSegmentSum,
+  backendName: "cpu",
+  kernelFunc: sparseSegmentSum2
+};
+function sparseToDense2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sparseIndices, sparseValues, defaultValue } = inputs;
+  const { outputShape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
+  const sumDupeIndices = false;
+  const indicesBuf = backend2.bufferSync(sparseIndices);
+  let outBuf;
+  switch (sparseValues.dtype) {
+    case "bool": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, $defaultValue, sumDupeIndices);
+      break;
+    }
+    case "float32": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, $defaultValue, sumDupeIndices);
+      break;
+    }
+    case "int32": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, $defaultValue, sumDupeIndices);
+      break;
+    }
+    case "string": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, $defaultValue, sumDupeIndices);
+      break;
+    }
+    default:
+      throw new Error(`Unsupported type ${sparseValues.dtype}`);
+  }
+  return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+}
+var sparseToDenseConfig = {
+  kernelName: SparseToDense,
+  backendName: "cpu",
+  kernelFunc: sparseToDense2
+};
+function splitV(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const begin = new Array(x.shape.length).fill(0);
+  const size2 = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const sliceSize = [...size2];
+    sliceSize[$axis] = s2;
+    const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
+    begin[$axis] += s2;
+    return sliceT;
+  });
+}
+var splitVConfig = {
+  kernelName: SplitV,
+  backendName: "cpu",
+  kernelFunc: splitV
+};
+var squareConfig = {
+  kernelName: Square,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const cpuBackend = backend2;
+    assertNotComplex(x, "square");
+    const values = cpuBackend.data.get(x.dataId).values;
+    const newValues = new Float32Array(values.length);
+    for (let i2 = 0; i2 < values.length; ++i2) {
+      const value = values[i2];
+      newValues[i2] = value * value;
+    }
+    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
+    return { dataId, shape: x.shape, dtype: x.dtype };
+  }
+};
+var step2 = unaryKernelFunc(Step, (xi, attrs) => {
+  const stepAttrs = attrs;
+  if (isNaN(xi)) {
+    return NaN;
+  } else {
+    return xi > 0 ? 1 : stepAttrs.alpha;
+  }
+});
+var stepConfig = {
+  kernelName: Step,
+  backendName: "cpu",
+  kernelFunc: step2
+};
+function stridedSlice2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides: strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  assertNotComplex(x, "stridedSlice");
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size2 = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size: size2 } });
+    result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeIntermediateTensorInfo(sliced);
+  } else {
+    const xBuf = backend2.bufferSync(x);
+    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);
+    result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);
+  }
+  return result;
+}
+var stridedSliceConfig = {
+  kernelName: StridedSlice,
+  backendName: "cpu",
+  kernelFunc: stridedSlice2
+};
+function stringNGrams2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
+  const { data, dataSplits } = inputs;
+  const $data = backend2.data.get(data.dataId).values;
+  const $dataSplits = backend2.data.get(dataSplits.dataId).values;
+  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
+  return [
+    backend2.makeTensorInfo([nGrams.length], "string", nGrams),
+    backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
+  ];
+}
+var stringNGramsConfig = {
+  kernelName: StringNGrams,
+  backendName: "cpu",
+  kernelFunc: stringNGrams2
+};
+function stringSplit2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { skipEmpty } = attrs;
+  const { input: input2, delimiter } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (input2.shape.length !== 1) {
+    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);
+  }
+  if (delimiter.shape.length !== 0) {
+    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
+  }
+  const $input = backend2.data.get(input2.dataId).values;
+  const $delimiter = backend2.data.get(delimiter.dataId).values[0];
+  const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);
+  const outputSize2 = values.length;
+  return [
+    backend2.makeTensorInfo([outputSize2, 2], "int32", indices),
+    backend2.makeTensorInfo([outputSize2], "string", values),
+    backend2.makeTensorInfo([2], "int32", new Int32Array(shape))
+  ];
+}
+var stringSplitConfig = {
+  kernelName: StringSplit,
+  backendName: "cpu",
+  kernelFunc: stringSplit2
+};
+function stringToHashBucketFast2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { numBuckets } = attrs;
+  const { input: input2 } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (numBuckets <= 0) {
+    throw new Error(`Number of buckets must be at least 1`);
+  }
+  const $input = backend2.data.get(input2.dataId).values;
+  const output = stringToHashBucketFastImpl($input, numBuckets);
+  return backend2.makeTensorInfo(input2.shape, "int32", output);
+}
+var stringToHashBucketFastConfig = {
+  kernelName: StringToHashBucketFast,
+  backendName: "cpu",
+  kernelFunc: stringToHashBucketFast2
+};
+var tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));
+var tanConfig = {
+  kernelName: Tan,
+  backendName: "cpu",
+  kernelFunc: tan2
+};
+var tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));
+var tanhConfig = {
+  kernelName: Tanh,
+  backendName: "cpu",
+  kernelFunc: tanh3
+};
+function tile3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reps } = attrs;
+  assertNotComplex(x, "tile");
+  const outBuf = tileImpl(backend2.bufferSync(x), reps);
+  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+}
+var tileConfig = {
+  kernelName: Tile,
+  backendName: "cpu",
+  kernelFunc: tile3
+};
+function topK(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  assertNotComplex(x, "topk");
+  const xVals = backend2.data.get(x.dataId).values;
+  const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);
+  return [
+    backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+    backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+  ];
+}
+var topKConfig = {
+  kernelName: TopK,
+  backendName: "cpu",
+  kernelFunc: topK
+};
+function transform2(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [batch, outHeight, outWidth, numChannels];
+  const strides2 = util_exports.computeStrides(image2.shape);
+  const batchStride = strides2[0];
+  const rowStride = strides2[1];
+  const colStride = strides2[2];
+  const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape));
+  outVals.fill(fillValue);
+  const imageVals = backend2.data.get(image2.dataId).values;
+  const transformVals = backend2.data.get(transforms.dataId).values;
+  for (let b = 0; b < batch; ++b) {
+    const transform6 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);
+    for (let outY = 0; outY < outHeight; ++outY) {
+      for (let outX = 0; outX < outWidth; ++outX) {
+        for (let channel = 0; channel < numChannels; ++channel) {
+          let val;
+          const projection = transform6[6] * outX + transform6[7] * outY + 1;
+          if (projection === 0) {
+            continue;
+          }
+          const inX = (transform6[0] * outX + transform6[1] * outY + transform6[2]) / projection;
+          const inY = (transform6[3] * outX + transform6[4] * outY + transform6[5]) / projection;
+          const x = mapCoord(inX, imageWidth, fillMode);
+          const y = mapCoord(inY, imageHeight, fillMode);
+          switch (interpolation) {
+            case "nearest":
+              val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
+              break;
+            case "bilinear":
+              val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
+              break;
+            default:
+              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);
+          }
+          const ind = b * batchStride + outY * rowStride + outX * colStride + channel;
+          outVals[ind] = val;
+        }
+      }
+    }
+    return backend2.makeTensorInfo(outShape, image2.dtype, outVals);
+  }
+  const dataId = backend2.write(outVals, outShape, image2.dtype);
+  return { dataId, shape: image2.shape, dtype: image2.dtype };
+}
+var transformConfig = {
+  kernelName: Transform,
+  backendName: "cpu",
+  kernelFunc: transform2
+};
+function mapCoord(outCoord, len, mode) {
+  switch (mode) {
+    case "reflect":
+      return mapCoordReflect(outCoord, len);
+    case "wrap":
+      return mapCoordWrap(outCoord, len);
+    case "nearest":
+      return mapCoordNearest(outCoord, len);
+    case "constant":
+    default:
+      return mapCoordConstant(outCoord, len);
+  }
+}
+function mapCoordReflect(outCoord, len) {
+  let inCoord = outCoord;
+  if (inCoord < 0) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz2 = 2 * len;
+      if (inCoord < sz2) {
+        inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
+      }
+      inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
+    }
+  } else if (inCoord > len - 1) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz2 = 2 * len;
+      inCoord -= sz2 * Math.trunc(inCoord / sz2);
+      if (inCoord >= len) {
+        inCoord = sz2 - inCoord - 1;
+      }
+    }
+  }
+  return util_exports.clamp(0, inCoord, len - 1);
+}
+function mapCoordWrap(outCoord, len) {
+  let inCoord = outCoord;
+  if (inCoord < 0) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz = len - 1;
+      inCoord += len * (Math.trunc(-inCoord / sz) + 1);
+    }
+  } else if (inCoord > len - 1) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz = len - 1;
+      inCoord -= len * Math.trunc(inCoord / sz);
+    }
+  }
+  return util_exports.clamp(0, inCoord, len - 1);
+}
+function mapCoordConstant(outCoord, len) {
+  return outCoord;
+}
+function mapCoordNearest(outCoord, len) {
+  return util_exports.clamp(0, outCoord, len - 1);
+}
+function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+  const ind = batch * batchStride + y * rowStride + x * colStride + channel;
+  if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
+    return imageVals[ind];
+  } else {
+    return fillValue;
+  }
+}
+function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+  const $y = Math.round(y);
+  const $x = Math.round(x);
+  return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
+}
+function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+  const yFloor = Math.floor(y);
+  const xFloor = Math.floor(x);
+  const yCeil = yFloor + 1;
+  const xCeil = xFloor + 1;
+  const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
+  const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
+  return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
+}
+function unique3(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { axis } = attrs;
+  const { x } = inputs;
+  assertNotComplex(x, "unique");
+  const values = backend2.data.get(x.dataId).values;
+  const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);
+  return [
+    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
+    backend2.makeTensorInfo([indices.length], "int32", indices)
+  ];
+}
+var uniqueConfig = {
+  kernelName: Unique,
+  backendName: "cpu",
+  kernelFunc: unique3
+};
+function unpack(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const valueRank = value.shape.length;
+  const num = value.shape[axis];
+  const outShape = new Array(valueRank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < valueRank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = value.shape[i2];
+    }
+  }
+  const begin = new Array(valueRank).fill(0);
+  const size2 = value.shape.slice();
+  size2[axis] = 1;
+  const res = new Array(num);
+  for (let i2 = 0; i2 < res.length; i2++) {
+    begin[axis] = i2;
+    const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size: size2 } });
+    res[i2] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
+    backend2.disposeIntermediateTensorInfo(tempRes);
+  }
+  return res;
+}
+var unpackConfig = {
+  kernelName: Unpack,
+  backendName: "cpu",
+  kernelFunc: unpack
+};
+function unsortedSegmentSum2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, segmentIds } = inputs;
+  const { numSegments } = attrs;
+  assertNotComplex(x, "unsortedSegmentSum");
+  const xRank = x.shape.length;
+  const segmentIdsRank = segmentIds.shape.length;
+  const res = [];
+  const intermediates = [];
+  const numIters = xRank - segmentIdsRank;
+  let $segmentIds = segmentIds;
+  for (let i2 = 0; i2 < numIters; ++i2) {
+    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i2 + 1 } });
+    $segmentIds = expanded;
+    intermediates.push(expanded);
+  }
+  for (let i2 = 0; i2 < numSegments; ++i2) {
+    const scalarValue = util_exports.createScalarValue(i2, "int32");
+    const segmentId = backend2.makeTensorInfo([], "int32", scalarValue);
+    const mask2 = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });
+    const maskCasted = cast3({ inputs: { x: mask2 }, backend: backend2, attrs: { dtype: "float32" } });
+    const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 });
+    const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });
+    res.push(sumTensorInfo);
+    intermediates.push(segmentId);
+    intermediates.push(mask2);
+    intermediates.push(maskCasted);
+    intermediates.push(mul2);
+    intermediates.push(sumTensorInfo);
+  }
+  const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });
+  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var unsortedSegmentSumConfig = {
+  kernelName: UnsortedSegmentSum,
+  backendName: "cpu",
+  kernelFunc: unsortedSegmentSum2
+};
+var kernelConfigs = [
+  _fusedMatMulConfig,
+  absConfig,
+  acosConfig,
+  acoshConfig,
+  addConfig,
+  addNConfig,
+  allConfig,
+  anyConfig,
+  argMaxConfig,
+  argMinConfig,
+  asinConfig,
+  asinhConfig,
+  atanConfig,
+  atan2Config,
+  atanhConfig,
+  avgPoolConfig,
+  avgPool3DConfig,
+  avgPool3DGradConfig2,
+  avgPoolGradConfig2,
+  batchMatMulConfig,
+  batchNormConfig,
+  batchToSpaceNDConfig,
+  bincountConfig,
+  broadcastArgsConfig,
+  castConfig,
+  ceilConfig,
+  clipByValueConfig,
+  complexConfig,
+  complexAbsConfig,
+  concatConfig,
+  conv2DConfig,
+  conv2DBackpropFilterConfig,
+  conv2DBackpropInputConfig,
+  conv3DConfig,
+  conv3DBackpropFilterV2Config,
+  conv3DBackpropInputV2Config,
+  cosConfig,
+  coshConfig,
+  cropAndResizeConfig,
+  cumprodConfig,
+  cumsumConfig,
+  denseBincountConfig,
+  depthToSpaceConfig,
+  depthwiseConv2dNativeConfig,
+  depthwiseConv2dNativeBackpropFilterConfig,
+  depthwiseConv2dNativeBackpropInputConfig,
+  diagConfig,
+  dilation2DConfig,
+  dilation2DBackpropFilterConfig,
+  dilation2DBackpropInputConfig,
+  einsumConfig,
+  eluConfig,
+  eluGradConfig2,
+  equalConfig,
+  erfConfig,
+  expConfig,
+  expandDimsConfig,
+  expm1Config,
+  fftConfig,
+  fillConfig,
+  flipLeftRightConfig,
+  floorConfig,
+  floorDivConfig,
+  fusedConv2DConfig,
+  fusedDepthwiseConv2DConfig,
+  gatherNdConfig,
+  gatherV2Config,
+  greaterConfig,
+  greaterEqualConfig,
+  identityConfig,
+  ifftConfig,
+  imagConfig,
+  isFiniteConfig,
+  isInfConfig,
+  isNaNConfig,
+  leakyReluConfig,
+  lessConfig,
+  lessEqualConfig,
+  linSpaceConfig,
+  logConfig,
+  log1pConfig,
+  logicalAndConfig,
+  logicalNotConfig,
+  logicalOrConfig,
+  LRNConfig,
+  LRNGradConfig,
+  maxConfig,
+  maximumConfig,
+  maxPoolConfig,
+  maxPool3DConfig,
+  maxPool3DGradConfig2,
+  maxPoolGradConfig2,
+  maxPoolWithArgmaxConfig,
+  meanConfig,
+  minConfig,
+  minimumConfig,
+  mirrorPadConfig,
+  modConfig,
+  multinomialConfig,
+  multiplyConfig,
+  negConfig,
+  nonMaxSuppressionV3Config,
+  nonMaxSuppressionV4Config,
+  nonMaxSuppressionV5Config,
+  notEqualConfig,
+  oneHotConfig,
+  onesLikeConfig,
+  packConfig,
+  padV2Config,
+  powConfig,
+  preluConfig,
+  prodConfig,
+  rangeConfig,
+  realConfig,
+  realDivConfig,
+  reciprocalConfig,
+  reluConfig,
+  relu6Config,
+  reshapeConfig,
+  resizeBilinearConfig,
+  resizeBilinearGradConfig2,
+  resizeNearestNeighborConfig,
+  resizeNearestNeighborGradConfig2,
+  reverseConfig,
+  rotateWithOffsetConfig,
+  roundConfig,
+  rsqrtConfig,
+  scatterNdConfig,
+  searchSortedConfig,
+  selectConfig,
+  seluConfig,
+  sigmoidConfig,
+  signConfig,
+  sinConfig,
+  sinhConfig,
+  sliceConfig,
+  softmaxConfig,
+  softplusConfig,
+  spaceToBatchNDConfig,
+  sparseFillEmptyRowsConfig,
+  sparseReshapeConfig,
+  sparseSegmentMeanConfig,
+  sparseSegmentSumConfig,
+  sparseToDenseConfig,
+  splitVConfig,
+  sqrtConfig,
+  squareConfig,
+  squaredDifferenceConfig,
+  stepConfig,
+  stridedSliceConfig,
+  stringNGramsConfig,
+  stringSplitConfig,
+  stringToHashBucketFastConfig,
+  subConfig,
+  sumConfig,
+  tanConfig,
+  tanhConfig,
+  tileConfig,
+  topKConfig,
+  transformConfig,
+  transposeConfig,
+  uniqueConfig,
+  unpackConfig,
+  unsortedSegmentSumConfig,
+  zerosLikeConfig
+];
+for (const kernelConfig of kernelConfigs) {
+  registerKernel(kernelConfig);
+}
+var webgl_util_exports = {};
+__export2(webgl_util_exports, {
+  assertNotComplex: () => assertNotComplex2,
+  bindCanvasToFramebuffer: () => bindCanvasToFramebuffer,
+  bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer,
+  bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler,
+  bindTextureUnit: () => bindTextureUnit,
+  bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute,
+  callAndCheck: () => callAndCheck,
+  canBeRepresented: () => canBeRepresented,
+  createFragmentShader: () => createFragmentShader,
+  createFramebuffer: () => createFramebuffer,
+  createProgram: () => createProgram,
+  createStaticIndexBuffer: () => createStaticIndexBuffer,
+  createStaticVertexBuffer: () => createStaticVertexBuffer,
+  createTexture: () => createTexture,
+  createVertexShader: () => createVertexShader,
+  getBatchDim: () => getBatchDim,
+  getExtensionOrThrow: () => getExtensionOrThrow,
+  getFramebufferErrorMessage: () => getFramebufferErrorMessage,
+  getMaxTexturesInShader: () => getMaxTexturesInShader,
+  getNumChannels: () => getNumChannels,
+  getProgramUniformLocation: () => getProgramUniformLocation,
+  getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow,
+  getRowsCols: () => getRowsCols,
+  getShapeAs3D: () => getShapeAs3D,
+  getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape,
+  getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion,
+  getWebGLErrorMessage: () => getWebGLErrorMessage,
+  getWebGLMaxTextureSize: () => getWebGLMaxTextureSize,
+  hasExtension: () => hasExtension,
+  isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture,
+  isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled,
+  isReshapeFree: () => isReshapeFree,
+  isWebGLFenceEnabled: () => isWebGLFenceEnabled,
+  isWebGLVersionEnabled: () => isWebGLVersionEnabled,
+  linkProgram: () => linkProgram,
+  logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog,
+  resetMaxTextureSize: () => resetMaxTextureSize,
+  resetMaxTexturesInShader: () => resetMaxTexturesInShader,
+  unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer,
+  unbindTextureUnit: () => unbindTextureUnit,
+  validateFramebuffer: () => validateFramebuffer,
+  validateProgram: () => validateProgram,
+  validateTextureSize: () => validateTextureSize
+});
+var contexts = {};
+var WEBGL_ATTRIBUTES = {
+  alpha: false,
+  antialias: false,
+  premultipliedAlpha: false,
+  preserveDrawingBuffer: false,
+  depth: false,
+  stencil: false,
+  failIfMajorPerformanceCaveat: true
+};
+function setWebGLContext(webGLVersion, gl) {
+  contexts[webGLVersion] = gl;
+}
+function getWebGLContext(webGLVersion, customCanvas) {
+  if (!(webGLVersion in contexts) || customCanvas != null) {
+    const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);
+    if (newCtx !== null) {
+      contexts[webGLVersion] = newCtx;
+    } else {
+      console.log("Could not get context for WebGL version", webGLVersion);
+      return null;
+    }
+  }
+  const gl = contexts[webGLVersion];
+  if (gl == null || gl.isContextLost()) {
+    delete contexts[webGLVersion];
+    return getWebGLContext(webGLVersion);
+  }
+  gl.disable(gl.DEPTH_TEST);
+  gl.disable(gl.STENCIL_TEST);
+  gl.disable(gl.BLEND);
+  gl.disable(gl.DITHER);
+  gl.disable(gl.POLYGON_OFFSET_FILL);
+  gl.disable(gl.SAMPLE_COVERAGE);
+  gl.enable(gl.SCISSOR_TEST);
+  gl.enable(gl.CULL_FACE);
+  gl.cullFace(gl.BACK);
+  return contexts[webGLVersion];
+}
+function createCanvas(webGLVersion) {
+  if (typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) {
+    return new OffscreenCanvas(300, 150);
+  } else if (typeof document !== "undefined") {
+    return document.createElement("canvas");
+  } else {
+    throw new Error("Cannot create a canvas in this context");
+  }
+}
+function getWebGLRenderingContext(webGLVersion, customCanvas) {
+  if (webGLVersion !== 1 && webGLVersion !== 2) {
+    throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
+  }
+  const canvas3 = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;
+  canvas3.addEventListener("webglcontextlost", (ev) => {
+    ev.preventDefault();
+    delete contexts[webGLVersion];
+  }, false);
+  if (webGLVersion === 1) {
+    return canvas3.getContext("webgl", WEBGL_ATTRIBUTES) || canvas3.getContext("experimental-webgl", WEBGL_ATTRIBUTES);
+  }
+  return canvas3.getContext("webgl2", WEBGL_ATTRIBUTES);
+}
+var PackingScheme;
+(function(PackingScheme2) {
+  PackingScheme2[PackingScheme2["DENSE"] = 0] = "DENSE";
+  PackingScheme2[PackingScheme2["SHARED_BATCH"] = 1] = "SHARED_BATCH";
+})(PackingScheme || (PackingScheme = {}));
+var TextureUsage;
+(function(TextureUsage2) {
+  TextureUsage2[TextureUsage2["RENDER"] = 0] = "RENDER";
+  TextureUsage2[TextureUsage2["UPLOAD"] = 1] = "UPLOAD";
+  TextureUsage2[TextureUsage2["PIXELS"] = 2] = "PIXELS";
+  TextureUsage2[TextureUsage2["DOWNLOAD"] = 3] = "DOWNLOAD";
+})(TextureUsage || (TextureUsage = {}));
+var PhysicalTextureType;
+(function(PhysicalTextureType2) {
+  PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
+  PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
+  PhysicalTextureType2[PhysicalTextureType2["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
+  PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
+  PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
+})(PhysicalTextureType || (PhysicalTextureType = {}));
+function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
+  return [columns, rows];
+}
+function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
+  return matrixSize * channelsPerTexture;
+}
+function getDenseTexShape(shape) {
+  const size2 = util_exports.sizeFromShape(shape);
+  const texelsNeeded = Math.ceil(size2 / 4);
+  return util_exports.sizeToSquarishShape(texelsNeeded);
+}
+function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
+  return [
+    Math.max(1, Math.ceil(columns / 2)),
+    Math.max(1, Math.ceil(rows / 2))
+  ];
+}
+function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
+  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+  return w * h * 4;
+}
+function getTextureConfig(gl, textureHalfFloatExtension) {
+  const glany = gl;
+  let internalFormatFloat;
+  let internalFormatHalfFloat;
+  let internalFormatPackedHalfFloat;
+  let internalFormatPackedFloat;
+  let textureFormatFloat;
+  let downloadTextureFormat;
+  let downloadUnpackNumChannels;
+  let defaultNumChannels;
+  let textureTypeHalfFloat;
+  let textureTypeFloat;
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    internalFormatFloat = glany.R32F;
+    internalFormatHalfFloat = glany.R16F;
+    internalFormatPackedHalfFloat = glany.RGBA16F;
+    internalFormatPackedFloat = glany.RGBA32F;
+    textureFormatFloat = glany.RED;
+    downloadUnpackNumChannels = 4;
+    defaultNumChannels = 1;
+    textureTypeHalfFloat = glany.HALF_FLOAT;
+    textureTypeFloat = glany.FLOAT;
+    downloadTextureFormat = glany.RGBA8;
+  } else {
+    internalFormatFloat = gl.RGBA;
+    internalFormatHalfFloat = gl.RGBA;
+    internalFormatPackedHalfFloat = gl.RGBA;
+    internalFormatPackedFloat = glany.RGBA;
+    textureFormatFloat = gl.RGBA;
+    downloadUnpackNumChannels = 4;
+    defaultNumChannels = 4;
+    textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;
+    textureTypeFloat = gl.FLOAT;
+    downloadTextureFormat = gl.RGBA;
+  }
+  return {
+    internalFormatFloat,
+    internalFormatHalfFloat,
+    internalFormatPackedHalfFloat,
+    internalFormatPackedFloat,
+    textureFormatFloat,
+    downloadTextureFormat,
+    downloadUnpackNumChannels,
+    defaultNumChannels,
+    textureTypeHalfFloat,
+    textureTypeFloat
+  };
+}
+function callAndCheck(gl, func2) {
+  const returnValue = func2();
+  if (env().getBool("DEBUG")) {
+    checkWebGLError(gl);
+  }
+  return returnValue;
+}
+function checkWebGLError(gl) {
+  const error = gl.getError();
+  if (error !== gl.NO_ERROR) {
+    throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error));
+  }
+}
+var MIN_FLOAT16 = 596e-10;
+var MAX_FLOAT16 = 65504;
+function canBeRepresented(num) {
+  if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {
+    return true;
+  }
+  return false;
+}
+function getWebGLErrorMessage(gl, status) {
+  switch (status) {
+    case gl.NO_ERROR:
+      return "NO_ERROR";
+    case gl.INVALID_ENUM:
+      return "INVALID_ENUM";
+    case gl.INVALID_VALUE:
+      return "INVALID_VALUE";
+    case gl.INVALID_OPERATION:
+      return "INVALID_OPERATION";
+    case gl.INVALID_FRAMEBUFFER_OPERATION:
+      return "INVALID_FRAMEBUFFER_OPERATION";
+    case gl.OUT_OF_MEMORY:
+      return "OUT_OF_MEMORY";
+    case gl.CONTEXT_LOST_WEBGL:
+      return "CONTEXT_LOST_WEBGL";
+    default:
+      return `Unknown error code ${status}`;
+  }
+}
+function getExtensionOrThrow(gl, extensionName) {
+  return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.');
+}
+function createVertexShader(gl, vertexShaderSource) {
+  const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
+  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));
+  callAndCheck(gl, () => gl.compileShader(vertexShader));
+  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
+    console.log(gl.getShaderInfoLog(vertexShader));
+    throw new Error("Failed to compile vertex shader.");
+  }
+  return vertexShader;
+}
+function createFragmentShader(gl, fragmentShaderSource) {
+  const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
+  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));
+  callAndCheck(gl, () => gl.compileShader(fragmentShader));
+  if (env().get("ENGINE_COMPILE_ONLY")) {
+    return fragmentShader;
+  }
+  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
+    logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
+    throw new Error("Failed to compile fragment shader.");
+  }
+  return fragmentShader;
+}
+var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
+function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
+  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
+  if (lineNumberRegexResult == null) {
+    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);
+    console.log(shaderSource);
+    return;
+  }
+  const lineNumber = +lineNumberRegexResult[1];
+  const shaderLines = shaderSource.split("\n");
+  const pad3 = shaderLines.length.toString().length + 2;
+  const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);
+  let maxLineLength = 0;
+  for (let i2 = 0; i2 < linesWithLineNumbers.length; i2++) {
+    maxLineLength = Math.max(linesWithLineNumbers[i2].length, maxLineLength);
+  }
+  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
+  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
+  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);
+  console.log(beforeErrorLines.join("\n"));
+  console.log(shaderInfoLog.split("\n")[0]);
+  console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717");
+  console.log(afterErrorLines.join("\n"));
+}
+function createProgram(gl) {
+  return throwIfNull(gl, () => gl.createProgram(), "Unable to create WebGLProgram.");
+}
+function linkProgram(gl, program) {
+  callAndCheck(gl, () => gl.linkProgram(program));
+  if (env().get("ENGINE_COMPILE_ONLY")) {
+    return;
+  }
+  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
+    console.log(gl.getProgramInfoLog(program));
+    throw new Error("Failed to link vertex and fragment shaders.");
+  }
+}
+function validateProgram(gl, program) {
+  callAndCheck(gl, () => gl.validateProgram(program));
+  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
+    console.log(gl.getProgramInfoLog(program));
+    throw new Error("Shader program validation failed.");
+  }
+}
+function createStaticVertexBuffer(gl, data) {
+  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
+  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
+  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));
+  return buffer2;
+}
+function createStaticIndexBuffer(gl, data) {
+  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
+  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));
+  callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));
+  return buffer2;
+}
+function getNumChannels() {
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    return 1;
+  }
+  return 4;
+}
+function createTexture(gl) {
+  return throwIfNull(gl, () => gl.createTexture(), "Unable to create WebGLTexture.");
+}
+function validateTextureSize(width, height) {
+  const maxTextureSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
+  if (width <= 0 || height <= 0) {
+    const requested = `[${width}x${height}]`;
+    throw new Error("Requested texture size " + requested + " is invalid.");
+  }
+  if (width > maxTextureSize || height > maxTextureSize) {
+    const requested = `[${width}x${height}]`;
+    const max7 = `[${maxTextureSize}x${maxTextureSize}]`;
+    throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max7 + ".");
+  }
+}
+function createFramebuffer(gl) {
+  return throwIfNull(gl, () => gl.createFramebuffer(), "Unable to create WebGLFramebuffer.");
+}
+function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
+  const loc = gl.getAttribLocation(program, attribute);
+  if (loc === -1) {
+    return false;
+  }
+  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
+  callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));
+  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));
+  return true;
+}
+function bindTextureUnit(gl, texture, textureUnit) {
+  validateTextureUnit(gl, textureUnit);
+  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
+}
+function unbindTextureUnit(gl, textureUnit) {
+  validateTextureUnit(gl, textureUnit);
+  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+}
+function getProgramUniformLocationOrThrow(gl, program, uniformName) {
+  return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.');
+}
+function getProgramUniformLocation(gl, program, uniformName) {
+  return gl.getUniformLocation(program, uniformName);
+}
+function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {
+  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));
+  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));
+}
+function bindCanvasToFramebuffer(gl) {
+  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
+  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));
+  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));
+}
+function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
+  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
+  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));
+}
+function unbindColorTextureFromFramebuffer(gl, framebuffer) {
+  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
+  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));
+}
+function validateFramebuffer(gl) {
+  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
+  if (status !== gl.FRAMEBUFFER_COMPLETE) {
+    throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status));
+  }
+}
+function getFramebufferErrorMessage(gl, status) {
+  switch (status) {
+    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
+      return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
+    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
+      return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
+    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
+      return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
+    case gl.FRAMEBUFFER_UNSUPPORTED:
+      return "FRAMEBUFFER_UNSUPPORTED";
+    default:
+      return `unknown error ${status}`;
+  }
+}
+function throwIfNull(gl, returnTOrNull, failureMessage) {
+  const tOrNull = callAndCheck(gl, () => returnTOrNull());
+  if (tOrNull == null) {
+    throw new Error(failureMessage);
+  }
+  return tOrNull;
+}
+function validateTextureUnit(gl, textureUnit) {
+  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
+  const glTextureUnit = textureUnit + gl.TEXTURE0;
+  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
+    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;
+    throw new Error(`textureUnit must be in ${textureUnitRange}.`);
+  }
+}
+function getBatchDim(shape, dimsToSkip = 2) {
+  return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
+}
+function getRowsCols(shape) {
+  if (shape.length === 0) {
+    throw Error("Cannot get rows and columns of an empty shape array.");
+  }
+  return [
+    shape.length > 1 ? shape[shape.length - 2] : 1,
+    shape[shape.length - 1]
+  ];
+}
+function getShapeAs3D(shape) {
+  let shapeAs3D = [1, 1, 1];
+  const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;
+  if (!isScalar) {
+    shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];
+  }
+  return shapeAs3D;
+}
+function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
+  let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
+  if (isPacked) {
+    maxTexSize = maxTexSize * 2;
+    logShape = logShape.map((d, i2) => i2 >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i2]) : logShape[i2]);
+    if (logShape.length === 1) {
+      logShape = [2, logShape[0]];
+    }
+  }
+  if (logShape.length !== 2) {
+    const squeezeResult = util_exports.squeezeShape(logShape);
+    logShape = squeezeResult.newShape;
+  }
+  let size2 = util_exports.sizeFromShape(logShape);
+  if (logShape.length <= 1 && size2 <= maxTexSize) {
+    return [1, size2];
+  } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {
+    return logShape;
+  } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {
+    return [logShape[0] * logShape[1], logShape[2]];
+  } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {
+    return [logShape[0], logShape[1] * logShape[2]];
+  } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {
+    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
+  } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
+    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
+  } else {
+    if (isPacked) {
+      const batchDim = getBatchDim(logShape);
+      let rows = 2, cols = 2;
+      if (logShape.length) {
+        [rows, cols] = getRowsCols(logShape);
+      }
+      size2 = batchDim * (rows / 2) * (cols / 2);
+      return util_exports.sizeToSquarishShape(size2).map((d) => d * 2);
+    }
+    return util_exports.sizeToSquarishShape(size2);
+  }
+}
+function isEven(n) {
+  return n % 2 === 0;
+}
+function isReshapeFree(shape1, shape2) {
+  shape1 = shape1.slice(-2);
+  shape2 = shape2.slice(-2);
+  if (util_exports.arraysEqual(shape1, shape2)) {
+    return true;
+  }
+  if (!shape1.length || !shape2.length) {
+    return true;
+  }
+  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {
+    return true;
+  }
+  if (shape1.length !== shape2.length) {
+    const shape1Cols = shape1.slice(-1)[0];
+    const shape2Cols = shape2.slice(-1)[0];
+    if (shape1Cols === shape2Cols) {
+      return true;
+    }
+    if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {
+      return true;
+    }
+  }
+  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
+}
+var MAX_TEXTURE_SIZE;
+var MAX_TEXTURES_IN_SHADER;
+function getWebGLMaxTextureSize(webGLVersion) {
+  if (MAX_TEXTURE_SIZE == null) {
+    const gl = getWebGLContext(webGLVersion);
+    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
+  }
+  return MAX_TEXTURE_SIZE;
+}
+function resetMaxTextureSize() {
+  MAX_TEXTURE_SIZE = null;
+}
+function resetMaxTexturesInShader() {
+  MAX_TEXTURES_IN_SHADER = null;
+}
+function getMaxTexturesInShader(webGLVersion) {
+  if (MAX_TEXTURES_IN_SHADER == null) {
+    const gl = getWebGLContext(webGLVersion);
+    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
+  }
+  return Math.min(16, MAX_TEXTURES_IN_SHADER);
+}
+function getWebGLDisjointQueryTimerVersion(webGLVersion) {
+  if (webGLVersion === 0) {
+    return 0;
+  }
+  let queryTimerVersion;
+  const gl = getWebGLContext(webGLVersion);
+  if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) {
+    queryTimerVersion = 2;
+  } else if (hasExtension(gl, "EXT_disjoint_timer_query")) {
+    queryTimerVersion = 1;
+  } else {
+    queryTimerVersion = 0;
+  }
+  return queryTimerVersion;
+}
+function hasExtension(gl, extensionName) {
+  const ext = gl.getExtension(extensionName);
+  return ext != null;
+}
+function isWebGLVersionEnabled(webGLVersion) {
+  try {
+    const gl = getWebGLContext(webGLVersion);
+    if (gl != null) {
+      return true;
+    }
+  } catch (e2) {
+    console.log("Error when getting WebGL context: ", e2);
+    return false;
+  }
+  return false;
+}
+function isCapableOfRenderingToFloatTexture(webGLVersion) {
+  if (webGLVersion === 0) {
+    return false;
+  }
+  const gl = getWebGLContext(webGLVersion);
+  if (webGLVersion === 1) {
+    if (!hasExtension(gl, "OES_texture_float")) {
+      return false;
+    }
+  } else {
+    if (!hasExtension(gl, "EXT_color_buffer_float")) {
+      return false;
+    }
+  }
+  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
+  return isFrameBufferComplete;
+}
+function isDownloadFloatTextureEnabled(webGLVersion) {
+  if (webGLVersion === 0) {
+    return false;
+  }
+  const gl = getWebGLContext(webGLVersion);
+  if (webGLVersion === 1) {
+    if (!hasExtension(gl, "OES_texture_float")) {
+      return false;
+    }
+    if (!hasExtension(gl, "WEBGL_color_buffer_float")) {
+      return false;
+    }
+  } else {
+    if (hasExtension(gl, "EXT_color_buffer_float")) {
+      return createFloatTextureAndBindToFramebuffer(gl);
+    }
+    const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
+    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
+      const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
+      return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
+    }
+    return false;
+  }
+  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
+  return isFrameBufferComplete;
+}
+function createFloatTextureAndBindToFramebuffer(gl) {
+  const texConfig = getTextureConfig(gl);
+  const texture = gl.createTexture();
+  gl.bindTexture(gl.TEXTURE_2D, texture);
+  const width = 1;
+  const height = 1;
+  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
+  const frameBuffer = gl.createFramebuffer();
+  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+  gl.bindTexture(gl.TEXTURE_2D, null);
+  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+  gl.deleteTexture(texture);
+  gl.deleteFramebuffer(frameBuffer);
+  return isFrameBufferComplete;
+}
+function createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {
+  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);
+  const texture = gl.createTexture();
+  gl.bindTexture(gl.TEXTURE_2D, texture);
+  const width = 1;
+  const height = 1;
+  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
+  const frameBuffer = gl.createFramebuffer();
+  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+  gl.bindTexture(gl.TEXTURE_2D, null);
+  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+  gl.deleteTexture(texture);
+  gl.deleteFramebuffer(frameBuffer);
+  return isFrameBufferComplete;
+}
+function isWebGLFenceEnabled(webGLVersion) {
+  if (webGLVersion !== 2) {
+    return false;
+  }
+  const gl = getWebGLContext(webGLVersion);
+  const isEnabled = gl.fenceSync != null;
+  return isEnabled;
+}
+function assertNotComplex2(tensor2, opName) {
+  if (!Array.isArray(tensor2)) {
+    tensor2 = [tensor2];
+  }
+  tensor2.forEach((t2) => {
+    if (t2 != null) {
+      util_exports.assert(t2.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`);
+    }
+  });
+}
+var ENV5 = env();
+ENV5.registerFlag("HAS_WEBGL", () => ENV5.getNumber("WEBGL_VERSION") > 0);
+ENV5.registerFlag("WEBGL_VERSION", () => {
+  if (isWebGLVersionEnabled(2)) {
+    return 2;
+  } else if (isWebGLVersionEnabled(1)) {
+    return 1;
+  }
+  return 0;
+});
+ENV5.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
+ENV5.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV5.get("WEBGL_VERSION") === 2);
+ENV5.registerFlag("WEBGL_CPU_FORWARD", () => true);
+ENV5.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
+ENV5.registerFlag("WEBGL_PACK", () => ENV5.getBool("HAS_WEBGL"));
+ENV5.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_CLIP", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_REDUCE", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_CONV_IM2COL", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
+  const webGLVersion = ENV5.getNumber("WEBGL_VERSION");
+  if (webGLVersion === 0) {
+    return 0;
+  }
+  return getWebGLDisjointQueryTimerVersion(webGLVersion);
+});
+ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV5.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile());
+ENV5.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => {
+  return ENV5.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV5.getBool("WEBGL_RENDER_FLOAT32_CAPABLE");
+});
+ENV5.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => {
+  const useUniforms = ENV5.getBool("WEBGL_RENDER_FLOAT32_ENABLED");
+  return useUniforms ? 4 : 0;
+});
+ENV5.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => {
+  return -1;
+}, (threshold3) => {
+  if (threshold3 < 0 && threshold3 !== -1) {
+    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);
+  }
+});
+ENV5.registerFlag("WEBGL_FLUSH_THRESHOLD", () => {
+  return device_util_exports.isMobile() ? 1 : -1;
+}, (threshold3) => {
+  if (threshold3 < 0 && threshold3 !== -1) {
+    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);
+  }
+});
+ENV5.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
+ENV5.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
+ENV5.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
+ENV5.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
+function getGlslDifferences() {
+  let version9;
+  let attribute;
+  let varyingVs;
+  let varyingFs;
+  let texture2D;
+  let output;
+  let defineOutput;
+  let defineSpecialNaN;
+  let defineSpecialInf;
+  let defineRound;
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    version9 = "#version 300 es";
+    attribute = "in";
+    varyingVs = "out";
+    varyingFs = "in";
+    texture2D = "texture";
+    output = "outputColor";
+    defineOutput = "out vec4 outputColor;";
+    defineSpecialNaN = `
+      bool isnan_custom(float val) {
+        uint floatToUint = floatBitsToUint(val);
+        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
+      }
+
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan_custom(val.x),
+          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
+      }
+
+      #define isnan(value) isnan_custom(value)
+    `;
+    defineSpecialInf = ``;
+    defineRound = `
+      #define round(value) newRound(value)
+      int newRound(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 newRound(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `;
+  } else {
+    version9 = "";
+    attribute = "attribute";
+    varyingVs = "varying";
+    varyingFs = "varying";
+    texture2D = "texture2D";
+    output = "gl_FragColor";
+    defineOutput = "";
+    defineSpecialNaN = `
+      #define isnan(value) isnan_custom(value)
+      bool isnan_custom(float val) {
+        return (val > 0. || val < 1. || val == 0.) ? false : true;
+      }
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
+      }
+    `;
+    defineSpecialInf = `
+      uniform float INFINITY;
+
+      bool isinf(float val) {
+        return abs(val) == INFINITY;
+      }
+      bvec4 isinf(vec4 val) {
+        return equal(abs(val), vec4(INFINITY));
+      }
+    `;
+    defineRound = `
+      int round(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 round(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `;
+  }
+  return {
+    version: version9,
+    attribute,
+    varyingVs,
+    varyingFs,
+    texture2D,
+    output,
+    defineOutput,
+    defineSpecialNaN,
+    defineSpecialInf,
+    defineRound
+  };
+}
+function getLogicalCoordinatesFromFlatIndex(coords3, shape, index2 = "index") {
+  const strides2 = util_exports.computeStrides(shape);
+  return strides2.map((stride, i2) => {
+    const line1 = `int ${coords3[i2]} = ${index2} / ${stride}`;
+    const line2 = i2 === strides2.length - 1 ? `int ${coords3[i2 + 1]} = ${index2} - ${coords3[i2]} * ${stride}` : `index -= ${coords3[i2]} * ${stride}`;
+    return `${line1}; ${line2};`;
+  }).join("");
+}
+function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index2 = "index") {
+  const strides2 = util_exports.computeStrides(shape);
+  return strides2.map((_, i2) => {
+    const line1 = `int ${coords3[i2]} = ${index2} / outShapeStrides[${i2}]`;
+    const line2 = i2 === strides2.length - 1 ? `int ${coords3[i2 + 1]} = ${index2} - ${coords3[i2]} * outShapeStrides[${i2}]` : `index -= ${coords3[i2]} * outShapeStrides[${i2}]`;
+    return `${line1}; ${line2};`;
+  }).join("");
+}
+function symbolicallyComputeStrides(indicesArr, variableName) {
+  const numCoords = indicesArr.length;
+  const shape = indicesArr.map((d) => `${variableName}[${d}]`);
+  const strides2 = new Array(numCoords - 1);
+  strides2[numCoords - 2] = shape[numCoords - 1];
+  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
+    strides2[i2] = `(${strides2[i2 + 1]} * ${shape[i2 + 1]})`;
+  }
+  return strides2;
+}
+function getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index2 = "index") {
+  const indicesArray = coords3.map((_, i2) => i2);
+  const strides2 = symbolicallyComputeStrides(indicesArray, variableName);
+  return strides2.map((_, i2) => {
+    const line1 = `int ${coords3[i2]} = ${index2} / ${strides2[i2]}`;
+    const line2 = i2 === strides2.length - 1 ? `int ${coords3[i2 + 1]} = ${index2} - ${coords3[i2]} * ${strides2[i2]}` : `index -= ${coords3[i2]} * ${strides2[i2]}`;
+    return `${line1}; ${line2};`;
+  }).join("");
+}
+function getFlatIndexFrom3D(shape) {
+  const strides2 = util_exports.computeStrides(shape).map((d) => d.toString());
+  return `
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * ${strides2[0]} + coords.y * ${strides2[1]} + coords.z;
+  }
+`;
+}
+function getFlatIndexFrom3DOutput() {
+  return `
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
+  }
+`;
+}
+var ENCODE_FLOAT_SNIPPET = `
+  const float FLOAT_MAX = 1.70141184e38;
+  const float FLOAT_MIN = 1.17549435e-38;
+
+  lowp vec4 encode_float(highp float v) {
+    if (isnan(v)) {
+      return vec4(255, 255, 255, 255);
+    }
+
+    highp float av = abs(v);
+
+    if(av < FLOAT_MIN) {
+      return vec4(0.0, 0.0, 0.0, 0.0);
+    } else if(v > FLOAT_MAX) {
+      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
+    } else if(v < -FLOAT_MAX) {
+      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
+    }
+
+    highp vec4 c = vec4(0,0,0,0);
+
+    highp float e = floor(log2(av));
+    highp float m = exp2(fract(log2(av))) - 1.0;
+
+    c[2] = floor(128.0 * m);
+    m -= c[2] / 128.0;
+    c[1] = floor(32768.0 * m);
+    m -= c[1] / 32768.0;
+    c[0] = floor(8388608.0 * m);
+
+    highp float ebias = e + 127.0;
+    c[3] = floor(ebias / 2.0);
+    ebias -= c[3] * 2.0;
+    c[2] += floor(ebias) * 128.0;
+
+    c[3] += 128.0 * step(0.0, -v);
+
+    return c / 255.0;
+  }
+`;
+var { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;
+function makeShader(inputsInfo, outputShape, program) {
+  const prefixSnippets = [];
+  inputsInfo.forEach((x) => {
+    const size2 = util_exports.sizeFromShape(x.shapeInfo.logicalShape);
+    if (x.shapeInfo.isUniform) {
+      prefixSnippets.push(`uniform float ${x.name}${size2 > 1 ? `[${size2}]` : ""};`);
+    } else {
+      prefixSnippets.push(`uniform sampler2D ${x.name};`);
+      prefixSnippets.push(`uniform int offset${x.name};`);
+    }
+    if (program.enableShapeUniforms) {
+      const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);
+      switch (uniformShape.length) {
+        case 1:
+          prefixSnippets.push(`uniform int ${x.name}Shape;`);
+          break;
+        case 2:
+          prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);
+          break;
+        case 3:
+          prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);
+          break;
+        case 4:
+          prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);
+          break;
+        default:
+          break;
+      }
+      prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);
+    }
+  });
+  if (program.enableShapeUniforms) {
+    switch (outputShape.logicalShape.length) {
+      case 1:
+        prefixSnippets.push(`uniform int outShape;`);
+        break;
+      case 2:
+        prefixSnippets.push(`uniform ivec2 outShape;`);
+        prefixSnippets.push(`uniform int outShapeStrides;`);
+        break;
+      case 3:
+        prefixSnippets.push(`uniform ivec3 outShape;`);
+        prefixSnippets.push(`uniform ivec2 outShapeStrides;`);
+        break;
+      case 4:
+        prefixSnippets.push(`uniform ivec4 outShape;`);
+        prefixSnippets.push(`uniform ivec3 outShapeStrides;`);
+        break;
+      default:
+        break;
+    }
+    prefixSnippets.push(`uniform ivec2 outTexShape;`);
+  }
+  if (program.customUniforms) {
+    program.customUniforms.forEach((d) => {
+      prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
+    });
+  }
+  const inputPrefixSnippet = prefixSnippets.join("\n");
+  const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join("\n");
+  const outTexShape = outputShape.texShape;
+  const glsl = getGlslDifferences();
+  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
+  let outputSamplingSnippet;
+  let floatTextureSetOutputSnippet;
+  let shaderPrefix = getShaderPrefix(glsl);
+  if (outputShape.isPacked) {
+    outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
+    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
+  } else {
+    outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
+    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
+  }
+  if (program.packedInputs) {
+    shaderPrefix += SHADER_PACKED_PREFIX;
+  }
+  const source = [
+    shaderPrefix,
+    floatTextureSampleSnippet,
+    floatTextureSetOutputSnippet,
+    inputPrefixSnippet,
+    outputSamplingSnippet,
+    inputSamplingSnippet,
+    program.userCode
+  ].join("\n");
+  return source;
+}
+function getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {
+  const shape = inInfo.shapeInfo.logicalShape;
+  switch (shape.length) {
+    case 0:
+      return getSamplerScalar(inInfo, enableShapeUniforms);
+    case 1:
+      return getSampler1D(inInfo, enableShapeUniforms);
+    case 2:
+      return getSampler2D(inInfo, enableShapeUniforms);
+    case 3:
+      return getSampler3D(inInfo, enableShapeUniforms);
+    case 4:
+      return getSampler4D(inInfo, enableShapeUniforms);
+    case 5:
+      return getSampler5D(inInfo);
+    case 6:
+      return getSampler6D(inInfo);
+    default:
+      throw new Error(`${shape.length}-D input sampling is not yet supported`);
+  }
+}
+function getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {
+  const shape = inInfo.shapeInfo.logicalShape;
+  switch (shape.length) {
+    case 0:
+      return getPackedSamplerScalar(inInfo);
+    case 1:
+      return getPackedSampler1D(inInfo, enableShapeUniforms);
+    case 2:
+      return getPackedSampler2D(inInfo, enableShapeUniforms);
+    case 3:
+      return getPackedSampler3D(inInfo, enableShapeUniforms);
+    default:
+      return getPackedSamplerND(inInfo, enableShapeUniforms);
+  }
+}
+function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {
+  let res = "";
+  if (usesPackedTextures) {
+    res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);
+  } else {
+    res += getSamplerFromInInfo(inInfo, enableShapeUniforms);
+  }
+  const inShape = inInfo.shapeInfo.logicalShape;
+  const outShape = outShapeInfo.logicalShape;
+  if (inShape.length <= outShape.length) {
+    if (usesPackedTextures) {
+      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
+    } else {
+      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
+    }
+  }
+  return res;
+}
+function getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
+  switch (outShape.length) {
+    case 0:
+      return getOutputScalarCoords();
+    case 1:
+      return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 2:
+      return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 3:
+      return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);
+    default:
+      return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);
+  }
+}
+function getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
+  switch (outShape.length) {
+    case 0:
+      return getOutputScalarCoords();
+    case 1:
+      return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 2:
+      return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 3:
+      return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 4:
+      return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 5:
+      return getOutput5DCoords(outShape, outTexShape);
+    case 6:
+      return getOutput6DCoords(outShape, outTexShape);
+    default:
+      throw new Error(`${outShape.length}-D output sampling is not yet supported`);
+  }
+}
+function getFloatTextureSampleSnippet(glsl) {
+  return `
+    float sampleTexture(sampler2D textureSampler, vec2 uv) {
+      return ${glsl.texture2D}(textureSampler, uv).r;
+    }
+  `;
+}
+function getFloatTextureSetRSnippet(glsl) {
+  return `
+    void setOutput(float val) {
+      ${glsl.output} = vec4(val, 0, 0, 0);
+    }
+  `;
+}
+function getFloatTextureSetRGBASnippet(glsl) {
+  return `
+    void setOutput(vec4 val) {
+      ${glsl.output} = val;
+    }
+  `;
+}
+function getShaderPrefix(glsl) {
+  const SHADER_PREFIX = `${glsl.version}
+    precision highp float;
+    precision highp int;
+    precision highp sampler2D;
+    ${glsl.varyingFs} vec2 resultUV;
+    ${glsl.defineOutput}
+    const vec2 halfCR = vec2(0.5, 0.5);
+
+    struct ivec5
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+    };
+
+    struct ivec6
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+      int v;
+    };
+
+    uniform float NAN;
+    ${glsl.defineSpecialNaN}
+    ${glsl.defineSpecialInf}
+    ${glsl.defineRound}
+
+    int imod(int x, int y) {
+      return x - y * (x / y);
+    }
+
+    int idiv(int a, int b, float sign) {
+      int res = a / b;
+      int mod = imod(a, b);
+      if (sign < 0. && mod != 0) {
+        res -= 1;
+      }
+      return res;
+    }
+
+    //Based on the work of Dave Hoskins
+    //https://www.shadertoy.com/view/4djSRW
+    #define HASHSCALE1 443.8975
+    float random(float seed){
+      vec2 p = resultUV * seed;
+      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
+      p3 += dot(p3, p3.yzx + 19.19);
+      return fract((p3.x + p3.y) * p3.z);
+    }
+
+    ${SAMPLE_1D_SNIPPET}
+    ${SAMPLE_2D_SNIPPET}
+    ${SAMPLE_3D_SNIPPET}
+  `;
+  return SHADER_PREFIX;
+}
+var SAMPLE_1D_SNIPPET = `
+vec2 uvFromFlat(int texNumR, int texNumC, int index) {
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
+  int texelIndex = index / 2;
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`;
+var SAMPLE_2D_SNIPPET = `
+vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
+  int texNumC, int row, int col) {
+  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`;
+var SAMPLE_3D_SNIPPET = `
+vec2 packedUVfrom3D(int texNumR, int texNumC,
+    int texelsInBatch, int texelsInLogicalRow, int b,
+    int row, int col) {
+  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`;
+var SHADER_PACKED_PREFIX = `
+  float getChannel(vec4 frag, vec2 innerDims) {
+    vec2 modCoord = mod(innerDims, 2.);
+    return modCoord.x == 0. ?
+      (modCoord.y == 0. ? frag.r : frag.g) :
+      (modCoord.y == 0. ? frag.b : frag.a);
+  }
+  float getChannel(vec4 frag, int dim) {
+    float modCoord = mod(float(dim), 2.);
+    return modCoord == 0. ? frag.r : frag.g;
+  }
+`;
+function getOutputScalarCoords() {
+  return `
+    int getOutputCoords() {
+      return 0;
+    }
+  `;
+}
+function getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  if (packedTexShape[0] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
+      }
+    `;
+  }
+  if (packedTexShape[1] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    int getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
+    }
+  `;
+  }
+  return `
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
+    }
+  `;
+}
+function getOutput1DCoords(shape, texShape, enableShapeUniforms) {
+  if (texShape[0] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return int(resultUV.x * float(outTexShape[1]));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return int(resultUV.x * ${texShape[1]}.0);
+      }
+    `;
+  }
+  if (texShape[1] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return int(resultUV.y * float(outTexShape[0]));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return int(resultUV.y * ${texShape[0]}.0);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(outTexShape[0], outTexShape[1]));
+      return resTexRC.x * outTexShape[1] + resTexRC.y;
+    }
+  `;
+  }
+  return `
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      return resTexRC.x * ${texShape[1]} + resTexRC.y;
+    }
+  `;
+}
+function getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    return `
+    ivec3 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
+      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+
+      int b = index / texelsInBatch;
+      index -= b * texelsInBatch;
+
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const texelsInLogicalRow = Math.ceil(shape[2] / 2);
+  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
+  return `
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
+
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `;
+}
+function getOutput3DCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], shape);
+    return `
+  ivec3 getOutputCoords() {
+    ivec2 resTexRC = ivec2(resultUV.yx *
+                           vec2(outTexShape[0], outTexShape[1]));
+    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+    ${coordsFromIndexSnippet2}
+    return ivec3(r, c, d);
+  }
+`;
+  }
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
+  return `
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
+      return ivec3(r, c, d);
+    }
+  `;
+}
+function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    return `
+    ivec4 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+
+      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
+      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
+      int texelsInBatchN = texelsInBatch * outShape[1];
+
+      int b2 = index / texelsInBatchN;
+      index -= b2 * texelsInBatchN;
+
+      int b = index / texelsInBatch;
+      index -= b * texelsInBatch;
+
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec4(b2, b, r, c);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
+  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
+  let texelsInBatchN = texelsInBatch;
+  let batches = ``;
+  let coords3 = "b, r, c";
+  for (let b = 2; b < shape.length - 1; b++) {
+    texelsInBatchN *= shape[shape.length - b - 1];
+    batches = `
+      int b${b} = index / ${texelsInBatchN};
+      index -= b${b} * ${texelsInBatchN};
+    ` + batches;
+    coords3 = `b${b}, ` + coords3;
+  }
+  return `
+    ivec${shape.length} getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+
+      ${batches}
+
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
+
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec${shape.length}(${coords3});
+    }
+  `;
+}
+function getOutput4DCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d", "d2"], shape);
+    return `
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(outTexShape[0], outTexShape[1]));
+      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+      ${coordsFromIndexSnippet2}
+      return ivec4(r, c, d, d2);
+    }
+  `;
+  }
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2"], shape);
+  return `
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
+      return ivec4(r, c, d, d2);
+    }
+  `;
+}
+function getOutput5DCoords(shape, texShape) {
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3"], shape);
+  return `
+    ivec5 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
+                             ${texShape[1]}));
+
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+
+      ${coordsFromIndexSnippet}
+
+      ivec5 outShape = ivec5(r, c, d, d2, d3);
+      return outShape;
+    }
+  `;
+}
+function getOutput6DCoords(shape, texShape) {
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3", "d4"], shape);
+  return `
+    ivec6 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+
+      ${coordsFromIndexSnippet}
+
+      ivec6 result = ivec6(r, c, d, d2, d3, d4);
+      return result;
+    }
+  `;
+}
+function getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  if (util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      }
+    `;
+  }
+  const texelsInLogicalRow = Math.ceil(shape[1] / 2);
+  if (enableShapeUniforms) {
+    return `
+    ivec2 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec2(r, c);
+    }
+  `;
+  }
+  return `
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec2(r, c);
+    }
+  `;
+}
+function getOutput2DCoords(shape, texShape, enableShapeUniforms) {
+  if (util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
+      }
+    `;
+  }
+  if (shape[1] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(outTexShape[0], outTexShape[1]));
+        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `;
+  }
+  if (shape[0] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(outTexShape[0], outTexShape[1]));
+        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(outTexShape[0], outTexShape[1]));
+      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+      int r = index / outShape[1];
+      int c = index - r * outShape[1];
+      return ivec2(r, c);
+    }
+  `;
+  }
+  return `
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      int r = index / ${shape[1]};
+      int c = index - r * ${shape[1]};
+      return ivec2(r, c);
+    }
+  `;
+}
+function getFlatOffsetUniformName(texName) {
+  return `offset${texName}`;
+}
+function getPackedSamplerScalar(inputInfo) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const glsl = getGlslDifferences();
+  return `
+    vec4 ${funcName}() {
+      return ${glsl.texture2D}(${texName}, halfCR);
+    }
+  `;
+}
+function getSamplerScalar(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  if (inputInfo.shapeInfo.isUniform) {
+    return `float ${funcName}() {return ${texName};}`;
+  }
+  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;
+  if (texNumR === 1 && texNumC === 1) {
+    return `
+      float ${funcName}() {
+        return sampleTexture(${texName}, halfCR);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;
+  return `
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getPackedSampler1D(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  const glsl = getGlslDifferences();
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int index) {
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      vec2 uv = packedUVfrom1D(
+        packedTexShape[0], packedTexShape[1], index);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  return `
+    vec4 ${funcName}(int index) {
+      vec2 uv = packedUVfrom1D(
+        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler1D(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int index) {
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const texShape = inputInfo.shapeInfo.texShape;
+  const tNumR = texShape[0];
+  const tNumC = texShape[1];
+  if (tNumC === 1 && tNumR === 1) {
+    return `
+      float ${funcName}(int index) {
+        return sampleTexture(${texName}, halfCR);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (tNumC === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (tNumR === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  return `
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getPackedSampler2D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const glsl = getGlslDifferences();
+  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+
+        return ${glsl.texture2D}(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+
+        return ${glsl.texture2D}(${texName}, uv);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int row, int col) {
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));
+      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const valuesPerRow = Math.ceil(shape[1] / 2);
+  return `
+    vec4 ${funcName}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler2D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    const texNumR2 = texShape[0];
+    const texNumC2 = texShape[1];
+    return `
+    float ${funcName}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  const squeezedShape = newShape;
+  if (squeezedShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+    const params = ["row", "col"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+      float ${funcName}(int row, int col) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const offset = getFlatOffsetUniformName(texName);
+  if (texNumC === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col) {
+        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
+        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  if (texNumR === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col) {
+        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
+        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  if (enableShapeUniforms) {
+    return `
+      float ${funcName}(int row, int col) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${texName}Shape[1] + col + ${offset};
+        vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  return `
+  float ${funcName}(int row, int col) {
+    // Explicitly use integer operations as dot() only works on floats.
+    int index = row * ${shape[1]} + col + ${offset};
+    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+    return sampleTexture(${texName}, uv);
+  }
+`;
+}
+function getPackedSampler3D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  if (shape[0] === 1) {
+    const squeezedShape = shape.slice(1);
+    const keptDims = [1, 2];
+    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+    const params = ["b", "row", "col"];
+    return `
+        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+        vec4 ${funcName}(int b, int row, int col) {
+          return ${funcName}(${getSqueezedParams(params, keptDims)});
+        }
+      `;
+  }
+  const glsl = getGlslDifferences();
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int b, int row, int col) {
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));
+      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));
+      vec2 uv = packedUVfrom3D(
+        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const texNumR = packedTexShape[0];
+  const texNumC = packedTexShape[1];
+  const valuesPerRow = Math.ceil(shape[2] / 2);
+  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
+  return `
+    vec4 ${funcName}(int b, int row, int col) {
+      vec2 uv = packedUVfrom3D(
+        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler3D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const stride0 = shape[1] * shape[2];
+  const stride1 = shape[2];
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  const squeezedShape = newShape;
+  if (squeezedShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+    const params = ["row", "col", "depth"];
+    return `
+        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+        float ${funcName}(int row, int col, int depth) {
+          return ${funcName}(${getSqueezedParams(params, keptDims)});
+        }
+      `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth) {
+        int index = round(dot(vec3(row, col, depth),
+                          vec3(${stride0}, ${stride1}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  if (texNumC === stride0 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth) {
+        int stride1 = ${texName}Shape[2];
+        float texR = float(row);
+        float texC = dot(vec2(col, depth), vec2(stride1, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+        float ${funcName}(int row, int col, int depth) {
+          float texR = float(row);
+          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
+          vec2 uv = (vec2(texC, texR) + halfCR) /
+                     vec2(${texNumC}.0, ${texNumR}.0);
+          return sampleTexture(${texName}, uv);
+        }
+      `;
+  }
+  if (texNumC === stride1 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth) {
+        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));
+        float texC = float(depth);
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+    float ${funcName}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
+      float texC = float(depth);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}(int row, int col, int depth) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];
+      int stride1 = ${texName}Shape[2];
+      int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
+      return sampleTexture(${texName}, uv);
+    }
+    `;
+  }
+  return `
+      float ${funcName}(int row, int col, int depth) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+        return sampleTexture(${texName}, uv);
+      }
+  `;
+}
+function getPackedSamplerND(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const glsl = getGlslDifferences();
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int b2, int b, int row, int col) {
+      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));
+      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));
+      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
+      texelsInBatch *= ${texName}Shape[1];
+      index = b2 * texelsInBatch + index;
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      int texR = index / packedTexShape[1];
+      int texC = index - texR * packedTexShape[1];
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const rank = shape.length;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const texNumR = packedTexShape[0];
+  const texNumC = packedTexShape[1];
+  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);
+  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
+  let params = `int b, int row, int col`;
+  let index2 = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;
+  for (let b = 2; b < rank - 1; b++) {
+    params = `int b${b}, ` + params;
+    texelsInBatch *= shape[rank - b - 1];
+    index2 = `b${b} * ${texelsInBatch} + ` + index2;
+  }
+  return `
+    vec4 ${funcName}(${params}) {
+      int index = ${index2};
+      int texR = index / ${texNumC};
+      int texC = index - texR * ${texNumC};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler4D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const stride2 = shape[3];
+  const stride1 = shape[2] * stride2;
+  const stride0 = shape[1] * stride1;
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  if (newShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
+    const params = ["row", "col", "depth", "depth2"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        int index = round(dot(vec4(row, col, depth, depth2),
+                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const stride2Str = `int stride2 = ${texName}Shape[3];`;
+  const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;
+  const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;
+  if (texNumC === stride0 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        ${stride2Str}
+        ${stride1Str}
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(stride1, stride2, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(${stride1}, ${stride2}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (texNumC === stride2 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      ${stride2Str}
+      ${stride1Str}
+      ${stride0Str}
+      int index = row * stride0 + col * stride1 +
+          depth * stride2 + depth2;
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  return `
+    float ${funcName}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} +
+          depth * ${stride2} + depth2;
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getSampler5D(inputInfo) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const stride3 = shape[4];
+  const stride2 = shape[3] * stride3;
+  const stride1 = shape[2] * stride2;
+  const stride0 = shape[1] * stride1;
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  if (newShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
+    const params = ["row", "col", "depth", "depth2", "depth3"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        float index = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
+          depth3;
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  if (texNumC === stride0 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (texNumC === stride3 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        float texR = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${shape[1] * shape[2] * shape[3]},
+               ${shape[2] * shape[3]}, ${shape[3]}, 1));
+        int texC = depth3;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  return `
+    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getSampler6D(inputInfo) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  if (newShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
+    const params = ["row", "col", "depth", "depth2", "depth3", "depth4"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  const stride4 = shape[5];
+  const stride3 = shape[4] * stride4;
+  const stride2 = shape[3] * stride3;
+  const stride1 = shape[2] * stride2;
+  const stride0 = shape[1] * stride1;
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+        int index = round(dot(
+          vec4(row, col, depth, depth2),
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
+          dot(
+            vec2(depth3, depth4),
+            vec2(${stride4}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  if (texNumC === stride0 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
+               float(depth4);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (texNumC === stride4 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        float texR = dot(vec4(row, col, depth, depth2),
+          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},
+               ${shape[2] * shape[3] * shape[4]},
+               ${shape[3] * shape[4]},
+               ${shape[4]})) + float(depth3);
+        int texC = depth4;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  return `
+    float ${funcName}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getUniformSampler(inputInfo) {
+  const texName = inputInfo.name;
+  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+  if (inSize < 2) {
+    return `return ${texName};`;
+  }
+  return `
+    for (int i = 0; i < ${inSize}; i++) {
+      if (i == index) {
+        return ${texName}[i];
+      }
+    }
+  `;
+}
+function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
+  const texName = inputInfo.name;
+  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+  const funcName = "get" + texFuncSnippet + "AtOutCoords";
+  const inRank = inputInfo.shapeInfo.logicalShape.length;
+  const outRank = outShapeInfo.logicalShape.length;
+  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+  const type = getCoordsDataType(outRank);
+  const rankDiff = outRank - inRank;
+  let coordsSnippet;
+  const fields = ["x", "y", "z", "w", "u", "v"];
+  if (inRank === 0) {
+    coordsSnippet = "";
+  } else if (outRank < 2 && broadcastDims.length >= 1) {
+    coordsSnippet = "coords = 0;";
+  } else {
+    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
+  }
+  let unpackedCoordsSnippet = "";
+  if (outRank < 2 && inRank > 0) {
+    unpackedCoordsSnippet = "coords";
+  } else {
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+  }
+  let output = `return outputValue;`;
+  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+  const isInputScalar = inSize === 1;
+  const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);
+  const isOutputScalar = outSize === 1;
+  if (inRank === 1 && !isInputScalar && !isOutputScalar) {
+    output = `
+      return vec4(outputValue.xy, outputValue.xy);
+    `;
+  } else if (isInputScalar && !isOutputScalar) {
+    if (outRank === 1) {
+      output = `
+        return vec4(outputValue.x, outputValue.x, 0., 0.);
+      `;
+    } else {
+      output = `
+        return vec4(outputValue.x);
+      `;
+    }
+  } else if (broadcastDims.length) {
+    const rows = inRank - 2;
+    const cols = inRank - 1;
+    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
+      output = `return vec4(outputValue.x);`;
+    } else if (broadcastDims.indexOf(rows) > -1) {
+      output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;
+    } else if (broadcastDims.indexOf(cols) > -1) {
+      output = `return vec4(outputValue.xx, outputValue.zz);`;
+    }
+  }
+  return `
+    vec4 ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
+      ${output}
+    }
+  `;
+}
+function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
+  const texName = inputInfo.name;
+  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+  const funcName = "get" + texFuncSnippet + "AtOutCoords";
+  const outTexShape = outShapeInfo.texShape;
+  const inTexShape = inputInfo.shapeInfo.texShape;
+  const inRank = inputInfo.shapeInfo.logicalShape.length;
+  const outRank = outShapeInfo.logicalShape.length;
+  if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {
+    return `
+      float ${funcName}() {
+        return sampleTexture(${texName}, resultUV);
+      }
+    `;
+  }
+  const type = getCoordsDataType(outRank);
+  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+  const rankDiff = outRank - inRank;
+  let coordsSnippet;
+  const fields = ["x", "y", "z", "w", "u", "v"];
+  if (inRank === 0) {
+    coordsSnippet = "";
+  } else if (outRank < 2 && broadcastDims.length >= 1) {
+    coordsSnippet = "coords = 0;";
+  } else {
+    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
+  }
+  let unpackedCoordsSnippet = "";
+  if (outRank < 2 && inRank > 0) {
+    unpackedCoordsSnippet = "coords";
+  } else {
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+  }
+  return `
+    float ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      return get${texFuncSnippet}(${unpackedCoordsSnippet});
+    }
+  `;
+}
+function getCoordsDataType(rank) {
+  if (rank <= 1) {
+    return "int";
+  } else if (rank === 2) {
+    return "ivec2";
+  } else if (rank === 3) {
+    return "ivec3";
+  } else if (rank === 4) {
+    return "ivec4";
+  } else if (rank === 5) {
+    return "ivec5";
+  } else if (rank === 6) {
+    return "ivec6";
+  } else {
+    throw Error(`GPU for rank ${rank} is not yet supported`);
+  }
+}
+function getUniformInfoFromShape(isPacked, shape, texShape) {
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  const rank = shape.length;
+  const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;
+  const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;
+  const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;
+  const uniformShape = useSqueezeShape ? squeezeShape2 : shape;
+  return { useSqueezeShape, uniformShape, keptDims };
+}
+function squeezeInputInfo(inInfo, squeezedShape) {
+  const newInputInfo = JSON.parse(JSON.stringify(inInfo));
+  newInputInfo.shapeInfo.logicalShape = squeezedShape;
+  return newInputInfo;
+}
+function getSqueezedParams(params, keptDims) {
+  return keptDims.map((d) => params[d]).join(", ");
+}
+function compileProgram(gpgpu, program, inputs, output) {
+  const inputInfos = inputs.map((input2, i2) => {
+    const shapeInfo = {
+      logicalShape: input2.shape,
+      texShape: input2.isUniform ? null : input2.texData.texShape,
+      isUniform: input2.isUniform,
+      isPacked: input2.isUniform ? false : input2.texData.isPacked,
+      flatOffset: null
+    };
+    if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {
+      shapeInfo.flatOffset = input2.texData.slice.flatOffset;
+    }
+    return { name: program.variableNames[i2], shapeInfo };
+  });
+  const inShapeInfos = inputInfos.map((x) => x.shapeInfo);
+  const outShapeInfo = {
+    logicalShape: output.shape,
+    texShape: output.texData.texShape,
+    isUniform: false,
+    isPacked: output.texData.isPacked,
+    flatOffset: null
+  };
+  const source = makeShader(inputInfos, outShapeInfo, program);
+  const fragmentShader = createFragmentShader(gpgpu.gl, source);
+  const webGLProgram = gpgpu.createProgram(fragmentShader);
+  if (!env().get("ENGINE_COMPILE_ONLY")) {
+    return Object.assign({
+      program,
+      fragmentShader,
+      source,
+      webGLProgram,
+      inShapeInfos,
+      outShapeInfo
+    }, getUniformLocations(gpgpu, program, webGLProgram));
+  } else {
+    return {
+      program,
+      fragmentShader,
+      source,
+      webGLProgram,
+      inShapeInfos,
+      outShapeInfo,
+      uniformLocations: null,
+      customUniformLocations: null,
+      infLoc: null,
+      nanLoc: null,
+      inShapesLocations: null,
+      inTexShapesLocations: null,
+      outShapeLocation: null,
+      outShapeStridesLocation: null,
+      outTexShapeLocation: null
+    };
+  }
+}
+function getUniformLocations(gpgpu, program, webGLProgram) {
+  const uniformLocations = {};
+  const inShapesLocations = {};
+  const inTexShapesLocations = {};
+  const customUniformLocations = [];
+  let outShapeLocation;
+  let outTexShapeLocation;
+  let outShapeStridesLocation;
+  let infLoc = null;
+  let nanLoc = null;
+  nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
+  if (env().getNumber("WEBGL_VERSION") === 1) {
+    infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
+  }
+  const shouldThrow = false;
+  for (let i2 = 0; i2 < program.variableNames.length; i2++) {
+    const varName = program.variableNames[i2];
+    uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
+    uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);
+    if (program.enableShapeUniforms) {
+      inShapesLocations[`${varName}Shape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);
+      inTexShapesLocations[`${varName}TexShape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);
+    }
+  }
+  if (program.enableShapeUniforms) {
+    outShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outShape", shouldThrow);
+    outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, "outShapeStrides", shouldThrow);
+    outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow);
+  }
+  if (program.customUniforms) {
+    program.customUniforms.forEach((d, i2) => {
+      customUniformLocations[i2] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
+    });
+  }
+  return {
+    uniformLocations,
+    customUniformLocations,
+    infLoc,
+    nanLoc,
+    inShapesLocations,
+    inTexShapesLocations,
+    outShapeLocation,
+    outShapeStridesLocation,
+    outTexShapeLocation
+  };
+}
+function validateBinaryAndProgram(shapeInfos, inputs) {
+  if (shapeInfos.length !== inputs.length) {
+    throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);
+  }
+  shapeInfos.forEach((s2, i2) => {
+    const shapeA = s2.logicalShape;
+    const input2 = inputs[i2];
+    const shapeB = input2.shape;
+    if (!util_exports.arraysEqual(shapeA, shapeB)) {
+      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);
+    }
+    if (s2.isUniform && input2.isUniform) {
+      return;
+    }
+    const texShapeA = s2.texShape;
+    const texShapeB = input2.isUniform ? null : input2.texData.texShape;
+    if (!util_exports.arraysEqual(texShapeA, texShapeB)) {
+      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);
+    }
+  });
+}
+function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
+  if (!binary.program.enableShapeUniforms) {
+    validateBinaryAndProgram(binary.inShapeInfos, inputs);
+    validateBinaryAndProgram([binary.outShapeInfo], [output]);
+  }
+  const outTex = output.texData.texture;
+  const outTexShape = output.texData.texShape;
+  if (output.texData.isPacked) {
+    gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
+  } else {
+    gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
+  }
+  gpgpu.setProgram(binary.webGLProgram);
+  if (env().getNumber("WEBGL_VERSION") === 1) {
+    if (binary.infLoc !== null) {
+      gpgpu.gl.uniform1f(binary.infLoc, Infinity);
+    }
+  }
+  if (binary.nanLoc !== null) {
+    gpgpu.gl.uniform1f(binary.nanLoc, NaN);
+  }
+  inputs.forEach((input2, i2) => {
+    const varName = binary.program.variableNames[i2];
+    const varLoc = binary.uniformLocations[varName];
+    const varOffsetLoc = binary.uniformLocations[`offset${varName}`];
+    const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];
+    const varTexShapeLoc = binary.inTexShapesLocations[`${varName}TexShape`];
+    if (varShapeLoc) {
+      const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);
+      switch (uniformShape.length) {
+        case 1:
+          gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        case 2:
+          gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        case 3:
+          gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        case 4:
+          gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        default:
+          break;
+      }
+    }
+    if (varTexShapeLoc) {
+      gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);
+    }
+    if (varLoc == null) {
+      return;
+    }
+    if (input2.isUniform) {
+      if (util_exports.sizeFromShape(input2.shape) < 2) {
+        gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);
+      } else {
+        let vals = input2.uniformValues;
+        if (!(vals instanceof Float32Array)) {
+          vals = new Float32Array(vals);
+        }
+        gpgpu.gl.uniform1fv(varLoc, vals);
+      }
+      return;
+    }
+    if (input2.texData.slice != null && varOffsetLoc != null) {
+      gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);
+    }
+    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i2);
+  });
+  const outShapeLoc = binary.outShapeLocation;
+  if (outShapeLoc) {
+    switch (output.shape.length) {
+      case 1:
+        gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      case 2:
+        gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      case 3:
+        gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      case 4:
+        gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      default:
+        break;
+    }
+  }
+  if (binary.outShapeStridesLocation) {
+    const strides2 = util_exports.computeStrides(output.shape);
+    switch (output.shape.length) {
+      case 2:
+        gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides2));
+        break;
+      case 3:
+        gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides2));
+        break;
+      case 4:
+        gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides2));
+        break;
+      default:
+        break;
+    }
+  }
+  if (binary.outTexShapeLocation) {
+    gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);
+  }
+  if (binary.program.customUniforms && customUniformValues) {
+    binary.program.customUniforms.forEach((d, i2) => {
+      const customLoc = binary.customUniformLocations[i2];
+      const customValue = customUniformValues[i2];
+      if (d.type === "float") {
+        gpgpu.gl.uniform1fv(customLoc, customValue);
+      } else if (d.type === "vec2") {
+        gpgpu.gl.uniform2fv(customLoc, customValue);
+      } else if (d.type === "vec3") {
+        gpgpu.gl.uniform3fv(customLoc, customValue);
+      } else if (d.type === "vec4") {
+        gpgpu.gl.uniform4fv(customLoc, customValue);
+      } else if (d.type === "int") {
+        gpgpu.gl.uniform1iv(customLoc, customValue);
+      } else if (d.type === "ivec2") {
+        gpgpu.gl.uniform2iv(customLoc, customValue);
+      } else if (d.type === "ivec3") {
+        gpgpu.gl.uniform3iv(customLoc, customValue);
+      } else if (d.type === "ivec4") {
+        gpgpu.gl.uniform4iv(customLoc, customValue);
+      } else {
+        throw Error(`uniform type ${d.type} is not supported yet.`);
+      }
+    });
+  }
+  gpgpu.executeProgram();
+}
+function makeShaderKey(program, inputs, output) {
+  let keyInputs = "";
+  inputs.concat(output).forEach((x) => {
+    const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;
+    if (program.enableShapeUniforms && !x.isUniform) {
+      const xTexShape = x.texData.texShape;
+      const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);
+      let rank1 = "", rank2 = "", rank34 = "";
+      if (uniformShape.length === 1 && program.packedInputs) {
+        const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];
+        rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;
+      } else if (uniformShape.length === 2 && !program.packedInputs) {
+        rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;
+      } else if (uniformShape.length > 2 && !program.packedInputs) {
+        const strides2 = util_exports.computeStrides(uniformShape);
+        rank34 = `${strides2[0] === xTexShape[1]}_${strides2[strides2.length - 1] === xTexShape[1]}`;
+      }
+      const xRank = x.shape.length;
+      const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);
+      const isScalar = util_exports.sizeFromShape(x.shape) === 1;
+      const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);
+      const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);
+      const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? "" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;
+      keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : ""}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;
+    } else {
+      const texShape = x.isUniform ? "uniform" : x.texData.texShape;
+      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;
+    }
+  });
+  const keyUserCode = program.userCode;
+  let key = program.constructor.name;
+  key += "_" + keyInputs + "_" + keyUserCode + `${env().getNumber("WEBGL_VERSION")}`;
+  return key;
+}
+function useShapeUniforms(rank) {
+  return env().getBool("WEBGL_USE_SHAPES_UNIFORMS") && rank <= 4;
+}
+var DecodeMatrixProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    this.outPackingScheme = PackingScheme.DENSE;
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
+        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getA(rc.x, rc.y, rc.z);
+        }
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+var DecodeMatrixPackedProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outPackingScheme = PackingScheme.DENSE;
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
+        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
+        }
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+var EncodeFloatProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.outTexUsage = TextureUsage.DOWNLOAD;
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.userCode = `
+      ${ENCODE_FLOAT_SNIPPET}
+
+      void main() {
+        float x = getAAtOutCoords();
+        ${glsl.output} = encode_float(x);
+      }
+    `;
+  }
+};
+var EncodeFloatPackedProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = false;
+    this.outTexUsage = TextureUsage.DOWNLOAD;
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.userCode = `
+      ${ENCODE_FLOAT_SNIPPET}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
+        ${glsl.output} = encode_float(x);
+      }
+    `;
+  }
+};
+var EncodeMatrixProgram = class {
+  constructor(outputShape, inputIsUnsignedByte = false) {
+    this.variableNames = ["A"];
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    let output = `result`;
+    if (inputIsUnsignedByte) {
+      output = `floor(result * 255. + 0.5)`;
+    }
+    this.userCode = `
+      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        int flatIndex = getFlatIndex(coords);
+        int offset = imod(flatIndex, 4);
+
+        flatIndex = idiv(flatIndex, 4, 1.);
+
+        int r = flatIndex / texShape[1];
+        int c = imod(flatIndex, texShape[1]);
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
+        vec4 values = ${glsl.texture2D}(A, uv);
+
+        float result;
+
+        if(offset == 0) {
+          result = values[0];
+        } else if(offset == 1) {
+          result = values[1];
+        } else if(offset == 2) {
+          result = values[2];
+        } else {
+          result = values[3];
+        }
+
+        ${glsl.output} = vec4(${output}, 0., 0., 0.);
+      }
+    `;
+  }
+};
+var EncodeMatrixPackedProgram = class {
+  constructor(outputShape, inputIsUnsignedByte = false) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    let mainLoop = "";
+    let output = "result";
+    if (inputIsUnsignedByte) {
+      output = "floor(result * 255. + 0.5)";
+    }
+    for (let row = 0; row <= 1; row++) {
+      for (let col = 0; col <= 1; col++) {
+        const channel = row * 2 + col;
+        mainLoop += `
+          localCoords = coords;
+          if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? "outShape[2]" : `${outputShape[2]}`}) {
+          localCoords[2] += ${col};
+          if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? "outShape[1]" : `${outputShape[1]}`}) {
+            localCoords[1] += ${row};
+
+            flatIndex = getFlatIndex(localCoords);
+            offset = imod(flatIndex, 4);
+
+            flatIndex = idiv(flatIndex, 4, 1.);
+
+            int r = flatIndex / texShape[1];
+            int c = imod(flatIndex, texShape[1]);
+            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
+            values = ${glsl.texture2D}(A, uv);
+
+            if (offset == 0) {
+              result[${channel}] = values[0];
+            } else if (offset == 1) {
+              result[${channel}] = values[1];
+            } else if (offset == 2) {
+              result[${channel}] = values[2];
+            } else {
+              result[${channel}] = values[3];
+            }
+          }
+        }
+        `;
+      }
+    }
+    this.userCode = `
+        ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
+
+        void main() {
+          ivec3 coords = getOutputCoords();
+
+          vec4 result = vec4(0.);
+          int flatIndex, r, c, offset;
+          ivec3 localCoords;
+          vec2 uv;
+          vec4 values;
+
+          ${mainLoop}
+
+          ${glsl.output} = ${output};
+        }
+    `;
+  }
+};
+var gpgpu_util_exports = {};
+__export2(gpgpu_util_exports, {
+  bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams,
+  createBufferFromOutputTexture: () => createBufferFromOutputTexture,
+  createFloat16MatrixTexture: () => createFloat16MatrixTexture,
+  createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture,
+  createFloat32MatrixTexture: () => createFloat32MatrixTexture,
+  createIndexBuffer: () => createIndexBuffer,
+  createPackedMatrixTexture: () => createPackedMatrixTexture,
+  createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture,
+  createVertexBuffer: () => createVertexBuffer,
+  createVertexShader: () => createVertexShader2,
+  downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture,
+  downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer,
+  downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture,
+  downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer,
+  getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture,
+  getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture,
+  getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture,
+  getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture,
+  getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture,
+  uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture,
+  uploadPixelDataToTexture: () => uploadPixelDataToTexture
+});
+function createVertexShader2(gl) {
+  const glsl = getGlslDifferences();
+  const vertexShaderSource = `${glsl.version}
+    precision highp float;
+    ${glsl.attribute} vec3 clipSpacePos;
+    ${glsl.attribute} vec2 uv;
+    ${glsl.varyingVs} vec2 resultUV;
+
+    void main() {
+      gl_Position = vec4(clipSpacePos, 1);
+      resultUV = uv;
+    }`;
+  return createVertexShader(gl, vertexShaderSource);
+}
+function createVertexBuffer(gl) {
+  const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
+  return createStaticVertexBuffer(gl, vertexArray);
+}
+function createIndexBuffer(gl) {
+  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
+  return createStaticIndexBuffer(gl, triangleVertexIndices);
+}
+function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
+  validateTextureSize(width, height);
+  const texture = createTexture(gl);
+  const tex2d = gl.TEXTURE_2D;
+  callAndCheck(gl, () => gl.bindTexture(tex2d, texture));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
+  if (env().getNumber("WEBGL_VERSION") === 1) {
+    callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));
+  } else {
+    callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));
+  }
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+  return { texture, texShape: [height, width] };
+}
+function getInternalFormatForFloat32MatrixTexture(textureConfig) {
+  return textureConfig.internalFormatFloat;
+}
+function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);
+}
+function getInternalFormatForFloat16MatrixTexture(textureConfig) {
+  return textureConfig.internalFormatHalfFloat;
+}
+function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
+}
+function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {
+  return textureConfig.downloadTextureFormat;
+}
+function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);
+}
+function getInternalFormatForPackedMatrixTexture(textureConfig) {
+  return textureConfig.internalFormatPackedFloat;
+}
+function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);
+}
+function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {
+  return textureConfig.internalFormatPackedHalfFloat;
+}
+function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);
+}
+function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
+  const posOffset = 0;
+  const uvOffset = 3 * 4;
+  const stride = 3 * 4 + 2 * 4;
+  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));
+  const success = bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset);
+  return success && bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset);
+}
+function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
+  let dataForUpload, texelDataType, internalFormat;
+  if (data instanceof Uint8Array) {
+    dataForUpload = new Uint8Array(width * height * 4);
+    texelDataType = gl.UNSIGNED_BYTE;
+    internalFormat = gl.RGBA;
+  } else {
+    dataForUpload = new Float32Array(width * height * 4);
+    texelDataType = gl.FLOAT;
+    internalFormat = textureConfig.internalFormatPackedFloat;
+  }
+  dataForUpload.set(data);
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));
+  } else {
+    callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));
+  }
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+}
+function uploadPixelDataToTexture(gl, texture, pixels) {
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
+  if (pixels.data instanceof Uint8Array) {
+    if (env().getNumber("WEBGL_VERSION") === 2) {
+      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
+    } else {
+      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
+    }
+  } else {
+    if (env().getNumber("WEBGL_VERSION") === 2) {
+      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
+    } else {
+      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
+    }
+  }
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+}
+function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {
+  const buffer2 = gl2.createBuffer();
+  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));
+  const bytesPerFloat = 4;
+  const valuesPerTexel = 4;
+  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
+  callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));
+  callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));
+  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));
+  return buffer2;
+}
+function downloadFloat32MatrixFromBuffer(gl, buffer2, size2) {
+  const gl2 = gl;
+  const downloadTarget = new Float32Array(size2);
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
+  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
+  return downloadTarget;
+}
+function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+  const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  const numChannels = 4;
+  const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
+  callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));
+  return new Float32Array(downloadTarget.buffer);
+}
+function downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+  const gl2 = gl;
+  const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
+  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
+  return downloadTarget;
+}
+function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
+  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
+  callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));
+  return packedRGBA;
+}
+var GPGPUContext = class {
+  constructor(gl) {
+    this.outputTexture = null;
+    this.program = null;
+    this.disposed = false;
+    this.vertexAttrsAreBound = false;
+    this.itemsToPoll = [];
+    const glVersion = env().getNumber("WEBGL_VERSION");
+    if (gl != null) {
+      this.gl = gl;
+      setWebGLContext(glVersion, gl);
+    } else {
+      this.gl = getWebGLContext(glVersion);
+    }
+    let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float";
+    const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
+    this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile");
+    if (env().getNumber("WEBGL_VERSION") === 1) {
+      const TEXTURE_FLOAT = "OES_texture_float";
+      const TEXTURE_HALF_FLOAT = "OES_texture_half_float";
+      this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
+      if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
+        this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
+      } else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
+        throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
+      }
+      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
+      if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
+        this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
+      } else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
+        throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
+      }
+    } else {
+      COLOR_BUFFER_FLOAT = "EXT_color_buffer_float";
+      if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
+        this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
+      } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
+        this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
+      } else {
+        throw new Error("GL context does not support color renderable floats");
+      }
+    }
+    this.vertexBuffer = createVertexBuffer(this.gl);
+    this.indexBuffer = createIndexBuffer(this.gl);
+    this.framebuffer = createFramebuffer(this.gl);
+    this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);
+  }
+  get debug() {
+    return env().getBool("DEBUG");
+  }
+  dispose() {
+    if (this.disposed) {
+      return;
+    }
+    if (this.program != null) {
+      console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.");
+    }
+    if (this.outputTexture != null) {
+      console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");
+    }
+    const gl = this.gl;
+    callAndCheck(gl, () => gl.finish());
+    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
+    callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));
+    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));
+    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));
+    callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));
+    this.disposed = true;
+  }
+  createFloat32MatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  createFloat16MatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  createUnsignedBytesMatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  uploadPixelDataToTexture(texture, pixels) {
+    this.throwIfDisposed();
+    uploadPixelDataToTexture(this.gl, texture, pixels);
+  }
+  uploadDenseMatrixToTexture(texture, width, height, data) {
+    this.throwIfDisposed();
+    uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);
+  }
+  createFloat16PackedMatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  createPackedMatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  deleteMatrixTexture(texture) {
+    this.throwIfDisposed();
+    if (this.outputTexture === texture) {
+      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+      this.outputTexture = null;
+    }
+    callAndCheck(this.gl, () => this.gl.deleteTexture(texture));
+  }
+  downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {
+    return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));
+  }
+  downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {
+    return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
+  }
+  downloadFloat32MatrixFromBuffer(buffer2, size2) {
+    return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size2);
+  }
+  createBufferFromTexture(texture, rows, columns) {
+    this.bindTextureToFrameBuffer(texture);
+    const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);
+    this.unbindTextureToFrameBuffer();
+    return result;
+  }
+  createAndWaitForFence() {
+    const fenceContext = this.createFence(this.gl);
+    return this.pollFence(fenceContext);
+  }
+  createFence(gl) {
+    let query;
+    let isFencePassed;
+    if (env().getBool("WEBGL_FENCE_API_ENABLED")) {
+      const gl2 = gl;
+      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);
+      gl.flush();
+      isFencePassed = () => {
+        const status = gl2.clientWaitSync(sync, 0, 0);
+        return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;
+      };
+      query = sync;
+    } else if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) {
+      query = this.beginQuery();
+      this.endQuery();
+      isFencePassed = () => this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
+    } else {
+      isFencePassed = () => true;
+    }
+    return { query, isFencePassed };
+  }
+  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
+    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
+  }
+  createProgram(fragmentShader) {
+    this.throwIfDisposed();
+    const gl = this.gl;
+    if (this.vertexShader == null) {
+      this.vertexShader = createVertexShader2(gl);
+    }
+    const program = createProgram(gl);
+    callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));
+    callAndCheck(gl, () => gl.attachShader(program, fragmentShader));
+    linkProgram(gl, program);
+    if (this.debug) {
+      validateProgram(gl, program);
+    }
+    if (!this.vertexAttrsAreBound) {
+      this.setProgram(program);
+      this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
+    }
+    return program;
+  }
+  deleteProgram(program) {
+    this.throwIfDisposed();
+    if (program === this.program) {
+      this.program = null;
+    }
+    if (program != null) {
+      callAndCheck(this.gl, () => this.gl.deleteProgram(program));
+    }
+  }
+  setProgram(program) {
+    this.throwIfDisposed();
+    this.program = program;
+    if (this.program != null && this.debug) {
+      validateProgram(this.gl, this.program);
+    }
+    callAndCheck(this.gl, () => this.gl.useProgram(program));
+  }
+  getUniformLocation(program, uniformName, shouldThrow = true) {
+    this.throwIfDisposed();
+    if (shouldThrow) {
+      return getProgramUniformLocationOrThrow(this.gl, program, uniformName);
+    } else {
+      return getProgramUniformLocation(this.gl, program, uniformName);
+    }
+  }
+  getAttributeLocation(program, attribute) {
+    this.throwIfDisposed();
+    return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));
+  }
+  getUniformLocationNoThrow(program, uniformName) {
+    this.throwIfDisposed();
+    return this.gl.getUniformLocation(program, uniformName);
+  }
+  setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {
+    this.throwIfDisposed();
+    this.throwIfNoProgram();
+    bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);
+  }
+  setOutputMatrixTexture(outputMatrixTexture, rows, columns) {
+    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
+  }
+  setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {
+    this.throwIfDisposed();
+    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
+  }
+  setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
+    this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
+  }
+  setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
+    throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
+  }
+  debugValidate() {
+    if (this.program != null) {
+      validateProgram(this.gl, this.program);
+    }
+    validateFramebuffer(this.gl);
+  }
+  executeProgram() {
+    this.throwIfDisposed();
+    this.throwIfNoProgram();
+    const gl = this.gl;
+    if (this.debug) {
+      this.debugValidate();
+    }
+    callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));
+  }
+  blockUntilAllProgramsCompleted() {
+    this.throwIfDisposed();
+    callAndCheck(this.gl, () => this.gl.finish());
+  }
+  getQueryTimerExtension() {
+    if (this.disjointQueryTimerExtension == null) {
+      this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query");
+    }
+    return this.disjointQueryTimerExtension;
+  }
+  getQueryTimerExtensionWebGL2() {
+    return this.getQueryTimerExtension();
+  }
+  getQueryTimerExtensionWebGL1() {
+    return this.getQueryTimerExtension();
+  }
+  beginQuery() {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
+      const gl2 = this.gl;
+      const ext2 = this.getQueryTimerExtensionWebGL2();
+      const query2 = gl2.createQuery();
+      gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);
+      return query2;
+    }
+    const ext = this.getQueryTimerExtensionWebGL1();
+    const query = ext.createQueryEXT();
+    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
+    return query;
+  }
+  endQuery() {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
+      const gl2 = this.gl;
+      const ext2 = this.getQueryTimerExtensionWebGL2();
+      gl2.endQuery(ext2.TIME_ELAPSED_EXT);
+      return;
+    }
+    const ext = this.getQueryTimerExtensionWebGL1();
+    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
+  }
+  async waitForQueryAndGetTime(query) {
+    await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")));
+    return this.getQueryTime(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
+  }
+  getQueryTime(query, queryTimerVersion) {
+    if (queryTimerVersion === 0) {
+      return null;
+    }
+    if (queryTimerVersion === 2) {
+      const gl2 = this.gl;
+      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
+      return timeElapsedNanos / 1e6;
+    } else {
+      const ext = this.getQueryTimerExtensionWebGL1();
+      const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
+      return timeElapsedNanos / 1e6;
+    }
+  }
+  isQueryAvailable(query, queryTimerVersion) {
+    if (queryTimerVersion === 0) {
+      return true;
+    }
+    if (queryTimerVersion === 2) {
+      const gl2 = this.gl;
+      const ext = this.getQueryTimerExtensionWebGL2();
+      const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
+      if (this.disjoint == null) {
+        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+      }
+      return available && !this.disjoint;
+    } else {
+      const ext = this.getQueryTimerExtensionWebGL1();
+      const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
+      if (this.disjoint == null) {
+        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+      }
+      return available && !this.disjoint;
+    }
+  }
+  pollFence(fenceContext) {
+    return new Promise((resolve) => {
+      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());
+    });
+  }
+  pollItems() {
+    const index2 = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));
+    for (let i2 = 0; i2 <= index2; ++i2) {
+      const { resolveFn } = this.itemsToPoll[i2];
+      resolveFn();
+    }
+    this.itemsToPoll = this.itemsToPoll.slice(index2 + 1);
+  }
+  addItemToPoll(isDoneFn, resolveFn) {
+    this.itemsToPoll.push({ isDoneFn, resolveFn });
+    if (this.itemsToPoll.length > 1) {
+      return;
+    }
+    util_exports.repeatedTry(() => {
+      this.pollItems();
+      return this.itemsToPoll.length === 0;
+    });
+  }
+  bindTextureToFrameBuffer(texture) {
+    this.throwIfDisposed();
+    bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
+    if (this.debug) {
+      validateFramebuffer(this.gl);
+    }
+  }
+  unbindTextureToFrameBuffer() {
+    if (this.outputTexture != null) {
+      bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
+      if (this.debug) {
+        validateFramebuffer(this.gl);
+      }
+    } else {
+      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+    }
+  }
+  downloadMatrixDriver(texture, downloadAndDecode) {
+    this.bindTextureToFrameBuffer(texture);
+    const result = downloadAndDecode();
+    this.unbindTextureToFrameBuffer();
+    return result;
+  }
+  setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {
+    this.throwIfDisposed();
+    const gl = this.gl;
+    bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
+    if (this.debug) {
+      validateFramebuffer(gl);
+    }
+    this.outputTexture = outputMatrixTextureMaybePacked;
+    callAndCheck(gl, () => gl.viewport(0, 0, width, height));
+    callAndCheck(gl, () => gl.scissor(0, 0, width, height));
+  }
+  setOutputMatrixWriteRegionDriver(x, y, width, height) {
+    this.throwIfDisposed();
+    callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));
+  }
+  throwIfDisposed() {
+    if (this.disposed) {
+      throw new Error("Attempted to use disposed GPGPUContext.");
+    }
+  }
+  throwIfNoProgram() {
+    if (this.program == null) {
+      throw new Error("No GPU program is currently set.");
+    }
+  }
+};
+function linearSearchLastTrue(arr) {
+  let i2 = 0;
+  for (; i2 < arr.length; ++i2) {
+    const isDone = arr[i2]();
+    if (!isDone) {
+      break;
+    }
+  }
+  return i2 - 1;
+}
+var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;
+function getVecChannels(name, rank) {
+  return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`);
+}
+function getChannels(name, rank) {
+  if (rank === 1) {
+    return [name];
+  }
+  return getVecChannels(name, rank);
+}
+function getSourceCoords(rank, dims) {
+  if (rank === 1) {
+    return "rc";
+  }
+  let coords3 = "";
+  for (let i2 = 0; i2 < rank; i2++) {
+    coords3 += dims[i2];
+    if (i2 < rank - 1) {
+      coords3 += ",";
+    }
+  }
+  return coords3;
+}
+var PackProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    if (this.rank === 0) {
+      this.userCode = `
+        void main() {
+          setOutput(vec4(getA(), 0., 0., 0.));
+        }
+      `;
+    } else {
+      const channels = getChannels("rc", this.rank);
+      const dtype = getCoordsDataType(this.rank);
+      const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);
+      const setup50 = this.getSetup(channels);
+      const output = this.getOutput(channels);
+      this.userCode = `
+        void main() {
+          ${dtype} rc = getOutputCoords();
+
+          if(${outOfBoundsCondition}) {
+            setOutput(vec4(0));
+          } else {
+            ${setup50}
+
+            setOutput(vec4(${output}));
+          }
+        }
+      `;
+    }
+  }
+  getSourceCoordsArr(dims) {
+    const coords3 = [];
+    for (let row = 0; row <= 1; row++) {
+      for (let col = 0; col <= 1; col++) {
+        let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`;
+        for (let d = 2; d < this.rank; d++) {
+          coord = `${dims[dims.length - 1 - d]},` + coord;
+        }
+        coords3.push(coord);
+      }
+    }
+    return coords3;
+  }
+  getOutOfBoundsCondition(dims) {
+    if (this.rank === 1) {
+      return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
+    }
+    let cond = "";
+    for (let i2 = this.rank - 2; i2 < this.rank; i2++) {
+      cond += `${dims[i2]} >= ${this.enableShapeUniforms ? `outShape[${i2}]` : this.outputShape[i2]}`;
+      if (i2 < this.rank - 1) {
+        cond += "||";
+      }
+    }
+    return cond;
+  }
+  getSetup(dims) {
+    if (this.rank === 1) {
+      return "";
+    }
+    const innerDims = dims.slice(-2);
+    const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];
+    const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];
+    return `
+      int r = ${innerDims[0]};
+      int c = ${innerDims[1]};
+      int rp1 = r + 1;
+      int cp1 = c + 1;
+
+      bool cEdge = cp1 >= ${col};
+      bool rEdge = rp1 >= ${row};
+    `;
+  }
+  getOutput(dims) {
+    const sourceCoords = this.getSourceCoordsArr(dims);
+    if (this.rank === 1) {
+      const outShape = this.enableShapeUniforms ? "outShape" : this.outputShape[0];
+      return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;
+    }
+    return `getA(${sourceCoords[0]}),
+            cEdge ? 0. : getA(${sourceCoords[1]}),
+            rEdge ? 0. : getA(${sourceCoords[2]}),
+            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
+  }
+};
+var ReshapePackedProgram = class {
+  constructor(outputShape, inputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [{ name: "inputShape", type: "ivec3" }];
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    let mainLoop = ``;
+    for (let i2 = 0; i2 < 4; i2++) {
+      let thisRC = `thisRC = rc;`;
+      if (i2 % 2 === 1) {
+        thisRC += `thisRC.z += 1;`;
+      }
+      if (i2 > 1) {
+        thisRC += `thisRC.y += 1;`;
+      }
+      mainLoop += `
+        ${thisRC}
+        ${i2 > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
+          int flatIndex = getFlatIndex(thisRC);
+
+          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
+          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
+
+          result[${i2}] =
+            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
+        ${i2 > 0 ? "}" : ""}
+      `;
+    }
+    this.userCode = `
+      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}
+      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+
+        vec4 result = vec4(0.);
+
+        ivec3 thisRC;
+        int rows = ${this.enableShapeUniforms ? "outShape[1]" : outputShape[1]};
+        int cols = ${this.enableShapeUniforms ? "outShape[2]" : outputShape[2]};
+
+        ${mainLoop}
+
+        setOutput(result);
+      }
+    `;
+  }
+};
+function getReshapedInputCoords(shape, enableShapeUniforms) {
+  const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], "inputShape") : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
+  return `
+    ivec3 inputCoordsFromReshapedOutCoords(int index) {
+      ${coordsFromIndexSnippet}
+      return ivec3(r, c, d);
+    }
+  `;
+}
+var TextureManager = class {
+  constructor(gpgpu) {
+    this.gpgpu = gpgpu;
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this._numBytesAllocated = 0;
+    this._numBytesFree = 0;
+    this.freeTextures = {};
+    this.logEnabled = false;
+    this.usedTextures = {};
+  }
+  acquireTexture(shapeRC, usage, isPacked) {
+    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
+    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
+    if (!(shapeKey in this.freeTextures)) {
+      this.freeTextures[shapeKey] = [];
+    }
+    if (!(shapeKey in this.usedTextures)) {
+      this.usedTextures[shapeKey] = [];
+    }
+    const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
+    if (this.freeTextures[shapeKey].length > 0) {
+      this.numFreeTextures--;
+      this.numUsedTextures++;
+      this._numBytesFree -= texBytes;
+      this.log();
+      const newTexture2 = this.freeTextures[shapeKey].shift();
+      this.usedTextures[shapeKey].push(newTexture2);
+      return newTexture2;
+    }
+    let newTexture;
+    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
+      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
+      newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
+      newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
+      newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
+      newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
+    }
+    this.usedTextures[shapeKey].push(newTexture);
+    this.numUsedTextures++;
+    this._numBytesAllocated += texBytes;
+    this.log();
+    return newTexture;
+  }
+  releaseTexture(texture, shape, logicalTexType, isPacked) {
+    if (this.freeTextures == null) {
+      return;
+    }
+    const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
+    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
+    if (!(shapeKey in this.freeTextures)) {
+      this.freeTextures[shapeKey] = [];
+    }
+    const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
+    const deleteTexThreshold = env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
+    if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {
+      this.gpgpu.deleteMatrixTexture(texture.texture);
+      this._numBytesAllocated -= texBytes;
+    } else {
+      this.freeTextures[shapeKey].push(texture);
+      this.numFreeTextures++;
+      this._numBytesFree += texBytes;
+    }
+    this.numUsedTextures--;
+    const texList = this.usedTextures[shapeKey];
+    const texIndex = texList.indexOf(texture);
+    if (texIndex < 0) {
+      throw new Error("Cannot release a texture that was never provided by this texture manager");
+    }
+    texList.splice(texIndex, 1);
+    this.log();
+  }
+  log() {
+    if (!this.logEnabled) {
+      return;
+    }
+    const total = this.numFreeTextures + this.numUsedTextures;
+    console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);
+    const freeRatio = this._numBytesFree / this._numBytesAllocated;
+    console.log(`Bytes allocated: ${this._numBytesAllocated}`);
+    console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);
+  }
+  get numBytesAllocated() {
+    return this._numBytesAllocated;
+  }
+  get numBytesFree() {
+    return this._numBytesFree;
+  }
+  getNumUsedTextures() {
+    return this.numUsedTextures;
+  }
+  getNumFreeTextures() {
+    return this.numFreeTextures;
+  }
+  dispose() {
+    if (this.freeTextures == null) {
+      return;
+    }
+    for (const texShape in this.freeTextures) {
+      this.freeTextures[texShape].forEach((tex) => {
+        this.gpgpu.deleteMatrixTexture(tex.texture);
+      });
+    }
+    for (const texShape in this.usedTextures) {
+      this.usedTextures[texShape].forEach((tex) => {
+        this.gpgpu.deleteMatrixTexture(tex.texture);
+      });
+    }
+    this.freeTextures = null;
+    this.usedTextures = null;
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this._numBytesAllocated = 0;
+    this._numBytesFree = 0;
+  }
+};
+function numBytesForInternalFormat(gl, internalFormat) {
+  const glany = gl;
+  if (internalFormat === glany.R32F) {
+    return 4;
+  } else if (internalFormat === glany.R16F) {
+    return 2;
+  } else if (internalFormat === glany.RGBA32F) {
+    return 16;
+  } else if (internalFormat === gl.RGBA) {
+    return 16;
+  } else if (internalFormat === glany.RGBA16F) {
+    return 8;
+  } else if (internalFormat === glany.RGBA8) {
+    return 4;
+  }
+  throw new Error(`Unknown internal format ${internalFormat}`);
+}
+function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {
+  const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);
+  let numElements;
+  if (isPacked) {
+    const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
+    numElements = packedWidth * packedHeight;
+  } else {
+    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
+    numElements = width * height;
+  }
+  const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);
+  return numElements * bytesPerElement2;
+}
+function internalFormatForPhysicalTexType(physicalTexType, textureConfig) {
+  switch (physicalTexType) {
+    case PhysicalTextureType.PACKED_2X2_FLOAT32:
+      return getInternalFormatForPackedMatrixTexture(textureConfig);
+    case PhysicalTextureType.PACKED_2X2_FLOAT16:
+      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);
+    case PhysicalTextureType.UNPACKED_FLOAT32:
+      return getInternalFormatForFloat32MatrixTexture(textureConfig);
+    case PhysicalTextureType.UNPACKED_FLOAT16:
+      return getInternalFormatForFloat16MatrixTexture(textureConfig);
+    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:
+      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);
+    default:
+      throw new Error(`Unknown physical texture type ${physicalTexType}`);
+  }
+}
+function getPhysicalTextureForRendering(isPacked) {
+  if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) {
+    if (isPacked) {
+      return PhysicalTextureType.PACKED_2X2_FLOAT32;
+    }
+    return PhysicalTextureType.UNPACKED_FLOAT32;
+  }
+  if (isPacked) {
+    return PhysicalTextureType.PACKED_2X2_FLOAT16;
+  }
+  return PhysicalTextureType.UNPACKED_FLOAT16;
+}
+function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
+  if (logicalTexType === TextureUsage.UPLOAD) {
+    return PhysicalTextureType.PACKED_2X2_FLOAT32;
+  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
+    return getPhysicalTextureForRendering(isPacked);
+  } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {
+    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
+  }
+  throw new Error(`Unknown logical texture type ${logicalTexType}`);
+}
+function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
+  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
+}
+var UnaryOpProgram = class {
+  constructor(aShape, opSnippet) {
+    this.variableNames = ["A"];
+    this.outputShape = aShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      float unaryOperation(float x) {
+        ${opSnippet}
+      }
+
+      void main() {
+        float x = getAAtOutCoords();
+        float y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `;
+  }
+};
+var CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;
+var LINEAR = `return x;`;
+var ABS = `return abs(x);`;
+var ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
+var RELU = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : x;
+`;
+var RELU6 = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`;
+var CLONE = "return x;";
+var SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
+var LINEAR2 = `return x;`;
+var ELU3 = `
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`;
+var RELU2 = `
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var RELU62 = `
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;
+var UnaryOpPackedProgram = class {
+  constructor(aShape, opSnippet) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = aShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      vec4 unaryOperation(vec4 x) {
+        ${opSnippet}
+      }
+
+      void main() {
+        vec4 x = getAAtOutCoords();
+        vec4 y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `;
+  }
+};
+var UnpackProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = false;
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const rank = outputShape.length;
+    const channels = getChannels("rc", rank);
+    const dtype = getCoordsDataType(rank);
+    const sourceCoords = getSourceCoords(rank, channels);
+    const innerDims = channels.slice(-2);
+    const coords3 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`;
+    this.userCode = `
+      void main() {
+        ${dtype} rc = getOutputCoords();
+        vec4 packedInput = getA(${sourceCoords});
+
+        setOutput(getChannel(packedInput, ${coords3}));
+      }
+    `;
+  }
+};
+var whereImpl3 = kernel_impls_exports.whereImpl;
+var EPSILON_FLOAT322 = 1e-7;
+var EPSILON_FLOAT162 = 1e-4;
+var binaryCaches = {};
+function getBinaryCache(webGLVersion) {
+  if (webGLVersion in binaryCaches) {
+    return binaryCaches[webGLVersion];
+  }
+  binaryCaches[webGLVersion] = {};
+  return binaryCaches[webGLVersion];
+}
+var CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
+var BEFORE_PAGING_CONSTANT = 600;
+function numMBBeforeWarning() {
+  if (env().global.screen == null) {
+    return 1024;
+  }
+  return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;
+}
+var MathBackendWebGL = class extends KernelBackend {
+  constructor(gpuResource) {
+    super();
+    this.pendingRead = /* @__PURE__ */ new WeakMap();
+    this.pendingDisposal = /* @__PURE__ */ new WeakSet();
+    this.dataRefCount = /* @__PURE__ */ new WeakMap();
+    this.numBytesInGPU = 0;
+    this.uploadWaitMs = 0;
+    this.downloadWaitMs = 0;
+    this.lastGlFlushTime = 0;
+    this.warnedAboutMemory = false;
+    this.pendingDeletes = 0;
+    this.disposed = false;
+    if (!env().getBool("HAS_WEBGL")) {
+      throw new Error("WebGL is not supported on this device");
+    }
+    let newGPGPU;
+    if (gpuResource != null) {
+      if (gpuResource instanceof GPGPUContext) {
+        newGPGPU = gpuResource;
+      } else {
+        const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"), gpuResource);
+        newGPGPU = new GPGPUContext(gl);
+      }
+      this.binaryCache = {};
+      this.gpgpuCreatedLocally = false;
+    } else {
+      const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"));
+      newGPGPU = new GPGPUContext(gl);
+      this.binaryCache = getBinaryCache(env().getNumber("WEBGL_VERSION"));
+      this.gpgpuCreatedLocally = true;
+    }
+    this.gpgpu = newGPGPU;
+    this.canvas = this.gpgpu.gl.canvas;
+    this.textureManager = new TextureManager(this.gpgpu);
+    this.numMBBeforeWarning = numMBBeforeWarning();
+    this.texData = new DataStorage(this, engine());
+  }
+  nextDataId() {
+    return MathBackendWebGL.nextDataId++;
+  }
+  numDataIds() {
+    return this.texData.numDataIds() - this.pendingDeletes;
+  }
+  write(values, shape, dtype) {
+    if (env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || env().getBool("DEBUG")) {
+      this.checkNumericalProblems(values);
+    }
+    if (dtype === "complex64" && values != null) {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    const dataId = { id: this.nextDataId() };
+    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });
+    return dataId;
+  }
+  refCount(dataId) {
+    if (this.texData.has(dataId)) {
+      const tensorData = this.texData.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const texData = this.texData.get(dataId);
+    texData.refCount++;
+  }
+  decRef(dataId) {
+    if (this.texData.has(dataId)) {
+      const texData = this.texData.get(dataId);
+      texData.refCount--;
+    }
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    if (env().getBool("DEBUG")) {
+      this.checkNumericalProblems(values);
+    }
+    if (dtype === "complex64") {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });
+  }
+  disposeIntermediateTensorInfo(tensorInfo) {
+    this.disposeData(tensorInfo.dataId);
+  }
+  readSync(dataId) {
+    const texData = this.texData.get(dataId);
+    const { values, dtype, complexTensorInfos, slice: slice6, shape, isPacked } = texData;
+    if (slice6 != null) {
+      let program;
+      if (isPacked) {
+        program = new UnaryOpPackedProgram(shape, CLONE);
+      } else {
+        program = new UnaryOpProgram(shape, CLONE);
+      }
+      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
+      const data = this.readSync(res.dataId);
+      this.disposeIntermediateTensorInfo(res);
+      return data;
+    }
+    if (values != null) {
+      return this.convertAndCacheOnCPU(dataId);
+    }
+    if (dtype === "string") {
+      return values;
+    }
+    const shouldTimeProgram = this.activeTimers != null;
+    let start;
+    if (shouldTimeProgram) {
+      start = util_exports.now();
+    }
+    let result;
+    if (dtype === "complex64") {
+      const realValues = this.readSync(complexTensorInfos.real.dataId);
+      const imagValues = this.readSync(complexTensorInfos.imag.dataId);
+      result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    } else {
+      result = this.getValuesFromTexture(dataId);
+    }
+    if (shouldTimeProgram) {
+      this.downloadWaitMs += util_exports.now() - start;
+    }
+    return this.convertAndCacheOnCPU(dataId, result);
+  }
+  async read(dataId) {
+    if (this.pendingRead.has(dataId)) {
+      const subscribers2 = this.pendingRead.get(dataId);
+      return new Promise((resolve) => subscribers2.push(resolve));
+    }
+    const texData = this.texData.get(dataId);
+    const { values, shape, slice: slice6, dtype, complexTensorInfos, isPacked } = texData;
+    if (slice6 != null) {
+      let program;
+      if (isPacked) {
+        program = new UnaryOpPackedProgram(shape, CLONE);
+      } else {
+        program = new UnaryOpProgram(shape, CLONE);
+      }
+      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
+      const data = this.read(res.dataId);
+      this.disposeIntermediateTensorInfo(res);
+      return data;
+    }
+    if (values != null) {
+      return this.convertAndCacheOnCPU(dataId);
+    }
+    if (env().getBool("DEBUG")) {
+      if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) {
+        throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);
+      }
+    }
+    let buffer2 = null;
+    let tmpDownloadTarget;
+    if (dtype !== "complex64" && env().get("WEBGL_BUFFER_SUPPORTED")) {
+      tmpDownloadTarget = this.decode(dataId);
+      const tmpData = this.texData.get(tmpDownloadTarget.dataId);
+      buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));
+    }
+    this.pendingRead.set(dataId, []);
+    if (dtype !== "complex64") {
+      await this.gpgpu.createAndWaitForFence();
+    }
+    let vals;
+    if (dtype === "complex64") {
+      const ps = await Promise.all([
+        this.read(complexTensorInfos.real.dataId),
+        this.read(complexTensorInfos.imag.dataId)
+      ]);
+      const realValues = ps[0];
+      const imagValues = ps[1];
+      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    } else if (buffer2 == null) {
+      vals = this.getValuesFromTexture(dataId);
+    } else {
+      const size2 = util_exports.sizeFromShape(shape);
+      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size2);
+    }
+    if (tmpDownloadTarget != null) {
+      this.disposeIntermediateTensorInfo(tmpDownloadTarget);
+    }
+    if (buffer2 != null) {
+      const gl = this.gpgpu.gl;
+      callAndCheck(gl, () => gl.deleteBuffer(buffer2));
+    }
+    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
+    const subscribers = this.pendingRead.get(dataId);
+    this.pendingRead.delete(dataId);
+    subscribers.forEach((resolve) => resolve(dTypeVals));
+    if (this.pendingDisposal.has(dataId)) {
+      this.pendingDisposal.delete(dataId);
+      if (this.disposeData(dataId)) {
+        engine().removeDataId(dataId, this);
+      }
+      this.pendingDeletes--;
+    }
+    return dTypeVals;
+  }
+  readToGPU(dataId, options4 = {}) {
+    const texData = this.texData.get(dataId);
+    const { values, shape, slice: slice6, dtype, isPacked, texture } = texData;
+    if (dtype === "complex64") {
+      throw new Error("Does not support reading texture for complex64 dtype.");
+    }
+    if (slice6 != null) {
+      let program;
+      if (isPacked) {
+        program = new UnaryOpPackedProgram(shape, CLONE);
+      } else {
+        program = new UnaryOpProgram(shape, CLONE);
+      }
+      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
+      const gpuResouorce = this.readToGPU(res, options4);
+      this.disposeIntermediateTensorInfo(res);
+      return gpuResouorce;
+    }
+    if (texture == null) {
+      if (values != null) {
+        throw new Error("Data is not on GPU but on CPU.");
+      } else {
+        throw new Error("There is no data on GPU or CPU.");
+      }
+    }
+    const tmpTarget = this.decode(dataId, options4.customTexShape);
+    const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);
+    const tmpData = this.texData.get(tmpTarget.dataId);
+    return Object.assign({ tensorRef }, tmpData.texture);
+  }
+  bufferSync(t2) {
+    const data = this.readSync(t2.dataId);
+    if (t2.dtype === "string") {
+      try {
+        const strings = data.map((d) => util_exports.decodeString(d));
+        return buffer(t2.shape, t2.dtype, strings);
+      } catch (_a) {
+        throw new Error("Failed to decode encoded string bytes into utf-8");
+      }
+    }
+    return buffer(t2.shape, t2.dtype, data);
+  }
+  checkNumericalProblems(values) {
+    if (values == null) {
+      return;
+    }
+    for (let i2 = 0; i2 < values.length; i2++) {
+      const num = values[i2];
+      if (!canBeRepresented(num)) {
+        if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) {
+          throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
+        }
+        throw Error(`The value ${num} cannot be represented on this device.`);
+      }
+    }
+  }
+  getValuesFromTexture(dataId) {
+    const { shape, dtype, isPacked } = this.texData.get(dataId);
+    const size2 = util_exports.sizeFromShape(shape);
+    if (env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
+      const tmpTarget = this.decode(dataId);
+      const tmpData2 = this.texData.get(tmpTarget.dataId);
+      const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size2);
+      this.disposeIntermediateTensorInfo(tmpTarget);
+      return vals2;
+    }
+    const shouldUsePackedProgram = env().getBool("WEBGL_PACK") && isPacked === true;
+    const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;
+    const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);
+    const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], "float32");
+    const tmpData = this.texData.get(output.dataId);
+    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size2);
+    this.disposeIntermediateTensorInfo(output);
+    return vals;
+  }
+  timerAvailable() {
+    return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
+  }
+  time(f) {
+    const oldActiveTimers = this.activeTimers;
+    const newActiveTimers = [];
+    let outerMostTime = false;
+    if (this.programTimersStack == null) {
+      this.programTimersStack = newActiveTimers;
+      outerMostTime = true;
+    } else {
+      this.activeTimers.push(newActiveTimers);
+    }
+    this.activeTimers = newActiveTimers;
+    f();
+    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);
+    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);
+    this.activeTimers = oldActiveTimers;
+    if (outerMostTime) {
+      this.programTimersStack = null;
+    }
+    const res = {
+      uploadWaitMs: this.uploadWaitMs,
+      downloadWaitMs: this.downloadWaitMs,
+      kernelMs: null,
+      wallMs: null
+    };
+    return (async () => {
+      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+        const kernelMs = await Promise.all(flattenedActiveTimerQueries);
+        res["kernelMs"] = util_exports.sum(kernelMs);
+        res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+      } else {
+        res["kernelMs"] = {
+          error: "WebGL query timers are not supported in this environment."
+        };
+      }
+      this.uploadWaitMs = 0;
+      this.downloadWaitMs = 0;
+      return res;
+    })();
+  }
+  memory() {
+    return {
+      unreliable: false,
+      numBytesInGPU: this.numBytesInGPU,
+      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
+      numBytesInGPUFree: this.textureManager.numBytesFree
+    };
+  }
+  startTimer() {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+      return this.gpgpu.beginQuery();
+    }
+    return { startMs: util_exports.now(), endMs: null };
+  }
+  endTimer(query) {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+      this.gpgpu.endQuery();
+      return query;
+    }
+    query.endMs = util_exports.now();
+    return query;
+  }
+  async getQueryTime(query) {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+      return this.gpgpu.waitForQueryAndGetTime(query);
+    }
+    const timerQuery = query;
+    return timerQuery.endMs - timerQuery.startMs;
+  }
+  disposeData(dataId, force = false) {
+    if (this.pendingDisposal.has(dataId)) {
+      return false;
+    }
+    if (!this.texData.has(dataId)) {
+      return true;
+    }
+    if (force) {
+      this.texData.get(dataId).refCount = 0;
+    } else {
+      this.texData.get(dataId).refCount--;
+    }
+    if (!force && this.texData.get(dataId).refCount > 0) {
+      return false;
+    }
+    if (this.pendingRead.has(dataId)) {
+      this.pendingDisposal.add(dataId);
+      this.pendingDeletes++;
+      return false;
+    }
+    this.releaseGPUData(dataId);
+    const { complexTensorInfos } = this.texData.get(dataId);
+    if (complexTensorInfos != null) {
+      this.disposeData(complexTensorInfos.real.dataId, force);
+      this.disposeData(complexTensorInfos.imag.dataId, force);
+    }
+    this.texData.delete(dataId);
+    return true;
+  }
+  releaseGPUData(dataId) {
+    const { texture, dtype, texShape, usage, isPacked, slice: slice6 } = this.texData.get(dataId);
+    const key = slice6 && slice6.origDataId || dataId;
+    const refCount = this.dataRefCount.get(key);
+    if (refCount > 1) {
+      this.dataRefCount.set(key, refCount - 1);
+    } else {
+      this.dataRefCount.delete(key);
+      if (texture != null) {
+        this.numBytesInGPU -= this.computeBytes(texShape, dtype);
+        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
+      }
+    }
+    const texData = this.texData.get(dataId);
+    texData.texture = null;
+    texData.texShape = null;
+    texData.isPacked = false;
+    texData.slice = null;
+  }
+  getTexture(dataId) {
+    this.uploadToGPU(dataId);
+    return this.texData.get(dataId).texture.texture;
+  }
+  getDataInfo(dataId) {
+    return this.texData.get(dataId);
+  }
+  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {
+    return env().getBool("WEBGL_CPU_FORWARD") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);
+  }
+  getGPGPUContext() {
+    return this.gpgpu;
+  }
+  where(condition) {
+    backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
+    const condVals = condition.dataSync();
+    return whereImpl3(condition.shape, condVals);
+  }
+  packedUnaryOp(x, op2, dtype) {
+    const program = new UnaryOpPackedProgram(x.shape, op2);
+    const outInfo = this.compileAndRun(program, [x], dtype);
+    return engine().makeTensorFromTensorInfo(outInfo);
+  }
+  abs(x) {
+    if (this.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
+      const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);
+      return this.makeOutput(x.shape, x.dtype, outValues);
+    }
+    if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
+      return this.packedUnaryOp(x, ABS, x.dtype);
+    }
+    const program = new UnaryOpProgram(x.shape, ABS);
+    const outInfo = this.compileAndRun(program, [x]);
+    return engine().makeTensorFromTensorInfo(outInfo);
+  }
+  makeTensorInfo(shape, dtype, values) {
+    let dataId;
+    if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
+      const encodedValues = values.map((d) => util_exports.encodeString(d));
+      dataId = this.write(encodedValues, shape, dtype);
+    } else {
+      dataId = this.write(values, shape, dtype);
+    }
+    this.texData.get(dataId).usage = null;
+    return { dataId, shape, dtype };
+  }
+  makeOutput(shape, dtype, values) {
+    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
+  }
+  unpackTensor(input2) {
+    const program = new UnpackProgram(input2.shape);
+    return this.runWebGLProgram(program, [input2], input2.dtype);
+  }
+  packTensor(input2) {
+    const program = new PackProgram(input2.shape);
+    const preventEagerUnpackingOutput = true;
+    return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);
+  }
+  packedReshape(input2, afterShape) {
+    const input3DShape = [
+      getBatchDim(input2.shape),
+      ...getRowsCols(input2.shape)
+    ];
+    const input3D = {
+      dtype: input2.dtype,
+      shape: input3DShape,
+      dataId: input2.dataId
+    };
+    const afterShapeAs3D = [
+      getBatchDim(afterShape),
+      ...getRowsCols(afterShape)
+    ];
+    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
+    const preventEagerUnpackingOfOutput = true;
+    const customValues = [input3DShape];
+    const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);
+    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
+  }
+  decode(dataId, customTexShape) {
+    const texData = this.texData.get(dataId);
+    const { isPacked, shape, dtype } = texData;
+    if (customTexShape != null) {
+      const size2 = util_exports.sizeFromShape(shape);
+      const texSize = customTexShape[0] * customTexShape[1] * 4;
+      util_exports.assert(size2 <= texSize, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
+    }
+    const shapeAs3D = getShapeAs3D(shape);
+    let program;
+    if (isPacked) {
+      program = new DecodeMatrixPackedProgram(shapeAs3D);
+    } else {
+      program = new DecodeMatrixProgram(shapeAs3D);
+    }
+    const preventEagerUnpackingOfOutput = true;
+    const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];
+    const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);
+    return { dtype, shape, dataId: out.dataId };
+  }
+  runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {
+    const output = this.makeTensorInfo(program.outputShape, outputDtype);
+    const outData = this.texData.get(output.dataId);
+    if (program.packedOutput) {
+      outData.isPacked = true;
+    }
+    if (program.outPackingScheme === PackingScheme.DENSE) {
+      const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);
+      outData.texShape = texelShape.map((d) => d * 2);
+    }
+    if (program.outTexUsage != null) {
+      outData.usage = program.outTexUsage;
+    }
+    if (util_exports.sizeFromShape(output.shape) === 0) {
+      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
+      return output;
+    }
+    const dataToDispose = [];
+    const inputsData = inputs.map((input2) => {
+      if (input2.dtype === "complex64") {
+        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
+      }
+      let texData = this.texData.get(input2.dataId);
+      if (texData.texture == null) {
+        if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) {
+          return {
+            shape: input2.shape,
+            texData: null,
+            isUniform: true,
+            uniformValues: texData.values
+          };
+        }
+        if (program.packedInputs) {
+          texData.isPacked = true;
+          texData.shape = input2.shape;
+        }
+      }
+      this.uploadToGPU(input2.dataId);
+      if (!!texData.isPacked !== !!program.packedInputs) {
+        input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);
+        dataToDispose.push(input2);
+        texData = this.texData.get(input2.dataId);
+      } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {
+        const savedInput = input2;
+        const targetShape = input2.shape;
+        input2.shape = texData.shape;
+        input2 = this.packedReshape(input2, targetShape);
+        dataToDispose.push(input2);
+        texData = this.texData.get(input2.dataId);
+        savedInput.shape = targetShape;
+      }
+      return { shape: input2.shape, texData, isUniform: false };
+    });
+    this.uploadToGPU(output.dataId);
+    const outputData = { shape: output.shape, texData: outData, isUniform: false };
+    const key = makeShaderKey(program, inputsData, outputData);
+    const binary = this.getAndSaveBinary(key, () => {
+      return compileProgram(this.gpgpu, program, inputsData, outputData);
+    });
+    const shouldTimeProgram = this.activeTimers != null;
+    let query;
+    if (shouldTimeProgram) {
+      query = this.startTimer();
+    }
+    if (!env().get("ENGINE_COMPILE_ONLY")) {
+      runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);
+    }
+    dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));
+    if (shouldTimeProgram) {
+      query = this.endTimer(query);
+      this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
+    }
+    const glFlushThreshold = env().get("WEBGL_FLUSH_THRESHOLD");
+    if (glFlushThreshold > 0) {
+      const time2 = util_exports.now();
+      if (time2 - this.lastGlFlushTime > glFlushThreshold) {
+        this.gpgpu.gl.flush();
+        this.lastGlFlushTime = time2;
+      }
+    }
+    if (!env().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) {
+      const unpacked = this.unpackTensor(output);
+      this.disposeIntermediateTensorInfo(output);
+      return unpacked;
+    }
+    return output;
+  }
+  compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {
+    outputDtype = outputDtype || inputs[0].dtype;
+    const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);
+    return outInfo;
+  }
+  getAndSaveBinary(key, getBinary) {
+    if (!(key in this.binaryCache)) {
+      this.binaryCache[key] = getBinary();
+    }
+    return this.binaryCache[key];
+  }
+  getTextureManager() {
+    return this.textureManager;
+  }
+  dispose() {
+    if (this.disposed) {
+      return;
+    }
+    if (!env().getBool("IS_TEST")) {
+      const allKeys = Object.keys(this.binaryCache);
+      allKeys.forEach((key) => {
+        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
+        delete this.binaryCache[key];
+      });
+    }
+    this.textureManager.dispose();
+    if (this.canvas != null && (typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement)) {
+      this.canvas.remove();
+    } else {
+      this.canvas = null;
+    }
+    if (this.gpgpuCreatedLocally) {
+      this.gpgpu.program = null;
+      this.gpgpu.dispose();
+    }
+    this.disposed = true;
+  }
+  floatPrecision() {
+    if (this.floatPrecisionValue == null) {
+      this.floatPrecisionValue = tidy(() => {
+        if (!env().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
+          const debugFlag = env().getBool("DEBUG");
+          env().set("DEBUG", false);
+          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];
+          env().set("DEBUG", debugFlag);
+          if (underflowCheckValue > 0) {
+            return 32;
+          }
+        }
+        return 16;
+      });
+    }
+    return this.floatPrecisionValue;
+  }
+  epsilon() {
+    return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;
+  }
+  uploadToGPU(dataId) {
+    const texData = this.texData.get(dataId);
+    const { shape, dtype, values, texture, usage, isPacked } = texData;
+    if (texture != null) {
+      return;
+    }
+    const shouldTimeProgram = this.activeTimers != null;
+    let start;
+    if (shouldTimeProgram) {
+      start = util_exports.now();
+    }
+    let texShape = texData.texShape;
+    if (texShape == null) {
+      texShape = getTextureShapeFromLogicalShape(shape, isPacked);
+      texData.texShape = texShape;
+    }
+    if (values != null) {
+      const shapeAs3D = getShapeAs3D(shape);
+      let program;
+      let width = texShape[1], height = texShape[0];
+      const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;
+      if (isPacked || !isByteArray) {
+        [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);
+      }
+      if (isPacked) {
+        program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);
+      } else {
+        program = new EncodeMatrixProgram(shapeAs3D, isByteArray);
+      }
+      const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;
+      const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);
+      const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);
+      if (isByteArray) {
+        tempDenseInputTexData.usage = TextureUsage.PIXELS;
+      } else {
+        tempDenseInputTexData.usage = TextureUsage.UPLOAD;
+      }
+      tempDenseInputTexData.texShape = tempDenseInputTexShape;
+      this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
+      const customValues = [[height, width]];
+      const preventEagerUnpacking = true;
+      const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);
+      const outputTexData = this.texData.get(encodedOutputTarget.dataId);
+      texData.texShape = outputTexData.texShape;
+      texData.isPacked = outputTexData.isPacked;
+      texData.usage = outputTexData.usage;
+      if (!env().get("ENGINE_COMPILE_ONLY")) {
+        texData.texture = outputTexData.texture;
+        texData.values = null;
+        this.texData.delete(encodedOutputTarget.dataId);
+      } else {
+        this.disposeData(encodedOutputTarget.dataId);
+      }
+      this.disposeIntermediateTensorInfo(tempDenseInputHandle);
+      if (shouldTimeProgram) {
+        this.uploadWaitMs += util_exports.now() - start;
+      }
+    } else {
+      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
+      texData.texture = newTexture;
+    }
+  }
+  convertAndCacheOnCPU(dataId, float32Values) {
+    const texData = this.texData.get(dataId);
+    const { dtype } = texData;
+    this.releaseGPUData(dataId);
+    if (float32Values != null) {
+      texData.values = float32ToTypedArray(float32Values, dtype);
+    }
+    return texData.values;
+  }
+  acquireTexture(texShape, texType, dtype, isPacked) {
+    this.numBytesInGPU += this.computeBytes(texShape, dtype);
+    if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
+      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
+      this.warnedAboutMemory = true;
+      console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);
+    }
+    return this.textureManager.acquireTexture(texShape, texType, isPacked);
+  }
+  computeBytes(shape, dtype) {
+    return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);
+  }
+  checkCompileCompletion() {
+    for (const [, binary] of Object.entries(this.binaryCache)) {
+      this.checkCompletion_(binary);
+    }
+  }
+  async checkCompileCompletionAsync() {
+    const ps = [];
+    if (this.gpgpu.parallelCompilationExtension) {
+      for (const [, binary] of Object.entries(this.binaryCache)) {
+        ps.push(this.checkCompletionAsync_(binary));
+      }
+      return Promise.all(ps);
+    } else {
+      for (const [, binary] of Object.entries(this.binaryCache)) {
+        const p2 = new Promise((resolve) => {
+          try {
+            this.checkCompletion_(binary);
+            resolve(true);
+          } catch (error) {
+            throw error;
+          }
+        });
+        ps.push(p2);
+      }
+      return Promise.all(ps);
+    }
+  }
+  async checkCompletionAsync_(binary) {
+    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {
+      return this.checkCompletion_(binary);
+    } else {
+      await nextFrame();
+      return this.checkCompletionAsync_(binary);
+    }
+  }
+  checkCompletion_(binary) {
+    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {
+      console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));
+      if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {
+        logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));
+        throw new Error("Failed to compile fragment shader.");
+      }
+      throw new Error("Failed to link vertex and fragment shaders.");
+    }
+    return true;
+  }
+  getUniformLocations() {
+    for (const [, binary] of Object.entries(this.binaryCache)) {
+      const { uniformLocations, customUniformLocations, infLoc, nanLoc, inShapesLocations, inTexShapesLocations, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);
+      binary.uniformLocations = uniformLocations;
+      binary.customUniformLocations = customUniformLocations;
+      binary.infLoc = infLoc;
+      binary.nanLoc = nanLoc;
+      binary.inShapesLocations = inShapesLocations;
+      binary.inTexShapesLocations = inTexShapesLocations;
+      binary.outShapeLocation = outShapeLocation;
+      binary.outShapeStridesLocation = outShapeStridesLocation;
+      binary.outTexShapeLocation = outTexShapeLocation;
+    }
+  }
+};
+MathBackendWebGL.nextDataId = 0;
+function float32ToTypedArray(a6, dtype) {
+  if (dtype === "float32" || dtype === "complex64") {
+    return a6;
+  } else if (dtype === "int32" || dtype === "bool") {
+    const result = dtype === "int32" ? new Int32Array(a6.length) : new Uint8Array(a6.length);
+    for (let i2 = 0; i2 < result.length; ++i2) {
+      result[i2] = Math.round(a6[i2]);
+    }
+    return result;
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+var version6 = "3.18.0";
+function forceHalfFloat() {
+  env().set("WEBGL_FORCE_F16_TEXTURES", true);
+}
+if (device_util_exports.isBrowser()) {
+  registerBackend("webgl", () => new MathBackendWebGL(), 2);
+}
+var webgl = { forceHalfFloat };
+var CHECK_NAN_SNIPPET2 = `
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`;
+var BinaryOpProgram = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["A", "B"];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      float binaryOperation(float a, float b) {
+        ${op2}
+      }
+
+      void main() {
+        float a = getAAtOutCoords();
+        float b = getBAtOutCoords();
+        setOutput(binaryOperation(a, b));
+      }
+    `;
+  }
+};
+var CHECK_NAN_SNIPPET3 = `
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`;
+var BinaryOpPackedProgram = class {
+  constructor(op2, aShape, bShape, checkOutOfBounds = false) {
+    this.variableNames = ["A", "B"];
+    this.supportsBroadcasting = true;
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    const rank = this.outputShape.length;
+    this.enableShapeUniforms = useShapeUniforms(rank);
+    let checkOutOfBoundsString = "";
+    if (checkOutOfBounds) {
+      if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {
+        checkOutOfBoundsString = `
+          result.y = 0.;
+          result.z = 0.;
+          result.w = 0.;
+        `;
+      } else {
+        const dtype = getCoordsDataType(rank);
+        checkOutOfBoundsString = `
+          ${dtype} coords = getOutputCoords();
+        `;
+        if (rank === 1) {
+          if (this.enableShapeUniforms) {
+            checkOutOfBoundsString += `
+            result.y = (coords + 1) >= outShape ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;
+          } else {
+            checkOutOfBoundsString += `
+            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;
+          }
+        } else {
+          const channels = getChannels("coords", rank);
+          if (this.enableShapeUniforms) {
+            checkOutOfBoundsString += `
+            bool nextRowOutOfBounds =
+              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];
+            bool nextColOutOfBounds =
+              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `;
+          } else {
+            checkOutOfBoundsString += `
+            bool nextRowOutOfBounds =
+              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};
+            bool nextColOutOfBounds =
+              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `;
+          }
+        }
+      }
+    }
+    this.userCode = `
+      vec4 binaryOperation(vec4 a, vec4 b) {
+        ${op2}
+      }
+
+      void main() {
+        vec4 a = getAAtOutCoords();
+        vec4 b = getBAtOutCoords();
+
+        vec4 result = binaryOperation(a, b);
+        ${checkOutOfBoundsString}
+
+        setOutput(result);
+      }
+    `;
+  }
+};
+function identity3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  backend2.incRef(x.dataId);
+  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+}
+var identityConfig2 = {
+  kernelName: Identity,
+  backendName: "webgl",
+  kernelFunc: identity3
+};
+function complex3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { real: real5, imag: imag5 } = inputs;
+  const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64");
+  const complex5 = backend2.texData.get(complexInfo.dataId);
+  const realTensorInfo = identity3({ inputs: { x: real5 }, backend: backend2 });
+  const imagTensorInfo = identity3({ inputs: { x: imag5 }, backend: backend2 });
+  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };
+  return complexInfo;
+}
+var complexConfig2 = {
+  kernelName: Complex,
+  backendName: "webgl",
+  kernelFunc: complex3
+};
+var LEAKYRELU = `return (a < 0.) ? b * a : a;`;
+var LEAKYRELU_PACKED = `
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`;
+function leakyRelu3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { alpha: alpha2 } = attrs;
+  const $alpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(alpha2, "float32"));
+  const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);
+  const result = backend2.runWebGLProgram(program, [x, $alpha], "float32");
+  backend2.disposeIntermediateTensorInfo($alpha);
+  return result;
+}
+var leakyReluConfig2 = {
+  kernelName: LeakyRelu,
+  backendName: "webgl",
+  kernelFunc: leakyRelu3
+};
+var PRELU = `return (a < 0.) ? b * a : a;`;
+var PRELU_PACKED = `
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`;
+function prelu4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha: alpha2 } = inputs;
+  const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha2.shape) : new BinaryOpProgram(PRELU, x.shape, alpha2.shape);
+  return backend2.runWebGLProgram(program, [x, alpha2], "float32");
+}
+var preluConfig2 = {
+  kernelName: Prelu,
+  backendName: "webgl",
+  kernelFunc: prelu4
+};
+var CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;
+var CHECK_NAN_SNIPPET_BINARY = `
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`;
+var CHECK_NAN_SNIPPET_BINARY_PACKED = `
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`;
+function unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const webglBackend = backend2;
+    const $dtype = dtype || x.dtype;
+    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
+      const xData = webglBackend.texData.get(x.dataId);
+      const outValues = cpuKernelImpl(xData.values, $dtype);
+      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
+    }
+    const shouldUsePackedProgram = env().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null;
+    let program;
+    if (shouldUsePackedProgram) {
+      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);
+    } else {
+      program = new UnaryOpProgram(x.shape, opSnippet);
+    }
+    return webglBackend.runWebGLProgram(program, [x], $dtype);
+  };
+}
+function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { a: a6, b } = inputs;
+    const webglBackend = backend2;
+    if (supportsComplex && a6.dtype === "complex64") {
+      const aData = webglBackend.texData.get(a6.dataId);
+      const bData = webglBackend.texData.get(b.dataId);
+      const [real5, imag5] = [
+        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
+        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
+      ].map((complexParts) => {
+        const [aPart, bPart] = complexParts;
+        const aHandle = {
+          dataId: aPart.dataId,
+          dtype: aPart.dtype,
+          shape: a6.shape
+        };
+        const bHandle = {
+          dataId: bPart.dataId,
+          dtype: bPart.dtype,
+          shape: b.shape
+        };
+        const program2 = new BinaryOpProgram(opSnippet, a6.shape, b.shape);
+        return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
+      });
+      const complexOutput = complex3({ inputs: { real: real5, imag: imag5 }, backend: webglBackend });
+      webglBackend.disposeIntermediateTensorInfo(real5);
+      webglBackend.disposeIntermediateTensorInfo(imag5);
+      return complexOutput;
+    }
+    const $dtype = dtype || upcastType(a6.dtype, b.dtype);
+    if ((a6.dtype === "string" || b.dtype === "string" || webglBackend.shouldExecuteOnCPU([a6, b])) && cpuKernelImpl != null) {
+      const aVals = webglBackend.texData.get(a6.dataId).values;
+      const bVals = webglBackend.texData.get(b.dataId).values;
+      const decodedAVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;
+      const decodedBVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;
+      const [outValues, outShape] = cpuKernelImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);
+      const out = webglBackend.makeTensorInfo(outShape, $dtype);
+      const outData = webglBackend.texData.get(out.dataId);
+      outData.values = outValues;
+      return out;
+    }
+    const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null;
+    let program;
+    if (shouldUsePackedProgram) {
+      program = new BinaryOpPackedProgram(packedOpSnippet, a6.shape, b.shape, checkOutOfBounds);
+    } else {
+      program = new BinaryOpProgram(opSnippet, a6.shape, b.shape);
+    }
+    return webglBackend.runWebGLProgram(program, [a6, b], $dtype);
+  };
+}
+function mapActivationToShaderProgram(activation2, packed = false) {
+  if (activation2 === "linear") {
+    if (packed) {
+      return LINEAR2;
+    }
+    return LINEAR;
+  } else if (activation2 === "relu") {
+    if (packed) {
+      return RELU2;
+    }
+    return RELU;
+  } else if (activation2 === "elu") {
+    if (packed) {
+      return ELU3;
+    }
+    return ELU2;
+  } else if (activation2 === "relu6") {
+    if (packed) {
+      return RELU62;
+    }
+    return RELU6;
+  } else if (activation2 === "prelu") {
+    if (packed) {
+      return PRELU_PACKED;
+    }
+    return PRELU;
+  } else if (activation2 === "leakyrelu") {
+    if (packed) {
+      return LEAKYRELU_PACKED;
+    }
+    return LEAKYRELU;
+  } else if (activation2 === "sigmoid") {
+    if (packed) {
+      return SIGMOID2;
+    }
+    return SIGMOID;
+  }
+  throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);
+}
+var MatMulPackedProgram = class {
+  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {
+    this.variableNames = ["matrixA", "matrixB"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const sharedDim = transposeA ? aShape[1] : aShape[2];
+    const sharedDimensionPacked = Math.ceil(sharedDim / 2);
+    const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2";
+    const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z";
+    const aSwizzle = transposeA ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"];
+    const bSwizzle = transposeB ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"];
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivation) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyreluActivation) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `vec4 activation(vec4 x) {
+          ${activation2}
+        }`;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyreluActivation) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    let batchASnippet = "rc.x";
+    let batchBSnippet = "rc.x";
+    if (aShape[0] < bShape[0]) {
+      batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;
+    } else if (bShape[0] < aShape[0]) {
+      batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;
+    }
+    this.userCode = `
+      ${activationSnippet}
+      // Don't use uniform for sharedDimensionPacked for performance.
+      const float sharedDimension = ${sharedDimensionPacked}.0;
+
+      vec4 dot2x2ARowBCol(ivec3 rc) {
+        vec4 result = vec4(0);
+        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
+          int batchA = ${batchASnippet};
+          int batchB = ${batchBSnippet};
+          vec4 a = getMatrixA(batchA, ${aSample});
+          vec4 b = getMatrixB(batchB, ${bSample});
+
+          // These swizzled products need to be separately added.
+          // See: https://github.com/tensorflow/tfjs/issues/1735
+          result += (${aSwizzle[0]} * ${bSwizzle[0]});
+          result += (${aSwizzle[1]} * ${bSwizzle[1]});
+        }
+        return result;
+      }
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+        vec4 result = dot2x2ARowBCol(rc);
+
+        ${addBiasSnippet}
+
+        ${applyActivationSnippet}
+
+        setOutput(result);
+      }
+    `;
+  }
+};
+var COMPLEX_MULTIPLY = {
+  REAL: "return areal * breal - aimag * bimag;",
+  IMAG: "return areal * bimag + aimag * breal;"
+};
+var BinaryOpComplexProgram = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["AReal", "AImag", "BReal", "BImag"];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.userCode = `
+      float binaryOpComplex(
+          float areal, float aimag, float breal, float bimag) {
+        ${op2}
+      }
+
+      void main() {
+        float areal = getARealAtOutCoords();
+        float aimag = getAImagAtOutCoords();
+        float breal = getBRealAtOutCoords();
+        float bimag = getBImagAtOutCoords();
+        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
+      }
+    `;
+  }
+};
+var MUL = "return a * b;";
+function multiply3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { a: a6, b } = inputs;
+  const dtype = backend_util_exports.upcastType(a6.dtype, b.dtype);
+  if (a6.dtype === "complex64") {
+    const aData = backend2.texData.get(a6.dataId);
+    const bData = backend2.texData.get(b.dataId);
+    const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a6.shape, b.shape);
+    const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a6.shape, b.shape);
+    const inputs2 = [
+      {
+        dataId: aData.complexTensorInfos.real.dataId,
+        dtype: aData.complexTensorInfos.real.dtype,
+        shape: a6.shape
+      },
+      {
+        dataId: aData.complexTensorInfos.imag.dataId,
+        dtype: aData.complexTensorInfos.imag.dtype,
+        shape: a6.shape
+      },
+      {
+        dataId: bData.complexTensorInfos.real.dataId,
+        dtype: bData.complexTensorInfos.real.dtype,
+        shape: b.shape
+      },
+      {
+        dataId: bData.complexTensorInfos.imag.dataId,
+        dtype: bData.complexTensorInfos.imag.dtype,
+        shape: b.shape
+      }
+    ];
+    const realPart = backend2.runWebGLProgram(realProgram, inputs2, "float32");
+    const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, "float32");
+    const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    return complexOutput;
+  }
+  if (backend2.shouldExecuteOnCPU([a6, b])) {
+    const aData = backend2.texData.get(a6.dataId);
+    const bData = backend2.texData.get(b.dataId);
+    const [outValues, outShape] = multiplyImplCPU(a6.shape, b.shape, aData.values, bData.values, dtype);
+    const out = backend2.makeTensorInfo(outShape, dtype);
+    const outData = backend2.texData.get(out.dataId);
+    outData.values = outValues;
+    return out;
+  }
+  let program;
+  if (env().getBool("WEBGL_PACK_BINARY_OPERATIONS")) {
+    program = new BinaryOpPackedProgram(MUL, a6.shape, b.shape);
+  } else {
+    program = new BinaryOpProgram(MUL, a6.shape, b.shape);
+  }
+  return backend2.runWebGLProgram(program, [a6, b], dtype);
+}
+var multiplyConfig2 = {
+  kernelName: Multiply,
+  backendName: "webgl",
+  kernelFunc: multiply3
+};
+function packedReshape(input2, afterShape, backend2) {
+  const input3DShape = [
+    getBatchDim(input2.shape),
+    ...getRowsCols(input2.shape)
+  ];
+  const input3D = {
+    dtype: input2.dtype,
+    shape: input3DShape,
+    dataId: input2.dataId
+  };
+  const afterShapeAs3D = [
+    getBatchDim(afterShape),
+    ...getRowsCols(afterShape)
+  ];
+  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
+  const preventEagerUnpackingOfOutput = true;
+  const customValues = [input3DShape];
+  const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);
+  return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
+}
+function reshape4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const webglBackend = backend2;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  const $xSize = util_exports.sizeFromShape($shape);
+  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
+  const xTexData = webglBackend.texData.get(x.dataId);
+  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {
+    return packedReshape(x, $shape, webglBackend);
+  }
+  webglBackend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig2 = {
+  kernelName: Reshape,
+  backendName: "webgl",
+  kernelFunc: reshape4
+};
+var MeanProgram = class {
+  constructor(reduceInfo, divisor) {
+    this.variableNames = ["x"];
+    const { windowSize, batchSize, inSize, outSize } = reduceInfo;
+    this.outputShape = [batchSize, outSize];
+    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+    const windowSizeVec4Remainder = windowSize % 4;
+    let updateSnippet = `sumValue += dot(values, ones);`;
+    if (divisor != null) {
+      const denominator = 1 / divisor;
+      updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;
+    }
+    let checkOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return 0.0;
+        }
+      `;
+    }
+    this.userCode = `
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${checkOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder === 1}) {
+          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1), 0.0, 0.0);
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2), 0.0);
+
+          ${updateSnippet}
+        }
+        setOutput(sumValue);
+      }
+    `;
+  }
+};
+var ReduceProgram = class {
+  constructor(reduceInfo, reduceType) {
+    this.variableNames = ["x"];
+    const { windowSize, batchSize, inSize, outSize } = reduceInfo;
+    this.outputShape = [batchSize, outSize];
+    let initializationValue = "0.0";
+    let compareOp = ``;
+    if (reduceType === "prod") {
+      initializationValue = "1.0";
+    } else if (reduceType === "min") {
+      initializationValue = "1.0 / 1e-20";
+      compareOp = `min`;
+    } else if (reduceType === "max") {
+      initializationValue = "-1.0 / 1e-20";
+      compareOp = `max`;
+    }
+    let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
+    if (reduceType === "sum") {
+      returnValue = `sumValue`;
+    } else if (reduceType === "prod") {
+      returnValue = `prodValue`;
+    } else if (reduceType === "all") {
+      returnValue = `allValue`;
+    } else if (reduceType === "any") {
+      returnValue = `anyValue`;
+    }
+    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+    const windowSizeVec4Remainder = windowSize % 4;
+    let updateSnippet = `
+      if (${reduceType === "sum"}) {
+        sumValue += dot(values, ones);
+      } else if (${reduceType === "prod"}) {
+        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
+        prodValue *= tmp[0] * tmp[1];
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+        if (${reduceType === "min"} || ${reduceType === "max"}) {
+          minMaxValue = ${compareOp}(values, minMaxValue);
+          bvec4 isNaN = isnan(values);
+          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
+            minMaxValue = vec4(NAN);
+          }
+        }
+      }
+    `;
+    let vecType = `vec4`;
+    if (reduceType === "all") {
+      initializationValue = "1.0";
+      updateSnippet = `
+        bool reducedAllValue = all(values);
+        float floatedReducedAllValue = float(reducedAllValue);
+        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
+      `;
+      vecType = `bvec4`;
+    } else if (reduceType === "any") {
+      initializationValue = "0.0";
+      updateSnippet = `
+        bool reducedAnyValue = any(values);
+        float floatedReducedAnyValue = float(reducedAnyValue);
+        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
+      `;
+      vecType = `bvec4`;
+    }
+    let checkOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return initializationValue;
+        }
+      `;
+    }
+    this.userCode = `
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${checkOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        vec4 minMaxValue = vec4(${initializationValue});
+        float prodValue = 1.0;
+        float sumValue = 0.0;
+        float allValue = 1.0;
+        float anyValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder === 1}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 2}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 3}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          ${updateSnippet}
+        }
+        setOutput(${returnValue});
+      }
+    `;
+  }
+};
+function getReductionStages(inShape) {
+  const stages = [];
+  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {
+    const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];
+    const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);
+    stages.push({
+      inSize: outSize,
+      windowSize,
+      outSize: Math.ceil(outSize / windowSize)
+    });
+  }
+  return stages;
+}
+function reduce(x, dtype, reductionType, backend2) {
+  const reductionStages = getReductionStages(x.shape);
+  let result = x;
+  for (let i2 = 0; i2 < reductionStages.length; i2++) {
+    const { inSize, windowSize, outSize } = reductionStages[i2];
+    let program;
+    let previousResult;
+    if (reductionType === "mean") {
+      program = i2 === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
+    } else {
+      program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);
+    }
+    previousResult = result;
+    result = backend2.runWebGLProgram(program, [result], dtype);
+    if (previousResult.dataId !== x.dataId) {
+      backend2.disposeIntermediateTensorInfo(previousResult);
+    }
+  }
+  return result;
+}
+var TransposeProgram = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    const dtype = getCoordsDataType(this.rank);
+    const switched = getSwitchedCoords(newDim);
+    this.userCode = `
+    void main() {
+      ${dtype} resRC = getOutputCoords();
+      setOutput(getA(${switched}));
+    }
+    `;
+  }
+};
+function getSwitchedCoords(newDim) {
+  const rank = newDim.length;
+  if (rank > 6) {
+    throw Error(`Transpose for rank ${rank} is not yet supported`);
+  }
+  const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"];
+  const switchedCoords = new Array(rank);
+  for (let i2 = 0; i2 < newDim.length; i2++) {
+    switchedCoords[newDim[i2]] = originalOrder[i2];
+  }
+  return switchedCoords.join();
+}
+var TransposePackedProgram = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    if (this.rank > 6) {
+      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
+    }
+    const dtype = getCoordsDataType(this.rank);
+    const outputOrder = getVecChannels("rc", this.rank);
+    const switchedOrder = new Array(this.rank);
+    for (let i2 = 0; i2 < newDim.length; i2++) {
+      switchedOrder[newDim[i2]] = outputOrder[i2];
+    }
+    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
+    const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
+    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;
+    this.userCode = `
+    void main() {
+      ${dtype} rc = getOutputCoords();
+      vec4 result = vec4(0.);
+      result[0] = ${getc};
+      if(${nextColumn}) {
+        result[1] = ${getc};
+      }
+      --${outputOrder[this.rank - 1]};
+      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {
+        result[2] = ${getc};
+        if(${nextColumn}) {
+          result[3] = ${getc};
+        }
+      }
+      setOutput(result);
+    }
+    `;
+  }
+};
+function transposeImpl2(x, perm, backend2) {
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+function sumImpl(x, axis, keepDims, backend2) {
+  const reductionIndices = axis;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  const sumInputIsTransposed = permutedAxes != null;
+  let sumInput = x;
+  if (sumInputIsTransposed) {
+    sumInput = transposeImpl2(x, permutedAxes, backend2);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
+  const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);
+  let outShape = sumOutShape;
+  if (keepDims) {
+    outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);
+  }
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const batchSize = xSize / inSize;
+  const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
+  const outType = sumOutType(x.dtype);
+  const reduced = reduce(reshapedInput, outType, "sum", backend2);
+  const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(reshapedInput);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (sumInputIsTransposed) {
+    backend2.disposeIntermediateTensorInfo(sumInput);
+  }
+  return out;
+}
+function sum4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return sumImpl(x, axis, keepDims, backend2);
+}
+var sumConfig2 = {
+  kernelName: Sum,
+  backendName: "webgl",
+  kernelFunc: sum4
+};
+function transpose3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { perm } = attrs;
+  const webglBackend = backend2;
+  const xRank = x.shape.length;
+  const newShape = new Array(xRank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[perm[i2]];
+  }
+  let out;
+  if (webglBackend.shouldExecuteOnCPU([x])) {
+    const xTexData = webglBackend.texData.get(x.dataId);
+    const values = xTexData.values;
+    const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);
+    out = webglBackend.makeTensorInfo(newShape, x.dtype);
+    const outData = webglBackend.texData.get(out.dataId);
+    outData.values = outValues;
+  } else {
+    out = transposeImpl2(x, perm, webglBackend);
+  }
+  return out;
+}
+var transposeConfig2 = {
+  kernelName: Transpose,
+  backendName: "webgl",
+  kernelFunc: transpose3
+};
+var MATMUL_SHARED_DIM_THRESHOLD = 1e3;
+function batchMatMulImpl({ a: a6, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape4({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const intermediates = [a3d, b3d];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const hasLeakyreluAlpha = activation2 === "leakyrelu";
+  const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;
+  const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;
+  let out;
+  if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
+    let aVec = a3d;
+    let bVec = b3d;
+    if (transposeA) {
+      aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });
+      intermediates.push(aVec);
+    }
+    if (transposeB) {
+      bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });
+      intermediates.push(bVec);
+    }
+    const shouldReshapeA = outerShapeB !== 1;
+    const shouldReshapeB = outerShapeB === 1;
+    let aVec3d = aVec;
+    if (shouldReshapeA) {
+      aVec3d = reshape4({
+        inputs: { x: aVec },
+        backend: backend2,
+        attrs: { shape: [batchDim, sharedDim, 1] }
+      });
+      intermediates.push(aVec3d);
+    }
+    const axis = outerShapeB === 1 ? 2 : 1;
+    let bVec3d = bVec;
+    if (shouldReshapeB) {
+      bVec3d = reshape4({
+        inputs: { x: bVec },
+        backend: backend2,
+        attrs: { shape: [batchDim, 1, sharedDim] }
+      });
+      intermediates.push(bVec3d);
+    }
+    const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });
+    out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });
+    intermediates.push(product);
+  } else {
+    const dtype = upcastType(a6.dtype, b.dtype);
+    const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+    const inputs = [a3d, b3d];
+    if (bias != null) {
+      inputs.push(bias);
+    }
+    if (hasPreluActivationWeights) {
+      inputs.push(preluActivationWeights);
+    }
+    if (hasLeakyreluAlpha) {
+      const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+      inputs.push($leakyreluAlpha);
+      intermediates.push($leakyreluAlpha);
+    }
+    out = backend2.runWebGLProgram(program, inputs, dtype);
+  }
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
+  intermediates.push(out);
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return outReshaped;
+}
+function _fusedMatMul2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  return batchMatMulImpl({
+    a: a6,
+    b,
+    transposeA,
+    transposeB,
+    backend: backend2,
+    bias,
+    preluActivationWeights,
+    leakyreluAlpha,
+    activation: activation2
+  });
+}
+var _fusedMatMulConfig2 = {
+  kernelName: _FusedMatMul,
+  backendName: "webgl",
+  kernelFunc: _fusedMatMul2
+};
+var ABS2 = `return abs(x);`;
+function abs3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
+    const xData = backend2.texData.get(x.dataId);
+    const outValues = simpleAbsImplCPU(xData.values);
+    return backend2.makeTensorInfo(x.shape, x.dtype, outValues);
+  }
+  let program;
+  if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
+    program = new UnaryOpPackedProgram(x.shape, ABS2);
+  } else {
+    program = new UnaryOpProgram(x.shape, ABS2);
+  }
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var absConfig2 = {
+  kernelName: Abs,
+  backendName: "webgl",
+  kernelFunc: abs3
+};
+var ACOS = CHECK_NAN_SNIPPET + `
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return acos(x);
+`;
+var acos3 = unaryKernelFunc2({ opSnippet: ACOS });
+var acosConfig2 = {
+  kernelName: Acos,
+  backendName: "webgl",
+  kernelFunc: acos3
+};
+var ACOSH = CHECK_NAN_SNIPPET + `
+  if (x < 1.0) return NAN;
+return log(x + sqrt(x * x - 1.0));`;
+var acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });
+var acoshConfig2 = {
+  kernelName: Acosh,
+  backendName: "webgl",
+  kernelFunc: acosh3
+};
+var ADD = "return a + b;";
+var addKernelFunc = binaryKernelFunc2({
+  opSnippet: ADD,
+  packedOpSnippet: ADD,
+  supportsComplex: true,
+  cpuKernelImpl: addImplCPU
+});
+var addConfig2 = {
+  kernelName: Add,
+  backendName: "webgl",
+  kernelFunc: addKernelFunc
+};
+var AddNProgram = class {
+  constructor(outputShape, shapes) {
+    this.outputShape = [];
+    this.outputShape = outputShape;
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const snippets = [];
+    this.variableNames.forEach((variable2) => {
+      snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);
+    });
+    const operation = this.variableNames.map((variable2) => {
+      return `v${variable2}`;
+    }).join(" + ");
+    this.userCode = `
+      void main() {
+        ${snippets.join("\n        ")}
+
+        float result = ${operation};
+        setOutput(result);
+      }
+    `;
+  }
+};
+var AddNPackedProgram = class {
+  constructor(outputShape, shapes) {
+    this.outputShape = [];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = outputShape;
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const snippets = [];
+    this.variableNames.forEach((variable2) => {
+      snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);
+    });
+    const operation = this.variableNames.map((variable2) => {
+      return `v${variable2}`;
+    }).join(" + ");
+    this.userCode = `
+      void main() {
+        ${snippets.join("\n        ")}
+
+        vec4 result = ${operation};
+        setOutput(result);
+      }
+    `;
+  }
+};
+function addN3(args) {
+  const { inputs, backend: backend2 } = args;
+  const tensors = inputs;
+  if (tensors.length === 1) {
+    return identity3({ inputs: { x: tensors[0] }, backend: backend2 });
+  }
+  if (tensors.length > env().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
+    const midIndex = Math.floor(tensors.length / 2);
+    const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 });
+    const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });
+    return addN3({ inputs: [leftSide, rightSide], backend: backend2 });
+  }
+  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));
+  const shapes = tensors.map((t2) => t2.shape);
+  const usePackedOp = env().getBool("WEBGL_PACK");
+  const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);
+  return backend2.runWebGLProgram(program, tensors, dtype);
+}
+var addNConfig2 = {
+  kernelName: AddN,
+  backendName: "webgl",
+  kernelFunc: addN3
+};
+function all3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("all", axes, xRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  const reduced = reduce(a2D, a2D.dtype, "all", backend2);
+  let res;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
+  } else {
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+  }
+  backend2.disposeIntermediateTensorInfo(a2D);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return res;
+}
+var allConfig2 = {
+  kernelName: All,
+  backendName: "webgl",
+  kernelFunc: all3
+};
+function any3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("any", axes, xRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  const reduced = reduce(a2D, a2D.dtype, "any", backend2);
+  let res;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
+  } else {
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+  }
+  backend2.disposeIntermediateTensorInfo(a2D);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return res;
+}
+var anyConfig2 = {
+  kernelName: Any,
+  backendName: "webgl",
+  kernelFunc: any3
+};
+var ArgMinMaxProgram = class {
+  constructor(reduceInfo, op2, firstPass) {
+    this.variableNames = ["A"];
+    const { windowSize, batchSize, outSize } = reduceInfo;
+    if (!firstPass) {
+      this.variableNames.push("bestIndicesA");
+    }
+    this.outputShape = [batchSize, outSize];
+    const compOp = op2 === "max" ? ">" : "<";
+    const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        int bestIndex = inOffset;
+        float bestValue = getA(batch, bestIndex);
+
+        for (int i = 0; i < ${windowSize}; i++) {
+          int inIdx = ${indexSnippet};
+          float candidate = getA(batch, inIdx);
+          if (candidate ${compOp} bestValue) {
+            bestValue = candidate;
+            bestIndex = inIdx;
+          }
+        }
+        setOutput(float(bestIndex));
+      }
+    `;
+  }
+};
+var ArgMinMaxPackedProgram = class {
+  constructor(shape, windowSize, op2, firstPass) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);
+    const inSize = shape[shape.length - 1];
+    const outSize = Math.ceil(inSize / windowSize);
+    this.outputShape = shape.slice(0, -1);
+    if (outSize > 1) {
+      this.outputShape.push(outSize);
+    }
+    if (!firstPass) {
+      this.variableNames.push("bestIndicesA");
+    }
+    const outShape = this.outputShape;
+    const rank = outShape.length;
+    const dtype = getCoordsDataType(rank);
+    const coords3 = getChannels("coords", rank);
+    let sourceLocSetup;
+    let sourceRank;
+    if (outSize === 1) {
+      sourceRank = rank + 1;
+      const sourceLocDType = getCoordsDataType(sourceRank);
+      sourceLocSetup = `
+        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords3.join()}, 0);
+        ++${coords3[rank - 1]};
+        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords3.join()}, 0);
+        ++${coords3[rank - 2]};
+        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords3.join()}, 0);
+        --${coords3[rank - 1]};
+        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords3.join()}, 0);
+        --${coords3[rank - 2]};`;
+    } else {
+      sourceRank = rank;
+      sourceLocSetup = `
+        ${dtype} sourceLocR = coords;
+        ++${coords3[rank - 1]};
+        ${dtype} sourceLocG = coords;
+        ++${coords3[rank - 2]};
+        ${dtype} sourceLocA = coords;
+        --${coords3[rank - 1]};
+        ${dtype} sourceLocB = coords;
+        --${coords3[rank - 2]};`;
+    }
+    const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank);
+    const inChannel = "." + channels[sourceRank - 1];
+    const intChannels = channels.map((x) => "int " + x);
+    const srcRCoords = getChannels("sourceLocR", sourceRank - 1).concat("inIdx.r");
+    const srcGCoords = getChannels("sourceLocG", sourceRank - 1).concat("inIdx.g");
+    const srcBCoords = getChannels("sourceLocB", sourceRank - 1).concat("inIdx.b");
+    const srcACoords = getChannels("sourceLocA", sourceRank - 1).concat("inIdx.a");
+    const compOp = op2 === "max" ? "greaterThan" : "lessThan";
+    const fetchCandidateIdx = firstPass ? "" : `
+          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
+                             getBestIndicesAChannel(${srcGCoords.join()}),
+                             getBestIndicesAChannel(${srcBCoords.join()}),
+                             getBestIndicesAChannel(${srcACoords.join()})));`;
+    const fetchValue = `vec4(
+            getAChannel(${srcRCoords.join()}),
+            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
+            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;
+    const getBestIndicesAChannelSnippet = firstPass ? "" : `
+      float getBestIndicesAChannel(${intChannels.join()}) {
+        return getChannel(getBestIndicesA(${channels.join()}),
+                                          vec2(${channels.slice(-2).join()}));
+      }`;
+    this.userCode = `
+      float getAChannel(${intChannels.join()}) {
+        return getChannel(getA(${channels.join()}),
+                               vec2(${channels.slice(-2).join()}));
+      }
+      ${getBestIndicesAChannelSnippet}
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        bool hasNextCol = ${coords3[rank - 1]} < ${outShape[rank - 1] - 1};
+        bool hasNextRow = ${coords3[rank - 2]} < ${outShape[rank - 2] - 1};
+        ${sourceLocSetup}
+        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
+          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
+        ivec4 inIdx = srcIdx;
+        vec4 bestIndex = vec4(inIdx);
+        vec4 bestValue = ${fetchValue};
+
+        for (int i = 0; i < ${windowSize}; i++) {
+          inIdx = srcIdx;
+          ${fetchCandidateIdx}
+          vec4 candidate = ${fetchValue};
+          bvec4 nan = isnan(candidate);
+          bvec4 replace = bvec4(
+            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+
+          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
+                           replace.y  ? candidate.y : bestValue.y,
+                           replace.z  ? candidate.z : bestValue.z,
+                           replace.w  ? candidate.w : bestValue.w);
+          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
+          srcIdx++;
+        }
+        setOutput(bestIndex);
+      }
+    `;
+  }
+};
+function argReduce(backend2, x, reduceType, bestIndicesA = null) {
+  let batchSize = x.shape[0];
+  let inSize = x.shape[1];
+  if (bestIndicesA != null) {
+    batchSize = bestIndicesA.shape[0];
+    inSize = bestIndicesA.shape[1];
+  }
+  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
+  const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };
+  const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
+  const inputs = [x];
+  if (bestIndicesA != null) {
+    inputs.push(bestIndicesA);
+  }
+  const output = backend2.runWebGLProgram(program, inputs, "int32");
+  if (output.shape[1] === 1) {
+    return output;
+  }
+  const result = argReduce(backend2, x, reduceType, output);
+  backend2.disposeIntermediateTensorInfo(output);
+  return result;
+}
+function argReducePacked(backend2, x, reduceType, bestIndicesA = null) {
+  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
+  const inSize = inShape[inShape.length - 1];
+  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
+  const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);
+  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
+  const output = backend2.runWebGLProgram(program, inputs, "int32");
+  if (output.shape.length === x.shape.length) {
+    const result = argReducePacked(backend2, x, reduceType, output);
+    backend2.disposeIntermediateTensorInfo(output);
+    return result;
+  }
+  return output;
+}
+function argMinMaxReduce(backend2, x, axis, reduceType) {
+  const axes = [axis];
+  backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);
+  if (!env().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) {
+    const intermediateTensorInfos = [];
+    const xtexData = backend2.texData.get(x.dataId);
+    const xIsPacked = xtexData !== null && xtexData.isPacked;
+    let xUnPacked = x;
+    if (xIsPacked) {
+      xUnPacked = backend2.unpackTensor(x);
+      intermediateTensorInfos.push(xUnPacked);
+    }
+    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);
+    const inSize = util_exports.sizeFromShape(reduceShape);
+    const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });
+    intermediateTensorInfos.push(a2D);
+    const reduced = argReduce(backend2, a2D, reduceType);
+    intermediateTensorInfos.push(reduced);
+    const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+    intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    return reshaped;
+  }
+  return argReducePacked(backend2, x, reduceType);
+}
+function argMax3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
+  const out = argMinMaxReduce(backend2, $x, axes[0], "max");
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return out;
+}
+var argMaxConfig2 = {
+  kernelName: ArgMax,
+  backendName: "webgl",
+  kernelFunc: argMax3
+};
+function argMin3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
+  const out = argMinMaxReduce(backend2, $x, axes[0], "min");
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return out;
+}
+var argMinConfig2 = {
+  kernelName: ArgMin,
+  backendName: "webgl",
+  kernelFunc: argMin3
+};
+var ASIN = CHECK_NAN_SNIPPET + `
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return asin(x);
+`;
+var asin3 = unaryKernelFunc2({ opSnippet: ASIN });
+var asinConfig2 = {
+  kernelName: Asin,
+  backendName: "webgl",
+  kernelFunc: asin3
+};
+var ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;
+var asinh3 = unaryKernelFunc2({ opSnippet: ASINH });
+var asinhConfig2 = {
+  kernelName: Asinh,
+  backendName: "webgl",
+  kernelFunc: asinh3
+};
+var ATAN = CHECK_NAN_SNIPPET + `
+  return atan(x);
+`;
+var atan4 = unaryKernelFunc2({ opSnippet: ATAN });
+var atanConfig2 = {
+  kernelName: Atan,
+  backendName: "webgl",
+  kernelFunc: atan4
+};
+var ATAN2 = CHECK_NAN_SNIPPET_BINARY + `
+  return atan(a, b);
+`;
+var ATAN2_PACKED = `
+  vec4 result = atan(a, b);
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  ` + CHECK_NAN_SNIPPET_BINARY_PACKED + `
+  return result;
+`;
+var atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });
+var atan2Config2 = {
+  kernelName: Atan2,
+  backendName: "webgl",
+  kernelFunc: atan23
+};
+var ATANH = CHECK_NAN_SNIPPET + `
+  if ((x < -1.0) || (x > 1.0)) return NAN;
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
+var atanh3 = unaryKernelFunc2({ opSnippet: ATANH });
+var atanhConfig2 = {
+  kernelName: Atanh,
+  backendName: "webgl",
+  kernelFunc: atanh3
+};
+var Pool2DProgram = class {
+  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
+    this.variableNames = ["x"];
+    if (poolType === "avg" && computePositions) {
+      throw new Error("Cannot compute positions for average pool.");
+    }
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    this.outputShape = convInfo.outShape;
+    const isAvgPool = poolType === "avg";
+    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
+    const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
+    let initializationValue = "0.0";
+    if (!isAvgPool) {
+      initializationValue = "-1.0 / 1e-20";
+    }
+    if (computePositions) {
+      const compareOp2 = ">=";
+      this.userCode = `
+        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+        const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int batch = coords[0];
+          int d = coords[3];
+
+          ivec2 xRCCorner = coords.yz * strides - pads;
+          int xRCorner = xRCCorner.x;
+          int xCCorner = xRCCorner.y;
+
+          // max/min x(?, ?, d) to get y(yR, yC, d).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+          float avgValue = 0.0;
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              int xC = xCCorner + wC;
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              float value = getX(batch, xR, xC, d);
+
+              // If a min / max value has already been found, use it. If not,
+              // use the current value.
+              float currMinMaxValue = mix(
+                  value, minMaxValue, minMaxValueFound);
+              if (value ${compareOp2} currMinMaxValue) {
+                minMaxValue = value;
+                minMaxValueFound = 1.0;
+                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;
+      return;
+    }
+    const compareOp = "max";
+    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
+    if (poolType === "avg") {
+      returnValue = `avgValue / count`;
+    }
+    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+    const filterWidthVec4Remainder = filterWidth % 4;
+    const updateSnippet = `
+      if (${isAvgPool}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `;
+    this.userCode = `
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xR, int xC, int d) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xR, xC, d);
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d = coords[3];
+
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // max/min x(?, ?, d) to get y(yR, yC, d).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${initializationValue});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          int xR = xRCorner + wR;
+
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
+            );
+
+            ${updateSnippet}
+          }
+
+          int xC = xCCorner + ${filterWidthNearestVec4};
+          if (${filterWidthVec4Remainder === 1}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              initializationValue,
+              initializationValue,
+              initializationValue
+            );
+
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder === 2}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              initializationValue,
+              initializationValue
+            );
+
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder === 3}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              initializationValue
+            );
+
+            ${updateSnippet}
+          }
+        }
+        setOutput(${returnValue});
+      }
+    `;
+  }
+};
+var Pool3DProgram = class {
+  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
+    this.variableNames = ["x"];
+    if (poolType === "avg" && computePositions) {
+      throw new Error("Cannot compute positions for average pool.");
+    }
+    const filterWidth = convInfo.filterWidth;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padFront = convInfo.padInfo.front;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    this.outputShape = convInfo.outShape;
+    const isAvgPool = poolType === "avg";
+    let initializationValue = "0.0";
+    if (!isAvgPool) {
+      initializationValue = "-1.0 / 1e-20";
+    }
+    if (computePositions) {
+      const compareOp2 = ">=";
+      this.userCode = `
+        const ivec3 strides =
+            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+        void main() {
+          ivec5 coords = getOutputCoords();
+          int batch = coords.x;
+          int ch = coords.u;
+
+          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+          int xDCorner = xCorner.x;
+          int xRCorner = xCorner.y;
+          int xCCorner = xCorner.z;
+
+          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+
+          for (int wD = 0; wD < ${effectiveFilterDepth};
+              wD += ${dilationDepth}) {
+            int xD = xDCorner + wD;
+
+            if (xD < 0 || xD >= ${convInfo.inDepth}) {
+              continue;
+            }
+
+            for (int wR = 0; wR < ${effectiveFilterHeight};
+                wR += ${dilationHeight}) {
+              int xR = xRCorner + wR;
+
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
+                continue;
+              }
+
+              for (int wC = 0; wC < ${effectiveFilterWidth};
+                  wC += ${dilationWidth}) {
+                int xC = xCCorner + wC;
+
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                  continue;
+                }
+
+                float value = getX(batch, xD, xR, xC, ch);
+
+                // If a min / max value has already been found, use it. If not,
+                // use the current value.
+                float currMinMaxValue = mix(
+                    value, minMaxValue, minMaxValueFound);
+                if (value ${compareOp2} currMinMaxValue) {
+                  minMaxValue = value;
+                  minMaxValueFound = 1.0;
+                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                      wR * ${effectiveFilterWidth} + wC`};
+                }
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;
+      return;
+    }
+    const compareOp = "max";
+    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
+    if (poolType === "avg") {
+      returnValue = `avgValue / count`;
+    }
+    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+    const filterWidthVec4Remainder = filterWidth % 4;
+    const updateSnippet = `
+      if (${isAvgPool}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `;
+    this.userCode = `
+      const ivec3 strides =
+        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xD, int xR, int xC, int ch) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xD, xR, xC, ch);
+      }
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xDCorner = xCorner.x;
+        int xRCorner = xCorner.y;
+        int xCCorner = xCorner.z;
+
+        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${initializationValue});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          int xD = xDCorner + wD;
+
+          if (xD < 0 || xD >= ${convInfo.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+              int xC = xCCorner + wC * ${dilationWidth};
+
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
+              );
+
+              ${updateSnippet}
+            }
+
+            int xC = xCCorner + ${filterWidthNearestVec4};
+            if (${filterWidthVec4Remainder === 1}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                initializationValue,
+                initializationValue,
+                initializationValue
+              );
+
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder === 2}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                initializationValue,
+                initializationValue
+              );
+
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder === 3}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                initializationValue
+              );
+
+              ${updateSnippet}
+            }
+          }
+          setOutput(${returnValue});
+        }
+      }
+    `;
+  }
+};
+function avgPool3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex2(x, "avgPool");
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    return identity3({ inputs: { x }, backend: backend2 });
+  }
+  const avgPoolProgram = new Pool2DProgram(convInfo, "avg", false);
+  return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
+}
+var avgPoolConfig2 = {
+  kernelName: AvgPool,
+  backendName: "webgl",
+  kernelFunc: avgPool3
+};
+function avgPool3D2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode, dataFormat);
+  const avgPoolProgram = new Pool3DProgram(convInfo, "avg", false);
+  return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
+}
+var avgPool3DConfig2 = {
+  kernelName: AvgPool3D,
+  backendName: "webgl",
+  kernelFunc: avgPool3D2
+};
+var AvgPool2DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy"];
+    this.outputShape = convInfo.inShape;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const avgMultiplier = 1 / (filterHeight * filterWidth);
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${effectiveFilterWidth};
+            wC+= ${dilationWidth}) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+
+            dotProd += dyValue * avgMultiplier;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var AvgPool3DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy"];
+    this.outputShape = convInfo.inShape;
+    const filterDepth = convInfo.filterDepth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
+    this.userCode = `
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
+
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+
+              dotProd += dyValue * avgMultiplier;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+function avgPool3DGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
+  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
+}
+var avgPool3DGradConfig3 = {
+  kernelName: AvgPool3DGrad,
+  backendName: "webgl",
+  kernelFunc: avgPool3DGrad2
+};
+function avgPoolGrad3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  assertNotComplex2([dy, input2], "avgPoolGrad");
+  const { filterSize, strides: strides2, pad: pad3 } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, 1, pad3);
+  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
+  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
+}
+var avgPoolGradConfig3 = {
+  kernelName: AvgPoolGrad,
+  backendName: "webgl",
+  kernelFunc: avgPoolGrad3
+};
+function batchMatMul2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  return batchMatMulImpl({ a: a6, b, transposeA, transposeB, backend: backend2 });
+}
+var batchMatMulConfig2 = {
+  kernelName: BatchMatMul,
+  backendName: "webgl",
+  kernelFunc: batchMatMul2
+};
+var BatchNormProgram = class {
+  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+    this.outputShape = [];
+    this.variableNames = ["x", "mean", "variance"];
+    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
+    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
+    let offsetSnippet = "0.0";
+    if (offsetShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
+      this.variableNames.push("offset");
+      offsetSnippet = "getOffsetAtOutCoords()";
+    }
+    let scaleSnippet = "1.0";
+    if (scaleShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
+      this.variableNames.push("scale");
+      scaleSnippet = "getScaleAtOutCoords()";
+    }
+    this.outputShape = xShape;
+    this.userCode = `
+      void main() {
+        float x = getXAtOutCoords();
+        float mean = getMeanAtOutCoords();
+        float variance = getVarianceAtOutCoords();
+        float offset = ${offsetSnippet};
+        float scale = ${scaleSnippet};
+        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
+        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
+      }
+    `;
+  }
+};
+var BatchNormPackedProgram = class {
+  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.variableNames = ["x", "mean", "variance"];
+    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
+    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
+    let offsetSnippet = "vec4(0.0)";
+    if (offsetShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
+      this.variableNames.push("offset");
+      offsetSnippet = "getOffsetAtOutCoords()";
+    }
+    let scaleSnippet = "vec4(1.0)";
+    if (scaleShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
+      this.variableNames.push("scale");
+      scaleSnippet = "getScaleAtOutCoords()";
+    }
+    this.outputShape = xShape;
+    this.userCode = `
+      void main() {
+        vec4 offset = ${offsetSnippet};
+        vec4 scale = ${scaleSnippet};
+
+        vec4 x = getXAtOutCoords();
+        vec4 mean = getMeanAtOutCoords();
+        vec4 variance = getVarianceAtOutCoords();
+
+        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
+
+        setOutput((x - mean) * inv + offset);
+      }
+    `;
+  }
+};
+var batchNorm3 = ({ inputs, backend: backend2, attrs }) => {
+  const { x, mean: mean5, variance, offset, scale: scale22 } = inputs;
+  util_exports.assert(mean5.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
+  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
+  util_exports.assert(scale22 == null || mean5.shape.length === scale22.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  let { varianceEpsilon } = attrs;
+  if (varianceEpsilon == null) {
+    varianceEpsilon = 1e-3;
+  }
+  const finalInputs = [x, mean5, variance];
+  let offsetShape = null;
+  if (offset != null) {
+    offsetShape = offset.shape;
+    finalInputs.push(offset);
+  }
+  let scaleShape = null;
+  if (scale22 != null) {
+    scaleShape = scale22.shape;
+    finalInputs.push(scale22);
+  }
+  const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+  const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
+  return output;
+};
+var batchNormConfig2 = {
+  kernelName: FusedBatchNorm,
+  backendName: "webgl",
+  kernelFunc: batchNorm3
+};
+var SliceProgram = class {
+  constructor(destSize) {
+    this.variableNames = ["source"];
+    this.outputShape = destSize;
+    this.rank = destSize.length;
+    const dtype = getCoordsDataType(this.rank);
+    this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
+    const sourceCoords = getCoords(this.rank);
+    let body4;
+    const coordSum = destSize.map((_, i2) => {
+      return `sourceLoc.${coords[i2]} = start[${i2}] + coords.${coords[i2]};`;
+    });
+    body4 = `
+        ${dtype} sourceLoc;
+        ${dtype} coords = getOutputCoords();
+        ${coordSum.join("\n")}
+      `;
+    this.userCode = `
+      void main() {
+        ${body4}
+        setOutput(getSource(${sourceCoords}));
+      }
+    `;
+  }
+};
+var coords = ["x", "y", "z", "w", "u", "v"];
+function getCoords(rank) {
+  if (rank === 1) {
+    return "sourceLoc";
+  } else if (rank <= 6) {
+    return coords.slice(0, rank).map((x) => "sourceLoc." + x).join(",");
+  } else {
+    throw Error(`Slicing for rank ${rank} is not yet supported`);
+  }
+}
+var SlicePackedProgram = class {
+  constructor(destSize) {
+    this.variableNames = ["source"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = destSize;
+    this.rank = destSize.length;
+    this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
+    const dtype = getCoordsDataType(this.rank);
+    const coords3 = getChannels("coords", this.rank);
+    const sourceLoc = getChannels("sourceLoc", this.rank);
+    const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`;
+    const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;
+    const upperRow = `
+      result.x = ${getChannel};
+      if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {
+        ++${sourceLoc[this.rank - 1]};
+        result.y = ${getChannel};
+        --${sourceLoc[this.rank - 1]};
+      }
+    `;
+    const lowerRow = this.rank === 1 ? "" : `
+      --${coords3[this.rank - 1]};
+      if (++${coords3[this.rank - 2]} < ${destSize[this.rank - 2]}) {
+        ++${sourceLoc[this.rank - 2]};
+        result.z = ${getChannel};
+        if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {
+          ++${sourceLoc[this.rank - 1]};
+          result.w = ${getChannel};
+        }
+      }
+    `;
+    const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
+            ${dtype}(${destSize.map((_, i2) => `start[${i2}]`).join()});` : destSize.map((_, i2) => `${sourceLoc[i2]} = ${coords3[i2]} + start[${i2}];`).join("\n");
+    this.userCode = `
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        ${dtype} sourceLoc;
+        ${sourceLocSetup}
+        vec4 result = vec4(0.);
+        ${upperRow}
+        ${lowerRow}
+        setOutput(result);
+      }
+    `;
+  }
+};
+function shallowSlice(x, begin, size2, backend2) {
+  const xTexData = backend2.texData.get(x.dataId);
+  const t2 = backend2.makeTensorInfo(size2, x.dtype);
+  const newTexData = backend2.texData.get(t2.dataId);
+  Object.assign(newTexData, xTexData);
+  newTexData.refCount = 1;
+  newTexData.shape = size2;
+  newTexData.dtype = x.dtype;
+  let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));
+  if (xTexData.slice) {
+    flatOffset += xTexData.slice.flatOffset;
+  }
+  newTexData.slice = {
+    flatOffset,
+    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
+  };
+  const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;
+  backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
+  return t2;
+}
+function slice3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, size: size2 } = attrs;
+  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size2);
+  slice_util_exports.assertParamsValid(x, $begin, $size);
+  if (util_exports.sizeFromShape($size) === 0) {
+    return backend2.makeTensorInfo($size, x.dtype, []);
+  }
+  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") {
+    const xTexData = backend2.texData.get(x.dataId);
+    const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);
+    return backend2.makeTensorInfo($size, x.dtype, outValues);
+  }
+  const { isPacked } = backend2.texData.get(x.dataId);
+  const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);
+  if (isPacked || !isContinous) {
+    const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new SlicePackedProgram($size) : new SliceProgram($size);
+    const customValues = [$begin];
+    return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
+  }
+  backend2.uploadToGPU(x.dataId);
+  return shallowSlice(x, $begin, $size, backend2);
+}
+var sliceConfig2 = {
+  kernelName: Slice,
+  backendName: "webgl",
+  kernelFunc: slice3
+};
+var batchToSpaceND3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const toDispose = [];
+  const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });
+  const reshapedIntermediate2 = reshape4({
+    inputs: { x: transposedIntermediate },
+    backend: backend2,
+    attrs: { shape: reshapedPermuted }
+  });
+  const sliced = slice3({
+    inputs: { x: reshapedIntermediate2 },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  toDispose.push(reshapedIntermediate);
+  toDispose.push(transposedIntermediate);
+  toDispose.push(reshapedIntermediate2);
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return sliced;
+};
+var batchToSpaceNDConfig2 = {
+  kernelName: BatchToSpaceND,
+  backendName: "webgl",
+  kernelFunc: batchToSpaceND3
+};
+function bincount3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size: size2 } = attrs;
+  const xVals = backend2.readSync(x.dataId);
+  const weightsVals = backend2.readSync(weights.dataId);
+  const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size2);
+  return backend2.makeTensorInfo([size2], weights.dtype, outVals);
+}
+var bincountConfig2 = {
+  kernelName: Bincount,
+  backendName: "webgl",
+  kernelFunc: bincount3
+};
+function broadcastArgs3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { s0, s1 } = inputs;
+  const s0Vals = backend2.readSync(s0.dataId);
+  const s1Vals = backend2.readSync(s1.dataId);
+  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
+  return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape));
+}
+var broadcastArgsConfig2 = {
+  kernelName: BroadcastArgs,
+  backendName: "webgl",
+  kernelFunc: broadcastArgs3
+};
+var NOT_EQUAL = `return float(a != b);`;
+var notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: "bool" });
+var notEqualConfig2 = {
+  kernelName: NotEqual,
+  backendName: "webgl",
+  kernelFunc: notEqual3
+};
+function real3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.texData.get(input2.dataId);
+  return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });
+}
+var realConfig2 = {
+  kernelName: Real,
+  backendName: "webgl",
+  kernelFunc: real3
+};
+var TO_INT = `return float(int(x));`;
+function int(input2, backend2) {
+  const program = new UnaryOpProgram(input2.shape, TO_INT);
+  const output = backend2.runWebGLProgram(program, [input2], "int32");
+  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };
+}
+function cast4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dtype } = attrs;
+  if (dtype === "complex64") {
+    if (x.dtype === "complex64") {
+      return identity3({ inputs: { x }, backend: backend2 });
+    }
+    const zerosTensor = zeros(x.shape);
+    const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+    const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });
+    zerosTensor.dispose();
+    backend2.disposeIntermediateTensorInfo(floatX);
+    return result;
+  }
+  if (x.dtype === "complex64") {
+    const realPart = real3({ inputs: { input: x }, backend: backend2 });
+    const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    return result;
+  }
+  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
+    const result = identity3({ inputs: { x }, backend: backend2 });
+    return { dataId: result.dataId, shape: result.shape, dtype };
+  }
+  if (dtype === "int32") {
+    return int(x, backend2);
+  }
+  if (dtype === "bool") {
+    const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1));
+    const binaryInputs = { a: x, b: zerosTensorInfo };
+    const result = notEqual3({ inputs: binaryInputs, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
+    return result;
+  }
+  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
+}
+var castConfig2 = {
+  kernelName: Cast,
+  backendName: "webgl",
+  kernelFunc: cast4
+};
+var CEIL = `return ceil(x);`;
+var ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });
+var ceilConfig2 = {
+  kernelName: Ceil,
+  backendName: "webgl",
+  kernelFunc: ceil3
+};
+var ClipProgram = class {
+  constructor(aShape) {
+    this.variableNames = ["A"];
+    this.customUniforms = [
+      { name: "minVal", type: "float" },
+      { name: "maxVal", type: "float" }
+    ];
+    this.outputShape = aShape;
+    this.userCode = `
+
+      void main() {
+        float value = getAAtOutCoords();
+        if (isnan(value)) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, minVal, maxVal));
+      }
+    `;
+  }
+};
+var ClipPackedProgram = class {
+  constructor(aShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [
+      { name: "minVal", type: "float" },
+      { name: "maxVal", type: "float" }
+    ];
+    this.outputShape = aShape;
+    this.userCode = `
+      void main() {
+        vec4 value = getAAtOutCoords();
+
+        if (any(isnan(value))) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
+      }
+    `;
+  }
+};
+function clipByValue3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { clipValueMin, clipValueMax } = attrs;
+  let program;
+  if (env().getBool("WEBGL_PACK_CLIP")) {
+    program = new ClipPackedProgram(x.shape);
+  } else {
+    program = new ClipProgram(x.shape);
+  }
+  const customValues = [[clipValueMin], [clipValueMax]];
+  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
+}
+var clipByValueConfig2 = {
+  kernelName: ClipByValue,
+  backendName: "webgl",
+  kernelFunc: clipByValue3
+};
+var ComplexAbsProgram = class {
+  constructor(shape) {
+    this.variableNames = ["real", "imag"];
+    this.outputShape = shape;
+    this.userCode = `
+      void main() {
+        float re = abs(getRealAtOutCoords());
+        float im = abs(getImagAtOutCoords());
+        float mx = max(re, im);
+
+        // sadly the length function in glsl is not underflow-safe
+        // (at least not on Intel GPUs). So the safe solution is
+        // to ensure underflow-safety in all cases.
+        setOutput(
+          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
+        );
+      }
+    `;
+  }
+};
+function makeComplexComponentTensorInfo(complexTensor, complexPart) {
+  return {
+    dataId: complexPart.dataId,
+    dtype: complexPart.dtype,
+    shape: complexTensor.shape
+  };
+}
+function complexAbs2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  const xData = backend2.texData.get(x.dataId);
+  const program = new ComplexAbsProgram(x.shape);
+  const programInputs = [
+    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
+    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)
+  ];
+  return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);
+}
+var complexAbsConfig2 = {
+  kernelName: ComplexAbs,
+  backendName: "webgl",
+  kernelFunc: complexAbs2
+};
+var ConcatProgram = class {
+  constructor(shapes) {
+    this.outputShape = [];
+    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const offsets = new Array(shapes.length - 1);
+    offsets[0] = shapes[0][1];
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
+    }
+    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      const shift = offsets[i2 - 1];
+      snippets.push(`else if (yC < ${offsets[i2]}) setOutput(getT${i2}(yR, yC-${shift}));`);
+    }
+    const lastIndex = offsets.length;
+    const lastShift = offsets[offsets.length - 1];
+    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int yR = coords.x;
+        int yC = coords.y;
+
+        ${snippets.join("\n        ")}
+      }
+    `;
+  }
+};
+var ConcatPackedProgram = class {
+  constructor(shapes, axis) {
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = [];
+    this.outputShape = backend_util_exports.computeOutShape(shapes, axis);
+    const shape = this.outputShape;
+    const rank = shape.length;
+    const dtype = getCoordsDataType(rank);
+    const coords3 = getChannels("coords", rank);
+    const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank);
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const offsets = new Array(shapes.length - 1);
+    offsets[0] = shapes[0][axis];
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      offsets[i2] = offsets[i2 - 1] + shapes[i2][axis];
+    }
+    const channel = channels[axis];
+    const lastChannels = channels.slice(-2);
+    const allChannels = channels.join();
+    let getValueSnippet = `if (${channel} < ${offsets[0]}) {
+        return getChannel(
+            getT0(${allChannels}), vec2(${lastChannels.join()}));
+        }`;
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      const shift2 = offsets[i2 - 1];
+      getValueSnippet += `
+        if (${channel} < ${offsets[i2]}  && ${channel} >= ${offsets[i2 - 1]}) {
+          return getChannel(
+            getT${i2}(${shiftedChannels(channels, channel, shift2)}),
+            vec2(${shiftedChannels(lastChannels, channel, shift2)}));
+        }`;
+    }
+    const lastIndex = offsets.length;
+    const shift = offsets[offsets.length - 1];
+    getValueSnippet += `
+        return getChannel(
+          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),
+          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;
+    this.userCode = `
+      float getValue(${channels.map((x) => "int " + x)}) {
+        ${getValueSnippet}
+      }
+
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${coords3}), 0., 0., 0.);
+
+        ${coords3[rank - 1]} = ${coords3[rank - 1]} + 1;
+        if (${coords3[rank - 1]} < ${shape[rank - 1]}) {
+          result.g = getValue(${coords3});
+        }
+
+        ${coords3[rank - 2]} = ${coords3[rank - 2]} + 1;
+        if (${coords3[rank - 2]} < ${shape[rank - 2]}) {
+          result.a = getValue(${coords3});
+        }
+
+        ${coords3[rank - 1]} = ${coords3[rank - 1]} - 1;
+        if (${coords3[rank - 2]} < ${shape[rank - 2]} &&
+            ${coords3[rank - 1]} < ${shape[rank - 1]}) {
+          result.b = getValue(${coords3});
+        }
+        setOutput(result);
+      }
+    `;
+  }
+};
+function shiftedChannels(channels, channel, shift) {
+  const channelIdx = channels.indexOf(channel);
+  const res = channels.map((c, idx) => {
+    if (idx === channelIdx) {
+      return `${c} - ${shift}`;
+    } else {
+      return c;
+    }
+  });
+  return res.join();
+}
+function imag3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.texData.get(input2.dataId);
+  return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });
+}
+var imagConfig2 = {
+  kernelName: Imag,
+  backendName: "webgl",
+  kernelFunc: imag3
+};
+function concatImpl2(inputs, axis, backend2) {
+  const dtype = inputs[0].dtype;
+  if (dtype === "complex64") {
+    const reals = inputs.map((t2) => real3({ inputs: { input: t2 }, backend: backend2 }));
+    const imags = inputs.map((t2) => imag3({ inputs: { input: t2 }, backend: backend2 }));
+    const realConcated = concatImpl2(reals, axis, backend2);
+    const imagConcated = concatImpl2(imags, axis, backend2);
+    const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
+    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    backend2.disposeIntermediateTensorInfo(realConcated);
+    backend2.disposeIntermediateTensorInfo(imagConcated);
+    return result2;
+  }
+  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);
+  if (dtype === "string") {
+    runOnCpu = true;
+  }
+  if (runOnCpu) {
+    const tensors2D2 = inputs.map((t2) => {
+      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+      const shape = [-1, innerSize];
+      return reshape4({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    });
+    const inputsValShapes = tensors2D2.map((t2) => {
+      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    });
+    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);
+    const simplyConcat = tensors2D2[0].shape[0] === 1;
+    const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);
+    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
+    tensors2D2.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    return outInfo;
+  }
+  if (inputs.length > env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
+    const midIndex = Math.floor(inputs.length / 2);
+    const leftSide = concatImpl2(inputs.slice(0, midIndex), axis, backend2);
+    const rightSide = concatImpl2(inputs.slice(midIndex), axis, backend2);
+    const result2 = concatImpl2([leftSide, rightSide], axis, backend2);
+    backend2.disposeIntermediateTensorInfo(leftSide);
+    backend2.disposeIntermediateTensorInfo(rightSide);
+    return result2;
+  }
+  if (env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && inputs[0].shape.length > 1) {
+    const program2 = new ConcatPackedProgram(inputs.map((t2) => t2.shape), axis);
+    return backend2.runWebGLProgram(program2, inputs, dtype);
+  }
+  const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);
+  const program = new ConcatProgram(tensors2D.map((t2) => t2.shape));
+  const result = backend2.runWebGLProgram(program, tensors2D, dtype);
+  tensors2D.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+  const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(result);
+  return reshapedResult;
+}
+function computeTensors2D(inputs, axis, backend2) {
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const tensors2D = inputs.map((x) => reshape4({
+    inputs: { x },
+    attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },
+    backend: backend2
+  }));
+  return { tensors2D, outShape };
+}
+function concat3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
+  }
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, $axis);
+  return concatImpl2($inputs, $axis, backend2);
+}
+var concatConfig2 = {
+  kernelName: Concat,
+  backendName: "webgl",
+  kernelFunc: concat3
+};
+var Conv2DProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {
+    this.variableNames = ["x", "W"];
+    this.outputShape = convInfo.outShape;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+    const inputDepthVec4Remainder = convInfo.inChannels % 4;
+    const isChannelsLast = convInfo.dataFormat === "channelsLast";
+    const rowDim = isChannelsLast ? 1 : 2;
+    const colDim = isChannelsLast ? 2 : 3;
+    const channelDim = isChannelsLast ? 3 : 1;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivationWeights) {
+        activationSnippet = `float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyreluAlpha) {
+        activationSnippet = `float activation(float a) {
+          float b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `
+          float activation(float x) {
+            ${activation2}
+          }
+        `;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyreluAlpha) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    this.userCode = `
+      ${activationSnippet}
+
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d2 = coords[${channelDim}];
+
+        ivec2 xRCCorner =
+            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
+
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            if (xC < 0 || xC >= ${convInfo.inWidth}) {
+              continue;
+            }
+
+            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
+              vec4 wValues = vec4(
+                getW(wR, wC, d1, d2),
+                getW(wR, wC, d1 + 1, d2),
+                getW(wR, wC, d1 + 2, d2),
+                getW(wR, wC, d1 + 3, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec4 xValues = vec4(
+                  getX(batch, xR, xC, d1),
+                  getX(batch, xR, xC, d1 + 1),
+                  getX(batch, xR, xC, d1 + 2),
+                  getX(batch, xR, xC, d1 + 3)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec4 xValues = vec4(
+                  getX(batch, d1, xR, xC),
+                  getX(batch, d1 + 1, xR, xC),
+                  getX(batch, d1 + 2, xR, xC),
+                  getX(batch, d1 + 3, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+
+            if (${inputDepthVec4Remainder === 1}) {
+
+              if (${isChannelsLast}) {
+                dotProd +=
+                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
+              } else {
+                dotProd +=
+                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
+              }
+
+            } else if (${inputDepthVec4Remainder === 2}) {
+              vec2 wValues = vec2(
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec2 xValues = vec2(
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec2 xValues = vec2(
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            } else if (${inputDepthVec4Remainder === 3}) {
+              vec3 wValues = vec3(
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec3 xValues = vec3(
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec3 xValues = vec3(
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            }
+          }
+        }
+
+        float result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `;
+  }
+};
+var Conv3DProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "W"];
+    this.outputShape = convInfo.outShape;
+    const padFront = convInfo.padInfo.front;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const filterDepth = convInfo.filterDepth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+    const inputDepthVec4Remainder = convInfo.inChannels % 4;
+    this.userCode = `
+      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d2 = coords.u;
+
+        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xFCorner = xFRCCorner.x;
+        int xRCorner = xFRCCorner.y;
+        int xCCorner = xFRCCorner.z;
+
+        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
+        // y(yF, yR, yC, d2). ? = to be determined. : = across all
+        // values in that axis.
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          int xF = xFCorner + wF * ${dilationDepth};
+
+          if (xF < 0 || xF >= ${convInfo.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            int xR = xRCorner + wR * ${dilationHeight};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              int xC = xCCorner + wC * ${dilationWidth};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
+                vec4 xValues = vec4(
+                  getX(batch, xF, xR, xC, d1),
+                  getX(batch, xF, xR, xC, d1 + 1),
+                  getX(batch, xF, xR, xC, d1 + 2),
+                  getX(batch, xF, xR, xC, d1 + 3)
+                );
+                vec4 wValues = vec4(
+                  getW(wF, wR, wC, d1, d2),
+                  getW(wF, wR, wC, d1 + 1, d2),
+                  getW(wF, wR, wC, d1 + 2, d2),
+                  getW(wF, wR, wC, d1 + 3, d2)
+                );
+
+                dotProd += dot(xValues, wValues);
+              }
+
+              if (${inputDepthVec4Remainder === 1}) {
+                dotProd +=
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
+              } else if (${inputDepthVec4Remainder === 2}) {
+                vec2 xValues = vec2(
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
+                );
+                vec2 wValues = vec2(
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else if (${inputDepthVec4Remainder === 3}) {
+                vec3 xValues = vec3(
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
+                );
+                vec3 wValues = vec3(
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Im2ColPackedProgram = class {
+  constructor(outputShape, convInfo) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [
+      { name: "inputShape", type: "ivec3" },
+      { name: "pad", type: "ivec2" },
+      { name: "stride", type: "ivec2" },
+      { name: "dilation", type: "ivec2" },
+      { name: "inChannels", type: "int" },
+      { name: "itemsPerBlockRow", type: "int" },
+      { name: "outWidth", type: "int" }
+    ];
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const { dataFormat } = convInfo;
+    const glsl = getGlslDifferences();
+    const isChannelsLast = dataFormat === "channelsLast";
+    const rowDim = isChannelsLast ? 0 : 1;
+    const colDim = isChannelsLast ? 1 : 2;
+    const boundsCheckingSnippet = this.enableShapeUniforms ? "if(blockIndex < outShape[1] && pos < outShape[0]) {" : `if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {`;
+    let unrolled = ``;
+    for (let row = 0; row <= 1; row++) {
+      for (let col = 0; col <= 1; col++) {
+        unrolled += `
+          blockIndex = rc.y + ${col};
+          pos = rc.x + ${row};
+
+          ${boundsCheckingSnippet}
+            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
+            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
+
+            if(d0 < inputShape[${rowDim}] && d0 >= 0) {
+              // Use custom imod instead mod. On Intel GPU, mod may generate
+              // unexpected value.
+              // https://github.com/tensorflow/tfjs/issues/5447
+              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
+              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
+                  inChannels);
+
+              if(d1 < inputShape[${colDim}] && d1 >= 0) {
+
+                ch = imod(pos, inChannels);
+
+                if (${isChannelsLast}) {
+                  innerDims = vec2(d1, ch);
+                  result[${row * 2 + col}] = getChannel(
+                    getA(d0, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                } else {
+                  innerDims = vec2(d0, d1);
+                  result[${row * 2 + col}] = getChannel(
+                    getA(ch, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                }
+              }
+            }
+          }
+        `;
+      }
+    }
+    this.userCode = `
+      void main() {
+        ivec2 rc = getOutputCoords();
+
+        vec4 result = vec4(0);
+
+        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
+        vec2 innerDims;
+
+        ${unrolled}
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const xShape = x.shape;
+  const xTexData = backend2.texData.get(x.dataId);
+  const sharedMatMulDim = convInfo.inChannels;
+  const outerShapeX = xShape[0] * xShape[1] * xShape[2];
+  const outerShapeFilter = convInfo.outChannels;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const transposeA = false;
+  const transposeB = false;
+  let out;
+  const intermediates = [];
+  if (preluActivationWeights != null && !isChannelsLast && preluActivationWeights.shape.length === 3) {
+    const preluActivationWeightsInNhwcFormat = transpose3({
+      inputs: { x: preluActivationWeights },
+      backend: backend2,
+      attrs: { perm: [1, 2, 0] }
+    });
+    intermediates.push(preluActivationWeightsInNhwcFormat);
+    preluActivationWeights = preluActivationWeightsInNhwcFormat;
+  }
+  const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
+  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));
+  if (canOptimize) {
+    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);
+    const xReshaped = {
+      dataId: x.dataId,
+      shape: [1, targetShape, convInfo.inChannels],
+      dtype: x.dtype
+    };
+    const originalXTexDataShape = xTexData.shape;
+    xTexData.shape = xTexData.shape.slice();
+    xTexData.shape[xTexData.shape.length - 2]++;
+    util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);
+    const filterReshaped = reshape4({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+    });
+    intermediates.push(filterReshaped);
+    const pointwiseConv = batchMatMulImpl({
+      a: xReshaped,
+      b: filterReshaped,
+      backend: backend2,
+      transposeA,
+      transposeB,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+    const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);
+    util_exports.assert(pointwiseConvTexData.isPacked, () => "batchMatMul result is expected to be packed");
+    xTexData.shape = originalXTexDataShape;
+    pointwiseConvTexData.shape = convInfo.outShape;
+    out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 });
+    out.shape = convInfo.outShape;
+    intermediates.push(pointwiseConv);
+  } else {
+    const xInNhwcFormat = isChannelsLast ? x : transpose3({ inputs: { x }, backend: backend2, attrs: { perm: [0, 2, 3, 1] } });
+    const xInNhwcFormatShape = xInNhwcFormat.shape;
+    const targetShape = xInNhwcFormatShape[0] * xInNhwcFormatShape[1] * xInNhwcFormatShape[2];
+    const xReshaped = reshape4({
+      inputs: { x: xInNhwcFormat },
+      backend: backend2,
+      attrs: { shape: [1, targetShape, convInfo.inChannels] }
+    });
+    const filterReshaped = reshape4({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+    });
+    const result = batchMatMulImpl({
+      a: xReshaped,
+      b: filterReshaped,
+      transposeA,
+      transposeB,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+    const outInNHWCFormatShape = [
+      convInfo.batchSize,
+      convInfo.outHeight,
+      convInfo.outWidth,
+      convInfo.outChannels
+    ];
+    const outInNHWCFormat = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outInNHWCFormatShape } });
+    out = isChannelsLast ? outInNHWCFormat : transpose3({
+      inputs: { x: outInNHWCFormat },
+      backend: backend2,
+      attrs: { perm: [0, 3, 1, 2] }
+    });
+    if (!isChannelsLast) {
+      intermediates.push(xInNhwcFormat);
+      intermediates.push(outInNHWCFormat);
+    }
+    intermediates.push(xReshaped);
+    intermediates.push(filterReshaped);
+    intermediates.push(result);
+  }
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return out;
+}
+function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;
+  const isChannelsLast = dataFormat === "channelsLast";
+  const sharedDim = filterWidth * filterHeight * inChannels;
+  const numCols = outHeight * outWidth;
+  const x2ColShape = [sharedDim, numCols];
+  const transposeA = true;
+  const transposeB = false;
+  const intermediates = [];
+  if (preluActivationWeights != null && !isChannelsLast && preluActivationWeights.shape.length === 3) {
+    const preluActivationWeightsInNhwcFormat = transpose3({
+      inputs: { x: preluActivationWeights },
+      backend: backend2,
+      attrs: { perm: [1, 2, 0] }
+    });
+    intermediates.push(preluActivationWeightsInNhwcFormat);
+    preluActivationWeights = preluActivationWeightsInNhwcFormat;
+  }
+  const xSqueezed = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: x.shape.slice(1) } });
+  const w2Row = reshape4({
+    inputs: { x: filter },
+    backend: backend2,
+    attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }
+  });
+  intermediates.push(xSqueezed);
+  intermediates.push(w2Row);
+  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);
+  const customValues = [
+    xSqueezed.shape,
+    [convInfo.padInfo.top, convInfo.padInfo.left],
+    [convInfo.strideHeight, convInfo.strideWidth],
+    [convInfo.dilationHeight, convInfo.dilationWidth],
+    [convInfo.inChannels],
+    [convInfo.filterWidth * convInfo.inChannels],
+    [convInfo.outWidth]
+  ];
+  const im2Col = backend2.runWebGLProgram(im2ColProgram, [xSqueezed], "float32", customValues);
+  const im2ColReshaped = reshape4({
+    inputs: { x: im2Col },
+    backend: backend2,
+    attrs: { shape: [1, x2ColShape[0], x2ColShape[1]] }
+  });
+  intermediates.push(im2Col);
+  intermediates.push(im2ColReshaped);
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const hasLeakyreluAlpha = activation2 === "leakyrelu";
+  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;
+  const matmulProgram = new MatMulPackedProgram(im2ColReshaped.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+  const inputs = [im2ColReshaped, w2Row];
+  if (bias) {
+    inputs.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    inputs.push(preluActivationWeights);
+  }
+  if (hasLeakyreluAlpha) {
+    const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+    inputs.push($leakyreluAlpha);
+    intermediates.push($leakyreluAlpha);
+  }
+  const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32");
+  const outInNHWCFormatShape = [1, outHeight, outWidth, convInfo.outChannels];
+  const outInNHWCFormat = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: outInNHWCFormatShape } });
+  const out = isChannelsLast ? outInNHWCFormat : transpose3({ inputs: { x: outInNHWCFormat }, backend: backend2, attrs: { perm: [0, 3, 1, 2] } });
+  if (!isChannelsLast) {
+    intermediates.push(outInNHWCFormat);
+  }
+  intermediates.push(product);
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return out;
+}
+function conv2d4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  let out;
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
+    out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });
+  } else if (env().getBool("WEBGL_CONV_IM2COL") && x.shape[0] === 1) {
+    out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });
+  } else {
+    const program = new Conv2DProgram(convInfo);
+    out = backend2.runWebGLProgram(program, [x, filter], "float32");
+  }
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  backend2.disposeIntermediateTensorInfo(out);
+  return outReshaped;
+}
+var conv2DConfig2 = {
+  kernelName: Conv2D,
+  backendName: "webgl",
+  kernelFunc: conv2d4
+};
+var Conv2DDerFilterProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "dy"];
+    this.outputShape = convInfo.filterShape;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const isChannelsLast = convInfo.dataFormat === "channelsLast";
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int d2 = coords.w;
+
+        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              if (${isChannelsLast}) {
+                float dyValue = getDy(b, yR, yC, d2);
+                float xValue = getX(b, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              } else {
+                float dyValue = getDy(b, d2, yR, yC);
+                float xValue = getX(b, d1, xR, xC);
+                dotProd += (xValue * dyValue);
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Conv2DDerInputProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.outputShape = convInfo.inShape;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const isChannelsLast = convInfo.dataFormat === "channelsLast";
+    const padTop = filterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
+    const rowDim = isChannelsLast ? 1 : 2;
+    const colDim = isChannelsLast ? 2 : 3;
+    const channelDim = isChannelsLast ? 3 : 1;
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[${channelDim}];
+
+        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${filterHeight} - 1 - wR;
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${filterWidth} - 1 - wC;
+
+            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
+
+              if (${isChannelsLast}) {
+                float xValue = getDy(batch, idyR, idyC, d2);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              } else {
+                float xValue = getDy(batch, d2, idyR, idyC);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Conv3DDerFilterProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "dy"];
+    this.outputShape = convInfo.filterShape;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padFront = convInfo.padInfo.front;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    this.userCode = `
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int wF = coords.x;
+        int wR = coords.y;
+        int wC = coords.z;
+        int d1 = coords.w;
+        int d2 = coords.u;
+
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
+            int xF = wF + yF * ${strideDepth} - ${padFront};
+
+            if (xF < 0 || xF >= ${convInfo.inDepth}) {
+              continue;
+            }
+
+            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+              int xR = wR + yR * ${strideHeight} - ${padTop};
+
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
+                continue;
+              }
+
+              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+                int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                  continue;
+                }
+
+                float dyValue = getDy(b, yF, yR, yC, d2);
+                float xValue = getX(b, xF, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Conv3DDerInputProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.outputShape = convInfo.inShape;
+    const filterDepth = convInfo.filterDepth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padFront = filterDepth - 1 - convInfo.padInfo.front;
+    const padTop = filterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
+    this.userCode = `
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.u;
+
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyFCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
+
+          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
+            continue;
+          }
+          int idyF = int(dyF);
+
+          int wFPerm = ${filterDepth} - 1 - wF;
+
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+              fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            int wRPerm = ${filterHeight} - 1 - wR;
+
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              int wCPerm = ${filterWidth} - 1 - wC;
+
+              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
+                float xValue = getDy(batch, idyF, idyR, idyC, d2);
+                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+function conv2DBackpropFilter3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides2, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const program = new Conv2DDerFilterProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, dy], "float32");
+}
+var conv2DBackpropFilterConfig2 = {
+  kernelName: Conv2DBackpropFilter,
+  backendName: "webgl",
+  kernelFunc: conv2DBackpropFilter3
+};
+function conv2DBackpropInput3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { inputShape, strides: strides2, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides2, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const program = new Conv2DDerInputProgram(convInfo);
+  return backend2.runWebGLProgram(program, [dy, filter], "float32");
+}
+var conv2DBackpropInputConfig2 = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "webgl",
+  kernelFunc: conv2DBackpropInput3
+};
+function conv3D2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dilations } = attrs;
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides2, dilations, pad3);
+  const program = new Conv3DProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, filter], "float32");
+}
+var conv3DConfig2 = {
+  kernelName: Conv3D,
+  backendName: "webgl",
+  kernelFunc: conv3D2
+};
+function conv3DBackpropFilterV22(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides: strides2, pad: pad3, filterShape } = attrs;
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides2, 1, pad3);
+  const program = new Conv3DDerFilterProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, dy], "float32");
+}
+var conv3DBackpropFilterV2Config2 = {
+  kernelName: Conv3DBackpropFilterV2,
+  backendName: "webgl",
+  kernelFunc: conv3DBackpropFilterV22
+};
+function conv3DBackpropInput2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { pad: pad3, strides: strides2, inputShape } = attrs;
+  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides2, 1, pad3);
+  const program = new Conv3DDerInputProgram(convInfo);
+  return backend2.runWebGLProgram(program, [dy, filter], "float32");
+}
+var conv3DBackpropInputConfig = {
+  kernelName: Conv3DBackpropInputV2,
+  backendName: "webgl",
+  kernelFunc: conv3DBackpropInput2
+};
+var COS = CHECK_NAN_SNIPPET_UNARY + `
+  return cos(x);
+`;
+var cos3 = unaryKernelFunc2({ opSnippet: COS });
+var cosConfig2 = {
+  kernelName: Cos,
+  backendName: "webgl",
+  kernelFunc: cos3
+};
+var COSH = `
+  float e2x = exp(-x);
+  return (e2x + 1.0 / e2x) / 2.0;
+`;
+var cosh3 = unaryKernelFunc2({ opSnippet: COSH });
+var coshConfig2 = {
+  kernelName: Cosh,
+  backendName: "webgl",
+  kernelFunc: cosh3
+};
+var CropAndResizeProgram = class {
+  constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {
+    this.variableNames = ["Image", "Boxes", "BoxInd"];
+    this.outputShape = [];
+    const [batch, imageHeight, imageWidth, depth] = imageShape;
+    const [numBoxes] = boxShape;
+    const [cropHeight, cropWidth] = cropSize;
+    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
+    const methodId = method === "bilinear" ? 1 : 0;
+    const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];
+    const [heightRatio, heightScale, inY] = cropHeight > 1 ? [
+      `${(imageHeight - 1) / (cropHeight - 1)}`,
+      "(y2-y1) * height_ratio",
+      `y1*${inputHeightFloat} + float(y)*(height_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (y1+y2) * ${inputHeightFloat}`
+    ];
+    const [widthRatio, widthScale, inX] = cropWidth > 1 ? [
+      `${(imageWidth - 1) / (cropWidth - 1)}`,
+      "(x2-x1) * width_ratio",
+      `x1*${inputWidthFloat} + float(x)*(width_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (x1+x2) * ${inputWidthFloat}`
+    ];
+    this.userCode = `
+      const float height_ratio = float(${heightRatio});
+      const float width_ratio = float(${widthRatio});
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int y = coords[1];
+        int x = coords[2];
+        int d = coords[3];
+
+        // get box vals
+        float y1 = getBoxes(b,0);
+        float x1 = getBoxes(b,1);
+        float y2 = getBoxes(b,2);
+        float x2 = getBoxes(b,3);
+
+        // get image in batch index
+        int bInd = round(getBoxInd(b));
+        if(bInd < 0 || bInd >= ${batch}) {
+          return;
+        }
+
+        float height_scale = ${heightScale};
+        float width_scale = ${widthScale};
+
+        float in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutput(float(${extrapolationValue}));
+          return;
+        }
+        float in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutput(float(${extrapolationValue}));
+          return;
+        }
+
+        vec2 sourceFracIndexCR = vec2(in_x,in_y);
+        if(${methodId} == 1) {
+          // Compute the four integer indices.
+          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
+          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
+
+          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
+          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
+          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
+          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
+
+          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
+
+          float top = topLeft + (topRight - topLeft) * fracCR.x;
+          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          float newValue = top + (bottom - top) * fracCR.y;
+          setOutput(newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          ivec2 sourceNearestCR = ivec2(floor(
+            sourceFracIndexCR + vec2(0.5,0.5)));
+          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutput(newValue);
+        }
+      }
+    `;
+  }
+};
+var cropAndResize3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, boxes, boxInd } = inputs;
+  const { cropSize, method, extrapolationValue } = attrs;
+  const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue);
+  return backend2.runWebGLProgram(program, [image2, boxes, boxInd], "float32");
+};
+var cropAndResizeConfig2 = {
+  kernelName: CropAndResize,
+  backendName: "webgl",
+  kernelFunc: cropAndResize3
+};
+var CumOpType;
+(function(CumOpType3) {
+  CumOpType3["Prod"] = "*";
+  CumOpType3["Sum"] = "+";
+})(CumOpType || (CumOpType = {}));
+var CumProgram = class {
+  constructor(op2, outputShape, exclusive, reverse5) {
+    this.op = op2;
+    this.outputShape = outputShape;
+    this.variableNames = ["x"];
+    this.customUniforms = [{ name: "index", type: "float" }];
+    const rank = this.outputShape.length;
+    const initVal = this.op === CumOpType.Prod ? "1.0" : "0.0";
+    const val = exclusive ? initVal : `getX(${getCoords2(rank, "coords", this.op)})`;
+    const length = this.outputShape[this.outputShape.length - 1];
+    let condition = "";
+    let idxString = "";
+    if (exclusive) {
+      condition = reverse5 ? `end != ${length - 1}` : "end != 0";
+      idxString = reverse5 ? "end + 1" : "end - 1";
+    } else {
+      condition = reverse5 ? `end + pow2 < ${length}` : "end >= pow2";
+      idxString = reverse5 ? "end + pow2" : "end - pow2";
+    }
+    this.userCode = `
+      void main() {
+        ${getCoordsDataType(rank)} coords = getOutputCoords();
+        int end = ${getFinalCoord(rank, "coords", this.op)};
+        float val = ${val};
+        int pow2 = int(pow(2.0, index));
+        if (${condition}) {
+          int idx = ${idxString};
+          ${getFinalCoord(rank, "coords", this.op)} = idx;
+          val ${this.op}= getX(${getCoords2(rank, "coords", this.op)});
+        }
+        setOutput(val);
+      }
+    `;
+  }
+};
+function getCoords2(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.x, ${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.x, ${name}.y, ${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
+  } else {
+    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+function getFinalCoord(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.w`;
+  } else {
+    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+function cumImpl(op2, x, backend2, axis, exclusive, reverse5) {
+  const xRank = x.shape.length;
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  if (permutedAxis !== xRank - 1) {
+    throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);
+  }
+  const size2 = permutedX.shape[permutedAxis];
+  let result = identity3({ inputs: { x: permutedX }, backend: backend2 });
+  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size2)) - 1; i2++) {
+    const program = new CumProgram(op2, permutedX.shape, false, reverse5);
+    const customValues = [[i2]];
+    const prevResult = result;
+    result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);
+    backend2.disposeIntermediateTensorInfo(prevResult);
+  }
+  if (exclusive) {
+    const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5);
+    const prevResult = result;
+    result = backend2.runWebGLProgram(program, [result], result.dtype);
+    backend2.disposeIntermediateTensorInfo(prevResult);
+  }
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeIntermediateTensorInfo(result);
+    backend2.disposeIntermediateTensorInfo(permutedX);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+function cumprod3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5);
+}
+var cumprodConfig2 = {
+  kernelName: Cumprod,
+  backendName: "webgl",
+  kernelFunc: cumprod3
+};
+function cumsum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5);
+}
+var cumsumConfig2 = {
+  kernelName: Cumsum,
+  backendName: "webgl",
+  kernelFunc: cumsum3
+};
+function denseBincount3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size: size2, binaryOutput } = attrs;
+  if (x.shape.length === 1) {
+    const xVals = backend2.readSync(x.dataId);
+    const weightsVals = backend2.readSync(weights.dataId);
+    const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size2);
+    return backend2.makeTensorInfo([size2], weights.dtype, outVals);
+  } else if (x.shape.length === 2) {
+    const xBuf = backend2.bufferSync(x);
+    const weightsBuf = backend2.bufferSync(weights);
+    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size2, binaryOutput);
+    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
+  }
+  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
+}
+var denseBincountConfig2 = {
+  kernelName: DenseBincount,
+  backendName: "webgl",
+  kernelFunc: denseBincount3
+};
+var DepthToSpaceProgram = class {
+  constructor(outputShape, blockSize, dataFormat) {
+    this.variableNames = ["x"];
+    this.outputShape = [];
+    this.outputShape = outputShape;
+    this.blockSize = blockSize;
+    this.dataFormat = dataFormat;
+    this.userCode = `
+    void main() {
+      ivec4 coords = getOutputCoords();
+      int b = coords[0];
+      int h = ${this.getHeightCoordString()};
+      int w = ${this.getWidthCoordString()};
+      int d = ${this.getDepthCoordString()};
+
+      int in_h = h / ${blockSize};
+      int offset_h = imod(h, ${blockSize});
+      int in_w = w / ${blockSize};
+      int offset_w = imod(w, ${blockSize});
+      int offset_d = (offset_h * ${blockSize} + offset_w) *
+        ${this.getOutputDepthSize()};
+      int in_d = d + offset_d;
+
+      float result = ${this.getInputSamplingString()};
+      setOutput(result);
+    }
+  `;
+  }
+  getHeightCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[1]`;
+    } else {
+      return `coords[2]`;
+    }
+  }
+  getWidthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[2]`;
+    } else {
+      return `coords[3]`;
+    }
+  }
+  getDepthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[3]`;
+    } else {
+      return `coords[1]`;
+    }
+  }
+  getOutputDepthSize() {
+    if (this.dataFormat === "NHWC") {
+      return this.outputShape[3];
+    } else {
+      return this.outputShape[1];
+    }
+  }
+  getInputSamplingString() {
+    if (this.dataFormat === "NHWC") {
+      return `getX(b, in_h, in_w, in_d)`;
+    } else {
+      return `getX(b, in_d, in_h, in_w)`;
+    }
+  }
+};
+function depthToSpace3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  const batchSize = x.shape[0];
+  const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
+  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var depthToSpaceConfig2 = {
+  kernelName: DepthToSpace,
+  backendName: "webgl",
+  kernelFunc: depthToSpace3
+};
+var DepthwiseConv2DProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
+    this.variableNames = ["x", "W"];
+    this.customUniforms = [
+      { name: "pads", type: "ivec2" },
+      { name: "strides", type: "ivec2" },
+      { name: "dilations", type: "ivec2" },
+      { name: "inDims", type: "ivec2" }
+    ];
+    this.outputShape = convInfo.outShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivation) {
+        activationSnippet = `float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyReluAlpha) {
+        activationSnippet = `float activation(float a) {
+          float b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `
+          float activation(float x) {
+            ${activation2}
+          }
+        `;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyReluAlpha) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    this.userCode = `
+      ${activationSnippet}
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
+
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * dilations[0];
+
+          if (xR < 0 || xR >= inDims[0]) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * dilations[1];
+
+            if (xC < 0 || xC >= inDims[1]) {
+              continue;
+            }
+
+            float xVal = getX(batch, xR, xC, d1);
+            float wVal = getW(wR, wC, d1, q);
+            dotProd += xVal * wVal;
+          }
+        }
+
+        float result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `;
+  }
+};
+var DepthwiseConvPacked2DProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
+    this.variableNames = ["x", "W"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [
+      { name: "pads", type: "ivec2" },
+      { name: "strides", type: "ivec2" },
+      { name: "dilations", type: "ivec2" },
+      { name: "inDims", type: "ivec2" }
+    ];
+    this.outputShape = convInfo.outShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    const padLeft = convInfo.padInfo.left;
+    const strideWidth = convInfo.strideWidth;
+    const dilationWidth = convInfo.dilationWidth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const texelsAcross = filterWidth;
+    let mainLoop = `
+      int xR; int xC; int xCOffset;
+      vec4 wTexel; vec4 previous; vec4 final;`;
+    for (let c = 0; c < filterWidth; c++) {
+      mainLoop += `
+          vec4 xTexelC${c * 2};
+          int xTexelC${c * 2}Ready;
+          vec4 xTexelC${c * 2 + 1};
+          int xTexelC${c * 2 + 1}Ready;
+          vec4 xC${c};`;
+    }
+    mainLoop += `
+    for (int r = 0; r < ${filterHeight}; r++) {
+      `;
+    for (let c = 0; c < filterWidth; c++) {
+      mainLoop += `
+          xTexelC${c * 2} = vec4(0.0);
+          xTexelC${c * 2}Ready = 0;
+          xTexelC${c * 2 + 1} = vec4(0.0);
+          xTexelC${c * 2 + 1}Ready = 0;
+          xC${c} = vec4(0.0);`;
+    }
+    mainLoop += `
+        xR = xRCorner + r * dilations[0];
+        if (xR >=0 && xR < inDims[0]) {
+      `;
+    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
+      const colIndex = texelC * 2;
+      mainLoop += `
+          xC = xCCorner + ${colIndex * dilationWidth};
+          `;
+      if (strideWidth === 1) {
+        if (colIndex < filterWidth) {
+          if (padLeft % 2 === 1) {
+            mainLoop += `
+                xCOffset = xC + 1;
+                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+              `;
+            if (dilationWidth === 1 && colIndex > 0) {
+              mainLoop += `
+                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);
+                `;
+            } else {
+              mainLoop += `
+                  xCOffset = xC + 1 - 2;
+
+                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
+                    previous = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= inDims[1]) {
+                      previous.zw = vec2(0.0);
+                    }
+
+                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
+                  } else {
+                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
+                  }
+                  `;
+            }
+          } else {
+            mainLoop += `
+                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+
+                xC${colIndex} = xTexelC${colIndex};
+                `;
+          }
+          if (colIndex + 1 < filterWidth) {
+            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;
+            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {
+              mainLoop += `
+                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};
+
+                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= inDims[1]) {
+                      xTexelC${colIndex + 1}.zw = vec2(0.0);
+                    }
+                    xTexelC${colIndex + 1}Ready = 1;
+                  }
+                  `;
+              if (dilationWidth > 1) {
+                mainLoop += `
+                    xCOffset -= 2;
+                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                      xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
+                      xTexelC${colIndex}Ready = 1;
+                    }
+                    `;
+              }
+              mainLoop += `
+                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);
+                  `;
+            } else {
+              if (nextTexelOffset === 1) {
+                mainLoop += `
+                    xC${colIndex + 1} = xTexelC${colIndex};
+                    `;
+              } else {
+                mainLoop += `
+                    xCOffset = xC + ${nextTexelOffset};
+
+                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
+                      if (xCOffset + 1 >= inDims[1]) {
+                        xTexelC${colIndex + 1}.zw = vec2(0.0);
+                      }
+                      xTexelC${colIndex + 1}Ready = 1;
+                    }
+
+                    xC${colIndex + 1} = xTexelC${colIndex + 1};
+                    `;
+              }
+            }
+          }
+        }
+      } else {
+        if (colIndex < filterWidth) {
+          if (padLeft % 2 === 1) {
+            mainLoop += `
+                xCOffset = xC + 1 - strides[1];
+                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+
+                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xC + 2 >= inDims[1]) {
+                    xTexelC${colIndex + 1}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex + 1}Ready = 1;
+                }
+
+                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
+              `;
+            if (colIndex + 1 < filterWidth) {
+              mainLoop += `
+                  final = vec4(0.0);
+                  xCOffset = xC + 1 + strides[1];
+                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
+                    final = getX(batch, xR, xCOffset, d1);
+                  }
+                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
+                `;
+            }
+          } else {
+            mainLoop += `
+                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+
+                xCOffset = xC + strides[1];
+                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${colIndex + 1}.zw = vec2(0.);
+                  }
+                  xTexelC${colIndex + 1}Ready = 1;
+                }
+
+                xC${colIndex} = vec4(
+                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
+              `;
+            if (colIndex + 1 < filterWidth) {
+              mainLoop += `
+                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
+                `;
+            }
+          }
+        }
+      }
+      if (colIndex < filterWidth) {
+        mainLoop += `
+            wTexel = getW(r, ${colIndex}, d1, q);
+            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);
+          `;
+        if (colIndex + 1 < filterWidth) {
+          mainLoop += `
+              wTexel = getW(r, ${colIndex + 1}, d1, q);
+              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);
+            `;
+        }
+      }
+    }
+    mainLoop += `
+    }
+  `;
+    mainLoop += `
+      }
+    `;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivation) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyReluAlpha) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `vec4 activation(vec4 x) {
+          ${activation2}
+        }`;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyReluAlpha) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    this.userCode = `
+      ${activationSnippet}
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
+        vec4 dotProd = vec4(0.000000000000001);
+
+        ${mainLoop}
+
+        vec4 result = dotProd - vec4(0.000000000000001);
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `;
+  }
+};
+function depthwiseConv2dNative2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dimRoundingMode } = attrs;
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, $dilations, pad3, dimRoundingMode, true);
+  let program;
+  if (env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {
+    program = new DepthwiseConvPacked2DProgram(convInfo);
+  } else {
+    program = new DepthwiseConv2DProgram(convInfo);
+  }
+  const customValues = [
+    [convInfo.padInfo.top, convInfo.padInfo.left],
+    [convInfo.strideHeight, convInfo.strideWidth],
+    [convInfo.dilationHeight, convInfo.dilationWidth],
+    [convInfo.inHeight, convInfo.inWidth]
+  ];
+  return backend2.runWebGLProgram(program, [x, filter], "float32", customValues);
+}
+var depthwiseConv2dNativeConfig2 = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "webgl",
+  kernelFunc: depthwiseConv2dNative2
+};
+var DepthwiseConv2DDerFilterProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "dy"];
+    this.outputShape = convInfo.filterShape;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int dm = coords.w;
+        int d2 = d1 * ${channelMul} + dm;
+
+        float dotProd = 0.0;
+
+        // TO DO: Vec4 over the batch size
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              float dyValue = getDy(b, yR, yC, d2);
+              float xValue = getX(b, xR, xC, d1);
+              dotProd += (xValue * dyValue);
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var DepthwiseConv2DDerInputProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.outputShape = convInfo.inShape;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padTop = filterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[3];
+        ivec2 dyCorner = coords.yz - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        float dotProd = 0.0;
+
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${filterHeight} - 1 - wR;
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${filterWidth} - 1 - wC;
+
+            // TO DO: Vec4 over the channelMul
+            for (int dm = 0; dm < ${channelMul}; dm++) {
+              int d2 = d1 * ${channelMul} + dm;
+              float xValue = getDy(batch, idyR, idyC, d2);
+              float wValue = getW(wRPerm, wCPerm, d1, dm);
+              dotProd += xValue * wValue;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+function depthwiseConv2dNativeBackpropFilter3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides: strides2, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides2, dilations, pad3, dimRoundingMode, true);
+  const program = new DepthwiseConv2DDerFilterProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, dy], "float32");
+}
+var depthwiseConv2dNativeBackpropFilterConfig2 = {
+  kernelName: DepthwiseConv2dNativeBackpropFilter,
+  backendName: "webgl",
+  kernelFunc: depthwiseConv2dNativeBackpropFilter3
+};
+function depthwiseConv2dNativeBackpropInput3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { strides: strides2, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides2, dilations, pad3, dimRoundingMode, true);
+  const program = new DepthwiseConv2DDerInputProgram(convInfo);
+  return backend2.runWebGLProgram(program, [dy, filter], "float32");
+}
+var depthwiseConv2dNativeBackpropInputConfig2 = {
+  kernelName: DepthwiseConv2dNativeBackpropInput,
+  backendName: "webgl",
+  kernelFunc: depthwiseConv2dNativeBackpropInput3
+};
+var DiagProgram = class {
+  constructor(size2) {
+    this.variableNames = ["X"];
+    this.outputShape = [size2, size2];
+    this.userCode = `
+      void main() {
+          ivec2 coords = getOutputCoords();
+          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
+          setOutput(val);
+      }
+    `;
+  }
+};
+function diag3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  const outShape = [...x.shape, ...x.shape];
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });
+  const program = new DiagProgram(xSize);
+  const res = backend2.runWebGLProgram(program, [flat], flat.dtype);
+  const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
+  backend2.disposeIntermediateTensorInfo(flat);
+  backend2.disposeIntermediateTensorInfo(res);
+  return out;
+}
+var diagConfig2 = {
+  kernelName: Diag,
+  backendName: "webgl",
+  kernelFunc: diag3
+};
+var Dilation2DProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "W"];
+    this.outputShape = convInfo.outShape;
+    const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;
+    const { top: padTop, left: padLeft } = padInfo;
+    this.userCode = `
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float neg_infinity = -3.4e38;
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.w;
+        ivec2 outTopLeftCorner =
+            coords.yz * strides - pads;
+        int hBeg = outTopLeftCorner.x;
+        int wBeg = outTopLeftCorner.y;
+
+        float curVal = neg_infinity;
+        for (int h = 0; h < ${filterHeight}; h++) {
+          int hIn = hBeg + h * ${dilationHeight};
+
+          if (hIn >= 0 && hIn < ${inHeight}) {
+            for (int w = 0; w < ${filterWidth}; w++) {
+              int wIn = wBeg + w * ${dilationWidth};
+
+              if (wIn >= 0 && wIn < ${inWidth}) {
+                float xVal = getX(batch, hIn, wIn, d1);
+                float wVal = getW(h, w, d1);
+
+                float val = xVal + wVal;
+                if (val > curVal) {
+                  curVal = val;
+                }
+              }
+            }
+          }
+        }
+
+        float result = curVal;
+        setOutput(result);
+      }
+    `;
+  }
+};
+function dilation2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dilations } = attrs;
+  const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides2, pad3, "NHWC", dilations);
+  let out;
+  const program = new Dilation2DProgram(convInfo);
+  out = backend2.runWebGLProgram(program, [x, filter], "float32");
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  backend2.disposeIntermediateTensorInfo(out);
+  return outReshaped;
+}
+var dilation2DConfig2 = {
+  kernelName: Dilation2D,
+  backendName: "webgl",
+  kernelFunc: dilation2D
+};
+function einsum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { equation } = attrs;
+  const tensors = inputs;
+  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
+  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
+  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
+  const nSteps = steps.length;
+  let out = null;
+  let numDimsRemaining = allDims.length;
+  const tensorsToDispose = [];
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    for (const idTerm of steps[i2]) {
+      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
+      let x;
+      if (backend_util_exports.isIdentityPermutation(perm)) {
+        x = tensors[idTerm];
+      } else {
+        x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
+        tensorsToDispose.push(x);
+      }
+      const targetShape = x.shape.slice();
+      for (let k = 0; k < dimsToExpand.length; ++k) {
+        targetShape.splice(dimsToExpand[k], 0, 1);
+      }
+      if (!util_exports.arraysEqual(x.shape, targetShape)) {
+        x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
+        tensorsToDispose.push(x);
+      }
+      if (out === null) {
+        out = x;
+      } else {
+        out = multiply3({ inputs: { a: x, b: out }, backend: backend2 });
+        tensorsToDispose.push(out);
+      }
+    }
+    if (i2 < nSteps - 1) {
+      if (path[i2] >= 0) {
+        out = sum4({
+          inputs: { x: out },
+          backend: backend2,
+          attrs: {
+            axis: path[i2] - (allDims.length - numDimsRemaining),
+            keepDims: false
+          }
+        });
+        tensorsToDispose.push(out);
+      }
+      numDimsRemaining--;
+    }
+  }
+  for (const tensorInfo of tensorsToDispose) {
+    if (tensorInfo === out) {
+      continue;
+    }
+    backend2.disposeIntermediateTensorInfo(tensorInfo);
+  }
+  return out;
+}
+var einsumConfig2 = {
+  kernelName: Einsum,
+  backendName: "webgl",
+  kernelFunc: einsum3
+};
+var ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
+var ELU_PACKED = `
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`;
+var elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });
+var eluConfig2 = {
+  kernelName: Elu,
+  backendName: "webgl",
+  kernelFunc: elu5
+};
+var ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;
+var ELU_DER_PACKED = `
+  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
+  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
+`;
+var eluGrad2 = (args) => {
+  const { inputs, backend: backend2 } = args;
+  const { dy, y } = inputs;
+  const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
+  return backend2.runWebGLProgram(program, [dy, y], dy.dtype);
+};
+var eluGradConfig3 = {
+  kernelName: EluGrad,
+  backendName: "webgl",
+  kernelFunc: eluGrad2
+};
+var PACKED_EQUAL = `
+  return vec4(equal(a, b));
+`;
+var EQUAL = `return float(a == b);`;
+var equal3 = binaryKernelFunc2({
+  opSnippet: EQUAL,
+  packedOpSnippet: PACKED_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: equalImplCPU
+});
+var equalConfig2 = {
+  kernelName: Equal,
+  backendName: "webgl",
+  kernelFunc: equal3
+};
+var ERF = `
+  // Error function is calculated approximately with elementary function.
+  // See "Handbook of Mathematical Functions with Formulas,
+  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
+  float p = ${backend_util_exports.ERF_P};
+  float a1 = ${backend_util_exports.ERF_A1};
+  float a2 = ${backend_util_exports.ERF_A2};
+  float a3 = ${backend_util_exports.ERF_A3};
+  float a4 = ${backend_util_exports.ERF_A4};
+  float a5 = ${backend_util_exports.ERF_A5};
+
+  float sign = sign(x);
+  x = abs(x);
+  float t = 1.0 / (1.0 + p * x);
+  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
+`;
+var erf3 = unaryKernelFunc2({ opSnippet: ERF });
+var erfConfig2 = {
+  kernelName: Erf,
+  backendName: "webgl",
+  kernelFunc: erf3
+};
+var EXP = CHECK_NAN_SNIPPET_UNARY + `
+  return exp(x);
+`;
+var EXP_PACKED = `
+  vec4 result = exp(x);
+  bvec4 isNaN = isnan(x);
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var exp3 = unaryKernelFunc2({
+  opSnippet: EXP,
+  packedOpSnippet: EXP_PACKED,
+  cpuKernelImpl: expImplCPU,
+  dtype: "float32"
+});
+var expConfig2 = {
+  kernelName: Exp,
+  backendName: "webgl",
+  kernelFunc: exp3
+};
+function expandDims4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { dim } = attrs;
+  const { input: input2 } = inputs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig2 = {
+  kernelName: ExpandDims,
+  backendName: "webgl",
+  kernelFunc: expandDims4
+};
+var EXPM1 = `return exp(x) - 1.0;`;
+var expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });
+var expm1Config2 = {
+  kernelName: Expm1,
+  backendName: "webgl",
+  kernelFunc: expm13
+};
+var FFTProgram = class {
+  constructor(component, inputShape, inverse) {
+    this.variableNames = ["real", "imag"];
+    const innerDim = inputShape[1];
+    this.outputShape = inputShape;
+    const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;
+    const resultDenominator = inverse ? `${innerDim}.0` : "1.0";
+    let opString;
+    if (component === "real") {
+      opString = "return real * expR - imag * expI;";
+    } else if (component === "imag") {
+      opString = "return real * expI + imag * expR;";
+    } else {
+      throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);
+    }
+    this.userCode = `
+      const float exponentMultiplier = ${exponentMultiplierSnippet};
+
+      float unaryOpComplex(float real, float expR, float imag, float expI) {
+        ${opString}
+      }
+
+      float mulMatDFT(int batch, int index) {
+        float indexRatio = float(index) / float(${innerDim});
+        float exponentMultiplierTimesIndexRatio =
+            exponentMultiplier * indexRatio;
+
+        float result = 0.0;
+
+        for (int i = 0; i < ${innerDim}; i++) {
+          // x = (-2|2 * PI / N) * index * i;
+          float x = exponentMultiplierTimesIndexRatio * float(i);
+          float expR = cos(x);
+          float expI = sin(x);
+          float real = getReal(batch, i);
+          float imag = getImag(batch, i);
+
+          result +=
+              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
+        }
+
+        return result;
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        setOutput(mulMatDFT(coords[0], coords[1]));
+      }
+    `;
+  }
+};
+function fftImpl2(x, inverse, backend2) {
+  const xData = backend2.texData.get(x.dataId);
+  const inputSize10 = util_exports.sizeFromShape(x.shape);
+  const innerDimensionSize = x.shape[x.shape.length - 1];
+  const batch = inputSize10 / innerDimensionSize;
+  const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });
+  const xShape = input2D.shape;
+  const realProgram = new FFTProgram("real", xShape, inverse);
+  const imagProgram = new FFTProgram("imag", xShape, inverse);
+  const inputs = [
+    {
+      dataId: xData.complexTensorInfos.real.dataId,
+      dtype: xData.complexTensorInfos.real.dtype,
+      shape: xShape
+    },
+    {
+      dataId: xData.complexTensorInfos.imag.dataId,
+      dtype: xData.complexTensorInfos.imag.dtype,
+      shape: xShape
+    }
+  ];
+  const realPart = backend2.runWebGLProgram(realProgram, inputs, "float32");
+  const imagPart = backend2.runWebGLProgram(imagProgram, inputs, "float32");
+  const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(realPart);
+  backend2.disposeIntermediateTensorInfo(imagPart);
+  const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });
+  backend2.disposeIntermediateTensorInfo(input2D);
+  backend2.disposeIntermediateTensorInfo(complexOutput);
+  return complexOutputReshaped;
+}
+function fft3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  return fftImpl2(input2, false, backend2);
+}
+var fftConfig2 = {
+  kernelName: FFT,
+  backendName: "webgl",
+  kernelFunc: fft3
+};
+var FillProgram = class {
+  constructor(shape, value) {
+    this.outputShape = [];
+    this.customUniforms = [{ name: "value", type: "float" }];
+    this.variableNames = ["x"];
+    this.outputShape = shape;
+    this.userCode = `
+      void main() {
+        // Input can be obtained from uniform value.
+        setOutput(value);
+      }
+    `;
+  }
+};
+function fill3(args) {
+  const { backend: backend2, attrs } = args;
+  const { shape, value } = attrs;
+  let { dtype } = attrs;
+  dtype = dtype || util_exports.inferDtype(value);
+  if (dtype === "string") {
+    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));
+    values.fill(value);
+    return backend2.makeTensorInfo(shape, dtype, values);
+  } else {
+    const program = new FillProgram(shape, value);
+    const customValues = [[value]];
+    return backend2.runWebGLProgram(program, [], dtype, customValues);
+  }
+}
+var fillConfig2 = {
+  kernelName: Fill,
+  backendName: "webgl",
+  kernelFunc: fill3
+};
+var FlipLeftRightProgram = class {
+  constructor(imageShape) {
+    this.variableNames = ["Image"];
+    this.outputShape = [];
+    const imageWidth = imageShape[2];
+    this.outputShape = imageShape;
+    this.userCode = `
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+
+          int coordX = ${imageWidth} - x - 1;
+          float outputValue;
+          if(coordX >= 0 && coordX < ${imageWidth}) {
+            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
+          } else {
+            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `;
+  }
+};
+var flipLeftRightConfig2 = {
+  kernelName: FlipLeftRight,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const webglBackend = backend2;
+    const program = new FlipLeftRightProgram(image2.shape);
+    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype);
+    return output;
+  }
+};
+var FLOOR = `return floor(x);`;
+var floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });
+var floorConfig2 = {
+  kernelName: Floor,
+  backendName: "webgl",
+  kernelFunc: floor3
+};
+var INT_DIV = `
+  float s = sign(a) * sign(b);
+  int ia = round(a);
+  int ib = round(b);
+  if (ib != 0) {
+    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+    return float(idiv(ia, ib, s));
+  } else {
+    return NAN;
+  }
+`;
+var INT_DIV_PACKED = `
+  ivec4 ia = round(a);
+  ivec4 ib = round(b);
+  bvec4 cond = notEqual(ib, ivec4(0));
+  ivec4 result = ivec4(0);
+  vec4 s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    result[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    result[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    result[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    result[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4(result);
+`;
+var floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: "int32" });
+var floorDivConfig2 = {
+  kernelName: FloorDiv,
+  backendName: "webgl",
+  kernelFunc: floorDiv3
+};
+var FromPixelsProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    const glsl = getGlslDifferences();
+    const [height, width] = outputShape;
+    this.outputShape = outputShape;
+    this.userCode = `
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
+
+        vec4 values = ${glsl.texture2D}(A, uv);
+        float value;
+        if (depth == 0) {
+          value = values.r;
+        } else if (depth == 1) {
+          value = values.g;
+        } else if (depth == 2) {
+          value = values.b;
+        } else if (depth == 3) {
+          value = values.a;
+        }
+
+        setOutput(floor(value * 255.0 + 0.5));
+      }
+    `;
+  }
+};
+var FromPixelsPackedProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    const glsl = getGlslDifferences();
+    const [height, width] = outputShape;
+    this.outputShape = outputShape;
+    this.userCode = `
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+
+        vec4 result = vec4(0.);
+
+        for(int row=0; row<=1; row++) {
+          for(int col=0; col<=1; col++) {
+            texC = coords[1] + row;
+            depth = coords[2] + col;
+
+            vec2 uv = (vec2(texC, texR) + halfCR) /
+                       vec2(${width}.0, ${height}.0);
+            vec4 values = ${glsl.texture2D}(A, uv);
+            float value;
+            if (depth == 0) {
+              value = values.r;
+            } else if (depth == 1) {
+              value = values.g;
+            } else if (depth == 2) {
+              value = values.b;
+            } else if (depth == 3) {
+              value = values.a;
+            }
+
+            result[row * 2 + col] = floor(value * 255.0 + 0.5);
+          }
+        }
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+var fromPixelsConfig = {
+  kernelName: FromPixels,
+  backendName: "webgl",
+  kernelFunc: fromPixels2
+};
+var fromPixels2DContext2;
+function fromPixels2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  let { pixels } = inputs;
+  const { numChannels } = attrs;
+  const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
+  const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
+  const [width, height] = isVideo ? [
+    pixels.videoWidth,
+    pixels.videoHeight
+  ] : [pixels.width, pixels.height];
+  const texShape = [height, width];
+  const outShape = [height, width, numChannels];
+  if (isImage || isVideo) {
+    if (fromPixels2DContext2 == null) {
+      fromPixels2DContext2 = document.createElement("canvas").getContext("2d");
+    }
+    fromPixels2DContext2.canvas.width = width;
+    fromPixels2DContext2.canvas.height = height;
+    fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);
+    pixels = fromPixels2DContext2.canvas;
+  }
+  const tempPixelHandle = backend2.makeTensorInfo(texShape, "int32");
+  backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
+  backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);
+  const program = env().getBool("WEBGL_PACK") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);
+  const res = backend2.runWebGLProgram(program, [tempPixelHandle], "int32");
+  backend2.disposeData(tempPixelHandle.dataId);
+  return res;
+}
+function fusedConv2d(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  let out;
+  const intermediates = [];
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
+    out = conv2dByMatMul({
+      x,
+      filter,
+      convInfo,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+  } else if (env().getBool("WEBGL_CONV_IM2COL") && x.shape[0] === 1) {
+    out = conv2dWithIm2Row({
+      x,
+      filter,
+      convInfo,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+  } else {
+    const hasBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    const hasLeakyreluAlpha = activation2 === "leakyrelu";
+    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;
+    const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+    const inputs2 = [x, filter];
+    const alignInputWithDataFormat = (input2, dataFormat2) => {
+      if (dataFormat2 === "NCHW" && input2.shape.length === 1 && input2.shape[0] !== 1) {
+        const alignedInput = reshape4({
+          inputs: { x: input2 },
+          backend: backend2,
+          attrs: { shape: [input2.shape[0], 1, 1] }
+        });
+        intermediates.push(alignedInput);
+        return alignedInput;
+      }
+      return input2;
+    };
+    if (hasBias) {
+      inputs2.push(alignInputWithDataFormat(bias, dataFormat));
+    }
+    if (hasPreluActivationWeights) {
+      inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));
+    }
+    if (hasLeakyreluAlpha) {
+      const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+      inputs2.push($leakyreluAlpha);
+      intermediates.push($leakyreluAlpha);
+    }
+    out = backend2.runWebGLProgram(program, inputs2, "float32");
+  }
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  intermediates.push(out);
+  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return outReshaped;
+}
+var fusedConv2DConfig2 = {
+  kernelName: FusedConv2D,
+  backendName: "webgl",
+  kernelFunc: fusedConv2d
+};
+function fusedDepthwiseConv2D2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const intermediates = [];
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, $dilations, pad3, dimRoundingMode, true);
+  const shouldPackDepthwiseConv = env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;
+  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;
+  const programInputs = [x, filter];
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const hasLeakyreluAlpha = activation2 === "leakyrelu";
+  if (hasBias) {
+    programInputs.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    programInputs.push(preluActivationWeights);
+  }
+  if (hasLeakyreluAlpha) {
+    const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+    programInputs.push($leakyreluAlpha);
+    intermediates.push($leakyreluAlpha);
+  }
+  let program;
+  if (shouldPackDepthwiseConv) {
+    program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+  } else {
+    program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+  }
+  const customValues = [
+    [convInfo.padInfo.top, convInfo.padInfo.left],
+    [convInfo.strideHeight, convInfo.strideWidth],
+    [convInfo.dilationHeight, convInfo.dilationWidth],
+    [convInfo.inHeight, convInfo.inWidth]
+  ];
+  const result = backend2.runWebGLProgram(program, programInputs, "float32", customValues);
+  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var fusedDepthwiseConv2DConfig2 = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "webgl",
+  kernelFunc: fusedDepthwiseConv2D2
+};
+var GatherNDProgram = class {
+  constructor(sliceDim, strides2, shape) {
+    this.sliceDim = sliceDim;
+    this.strides = strides2;
+    this.variableNames = ["x", "indices"];
+    this.outputShape = shape;
+    const stridesType = getCoordsDataType(strides2.length);
+    const dtype = getCoordsDataType(shape.length);
+    const strideString = this.sliceDim > 1 ? "strides[j]" : "strides";
+    this.userCode = `
+        ${stridesType} strides = ${stridesType}(${this.strides});
+         void main() {
+          ${dtype} coords = getOutputCoords();
+          int flattenIndex = 0;
+          for (int j = 0; j < ${this.sliceDim}; j++) {
+            int index = round(getIndices(coords[0], j));
+            flattenIndex += index * ${strideString};
+          }
+          setOutput(getX(flattenIndex, coords[1]));
+        }
+      `;
+  }
+};
+function gatherNd2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { params, indices } = inputs;
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const paramsSize = util_exports.sizeFromShape(params.shape);
+  const [resultShape, numSlices, sliceSize, strides2] = backend_util_exports.prepareAndValidate(params, indices);
+  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });
+  const flattenX = reshape4({
+    inputs: { x: params },
+    backend: backend2,
+    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }
+  });
+  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") {
+    const indicesData = backend2.readSync(indices.dataId);
+    const paramsBuf = backend2.bufferSync(params);
+    const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides2, params.shape, paramsSize);
+    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);
+  }
+  const program = new GatherNDProgram(sliceRank, strides2, [numSlices, sliceSize]);
+  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });
+  backend2.disposeIntermediateTensorInfo(flattenIndices);
+  backend2.disposeIntermediateTensorInfo(flattenX);
+  backend2.disposeIntermediateTensorInfo(res);
+  return reshaped;
+}
+var gatherNdConfig2 = {
+  kernelName: GatherNd,
+  backendName: "webgl",
+  kernelFunc: gatherNd2
+};
+var GatherProgram = class {
+  constructor(aShape, outputShape) {
+    this.variableNames = ["A", "indices"];
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    const dtype = getCoordsDataType(this.rank);
+    const sourceCoords = getSourceCoords2(aShape, 2);
+    this.userCode = `
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        int index = int(getIndices(resRC.x, resRC.z));
+        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;
+        setOutput(inBounds * getA(${sourceCoords}));
+      }
+    `;
+  }
+};
+function getSourceCoords2(aShape, axis) {
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < aShape.length; i2++) {
+    if (i2 === 2) {
+      sourceCoords.push("index");
+    } else {
+      sourceCoords.push(`${currentCoords[i2]}`);
+    }
+  }
+  return sourceCoords.join();
+}
+function gatherV22(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  if (env().get("DEBUG")) {
+    const indicesVals = backend2.readSync(indices.dataId);
+    const axisDim = x.shape[parsedAxis];
+    for (let i2 = 0; i2 < indicesVals.length; ++i2) {
+      const index2 = indicesVals[i2];
+      util_exports.assert(index2 <= axisDim - 1 && index2 >= 0, () => `GatherV2: the index value ${index2} is not in [0, ${axisDim - 1}]`);
+    }
+  }
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const toDispose = [];
+  const flattenX = reshape4({
+    inputs: { x },
+    backend: backend2,
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    }
+  });
+  const flattenIndex = reshape4({
+    inputs: { x: indices },
+    backend: backend2,
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+  });
+  toDispose.push(flattenX);
+  toDispose.push(flattenIndex);
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === "string") {
+    const indicesBuf = backend2.bufferSync(flattenIndex);
+    const xBuf = backend2.bufferSync(flattenX);
+    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
+    toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+  }
+  const program = new GatherProgram(flattenX.shape, flattenOutputShape);
+  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);
+  toDispose.push(res);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return reshaped;
+}
+var gatherV2Config2 = {
+  kernelName: GatherV2,
+  backendName: "webgl",
+  kernelFunc: gatherV22
+};
+var GREATER = `return float(a > b);`;
+var GREATER_PACKED = `
+  return vec4(greaterThan(a, b));
+`;
+var greater4 = binaryKernelFunc2({
+  opSnippet: GREATER,
+  packedOpSnippet: GREATER_PACKED,
+  cpuKernelImpl: greaterImplCPU,
+  dtype: "bool"
+});
+var greaterConfig2 = {
+  kernelName: Greater,
+  backendName: "webgl",
+  kernelFunc: greater4
+};
+var GREATER_EQUAL = `return float(a >= b);`;
+var GREATER_EQUAL_PACKED = `
+  return vec4(greaterThanEqual(a, b));
+`;
+var greaterEqual3 = binaryKernelFunc2({
+  opSnippet: GREATER_EQUAL,
+  packedOpSnippet: GREATER_EQUAL_PACKED,
+  dtype: "bool",
+  cpuKernelImpl: greaterEqualImplCPU
+});
+var greaterEqualConfig2 = {
+  kernelName: GreaterEqual,
+  backendName: "webgl",
+  kernelFunc: greaterEqual3
+};
+function ifft3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  return fftImpl2(input2, true, backend2);
+}
+var ifftConfig2 = {
+  kernelName: IFFT,
+  backendName: "webgl",
+  kernelFunc: ifft3
+};
+var IS_FINITE = `return float(!isnan(x) && !isinf(x));`;
+var isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: "bool" });
+var isFiniteConfig2 = {
+  kernelName: IsFinite,
+  backendName: "webgl",
+  kernelFunc: isFinite4
+};
+var IS_INF = `return float(isinf(x));`;
+var isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: "bool" });
+var isInfConfig2 = {
+  kernelName: IsInf,
+  backendName: "webgl",
+  kernelFunc: isInf3
+};
+var IS_NAN = `return float(isnan(x));`;
+var isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: "bool" });
+var isNaNConfig2 = {
+  kernelName: IsNan,
+  backendName: "webgl",
+  kernelFunc: isNaN4
+};
+var LESS = `return float(a < b);`;
+var LESS_PACKED = `
+  return vec4(lessThan(a, b));
+`;
+var less4 = binaryKernelFunc2({
+  opSnippet: LESS,
+  packedOpSnippet: LESS_PACKED,
+  cpuKernelImpl: lessImplCPU,
+  dtype: "bool"
+});
+var lessConfig2 = {
+  kernelName: Less,
+  backendName: "webgl",
+  kernelFunc: less4
+};
+var LESS_EQUAL = `return float(a <= b);`;
+var LESS_EQUAL_PACKED = `
+  return vec4(lessThanEqual(a, b));
+`;
+var lessEqual3 = binaryKernelFunc2({
+  opSnippet: LESS_EQUAL,
+  packedOpSnippet: LESS_EQUAL_PACKED,
+  cpuKernelImpl: lessEqualImplCPU,
+  dtype: "bool"
+});
+var lessEqualConfig2 = {
+  kernelName: LessEqual,
+  backendName: "webgl",
+  kernelFunc: lessEqual3
+};
+function linSpace2(args) {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, num } = attrs;
+  const outVals = linSpaceImplCPU(start, stop, num);
+  return backend2.makeTensorInfo([outVals.length], "float32", outVals);
+}
+var linSpaceConfig2 = {
+  kernelName: LinSpace,
+  backendName: "webgl",
+  kernelFunc: linSpace2
+};
+var LOG = CHECK_NAN_SNIPPET_UNARY + `
+  return x < 0.0 ? 0./0. : log(x);
+`;
+var LOG_PACKED = `
+  vec4 result = log(x);
+  bvec4 isNaN = isnan(x);
+  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
+  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
+  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
+  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
+  return result;
+`;
+var log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });
+var logConfig2 = {
+  kernelName: Log,
+  backendName: "webgl",
+  kernelFunc: log4
+};
+var LOG1P = CHECK_NAN_SNIPPET_UNARY + `
+  return log(1.0 + x);
+`;
+var log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });
+var log1pConfig2 = {
+  kernelName: Log1p,
+  backendName: "webgl",
+  kernelFunc: log1p3
+};
+var LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;
+var LOGICAL_AND_PACKED = `
+  return vec4(
+    vec4(greaterThanEqual(a, vec4(1.0))) *
+    vec4(greaterThanEqual(b, vec4(1.0))));
+`;
+var logicalAnd3 = binaryKernelFunc2({
+  opSnippet: LOGICAL_AND,
+  packedOpSnippet: LOGICAL_AND_PACKED,
+  dtype: "bool"
+});
+var logicalAndConfig2 = {
+  kernelName: LogicalAnd,
+  backendName: "webgl",
+  kernelFunc: logicalAnd3
+};
+var LOGICAL_NOT = `return float(!(x >= 1.0));`;
+var logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });
+var logicalNotConfig2 = {
+  kernelName: LogicalNot,
+  backendName: "webgl",
+  kernelFunc: logicalNot3
+};
+var LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;
+var LOGICAL_OR_PACKED = `
+  return min(
+    vec4(greaterThanEqual(a, vec4(1.0))) +
+    vec4(greaterThanEqual(b, vec4(1.0))),
+    vec4(1.0));
+`;
+var logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: "bool" });
+var logicalOrConfig2 = {
+  kernelName: LogicalOr,
+  backendName: "webgl",
+  kernelFunc: logicalOr3
+};
+var LRNProgram = class {
+  constructor(xShape, radius, bias, alpha2, beta) {
+    this.variableNames = ["x"];
+    this.outputShape = [];
+    const rad = radius;
+    const maxD = xShape[3] - 1;
+    this.outputShape = xShape;
+    let powOperator;
+    const basis = `float(${bias}) + float(${alpha2}) * sum`;
+    if (beta === 0.5) {
+      powOperator = `inversesqrt(${basis})`;
+    } else if (beta === 1) {
+      powOperator = `1.0/(${basis})`;
+    } else {
+      powOperator = `exp(log(${basis}) * float(-${beta}));`;
+    }
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+        int d = coords[3];
+        float x = getX(b, r, c, d);
+        float sum = 0.0;
+        for (int j = -${rad}; j <= ${rad}; j++) {
+          int idx = d + j;
+          if (idx >= 0 && idx <=  ${maxD}) {
+            float z = getX(b, r, c, idx);
+            sum += z * z;
+          }
+        }
+        float val = x * ${powOperator};
+        setOutput(val);
+      }
+    `;
+  }
+};
+var LRNPackedProgram = class {
+  constructor(xShape, radius, bias, alpha2, beta) {
+    this.variableNames = ["x"];
+    this.outputShape = [];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    const rad = radius;
+    const maxD = xShape[3] - 1;
+    this.outputShape = xShape;
+    let powOperator;
+    const basis = `float(${bias}) + float(${alpha2}) * sum`;
+    if (beta === 0.5) {
+      powOperator = `inversesqrt(${basis})`;
+    } else if (beta === 1) {
+      powOperator = `1.0/(${basis})`;
+    } else {
+      powOperator = `exp(log(${basis}) * float(-${beta}));`;
+    }
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords.x;
+        int r = coords.y;
+        int c = coords.z;
+        int d = coords.w;
+
+        bool hasNextCol = d < ${this.outputShape[3]};
+        bool hasNextRow = c < ${this.outputShape[2]};
+
+        vec4 sum = vec4(0.);
+        vec4 xFragAtOutputCoords = getX(b, r, c, d);
+
+        vec4 xAtOutputCoords = vec4(
+          getChannel(xFragAtOutputCoords, vec2(c, d)),
+          hasNextCol ?
+            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
+          hasNextRow ?
+            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
+        );
+
+        int firstChannel = d - ${rad};
+        vec2 cache = vec2(0.);
+        if(firstChannel >= 0){
+          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
+          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
+            if(hasNextRow){
+              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
+            }
+        }
+
+        ivec2 depth = ivec2(d, d + 1);
+        for (int j = - ${rad}; j <= ${rad}; j++) {
+          ivec2 idx = depth + j;
+          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
+
+          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
+          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
+
+          if(depthInRange || depthPlusOneInRange){
+            vec4 z = vec4(0.);
+            vec4 xFragAtCurrentDepth;
+            z.xz = cache.xy;
+            if(depthPlusOneInRange && hasNextCol){
+              xFragAtCurrentDepth = idx.y != d ?
+                getX(b, r, c, idx.y) : xFragAtOutputCoords;
+              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
+              if(hasNextRow){
+                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
+              }
+            }
+            cache.xy = z.yw;
+            sum += z * z;
+          }
+        }
+        vec4 result = xAtOutputCoords * ${powOperator};
+        setOutput(result);
+      }
+    `;
+  }
+};
+var lrn = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { depthRadius, bias, alpha: alpha2, beta } = attrs;
+  const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha2, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha2, beta);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+};
+var LRNConfig2 = {
+  kernelName: LRN,
+  backendName: "webgl",
+  kernelFunc: lrn
+};
+var LRNGradProgram = class {
+  constructor(inputShape, depthRadius, bias, alpha2, beta) {
+    this.variableNames = ["inputImage", "outputImage", "dy"];
+    this.outputShape = [];
+    this.outputShape = inputShape;
+    this.depth = inputShape[3];
+    this.depthRadius = depthRadius;
+    this.bias = bias;
+    this.alpha = alpha2;
+    this.beta = beta;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+
+        float result = 0.0;
+        for (int d = 0; d < ${this.depth}; ++d) {
+          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
+          int depthEnd = int(min(float(${this.depth}),
+              float(d + ${depthRadius} + 1)));
+
+          const int MIN_DEPTH_BEGIN = 0;
+          const int MAX_DEPTH_END = ${this.depth};
+
+          float norm = 0.0;
+          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd) {
+              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
+            }
+            else {
+              break;
+            }
+          }
+
+          norm = float(${alpha2}) * norm + float(${bias});
+
+          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd){
+              float dyi = -2.0 * float(${alpha2})
+                * float(${beta})
+                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
+                / norm;
+              if (k == d) {
+                dyi += pow(norm, -1.0 * ${beta});
+              }
+              if (k == coords[3]) {
+                dyi *= getDy(b, r, c, d);
+                result += dyi;
+              }
+            }
+            else {
+              break;
+            }
+          }
+      }
+      setOutput(result);
+      }
+    `;
+  }
+};
+var lrnGrad = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, y, dy } = inputs;
+  const { depthRadius, bias, alpha: alpha2, beta } = attrs;
+  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha2, beta);
+  return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);
+};
+var LRNGradConfig2 = {
+  kernelName: LRNGrad,
+  backendName: "webgl",
+  kernelFunc: lrnGrad
+};
+function maxImpl2(x, reduceShape, outShape, backend2) {
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const batchSize = xSize / inSize;
+  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
+  const reduced = reduce(reshapedInput, x.dtype, "max", backend2);
+  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(reshapedInput);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  return reshapedOutput;
+}
+function max4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reductionIndices, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  const maxInputIsTransposed = permutedAxes != null;
+  const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
+  let maxInput = x;
+  if (maxInputIsTransposed) {
+    if (shouldExecuteOnCPU) {
+      const xTexData = backend2.texData.get(maxInput.dataId);
+      const values = xTexData.values;
+      const newShape = new Array(xRank);
+      for (let i2 = 0; i2 < newShape.length; i2++) {
+        newShape[i2] = x.shape[permutedAxes[i2]];
+      }
+      const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
+      maxInput = backend2.makeTensorInfo(newShape, x.dtype);
+      const maxInputData = backend2.texData.get(maxInput.dataId);
+      maxInputData.values = maxInputValues;
+    } else {
+      maxInput = transposeImpl2(x, permutedAxes, backend2);
+    }
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
+  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);
+  let outShape = maxOutShape;
+  if (keepDims) {
+    outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
+  }
+  let out;
+  if (shouldExecuteOnCPU) {
+    const xTexData = backend2.texData.get(maxInput.dataId);
+    const values = xTexData.values;
+    const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);
+    out = backend2.makeTensorInfo(outShape, x.dtype);
+    const outData = backend2.texData.get(out.dataId);
+    outData.values = outValues;
+  } else {
+    out = maxImpl2(maxInput, reduceShape, outShape, backend2);
+  }
+  if (maxInputIsTransposed) {
+    backend2.disposeIntermediateTensorInfo(maxInput);
+  }
+  return out;
+}
+var maxConfig2 = {
+  kernelName: Max,
+  backendName: "webgl",
+  kernelFunc: max4
+};
+var MAXIMUM = CHECK_NAN_SNIPPET2 + `
+  return max(a, b);
+`;
+var MAXIMUM_PACKED = `
+  vec4 result = vec4(max(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var maximum4 = binaryKernelFunc2({
+  opSnippet: MAXIMUM,
+  packedOpSnippet: MAXIMUM_PACKED,
+  cpuKernelImpl: maximumImplCPU
+});
+var maximumConfig2 = {
+  kernelName: Maximum,
+  backendName: "webgl",
+  kernelFunc: maximum4
+};
+function maxPool3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex2(x, "maxPool");
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    return identity3({ inputs: { x }, backend: backend2 });
+  }
+  const maxPoolProgram = new Pool2DProgram(convInfo, "max", false);
+  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
+}
+var maxPoolConfig2 = {
+  kernelName: MaxPool,
+  backendName: "webgl",
+  kernelFunc: maxPool3
+};
+function maxPool3d2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode, dataFormat);
+  const maxPoolProgram = new Pool3DProgram(convInfo, "max", false);
+  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
+}
+var maxPool3DConfig2 = {
+  kernelName: MaxPool3D,
+  backendName: "webgl",
+  kernelFunc: maxPool3d2
+};
+var MaxPool2DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "maxPos"];
+    this.outputShape = convInfo.inShape;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+          wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
+
+            // Get the current value, check it against the value from the
+            // position matrix.
+            int curPosValue = wR * ${effectiveFilterWidth} + wC;
+            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+            dotProd += dyValue * mask;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var MaxPool3DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "maxPos"];
+    this.outputShape = convInfo.inShape;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
+    this.userCode = `
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+           wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
+
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+              int maxPosValue = ${lastIndex} -
+                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
+
+              // Get the current value, check it against the value from the
+              // position matrix.
+              int curPosValue =
+                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                  wR * ${effectiveFilterWidth} + wC;
+              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+              dotProd += dyValue * mask;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+function maxPool3DGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, "max", true);
+  const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);
+  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
+  const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);
+  backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);
+  return result;
+}
+var maxPool3DGradConfig3 = {
+  kernelName: MaxPool3DGrad,
+  backendName: "webgl",
+  kernelFunc: maxPool3DGrad2
+};
+function maxPoolGrad3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2, output } = inputs;
+  const x = input2;
+  assertNotComplex2([input2, output], "maxPoolGrad");
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, 1, pad3, dimRoundingMode);
+  const getPositions = true;
+  const maxPoolPositionsProgram = new Pool2DProgram(convInfo, "max", getPositions);
+  const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);
+  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
+  const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);
+  backend2.disposeIntermediateTensorInfo(maxPoolPositions2);
+  return result;
+}
+var maxPoolGradConfig3 = {
+  kernelName: MaxPoolGrad,
+  backendName: "webgl",
+  kernelFunc: maxPoolGrad3
+};
+function maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {
+  let program = new Pool2DProgram(convInfo, "max", false);
+  const poolOutput = backend2.runWebGLProgram(program, [x], "float32");
+  program = new Pool2DProgram(convInfo, "max", true, true, includeBatchInIndex);
+  const indexOutput = backend2.runWebGLProgram(program, [x], "float32");
+  return [poolOutput, indexOutput];
+}
+var maxPoolWithArgmaxConfig2 = {
+  kernelName: MaxPoolWithArgmax,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { filterSize, strides: strides2, pad: pad3, includeBatchInIndex } = attrs;
+    const webglBackend = backend2;
+    util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);
+    const dilations = [1, 1];
+    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${dilations}'`);
+    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3);
+    const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);
+    return [result, indexes];
+  }
+};
+function meanImpl(x, reduceShape, outShape, backend2) {
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const batchSize = xSize / inSize;
+  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
+  const reduced = reduce(reshapedInput, "float32", "mean", backend2);
+  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(reshapedInput);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  return reshapedOutput;
+}
+var meanConfig2 = {
+  kernelName: Mean,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { keepDims, axis } = attrs;
+    const webglBackend = backend2;
+    const xRank = x.shape.length;
+    const origAxes = util_exports.parseAxisParam(axis, x.shape);
+    let axes = origAxes;
+    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+    const meanInputIsTransposed = permutedAxes != null;
+    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
+    const intermediates = [];
+    let meanInput = x;
+    if (meanInputIsTransposed) {
+      if (shouldExecuteOnCPU) {
+        const xTexData = webglBackend.texData.get(meanInput.dataId);
+        const values = xTexData.values;
+        const newShape = new Array(xRank);
+        for (let i2 = 0; i2 < newShape.length; i2++) {
+          newShape[i2] = x.shape[permutedAxes[i2]];
+        }
+        const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
+        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
+        const meanInputData = webglBackend.texData.get(meanInput.dataId);
+        meanInputData.values = meanInputValues;
+      } else {
+        meanInput = transposeImpl2(x, permutedAxes, webglBackend);
+      }
+      intermediates.push(meanInput);
+      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    }
+    backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
+    const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);
+    let outShape = meanOutShape;
+    if (keepDims) {
+      outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);
+    }
+    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
+    for (const i2 of intermediates) {
+      webglBackend.disposeIntermediateTensorInfo(i2);
+    }
+    return out;
+  }
+};
+function min4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("min", axes, xRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  const reduced = reduce(a2D, a2D.dtype, "min", backend2);
+  let res;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
+  } else {
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+  }
+  backend2.disposeIntermediateTensorInfo(a2D);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return res;
+}
+var minConfig2 = {
+  kernelName: Min,
+  backendName: "webgl",
+  kernelFunc: min4
+};
+var MINIMUM = CHECK_NAN_SNIPPET2 + `
+  return min(a, b);
+`;
+var MINIMUM_PACKED = `
+  vec4 result = vec4(min(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var minimum4 = binaryKernelFunc2({
+  opSnippet: MINIMUM,
+  packedOpSnippet: MINIMUM_PACKED,
+  cpuKernelImpl: minimumImplCPU
+});
+var minimumConfig2 = {
+  kernelName: Minimum,
+  backendName: "webgl",
+  kernelFunc: minimum4
+};
+var MirrorPadProgram = class {
+  constructor(xShape, paddings, mode) {
+    this.variableNames = ["x"];
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const dtype = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
+    const offset = mode === "reflect" ? 0 : 1;
+    if (rank === 1) {
+      this.userCode = `
+        int start = ${start};
+        int end = ${end};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start) {
+            outC = start * 2 - outC - ${offset};
+          } else if(outC >= end) {
+            outC = (end - 1) * 2 - outC + ${offset};
+          }
+          setOutput(getX(outC - start));
+        }
+      `;
+      return;
+    }
+    this.userCode = `
+      ${dtype} start = ${dtype}(${start});
+      ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outC = getOutputCoords();
+        for (int i = 0; i < ${rank}; i++) {
+          if (outC[i] < start[i]) {
+            outC[i] = start[i] * 2 - outC[i] - ${offset};
+          } else if(outC[i] >= end[i]) {
+            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
+          }
+        }
+        ${dtype} coords = outC - start;
+        setOutput(getX(${unpackedCoords}));
+      }
+    `;
+  }
+};
+var MirrorPadPackedProgram = class {
+  constructor(xShape, paddings, mode) {
+    this.variableNames = ["x"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const dtype = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const coords3 = getChannels("rc", rank);
+    const source = getChannels("source", rank);
+    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
+    const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
+    const offset = mode === "reflect" ? 0 : 1;
+    let mainLoop = "";
+    if (rank === 1) {
+      const padSetup = `
+        ${dtype} source = rc;
+        if (source < start) {
+          source = start * 2 - source - ${offset};
+        } else if (source >= end) {
+          source = (end - 1) * 2 - source + ${offset};
+        }
+        source -= start;
+      `;
+      mainLoop = `
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords3[rank - 1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+      `;
+    } else {
+      const padSetup = `
+        ${dtype} source = rc;
+        ${dtype} lt = ${dtype}(lessThan(source, start));
+        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
+        ${dtype} orig = 1 - (lt + gte);
+        source = orig * source +
+                lt * (start * 2 - source - ${offset}) +
+                gte * ((end - 1) * 2 - source + ${offset});
+        source -= start;
+      `;
+      mainLoop = `
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords3[rank - 1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+        rc = outputLoc;
+        ${coords3[rank - 2]} += 1;
+        if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {
+          ${padSetup}
+          result[2] = getChannel(getX(${source.join()}), ${innerDims});
+          ${coords3[rank - 1]} += 1;
+          if(${cLimit}) {
+            ${padSetup}
+            result[3] = getChannel(getX(${source.join()}), ${innerDims});
+          }
+        }
+      `;
+    }
+    this.userCode = `
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${mainLoop}
+        setOutput(result);
+      }
+    `;
+  }
+};
+var mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {
+  const { x } = inputs;
+  const { paddings, mode } = attrs;
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);
+  const output = backend2.runWebGLProgram(program, [x], x.dtype);
+  return output;
+};
+var mirrorPadConfig2 = {
+  kernelName: MirrorPad,
+  backendName: "webgl",
+  kernelFunc: mirrorPadKernelFunc
+};
+var MOD = `if (b == 0.0) return NAN;
+  return mod(a, b);`;
+var MOD_PACKED = `
+  vec4 result = mod(a, b);
+  vec4 isNaN = vec4(equal(b, vec4(0.0)));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var mod3 = binaryKernelFunc2({
+  opSnippet: MOD,
+  packedOpSnippet: MOD_PACKED
+});
+var modConfig2 = {
+  kernelName: Mod,
+  backendName: "webgl",
+  kernelFunc: mod3
+};
+var MultinomialProgram = class {
+  constructor(batchSize, numOutcomes, numSamples) {
+    this.variableNames = ["probs"];
+    this.customUniforms = [{ name: "seed", type: "float" }];
+    this.outputShape = [batchSize, numSamples];
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+
+        float r = random(seed);
+        float cdf = 0.0;
+
+        for (int i = 0; i < ${numOutcomes - 1}; i++) {
+          cdf += getProbs(batch, i);
+
+          if (r < cdf) {
+            setOutput(float(i));
+            return;
+          }
+        }
+
+        // If no other event happened, last event happened.
+        setOutput(float(${numOutcomes - 1}));
+      }
+    `;
+  }
+};
+var DIV = `
+if (a == b) {
+  return 1.0;
+};
+return a / b;`;
+var DIV_PACKED = `
+  // vec4 one = vec4(equal(a, b));
+  // return one + (vec4(1.0) - one) * a / b;
+  vec4 result = a / b;
+  if(a.x == b.x) {
+    result.x = 1.;
+  }
+  if(a.y == b.y) {
+    result.y = 1.;
+  }
+  if(a.z == b.z) {
+    result.z = 1.;
+  }
+  if(a.w == b.w) {
+    result.w = 1.;
+  }
+
+  return result;
+`;
+var realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });
+var realDivConfig2 = {
+  kernelName: RealDiv,
+  backendName: "webgl",
+  kernelFunc: realDiv
+};
+var SUB = "return a - b;";
+var sub3 = binaryKernelFunc2({
+  opSnippet: SUB,
+  packedOpSnippet: SUB,
+  supportsComplex: true,
+  cpuKernelImpl: subImplCPU
+});
+var subConfig2 = {
+  kernelName: Sub,
+  backendName: "webgl",
+  kernelFunc: sub3
+};
+function softmax4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { dim } = attrs;
+  const axes = util_exports.parseAxisParam([dim], logits.shape);
+  const maxLogit = max4({
+    inputs: { x: logits },
+    backend: backend2,
+    attrs: { reductionIndices: axes, keepDims: false }
+  });
+  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
+  const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
+  const a6 = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });
+  const b = exp3({ inputs: { x: a6 }, backend: backend2 });
+  const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
+  const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
+  const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(maxLogit);
+  backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);
+  backend2.disposeIntermediateTensorInfo(a6);
+  backend2.disposeIntermediateTensorInfo(b);
+  backend2.disposeIntermediateTensorInfo(sumExp);
+  backend2.disposeIntermediateTensorInfo(sumExpReshaped);
+  return res;
+}
+var softmaxConfig2 = {
+  kernelName: Softmax,
+  backendName: "webgl",
+  kernelFunc: softmax4
+};
+function multinomial3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { numSamples, seed, normalized } = attrs;
+  const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });
+  const batchSize = probs.shape[0];
+  const numOutcomes = probs.shape[1];
+  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
+  const customValues = [[seed]];
+  const res = backend2.runWebGLProgram(program, [probs], "int32", customValues);
+  if (!normalized) {
+    backend2.disposeIntermediateTensorInfo(probs);
+  }
+  return res;
+}
+var multinomialConfig2 = {
+  kernelName: Multinomial,
+  backendName: "webgl",
+  kernelFunc: multinomial3
+};
+var NEG = CHECK_NAN_SNIPPET + `
+  return -x;
+`;
+var NEG_PACKED = `
+  vec4 result = -x;
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+function neg3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xData = backend2.texData.get(x.dataId);
+    const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);
+    return backend2.makeTensorInfo(newShape, x.dtype, outValues);
+  }
+  let program;
+  if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
+    program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);
+  } else {
+    program = new UnaryOpProgram(x.shape, NEG);
+  }
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var negConfig2 = {
+  kernelName: Neg,
+  backendName: "webgl",
+  kernelFunc: neg3
+};
+var nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;
+function nonMaxSuppressionV32(args) {
+  backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
+}
+var nonMaxSuppressionV3Config2 = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "webgl",
+  kernelFunc: nonMaxSuppressionV32
+};
+var nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;
+function nonMaxSuppressionV42(args) {
+  backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
+  ];
+}
+var nonMaxSuppressionV4Config2 = {
+  kernelName: NonMaxSuppressionV4,
+  backendName: "webgl",
+  kernelFunc: nonMaxSuppressionV42
+};
+var nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;
+function nonMaxSuppressionV52(args) {
+  backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const maxOutputSizeVal = maxOutputSize;
+  const iouThresholdVal = iouThreshold;
+  const scoreThresholdVal = scoreThreshold;
+  const softNmsSigmaVal = softNmsSigma;
+  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
+  ];
+}
+var nonMaxSuppressionV5Config2 = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "webgl",
+  kernelFunc: nonMaxSuppressionV52
+};
+var OneHotProgram = class {
+  constructor(numIndices, depth, onValue, offValue) {
+    this.variableNames = ["indices"];
+    this.outputShape = [numIndices, depth];
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int index = round(getIndices(coords.x));
+        setOutput(mix(float(${offValue}), float(${onValue}),
+                      float(index == coords.y)));
+      }
+    `;
+  }
+};
+var oneHot3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices } = inputs;
+  const { depth, onValue, offValue } = attrs;
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);
+  const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });
+  const result = backend2.runWebGLProgram(program, [reshaped], indices.dtype);
+  backend2.disposeIntermediateTensorInfo(reshaped);
+  const outShape = [...indices.shape, depth];
+  const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });
+  backend2.disposeIntermediateTensorInfo(result);
+  return out;
+};
+var oneHotConfig2 = {
+  kernelName: OneHot,
+  backendName: "webgl",
+  kernelFunc: oneHot3
+};
+function zerosLike3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "complex64") {
+    const realPart = real3({ inputs: { input: x }, backend: backend2 });
+    const r2 = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill3({
+      attrs: {
+        shape: x.shape,
+        dtype: x.dtype,
+        value: x.dtype === "string" ? "" : 0
+      },
+      backend: backend2
+    });
+  }
+}
+var zerosLikeConfig2 = {
+  kernelName: ZerosLike,
+  backendName: "webgl",
+  kernelFunc: zerosLike3
+};
+function onesLike3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("onesLike is not supported under string dtype");
+  } else if (x.dtype === "complex64") {
+    const realPart = real3({ inputs: { input: x }, backend: backend2 });
+    const r2 = onesLike3({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
+  }
+}
+var onesLikeConfig2 = {
+  kernelName: OnesLike,
+  backendName: "webgl",
+  kernelFunc: onesLike3
+};
+function pack2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims4({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var packConfig2 = {
+  kernelName: Pack,
+  backendName: "webgl",
+  kernelFunc: pack2
+};
+var PadProgram = class {
+  constructor(xShape, paddings, constantValue) {
+    this.variableNames = ["x"];
+    this.customUniforms = [{ name: "value", type: "float" }];
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const type = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
+    if (rank === 1) {
+      this.userCode = `
+        int start = ${start};
+        int end = ${end};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start || outC >= end) {
+            setOutput(value);
+          } else {
+            setOutput(getX(outC - start));
+          }
+        }
+      `;
+      return;
+    }
+    this.userCode = `
+      ${type} start = ${type}(${start});
+      ${type} end = ${type}(${end});
+
+      void main() {
+        ${type} outC = getOutputCoords();
+        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
+          setOutput(value);
+        } else {
+          ${type} coords = outC - start;
+          setOutput(getX(${unpackedCoords}));
+        }
+      }
+    `;
+  }
+};
+var PadPackedProgram = class {
+  constructor(xShape, paddings, constantValue) {
+    this.variableNames = ["x"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [{ name: "value", type: "float" }];
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const dtype = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const coords3 = getChannels("rc", rank);
+    const source = getChannels("source", rank);
+    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
+    const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
+    const componentSetup = [
+      `${dtype} rc = outputLoc;`,
+      `${coords3[rank - 1]} += 1;
+       if(${cLimit}) {
+      `,
+      rank === 1 ? "" : `}
+       rc = outputLoc;
+       ${coords3[rank - 2]} += 1;
+       if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {`,
+      rank === 1 ? "" : `  ${coords3[rank - 1]} += 1;
+         if(${cLimit}) {`
+    ];
+    const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
+    let mainLoop = "";
+    for (let i2 = 0, j = rank === 1 ? 2 : 4; i2 < j; i2++) {
+      mainLoop += `
+        ${componentSetup[i2]}
+        if (${paddingArea}) {
+          result[${i2}] = float(value);
+        } else {
+          ${dtype} source = rc - start;
+          result[${i2}] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+      `;
+    }
+    mainLoop += rank === 1 ? `} ` : `}}`;
+    this.userCode = `
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${mainLoop}
+        setOutput(result);
+      }
+    `;
+  }
+};
+var padV22 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, constantValue } = attrs;
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    return fill3({
+      backend: backend2,
+      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
+    });
+  }
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);
+  const customValues = [[constantValue]];
+  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
+};
+var padV2Config2 = {
+  kernelName: PadV2,
+  backendName: "webgl",
+  kernelFunc: padV22
+};
+var POW = `
+  if(a < 0.0 && floor(b) < b){
+    return NAN;
+  }
+  if (b == 0.0) {
+    return 1.0;
+  }
+  return (round(mod(b, 2.0)) != 1) ?
+      pow(abs(a), b) : sign(a) * pow(abs(a), b);
+`;
+var POW_PACKED = `
+  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
+  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
+  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
+  vec4 result = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  bvec4 isExpZero = equal(b, vec4(0.0));
+  result.r = isExpZero.r ? 1.0 : result.r;
+  result.g = isExpZero.g ? 1.0 : result.g;
+  result.b = isExpZero.b ? 1.0 : result.b;
+  result.a = isExpZero.a ? 1.0 : result.a;
+
+  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });
+var powConfig2 = {
+  kernelName: Pow,
+  backendName: "webgl",
+  kernelFunc: pow3
+};
+function prod3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const toDispose = [];
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    toDispose.push(permutedX);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("prod", axes, xRank);
+  let res;
+  if (backend2.shouldExecuteOnCPU([permutedX])) {
+    const xVals = backend2.texData.get(permutedX.dataId).values;
+    const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);
+    res = backend2.makeTensorInfo(outShape, outDtype, outVals);
+  } else {
+    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+    const inSize = util_exports.sizeFromShape(reduceShape);
+    const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+    const outputDType = sumOutType(x.dtype);
+    const reduced = reduce(a2D, outputDType, "prod", backend2);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+    toDispose.push(a2D);
+    toDispose.push(reduced);
+  }
+  if (keepDims) {
+    toDispose.push(res);
+    const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);
+    res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });
+  }
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return res;
+}
+var prodConfig2 = {
+  kernelName: Prod,
+  backendName: "webgl",
+  kernelFunc: prod3
+};
+var range4 = (args) => {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, step: step5, dtype } = attrs;
+  const values = rangeImplCPU(start, stop, step5, dtype);
+  return backend2.makeTensorInfo([values.length], dtype, values);
+};
+var rangeConfig2 = {
+  kernelName: Range,
+  backendName: "webgl",
+  kernelFunc: range4
+};
+var RECIPROCAL = `return 1.0 / x;`;
+var reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });
+var reciprocalConfig2 = {
+  kernelName: Reciprocal,
+  backendName: "webgl",
+  kernelFunc: reciprocal3
+};
+var RELU3 = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : x;
+`;
+var RELU_PACKED = `
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });
+var reluConfig2 = {
+  kernelName: Relu,
+  backendName: "webgl",
+  kernelFunc: relu3
+};
+var RELU63 = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`;
+var RELU6_PACKED = `
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });
+var relu6Config2 = {
+  kernelName: Relu6,
+  backendName: "webgl",
+  kernelFunc: relu63
+};
+var ResizeBilinearProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;
+    } else {
+      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the four integer indices.
+        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
+        ivec2 sourceCeilRC = ivec2(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
+        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
+        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
+        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
+
+        float top = topLeft + (topRight - topLeft) * fracRC.y;
+        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+        float newValue = top + (bottom - top) * fracRC.x;
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+var ResizeBilinearPackedProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;
+    } else {
+      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the four integer indices.
+        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
+        ivec3 sourceCeilRC = ivec3(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${depth - 1};
+        bool hasNextRow = coords.z < ${newWidth - 1};
+
+        // In parallel, construct four corners for all four components in
+        // packed 2x2 cell.
+        vec4 topLeft = vec4(
+          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 bottomLeft = vec4(
+          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 topRight = vec4(
+          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec4 bottomRight = vec4(
+          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
+
+        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
+        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
+        vec4 newValue = mix(top, bottom, fracRC.x);
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+function resizeBilinear3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size: size2 } = attrs;
+  const [newHeight, newWidth] = size2;
+  const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
+  return backend2.runWebGLProgram(program, [images], "float32");
+}
+var resizeBilinearConfig2 = {
+  kernelName: ResizeBilinear,
+  backendName: "webgl",
+  kernelFunc: resizeBilinear3
+};
+var ResizeBilinearBackpropProgram = class {
+  constructor(dyShape, inputShape, alignCorners) {
+    this.variableNames = ["dy"];
+    this.outputShape = [];
+    this.outputShape = inputShape;
+    const [, xHeight, xWidth] = inputShape;
+    const [, yHeight, yWidth] = dyShape;
+    const effectiveXSize = [
+      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+    ];
+    const effectiveYSize = [
+      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+    ];
+    const heightScale = effectiveXSize[0] / effectiveYSize[0];
+    const widthScale = effectiveXSize[1] / effectiveYSize[1];
+    const invHeightScale = 1 / heightScale;
+    const invWidthScale = 1 / widthScale;
+    const winHeight = Math.ceil(invHeightScale) * 2 + 2;
+    const winWidth = Math.ceil(invWidthScale) * 2 + 2;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
+
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
+
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(startRLerp - float(winHeight / 2));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(startCLerp - float(winWidth / 2));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${yHeight}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${yWidth}) {
+              continue;
+            }
+
+            float dxR = float(dyR) * heightScale;
+            int topDxRIndex = int(floor(dxR));
+            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));
+            float dxRLerp = dxR - float(topDxRIndex);
+            float inverseDxRLerp = 1.0 - dxRLerp;
+
+            float dxC = float(dyC) * widthScale;
+            int leftDxCIndex = int(floor(dxC));
+            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));
+            float dxCLerp = dxC - float(leftDxCIndex);
+            float inverseDxCLerp = 1.0 - dxCLerp;
+
+            if (r == topDxRIndex && c == leftDxCIndex) {
+              // topLeft
+              accumulator +=
+                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
+            }
+
+            if (r == topDxRIndex && c == rightDxCIndex) {
+              // topRight
+              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == leftDxCIndex) {
+              // bottomLeft
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == rightDxCIndex) {
+              // bottomRight
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `;
+  }
+};
+function resizeBilinearGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);
+  return backend2.runWebGLProgram(program, [dy], dy.dtype);
+}
+var resizeBilinearGradConfig3 = {
+  kernelName: ResizeBilinearGrad,
+  backendName: "webgl",
+  kernelFunc: resizeBilinearGrad2
+};
+var ResizeNearestNeighborProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    const roundBase = alignCorners ? "0.5" : "0.0";
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;
+    } else {
+      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec2 sourceNearestRC = ivec2(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
+        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+var ResizeNearestNeighborPackedProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    const roundBase = alignCorners ? "0.5" : "0.0";
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;
+    } else {
+      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec3 sourceNearestRC = ivec3(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${depth - 1};
+        bool hasNextRow = coords.z < ${newWidth - 1};
+
+        vec4 newValue = vec4(
+          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
+          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+function resizeNearestNeighbor3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size: size2 } = attrs;
+  const [newHeight, newWidth] = size2;
+  const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
+  return backend2.runWebGLProgram(program, [images], images.dtype);
+}
+var resizeNearestNeighborConfig2 = {
+  kernelName: ResizeNearestNeighbor,
+  backendName: "webgl",
+  kernelFunc: resizeNearestNeighbor3
+};
+var ResizeNearestNeigborBackpropProgram = class {
+  constructor(dyShape, inputShape, alignCorners) {
+    this.variableNames = ["dy"];
+    this.outputShape = [];
+    this.outputShape = inputShape;
+    const [, xHeight, xWidth] = inputShape;
+    const [, yHeight, yWidth] = dyShape;
+    const effectiveXSize = [
+      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+    ];
+    const effectiveYSize = [
+      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+    ];
+    const heightScale = effectiveXSize[0] / effectiveYSize[0];
+    const widthScale = effectiveXSize[1] / effectiveYSize[1];
+    const invHeightScale = 1 / heightScale;
+    const invWidthScale = 1 / widthScale;
+    const winHeight = Math.ceil(invHeightScale) * 2 + 2;
+    const winWidth = Math.ceil(invWidthScale) * 2 + 2;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
+
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
+
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(floor(startRLerp - float(winHeight / 2)));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(floor(startCLerp - float(winWidth / 2)));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${yHeight}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${yWidth}) {
+              continue;
+            }
+
+            float sourceFracRow =
+              float(${effectiveXSize[0]}) *
+                (float(dyR) / float(${effectiveYSize[0]}));
+
+            float sourceFracCol =
+                float(${effectiveXSize[1]}) *
+                  (float(dyC) / float(${effectiveYSize[1]}));
+
+            int sourceNearestRow = int(min(
+                float(int(${xHeight}) - 1),
+                ${alignCorners} ? float(round(sourceFracRow)) :
+                                  float(floor(sourceFracRow))));
+
+            int sourceNearestCol = int(min(
+                float(int(${xWidth}) - 1),
+                ${alignCorners} ? float(round(sourceFracCol)) :
+                                  float(floor(sourceFracCol))));
+
+            if (r == sourceNearestRow && c == sourceNearestCol) {
+              accumulator += getDy(b, dyR, dyC, d);
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `;
+  }
+};
+function resizeNearestNeighborGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);
+  return backend2.runWebGLProgram(program, [dy], dy.dtype);
+}
+var resizeNearestNeighborGradConfig3 = {
+  kernelName: ResizeNearestNeighborGrad,
+  backendName: "webgl",
+  kernelFunc: resizeNearestNeighborGrad2
+};
+var ReverseProgram = class {
+  constructor(xShape, axis) {
+    this.variableNames = ["x"];
+    const rank = xShape.length;
+    if (rank > 4) {
+      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
+    }
+    this.outputShape = xShape;
+    if (rank === 1) {
+      this.userCode = `
+        void main() {
+          int coord = getOutputCoords();
+          setOutput(getX(${xShape[0]} - coord - 1));
+        }
+      `;
+      return;
+    }
+    const getInCoord = (i2) => {
+      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
+        return `${xShape[i2]} - coords[${i2}] - 1`;
+      }
+      return `coords[${i2}]`;
+    };
+    const inCoords = xShape.map((_, i2) => getInCoord(i2)).join(",");
+    const type = getCoordsDataType(rank);
+    this.userCode = `
+      void main() {
+        ${type} coords = getOutputCoords();
+        setOutput(getX(${inCoords}));
+      }
+    `;
+  }
+};
+var ReversePackedProgram = class {
+  constructor(xShape, axis) {
+    this.variableNames = ["x"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    const rank = xShape.length;
+    if (rank > 4) {
+      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
+    }
+    this.outputShape = xShape;
+    const channels = getChannels("rc", rank);
+    const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;
+    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;
+    const type = getCoordsDataType(rank);
+    if (rank === 1) {
+      this.userCode = `
+        void main(){
+          int rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = getChannel(getX(${xShape[0]} - rc - 1),
+            ${xShape[0]} - rc - 1);
+          if(${nextColumn}){
+              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
+                ${xShape[0]} - (rc  + 1) - 1);
+          }
+          setOutput(result);
+        }
+      `;
+    } else {
+      this.userCode = `
+        void main() {
+          ${type} rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = ${getR(channels.slice())};
+          if(${nextColumn}){
+            result.g = ${getG(channels.slice())};
+          }
+          if(${nextRow}) {
+            result.b = ${getB(channels.slice())};
+            if(${nextColumn}) {
+              result.a = ${getA(channels.slice())};
+            }
+          }
+          setOutput(result);
+        }
+    `;
+    }
+    function getR(channels2) {
+      return getChannel(channels2);
+    }
+    function getG(channels2) {
+      channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
+      return getChannel(channels2);
+    }
+    function getB(channels2) {
+      channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
+      return getChannel(channels2);
+    }
+    function getA(channels2) {
+      channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
+      channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
+      return getChannel(channels2);
+    }
+    function getChannel(channels2) {
+      const inCoordsArray = xShape.map((_, i2) => getInCoord(i2, channels2));
+      const inCoords = inCoordsArray.join(",");
+      const innerDims = inCoordsArray.slice(-2).join(",");
+      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
+    }
+    function getInCoord(i2, channels1) {
+      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
+        return `${xShape[i2]} - ${channels1[i2]} - 1`;
+      } else {
+        return `${channels1[i2]}`;
+      }
+    }
+  }
+};
+function reverse3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dims } = attrs;
+  const xRank = x.shape.length;
+  const $dims = util_exports.parseAxisParam(dims, x.shape);
+  if (xRank === 0) {
+    return identity3({ inputs: { x }, backend: backend2 });
+  }
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var reverseConfig2 = {
+  kernelName: Reverse,
+  backendName: "webgl",
+  kernelFunc: reverse3
+};
+var RotateProgram = class {
+  constructor(imageShape, fillValue) {
+    this.variableNames = ["Image"];
+    this.outputShape = [];
+    this.customUniforms = [{ name: "params", type: "vec4" }];
+    const imageHeight = imageShape[1];
+    const imageWidth = imageShape[2];
+    this.outputShape = imageShape;
+    let fillSnippet = "";
+    if (typeof fillValue === "number") {
+      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;
+    } else {
+      fillSnippet = `
+        vec3 fill = vec3(${fillValue.join(",")});
+        float outputValue = fill[coords[3]];`;
+    }
+    this.userCode = `
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+          int y = coords[1];
+          float coordXFloat = (float(x) - params[0]) * params[3] -
+            (float(y) - params[1]) * params[2];
+          float coordYFloat = (float(x) - params[0]) * params[2] +
+            (float(y) - params[1]) * params[3];
+          int coordX = int(round(coordXFloat + params[0]));
+          int coordY = int(round(coordYFloat + params[1]));
+          ${fillSnippet}
+          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
+            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `;
+  }
+};
+var rotateWithOffsetConfig2 = {
+  kernelName: RotateWithOffset,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const { radians, fillValue, center } = attrs;
+    const webglBackend = backend2;
+    const program = new RotateProgram(image2.shape, fillValue);
+    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);
+    const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];
+    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues);
+    return output;
+  }
+};
+var ROUND = `
+  // OpenGL ES does not support round function.
+  // The algorithm is based on banker's rounding.
+  float base = floor(x);
+  if ((x - base) < 0.5) {
+    return floor(x);
+  } else if ((x - base) > 0.5) {
+    return ceil(x);
+  } else {
+    if (mod(base, 2.0) == 0.0) {
+      return base;
+    } else {
+      return base + 1.0;
+    }
+  }
+`;
+var round4 = unaryKernelFunc2({ opSnippet: ROUND });
+var roundConfig2 = {
+  kernelName: Round,
+  backendName: "webgl",
+  kernelFunc: round4
+};
+var RSQRT = `return inversesqrt(x);`;
+var rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });
+var rsqrtConfig2 = {
+  kernelName: Rsqrt,
+  backendName: "webgl",
+  kernelFunc: rsqrt3
+};
+var ScatterProgram = class {
+  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides2, shape, summingDupeIndex = true) {
+    this.variableNames = ["updates", "indices", "defaultValue"];
+    this.outputShape = shape;
+    const stridesType = getCoordsDataType(strides2.length);
+    const dtype = getCoordsDataType(shape.length);
+    let indicesString = "";
+    if (indicesRank === 1) {
+      indicesString = "i";
+    } else if (indicesRank === 2) {
+      indicesString = "i, j";
+    }
+    const indicesSnippet = `getIndices(${indicesString})`;
+    let updatesString = "";
+    if (updatesRank === 1) {
+      updatesString = "i";
+    } else if (updatesRank === 2) {
+      updatesString = "i, coords[1]";
+    }
+    const updatesSnippet = `getUpdates(${updatesString})`;
+    const strideString = sliceDim > 1 ? "strides[j]" : "strides";
+    this.userCode = `
+        ${stridesType} strides = ${stridesType}(${strides2});
+
+        void main() {
+          ${dtype} coords = getOutputCoords();
+          float sum = 0.0;
+          bool found = false;
+          for (int i = 0; i < ${updateSize}; i++) {
+            int flattenedIndex = 0;
+            for (int j = 0; j < ${sliceDim}; j++) {
+              int index = round(${indicesSnippet});
+              flattenedIndex += index * ${strideString};
+            }
+            if (flattenedIndex == coords[0]) {
+              sum += ${updatesSnippet};
+              found = true;
+            }
+          }
+          setOutput(mix(getDefaultValue(), sum, float(found)));
+        }
+      `;
+  }
+};
+function scatterNd2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = backend_util_exports.calculateShapes(updates, indices, shape);
+  const flattenShape = [outputSize2 / sliceSize, sliceSize];
+  if (outputSize2 === 0) {
+    return backend2.makeTensorInfo(shape, indices.dtype);
+  }
+  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });
+  const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });
+  const defaultValue = backend2.makeTensorInfo([], "float32", new Float32Array([0]));
+  const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides2, flattenShape);
+  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } });
+  backend2.disposeIntermediateTensorInfo(flattenIndices);
+  backend2.disposeIntermediateTensorInfo(flattenX);
+  backend2.disposeIntermediateTensorInfo(res);
+  backend2.disposeIntermediateTensorInfo(defaultValue);
+  return reshaped;
+}
+var scatterNdConfig2 = {
+  kernelName: ScatterNd,
+  backendName: "webgl",
+  kernelFunc: scatterNd2
+};
+var SearchSortedProgram = class {
+  constructor(batchSize, numInputs, numValues, side) {
+    this.variableNames = ["sortedSequence", "values"];
+    this.customUniforms = [{ name: "numInputs", type: "int" }];
+    this.outputShape = [batchSize, numValues];
+    const webGL2LoopHead = "while (left < right) {";
+    const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;
+    const loopHead = env().getNumber("WEBGL_VERSION") === 2 ? webGL2LoopHead : webGL1LoopHead;
+    const boundComparator = side === "left" ? "<" : "<=";
+    this.userCode = `
+       int findBound(int batch, float value) {
+         int left = 0;
+         int right = numInputs;
+         int mid;
+         ${loopHead}
+           mid = (left + right) / 2;
+           if (getSortedSequence(batch, mid) ${boundComparator} value) {
+             left = mid + 1;
+           } else {
+             right = mid;
+           }
+         }
+         return right;
+       }
+
+       void main() {
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int valueIndex = coords[1];
+
+         float value = getValues(batch, valueIndex);
+
+         setOutput(float(findBound(batch, value)));
+       }
+     `;
+  }
+};
+function searchSorted3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sortedSequence, values } = inputs;
+  const { side } = attrs;
+  const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
+  const customValues = [[sortedSequence.shape[1]]];
+  return backend2.runWebGLProgram(program, [sortedSequence, values], "int32", customValues);
+}
+var searchSortedConfig2 = {
+  kernelName: SearchSorted,
+  backendName: "webgl",
+  kernelFunc: searchSorted3
+};
+var SelectProgram = class {
+  constructor(cRank, shape, rank) {
+    this.variableNames = ["c", "a", "b"];
+    this.outputShape = shape;
+    let cCoords;
+    let abCoords;
+    if (rank > 4) {
+      throw Error(`Where for rank ${rank} is not yet supported`);
+    }
+    if (rank === 1) {
+      abCoords = `resRC`;
+      cCoords = `resRC`;
+    } else {
+      const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+      const cCoordVars = [];
+      const abCoordVars = [];
+      for (let i2 = 0; i2 < shape.length; i2++) {
+        abCoordVars.push(`${currentCoords[i2]}`);
+        if (i2 < cRank) {
+          cCoordVars.push(`${currentCoords[i2]}`);
+        }
+      }
+      cCoords = cCoordVars.join();
+      abCoords = abCoordVars.join();
+    }
+    const dtype = getCoordsDataType(rank);
+    this.userCode = `
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        float cVal = getC(${cCoords});
+        if (cVal >= 1.0) {
+          setOutput(getA(${abCoords}));
+        } else {
+          setOutput(getB(${abCoords}));
+        }
+      }
+    `;
+  }
+};
+function select3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  const program = new SelectProgram(condition.shape.length, t2.shape, t2.shape.length);
+  return backend2.runWebGLProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+}
+var selectConfig2 = {
+  kernelName: Select,
+  backendName: "webgl",
+  kernelFunc: select3
+};
+var SELU = `
+  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
+  // see: https://arxiv.org/abs/1706.02515
+  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
+  float scale = ${backend_util_exports.SELU_SCALE};
+  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
+`;
+var selu3 = unaryKernelFunc2({ opSnippet: SELU });
+var seluConfig2 = {
+  kernelName: Selu,
+  backendName: "webgl",
+  kernelFunc: selu3
+};
+var SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `
+  return 1.0 / (1.0 + exp(-1.0 * x));
+`;
+var SIGMOID_PACKED = `
+  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var sigmoid3 = unaryKernelFunc2({
+  opSnippet: SIGMOID3,
+  packedOpSnippet: SIGMOID_PACKED,
+  cpuKernelImpl: sigmoidImplCPU
+});
+var sigmoidConfig2 = {
+  kernelName: Sigmoid,
+  backendName: "webgl",
+  kernelFunc: sigmoid3
+};
+var SIGN = `
+  if (isnan(x)) { return 0.0; }
+  return sign(x);
+`;
+var sign3 = unaryKernelFunc2({ opSnippet: SIGN });
+var signConfig2 = {
+  kernelName: Sign,
+  backendName: "webgl",
+  kernelFunc: sign3
+};
+var SIN = CHECK_NAN_SNIPPET_UNARY + `
+  return sin(x);
+`;
+var sin3 = unaryKernelFunc2({ opSnippet: SIN });
+var sinConfig2 = {
+  kernelName: Sin,
+  backendName: "webgl",
+  kernelFunc: sin3
+};
+var SINH = `
+  float e2x = exp(x);
+  return (e2x - 1.0 / e2x) / 2.0;
+`;
+var sinh3 = unaryKernelFunc2({ opSnippet: SINH });
+var sinhConfig2 = {
+  kernelName: Sinh,
+  backendName: "webgl",
+  kernelFunc: sinh3
+};
+var SOFTPLUS = `
+  float epsilon = 1.1920928955078125e-7;
+  float threshold = log(epsilon) + 2.0;
+
+  bool too_large = x > -threshold;
+  bool too_small = x < threshold;
+
+  float result;
+  float exp_x = exp(x);
+
+  if (too_large){
+    result = x;
+  }
+  else if (too_small){
+    result = exp_x;
+  }
+  else{
+    result = log(exp_x + 1.0);
+  }
+  return result;
+`;
+var softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });
+var softplusConfig2 = {
+  kernelName: Softplus,
+  backendName: "webgl",
+  kernelFunc: softplus3
+};
+var spaceToBatchND3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const toDispose = [];
+  const paddedX = padV22({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });
+  const paddedXT = transpose3({
+    inputs: { x: reshapedPaddedX },
+    backend: backend2,
+    attrs: { perm: permutedReshapedPaddedPermutation }
+  });
+  const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });
+  toDispose.push(paddedX);
+  toDispose.push(reshapedPaddedX);
+  toDispose.push(paddedXT);
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+};
+var spaceToBatchNDConfig2 = {
+  kernelName: SpaceToBatchND,
+  backendName: "webgl",
+  kernelFunc: spaceToBatchND3
+};
+function sparseFillEmptyRows3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { indices, values, denseShape, defaultValue } = inputs;
+  if (denseShape.shape.length !== 1) {
+    throw new Error(`Dense shape must be a vector, saw:
+         ${denseShape.shape}`);
+  }
+  if (indices.shape.length !== 2) {
+    throw new Error(`Indices must be a matrix, saw:
+         ${indices.shape}`);
+  }
+  if (values.shape.length !== 1) {
+    throw new Error(`Values must be a vector, saw:
+         ${values.shape}`);
+  }
+  if (defaultValue.shape.length !== 0) {
+    throw new Error(`Default value must be a scalar, saw:
+        ${defaultValue.shape}`);
+  }
+  const $indices = backend2.readSync(indices.dataId);
+  const $values = backend2.readSync(values.dataId);
+  const $denseShape = backend2.readSync(denseShape.dataId);
+  const $defaultValue = backend2.readSync(defaultValue.dataId)[0];
+  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
+  return [
+    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
+    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
+    backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),
+    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))
+  ];
+}
+var sparseFillEmptyRowsConfig2 = {
+  kernelName: SparseFillEmptyRows,
+  backendName: "webgl",
+  kernelFunc: sparseFillEmptyRows3
+};
+function sparseReshape3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { inputIndices, inputShape, newShape } = inputs;
+  if (inputIndices.shape.length !== 2) {
+    throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);
+  }
+  if (inputShape.shape.length !== 1) {
+    throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);
+  }
+  if (newShape.shape.length !== 1) {
+    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
+  }
+  const $inputShape = Array.from(backend2.readSync(inputShape.dataId));
+  const $inputIndices = backend2.readSync(inputIndices.dataId);
+  const targetShape = Array.from(backend2.readSync(newShape.dataId));
+  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
+  return [
+    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
+    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))
+  ];
+}
+var sparseReshapeConfig2 = {
+  kernelName: SparseReshape,
+  backendName: "webgl",
+  kernelFunc: sparseReshape3
+};
+function sparseSegmentMean3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+              ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+              ${segmentIds.shape}`);
+  }
+  const $data = backend2.readSync(data.dataId);
+  const $indices = backend2.readSync(indices.dataId);
+  const $segmentIds = backend2.readSync(segmentIds.dataId);
+  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentMeanConfig2 = {
+  kernelName: SparseSegmentMean,
+  backendName: "webgl",
+  kernelFunc: sparseSegmentMean3
+};
+function sparseSegmentSum3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+             ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+             ${segmentIds.shape}`);
+  }
+  const $data = backend2.readSync(data.dataId);
+  const $indices = backend2.readSync(indices.dataId);
+  const $segmentIds = backend2.readSync(segmentIds.dataId);
+  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentSumConfig2 = {
+  kernelName: SparseSegmentSum,
+  backendName: "webgl",
+  kernelFunc: sparseSegmentSum3
+};
+function sparseToDense3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sparseIndices, sparseValues, defaultValue } = inputs;
+  const { outputShape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
+  const sumDupeIndices = false;
+  if (sparseValues.dtype === "string") {
+    const indicesBuf = backend2.bufferSync(sparseIndices);
+    const updatesBuf = backend2.bufferSync(sparseValues);
+    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);
+    const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, $defaultValue, sumDupeIndices);
+    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+  }
+  const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides2, [outputSize2, 1], sumDupeIndices);
+  const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });
+  backend2.disposeIntermediateTensorInfo(res);
+  return reshaped;
+}
+var sparseToDenseConfig2 = {
+  kernelName: SparseToDense,
+  backendName: "webgl",
+  kernelFunc: sparseToDense3
+};
+function splitV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const xRank = x.shape.length;
+  const begin = new Array(xRank).fill(0);
+  const size2 = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const sliceSize = [...size2];
+    sliceSize[$axis] = s2;
+    const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
+    begin[$axis] += s2;
+    return sliceT;
+  });
+}
+var splitVConfig2 = {
+  kernelName: SplitV,
+  backendName: "webgl",
+  kernelFunc: splitV2
+};
+var SQRT = `return sqrt(x);`;
+var sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });
+var sqrtConfig2 = {
+  kernelName: Sqrt,
+  backendName: "webgl",
+  kernelFunc: sqrt3
+};
+var SQUARE = `return x * x;`;
+var square3 = unaryKernelFunc2({ opSnippet: SQUARE });
+var squareConfig2 = {
+  kernelName: Square,
+  backendName: "webgl",
+  kernelFunc: square3
+};
+var SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
+var squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });
+var squaredDifferenceConfig2 = {
+  kernelName: SquaredDifference,
+  backendName: "webgl",
+  kernelFunc: squaredDifference3
+};
+function step3({ inputs, attrs, backend: backend2 }) {
+  const { x } = inputs;
+  const opSnippet = CHECK_NAN_SNIPPET + `
+    return x > 0.0 ? 1.0 : float(${attrs.alpha});
+  `;
+  const program = new UnaryOpProgram(x.shape, opSnippet);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var stepConfig2 = {
+  kernelName: Step,
+  backendName: "webgl",
+  kernelFunc: step3
+};
+var StridedSliceProgram = class {
+  constructor(begin, strides2, size2) {
+    this.variableNames = ["x"];
+    this.outputShape = size2;
+    const rank = size2.length;
+    const inputDtype = getCoordsDataType(size2.length);
+    const dtype = getCoordsDataType(size2.length);
+    let newCoords = "";
+    if (rank === 1) {
+      newCoords = "coords * strides + begin";
+    } else {
+      let outputAxis = 0;
+      newCoords = size2.map((_, i2) => {
+        outputAxis++;
+        return size2.length === 1 ? `coords * strides[${i2}] + begin[${i2}]` : `coords[${outputAxis - 1}] * strides[${i2}] + begin[${i2}]`;
+      }).join(",");
+    }
+    this.userCode = `
+      ${inputDtype} begin = ${inputDtype}(${begin});
+      ${inputDtype} strides = ${inputDtype}(${strides2});
+
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        setOutput(getX(${newCoords}));
+      }
+    `;
+  }
+};
+function stridedSlice3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides: strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size2 = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size: size2 } });
+    result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeIntermediateTensorInfo(sliced);
+  } else {
+    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
+    if (shouldExecuteOnCPU) {
+      const values = backend2.readSync(x.dataId);
+      const xBuf = buffer(x.shape, x.dtype, values);
+      const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);
+      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);
+    } else {
+      const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);
+      result = backend2.runWebGLProgram(program, [x], x.dtype);
+    }
+  }
+  const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });
+  backend2.disposeIntermediateTensorInfo(result);
+  return resultReshaped;
+}
+var stridedSliceConfig2 = {
+  kernelName: StridedSlice,
+  backendName: "webgl",
+  kernelFunc: stridedSlice3
+};
+function stringNGrams3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
+  const { data, dataSplits } = inputs;
+  const $data = backend2.readSync(data.dataId);
+  const $dataSplits = backend2.readSync(dataSplits.dataId);
+  const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
+  return [
+    backend2.makeTensorInfo([nGrams.length], "string", nGrams),
+    backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
+  ];
+}
+var stringNGramsConfig2 = {
+  kernelName: StringNGrams,
+  backendName: "webgl",
+  kernelFunc: stringNGrams3
+};
+function stringSplit3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { skipEmpty } = attrs;
+  const { input: input2, delimiter } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (input2.shape.length !== 1) {
+    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);
+  }
+  if (delimiter.shape.length !== 0) {
+    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
+  }
+  const $input = backend2.readSync(input2.dataId);
+  const $delimiter = backend2.readSync(delimiter.dataId)[0];
+  const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);
+  const outputSize2 = values.length;
+  return [
+    backend2.makeTensorInfo([outputSize2, 2], "int32", indices),
+    backend2.makeTensorInfo([outputSize2], "string", values),
+    backend2.makeTensorInfo([2], "int32", new Int32Array(shape))
+  ];
+}
+var stringSplitConfig2 = {
+  kernelName: StringSplit,
+  backendName: "webgl",
+  kernelFunc: stringSplit3
+};
+function stringToHashBucketFast3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { numBuckets } = attrs;
+  const { input: input2 } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (numBuckets <= 0) {
+    throw new Error(`Number of buckets must be at least 1`);
+  }
+  const $input = backend2.readSync(input2.dataId);
+  const output = stringToHashBucketFastImplCPU($input, numBuckets);
+  return backend2.makeTensorInfo(input2.shape, "int32", output);
+}
+var stringToHashBucketFastConfig2 = {
+  kernelName: StringToHashBucketFast,
+  backendName: "webgl",
+  kernelFunc: stringToHashBucketFast3
+};
+var TAN = `return tan(x);`;
+var tan3 = unaryKernelFunc2({ opSnippet: TAN });
+var tanConfig2 = {
+  kernelName: Tan,
+  backendName: "webgl",
+  kernelFunc: tan3
+};
+var TANH = `
+  float e2x = exp(-2.0 * abs(x));
+  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
+`;
+var tanh4 = unaryKernelFunc2({ opSnippet: TANH });
+var tanhConfig2 = {
+  kernelName: Tanh,
+  backendName: "webgl",
+  kernelFunc: tanh4
+};
+var TileProgram = class {
+  constructor(aShape, reps) {
+    this.variableNames = ["A"];
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[i2] * reps[i2];
+    }
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    const dtype = getCoordsDataType(this.rank);
+    const sourceCoords = getSourceCoords3(aShape);
+    this.userCode = `
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
+      }
+    `;
+  }
+};
+function getSourceCoords3(aShape) {
+  const rank = aShape.length;
+  if (rank > 5) {
+    throw Error(`Tile for rank ${rank} is not yet supported`);
+  }
+  if (rank === 1) {
+    return `imod(resRC, ${aShape[0]})`;
+  }
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < aShape.length; i2++) {
+    sourceCoords.push(`imod(${currentCoords[i2]}, ${aShape[i2]})`);
+  }
+  return sourceCoords.join();
+}
+function tile4(params) {
+  const { inputs, backend: backend2, attrs } = params;
+  const { x } = inputs;
+  const { reps } = attrs;
+  if (x.dtype === "string" || x.shape.length > 5) {
+    const data = backend2.readSync(x.dataId);
+    const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data;
+    const buf = buffer(x.shape, x.dtype, value);
+    const outBuf = tileImplCPU(buf, reps);
+    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+  }
+  const program = new TileProgram(x.shape, reps);
+  const output = backend2.runWebGLProgram(program, [x], x.dtype);
+  return output;
+}
+var tileConfig2 = {
+  kernelName: Tile,
+  backendName: "webgl",
+  kernelFunc: tile4
+};
+var SwapProgram = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.customUniforms = [
+      { name: "n", type: "int" },
+      { name: "firstPass", type: "int" },
+      { name: "negativeInf", type: "float" },
+      { name: "dir", type: "int" },
+      { name: "inc", type: "int" }
+    ];
+    this.outputShape = shape;
+    this.userCode = `
+       void main() {
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int elemIdx = coords[1];
+
+         // We compare elements pair-wise within a group of size 2 * inc.
+         // The comparing rule for each group alternates between ascending
+         // and descending. Within each group, we compare each pair at
+         // positions i and i+inc. To decide whether an element at position i
+         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
+         // inc, it is in the first half of the group, we denote it as x0,
+         // otherwise we denote it as x1.
+         // For example, as shown in the Bitonic top K paper referenced above,
+         // Figure5(a) shows that element[1] is in the
+         // second half of the group when group size is 2, but it is in the
+         // first half of the group when group size is 4.
+
+         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
+         int i = isFirstInPair ? elemIdx : elemIdx - inc;
+
+         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
+         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
+         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
+         float x1 = i1 < n ? getX(batch, i1) : negativeInf;
+
+         // Denotes which direction indices are in (ascending or descending).
+         bool reverse = imod(elemIdx, 2 * dir) >= dir;
+         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
+         if (reverse == isGreater) { // Elements in opposite order of direction
+           int iTemp = i0;
+           i0 = i1;
+           i1 = iTemp;
+         }
+         if (isFirstInPair) {
+            setOutput(float(i0));
+         } else {
+            setOutput(float(i1));
+         }
+       }
+     `;
+  }
+};
+var MergeProgram = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.customUniforms = [
+      { name: "n", type: "int" },
+      { name: "firstPass", type: "int" },
+      { name: "k", type: "int" }
+    ];
+    this.outputShape = shape;
+    this.userCode = `
+    void main() {
+         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int elemIdx = coords[1];
+
+         // The output size is half of the previous size.
+         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
+         // we only need to output the indices at positions |, the indices at
+         // positions _ can be thrown away, see Figure5(b) After Phase 2
+         // (Merge phase) in the Bitonic Top K paper referenced above.
+         // For example, the paper shows we only need to output the orange bars.
+         // The output sequence should look like this | | | | | | | |.
+         // Because the sequence is halved, to map the output index back
+         // to the previous sequence to find the corresponding value,
+         // we need to double the index. When we double the index,
+         // we basically interpolate a position, so 2i looks like
+         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
+         // of each 2k positions by - elemIdx % k. E.g. for output at
+         // index 4,5,6,7, we want to get the corresponding element at
+         // original index 8,9,10,11, for output at index 8,9,10,11,
+         // we want to get the corresponding element at original index
+         // 16,17,18,19, so on and so forth.
+
+         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
+         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
+         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
+
+         float x0 = getX(batch, i0);
+         float x1 = i1 < n ? getX(batch, i1) : x0;
+
+         setOutput(x0 >= x1 ? float(i0) : float(i1));
+       }
+     `;
+  }
+};
+function disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {
+  if (tensorInfo !== null) {
+    backend2.disposeIntermediateTensorInfo(tensorInfo);
+  }
+}
+function roundUpToPow2(num) {
+  let pow22 = 1;
+  while (pow22 < num) {
+    pow22 *= 2;
+  }
+  return pow22;
+}
+function topK2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD");
+  const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD");
+  const xShape = x.shape;
+  const lastDim = xShape[xShape.length - 1];
+  if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {
+    const xVals = backend2.readSync(x.dataId);
+    const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);
+    return [
+      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+    ];
+  }
+  if (k === 0) {
+    xShape[xShape.length - 1] = 0;
+    return [
+      backend2.makeTensorInfo(xShape, x.dtype, []),
+      backend2.makeTensorInfo(xShape, "int32", [])
+    ];
+  }
+  if (lastDim === 1) {
+    return [
+      x,
+      fill3({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 })
+    ];
+  }
+  const xtexData = backend2.texData.get(x.dataId);
+  const xIsPacked = xtexData !== null && xtexData.isPacked;
+  const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;
+  const xSize = util_exports.sizeFromShape(xShape);
+  const batch = xSize / lastDim;
+  const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });
+  if (xIsPacked) {
+    disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);
+  }
+  const kPow2 = roundUpToPow2(k);
+  const lastDimPow2 = roundUpToPow2(lastDim);
+  let indices = null;
+  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
+  const runSwap = (dir, inc, shape) => {
+    const inputs2 = getInputs();
+    const program = new SwapProgram(shape);
+    const fistPass = indices === null ? 1 : 0;
+    const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGLProgram(program, inputs2, "int32", customValues);
+    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);
+  };
+  for (let len = 1; len < kPow2; len *= 2) {
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, [batch, lastDimPow2]);
+    }
+  }
+  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
+    const inputs2 = getInputs();
+    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);
+    const firstPass = indices === null ? 1 : 0;
+    const customValues = [[lastDim], [firstPass], [kPow2]];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGLProgram(mergeProgram, inputs2, "int32", customValues);
+    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);
+    const len = kPow2 / 2;
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, indices.shape);
+    }
+  }
+  let prevIndices = indices;
+  indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });
+  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);
+  let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });
+  disposeIntermediateTensorInfoOrNull(backend2, x2D);
+  const newShape = xShape.slice(0, -1);
+  newShape.push(k);
+  prevIndices = indices;
+  indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);
+  const prevValues = values;
+  values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull(backend2, prevValues);
+  return [values, indices];
+}
+var topKConfig2 = {
+  kernelName: TopK,
+  backendName: "webgl",
+  kernelFunc: topK2
+};
+var TransformProgram = class {
+  constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {
+    this.variableNames = ["Image", "Transforms"];
+    this.outputShape = outShape;
+    const interpolationModeId = interpolation === "nearest" ? 1 : 2;
+    let fillModeId;
+    switch (fillMode) {
+      case "constant":
+        fillModeId = 1;
+        break;
+      case "reflect":
+        fillModeId = 2;
+        break;
+      case "wrap":
+        fillModeId = 3;
+        break;
+      case "nearest":
+        fillModeId = 4;
+        break;
+      default:
+        fillModeId = 1;
+        break;
+    }
+    this.userCode = `
+            float mapCoord(float outCoord, float len) {
+              float inCoord = outCoord;
+              if(${fillModeId} == 2) {
+                if (inCoord < 0.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz2 = 2.0 * len;
+                    if (inCoord < sz2) {
+                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
+                      inCoord;
+                    }
+                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
+                  }
+                } else if (inCoord > len - 1.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz2 = 2.0 * len;
+                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
+                    if (inCoord >= len) {
+                      inCoord = sz2 - inCoord - 1.0;
+                    }
+                  }
+                }
+                return clamp(inCoord, 0.0, len - 1.0);
+              } else if (${fillModeId} == 3) {
+                if (inCoord < 0.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz = len - 1.0;
+                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
+                  }
+                } else if (inCoord > len - 1.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz = len - 1.0;
+                    inCoord -= len * float(int(float(inCoord / sz)));
+                  }
+                }
+                return clamp(inCoord, 0.0, len - 1.0);
+              } else if (${fillModeId} == 4) {
+                return clamp(outCoord, 0.0, len - 1.0);
+              } else {
+                return outCoord;
+              }
+            }
+
+            float readWithFillValue(int batch, int coordY, int coordX,
+              int channel) {
+              float outputValue;
+              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {
+                  outputValue = getImage(batch, coordY, coordX, channel);
+              } else {
+                outputValue = float(${fillValue});
+              }
+              return outputValue;
+            }
+
+            void main() {
+              ivec4 coords = getOutputCoords();
+              float outputValue;
+              int batch = coords[0];
+              int x = coords[2];
+              int y = coords[1];
+              int channel = coords[3];
+              float xf = float(x);
+              float yf = float(y);
+              float a1 = getTransforms(batch, 0);
+              float a2 = getTransforms(batch, 1);
+              float a3 = getTransforms(batch, 2);
+              float b1 = getTransforms(batch, 3);
+              float b2 = getTransforms(batch, 4);
+              float b3 = getTransforms(batch, 5);
+              float c1 = getTransforms(batch, 6);
+              float c2 = getTransforms(batch, 7);
+              float projection = c1 * xf + c2 * yf + 1.0;
+              if (projection == 0.0) {
+                outputValue = float(${fillValue});
+              } else {
+                float inX = (a1 * xf + a2 * yf + a3) / projection;
+                float inY = (b1 * xf + b2 * yf + b3) / projection;
+                float mapX = mapCoord(inX, float(${imageWidth}));
+                float mapY = mapCoord(inY, float(${imageHeight}));
+
+                if (${interpolationModeId} == 1) {
+                  int coordY = int(round(mapY));
+                  int coordX = int(round(mapX));
+                  outputValue = readWithFillValue(batch, coordY, coordX,
+                    channel);
+                } else {
+                  float yFloor = floor(mapY);
+                  float xFloor = floor(mapX);
+                  float yCeil = yFloor + 1.0;
+                  float xCeil = xFloor + 1.0;
+                  float valueYFloor = (xCeil - mapX) *
+                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
+                  float valueYCeil = (xCeil - mapX) *
+                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
+                  outputValue = (yCeil - mapY) * valueYFloor +
+                  (mapY - yFloor) * valueYCeil;
+                }
+              }
+              setOutput(outputValue);
+            }
+        `;
+  }
+};
+function transform3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [
+    batch,
+    outHeight,
+    outWidth,
+    numChannels
+  ];
+  const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
+  return backend2.runWebGLProgram(program, [image2, transforms], "float32");
+}
+var transformConfig2 = {
+  kernelName: Transform,
+  backendName: "webgl",
+  kernelFunc: transform3
+};
+function unique4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { axis } = attrs;
+  const { x } = inputs;
+  assertNotComplex2(x, "unique");
+  console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
+  const values = backend2.readSync(x.dataId);
+  const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);
+  return [
+    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
+    backend2.makeTensorInfo([indices.length], "int32", indices)
+  ];
+}
+var uniqueConfig2 = {
+  kernelName: Unique,
+  backendName: "webgl",
+  kernelFunc: unique4
+};
+function unpack2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const x = value;
+  const xRank = x.shape.length;
+  const num = value.shape[axis];
+  const outShape = new Array(xRank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < xRank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = x.shape[i2];
+    }
+  }
+  const toDispose = [];
+  const begin = new Array(xRank).fill(0);
+  const size2 = x.shape.slice();
+  size2[axis] = 1;
+  const res = new Array(num);
+  for (let i2 = 0; i2 < res.length; i2++) {
+    begin[axis] = i2;
+    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: size2 } });
+    const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
+    res[i2] = reshaped;
+    toDispose.push(sliced);
+  }
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return res;
+}
+var unpackConfig2 = {
+  kernelName: Unpack,
+  backendName: "webgl",
+  kernelFunc: unpack2
+};
+var SegmentOpProgram = class {
+  constructor(segOpInfo, segOpType) {
+    this.variableNames = ["x", "segmentIds"];
+    const windowSize = segOpInfo.windowSize;
+    const batchSize = segOpInfo.batchSize;
+    const inSize = segOpInfo.inSize;
+    const numSegments = segOpInfo.numSegments;
+    const outSize = numSegments * Math.ceil(inSize / windowSize);
+    this.outputShape = [batchSize, outSize];
+    const initializationValue = "0.0";
+    const returnValue = `sumValue`;
+    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+    const windowSizeVec4Remainder = windowSize % 4;
+    const updateSnippet = `
+        sumValue += dot(values, segFilter);
+    `;
+    let checkValueOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkValueOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return initializationValue;
+        }
+      `;
+    }
+    let checkSegmentIdOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkSegmentIdOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return -1.0;
+        }
+      `;
+    }
+    this.userCode = `
+      const float initializationValue = ${initializationValue};
+
+      float getValue(int batch, int inIdx) {
+        ${checkValueOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      float getSegmentIdAtIndex(int inIdx) {
+        ${checkSegmentIdOutOfBounds}
+        return getSegmentIds(inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = int(floor(float(outIdx) / float(
+          ${numSegments})) * float(${windowSize}));
+        int currentSeg = int(mod(float(outIdx), float(${numSegments})));
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder === 1}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            0,
+            0,
+            0
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+              0,
+              0
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            0
+          );
+
+          ${updateSnippet}
+        }
+        setOutput(${returnValue});
+      }
+    `;
+  }
+};
+function unsortedSegmentSum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, segmentIds } = inputs;
+  const { numSegments } = attrs;
+  const xRank = x.shape.length;
+  const toDispose = [];
+  let axis = 0;
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+    toDispose.push(permutedX);
+    axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  }
+  const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);
+  const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  toDispose.push(a2D);
+  const outputDType = sumOutType(x.dtype);
+  const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {
+    const batchSize = x2.shape[0];
+    const inSize2 = x2.shape[1];
+    const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);
+    const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };
+    const program = new SegmentOpProgram(segOpInfo, segOpType);
+    const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);
+    toDispose.push(output);
+    if (output.shape[1] === numSegments2) {
+      return output;
+    }
+    const rangeInfo = range4({
+      backend: backend2,
+      attrs: { start: 0, stop: numSegments2, step: 1, dtype: "float32" }
+    });
+    const tileInfo = tile4({
+      inputs: { x: rangeInfo },
+      backend: backend2,
+      attrs: { reps: [inSize2 / windowSize] }
+    });
+    toDispose.push(rangeInfo);
+    toDispose.push(tileInfo);
+    const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);
+    return result2;
+  };
+  const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments);
+  const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });
+  let result = reshaped;
+  if (permutation != null) {
+    toDispose.push(reshaped);
+    const perm = backend_util_exports.getUndoAxesPermutation(permutation);
+    result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });
+  }
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var unsortedSegmentSumConfig2 = {
+  kernelName: UnsortedSegmentSum,
+  backendName: "webgl",
+  kernelFunc: unsortedSegmentSum3
+};
+var kernelConfigs2 = [
+  _fusedMatMulConfig2,
+  absConfig2,
+  acosConfig2,
+  acoshConfig2,
+  addConfig2,
+  addNConfig2,
+  allConfig2,
+  anyConfig2,
+  argMaxConfig2,
+  argMinConfig2,
+  asinConfig2,
+  asinhConfig2,
+  atanConfig2,
+  atan2Config2,
+  atanhConfig2,
+  avgPoolConfig2,
+  avgPool3DConfig2,
+  avgPool3DGradConfig3,
+  avgPoolGradConfig3,
+  batchMatMulConfig2,
+  batchNormConfig2,
+  batchToSpaceNDConfig2,
+  bincountConfig2,
+  broadcastArgsConfig2,
+  castConfig2,
+  ceilConfig2,
+  clipByValueConfig2,
+  complexConfig2,
+  complexAbsConfig2,
+  concatConfig2,
+  conv2DConfig2,
+  conv2DBackpropFilterConfig2,
+  conv2DBackpropInputConfig2,
+  conv3DConfig2,
+  conv3DBackpropFilterV2Config2,
+  conv3DBackpropInputConfig,
+  cosConfig2,
+  coshConfig2,
+  cropAndResizeConfig2,
+  cumprodConfig2,
+  cumsumConfig2,
+  denseBincountConfig2,
+  depthToSpaceConfig2,
+  depthwiseConv2dNativeConfig2,
+  depthwiseConv2dNativeBackpropFilterConfig2,
+  depthwiseConv2dNativeBackpropInputConfig2,
+  diagConfig2,
+  dilation2DConfig2,
+  einsumConfig2,
+  eluConfig2,
+  eluGradConfig3,
+  equalConfig2,
+  erfConfig2,
+  expConfig2,
+  expandDimsConfig2,
+  expm1Config2,
+  fftConfig2,
+  fillConfig2,
+  flipLeftRightConfig2,
+  floorConfig2,
+  floorDivConfig2,
+  fromPixelsConfig,
+  fusedConv2DConfig2,
+  fusedDepthwiseConv2DConfig2,
+  gatherNdConfig2,
+  gatherV2Config2,
+  greaterConfig2,
+  greaterEqualConfig2,
+  identityConfig2,
+  ifftConfig2,
+  imagConfig2,
+  isFiniteConfig2,
+  isInfConfig2,
+  isNaNConfig2,
+  leakyReluConfig2,
+  lessConfig2,
+  lessEqualConfig2,
+  linSpaceConfig2,
+  logConfig2,
+  log1pConfig2,
+  logicalAndConfig2,
+  logicalNotConfig2,
+  logicalOrConfig2,
+  LRNConfig2,
+  LRNGradConfig2,
+  maxConfig2,
+  maximumConfig2,
+  maxPoolConfig2,
+  maxPool3DConfig2,
+  maxPool3DGradConfig3,
+  maxPoolGradConfig3,
+  maxPoolWithArgmaxConfig2,
+  meanConfig2,
+  minConfig2,
+  minimumConfig2,
+  mirrorPadConfig2,
+  modConfig2,
+  multinomialConfig2,
+  multiplyConfig2,
+  negConfig2,
+  nonMaxSuppressionV3Config2,
+  nonMaxSuppressionV4Config2,
+  nonMaxSuppressionV5Config2,
+  notEqualConfig2,
+  oneHotConfig2,
+  onesLikeConfig2,
+  packConfig2,
+  padV2Config2,
+  powConfig2,
+  preluConfig2,
+  prodConfig2,
+  rangeConfig2,
+  realConfig2,
+  realDivConfig2,
+  reciprocalConfig2,
+  reluConfig2,
+  relu6Config2,
+  reshapeConfig2,
+  resizeBilinearConfig2,
+  resizeBilinearGradConfig3,
+  resizeNearestNeighborConfig2,
+  resizeNearestNeighborGradConfig3,
+  reverseConfig2,
+  rotateWithOffsetConfig2,
+  roundConfig2,
+  rsqrtConfig2,
+  scatterNdConfig2,
+  searchSortedConfig2,
+  selectConfig2,
+  seluConfig2,
+  sigmoidConfig2,
+  signConfig2,
+  sinConfig2,
+  sinhConfig2,
+  sliceConfig2,
+  softmaxConfig2,
+  softplusConfig2,
+  spaceToBatchNDConfig2,
+  sparseFillEmptyRowsConfig2,
+  sparseReshapeConfig2,
+  sparseSegmentMeanConfig2,
+  sparseSegmentSumConfig2,
+  sparseToDenseConfig2,
+  splitVConfig2,
+  sqrtConfig2,
+  squareConfig2,
+  squaredDifferenceConfig2,
+  stepConfig2,
+  stridedSliceConfig2,
+  stringNGramsConfig2,
+  stringSplitConfig2,
+  stringToHashBucketFastConfig2,
+  subConfig2,
+  sumConfig2,
+  tanConfig2,
+  tanhConfig2,
+  tileConfig2,
+  topKConfig2,
+  transformConfig2,
+  transposeConfig2,
+  uniqueConfig2,
+  unpackConfig2,
+  unsortedSegmentSumConfig2,
+  zerosLikeConfig2
+];
+for (const kernelConfig of kernelConfigs2) {
+  registerKernel(kernelConfig);
+}
+var CppDType;
+(function(CppDType2) {
+  CppDType2[CppDType2["float32"] = 0] = "float32";
+  CppDType2[CppDType2["int32"] = 1] = "int32";
+  CppDType2[CppDType2["bool"] = 2] = "bool";
+  CppDType2[CppDType2["string"] = 3] = "string";
+  CppDType2[CppDType2["complex64"] = 4] = "complex64";
+})(CppDType || (CppDType = {}));
+var FusableActivation;
+(function(FusableActivation2) {
+  FusableActivation2[FusableActivation2["linear"] = 0] = "linear";
+  FusableActivation2[FusableActivation2["relu"] = 1] = "relu";
+  FusableActivation2[FusableActivation2["relu6"] = 2] = "relu6";
+  FusableActivation2[FusableActivation2["prelu"] = 3] = "prelu";
+  FusableActivation2[FusableActivation2["leakyrelu"] = 4] = "leakyrelu";
+  FusableActivation2[FusableActivation2["sigmoid"] = 5] = "sigmoid";
+  FusableActivation2[FusableActivation2["elu"] = 6] = "elu";
+})(FusableActivation || (FusableActivation = {}));
+var wasmFusedMatMul;
+function setup(backend2) {
+  wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function fusedBatchMatMul(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  if (a6.dtype !== "float32" || b.dtype !== "float32") {
+    throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);
+  }
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  const aId = backend2.dataIdMap.get(a6.dataId).id;
+  const bId = backend2.dataIdMap.get(b.dataId).id;
+  let biasId = 0;
+  if (bias != null) {
+    const biasData = backend2.dataIdMap.get(bias.dataId);
+    if (biasData.shape.length !== 1) {
+      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);
+    }
+    biasId = biasData.id;
+  }
+  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
+  const fusedActivation = FusableActivation[activation2];
+  if (fusedActivation == null) {
+    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
+  }
+  const leftDim = transposeA ? a6.shape[2] : a6.shape[1];
+  const rightDim = transposeB ? b.shape[1] : b.shape[2];
+  const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a6.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const aShapeBytes = new Uint8Array(new Int32Array(a6.shape).buffer);
+  const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
+  wasmFusedMatMul(aId, aShapeBytes, a6.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);
+  return out;
+}
+var _fusedMatMulConfig3 = {
+  kernelName: _FusedMatMul,
+  backendName: "wasm",
+  setupFunc: setup,
+  kernelFunc: fusedBatchMatMul
+};
+function createUnaryKernelConfig(kernelName, outType) {
+  let wasmFunc9;
+  function setupFunc3(backend2) {
+    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [
+      "number",
+      "number",
+      "number"
+    ]);
+  }
+  function kernelFunc3(args) {
+    const { backend: backend2, inputs: { x } } = args;
+    const xId = backend2.dataIdMap.get(x.dataId).id;
+    const out = backend2.makeOutput(x.shape, outType || x.dtype);
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    if (util_exports.sizeFromShape(out.shape) === 0) {
+      return out;
+    }
+    wasmFunc9(xId, CppDType[x.dtype], outId);
+    return out;
+  }
+  return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };
+}
+var absConfig3 = createUnaryKernelConfig(Abs);
+function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {
+  let wasmFunc9;
+  function setupFunc3(backend2) {
+    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [
+      "number",
+      "array",
+      "number",
+      "number",
+      "array",
+      "number",
+      "number",
+      "number"
+    ]);
+  }
+  function kernelFunc3(args) {
+    const { backend: backend2, inputs } = args;
+    const { a: a6, b } = inputs;
+    const aId = backend2.dataIdMap.get(a6.dataId).id;
+    const bId = backend2.dataIdMap.get(b.dataId).id;
+    const outputType = dtype != null ? dtype : a6.dtype;
+    const newShape = backend_util_exports.assertAndGetBroadcastShape(a6.shape, b.shape);
+    const out = backend2.makeOutput(newShape, outputType);
+    if (util_exports.sizeFromShape(newShape) === 0) {
+      return out;
+    }
+    const aShapeBytes = new Uint8Array(new Int32Array(a6.shape).buffer);
+    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a6.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a6.dtype], outId);
+    kernelFunc4();
+    return out;
+  }
+  return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };
+}
+var supportsFullBroadcast = true;
+var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);
+var wasmFunc;
+function setupFunc(backend2) {
+  wasmFunc = backend2.wasm.cwrap(AddN, null, [
+    "array",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function addn(args) {
+  const { inputs, backend: backend2 } = args;
+  const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);
+  if (util_exports.sizeFromShape(out.shape) === 0) {
+    return out;
+  }
+  const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);
+  const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
+  return out;
+}
+var addNConfig3 = {
+  kernelName: AddN,
+  backendName: "wasm",
+  setupFunc,
+  kernelFunc: addn
+};
+function identity4(args) {
+  const { inputs: { x }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const inVals = backend2.typedArrayFromHeap(x);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.set(inVals);
+  return out;
+}
+var identityConfig3 = {
+  kernelName: Identity,
+  backendName: "wasm",
+  kernelFunc: identity4
+};
+var wasmTranspose;
+function setup2(backend2) {
+  wasmTranspose = backend2.wasm.cwrap(Transpose, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number"
+  ]);
+}
+function transpose4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
+  let permIsNoOp = true;
+  for (let i2 = 0; i2 < perm.length; i2++) {
+    if (perm[i2] !== i2) {
+      permIsNoOp = false;
+    }
+  }
+  const outShape = computeOutShape4(inputs.x.shape, attrs.perm);
+  const x = {
+    dataId: inputs.x.dataId,
+    shape: reducedShape,
+    dtype: inputs.x.dtype
+  };
+  if (permIsNoOp) {
+    const cloned = identity4({ inputs, backend: backend2 });
+    cloned.shape = outShape;
+    return cloned;
+  }
+  const out = backend2.makeOutput(outShape, x.dtype);
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const permBytes = new Uint8Array(new Int32Array(perm).buffer);
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);
+  return out;
+}
+function computeOutShape4(inShape, perm) {
+  const outShape = new Array(inShape.length);
+  for (let i2 = 0; i2 < outShape.length; i2++) {
+    outShape[i2] = inShape[perm[i2]];
+  }
+  return outShape;
+}
+function removeOneSizeDims(shape, perm) {
+  const newShape = [];
+  const newPerm = [];
+  for (let i2 = 0; i2 < shape.length; ++i2) {
+    if (shape[i2] !== 1) {
+      newShape.push(shape[i2]);
+    }
+    if (shape[perm[i2]] !== 1) {
+      newPerm.push(perm[i2]);
+    }
+  }
+  for (let i2 = 0; i2 < newPerm.length; ++i2) {
+    let minValIdx = -1;
+    for (let j = 0; j < newPerm.length; ++j) {
+      if (newPerm[j] >= i2 && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
+        minValIdx = j;
+      }
+    }
+    newPerm[minValIdx] = i2;
+  }
+  return [newShape, newPerm];
+}
+var transposeConfig3 = {
+  kernelName: Transpose,
+  backendName: "wasm",
+  kernelFunc: transpose4,
+  setupFunc: setup2
+};
+function permuteAxesAndTranspose(x, axis, backend2) {
+  const xShape = x.shape;
+  const xRank = x.shape.length;
+  const originalAxes = util_exports.parseAxisParam(axis, xShape);
+  let axes = originalAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let xTransposed = null;
+  let inputWasTransposed = false;
+  if (permutedAxes != null) {
+    const newShape = new Array(xRank);
+    for (let i2 = 0; i2 < newShape.length; i2++) {
+      newShape[i2] = xShape[permutedAxes[i2]];
+    }
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });
+    const xId = backend2.dataIdMap.get(x.dataId).id;
+    const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;
+    if (transposedId !== xId) {
+      inputWasTransposed = true;
+    }
+  }
+  return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };
+}
+var wasmAll;
+function setup3(backend2) {
+  wasmAll = backend2.wasm.cwrap(All, null, ["number, number, number"]);
+}
+function all4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    input2 = transposed;
+    inputId = transposedId;
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("all", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, x.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmAll(inputId, reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var allConfig3 = {
+  kernelName: All,
+  backendName: "wasm",
+  setupFunc: setup3,
+  kernelFunc: all4
+};
+var wasmAny;
+function setup4(backend2) {
+  wasmAny = backend2.wasm.cwrap(Any, null, ["number, number, number"]);
+}
+function any4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    input2 = transposed;
+    inputId = transposedId;
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("any", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, x.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmAny(inputId, reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var anyConfig3 = {
+  kernelName: Any,
+  backendName: "wasm",
+  setupFunc: setup4,
+  kernelFunc: any4
+};
+var wasmFunc2;
+function setup5(backend2) {
+  wasmFunc2 = backend2.wasm.cwrap(ArgMax, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function argmax(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+    }
+  }
+  const outShape = input2.shape.slice(0, -1);
+  const out = backend2.makeOutput(outShape, "int32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const outerSize = util_exports.sizeFromShape(out.shape);
+  const innerSize = input2.shape[axes[0]];
+  wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  return out;
+}
+var argMaxConfig3 = {
+  kernelName: ArgMax,
+  backendName: "wasm",
+  kernelFunc: argmax,
+  setupFunc: setup5
+};
+var wasmAvgPool;
+function setup6(backend2) {
+  wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function avgPool4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const x = inputs.x;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, 1, pad3, dimRoundingMode);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const channels = convInfo.inChannels;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {
+    throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);
+  return out;
+}
+var avgPoolConfig3 = {
+  kernelName: AvgPool,
+  backendName: "wasm",
+  setupFunc: setup6,
+  kernelFunc: avgPool4
+};
+function reshape5(args) {
+  const { inputs, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);
+  args.backend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig3 = {
+  kernelName: Reshape,
+  backendName: "wasm",
+  kernelFunc: reshape5
+};
+var wasmBatchMatMul;
+function setup7(backend2) {
+  wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function batchMatMul3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  if (a6.dtype !== "float32" || b.dtype !== "float32") {
+    throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);
+  }
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape5({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const a3dId = backend2.dataIdMap.get(a3d.dataId).id;
+  const b3dId = backend2.dataIdMap.get(b3d.dataId).id;
+  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
+  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);
+  const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);
+  wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);
+  backend2.disposeData(a3d.dataId);
+  backend2.disposeData(b3d.dataId);
+  out.shape = outShape;
+  return out;
+}
+var batchMatMulConfig3 = {
+  kernelName: BatchMatMul,
+  backendName: "wasm",
+  setupFunc: setup7,
+  kernelFunc: batchMatMul3
+};
+function slice4(args) {
+  const { inputs: { x }, attrs: { begin, size: size2 }, backend: backend2 } = args;
+  const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size2);
+  const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);
+  const xVals = backend2.readSync(x.dataId);
+  const out = backend2.makeOutput(size_, x.dtype);
+  const xStrides = util_exports.computeStrides(x.shape);
+  const outData = backend2.dataIdMap.get(out.dataId);
+  if (isContinous) {
+    const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);
+    if (x.dtype === "string") {
+      outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));
+    } else {
+      const outVals2 = backend2.typedArrayFromHeap(out);
+      outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));
+    }
+    return out;
+  }
+  if (x.dtype === "string") {
+    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);
+    outData.stringBytes = res;
+    return out;
+  }
+  const outVals = backend2.typedArrayFromHeap(out);
+  const rank = x.shape.length;
+  if (rank === 2) {
+    slice2d2(xVals, xStrides[0], outVals, begin_, size_);
+  } else if (rank === 3) {
+    slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);
+  } else if (rank === 4) {
+    slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);
+  } else {
+    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);
+    outVals.set(res);
+  }
+  return out;
+}
+function slice2d2(xVals, xStride, outVals, begin, size2) {
+  let outOffset = 0;
+  const beginI = begin[0];
+  const beginJ = begin[1];
+  const endI = beginI + size2[0];
+  for (let i2 = beginI; i2 < endI; i2++) {
+    const xOffset = i2 * xStride + beginJ;
+    outVals.set(xVals.subarray(xOffset, xOffset + size2[1]), outOffset);
+    outOffset += size2[1];
+  }
+}
+function slice3d2(xVals, xStride1, xStride2, outVals, begin, size2) {
+  let outOffset = 0;
+  const beginI = begin[0];
+  const beginJ = begin[1];
+  const beginK = begin[2];
+  const endI = beginI + size2[0];
+  const endJ = beginJ + size2[1];
+  for (let i2 = beginI; i2 < endI; i2++) {
+    for (let j = beginJ; j < endJ; j++) {
+      const xOffset = i2 * xStride1 + j * xStride2 + beginK;
+      outVals.set(xVals.subarray(xOffset, xOffset + size2[2]), outOffset);
+      outOffset += size2[2];
+    }
+  }
+}
+function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size2) {
+  let outOffset = 0;
+  const beginI = begin[0];
+  const beginJ = begin[1];
+  const beginK = begin[2];
+  const endI = beginI + size2[0];
+  const endJ = beginJ + size2[1];
+  const endK = beginK + size2[2];
+  const beginL = begin[3];
+  for (let i2 = beginI; i2 < endI; i2++) {
+    for (let j = beginJ; j < endJ; j++) {
+      for (let k = beginK; k < endK; k++) {
+        const xOffset = i2 * xStride1 + j * xStride2 + k * xStride3 + beginL;
+        outVals.set(xVals.subarray(xOffset, xOffset + size2[3]), outOffset);
+        outOffset += size2[3];
+      }
+    }
+  }
+}
+var sliceConfig3 = {
+  kernelName: Slice,
+  backendName: "wasm",
+  kernelFunc: slice4
+};
+function batchToSpaceND4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });
+  const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });
+  const result = slice4({
+    inputs: { x: xTransposedReshaped },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  backend2.disposeData(xReshaped.dataId);
+  backend2.disposeData(xTransposed.dataId);
+  backend2.disposeData(xReshaped.dataId);
+  return result;
+}
+var batchToSpaceNDConfig3 = {
+  kernelName: BatchToSpaceND,
+  backendName: "wasm",
+  kernelFunc: batchToSpaceND4
+};
+function cast5(args) {
+  const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, dtype);
+  const inVals = backend2.typedArrayFromHeap(x);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.set(inVals);
+  return out;
+}
+var castConfig3 = {
+  kernelName: Cast,
+  backendName: "wasm",
+  kernelFunc: cast5
+};
+var ceilConfig3 = createUnaryKernelConfig(Ceil);
+var wasmClip;
+function setup8(backend2) {
+  wasmClip = backend2.wasm.cwrap(ClipByValue, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function clip(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { clipValueMin, clipValueMax } = attrs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmClip(xId, clipValueMin, clipValueMax, outId);
+  return out;
+}
+var clipByValueConfig3 = {
+  kernelName: ClipByValue,
+  backendName: "wasm",
+  setupFunc: setup8,
+  kernelFunc: clip
+};
+function concat4(args) {
+  const { inputs, backend: backend2 } = args;
+  const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
+  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const out = backend2.makeOutput(outShape, inputs[0].dtype);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return out;
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, axis);
+  if ($inputs[0].dtype === "string") {
+    const inputs2D = $inputs.map((t2) => {
+      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+      const shape = [-1, innerSize];
+      return reshape5({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    });
+    const inputsValShapes = inputs2D.map((t2) => {
+      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    });
+    outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);
+    const simplyConcat = inputs2D[0].shape[0] === 1;
+    const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
+    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), axis);
+    out.shape = finalOutShape;
+    const outData = backend2.dataIdMap.get(out.dataId);
+    outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);
+    inputs2D.forEach((t2) => backend2.disposeData(t2.dataId));
+    return out;
+  }
+  const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));
+  let sumInnerDims = 0;
+  const innerDims = $inputs.map((input2) => {
+    const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));
+    sumInnerDims += innerDim;
+    return innerDim;
+  });
+  const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));
+  const outVals = backend2.typedArrayFromHeap(out);
+  for (let b = 0; b < batchDim; b++) {
+    let outOffset = b * sumInnerDims;
+    for (let i2 = 0; i2 < inVals.length; i2++) {
+      const innerDim = innerDims[i2];
+      const inOffset = b * innerDim;
+      const vals = inVals[i2].subarray(inOffset, inOffset + innerDim);
+      outVals.set(vals, outOffset);
+      outOffset += innerDim;
+    }
+  }
+  return out;
+}
+var concatConfig3 = {
+  kernelName: Concat,
+  backendName: "wasm",
+  kernelFunc: concat4
+};
+var wasmConv2d;
+function setup9(backend2) {
+  wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function conv2d5(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const { strides: strides2, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const outputChannels = convInfo.outChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
+  return out;
+}
+var conv2DConfig3 = {
+  kernelName: Conv2D,
+  backendName: "wasm",
+  setupFunc: setup9,
+  kernelFunc: conv2d5
+};
+var wasmConv2DBackpropInput;
+function setup10(backend2) {
+  wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function conv2DBackpropInput4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { dy, filter } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs;
+  const dilations = 1;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const dxStrides = util_exports.computeStrides(convInfo.inShape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);
+  const xBatchStride = dxStrides[0];
+  const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];
+  const xColStride = isChannelsLast ? dxStrides[2] : 1;
+  const xChannelStride = isChannelsLast ? 1 : dxStrides[1];
+  const yBatchStride = dyStrides[0];
+  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
+  const yColStride = isChannelsLast ? dyStrides[2] : 1;
+  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
+  const out = backend2.makeOutput(convInfo.inShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const dyId = backend2.dataIdMap.get(dy.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);
+  return out;
+}
+var conv2DBackpropInputConfig3 = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "wasm",
+  setupFunc: setup10,
+  kernelFunc: conv2DBackpropInput4
+};
+var cosConfig3 = createUnaryKernelConfig(Cos);
+var coshConfig3 = createUnaryKernelConfig(Cosh);
+var InterpolationMethod;
+(function(InterpolationMethod2) {
+  InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear";
+  InterpolationMethod2[InterpolationMethod2["nearest"] = 1] = "nearest";
+})(InterpolationMethod || (InterpolationMethod = {}));
+var wasmCropAndResize;
+function setup11(backend2) {
+  wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function cropAndResize4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { method, extrapolationValue, cropSize } = attrs;
+  const { image: image2, boxes, boxInd } = inputs;
+  const numBoxes = boxes.shape[0];
+  const [cropHeight, cropWidth] = cropSize;
+  const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]];
+  let imagesData = backend2.dataIdMap.get(image2.dataId);
+  let castedData;
+  if (image2.dtype !== "float32") {
+    castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: "float32" } });
+    imagesData = backend2.dataIdMap.get(castedData.dataId);
+  }
+  const imagesId = imagesData.id;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;
+  const out = backend2.makeOutput(outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer);
+  wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);
+  if (castedData != null) {
+    backend2.disposeData(castedData.dataId);
+  }
+  return out;
+}
+var cropAndResizeConfig3 = {
+  kernelName: CropAndResize,
+  backendName: "wasm",
+  setupFunc: setup11,
+  kernelFunc: cropAndResize4
+};
+var wasmCumprod;
+function setup12(backend2) {
+  wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function cumprod4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  const xRank = x.shape.length;
+  util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation !== null) {
+    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  backend_util_exports.assertAxesAreInnerMostDims("cumprod", [permutedAxis], xRank);
+  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);
+  const finalDim = permutedX.shape[permutedAxis];
+  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;
+  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;
+  wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
+  let out = permutedOut;
+  if (permutation !== null) {
+    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });
+    backend2.disposeData(permutedX.dataId);
+    backend2.disposeData(permutedOut.dataId);
+  }
+  return out;
+}
+var cumprodConfig3 = {
+  kernelName: Cumprod,
+  backendName: "wasm",
+  setupFunc: setup12,
+  kernelFunc: cumprod4
+};
+var wasmCumsum;
+function setup13(backend2) {
+  wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function cumsum4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  const xRank = x.shape.length;
+  util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation !== null) {
+    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank);
+  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);
+  const finalDim = permutedX.shape[permutedAxis];
+  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;
+  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;
+  wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
+  let out = permutedOut;
+  if (permutation !== null) {
+    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });
+    backend2.disposeData(permutedX.dataId);
+    backend2.disposeData(permutedOut.dataId);
+  }
+  return out;
+}
+var cumsumConfig3 = {
+  kernelName: Cumsum,
+  backendName: "wasm",
+  setupFunc: setup13,
+  kernelFunc: cumsum4
+};
+var wasmDepthToSpace;
+function setup14(backend2) {
+  wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function depthToSpace4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  const batchSize = x.shape[0];
+  const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
+  const out = backend2.makeOutput(outputShape, "float32");
+  const xData = backend2.dataIdMap.get(x.dataId);
+  const xId = xData.id;
+  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
+  const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);
+  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const channelsLast = dataFormat === "NHWC" ? 1 : 0;
+  wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);
+  return out;
+}
+var depthToSpaceConfig3 = {
+  kernelName: DepthToSpace,
+  backendName: "wasm",
+  setupFunc: setup14,
+  kernelFunc: depthToSpace4
+};
+var wasmDepthwiseConv2d;
+function setup15(backend2) {
+  wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function depthwiseConv2d5(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const { strides: strides2, dilations, pad: pad3, dimRoundingMode } = attrs;
+  const $dilations = dilations == null ? [1, 1] : dilations;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, $dilations, pad3, dimRoundingMode, true);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const outputChannels = convInfo.outChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
+  return out;
+}
+var depthwiseConv2dNativeConfig3 = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "wasm",
+  setupFunc: setup15,
+  kernelFunc: depthwiseConv2d5
+};
+var eluConfig3 = createUnaryKernelConfig(Elu);
+var supportsFullBroadcast2 = false;
+var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool");
+var expConfig3 = createUnaryKernelConfig(Exp, "float32");
+function expandDims5(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const { dim } = attrs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig3 = {
+  kernelName: ExpandDims,
+  backendName: "wasm",
+  kernelFunc: expandDims5
+};
+function fill4(args) {
+  const { attrs: { shape, value, dtype }, backend: backend2 } = args;
+  const out = backend2.makeOutput(shape, dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.fill(value);
+  return out;
+}
+var fillConfig3 = {
+  kernelName: Fill,
+  backendName: "wasm",
+  kernelFunc: fill4
+};
+var wasmFlipLeftRight;
+function setup16(backend2) {
+  wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function flipLeftRight2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { image: image2 } = inputs;
+  const out = backend2.makeOutput(image2.shape, image2.dtype);
+  const imageId = backend2.dataIdMap.get(image2.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);
+  return out;
+}
+var flipLeftRightConfig3 = {
+  kernelName: FlipLeftRight,
+  backendName: "wasm",
+  kernelFunc: flipLeftRight2,
+  setupFunc: setup16
+};
+var floorConfig3 = createUnaryKernelConfig(Floor);
+var supportsFullBroadcast3 = false;
+var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);
+var wasmBatchNorm;
+function setup17(backend2) {
+  wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]);
+}
+function fusedBatchNorm(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { varianceEpsilon } = attrs;
+  const { x, mean: mean5, variance, offset, scale: scale22 } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const meanId = backend2.dataIdMap.get(mean5.dataId).id;
+  const varianceId = backend2.dataIdMap.get(variance.dataId).id;
+  const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;
+  const scaleId = scale22 != null ? backend2.dataIdMap.get(scale22.dataId).id : 0;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    return out;
+  }
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);
+  return out;
+}
+var fusedBatchNormConfig = {
+  kernelName: FusedBatchNorm,
+  backendName: "wasm",
+  setupFunc: setup17,
+  kernelFunc: fusedBatchNorm
+};
+var wasmFusedConv2d;
+function setup18(backend2) {
+  wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function fusedConv2d2(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode);
+  const fusedActivation = FusableActivation[activation2];
+  if (fusedActivation == null) {
+    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
+  }
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const outputChannels = convInfo.outChannels;
+  let biasId = 0;
+  if (bias != null) {
+    const biasData = backend2.dataIdMap.get(bias.dataId);
+    if (biasData.shape.length !== 1) {
+      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
+    }
+    if (biasData.shape[0] !== outputChannels) {
+      throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
+    }
+    biasId = biasData.id;
+  }
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  const batchSize = convInfo.batchSize;
+  const inHeight = convInfo.inHeight;
+  const inWidth = convInfo.inWidth;
+  if (dataFormat !== "NHWC") {
+    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
+  wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
+  return out;
+}
+var fusedConv2DConfig3 = {
+  kernelName: FusedConv2D,
+  backendName: "wasm",
+  setupFunc: setup18,
+  kernelFunc: fusedConv2d2
+};
+var wasmFusedDepthwiseConv2d;
+function setup19(backend2) {
+  wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function fusedDepthwiseConv2d(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, true);
+  const fusedActivation = FusableActivation[activation2];
+  if (fusedActivation == null) {
+    throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
+  }
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const outputChannels = convInfo.outChannels;
+  let biasId = 0;
+  if (bias != null) {
+    const biasData = backend2.dataIdMap.get(bias.dataId);
+    if (biasData.shape.length !== 1) {
+      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
+    }
+    if (biasData.shape[0] !== outputChannels) {
+      throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
+    }
+    biasId = biasData.id;
+  }
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  const batchSize = convInfo.batchSize;
+  const inHeight = convInfo.inHeight;
+  const inWidth = convInfo.inWidth;
+  if (dataFormat !== "NHWC") {
+    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
+  wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
+  return out;
+}
+var fusedDepthwiseConv2DConfig3 = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "wasm",
+  setupFunc: setup19,
+  kernelFunc: fusedDepthwiseConv2d
+};
+var wasmGatherNd;
+function setup20(backend2) {
+  wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number"
+  ]);
+}
+function gatherNd3(args) {
+  const { backend: backend2, inputs } = args;
+  const { params, indices } = inputs;
+  const [resultShape, numSlices, sliceSize, strides2] = gather_nd_util_exports.prepareAndValidate(params, indices);
+  const out = backend2.makeOutput(resultShape, params.dtype);
+  if (numSlices === 0) {
+    return out;
+  }
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const xData = backend2.dataIdMap.get(params.dataId);
+  const xId = xData.id;
+  const indicesData = backend2.dataIdMap.get(indices.dataId);
+  const indicesId = indicesData.id;
+  const stridesBytes = new Uint8Array(new Int32Array(strides2).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);
+  return out;
+}
+var gatherNdConfig3 = {
+  kernelName: GatherNd,
+  backendName: "wasm",
+  setupFunc: setup20,
+  kernelFunc: gatherNd3
+};
+var wasmGather;
+function setup21(backend2) {
+  wasmGather = backend2.wasm.cwrap("Gather", null, [
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number"
+  ]);
+}
+function gatherV23(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const indicesVals = backend2.readSync(indices.dataId);
+  const axisDim = x.shape[parsedAxis];
+  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
+    const index2 = indicesVals[i2];
+    util_exports.assert(index2 <= axisDim - 1 && index2 >= 0, () => `GatherV2: the index value ${index2} is not in [0, ${axisDim - 1}]`);
+  }
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+  const flattenX = reshape5({
+    inputs: { x },
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    },
+    backend: backend2
+  });
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const flattenIndex = reshape5({
+    inputs: { x: indices },
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] },
+    backend: backend2
+  });
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  const out = backend2.makeOutput(flattenOutputShape, x.dtype);
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    return out;
+  }
+  const stridesSize = flattenX.shape.length - 1;
+  const xData = backend2.dataIdMap.get(flattenX.dataId);
+  const xId = xData.id;
+  const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);
+  const indicesId = indicesData.id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);
+  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);
+  wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);
+  backend2.disposeData(flattenX.dataId);
+  backend2.disposeData(flattenIndex.dataId);
+  out.shape = shapeInfo.outputShape;
+  return out;
+}
+var gatherV2Config3 = {
+  kernelName: GatherV2,
+  backendName: "wasm",
+  setupFunc: setup21,
+  kernelFunc: gatherV23
+};
+var supportsFullBroadcast4 = false;
+var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "bool");
+var supportsFullBroadcast5 = false;
+var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, "bool");
+var wasmFunc3;
+function setupFunc2(backend2) {
+  wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function leakyRelu4(args) {
+  const { inputs: { x }, attrs: { alpha: alpha2 }, backend: backend2 } = args;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, "float32");
+  if (util_exports.sizeFromShape(x.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmFunc3(xId, CppDType[x.dtype], alpha2, outId);
+  }
+  return out;
+}
+var leakyReluConfig3 = {
+  kernelName: LeakyRelu,
+  backendName: "wasm",
+  setupFunc: setupFunc2,
+  kernelFunc: leakyRelu4
+};
+var supportsFullBroadcast6 = false;
+var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool");
+var supportsFullBroadcast7 = false;
+var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, "bool");
+var logConfig3 = createUnaryKernelConfig(Log);
+var supportsFullBroadcast8 = false;
+var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, "bool");
+var wasmMax;
+function setup22(backend2) {
+  wasmMax = backend2.wasm.cwrap(Max, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function max5(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { reductionIndices: axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    input2 = transposed;
+    inputId = transposedId;
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("max", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, x.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var maxConfig3 = {
+  kernelName: Max,
+  backendName: "wasm",
+  setupFunc: setup22,
+  kernelFunc: max5
+};
+var supportsFullBroadcast9 = false;
+var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9);
+var wasmMaxPool;
+function setup23(backend2) {
+  wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function maxPool4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const x = inputs.x;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  util_exports.assert(x.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, 1, pad3, dimRoundingMode);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const outputChannels = convInfo.outChannels;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
+  return out;
+}
+var maxPoolConfig3 = {
+  kernelName: MaxPool,
+  backendName: "wasm",
+  setupFunc: setup23,
+  kernelFunc: maxPool4
+};
+var wasmMean;
+function setup24(backend2) {
+  wasmMean = backend2.wasm.cwrap(Mean, null, ["number, number, number"]);
+}
+function mean3(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  let reductionAxes = axes;
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
+    }
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("mean", reductionAxes, input2.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  let castedInput = input2;
+  if (input2.dtype !== "float32") {
+    castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: "float32" } });
+    inputId = backend2.dataIdMap.get(castedInput.dataId).id;
+  }
+  const out = backend2.makeOutput(outShape, "float32");
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmMean(inputId, reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  if (input2.dtype !== "float32") {
+    backend2.disposeData(castedInput.dataId);
+  }
+  return out;
+}
+var meanConfig3 = {
+  kernelName: Mean,
+  backendName: "wasm",
+  setupFunc: setup24,
+  kernelFunc: mean3
+};
+var wasmMin;
+function setup25(backend2) {
+  wasmMin = backend2.wasm.cwrap(Min, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function min5(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+    }
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("min", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, input2.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var minConfig3 = {
+  kernelName: Min,
+  backendName: "wasm",
+  setupFunc: setup25,
+  kernelFunc: min5
+};
+var supportsFullBroadcast10 = false;
+var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10);
+var MirrorPaddingMode;
+(function(MirrorPaddingMode2) {
+  MirrorPaddingMode2[MirrorPaddingMode2["reflect"] = 0] = "reflect";
+  MirrorPaddingMode2[MirrorPaddingMode2["symmetric"] = 1] = "symmetric";
+})(MirrorPaddingMode || (MirrorPaddingMode = {}));
+var wasmMirrorPad;
+function setup26(backend2) {
+  wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function mirrorPad3(args) {
+  const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(outShape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);
+  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);
+  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
+  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
+  wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId);
+  return out;
+}
+var mirrorPadConfig3 = {
+  kernelName: MirrorPad,
+  backendName: "wasm",
+  kernelFunc: mirrorPad3,
+  setupFunc: setup26
+};
+var supportsFullBroadcast11 = true;
+var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11);
+var negConfig3 = createUnaryKernelConfig(Neg);
+function parseResultStruct(backend2, resOffset) {
+  const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);
+  const pSelectedIndices = result[0];
+  const selectedSize = result[1];
+  const pSelectedScores = result[2];
+  const pValidOutputs = result[3];
+  backend2.wasm._free(resOffset);
+  return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs };
+}
+var wasmFunc4;
+function setup27(backend2) {
+  wasmFunc4 = backend2.wasm.cwrap(NonMaxSuppressionV3, "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function kernelFunc(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;
+  const { boxes, scores } = inputs;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const scoresId = backend2.dataIdMap.get(scores.dataId).id;
+  const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);
+  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);
+  backend2.wasm._free(pSelectedScores);
+  backend2.wasm._free(pValidOutputs);
+  const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
+  return selectedIndicesTensor;
+}
+var nonMaxSuppressionV3Config3 = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "wasm",
+  setupFunc: setup27,
+  kernelFunc
+};
+var wasmFunc5;
+function setup28(backend2) {
+  wasmFunc5 = backend2.wasm.cwrap(NonMaxSuppressionV4, "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "bool"
+  ]);
+}
+function nonMaxSuppressionV43(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;
+  const { boxes, scores } = inputs;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const scoresId = backend2.dataIdMap.get(scores.dataId).id;
+  const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);
+  backend2.wasm._free(pSelectedScores);
+  const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
+  const validOutputsTensor = backend2.makeOutput([], "int32", pValidOutputs);
+  return [selectedIndicesTensor, validOutputsTensor];
+}
+var nonMaxSuppressionV4Config3 = {
+  kernelName: NonMaxSuppressionV4,
+  backendName: "wasm",
+  setupFunc: setup28,
+  kernelFunc: nonMaxSuppressionV43
+};
+var wasmFunc6;
+function setup29(backend2) {
+  wasmFunc6 = backend2.wasm.cwrap(NonMaxSuppressionV5, "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function kernelFunc2(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;
+  const { boxes, scores } = inputs;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const scoresId = backend2.dataIdMap.get(scores.dataId).id;
+  const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);
+  backend2.wasm._free(pValidOutputs);
+  const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
+  const selectedScoresTensor = backend2.makeOutput([selectedSize], "float32", pSelectedScores);
+  return [selectedIndicesTensor, selectedScoresTensor];
+}
+var nonMaxSuppressionV5Config3 = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "wasm",
+  setupFunc: setup29,
+  kernelFunc: kernelFunc2
+};
+var supportsFullBroadcast12 = false;
+var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, "bool");
+var wasmOneHot;
+function setup30(backend2) {
+  wasmOneHot = backend2.wasm.cwrap(OneHot, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function oneHot4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices } = inputs;
+  const { depth, onValue, offValue } = attrs;
+  const out = backend2.makeOutput([...indices.shape, depth], "int32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const indicesData = backend2.dataIdMap.get(indices.dataId);
+  const indicesId = indicesData.id;
+  wasmOneHot(indicesId, depth, onValue, offValue, outId);
+  return out;
+}
+var oneHotConfig3 = {
+  kernelName: OneHot,
+  backendName: "wasm",
+  setupFunc: setup30,
+  kernelFunc: oneHot4
+};
+function onesLike4(args) {
+  const { inputs: { x }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.fill(1);
+  return out;
+}
+var onesLikeConfig3 = {
+  kernelName: OnesLike,
+  backendName: "wasm",
+  kernelFunc: onesLike4
+};
+function pack3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims5({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return result;
+}
+var packConfig3 = {
+  kernelName: Pack,
+  backendName: "wasm",
+  kernelFunc: pack3
+};
+var wasmPadV2;
+function setup31(backend2) {
+  wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function pad2(args) {
+  const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    return fill4({
+      backend: backend2,
+      attrs: { shape: outShape, value: constantValue, dtype: x.dtype }
+    });
+  }
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(outShape, x.dtype);
+  const outTensorData = backend2.dataIdMap.get(out.dataId);
+  const outId = outTensorData.id;
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);
+  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);
+  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
+  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
+  wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);
+  return out;
+}
+var padV2Config3 = {
+  kernelName: PadV2,
+  backendName: "wasm",
+  kernelFunc: pad2,
+  setupFunc: setup31
+};
+var supportsFullBroadcast13 = false;
+var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast13);
+var wasmPrelu;
+function setup32(backend2) {
+  wasmPrelu = backend2.wasm.cwrap(Prelu, null, [
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function prelu5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha: alpha2 } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const weightsId = backend2.dataIdMap.get(alpha2.dataId).id;
+  let inputId = xId;
+  const input2 = x;
+  let castedInput = input2;
+  if (input2.dtype !== "float32") {
+    castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: "float32" } });
+    inputId = backend2.dataIdMap.get(castedInput.dataId).id;
+  }
+  const out = backend2.makeOutput(x.shape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmPrelu(inputId, weightsId, outId);
+  if (input2.dtype !== "float32") {
+    backend2.disposeData(castedInput.dataId);
+  }
+  return out;
+}
+var preluConfig3 = {
+  kernelName: Prelu,
+  backendName: "wasm",
+  setupFunc: setup32,
+  kernelFunc: prelu5
+};
+var wasmProd;
+function setup33(backend2) {
+  wasmProd = backend2.wasm.cwrap(Prod, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function prod4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  let reductionAxes = axes;
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
+    }
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("prod", reductionAxes, input2.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, input2.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var prodConfig3 = {
+  kernelName: Prod,
+  backendName: "wasm",
+  setupFunc: setup33,
+  kernelFunc: prod4
+};
+var range5 = (args) => {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, step: step5, dtype } = attrs;
+  const values = rangeImpl(start, stop, step5, dtype);
+  const out = backend2.makeOutput([values.length], dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.set(values);
+  return out;
+};
+var rangeConfig3 = {
+  kernelName: Range,
+  backendName: "wasm",
+  kernelFunc: range5
+};
+var supportsFullBroadcast14 = true;
+var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14);
+var reluConfig3 = createUnaryKernelConfig(Relu);
+var relu6Config3 = createUnaryKernelConfig(Relu6);
+var wasmResizeBilinear;
+function setup34(backend2) {
+  wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function resizeBilinear4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size: size2 } = attrs;
+  const [newHeight, newWidth] = size2;
+  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
+  const outShape = [batch, newHeight, newWidth, numChannels];
+  let xData = backend2.dataIdMap.get(images.dataId);
+  let castedData;
+  if (xData.dtype !== "float32") {
+    castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: "float32" } });
+    xData = backend2.dataIdMap.get(castedData.dataId);
+  }
+  const xId = xData.id;
+  const out = backend2.makeOutput(outShape, "float32");
+  if (util_exports.sizeFromShape(images.shape) === 0) {
+    return out;
+  }
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);
+  if (castedData != null) {
+    backend2.disposeData(castedData.dataId);
+  }
+  return out;
+}
+var resizeBilinearConfig3 = {
+  kernelName: ResizeBilinear,
+  backendName: "wasm",
+  setupFunc: setup34,
+  kernelFunc: resizeBilinear4
+};
+var wasmReverse;
+function setup35(backend2) {
+  wasmReverse = backend2.wasm.cwrap(Reverse, null, [
+    "number",
+    "array",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function reverse4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dims } = attrs;
+  const axes = util_exports.parseAxisParam(dims, x.shape);
+  if (x.shape.length === 0) {
+    return identity4({ inputs: { x }, backend: backend2 });
+  }
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const axesBytes = new Uint8Array(new Int32Array(axes).buffer);
+  const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);
+  const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 });
+  backend2.disposeData(out.dataId);
+  return reshaped;
+}
+var reverseConfig3 = {
+  kernelName: Reverse,
+  backendName: "wasm",
+  kernelFunc: reverse4,
+  setupFunc: setup35
+};
+var wasmRotate;
+function setup36(backend2) {
+  wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function rotateWithOffset2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2 } = inputs;
+  const { radians, fillValue, center } = attrs;
+  const out = backend2.makeOutput(image2.shape, image2.dtype);
+  const imageId = backend2.dataIdMap.get(image2.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
+  const fillIsBlack = fillValue === 0;
+  const fullOpacityValue = 255;
+  const fillValues2 = typeof fillValue === "number" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];
+  const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);
+  wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);
+  return out;
+}
+var rotateWithOffsetConfig3 = {
+  kernelName: RotateWithOffset,
+  backendName: "wasm",
+  kernelFunc: rotateWithOffset2,
+  setupFunc: setup36
+};
+var roundConfig3 = createUnaryKernelConfig(Round);
+var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);
+var wasmScatterNd;
+function setup37(backend2) {
+  wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function scatterNd3(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const out = backend2.makeOutput(shape, updates.dtype);
+  if (util_exports.sizeFromShape(shape) === 0) {
+    return out;
+  }
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = scatter_nd_util_exports.calculateShapes(updates, indices, shape);
+  const indicesData = backend2.dataIdMap.get(indices.dataId);
+  const indicesId = indicesData.id;
+  const updatesData = backend2.dataIdMap.get(updates.dataId);
+  const updatesId = updatesData.id;
+  const stridesBytes = new Uint8Array(new Int32Array(strides2).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize2, outId);
+  return out;
+}
+var scatterNdConfig3 = {
+  kernelName: ScatterNd,
+  backendName: "wasm",
+  setupFunc: setup37,
+  kernelFunc: scatterNd3
+};
+var wasmSelect;
+function setup38(backend2) {
+  wasmSelect = backend2.wasm.cwrap("SelectV2", null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function select4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  const conditionId = backend2.dataIdMap.get(condition.dataId).id;
+  const tId = backend2.dataIdMap.get(t2.dataId).id;
+  const eId = backend2.dataIdMap.get(e2.dataId).id;
+  const out = backend2.makeOutput(t2.shape, t2.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const cRank = condition.shape.length;
+  const tRank = t2.shape.length;
+  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
+  wasmSelect(conditionId, tId, eId, offset, outId);
+  return out;
+}
+var selectConfig3 = {
+  kernelName: Select,
+  backendName: "wasm",
+  kernelFunc: select4,
+  setupFunc: setup38
+};
+var wasmFunc7;
+function setup39(backend2) {
+  wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, ["number", "number"]);
+}
+function sigmoid4(args) {
+  const { backend: backend2, inputs: { x } } = args;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  if (util_exports.sizeFromShape(out.shape) === 0) {
+    return out;
+  }
+  wasmFunc7(xId, outId);
+  return out;
+}
+var sigmoidConfig3 = {
+  kernelName: "Sigmoid",
+  backendName: "wasm",
+  setupFunc: setup39,
+  kernelFunc: sigmoid4
+};
+var sinConfig3 = createUnaryKernelConfig(Sin);
+var wasmFunc8;
+function setup40(backend2) {
+  wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function softmax5(args) {
+  const { backend: backend2, inputs: { logits }, attrs: { dim } } = args;
+  const xId = backend2.dataIdMap.get(logits.dataId).id;
+  const out = backend2.makeOutput(logits.shape, logits.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const channels = logits.shape[dim];
+  const batch = util_exports.sizeFromShape(logits.shape) / channels;
+  if (util_exports.sizeFromShape(out.shape) === 0) {
+    return out;
+  }
+  wasmFunc8(xId, outId, channels, batch);
+  return out;
+}
+var softmaxConfig3 = {
+  kernelName: Softmax,
+  backendName: "wasm",
+  setupFunc: setup40,
+  kernelFunc: softmax5
+};
+function spaceToBatchND4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  const prod6 = util_exports.sizeFromShape(blockShape);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const paddedX = padV2Config3.kernelFunc({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapeInputs = { x: paddedX };
+  const reshapeAttrs = { shape: reshapedPaddedShape };
+  const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });
+  const transposeInputs = { x: paddedXReshaped };
+  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };
+  const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });
+  const resultReshapeInputs = { x: paddedXT };
+  const resultReshapeAttrs = { shape: flattenShape };
+  const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });
+  backend2.disposeData(paddedX.dataId);
+  backend2.disposeData(paddedXReshaped.dataId);
+  backend2.disposeData(paddedXT.dataId);
+  return result;
+}
+var spaceToBatchNDConfig3 = {
+  kernelName: SpaceToBatchND,
+  backendName: "wasm",
+  kernelFunc: spaceToBatchND4
+};
+var wasmSparseFillEmptyRows;
+function setup41(backend2) {
+  wasmSparseFillEmptyRows = backend2.wasm.cwrap("SparseFillEmptyRows", "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sparseFillEmptyRows4(args) {
+  const { backend: backend2, inputs } = args;
+  const { indices, values, denseShape, defaultValue } = inputs;
+  const indicesCount = indices.shape[0];
+  const rank = indices.shape[1];
+  const denseRows = backend2.readSync(denseShape.dataId)[0];
+  const maxOutputIndicesShape = [indicesCount + denseRows, rank];
+  const indicesId = backend2.dataIdMap.get(indices.dataId).id;
+  const valuesId = backend2.dataIdMap.get(values.dataId).id;
+  const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;
+  const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype);
+  const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id;
+  const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);
+  const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id;
+  const emptyRowIndicator = backend2.makeOutput([denseRows], "bool");
+  const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id;
+  const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype);
+  const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id;
+  const exceptionValues = backend2.makeOutput([4], "int32");
+  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;
+  const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);
+  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);
+  let exceptionMessage;
+  switch (exceptionValuesArray[0]) {
+    case 1: {
+      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]);
+      break;
+    }
+    case 2: {
+      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    }
+    case 3:
+      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);
+      break;
+    default:
+      exceptionMessage = "";
+  }
+  backend2.disposeData(exceptionValues.dataId);
+  if (exceptionMessage) {
+    backend2.disposeData(outputIndices.dataId);
+    backend2.disposeData(outputValues.dataId);
+    backend2.disposeData(emptyRowIndicator.dataId);
+    backend2.disposeData(reverseIndexMap.dataId);
+    throw new Error(exceptionMessage);
+  }
+  let resizedIndices = outputIndices;
+  let resizedValues = outputValues;
+  if (outputRows !== maxOutputIndicesShape[0]) {
+    resizedIndices = slice4({
+      inputs: { x: outputIndices },
+      attrs: { begin: 0, size: [outputRows, rank] },
+      backend: backend2
+    });
+    resizedValues = slice4({
+      inputs: { x: outputValues },
+      attrs: { begin: 0, size: outputRows },
+      backend: backend2
+    });
+    backend2.disposeData(outputIndices.dataId);
+    backend2.disposeData(outputValues.dataId);
+  }
+  return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];
+}
+var sparseFillEmptyRowsConfig3 = {
+  kernelName: SparseFillEmptyRows,
+  backendName: "wasm",
+  setupFunc: setup41,
+  kernelFunc: sparseFillEmptyRows4
+};
+var wasmSparseReshape;
+function setup42(backend2) {
+  wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sparseReshape4(args) {
+  const { backend: backend2, inputs } = args;
+  const { inputIndices, inputShape, newShape } = inputs;
+  if (inputIndices.shape.length !== 2) {
+    throw new Error(`Input indices should be a matrix but received shape
+        ${inputIndices.shape}`);
+  }
+  if (inputShape.shape.length !== 1) {
+    throw new Error(`Input shape should be a vector but received shape
+        ${inputShape.shape}`);
+  }
+  if (newShape.shape.length !== 1) {
+    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
+  }
+  const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id;
+  const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id;
+  const newShapeId = backend2.dataIdMap.get(newShape.dataId).id;
+  const nnz = inputIndices.shape[0];
+  const outputRank = util_exports.sizeFromShape(newShape.shape);
+  const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype);
+  const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id;
+  const outputShape = backend2.makeOutput([outputRank], newShape.dtype);
+  const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id;
+  const exceptionValues = backend2.makeOutput([3], "int32");
+  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;
+  wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId);
+  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);
+  let exceptionMessage;
+  switch (exceptionValuesArray[0]) {
+    case 0: {
+      exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    }
+    case 1: {
+      exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    }
+    case 2:
+      exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
+      break;
+    case 3: {
+      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));
+      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues);
+      break;
+    }
+    case 4: {
+      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));
+      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues);
+      break;
+    }
+    default:
+      exceptionMessage = "";
+  }
+  backend2.disposeData(exceptionValues.dataId);
+  if (exceptionMessage) {
+    backend2.disposeData(newIndices.dataId);
+    backend2.disposeData(outputShape.dataId);
+    throw new Error(exceptionMessage);
+  }
+  return [newIndices, outputShape];
+}
+var sparseReshapeConfig3 = {
+  kernelName: SparseReshape,
+  backendName: "wasm",
+  setupFunc: setup42,
+  kernelFunc: sparseReshape4
+};
+var wasmSparseSegmentReduction;
+function setup43(backend2) {
+  wasmSparseSegmentReduction = backend2.wasm.cwrap("SparseSegmentReduction", null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sparseSegmentReduction(args, isMean) {
+  const { backend: backend2, inputs } = args;
+  const { data, indices, segmentIds } = inputs;
+  const numIndices = indices.shape[0];
+  const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0];
+  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;
+  const outputRows = lastSegmentIdPlusOne;
+  if (outputRows < 0) {
+    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+  }
+  const outputShape = data.shape.slice();
+  outputShape[0] = outputRows;
+  const dataId = backend2.dataIdMap.get(data.dataId).id;
+  const indicesId = backend2.dataIdMap.get(indices.dataId).id;
+  const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id;
+  const output = backend2.makeOutput(outputShape, data.dtype);
+  const outputId = backend2.dataIdMap.get(output.dataId).id;
+  const exceptionValues = backend2.makeOutput([4], "int32");
+  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;
+  wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0);
+  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);
+  let exceptionMessage;
+  switch (exceptionValuesArray[0]) {
+    case 0: {
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
+      break;
+    }
+    case 1: {
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
+      break;
+    }
+    case 2:
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    case 3:
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);
+      break;
+    default:
+      exceptionMessage = "";
+  }
+  backend2.disposeData(exceptionValues.dataId);
+  if (exceptionMessage) {
+    backend2.disposeData(output.dataId);
+    throw new Error(exceptionMessage);
+  }
+  return output;
+}
+function sparseSegmentMean4(args) {
+  return sparseSegmentReduction(args, true);
+}
+var sparseSegmentMeanConfig3 = {
+  kernelName: SparseSegmentMean,
+  backendName: "wasm",
+  setupFunc: setup43,
+  kernelFunc: sparseSegmentMean4
+};
+function sparseSegmentSum4(args) {
+  return sparseSegmentReduction(args, false);
+}
+var sparseSegmentSumConfig3 = {
+  kernelName: SparseSegmentSum,
+  backendName: "wasm",
+  setupFunc: setup43,
+  kernelFunc: sparseSegmentSum4
+};
+function splitV3(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const begin = new Array(x.shape.length).fill(0);
+  const size2 = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const xSliceSize = [...size2];
+    xSliceSize[$axis] = s2;
+    const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });
+    begin[$axis] += s2;
+    return xSlice;
+  });
+}
+var splitVConfig3 = {
+  kernelName: SplitV,
+  backendName: "wasm",
+  kernelFunc: splitV3
+};
+var sqrtConfig3 = createUnaryKernelConfig(Sqrt);
+var squareConfig3 = createUnaryKernelConfig(Square);
+var supportsFullBroadcast15 = true;
+var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15);
+var wasmStep;
+function setup44(backend2) {
+  wasmStep = backend2.wasm.cwrap(Step, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function step4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { alpha: alpha2 } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmStep(xId, alpha2, CppDType[x.dtype], outId);
+  return out;
+}
+var stepConfig3 = {
+  kernelName: Step,
+  backendName: "wasm",
+  setupFunc: setup44,
+  kernelFunc: step4
+};
+var wasmStridedSlice;
+function setup45(backend2) {
+  wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [
+    "number",
+    "array",
+    "number",
+    "array",
+    "array",
+    "array",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function stridedSlice4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides: strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size2 = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size: size2 } });
+    result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeData(sliced.dataId);
+  } else {
+    const out = backend2.makeOutput(finalShapeSparse, "float32");
+    const xId = backend2.dataIdMap.get(x.dataId).id;
+    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
+    const beginBytes = new Uint8Array(new Int32Array($begin).buffer);
+    const endBytes = new Uint8Array(new Int32Array($end).buffer);
+    const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);
+    const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer);
+    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer);
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);
+    result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeData(out.dataId);
+  }
+  return result;
+}
+var stridedSliceConfig3 = {
+  kernelName: StridedSlice,
+  backendName: "wasm",
+  setupFunc: setup45,
+  kernelFunc: stridedSlice4
+};
+var supportsFullBroadcast16 = true;
+var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast16);
+var wasmSum;
+function setup46(backend2) {
+  wasmSum = backend2.wasm.cwrap(Sum, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sum5(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  let reductionAxes = axes;
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
+    }
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, input2.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, input2.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var sumConfig3 = {
+  kernelName: Sum,
+  backendName: "wasm",
+  setupFunc: setup46,
+  kernelFunc: sum5
+};
+var tanConfig3 = createUnaryKernelConfig(Tan);
+var tanhConfig3 = createUnaryKernelConfig(Tanh);
+var wasmTile;
+function setup47(backend2) {
+  wasmTile = backend2.wasm.cwrap(Tile, null, [
+    "number",
+    "array",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function tile5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const { reps } = attrs;
+  const newShape = new Array(x.shape.length);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[i2] * reps[i2];
+  }
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
+  const out = backend2.makeOutput(newShape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);
+  return out;
+}
+var tileConfig3 = {
+  kernelName: Tile,
+  backendName: "wasm",
+  setupFunc: setup47,
+  kernelFunc: tile5
+};
+var wasmTopK;
+function setup48(backend2) {
+  wasmTopK = backend2.wasm.cwrap(TopK, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "bool",
+    "number",
+    "number"
+  ]);
+}
+var topk2 = ({ inputs, backend: backend2, attrs }) => {
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const outputShape = x.shape.slice();
+  outputShape[outputShape.length - 1] = k;
+  const outValues = backend2.makeOutput(outputShape, x.dtype);
+  const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;
+  const outIndices = backend2.makeOutput(outputShape, "int32");
+  const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;
+  wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);
+  return [outValues, outIndices];
+};
+var topKConfig3 = {
+  kernelName: TopK,
+  backendName: "wasm",
+  setupFunc: setup48,
+  kernelFunc: topk2
+};
+var wasmTransform;
+function setup49(backend2) {
+  wasmTransform = backend2.wasm.cwrap(Transform, null, [
+    "number",
+    "number",
+    "bool",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function transform4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [
+    batch,
+    outHeight,
+    outWidth,
+    numChannels
+  ];
+  const strides2 = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer);
+  const out = backend2.makeOutput(outShape, image2.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const imageData = backend2.dataIdMap.get(image2.dataId);
+  const imageId = imageData.id;
+  const transformsData = backend2.dataIdMap.get(transforms.dataId);
+  const transformsId = transformsData.id;
+  const interpolationModeId = interpolation === "nearest" ? 1 : 2;
+  let fillModeId;
+  switch (fillMode) {
+    case "constant":
+      fillModeId = 1;
+      break;
+    case "reflect":
+      fillModeId = 2;
+      break;
+    case "wrap":
+      fillModeId = 3;
+      break;
+    case "nearest":
+      fillModeId = 4;
+      break;
+    default:
+      fillModeId = 1;
+      break;
+  }
+  wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, strides2, image2.shape.length - 1, interpolationModeId, fillModeId, fillValue, outId);
+  return out;
+}
+var transformConfig3 = {
+  kernelName: Transform,
+  backendName: "wasm",
+  setupFunc: setup49,
+  kernelFunc: transform4
+};
+function unpack3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const numOutputs = value.shape[axis];
+  const rank = value.shape.length;
+  const outShape = new Array(rank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < rank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = value.shape[i2];
+    }
+  }
+  const outs = new Array(numOutputs);
+  const begin = new Array(rank).fill(0);
+  const size2 = value.shape.slice();
+  size2[axis] = 1;
+  for (let i2 = 0; i2 < outs.length; i2++) {
+    begin[axis] = i2;
+    outs[i2] = slice4({ inputs: { x: value }, attrs: { begin, size: size2 }, backend: backend2 });
+  }
+  return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));
+}
+var unpackConfig3 = {
+  kernelName: Unpack,
+  backendName: "wasm",
+  kernelFunc: unpack3
+};
+function zerosLike4(args) {
+  const { inputs: { x }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.fill(0);
+  return out;
+}
+var zerosLikeConfig3 = {
+  kernelName: ZerosLike,
+  backendName: "wasm",
+  kernelFunc: zerosLike4
+};
+var kernelConfigs3 = [
+  _fusedMatMulConfig3,
+  absConfig3,
+  addConfig3,
+  addNConfig3,
+  allConfig3,
+  anyConfig3,
+  argMaxConfig3,
+  avgPoolConfig3,
+  batchMatMulConfig3,
+  batchToSpaceNDConfig3,
+  castConfig3,
+  ceilConfig3,
+  clipByValueConfig3,
+  concatConfig3,
+  conv2DConfig3,
+  conv2DBackpropInputConfig3,
+  cosConfig3,
+  coshConfig3,
+  cropAndResizeConfig3,
+  cumprodConfig3,
+  cumsumConfig3,
+  depthToSpaceConfig3,
+  depthwiseConv2dNativeConfig3,
+  eluConfig3,
+  equalConfig3,
+  expConfig3,
+  expandDimsConfig3,
+  fillConfig3,
+  flipLeftRightConfig3,
+  floorConfig3,
+  floorDivConfig3,
+  fusedBatchNormConfig,
+  fusedConv2DConfig3,
+  fusedDepthwiseConv2DConfig3,
+  gatherNdConfig3,
+  gatherV2Config3,
+  greaterConfig3,
+  greaterEqualConfig3,
+  identityConfig3,
+  leakyReluConfig3,
+  lessConfig3,
+  lessEqualConfig3,
+  logConfig3,
+  logicalAndConfig3,
+  maxConfig3,
+  maximumConfig3,
+  maxPoolConfig3,
+  meanConfig3,
+  minConfig3,
+  minimumConfig3,
+  mirrorPadConfig3,
+  multiplyConfig3,
+  negConfig3,
+  nonMaxSuppressionV3Config3,
+  nonMaxSuppressionV4Config3,
+  nonMaxSuppressionV5Config3,
+  notEqualConfig3,
+  oneHotConfig3,
+  onesLikeConfig3,
+  packConfig3,
+  padV2Config3,
+  powConfig3,
+  preluConfig3,
+  prodConfig3,
+  rangeConfig3,
+  realDivConfig3,
+  reluConfig3,
+  relu6Config3,
+  reshapeConfig3,
+  resizeBilinearConfig3,
+  reverseConfig3,
+  rotateWithOffsetConfig3,
+  roundConfig3,
+  rsqrtConfig3,
+  scatterNdConfig3,
+  selectConfig3,
+  sigmoidConfig3,
+  sinConfig3,
+  sliceConfig3,
+  softmaxConfig3,
+  spaceToBatchNDConfig3,
+  sparseFillEmptyRowsConfig3,
+  sparseReshapeConfig3,
+  sparseSegmentMeanConfig3,
+  sparseSegmentSumConfig3,
+  splitVConfig3,
+  sqrtConfig3,
+  squareConfig3,
+  squaredDifferenceConfig3,
+  stepConfig3,
+  stridedSliceConfig3,
+  subConfig3,
+  sumConfig3,
+  tanConfig3,
+  tanhConfig3,
+  tileConfig3,
+  topKConfig3,
+  transformConfig3,
+  transposeConfig3,
+  unpackConfig3,
+  zerosLikeConfig3
+];
+for (const kernelConfig of kernelConfigs3) {
+  registerKernel(kernelConfig);
+}
+var ENV6 = env();
+ENV6.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([
+  0,
+  97,
+  115,
+  109,
+  1,
+  0,
+  0,
+  0,
+  1,
+  4,
+  1,
+  96,
+  0,
+  0,
+  3,
+  2,
+  1,
+  0,
+  10,
+  9,
+  1,
+  7,
+  0,
+  65,
+  0,
+  253,
+  15,
+  26,
+  11
+])));
+ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
+  if (ENV6.get("IS_NODE")) {
+    return false;
+  }
+  try {
+    new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));
+    return WebAssembly.validate(new Uint8Array([
+      0,
+      97,
+      115,
+      109,
+      1,
+      0,
+      0,
+      0,
+      1,
+      4,
+      1,
+      96,
+      0,
+      0,
+      3,
+      2,
+      1,
+      0,
+      5,
+      4,
+      1,
+      3,
+      1,
+      1,
+      10,
+      11,
+      1,
+      9,
+      0,
+      65,
+      0,
+      254,
+      16,
+      2,
+      0,
+      26,
+      11
+    ]));
+  } catch (e2) {
+    return false;
+  }
+});
+var import_tfjs_backend_wasm_threaded_simd = __toESM(require_tfjs_backend_wasm_threaded_simd());
+var wasmWorkerContents = '"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"\n");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});';
+var import_tfjs_backend_wasm = __toESM(require_tfjs_backend_wasm());
+var BackendWasm = class extends KernelBackend {
+  constructor(wasm) {
+    super();
+    this.wasm = wasm;
+    this.dataIdNextNumber = 1;
+    this.wasm.tfjs.initWithThreadsCount(threadsCount);
+    actualThreadsCount = this.wasm.tfjs.getThreadsCount();
+    this.dataIdMap = new DataStorage(this, engine());
+  }
+  write(values, shape, dtype) {
+    const dataId = { id: this.dataIdNextNumber++ };
+    this.move(dataId, values, shape, dtype, 1);
+    return dataId;
+  }
+  numDataIds() {
+    return this.dataIdMap.numDataIds();
+  }
+  async time(f) {
+    const start = util_exports.now();
+    f();
+    const kernelMs = util_exports.now() - start;
+    return { kernelMs };
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    const id = this.dataIdNextNumber++;
+    if (dtype === "string") {
+      const stringBytes = values;
+      this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount });
+      return;
+    }
+    const size2 = util_exports.sizeFromShape(shape);
+    const numBytes = size2 * util_exports.bytesPerElement(dtype);
+    const memoryOffset = this.wasm._malloc(numBytes);
+    this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount });
+    this.wasm.tfjs.registerTensor(id, size2, memoryOffset);
+    if (values != null) {
+      this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);
+    }
+  }
+  async read(dataId) {
+    return this.readSync(dataId);
+  }
+  readSync(dataId, start, end) {
+    const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId);
+    if (dtype === "string") {
+      if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) {
+        return stringBytes;
+      }
+      return stringBytes.slice(start, end);
+    }
+    start = start || 0;
+    end = end || util_exports.sizeFromShape(shape);
+    const bytesPerElement2 = util_exports.bytesPerElement(dtype);
+    const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2);
+    return typedArrayFromBuffer(bytes.buffer, dtype);
+  }
+  disposeData(dataId, force = false) {
+    if (this.dataIdMap.has(dataId)) {
+      const data = this.dataIdMap.get(dataId);
+      data.refCount--;
+      if (!force && data.refCount > 0) {
+        return false;
+      }
+      this.wasm._free(data.memoryOffset);
+      this.wasm.tfjs.disposeData(data.id);
+      this.dataIdMap.delete(dataId);
+    }
+    return true;
+  }
+  refCount(dataId) {
+    if (this.dataIdMap.has(dataId)) {
+      const tensorData = this.dataIdMap.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const data = this.dataIdMap.get(dataId);
+    if (data != null) {
+      data.refCount++;
+    }
+  }
+  floatPrecision() {
+    return 32;
+  }
+  getMemoryOffset(dataId) {
+    return this.dataIdMap.get(dataId).memoryOffset;
+  }
+  dispose() {
+    this.wasm.tfjs.dispose();
+    if ("PThread" in this.wasm) {
+      this.wasm.PThread.terminateAllThreads();
+    }
+    this.wasm = null;
+  }
+  memory() {
+    return { unreliable: false };
+  }
+  makeOutput(shape, dtype, memoryOffset) {
+    let dataId;
+    if (memoryOffset == null) {
+      dataId = this.write(null, shape, dtype);
+    } else {
+      const id = this.dataIdNextNumber++;
+      dataId = { id };
+      this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 });
+      const size2 = util_exports.sizeFromShape(shape);
+      this.wasm.tfjs.registerTensor(id, size2, memoryOffset);
+    }
+    return { dataId, shape, dtype };
+  }
+  typedArrayFromHeap({ shape, dtype, dataId }) {
+    const buffer2 = this.wasm.HEAPU8.buffer;
+    const { memoryOffset } = this.dataIdMap.get(dataId);
+    const size2 = util_exports.sizeFromShape(shape);
+    switch (dtype) {
+      case "float32":
+        return new Float32Array(buffer2, memoryOffset, size2);
+      case "int32":
+        return new Int32Array(buffer2, memoryOffset, size2);
+      case "bool":
+        return new Uint8Array(buffer2, memoryOffset, size2);
+      default:
+        throw new Error(`Unknown dtype ${dtype}`);
+    }
+  }
+};
+function createInstantiateWasmFunc(path) {
+  return (imports, callback) => {
+    util_exports.fetch(path, { credentials: "same-origin" }).then((response) => {
+      if (!response["ok"]) {
+        imports.env.a(`failed to load wasm binary file at '${path}'`);
+      }
+      response.arrayBuffer().then((binary) => {
+        WebAssembly.instantiate(binary, imports).then((output) => {
+          callback(output.instance, output.module);
+        });
+      });
+    });
+    return {};
+  };
+}
+function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {
+  if (wasmPath != null) {
+    return wasmPath;
+  }
+  let path = "tfjs-backend-wasm.wasm";
+  if (simdSupported && threadsSupported) {
+    path = "tfjs-backend-wasm-threaded-simd.wasm";
+  } else if (simdSupported) {
+    path = "tfjs-backend-wasm-simd.wasm";
+  }
+  if (wasmFileMap != null) {
+    if (wasmFileMap[path] != null) {
+      return wasmFileMap[path];
+    }
+  }
+  return wasmModuleFolder + path;
+}
+async function init() {
+  const [simdSupported, threadsSupported] = await Promise.all([
+    env().getAsync("WASM_HAS_SIMD_SUPPORT"),
+    env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")
+  ]);
+  return new Promise((resolve, reject) => {
+    const factoryConfig = {};
+    factoryConfig.locateFile = (path, prefix) => {
+      if (path.endsWith(".worker.js")) {
+        const response = wasmWorkerContents.replace(/\n/g, "\\n");
+        const blob = new Blob([response], { type: "application/javascript" });
+        return URL.createObjectURL(blob);
+      }
+      if (path.endsWith(".wasm")) {
+        return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);
+      }
+      return prefix + path;
+    };
+    if (customFetch) {
+      factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : ""));
+    }
+    let initialized = false;
+    factoryConfig.onAbort = () => {
+      if (initialized) {
+        return;
+      }
+      if (initAborted) {
+        return;
+      }
+      initAborted = true;
+      const rejectMsg = "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";
+      reject({ message: rejectMsg });
+    };
+    let wasm;
+    if (threadsSupported && simdSupported && wasmPath == null) {
+      factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + import_tfjs_backend_wasm_threaded_simd.default.toString()], { type: "text/javascript" });
+      wasm = (0, import_tfjs_backend_wasm_threaded_simd.default)(factoryConfig);
+    } else {
+      wasm = (0, import_tfjs_backend_wasm.default)(factoryConfig);
+    }
+    wasm.then((module) => {
+      initialized = true;
+      initAborted = false;
+      const voidReturnType = null;
+      module.tfjs = {
+        init: module.cwrap("init", null, []),
+        initWithThreadsCount: module.cwrap("init_with_threads_count", null, ["number"]),
+        getThreadsCount: module.cwrap("get_threads_count", "number", []),
+        registerTensor: module.cwrap("register_tensor", null, [
+          "number",
+          "number",
+          "number"
+        ]),
+        disposeData: module.cwrap("dispose_data", voidReturnType, ["number"]),
+        dispose: module.cwrap("dispose", voidReturnType, [])
+      };
+      resolve({ wasm: module });
+    });
+  });
+}
+function typedArrayFromBuffer(buffer2, dtype) {
+  switch (dtype) {
+    case "float32":
+      return new Float32Array(buffer2);
+    case "int32":
+      return new Int32Array(buffer2);
+    case "bool":
+      return new Uint8Array(buffer2);
+    default:
+      throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+var wasmBinaryNames = [
+  "tfjs-backend-wasm.wasm",
+  "tfjs-backend-wasm-simd.wasm",
+  "tfjs-backend-wasm-threaded-simd.wasm"
+];
+var wasmPath = null;
+var wasmPathPrefix = null;
+var wasmFileMap = {};
+var initAborted = false;
+var customFetch = false;
+function setWasmPath(path, usePlatformFetch = false) {
+  deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.");
+  if (initAborted) {
+    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
+  }
+  wasmPath = path;
+  customFetch = usePlatformFetch;
+}
+function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {
+  if (initAborted) {
+    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
+  }
+  if (typeof prefixOrFileMap === "string") {
+    wasmPathPrefix = prefixOrFileMap;
+  } else {
+    wasmFileMap = prefixOrFileMap;
+    const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);
+    if (missingPaths.length > 0) {
+      throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
+    }
+  }
+  customFetch = usePlatformFetch;
+}
+var threadsCount = -1;
+var actualThreadsCount = -1;
+function setThreadsCount(numThreads) {
+  threadsCount = numThreads;
+}
+function getThreadsCount() {
+  if (actualThreadsCount === -1) {
+    throw new Error(`WASM backend not initialized.`);
+  }
+  return actualThreadsCount;
+}
+var version8 = "3.18.0";
+var WASM_PRIORITY = 2;
+registerBackend("wasm", async () => {
+  const { wasm } = await init();
+  return new BackendWasm(wasm);
+}, WASM_PRIORITY);
+var ENV7 = env();
+ENV7.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
+ENV7.registerFlag("WEBGPU_CPU_FORWARD", () => true);
+ENV7.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
+ENV7.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
+ENV7.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
+ENV7.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
+ENV7.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
+ENV7.registerFlag("WEBGPU_USE_IMPORT", () => false);
+var BinaryOpType;
+(function(BinaryOpType2) {
+  BinaryOpType2[BinaryOpType2["MUL"] = 0] = "MUL";
+  BinaryOpType2[BinaryOpType2["ADD"] = 1] = "ADD";
+  BinaryOpType2[BinaryOpType2["SUB"] = 2] = "SUB";
+  BinaryOpType2[BinaryOpType2["DIV"] = 3] = "DIV";
+  BinaryOpType2[BinaryOpType2["EQUAL"] = 4] = "EQUAL";
+  BinaryOpType2[BinaryOpType2["GREATER"] = 5] = "GREATER";
+  BinaryOpType2[BinaryOpType2["GREATER_EQUAL"] = 6] = "GREATER_EQUAL";
+  BinaryOpType2[BinaryOpType2["LESS"] = 7] = "LESS";
+  BinaryOpType2[BinaryOpType2["LESS_EQUAL"] = 8] = "LESS_EQUAL";
+  BinaryOpType2[BinaryOpType2["LOGICAL_AND"] = 9] = "LOGICAL_AND";
+  BinaryOpType2[BinaryOpType2["NOT_EQUAL"] = 10] = "NOT_EQUAL";
+  BinaryOpType2[BinaryOpType2["SQUARED_DIFFERENCE"] = 11] = "SQUARED_DIFFERENCE";
+  BinaryOpType2[BinaryOpType2["INT_DIV"] = 12] = "INT_DIV";
+  BinaryOpType2[BinaryOpType2["POW"] = 13] = "POW";
+  BinaryOpType2[BinaryOpType2["PRELU"] = 14] = "PRELU";
+  BinaryOpType2[BinaryOpType2["MAX"] = 15] = "MAX";
+  BinaryOpType2[BinaryOpType2["MIN"] = 16] = "MIN";
+  BinaryOpType2[BinaryOpType2["COMPLEX_MULTIPLY_REAL"] = 17] = "COMPLEX_MULTIPLY_REAL";
+  BinaryOpType2[BinaryOpType2["COMPLEX_MULTIPLY_IMAG"] = 18] = "COMPLEX_MULTIPLY_IMAG";
+})(BinaryOpType || (BinaryOpType = {}));
+var ADD2 = "return a + b;";
+var COMPLEX_MULTIPLY_REAL = "return areal * breal - aimag * bimag;";
+var COMPLEX_MULTIPLY_IMAG = "return areal * bimag + aimag * breal;";
+var DIV2 = "return a / b;";
+var MUL2 = "return a * b;";
+var SQUARED_DIFFERENCE2 = "return (a - b) * (a - b);";
+var SUB2 = "return a - b;";
+var EQUAL2 = "return f32(a == b);";
+var EQUAL_VEC4 = "return vec4<f32>(a == b);";
+var GREATER2 = "return f32(a > b);";
+var GREATER_VEC4 = "return vec4<f32>(a > b);";
+var GREATER_EQUAL2 = "return f32(a >= b);";
+var GREATER_EQUAL_VEC4 = "return vec4<f32>(a >= b);";
+var LESS2 = "return f32(a < b);";
+var LESS_VEC4 = "return vec4<f32>(a < b);";
+var LESS_EQUAL2 = "return f32(a <= b);";
+var LESS_EQUAL_VEC4 = "return vec4<f32>(a <= b);";
+var LOGICAL_AND2 = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
+var LOGICAL_AND_VEC4 = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`;
+var CHECK_NAN_SNIPPET4 = `
+  if (isnan(a)) { return a; }
+  if (isnan(b)) { return b; }
+  `;
+var CHECK_NAN_SNIPPET_VEC4 = `
+  if (isNaN.r) {
+    resultTemp.r = uniforms.NAN;
+  }
+  if (isNaN.g) {
+    resultTemp.g = uniforms.NAN;
+  }
+  if (isNaN.b) {
+    resultTemp.b = uniforms.NAN;
+  }
+  if (isNaN.a) {
+    resultTemp.a = uniforms.NAN;
+  }
+  `;
+var INT_DIV2 = `
+  let s = sign(a) * sign(b);
+  let ia = i32(round(a));
+  let ib = i32(round(b));
+  return f32(idiv(ia, ib, s));
+  `;
+var INT_DIV_VEC4 = `
+  let ia = vec4<i32>(round(a));
+  let ib = vec4<i32>(round(b));
+  let cond = ib != vec4<i32>(0);
+  var resultTemp = vec4<i32>(0);
+  let s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    resultTemp[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    resultTemp[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    resultTemp[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    resultTemp[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4<f32>(resultTemp);
+  `;
+var NOT_EQUAL2 = "return f32(a != b);";
+var NOT_EQUAL_VEC4 = "return vec4<f32>(a != b);";
+var POW2 = `
+  if(a < 0.0 && floor(b) < b) {
+    return uniforms.NAN;
+  }
+  if (b == 0.0) {
+    return 1.0;
+  }
+  if (round(abs(b) % 2.0) != 1.0) {
+    return pow(abs(a), b);
+  }
+  return sign(a) * pow(abs(a), b);
+  `;
+var POW_VEC4 = `
+  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
+  let isModRound1 = vec4<f32>(isModRound1Bool);
+  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
+  var resultTemp = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  let isExpZero = b == vec4<f32>(0.0);
+  if (isExpZero.r) {
+    resultTemp.r = 1.0;
+  }
+  if (isExpZero.g) {
+    resultTemp.g = 1.0;
+  }
+  if (isExpZero.b) {
+    resultTemp.b = 1.0;
+  }
+  if (isExpZero.a) {
+    resultTemp.a = 1.0;
+  }
+  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
+  ${CHECK_NAN_SNIPPET_VEC4}
+  return resultTemp;
+  `;
+var PRELU2 = `if (a < 0.0) { return b * a; }  return a;`;
+var PRELU_VEC4 = `
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+  `;
+function getMinMaxString(op2, useVec4) {
+  const checkNanSnippet = useVec4 ? CHECK_NAN_SNIPPET_VEC4 : CHECK_NAN_SNIPPET4;
+  return useVec4 ? `
+    var resultTemp = vec4<f32>(${op2}(a, b));
+    let isNaN = isnanVec4(a) | isnanVec4(b);
+    ` + checkNanSnippet + `
+    return resultTemp;
+  ` : checkNanSnippet + `
+    return ${op2}(a, b);
+  `;
+}
+function getBinaryOpString(type, useVec4) {
+  switch (type) {
+    case BinaryOpType.MUL:
+      return MUL2;
+    case BinaryOpType.ADD:
+      return ADD2;
+    case BinaryOpType.SUB:
+      return SUB2;
+    case BinaryOpType.DIV:
+      return DIV2;
+    case BinaryOpType.EQUAL:
+      return useVec4 ? EQUAL_VEC4 : EQUAL2;
+    case BinaryOpType.GREATER:
+      return useVec4 ? GREATER_VEC4 : GREATER2;
+    case BinaryOpType.GREATER_EQUAL:
+      return useVec4 ? GREATER_EQUAL_VEC4 : GREATER_EQUAL2;
+    case BinaryOpType.LESS:
+      return useVec4 ? LESS_VEC4 : LESS2;
+    case BinaryOpType.LESS_EQUAL:
+      return useVec4 ? LESS_EQUAL_VEC4 : LESS_EQUAL2;
+    case BinaryOpType.LOGICAL_AND:
+      return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND2;
+    case BinaryOpType.NOT_EQUAL:
+      return useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL2;
+    case BinaryOpType.SQUARED_DIFFERENCE:
+      return SQUARED_DIFFERENCE2;
+    case BinaryOpType.INT_DIV:
+      return useVec4 ? INT_DIV_VEC4 : INT_DIV2;
+    case BinaryOpType.PRELU:
+      return useVec4 ? PRELU_VEC4 : PRELU2;
+    case BinaryOpType.MAX:
+      return getMinMaxString("max", useVec4);
+    case BinaryOpType.MIN:
+      return getMinMaxString("min", useVec4);
+    case BinaryOpType.POW:
+      return useVec4 ? POW_VEC4 : POW2;
+    case BinaryOpType.COMPLEX_MULTIPLY_REAL:
+      return COMPLEX_MULTIPLY_REAL;
+    case BinaryOpType.COMPLEX_MULTIPLY_IMAG:
+      return COMPLEX_MULTIPLY_IMAG;
+    default:
+      throw new Error(`BinaryType ${type} is not implemented!`);
+  }
+}
+var UnaryOpType;
+(function(UnaryOpType2) {
+  UnaryOpType2[UnaryOpType2["ABS"] = 0] = "ABS";
+  UnaryOpType2[UnaryOpType2["CEIL"] = 1] = "CEIL";
+  UnaryOpType2[UnaryOpType2["COS"] = 2] = "COS";
+  UnaryOpType2[UnaryOpType2["COSH"] = 3] = "COSH";
+  UnaryOpType2[UnaryOpType2["ELU"] = 4] = "ELU";
+  UnaryOpType2[UnaryOpType2["EXP"] = 5] = "EXP";
+  UnaryOpType2[UnaryOpType2["EXPM1"] = 6] = "EXPM1";
+  UnaryOpType2[UnaryOpType2["FLOOR"] = 7] = "FLOOR";
+  UnaryOpType2[UnaryOpType2["LINEAR"] = 8] = "LINEAR";
+  UnaryOpType2[UnaryOpType2["LOG"] = 9] = "LOG";
+  UnaryOpType2[UnaryOpType2["LOGICAL_NOT"] = 10] = "LOGICAL_NOT";
+  UnaryOpType2[UnaryOpType2["NEG"] = 11] = "NEG";
+  UnaryOpType2[UnaryOpType2["RELU"] = 12] = "RELU";
+  UnaryOpType2[UnaryOpType2["RELU6"] = 13] = "RELU6";
+  UnaryOpType2[UnaryOpType2["LEAKYRELU"] = 14] = "LEAKYRELU";
+  UnaryOpType2[UnaryOpType2["RSQRT"] = 15] = "RSQRT";
+  UnaryOpType2[UnaryOpType2["SIN"] = 16] = "SIN";
+  UnaryOpType2[UnaryOpType2["SINH"] = 17] = "SINH";
+  UnaryOpType2[UnaryOpType2["SIGMOID"] = 18] = "SIGMOID";
+  UnaryOpType2[UnaryOpType2["SQRT"] = 19] = "SQRT";
+  UnaryOpType2[UnaryOpType2["SQUARE"] = 20] = "SQUARE";
+  UnaryOpType2[UnaryOpType2["TANH"] = 21] = "TANH";
+  UnaryOpType2[UnaryOpType2["TO_INT"] = 22] = "TO_INT";
+})(UnaryOpType || (UnaryOpType = {}));
+var ABS3 = `return abs(a);`;
+var CEIL2 = `return ceil(a);`;
+var COS2 = `return cos(a);`;
+var COSH2 = `
+  let e2x = exp(-a);
+  return (e2x + 1.0 / e2x) / 2.0;
+`;
+var EXPM12 = `return exp(a) - 1.0;`;
+var ELU5 = `if (a >= 0.0) { return a; }  return (exp(a) - 1.0);`;
+var ELU_VEC4 = `
+  var resFloat = exp(a) - vec4<f32>(1.0);
+  if (a.r >= 0.0) {
+    resFloat.r = a.r;
+  }
+  if (a.g >= 0.0) {
+    resFloat.g = a.g;
+  }
+  if (a.b >= 0.0) {
+    resFloat.b = a.b;
+  }
+  if (a.a >= 0.0) {
+    resFloat.a = a.a;
+  }
+  return resFloat;
+`;
+var EXP2 = `return exp(a);`;
+var FLOOR2 = `return floor(a);`;
+var LINEAR3 = `return a;`;
+var LOG2 = `if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`;
+var LOGICAL_NOT2 = `return f32(!(a >= 1.0));`;
+var NEG2 = `return -a;`;
+var LEAKYRELU2 = `if (a < 0.0) { return uniforms.alpha * a; } return a;`;
+var LEAKYRELU_VEC4 = `
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+`;
+var RELU4 = `return select(a, 0.0, a < 0.0);`;
+var RELU64 = "return clamp(a, 0.0, 6.0);";
+var RELU6_VEC4 = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
+var RELU_VEC4 = `
+  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
+`;
+var RSQRT2 = `return 1.0/sqrt(a);`;
+var SIGMOID4 = `return 1.0 / (1.0 + exp(-1.0 * a));`;
+var SIN2 = `return sin(a);`;
+var SINH2 = `
+  let e2x = exp(a);
+  return (e2x - 1.0 / e2x) / 2.0;
+`;
+var SQRT2 = `return sqrt(a);`;
+var SQUARE2 = `return a * a;`;
+var TANH2 = `
+  let e2x = exp(-2.0 * abs(a));
+  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
+`;
+var TO_INT2 = `return f32(i32((a)));`;
+function getUnaryOpString(type, useVec4) {
+  switch (type) {
+    case UnaryOpType.ABS:
+      return ABS3;
+    case UnaryOpType.COS:
+      return COS2;
+    case UnaryOpType.COSH:
+      return COSH2;
+    case UnaryOpType.CEIL:
+      return CEIL2;
+    case UnaryOpType.ELU:
+      return useVec4 ? ELU_VEC4 : ELU5;
+    case UnaryOpType.EXP:
+      return EXP2;
+    case UnaryOpType.EXPM1:
+      return EXPM12;
+    case UnaryOpType.FLOOR:
+      return FLOOR2;
+    case UnaryOpType.LINEAR:
+      return LINEAR3;
+    case UnaryOpType.LOG:
+      return LOG2;
+    case UnaryOpType.LOGICAL_NOT:
+      return LOGICAL_NOT2;
+    case UnaryOpType.NEG:
+      return NEG2;
+    case UnaryOpType.LEAKYRELU:
+      return useVec4 ? LEAKYRELU_VEC4 : LEAKYRELU2;
+    case UnaryOpType.RELU:
+      return useVec4 ? RELU_VEC4 : RELU4;
+    case UnaryOpType.RELU6:
+      return useVec4 ? RELU6_VEC4 : RELU64;
+    case UnaryOpType.RSQRT:
+      return RSQRT2;
+    case UnaryOpType.SIGMOID:
+      return SIGMOID4;
+    case UnaryOpType.SIN:
+      return SIN2;
+    case UnaryOpType.SINH:
+      return SINH2;
+    case UnaryOpType.SQRT:
+      return SQRT2;
+    case UnaryOpType.SQUARE:
+      return SQUARE2;
+    case UnaryOpType.TANH:
+      return TANH2;
+    case UnaryOpType.TO_INT:
+      return TO_INT2;
+    default:
+      throw new Error(`BinaryType ${type} is not implemented!`);
+  }
+}
+function mapActivationToShaderProgram2(activation2, packed = false) {
+  if (activation2 === null) {
+    return null;
+  } else if (activation2 === "linear") {
+    return getUnaryOpString(UnaryOpType.LINEAR);
+  } else if (activation2 === "relu") {
+    return getUnaryOpString(UnaryOpType.RELU, packed);
+  } else if (activation2 === "elu") {
+    return getUnaryOpString(UnaryOpType.ELU, packed);
+  } else if (activation2 === "relu6") {
+    return getUnaryOpString(UnaryOpType.RELU6, packed);
+  } else if (activation2 === "prelu") {
+    return getBinaryOpString(BinaryOpType.PRELU, packed);
+  } else if (activation2 === "sigmoid") {
+    return getUnaryOpString(UnaryOpType.SIGMOID, packed);
+  } else if (activation2 === "leakyrelu") {
+    return getUnaryOpString(UnaryOpType.LEAKYRELU, packed);
+  }
+  throw new Error(`Activation ${activation2} has not been implemented for the WebGPU backend.`);
+}
+function symbolicallyComputeStrides2(indicesArr, variableName) {
+  if (Math.max(...indicesArr) > 3) {
+    throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
+  }
+  const numCoords = indicesArr.length;
+  const shape = indicesArr.map((d) => `${variableName}[${d}]`);
+  const strides2 = new Array(numCoords - 1);
+  strides2[numCoords - 2] = shape[numCoords - 1];
+  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
+    strides2[i2] = `(${strides2[i2 + 1]} * ${shape[i2 + 1]})`;
+  }
+  return strides2;
+}
+function getCoordsDataType2(rank) {
+  if (rank <= 1) {
+    return "i32";
+  } else if (rank === 2) {
+    return `vec2<i32>`;
+  } else if (rank === 3) {
+    return `vec3<i32>`;
+  } else if (rank === 4) {
+    return `vec4<i32>`;
+  } else if (rank === 5) {
+    return `vec5`;
+  } else if (rank === 6) {
+    return `vec6`;
+  } else {
+    throw Error(`GPU for rank ${rank} is not yet supported`);
+  }
+}
+function getCoordsXYZ(index2) {
+  if (index2 === 0) {
+    return "x";
+  } else if (index2 === 1) {
+    return "y";
+  } else if (index2 === 2) {
+    return "z";
+  } else if (index2 === 3) {
+    return "w";
+  } else if (index2 === 4) {
+    return "u";
+  } else if (index2 === 5) {
+    return "v";
+  } else {
+    throw Error(`Index ${index2} is not yet supported`);
+  }
+}
+function mapToWgslTypes(type, isVec4) {
+  if (type === "float32") {
+    return isVec4 ? "vec4<f32>" : "f32";
+  } else if (type === "int32") {
+    return isVec4 ? "vec4<i32>" : "i32";
+  } else if (type === "bool") {
+    return isVec4 ? "vec4<i32>" : "i32";
+  }
+  return type;
+}
+function getWorkGroupSizeString() {
+  return `
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+`;
+}
+function getMainHeaderString() {
+  return `
+  ${getWorkGroupSizeString()}
+  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
+          @builtin(global_invocation_id) GlobalId : vec3<u32>,
+          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
+    localId = LocalId;
+    globalId = GlobalId;
+    numWorkgroups = NumWorkgroups;
+`;
+}
+function getMainHeaderAndGlobalIndexString() {
+  return `
+    ${getMainHeaderString()}
+      let index = getGlobalIndex();
+`;
+}
+function makeShader2(inputInfo, outputData, program, isFromPixel = false) {
+  const prefixSnippets = [];
+  prefixSnippets.push(`
+    let workGroupSizeX = ${program.workGroupSize[0]}u;
+    let workGroupSizeY = ${program.workGroupSize[1]}u;
+    let workGroupSizeZ = ${program.workGroupSize[2]}u;
+
+    var<private> localId: vec3<u32>;
+    var<private> globalId: vec3<u32>;
+    var<private> numWorkgroups: vec3<u32>;
+
+    // Only used when the y/z dimension of workgroup size is 1.
+    fn getGlobalIndex() -> i32 {
+      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
+        return i32(globalId.x);
+      }
+
+      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+          localId.y * workGroupSizeX + localId.x;
+      let workGroupID = (globalId - localId)/vec3<u32>(
+          workGroupSizeX, workGroupSizeY, workGroupSizeZ);
+
+      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
+        workGroupID.y * numWorkgroups.x + workGroupID.x) *
+        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
+        localInvocationIndex);
+    }
+  `);
+  if (isFromPixel === true) {
+    prefixSnippets.push(`
+      struct Uniform {
+        size            : i32,
+        numChannels     : i32,
+        outShapeStrides : vec2<i32>,
+        dispatchSize    : vec3<u32>,
+      };
+
+      @group(0) @binding(0) var<storage, write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;
+      @group(0) @binding(2) var<uniform> uniforms: Uniform;
+    `);
+    return [
+      commonSnippet,
+      prefixSnippets.join("\n"),
+      getCoordsFromIndexSnippet(outputData.shape),
+      program.getUserCode()
+    ].join("\n");
+  }
+  let preMemberIsStruct = false;
+  let currentMemberIsStruct = false;
+  let uniformDeclaration = "struct Uniforms { NAN : f32, ";
+  program.variableNames.forEach((x, i2) => {
+    const perDataType = getCoordsDataType2(inputInfo[i2].shape.length);
+    if (perDataType === "vec5" || perDataType === "vec6") {
+      currentMemberIsStruct = true;
+    }
+    if (preMemberIsStruct || currentMemberIsStruct) {
+      uniformDeclaration += `@align(16) `;
+    }
+    preMemberIsStruct = currentMemberIsStruct;
+    uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;
+  });
+  const outputDataType = getCoordsDataType2(outputData.shape.length);
+  currentMemberIsStruct = outputDataType === "vec5" || outputDataType === "vec6";
+  if (preMemberIsStruct || currentMemberIsStruct) {
+    uniformDeclaration += `@align(16) `;
+  }
+  preMemberIsStruct = currentMemberIsStruct;
+  uniformDeclaration += `outShape : ${outputDataType}, `;
+  const stridesLength = outputData.shape.length - 1;
+  const stridesDataType = getCoordsDataType2(stridesLength);
+  currentMemberIsStruct = stridesDataType === "vec5" || stridesDataType === "vec6";
+  if (preMemberIsStruct || currentMemberIsStruct) {
+    uniformDeclaration += `@align(16) `;
+  }
+  preMemberIsStruct = currentMemberIsStruct;
+  uniformDeclaration += `
+       outShapeStrides: ${stridesDataType}, `;
+  if (program.size) {
+    if (preMemberIsStruct) {
+      uniformDeclaration += `@align(16) `;
+    }
+    preMemberIsStruct = false;
+    uniformDeclaration += "size : i32, ";
+  }
+  if (program.uniforms) {
+    if (preMemberIsStruct) {
+      uniformDeclaration += `@align(16) `;
+    }
+    uniformDeclaration += program.uniforms;
+  }
+  uniformDeclaration += "};";
+  prefixSnippets.push(uniformDeclaration);
+  if (program.atomic) {
+    prefixSnippets.push(`
+    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
+  `);
+  } else {
+    prefixSnippets.push(`
+    @group(0) @binding(0) var<storage, write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;
+  `);
+  }
+  program.variableNames.forEach((x, i2) => {
+    prefixSnippets.push(`
+    @group(0) @binding(${1 + i2}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i2] : mapToWgslTypes(inputInfo[i2].dtype, program.isVec4)}>;
+      `);
+  });
+  if (uniformDeclaration !== "") {
+    prefixSnippets.push(`
+    @group(0) @binding(${1 + program.variableNames.length}) var<uniform> uniforms: Uniforms;
+    `);
+  }
+  const [coordsSnippet, dispatchLayoutRank] = getOutputCoordsSnippet(outputData.shape, program.dispatchLayout);
+  const sources = [
+    commonSnippet,
+    prefixSnippets.join("\n"),
+    getCoordsFromIndexSnippet(outputData.shape),
+    coordsSnippet,
+    getOutputIndexFromCoordsSnippet(outputData.shape.length)
+  ];
+  if (!program.atomic) {
+    sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));
+  }
+  if (dispatchLayoutRank === outputData.shape.length) {
+    const inputSnippet = inputInfo.map((x, i2) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i2] === "vec4<f32>" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n");
+    sources.push(inputSnippet);
+  }
+  sources.push(program.getUserCode());
+  const source = sources.join("\n");
+  return source;
+}
+var commonSnippet = `
+  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
+  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
+
+  // Checks whether coordinates lie within the bounds of the shape.
+  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
+    return all(coord >= vec2<i32>(0)) && all(coord < shape);
+  }
+  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
+    return all(coord >= vec3<i32>(0)) && all(coord < shape);
+  }
+  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
+    return all(coord >= vec4<i32>(0)) && all(coord < shape);
+  }
+
+  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
+    return coord;
+  }
+  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
+    return dot(coords, vec2<i32>(shape.y, 1));
+  }
+  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
+    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
+  }
+  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
+    return dot(coords, vec4<i32>(
+        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
+  }
+  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
+    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
+    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
+  }
+  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
+    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
+    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
+  }
+
+  fn idiv(a: i32, b: i32, sign: f32) -> i32 {
+    var res: i32 = a / b;
+    let mod: i32 = a % b;
+    if (sign < 0. && mod != 0) {
+      res = res - 1;
+    }
+    return res;
+  }
+
+  // NaN defination in IEEE 754-1985 is :
+  //   - sign = either 0 or 1.
+  //   - biased exponent = all 1 bits.
+  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
+  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
+  fn isnan(val: f32) -> bool {
+    let floatToUint: u32 = bitcast<u32>(val);
+    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
+  }
+  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
+    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
+  }
+`;
+function getOutputIndexFromCoordsSnippet(outRank) {
+  let snippet = "";
+  switch (outRank) {
+    case 0:
+    case 1:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : i32) -> i32 {
+          return coords;
+        }
+        `;
+      break;
+    case 2:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
+          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
+        }
+        `;
+      break;
+    case 3:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
+          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
+        }
+        `;
+      break;
+    case 4:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
+          return dot(coords, vec4<i32>(
+            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
+        }
+        `;
+      break;
+    case 5:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec5) -> i32 {
+          return coords.x * uniforms.outShapeStrides.x +
+              coords.y * uniforms.outShapeStrides.y +
+              coords.z * uniforms.outShapeStrides.z +
+              coords.w * uniforms.outShapeStrides.w +
+              coords.u;
+        }
+        `;
+      break;
+    case 6:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec6) -> i32 {
+          return coords.x * uniforms.outShapeStrides.x +
+              coords.y * uniforms.outShapeStrides.y +
+              coords.z * uniforms.outShapeStrides.z +
+              coords.w * uniforms.outShapeStrides.w +
+              coords.u * uniforms.outShapeStrides.u +
+              coords.v;
+        }
+        `;
+      break;
+    default:
+      util_exports.assert(false, () => `Unsupported ${outRank}D shape`);
+      break;
+  }
+  return snippet;
+}
+function setOutputSnippet(outShape, outBufferType, isVec4) {
+  const outRank = outShape.length;
+  const wgslType = mapToWgslTypes(outBufferType, isVec4);
+  let snippet;
+  if (isVec4) {
+    snippet = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+      result[flatIndex] = ${wgslType}(value);
+    }
+    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
+      result[flatIndex] = ${wgslType}(value);
+    }`;
+  } else {
+    snippet = `fn setOutputAtIndex(flatIndex : i32, value : f32) {
+      result[flatIndex] = ${wgslType}(value);
+    }
+    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
+      result[flatIndex] = ${wgslType}(value);
+    }`;
+  }
+  if (outRank >= 2) {
+    const dims = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, outRank);
+    const type = getCoordsDataType2(outRank);
+    if (isVec4) {
+      snippet += `
+      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(", ")}, value : vec4<f32>) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndex(flatIndex / 4, value);
+      }
+      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(", ")}, value : vec4<i32>) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndexI32(flatIndex / 4, value);
+      }
+    `;
+    } else {
+      snippet += `
+      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(", ")}, value : f32) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndex(flatIndex, value);
+      }
+      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(", ")}, value : i32) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndexI32(flatIndex, value);
+      }
+    `;
+    }
+  }
+  return snippet;
+}
+function getInputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {
+  let res = getInputAtCoordsSnippet(inputInfo, isVec4);
+  const inShape = inputInfo.shape;
+  if (inShape.length <= outShape.length) {
+    res += getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout);
+  }
+  return res;
+}
+function getInputAtCoordsSnippet(inputInfo, isVec4) {
+  const texName = inputInfo.name;
+  const rank = inputInfo.shape.length;
+  const type = getCoordsDataType2(rank);
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const dims = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, rank);
+  const inputs = dims.map((d) => `${d} : i32`).join(", ");
+  if (rank < 1) {
+    if (isVec4) {
+      return `
+        fn ${funcName}() -> vec4<f32> {
+          return vec4<f32>(${texName}[0]);
+        }
+      `;
+    }
+    return `
+      fn ${funcName}() ->f32 {
+        return f32(${texName}[0]);
+      }
+    `;
+  }
+  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;
+  let rankStr = `${rank}D`;
+  if (rank === 0) {
+    rankStr = "1D";
+  }
+  if (isVec4) {
+    return `
+      fn ${funcName}(${inputs}) -> vec4<f32> {
+        return vec4<f32>(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(",")}),
+          ${shapeStr}) / 4]);
+      }
+      `;
+  }
+  return `
+    fn ${funcName}(${inputs}) -> f32 {
+      return f32(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(",")}),
+        ${shapeStr})]);
+    }
+   `;
+}
+function getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {
+  const texName = inputInfo.name;
+  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+  const funcName = "get" + texFuncSnippet + "ByOutput";
+  const inRank = inputInfo.shape.length;
+  const outRank = outShape.length;
+  const type = getCoordsDataType2(outRank);
+  if (util_exports.arraysEqual(inputInfo.shape, outShape) && isFlatDispatchLayout) {
+    if (isVec4) {
+      return `
+        fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {
+          return vec4<f32>(${texName}[globalIndex]);
+        }
+
+        fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {
+          return vec4<f32>(${texName}[${outRank > 1 ? "getOutputIndexFromCoords(coords)" : "coords"} / 4]);
+        }
+        `;
+    } else {
+      return `
+      fn ${funcName}Index(globalIndex : i32) -> f32 {
+        return f32(${texName}[globalIndex]);
+      }
+
+      fn ${funcName}Coords(coords : ${type}) -> f32 {
+        return f32(${texName}[${outRank > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}]);
+      }
+      `;
+    }
+  }
+  const broadcastDims = backend_util_exports.getBroadcastDims(inputInfo.shape, outShape);
+  const rankDiff = outRank - inRank;
+  let coordsSnippet = "";
+  if (inRank === 0) {
+    if (isVec4) {
+      return `
+      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {
+        return get${texFuncSnippet}();
+      }
+
+      fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {
+        return get${texFuncSnippet}();
+      }
+    `;
+    }
+    return `
+      fn ${funcName}Index(globalIndex : i32) -> f32{
+        return get${texFuncSnippet}();
+      }
+
+      fn ${funcName}Coords(coords : ${type}) -> f32{
+        return get${texFuncSnippet}();
+      }
+    `;
+  } else {
+    if (outRank < 2 && broadcastDims.length >= 1) {
+      coordsSnippet = "coords = 0;";
+    } else {
+      coordsSnippet = broadcastDims.map((d) => `coords.${getCoordsXYZ(d + rankDiff)} = 0;`).join("\n");
+    }
+  }
+  let unpackedCoordsSnippet = "";
+  if (outRank < 2 && inRank > 0) {
+    unpackedCoordsSnippet = "coords";
+  } else {
+    if (outRank > 1) {
+      const coordsType = getCoordsDataType2(inRank);
+      const coordsValues = inputInfo.shape.map((s2, i2) => `coords.${getCoordsXYZ(i2 + rankDiff)}`).join(", ");
+      unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;
+    } else {
+      unpackedCoordsSnippet = "coords";
+    }
+  }
+  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;
+  const rankStr = `${inRank}D`;
+  if (isVec4) {
+    return `
+      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {
+        var coords = getCoordsFromIndex(globalIndex);
+        ${coordsSnippet}
+        return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];
+      }
+
+      fn ${funcName}Coords(coordsIn : ${type}) -> vec4<f32> {
+        var coords = coordsIn;
+        ${coordsSnippet}
+        return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];
+      }
+    `;
+  }
+  return `
+    fn ${funcName}Index(globalIndex : i32) -> f32 {
+      var coords = getCoordsFromIndex(globalIndex);
+      ${coordsSnippet}
+      return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);
+    }
+
+    fn ${funcName}Coords(coordsIn : ${type}) -> f32 {
+      var coords = coordsIn;
+      ${coordsSnippet}
+      return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);
+    }
+  `;
+}
+function getOutputCoordsSnippet(outShape, dispatchLayout) {
+  const { x, y = [], z = [] } = dispatchLayout;
+  const outRank = outShape.length;
+  if (x.length === outRank) {
+    const dtype2 = getCoordsDataType2(outRank);
+    const snippet2 = `fn getOutputCoords() -> ${dtype2}{
+      let globalIndex = getGlobalIndex();
+      return getCoordsFromIndex(globalIndex);
+    }
+    `;
+    return [snippet2, outRank];
+  }
+  let gatherDimensionsStr = "";
+  const dims = [x, y, z];
+  let rank = 0;
+  for (let i2 = 0; i2 < dims.length; i2++) {
+    const arr = dims[i2];
+    if (arr.length === 0) {
+      continue;
+    }
+    rank += arr.length;
+    if (arr.length === 1) {
+      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i2}]);`;
+    } else {
+      const strides2 = symbolicallyComputeStrides2(arr, "uniforms.outShape");
+      gatherDimensionsStr += `var index${i2} = i32(globalId[${i2}]);`;
+      for (let j = 0; j < strides2.length; j++) {
+        gatherDimensionsStr += `let d${arr[j]} = index${i2} / ${strides2[j]};`;
+        if (j === strides2.length - 1) {
+          gatherDimensionsStr += `let d${arr[j + 1]} = index${i2} - d${arr[j]} * ${strides2[j]};`;
+        } else {
+          gatherDimensionsStr += `index${i2} = index${i2} - d${arr[j]} * ${strides2[j]};`;
+        }
+      }
+    }
+  }
+  const dimensions = [];
+  for (let i2 = 0; i2 < rank; i2++) {
+    dimensions.push(`d${i2}`);
+  }
+  const dtype = getCoordsDataType2(rank);
+  let snippet = `fn getOutputCoords() -> ${dtype} {
+    ${gatherDimensionsStr}
+  `;
+  if (dimensions.length === 0) {
+    snippet += `return ${dtype}(0); }`;
+  } else {
+    snippet += `return ${dtype}(${dimensions.join(",")}); }`;
+  }
+  return [snippet, rank];
+}
+function getCoordsFromIndexSnippet(shape) {
+  const rank = shape.length;
+  if (rank <= 1) {
+    return `fn getCoordsFromIndex(index : i32) -> i32 { return index; }`;
+  }
+  const strides2 = util_exports.computeStrides(shape);
+  const dtype = getCoordsDataType2(rank);
+  const coords3 = [];
+  for (let i2 = 0; i2 < rank; i2++) {
+    coords3.push(`d${i2}`);
+  }
+  if (strides2.length === 1) {
+    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
+      return vec2<i32>(d0, d1);
+    }`;
+  }
+  let snippet;
+  snippet = "var index2 = index;" + strides2.map((_, i2) => {
+    const line1 = `let ${coords3[i2]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
+    const line2 = i2 === strides2.length - 1 ? `let ${coords3[i2 + 1]} = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}` : `index2 = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
+    return `${line1}; ${line2};`;
+  }).join("");
+  return `
+    fn getCoordsFromIndex(index : i32) -> ${dtype} {
+      ${snippet}
+      return ${dtype}(${coords3.join(",")});
+    }
+  `;
+}
+var webgpu_util_exports = {};
+__export2(webgpu_util_exports, {
+  ArrayBufferToTypedArray: () => ArrayBufferToTypedArray,
+  GPUBytesPerElement: () => GPUBytesPerElement,
+  computeDispatch: () => computeDispatch,
+  computeWorkGroupSizeForConv2d: () => computeWorkGroupSizeForConv2d,
+  computeWorkGroupSizeForMatMul: () => computeWorkGroupSizeForMatMul,
+  computeWorkPerThreadForConv2d: () => computeWorkPerThreadForConv2d,
+  flatDispatchLayout: () => flatDispatchLayout,
+  isWebGPUSupported: () => isWebGPUSupported,
+  tilesFitEvenlyIntoShape: () => tilesFitEvenlyIntoShape
+});
+var arrayProduct = (arr) => {
+  let product = 1;
+  for (let i2 = 0; i2 < arr.length; i2++) {
+    product *= arr[i2];
+  }
+  return product;
+};
+function tilesFitEvenlyIntoShape(tileSize, shape) {
+  if (tileSize.length !== shape.length) {
+    throw new Error(`Cannot compute whether rank ${tileSize.length} tiles fit evenly into rank ${shape.length} shape - ranks must match.`);
+  }
+  return shape.every((dim, dimIdx) => dim % tileSize[dimIdx] === 0);
+}
+function computeDispatch(layout, outputShape, workGroupSize = [1, 1, 1], elementsPerThread = [1, 1, 1]) {
+  const [dispatchX, dispatchY, dispatchZ] = [
+    Math.ceil(arrayProduct(layout.x.map((d) => outputShape[d])) / (workGroupSize[0] * elementsPerThread[0])),
+    layout.y ? Math.ceil(arrayProduct(layout.y.map((d) => outputShape[d])) / (workGroupSize[1] * elementsPerThread[1])) : 1,
+    layout.z ? Math.ceil(arrayProduct(layout.z.map((d) => outputShape[d])) / (workGroupSize[2] * elementsPerThread[2])) : 1
+  ];
+  return [dispatchX, dispatchY, dispatchZ];
+}
+function computeWorkGroupSizeForConv2d(layout, outputShape) {
+  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));
+  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));
+  if (dim0 <= 4) {
+    return [4, 16, 1];
+  }
+  if (dim1 <= 4) {
+    return [16, 4, 1];
+  }
+  return [16, 16, 1];
+}
+function computeWorkGroupSizeForMatMul(dimAOuter, dimInner, dimBOuter) {
+  if (dimAOuter === 1) {
+    return [32, 1, 1];
+  } else if (dimBOuter === 1) {
+    return [1, 32, 1];
+  }
+  return [8, 8, 1];
+}
+function computeWorkPerThreadForConv2d(layout, outputShape) {
+  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));
+  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));
+  if (dim0 <= 4) {
+    return [1, 2, 1];
+  }
+  if (dim1 <= 4) {
+    return [2, 1, 1];
+  }
+  return [2, 2, 1];
+}
+function flatDispatchLayout(shape) {
+  return { x: shape.map((d, i2) => i2) };
+}
+function GPUBytesPerElement(dtype) {
+  if (dtype === "float32" || dtype === "int32" || dtype === "bool" || dtype === "string") {
+    return 4;
+  } else if (dtype === "complex64") {
+    return 8;
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+function ArrayBufferToTypedArray(data, dtype) {
+  if (dtype === "float32") {
+    return new Float32Array(data);
+  } else if (dtype === "int32") {
+    return new Int32Array(data);
+  } else if (dtype === "bool" || dtype === "string") {
+    return Uint8Array.from(new Int32Array(data));
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+function isWebGPUSupported() {
+  return (typeof window !== "undefined" || typeof WorkerGlobalScope !== "undefined") && !!navigator.gpu;
+}
+function makeMatMulPackedVec4Source(workPerThread, tileAOuter, tileBOuter, tileInner, innerElementSize = 4) {
+  util_exports.assert((tileInner % 4 === 0 || tileInner % 3 === 0) && workPerThread[0] === 4 && (innerElementSize === 3 || innerElementSize === 4), () => `tileInner must be divisible by 4|3. ColPerThread must be 4.
+           innerElementSize must be 3|4.`);
+  return `
+  var<workgroup> mm_Asub : array<array<vec${innerElementSize}<f32>, ${tileInner / innerElementSize}>, ${tileAOuter}>;
+  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${tileBOuter / workPerThread[0]}>, ${tileInner}>;
+
+  let RowPerThread = ${workPerThread[1]};
+  let ColPerThread = ${workPerThread[0]};
+  let InnerElementSize = ${innerElementSize};
+  let TileInner = ${tileInner};
+
+  ${getMainHeaderString()}
+
+    let tileRow = ${tileAOuter === 1 ? "0" : "i32(localId.y) * RowPerThread"};
+    let tileCol = i32(localId.x);
+
+    let globalRow = ${tileAOuter === 1 ? "0" : "i32(globalId.y) * RowPerThread"};
+    let globalCol = i32(globalId.x);
+    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
+
+    var acc: array<vec4<f32>, RowPerThread>;
+    var BCached : array<vec4<f32>, 4>;
+
+    // Loop over shared dimension.
+    var globalColA = tileCol;
+    let RowPerThreadB = TileInner / i32(workGroupSizeY);
+    let tileRowB = i32(localId.y) * RowPerThreadB;
+    for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileCol;
+            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
+        }
+        globalColA = globalColA + TileInner / InnerElementSize;
+
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol;
+            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
+            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
+            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
+            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
+            ${innerElementSize === 3 ? "" : "BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
+
+            for (var i = 0; i < RowPerThread; i = i + 1) {
+                let ACached = mm_Asub[tileRow + i][k];
+                acc[i] = BCached[0] * ACached.x + acc[i];
+                acc[i] = BCached[1] * ACached.y + acc[i];
+                acc[i] = BCached[2] * ACached.z + acc[i];
+                ${innerElementSize === 3 ? "" : "acc[i] = BCached[3] * ACached.w + acc[i];"}
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+        mm_write(globalRow + innerRow,
+                 globalCol,
+                 acc[innerRow], globalId);
+    }
+  }`;
+}
+var MatMulPackedVec4Program = class {
+  constructor(aShape, outputShape, rowPerThread, batchAEqualOne, batchBEqualOne, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [8, 8, 1];
+    this.isVec4 = true;
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [2], y: [1], z: [0] };
+    if (outputShape[1] === 1) {
+      this.elementsPerThread = [4, 1, 1];
+    } else {
+      this.elementsPerThread = [4, 4, 1];
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    const addBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.tileAOuter = outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1];
+    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];
+    this.tileInner = this.tileBOuter;
+    this.aShape = aShape;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = batchAEqualOne;
+    this.batchBEqualOne = batchBEqualOne;
+    [this.fitA, this.fitB] = this.getShapeFit();
+    this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getShapeFit() {
+    const dimInner = this.aShape[2];
+    const dimBOuter = this.outputShape[2];
+    const bShape = [this.outputShape[0], dimInner, dimBOuter];
+    const tileSizeA = [this.tileAOuter, this.tileInner];
+    const tileSizeB = [this.tileInner, this.tileBOuter];
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)),
+      tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1))
+    ];
+  }
+  getUserCode() {
+    const sampleA = this.fitA ? `return A[batch * batchASize + row * uniforms.dimInner / 4 + col]` : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
+        }
+        return vec4<f32>(0.0)`;
+    const sampleB = this.fitB ? `return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]` : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
+        }
+        return vec4<f32>(0.0)`;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${activationOp}
+                }`;
+      } else {
+        activationSnippet = `
+            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+              ${activationOp}
+            }`;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchAEqualOne ? `
+          let batchASize = 0;
+          let batch = 0;
+        ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
+          let batch = i32(globalId.z);
+        `}
+
+        ${sampleA};
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchBEqualOne ? `
+          let batchBSize = 0;
+          let batch = 0;
+          ` : `
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
+          let batch = i32(globalId.z);
+       `}
+        ${sampleB};
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
+        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
+        {
+          var value = valueIn;
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col * 4);
+          ${addBiasSnippet}
+          ${applyActivationSnippet}
+          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
+        }
+      }
+      ${makeMatMulPackedVec4Source(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
+    `;
+    return userCode;
+  }
+};
+function makeMatMulPackedSource(workPerThread, workGroupSize) {
+  const tileAOuter = workGroupSize[1] * workPerThread[1];
+  const tileBOuter = workGroupSize[0] * workPerThread[0];
+  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+  return `
+    var<workgroup> mm_Asub : array<array<f32, ${tileInner}>, ${tileAOuter}>;
+    var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;
+    ${getMainHeaderString()}
+      let tileRow = i32(localId.y) * ${workPerThread[1]};
+      let tileCol = i32(localId.x) * ${workPerThread[0]};
+
+      let globalRow = i32(globalId.y) * ${workPerThread[1]};
+      let globalCol = i32(globalId.x) * ${workPerThread[0]};
+
+      let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;
+
+      var acc : array<array<f32, ${workPerThread[0]}>, ${workPerThread[1]}>;
+      var ACached : f32;
+      var BCached : array<f32, ${workPerThread[0]}>;
+
+      // Without this initialization strange values show up in acc.
+      for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+        for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+          acc[innerRow][innerCol] = 0.0;
+        }
+      }
+
+      let ColPerThreadA = ${tileInner} / ${workGroupSize[0]};
+      let tileColA = i32(localId.x) * ColPerThreadA;
+      let RowPerThreadB = ${tileInner} / ${workGroupSize[1]};
+      let tileRowB = i32(localId.y) * RowPerThreadB;
+
+      // Loop over shared dimension.
+      for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileColA + innerCol;
+
+            mm_Asub[inputRow][inputCol] = mm_readA(
+                globalRow + innerRow,
+                t * ${tileInner} + inputCol, globalId);
+          }
+        }
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+          for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol + innerCol;
+
+            mm_Bsub[inputRow][inputCol] = mm_readB(
+              t * ${tileInner} + inputRow,
+              globalCol + innerCol, globalId);
+          }
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < ${tileInner}; k = k + 1) {
+          for (var inner = 0; inner < ${workPerThread[0]}; inner = inner + 1) {
+            BCached[inner] = mm_Bsub[k][tileCol + inner];
+          }
+
+          for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+            ACached = mm_Asub[tileRow + innerRow][k];
+            for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
+            }
+          }
+        }
+
+        workgroupBarrier();
+      }
+
+      for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+        for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+
+          if ((globalCol + innerCol) < uniforms.dimBOuter &&
+              (globalRow + innerRow) < uniforms.dimAOuter) {
+            mm_write(globalRow + innerRow,
+                     globalCol + innerCol,
+                     acc[innerRow][innerCol], globalId);
+          }
+        }
+      }
+    }
+  `;
+}
+function makeMatMulVectorSource(workGroupSize) {
+  return `
+    let TileSize = ${workGroupSize[0] * 4};
+    var<workgroup> mm_Asub : array<vec4<f32>, ${workGroupSize[0]}>;
+
+    ${getMainHeaderString()}
+      let tileCol = i32(localId.x);
+      let globalCol = i32(globalId.x);
+      let globalRow = i32(globalId.y);
+
+      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
+
+      // Without this initialization strange values show up in acc.
+      var acc = 0.0;
+
+      // Loop over shared dimension.
+      for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        let colA = t * TileSize + tileCol * 4;
+        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
+                                mm_readA(globalRow, colA + 1, globalId),
+                                mm_readA(globalRow, colA + 2, globalId),
+                                mm_readA(globalRow, colA + 3, globalId));
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileSize / 4; k = k + 1) {
+          let rowB = t * TileSize + k * 4;
+          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
+                              mm_readB(rowB + 1, globalCol, globalId),
+                              mm_readB(rowB + 2, globalCol, globalId),
+                              mm_readB(rowB + 3, globalCol, globalId));
+
+          let ACached = mm_Asub[k];
+          acc = acc + dot(ACached, BCached);
+        }
+
+        workgroupBarrier();
+      }
+
+      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
+        mm_write(globalRow, globalCol, acc, globalId);
+      }
+    }
+  `;
+}
+var MatMulPackedProgram2 = class {
+  constructor(aShape, outputShape, workPerThread, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [16, 16, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [2], y: [1], z: [0] };
+    const dimInner = transposeA ? aShape[1] : aShape[2];
+    this.workGroupSize = computeWorkGroupSizeForMatMul(outputShape[1], dimInner, outputShape[2]);
+    if (outputShape[1] === 1 || outputShape[2] === 1) {
+      workPerThread = 1;
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]);
+    if (util_exports.arraysEqual(this.dispatch, [1, 1, 1])) {
+      workPerThread = 1;
+      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]);
+    }
+    const addBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.workPerThread = workPerThread;
+    this.aShape = aShape;
+    this.transposeA = transposeA;
+    this.transposeB = transposeB;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = batchAEqualOne;
+    this.batchBEqualOne = batchBEqualOne;
+    const dimBOuter = this.outputShape[2];
+    const bShape = this.transposeB ? [this.outputShape[0], dimBOuter, dimInner] : [this.outputShape[0], dimInner, dimBOuter];
+    [this.fitA, this.fitB] = this.getShapeFit(bShape);
+    this.shaderKey = `matMulPacked_${this.workPerThread}_${transposeA}_${transposeB}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getShapeFit(bShape) {
+    const tileAOuter = this.workGroupSize[1] * this.workPerThread;
+    const tileBOuter = this.workGroupSize[0] * this.workPerThread;
+    let tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+    if (this.outputShape[1] === 1) {
+      tileInner *= 4;
+    }
+    util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => `tileInner must be multiple of workgroupsize.x and workgroupsize.y`);
+    const tileSizeA = [tileAOuter, tileInner];
+    const tileSizeB = [tileInner, tileBOuter];
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)),
+      tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1))
+    ];
+  }
+  getUserCode() {
+    let sampleA;
+    if (this.transposeA === false) {
+      sampleA = this.fitA ? `return A[batch * batchASize + row * uniforms.dimInner + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+             return A[batch * batchASize + row * uniforms.dimInner + col];
+           }
+           return 0.0;`;
+    } else {
+      sampleA = this.fitA ? `return A[batch * batchASize + col * uniforms.dimAOuter + row];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+             return A[batch* batchASize + col * uniforms.dimAOuter + row];
+           }
+           return 0.0;`;
+    }
+    let sampleB;
+    if (this.transposeB === false) {
+      sampleB = this.fitB ? `return B[batch * batchBSize + row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter + col];
+           }
+           return 0.0;`;
+    } else {
+      sampleB = this.fitB ? `return B[batch * batchBSize + col * uniforms.dimInner + row];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + col * uniforms.dimInner + row];
+           }
+           return 0.0;`;
+    }
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+               let b = getPreluActivationWeightsByOutputCoords(outCoord);
+               ${activationOp}
+            }`;
+      } else {
+        activationSnippet = `
+              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+                ${activationOp}
+              }
+            `;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchAEqualOne ? `
+        let batch = 0;
+        let batchASize = 0;
+        ` : `
+        let batch = i32(globalId.z);
+        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+        `}
+        ${sampleA}
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchBEqualOne ? `
+        let batch = 0;
+        let batchBSize = 0;
+        ` : `
+        let batch = i32(globalId.z);
+        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+        `}
+        ${sampleB}
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        var value = valueIn;
+        let batch = i32(globalId.z);
+        let outCoord = vec3<i32>(batch, row, col);
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutputAtCoords(batch, row, col, value);
+      }
+      ${this.outputShape[1] > 1 ? makeMatMulPackedSource([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : makeMatMulVectorSource(this.workGroupSize)}
+    `;
+    return userCode;
+  }
+};
+function makeMatMulReduceSource() {
+  return `
+    var<workgroup> sumValues : array<f32, workGroupSizeX>;
+    ${getMainHeaderString()}
+      let coords = getOutputCoords();
+      let batch = coords[0];
+      let row = coords[1];
+      let col = coords[2];
+      var sum = 0.0;
+      let Length = uniforms.dimInner;
+      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
+        let dataA = mm_readA(batch, row, k);
+        let dataB = mm_readB(batch, k, col);
+        sum = sum + dataA * dataB;
+      }
+      sumValues[localId.x] = sum;
+      workgroupBarrier();
+
+      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
+          currentSize = currentSize / 2u) {
+        if (localId.x < currentSize)
+        {
+          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
+        }
+        workgroupBarrier();
+      }
+
+      if (localId.x == 0u) {
+        sum = sumValues[0] + sumValues[1];
+        mm_write(batch, row, col, sum);
+      }
+    }
+  `;
+}
+var MatMulReduceProgram = class {
+  constructor(outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [256, 1, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [], y: [1, 2], z: [0] };
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    const addBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.transposeA = transposeA;
+    this.transposeB = transposeB;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = batchAEqualOne;
+    this.batchBEqualOne = batchBEqualOne;
+    this.shaderKey = `matMulReduce_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getUserCode() {
+    let sampleA;
+    if (this.transposeA === false) {
+      sampleA = `return f32(A[batch * batchASize + row * uniforms.dimInner + col]);`;
+    } else {
+      sampleA = `return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);`;
+    }
+    let sampleB;
+    if (this.transposeB === false) {
+      sampleB = `return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);`;
+    } else {
+      sampleB = `return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);`;
+    }
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+               let b = getPreluActivationWeightsByOutputCoords(outCoord);
+               ${activationOp}
+            }`;
+      } else {
+        activationSnippet = `
+              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+                ${activationOp}
+              }
+            `;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {
+        ${this.batchAEqualOne ? `
+          let batchASize = 0;
+          let batch = 0;
+          ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+          let batch = batchIn;
+          `}
+        ${sampleA}
+      }
+
+      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
+        ${this.batchBEqualOne ? `
+          let batch = 0;
+          let batchBSize = 0;
+          ` : `
+          let batch = batchIn;
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+          `}
+        ${sampleB}
+      }
+
+      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
+        var value = valueIn;
+        let outCoord = vec3<i32>(batch, row, col);
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutputAtCoords(batch, row, col, value);
+      }
+      ${makeMatMulReduceSource()}
+    `;
+    return userCode;
+  }
+};
+function makeMatMulSmallOutputSizeSource(workGroupSize) {
+  const tileAOuter = workGroupSize[1] / 2;
+  const tileBOuter = workGroupSize[0];
+  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+  return `
+  var<workgroup> mm_Asub1 : array<array<f32, ${tileInner}>, ${tileAOuter}>;
+  var<workgroup> mm_Bsub1 : array<array<f32, ${tileBOuter}>, ${tileInner}>;
+  var<workgroup> mm_Asub2 : array<array<f32, ${tileInner}>, ${tileAOuter}>;
+  var<workgroup> mm_Bsub2 : array<array<f32, ${tileBOuter}>, ${tileInner}>;
+
+  // If the output size is small for matrix multiplication, avoid to use vec4
+  // and handle some elements per thread to optimally utilize the ALU.
+  // Introduces two shared memory buffers, some logical threads could handle
+  // arithmetic operations and others handle IO operations between barrier api,
+  // makes ALUs and load/store units work simultaneously, could improves
+  // the performance.
+  ${getMainHeaderString()}
+    let tileRow = i32(localId.y);
+    let tileCol = i32(localId.x);
+    let globalRow = i32(globalId.y);
+    let globalCol = i32(globalId.x);
+
+    // uniforms.dimInner should be greater than 0.
+    let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;
+    var acc = 0.0;
+
+    var globalColA = tileCol;
+    var globalRowB = tileRow;
+    for (var t = 0; t < numTiles; t = t + 1) {
+      if (t == 0) {
+        if (tileRow < ${tileAOuter}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub1[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${tileInner};
+          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${tileInner};
+        }
+      } else {
+        if (tileRow < ${tileAOuter}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub1[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${tileInner};
+          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${tileInner};
+        } else {
+          // Compute acc values for a single thread.
+          for (var k = 0; k < ${tileInner}; k = k + 1) {
+            let subRow = tileRow - ${tileAOuter};
+            if (subRow < 0) {
+              continue;
+            }
+            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
+          }
+        }
+      }
+      workgroupBarrier();
+      if (t != 0) {
+        t = t + 1;
+      }
+
+      if (t < numTiles) {
+        if (tileRow < ${tileAOuter}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub2[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${tileInner};
+          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${tileInner};
+        } else {
+          // Compute acc values for a single thread.
+          for (var k = 0; k < ${tileInner}; k = k + 1) {
+            let subRow = tileRow - ${tileAOuter};
+            if (subRow < 0) {
+              continue;
+            }
+            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
+          }
+        }
+      }
+      workgroupBarrier();
+    }
+    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${tileAOuter};
+    if (tileRow >= ${tileAOuter} && writeCol >= 0) {
+      mm_write(writeCol, globalCol, acc, globalId);
+    }
+  }
+  `;
+}
+var MatMulSmallOutputSizeProgram = class {
+  constructor(aShape, bShape, outputShape, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [8, 16, 1];
+    util_exports.assert(aShape[1] <= 16 || bShape[2] <= 16, () => "This program can be only used when A width or B Height are small");
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [2], y: [1], z: [0] };
+    this.dispatch = [
+      Math.ceil(outputShape[2] / this.workGroupSize[0]),
+      Math.ceil(outputShape[1] * 2 / this.workGroupSize[1]),
+      outputShape[0]
+    ];
+    const addBias = bias != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = aShape[0] === 1;
+    this.batchBEqualOne = bShape[0] === 1;
+    this.shaderKey = `matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getUserCode() {
+    const sampleA = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+          return A[batch * batchASize + row * uniforms.dimInner + col];
+        }
+        return 0.0;`;
+    const sampleB = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return B[batch * batchBSize + row * uniforms.dimBOuter + col];
+         }
+         return 0.0;`;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+            let b = getPreluActivationWeightsByOutputCoords(outCoord);
+            ${activationOp}
+            }`;
+      } else {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+            ${activationOp}
+        }`;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchAEqualOne ? `
+          let batch = 0;
+          let batchASize = 0;
+          ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+          let batch = i32(globalId.z);
+          `}
+        ${sampleA}
+      }
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchBEqualOne ? `
+          let batch = 0;
+          let batchBSize = 0;
+          ` : `
+          let batch = i32(globalId.z);
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+          `}
+        ${sampleB}
+      }
+      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col);
+          var value = valueIn;
+          ${addBiasSnippet}
+          ${applyActivationSnippet}
+          setOutputAtCoords(batch, row, col, value);
+        }
+      }
+      ${makeMatMulSmallOutputSizeSource(this.workGroupSize)}
+    `;
+    return userCode;
+  }
+};
+function reshape6(args) {
+  const { inputs, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  const $xSize = util_exports.sizeFromShape($shape);
+  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
+  args.backend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig4 = {
+  kernelName: Reshape,
+  backendName: "webgpu",
+  kernelFunc: reshape6
+};
+function batchMatMulImpl2({ a: a6, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape6({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape6({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const intermediates = [a3d, b3d];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const batchAEqualOne = batchDimA === 1;
+  const batchBEqualOne = batchDimB === 1;
+  const useVec4 = innerShapeA % 4 === 0 && outerShapeB % 4 === 0 && !transposeA && !transposeB;
+  let program;
+  if (outerShapeA * outerShapeB <= 32) {
+    program = new MatMulReduceProgram([batchDim, outerShapeA, outerShapeB], batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);
+  } else if (!transposeA && !transposeB && (outerShapeA <= 16 && (outerShapeB <= 512 || innerShapeB >= 2 * outerShapeB) || outerShapeB <= 16 && (outerShapeA <= 512 || innerShapeA >= 2 * outerShapeA))) {
+    program = new MatMulSmallOutputSizeProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], bias, activation2, preluActivationWeights);
+  } else if (useVec4) {
+    program = new MatMulPackedVec4Program(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), batchAEqualOne, batchBEqualOne, bias, activation2, preluActivationWeights);
+  } else {
+    program = new MatMulPackedProgram2(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);
+  }
+  const inputs = [a3d, b3d];
+  if (bias) {
+    inputs.push(bias);
+  }
+  if (preluActivationWeights) {
+    inputs.push(preluActivationWeights);
+  }
+  const dimensions = [
+    { type: "int32", data: [outerShapeA] },
+    { type: "int32", data: [outerShapeB] },
+    { type: "int32", data: [innerShapeA] }
+  ];
+  if (activation2 === "leakyrelu") {
+    dimensions.push({ type: "float32", data: [leakyreluAlpha] });
+    program.uniforms += " alpha : f32,";
+  }
+  const out = backend2.runWebGPUProgram(program, inputs, a6.dtype, dimensions);
+  const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
+  intermediates.push(out);
+  for (const i2 of intermediates) {
+    backend2.disposeData(i2.dataId);
+  }
+  return outReshaped;
+}
+function _fusedMatMul3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  return batchMatMulImpl2({
+    a: a6,
+    b,
+    transposeA,
+    transposeB,
+    backend: backend2,
+    bias,
+    preluActivationWeights,
+    leakyreluAlpha,
+    activation: activation2
+  });
+}
+var _fusedMatMulConfig4 = {
+  kernelName: _FusedMatMul,
+  backendName: "webgpu",
+  kernelFunc: _fusedMatMul3
+};
+var BinaryOpComplexProgram2 = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["AReal", "AImag", "BReal", "BImag"];
+    this.workGroupSize = [128, 1, 1];
+    this.size = true;
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `binaryOpComplex_${op2}`;
+    this.op = op2;
+  }
+  getUserCode() {
+    const opStr = getBinaryOpString(this.op, false);
+    const userCode = `
+      fn binaryOpComplex(
+          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
+        ${opStr}
+      }
+
+      ${getMainHeaderAndGlobalIndexString()}
+        if(index < uniforms.size) {
+          let areal = getARealByOutputIndex(index);
+          let aimag = getAImagByOutputIndex(index);
+          let breal = getBRealByOutputIndex(index);
+          let bimag = getBImagByOutputIndex(index);
+          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var BinaryOpSharedProgram = class {
+  constructor(op2, aShape, bShape, useSharedMemoryWithB) {
+    this.variableNames = ["A", "B"];
+    this.size = true;
+    const workGroupSizeX = 256;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.lastDimensionSize = useSharedMemoryWithB ? bShape[0] : aShape[0];
+    if (this.lastDimensionSize < 256) {
+      this.workPerThread = 1;
+    } else if (this.lastDimensionSize < 512) {
+      this.workPerThread = 2;
+    } else {
+      this.workPerThread = 4;
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.useSharedMemoryWithB = useSharedMemoryWithB;
+    this.op = op2;
+    this.shaderKey = `binaryShared_${op2}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;
+  }
+  getUserCode() {
+    const sharedIndexSnippet = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0";
+    const accessDataSnippet = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);
+         let b = sharedBuf[${sharedIndexSnippet}];` : `let a = sharedBuf[${sharedIndexSnippet}];
+         let b = getBByOutputCoords(coords);`;
+    const opStr = getBinaryOpString(this.op, false);
+    const userCode = `
+        fn binaryOperation(a : f32, b : f32) -> f32 {
+          ${opStr}
+        }
+        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
+        ${getMainHeaderAndGlobalIndexString()}
+
+          // Fill in the shared memory buffer. Here we need a loop to make sure
+          // that all data in A|B are uploaded when |sharedMemorySize| is larger
+          // than work group size.
+          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
+            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? "B" : "A"}[localIndex]);
+          }
+          workgroupBarrier();
+
+          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+            let flatIndex = index * ${this.workPerThread} + i;
+            if(flatIndex < uniforms.size) {
+              let coords = getCoordsFromIndex(flatIndex);
+
+              ${accessDataSnippet}
+              setOutputAtIndex(flatIndex, binaryOperation(a, b));
+            }
+          }
+        }
+        `;
+    return userCode;
+  }
+};
+var BinaryOpVec4Program = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["A", "B"];
+    this.workPerThread = 4;
+    this.isVec4 = true;
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.op = op2;
+    this.shaderKey = `binaryVec4_${op2}`;
+  }
+  getUserCode() {
+    const opStr = getBinaryOpString(this.op, this.isVec4);
+    const userCode = `
+      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
+        ${opStr}
+      }
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          let b = getBByOutputIndex(index);
+          setOutputAtIndex(index, binaryOperation(a, b));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var BinaryOpProgram2 = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["A", "B"];
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `binary_${op2}`;
+    this.op = op2;
+  }
+  getUserCode() {
+    const opStr = getBinaryOpString(this.op, false);
+    const userCode = `
+      fn binaryOperation(a : f32, b : f32) -> f32 {
+        ${opStr}
+      }
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          let b = getBByOutputIndex(index);
+          setOutputAtIndex(index, binaryOperation(a, b));
+        }
+      }
+      `;
+    return userCode;
+  }
+};
+function getBinaryProgram(op2, aShape, bShape) {
+  const useVec4 = util_exports.arraysEqual(aShape, bShape) && util_exports.sizeFromShape(aShape) % 4 === 0;
+  if (useVec4) {
+    return new BinaryOpVec4Program(op2, aShape, bShape);
+  }
+  const useSharedMemoryWithA = aShape.length === 1 && bShape.length > 1 && aShape[0] < 1024;
+  const useSharedMemoryWithB = bShape.length === 1 && aShape.length > 1 && bShape[0] < 1024;
+  if (useSharedMemoryWithA || useSharedMemoryWithB) {
+    return new BinaryOpSharedProgram(op2, aShape, bShape, useSharedMemoryWithB);
+  } else {
+    return new BinaryOpProgram2(op2, aShape, bShape);
+  }
+}
+function identity5(args) {
+  const { inputs } = args;
+  const { x } = inputs;
+  args.backend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+}
+var identityConfig4 = {
+  kernelName: Identity,
+  backendName: "webgpu",
+  kernelFunc: identity5
+};
+function complex4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { real: real5, imag: imag5 } = inputs;
+  const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64");
+  const complex5 = backend2.tensorMap.get(complexInfo.dataId);
+  const realTensorInfo = identity5({ inputs: { x: real5 }, backend: backend2 });
+  const imagTensorInfo = identity5({ inputs: { x: imag5 }, backend: backend2 });
+  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };
+  return complexInfo;
+}
+var complexConfig3 = {
+  kernelName: Complex,
+  backendName: "webgpu",
+  kernelFunc: complex4
+};
+var UnaryOpProgram2 = class {
+  constructor(outputShape, op2) {
+    this.variableNames = ["A"];
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.op = op2;
+    this.shaderKey = `unary_${op2}`;
+  }
+  getUserCode() {
+    return `
+      fn unaryOperation(a : f32) -> f32 {
+        ${getUnaryOpString(this.op, false)}
+      }
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          setOutputAtIndex(index, unaryOperation(a));
+        }
+      }
+      `;
+  }
+};
+function unaryKernelFunc3({ opType, cpuKernelImpl, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const webgpuBackend = backend2;
+    const $dtype = dtype || x.dtype;
+    if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
+      const xData = webgpuBackend.tensorMap.get(x.dataId);
+      const outValues = cpuKernelImpl(xData.values, $dtype);
+      return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues);
+    }
+    const program = new UnaryOpProgram2(x.shape, opType);
+    return webgpuBackend.runWebGPUProgram(program, [x], $dtype);
+  };
+}
+function binaryKernelFunc3({ opSnippet, cpuKernelImpl, supportsComplex = false, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { a: a6, b } = inputs;
+    const webgpuBackend = backend2;
+    if (supportsComplex && a6.dtype === "complex64") {
+      const aData = webgpuBackend.tensorMap.get(a6.dataId);
+      const bData = webgpuBackend.tensorMap.get(b.dataId);
+      let real5, imag5;
+      if (opSnippet !== BinaryOpType.MUL) {
+        [real5, imag5] = [
+          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
+          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
+        ].map((complexParts) => {
+          const [aPart, bPart] = complexParts;
+          const aHandle = {
+            dataId: aPart.dataId,
+            dtype: aPart.dtype,
+            shape: a6.shape
+          };
+          const bHandle = {
+            dataId: bPart.dataId,
+            dtype: bPart.dtype,
+            shape: b.shape
+          };
+          const program2 = getBinaryProgram(opSnippet, a6.shape, b.shape);
+          return webgpuBackend.runWebGPUProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
+        });
+      } else {
+        const realProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_REAL, a6.shape, b.shape);
+        const imagProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_IMAG, a6.shape, b.shape);
+        const inputs2 = [
+          {
+            dataId: aData.complexTensorInfos.real.dataId,
+            dtype: aData.complexTensorInfos.real.dtype,
+            shape: a6.shape
+          },
+          {
+            dataId: aData.complexTensorInfos.imag.dataId,
+            dtype: aData.complexTensorInfos.imag.dtype,
+            shape: a6.shape
+          },
+          {
+            dataId: bData.complexTensorInfos.real.dataId,
+            dtype: bData.complexTensorInfos.real.dtype,
+            shape: b.shape
+          },
+          {
+            dataId: bData.complexTensorInfos.imag.dataId,
+            dtype: bData.complexTensorInfos.imag.dtype,
+            shape: b.shape
+          }
+        ];
+        real5 = webgpuBackend.runWebGPUProgram(realProgram, inputs2, "float32");
+        imag5 = webgpuBackend.runWebGPUProgram(imagProgram, inputs2, "float32");
+      }
+      const complexOutput = complex4({ inputs: { real: real5, imag: imag5 }, backend: webgpuBackend });
+      webgpuBackend.disposeData(real5.dataId);
+      webgpuBackend.disposeData(imag5.dataId);
+      return complexOutput;
+    }
+    const $dtype = dtype || upcastType(a6.dtype, b.dtype);
+    if ((a6.dtype === "string" || b.dtype === "string" || webgpuBackend.shouldExecuteOnCPU([a6, b])) && cpuKernelImpl != null) {
+      const aData = webgpuBackend.tensorMap.get(a6.dataId).values;
+      const bData = webgpuBackend.tensorMap.get(b.dataId).values;
+      const decodedAVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aData) : aData;
+      const decodedBVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bData) : bData;
+      const [outValues, outShape] = cpuKernelImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);
+      return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues);
+    }
+    const program = getBinaryProgram(opSnippet, a6.shape, b.shape);
+    return webgpuBackend.runWebGPUProgram(program, [a6, b], $dtype);
+  };
+}
+var { addImpl: addImplCPU2, ceilImpl: ceilImplCPU2, concatImpl: concatImplCPU2, equalImpl: equalImplCPU2, expImpl: expImplCPU2, expm1Impl: expm1ImplCPU2, floorImpl: floorImplCPU2, gatherNdImpl: gatherNdImplCPU2, gatherV2Impl: gatherV2ImplCPU2, greaterEqualImpl: greaterEqualImplCPU2, greaterImpl: greaterImplCPU2, lessEqualImpl: lessEqualImplCPU2, lessImpl: lessImplCPU2, logImpl: logImplCPU2, maxImpl: maxImplCPU2, maximumImpl: maximumImplCPU2, minimumImpl: minimumImplCPU2, multiplyImpl: multiplyImplCPU2, negImpl: negImplCPU2, notEqualImpl: notEqualImplCPU2, prodImpl: prodImplCPU2, rangeImpl: rangeImplCPU2, rsqrtImpl: rsqrtImplCPU2, scatterImpl: scatterImplCPU2, simpleAbsImpl: simpleAbsImplCPU2, sliceImpl: sliceImplCPU2, stridedSliceImpl: stridedSliceImplCPU2, stringNGramsImpl: stringNGramsImplCPU2, subImpl: subImplCPU2, tileImpl: tileImplCPU2, topKImpl: topKImplCPU2, transposeImpl: transposeImplCPU2, uniqueImpl: uniqueImplCPU2 } = shared_exports;
+var abs4 = unaryKernelFunc3({ opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU2 });
+var absConfig4 = {
+  kernelName: Abs,
+  backendName: "webgpu",
+  kernelFunc: abs4
+};
+var addKernelFunc2 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.ADD,
+  cpuKernelImpl: addImplCPU2,
+  supportsComplex: true
+});
+var addConfig4 = {
+  kernelName: Add,
+  backendName: "webgpu",
+  kernelFunc: addKernelFunc2
+};
+var AddNPackedProgram2 = class {
+  constructor(shapes) {
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shapes[0];
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.shaderKey = "addN";
+  }
+  getUserCode() {
+    const snippets = [];
+    this.variableNames.forEach((variable2) => {
+      snippets.push(`let v${variable2} = get${variable2}ByOutputCoords(coords);`);
+    });
+    const operation = this.variableNames.map((variable2) => {
+      return `v${variable2}`;
+    }).join(" + ");
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if (flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndex);
+            ${snippets.join("\n        ")}
+            setOutputAtIndex(flatIndex, ${operation});
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function addN4(args) {
+  const { inputs, backend: backend2 } = args;
+  const tensors = inputs;
+  if (tensors.length === 1) {
+    return identity5({ inputs: { x: tensors[0] }, backend: backend2 });
+  }
+  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));
+  const shapes = tensors.map((t2) => t2.shape);
+  const program = new AddNPackedProgram2(shapes);
+  return backend2.runWebGPUProgram(program, tensors, dtype);
+}
+var addNConfig4 = {
+  kernelName: AddN,
+  backendName: "webgpu",
+  kernelFunc: addN4
+};
+var ArgMinMaxProgram2 = class {
+  constructor(inputShape, axis, reduceType) {
+    this.workGroupSize = [64, 1, 1];
+    this.variableNames = ["x"];
+    this.uniforms = "infinityValue : f32,";
+    this.size = true;
+    const axes = [axis];
+    backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, inputShape.length);
+    this.op = reduceType === "min" ? "<" : ">";
+    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(inputShape, axes);
+    this.outputShape = outputShape.length === 0 ? [1] : outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);
+    this.inputShape = inputShape;
+    this.shaderKey = `argMinMax${this.op}`;
+  }
+  getUserCode() {
+    const sharedMemorySnippet = `
+      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
+      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
+    `;
+    const getInputShapeLastDim = () => {
+      if (this.inputShape.length === 1) {
+        return "uniforms.xShape";
+      } else {
+        return `uniforms.xShape.${getCoordsXYZ(this.inputShape.length - 1)}`;
+      }
+    };
+    const splitOutputCoords = () => {
+      let snippet = "";
+      if (this.outputShape.length === 1) {
+        if (this.inputShape.length !== 1) {
+          snippet += "outputCoords,";
+        }
+      } else {
+        for (let i2 = 0; i2 < this.outputShape.length; i2++) {
+          snippet += `outputCoords.${getCoordsXYZ(i2)},`;
+        }
+      }
+      return snippet;
+    };
+    const userCode = `
+      fn DIV_CEIL(a : u32, b : u32) -> u32 {
+        return ((a - 1u) / b + 1u);
+      }
+
+      ${sharedMemorySnippet}
+
+      ${getMainHeaderAndGlobalIndexString()}
+        let outputIndex = index / i32(workGroupSizeX);
+        let reduceLength = ${getInputShapeLastDim()};
+
+        var bestIndex = i32(localId.x);
+        var bestValue = uniforms.infinityValue;
+        let outputCoords = getCoordsFromIndex(outputIndex);
+        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
+            k = k + i32(workGroupSizeX)) {
+          let candidate = getX(${splitOutputCoords()} k);
+          if (!isnan(candidate) && candidate ${this.op} bestValue) {
+            bestValue = candidate;
+            bestIndex = k;
+          }
+        }
+        xBestValues[localId.x] = bestValue;
+        xBestIndices[localId.x] = bestIndex;
+        workgroupBarrier();
+
+        var reduceSize = min(u32(reduceLength), workGroupSizeX);
+        for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+            currentSize = reduceSize / 2u) {
+          let interval = DIV_CEIL(reduceSize, 2u);
+          if (localId.x < currentSize) {
+            let candidate = xBestValues[localId.x + interval];
+            if (candidate ${this.op} bestValue) {
+              bestValue = candidate;
+              xBestValues[localId.x] = bestValue;
+              xBestIndices[localId.x] = xBestIndices[localId.x + interval];
+            }
+          }
+          reduceSize = interval;
+          workgroupBarrier();
+        }
+
+        if (localId.x == 0u && outputIndex < uniforms.size) {
+          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var TransposeSharedProgram = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    this.workGroupSize = [16, 16, 1];
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [0], y: [1] };
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]);
+    this.shaderKey = "transposeShared";
+  }
+  getUserCode() {
+    const userCode = `
+      let TILE_DIM = ${this.workGroupSize[0]};
+      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
+      ${getWorkGroupSizeString()}
+      fn main(@builtin(local_invocation_id) localId : vec3<u32>,
+              @builtin(workgroup_id) workgroupId : vec3<u32>) {
+        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
+        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
+        let width = uniforms.outShape[0];
+        let height = uniforms.outShape[1];
+        if (x < width && y < height) {
+          tile[localId.y][localId.x] = A[y * width + x];
+        }
+        workgroupBarrier();
+
+        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
+        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
+        if (x < height && y < width) {
+          setOutputAtIndex((y * height + x), tile[localId.x]
+            [localId.y]);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var TransposeProgram2 = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.newDim = newDim;
+    this.shaderKey = `transpose_${newDim}`;
+  }
+  getUserCode() {
+    const dtype = getCoordsDataType2(this.outputShape.length);
+    const switched = getSwitchedCoords2(this.newDim);
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+
+        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if(flatIndex < uniforms.size) {
+            let resRC = getCoordsFromIndex(flatIndex);
+            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
+              ${dtype}(${switched}), uniforms.aShape)]);
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function getSwitchedCoords2(newDim) {
+  const rank = newDim.length;
+  if (rank > 6) {
+    throw Error(`Transpose for rank ${rank} is not yet supported`);
+  }
+  const switchedCoords = new Array(rank);
+  for (let i2 = 0; i2 < newDim.length; i2++) {
+    switchedCoords[newDim[i2]] = `resRC.${getCoordsXYZ(i2)}`;
+  }
+  return switchedCoords.join();
+}
+function transpose5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { perm } = attrs;
+  const webgpuBackend = backend2;
+  const xRank = x.shape.length;
+  const newShape = new Array(xRank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[perm[i2]];
+  }
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xData = webgpuBackend.tensorMap.get(x.dataId);
+    const values = xData.values;
+    const outValues = transposeImplCPU2(values, x.shape, x.dtype, perm, newShape);
+    return backend2.makeTensorInfo(newShape, x.dtype, outValues);
+  }
+  if (x.shape.length === 2 && util_exports.arraysEqual(perm, [1, 0])) {
+    const program2 = new TransposeSharedProgram(x.shape, perm);
+    return webgpuBackend.runWebGPUProgram(program2, [x], x.dtype);
+  }
+  const program = new TransposeProgram2(x.shape, perm);
+  return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);
+}
+var transposeConfig4 = {
+  kernelName: Transpose,
+  backendName: "webgpu",
+  kernelFunc: transpose5
+};
+function argMax4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
+  const program = new ArgMinMaxProgram2($x.shape, axes[0], "max");
+  const uniformData = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }];
+  const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData);
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return out;
+}
+var argMaxConfig4 = {
+  kernelName: ArgMax,
+  backendName: "webgpu",
+  kernelFunc: argMax4
+};
+function argMin4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
+  const program = new ArgMinMaxProgram2($x.shape, axes[0], "min");
+  const uniformData = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }];
+  const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData);
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return out;
+}
+var argMinConfig3 = {
+  kernelName: ArgMin,
+  backendName: "webgpu",
+  kernelFunc: argMin4
+};
+var Pool2DProgram2 = class {
+  constructor(convInfo, poolType) {
+    this.variableNames = ["x"];
+    this.uniforms = `stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,`;
+    this.workGroupSize = [128, 1, 1];
+    this.size = true;
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `pool2D_${poolType}`;
+    this.poolType = poolType;
+  }
+  getUserCode() {
+    let updateSnippet = `resultValue = max(value, resultValue);`;
+    if (this.poolType === "avg") {
+      updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`;
+    }
+    let returnValue = `resultValue`;
+    if (this.poolType === "avg") {
+      returnValue = `resultValue / count`;
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+      if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+          let batch = coords[0];
+          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
+          let xRCorner = xRCCorner.x;
+          let xCCorner = xRCCorner.y;
+
+          var resultValue = ${this.poolType === "avg" ? "0.0" : "-1.0 / pow(10.0, -20.0)"};
+          var count = 0.0;
+
+          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
+            let xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= uniforms.convDims.x) {
+              continue;
+            }
+
+            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
+              let xC = xCCorner + wC;
+              if (xC < 0 || xC >= uniforms.convDims.y) {
+                continue;
+              }
+
+              let value = getX(batch, xR, xC, coords[3]);
+              ${updateSnippet}
+            }
+          }
+
+          setOutputAtIndex(index, ${returnValue});
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var PoolWithFilterSizeEqualsOneProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x"];
+    this.uniforms = `stride : vec2<i32>,`;
+    this.workGroupSize = [256, 1, 1];
+    this.size = true;
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "poolWithFilterSizeEqualsOne";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let batch = coords[0];
+          let d = coords[3];
+
+          let xRCCorner = coords.yz * uniforms.stride;
+          let xRCorner = xRCCorner.x;
+          let xCCorner = xRCCorner.y;
+
+          let value = getX(batch, xRCorner, xCCorner, d);
+          setOutputAtIndex(index, value);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function avgPool5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    return identity5({ inputs: { x }, backend: backend2 });
+  }
+  let program;
+  const dimensions = [{ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }];
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {
+    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);
+  } else {
+    program = new Pool2DProgram2(convInfo, "avg");
+    dimensions.push({ type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {
+      type: "int32",
+      data: [convInfo.dilationHeight, convInfo.dilationWidth]
+    }, { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }, {
+      type: "int32",
+      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
+    });
+  }
+  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);
+}
+var avgPoolConfig4 = {
+  kernelName: AvgPool,
+  backendName: "webgpu",
+  kernelFunc: avgPool5
+};
+function batchMatMul4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  return batchMatMulImpl2({ a: a6, b, transposeA, transposeB, backend: backend2 });
+}
+var batchMatMulConfig4 = {
+  kernelName: BatchMatMul,
+  backendName: "webgpu",
+  kernelFunc: batchMatMul4
+};
+var SliceProgram2 = class {
+  constructor(start, destSize) {
+    this.variableNames = ["source"];
+    this.workPerThread = 1;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = destSize;
+    this.rank = destSize.length;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.start = start;
+    this.uniforms = `start : ${getCoordsDataType2(start.length)}, `;
+    this.shaderKey = "slice";
+  }
+  getUserCode() {
+    const dtype = getCoordsDataType2(this.rank);
+    const sourceCoords = getCoords3(this.rank);
+    let coordSum;
+    if (this.start.length === 1) {
+      coordSum = this.outputShape.map((_, i2) => {
+        return `sourceLoc = uniforms.start + coords;`;
+      });
+    } else {
+      coordSum = this.outputShape.map((_, i2) => {
+        return `sourceLoc.${coords2[i2]} = uniforms.start[${i2}] + coords.${coords2[i2]};`;
+      });
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          var sourceLoc : ${dtype};
+          let coords = getCoordsFromIndex(index);
+          ${coordSum.join("\n")}
+          setOutputAtIndex(index, getSource(${sourceCoords}));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var coords2 = ["x", "y", "z", "w", "u", "v"];
+function getCoords3(rank) {
+  if (rank === 1) {
+    return "sourceLoc";
+  } else if (rank <= 6) {
+    return coords2.slice(0, rank).map((coord) => `sourceLoc.${coord}`).join(",");
+  } else {
+    throw Error(`Slicing for rank ${rank} is not yet supported`);
+  }
+}
+function slice5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, size: size2 } = attrs;
+  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size2);
+  slice_util_exports.assertParamsValid(x, $begin, $size);
+  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") {
+    const xBufferInfo = backend2.tensorMap.get(x.dataId);
+    const outValues = sliceImplCPU2(xBufferInfo.values, $begin, $size, x.shape, x.dtype);
+    return backend2.makeTensorInfo($size, x.dtype, outValues);
+  }
+  if (util_exports.sizeFromShape($size) === 0) {
+    return backend2.makeTensorInfo($size, x.dtype, []);
+  }
+  const program = new SliceProgram2($begin, $size);
+  const uniformData = [{ type: "int32", data: $begin }];
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+}
+var sliceConfig4 = {
+  kernelName: Slice,
+  backendName: "webgpu",
+  kernelFunc: slice5
+};
+var batchToSpaceND5 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const toDispose = [];
+  const reshapedIntermediate = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const transposedIntermediate = transpose5({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });
+  const reshapedIntermediate2 = reshape6({
+    inputs: { x: transposedIntermediate },
+    backend: backend2,
+    attrs: { shape: reshapedPermuted }
+  });
+  const sliced = slice5({
+    inputs: { x: reshapedIntermediate2 },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  toDispose.push(reshapedIntermediate);
+  toDispose.push(transposedIntermediate);
+  toDispose.push(reshapedIntermediate2);
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return sliced;
+};
+var batchToSpaceNDConfig4 = {
+  kernelName: BatchToSpaceND,
+  backendName: "webgpu",
+  kernelFunc: batchToSpaceND5
+};
+var notEqual4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.NOT_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: notEqualImplCPU2
+});
+var notEqualConfig4 = {
+  kernelName: NotEqual,
+  backendName: "webgpu",
+  kernelFunc: notEqual4
+};
+function real4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.tensorMap.get(input2.dataId);
+  return identity5({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });
+}
+var realConfig3 = {
+  kernelName: Real,
+  backendName: "webgpu",
+  kernelFunc: real4
+};
+function int2(input2, backend2) {
+  const program = new UnaryOpProgram2(input2.shape, UnaryOpType.TO_INT);
+  const output = backend2.runWebGPUProgram(program, [input2], "int32");
+  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };
+}
+function cast6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dtype } = attrs;
+  if (dtype === "complex64") {
+    if (x.dtype === "complex64") {
+      return identity5({ inputs: { x }, backend: backend2 });
+    }
+    const zerosTensor = zeros(x.shape);
+    const floatX = cast6({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+    const result = complex4({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });
+    zerosTensor.dispose();
+    backend2.disposeData(floatX.dataId);
+    return result;
+  }
+  if (x.dtype === "complex64") {
+    const realPart = real4({ inputs: { input: x }, backend: backend2 });
+    const result = cast6({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
+    backend2.disposeData(realPart.dataId);
+    return result;
+  }
+  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
+    const result = identity5({ inputs: { x }, backend: backend2 });
+    return { dataId: result.dataId, shape: result.shape, dtype };
+  }
+  if (dtype === "int32") {
+    return int2(x, backend2);
+  }
+  if (dtype === "bool") {
+    const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1));
+    const binaryInputs = { a: x, b: zerosTensorInfo };
+    const result = notEqual4({ inputs: binaryInputs, backend: backend2 });
+    backend2.disposeData(zerosTensorInfo.dataId);
+    return result;
+  }
+  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
+}
+var castConfig4 = {
+  kernelName: Cast,
+  backendName: "webgpu",
+  kernelFunc: cast6
+};
+var ceil4 = unaryKernelFunc3({ opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU2 });
+var ceilConfig4 = {
+  kernelName: Ceil,
+  backendName: "webgpu",
+  kernelFunc: ceil4
+};
+var ClipVec4Program = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.uniforms = "minVal : f32, maxVal : f32,";
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.isVec4 = true;
+    this.size = true;
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.shaderKey = "clipVec4";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if(index < uniforms.size) {
+          let value = getAByOutputIndex(index);
+          var clampedValue : vec4<f32>;
+          for (var i = 0; i < 4; i = i + 1) {
+            if (isnan(value[i])) {
+              clampedValue[i] = value[i];
+            } else {
+              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
+            }
+          }
+
+          setOutputAtIndex(index, clampedValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var ClipProgram2 = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.uniforms = "minVal : f32, maxVal : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "clip";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if(index < uniforms.size) {
+          let value = getAByOutputIndex(index);
+          if (isnan(value)) {
+            setOutputAtIndex(index, value);
+            return;
+          }
+          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function clipByValue4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { clipValueMin, clipValueMax } = attrs;
+  let program;
+  const uniformData = [
+    { type: "float32", data: [clipValueMin] },
+    { type: "float32", data: [clipValueMax] }
+  ];
+  if (util_exports.sizeFromShape(x.shape) % 4 === 0) {
+    program = new ClipVec4Program(x.shape);
+  } else {
+    program = new ClipProgram2(x.shape);
+  }
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+}
+var clipByValueConfig4 = {
+  kernelName: ClipByValue,
+  backendName: "webgpu",
+  kernelFunc: clipByValue4
+};
+var ConcatProgram2 = class {
+  constructor(shapes) {
+    this.uniforms = "";
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.offsetLength = shapes.length - 1;
+    for (let i2 = 0; i2 < this.offsetLength; i2++) {
+      this.uniforms += `offset${i2} : i32,`;
+    }
+    this.shaderKey = "concat";
+  }
+  getUserCode() {
+    const snippets = [];
+    if (this.offsetLength > 0) {
+      snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);
+      for (let i2 = 1; i2 < this.offsetLength; i2++) {
+        snippets.push(`else if (yC < uniforms.offset${[i2]}){ setOutputAtCoords(coords.x, coords.y, getT${i2}(yR, yC - uniforms.offset${i2 - 1})); }`);
+      }
+      const lastIndex = this.offsetLength;
+      const lastShiftIndex = this.offsetLength - 1;
+      snippets.push(`else { setOutputAtCoords(coords.x, coords.y, getT${lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`);
+    } else {
+      snippets.push(`setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));`);
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if(flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndex);
+            let yR = coords.x;
+            let yC = coords.y;
+
+            ${snippets.join("\n        ")}
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function imag4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.tensorMap.get(input2.dataId);
+  return identity5({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });
+}
+var imagConfig3 = {
+  kernelName: Imag,
+  backendName: "webgpu",
+  kernelFunc: imag4
+};
+function concatImpl3(inputs, axis, backend2) {
+  const dtype = inputs[0].dtype;
+  if (dtype === "complex64") {
+    const reals = inputs.map((t2) => real4({ inputs: { input: t2 }, backend: backend2 }));
+    const imags = inputs.map((t2) => imag4({ inputs: { input: t2 }, backend: backend2 }));
+    const realConcated = concatImpl3(reals, axis, backend2);
+    const imagConcated = concatImpl3(imags, axis, backend2);
+    const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
+    reals.forEach((r2) => backend2.disposeData(r2.dataId));
+    imags.forEach((i2) => backend2.disposeData(i2.dataId));
+    backend2.disposeData(realConcated.dataId);
+    backend2.disposeData(imagConcated.dataId);
+    return result;
+  }
+  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);
+  if (dtype === "string") {
+    runOnCpu = true;
+  }
+  if (runOnCpu) {
+    const tensors2D2 = inputs.map((t2) => {
+      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+      const shape = [-1, innerSize];
+      return reshape6({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    });
+    const inputsValShapes = tensors2D2.map((t2) => {
+      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    });
+    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);
+    const simplyConcat = tensors2D2[0].shape[0] === 1;
+    const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat);
+    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
+    tensors2D2.forEach((t2) => backend2.disposeData(t2.dataId));
+    return outInfo;
+  }
+  const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2);
+  const shapes = tensors2D.map((t2) => t2.shape);
+  const program = new ConcatProgram2(shapes);
+  const uniformData = [];
+  const offsets = new Array(shapes.length - 1);
+  if (offsets.length > 0) {
+    offsets[0] = shapes[0][1];
+    uniformData.push({ type: "int32", data: [offsets[0]] });
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
+      uniformData.push({ type: "int32", data: [offsets[i2]] });
+    }
+  }
+  const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);
+  tensors2D.forEach((r2) => backend2.disposeData(r2.dataId));
+  const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
+  backend2.disposeData(res.dataId);
+  return reshapedResult;
+}
+function computeTensors2D2(inputs, axis, backend2) {
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const tensors2D = inputs.map((t2) => reshape6({
+    inputs: { x: t2 },
+    backend: backend2,
+    attrs: {
+      shape: [
+        util_exports.sizeFromShape(t2.shape.slice(0, axis)),
+        util_exports.sizeFromShape(t2.shape.slice(axis))
+      ]
+    }
+  }));
+  return { tensors2D, outShape };
+}
+function concat5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
+  }
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity5({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, $axis);
+  return concatImpl3($inputs, $axis, backend2);
+}
+var concatConfig4 = {
+  kernelName: Concat,
+  backendName: "webgpu",
+  kernelFunc: concat5
+};
+var Conv2DMMVec4Program = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [8, 8, 1];
+    this.isVec4 = true;
+    this.outputShape = convInfo.outShape;
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };
+    if (this.outputShape[1] === 1) {
+      this.elementsPerThread = [4, 1, 1];
+    } else {
+      this.elementsPerThread = [4, 4, 1];
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.innerElementSize = this.convInfo.inChannels % 4 === 0 ? 4 : 3;
+    if (this.innerElementSize === 3) {
+      this.variableTypes = ["f32", "vec4<f32>"];
+    } else {
+      this.variableTypes = ["vec4<f32>", "vec4<f32>"];
+    }
+    if (this.addBias) {
+      this.variableNames.push("bias");
+      this.variableTypes.push("vec4<f32>");
+    }
+    if (this.hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+      this.variableTypes.push("vec4<f32>");
+    }
+    this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1];
+    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];
+    this.tileInner = this.workGroupSize[0] * this.innerElementSize;
+    [this.fitA, this.fitB] = this.getShapeFit();
+    this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`;
+  }
+  getShapeFit() {
+    const tileSizeA = [this.tileAOuter, this.tileInner];
+    const tileSizeB = [this.tileInner, this.tileBOuter];
+    const dimAOuter = this.outputShape[1] * this.outputShape[2];
+    const dimBOuter = this.outputShape[3];
+    const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]),
+      tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter])
+    ];
+  }
+  getUserCode() {
+    const matMulSource = makeMatMulPackedVec4Source(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, this.innerElementSize);
+    const readASnippet = `let outRow = r / uniforms.outShape[2];
+        let outCol = r % uniforms.outShape[2];
+        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
+        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
+        let inChCoord = c % uniforms.xShape[3];
+        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+
+        var resData = vec${this.innerElementSize}<f32>(0.0);
+        // The bounds checking is always needed since we use it to pad zero for
+        // the 'same' padding type.
+        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
+          var coord = vec4<i32>(
+            batch,
+            xRow,
+            xCol,
+            inChCoord);
+          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
+          ${this.innerElementSize === 3 ? "resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);" : "resData = x[xIndex / 4];"}
+        }
+        return resData;`;
+    const sampleA = this.fitA ? `${readASnippet}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
+          ${readASnippet}
+         }
+         return vec${this.innerElementSize}<f32>(0.0);
+        `;
+    const sampleB = this.fitB ? `return W[row * uniforms.dimBOuter / 4 + col];` : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return W[row * uniforms.dimBOuter / 4 + col];
+         }
+         return vec4<f32>(0.0);
+        `;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${activationOp}
+        }`;
+      } else {
+        activationSnippet = `
+        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          ${activationOp}
+        }`;
+      }
+      applyActivationSnippet = `value = activation(value, outCoord);`;
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+        ${activationSnippet}
+        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
+          let r = row;
+          let c = col * ${this.innerElementSize};
+          var batch = i32(globalId.z);
+          ${sampleA}
+        }
+
+        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
+          ${sampleB}
+        }
+
+        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
+          var batch = i32(globalId.z);
+          var value = valueInput;
+          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
+          {
+            let outCoord = vec4<i32>(
+              batch,
+              row / uniforms.outShape[2],
+              row % uniforms.outShape[2],
+              col * 4);
+            ${addBiasSnippet}
+            ${applyActivationSnippet}
+            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
+              value);
+          }
+        }
+        ${matMulSource}
+      `;
+    return userCode;
+  }
+};
+var Conv2DMMProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.outputShape = convInfo.outShape;
+    this.isChannelsLast = convInfo.dataFormat === "channelsLast";
+    this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [1], y: [2, 3], z: [0] };
+    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape);
+    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    [this.fitA, this.fitB] = this.getShapeFit();
+    this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`;
+  }
+  getShapeFit() {
+    const tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];
+    const tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];
+    const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+    util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => "tileInner must be multiple of workgroupsize.x and workgroupsize.y");
+    const tileSizeA = [tileAOuter, tileInner];
+    const tileSizeB = [tileInner, tileBOuter];
+    const dimAOuter = this.convInfo.outHeight * this.convInfo.outWidth;
+    const dimBOuter = this.convInfo.outChannels;
+    const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]),
+      tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter])
+    ];
+  }
+  getUserCode() {
+    const coordASnippet = this.isChannelsLast ? `
+    let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);
+    ` : `
+    let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);
+    `;
+    const coordResSnippet = this.isChannelsLast ? `
+    let outCoord = vec4<i32>(
+      batch,
+      row / outWidth,
+      row % outWidth,
+      col);
+    ` : `
+    let outCoord = vec4<i32>(
+      batch,
+      col,
+      row / outWidth,
+      row % outWidth);
+    `;
+    const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);
+    const readASnippet = `
+    let inChannels = uniforms.wShape[2];
+    let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
+    let outRow = row / outWidth;
+    let outCol = row % outWidth;
+
+    let WRow = col / (uniforms.filterDims[1] * inChannels);
+    let WCol = col / inChannels % uniforms.filterDims[1];
+    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+    ${coordASnippet}
+    // The bounds checking is always needed since we use it to pad zero for the
+    // 'same' padding type.
+    if(coordsInBounds4D(coord, uniforms.xShape)) {
+      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
+    }
+    return 0.0;`;
+    const sampleA = this.fitA ? `${readASnippet}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+      ${readASnippet}
+    }
+    return 0.0;
+    `;
+    const sampleB = this.fitB ? `return W[row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return W[row * uniforms.dimBOuter + col];
+	 }
+	 return 0.0;
+	 `;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${activationOp}
+                }`;
+      } else {
+        activationSnippet = `
+                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+                    ${activationOp}
+                  }
+                `;
+      }
+      applyActivationSnippet = `value = activation(value, outCoord);`;
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+    ${activationSnippet}
+    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      var batch = i32(globalId.z);
+      ${sampleA}
+    }
+
+    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      ${sampleB}
+    }
+
+    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      var batch = i32(globalId.z);
+      var value = valueInput;
+      let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
+      ${coordResSnippet}
+      ${addBiasSnippet}
+      ${applyActivationSnippet}
+      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+    }
+    ${matMulSource}
+  `;
+    return userCode;
+  }
+};
+function conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const transposeA = isChannelsLast ? false : true;
+  const transposeB = false;
+  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === "VALID";
+  let xReshaped;
+  let filterReshaped;
+  if (sameSize) {
+    const sharedDim = convInfo.inHeight * convInfo.inWidth * convInfo.inChannels;
+    xReshaped = reshape6({
+      inputs: { x },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.batchSize, sharedDim] }
+    });
+    filterReshaped = reshape6({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, sharedDim, convInfo.outChannels] }
+    });
+  } else {
+    xReshaped = reshape6({
+      inputs: { x },
+      backend: backend2,
+      attrs: {
+        shape: isChannelsLast ? [
+          convInfo.batchSize,
+          convInfo.inHeight * convInfo.inWidth,
+          convInfo.inChannels
+        ] : [
+          convInfo.batchSize,
+          convInfo.inChannels,
+          convInfo.inHeight * convInfo.inWidth
+        ]
+      }
+    });
+    filterReshaped = reshape6({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+    });
+  }
+  const result = batchMatMulImpl2({
+    a: isChannelsLast ? xReshaped : filterReshaped,
+    b: isChannelsLast ? filterReshaped : xReshaped,
+    transposeA,
+    transposeB,
+    backend: backend2,
+    bias,
+    activation: activation2,
+    preluActivationWeights,
+    leakyreluAlpha
+  });
+  const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  backend2.disposeData(xReshaped.dataId);
+  backend2.disposeData(filterReshaped.dataId);
+  backend2.disposeData(result.dataId);
+  return out;
+}
+function conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  let program;
+  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === "VALID";
+  if (sameSize || convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
+    return conv2dByMatMul2({
+      x,
+      filter,
+      convInfo,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+  }
+  const useVec4 = (convInfo.inChannels % 4 === 0 || convInfo.inChannels % 3 === 0) && convInfo.outChannels % 4 === 0 && isChannelsLast;
+  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];
+  const dimensions = [
+    { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] },
+    { type: "int32", data: [...padInfo] },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] }
+  ];
+  if (useVec4) {
+    program = new Conv2DMMVec4Program(convInfo, hasBias, activation2, hasPreluActivationWeights);
+  } else {
+    program = new Conv2DMMProgram(convInfo, hasBias, activation2, hasPreluActivationWeights);
+  }
+  const dimAOuter = convInfo.outHeight * convInfo.outWidth;
+  const dimBOuter = convInfo.outChannels;
+  const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inChannels;
+  dimensions.push({ type: "int32", data: [dimAOuter] }, { type: "int32", data: [dimBOuter] }, { type: "int32", data: [dimInner] });
+  const inputVar = [x, filter];
+  if (hasBias) {
+    inputVar.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    inputVar.push(preluActivationWeights);
+  }
+  if (activation2 === "leakyrelu") {
+    dimensions.push({ type: "float32", data: [leakyreluAlpha] });
+    program.uniforms += " alpha : f32,";
+  }
+  return backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);
+}
+function conv2d6(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  return conv2DImpl({ x, filter, convInfo, backend: backend2 });
+}
+var conv2DConfig4 = {
+  kernelName: Conv2D,
+  backendName: "webgpu",
+  kernelFunc: conv2d6
+};
+var Conv2DDerInputMMProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,";
+    this.outputShape = convInfo.inShape;
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };
+    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape);
+    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
+  }
+  getUserCode() {
+    const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);
+    const readASnippet = `
+    let outRow = row / uniforms.outShape[2];
+    let outCol = row % uniforms.outShape[2];
+
+    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
+    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
+    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
+    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
+    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
+      return 0.0;
+    }
+    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
+      return 0.0;
+    }
+    let coord = vec4<i32>(
+        batch,
+        i32(xR),
+        i32(xC),
+        col % uniforms.outBackprop[3]);
+    return x[getIndexFromCoords4D(coord, uniforms.xShape)];`;
+    const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+      ${readASnippet}
+    }
+    return 0.0;`;
+    const userCode = `
+    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      var batch = i32(globalId.z);
+      ${sampleA}
+    }
+
+    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      let coordX = uniforms.filterDims.x - 1 -
+          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
+      let coordY = uniforms.filterDims.y - 1 -
+          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
+      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
+          coordX >= 0 && coordY >= 0) {
+        let coord = vec4<i32>(coordX, coordY, col,
+            row % uniforms.outBackprop[3]);
+        return W[getIndexFromCoords4D(coord, uniforms.wShape)];
+      }
+      return 0.0;
+    }
+
+    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      var batch = i32(globalId.z);
+      var value = valueInput;
+      let outCoord = vec4<i32>(
+          batch,
+          row / uniforms.outShape[2],
+          row % uniforms.outShape[2],
+          col);
+      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+    }
+
+    ${matMulSource}
+  `;
+    return userCode;
+  }
+};
+var Conv2DDerInputProgram2 = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = convInfo.inShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.isChannelsLast = convInfo.dataFormat === "channelsLast";
+    this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
+  }
+  getUserCode() {
+    const rowDim = this.isChannelsLast ? 1 : 2;
+    const colDim = this.isChannelsLast ? 2 : 3;
+    const channelDim = this.isChannelsLast ? 3 : 1;
+    return `
+    ${getMainHeaderAndGlobalIndexString()} {
+      if(index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+        let batch = coords[0];
+        let d1 = coords[${channelDim}];
+
+        let dyCorner = vec2<i32>(coords[${rowDim}]), coords[${colDim}]) - uniforms.pads;
+        let dyRCorner = dyCorner.x;
+        let dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        var dotProd = 0.0;
+        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
+          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
+          let wRPerm = uniforms.filterDims.x - 1 - wR;
+          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
+              wRPerm < 0) {
+            continue;
+          }
+          let idyR = dyR;
+
+          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
+            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
+            let wCPerm = uniforms.filterDims.y - 1 - wC;
+            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
+                fract(dyC) > 0.0 || wCPerm < 0) {
+              continue;
+            }
+            let idyC = dyC;
+
+            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
+              if (${this.isChannelsLast}) {
+                let xValue = getDy(batch, idyR, idyC, d2);
+                let wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd = dotProd + xValue * wValue;
+              } else {
+                let xValue = getDy(batch, d2, idyR, idyC);
+                let wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd = dotProd + xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutputAtIndex(index, dotProd);
+      }
+    }
+  `;
+  }
+};
+function conv2DBackpropInput5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { inputShape, strides: strides2, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides2, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const dimensions = [
+    { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] },
+    {
+      type: "int32",
+      data: [
+        convInfo.filterHeight - 1 - convInfo.padInfo.top,
+        convInfo.filterWidth - 1 - convInfo.padInfo.left
+      ]
+    },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    {
+      type: "int32",
+      data: [
+        convInfo.batchSize,
+        convInfo.outHeight,
+        convInfo.outWidth,
+        convInfo.outChannels
+      ]
+    }
+  ];
+  let program;
+  if (env().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")) {
+    program = new Conv2DDerInputProgram2(convInfo);
+  } else {
+    program = new Conv2DDerInputMMProgram(convInfo);
+    const dimAOuter = convInfo.inShape[1] * convInfo.inShape[2];
+    const dimBOuter = convInfo.inShape[3];
+    const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels;
+    dimensions.push({ type: "uint32", data: [dimAOuter] }, { type: "uint32", data: [dimBOuter] }, { type: "uint32", data: [dimInner] });
+  }
+  return backend2.runWebGPUProgram(program, [dy, filter], "float32", dimensions);
+}
+var conv2DBackpropInputConfig4 = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "webgpu",
+  kernelFunc: conv2DBackpropInput5
+};
+var cos4 = unaryKernelFunc3({ opType: UnaryOpType.COS });
+var cosConfig4 = {
+  kernelName: Cos,
+  backendName: "webgpu",
+  kernelFunc: cos4
+};
+var cosh4 = unaryKernelFunc3({ opType: UnaryOpType.COSH });
+var coshConfig4 = {
+  kernelName: Cosh,
+  backendName: "webgpu",
+  kernelFunc: cosh4
+};
+var CropAndResizeProgram2 = class {
+  constructor(channnel, boxShape, cropSize, method) {
+    this.variableNames = ["Image", "Boxes", "BoxInd"];
+    this.uniforms = "extrapolationValue : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    const [numBoxes] = boxShape;
+    this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.methodId = method === "bilinear" ? 1 : 0;
+    this.cropHeightBiggerThan1 = this.outputShape[1] > 1;
+    this.cropWidthBiggerThan1 = this.outputShape[2] > 1;
+    this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
+  }
+  getUserCode() {
+    const [inputHeightFloat, inputWidthFloat] = [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`];
+    const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ? [
+      `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`,
+      "(y2-y1) * height_ratio",
+      `y1*${inputHeightFloat} + f32(y)*(height_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (y1+y2) * ${inputHeightFloat}`
+    ];
+    const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ? [
+      `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`,
+      "(x2-x1) * width_ratio",
+      `x1*${inputWidthFloat} + f32(x)*(width_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (x1+x2) * ${inputWidthFloat}`
+    ];
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+      if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+        let height_ratio = f32(${heightRatio});
+        let width_ratio = f32(${widthRatio});
+        let b = coords[0];
+        let y = coords[1];
+        let x = coords[2];
+        let d = coords[3];
+        // get box vals
+        let y1 = getBoxes(b, 0);
+        let x1 = getBoxes(b, 1);
+        let y2 = getBoxes(b, 2);
+        let x2 = getBoxes(b, 3);
+        // get image in batch index
+        let bInd = i32(round(getBoxInd(b)));
+        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
+          return;
+        }
+        let height_scale = ${heightScale};
+        let width_scale = ${widthScale};
+        let in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutputAtIndex(index, uniforms.extrapolationValue);
+          return;
+        }
+        let in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutputAtIndex(index, uniforms.extrapolationValue);
+          return;
+        }
+        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
+        if(${this.methodId} == 1) {
+          // Compute the four integer indices.
+          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
+          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
+          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
+          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
+          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
+          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
+          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
+          let top = topLeft + (topRight - topLeft) * fracCR.x;
+          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          let newValue = top + (bottom - top) * fracCR.y;
+          setOutputAtIndex(index, newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          let sourceNearestCR = vec2<i32>(floor(
+            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
+          let newValue = getImage(
+            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    }
+    `;
+    return userCode;
+  }
+};
+var cropAndResize5 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, boxes, boxInd } = inputs;
+  const { cropSize, method, extrapolationValue } = attrs;
+  const program = new CropAndResizeProgram2(image2.shape[3], boxes.shape, cropSize, method);
+  const uniformData = [{ type: "float32", data: [extrapolationValue] }];
+  return backend2.runWebGPUProgram(program, [image2, boxes, boxInd], "float32", uniformData);
+};
+var cropAndResizeConfig4 = {
+  kernelName: CropAndResize,
+  backendName: "webgpu",
+  kernelFunc: cropAndResize5
+};
+var CumOpType2;
+(function(CumOpType3) {
+  CumOpType3["Prod"] = "*";
+  CumOpType3["Sum"] = "+";
+})(CumOpType2 || (CumOpType2 = {}));
+var CumProgram2 = class {
+  constructor(op2, shape, exclusive, reverse5) {
+    this.variableNames = ["x"];
+    this.uniforms = "index : f32,";
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.exclusive = exclusive;
+    this.reverse = reverse5;
+    this.op = op2;
+    this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
+  }
+  getUserCode() {
+    const rank = this.outputShape.length;
+    const initVal = this.op === CumOpType2.Prod ? "1.0" : "0.0";
+    const val = this.exclusive ? initVal : `getX(${getCoords4(rank, "coords", this.op)})`;
+    const length = this.outputShape[this.outputShape.length - 1];
+    let condition = "";
+    let idxString = "";
+    if (this.exclusive) {
+      condition = this.reverse ? `end != ${length - 1}` : "end != 0";
+      idxString = this.reverse ? "end + 1" : "end - 1";
+    } else {
+      condition = this.reverse ? `end + pow2 < ${length}` : "end >= pow2";
+      idxString = this.reverse ? "end + pow2" : "end - pow2";
+    }
+    return `
+      ${getMainHeaderAndGlobalIndexString()}
+       if (index < uniforms.size) {
+         var coords = getCoordsFromIndex(index);
+
+         let end = ${getFinalCoord2(rank, "coords", this.op)};
+         var val = ${val};
+         let pow2 = i32(pow(2.0, uniforms.index));
+         if (${condition}) {
+           let idx = ${idxString};
+           ${getFinalCoord2(rank, "coords", this.op)} = idx;
+           val ${this.op}= getX(${getCoords4(rank, "coords", this.op)});
+         }
+         setOutputAtIndex(index, val);
+       }
+      }
+    `;
+  }
+};
+function getCoords4(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.x, ${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.x, ${name}.y, ${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
+  } else {
+    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+function getFinalCoord2(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.w`;
+  } else {
+    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+function cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {
+  const xRank = x.shape.length;
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation != null) {
+    permutedX = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  if (permutedAxis !== xRank - 1) {
+    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);
+  }
+  const size2 = permutedX.shape[permutedAxis];
+  let result = identity5({ inputs: { x: permutedX }, backend: backend2 });
+  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size2)) - 1; i2++) {
+    const program = new CumProgram2(op2, permutedX.shape, false, reverse5);
+    const prevResult = result;
+    const uniformData = [{ type: "float32", data: [i2] }];
+    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);
+    backend2.disposeData(prevResult.dataId);
+  }
+  if (exclusive) {
+    const program = new CumProgram2(op2, permutedX.shape, exclusive, reverse5);
+    const prevResult = result;
+    const uniformData = [{ type: "float32", data: [0] }];
+    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);
+    backend2.disposeData(prevResult.dataId);
+  }
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose5({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeData(result.dataId);
+    backend2.disposeData(permutedX.dataId);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+function cumprod5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl2(CumOpType2.Prod, x, backend2, axis, exclusive, reverse5);
+}
+var cumprodConfig4 = {
+  kernelName: Cumprod,
+  backendName: "webgpu",
+  kernelFunc: cumprod5
+};
+function cumsum5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl2(CumOpType2.Sum, x, backend2, axis, exclusive, reverse5);
+}
+var cumsumConfig4 = {
+  kernelName: Cumsum,
+  backendName: "webgpu",
+  kernelFunc: cumsum5
+};
+var DepthToSpaceProgram2 = class {
+  constructor(outputShape, dataFormat) {
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.uniforms = "blockSize : i32,";
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `depthToSpace_${dataFormat}`;
+    this.dataFormat = dataFormat;
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let h = ${this.getHeightCoordString()};
+          let w = ${this.getWidthCoordString()};
+          let d = ${this.getDepthCoordString()};
+
+          let in_h = h / uniforms.blockSize;
+          let offset_h = h % uniforms.blockSize;
+          let in_w = w / uniforms.blockSize;
+          let offset_w = w % uniforms.blockSize;
+          let offset_d = (offset_h * uniforms.blockSize + offset_w) *
+            ${this.getOutputDepthSize()};
+          let in_d = d + offset_d;
+
+          let rlt = ${this.getInputSamplingString()};
+          setOutputAtIndex(index, rlt);
+        }
+      }`;
+    return userCode;
+  }
+  getHeightCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[1]`;
+    } else {
+      return `coords[2]`;
+    }
+  }
+  getWidthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[2]`;
+    } else {
+      return `coords[3]`;
+    }
+  }
+  getDepthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[3]`;
+    } else {
+      return `coords[1]`;
+    }
+  }
+  getOutputDepthSize() {
+    if (this.dataFormat === "NHWC") {
+      return `uniforms.outShape[3]`;
+    } else {
+      return `uniforms.outShape[1]`;
+    }
+  }
+  getInputSamplingString() {
+    if (this.dataFormat === "NHWC") {
+      return `getX(b, in_h, in_w, in_d)`;
+    } else {
+      return `getX(b, in_d, in_h, in_w)`;
+    }
+  }
+};
+function depthToSpace5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  const batchSize = x.shape[0];
+  const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
+  const uniformData = [
+    { type: "int32", data: [blockSize] }
+  ];
+  const program = new DepthToSpaceProgram2(outputShape, dataFormat);
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+}
+var depthToSpaceConfig4 = {
+  kernelName: DepthToSpace,
+  backendName: "webgpu",
+  kernelFunc: depthToSpace5
+};
+var DepthwiseConv2D3x3Program = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,";
+    this.workGroupSize = [4, 4, 4];
+    this.isVec4 = true;
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = { x: [0, 1], y: [2], z: [3] };
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]);
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivation = hasPreluActivation;
+    this.shaderKey = `depthwise3x3_${activation2}`;
+  }
+  getUserCode() {
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);
+      if (this.hasPreluActivation) {
+        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${activationOp}
+        }`;
+      } else {
+        activationSnippet = `
+        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+            ${activationOp}
+          }
+        `;
+      }
+      applyActivationSnippet = `dotProd[i] = activation(dotProd[i], coords);`;
+    }
+    const addBiasSnippet = this.addBias ? "dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      ${getWorkGroupSizeString()}
+      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
+        let batch = 0;
+        let r = i32(globalId.x);
+        let c = i32(globalId.y) * 4;
+        let d2 = i32(globalId.z) * 4;
+        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
+        let d1 = d2;
+        let q = 0;
+
+        let xRCorner = xRCCorner.x;
+        let xCCorner = xRCCorner.y;
+
+        var wVals : array<vec4<f32>, 9>;
+        wVals[0] = getW(0, 0, d1, q);
+        wVals[1] = getW(0, 1, d1, q);
+        wVals[2] = getW(0, 2, d1, q);
+        wVals[3] = getW(1, 0, d1, q);
+        wVals[4] = getW(1, 1, d1, q);
+        wVals[5] = getW(1, 2, d1, q);
+        wVals[6] = getW(2, 0, d1, q);
+        wVals[7] = getW(2, 1, d1, q);
+        wVals[8] = getW(2, 2, d1, q);
+
+        var xVals : array<array<vec4<f32>, 6>, 3>;
+        for (var wR = 0; wR < 3; wR = wR + 1) {
+          let xR = xRCorner + wR * uniforms.dilation[0];
+          for (var wC = 0; wC < 6; wC = wC + 1) {
+            let xC = xCCorner + wC * uniforms.dilation[1];
+            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
+              xVals[wR][wC] = vec4<f32>(0.0);
+            } else {
+              xVals[wR][wC] = getX(batch, xR, xC, d1);
+            }
+          }
+        }
+
+        var dotProd : array<vec4<f32>, 4>;
+        dotProd[0] = vec4<f32>(0.0);
+        dotProd[1] = vec4<f32>(0.0);
+        dotProd[2] = vec4<f32>(0.0);
+        dotProd[3] = vec4<f32>(0.0);
+
+        for (var wR = 0; wR < 3; wR = wR + 1) {
+          for (var wC = 0; wC < 3; wC = wC + 1) {
+            let indexW = wR * 3 + wC;
+            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
+            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
+            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
+            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
+          }
+        }
+
+        for (var i = 0; i < 4; i = i + 1) {
+          let coords = vec4<i32>(batch, r, c + i, d2);
+          if (coordsInBounds4D(coords, uniforms.outShape)) {
+            ${addBiasSnippet}
+            ${applyActivationSnippet}
+            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var DepthwiseConv2DProgram2 = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
+      channelMul : i32,`;
+    this.workGroupSize = [256, 1, 1];
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivation = hasPreluActivation;
+    this.shaderKey = `depthwise_${this.activation}`;
+  }
+  getUserCode() {
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivation) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${activationOp}
+        }`;
+      } else {
+        activationSnippet = `
+          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+            ${activationOp}
+          }
+        `;
+      }
+      applyActivationSnippet = `dotProd = activation(dotProd, coords);`;
+    }
+    const addBiasSnippet = this.addBias ? "dotProd = dotProd + getBiasByOutputCoords(coords);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
+          value : f32) {
+        let coord = vec4<i32>(batch, row, col, chan);
+        if (coordsInBounds4D(coord, uniforms.outShape)) {
+          setOutputAtCoords(batch, row, col, chan, value);
+        }
+      }
+
+      ${getMainHeaderString()}
+        let coords = getOutputCoords();
+        let batch = coords[0];
+        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
+        let d2 = coords[3];
+        let d1 = d2 / uniforms.channelMul;
+        let q = d2 - d1 * uniforms.channelMul;
+
+        let inputRowStart = xRCCorner.x;
+        let inputColStart = xRCCorner.y;
+        let inputRowEnd = inputRowStart + uniforms.filterHeight *
+            uniforms.dilation[0];
+        let inputColEnd = inputColStart + uniforms.filterWidth *
+            uniforms.dilation[1];
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        var dotProd = 0.0;
+
+        // Extract if checking out of for loop for performance.
+        if (inputRowStart >= 0 && inputColStart >= 0 &&
+          inputRowEnd < uniforms.inDims[0] &&
+              inputColEnd < uniforms.inDims[1]) {
+            // Here using a constant value |this.convInfo.filterHeight| instead
+            // of uniform value is in order to loop unrolling.
+            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
+              let xR = inputRowStart + wR * uniforms.dilation[0];
+
+              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
+                let xC = inputColStart + wC * uniforms.dilation[1];
+
+                let xVal = getX(batch, xR, xC, d1);
+                let wVal = getW(wR, wC, d1, q);
+                dotProd = dotProd + xVal * wVal;
+              }
+            }
+          } else {
+            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
+              let xR = inputRowStart + wR * uniforms.dilation[0];
+
+              if (xR < 0 || xR >= uniforms.inDims[0]) {
+                continue;
+              }
+
+              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
+                let xC = inputColStart + wC * uniforms.dilation[1];
+
+                if (xC < 0 || xC >= uniforms.inDims[1]) {
+                  continue;
+                }
+
+                let xVal = getX(batch, xR, xC, d1);
+                let wVal = getW(wR, wC, d1, q);
+                dotProd = dotProd + xVal * wVal;
+              }
+            }
+          }
+
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        writeResult(batch, coords[1], coords[2], d2, dotProd);
+      }
+    `;
+    return userCode;
+  }
+};
+function depthwiseConv2dNative3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dimRoundingMode } = attrs;
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, $dilations, pad3, dimRoundingMode, true);
+  const dimensions = [
+    { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] },
+    { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }
+  ];
+  let program;
+  if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) {
+    program = new DepthwiseConv2D3x3Program(convInfo);
+  } else {
+    program = new DepthwiseConv2DProgram2(convInfo);
+    dimensions.push({ type: "int32", data: [convInfo.filterHeight] }, { type: "int32", data: [convInfo.filterWidth] }, { type: "int32", data: [convInfo.outChannels / convInfo.inChannels] });
+  }
+  return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions);
+}
+var depthwiseConv2dNativeConfig4 = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "webgpu",
+  kernelFunc: depthwiseConv2dNative3
+};
+var multiplyKernelFunc = binaryKernelFunc3({
+  opSnippet: BinaryOpType.MUL,
+  cpuKernelImpl: multiplyImplCPU2,
+  supportsComplex: true
+});
+var multiplyConfig4 = {
+  kernelName: Multiply,
+  backendName: "webgpu",
+  kernelFunc: multiplyKernelFunc
+};
+var ReduceProgram2 = class {
+  constructor(reduceInfo, reduceType) {
+    this.workGroupSize = [64, 1, 1];
+    this.variableNames = ["x"];
+    this.uniforms = "reduceSize : i32,";
+    this.size = true;
+    this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize];
+    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(this.inputShape, [1]);
+    this.outputShape = outputShape.length === 0 ? [1] : outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);
+    this.reduceType = reduceType;
+    this.shaderKey = `reduce_${reduceType}`;
+  }
+  getUserCode() {
+    let reduceOp = ``;
+    let initValue = "0.0";
+    if (this.reduceType === "min" || this.reduceType === "max") {
+      reduceOp = `
+         if (isnan(candidate)) {
+          bestValue = uniforms.NAN;
+         } else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue)
+           {  bestValue = candidate; }`;
+      initValue = "f32(x[offset])";
+    } else if (this.reduceType === "sum" || this.reduceType === "mean") {
+      reduceOp = " bestValue = bestValue + candidate; ";
+    } else if (this.reduceType === "prod") {
+      reduceOp = " bestValue = bestValue * candidate; ";
+      initValue = "1.0";
+    }
+    const outputSnippet = this.reduceType === "mean" ? `setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));` : `setOutputAtIndex(outputIndex, bestValue);`;
+    const sharedMemorySnippet = `
+         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
+       `;
+    const userCode = `
+       fn DIV_CEIL(a : u32, b : u32) -> u32 {
+        return ((a - 1u) / b + 1u);
+       }
+
+       ${sharedMemorySnippet}
+       fn getOffset(outputIndex : i32) -> i32 {
+         let outputCoords = getCoordsFromIndex(outputIndex);
+         let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize;
+          return offset;
+       }
+       ${getMainHeaderAndGlobalIndexString()}
+         let outputIndex = index / i32(workGroupSizeX);
+         let offset = getOffset(outputIndex);
+         var bestValue = ${initValue};
+         let Length = uniforms.reduceSize;
+         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
+         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
+             k = k + i32(workGroupSizeX)) {
+           let candidate = f32(x[offset + k]);
+           ${reduceOp}
+         }
+         xBestValues[localId.x] = bestValue;
+         workgroupBarrier();
+
+         var reduceSize = min(u32(Length), workGroupSizeX);
+         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+             currentSize = reduceSize / 2u) {
+           let interval = DIV_CEIL(reduceSize, 2u);
+           if (localId.x < currentSize) {
+            let candidate = xBestValues[localId.x + interval];
+            ${reduceOp}
+            xBestValues[localId.x] = bestValue;
+           }
+           reduceSize = interval;
+           workgroupBarrier();
+         }
+
+         if (localId.x == 0u && outputIndex < uniforms.size) {
+          ${outputSnippet}
+        }
+       }
+     `;
+    return userCode;
+  }
+};
+function reduce2(x, axis, keepDims, reduceType, backend2) {
+  const xRank = x.shape.length;
+  const toDispose = [];
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let input2 = x;
+  if (permutedAxes != null) {
+    input2 = transpose5({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    toDispose.push(input2);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims(reduceType, axes, xRank);
+  const [reduceOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  let resOutShape = reduceOutShape;
+  if (keepDims) {
+    resOutShape = backend_util_exports.expandShapeToKeepDim(reduceOutShape, origAxes);
+  }
+  let res;
+  if ((reduceType === "max" || reduceType === "prod") && backend2.shouldExecuteOnCPU([input2])) {
+    const xVals = backend2.tensorMap.get(input2.dataId).values;
+    switch (reduceType) {
+      case "max":
+        const outValues = maxImplCPU2(xVals, util_exports.sizeFromShape(reduceShape), resOutShape, x.dtype);
+        res = backend2.makeTensorInfo(resOutShape, x.dtype, outValues);
+        break;
+      case "prod":
+        const { outVals, outShape, outDtype } = prodImplCPU2(input2.shape, input2.dtype, xVals, axes);
+        res = backend2.makeTensorInfo(outShape, outDtype, outVals);
+        break;
+      default:
+        throw new Error(`${reduceType} CPU implementation is not yet supported.`);
+    }
+  } else {
+    const inSize = util_exports.sizeFromShape(reduceShape);
+    const xSize = util_exports.sizeFromShape(input2.shape);
+    const batchSize = xSize / inSize;
+    const reduceInfo = { windowSize: inSize, inSize, batchSize, outSize: 1 };
+    const dtype = reduceType === "mean" ? "float32" : sumOutType(x.dtype);
+    const uniformData = [
+      { type: "int32", data: [inSize] }
+    ];
+    const program = new ReduceProgram2(reduceInfo, reduceType);
+    const reduced = backend2.runWebGPUProgram(program, [input2], dtype, uniformData);
+    toDispose.push(reduced);
+    res = reshape6({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 });
+  }
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return res;
+}
+function sum6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return reduce2(x, axis, keepDims, "sum", backend2);
+}
+var sumConfig4 = {
+  kernelName: Sum,
+  backendName: "webgpu",
+  kernelFunc: sum6
+};
+function einsum4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { equation } = attrs;
+  const tensors = inputs;
+  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
+  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
+  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
+  const nSteps = steps.length;
+  let out = null;
+  let numDimsRemaining = allDims.length;
+  const tensorsToDispose = [];
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    for (const idTerm of steps[i2]) {
+      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
+      let x;
+      if (backend_util_exports.isIdentityPermutation(perm)) {
+        x = tensors[idTerm];
+      } else {
+        x = transpose5({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
+        tensorsToDispose.push(x);
+      }
+      const targetShape = x.shape.slice();
+      for (let k = 0; k < dimsToExpand.length; ++k) {
+        targetShape.splice(dimsToExpand[k], 0, 1);
+      }
+      if (!util_exports.arraysEqual(x.shape, targetShape)) {
+        x = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
+        tensorsToDispose.push(x);
+      }
+      if (out === null) {
+        out = x;
+      } else {
+        out = multiplyKernelFunc({ inputs: { a: x, b: out }, backend: backend2 });
+        tensorsToDispose.push(out);
+      }
+    }
+    if (i2 < nSteps - 1) {
+      if (path[i2] >= 0) {
+        out = sum6({
+          inputs: { x: out },
+          backend: backend2,
+          attrs: {
+            axis: path[i2] - (allDims.length - numDimsRemaining),
+            keepDims: false
+          }
+        });
+        tensorsToDispose.push(out);
+      }
+      numDimsRemaining--;
+    }
+  }
+  for (const tensorInfo of tensorsToDispose) {
+    if (tensorInfo === out) {
+      continue;
+    }
+    backend2.disposeData(tensorInfo.dataId);
+  }
+  return out;
+}
+var einsumConfig3 = {
+  kernelName: Einsum,
+  backendName: "webgpu",
+  kernelFunc: einsum4
+};
+var elu6 = unaryKernelFunc3({ opType: UnaryOpType.ELU });
+var eluConfig4 = {
+  kernelName: Elu,
+  backendName: "webgpu",
+  kernelFunc: elu6
+};
+var equal4 = binaryKernelFunc3({ opSnippet: BinaryOpType.EQUAL, dtype: "bool", cpuKernelImpl: equalImplCPU2 });
+var equalConfig4 = {
+  kernelName: Equal,
+  backendName: "webgpu",
+  kernelFunc: equal4
+};
+var exp4 = unaryKernelFunc3({
+  opType: UnaryOpType.EXP,
+  cpuKernelImpl: expImplCPU2,
+  dtype: "float32"
+});
+var expConfig4 = {
+  kernelName: Exp,
+  backendName: "webgpu",
+  kernelFunc: exp4
+};
+function expandDims6(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { dim } = attrs;
+  const { input: input2 } = inputs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape6({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig4 = {
+  kernelName: ExpandDims,
+  backendName: "webgpu",
+  kernelFunc: expandDims6
+};
+var expm14 = unaryKernelFunc3({ opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU2 });
+var expm1Config3 = {
+  kernelName: Expm1,
+  backendName: "webgpu",
+  kernelFunc: expm14
+};
+var FillProgram2 = class {
+  constructor(shape) {
+    this.variableNames = [];
+    this.outputShape = [];
+    this.uniforms = "value : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "fill";
+  }
+  getUserCode() {
+    const userCode = `
+    ${getMainHeaderAndGlobalIndexString()}
+      if (index < uniforms.size) {
+        setOutputAtIndex(index, uniforms.value);
+      }
+    }
+  `;
+    return userCode;
+  }
+};
+function fill5(args) {
+  const { backend: backend2, attrs } = args;
+  const { shape, value } = attrs;
+  let { dtype } = attrs;
+  dtype = dtype || util_exports.inferDtype(value);
+  if (dtype === "string") {
+    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));
+    values.fill(value);
+    return backend2.makeTensorInfo(shape, dtype, values);
+  } else {
+    const program = new FillProgram2(shape);
+    const uniformData = [{ type: "float32", data: [value] }];
+    return backend2.runWebGPUProgram(program, [], dtype, uniformData);
+  }
+}
+var fillConfig4 = {
+  kernelName: Fill,
+  backendName: "webgpu",
+  kernelFunc: fill5
+};
+var FlipLeftRightProgram2 = class {
+  constructor(imageShape) {
+    this.outputShape = [];
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = imageShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "flipLeftRight";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let coordX = uniforms.xShape[2] - coords[2] - 1;
+          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
+          setOutputAtIndex(index, outputValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var flipLeftRightConfig4 = {
+  kernelName: FlipLeftRight,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const webgpuBackend = backend2;
+    const program = new FlipLeftRightProgram2(image2.shape);
+    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype);
+    return output;
+  }
+};
+var floor4 = unaryKernelFunc3({ opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU2 });
+var floorConfig4 = {
+  kernelName: Floor,
+  backendName: "webgpu",
+  kernelFunc: floor4
+};
+var floorDiv4 = binaryKernelFunc3({ opSnippet: BinaryOpType.INT_DIV, dtype: "int32" });
+var floorDivConfig4 = {
+  kernelName: FloorDiv,
+  backendName: "webgpu",
+  kernelFunc: floorDiv4
+};
+var FromPixelsProgram2 = class {
+  constructor(outputShape, useImport = false) {
+    this.outputShape = [0];
+    this.variableNames = [];
+    this.workGroupSize = [256, 1, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.useImport = useImport;
+    this.shaderKey = `fromPixels_${this.useImport}`;
+  }
+  getUserCode() {
+    const textureLoad = this.useImport ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
+    const textureType = this.useImport ? "texture_external" : "texture_2d<f32>";
+    return `
+      @binding(1) @group(0) var src: ${textureType};
+
+      ${getMainHeaderAndGlobalIndexString()}
+        let flatIndexBase = index * uniforms.numChannels;
+        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
+          let flatIndex = flatIndexBase + i;
+          if (flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndexBase);
+            let values = ${textureLoad};
+            result[flatIndex] = i32(floor(255.0 * values[i]));
+          }
+        }
+      }
+  `;
+  }
+};
+var fromPixelsConfig2 = {
+  kernelName: FromPixels,
+  backendName: "webgpu",
+  kernelFunc: fromPixels3
+};
+var fromPixels2DContext3;
+function fromPixels3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  let { pixels } = inputs;
+  const { numChannels } = attrs;
+  if (pixels == null) {
+    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
+  }
+  const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
+  const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
+  const isCanvas = typeof HTMLCanvasElement !== "undefined" && pixels instanceof HTMLCanvasElement || typeof OffscreenCanvas !== "undefined" && pixels instanceof OffscreenCanvas;
+  const isImageBitmap = typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap;
+  const [width, height] = isVideo ? [
+    pixels.videoWidth,
+    pixels.videoHeight
+  ] : [pixels.width, pixels.height];
+  const outShape = [height, width, numChannels];
+  if (env().getBool("WEBGPU_USE_IMPORT")) {
+    if (isVideo) {
+      return fromPixelsExternalImage({
+        externalImage: pixels,
+        backend: backend2,
+        attrs,
+        outShape,
+        useImport: true
+      });
+    }
+  }
+  if (isVideo || isImage) {
+    if (fromPixels2DContext3 == null) {
+      fromPixels2DContext3 = document.createElement("canvas").getContext("2d");
+    }
+    fromPixels2DContext3.canvas.width = width;
+    fromPixels2DContext3.canvas.height = height;
+    fromPixels2DContext3.drawImage(pixels, 0, 0, width, height);
+    pixels = fromPixels2DContext3.canvas;
+  }
+  if (isImageBitmap || isCanvas || isVideo || isImage) {
+    return fromPixelsExternalImage({
+      externalImage: pixels,
+      backend: backend2,
+      attrs,
+      outShape,
+      useImport: false
+    });
+  }
+  const imageData = pixels.data;
+  let pixelArray = imageData;
+  if (numChannels != null && numChannels !== 4) {
+    pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);
+    const dataLength = imageData.length;
+    let j = 0;
+    for (let i2 = 0; i2 < dataLength; i2++) {
+      if (i2 % 4 < numChannels) {
+        pixelArray[j++] = imageData[i2];
+      }
+    }
+  }
+  const output = backend2.makeTensorInfo(outShape, "int32");
+  const info = backend2.tensorMap.get(output.dataId);
+  info.values = new Int32Array(pixelArray);
+  backend2.maybeReleaseBuffer(output.dataId);
+  backend2.uploadToGPU(output.dataId);
+  return output;
+}
+function fromPixelsExternalImage(args) {
+  const { externalImage, backend: backend2, attrs, outShape, useImport } = args;
+  const { numChannels } = attrs;
+  const size2 = util_exports.sizeFromShape(outShape);
+  const strides2 = util_exports.computeStrides(outShape);
+  const program = new FromPixelsProgram2(outShape, useImport);
+  const uniformData = [
+    { type: "uint32", data: [size2] },
+    { type: "uint32", data: [numChannels] },
+    { type: "uint32", data: [...strides2] },
+    { type: "uint32", data: [...program.dispatch] }
+  ];
+  const output = backend2.runFromPixelsProgram(program, outShape, uniformData, useImport, externalImage);
+  return output;
+}
+var BatchNormProgram2 = class {
+  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape) {
+    this.uniforms = "varianceEpsilon : f32,";
+    this.workGroupSize = [128, 1, 1];
+    this.size = true;
+    this.variableNames = ["x", "mean", "variance"];
+    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
+    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
+    this.outputShape = xShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    if (offsetShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
+      this.variableNames.push("offset");
+    }
+    if (scaleShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
+      this.variableNames.push("scale");
+    }
+    this.offsetShape = offsetShape;
+    this.scaleShape = scaleShape;
+    this.shaderKey = "batchNorm";
+  }
+  getUserCode() {
+    let offsetSnippet = "0.0";
+    if (this.offsetShape != null) {
+      offsetSnippet = "getOffsetByOutputIndex(index)";
+    }
+    let scaleSnippet = "1.0";
+    if (this.scaleShape != null) {
+      scaleSnippet = "getScaleByOutputIndex(index)";
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size)
+        {
+          let xValue = getXByOutputIndex(index);
+          let meanValue = getMeanByOutputIndex(index);
+          let varianValue = getVarianceByOutputIndex(index);
+          let offsetValue = ${offsetSnippet};
+          let scaleValue = ${scaleSnippet};
+          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
+          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
+        }
+      }
+  `;
+    return userCode;
+  }
+};
+var fusedBatchNormConfig2 = {
+  kernelName: FusedBatchNorm,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x, scale: scale22, offset, mean: mean5, variance } = inputs;
+    const { varianceEpsilon } = attrs;
+    const webGPUBackend = backend2;
+    const batchNormInputs = [x, mean5, variance];
+    let offsetShape = null;
+    if (offset != null) {
+      offsetShape = offset.shape;
+      batchNormInputs.push(offset);
+    }
+    let scaleShape = null;
+    if (scale22 != null) {
+      scaleShape = scale22.shape;
+      batchNormInputs.push(scale22);
+    }
+    const program = new BatchNormProgram2(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape);
+    const uniformData = [{ type: "float32", data: [varianceEpsilon] }];
+    return webGPUBackend.runWebGPUProgram(program, batchNormInputs, x.dtype, uniformData);
+  }
+};
+function fusedConv2d3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  if (dataFormat !== "NHWC") {
+    throw new Error(`WebGPU backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
+  }
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  return conv2DImpl({
+    x,
+    filter,
+    convInfo,
+    backend: backend2,
+    bias,
+    preluActivationWeights,
+    leakyreluAlpha,
+    activation: activation2
+  });
+}
+var fusedConv2DConfig4 = {
+  kernelName: FusedConv2D,
+  backendName: "webgpu",
+  kernelFunc: fusedConv2d3
+};
+function fusedDepthwiseConv2D3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides: strides2, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides2, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides2} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides2, $dilations, pad3, dimRoundingMode, true);
+  const programInputs = [x, filter];
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  if (hasBias) {
+    programInputs.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    programInputs.push(preluActivationWeights);
+  }
+  const dimensions = [
+    { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] },
+    { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }
+  ];
+  let program;
+  if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) {
+    program = new DepthwiseConv2D3x3Program(convInfo, hasBias, activation2, hasPreluActivationWeights);
+  } else {
+    program = new DepthwiseConv2DProgram2(convInfo, hasBias, activation2, hasPreluActivationWeights);
+    dimensions.push({ type: "int32", data: [convInfo.filterHeight] }, { type: "int32", data: [convInfo.filterWidth] }, { type: "int32", data: [convInfo.outChannels / convInfo.inChannels] });
+  }
+  if (activation2 === "leakyrelu") {
+    dimensions.push({ type: "float32", data: [leakyreluAlpha] });
+    program.uniforms += " alpha : f32,";
+  }
+  const result = backend2.runWebGPUProgram(program, programInputs, "float32", dimensions);
+  return result;
+}
+var fusedDepthwiseConv2DConfig4 = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "webgpu",
+  kernelFunc: fusedDepthwiseConv2D3
+};
+var GatherNDProgram2 = class {
+  constructor(sliceDim, shape) {
+    this.variableNames = ["A", "indices"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `gathernd_${sliceDim}`;
+    this.sliceDim = sliceDim;
+    this.uniforms = `sliceDim : i32, strides : ${getCoordsDataType2(sliceDim)},`;
+  }
+  getUserCode() {
+    let strideString;
+    if (this.sliceDim > 1) {
+      strideString = "uniforms.strides[j]";
+    } else {
+      strideString = "uniforms.strides";
+    }
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          var flattenIndex = 0;
+          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+            let indexTemp = i32(round(getIndices(coords[0], j)));
+            let strideNum = ${strideString};
+            flattenIndex = flattenIndex + indexTemp * strideNum;
+          }
+
+          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
+        }
+      }
+      `;
+    return userCode;
+  }
+};
+function gatherNd4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { params, indices } = inputs;
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const paramsSize = util_exports.sizeFromShape(params.shape);
+  const [resultShape, numSlices, sliceSize, strides2] = backend_util_exports.prepareAndValidate(params, indices);
+  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });
+  const flattenX = reshape6({
+    inputs: { x: params },
+    backend: backend2,
+    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }
+  });
+  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") {
+    const indicesData = backend2.readSync(indices.dataId);
+    const paramsBuf = backend2.bufferSync(params);
+    const outValue = gatherNdImplCPU2(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides2, params.shape, paramsSize);
+    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);
+  }
+  const program = new GatherNDProgram2(sliceRank, [numSlices, sliceSize]);
+  const uniformData = [{ type: "int32", data: [sliceRank] }, { type: "int32", data: strides2 }];
+  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], flattenX.dtype, uniformData);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });
+  backend2.disposeData(flattenIndices.dataId);
+  backend2.disposeData(flattenX.dataId);
+  backend2.disposeData(res.dataId);
+  return reshaped;
+}
+var gatherNdConfig4 = {
+  kernelName: GatherNd,
+  backendName: "webgpu",
+  kernelFunc: gatherNd4
+};
+var GatherProgram2 = class {
+  constructor(aShape, outputShape) {
+    this.variableNames = ["A", "indices"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = aShape.slice();
+    this.aShape = aShape;
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `gather`;
+  }
+  getUserCode() {
+    const sourceCoords = getSourceCoords4(this.aShape);
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          let indexZ = i32(getIndices(resRC.x, resRC.z));
+          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
+          setOutputAtIndex(index, inBounds * getA(${sourceCoords}));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function getSourceCoords4(aShape) {
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < aShape.length; i2++) {
+    if (i2 === 2) {
+      sourceCoords.push("indexZ");
+    } else {
+      sourceCoords.push(`${currentCoords[i2]}`);
+    }
+  }
+  return sourceCoords.join();
+}
+function gatherV24(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const toDispose = [];
+  const flattenX = reshape6({
+    inputs: { x },
+    backend: backend2,
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    }
+  });
+  const flattenIndex = reshape6({
+    inputs: { x: indices },
+    backend: backend2,
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+  });
+  toDispose.push(flattenX);
+  toDispose.push(flattenIndex);
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  if (backend2.shouldExecuteOnCPU([x, indices])) {
+    const indicesBufferInfo = backend2.tensorMap.get(flattenIndex.dataId);
+    const indicesValues = indicesBufferInfo.values;
+    const indicesBuf = buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues);
+    const xBufferInfo = backend2.tensorMap.get(flattenX.dataId);
+    const xValues = xBufferInfo.values;
+    const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues);
+    const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape);
+    toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+  }
+  const program = new GatherProgram2(flattenX.shape, flattenOutputShape);
+  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype);
+  toDispose.push(res);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return reshaped;
+}
+var gatherV2Config4 = {
+  kernelName: GatherV2,
+  backendName: "webgpu",
+  kernelFunc: gatherV24
+};
+var greater5 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.GREATER,
+  cpuKernelImpl: greaterImplCPU2,
+  dtype: "bool"
+});
+var greaterConfig4 = {
+  kernelName: Greater,
+  backendName: "webgpu",
+  kernelFunc: greater5
+};
+var greaterEqual4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.GREATER_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: greaterEqualImplCPU2
+});
+var greaterEqualConfig4 = {
+  kernelName: GreaterEqual,
+  backendName: "webgpu",
+  kernelFunc: greaterEqual4
+};
+function leakyRelu5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { alpha: alpha2 } = attrs;
+  const uniformData = [{ type: "float32", data: [alpha2] }];
+  const program = new UnaryOpProgram2(x.shape, UnaryOpType.LEAKYRELU);
+  program.uniforms = "alpha : f32,";
+  return backend2.runWebGPUProgram(program, [x], "float32", uniformData);
+}
+var leakyReluConfig4 = {
+  kernelName: LeakyRelu,
+  backendName: "webgpu",
+  kernelFunc: leakyRelu5
+};
+var less5 = binaryKernelFunc3({ opSnippet: BinaryOpType.LESS, dtype: "bool", cpuKernelImpl: lessImplCPU2 });
+var lessConfig4 = {
+  kernelName: Less,
+  backendName: "webgpu",
+  kernelFunc: less5
+};
+var lessEqual4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.LESS_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: lessEqualImplCPU2
+});
+var lessEqualConfig4 = {
+  kernelName: LessEqual,
+  backendName: "webgpu",
+  kernelFunc: lessEqual4
+};
+var log5 = unaryKernelFunc3({ opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU2 });
+var logConfig4 = {
+  kernelName: Log,
+  backendName: "webgpu",
+  kernelFunc: log5
+};
+var logicalAnd4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.LOGICAL_AND,
+  dtype: "bool"
+});
+var logicalAndConfig4 = {
+  kernelName: LogicalAnd,
+  backendName: "webgpu",
+  kernelFunc: logicalAnd4
+};
+var logicalNot4 = unaryKernelFunc3({ opType: UnaryOpType.LOGICAL_NOT });
+var logicalNotConfig3 = {
+  kernelName: LogicalNot,
+  backendName: "webgpu",
+  kernelFunc: logicalNot4
+};
+function max6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reductionIndices, keepDims } = attrs;
+  return reduce2(x, reductionIndices, keepDims, "max", backend2);
+}
+var maxConfig4 = {
+  kernelName: Max,
+  backendName: "webgpu",
+  kernelFunc: max6
+};
+var maximum5 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.MAX,
+  cpuKernelImpl: maximumImplCPU2
+});
+var maximumConfig4 = {
+  kernelName: Maximum,
+  backendName: "webgpu",
+  kernelFunc: maximum5
+};
+function maxPool5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides: strides2, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides2, dilations, pad3, dimRoundingMode);
+  let program;
+  const dimensions = [];
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {
+    if (util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+      return identity5({ inputs: { x }, backend: backend2 });
+    }
+    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);
+    dimensions.push({ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] });
+  } else {
+    program = new Pool2DProgram2(convInfo, "max");
+    dimensions.push({ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {
+      type: "int32",
+      data: [convInfo.dilationHeight, convInfo.dilationWidth]
+    }, { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }, {
+      type: "int32",
+      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
+    });
+  }
+  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);
+}
+var maxPoolConfig4 = {
+  kernelName: MaxPool,
+  backendName: "webgpu",
+  kernelFunc: maxPool5
+};
+function mean4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { keepDims, axis } = attrs;
+  return reduce2(x, axis, keepDims, "mean", backend2);
+}
+var meanConfig4 = {
+  kernelName: Mean,
+  backendName: "webgpu",
+  kernelFunc: mean4
+};
+function min6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return reduce2(x, axis, keepDims, "min", backend2);
+}
+var minConfig4 = {
+  kernelName: Min,
+  backendName: "webgpu",
+  kernelFunc: min6
+};
+var minimum5 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.MIN,
+  cpuKernelImpl: minimumImplCPU2
+});
+var minimumConfig4 = {
+  kernelName: Minimum,
+  backendName: "webgpu",
+  kernelFunc: minimum5
+};
+var MirrorPadProgram2 = class {
+  constructor(xShape, paddings, mode) {
+    this.uniforms = "";
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.xShape = xShape;
+    paddings.map((_, i2) => {
+      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    });
+    this.offset = mode === "reflect" ? 0 : 1;
+    this.shaderKey = `mirrorPad_${mode}`;
+  }
+  getUserCode() {
+    const rank = this.xShape.length;
+    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
+    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const shaderStart = rank === 1 ? "start" : "start[i]";
+    const shaderEnd = rank === 1 ? "end" : "end[i]";
+    const shaderOutC = rank === 1 ? "outC" : "outC[i]";
+    const dtype = getCoordsDataType2(rank);
+    const unpackedCoords = rank > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank) : "coords";
+    return `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let start = ${dtype}(${start});
+          let end = ${dtype}(${end});
+          var outC = getCoordsFromIndex(index);
+          for (var i = 0; i < ${rank}; i = i + 1) {
+            if (${shaderOutC} < ${shaderStart}) {
+              ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${this.offset};
+            } else if(${shaderOutC} >= ${shaderEnd}) {
+              ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${this.offset};
+            }
+          }
+          let coords = outC - start;
+          setOutputAtIndex(index, getX(${unpackedCoords}));
+        }
+      }
+    `;
+  }
+};
+var mirrorPadConfig4 = {
+  kernelName: MirrorPad,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { paddings, mode } = attrs;
+    const webGPUBackend = backend2;
+    const uniformData = paddings.map((p2) => {
+      return { type: "int32", data: [p2[0], p2[1]] };
+    });
+    const program = new MirrorPadProgram2(x.shape, paddings, mode);
+    const output = webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData);
+    return output;
+  }
+};
+function neg4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xData = backend2.tensorMap.get(x.dataId);
+    const [outValues, newShape] = negImplCPU2(xData.values, x.shape, x.dtype);
+    return backend2.makeTensorInfo(newShape, x.dtype, outValues);
+  }
+  const program = new UnaryOpProgram2(x.shape, UnaryOpType.NEG);
+  return backend2.runWebGPUProgram(program, [x], x.dtype);
+}
+var negConfig4 = {
+  kernelName: Neg,
+  backendName: "webgpu",
+  kernelFunc: neg4
+};
+function nonMaxSuppressionV33(args) {
+  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const { selectedIndices } = kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
+}
+var nonMaxSuppressionV3Config4 = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "webgpu",
+  kernelFunc: nonMaxSuppressionV33
+};
+function nonMaxSuppressionV53(args) {
+  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const maxOutputSizeVal = maxOutputSize;
+  const iouThresholdVal = iouThreshold;
+  const scoreThresholdVal = scoreThreshold;
+  const softNmsSigmaVal = softNmsSigma;
+  const { selectedIndices, selectedScores } = kernel_impls_exports.nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
+  ];
+}
+var nonMaxSuppressionV5Config4 = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "webgpu",
+  kernelFunc: nonMaxSuppressionV53
+};
+function zerosLike5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "complex64") {
+    const realPart = real4({ inputs: { input: x }, backend: backend2 });
+    const r2 = zerosLike5({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeData(realPart.dataId);
+    backend2.disposeData(r2.dataId);
+    backend2.disposeData(imagPart.dataId);
+    backend2.disposeData(i2.dataId);
+    return result;
+  } else {
+    return fill5({
+      attrs: {
+        shape: x.shape,
+        dtype: x.dtype,
+        value: x.dtype === "string" ? "" : 0
+      },
+      backend: backend2
+    });
+  }
+}
+var zerosLikeConfig4 = {
+  kernelName: ZerosLike,
+  backendName: "webgpu",
+  kernelFunc: zerosLike5
+};
+function onesLike5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("onesLike is not supported under string dtype");
+  } else if (x.dtype === "complex64") {
+    const realPart = real4({ inputs: { input: x }, backend: backend2 });
+    const r2 = onesLike5({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeData(realPart.dataId);
+    backend2.disposeData(r2.dataId);
+    backend2.disposeData(imagPart.dataId);
+    backend2.disposeData(i2.dataId);
+    return result;
+  } else {
+    return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
+  }
+}
+var onesLikeConfig4 = {
+  kernelName: OnesLike,
+  backendName: "webgpu",
+  kernelFunc: onesLike5
+};
+function pack4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims6({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims6({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return result;
+}
+var packConfig4 = {
+  kernelName: Pack,
+  backendName: "webgpu",
+  kernelFunc: pack4
+};
+var PadProgram2 = class {
+  constructor(xShape, paddings) {
+    this.variableNames = ["x"];
+    this.uniforms = "constantValue : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    paddings.map((_, i2) => {
+      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    });
+    this.xShape = xShape;
+    this.shaderKey = "pad";
+  }
+  getUserCode() {
+    const rank = this.xShape.length;
+    const type = getCoordsDataType2(rank);
+    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
+    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const startValue = rank > 1 ? `${type}(${start})` : `${start}`;
+    const endValue = rank > 1 ? `${type}(${end})` : `${end}`;
+    const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;
+    const rightPadCondition = rank > 1 ? `any(outC >= end)` : `outC >= end`;
+    const unpackedCoords = rank > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank) : "coords";
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let start = ${startValue};
+          let end = ${endValue};
+          let outC = getCoordsFromIndex(index);
+
+          if (${leftPadCondition} || ${rightPadCondition}) {
+            setOutputAtIndex(index, uniforms.constantValue);
+          } else {
+            let coords = outC - start;
+            setOutputAtIndex(index, getX(${unpackedCoords}));
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var padV23 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, constantValue } = attrs;
+  if (paddings.every((p2) => util_exports.arraysEqual(p2, [0, 0]))) {
+    return identity5({ inputs: { x }, backend: backend2 });
+  }
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    return fill5({
+      backend: backend2,
+      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
+    });
+  }
+  const uniformData = [{ type: "float32", data: [constantValue] }];
+  paddings.map((p2) => uniformData.push({ type: "int32", data: [p2[0], p2[1]] }));
+  const program = new PadProgram2(x.shape, paddings);
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+};
+var padV2Config4 = {
+  kernelName: PadV2,
+  backendName: "webgpu",
+  kernelFunc: padV23
+};
+var pow4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.POW
+});
+var powConfig4 = {
+  kernelName: Pow,
+  backendName: "webgpu",
+  kernelFunc: pow4
+};
+function prelu6(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha: alpha2 } = inputs;
+  const program = new BinaryOpProgram2(BinaryOpType.PRELU, x.shape, alpha2.shape);
+  return backend2.runWebGPUProgram(program, [x, alpha2], "float32");
+}
+var preluConfig4 = {
+  kernelName: Prelu,
+  backendName: "webgpu",
+  kernelFunc: prelu6
+};
+function prod5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return reduce2(x, axis, keepDims, "prod", backend2);
+}
+var prodConfig4 = {
+  kernelName: Prod,
+  backendName: "webgpu",
+  kernelFunc: prod5
+};
+var range6 = (args) => {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, step: step5, dtype } = attrs;
+  const values = rangeImplCPU2(start, stop, step5, dtype);
+  return backend2.makeTensorInfo([values.length], dtype, values);
+};
+var rangeConfig4 = {
+  kernelName: Range,
+  backendName: "webgpu",
+  kernelFunc: range6
+};
+var realDiv2 = binaryKernelFunc3({ opSnippet: BinaryOpType.DIV });
+var realDivConfig4 = {
+  kernelName: RealDiv,
+  backendName: "webgpu",
+  kernelFunc: realDiv2
+};
+var relu4 = unaryKernelFunc3({ opType: UnaryOpType.RELU });
+var reluConfig4 = {
+  kernelName: Relu,
+  backendName: "webgpu",
+  kernelFunc: relu4
+};
+var relu64 = unaryKernelFunc3({ opType: UnaryOpType.RELU6 });
+var relu6Config4 = {
+  kernelName: Relu6,
+  backendName: "webgpu",
+  kernelFunc: relu64
+};
+var ResizeBilinearProgram2 = class {
+  constructor(inputShape, newHeight, newWidth) {
+    this.variableNames = ["x"];
+    this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `resizeBilinear`;
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let d = coords[3];
+          let rc = coords.yz;
+
+          let effectiveInSize = vec2<f32>(
+            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveOutSize = vec2<f32>(
+            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveInputOverOutputRatioRC =
+              effectiveInSize / effectiveOutSize;
+
+          // Fractional source index
+          let sourceFracIndexRC =
+            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
+            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
+
+          // Compute the four integer indices.
+          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
+          let sourceCeilRC = vec2<i32>(
+            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
+
+          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
+          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
+          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
+          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
+
+          let top = topLeft + (topRight - topLeft) * fracRC.y;
+          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+          let newValue = top + (bottom - top) * fracRC.x;
+
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function resizeBilinear5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, size: size2, halfPixelCenters } = attrs;
+  const [newHeight, newWidth] = size2;
+  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;
+  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;
+  const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0;
+  const uniformData = [
+    { type: "float32", data: [adjustHeight, adjustWidth] },
+    { type: "float32", data: [halfPixelCentersValue] }
+  ];
+  const program = new ResizeBilinearProgram2(images.shape, newHeight, newWidth);
+  return backend2.runWebGPUProgram(program, [images], "float32", uniformData);
+}
+var resizeBilinearConfig4 = {
+  kernelName: ResizeBilinear,
+  backendName: "webgpu",
+  kernelFunc: resizeBilinear5
+};
+var ResizeNearestNeighborProgram2 = class {
+  constructor(inputShape, newHeight, newWidth, halfPixelCenters) {
+    this.variableNames = ["x"];
+    this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.halfPixelCenters = halfPixelCenters;
+    this.shaderKey = `resizeNearest_${halfPixelCenters}`;
+  }
+  getUserCode() {
+    let sourceFracIndexRC;
+    if (this.halfPixelCenters) {
+      sourceFracIndexRC = `max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))`;
+    } else {
+      sourceFracIndexRC = `vec2<f32>(rc) * effectiveInputOverOutputRatioRC`;
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let d = coords[3];
+          let rc = coords.yz;
+
+          let effectiveInSize = vec2<f32>(
+            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveOutSize = vec2<f32>(
+            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveInputOverOutputRatioRC =
+              effectiveInSize / effectiveOutSize;
+
+          // Fractional source index
+          let sourceFracIndexRC = ${sourceFracIndexRC};
+
+          // Compute the coordinators of nearest neighbor point.
+          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
+          let sourceNearestRC = vec2<i32>(
+            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
+          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function resizeNearestNeighbor4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size: size2 } = attrs;
+  const [newHeight, newWidth] = size2;
+  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;
+  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;
+  const roundBase = alignCorners ? 0.5 : 0;
+  const uniformData = [
+    { type: "float32", data: [adjustHeight, adjustWidth] },
+    { type: "float32", data: [roundBase] }
+  ];
+  const program = new ResizeNearestNeighborProgram2(images.shape, newHeight, newWidth, halfPixelCenters);
+  return backend2.runWebGPUProgram(program, [images], images.dtype, uniformData);
+}
+var resizeNearestNeighborConfig3 = {
+  kernelName: ResizeNearestNeighbor,
+  backendName: "webgpu",
+  kernelFunc: resizeNearestNeighbor4
+};
+var RotateProgram2 = class {
+  constructor(imageShape, fillValue) {
+    this.outputShape = [];
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = imageShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
+          cosRadians : f32,`;
+    this.shaderKey = "rotate";
+    this.outputShape = imageShape;
+    if (typeof fillValue === "number") {
+      this.uniforms += ` fillValue : f32,`;
+      this.fillSnippet = `var outputValue = uniforms.fillValue;`;
+      this.shaderKey += "_float";
+    } else {
+      this.uniforms += ` fillValue : vec3<f32>,`;
+      this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`;
+      this.shaderKey += "_vec3";
+    }
+  }
+  getUserCode() {
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+
+          if (index < uniforms.size) {
+            let coords = getCoordsFromIndex(index);
+            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
+                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
+                uniforms.sinRadians;
+            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
+                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
+                uniforms.cosRadians;
+            let coordX = i32(round(coordXFloat + uniforms.centerX));
+            let coordY = i32(round(coordYFloat + uniforms.centerY));
+            ${this.fillSnippet}
+            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
+                coordY < uniforms.xShape[1]) {
+              outputValue = getX(coords[0], coordY, coordX, coords[3]);
+            }
+            setOutputAtIndex(index, outputValue);
+          }
+        }
+      `;
+    return userCode;
+  }
+};
+var rotateWithOffsetConfig4 = {
+  kernelName: RotateWithOffset,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const { radians, fillValue, center } = attrs;
+    const webgpuBackend = backend2;
+    const program = new RotateProgram2(image2.shape, fillValue);
+    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);
+    const uniformData = [
+      { type: "float32", data: [centerX] },
+      { type: "float32", data: [centerY] },
+      { type: "float32", data: [Math.sin(radians)] },
+      { type: "float32", data: [Math.cos(radians)] }
+    ];
+    if (typeof fillValue === "number") {
+      uniformData.push({ type: "float32", data: [Number.parseFloat(fillValue.toFixed(2))] });
+    } else {
+      uniformData.push({ type: "float32", data: fillValue });
+    }
+    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype, uniformData);
+    return output;
+  }
+};
+var rsqrt4 = unaryKernelFunc3({ opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU2 });
+var rsqrtConfig4 = {
+  kernelName: Rsqrt,
+  backendName: "webgpu",
+  kernelFunc: rsqrt4
+};
+var ScatterOptimizedProgram = class {
+  constructor(flattenXShape, sliceDim, indicesRank, updatesRank, strides2, shape, outputDtype) {
+    this.variableNames = ["updates", "indices"];
+    this.workGroupSize = [64, 1, 1];
+    this.atomic = true;
+    this.outputShape = shape;
+    this.type = outputDtype;
+    this.dispatchLayout = flatDispatchLayout(flattenXShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, flattenXShape, this.workGroupSize);
+    this.sliceDimGreaterThanOne = sliceDim > 1;
+    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${outputDtype}`;
+    const stridesType = getCoordsDataType2(strides2.length);
+    this.uniforms = `sliceDim : i32, strides: ${stridesType}, size: i32,`;
+    this.updatesRank = updatesRank;
+    this.indicesRank = indicesRank;
+  }
+  getUserCode() {
+    let indicesString = "";
+    if (this.indicesRank === 1) {
+      indicesString = "coords[0]";
+    } else if (this.indicesRank === 2) {
+      indicesString = "coords[0], j";
+    }
+    const indicesSnippet = `getIndices(${indicesString})`;
+    const strideString = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides";
+    let updatesString = "";
+    let outCoordsString = "";
+    let getUpdatesCoordsFromFlatIndex = "";
+    if (this.updatesRank === 1) {
+      updatesString = "coords[0]";
+      outCoordsString = "flattenedIndex";
+      getUpdatesCoordsFromFlatIndex = `
+      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
+        return index;
+      }
+      `;
+    } else if (this.updatesRank === 2) {
+      updatesString = "coords[0], coords[1]";
+      outCoordsString = "vec2<i32>(flattenedIndex, coords[1])";
+      getUpdatesCoordsFromFlatIndex = `
+      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
+        let d0 = index / uniforms.updatesShape[1];
+        let d1 = index - d0 * uniforms.updatesShape[1];
+        return vec2<i32>(d0, d1);
+      }
+      `;
+    }
+    const updatesSnippet = `getUpdates(${updatesString})`;
+    const atomicAddSnippet = this.type === "int32" ? `atomicAdd(&(result[flatIndex]), i32(updateValue));` : `
+     var assumed = atomicLoad(&(result[flatIndex]));
+     var success = 0;
+     for (; success == 0;) {
+       let new = bitcast<f32>(assumed) + updateValue;
+       let newI32 = bitcast<i32>(new);
+       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
+       assumed = resValue[0];
+       success = resValue[1];
+     }
+     `;
+    const userCode = `
+    ${getUpdatesCoordsFromFlatIndex}
+
+      ${getMainHeaderAndGlobalIndexString()}
+
+        if (index < uniforms.size) {
+          let coords = getUpdatesCoordsFromFlatIndex(index);
+          var flattenedIndex = 0;
+          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+            let indexInside = i32(round(${indicesSnippet}));
+            flattenedIndex = flattenedIndex + indexInside * ${strideString};
+          }
+          let updateValue = ${updatesSnippet};
+          let flatIndex = getOutputIndexFromCoords(${outCoordsString});
+
+         ${atomicAddSnippet}
+        }
+      }`;
+    return userCode;
+  }
+};
+function scatterNd4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = backend_util_exports.calculateShapes(updates, indices, shape);
+  const flattenShape = [outputSize2 / sliceSize, sliceSize];
+  if (outputSize2 === 0) {
+    return backend2.makeTensorInfo(shape, indices.dtype);
+  }
+  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });
+  const flattenX = reshape6({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });
+  const type = flattenX.dtype;
+  const output = fill5({ backend: backend2, attrs: { shape: flattenShape, value: 0, dtype: type } });
+  const size2 = util_exports.sizeFromShape(flattenX.shape);
+  const uniformData = [
+    { type: "int32", data: [sliceRank] },
+    { type: "int32", data: strides2 },
+    { type: "int32", data: [size2] }
+  ];
+  const program = new ScatterOptimizedProgram(flattenX.shape, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides2, flattenShape, type);
+  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], type, uniformData, output);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape } });
+  backend2.disposeData(flattenIndices.dataId);
+  backend2.disposeData(flattenX.dataId);
+  backend2.disposeData(res.dataId);
+  return reshaped;
+}
+var scatterNdConfig4 = {
+  kernelName: ScatterNd,
+  backendName: "webgpu",
+  kernelFunc: scatterNd4
+};
+var SelectProgram2 = class {
+  constructor(cRank, shape, rank) {
+    this.variableNames = ["c", "a", "b"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.cRank = cRank;
+    this.rank = rank;
+    this.shaderKey = "select";
+  }
+  getUserCode() {
+    let cCoords;
+    let abCoords;
+    if (this.rank > 4) {
+      throw Error(`Where for rank ${this.rank} is not yet supported`);
+    }
+    if (this.rank === 1) {
+      abCoords = `resRC`;
+      cCoords = `resRC`;
+    } else {
+      const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+      const cCoordVars = [];
+      const abCoordVars = [];
+      for (let i2 = 0; i2 < this.outputShape.length; i2++) {
+        abCoordVars.push(`${currentCoords[i2]}`);
+        if (i2 < this.cRank) {
+          cCoordVars.push(`${currentCoords[i2]}`);
+        }
+      }
+      cCoords = cCoordVars.join();
+      abCoords = abCoordVars.join();
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          let cVal = getC(${cCoords});
+          if (cVal >= 1.0) {
+            setOutputAtIndex(index, getA(${abCoords}));
+          } else {
+            setOutputAtIndex(index, getB(${abCoords}));
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function select5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  const program = new SelectProgram2(condition.shape.length, t2.shape, t2.shape.length);
+  return backend2.runWebGPUProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+}
+var selectConfig4 = {
+  kernelName: Select,
+  backendName: "webgpu",
+  kernelFunc: select5
+};
+var sigmoid5 = unaryKernelFunc3({ opType: UnaryOpType.SIGMOID });
+var sigmoidConfig4 = {
+  kernelName: Sigmoid,
+  backendName: "webgpu",
+  kernelFunc: sigmoid5
+};
+var sin4 = unaryKernelFunc3({ opType: UnaryOpType.SIN });
+var sinConfig4 = {
+  kernelName: Sin,
+  backendName: "webgpu",
+  kernelFunc: sin4
+};
+var sinh4 = unaryKernelFunc3({ opType: UnaryOpType.SINH });
+var sinhConfig3 = {
+  kernelName: Sinh,
+  backendName: "webgpu",
+  kernelFunc: sinh4
+};
+var sub4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.SUB,
+  cpuKernelImpl: subImplCPU2,
+  supportsComplex: true
+});
+var subConfig4 = {
+  kernelName: Sub,
+  backendName: "webgpu",
+  kernelFunc: sub4
+};
+function softmax6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { dim } = attrs;
+  const axes = util_exports.parseAxisParam([dim], logits.shape);
+  const maxLogit = max6({
+    inputs: { x: logits },
+    backend: backend2,
+    attrs: { reductionIndices: axes, keepDims: false }
+  });
+  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
+  const maxLogitsReshaped = reshape6({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
+  const a6 = sub4({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });
+  const b = exp4({ inputs: { x: a6 }, backend: backend2 });
+  const sumExp = sum6({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
+  const sumExpReshaped = reshape6({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
+  const res = realDiv2({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });
+  backend2.disposeData(maxLogit.dataId);
+  backend2.disposeData(maxLogitsReshaped.dataId);
+  backend2.disposeData(a6.dataId);
+  backend2.disposeData(b.dataId);
+  backend2.disposeData(sumExp.dataId);
+  backend2.disposeData(sumExpReshaped.dataId);
+  return res;
+}
+var softmaxConfig4 = {
+  kernelName: Softmax,
+  backendName: "webgpu",
+  kernelFunc: softmax6
+};
+var spaceToBatchND5 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const toDispose = [];
+  const paddedX = padV23({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapedPaddedX = reshape6({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });
+  const paddedXT = transpose5({
+    inputs: { x: reshapedPaddedX },
+    backend: backend2,
+    attrs: { perm: permutedReshapedPaddedPermutation }
+  });
+  const result = reshape6({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });
+  toDispose.push(paddedX);
+  toDispose.push(reshapedPaddedX);
+  toDispose.push(paddedXT);
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return result;
+};
+var spaceToBatchNDConfig4 = {
+  kernelName: SpaceToBatchND,
+  backendName: "webgpu",
+  kernelFunc: spaceToBatchND5
+};
+var ScatterProgram2 = class {
+  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides2, shape, summingDupeIndex = true) {
+    this.variableNames = ["updates", "indices", "defaultValue"];
+    this.workGroupSize = [64, 1, 1];
+    this.workPerThread = 4;
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    const sliceDimGreaterThanOne = sliceDim > 1;
+    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${sliceDimGreaterThanOne}`;
+    const stridesType = getCoordsDataType2(strides2.length);
+    this.uniforms = `updateSize : i32, sliceDim : i32, strides: ${stridesType},`;
+    let indicesString = "";
+    if (indicesRank === 1) {
+      indicesString = "i";
+    } else if (indicesRank === 2) {
+      indicesString = "i, j";
+    }
+    this.indicesSnippet = `getIndices(${indicesString})`;
+    let updatesString = "";
+    if (updatesRank === 1) {
+      updatesString = "i";
+    } else if (updatesRank === 2) {
+      updatesString = "i, coords[1]";
+    }
+    this.updatesSnippet = `getUpdates(${updatesString})`;
+    this.strideString = sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+
+        let globalIndex = index * ${this.workPerThread};
+        if (globalIndex < uniforms.size) {
+          var sum = vec4<f32>(0.0);
+          var found = vec4<bool>(false);
+          for (var i = 0; i < uniforms.updateSize; i = i + 1) {
+            var flattenedIndex = 0;
+            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+              let indexInside = i32(round(${this.indicesSnippet}));
+              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
+            }
+            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
+              let curIndex = globalIndex + innerIndex;
+              let coords = getCoordsFromIndex(curIndex);
+              if (flattenedIndex == coords[0]) {
+                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
+                found[innerIndex] = true;
+              }
+            }
+          }
+          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
+            let curIndex = globalIndex + innerIndex;
+            if (curIndex < uniforms.size)
+            {
+              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
+            }
+          }
+        }
+      }`;
+    return userCode;
+  }
+};
+function sparseToDense4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sparseIndices, sparseValues, defaultValue } = inputs;
+  const { outputShape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides: strides2, outputSize: outputSize2 } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
+  const sumDupeIndices = false;
+  if (sparseValues.dtype === "string") {
+    const indicesBuf = backend2.bufferSync(sparseIndices);
+    const updatesBuf = backend2.bufferSync(sparseValues);
+    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);
+    const outBuf = scatterImplCPU2(indicesBuf, updatesBuf, outputShape, outputSize2, sliceSize, numUpdates, sliceRank, strides2, $defaultValue, sumDupeIndices);
+    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+  }
+  const uniformData = [
+    { type: "int32", data: [numUpdates] },
+    { type: "int32", data: [sliceRank] },
+    { type: "int32", data: strides2 }
+  ];
+  const program = new ScatterProgram2(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides2, [outputSize2, 1], sumDupeIndices);
+  const res = backend2.runWebGPUProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype, uniformData);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });
+  backend2.disposeData(res.dataId);
+  return reshaped;
+}
+var sparseToDenseConfig3 = {
+  kernelName: SparseToDense,
+  backendName: "webgpu",
+  kernelFunc: sparseToDense4
+};
+function splitV4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const xRank = x.shape.length;
+  const begin = new Array(xRank).fill(0);
+  const size2 = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const sliceSize = [...size2];
+    sliceSize[$axis] = s2;
+    const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
+    begin[$axis] += s2;
+    return sliceT;
+  });
+}
+var splitVConfig4 = {
+  kernelName: SplitV,
+  backendName: "webgpu",
+  kernelFunc: splitV4
+};
+var sqrt4 = unaryKernelFunc3({ opType: UnaryOpType.SQRT });
+var sqrtConfig4 = {
+  kernelName: Sqrt,
+  backendName: "webgpu",
+  kernelFunc: sqrt4
+};
+var squareConfig4 = {
+  kernelName: Square,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const webGPUBackend = backend2;
+    const program = new UnaryOpProgram2(x.shape, UnaryOpType.SQUARE);
+    return webGPUBackend.runWebGPUProgram(program, [x], x.dtype);
+  }
+};
+var squaredDifference4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.SQUARED_DIFFERENCE
+});
+var squaredDifferenceConfig4 = {
+  kernelName: SquaredDifference,
+  backendName: "webgpu",
+  kernelFunc: squaredDifference4
+};
+var StridedSliceProgram2 = class {
+  constructor(destSize) {
+    this.variableNames = ["x"];
+    this.workPerThread = 1;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = destSize;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    const dtype = getCoordsDataType2(this.outputShape.length);
+    this.uniforms = `begin : ${dtype},  strides : ${dtype}, `;
+    this.shaderKey = "stridedSlice";
+  }
+  getUserCode() {
+    const rank = this.outputShape.length;
+    let newCoords = "";
+    if (rank === 1) {
+      newCoords = "coords * uniforms.strides + uniforms.begin";
+    } else {
+      let outputAxis = 0;
+      newCoords = this.outputShape.map((_, i2) => {
+        outputAxis++;
+        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i2}] + uniforms.begin[${i2}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i2}] + uniforms.begin[${i2}]`;
+      }).join(",");
+    }
+    const userCode = `
+       ${getMainHeaderAndGlobalIndexString()}
+         if (index < uniforms.size) {
+           let coords = getCoordsFromIndex(index);
+           setOutputAtIndex(index, getX(${newCoords}));
+         }
+       }
+     `;
+    return userCode;
+  }
+};
+function stridedSlice5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides: strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides2, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size2 = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size: size2 } });
+    result = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeData(sliced.dataId);
+  } else {
+    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
+    if (shouldExecuteOnCPU) {
+      const values = backend2.readSync(x.dataId);
+      const xBuf = buffer(x.shape, x.dtype, values);
+      const resultValues = stridedSliceImplCPU2(finalShapeSparse, xBuf, $strides, $begin);
+      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);
+    } else {
+      const program = new StridedSliceProgram2(finalShapeSparse);
+      const uniformData = [{ type: "int32", data: $begin }, { type: "int32", data: $strides }];
+      const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+      result = reshape6({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } });
+      backend2.disposeData(resultValues.dataId);
+    }
+  }
+  return result;
+}
+var stridedSliceConfig4 = {
+  kernelName: StridedSlice,
+  backendName: "webgpu",
+  kernelFunc: stridedSlice5
+};
+function stringNGrams4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
+  const { data, dataSplits } = inputs;
+  const $data = backend2.readSync(data.dataId);
+  const $dataSplits = backend2.readSync(dataSplits.dataId);
+  const [nGrams, nGramsSplits] = stringNGramsImplCPU2($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
+  return [
+    backend2.makeTensorInfo([nGrams.length], "string", nGrams),
+    backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
+  ];
+}
+var stringNGramsConfig3 = {
+  kernelName: StringNGrams,
+  backendName: "webgpu",
+  kernelFunc: stringNGrams4
+};
+var tanh5 = unaryKernelFunc3({ opType: UnaryOpType.TANH });
+var tanhConfig4 = {
+  kernelName: Tanh,
+  backendName: "webgpu",
+  kernelFunc: tanh5
+};
+var TileProgram2 = class {
+  constructor(aShape, reps) {
+    this.variableNames = ["A"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[i2] * reps[i2];
+    }
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.rank = this.outputShape.length;
+    this.shaderKey = "tile";
+  }
+  getUserCode() {
+    const sourceCoords = getSourceCoords5(this.rank, "uniforms.");
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          setOutputAtIndex(index, getA(${sourceCoords}));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function getSourceCoords5(rank, uniformPrefix = "") {
+  if (rank >= 5) {
+    throw Error(`Tile for rank ${rank} is not yet supported`);
+  }
+  if (rank === 1) {
+    return `(resRC % ${uniformPrefix}aShape)`;
+  }
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < rank; i2++) {
+    sourceCoords.push(`(${currentCoords[i2]} % ${uniformPrefix}aShape[${i2}])`);
+  }
+  return sourceCoords.join();
+}
+function tile6(params) {
+  const { inputs, backend: backend2, attrs } = params;
+  const { x } = inputs;
+  const { reps } = attrs;
+  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string" || x.shape.length >= 5) {
+    const data = backend2.readSync(x.dataId);
+    const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data;
+    const buf = buffer(x.shape, x.dtype, value);
+    const outBuf = tileImplCPU2(buf, reps);
+    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+  }
+  const program = new TileProgram2(x.shape, reps);
+  const output = backend2.runWebGPUProgram(program, [x], x.dtype);
+  return output;
+}
+var tileConfig4 = {
+  kernelName: Tile,
+  backendName: "webgpu",
+  kernelFunc: tile6
+};
+var SwapProgram2 = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.workGroupSize = [256, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
+        dir : i32, inc : i32,`;
+    this.shaderKey = "swap";
+  }
+  getUserCode() {
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+          if (index < uniforms.size) {
+            let outC = getCoordsFromIndex(index);
+            let batch = outC[0];
+            let elemIdx = outC[1];
+            // We compare elements pair-wise within a group of size 2 * inc.
+            // The comparing rule for each group alternates between ascending
+            // and descending. Within each group, we compare each pair at
+            // positions i and i+inc. To decide whether an element at position i
+            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
+            // inc, it is in the first half of the group, we denote it as x0,
+            // otherwise we denote it as x1.
+            // For example, as shown in the Bitonic top K paper referenced
+            // above, Figure5(a) shows that element[1] is in the second half of
+            // the group when group size is 2, but it is in the first half of
+            // the group when group size is 4.
+            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
+            var i = 0;
+            if (isFirstInPair) {
+              i = elemIdx;
+            } else {
+              i = elemIdx - uniforms.inc;
+            }
+
+            var i0 = 0;
+            if (uniforms.firstPass == 1) {
+              i0 = i;
+            } else {
+              i0 = i32(getIndices(batch, i));
+            }
+
+            var i1 = 0;
+            if (uniforms.firstPass == 1) {
+              i1 = i + uniforms.inc;
+            } else {
+              i1 = i32(getIndices(batch, i + uniforms.inc));
+            }
+
+            var x0 = f32(0.0);
+            var x1 = f32(0.0);
+            if (i0 < uniforms.inputSize) {
+              x0 = getX(batch, i0);
+            } else {
+              x0 = uniforms.negativeInf;
+            }
+            if (i1 < uniforms.inputSize) {
+              x1 = getX(batch, i1);
+            } else {
+              x1 = uniforms.negativeInf;
+            }
+
+            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
+            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
+            if (reverse == isGreater) {
+              // Elements in opposite order of direction
+              let iTemp = i0;
+              i0 = i1;
+              i1 = iTemp;
+            }
+            if (isFirstInPair) {
+              setOutputAtIndex(index, f32(i0));
+            } else {
+              setOutputAtIndex(index, f32(i1));
+            }
+          }
+        }
+      `;
+    return userCode;
+  }
+};
+var MergeProgram2 = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.workGroupSize = [256, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.uniforms = `inputSize : i32, firstPass : i32, k : i32,`;
+    this.shaderKey = "merge";
+  }
+  getUserCode() {
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+          if (index < uniforms.size) {
+            let outC = getCoordsFromIndex(index);
+            let batch = outC[0];
+            let elemIdx = outC[1];
+            // The output size is half of the previous size.
+            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _
+            // (k=4), we only need to output the indices at positions |, the
+            // indices at positions _ can be thrown away, see Figure5(b) After
+            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced
+            // above.
+            // For example, the paper shows we only need to output the orange
+            // bars. The output sequence should look like this | | | | | | | |.
+            // Because the sequence is halved, to map the output index back to
+            // the previous sequence to find the corresponding value, we need
+            // to double the index. When we double the index, we basically
+            // interpolate a position, so 2i looks like
+            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
+            // position of each 2k positions by - elemIdx % k. E.g. for output
+            // at index 4,5,6,7, we want to get the corresponding element at
+            // original index 8,9,10,11, for output at index 8,9,10,11,
+            // we want to get the corresponding element at original index
+            // 16,17,18,19, so on and so forth.
+
+            var i = 0;
+            if (elemIdx < uniforms.k) {
+              i = elemIdx;
+            } else {
+              i = elemIdx * 2 - elemIdx % uniforms.k;
+            }
+            var i0 = 0;
+            if (uniforms.firstPass == 1) {
+              i0 = i;
+            } else {
+              i0 = i32(getIndices(batch, i));
+            }
+            var i1 = 0;
+            if (uniforms.firstPass == 1) {
+              i1 = i + uniforms.k;
+            } else {
+              i1 = i32(getIndices(batch, i + uniforms.k));
+            }
+
+            let x0 = getX(batch, i0);
+            var x1 = f32(0.0);
+            if (i1 < uniforms.inputSize) {
+              x1 = getX(batch, i1);
+            } else {
+              x1 = x0;
+            }
+
+            if (x0 >= x1) {
+              setOutputAtIndex(index, f32(i0));
+            } else {
+              setOutputAtIndex(index, f32(i1));
+            }
+          }
+        }
+      `;
+    return userCode;
+  }
+};
+function disposeIntermediateTensorInfoOrNull2(backend2, tensorInfo) {
+  if (tensorInfo !== null) {
+    backend2.disposeData(tensorInfo.dataId);
+  }
+}
+function roundUpToPow22(num) {
+  let pow22 = 1;
+  while (pow22 < num) {
+    pow22 *= 2;
+  }
+  return pow22;
+}
+function topK3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  const xShape = x.shape;
+  const lastDim = xShape[xShape.length - 1];
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xVals = backend2.readSync(x.dataId);
+    const [allTopKVals, allTopKIndices] = topKImplCPU2(xVals, xShape, x.dtype, k, sorted);
+    return [
+      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+    ];
+  }
+  if (k === 0) {
+    xShape[xShape.length - 1] = 0;
+    return [
+      backend2.makeTensorInfo(xShape, x.dtype, []),
+      backend2.makeTensorInfo(xShape, "int32", [])
+    ];
+  }
+  if (lastDim === 1) {
+    return [
+      x,
+      fill5({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 })
+    ];
+  }
+  const xSize = util_exports.sizeFromShape(xShape);
+  const batch = xSize / lastDim;
+  const x2D = reshape6({ inputs: { x }, attrs: { shape: [batch, lastDim] }, backend: backend2 });
+  const kPow2 = roundUpToPow22(k);
+  const lastDimPow2 = roundUpToPow22(lastDim);
+  let indices = null;
+  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
+  const runSwap = (dir, inc, shape) => {
+    const inputs2 = getInputs();
+    const program = new SwapProgram2(shape);
+    const firstPass = indices === null ? 1 : 0;
+    const uniformDataSwap = [
+      { type: "int32", data: [lastDim] },
+      { type: "int32", data: [firstPass] },
+      { type: "float32", data: [Number.NEGATIVE_INFINITY] },
+      { type: "int32", data: [dir] },
+      { type: "int32", data: [inc] }
+    ];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGPUProgram(program, inputs2, "int32", uniformDataSwap);
+    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);
+  };
+  for (let len = 1; len < kPow2; len *= 2) {
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, [batch, lastDimPow2]);
+    }
+  }
+  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
+    const inputs2 = getInputs();
+    const mergeProgram = new MergeProgram2([batch, indicesSize / 2]);
+    const firstPass = indices === null ? 1 : 0;
+    const uniformDataMerge = [
+      { type: "int32", data: [lastDim] },
+      { type: "int32", data: [firstPass] },
+      { type: "int32", data: [kPow2] }
+    ];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGPUProgram(mergeProgram, inputs2, "int32", uniformDataMerge);
+    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);
+    const len = kPow2 / 2;
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, indices.shape);
+    }
+  }
+  let prevIndices = indices;
+  indices = slice5({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });
+  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);
+  let values = gatherV24({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });
+  disposeIntermediateTensorInfoOrNull2(backend2, x2D);
+  const newShape = xShape.slice(0, -1);
+  newShape.push(k);
+  prevIndices = indices;
+  indices = reshape6({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);
+  const prevValues = values;
+  values = reshape6({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull2(backend2, prevValues);
+  return [values, indices];
+}
+var topKConfig4 = {
+  kernelName: TopK,
+  backendName: "webgpu",
+  kernelFunc: topK3
+};
+var TransformProgram2 = class {
+  constructor(outShape) {
+    this.variableNames = ["Image", "Transforms"];
+    this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "transform";
+  }
+  getUserCode() {
+    const userCode = `
+          fn mapCoord(outCoord : f32, len : f32) -> f32{
+            var inCoord = outCoord;
+            if(uniforms.fillModeId == 2) {
+              if (inCoord < 0.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz2 = 2.0 * len;
+                  if (inCoord < sz2) {
+                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
+                    inCoord;
+                  }
+                  if (inCoord < -len) {
+                    inCoord = inCoord + sz2;
+                  } else {
+                    inCoord = -inCoord - 1.0;
+                  }
+                }
+              } else if (inCoord > len - 1.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz2 = 2.0 * len;
+                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
+                  if (inCoord >= len) {
+                    inCoord = sz2 - inCoord - 1.0;
+                  }
+                }
+              }
+              return clamp(inCoord, 0.0, len - 1.0);
+            } else if (uniforms.fillModeId == 3) {
+              if (inCoord < 0.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz = len - 1.0;
+                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
+                }
+              } else if (inCoord > len - 1.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz = len - 1.0;
+                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
+                }
+              }
+              return clamp(inCoord, 0.0, len - 1.0);
+            } else if (uniforms.fillModeId == 4) {
+              return clamp(outCoord, 0.0, len - 1.0);
+            }
+            return outCoord;
+          }
+          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
+            channel : i32) -> f32 {
+            var outputValue : f32;
+            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
+                outputValue = getImage(batch, coordY, coordX, channel);
+            } else {
+              outputValue = uniforms.fillValue;
+            }
+            return outputValue;
+          }
+
+          ${getMainHeaderAndGlobalIndexString()}
+            if (index < uniforms.size) {
+              let coords = getCoordsFromIndex(index);
+              var outputValue : f32;
+              let batch = coords[0];
+              let x = coords[2];
+              let y = coords[1];
+              let channel = coords[3];
+              let xf = f32(x);
+              let yf = f32(y);
+              let a1 = getTransforms(batch, 0);
+              let a2 = getTransforms(batch, 1);
+              let a3 = getTransforms(batch, 2);
+              let b1 = getTransforms(batch, 3);
+              let b2 = getTransforms(batch, 4);
+              let b3 = getTransforms(batch, 5);
+              let c1 = getTransforms(batch, 6);
+              let c2 = getTransforms(batch, 7);
+              let projection = c1 * xf + c2 * yf + 1.0;
+              if (projection == 0.0) {
+                outputValue = uniforms.fillValue;
+              } else {
+                let inX = (a1 * xf + a2 * yf + a3) / projection;
+                let inY = (b1 * xf + b2 * yf + b3) / projection;
+                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
+                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
+
+                if (uniforms.interpolationModeId == 1) {
+                  let coordY = i32(round(mapY));
+                  let coordX = i32(round(mapX));
+                  outputValue = readWithFillValue(batch, coordY, coordX,
+                    channel);
+                } else {
+                  let yFloor = floor(mapY);
+                  let xFloor = floor(mapX);
+                  let yCeil = yFloor + 1.0;
+                  let xCeil = xFloor + 1.0;
+                  let valueYFloor = (xCeil - mapX) *
+                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
+                  let valueYCeil = (xCeil - mapX) *
+                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
+                  outputValue = (yCeil - mapY) * valueYFloor +
+                  (mapY - yFloor) * valueYCeil;
+                }
+              }
+              setOutputAtIndex(index, outputValue);
+            }
+          }
+        `;
+    return userCode;
+  }
+};
+function transform5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [
+    batch,
+    outHeight,
+    outWidth,
+    numChannels
+  ];
+  const program = new TransformProgram2(outShape);
+  const interpolationModeId = interpolation === "nearest" ? 1 : 2;
+  let fillModeId;
+  switch (fillMode) {
+    case "constant":
+      fillModeId = 1;
+      break;
+    case "reflect":
+      fillModeId = 2;
+      break;
+    case "wrap":
+      fillModeId = 3;
+      break;
+    case "nearest":
+      fillModeId = 4;
+      break;
+    default:
+      fillModeId = 1;
+      break;
+  }
+  const uniformData = [
+    { type: "int32", data: [interpolationModeId] },
+    { type: "int32", data: [fillModeId] },
+    { type: "float32", data: [fillValue] }
+  ];
+  return backend2.runWebGPUProgram(program, [image2, transforms], "float32", uniformData);
+}
+var transformConfig4 = {
+  kernelName: Transform,
+  backendName: "webgpu",
+  kernelFunc: transform5
+};
+function unpack4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const x = value;
+  const xRank = x.shape.length;
+  const num = value.shape[axis];
+  const outShape = new Array(xRank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < xRank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = x.shape[i2];
+    }
+  }
+  const toDispose = [];
+  const begin = new Array(xRank).fill(0);
+  const size2 = x.shape.slice();
+  size2[axis] = 1;
+  const res = new Array(num);
+  for (let i2 = 0; i2 < res.length; i2++) {
+    begin[axis] = i2;
+    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: size2 } });
+    const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
+    res[i2] = reshaped;
+    toDispose.push(sliced);
+  }
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return res;
+}
+var unpackConfig4 = {
+  kernelName: Unpack,
+  backendName: "webgpu",
+  kernelFunc: unpack4
+};
+var kernelConfigs4 = [
+  _fusedMatMulConfig4,
+  absConfig4,
+  addConfig4,
+  addNConfig4,
+  argMaxConfig4,
+  argMinConfig3,
+  avgPoolConfig4,
+  batchMatMulConfig4,
+  batchToSpaceNDConfig4,
+  castConfig4,
+  ceilConfig4,
+  clipByValueConfig4,
+  complexConfig3,
+  concatConfig4,
+  conv2DConfig4,
+  conv2DBackpropInputConfig4,
+  cosConfig4,
+  coshConfig4,
+  cropAndResizeConfig4,
+  cumprodConfig4,
+  cumsumConfig4,
+  depthToSpaceConfig4,
+  depthwiseConv2dNativeConfig4,
+  einsumConfig3,
+  eluConfig4,
+  equalConfig4,
+  expConfig4,
+  expandDimsConfig4,
+  expm1Config3,
+  fillConfig4,
+  flipLeftRightConfig4,
+  fromPixelsConfig2,
+  floorConfig4,
+  floorDivConfig4,
+  fusedBatchNormConfig2,
+  fusedConv2DConfig4,
+  fusedDepthwiseConv2DConfig4,
+  gatherNdConfig4,
+  gatherV2Config4,
+  greaterConfig4,
+  greaterEqualConfig4,
+  identityConfig4,
+  imagConfig3,
+  leakyReluConfig4,
+  lessConfig4,
+  lessEqualConfig4,
+  logConfig4,
+  logicalAndConfig4,
+  logicalNotConfig3,
+  maxConfig4,
+  maximumConfig4,
+  maxPoolConfig4,
+  meanConfig4,
+  minConfig4,
+  minimumConfig4,
+  mirrorPadConfig4,
+  multiplyConfig4,
+  negConfig4,
+  nonMaxSuppressionV3Config4,
+  nonMaxSuppressionV5Config4,
+  notEqualConfig4,
+  onesLikeConfig4,
+  packConfig4,
+  padV2Config4,
+  powConfig4,
+  preluConfig4,
+  prodConfig4,
+  rangeConfig4,
+  realConfig3,
+  realDivConfig4,
+  reluConfig4,
+  relu6Config4,
+  reshapeConfig4,
+  resizeBilinearConfig4,
+  resizeNearestNeighborConfig3,
+  rotateWithOffsetConfig4,
+  rsqrtConfig4,
+  scatterNdConfig4,
+  selectConfig4,
+  sigmoidConfig4,
+  sinConfig4,
+  sinhConfig3,
+  sliceConfig4,
+  stridedSliceConfig4,
+  stringNGramsConfig3,
+  softmaxConfig4,
+  spaceToBatchNDConfig4,
+  sparseToDenseConfig3,
+  splitVConfig4,
+  sqrtConfig4,
+  squareConfig4,
+  squaredDifferenceConfig4,
+  subConfig4,
+  sumConfig4,
+  tanhConfig4,
+  tileConfig4,
+  topKConfig4,
+  transformConfig4,
+  transposeConfig4,
+  unpackConfig4,
+  zerosLikeConfig4
+];
+for (const kernelConfig of kernelConfigs4) {
+  registerKernel(kernelConfig);
+}
+var BufferManager = class {
+  constructor(device) {
+    this.device = device;
+    this.numUsedBuffers = 0;
+    this.numFreeBuffers = 0;
+    this.freeBuffers = /* @__PURE__ */ new Map();
+    this.usedBuffers = /* @__PURE__ */ new Map();
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+  acquireUploadBuffer(byteSize, usage) {
+    return this.acquireBuffer(byteSize, usage, true);
+  }
+  acquireBuffer(byteSize, usage, mappedAtCreation = false) {
+    const key = getBufferKey(byteSize, usage);
+    if (!this.freeBuffers.has(key)) {
+      this.freeBuffers.set(key, []);
+    }
+    if (!this.usedBuffers.has(key)) {
+      this.usedBuffers.set(key, []);
+    }
+    this.numBytesUsed += byteSize;
+    this.numUsedBuffers++;
+    if (this.freeBuffers.get(key).length > 0) {
+      this.numFreeBuffers--;
+      const newBuffer2 = this.freeBuffers.get(key).shift();
+      this.usedBuffers.get(key).push(newBuffer2);
+      return newBuffer2;
+    }
+    this.numBytesAllocated += byteSize;
+    const newBuffer = this.device.createBuffer({ mappedAtCreation, size: byteSize, usage });
+    this.usedBuffers.get(key).push(newBuffer);
+    return newBuffer;
+  }
+  releaseBuffer(buffer2, byteSize, usage) {
+    if (this.freeBuffers.size === 0) {
+      return;
+    }
+    const key = getBufferKey(byteSize, usage);
+    if (!this.freeBuffers.has(key)) {
+      this.freeBuffers.set(key, []);
+    }
+    this.freeBuffers.get(key).push(buffer2);
+    this.numFreeBuffers++;
+    this.numUsedBuffers--;
+    const bufferList = this.usedBuffers.get(key);
+    const bufferIndex = bufferList.indexOf(buffer2);
+    if (bufferIndex < 0) {
+      throw new Error("Cannot release a buffer that was never provided by this buffer manager");
+    }
+    bufferList.splice(bufferIndex, 1);
+    this.numBytesUsed -= byteSize;
+  }
+  releaseUploadBuffer(buffer2, byteSize, usage) {
+    buffer2.mapAsync(GPUMapMode.WRITE).then(() => {
+      this.releaseBuffer(buffer2, byteSize, usage);
+    }, (err) => {
+    });
+  }
+  getNumUsedBuffers() {
+    return this.numUsedBuffers;
+  }
+  getNumFreeBuffers() {
+    return this.numFreeBuffers;
+  }
+  dispose() {
+    this.freeBuffers.forEach((buffers, key) => {
+      buffers.forEach((buff) => {
+        buff.destroy();
+      });
+    });
+    this.usedBuffers.forEach((buffers, key) => {
+      buffers.forEach((buff) => {
+        buff.destroy();
+      });
+    });
+    this.freeBuffers = /* @__PURE__ */ new Map();
+    this.usedBuffers = /* @__PURE__ */ new Map();
+    this.numUsedBuffers = 0;
+    this.numFreeBuffers = 0;
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+};
+function getBufferKey(byteSize, usage) {
+  return `${byteSize}_${usage}`;
+}
+var TextureManager2 = class {
+  constructor(device) {
+    this.device = device;
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this.freeTextures = /* @__PURE__ */ new Map();
+    this.usedTextures = /* @__PURE__ */ new Map();
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+  acquireTexture(width, height, format, usage) {
+    const bytesPerElement2 = getBytesPerElement(format);
+    const byteSize = width * height * bytesPerElement2;
+    const key = getTextureKey(width, height, format, usage);
+    if (!this.freeTextures.has(key)) {
+      this.freeTextures.set(key, []);
+    }
+    if (!this.usedTextures.has(key)) {
+      this.usedTextures.set(key, []);
+    }
+    this.numBytesUsed += byteSize;
+    this.numUsedTextures++;
+    if (this.freeTextures.get(key).length > 0) {
+      this.numFreeTextures--;
+      const newTexture2 = this.freeTextures.get(key).shift();
+      this.usedTextures.get(key).push(newTexture2);
+      return newTexture2;
+    }
+    this.numBytesAllocated += byteSize;
+    const newTexture = this.device.createTexture({
+      size: [width, height],
+      format,
+      usage
+    });
+    this.usedTextures.get(key).push(newTexture);
+    return newTexture;
+  }
+  releaseTexture(texture, width, height, format, usage) {
+    if (this.freeTextures.size === 0) {
+      return;
+    }
+    const key = getTextureKey(width, height, format, usage);
+    if (!this.freeTextures.has(key)) {
+      this.freeTextures.set(key, []);
+    }
+    this.freeTextures.get(key).push(texture);
+    this.numFreeTextures++;
+    this.numUsedTextures--;
+    const textureList = this.usedTextures.get(key);
+    const textureIndex = textureList.indexOf(texture);
+    if (textureIndex < 0) {
+      throw new Error("Cannot release a texture that was never provided by this texture manager");
+    }
+    textureList.splice(textureIndex, 1);
+    const bytesPerElement2 = getBytesPerElement(format);
+    const byteSize = width * height * bytesPerElement2;
+    this.numBytesUsed -= byteSize;
+  }
+  getNumUsedTextures() {
+    return this.numUsedTextures;
+  }
+  getNumFreeTextures() {
+    return this.numFreeTextures;
+  }
+  dispose() {
+    this.freeTextures.forEach((textures, key) => {
+      textures.forEach((texture) => {
+        texture.destroy();
+      });
+    });
+    this.usedTextures.forEach((textures, key) => {
+      textures.forEach((texture) => {
+        texture.destroy();
+      });
+    });
+    this.freeTextures = /* @__PURE__ */ new Map();
+    this.usedTextures = /* @__PURE__ */ new Map();
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+};
+function getTextureKey(width, height, format, usage) {
+  return `${width}_${height}_${format}_${usage}`;
+}
+function getBytesPerElement(format) {
+  if (format === "rgba8unorm") {
+    return 16;
+  } else {
+    throw new Error(`${format} is not supported!`);
+  }
+}
+var makeBindGroup = (device, bindGroupLayout, inputs, output, uniforms) => {
+  const bindings = [output, ...inputs];
+  if (uniforms) {
+    bindings.push(uniforms);
+  }
+  return device.createBindGroup({
+    layout: bindGroupLayout,
+    entries: bindings.map((b, i2) => ({ binding: i2, resource: b }))
+  });
+};
+var compileProgram2 = (device, program, pipelineLayout, inputsData, output, isFromPixel = false) => {
+  const outputData = { dtype: output.dtype, shape: output.shape };
+  const source = makeShader2(inputsData, outputData, program, isFromPixel);
+  const module = device.createShaderModule({ code: source, label: program.constructor.name });
+  const pipeline = device.createComputePipeline({
+    layout: pipelineLayout,
+    compute: { module, entryPoint: "main" },
+    label: program.constructor.name
+  });
+  return pipeline;
+};
+function makeShaderKey2(program, shapes, types = [], broadcastDimsKey = "", inputShapesEqualsOutShape = "") {
+  const key = program.shaderKey + "_" + (program.workGroupSize ? program.workGroupSize.join(",") : "") + shapes.map((shape) => shape.length).join(",") + types.join(",") + program.variableNames.join(",") + broadcastDimsKey + inputShapesEqualsOutShape;
+  return key;
+}
+var CPU_HANDOFF_SIZE_THRESHOLD2 = env().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
+var reshapeDispatch = (device, program) => {
+  const MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE = device.limits.maxComputeWorkgroupsPerDimension;
+  const layout = program["dispatchLayout"];
+  const dispatch = program["dispatch"];
+  if (dispatch.every((d) => d <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE)) {
+    return dispatch;
+  }
+  util_exports.assert(dispatch[0] > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && layout.y === void 0 && layout.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
+  let dispatchAverage = Math.ceil(Math.sqrt(dispatch[0]));
+  if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) {
+    dispatchAverage = Math.ceil(Math.cbrt(dispatch[0]));
+    util_exports.assert(dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE, () => "Total dispatch size exceeds WebGPU maximum.");
+    return [dispatchAverage, dispatchAverage, dispatchAverage];
+  } else {
+    return [dispatchAverage, dispatchAverage, 1];
+  }
+};
+var WebGPUBackend = class extends KernelBackend {
+  constructor(device, supportTimeQuery = false) {
+    super();
+    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();
+    this.tensorDisposalQueue = [];
+    this.uniformDisposalQueue = [];
+    this.stagingDisposalQueue = [];
+    this.textureDisposalQueue = [];
+    this.disposed = false;
+    this.uploadWaitMs = 0;
+    this.downloadWaitMs = 0;
+    this.dispatchNumberInEncoder = 0;
+    this.fromPixelTextureLayout = null;
+    this.fromPixelImportTextureLayout = null;
+    if (!isWebGPUSupported()) {
+      throw new Error("WebGPU is not supported on this device");
+    }
+    this.layoutCache = {};
+    this.pipelineCache = {};
+    this.device = device;
+    this.queue = device.queue;
+    this.currentCommandEncoder = null;
+    this.currentComputePass = null;
+    this.supportTimeQuery = supportTimeQuery;
+    this.bufferManager = new BufferManager(this.device);
+    this.textureManager = new TextureManager2(this.device);
+    this.tensorMap = new DataStorage(this, engine());
+    if (this.supportTimeQuery) {
+      this.querySet = this.device.createQuerySet({
+        type: "timestamp",
+        count: 2
+      });
+    }
+    if (env().getBool("WEBGPU_USE_PROFILE_TOOL")) {
+      this.dummyCanvas = document.createElement("canvas");
+      this.dummyCanvas.width = 1;
+      this.dummyCanvas.height = 1;
+      this.dummyContext = this.dummyCanvas.getContext("webgpu");
+      this.dummyContext.configure({
+        device,
+        format: "bgra8unorm"
+      });
+      document.body.appendChild(this.dummyCanvas);
+    }
+  }
+  nextDataId() {
+    return WebGPUBackend.nextDataId++;
+  }
+  floatPrecision() {
+    return 32;
+  }
+  defaultGpuBufferUsage() {
+    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;
+  }
+  flushDisposalQueue() {
+    this.tensorDisposalQueue.forEach((d) => {
+      this.maybeReleaseBuffer(d);
+      this.tensorMap.delete(d);
+    });
+    this.uniformDisposalQueue.forEach((d) => this.bufferManager.releaseBuffer(d.buffer, d.byteSize, d.usage));
+    this.stagingDisposalQueue.forEach((d) => this.bufferManager.releaseUploadBuffer(d.buffer, d.byteSize, d.usage));
+    this.textureDisposalQueue.forEach((d) => this.textureManager.releaseTexture(d.texture, d.width, d.height, d.format, d.usage));
+    this.tensorDisposalQueue = [];
+    this.uniformDisposalQueue = [];
+    this.stagingDisposalQueue = [];
+    this.textureDisposalQueue = [];
+  }
+  disposeData(dataId, force = false) {
+    if (this.tensorMap.has(dataId)) {
+      const data = this.tensorMap.get(dataId);
+      data.refCount--;
+      if (!force && data.refCount > 0) {
+        return false;
+      }
+      if (this.commandQueueOwnedIds.has(dataId)) {
+        this.tensorDisposalQueue.push(dataId);
+        return false;
+      } else {
+        this.maybeReleaseBuffer(dataId);
+      }
+      const { complexTensorInfos } = this.tensorMap.get(dataId);
+      if (complexTensorInfos != null) {
+        this.disposeData(complexTensorInfos.real.dataId, true);
+        this.disposeData(complexTensorInfos.imag.dataId, true);
+      }
+      this.tensorMap.delete(dataId);
+    }
+    return true;
+  }
+  memory() {
+    return {
+      numBytesInGPU: this.bufferManager.numBytesUsed,
+      numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated,
+      unreliable: false
+    };
+  }
+  getBufferManager() {
+    return this.bufferManager;
+  }
+  getTextureManager() {
+    return this.textureManager;
+  }
+  acquireBuffer(byteSize, usage = this.defaultGpuBufferUsage()) {
+    return this.bufferManager.acquireBuffer(byteSize, usage);
+  }
+  maybeReleaseBuffer(dataId) {
+    const info = this.tensorMap.get(dataId);
+    if (info != null && info.bufferInfo.buffer != null) {
+      this.bufferManager.releaseBuffer(info.bufferInfo.buffer, info.bufferInfo.byteSize, info.bufferInfo.usage);
+      info.bufferInfo.buffer = null;
+    }
+  }
+  refCount(dataId) {
+    if (this.tensorMap.has(dataId)) {
+      const tensorData = this.tensorMap.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const tensorData = this.tensorMap.get(dataId);
+    tensorData.refCount++;
+  }
+  decRef(dataId) {
+    if (this.tensorMap.has(dataId)) {
+      const tensorData = this.tensorMap.get(dataId);
+      tensorData.refCount--;
+    }
+  }
+  write(values, shape, dtype) {
+    if (dtype === "complex64" && values != null) {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    const dataId = { id: this.nextDataId() };
+    const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);
+    this.tensorMap.set(dataId, {
+      dtype,
+      shape,
+      values,
+      bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() },
+      refCount: 1
+    });
+    return dataId;
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    if (dtype === "complex64") {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);
+    this.tensorMap.set(dataId, {
+      dtype,
+      shape,
+      values,
+      bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() },
+      refCount
+    });
+  }
+  submitQueue() {
+    this.ensureComputePassEnded();
+    this.queue.submit([this.currentCommandEncoder.finish()]);
+    this.currentCommandEncoder = null;
+    this.dispatchNumberInEncoder = 0;
+    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();
+    this.flushDisposalQueue();
+  }
+  getBuffer(dataId) {
+    this.uploadToGPU(dataId);
+    return this.tensorMap.get(dataId).bufferInfo.buffer;
+  }
+  ensureCommandEncoderReady() {
+    if (!this.currentCommandEncoder) {
+      this.currentCommandEncoder = this.device.createCommandEncoder();
+    }
+  }
+  ensureComputePassEnded() {
+    if (this.currentComputePass) {
+      this.currentComputePass.end();
+      this.currentComputePass = null;
+    }
+  }
+  getComputePass() {
+    if (!this.currentComputePass) {
+      this.currentComputePass = this.currentCommandEncoder.beginComputePass();
+    }
+    return this.currentComputePass;
+  }
+  async getBufferData(buffer2, size2) {
+    const staging = this.acquireBuffer(size2, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
+    this.ensureCommandEncoderReady();
+    this.ensureComputePassEnded();
+    this.currentCommandEncoder.copyBufferToBuffer(buffer2, 0, staging, 0, size2);
+    this.submitQueue();
+    await staging.mapAsync(GPUMapMode.READ);
+    const values = staging.getMappedRange().slice(0);
+    staging.unmap();
+    if (staging != null) {
+      this.bufferManager.releaseBuffer(staging, size2, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
+    }
+    if (env().getBool("WEBGPU_USE_PROFILE_TOOL")) {
+      util_exports.assert(this.dummyContext !== void 0, () => `Fail to get context for profiling tool`);
+      this.dummyContext.getCurrentTexture();
+    }
+    return values;
+  }
+  convertAndCacheOnCPU(dataId, data) {
+    const info = this.tensorMap.get(dataId);
+    this.maybeReleaseBuffer(dataId);
+    info.values = data;
+    return info.values;
+  }
+  readSync(dataId) {
+    const texData = this.tensorMap.get(dataId);
+    const { values } = texData;
+    if (values == null) {
+      throw new Error("WebGPU readSync is only available for CPU-resident tensors.");
+    }
+    return values;
+  }
+  async read(dataId) {
+    if (!this.tensorMap.has(dataId)) {
+      throw new Error(`Tensor ${dataId} was not registered!`);
+    }
+    const info = this.tensorMap.get(dataId);
+    const { values } = info;
+    if (values != null) {
+      return this.convertAndCacheOnCPU(dataId, values);
+    }
+    let vals;
+    if (info.dtype === "complex64") {
+      const ps = await Promise.all([
+        this.read(info.complexTensorInfos.real.dataId),
+        this.read(info.complexTensorInfos.imag.dataId)
+      ]);
+      const realValues = ps[0];
+      const imagValues = ps[1];
+      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    } else {
+      const data = info.values != null ? info.values : await this.getBufferData(info.bufferInfo.buffer, info.bufferInfo.byteSize);
+      vals = ArrayBufferToTypedArray(data, info.dtype);
+    }
+    this.convertAndCacheOnCPU(dataId, vals);
+    return vals;
+  }
+  readToGPU(dataId) {
+    const srcData = this.tensorMap.get(dataId);
+    const { values, dtype, shape, bufferInfo } = srcData;
+    if (dtype === "complex64") {
+      throw new Error("Does not support reading buffer for complex64 dtype.");
+    }
+    if (bufferInfo.buffer == null) {
+      if (values != null) {
+        throw new Error("Data is not on GPU but on CPU.");
+      } else {
+        throw new Error("There is no data on GPU or CPU.");
+      }
+    }
+    const size2 = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);
+    const resBuffer = this.acquireBuffer(size2);
+    this.ensureCommandEncoderReady();
+    this.ensureComputePassEnded();
+    this.currentCommandEncoder.copyBufferToBuffer(bufferInfo.buffer, 0, resBuffer, 0, size2);
+    this.submitQueue();
+    const tensorInfo = this.makeTensorInfo(shape, dtype);
+    const tensorRef = engine().makeTensorFromTensorInfo(tensorInfo);
+    const info = this.tensorMap.get(tensorInfo.dataId);
+    info.bufferInfo.buffer = resBuffer;
+    return { tensorRef, buffer: resBuffer, bufSize: size2 };
+  }
+  bufferSync(t2) {
+    const data = this.readSync(t2.dataId);
+    if (t2.dtype === "string") {
+      try {
+        const strings = data.map((d) => util_exports.decodeString(d));
+        return buffer(t2.shape, t2.dtype, strings);
+      } catch (_a) {
+        throw new Error("Failed to decode encoded string bytes into utf-8");
+      }
+    }
+    return buffer(t2.shape, t2.dtype, data);
+  }
+  async time(f) {
+    const oldActiveTimers = this.activeTimers;
+    const newActiveTimers = [];
+    let outerMostTime = false;
+    if (this.programTimersStack == null) {
+      this.programTimersStack = newActiveTimers;
+      outerMostTime = true;
+    } else {
+      this.activeTimers.push(newActiveTimers);
+    }
+    this.activeTimers = newActiveTimers;
+    f();
+    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);
+    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);
+    this.activeTimers = oldActiveTimers;
+    if (outerMostTime) {
+      this.programTimersStack = null;
+    }
+    const res = {
+      uploadWaitMs: this.uploadWaitMs,
+      downloadWaitMs: this.downloadWaitMs,
+      kernelMs: null,
+      wallMs: null
+    };
+    const kernelMs = await Promise.all(flattenedActiveTimerQueries);
+    res["kernelMs"] = util_exports.sum(kernelMs);
+    res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+    this.uploadWaitMs = 0;
+    this.downloadWaitMs = 0;
+    return res;
+  }
+  getAndSavePipeline(key, getPipeline) {
+    if (!(key in this.pipelineCache)) {
+      this.pipelineCache[key] = getPipeline();
+    }
+    return this.pipelineCache[key];
+  }
+  makeTensorInfo(shape, dtype, values) {
+    let dataId;
+    if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
+      const encodedValues = values.map((d) => util_exports.encodeString(d));
+      dataId = this.write(encodedValues, shape, dtype);
+    } else {
+      dataId = this.write(values, shape, dtype);
+    }
+    return { dataId, shape, dtype };
+  }
+  tensorToBinding(tensor2) {
+    if (!tensor2) {
+      return null;
+    }
+    const tensorData = this.tensorMap.get(tensor2.dataId);
+    return {
+      offset: 0,
+      size: tensorData.bufferInfo.byteSize,
+      buffer: tensorData.bufferInfo.buffer
+    };
+  }
+  async getQueryTime(query) {
+    if (this.supportTimeQuery) {
+      return this.getTimeFromQuerySet(query);
+    } else {
+      return 0;
+    }
+  }
+  uploadToGPU(dataId) {
+    const info = this.tensorMap.get(dataId);
+    if (info.bufferInfo.buffer != null) {
+      return;
+    }
+    info.bufferInfo.buffer = this.acquireBuffer(info.bufferInfo.byteSize);
+    if (info.values) {
+      const stagingBuffer = this.bufferManager.acquireUploadBuffer(info.bufferInfo.byteSize, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC);
+      const arrayBuffer = stagingBuffer.getMappedRange();
+      if (info.dtype === "int32" || info.dtype === "bool") {
+        new Int32Array(arrayBuffer).set(info.values);
+      } else {
+        new Float32Array(arrayBuffer).set(info.values);
+      }
+      stagingBuffer.unmap();
+      this.ensureCommandEncoderReady();
+      this.ensureComputePassEnded();
+      this.currentCommandEncoder.copyBufferToBuffer(stagingBuffer, 0, info.bufferInfo.buffer, 0, info.bufferInfo.byteSize);
+      const stagingInfo = {
+        byteSize: info.bufferInfo.byteSize,
+        usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC,
+        buffer: stagingBuffer
+      };
+      this.stagingDisposalQueue.push(stagingInfo);
+    }
+  }
+  makeUniforms(uniformsWithType) {
+    let currentOffset = 0;
+    let preLength = 0;
+    const offsets = [];
+    uniformsWithType.forEach((d) => {
+      if (d.data.length === 0) {
+        d.data = [1];
+      }
+      let baseAlignment;
+      switch (d.data.length) {
+        case 1:
+          baseAlignment = 4;
+          break;
+        case 2:
+          baseAlignment = 8;
+          break;
+        case 3:
+          baseAlignment = 16;
+          break;
+        case 4:
+          baseAlignment = 16;
+          break;
+        case 5:
+          baseAlignment = 16;
+          break;
+        case 6:
+          baseAlignment = 16;
+          break;
+        default:
+          util_exports.assert(false, () => `Unsupported ${d.data.length}D shape`);
+      }
+      if (preLength === 5 || preLength === 6) {
+        baseAlignment = 16;
+      }
+      currentOffset = Math.ceil(currentOffset / baseAlignment) * baseAlignment;
+      preLength = d.data.length;
+      offsets.push(currentOffset);
+      currentOffset += d.data.length * 4;
+    });
+    const arrayBuffer = new ArrayBuffer(currentOffset);
+    uniformsWithType.forEach((d, i2) => {
+      const offset = offsets[i2];
+      if (d.type === "int32") {
+        new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);
+      } else if (d.type === "uint32") {
+        new Uint32Array(arrayBuffer, offset, d.data.length).set(d.data);
+      } else {
+        new Float32Array(arrayBuffer, offset, d.data.length).set(d.data);
+      }
+    });
+    const uniformBuffer = this.acquireBuffer(currentOffset, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
+    this.queue.writeBuffer(uniformBuffer, 0, arrayBuffer, 0, currentOffset);
+    const uniformInfo = {
+      byteSize: currentOffset,
+      usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM,
+      buffer: uniformBuffer
+    };
+    this.uniformDisposalQueue.push(uniformInfo);
+    return { offset: 0, size: currentOffset, buffer: uniformBuffer };
+  }
+  createLayout(inputEntrySize) {
+    const bindGroupLayoutEntries = [];
+    bindGroupLayoutEntries.push({
+      binding: 0,
+      visibility: GPUShaderStage.COMPUTE,
+      buffer: { type: "storage" }
+    });
+    for (let i2 = 0; i2 < inputEntrySize; i2++) {
+      bindGroupLayoutEntries.push({
+        binding: i2 + 1,
+        visibility: GPUShaderStage.COMPUTE,
+        buffer: { type: "read-only-storage" }
+      });
+    }
+    bindGroupLayoutEntries.push({
+      binding: inputEntrySize + 1,
+      visibility: GPUShaderStage.COMPUTE,
+      buffer: { type: "uniform" }
+    });
+    const bindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries });
+    const pipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [bindGroupLayout] });
+    return { bindGroupLayout, pipelineLayout };
+  }
+  getCachedOrCreateLayout(inputEntrySize) {
+    if (!(inputEntrySize in this.layoutCache)) {
+      this.layoutCache[inputEntrySize] = this.createLayout(inputEntrySize);
+    }
+    return this.layoutCache[inputEntrySize];
+  }
+  runWebGPUProgram(program, inputs, outputDtype, programUniforms, output) {
+    if (!output) {
+      output = this.makeTensorInfo(program.outputShape, outputDtype);
+      if (util_exports.sizeFromShape(output.shape) === 0) {
+        const outData = this.tensorMap.get(output.dataId);
+        outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
+        return output;
+      }
+      this.uploadToGPU(output.dataId);
+    }
+    program.dispatch = reshapeDispatch(this.device, program);
+    let uniformsWithType = [{ type: "float32", data: [NaN] }];
+    const bufferShapes = inputs.concat(output).map((d) => d.shape);
+    const uniformsType = "int32";
+    bufferShapes.map((d) => {
+      uniformsWithType.push({ type: uniformsType, data: d });
+    });
+    const strides2 = util_exports.computeStrides(output.shape);
+    uniformsWithType.push({ type: uniformsType, data: strides2 });
+    if (program.size) {
+      const size2 = util_exports.sizeFromShape(program.outputShape);
+      uniformsWithType.push({ type: uniformsType, data: [program.isVec4 ? size2 / 4 : size2] });
+    }
+    if (programUniforms) {
+      uniformsWithType = [...uniformsWithType, ...programUniforms];
+    }
+    const uniforms = this.makeUniforms(uniformsWithType);
+    const inputsData = inputs.map((input2, i2) => {
+      if (input2.dtype === "complex64") {
+        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
+      }
+      this.uploadToGPU(input2.dataId);
+      return {
+        dtype: this.tensorMap.get(input2.dataId).dtype,
+        shape: input2.shape,
+        name: program.variableNames[i2]
+      };
+    });
+    const bufferTypes = inputsData.map((d) => d.dtype).concat(output.dtype);
+    const broadcastDims = inputsData.map((d) => backend_util_exports.getBroadcastDims(d.shape, output.shape));
+    const inputShapesEqualsOutShape = inputsData.map((d) => util_exports.arraysEqual(d.shape, output.shape)).join("_");
+    const broadcastDimsKey = broadcastDims.map((d) => d.join("_")).join(";");
+    const key = makeShaderKey2(program, bufferShapes, bufferTypes, broadcastDimsKey, inputShapesEqualsOutShape);
+    const { bindGroupLayout, pipelineLayout } = this.getCachedOrCreateLayout(program.variableNames.length);
+    const pipeline = this.getAndSavePipeline(key, () => {
+      return compileProgram2(this.device, program, pipelineLayout, inputsData, output);
+    });
+    const shouldTimeProgram = this.activeTimers != null;
+    const bg = makeBindGroup(this.device, bindGroupLayout, inputs.map((t2) => this.tensorToBinding(t2)), this.tensorToBinding(output), uniforms);
+    this.ensureCommandEncoderReady();
+    const pass = this.getComputePass();
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 0);
+      }
+    }
+    pass.setPipeline(pipeline);
+    pass.setBindGroup(0, bg);
+    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 1);
+      }
+    }
+    this.dispatchNumberInEncoder++;
+    inputs.forEach((input2) => {
+      this.commandQueueOwnedIds.add(input2.dataId);
+    });
+    this.commandQueueOwnedIds.add(output.dataId);
+    if (env().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder) {
+      this.submitQueue();
+    }
+    if (shouldTimeProgram) {
+      this.activeTimers.push({
+        name: program.constructor.name,
+        query: this.getQueryTime(this.querySet)
+      });
+    }
+    return output;
+  }
+  getFromPixelTextureLayout(useImport) {
+    if (useImport) {
+      if (this.fromPixelImportTextureLayout === null) {
+        this.fromPixelImportTextureLayout = this.createFromPixelTextureLayout(true);
+      }
+      return this.fromPixelImportTextureLayout;
+    }
+    if (this.fromPixelTextureLayout === null) {
+      this.fromPixelTextureLayout = this.createFromPixelTextureLayout(false);
+    }
+    return this.fromPixelTextureLayout;
+  }
+  createFromPixelTextureLayout(useImport) {
+    const bindGroupLayoutEntries = [];
+    bindGroupLayoutEntries.push({
+      binding: 0,
+      visibility: GPUShaderStage.COMPUTE,
+      buffer: { type: "storage" }
+    });
+    if (useImport) {
+      bindGroupLayoutEntries.push({
+        binding: 1,
+        visibility: GPUShaderStage.COMPUTE,
+        externalTexture: {}
+      });
+    } else {
+      bindGroupLayoutEntries.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, texture: {} });
+    }
+    bindGroupLayoutEntries.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} });
+    const fromPixelBindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries });
+    const fromPixelPipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [fromPixelBindGroupLayout] });
+    return {
+      bindGroupLayout: fromPixelBindGroupLayout,
+      pipelineLayout: fromPixelPipelineLayout
+    };
+  }
+  copyExternalImageToTexture(externalImage, outShape) {
+    const textureUsage = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING;
+    const textureFormat = "rgba8unorm";
+    const texture = this.textureManager.acquireTexture(outShape[1], outShape[0], textureFormat, textureUsage);
+    const externalResource = texture.createView();
+    this.queue.copyExternalImageToTexture({ source: externalImage }, { texture }, [outShape[1], outShape[0]]);
+    const textureInfo = {
+      width: outShape[1],
+      height: outShape[0],
+      format: textureFormat,
+      usage: textureUsage,
+      texture
+    };
+    this.textureDisposalQueue.push(textureInfo);
+    return externalResource;
+  }
+  runFromPixelsProgram(program, outShape, programUniforms, useImport, externalImage) {
+    program.dispatch = reshapeDispatch(this.device, program);
+    const output = this.makeTensorInfo(outShape, "int32");
+    if (util_exports.sizeFromShape(output.shape) === 0) {
+      const outData = this.tensorMap.get(output.dataId);
+      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
+      return output;
+    }
+    this.uploadToGPU(output.dataId);
+    const key = makeShaderKey2(program, [output.shape]);
+    const layout = this.getFromPixelTextureLayout(useImport);
+    const pipeline = this.getAndSavePipeline(key, () => {
+      return compileProgram2(this.device, program, layout.pipelineLayout, [], output, true);
+    });
+    let externalResource;
+    if (useImport) {
+      const externalTextureDescriptor = {
+        source: externalImage
+      };
+      externalResource = this.device.importExternalTexture(externalTextureDescriptor);
+    } else {
+      externalResource = this.copyExternalImageToTexture(externalImage, output.shape);
+    }
+    const binding = this.tensorToBinding(output);
+    const uniforms = this.makeUniforms(programUniforms);
+    const bindGroup = this.device.createBindGroup({
+      layout: layout.bindGroupLayout,
+      entries: [
+        {
+          binding: 0,
+          resource: {
+            buffer: binding.buffer
+          }
+        },
+        {
+          binding: 1,
+          resource: externalResource
+        },
+        {
+          binding: 2,
+          resource: {
+            buffer: uniforms.buffer
+          }
+        }
+      ]
+    });
+    this.ensureCommandEncoderReady();
+    const pass = this.getComputePass();
+    const shouldTimeProgram = this.activeTimers != null;
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 0);
+      }
+    }
+    pass.setPipeline(pipeline);
+    pass.setBindGroup(0, bindGroup);
+    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 1);
+      }
+    }
+    this.commandQueueOwnedIds.add(output.dataId);
+    this.dispatchNumberInEncoder++;
+    if (env().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder) {
+      this.submitQueue();
+    }
+    if (shouldTimeProgram) {
+      this.activeTimers.push({
+        name: program.constructor.name,
+        query: this.getQueryTime(this.querySet)
+      });
+    }
+    return output;
+  }
+  async getTimeFromQuerySet(querySet) {
+    const queryBuffer = this.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);
+    const dst = this.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
+    this.ensureCommandEncoderReady();
+    this.ensureComputePassEnded();
+    this.currentCommandEncoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0);
+    this.currentCommandEncoder.copyBufferToBuffer(queryBuffer, 0, dst, 0, 16);
+    this.submitQueue();
+    await dst.mapAsync(GPUMapMode.READ);
+    const arrayBuf = new BigUint64Array(dst.getMappedRange());
+    const timeElapsedNanos = Number(arrayBuf[1] - arrayBuf[0]);
+    dst.unmap();
+    this.bufferManager.releaseBuffer(dst, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
+    this.bufferManager.releaseBuffer(queryBuffer, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);
+    return timeElapsedNanos / 1e6;
+  }
+  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD2) {
+    return env().getBool("WEBGPU_CPU_FORWARD") && inputs.every((input2) => this.tensorMap.get(input2.dataId).bufferInfo.buffer == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);
+  }
+  numDataIds() {
+    return this.tensorMap.numDataIds() - this.tensorDisposalQueue.length;
+  }
+  dispose() {
+    if (this.disposed) {
+      return;
+    }
+    this.bufferManager.dispose();
+    this.textureManager.dispose();
+    this.disposed = true;
+  }
+};
+WebGPUBackend.nextDataId = 0;
+var webgpu_exports = {};
+__export2(webgpu_exports, {
+  WebGPUBackend: () => WebGPUBackend,
+  webgpu_util: () => webgpu_util_exports
+});
+if (isWebGPUSupported()) {
+  registerBackend("webgpu", async () => {
+    env().set("CHECK_COMPUTATION_FOR_ERRORS", false);
+    const gpuDescriptor = {
+      powerPreference: env().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance"
+    };
+    const adapter = await navigator.gpu.requestAdapter(gpuDescriptor);
+    const adapterLimits = adapter.limits;
+    const deviceDescriptor = {};
+    const supportTimeQuery = adapter.features.has("timestamp-query");
+    deviceDescriptor.requiredLimits = {
+      "maxComputeWorkgroupStorageSize": adapterLimits.maxComputeWorkgroupStorageSize,
+      "maxComputeWorkgroupsPerDimension": adapterLimits.maxComputeWorkgroupsPerDimension
+    };
+    if (supportTimeQuery) {
+      deviceDescriptor.requiredFeatures = ["timestamp-query"];
+    } else {
+      console.warn(`This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.`);
+    }
+    const device = await adapter.requestDevice(deviceDescriptor);
+    return new WebGPUBackend(device, supportTimeQuery);
+  }, 3);
+}
+var e = "3.18.0";
+var s = "3.18.0";
+var t = "3.18.0";
+var r = "3.18.0";
+var l = "3.18.0";
+var i = "3.18.0";
+var a = "3.18.0";
+var V = { tfjs: e, "tfjs-core": s, "tfjs-data": t, "tfjs-layers": r, "tfjs-converter": l, "tfjs-backend-webgl": i, "tfjs-backend-wasm": a };
+
+// src/image/imagefxshaders.ts
+var vertexIdentity = `
+  precision highp float;
+  attribute vec2 pos;
+  attribute vec2 uv;
+  varying vec2 vUv;
+  uniform float flipY;
+  void main(void) {
+    vUv = uv;
+    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
+  }
+`;
+var colorMatrixWithAlpha = `
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
+    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
+  }
+`;
+var colorMatrixWithoutAlpha = `
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
+    gl_FragColor.a = c.a;
+  }
+`;
+var pixelate = `
+  precision highp float;
+  varying vec2 vUv;
+  uniform vec2 size;
+  uniform sampler2D texture;
+  vec2 pixelate(vec2 coord, vec2 size) {
+    return floor( coord / size ) * size;
+  }
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    vec2 coord = pixelate(vUv, size);
+    gl_FragColor += texture2D(texture, coord);
+  }
+`;
+var blur = `
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
+    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;
+    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
+  }
+`;
+var convolution = `
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  uniform float m[9];
+  void main(void) {
+    vec4 c11 = texture2D(texture, vUv - px); // top left
+    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
+    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
+    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
+    vec4 c22 = texture2D(texture, vUv); // mid center
+    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
+    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
+    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
+    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
+    gl_FragColor = 
+    c11 * m[0] + c12 * m[1] + c22 * m[2] +
+    c21 * m[3] + c22 * m[4] + c23 * m[5] +
+    c31 * m[6] + c32 * m[7] + c33 * m[8];
+    gl_FragColor.a = c22.a;
+  }
+`;
+
+// src/image/imagefx.ts
+var collect = (source, prefix, collection) => {
+  const r2 = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
+  source.replace(r2, (match3, name) => {
+    collection[name] = 0;
+    return match3;
+  });
+};
+var GLProgram = class {
+  constructor(gl, vertexSource, fragmentSource) {
+    __publicField(this, "uniform", {});
+    __publicField(this, "attribute", {});
+    __publicField(this, "gl");
+    __publicField(this, "id");
+    __publicField(this, "compile", (source, type) => {
+      const shader = this.gl.createShader(type);
+      if (!shader) {
+        log("filter: could not create shader");
+        return null;
+      }
+      this.gl.shaderSource(shader, source);
+      this.gl.compileShader(shader);
+      if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
+        log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);
+        return null;
+      }
+      return shader;
+    });
+    this.gl = gl;
+    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
+    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
+    this.id = this.gl.createProgram();
+    if (!vertexShader || !fragmentShader)
+      return;
+    if (!this.id) {
+      log("filter: could not create webgl program");
+      return;
+    }
+    this.gl.attachShader(this.id, vertexShader);
+    this.gl.attachShader(this.id, fragmentShader);
+    this.gl.linkProgram(this.id);
+    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
+      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);
+      return;
+    }
+    this.gl.useProgram(this.id);
+    collect(vertexSource, "attribute", this.attribute);
+    for (const a6 in this.attribute)
+      this.attribute[a6] = this.gl.getAttribLocation(this.id, a6);
+    collect(vertexSource, "uniform", this.uniform);
+    collect(fragmentSource, "uniform", this.uniform);
+    for (const u in this.uniform)
+      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
+  }
+};
+function GLImageFilter() {
+  let drawCount = 0;
+  let sourceTexture = null;
+  let lastInChain = false;
+  let currentFramebufferIndex = -1;
+  let tempFramebuffers = [null, null];
+  let filterChain = [];
+  let vertexBuffer = null;
+  let currentProgram = null;
+  const fxcanvas = canvas(100, 100);
+  const shaderProgramCache = {};
+  const DRAW = { INTERMEDIATE: 1 };
+  const gl = fxcanvas.getContext("webgl");
+  if (!gl) {
+    log("filter: cannot get webgl context");
+    return;
+  }
+  this.gl = gl;
+  function resize(width, height) {
+    if (width === fxcanvas.width && height === fxcanvas.height)
+      return;
+    fxcanvas.width = width;
+    fxcanvas.height = height;
+    if (!vertexBuffer) {
+      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
+      vertexBuffer = gl.createBuffer();
+      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
+      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
+      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
+    }
+    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);
+    tempFramebuffers = [null, null];
+  }
+  function createFramebufferTexture(width, height) {
+    const fbo = gl.createFramebuffer();
+    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
+    const renderbuffer = gl.createRenderbuffer();
+    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
+    const texture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, texture);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+    gl.bindTexture(gl.TEXTURE_2D, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+    return { fbo, texture };
+  }
+  function getTempFramebuffer(index2) {
+    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
+    return tempFramebuffers[index2];
+  }
+  function draw(flags = 0) {
+    if (!currentProgram)
+      return;
+    let source = null;
+    let target = null;
+    let flipY = false;
+    if (drawCount === 0)
+      source = sourceTexture;
+    else
+      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
+    drawCount++;
+    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
+      target = null;
+      flipY = drawCount % 2 === 0;
+    } else {
+      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
+      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
+    }
+    gl.bindTexture(gl.TEXTURE_2D, source);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, target);
+    gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
+    gl.drawArrays(gl.TRIANGLES, 0, 6);
+  }
+  function compileShader(fragmentSource) {
+    if (shaderProgramCache[fragmentSource]) {
+      currentProgram = shaderProgramCache[fragmentSource];
+      gl.useProgram((currentProgram ? currentProgram.id : null) || null);
+      return currentProgram;
+    }
+    currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
+    if (!currentProgram) {
+      log("filter: could not get webgl program");
+      return null;
+    }
+    const floatSize = Float32Array.BYTES_PER_ELEMENT;
+    const vertSize = 4 * floatSize;
+    gl.enableVertexAttribArray(currentProgram.attribute["pos"]);
+    gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize);
+    gl.enableVertexAttribArray(currentProgram.attribute["uv"]);
+    gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
+    shaderProgramCache[fragmentSource] = currentProgram;
+    return currentProgram;
+  }
+  const filter = {
+    colorMatrix: (matrix) => {
+      const m = new Float32Array(matrix);
+      m[4] /= 255;
+      m[9] /= 255;
+      m[14] /= 255;
+      m[19] /= 255;
+      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
+      const program = compileShader(shader);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      draw();
+    },
+    brightness: (brightness) => {
+      const b = (brightness || 0) + 1;
+      filter.colorMatrix([
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    saturation: (amount) => {
+      const x = (amount || 0) * 2 / 3 + 1;
+      const y = (x - 1) * -0.5;
+      filter.colorMatrix([
+        x,
+        y,
+        y,
+        0,
+        0,
+        y,
+        x,
+        y,
+        0,
+        0,
+        y,
+        y,
+        x,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturate: () => {
+      filter.saturation(-1);
+    },
+    contrast: (amount) => {
+      const v = (amount || 0) + 1;
+      const o = -128 * (v - 1);
+      filter.colorMatrix([
+        v,
+        0,
+        0,
+        0,
+        o,
+        0,
+        v,
+        0,
+        0,
+        o,
+        0,
+        0,
+        v,
+        0,
+        o,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    negative: () => {
+      filter.contrast(-2);
+    },
+    hue: (rotation) => {
+      rotation = (rotation || 0) / 180 * Math.PI;
+      const cos5 = Math.cos(rotation);
+      const sin5 = Math.sin(rotation);
+      const lumR = 0.213;
+      const lumG = 0.715;
+      const lumB = 0.072;
+      filter.colorMatrix([
+        lumR + cos5 * (1 - lumR) + sin5 * -lumR,
+        lumG + cos5 * -lumG + sin5 * -lumG,
+        lumB + cos5 * -lumB + sin5 * (1 - lumB),
+        0,
+        0,
+        lumR + cos5 * -lumR + sin5 * 0.143,
+        lumG + cos5 * (1 - lumG) + sin5 * 0.14,
+        lumB + cos5 * -lumB + sin5 * -0.283,
+        0,
+        0,
+        lumR + cos5 * -lumR + sin5 * -(1 - lumR),
+        lumG + cos5 * -lumG + sin5 * lumG,
+        lumB + cos5 * (1 - lumB) + sin5 * lumB,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturateLuminance: () => {
+      filter.colorMatrix([
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    sepia: () => {
+      filter.colorMatrix([
+        0.393,
+        0.7689999,
+        0.18899999,
+        0,
+        0,
+        0.349,
+        0.6859999,
+        0.16799999,
+        0,
+        0,
+        0.272,
+        0.5339999,
+        0.13099999,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    brownie: () => {
+      filter.colorMatrix([
+        0.5997023498159715,
+        0.34553243048391263,
+        -0.2708298674538042,
+        0,
+        47.43192855600873,
+        -0.037703249837783157,
+        0.8609577587992641,
+        0.15059552388459913,
+        0,
+        -36.96841498319127,
+        0.24113635128153335,
+        -0.07441037908422492,
+        0.44972182064877153,
+        0,
+        -7.562075277591283,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    vintagePinhole: () => {
+      filter.colorMatrix([
+        0.6279345635605994,
+        0.3202183420819367,
+        -0.03965408211312453,
+        0,
+        9.651285835294123,
+        0.02578397704808868,
+        0.6441188644374771,
+        0.03259127616149294,
+        0,
+        7.462829176470591,
+        0.0466055556782719,
+        -0.0851232987247891,
+        0.5241648018700465,
+        0,
+        5.159190588235296,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    kodachrome: () => {
+      filter.colorMatrix([
+        1.1285582396593525,
+        -0.3967382283601348,
+        -0.03992559172921793,
+        0,
+        63.72958762196502,
+        -0.16404339962244616,
+        1.0835251566291304,
+        -0.05498805115633132,
+        0,
+        24.732407896706203,
+        -0.16786010706155763,
+        -0.5603416277695248,
+        1.6014850761964943,
+        0,
+        35.62982807460946,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    technicolor: () => {
+      filter.colorMatrix([
+        1.9125277891456083,
+        -0.8545344976951645,
+        -0.09155508482755585,
+        0,
+        11.793603434377337,
+        -0.3087833385928097,
+        1.7658908555458428,
+        -0.10601743074722245,
+        0,
+        -70.35205161461398,
+        -0.231103377548616,
+        -0.7501899197440212,
+        1.847597816108189,
+        0,
+        30.950940869491138,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    polaroid: () => {
+      filter.colorMatrix([
+        1.438,
+        -0.062,
+        -0.062,
+        0,
+        0,
+        -0.122,
+        1.378,
+        -0.122,
+        0,
+        0,
+        -0.016,
+        -0.016,
+        1.483,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    shiftToBGR: () => {
+      filter.colorMatrix([
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    convolution: (matrix) => {
+      const m = new Float32Array(matrix);
+      const pixelSizeX = 1 / fxcanvas.width;
+      const pixelSizeY = 1 / fxcanvas.height;
+      const program = compileShader(convolution);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
+      draw();
+    },
+    detectEdges: () => {
+      filter.convolution.call(this, [
+        0,
+        1,
+        0,
+        1,
+        -4,
+        1,
+        0,
+        1,
+        0
+      ]);
+    },
+    sobelX: () => {
+      filter.convolution.call(this, [
+        -1,
+        0,
+        1,
+        -2,
+        0,
+        2,
+        -1,
+        0,
+        1
+      ]);
+    },
+    sobelY: () => {
+      filter.convolution.call(this, [
+        -1,
+        -2,
+        -1,
+        0,
+        0,
+        0,
+        1,
+        2,
+        1
+      ]);
+    },
+    sharpen: (amount) => {
+      const a6 = amount || 1;
+      filter.convolution.call(this, [
+        0,
+        -1 * a6,
+        0,
+        -1 * a6,
+        1 + 4 * a6,
+        -1 * a6,
+        0,
+        -1 * a6,
+        0
+      ]);
+    },
+    emboss: (size2) => {
+      const s2 = size2 || 1;
+      filter.convolution.call(this, [
+        -2 * s2,
+        -1 * s2,
+        0,
+        -1 * s2,
+        1,
+        1 * s2,
+        0,
+        1 * s2,
+        2 * s2
+      ]);
+    },
+    blur: (size2) => {
+      const blurSizeX = size2 / 7 / fxcanvas.width;
+      const blurSizeY = size2 / 7 / fxcanvas.height;
+      const program = compileShader(blur);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["px"], 0, blurSizeY);
+      draw(DRAW.INTERMEDIATE);
+      gl.uniform2f(program.uniform["px"], blurSizeX, 0);
+      draw();
+    },
+    pixelate: (size2) => {
+      const blurSizeX = size2 / fxcanvas.width;
+      const blurSizeY = size2 / fxcanvas.height;
+      const program = compileShader(pixelate);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
+      draw();
+    }
+  };
+  this.add = function(name) {
+    const args = Array.prototype.slice.call(arguments, 1);
+    const func2 = filter[name];
+    filterChain.push({ func: func2, args });
+  };
+  this.reset = function() {
+    filterChain = [];
+  };
+  this.get = function() {
+    return filterChain;
+  };
+  this.apply = function(image2) {
+    resize(image2.width, image2.height);
+    drawCount = 0;
+    if (!sourceTexture)
+      sourceTexture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image2);
+    for (let i2 = 0; i2 < filterChain.length; i2++) {
+      lastInChain = i2 === filterChain.length - 1;
+      const f = filterChain[i2];
+      f.func.apply(this, f.args || []);
+    }
+    return fxcanvas;
+  };
+  this.draw = function(image2) {
+    this.add("brightness", 0);
+    return this.apply(image2);
+  };
+}
+
+// src/image/enhance.ts
+async function histogramEqualization(inputImage) {
+  const squeeze2 = inputImage.shape.length === 4 ? squeeze(inputImage) : inputImage;
+  const channels = split(squeeze2, 3, 2);
+  const min7 = [min(channels[0]), min(channels[1]), min(channels[2])];
+  const max7 = [max(channels[0]), max(channels[1]), max(channels[2])];
+  const absMax = await Promise.all(max7.map((channel) => channel.data()));
+  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
+  const sub5 = [sub(channels[0], min7[0]), sub(channels[1], min7[1]), sub(channels[2], min7[2])];
+  const range7 = [sub(max7[0], min7[0]), sub(max7[1], min7[1]), sub(max7[2], min7[2])];
+  const fact = [div(maxValue, range7[0]), div(maxValue, range7[1]), div(maxValue, range7[2])];
+  const enh = [mul(sub5[0], fact[0]), mul(sub5[1], fact[1]), mul(sub5[2], fact[2])];
+  const rgb2 = stack([enh[0], enh[1], enh[2]], 2);
+  const reshape7 = reshape(rgb2, [1, squeeze2.shape[0], squeeze2.shape[1], 3]);
+  dispose([...channels, ...min7, ...max7, ...sub5, ...range7, ...fact, ...enh, rgb2, squeeze2]);
+  return reshape7;
+}
+
+// src/image/image.ts
+var maxSize = 3840;
+var inCanvas = null;
+var outCanvas = null;
+var tmpCanvas = null;
+var fx;
+var last = {
+  inputSum: 0,
+  cacheDiff: 1,
+  sumMethod: 0,
+  inputTensor: void 0
+};
+function canvas(width, height) {
+  let c;
+  if (env2.browser) {
+    if (env2.worker) {
+      if (typeof OffscreenCanvas === "undefined")
+        throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
+      c = new OffscreenCanvas(width, height);
+    } else {
+      if (typeof document === "undefined")
+        throw new Error("canvas error: attempted to run in browser but DOM is not defined");
+      c = document.createElement("canvas");
+      c.width = width;
+      c.height = height;
+    }
+  } else {
+    if (typeof env2.Canvas !== "undefined")
+      c = new env2.Canvas(width, height);
+    else if (typeof globalThis.Canvas !== "undefined")
+      c = new globalThis.Canvas(width, height);
+  }
+  return c;
+}
+function copy(input2, output) {
+  const outputCanvas = output || canvas(input2.width, input2.height);
+  const ctx = outputCanvas.getContext("2d");
+  ctx.drawImage(input2, 0, 0);
+  return outputCanvas;
+}
+async function process2(input2, config3, getTensor2 = true) {
+  if (!input2) {
+    if (config3.debug)
+      log("input error: input is missing");
+    return { tensor: null, canvas: null };
+  }
+  if (!(input2 instanceof Tensor) && !(typeof Image !== "undefined" && input2 instanceof Image) && !(typeof env2.Canvas !== "undefined" && input2 instanceof env2.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input2 instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input2 instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input2 instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input2 instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input2 instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input2 instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input2 instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input2 instanceof OffscreenCanvas)) {
+    throw new Error("input error: type is not recognized");
+  }
+  if (input2 instanceof Tensor) {
+    let tensor2 = null;
+    if (input2["isDisposedInternal"])
+      throw new Error("input error: attempted to use tensor but it is disposed");
+    if (!input2["shape"])
+      throw new Error("input error: attempted to use tensor without a shape");
+    if (input2.shape.length === 3) {
+      if (input2.shape[2] === 3) {
+        tensor2 = expandDims(input2, 0);
+      } else if (input2.shape[2] === 4) {
+        const rgb2 = slice3d(input2, [0, 0, 0], [-1, -1, 3]);
+        tensor2 = expandDims(rgb2, 0);
+        dispose(rgb2);
+      }
+    } else if (input2.shape.length === 4) {
+      if (input2.shape[3] === 3) {
+        tensor2 = clone(input2);
+      } else if (input2.shape[3] === 4) {
+        tensor2 = slice4d(input2, [0, 0, 0, 0], [-1, -1, -1, 3]);
+      }
+    }
+    if (tensor2 == null || tensor2.shape.length !== 4 || tensor2.shape[0] !== 1 || tensor2.shape[3] !== 3)
+      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input2["shape"]}`);
+    if (tensor2.dtype === "int32") {
+      const cast7 = cast(tensor2, "float32");
+      dispose(tensor2);
+      tensor2 = cast7;
+    }
+    return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null };
+  } else {
+    if (typeof input2["readyState"] !== "undefined" && input2["readyState"] <= 2) {
+      if (config3.debug)
+        log("input stream is not ready");
+      return { tensor: null, canvas: inCanvas };
+    }
+    const originalWidth = input2["naturalWidth"] || input2["videoWidth"] || input2["width"] || input2["shape"] && input2["shape"][1] > 0;
+    const originalHeight = input2["naturalHeight"] || input2["videoHeight"] || input2["height"] || input2["shape"] && input2["shape"][2] > 0;
+    if (!originalWidth || !originalHeight) {
+      if (config3.debug)
+        log("cannot determine input dimensions");
+      return { tensor: null, canvas: inCanvas };
+    }
+    let targetWidth = originalWidth;
+    let targetHeight = originalHeight;
+    if (targetWidth > maxSize) {
+      targetWidth = maxSize;
+      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
+    }
+    if (targetHeight > maxSize) {
+      targetHeight = maxSize;
+      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
+    }
+    if ((config3.filter.width || 0) > 0)
+      targetWidth = config3.filter.width;
+    else if ((config3.filter.height || 0) > 0)
+      targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
+    if ((config3.filter.height || 0) > 0)
+      targetHeight = config3.filter.height;
+    else if ((config3.filter.width || 0) > 0)
+      targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
+    if (!targetWidth || !targetHeight)
+      throw new Error("input error: cannot determine dimension");
+    if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
+      inCanvas = canvas(targetWidth, targetHeight);
+    const inCtx = inCanvas.getContext("2d");
+    if (typeof ImageData !== "undefined" && input2 instanceof ImageData) {
+      inCtx.putImageData(input2, 0, 0);
+    } else {
+      if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
+        inCtx.translate(originalWidth, 0);
+        inCtx.scale(-1, 1);
+        inCtx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
+        inCtx.setTransform(1, 0, 0, 1, 0, 0);
+      } else {
+        inCtx.drawImage(input2, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
+      }
+    }
+    if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
+      outCanvas = canvas(inCanvas.width, inCanvas.height);
+    if (config3.filter.enabled && env2.webgl.supported) {
+      if (!fx)
+        fx = env2.browser ? new GLImageFilter() : null;
+      env2.filter = !!fx;
+      if (!fx || !fx.add) {
+        if (config3.debug)
+          log("input process error: cannot initialize filters");
+        env2.webgl.supported = false;
+        config3.filter.enabled = false;
+        copy(inCanvas, outCanvas);
+      } else {
+        fx.reset();
+        if (config3.filter.brightness !== 0)
+          fx.add("brightness", config3.filter.brightness);
+        if (config3.filter.contrast !== 0)
+          fx.add("contrast", config3.filter.contrast);
+        if (config3.filter.sharpness !== 0)
+          fx.add("sharpen", config3.filter.sharpness);
+        if (config3.filter.blur !== 0)
+          fx.add("blur", config3.filter.blur);
+        if (config3.filter.saturation !== 0)
+          fx.add("saturation", config3.filter.saturation);
+        if (config3.filter.hue !== 0)
+          fx.add("hue", config3.filter.hue);
+        if (config3.filter.negative)
+          fx.add("negative");
+        if (config3.filter.sepia)
+          fx.add("sepia");
+        if (config3.filter.vintage)
+          fx.add("brownie");
+        if (config3.filter.sepia)
+          fx.add("sepia");
+        if (config3.filter.kodachrome)
+          fx.add("kodachrome");
+        if (config3.filter.technicolor)
+          fx.add("technicolor");
+        if (config3.filter.polaroid)
+          fx.add("polaroid");
+        if (config3.filter.pixelate !== 0)
+          fx.add("pixelate", config3.filter.pixelate);
+        if (fx.get() > 0)
+          outCanvas = fx.apply(inCanvas);
+        else
+          outCanvas = fx.draw(inCanvas);
+      }
+    } else {
+      copy(inCanvas, outCanvas);
+      if (fx)
+        fx = null;
+      env2.filter = !!fx;
+    }
+    if (!getTensor2)
+      return { tensor: null, canvas: outCanvas };
+    if (!outCanvas)
+      throw new Error("canvas error: cannot create output");
+    let pixels;
+    let depth = 3;
+    if (typeof ImageData !== "undefined" && input2 instanceof ImageData || input2["data"] && input2["width"] && input2["height"]) {
+      if (env2.browser && browser_exports) {
+        pixels = browser_exports ? browser_exports.fromPixels(input2) : null;
+      } else {
+        depth = input2["data"].length / input2["height"] / input2["width"];
+        const arr = new Uint8Array(input2["data"]["buffer"]);
+        pixels = tensor(arr, [input2["height"], input2["width"], depth], "int32");
+      }
+    } else {
+      if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
+        tmpCanvas = canvas(outCanvas.width, outCanvas.height);
+      if (browser_exports && env2.browser) {
+        if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
+          pixels = browser_exports.fromPixels(outCanvas);
+        } else {
+          tmpCanvas = copy(outCanvas);
+          pixels = browser_exports.fromPixels(tmpCanvas);
+        }
+      } else {
+        const tempCanvas = copy(outCanvas);
+        const tempCtx = tempCanvas.getContext("2d");
+        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
+        depth = tempData.data.length / targetWidth / targetHeight;
+        const arr = new Uint8Array(tempData.data.buffer);
+        pixels = tensor(arr, [targetWidth, targetHeight, depth]);
+      }
+    }
+    if (depth === 4) {
+      const rgb2 = slice3d(pixels, [0, 0, 0], [-1, -1, 3]);
+      dispose(pixels);
+      pixels = rgb2;
+    }
+    if (!pixels)
+      throw new Error("input error: cannot create tensor");
+    const casted = cast(pixels, "float32");
+    const tensor2 = config3.filter.equalization ? await histogramEqualization(casted) : expandDims(casted, 0);
+    dispose([pixels, casted]);
+    return { tensor: tensor2, canvas: config3.filter.return ? outCanvas : null };
+  }
+}
+async function skip(config3, input2) {
+  let skipFrame = false;
+  if (config3.cacheSensitivity === 0 || !input2.shape || input2.shape.length !== 4 || input2.shape[1] > 2048 || input2.shape[2] > 2048)
+    return skipFrame;
+  if (!last.inputTensor) {
+    last.inputTensor = clone(input2);
+  } else if (last.inputTensor.shape[1] !== input2.shape[1] || last.inputTensor.shape[2] !== input2.shape[2]) {
+    dispose(last.inputTensor);
+    last.inputTensor = clone(input2);
+  } else {
+    const t2 = {};
+    t2.diff = sub(input2, last.inputTensor);
+    t2.squared = mul(t2.diff, t2.diff);
+    t2.sum = sum2(t2.squared);
+    const diffSum = await t2.sum.data();
+    const diffRelative = diffSum[0] / (input2.shape[1] || 1) / (input2.shape[2] || 1) / 255 / 3;
+    dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]);
+    last.inputTensor = clone(input2);
+    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
+  }
+  return skipFrame;
+}
+async function compare(config3, input1, input2) {
+  const t2 = {};
+  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
+    if (!config3.debug)
+      log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
+    return 0;
+  }
+  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
+    if (!config3.debug)
+      log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
+    return 0;
+  }
+  t2.input1 = clone(input1);
+  t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : clone(input2);
+  t2.diff = sub(t2.input1, t2.input2);
+  t2.squared = mul(t2.diff, t2.diff);
+  t2.sum = sum2(t2.squared);
+  const diffSum = await t2.sum.data();
+  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
+  dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]);
+  return diffRelative;
+}
+
+// src/util/env.ts
+var Env = class {
+  constructor() {
+    __publicField(this, "browser");
+    __publicField(this, "node");
+    __publicField(this, "worker");
+    __publicField(this, "platform", "");
+    __publicField(this, "agent", "");
+    __publicField(this, "backends", []);
+    __publicField(this, "initial");
+    __publicField(this, "filter");
+    __publicField(this, "tfjs");
+    __publicField(this, "offscreen");
+    __publicField(this, "perfadd", false);
+    __publicField(this, "wasm", {
+      supported: void 0,
+      backend: void 0,
+      simd: void 0,
+      multithread: void 0
+    });
+    __publicField(this, "webgl", {
+      supported: void 0,
+      backend: void 0,
+      version: void 0,
+      renderer: void 0
+    });
+    __publicField(this, "webgpu", {
+      supported: void 0,
+      backend: void 0,
+      adapter: void 0
+    });
+    __publicField(this, "cpu", {
+      model: void 0,
+      flags: []
+    });
+    __publicField(this, "kernels", []);
+    __publicField(this, "Canvas");
+    __publicField(this, "Image");
+    __publicField(this, "ImageData");
+    this.browser = typeof navigator !== "undefined";
+    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
+    this.tfjs = { version: V["tfjs-core"] };
+    this.offscreen = typeof OffscreenCanvas !== "undefined";
+    this.initial = true;
+    this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
+    if (typeof navigator !== "undefined") {
+      const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
+      if (raw && raw[0]) {
+        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
+        this.platform = platformMatch && platformMatch[0] ? platformMatch[0].replace(/\(|\)/g, "") : "";
+        this.agent = navigator.userAgent.replace(raw[0], "");
+        if (this.platform[1])
+          this.agent = this.agent.replace(raw[1], "");
+        this.agent = this.agent.replace(/  /g, " ");
+      }
+    } else if (typeof process !== "undefined") {
+      this.platform = `${process.platform} ${process.arch}`;
+      this.agent = `NodeJS ${process.version}`;
+    }
+  }
+  async updateBackend() {
+    this.backends = Object.keys(engine().registryFactory);
+    this.wasm.supported = typeof WebAssembly !== "undefined";
+    this.wasm.backend = this.backends.includes("wasm");
+    if (this.wasm.supported && this.wasm.backend && getBackend() === "wasm") {
+      this.wasm.simd = await env().getAsync("WASM_HAS_SIMD_SUPPORT");
+      this.wasm.multithread = await env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
+    }
+    const c = canvas(100, 100);
+    const ctx = c ? c.getContext("webgl2") : void 0;
+    this.webgl.supported = typeof ctx !== "undefined";
+    this.webgl.backend = this.backends.includes("webgl");
+    if (this.webgl.supported && this.webgl.backend && (getBackend() === "webgl" || getBackend() === "humangl")) {
+      const gl = backend().gpgpu !== "undefined" ? await backend().getGPGPUContext().gl : null;
+      if (gl) {
+        this.webgl.version = gl.getParameter(gl.VERSION);
+        this.webgl.renderer = gl.getParameter(gl.RENDERER);
+      }
+    }
+    this.webgpu.supported = this.browser && typeof navigator["gpu"] !== "undefined";
+    this.webgpu.backend = this.backends.includes("webgpu");
+    try {
+      if (this.webgpu.supported)
+        this.webgpu.adapter = (await navigator["gpu"].requestAdapter()).name;
+    } catch (e2) {
+      this.webgpu.supported = false;
+    }
+    try {
+      this.kernels = getKernelsForBackend(getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+    } catch (e2) {
+    }
+  }
+  async updateCPU() {
+    const cpu = { model: "", flags: [] };
+    if (this.node && this.platform.startsWith("linux")) {
+    }
+    if (!this["cpu"])
+      Object.defineProperty(this, "cpu", { value: cpu });
+    else
+      this["cpu"] = cpu;
+  }
+};
+var env2 = new Env();
+
+// models/models.json
+var models_exports = {};
+__export(models_exports, {
+  age: () => age,
+  antispoof: () => antispoof,
+  blazeface: () => blazeface,
+  "blazeface-back": () => blazeface_back,
+  "blazeface-front": () => blazeface_front,
+  "blazepose-detector2d": () => blazepose_detector2d,
+  "blazepose-detector3d": () => blazepose_detector3d,
+  "blazepose-full": () => blazepose_full,
+  "blazepose-heavy": () => blazepose_heavy,
+  "blazepose-lite": () => blazepose_lite,
+  default: () => models_default,
+  efficientpose: () => efficientpose,
+  emotion: () => emotion,
+  faceboxes: () => faceboxes,
+  facemesh: () => facemesh,
+  "facemesh-attention": () => facemesh_attention,
+  "facemesh-attention-alt": () => facemesh_attention_alt,
+  "facemesh-detection-full": () => facemesh_detection_full,
+  "facemesh-detection-short": () => facemesh_detection_short,
+  "facemesh-orig": () => facemesh_orig,
+  faceres: () => faceres,
+  "faceres-deep": () => faceres_deep,
+  gear: () => gear,
+  gender: () => gender,
+  "gender-ssrnet-imdb": () => gender_ssrnet_imdb,
+  handdetect: () => handdetect,
+  "handlandmark-full": () => handlandmark_full,
+  "handlandmark-lite": () => handlandmark_lite,
+  "handlandmark-sparse": () => handlandmark_sparse,
+  handskeleton: () => handskeleton,
+  handtrack: () => handtrack,
+  iris: () => iris,
+  liveness: () => liveness,
+  "mb3-centernet": () => mb3_centernet,
+  meet: () => meet,
+  mobileface: () => mobileface,
+  mobilefacenet: () => mobilefacenet,
+  "movenet-lightning": () => movenet_lightning,
+  "movenet-multipose": () => movenet_multipose,
+  "movenet-thunder": () => movenet_thunder,
+  nanodet: () => nanodet,
+  posenet: () => posenet,
+  selfie: () => selfie
+});
+var age = 161240;
+var antispoof = 853098;
+var blazeface_back = 538928;
+var blazeface_front = 402048;
+var blazeface = 538928;
+var blazepose_detector2d = 7499400;
+var blazepose_detector3d = 5928856;
+var blazepose_full = 6338290;
+var blazepose_heavy = 27501554;
+var blazepose_lite = 2725490;
+var efficientpose = 5651240;
+var emotion = 820516;
+var faceboxes = 2013002;
+var facemesh_attention_alt = 2387598;
+var facemesh_attention = 2382414;
+var facemesh_detection_full = 1026192;
+var facemesh_detection_short = 201268;
+var facemesh_orig = 2955780;
+var facemesh = 1477958;
+var faceres_deep = 13957620;
+var faceres = 6978814;
+var gear = 1498916;
+var gender_ssrnet_imdb = 161236;
+var gender = 201808;
+var handdetect = 3515612;
+var handlandmark_full = 5431368;
+var handlandmark_lite = 2023432;
+var handlandmark_sparse = 5286322;
+var handskeleton = 5502280;
+var handtrack = 2964837;
+var iris = 2599092;
+var liveness = 592976;
+var mb3_centernet = 4030290;
+var meet = 372228;
+var mobileface = 2183192;
+var mobilefacenet = 5171976;
+var movenet_lightning = 4650216;
+var movenet_multipose = 9448838;
+var movenet_thunder = 12477112;
+var nanodet = 7574558;
+var posenet = 5032780;
+var selfie = 212886;
+var models_default = {
+  age,
+  antispoof,
+  "blazeface-back": blazeface_back,
+  "blazeface-front": blazeface_front,
+  blazeface,
+  "blazepose-detector2d": blazepose_detector2d,
+  "blazepose-detector3d": blazepose_detector3d,
+  "blazepose-full": blazepose_full,
+  "blazepose-heavy": blazepose_heavy,
+  "blazepose-lite": blazepose_lite,
+  efficientpose,
+  emotion,
+  faceboxes,
+  "facemesh-attention-alt": facemesh_attention_alt,
+  "facemesh-attention": facemesh_attention,
+  "facemesh-detection-full": facemesh_detection_full,
+  "facemesh-detection-short": facemesh_detection_short,
+  "facemesh-orig": facemesh_orig,
+  facemesh,
+  "faceres-deep": faceres_deep,
+  faceres,
+  gear,
+  "gender-ssrnet-imdb": gender_ssrnet_imdb,
+  gender,
+  handdetect,
+  "handlandmark-full": handlandmark_full,
+  "handlandmark-lite": handlandmark_lite,
+  "handlandmark-sparse": handlandmark_sparse,
+  handskeleton,
+  handtrack,
+  iris,
+  liveness,
+  "mb3-centernet": mb3_centernet,
+  meet,
+  mobileface,
+  mobilefacenet,
+  "movenet-lightning": movenet_lightning,
+  "movenet-multipose": movenet_multipose,
+  "movenet-thunder": movenet_thunder,
+  nanodet,
+  posenet,
+  selfie
+};
+
+// src/tfjs/load.ts
+var options = {
+  cacheModels: true,
+  cacheSupported: true,
+  verbose: true,
+  debug: false,
+  modelBasePath: ""
+};
+var modelStats = {};
+async function httpHandler(url, init3) {
+  if (options.debug)
+    log("load model fetch:", url, init3);
+  return fetch(url, init3);
+}
+function setModelLoadOptions(config3) {
+  options.cacheModels = config3.cacheModels;
+  options.verbose = config3.debug;
+  options.modelBasePath = config3.modelBasePath;
+}
+async function loadModel(modelPath) {
+  var _a, _b, _c;
+  let modelUrl = join(options.modelBasePath, modelPath || "");
+  if (!modelUrl.toLowerCase().endsWith(".json"))
+    modelUrl += ".json";
+  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
+  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
+  const cachedModelName = "indexeddb://" + shortModelName;
+  modelStats[shortModelName] = {
+    name: shortModelName,
+    sizeFromManifest: 0,
+    sizeLoadedWeights: 0,
+    sizeDesired: models_exports[shortModelName],
+    inCache: false
+  };
+  options.cacheSupported = typeof window !== "undefined" && typeof window.localStorage !== "undefined" && typeof window.indexedDB !== "undefined";
+  let cachedModels = {};
+  try {
+    cachedModels = options.cacheSupported && options.cacheModels ? await io_exports.listModels() : {};
+  } catch (e2) {
+    options.cacheSupported = false;
+  }
+  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
+  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
+  const model19 = new GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+  let loaded = false;
+  try {
+    model19.findIOHandler();
+    if (options.debug)
+      log("model load handler:", model19["handler"]);
+    const artifacts = await model19.handler.load();
+    modelStats[shortModelName].sizeFromManifest = ((_a = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _a.byteLength) || 0;
+    model19.loadSync(artifacts);
+    modelStats[shortModelName].sizeLoadedWeights = ((_c = (_b = model19 == null ? void 0 : model19.artifacts) == null ? void 0 : _b.weightData) == null ? void 0 : _c.byteLength) || 0;
+    if (options.verbose)
+      log("load model:", model19["modelUrl"], { bytes: modelStats[shortModelName].sizeLoadedWeights }, options);
+    loaded = true;
+  } catch (err) {
+    log("error loading model:", modelUrl, err);
+  }
+  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
+    try {
+      const saveResult = await model19.save(cachedModelName);
+      log("model saved:", cachedModelName, saveResult);
+    } catch (err) {
+      log("error saving model:", modelUrl, err);
+    }
+  }
+  return model19;
+}
+
+// package.json
+var version5 = "2.9.0";
+
+// src/models.ts
+var models_exports2 = {};
+__export(models_exports2, {
+  Models: () => Models,
+  getModelStats: () => getModelStats,
+  load: () => load19,
+  reset: () => reset,
+  validate: () => validate2
+});
+
+// src/gear/gear.ts
+var model2;
+var last2 = [];
+var raceNames = ["white", "black", "asian", "indian", "other"];
+var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var lastCount = 0;
+var lastTime = 0;
+var skipped = Number.MAX_SAFE_INTEGER;
+async function load(config3) {
+  var _a;
+  if (env2.initial)
+    model2 = null;
+  if (!model2)
+    model2 = await loadModel((_a = config3.face["gear"]) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model2["modelUrl"]);
+  return model2;
+}
+async function predict(image2, config3, idx, count3) {
+  var _a, _b;
+  if (!model2)
+    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
+  const skipFrame = skipped < (((_a = config3.face["gear"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["gear"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count3 && last2[idx]) {
+    skipped++;
+    return last2[idx];
+  }
+  skipped = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    if (!(model2 == null ? void 0 : model2.inputs[0].shape))
+      return;
+    const t2 = {};
+    const box = [[0, 0.1, 0.9, 0.9]];
+    t2.resize = image.cropAndResize(image2, box, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
+    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
+    if ((_a2 = config3.face["gear"]) == null ? void 0 : _a2.enabled)
+      [t2.age, t2.gender, t2.race] = model2.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
+    const gender2 = await t2.gender.data();
+    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
+    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
+    const race = await t2.race.data();
+    for (let i2 = 0; i2 < race.length; i2++) {
+      if (race[i2] > (((_b2 = config3.face["gear"]) == null ? void 0 : _b2.minConfidence) || 0.2))
+        obj.race.push({ score: Math.round(100 * race[i2]) / 100, race: raceNames[i2] });
+    }
+    obj.race.sort((a6, b) => b.score - a6.score);
+    const ageDistribution = Array.from(await t2.age.data());
+    const ageSorted = ageDistribution.map((a6, i2) => [ageWeights[i2], a6]).sort((a6, b) => b[1] - a6[1]);
+    let age2 = ageSorted[0][0];
+    for (let i2 = 1; i2 < ageSorted.length; i2++)
+      age2 += ageSorted[i2][1] * (ageSorted[i2][0] - age2);
+    obj.age = Math.round(10 * age2) / 10;
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    last2[idx] = obj;
+    lastCount = count3;
+    lastTime = now();
+    resolve(obj);
+  });
+}
+
+// src/tfjs/constants.ts
+var constants = {
+  tf255: 255,
+  tf1: 1,
+  tf2: 2,
+  tf05: 0.5,
+  tf127: 127.5,
+  rgb: [0.2989, 0.587, 0.114]
+};
+function init2() {
+  constants.tf255 = scalar(255, "float32");
+  constants.tf1 = scalar(1, "float32");
+  constants.tf2 = scalar(2, "float32");
+  constants.tf05 = scalar(0.5, "float32");
+  constants.tf127 = scalar(127.5, "float32");
+  constants.rgb = tensor1d([0.2989, 0.587, 0.114], "float32");
+}
+
+// src/gear/ssrnet-age.ts
+var model3;
+var last3 = [];
+var lastCount2 = 0;
+var lastTime2 = 0;
+var skipped2 = Number.MAX_SAFE_INTEGER;
+async function load2(config3) {
+  if (env2.initial)
+    model3 = null;
+  if (!model3)
+    model3 = await loadModel(config3.face["ssrnet"].modelPathAge);
+  else if (config3.debug)
+    log("cached model:", model3["modelUrl"]);
+  return model3;
+}
+async function predict2(image2, config3, idx, count3) {
+  var _a, _b, _c, _d;
+  if (!model3)
+    return { age: 0 };
+  const skipFrame = skipped2 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime2;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count3 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped2++;
+    return last3[idx];
+  }
+  skipped2 = 0;
+  return new Promise(async (resolve) => {
+    if (!(model3 == null ? void 0 : model3.inputs) || !model3.inputs[0] || !model3.inputs[0].shape)
+      return;
+    const t2 = {};
+    t2.resize = image.resizeBilinear(image2, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
+    t2.enhance = mul(t2.resize, constants.tf255);
+    const obj = { age: 0 };
+    if (config3.face["ssrnet"].enabled)
+      t2.age = model3.execute(t2.enhance);
+    if (t2.age) {
+      const data = await t2.age.data();
+      obj.age = Math.trunc(10 * data[0]) / 10;
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    last3[idx] = obj;
+    lastCount2 = count3;
+    lastTime2 = now();
+    resolve(obj);
+  });
+}
+
+// src/gear/ssrnet-gender.ts
+var model4;
+var last4 = [];
+var lastCount3 = 0;
+var lastTime3 = 0;
+var skipped3 = Number.MAX_SAFE_INTEGER;
+var rgb = [0.2989, 0.587, 0.114];
+async function load3(config3) {
+  if (env2.initial)
+    model4 = null;
+  if (!model4)
+    model4 = await loadModel(config3.face["ssrnet"].modelPathGender);
+  else if (config3.debug)
+    log("cached model:", model4["modelUrl"]);
+  return model4;
+}
+async function predict3(image2, config3, idx, count3) {
+  var _a, _b, _c, _d;
+  if (!model4)
+    return { gender: "unknown", genderScore: 0 };
+  const skipFrame = skipped3 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime3;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count3 && ((_c = last4[idx]) == null ? void 0 : _c.gender) && ((_d = last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped3++;
+    return last4[idx];
+  }
+  skipped3 = 0;
+  return new Promise(async (resolve) => {
+    if (!(model4 == null ? void 0 : model4.inputs[0].shape))
+      return;
+    const t2 = {};
+    t2.resize = image.resizeBilinear(image2, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
+    t2.enhance = tidy(() => {
+      const [red, green, blue] = split(t2.resize, 3, 3);
+      const redNorm = mul(red, rgb[0]);
+      const greenNorm = mul(green, rgb[1]);
+      const blueNorm = mul(blue, rgb[2]);
+      const grayscale = addN([redNorm, greenNorm, blueNorm]);
+      const normalize = mul(sub(grayscale, constants.tf05), 2);
+      return normalize;
+    });
+    const obj = { gender: "unknown", genderScore: 0 };
+    if (config3.face["ssrnet"].enabled)
+      t2.gender = model4.execute(t2.enhance);
+    const data = await t2.gender.data();
+    obj.gender = data[0] > data[1] ? "female" : "male";
+    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    last4[idx] = obj;
+    lastCount3 = count3;
+    lastTime3 = now();
+    resolve(obj);
+  });
+}
+
+// src/face/antispoof.ts
+var model5;
+var cached = [];
+var skipped4 = Number.MAX_SAFE_INTEGER;
+var lastCount4 = 0;
+var lastTime4 = 0;
+async function load4(config3) {
+  var _a;
+  if (env2.initial)
+    model5 = null;
+  if (!model5)
+    model5 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model5["modelUrl"]);
+  return model5;
+}
+async function predict4(image2, config3, idx, count3) {
+  var _a, _b;
+  if (!model5)
+    return 0;
+  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime4;
+  const skipFrame = skipped4 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count3 && cached[idx]) {
+    skipped4++;
+    return cached[idx];
+  }
+  skipped4 = 0;
+  return new Promise(async (resolve) => {
+    const resize = image.resizeBilinear(image2, [(model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0, (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[1] : 0], false);
+    const res = model5 == null ? void 0 : model5.execute(resize);
+    const num = (await res.data())[0];
+    cached[idx] = Math.round(100 * num) / 100;
+    lastCount4 = count3;
+    lastTime4 = now();
+    dispose([resize, res]);
+    resolve(cached[idx]);
+  });
+}
+
+// src/face/facemeshcoords.ts
+var meshAnnotations = {
+  silhouette: [
+    10,
+    338,
+    297,
+    332,
+    284,
+    251,
+    389,
+    356,
+    454,
+    323,
+    361,
+    288,
+    397,
+    365,
+    379,
+    378,
+    400,
+    377,
+    152,
+    148,
+    176,
+    149,
+    150,
+    136,
+    172,
+    58,
+    132,
+    93,
+    234,
+    127,
+    162,
+    21,
+    54,
+    103,
+    67,
+    109
+  ],
+  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
+  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
+  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
+  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
+  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
+  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
+  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
+  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
+  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
+  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
+  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
+  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
+  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
+  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
+  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
+  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
+  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
+  rightEyeIris: [473, 474, 475, 476, 477],
+  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
+  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
+  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
+  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
+  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
+  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
+  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
+  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
+  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
+  leftEyeIris: [468, 469, 470, 471, 472],
+  midwayBetweenEyes: [168],
+  noseTip: [1],
+  noseBottom: [2],
+  noseRightCorner: [98],
+  noseLeftCorner: [327],
+  rightCheek: [205],
+  leftCheek: [425]
+};
+var meshLandmarks = {
+  count: 468,
+  mouth: 13,
+  symmetryLine: [13, meshAnnotations["midwayBetweenEyes"][0]]
+};
+var blazeFaceLandmarks = {
+  leftEye: 0,
+  rightEye: 1,
+  nose: 2,
+  mouth: 3,
+  leftEar: 4,
+  rightEar: 5,
+  symmetryLine: [3, 2]
+};
+var irisIndices = [
+  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
+  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
+  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
+  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
+  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
+  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
+  { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
+  { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] },
+  { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] }
+];
+var UV468 = [
+  [0.499976992607117, 0.652534008026123],
+  [0.500025987625122, 0.547487020492554],
+  [0.499974012374878, 0.602371990680695],
+  [0.482113003730774, 0.471979022026062],
+  [0.500150978565216, 0.527155995368958],
+  [0.499909996986389, 0.498252987861633],
+  [0.499523013830185, 0.40106201171875],
+  [0.289712011814117, 0.380764007568359],
+  [0.499954998493195, 0.312398016452789],
+  [0.499987006187439, 0.269918978214264],
+  [0.500023007392883, 0.107050001621246],
+  [0.500023007392883, 0.666234016418457],
+  [0.5000159740448, 0.679224014282227],
+  [0.500023007392883, 0.692348003387451],
+  [0.499976992607117, 0.695277988910675],
+  [0.499976992607117, 0.70593398809433],
+  [0.499976992607117, 0.719385027885437],
+  [0.499976992607117, 0.737019002437592],
+  [0.499967992305756, 0.781370997428894],
+  [0.499816000461578, 0.562981009483337],
+  [0.473773002624512, 0.573909997940063],
+  [0.104906998574734, 0.254140973091125],
+  [0.365929991006851, 0.409575998783112],
+  [0.338757991790771, 0.41302502155304],
+  [0.311120003461838, 0.409460008144379],
+  [0.274657994508743, 0.389131009578705],
+  [0.393361985683441, 0.403706014156342],
+  [0.345234006643295, 0.344011008739471],
+  [0.370094001293182, 0.346076011657715],
+  [0.319321990013123, 0.347265005111694],
+  [0.297903001308441, 0.353591024875641],
+  [0.24779200553894, 0.410809993743896],
+  [0.396889001131058, 0.842755019664764],
+  [0.280097991228104, 0.375599980354309],
+  [0.106310002505779, 0.399955987930298],
+  [0.2099249958992, 0.391353011131287],
+  [0.355807989835739, 0.534406006336212],
+  [0.471751004457474, 0.65040397644043],
+  [0.474155008792877, 0.680191993713379],
+  [0.439785003662109, 0.657229006290436],
+  [0.414617002010345, 0.66654098033905],
+  [0.450374007225037, 0.680860996246338],
+  [0.428770989179611, 0.682690978050232],
+  [0.374971002340317, 0.727805018424988],
+  [0.486716985702515, 0.547628998756409],
+  [0.485300987958908, 0.527395009994507],
+  [0.257764995098114, 0.314490020275116],
+  [0.401223003864288, 0.455172002315521],
+  [0.429818987846375, 0.548614978790283],
+  [0.421351999044418, 0.533740997314453],
+  [0.276895999908447, 0.532056987285614],
+  [0.483370006084442, 0.499586999416351],
+  [0.33721199631691, 0.282882988452911],
+  [0.296391993761063, 0.293242990970612],
+  [0.169294998049736, 0.193813979625702],
+  [0.447580009698868, 0.302609980106354],
+  [0.392390012741089, 0.353887975215912],
+  [0.354490011930466, 0.696784019470215],
+  [0.067304998636246, 0.730105042457581],
+  [0.442739009857178, 0.572826027870178],
+  [0.457098007202148, 0.584792017936707],
+  [0.381974011659622, 0.694710969924927],
+  [0.392388999462128, 0.694203019142151],
+  [0.277076005935669, 0.271932005882263],
+  [0.422551989555359, 0.563233017921448],
+  [0.385919004678726, 0.281364023685455],
+  [0.383103013038635, 0.255840003490448],
+  [0.331431001424789, 0.119714021682739],
+  [0.229923993349075, 0.232002973556519],
+  [0.364500999450684, 0.189113974571228],
+  [0.229622006416321, 0.299540996551514],
+  [0.173287004232407, 0.278747975826263],
+  [0.472878992557526, 0.666198015213013],
+  [0.446828007698059, 0.668527007102966],
+  [0.422762006521225, 0.673889994621277],
+  [0.445307999849319, 0.580065965652466],
+  [0.388103008270264, 0.693961024284363],
+  [0.403039008378983, 0.706539988517761],
+  [0.403629004955292, 0.693953037261963],
+  [0.460041999816895, 0.557139039039612],
+  [0.431158006191254, 0.692366003990173],
+  [0.452181994915009, 0.692366003990173],
+  [0.475387006998062, 0.692366003990173],
+  [0.465828001499176, 0.779190003871918],
+  [0.472328990697861, 0.736225962638855],
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+  [0.473122000694275, 0.704625964164734],
+  [0.473033010959625, 0.695277988910675],
+  [0.427942007780075, 0.695277988910675],
+  [0.426479011774063, 0.703539967536926],
+  [0.423162013292313, 0.711845993995667],
+  [0.4183090031147, 0.720062971115112],
+  [0.390094995498657, 0.639572978019714],
+  [0.013953999616206, 0.560034036636353],
+  [0.499913990497589, 0.58014702796936],
+  [0.413199990987778, 0.69539999961853],
+  [0.409626007080078, 0.701822996139526],
+  [0.468080013990402, 0.601534962654114],
+  [0.422728985548019, 0.585985004901886],
+  [0.463079988956451, 0.593783974647522],
+  [0.37211999297142, 0.47341400384903],
+  [0.334562003612518, 0.496073007583618],
+  [0.411671012639999, 0.546965003013611],
+  [0.242175996303558, 0.14767599105835],
+  [0.290776997804642, 0.201445996761322],
+  [0.327338010072708, 0.256527006626129],
+  [0.399509996175766, 0.748921036720276],
+  [0.441727995872498, 0.261676013469696],
+  [0.429764986038208, 0.187834024429321],
+  [0.412198007106781, 0.108901023864746],
+  [0.288955003023148, 0.398952007293701],
+  [0.218936994671822, 0.435410976409912],
+  [0.41278201341629, 0.398970007896423],
+  [0.257135003805161, 0.355440020561218],
+  [0.427684992551804, 0.437960982322693],
+  [0.448339998722076, 0.536936044692993],
+  [0.178560003638268, 0.45755398273468],
+  [0.247308000922203, 0.457193970680237],
+  [0.286267012357712, 0.467674970626831],
+  [0.332827985286713, 0.460712015628815],
+  [0.368755996227264, 0.447206974029541],
+  [0.398963987827301, 0.432654976844788],
+  [0.476410001516342, 0.405806005001068],
+  [0.189241006970406, 0.523923993110657],
+  [0.228962004184723, 0.348950982093811],
+  [0.490725994110107, 0.562400996685028],
+  [0.404670000076294, 0.485132992267609],
+  [0.019469000399113, 0.401564002037048],
+  [0.426243007183075, 0.420431017875671],
+  [0.396993011236191, 0.548797011375427],
+  [0.266469985246658, 0.376977026462555],
+  [0.439121007919312, 0.51895797252655],
+  [0.032313998788595, 0.644356966018677],
+  [0.419054001569748, 0.387154996395111],
+  [0.462783008813858, 0.505746960639954],
+  [0.238978996872902, 0.779744982719421],
+  [0.198220998048782, 0.831938028335571],
+  [0.107550002634525, 0.540755033493042],
+  [0.183610007166862, 0.740257024765015],
+  [0.134409993886948, 0.333683013916016],
+  [0.385764002799988, 0.883153975009918],
+  [0.490967005491257, 0.579378008842468],
+  [0.382384985685349, 0.508572995662689],
+  [0.174399003386497, 0.397670984268188],
+  [0.318785011768341, 0.39623498916626],
+  [0.343364000320435, 0.400596976280212],
+  [0.396100014448166, 0.710216999053955],
+  [0.187885001301765, 0.588537991046906],
+  [0.430987000465393, 0.944064974784851],
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+  [0.499976992607117, 0.954452991485596],
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+  [0.410373002290726, 0.391080021858215],
+  [0.194993004202843, 0.342101991176605],
+  [0.388664990663528, 0.362284004688263],
+  [0.365961998701096, 0.355970978736877],
+  [0.343364000320435, 0.355356991291046],
+  [0.318785011768341, 0.35834002494812],
+  [0.301414996385574, 0.363156020641327],
+  [0.058132998645306, 0.319076001644135],
+  [0.301414996385574, 0.387449026107788],
+  [0.499987989664078, 0.618434011936188],
+  [0.415838003158569, 0.624195992946625],
+  [0.445681989192963, 0.566076993942261],
+  [0.465844005346298, 0.620640993118286],
+  [0.49992299079895, 0.351523995399475],
+  [0.288718998432159, 0.819945991039276],
+  [0.335278987884521, 0.852819979190826],
+  [0.440512001514435, 0.902418971061707],
+  [0.128294005990028, 0.791940987110138],
+  [0.408771991729736, 0.373893976211548],
+  [0.455606997013092, 0.451801002025604],
+  [0.499877005815506, 0.908990025520325],
+  [0.375436991453171, 0.924192011356354],
+  [0.11421000212431, 0.615022003650665],
+  [0.448662012815475, 0.695277988910675],
+  [0.4480200111866, 0.704632043838501],
+  [0.447111994028091, 0.715808033943176],
+  [0.444831997156143, 0.730794012546539],
+  [0.430011987686157, 0.766808986663818],
+  [0.406787008047104, 0.685672998428345],
+  [0.400738000869751, 0.681069016456604],
+  [0.392399996519089, 0.677703022956848],
+  [0.367855995893478, 0.663918972015381],
+  [0.247923001646996, 0.601333022117615],
+  [0.452769994735718, 0.420849978923798],
+  [0.43639200925827, 0.359887003898621],
+  [0.416164010763168, 0.368713974952698],
+  [0.413385987281799, 0.692366003990173],
+  [0.228018000721931, 0.683571994304657],
+  [0.468268007040024, 0.352671027183533],
+  [0.411361992359161, 0.804327011108398],
+  [0.499989002943039, 0.469825029373169],
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+];
+var VTX68 = [
+  127,
+  234,
+  132,
+  58,
+  172,
+  150,
+  149,
+  148,
+  152,
+  377,
+  378,
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+  17,
+  84,
+  91,
+  78,
+  81,
+  13,
+  311,
+  308,
+  402,
+  14,
+  178
+];
+var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
+var VTX7 = [33, 133, 362, 263, 1, 78, 308];
+var UV68 = VTX68.map((x) => UV468[x]);
+var UV33 = VTX33.map((x) => UV468[x]);
+var UV7 = VTX7.map((x) => UV468[x]);
+function connectionsToIndices(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var pairsLips = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var pairsFaceContour = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+var contourKeypoints = {
+  lips: connectionsToIndices(pairsLips),
+  leftEye: connectionsToIndices(pairsLeftEye),
+  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
+  leftIris: connectionsToIndices(pairsLeftIris),
+  rightEye: connectionsToIndices(pairsRightEye),
+  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
+  rightIris: connectionsToIndices(pairsRightIris),
+  faceOval: connectionsToIndices(pairsFaceContour)
+};
+
+// src/face/facemeshutil.ts
+var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
+var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
+var clampBox = (box, input2) => box ? [
+  Math.trunc(Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.max(0, box.startPoint[1])),
+  Math.trunc(Math.min(input2.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.min(input2.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
+] : [0, 0, 0, 0];
+var getRawBox = (box, input2) => box ? [
+  box.startPoint[0] / (input2.shape[2] || 0),
+  box.startPoint[1] / (input2.shape[1] || 0),
+  (box.endPoint[0] - box.startPoint[0]) / (input2.shape[2] || 0),
+  (box.endPoint[1] - box.startPoint[1]) / (input2.shape[1] || 0)
+] : [0, 0, 0, 0];
+var scaleBoxCoordinates = (box, factor) => {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
+};
+var cutAndResize = (box, image2, cropSize) => {
+  const h = image2.shape[1];
+  const w = image2.shape[2];
+  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
+  const crop = image.cropAndResize(image2, [cutBox], [0], cropSize);
+  const norm2 = div(crop, constants.tf255);
+  dispose(crop);
+  return norm2;
+};
+var enlargeBox = (box, factor) => {
+  const center = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
+};
+var squarifyBox = (box) => {
+  const centers = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = Math.max(...size2) / 2;
+  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
+};
+var calculateLandmarksBoundingBox = (landmarks) => {
+  const x = landmarks.map((d) => d[0]);
+  const y = landmarks.map((d) => d[1]);
+  return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks };
+};
+var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
+var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
+var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+var dot4 = (v1, v2) => {
+  let product = 0;
+  for (let i2 = 0; i2 < v1.length; i2++)
+    product += v1[i2] * v2[i2];
+  return product;
+};
+var getColumnFrom2DArr = (arr, columnIndex) => {
+  const column = [];
+  for (let i2 = 0; i2 < arr.length; i2++)
+    column.push(arr[i2][columnIndex]);
+  return column;
+};
+var multiplyTransformMatrices = (mat1, mat2) => {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++)
+      product[row].push(dot4(mat1[row], getColumnFrom2DArr(mat2, col)));
+  }
+  return product;
+};
+var buildRotationMatrix = (rotation, center) => {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
+  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
+};
+var invertTransformMatrix = (matrix) => {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [-dot4(rotationComponent[0], translationComponent), -dot4(rotationComponent[1], translationComponent)];
+  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
+};
+var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot4(homogeneousCoordinate, rotationMatrix[0]), dot4(homogeneousCoordinate, rotationMatrix[1])];
+function generateAnchors(inputSize10) {
+  const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
+  const anchors3 = [];
+  for (let i2 = 0; i2 < spec.strides.length; i2++) {
+    const stride = spec.strides[i2];
+    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
+    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
+    const anchorsNum = spec.anchors[i2];
+    for (let gridY = 0; gridY < gridRows; gridY++) {
+      const anchorY = stride * (gridY + 0.5);
+      for (let gridX = 0; gridX < gridCols; gridX++) {
+        const anchorX = stride * (gridX + 0.5);
+        for (let n = 0; n < anchorsNum; n++)
+          anchors3.push([anchorX, anchorY]);
+      }
+    }
+  }
+  return anchors3;
+}
+function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
+  const boxSize = getBoxSize(box);
+  const coordsScaled = coordsRaw.map((coord) => [
+    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
+    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
+    coord[2] || 0
+  ]);
+  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
+  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
+  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
+  const boxCenter = getBoxCenter(box);
+  const offsets = [dot4(boxCenter, inverseRotationMatrix[0]), dot4(boxCenter, inverseRotationMatrix[1])];
+  return coordsRotated.map((coord) => [
+    Math.trunc(coord[0] + offsets[0]),
+    Math.trunc(coord[1] + offsets[1]),
+    Math.trunc(coord[2] || 0)
+  ]);
+}
+function correctFaceRotation(rotate, box, input2, inputSize10) {
+  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
+  let angle = 0;
+  let rotationMatrix = fixedRotationMatrix;
+  let face4;
+  if (rotate && env2.kernels.includes("rotatewithoffset")) {
+    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
+    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+    if (largeAngle) {
+      const center = getBoxCenter(box);
+      const centerRaw = [center[0] / input2.shape[2], center[1] / input2.shape[1]];
+      const rotated = image.rotateWithOffset(input2, angle, 0, centerRaw);
+      rotationMatrix = buildRotationMatrix(-angle, center);
+      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
+      dispose(rotated);
+    } else {
+      face4 = cutAndResize(box, input2, [inputSize10, inputSize10]);
+    }
+  } else {
+    face4 = cutAndResize(box, input2, [inputSize10, inputSize10]);
+  }
+  return [angle, rotationMatrix, face4];
+}
+var findFaceCenter = (mesh) => {
+  const x = mesh.map((m) => m[0]);
+  const y = mesh.map((m) => m[1]);
+  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];
+};
+var calculateFaceBox = (mesh, previousBox) => {
+  const center = findFaceCenter(mesh);
+  const boxSize = getBoxSize(previousBox);
+  const calculatedBox = {
+    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
+    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
+  };
+  return calculatedBox;
+};
+
+// src/face/blazeface.ts
+var keypointsCount = 6;
+var faceBoxScaleFactor = 1.4;
+var model6;
+var anchors = null;
+var inputSize = 0;
+var inputSizeT = null;
+var size = () => inputSize;
+async function load5(config3) {
+  var _a;
+  if (env2.initial)
+    model6 = null;
+  if (!model6)
+    model6 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model6["modelUrl"]);
+  inputSize = model6.inputs[0].shape ? model6.inputs[0].shape[2] : 0;
+  inputSizeT = scalar(inputSize, "int32");
+  anchors = tensor2d(generateAnchors(inputSize));
+  return model6;
+}
+function decodeBoxes(boxOutputs) {
+  const t2 = {};
+  t2.boxStarts = slice(boxOutputs, [0, 1], [-1, 2]);
+  t2.centers = add2(t2.boxStarts, anchors);
+  t2.boxSizes = slice(boxOutputs, [0, 3], [-1, 2]);
+  t2.boxSizesNormalized = div(t2.boxSizes, inputSizeT);
+  t2.centersNormalized = div(t2.centers, inputSizeT);
+  t2.halfBoxSize = div(t2.boxSizesNormalized, constants.tf2);
+  t2.starts = sub(t2.centersNormalized, t2.halfBoxSize);
+  t2.ends = add2(t2.centersNormalized, t2.halfBoxSize);
+  t2.startNormalized = mul(t2.starts, inputSizeT);
+  t2.endNormalized = mul(t2.ends, inputSizeT);
+  const boxes = concat2d([t2.startNormalized, t2.endNormalized], 1);
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return boxes;
+}
+async function getBoxes(inputImage, config3) {
+  var _a, _b, _c, _d;
+  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
+    return [];
+  const t2 = {};
+  t2.resized = image.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t2.div = div(t2.resized, constants.tf127);
+  t2.normalized = sub(t2.div, constants.tf05);
+  const res = model6 == null ? void 0 : model6.execute(t2.normalized);
+  if (Array.isArray(res) && res.length > 2) {
+    const sorted = res.sort((a6, b) => a6.size - b.size);
+    t2.concat384 = concat([sorted[0], sorted[2]], 2);
+    t2.concat512 = concat([sorted[1], sorted[3]], 2);
+    t2.concat = concat([t2.concat512, t2.concat384], 1);
+    t2.batch = squeeze(t2.concat, 0);
+  } else if (Array.isArray(res)) {
+    t2.batch = squeeze(res[0]);
+  } else {
+    t2.batch = squeeze(res);
+  }
+  dispose(res);
+  t2.boxes = decodeBoxes(t2.batch);
+  t2.logits = slice(t2.batch, [0, 0], [-1, 1]);
+  t2.sigmoid = sigmoid(t2.logits);
+  t2.scores = squeeze(t2.sigmoid);
+  t2.nms = await image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  const nms = await t2.nms.array();
+  const boxes = [];
+  const scores = await t2.scores.data();
+  for (let i2 = 0; i2 < nms.length; i2++) {
+    const confidence = scores[nms[i2]];
+    if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
+      const b = {};
+      b.bbox = slice(t2.boxes, [nms[i2], 0], [1, -1]);
+      b.slice = slice(t2.batch, [nms[i2], keypointsCount - 1], [1, -1]);
+      b.squeeze = squeeze(b.slice);
+      b.landmarks = reshape(b.squeeze, [keypointsCount, -1]);
+      const points = await b.bbox.data();
+      const rawBox = {
+        startPoint: [points[0], points[1]],
+        endPoint: [points[2], points[3]],
+        landmarks: await b.landmarks.array(),
+        confidence
+      };
+      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
+      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
+      const squaredBox = squarifyBox(enlargedBox);
+      boxes.push(squaredBox);
+      Object.keys(b).forEach((tensor2) => dispose(b[tensor2]));
+    }
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return boxes;
+}
+
+// src/body/blazeposecoords.ts
+var blazeposecoords_exports = {};
+__export(blazeposecoords_exports, {
+  connected: () => connected,
+  kpt: () => kpt
+});
+var kpt = [
+  "nose",
+  "leftEyeInside",
+  "leftEye",
+  "leftEyeOutside",
+  "rightEyeInside",
+  "rightEye",
+  "rightEyeOutside",
+  "leftEar",
+  "rightEar",
+  "leftMouth",
+  "rightMouth",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftPinky",
+  "rightPinky",
+  "leftIndex",
+  "rightIndex",
+  "leftThumb",
+  "rightThumb",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle",
+  "leftHeel",
+  "rightHeel",
+  "leftFoot",
+  "rightFoot",
+  "bodyCenter",
+  "bodyTop",
+  "leftPalm",
+  "leftHand",
+  "rightPalm",
+  "rightHand"
+];
+var connected = {
+  shoulders: ["leftShoulder", "rightShoulder"],
+  hips: ["rightHip", "leftHip"],
+  mouth: ["leftMouth", "rightMouth"],
+  leftLegUpper: ["leftHip", "leftKnee"],
+  leftLegLower: ["leftKnee", "leftAnkle"],
+  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
+  leftTorso: ["leftShoulder", "leftHip"],
+  leftArmUpper: ["leftShoulder", "leftElbow"],
+  leftArmLower: ["leftElbow", "leftWrist"],
+  leftHand: ["leftWrist", "leftPalm"],
+  leftHandPinky: ["leftPalm", "leftPinky"],
+  leftHandIndex: ["leftPalm", "leftIndex"],
+  leftHandThumb: ["leftPalm", "leftThumb"],
+  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
+  rightLegUpper: ["rightHip", "rightKnee"],
+  rightLegLower: ["rightKnee", "rightAnkle"],
+  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
+  rightTorso: ["rightShoulder", "rightHip"],
+  rightArmUpper: ["rightShoulder", "rightElbow"],
+  rightArmLower: ["rightElbow", "rightWrist"],
+  rightHand: ["rightWrist", "rightPalm"],
+  rightHandPinky: ["rightPalm", "rightPinky"],
+  rightHandIndex: ["rightPalm", "rightIndex"],
+  rightHandThumb: ["rightPalm", "rightThumb"],
+  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
+};
+
+// src/body/blazeposedetector.ts
+var inputSize2 = 224;
+var anchorTensor;
+var numLayers = 5;
+var strides = [8, 16, 32, 32, 32];
+async function createAnchors() {
+  const anchors3 = [];
+  let layerId = 0;
+  while (layerId < numLayers) {
+    let anchorCount = 0;
+    let lastSameStrideLayer = layerId;
+    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
+      anchorCount += 2;
+      lastSameStrideLayer++;
+    }
+    const stride = strides[layerId];
+    const featureMapHeight = Math.ceil(inputSize2 / stride);
+    const featureMapWidth = Math.ceil(inputSize2 / stride);
+    for (let y = 0; y < featureMapHeight; ++y) {
+      for (let x = 0; x < featureMapWidth; ++x) {
+        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
+          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
+        }
+      }
+    }
+    layerId = lastSameStrideLayer;
+  }
+  anchorTensor = { x: tensor1d(anchors3.map((a6) => a6.x)), y: tensor1d(anchors3.map((a6) => a6.y)) };
+}
+
+// src/util/box.ts
+function calc(keypoints, outputSize2 = [1, 1]) {
+  const coords3 = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min7 = [Math.min(...coords3[0]), Math.min(...coords3[1])];
+  const max7 = [Math.max(...coords3[0]), Math.max(...coords3[1])];
+  const box = [min7[0], min7[1], max7[0] - min7[0], max7[1] - min7[1]];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function square4(keypoints, outputSize2 = [1, 1]) {
+  const coords3 = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min7 = [Math.min(...coords3[0]), Math.min(...coords3[1])];
+  const max7 = [Math.max(...coords3[0]), Math.max(...coords3[1])];
+  const center = [(min7[0] + max7[0]) / 2, (min7[1] + max7[1]) / 2];
+  const dist = Math.max(center[0] - min7[0], center[1] - min7[1], -center[0] + max7[0], -center[1] + max7[1]);
+  const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function scale2(box, scaleFact) {
+  const dist = [box[2] * scaleFact, box[3] * scaleFact];
+  const newBox = [
+    box[0] - (dist[0] - box[2]) / 2,
+    box[1] - (dist[1] - box[3]) / 2,
+    dist[0],
+    dist[1]
+  ];
+  return newBox;
+}
+
+// src/body/blazepose.ts
+var env3 = { initial: true };
+var models = { detector: null, landmarks: null };
+var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
+var skipped5 = Number.MAX_SAFE_INTEGER;
+var outputNodes = {
+  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
+  detector: []
+};
+var cache = null;
+var cropBox;
+var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+var lastTime5 = 0;
+var sigmoid6 = (x) => 1 - 1 / (1 + Math.exp(x));
+async function loadDetect(config3) {
+  if (env3.initial)
+    models.detector = null;
+  if (!models.detector && config3.body["detector"] && config3.body["detector"]["modelPath"] || "") {
+    models.detector = await loadModel(config3.body["detector"]["modelPath"]);
+    const inputs = Object.values(models.detector.modelSignature["inputs"]);
+    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug && models.detector)
+    log("cached model:", models.detector["modelUrl"]);
+  await createAnchors();
+  return models.detector;
+}
+async function loadPose(config3) {
+  if (env3.initial)
+    models.landmarks = null;
+  if (!models.landmarks) {
+    models.landmarks = await loadModel(config3.body.modelPath);
+    const inputs = Object.values(models.landmarks.modelSignature["inputs"]);
+    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models.landmarks["modelUrl"]);
+  return models.landmarks;
+}
+async function prepareImage(input2, size2) {
+  const t2 = {};
+  if (!input2.shape || !input2.shape[1] || !input2.shape[2])
+    return input2;
+  let final;
+  if (cropBox) {
+    t2.cropped = image.cropAndResize(input2, [cropBox], [0], [input2.shape[1], input2.shape[2]]);
+  }
+  if (input2.shape[1] !== input2.shape[2]) {
+    const height = [
+      input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0,
+      input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0
+    ];
+    const width = [
+      input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0,
+      input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0
+    ];
+    padding = [
+      [0, 0],
+      height,
+      width,
+      [0, 0]
+    ];
+    t2.pad = pad(t2.cropped || input2, padding);
+    t2.resize = image.resizeBilinear(t2.pad, [size2, size2]);
+    final = div(t2.resize, constants.tf255);
+  } else if (input2.shape[1] !== size2) {
+    t2.resize = image.resizeBilinear(t2.cropped || input2, [size2, size2]);
+    final = div(t2.resize, constants.tf255);
+  } else {
+    final = div(t2.cropped || input2, constants.tf255);
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return final;
+}
+function rescaleKeypoints(keypoints, outputSize2) {
+  for (const kpt4 of keypoints) {
+    kpt4.position = [
+      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
+      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
+      kpt4.position[2]
+    ];
+    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
+  }
+  if (cropBox) {
+    for (const kpt4 of keypoints) {
+      kpt4.positionRaw = [
+        kpt4.positionRaw[0] + cropBox[1],
+        kpt4.positionRaw[1] + cropBox[0],
+        kpt4.positionRaw[2]
+      ];
+      kpt4.position = [
+        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
+        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
+        kpt4.positionRaw[2]
+      ];
+    }
+  }
+  return keypoints;
+}
+async function fixKeypoints(keypoints) {
+  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
+  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
+  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
+  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
+  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
+  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
+  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
+  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
+}
+async function detectLandmarks(input2, config3, outputSize2) {
+  var _a;
+  const t2 = {};
+  [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = (_a = models.landmarks) == null ? void 0 : _a.execute(input2, outputNodes.landmarks);
+  const poseScore = (await t2.poseflag.data())[0];
+  const points = await t2.ld.data();
+  const distances = await t2.world.data();
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  const keypointsRelative = [];
+  const depth = 5;
+  for (let i2 = 0; i2 < points.length / depth; i2++) {
+    const score = sigmoid6(points[depth * i2 + 3]);
+    const presence = sigmoid6(points[depth * i2 + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
+    const positionRaw = [points[depth * i2 + 0] / inputSize3.landmarks[0], points[depth * i2 + 1] / inputSize3.landmarks[1], points[depth * i2 + 2] + 0];
+    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
+    const distance2 = [distances[depth * i2 + 0], distances[depth * i2 + 1], distances[depth * i2 + 2] + 0];
+    keypointsRelative.push({ part: kpt[i2], positionRaw, position, distance: distance2, score: adjScore });
+  }
+  if (poseScore < (config3.body.minConfidence || 0))
+    return null;
+  fixKeypoints(keypointsRelative);
+  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
+  const kpts = keypoints.map((k) => k.position);
+  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected)) {
+    const pt = [];
+    for (let i2 = 0; i2 < indexes.length - 1; i2++) {
+      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i2]);
+      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i2 + 1]);
+      if (pt0 && pt1)
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
+  return body4;
+}
+async function predict5(input2, config3) {
+  const outputSize2 = [input2.shape[2] || 0, input2.shape[1] || 0];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime5;
+  const skipFrame = skipped5 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
+    skipped5++;
+  } else {
+    const t2 = {};
+    t2.landmarks = await prepareImage(input2, 256);
+    cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    lastTime5 = now();
+    skipped5 = 0;
+  }
+  return cache ? [cache] : [];
+}
+
+// src/object/labels.ts
+var labels = [
+  { class: 1, label: "person" },
+  { class: 2, label: "bicycle" },
+  { class: 3, label: "car" },
+  { class: 4, label: "motorcycle" },
+  { class: 5, label: "airplane" },
+  { class: 6, label: "bus" },
+  { class: 7, label: "train" },
+  { class: 8, label: "truck" },
+  { class: 9, label: "boat" },
+  { class: 10, label: "traffic light" },
+  { class: 11, label: "fire hydrant" },
+  { class: 12, label: "stop sign" },
+  { class: 13, label: "parking meter" },
+  { class: 14, label: "bench" },
+  { class: 15, label: "bird" },
+  { class: 16, label: "cat" },
+  { class: 17, label: "dog" },
+  { class: 18, label: "horse" },
+  { class: 19, label: "sheep" },
+  { class: 20, label: "cow" },
+  { class: 21, label: "elephant" },
+  { class: 22, label: "bear" },
+  { class: 23, label: "zebra" },
+  { class: 24, label: "giraffe" },
+  { class: 25, label: "backpack" },
+  { class: 26, label: "umbrella" },
+  { class: 27, label: "handbag" },
+  { class: 28, label: "tie" },
+  { class: 29, label: "suitcase" },
+  { class: 30, label: "frisbee" },
+  { class: 31, label: "skis" },
+  { class: 32, label: "snowboard" },
+  { class: 33, label: "sports ball" },
+  { class: 34, label: "kite" },
+  { class: 35, label: "baseball bat" },
+  { class: 36, label: "baseball glove" },
+  { class: 37, label: "skateboard" },
+  { class: 38, label: "surfboard" },
+  { class: 39, label: "tennis racket" },
+  { class: 40, label: "bottle" },
+  { class: 41, label: "wine glass" },
+  { class: 42, label: "cup" },
+  { class: 43, label: "fork" },
+  { class: 44, label: "knife" },
+  { class: 45, label: "spoon" },
+  { class: 46, label: "bowl" },
+  { class: 47, label: "banana" },
+  { class: 48, label: "apple" },
+  { class: 49, label: "sandwich" },
+  { class: 50, label: "orange" },
+  { class: 51, label: "broccoli" },
+  { class: 52, label: "carrot" },
+  { class: 53, label: "hot dog" },
+  { class: 54, label: "pizza" },
+  { class: 55, label: "donut" },
+  { class: 56, label: "cake" },
+  { class: 57, label: "chair" },
+  { class: 58, label: "couch" },
+  { class: 59, label: "potted plant" },
+  { class: 60, label: "bed" },
+  { class: 61, label: "dining table" },
+  { class: 62, label: "toilet" },
+  { class: 63, label: "tv" },
+  { class: 64, label: "laptop" },
+  { class: 65, label: "mouse" },
+  { class: 66, label: "remote" },
+  { class: 67, label: "keyboard" },
+  { class: 68, label: "cell phone" },
+  { class: 69, label: "microwave" },
+  { class: 70, label: "oven" },
+  { class: 71, label: "toaster" },
+  { class: 72, label: "sink" },
+  { class: 73, label: "refrigerator" },
+  { class: 74, label: "book" },
+  { class: 75, label: "clock" },
+  { class: 76, label: "vase" },
+  { class: 77, label: "scissors" },
+  { class: 78, label: "teddy bear" },
+  { class: 79, label: "hair drier" },
+  { class: 80, label: "toothbrush" }
+];
+
+// src/object/centernet.ts
+var model7;
+var inputSize4 = 0;
+var last5 = [];
+var lastTime6 = 0;
+var skipped6 = Number.MAX_SAFE_INTEGER;
+async function load6(config3) {
+  if (env2.initial)
+    model7 = null;
+  if (!model7) {
+    model7 = await loadModel(config3.object.modelPath);
+    const inputs = Object.values(model7.modelSignature["inputs"]);
+    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", model7["modelUrl"]);
+  return model7;
+}
+async function process3(res, outputShape, config3) {
+  if (!res)
+    return [];
+  const t2 = {};
+  const results = [];
+  const detections = await res.array();
+  t2.squeeze = squeeze(res);
+  const arr = split(t2.squeeze, 6, 1);
+  t2.stack = stack([arr[1], arr[0], arr[3], arr[2]], 1);
+  t2.boxes = squeeze(t2.stack);
+  t2.scores = squeeze(arr[4]);
+  t2.classes = squeeze(arr[5]);
+  dispose([res, ...arr]);
+  t2.nms = await image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  const nms = await t2.nms.data();
+  let i2 = 0;
+  for (const id of Array.from(nms)) {
+    const score = Math.trunc(100 * detections[0][id][4]) / 100;
+    const classVal = detections[0][id][5];
+    const label = labels[classVal].label;
+    const [x, y] = [
+      detections[0][id][0] / inputSize4,
+      detections[0][id][1] / inputSize4
+    ];
+    const boxRaw = [
+      x,
+      y,
+      detections[0][id][2] / inputSize4 - x,
+      detections[0][id][3] / inputSize4 - y
+    ];
+    const box = [
+      Math.trunc(boxRaw[0] * outputShape[0]),
+      Math.trunc(boxRaw[1] * outputShape[1]),
+      Math.trunc(boxRaw[2] * outputShape[0]),
+      Math.trunc(boxRaw[3] * outputShape[1])
+    ];
+    results.push({ id: i2++, score, class: classVal, label, box, boxRaw });
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return results;
+}
+async function predict6(input2, config3) {
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime6;
+  const skipFrame = skipped6 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
+    skipped6++;
+    return last5;
+  }
+  skipped6 = 0;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [input2.shape[2] || 0, input2.shape[1] || 0];
+    const resize = image.resizeBilinear(input2, [inputSize4, inputSize4]);
+    const objectT = config3.object.enabled ? model7 == null ? void 0 : model7.execute(resize, ["tower_0/detections"]) : null;
+    lastTime6 = now();
+    dispose(resize);
+    const obj = await process3(objectT, outputSize2, config3);
+    last5 = obj;
+    resolve(obj);
+  });
+}
+
+// src/body/efficientposecoords.ts
+var efficientposecoords_exports = {};
+__export(efficientposecoords_exports, {
+  connected: () => connected2,
+  kpt: () => kpt2
+});
+var kpt2 = [
+  "head",
+  "neck",
+  "rightShoulder",
+  "rightElbow",
+  "rightWrist",
+  "chest",
+  "leftShoulder",
+  "leftElbow",
+  "leftWrist",
+  "bodyCenter",
+  "rightHip",
+  "rightKnee",
+  "rightAnkle",
+  "leftHip",
+  "leftKnee",
+  "leftAnkle"
+];
+var connected2 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/efficientpose.ts
+var model8;
+var lastTime7 = 0;
+var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
+var skipped7 = Number.MAX_SAFE_INTEGER;
+async function load7(config3) {
+  if (env2.initial)
+    model8 = null;
+  if (!model8)
+    model8 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model8["modelUrl"]);
+  return model8;
+}
+async function max2d(inputs, minScore) {
+  const [width, height] = inputs.shape;
+  const reshaped = reshape(inputs, [height * width]);
+  const max7 = max(reshaped, 0);
+  const newScore = (await max7.data())[0];
+  dispose([reshaped, max7]);
+  if (newScore > minScore) {
+    const coordinates = argMax(reshaped, 0);
+    const mod4 = mod(coordinates, width);
+    const x = (await mod4.data())[0];
+    const div3 = div(coordinates, scalar(width, "int32"));
+    const y = (await div3.data())[0];
+    dispose([mod4, div3]);
+    return [x, y, newScore];
+  }
+  return [0, 0, newScore];
+}
+async function predict7(image2, config3) {
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime7;
+  const skipFrame = skipped7 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
+    skipped7++;
+    return [cache2];
+  }
+  skipped7 = 0;
+  return new Promise(async (resolve) => {
+    var _a;
+    const tensor2 = tidy(() => {
+      if (!(model8 == null ? void 0 : model8.inputs[0].shape))
+        return null;
+      const resize = image.resizeBilinear(image2, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
+      const enhance2 = mul(resize, constants.tf2);
+      const norm2 = sub(enhance2, constants.tf1);
+      return norm2;
+    });
+    let resT;
+    if (config3.body.enabled)
+      resT = model8 == null ? void 0 : model8.execute(tensor2);
+    lastTime7 = now();
+    dispose(tensor2);
+    if (resT) {
+      cache2.keypoints.length = 0;
+      const squeeze2 = resT.squeeze();
+      dispose(resT);
+      const stack2 = squeeze2.unstack(2);
+      dispose(squeeze2);
+      for (let id = 0; id < stack2.length; id++) {
+        const [x2, y2, partScore] = await max2d(stack2[id], config3.body.minConfidence);
+        if (partScore > (((_a = config3.body) == null ? void 0 : _a.minConfidence) || 0)) {
+          cache2.keypoints.push({
+            score: Math.round(100 * partScore) / 100,
+            part: kpt2[id],
+            positionRaw: [
+              x2 / model8.inputs[0].shape[2],
+              y2 / model8.inputs[0].shape[1]
+            ],
+            position: [
+              Math.round(image2.shape[2] * x2 / model8.inputs[0].shape[2]),
+              Math.round(image2.shape[1] * y2 / model8.inputs[0].shape[1])
+            ]
+          });
+        }
+      }
+      stack2.forEach((s2) => dispose(s2));
+    }
+    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+    const x = cache2.keypoints.map((a6) => a6.position[0]);
+    const y = cache2.keypoints.map((a6) => a6.position[1]);
+    cache2.box = [
+      Math.min(...x),
+      Math.min(...y),
+      Math.max(...x) - Math.min(...x),
+      Math.max(...y) - Math.min(...y)
+    ];
+    const xRaw = cache2.keypoints.map((a6) => a6.positionRaw[0]);
+    const yRaw = cache2.keypoints.map((a6) => a6.positionRaw[1]);
+    cache2.boxRaw = [
+      Math.min(...xRaw),
+      Math.min(...yRaw),
+      Math.max(...xRaw) - Math.min(...xRaw),
+      Math.max(...yRaw) - Math.min(...yRaw)
+    ];
+    for (const [name, indexes] of Object.entries(connected2)) {
+      const pt = [];
+      for (let i2 = 0; i2 < indexes.length - 1; i2++) {
+        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i2]);
+        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i2 + 1]);
+        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+          pt.push([pt0.position, pt1.position]);
+      }
+      cache2.annotations[name] = pt;
+    }
+    resolve([cache2]);
+  });
+}
+
+// src/gear/emotion.ts
+var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
+var model9;
+var last6 = [];
+var lastCount5 = 0;
+var lastTime8 = 0;
+var skipped8 = Number.MAX_SAFE_INTEGER;
+async function load8(config3) {
+  var _a;
+  if (env2.initial)
+    model9 = null;
+  if (!model9)
+    model9 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model9["modelUrl"]);
+  return model9;
+}
+async function predict8(image2, config3, idx, count3) {
+  var _a, _b;
+  if (!model9)
+    return [];
+  const skipFrame = skipped8 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count3 && last6[idx] && last6[idx].length > 0) {
+    skipped8++;
+    return last6[idx];
+  }
+  skipped8 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    const obj = [];
+    if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
+      const t2 = {};
+      const inputSize10 = (model9 == null ? void 0 : model9.inputs[0].shape) ? model9.inputs[0].shape[2] : 0;
+      t2.resize = image.resizeBilinear(image2, [inputSize10, inputSize10], false);
+      t2.channels = mul(t2.resize, constants.rgb);
+      t2.grayscale = sum2(t2.channels, 3, true);
+      t2.grayscaleSub = sub(t2.grayscale, constants.tf05);
+      t2.grayscaleMul = mul(t2.grayscaleSub, constants.tf2);
+      t2.emotion = model9 == null ? void 0 : model9.execute(t2.grayscaleMul);
+      lastTime8 = now();
+      const data = await t2.emotion.data();
+      for (let i2 = 0; i2 < data.length; i2++) {
+        if (data[i2] > (((_b2 = config3.face.emotion) == null ? void 0 : _b2.minConfidence) || 0))
+          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i2]) / 100), emotion: annotations[i2] });
+      }
+      obj.sort((a6, b) => b.score - a6.score);
+      Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    }
+    last6[idx] = obj;
+    lastCount5 = count3;
+    resolve(obj);
+  });
+}
+
+// src/face/mobilefacenet.ts
+var model10;
+var last7 = [];
+var lastCount6 = 0;
+var lastTime9 = 0;
+var skipped9 = Number.MAX_SAFE_INTEGER;
+async function load9(config3) {
+  if (env2.initial)
+    model10 = null;
+  if (!model10)
+    model10 = await loadModel(config3.face["mobilefacenet"].modelPath);
+  else if (config3.debug)
+    log("cached model:", model10["modelUrl"]);
+  return model10;
+}
+async function predict9(input2, config3, idx, count3) {
+  var _a, _b;
+  if (!model10)
+    return [];
+  const skipFrame = skipped9 < (((_a = config3.face["embedding"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["embedding"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count3 && last7[idx]) {
+    skipped9++;
+    return last7[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["embedding"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = image.resizeBilinear(input2, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
+      t2.data = model10 == null ? void 0 : model10.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+    }
+    last7[idx] = data;
+    lastCount6 = count3;
+    lastTime9 = now();
+    resolve(data);
+  });
+}
+
+// src/face/iris.ts
+var model11;
+var inputSize5 = 0;
+var irisEnlarge = 2.3;
+var leftOutline = meshAnnotations["leftEyeLower0"];
+var rightOutline = meshAnnotations["rightEyeLower0"];
+var eyeLandmarks = {
+  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
+  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
+};
+var irisLandmarks = {
+  upperCenter: 3,
+  lowerCenter: 4,
+  index: 71,
+  numCoordinates: 76
+};
+async function load10(config3) {
+  var _a;
+  if (env2.initial)
+    model11 = null;
+  if (!model11)
+    model11 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model11["modelUrl"]);
+  inputSize5 = model11.inputs[0].shape ? model11.inputs[0].shape[2] : 0;
+  if (inputSize5 === -1)
+    inputSize5 = 64;
+  return model11;
+}
+function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
+  for (let i2 = 0; i2 < irisIndices.length; i2++) {
+    const { key, indices } = irisIndices[i2];
+    const originalIndices = meshAnnotations[`${prefix}${key}`];
+    if (!keys || keys.includes(key)) {
+      for (let j = 0; j < indices.length; j++) {
+        const index2 = indices[j];
+        rawCoords[originalIndices[j]] = [
+          newCoords[index2][0],
+          newCoords[index2][1],
+          (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
+        ];
+      }
+    }
+  }
+}
+var getLeftToRightEyeDepthDifference = (rawCoords) => {
+  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
+  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
+  return leftEyeZ - rightEyeZ;
+};
+var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
+  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
+  const boxSize = getBoxSize(box);
+  let crop = image.cropAndResize(face4, [[
+    box.startPoint[1] / meshSize,
+    box.startPoint[0] / meshSize,
+    box.endPoint[1] / meshSize,
+    box.endPoint[0] / meshSize
+  ]], [0], [inputSize5, inputSize5]);
+  if (flip && env2.kernels.includes("flipleftright")) {
+    const flipped = image.flipLeftRight(crop);
+    dispose(crop);
+    crop = flipped;
+  }
+  return { box, boxSize, crop };
+};
+var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
+  const eyeRawCoords = [];
+  for (let i2 = 0; i2 < irisLandmarks.numCoordinates; i2++) {
+    const x = eyeData[i2 * 3];
+    const y = eyeData[i2 * 3 + 1];
+    const z = eyeData[i2 * 3 + 2];
+    eyeRawCoords.push([
+      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
+      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
+      z
+    ]);
+  }
+  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
+};
+var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
+  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
+  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
+  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
+  return irisCoords.map((coord, i2) => {
+    let z = averageZ;
+    if (i2 === 2) {
+      z = upperCenterZ;
+    } else if (i2 === 4) {
+      z = lowerCenterZ;
+    }
+    return [coord[0], coord[1], z];
+  });
+};
+async function augmentIris(rawCoords, face4, config3, meshSize) {
+  if (!model11) {
+    if (config3.debug)
+      log("face mesh iris detection requested, but model is not loaded");
+    return rawCoords;
+  }
+  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
+  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
+  const combined = concat([leftEyeCrop, rightEyeCrop]);
+  dispose(leftEyeCrop);
+  dispose(rightEyeCrop);
+  const eyePredictions = model11.execute(combined);
+  dispose(combined);
+  const eyePredictionsData = await eyePredictions.data();
+  dispose(eyePredictions);
+  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
+  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
+  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
+  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
+  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
+  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
+  } else if (leftToRightEyeDepthDifference < 1) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
+  } else {
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
+  }
+  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
+  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
+  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
+  return newCoords;
+}
+
+// src/face/constants.ts
+var LIPS_CONNECTIONS = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var FACE_OVAL_CONNECTIONS = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+function connectionsToIndices2(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
+  lips: connectionsToIndices2(LIPS_CONNECTIONS),
+  leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
+  leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
+  leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
+  rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
+  rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
+  rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
+  faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
+};
+var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
+var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
+var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
+  61,
+  146,
+  91,
+  181,
+  84,
+  17,
+  314,
+  405,
+  321,
+  375,
+  291,
+  185,
+  40,
+  39,
+  37,
+  0,
+  267,
+  269,
+  270,
+  409,
+  78,
+  95,
+  88,
+  178,
+  87,
+  14,
+  317,
+  402,
+  318,
+  324,
+  308,
+  191,
+  80,
+  81,
+  82,
+  13,
+  312,
+  311,
+  310,
+  415,
+  76,
+  77,
+  90,
+  180,
+  85,
+  16,
+  315,
+  404,
+  320,
+  307,
+  306,
+  184,
+  74,
+  73,
+  72,
+  11,
+  302,
+  303,
+  304,
+  408,
+  62,
+  96,
+  89,
+  179,
+  86,
+  15,
+  316,
+  403,
+  319,
+  325,
+  292,
+  183,
+  42,
+  41,
+  38,
+  12,
+  268,
+  271,
+  272,
+  407
+];
+var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
+  33,
+  7,
+  163,
+  144,
+  145,
+  153,
+  154,
+  155,
+  133,
+  246,
+  161,
+  160,
+  159,
+  158,
+  157,
+  173,
+  130,
+  25,
+  110,
+  24,
+  23,
+  22,
+  26,
+  112,
+  243,
+  247,
+  30,
+  29,
+  27,
+  28,
+  56,
+  190,
+  226,
+  31,
+  228,
+  229,
+  230,
+  231,
+  232,
+  233,
+  244,
+  113,
+  225,
+  224,
+  223,
+  222,
+  221,
+  189,
+  35,
+  124,
+  46,
+  53,
+  52,
+  65,
+  143,
+  111,
+  117,
+  118,
+  119,
+  120,
+  121,
+  128,
+  245,
+  156,
+  70,
+  63,
+  105,
+  66,
+  107,
+  55,
+  193
+];
+var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
+  263,
+  249,
+  390,
+  373,
+  374,
+  380,
+  381,
+  382,
+  362,
+  466,
+  388,
+  387,
+  386,
+  385,
+  384,
+  398,
+  359,
+  255,
+  339,
+  254,
+  253,
+  252,
+  256,
+  341,
+  463,
+  467,
+  260,
+  259,
+  257,
+  258,
+  286,
+  414,
+  446,
+  261,
+  448,
+  449,
+  450,
+  451,
+  452,
+  453,
+  464,
+  342,
+  445,
+  444,
+  443,
+  442,
+  441,
+  413,
+  265,
+  353,
+  276,
+  283,
+  282,
+  295,
+  372,
+  340,
+  346,
+  347,
+  348,
+  349,
+  350,
+  357,
+  465,
+  383,
+  300,
+  293,
+  334,
+  296,
+  336,
+  285,
+  417
+];
+
+// src/face/attention.ts
+async function augment(rawCoords, results) {
+  const t2 = {
+    lips: await results.filter((r2) => r2.size === 160)[0].data(),
+    irisL: await results.filter((r2) => r2.size === 10)[0].data(),
+    eyeL: await results.filter((r2) => r2.size === 142)[0].data(),
+    irisR: await results.filter((r2) => r2.size === 10)[1].data(),
+    eyeR: await results.filter((r2) => r2.size === 142)[1].data()
+  };
+  const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
+  for (let i2 = 0; i2 < t2.irisL.length / 2; i2++)
+    rawCoords.push([t2.irisL[2 * i2 + 0], t2.irisL[2 * i2 + 1], irisLDepth]);
+  const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
+  for (let i2 = 0; i2 < t2.irisR.length / 2; i2++)
+    rawCoords.push([t2.irisR[2 * i2 + 0], t2.irisR[2 * i2 + 1], irisRDepth]);
+  for (let i2 = 0; i2 < t2.eyeL.length / 2; i2++)
+    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i2]] = [t2.eyeL[2 * i2 + 0], t2.eyeL[2 * i2 + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i2]][2]];
+  for (let i2 = 0; i2 < t2.eyeR.length / 2; i2++)
+    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i2]] = [t2.eyeR[2 * i2 + 0], t2.eyeR[2 * i2 + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i2]][2]];
+  for (let i2 = 0; i2 < t2.lips.length / 2; i2++)
+    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i2]] = [t2.lips[2 * i2 + 0], t2.lips[2 * i2 + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i2]][2]];
+  return rawCoords;
+}
+
+// src/face/facemesh.ts
+var cache3 = {
+  boxes: [],
+  skipped: Number.MAX_SAFE_INTEGER,
+  timestamp: 0
+};
+var model12 = null;
+var inputSize6 = 0;
+async function predict10(input2, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k;
+  const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
+  const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
+  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
+    cache3.boxes = await getBoxes(input2, config3);
+    cache3.timestamp = now();
+    cache3.skipped = 0;
+  } else {
+    cache3.skipped++;
+  }
+  const faces = [];
+  const newCache = [];
+  let id = 0;
+  for (let i2 = 0; i2 < cache3.boxes.length; i2++) {
+    const box = cache3.boxes[i2];
+    let angle = 0;
+    let rotationMatrix;
+    const face4 = {
+      id: id++,
+      mesh: [],
+      meshRaw: [],
+      box: [0, 0, 0, 0],
+      boxRaw: [0, 0, 0, 0],
+      score: 0,
+      boxScore: 0,
+      faceScore: 0,
+      annotations: {}
+    };
+    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input2, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size());
+    if ((_e = config3 == null ? void 0 : config3.filter) == null ? void 0 : _e.equalization) {
+      const equilized = await histogramEqualization(face4.tensor);
+      dispose(face4.tensor);
+      face4.tensor = equilized;
+    }
+    face4.boxScore = Math.round(100 * box.confidence) / 100;
+    if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
+      face4.box = clampBox(box, input2);
+      face4.boxRaw = getRawBox(box, input2);
+      face4.score = face4.boxScore;
+      face4.mesh = box.landmarks.map((pt) => [
+        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+      ]);
+      face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input2.shape[2] || 0), pt[1] / (input2.shape[1] || 0), (pt[2] || 0) / inputSize6]);
+      for (const key of Object.keys(blazeFaceLandmarks)) {
+        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+      }
+    } else if (!model12) {
+      if (config3.debug)
+        log("face mesh detection requested, but model is not loaded");
+    } else {
+      const results = model12.execute(face4.tensor);
+      const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
+      const faceConfidence = await confidenceT.data();
+      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
+      if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
+        box.confidence = face4.faceScore;
+        if ((_h = config3.face.mesh) == null ? void 0 : _h.keepInvalid) {
+          face4.box = clampBox(box, input2);
+          face4.boxRaw = getRawBox(box, input2);
+          face4.score = face4.boxScore;
+          face4.mesh = box.landmarks.map((pt) => [
+            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+          ]);
+          face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input2.shape[2] || 1), pt[1] / (input2.shape[1] || 1), (pt[2] || 0) / inputSize6]);
+          for (const key of Object.keys(blazeFaceLandmarks)) {
+            face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+          }
+        }
+      } else {
+        const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404);
+        const coordsReshaped = reshape(meshT, [-1, 3]);
+        let rawCoords = await coordsReshaped.array();
+        dispose(coordsReshaped);
+        if ((_i = config3.face.attention) == null ? void 0 : _i.enabled) {
+          rawCoords = await augment(rawCoords, results);
+        } else if ((_j = config3.face.iris) == null ? void 0 : _j.enabled) {
+          rawCoords = await augmentIris(rawCoords, face4.tensor, config3, inputSize6);
+        }
+        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
+        face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input2.shape[2] || 0), pt[1] / (input2.shape[1] || 0), (pt[2] || 0) / inputSize6]);
+        for (const key of Object.keys(meshAnnotations))
+          face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
+        face4.score = face4.faceScore;
+        const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
+        face4.box = clampBox(calculatedBox, input2);
+        face4.boxRaw = getRawBox(calculatedBox, input2);
+        newCache.push(calculatedBox);
+      }
+      dispose(results);
+    }
+    if (face4.score > (((_k = config3.face.detector) == null ? void 0 : _k.minConfidence) || 1))
+      faces.push(face4);
+    else
+      dispose(face4.tensor);
+  }
+  cache3.boxes = newCache;
+  return faces;
+}
+async function load11(config3) {
+  var _a, _b, _c, _d, _e, _f;
+  if (env2.initial)
+    model12 = null;
+  if (((_b = (_a = config3 == null ? void 0 : config3.face) == null ? void 0 : _a.attention) == null ? void 0 : _b.enabled) && (model12 == null ? void 0 : model12.signature)) {
+    if (Object.keys(((_c = model12 == null ? void 0 : model12.signature) == null ? void 0 : _c.outputs) || {}).length < 6)
+      model12 = null;
+  }
+  if (!model12) {
+    if ((_d = config3.face.attention) == null ? void 0 : _d.enabled)
+      model12 = await loadModel((_e = config3.face.attention) == null ? void 0 : _e.modelPath);
+    else
+      model12 = await loadModel((_f = config3.face.mesh) == null ? void 0 : _f.modelPath);
+  } else if (config3.debug) {
+    log("cached model:", model12["modelUrl"]);
+  }
+  inputSize6 = model12.inputs[0].shape ? model12.inputs[0].shape[2] : 0;
+  return model12;
+}
+var triangulation = TRI468;
+var uvmap = UV468;
+
+// src/face/faceres.ts
+var model13;
+var last8 = [];
+var lastTime10 = 0;
+var lastCount7 = 0;
+var skipped10 = Number.MAX_SAFE_INTEGER;
+async function load12(config3) {
+  var _a;
+  if (env2.initial)
+    model13 = null;
+  if (!model13)
+    model13 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model13["modelUrl"]);
+  return model13;
+}
+function enhance(input2) {
+  const tensor2 = input2.image || input2.tensor || input2;
+  if (!(model13 == null ? void 0 : model13.inputs[0].shape))
+    return tensor2;
+  const crop = image.resizeBilinear(tensor2, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+  const norm2 = mul(crop, constants.tf255);
+  dispose(crop);
+  return norm2;
+}
+async function predict11(image2, config3, idx, count3) {
+  var _a, _b, _c, _d;
+  if (!model13)
+    return { age: 0, gender: "unknown", genderScore: 0, descriptor: [] };
+  const skipFrame = skipped10 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count3 && ((_c = last8[idx]) == null ? void 0 : _c.age) && ((_d = last8[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped10++;
+    return last8[idx];
+  }
+  skipped10 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    const obj = {
+      age: 0,
+      gender: "unknown",
+      genderScore: 0,
+      descriptor: []
+    };
+    if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
+      const enhanced = enhance(image2);
+      const resT = model13 == null ? void 0 : model13.execute(enhanced);
+      lastTime10 = now();
+      dispose(enhanced);
+      const genderT = await resT.find((t2) => t2.shape[1] === 1);
+      const gender2 = await genderT.data();
+      const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
+      if (confidence > (((_b2 = config3.face.description) == null ? void 0 : _b2.minConfidence) || 0)) {
+        obj.gender = gender2[0] <= 0.5 ? "female" : "male";
+        obj.genderScore = Math.min(0.99, confidence);
+      }
+      const argmax2 = argMax(resT.find((t2) => t2.shape[1] === 100), 1);
+      const age2 = (await argmax2.data())[0];
+      dispose(argmax2);
+      const ageT = resT.find((t2) => t2.shape[1] === 100);
+      const all6 = await ageT.data();
+      obj.age = Math.round(all6[age2 - 1] > all6[age2 + 1] ? 10 * age2 - 100 * all6[age2 - 1] : 10 * age2 + 100 * all6[age2 + 1]) / 10;
+      const desc = resT.find((t2) => t2.shape[1] === 1024);
+      const descriptor = desc ? await desc.data() : [];
+      obj.descriptor = Array.from(descriptor);
+      resT.forEach((t2) => dispose(t2));
+    }
+    last8[idx] = obj;
+    lastCount7 = count3;
+    resolve(obj);
+  });
+}
+
+// src/hand/handposeutil.ts
+function getBoxSize2(box) {
+  return [
+    Math.abs(box.endPoint[0] - box.startPoint[0]),
+    Math.abs(box.endPoint[1] - box.startPoint[1])
+  ];
+}
+function getBoxCenter2(box) {
+  return [
+    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+  ];
+}
+function cutBoxFromImageAndResize(box, image2, cropSize) {
+  const h = image2.shape[1];
+  const w = image2.shape[2];
+  const boxes = [[
+    box.startPoint[1] / h,
+    box.startPoint[0] / w,
+    box.endPoint[1] / h,
+    box.endPoint[0] / w
+  ]];
+  return image.cropAndResize(image2, boxes, [0], cropSize);
+}
+function scaleBoxCoordinates2(box, factor) {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  const palmLandmarks = box.palmLandmarks.map((coord) => {
+    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
+    return scaledCoord;
+  });
+  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
+}
+function enlargeBox2(box, factor = 1.5) {
+  const center = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function squarifyBox2(box) {
+  const centers = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const maxEdge = Math.max(...size2);
+  const halfSize = maxEdge / 2;
+  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
+  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function normalizeRadians2(angle) {
+  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+}
+function computeRotation2(point1, point2) {
+  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
+  return normalizeRadians2(radians);
+}
+var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+function dot5(v1, v2) {
+  let product = 0;
+  for (let i2 = 0; i2 < v1.length; i2++) {
+    product += v1[i2] * v2[i2];
+  }
+  return product;
+}
+function getColumnFrom2DArr2(arr, columnIndex) {
+  const column = [];
+  for (let i2 = 0; i2 < arr.length; i2++) {
+    column.push(arr[i2][columnIndex]);
+  }
+  return column;
+}
+function multiplyTransformMatrices2(mat1, mat2) {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++) {
+      product[row].push(dot5(mat1[row], getColumnFrom2DArr2(mat2, col)));
+    }
+  }
+  return product;
+}
+function buildRotationMatrix2(rotation, center) {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
+  return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
+}
+function invertTransformMatrix2(matrix) {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [
+    -dot5(rotationComponent[0], translationComponent),
+    -dot5(rotationComponent[1], translationComponent)
+  ];
+  return [
+    rotationComponent[0].concat(invertedTranslation[0]),
+    rotationComponent[1].concat(invertedTranslation[1]),
+    [0, 0, 1]
+  ];
+}
+function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
+  return [
+    dot5(homogeneousCoordinate, rotationMatrix[0]),
+    dot5(homogeneousCoordinate, rotationMatrix[1])
+  ];
+}
+
+// src/hand/handposeanchors.ts
+var anchors2 = [
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
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+  { x: 0.9375, y: 0.4375 },
+  { x: 0.9375, y: 0.4375 },
+  { x: 0.9375, y: 0.4375 },
+  { x: 0.0625, y: 0.5625 },
+  { x: 0.0625, y: 0.5625 },
+  { x: 0.0625, y: 0.5625 },
+  { x: 0.0625, y: 0.5625 },
+  { x: 0.0625, y: 0.5625 },
+  { x: 0.0625, y: 0.5625 },
+  { x: 0.1875, y: 0.5625 },
+  { x: 0.1875, y: 0.5625 },
+  { x: 0.1875, y: 0.5625 },
+  { x: 0.1875, y: 0.5625 },
+  { x: 0.1875, y: 0.5625 },
+  { x: 0.1875, y: 0.5625 },
+  { x: 0.3125, y: 0.5625 },
+  { x: 0.3125, y: 0.5625 },
+  { x: 0.3125, y: 0.5625 },
+  { x: 0.3125, y: 0.5625 },
+  { x: 0.3125, y: 0.5625 },
+  { x: 0.3125, y: 0.5625 },
+  { x: 0.4375, y: 0.5625 },
+  { x: 0.4375, y: 0.5625 },
+  { x: 0.4375, y: 0.5625 },
+  { x: 0.4375, y: 0.5625 },
+  { x: 0.4375, y: 0.5625 },
+  { x: 0.4375, y: 0.5625 },
+  { x: 0.5625, y: 0.5625 },
+  { x: 0.5625, y: 0.5625 },
+  { x: 0.5625, y: 0.5625 },
+  { x: 0.5625, y: 0.5625 },
+  { x: 0.5625, y: 0.5625 },
+  { x: 0.5625, y: 0.5625 },
+  { x: 0.6875, y: 0.5625 },
+  { x: 0.6875, y: 0.5625 },
+  { x: 0.6875, y: 0.5625 },
+  { x: 0.6875, y: 0.5625 },
+  { x: 0.6875, y: 0.5625 },
+  { x: 0.6875, y: 0.5625 },
+  { x: 0.8125, y: 0.5625 },
+  { x: 0.8125, y: 0.5625 },
+  { x: 0.8125, y: 0.5625 },
+  { x: 0.8125, y: 0.5625 },
+  { x: 0.8125, y: 0.5625 },
+  { x: 0.8125, y: 0.5625 },
+  { x: 0.9375, y: 0.5625 },
+  { x: 0.9375, y: 0.5625 },
+  { x: 0.9375, y: 0.5625 },
+  { x: 0.9375, y: 0.5625 },
+  { x: 0.9375, y: 0.5625 },
+  { x: 0.9375, y: 0.5625 },
+  { x: 0.0625, y: 0.6875 },
+  { x: 0.0625, y: 0.6875 },
+  { x: 0.0625, y: 0.6875 },
+  { x: 0.0625, y: 0.6875 },
+  { x: 0.0625, y: 0.6875 },
+  { x: 0.0625, y: 0.6875 },
+  { x: 0.1875, y: 0.6875 },
+  { x: 0.1875, y: 0.6875 },
+  { x: 0.1875, y: 0.6875 },
+  { x: 0.1875, y: 0.6875 },
+  { x: 0.1875, y: 0.6875 },
+  { x: 0.1875, y: 0.6875 },
+  { x: 0.3125, y: 0.6875 },
+  { x: 0.3125, y: 0.6875 },
+  { x: 0.3125, y: 0.6875 },
+  { x: 0.3125, y: 0.6875 },
+  { x: 0.3125, y: 0.6875 },
+  { x: 0.3125, y: 0.6875 },
+  { x: 0.4375, y: 0.6875 },
+  { x: 0.4375, y: 0.6875 },
+  { x: 0.4375, y: 0.6875 },
+  { x: 0.4375, y: 0.6875 },
+  { x: 0.4375, y: 0.6875 },
+  { x: 0.4375, y: 0.6875 },
+  { x: 0.5625, y: 0.6875 },
+  { x: 0.5625, y: 0.6875 },
+  { x: 0.5625, y: 0.6875 },
+  { x: 0.5625, y: 0.6875 },
+  { x: 0.5625, y: 0.6875 },
+  { x: 0.5625, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 }
+];
+
+// src/hand/handposedetector.ts
+var HandDetector = class {
+  constructor(model19) {
+    __publicField(this, "model");
+    __publicField(this, "anchors");
+    __publicField(this, "anchorsTensor");
+    __publicField(this, "inputSize");
+    __publicField(this, "inputSizeTensor");
+    __publicField(this, "doubleInputSizeTensor");
+    this.model = model19;
+    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
+    this.anchorsTensor = tensor2d(this.anchors);
+    this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
+    this.inputSizeTensor = tensor1d([this.inputSize, this.inputSize]);
+    this.doubleInputSizeTensor = tensor1d([this.inputSize * 2, this.inputSize * 2]);
+  }
+  normalizeBoxes(boxes) {
+    const t2 = {};
+    t2.boxOffsets = slice(boxes, [0, 0], [-1, 2]);
+    t2.boxSizes = slice(boxes, [0, 2], [-1, 2]);
+    t2.div = div(t2.boxOffsets, this.inputSizeTensor);
+    t2.boxCenterPoints = add2(t2.div, this.anchorsTensor);
+    t2.halfBoxSizes = div(t2.boxSizes, this.doubleInputSizeTensor);
+    t2.sub = sub(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.startPoints = mul(t2.sub, this.inputSizeTensor);
+    t2.add = add2(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.endPoints = mul(t2.add, this.inputSizeTensor);
+    const res = concat2d([t2.startPoints, t2.endPoints], 1);
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    return res;
+  }
+  normalizeLandmarks(rawPalmLandmarks, index2) {
+    const t2 = {};
+    t2.reshape = reshape(rawPalmLandmarks, [-1, 7, 2]);
+    t2.div = div(t2.reshape, this.inputSizeTensor);
+    t2.landmarks = add2(t2.div, this.anchors[index2]);
+    const res = mul(t2.landmarks, this.inputSizeTensor);
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    return res;
+  }
+  async predict(input2, config3) {
+    const t2 = {};
+    t2.resize = image.resizeBilinear(input2, [this.inputSize, this.inputSize]);
+    t2.div = div(t2.resize, constants.tf127);
+    t2.image = sub(t2.div, constants.tf1);
+    t2.batched = this.model.execute(t2.image);
+    t2.predictions = squeeze(t2.batched);
+    t2.slice = slice(t2.predictions, [0, 0], [-1, 1]);
+    t2.sigmoid = sigmoid(t2.slice);
+    t2.scores = squeeze(t2.sigmoid);
+    const scores = await t2.scores.data();
+    t2.boxes = slice(t2.predictions, [0, 1], [-1, 4]);
+    t2.norm = this.normalizeBoxes(t2.boxes);
+    t2.nms = await image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
+    const nms = await t2.nms.array();
+    const hands = [];
+    for (const index2 of nms) {
+      const p2 = {};
+      p2.box = slice(t2.norm, [index2, 0], [1, -1]);
+      p2.slice = slice(t2.predictions, [index2, 5], [1, 14]);
+      p2.norm = this.normalizeLandmarks(p2.slice, index2);
+      p2.palmLandmarks = reshape(p2.norm, [-1, 2]);
+      const box = await p2.box.data();
+      const startPoint = box.slice(0, 2);
+      const endPoint = box.slice(2, 4);
+      const palmLandmarks = await p2.palmLandmarks.array();
+      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
+      const scaled = scaleBoxCoordinates2(hand3, [input2.shape[2] / this.inputSize, input2.shape[1] / this.inputSize]);
+      hands.push(scaled);
+      Object.keys(p2).forEach((tensor2) => dispose(p2[tensor2]));
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    return hands;
+  }
+};
+
+// src/hand/handposepipeline.ts
+var palmBoxEnlargeFactor = 5;
+var handBoxEnlargeFactor = 1.65;
+var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
+var palmLandmarksPalmBase = 0;
+var palmLandmarksMiddleFingerBase = 2;
+var lastTime11 = 0;
+var HandPipeline = class {
+  constructor(handDetector, handPoseModel2) {
+    __publicField(this, "handDetector");
+    __publicField(this, "handPoseModel");
+    __publicField(this, "inputSize");
+    __publicField(this, "storedBoxes");
+    __publicField(this, "skipped");
+    __publicField(this, "detectedHands");
+    this.handDetector = handDetector;
+    this.handPoseModel = handPoseModel2;
+    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;
+    this.storedBoxes = [];
+    this.skipped = Number.MAX_SAFE_INTEGER;
+    this.detectedHands = 0;
+  }
+  calculateLandmarksBoundingBox(landmarks) {
+    const xs = landmarks.map((d) => d[0]);
+    const ys = landmarks.map((d) => d[1]);
+    const startPoint = [Math.min(...xs), Math.min(...ys)];
+    const endPoint = [Math.max(...xs), Math.max(...ys)];
+    return { startPoint, endPoint };
+  }
+  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
+    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
+    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
+    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
+  }
+  getBoxForHandLandmarks(landmarks) {
+    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
+    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
+    boxAroundHand.palmLandmarks = [];
+    for (let i2 = 0; i2 < palmLandmarkIds.length; i2++) {
+      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i2]].slice(0, 2));
+    }
+    return boxAroundHand;
+  }
+  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
+    const boxSize = getBoxSize2(box2);
+    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
+    const coordsScaled = rawCoords.map((coord) => [
+      scaleFactor[0] * (coord[0] - this.inputSize / 2),
+      scaleFactor[1] * (coord[1] - this.inputSize / 2),
+      scaleFactor[2] * coord[2]
+    ]);
+    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
+    const coordsRotated = coordsScaled.map((coord) => {
+      const rotated = rotatePoint2(coord, coordsRotationMatrix);
+      return [...rotated, coord[2]];
+    });
+    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
+    const boxCenter = [...getBoxCenter2(box2), 1];
+    const originalBoxCenter = [
+      dot5(boxCenter, inverseRotationMatrix[0]),
+      dot5(boxCenter, inverseRotationMatrix[1])
+    ];
+    return coordsRotated.map((coord) => [
+      Math.trunc(coord[0] + originalBoxCenter[0]),
+      Math.trunc(coord[1] + originalBoxCenter[1]),
+      Math.trunc(coord[2])
+    ]);
+  }
+  async estimateHands(image2, config3) {
+    let useFreshBox = false;
+    let boxes;
+    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime11;
+    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && skipTime && skipFrame) {
+      boxes = await this.handDetector.predict(image2, config3);
+      this.skipped = 0;
+    }
+    if (config3.skipAllowed)
+      this.skipped++;
+    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
+      this.detectedHands = 0;
+      this.storedBoxes = [...boxes];
+      if (this.storedBoxes.length > 0)
+        useFreshBox = true;
+    }
+    const hands = [];
+    for (let i2 = 0; i2 < this.storedBoxes.length; i2++) {
+      const currentBox = this.storedBoxes[i2];
+      if (!currentBox)
+        continue;
+      if (config3.hand.landmarks) {
+        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
+        const palmCenter = getBoxCenter2(currentBox);
+        const palmCenterNormalized = [palmCenter[0] / image2.shape[2], palmCenter[1] / image2.shape[1]];
+        const rotatedImage = config3.hand.rotation && env2.kernels.includes("rotatewithoffset") ? image.rotateWithOffset(image2, angle, 0, palmCenterNormalized) : image2.clone();
+        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
+        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
+        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
+        const handImage = div(croppedInput, constants.tf255);
+        dispose(croppedInput);
+        dispose(rotatedImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
+        lastTime11 = now();
+        dispose(handImage);
+        const confidence = (await confidenceT.data())[0];
+        dispose(confidenceT);
+        if (confidence >= config3.hand.minConfidence / 4) {
+          const keypointsReshaped = reshape(keypoints, [-1, 3]);
+          const rawCoords = await keypointsReshaped.array();
+          dispose(keypoints);
+          dispose(keypointsReshaped);
+          const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
+          const nextBoundingBox = this.getBoxForHandLandmarks(coords3);
+          this.storedBoxes[i2] = { ...nextBoundingBox, confidence };
+          const result = {
+            landmarks: coords3,
+            confidence,
+            boxConfidence: currentBox.confidence,
+            fingerConfidence: confidence,
+            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
+          };
+          hands.push(result);
+        } else {
+          this.storedBoxes[i2] = null;
+        }
+        dispose(keypoints);
+      } else {
+        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
+        const result = {
+          confidence: currentBox.confidence,
+          boxConfidence: currentBox.confidence,
+          fingerConfidence: 0,
+          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
+          landmarks: []
+        };
+        hands.push(result);
+      }
+    }
+    this.storedBoxes = this.storedBoxes.filter((a6) => a6 !== null);
+    this.detectedHands = hands.length;
+    if (hands.length > config3.hand.maxDetected)
+      hands.length = config3.hand.maxDetected;
+    return hands;
+  }
+};
+
+// src/hand/fingerdef.ts
+var Finger = {
+  thumb: 0,
+  index: 1,
+  middle: 2,
+  ring: 3,
+  pinky: 4,
+  all: [0, 1, 2, 3, 4],
+  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
+  pointsMapping: {
+    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
+    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
+    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
+    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
+    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
+  },
+  getName: (value) => Finger.nameMapping[value],
+  getPoints: (value) => Finger.pointsMapping[value]
+};
+var FingerCurl = {
+  none: 0,
+  half: 1,
+  full: 2,
+  nameMapping: { 0: "none", 1: "half", 2: "full" },
+  getName: (value) => FingerCurl.nameMapping[value]
+};
+var FingerDirection = {
+  verticalUp: 0,
+  verticalDown: 1,
+  horizontalLeft: 2,
+  horizontalRight: 3,
+  diagonalUpRight: 4,
+  diagonalUpLeft: 5,
+  diagonalDownRight: 6,
+  diagonalDownLeft: 7,
+  nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
+  getName: (value) => FingerDirection.nameMapping[value]
+};
+var FingerGesture = class {
+  constructor(name) {
+    __publicField(this, "name");
+    __publicField(this, "curls");
+    __publicField(this, "directions");
+    __publicField(this, "weights");
+    __publicField(this, "weightsRelative");
+    this.name = name;
+    this.curls = {};
+    this.directions = {};
+    this.weights = [1, 1, 1, 1, 1];
+    this.weightsRelative = [1, 1, 1, 1, 1];
+  }
+  curl(finger, curl, confidence) {
+    if (typeof this.curls[finger] === "undefined")
+      this.curls[finger] = [];
+    this.curls[finger].push([curl, confidence]);
+  }
+  direction(finger, position, confidence) {
+    if (!this.directions[finger])
+      this.directions[finger] = [];
+    this.directions[finger].push([position, confidence]);
+  }
+  weight(finger, weight) {
+    this.weights[finger] = weight;
+    const total = this.weights.reduce((a6, b) => a6 + b, 0);
+    this.weightsRelative = this.weights.map((el) => el * 5 / total);
+  }
+  matchAgainst(detectedCurls, detectedDirections) {
+    let confidence = 0;
+    for (const fingerIdx in detectedCurls) {
+      const detectedCurl = detectedCurls[fingerIdx];
+      const expectedCurls = this.curls[fingerIdx];
+      if (typeof expectedCurls === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedCurl, score] of expectedCurls) {
+        if (detectedCurl === expectedCurl) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    for (const fingerIdx in detectedDirections) {
+      const detectedDirection = detectedDirections[fingerIdx];
+      const expectedDirections = this.directions[fingerIdx];
+      if (typeof expectedDirections === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedDirection, score] of expectedDirections) {
+        if (detectedDirection === expectedDirection) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    return confidence / 10;
+  }
+};
+
+// src/hand/fingergesture.ts
+var { thumb, index, middle, ring, pinky } = Finger;
+var { none, half, full } = FingerCurl;
+var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
+var ThumbsUp = new FingerGesture("thumbs up");
+ThumbsUp.curl(thumb, none, 1);
+ThumbsUp.direction(thumb, verticalUp, 1);
+ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
+ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
+for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
+  ThumbsUp.curl(finger, full, 1);
+  ThumbsUp.direction(finger, horizontalLeft, 1);
+  ThumbsUp.direction(finger, horizontalRight, 1);
+}
+var Victory = new FingerGesture("victory");
+Victory.curl(thumb, half, 0.5);
+Victory.curl(thumb, none, 0.5);
+Victory.direction(thumb, verticalUp, 1);
+Victory.direction(thumb, diagonalUpLeft, 1);
+Victory.curl(index, none, 1);
+Victory.direction(index, verticalUp, 0.75);
+Victory.direction(index, diagonalUpLeft, 1);
+Victory.curl(middle, none, 1);
+Victory.direction(middle, verticalUp, 1);
+Victory.direction(middle, diagonalUpLeft, 0.75);
+Victory.curl(ring, full, 1);
+Victory.direction(ring, verticalUp, 0.2);
+Victory.direction(ring, diagonalUpLeft, 1);
+Victory.direction(ring, horizontalLeft, 0.2);
+Victory.curl(pinky, full, 1);
+Victory.direction(pinky, verticalUp, 0.2);
+Victory.direction(pinky, diagonalUpLeft, 1);
+Victory.direction(pinky, horizontalLeft, 0.2);
+Victory.weight(index, 2);
+Victory.weight(middle, 2);
+var Point = new FingerGesture("point");
+Point.curl(thumb, full, 1);
+Point.curl(index, none, 0.5);
+Point.curl(middle, full, 0.5);
+Point.curl(ring, full, 0.5);
+Point.curl(pinky, full, 0.5);
+Point.weight(index, 2);
+Point.weight(middle, 2);
+var MiddleFinger = new FingerGesture("middle finger");
+MiddleFinger.curl(thumb, none, 1);
+MiddleFinger.curl(index, full, 0.5);
+MiddleFinger.curl(middle, full, 0.5);
+MiddleFinger.curl(ring, full, 0.5);
+MiddleFinger.curl(pinky, full, 0.5);
+MiddleFinger.weight(index, 2);
+MiddleFinger.weight(middle, 2);
+var OpenPalm = new FingerGesture("open palm");
+OpenPalm.curl(thumb, none, 0.75);
+OpenPalm.curl(index, none, 0.75);
+OpenPalm.curl(middle, none, 0.75);
+OpenPalm.curl(ring, none, 0.75);
+OpenPalm.curl(pinky, none, 0.75);
+var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
+
+// src/hand/fingerpose.ts
+var minConfidence = 0.7;
+var options2 = {
+  HALF_CURL_START_LIMIT: 60,
+  NO_CURL_START_LIMIT: 130,
+  DISTANCE_VOTE_POWER: 1.1,
+  SINGLE_ANGLE_VOTE_POWER: 0.9,
+  TOTAL_ANGLE_VOTE_POWER: 1.6
+};
+function calculateSlope(point1x, point1y, point2x, point2y) {
+  const value = (point1y - point2y) / (point1x - point2x);
+  let slope = Math.atan(value) * 180 / Math.PI;
+  if (slope <= 0)
+    slope = -slope;
+  else if (slope > 0)
+    slope = 180 - slope;
+  return slope;
+}
+function getSlopes(point1, point2) {
+  if (!point1 || !point2)
+    return [0, 0];
+  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
+  if (point1.length === 2)
+    return slopeXY;
+  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
+  return [slopeXY, slopeYZ];
+}
+function angleOrientationAt(angle, weightageAt = 1) {
+  let isVertical = 0;
+  let isDiagonal = 0;
+  let isHorizontal = 0;
+  if (angle >= 75 && angle <= 105)
+    isVertical = 1 * weightageAt;
+  else if (angle >= 25 && angle <= 155)
+    isDiagonal = 1 * weightageAt;
+  else
+    isHorizontal = 1 * weightageAt;
+  return [isVertical, isDiagonal, isHorizontal];
+}
+function estimateFingerCurl(startPoint, midPoint, endPoint) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const start_mid_z_dist = startPoint[2] - midPoint[2];
+  const start_end_z_dist = startPoint[2] - endPoint[2];
+  const mid_end_z_dist = midPoint[2] - endPoint[2];
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
+  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
+  if (cos_in > 1)
+    cos_in = 1;
+  else if (cos_in < -1)
+    cos_in = -1;
+  let angleOfCurve = Math.acos(cos_in);
+  angleOfCurve = 57.2958 * angleOfCurve % 180;
+  let fingerCurl;
+  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.none;
+  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.half;
+  else
+    fingerCurl = FingerCurl.full;
+  return fingerCurl;
+}
+function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  if (max_dist_x === Math.abs(start_end_x_dist)) {
+    if (start_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
+    if (start_mid_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else {
+    if (mid_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  }
+  return estimatedDirection;
+}
+function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
+  let estimatedDirection;
+  if (max_dist_y === Math.abs(start_end_y_dist)) {
+    if (start_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
+    if (start_mid_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else {
+    if (mid_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  }
+  return estimatedDirection;
+}
+function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  if (reqd_vertical_direction === FingerDirection.verticalUp) {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalUpLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalUpRight;
+  } else {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalDownLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalDownRight;
+  }
+  return estimatedDirection;
+}
+function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
+  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
+  let voteVertical = 0;
+  let voteDiagonal = 0;
+  let voteHorizontal = 0;
+  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
+  if (start_end_x_y_dist_ratio > 1.5)
+    voteVertical += options2.DISTANCE_VOTE_POWER;
+  else if (start_end_x_y_dist_ratio > 0.66)
+    voteDiagonal += options2.DISTANCE_VOTE_POWER;
+  else
+    voteHorizontal += options2.DISTANCE_VOTE_POWER;
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
+  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
+  let calc_start_point_x = startPoint[0];
+  let calc_start_point_y = startPoint[1];
+  let calc_end_point_x = endPoint[0];
+  let calc_end_point_y = endPoint[1];
+  if (max_dist === start_mid_dist) {
+    calc_end_point_x = endPoint[0];
+    calc_end_point_y = endPoint[1];
+  } else if (max_dist === mid_end_dist) {
+    calc_start_point_x = midPoint[0];
+    calc_start_point_y = midPoint[1];
+  }
+  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
+  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
+  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
+  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
+  voteVertical += votes[0];
+  voteDiagonal += votes[1];
+  voteHorizontal += votes[2];
+  for (const fingerSlope of fingerSlopes) {
+    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
+    voteVertical += fingerVotes[0];
+    voteDiagonal += fingerVotes[1];
+    voteHorizontal += fingerVotes[2];
+  }
+  let estimatedDirection;
+  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  } else {
+    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  }
+  return estimatedDirection;
+}
+function estimate(landmarks) {
+  const slopesXY = [];
+  const slopesYZ = [];
+  const fingerCurls = [];
+  const fingerDirections = [];
+  if (!landmarks)
+    return { curls: fingerCurls, directions: fingerDirections };
+  for (const finger of Finger.all) {
+    const points = Finger.getPoints(finger);
+    const slopeAtXY = [];
+    const slopeAtYZ = [];
+    for (const point2 of points) {
+      const point1 = landmarks[point2[0]];
+      const point22 = landmarks[point2[1]];
+      const slopes = getSlopes(point1, point22);
+      const slopeXY = slopes[0];
+      const slopeYZ = slopes[1];
+      slopeAtXY.push(slopeXY);
+      slopeAtYZ.push(slopeYZ);
+    }
+    slopesXY.push(slopeAtXY);
+    slopesYZ.push(slopeAtYZ);
+  }
+  for (const finger of Finger.all) {
+    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
+    const fingerPointsAt = Finger.getPoints(finger);
+    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
+    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
+    const endPoint = landmarks[fingerPointsAt[3][1]];
+    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
+    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
+    fingerCurls[finger] = fingerCurled;
+    fingerDirections[finger] = fingerPosition;
+  }
+  return { curls: fingerCurls, directions: fingerDirections };
+}
+function analyze(keypoints) {
+  if (!keypoints || keypoints.length === 0)
+    return null;
+  const estimatorRes = estimate(keypoints);
+  const landmarks = {};
+  for (const fingerIdx of Finger.all) {
+    landmarks[Finger.getName(fingerIdx)] = {
+      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
+      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
+    };
+  }
+  return landmarks;
+}
+function match(keypoints) {
+  const poses = [];
+  if (!keypoints || keypoints.length === 0)
+    return poses;
+  const estimatorRes = estimate(keypoints);
+  for (const gesture2 of fingergesture_default) {
+    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
+    if (confidence >= minConfidence)
+      poses.push({ name: gesture2.name, confidence });
+  }
+  return poses;
+}
+
+// src/hand/handpose.ts
+var meshAnnotations2 = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  palm: [0]
+};
+var handDetectorModel;
+var handPoseModel;
+var handPipeline;
+async function predict12(input2, config3) {
+  const predictions = await handPipeline.estimateHands(input2, config3);
+  if (!predictions)
+    return [];
+  const hands = [];
+  for (let i2 = 0; i2 < predictions.length; i2++) {
+    const annotations2 = {};
+    if (predictions[i2].landmarks) {
+      for (const key of Object.keys(meshAnnotations2)) {
+        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i2].landmarks[index2]);
+      }
+    }
+    const keypoints = predictions[i2].landmarks;
+    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
+    let boxRaw = [0, 0, 0, 0];
+    if (keypoints && keypoints.length > 0) {
+      for (const pt of keypoints) {
+        if (pt[0] < box[0])
+          box[0] = pt[0];
+        if (pt[1] < box[1])
+          box[1] = pt[1];
+        if (pt[0] > box[2])
+          box[2] = pt[0];
+        if (pt[1] > box[3])
+          box[3] = pt[1];
+      }
+      box[2] -= box[0];
+      box[3] -= box[1];
+      boxRaw = [box[0] / (input2.shape[2] || 0), box[1] / (input2.shape[1] || 0), box[2] / (input2.shape[2] || 0), box[3] / (input2.shape[1] || 0)];
+    } else {
+      box = predictions[i2].box ? [
+        Math.trunc(Math.max(0, predictions[i2].box.topLeft[0])),
+        Math.trunc(Math.max(0, predictions[i2].box.topLeft[1])),
+        Math.trunc(Math.min(input2.shape[2] || 0, predictions[i2].box.bottomRight[0]) - Math.max(0, predictions[i2].box.topLeft[0])),
+        Math.trunc(Math.min(input2.shape[1] || 0, predictions[i2].box.bottomRight[1]) - Math.max(0, predictions[i2].box.topLeft[1]))
+      ] : [0, 0, 0, 0];
+      boxRaw = [
+        predictions[i2].box.topLeft[0] / (input2.shape[2] || 0),
+        predictions[i2].box.topLeft[1] / (input2.shape[1] || 0),
+        (predictions[i2].box.bottomRight[0] - predictions[i2].box.topLeft[0]) / (input2.shape[2] || 0),
+        (predictions[i2].box.bottomRight[1] - predictions[i2].box.topLeft[1]) / (input2.shape[1] || 0)
+      ];
+    }
+    const landmarks = analyze(keypoints);
+    hands.push({
+      id: i2,
+      score: Math.round(100 * predictions[i2].confidence) / 100,
+      boxScore: Math.round(100 * predictions[i2].boxConfidence) / 100,
+      fingerScore: Math.round(100 * predictions[i2].fingerConfidence) / 100,
+      label: "hand",
+      box,
+      boxRaw,
+      keypoints,
+      annotations: annotations2,
+      landmarks
+    });
+  }
+  return hands;
+}
+async function load13(config3) {
+  var _a, _b;
+  if (env2.initial) {
+    handDetectorModel = null;
+    handPoseModel = null;
+  }
+  if (!handDetectorModel || !handPoseModel) {
+    [handDetectorModel, handPoseModel] = await Promise.all([
+      config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null,
+      config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
+    ]);
+  } else {
+    if (config3.debug)
+      log("cached model:", handDetectorModel["modelUrl"]);
+    if (config3.debug)
+      log("cached model:", handPoseModel["modelUrl"]);
+  }
+  const handDetector = new HandDetector(handDetectorModel);
+  handPipeline = new HandPipeline(handDetector, handPoseModel);
+  return [handDetectorModel, handPoseModel];
+}
+
+// src/hand/handtrack.ts
+var models2 = [null, null];
+var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
+var inputSize7 = [[0, 0], [0, 0]];
+var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
+var faceIndex = 4;
+var boxExpandFact = 1.6;
+var maxDetectorResolution = 512;
+var detectorExpandFact = 1.4;
+var skipped11 = Number.MAX_SAFE_INTEGER;
+var lastTime12 = 0;
+var outputSize = [0, 0];
+var cache4 = {
+  boxes: [],
+  hands: []
+};
+var fingerMap = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  base: [0],
+  palm: [0, 17, 13, 9, 5, 1, 0]
+};
+async function loadDetect2(config3) {
+  var _a;
+  if (env2.initial)
+    models2[0] = null;
+  if (!models2[0]) {
+    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
+    models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
+    const inputs = Object.values(models2[0].modelSignature["inputs"]);
+    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models2[0]["modelUrl"]);
+  return models2[0];
+}
+async function loadSkeleton(config3) {
+  var _a;
+  if (env2.initial)
+    models2[1] = null;
+  if (!models2[1]) {
+    models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
+    const inputs = Object.values(models2[1].modelSignature["inputs"]);
+    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models2[1]["modelUrl"]);
+  return models2[1];
+}
+async function detectHands(input2, config3) {
+  const hands = [];
+  if (!input2 || !models2[0])
+    return hands;
+  const t2 = {};
+  const ratio = (input2.shape[2] || 1) / (input2.shape[1] || 1);
+  const height = Math.min(Math.round((input2.shape[1] || 0) / 8) * 8, maxDetectorResolution);
+  const width = Math.round(height * ratio / 8) * 8;
+  t2.resize = image.resizeBilinear(input2, [height, width]);
+  t2.cast = cast(t2.resize, "int32");
+  [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes);
+  t2.boxes = squeeze(t2.rawBoxes, [0, 2]);
+  t2.scores = squeeze(t2.rawScores, [0]);
+  const classScores = unstack(t2.scores, 1);
+  dispose(classScores[faceIndex]);
+  classScores.splice(faceIndex, 1);
+  t2.filtered = stack(classScores, 1);
+  dispose(classScores);
+  t2.max = max(t2.filtered, 1);
+  t2.argmax = argMax(t2.filtered, 1);
+  let id = 0;
+  t2.nms = await image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  const nms = await t2.nms.data();
+  const scores = await t2.max.data();
+  const classNum = await t2.argmax.data();
+  for (const nmsIndex of Array.from(nms)) {
+    const boxSlice = slice(t2.boxes, nmsIndex, 1);
+    const boxYX = await boxSlice.data();
+    dispose(boxSlice);
+    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
+    const boxRaw = scale2(boxData, detectorExpandFact);
+    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
+    const score = scores[nmsIndex];
+    const label = classes[classNum[nmsIndex]];
+    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
+    hands.push(hand3);
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  hands.sort((a6, b) => b.score - a6.score);
+  if (hands.length > (config3.hand.maxDetected || 1))
+    hands.length = config3.hand.maxDetected || 1;
+  return hands;
+}
+async function detectFingers(input2, h, config3) {
+  const hand3 = {
+    id: h.id,
+    score: Math.round(100 * h.score) / 100,
+    boxScore: Math.round(100 * h.score) / 100,
+    fingerScore: 0,
+    box: h.box,
+    boxRaw: h.boxRaw,
+    label: h.label,
+    keypoints: [],
+    landmarks: {},
+    annotations: {}
+  };
+  if (input2 && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
+    const t2 = {};
+    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
+    t2.crop = image.cropAndResize(input2, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t2.div = div(t2.crop, constants.tf255);
+    [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]);
+    const rawScore = (await t2.score.data())[0];
+    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
+    if (score >= (config3.hand.minConfidence || 0)) {
+      hand3.fingerScore = score;
+      t2.reshaped = reshape(t2.keypoints, [-1, 3]);
+      const coordsData = await t2.reshaped.array();
+      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
+      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
+      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
+      hand3.landmarks = analyze(hand3.keypoints);
+      for (const key of Object.keys(fingerMap)) {
+        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
+      }
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  }
+  return hand3;
+}
+async function predict13(input2, config3) {
+  var _a, _b;
+  if (!models2[0] || !models2[1] || !((_a = models2[0]) == null ? void 0 : _a.inputs[0].shape) || !((_b = models2[1]) == null ? void 0 : _b.inputs[0].shape))
+    return [];
+  outputSize = [input2.shape[2] || 0, input2.shape[1] || 0];
+  skipped11++;
+  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime12;
+  const skipFrame = skipped11 < (config3.hand.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache4.hands;
+  }
+  return new Promise(async (resolve) => {
+    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime12;
+    const skipFrameExtended = skipped11 < 3 * (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
+    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
+    } else {
+      cache4.boxes = await detectHands(input2, config3);
+      lastTime12 = now();
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
+      skipped11 = 0;
+    }
+    const oldCache = [...cache4.boxes];
+    cache4.boxes.length = 0;
+    if (config3.cacheSensitivity > 0) {
+      for (let i2 = 0; i2 < cache4.hands.length; i2++) {
+        const boxKpt = square4(cache4.hands[i2].keypoints, outputSize);
+        if (boxKpt.box[2] / (input2.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input2.shape[1] || 1) > 0.05 && cache4.hands[i2].fingerScore && cache4.hands[i2].fingerScore > (config3.hand.minConfidence || 0)) {
+          const boxScale = scale2(boxKpt.box, boxExpandFact);
+          const boxScaleRaw = scale2(boxKpt.boxRaw, boxExpandFact);
+          cache4.boxes.push({ ...oldCache[i2], box: boxScale, boxRaw: boxScaleRaw });
+        }
+      }
+    }
+    for (let i2 = 0; i2 < cache4.hands.length; i2++) {
+      const bbox = calc(cache4.hands[i2].keypoints, outputSize);
+      cache4.hands[i2].box = bbox.box;
+      cache4.hands[i2].boxRaw = bbox.boxRaw;
+    }
+    resolve(cache4.hands);
+  });
+}
+
+// src/face/liveness.ts
+var model14;
+var cached2 = [];
+var skipped12 = Number.MAX_SAFE_INTEGER;
+var lastCount8 = 0;
+var lastTime13 = 0;
+async function load14(config3) {
+  var _a;
+  if (env2.initial)
+    model14 = null;
+  if (!model14)
+    model14 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model14["modelUrl"]);
+  return model14;
+}
+async function predict14(image2, config3, idx, count3) {
+  var _a, _b;
+  if (!model14)
+    return 0;
+  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime13;
+  const skipFrame = skipped12 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count3 && cached2[idx]) {
+    skipped12++;
+    return cached2[idx];
+  }
+  skipped12 = 0;
+  return new Promise(async (resolve) => {
+    const resize = image.resizeBilinear(image2, [(model14 == null ? void 0 : model14.inputs[0].shape) ? model14.inputs[0].shape[2] : 0, (model14 == null ? void 0 : model14.inputs[0].shape) ? model14.inputs[0].shape[1] : 0], false);
+    const res = model14 == null ? void 0 : model14.execute(resize);
+    const num = (await res.data())[0];
+    cached2[idx] = Math.round(100 * num) / 100;
+    lastCount8 = count3;
+    lastTime13 = now();
+    dispose([resize, res]);
+    resolve(cached2[idx]);
+  });
+}
+
+// src/body/movenetcoords.ts
+var movenetcoords_exports = {};
+__export(movenetcoords_exports, {
+  connected: () => connected3,
+  horizontal: () => horizontal,
+  kpt: () => kpt3,
+  relative: () => relative,
+  vertical: () => vertical
+});
+var kpt3 = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var horizontal = [
+  ["leftEye", "rightEye"],
+  ["leftEar", "rightEar"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftElbow", "rightElbow"],
+  ["leftWrist", "rightWrist"],
+  ["leftHip", "rightHip"],
+  ["leftKnee", "rightKnee"],
+  ["leftAnkle", "rightAnkle"]
+];
+var vertical = [
+  ["leftKnee", "leftShoulder"],
+  ["rightKnee", "rightShoulder"],
+  ["leftAnkle", "leftKnee"],
+  ["rightAnkle", "rightKnee"]
+];
+var relative = [
+  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
+  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
+];
+var connected3 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/movenetfix.ts
+var maxJitter = 5e-3;
+var cache5 = {
+  keypoints: [],
+  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
+};
+function bodyParts(body4) {
+  for (const pair of horizontal) {
+    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
+    if (body4.keypoints[left] && body4.keypoints[right]) {
+      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
+        const tmp = body4.keypoints[left];
+        body4.keypoints[left] = body4.keypoints[right];
+        body4.keypoints[right] = tmp;
+      }
+    }
+  }
+  for (const pair of vertical) {
+    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    if (body4.keypoints[lower] && body4.keypoints[higher]) {
+      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
+        body4.keypoints.splice(lower, 1);
+      }
+    }
+  }
+  for (const [pair, compare2] of relative) {
+    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
+    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
+    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
+      continue;
+    const distanceLeft = body4.keypoints[left] ? [
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
+    ] : [0, 0];
+    const distanceRight = body4.keypoints[right] ? [
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
+    ] : [0, 0];
+    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
+      const tmp = body4.keypoints[left];
+      body4.keypoints[left] = body4.keypoints[right];
+      body4.keypoints[right] = tmp;
+    }
+  }
+}
+function jitter(keypoints) {
+  for (let i2 = 0; i2 < keypoints.length; i2++) {
+    if (keypoints[i2] && cache5.keypoints[i2]) {
+      const diff = [Math.abs(keypoints[i2].positionRaw[0] - cache5.keypoints[i2].positionRaw[0]), Math.abs(keypoints[i2].positionRaw[1] - cache5.keypoints[i2].positionRaw[1])];
+      if (diff[0] < maxJitter && diff[1] < maxJitter) {
+        keypoints[i2] = cache5.keypoints[i2];
+      } else {
+        cache5.keypoints[i2] = keypoints[i2];
+      }
+    } else {
+      cache5.keypoints[i2] = keypoints[i2];
+    }
+  }
+  return keypoints;
+}
+function padInput(input2, inputSize10) {
+  const t2 = {};
+  if (!input2.shape || !input2.shape[1] || !input2.shape[2])
+    return input2;
+  cache5.padding = [
+    [0, 0],
+    [input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0, input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0],
+    [input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0, input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0],
+    [0, 0]
+  ];
+  t2.pad = pad(input2, cache5.padding);
+  t2.resize = image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
+  const final = cast(t2.resize, "int32");
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return final;
+}
+function rescaleBody(body4, outputSize2) {
+  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 && kpt4.position);
+  for (const kpt4 of body4.keypoints) {
+    kpt4.position = [
+      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
+      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
+    ];
+    kpt4.positionRaw = [
+      kpt4.position[0] / outputSize2[0],
+      kpt4.position[1] / outputSize2[1]
+    ];
+  }
+  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
+  body4.box = rescaledBoxes.box;
+  body4.boxRaw = rescaledBoxes.boxRaw;
+  return body4;
+}
+
+// src/body/movenet.ts
+var model15;
+var inputSize8 = 0;
+var skipped13 = Number.MAX_SAFE_INTEGER;
+var cache6 = {
+  boxes: [],
+  bodies: [],
+  last: 0
+};
+async function load15(config3) {
+  if (env2.initial)
+    model15 = null;
+  if (!model15) {
+    fakeOps(["size"], config3);
+    model15 = await loadModel(config3.body.modelPath);
+  } else if (config3.debug)
+    log("cached model:", model15["modelUrl"]);
+  inputSize8 = model15.inputs[0].shape ? model15.inputs[0].shape[2] : 0;
+  if (inputSize8 < 64)
+    inputSize8 = 256;
+  return model15;
+}
+async function parseSinglePose(res, config3, image2) {
+  const kpt4 = res[0][0];
+  const keypoints = [];
+  let score = 0;
+  for (let id = 0; id < kpt4.length; id++) {
+    score = kpt4[id][2];
+    if (score > config3.body.minConfidence) {
+      const positionRaw = [kpt4[id][1], kpt4[id][0]];
+      keypoints.push({
+        score: Math.round(100 * score) / 100,
+        part: kpt3[id],
+        positionRaw,
+        position: [
+          Math.round((image2.shape[2] || 0) * positionRaw[0]),
+          Math.round((image2.shape[1] || 0) * positionRaw[1])
+        ]
+      });
+    }
+  }
+  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+  const bodies = [];
+  const newBox = calc(keypoints.map((pt) => pt.position), [image2.shape[2], image2.shape[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected3)) {
+    const pt = [];
+    for (let i2 = 0; i2 < indexes.length - 1; i2++) {
+      const pt0 = keypoints.find((kp) => kp.part === indexes[i2]);
+      const pt1 = keypoints.find((kp) => kp.part === indexes[i2 + 1]);
+      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
+  bodyParts(body4);
+  bodies.push(body4);
+  return bodies;
+}
+async function parseMultiPose(res, config3, image2) {
+  const bodies = [];
+  for (let id = 0; id < res[0].length; id++) {
+    const kpt4 = res[0][id];
+    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
+    if (totalScore > config3.body.minConfidence) {
+      const keypoints = [];
+      for (let i2 = 0; i2 < 17; i2++) {
+        const score = kpt4[3 * i2 + 2];
+        if (score > config3.body.minConfidence) {
+          const positionRaw = [kpt4[3 * i2 + 1], kpt4[3 * i2 + 0]];
+          keypoints.push({
+            part: kpt3[i2],
+            score: Math.round(100 * score) / 100,
+            positionRaw,
+            position: [Math.round((image2.shape[2] || 0) * positionRaw[0]), Math.round((image2.shape[1] || 0) * positionRaw[1])]
+          });
+        }
+      }
+      const newBox = calc(keypoints.map((pt) => pt.position), [image2.shape[2], image2.shape[1]]);
+      const annotations2 = {};
+      for (const [name, indexes] of Object.entries(connected3)) {
+        const pt = [];
+        for (let i2 = 0; i2 < indexes.length - 1; i2++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[i2]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[i2 + 1]);
+          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
+      bodyParts(body4);
+      bodies.push(body4);
+    }
+  }
+  bodies.sort((a6, b) => b.score - a6.score);
+  if (bodies.length > config3.body.maxDetected)
+    bodies.length = config3.body.maxDetected;
+  return bodies;
+}
+async function predict15(input2, config3) {
+  if (!model15 || !(model15 == null ? void 0 : model15.inputs[0].shape))
+    return [];
+  if (!config3.skipAllowed)
+    cache6.boxes.length = 0;
+  skipped13++;
+  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
+  const skipFrame = skipped13 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache6.bodies;
+  }
+  return new Promise(async (resolve) => {
+    const t2 = {};
+    skipped13 = 0;
+    t2.input = padInput(input2, inputSize8);
+    t2.res = model15 == null ? void 0 : model15.execute(t2.input);
+    cache6.last = now();
+    const res = await t2.res.array();
+    cache6.bodies = t2.res.shape[2] === 17 ? await parseSinglePose(res, config3, input2) : await parseMultiPose(res, config3, input2);
+    for (const body4 of cache6.bodies) {
+      rescaleBody(body4, [input2.shape[2] || 1, input2.shape[1] || 1]);
+      jitter(body4.keypoints);
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    resolve(cache6.bodies);
+  });
+}
+
+// src/object/nanodet.ts
+var model16;
+var last9 = [];
+var lastTime14 = 0;
+var skipped14 = Number.MAX_SAFE_INTEGER;
+var inputSize9 = 0;
+var scaleBox = 2.5;
+async function load16(config3) {
+  if (!model16 || env2.initial) {
+    model16 = await loadModel(config3.object.modelPath);
+    const inputs = Object.values(model16.modelSignature["inputs"]);
+    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", model16["modelUrl"]);
+  return model16;
+}
+async function process4(res, outputShape, config3) {
+  let id = 0;
+  let results = [];
+  for (const strideSize of [1, 2, 4]) {
+    tidy(async () => {
+      const baseSize = strideSize * 13;
+      const scoresT = squeeze(res.find((a6) => a6.shape[1] === baseSize ** 2 && (a6.shape[2] || 0) === labels.length));
+      const featuresT = squeeze(res.find((a6) => a6.shape[1] === baseSize ** 2 && (a6.shape[2] || 0) < labels.length));
+      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
+      const boxIdx = await boxesMax.argMax(2).array();
+      const scores = await scoresT.array();
+      for (let i2 = 0; i2 < scoresT.shape[0]; i2++) {
+        for (let j = 0; j < scoresT.shape[1]; j++) {
+          const score = scores[i2][j];
+          if (score > (config3.object.minConfidence || 0) && j !== 61) {
+            const cx = (0.5 + Math.trunc(i2 % baseSize)) / baseSize;
+            const cy = (0.5 + Math.trunc(i2 / baseSize)) / baseSize;
+            const boxOffset = boxIdx[i2].map((a6) => a6 * (baseSize / strideSize / inputSize9));
+            const [x, y] = [
+              cx - scaleBox / strideSize * boxOffset[0],
+              cy - scaleBox / strideSize * boxOffset[1]
+            ];
+            const [w, h] = [
+              cx + scaleBox / strideSize * boxOffset[2] - x,
+              cy + scaleBox / strideSize * boxOffset[3] - y
+            ];
+            let boxRaw = [x, y, w, h];
+            boxRaw = boxRaw.map((a6) => Math.max(0, Math.min(a6, 1)));
+            const box = [
+              boxRaw[0] * outputShape[0],
+              boxRaw[1] * outputShape[1],
+              boxRaw[2] * outputShape[0],
+              boxRaw[3] * outputShape[1]
+            ];
+            const result = {
+              id: id++,
+              score: Math.round(100 * score) / 100,
+              class: j + 1,
+              label: labels[j].label,
+              box: box.map((a6) => Math.trunc(a6)),
+              boxRaw
+            };
+            results.push(result);
+          }
+        }
+      }
+    });
+  }
+  res.forEach((t2) => dispose(t2));
+  const nmsBoxes = results.map((a6) => [a6.boxRaw[1], a6.boxRaw[0], a6.boxRaw[3], a6.boxRaw[2]]);
+  const nmsScores = results.map((a6) => a6.score);
+  let nmsIdx = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    nmsIdx = await nms.data();
+    dispose(nms);
+  }
+  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a6, b) => b.score - a6.score);
+  return results;
+}
+async function predict16(image2, config3) {
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime14;
+  const skipFrame = skipped14 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last9.length > 0) {
+    skipped14++;
+    return last9;
+  }
+  skipped14 = 0;
+  if (!env2.kernels.includes("mod") || !env2.kernels.includes("sparsetodense"))
+    return last9;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [image2.shape[2] || 0, image2.shape[1] || 0];
+    const resize = image.resizeBilinear(image2, [inputSize9, inputSize9], false);
+    const norm2 = div(resize, constants.tf255);
+    const transpose6 = norm2.transpose([0, 3, 1, 2]);
+    dispose(norm2);
+    dispose(resize);
+    let objectT;
+    if (config3.object.enabled)
+      objectT = model16.execute(transpose6);
+    lastTime14 = now();
+    dispose(transpose6);
+    const obj = await process4(objectT, outputSize2, config3);
+    last9 = obj;
+    resolve(obj);
+  });
+}
+
+// src/body/posenetutils.ts
+var partNames = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var count2 = partNames.length;
+var partIds = partNames.reduce((result, jointName, i2) => {
+  result[jointName] = i2;
+  return result;
+}, {});
+var connectedPartNames = [
+  ["leftHip", "leftShoulder"],
+  ["leftElbow", "leftShoulder"],
+  ["leftElbow", "leftWrist"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["rightHip", "rightShoulder"],
+  ["rightElbow", "rightShoulder"],
+  ["rightElbow", "rightWrist"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftHip", "rightHip"]
+];
+var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
+var poseChain = [
+  ["nose", "leftEye"],
+  ["leftEye", "leftEar"],
+  ["nose", "rightEye"],
+  ["rightEye", "rightEar"],
+  ["nose", "leftShoulder"],
+  ["leftShoulder", "leftElbow"],
+  ["leftElbow", "leftWrist"],
+  ["leftShoulder", "leftHip"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["nose", "rightShoulder"],
+  ["rightShoulder", "rightElbow"],
+  ["rightElbow", "rightWrist"],
+  ["rightShoulder", "rightHip"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"]
+];
+function getBoundingBox(keypoints) {
+  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
+    maxX: Math.max(maxX, x),
+    maxY: Math.max(maxY, y),
+    minX: Math.min(minX, x),
+    minY: Math.min(minY, y)
+  }), {
+    maxX: Number.NEGATIVE_INFINITY,
+    maxY: Number.NEGATIVE_INFINITY,
+    minX: Number.POSITIVE_INFINITY,
+    minY: Number.POSITIVE_INFINITY
+  });
+  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
+}
+function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
+  const scaleY = height / inputResolutionHeight;
+  const scaleX = width / inputResolutionWidth;
+  const scalePose = (pose, i2) => ({
+    id: i2,
+    score: pose.score,
+    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
+    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
+    keypoints: pose.keypoints.map(({ score, part, position }) => ({
+      score,
+      part,
+      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
+      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
+    })),
+    annotations: {}
+  });
+  const scaledPoses = poses.map((pose, i2) => scalePose(pose, i2));
+  return scaledPoses;
+}
+var MaxHeap = class {
+  constructor(maxSize2, getElementValue) {
+    __publicField(this, "priorityQueue");
+    __publicField(this, "numberOfElements");
+    __publicField(this, "getElementValue");
+    this.priorityQueue = new Array(maxSize2);
+    this.numberOfElements = -1;
+    this.getElementValue = getElementValue;
+  }
+  enqueue(x) {
+    this.priorityQueue[++this.numberOfElements] = x;
+    this.swim(this.numberOfElements);
+  }
+  dequeue() {
+    const max7 = this.priorityQueue[0];
+    this.exchange(0, this.numberOfElements--);
+    this.sink(0);
+    this.priorityQueue[this.numberOfElements + 1] = null;
+    return max7;
+  }
+  empty() {
+    return this.numberOfElements === -1;
+  }
+  size() {
+    return this.numberOfElements + 1;
+  }
+  all() {
+    return this.priorityQueue.slice(0, this.numberOfElements + 1);
+  }
+  max() {
+    return this.priorityQueue[0];
+  }
+  swim(k) {
+    while (k > 0 && this.less(Math.floor(k / 2), k)) {
+      this.exchange(k, Math.floor(k / 2));
+      k = Math.floor(k / 2);
+    }
+  }
+  sink(k) {
+    while (2 * k <= this.numberOfElements) {
+      let j = 2 * k;
+      if (j < this.numberOfElements && this.less(j, j + 1))
+        j++;
+      if (!this.less(k, j))
+        break;
+      this.exchange(k, j);
+      k = j;
+    }
+  }
+  getValueAt(i2) {
+    return this.getElementValue(this.priorityQueue[i2]);
+  }
+  less(i2, j) {
+    return this.getValueAt(i2) < this.getValueAt(j);
+  }
+  exchange(i2, j) {
+    const t2 = this.priorityQueue[i2];
+    this.priorityQueue[i2] = this.priorityQueue[j];
+    this.priorityQueue[j] = t2;
+  }
+};
+function getOffsetPoint(y, x, keypoint, offsets) {
+  return {
+    y: offsets.get(y, x, keypoint),
+    x: offsets.get(y, x, keypoint + count2)
+  };
+}
+function getImageCoords(part, outputStride2, offsets) {
+  const { heatmapY, heatmapX, id: keypoint } = part;
+  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
+  return {
+    x: part.heatmapX * outputStride2 + x,
+    y: part.heatmapY * outputStride2 + y
+  };
+}
+function clamp2(a6, min7, max7) {
+  if (a6 < min7)
+    return min7;
+  if (a6 > max7)
+    return max7;
+  return a6;
+}
+function squaredDistance(y1, x1, y2, x2) {
+  const dy = y2 - y1;
+  const dx = x2 - x1;
+  return dy * dy + dx * dx;
+}
+function addVectors(a6, b) {
+  return { x: a6.x + b.x, y: a6.y + b.y };
+}
+
+// src/body/posenet.ts
+var model17;
+var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
+var localMaximumRadius = 1;
+var outputStride = 16;
+var squaredNmsRadius = 50 ** 2;
+function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
+  const getDisplacement = (point2) => ({
+    y: displacements.get(point2.y, point2.x, edgeId),
+    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
+  });
+  const getStridedIndexNearPoint = (point2, height2, width2) => ({
+    y: clamp2(Math.round(point2.y / outputStride), 0, height2 - 1),
+    x: clamp2(Math.round(point2.x / outputStride), 0, width2 - 1)
+  });
+  const [height, width] = scores.shape;
+  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
+  const displacement = getDisplacement(sourceKeypointIndices);
+  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
+  let targetKeypoint = displacedPoint;
+  for (let i2 = 0; i2 < offsetRefineStep; i2++) {
+    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
+    targetKeypoint = addVectors({ x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, { x: offsetPoint.x, y: offsetPoint.y });
+  }
+  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
+  return { position: targetKeypoint, part: partNames[targetId], score };
+}
+function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
+  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
+  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
+  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
+  const numParts = scores.shape[2];
+  const numEdges = edgesFwd.length;
+  const keypoints = new Array(numParts);
+  const rootPoint = getImageCoords(root.part, outputStride, offsets);
+  keypoints[root.part.id] = {
+    score: root.score,
+    part: partNames[root.part.id],
+    position: rootPoint
+  };
+  for (let edge = numEdges - 1; edge >= 0; --edge) {
+    const sourceId = edgesFwd[edge];
+    const targetId = edgesBwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
+    }
+  }
+  for (let edge = 0; edge < numEdges; ++edge) {
+    const sourceId = edgesBwd[edge];
+    const targetId = edgesFwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
+    }
+  }
+  return keypoints;
+}
+function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
+  const [height, width] = scores.shape;
+  let localMaximum = true;
+  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
+  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
+  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
+    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
+    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
+    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
+      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
+        localMaximum = false;
+        break;
+      }
+    }
+    if (!localMaximum)
+      break;
+  }
+  return localMaximum;
+}
+function buildPartWithScoreQueue(minConfidence2, scores) {
+  const [height, width, numKeypoints] = scores.shape;
+  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
+  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
+    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
+      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
+        const score = scores.get(heatmapY, heatmapX, keypointId);
+        if (score < minConfidence2)
+          continue;
+        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
+          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
+      }
+    }
+  }
+  return queue;
+}
+function withinRadius(poses, { x, y }, keypointId) {
+  return poses.some(({ keypoints }) => {
+    var _a;
+    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
+    if (!correspondingKeypoint)
+      return false;
+    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
+  });
+}
+function getInstanceScore(existingPoses, keypoints) {
+  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
+    if (!withinRadius(existingPoses, position, keypointId))
+      result += score;
+    return result;
+  }, 0);
+  return notOverlappedKeypointScores / keypoints.length;
+}
+function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
+  const poses = [];
+  const queue = buildPartWithScoreQueue(minConfidence2, scores);
+  while (poses.length < maxDetected && !queue.empty()) {
+    const root = queue.dequeue();
+    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
+    if (withinRadius(poses, rootImageCoords, root.part.id))
+      continue;
+    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
+    keypoints = keypoints.filter((a6) => a6.score > minConfidence2);
+    const score = getInstanceScore(poses, keypoints);
+    const box = getBoundingBox(keypoints);
+    if (score > minConfidence2)
+      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
+  }
+  return poses;
+}
+async function predict17(input2, config3) {
+  const res = tidy(() => {
+    if (!model17.inputs[0].shape)
+      return [];
+    const resized = image.resizeBilinear(input2, [model17.inputs[0].shape[2], model17.inputs[0].shape[1]]);
+    const normalized = sub(div(cast(resized, "float32"), 127.5), 1);
+    const results = model17.execute(normalized, poseNetOutputs);
+    const results3d = results.map((y) => squeeze(y, [0]));
+    results3d[1] = sigmoid(results3d[1]);
+    return results3d;
+  });
+  const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer()));
+  for (const t2 of res)
+    dispose(t2);
+  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
+  if (!model17.inputs[0].shape)
+    return [];
+  const scaled = scalePoses(decoded, [input2.shape[1], input2.shape[2]], [model17.inputs[0].shape[2], model17.inputs[0].shape[1]]);
+  return scaled;
+}
+async function load17(config3) {
+  if (!model17 || env2.initial)
+    model17 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model17["modelUrl"]);
+  return model17;
+}
+
+// src/segmentation/segmentation.ts
+var model18;
+var busy = false;
+async function load18(config3) {
+  if (!model18 || env2.initial)
+    model18 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model18["modelUrl"]);
+  return model18;
+}
+async function process5(input2, background, config3) {
+  var _a, _b;
+  if (busy)
+    return { data: [], canvas: null, alpha: null };
+  busy = true;
+  if (!model18)
+    await load18(config3);
+  const inputImage = await process2(input2, config3);
+  const width = ((_a = inputImage.tensor) == null ? void 0 : _a.shape[2]) || 0;
+  const height = ((_b = inputImage.tensor) == null ? void 0 : _b.shape[1]) || 0;
+  if (!inputImage.tensor)
+    return { data: [], canvas: null, alpha: null };
+  const t2 = {};
+  t2.resize = image.resizeBilinear(inputImage.tensor, [model18.inputs[0].shape ? model18.inputs[0].shape[1] : 0, model18.inputs[0].shape ? model18.inputs[0].shape[2] : 0], false);
+  dispose(inputImage.tensor);
+  t2.norm = div(t2.resize, constants.tf255);
+  t2.res = model18.execute(t2.norm);
+  t2.squeeze = squeeze(t2.res, 0);
+  if (t2.squeeze.shape[2] === 2) {
+    t2.softmax = softmax(t2.squeeze);
+    [t2.bg, t2.fg] = unstack(t2.softmax, 2);
+    t2.expand = expandDims(t2.fg, 2);
+    t2.pad = expandDims(t2.expand, 0);
+    t2.crop = image.cropAndResize(t2.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
+    t2.data = squeeze(t2.crop, 0);
+  } else {
+    t2.data = image.resizeBilinear(t2.squeeze, [height, width]);
+  }
+  const data = Array.from(await t2.data.data());
+  if (env2.node && !env2.Canvas && typeof ImageData === "undefined") {
+    if (config3.debug)
+      log("canvas support missing");
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    return { data, canvas: null, alpha: null };
+  }
+  const alphaCanvas = canvas(width, height);
+  if (browser_exports)
+    await browser_exports.toPixels(t2.data, alphaCanvas);
+  const alphaCtx = alphaCanvas.getContext("2d");
+  if (config3.segmentation.blur && config3.segmentation.blur > 0)
+    alphaCtx.filter = `blur(${config3.segmentation.blur}px)`;
+  const alphaData = alphaCtx.getImageData(0, 0, width, height);
+  const compositeCanvas = canvas(width, height);
+  const compositeCtx = compositeCanvas.getContext("2d");
+  if (inputImage.canvas)
+    compositeCtx.drawImage(inputImage.canvas, 0, 0);
+  compositeCtx.globalCompositeOperation = "darken";
+  if (config3.segmentation.blur && config3.segmentation.blur > 0)
+    compositeCtx.filter = `blur(${config3.segmentation.blur}px)`;
+  compositeCtx.drawImage(alphaCanvas, 0, 0);
+  compositeCtx.globalCompositeOperation = "source-over";
+  compositeCtx.filter = "none";
+  const compositeData = compositeCtx.getImageData(0, 0, width, height);
+  for (let i2 = 0; i2 < width * height; i2++)
+    compositeData.data[4 * i2 + 3] = alphaData.data[4 * i2 + 0];
+  compositeCtx.putImageData(compositeData, 0, 0);
+  let mergedCanvas = null;
+  if (background && compositeCanvas) {
+    mergedCanvas = canvas(width, height);
+    const bgImage = await process2(background, config3);
+    dispose(bgImage.tensor);
+    const ctxMerge = mergedCanvas.getContext("2d");
+    ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
+    ctxMerge.drawImage(compositeCanvas, 0, 0);
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  busy = false;
+  return { data, canvas: compositeCanvas, alpha: alphaCanvas };
+}
+
+// src/models.ts
+var Models = class {
+  constructor() {
+    __publicField(this, "ssrnetage", null);
+    __publicField(this, "gear", null);
+    __publicField(this, "blazeposedetect", null);
+    __publicField(this, "blazepose", null);
+    __publicField(this, "centernet", null);
+    __publicField(this, "efficientpose", null);
+    __publicField(this, "mobilefacenet", null);
+    __publicField(this, "emotion", null);
+    __publicField(this, "facedetect", null);
+    __publicField(this, "faceiris", null);
+    __publicField(this, "facemesh", null);
+    __publicField(this, "faceres", null);
+    __publicField(this, "ssrnetgender", null);
+    __publicField(this, "handpose", null);
+    __publicField(this, "handskeleton", null);
+    __publicField(this, "handtrack", null);
+    __publicField(this, "liveness", null);
+    __publicField(this, "movenet", null);
+    __publicField(this, "nanodet", null);
+    __publicField(this, "posenet", null);
+    __publicField(this, "segmentation", null);
+    __publicField(this, "antispoof", null);
+  }
+};
+var getModelStats = (instance) => {
+  let totalSizeFromManifest = 0;
+  let totalSizeWeights = 0;
+  let totalSizeLoading = 0;
+  for (const m of Object.values(modelStats)) {
+    totalSizeFromManifest += m.sizeFromManifest;
+    totalSizeWeights += m.sizeLoadedWeights;
+    totalSizeLoading += m.sizeDesired;
+  }
+  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+  return {
+    numLoadedModels: Object.values(modelStats).length,
+    numEnabledModels: void 0,
+    numDefinedModels: Object.keys(instance.models).length,
+    percentageLoaded,
+    totalSizeFromManifest,
+    totalSizeWeights,
+    totalSizeLoading,
+    totalSizeEnabled: void 0,
+    modelStats: Object.values(modelStats)
+  };
+};
+function reset(instance) {
+  for (const model19 of Object.keys(instance.models))
+    instance.models[model19] = null;
+}
+async function load19(instance) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E;
+  if (env2.initial)
+    reset(instance);
+  if (instance.config.hand.enabled) {
+    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config);
+    }
+    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load13(instance.config);
+    }
+  }
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_f = (_e = instance.config.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("blazepose")))
+    instance.models.blazepose = loadPose(instance.config);
+  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"]["modelPath"])
+    instance.models.blazeposedetect = loadDetect(instance.config);
+  if (instance.config.body.enabled && !instance.models.efficientpose && ((_h = (_g = instance.config.body) == null ? void 0 : _g.modelPath) == null ? void 0 : _h.includes("efficientpose")))
+    instance.models.efficientpose = load7(instance.config);
+  if (instance.config.body.enabled && !instance.models.movenet && ((_j = (_i = instance.config.body) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("movenet")))
+    instance.models.movenet = load15(instance.config);
+  if (instance.config.body.enabled && !instance.models.posenet && ((_l = (_k = instance.config.body) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("posenet")))
+    instance.models.posenet = load17(instance.config);
+  if (instance.config.face.enabled && !instance.models.facedetect)
+    instance.models.facedetect = load5(instance.config);
+  if (instance.config.face.enabled && ((_m = instance.config.face.antispoof) == null ? void 0 : _m.enabled) && !instance.models.antispoof)
+    instance.models.antispoof = load4(instance.config);
+  if (instance.config.face.enabled && ((_n = instance.config.face.liveness) == null ? void 0 : _n.enabled) && !instance.models.liveness)
+    instance.models.liveness = load14(instance.config);
+  if (instance.config.face.enabled && ((_o = instance.config.face.description) == null ? void 0 : _o.enabled) && !instance.models.faceres)
+    instance.models.faceres = load12(instance.config);
+  if (instance.config.face.enabled && ((_p = instance.config.face.emotion) == null ? void 0 : _p.enabled) && !instance.models.emotion)
+    instance.models.emotion = load8(instance.config);
+  if (instance.config.face.enabled && ((_q = instance.config.face.iris) == null ? void 0 : _q.enabled) && !((_r = instance.config.face.attention) == null ? void 0 : _r.enabled) && !instance.models.faceiris)
+    instance.models.faceiris = load10(instance.config);
+  if (instance.config.face.enabled && ((_s = instance.config.face.mesh) == null ? void 0 : _s.enabled) && !instance.models.facemesh)
+    instance.models.facemesh = load11(instance.config);
+  if (instance.config.face.enabled && ((_t = instance.config.face["gear"]) == null ? void 0 : _t.enabled) && !instance.models.gear)
+    instance.models.gear = load(instance.config);
+  if (instance.config.face.enabled && ((_u = instance.config.face["ssrnet"]) == null ? void 0 : _u.enabled) && !instance.models.ssrnetage)
+    instance.models.ssrnetage = load2(instance.config);
+  if (instance.config.face.enabled && ((_v = instance.config.face["ssrnet"]) == null ? void 0 : _v.enabled) && !instance.models.ssrnetgender)
+    instance.models.ssrnetgender = load3(instance.config);
+  if (instance.config.face.enabled && ((_w = instance.config.face["mobilefacenet"]) == null ? void 0 : _w.enabled) && !instance.models.mobilefacenet)
+    instance.models.mobilefacenet = load9(instance.config);
+  if (instance.config.hand.enabled && !instance.models.handtrack && ((_y = (_x = instance.config.hand.detector) == null ? void 0 : _x.modelPath) == null ? void 0 : _y.includes("handtrack")))
+    instance.models.handtrack = loadDetect2(instance.config);
+  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_A = (_z = instance.config.hand.detector) == null ? void 0 : _z.modelPath) == null ? void 0 : _A.includes("handtrack")))
+    instance.models.handskeleton = loadSkeleton(instance.config);
+  if (instance.config.object.enabled && !instance.models.centernet && ((_C = (_B = instance.config.object) == null ? void 0 : _B.modelPath) == null ? void 0 : _C.includes("centernet")))
+    instance.models.centernet = load6(instance.config);
+  if (instance.config.object.enabled && !instance.models.nanodet && ((_E = (_D = instance.config.object) == null ? void 0 : _D.modelPath) == null ? void 0 : _E.includes("nanodet")))
+    instance.models.nanodet = load16(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.segmentation)
+    instance.models.segmentation = load18(instance.config);
+  for await (const model19 of Object.keys(instance.models)) {
+    if (instance.models[model19] && typeof instance.models[model19] !== "undefined") {
+      instance.models[model19] = await instance.models[model19];
+    }
+  }
+}
+async function validate2(instance) {
+  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
+  for (const defined of Object.keys(instance.models)) {
+    const model19 = instance.models[defined];
+    if (!model19)
+      continue;
+    const ops = [];
+    const executor = model19 == null ? void 0 : model19.executor;
+    if (executor && executor.graph.nodes) {
+      for (const kernel of Object.values(executor.graph.nodes)) {
+        const op2 = kernel.op.toLowerCase();
+        if (!ops.includes(op2))
+          ops.push(op2);
+      }
+    } else {
+      if (!executor && instance.config.debug)
+        log("model signature not determined:", defined);
+    }
+    const missing = [];
+    for (const op2 of ops) {
+      if (!simpleOps.includes(op2) && !instance.env.kernels.includes(op2) && !instance.env.kernels.includes(op2.replace("_", "")) && !instance.env.kernels.includes(op2.replace("native", "")) && !instance.env.kernels.includes(op2.replace("v2", ""))) {
+        missing.push(op2);
+      }
+    }
+    if (instance.config.debug && missing.length > 0)
+      log("model validation failed:", defined, missing);
+  }
+}
+
+// src/tfjs/humangl.ts
+var config2 = {
+  name: "humangl",
+  priority: 999,
+  canvas: null,
+  gl: null,
+  extensions: [],
+  webGLattr: {
+    alpha: false,
+    antialias: false,
+    premultipliedAlpha: false,
+    preserveDrawingBuffer: false,
+    depth: false,
+    stencil: false,
+    failIfMajorPerformanceCaveat: false,
+    desynchronized: true
+  }
+};
+function extensions() {
+  const gl = config2.gl;
+  if (!gl)
+    return;
+  config2.extensions = gl.getSupportedExtensions();
+}
+async function register(instance) {
+  var _a;
+  if (instance.config.backend !== "humangl")
+    return;
+  if (config2.name in engine().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
+    log("error: humangl backend invalid context");
+    reset(instance);
+  }
+  if (!findBackend(config2.name)) {
+    try {
+      config2.canvas = await canvas(100, 100);
+    } catch (err) {
+      log("error: cannot create canvas:", err);
+      return;
+    }
+    try {
+      config2.gl = (_a = config2.canvas) == null ? void 0 : _a.getContext("webgl2", config2.webGLattr);
+      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
+      if (!glv2) {
+        log("override: using fallback webgl backend as webgl 2.0 is not detected");
+        instance.config.backend = "webgl";
+        return;
+      }
+      if (config2.canvas) {
+        config2.canvas.addEventListener("webglcontextlost", async (e2) => {
+          log("error: humangl:", e2.type);
+          log("possible browser memory leak using webgl or conflict with multiple backend registrations");
+          instance.emit("error");
+          throw new Error("backend error: webgl context lost");
+        });
+        config2.canvas.addEventListener("webglcontextrestored", (e2) => {
+          log("error: humangl context restored:", e2);
+        });
+        config2.canvas.addEventListener("webglcontextcreationerror", (e2) => {
+          log("error: humangl context create:", e2);
+        });
+      }
+    } catch (err) {
+      log("error: cannot get WebGL context:", err);
+      return;
+    }
+    try {
+      setWebGLContext(2, config2.gl);
+    } catch (err) {
+      log("error: cannot set WebGL context:", err);
+      return;
+    }
+    try {
+      const ctx = new GPGPUContext(config2.gl);
+      registerBackend(config2.name, () => new MathBackendWebGL(ctx), config2.priority);
+    } catch (err) {
+      log("error: cannot register WebGL backend:", err);
+      return;
+    }
+    try {
+      const kernels = getKernelsForBackend("webgl");
+      kernels.forEach((kernelConfig) => {
+        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
+        registerKernel(newKernelConfig);
+      });
+    } catch (err) {
+      log("error: cannot update WebGL backend registration:", err);
+      return;
+    }
+    const current = backend().getGPGPUContext ? backend().getGPGPUContext().gl : null;
+    if (current) {
+      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);
+    } else {
+      log("error: no current gl context:", current, config2.gl);
+      return;
+    }
+    try {
+      ENV.set("WEBGL_VERSION", 2);
+    } catch (err) {
+      log("error: cannot set WebGL backend flags:", err);
+      return;
+    }
+    extensions();
+    log("backend registered:", config2.name);
+  }
+}
+
+// src/tfjs/backend.ts
+function registerCustomOps() {
+  if (!env2.kernels.includes("mod")) {
+    const kernelMod = {
+      kernelName: "Mod",
+      backendName: getBackend(),
+      kernelFunc: (op2) => tidy(() => sub(op2.inputs.a, mul(div(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
+    };
+    registerKernel(kernelMod);
+    env2.kernels.push("mod");
+  }
+  if (!env2.kernels.includes("floormod")) {
+    const kernelMod = {
+      kernelName: "FloorMod",
+      backendName: getBackend(),
+      kernelFunc: (op2) => tidy(() => floorDiv(op2.inputs.a / op2.inputs.b) * op2.inputs.b + mod(op2.inputs.a, op2.inputs.b))
+    };
+    registerKernel(kernelMod);
+    env2.kernels.push("floormod");
+  }
+}
+async function check(instance, force = false) {
+  instance.state = "backend";
+  if (force || env2.initial || instance.config.backend && instance.config.backend.length > 0 && getBackend() !== instance.config.backend) {
+    const timeStamp = now();
+    if (instance.config.backend && instance.config.backend.length > 0) {
+      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) {
+        if (instance.config.debug)
+          log("running inside web worker");
+      }
+      if (env2.browser && instance.config.backend === "tensorflow") {
+        if (instance.config.debug)
+          log("override: backend set to tensorflow while running in browser");
+        instance.config.backend = "humangl";
+      }
+      if (env2.node && (instance.config.backend === "webgl" || instance.config.backend === "humangl")) {
+        if (instance.config.debug)
+          log(`override: backend set to ${instance.config.backend} while running in nodejs`);
+        instance.config.backend = "tensorflow";
+      }
+      if (env2.browser && instance.config.backend === "webgpu") {
+        if (typeof navigator === "undefined" || typeof navigator["gpu"] === "undefined") {
+          log("override: backend set to webgpu but browser does not support webgpu");
+          instance.config.backend = "humangl";
+        } else {
+          const adapter = await navigator["gpu"].requestAdapter();
+          if (instance.config.debug)
+            log("enumerated webgpu adapter:", adapter);
+          if (!adapter) {
+            log("override: backend set to webgpu but browser reports no available gpu");
+            instance.config.backend = "humangl";
+          } else {
+            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
+            log("webgpu adapter info:", adapterInfo);
+          }
+        }
+      }
+      if (instance.config.backend === "humangl")
+        await register(instance);
+      const available = Object.keys(engine().registryFactory);
+      if (instance.config.debug)
+        log("available backends:", available);
+      if (!available.includes(instance.config.backend)) {
+        log(`error: backend ${instance.config.backend} not found in registry`);
+        instance.config.backend = env2.node ? "tensorflow" : "webgl";
+        if (instance.config.debug)
+          log(`override: setting backend ${instance.config.backend}`);
+      }
+      if (instance.config.debug)
+        log("setting backend:", instance.config.backend);
+      if (instance.config.backend === "wasm") {
+        try {
+          env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
+        } catch (e2) {
+        }
+        if (instance.config.debug)
+          log("wasm path:", instance.config.wasmPath);
+        if (typeof (tfjs_esm_exports == null ? void 0 : tfjs_esm_exports.setWasmPaths) !== "undefined")
+          await setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);
+        else
+          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
+        const simd = await env().getAsync("WASM_HAS_SIMD_SUPPORT");
+        const mt = await env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
+        if (instance.config.debug)
+          log(`wasm execution: ${simd ? "SIMD" : "no SIMD"} ${mt ? "multithreaded" : "singlethreaded"}`);
+        if (instance.config.debug && !simd)
+          log("warning: wasm simd support is not enabled");
+      }
+      try {
+        await setBackend(instance.config.backend);
+        await ready();
+        init2();
+      } catch (err) {
+        log("error: cannot set backend:", instance.config.backend, err);
+        return false;
+      }
+    }
+    if (getBackend() === "humangl") {
+      ENV.set("CHECK_COMPUTATION_FOR_ERRORS", false);
+      ENV.set("WEBGL_CPU_FORWARD", true);
+      ENV.set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      ENV.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
+      if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
+        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
+        ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+      }
+      if (backend().getGPGPUContext) {
+        const gl = await backend().getGPGPUContext().gl;
+        if (instance.config.debug)
+          log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);
+      }
+    }
+    if (getBackend() === "webgpu") {
+    }
+    enableProdMode();
+    await ready();
+    instance.performance.initBackend = Math.trunc(now() - timeStamp);
+    instance.config.backend = getBackend();
+    await env2.updateBackend();
+    registerCustomOps();
+  }
+  return true;
+}
+function fakeOps(kernelNames, config3) {
+  for (const kernelName of kernelNames) {
+    const kernelConfig = {
+      kernelName,
+      backendName: config3.backend,
+      kernelFunc: () => {
+        if (config3.debug)
+          log("kernelFunc", kernelName, config3.backend);
+      }
+    };
+    registerKernel(kernelConfig);
+  }
+  env2.kernels = getKernelsForBackend(getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+}
+
+// src/draw/draw.ts
+var draw_exports = {};
+__export(draw_exports, {
+  all: () => all5,
+  body: () => body,
+  canvas: () => canvas2,
+  face: () => face,
+  gesture: () => gesture,
+  hand: () => hand,
+  object: () => object,
+  options: () => options3,
+  person: () => person
+});
+
+// src/draw/primitives.ts
+var getCanvasContext = (input2) => {
+  if (!input2)
+    log("draw error: invalid canvas");
+  else if (!input2.getContext)
+    log("draw error: canvas context not defined");
+  else {
+    const ctx = input2.getContext("2d");
+    if (!ctx)
+      log("draw error: cannot get canvas context");
+    else
+      return ctx;
+  }
+  return null;
+};
+var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
+var colorDepth = (z, opt2) => {
+  if (!opt2.useDepth || typeof z === "undefined")
+    return opt2.color;
+  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
+  const color = `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
+  return color;
+};
+function point(ctx, x, y, z, localOptions) {
+  ctx.fillStyle = colorDepth(z, localOptions);
+  ctx.beginPath();
+  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
+  ctx.fill();
+}
+function rect(ctx, x, y, width, height, localOptions) {
+  ctx.beginPath();
+  ctx.lineWidth = localOptions.lineWidth;
+  if (localOptions.useCurves) {
+    const cx = (x + x + width) / 2;
+    const cy = (y + y + height) / 2;
+    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
+  } else {
+    ctx.moveTo(x + localOptions.roundRect, y);
+    ctx.lineTo(x + width - localOptions.roundRect, y);
+    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
+    ctx.lineTo(x + width, y + height - localOptions.roundRect);
+    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
+    ctx.lineTo(x + localOptions.roundRect, y + height);
+    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
+    ctx.lineTo(x, y + localOptions.roundRect);
+    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
+    ctx.closePath();
+  }
+  ctx.stroke();
+}
+function lines(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.beginPath();
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (const pt of points) {
+    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);
+    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
+  }
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function curves(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.lineWidth = localOptions.lineWidth;
+  if (!localOptions.useCurves || points.length <= 2) {
+    lines(ctx, points, localOptions);
+    return;
+  }
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (let i2 = 0; i2 < points.length - 2; i2++) {
+    const xc = (points[i2][0] + points[i2 + 1][0]) / 2;
+    const yc = (points[i2][1] + points[i2 + 1][1]) / 2;
+    ctx.quadraticCurveTo(points[i2][0], points[i2][1], xc, yc);
+  }
+  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function arrow(ctx, from, to, radius = 5) {
+  let angle;
+  let x;
+  let y;
+  ctx.beginPath();
+  ctx.moveTo(from[0], from[1]);
+  ctx.lineTo(to[0], to[1]);
+  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.moveTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  ctx.closePath();
+  ctx.stroke();
+  ctx.fill();
+}
+
+// src/draw/options.ts
+var options3 = {
+  color: "rgba(173, 216, 230, 0.6)",
+  labelColor: "rgba(173, 216, 230, 1)",
+  shadowColor: "black",
+  alpha: 0.5,
+  font: 'small-caps 16px "Segoe UI"',
+  lineHeight: 18,
+  lineWidth: 4,
+  pointSize: 2,
+  roundRect: 8,
+  drawPoints: false,
+  drawLabels: true,
+  drawBoxes: true,
+  drawAttention: true,
+  drawGestures: true,
+  drawPolygons: true,
+  drawGaze: true,
+  fillPolygons: false,
+  useDepth: true,
+  useCurves: false
+};
+
+// src/draw/face.ts
+var opt;
+function drawLabels(f, ctx) {
+  if (opt.drawLabels) {
+    const labels2 = [];
+    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
+    if (f.genderScore)
+      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
+    if (f.age)
+      labels2.push(`age: ${f.age || ""}`);
+    if (f.iris)
+      labels2.push(`distance: ${f.iris}`);
+    if (f.real)
+      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
+    if (f.live)
+      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
+    if (f.emotion && f.emotion.length > 0) {
+      const emotion2 = f.emotion.map((a6) => `${Math.trunc(100 * a6.score)}% ${a6.emotion}`);
+      if (emotion2.length > 3)
+        emotion2.length = 3;
+      labels2.push(emotion2.join(" "));
+    }
+    if (f.rotation && f.rotation.angle && f.rotation.gaze) {
+      if (f.rotation.angle.roll)
+        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
+      if (f.rotation.gaze.bearing)
+        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
+    }
+    if (labels2.length === 0)
+      labels2.push("face");
+    ctx.fillStyle = opt.color;
+    for (let i2 = labels2.length - 1; i2 >= 0; i2--) {
+      const x = Math.max(f.box[0], 0);
+      const y = i2 * opt.lineHeight + f.box[1];
+      if (opt.shadowColor && opt.shadowColor !== "") {
+        ctx.fillStyle = opt.shadowColor;
+        ctx.fillText(labels2[i2], x + 5, y + 16);
+      }
+      ctx.fillStyle = opt.labelColor;
+      ctx.fillText(labels2[i2], x + 4, y + 15);
+    }
+  }
+}
+function drawIrisElipse(f, ctx) {
+  if (f.annotations && f.annotations["leftEyeIris"] && f.annotations["leftEyeIris"][0]) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations["leftEyeIris"][3][0] - f.annotations["leftEyeIris"][1][0]) / 2;
+    const sizeY = Math.abs(f.annotations["leftEyeIris"][4][1] - f.annotations["leftEyeIris"][2][1]) / 2;
+    ctx.ellipse(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+  if (f.annotations && f.annotations["rightEyeIris"] && f.annotations["rightEyeIris"][0]) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations["rightEyeIris"][3][0] - f.annotations["rightEyeIris"][1][0]) / 2;
+    const sizeY = Math.abs(f.annotations["rightEyeIris"][4][1] - f.annotations["rightEyeIris"][2][1]) / 2;
+    ctx.ellipse(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+}
+function drawGazeSpheres(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") {
+    ctx.strokeStyle = "pink";
+    const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
+    const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
+    const pathV = new Path2D(`
+      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
+      C
+        ${valX} ${f.box[1]},
+        ${valX} ${f.box[1] + f.box[3]},
+        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
+    `);
+    const pathH = new Path2D(`
+      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
+      C 
+        ${f.box[0]} ${valY},
+        ${f.box[0] + f.box[2]} ${valY},
+        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
+    `);
+    ctx.stroke(pathH);
+    ctx.stroke(pathV);
+  }
+}
+function drawGazeArrows(f, ctx) {
+  var _a, _b, _c, _d;
+  if (opt.drawGaze && ((_b = (_a = f.rotation) == null ? void 0 : _a.gaze) == null ? void 0 : _b.strength) && ((_d = (_c = f.rotation) == null ? void 0 : _c.gaze) == null ? void 0 : _d.bearing) && f.annotations["leftEyeIris"] && f.annotations["rightEyeIris"] && f.annotations["leftEyeIris"][0] && f.annotations["rightEyeIris"][0]) {
+    ctx.strokeStyle = "pink";
+    ctx.fillStyle = "pink";
+    const leftGaze = [
+      f.annotations["leftEyeIris"][0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations["leftEyeIris"][0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1]], [leftGaze[0], leftGaze[1]], 4);
+    const rightGaze = [
+      f.annotations["rightEyeIris"][0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations["rightEyeIris"][0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1]], [rightGaze[0], rightGaze[1]], 4);
+  }
+}
+function drawFacePolygons(f, ctx) {
+  if (opt.drawPolygons && f.mesh.length >= 468) {
+    ctx.lineWidth = 1;
+    for (let i2 = 0; i2 < TRI468.length / 3; i2++) {
+      const points = [TRI468[i2 * 3 + 0], TRI468[i2 * 3 + 1], TRI468[i2 * 3 + 2]].map((index2) => f.mesh[index2]);
+      lines(ctx, points, opt);
+    }
+    drawIrisElipse(f, ctx);
+  }
+}
+function drawFacePoints(f, ctx) {
+  if (opt.drawPoints && f.mesh.length >= 468) {
+    for (let i2 = 0; i2 < f.mesh.length; i2++) {
+      point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2], opt);
+      if (opt.drawAttention) {
+        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i2))
+          point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2] + 127, opt);
+        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i2))
+          point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i2))
+          point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2] - 127, opt);
+      }
+    }
+  }
+}
+function drawFaceBoxes(f, ctx) {
+  if (opt.drawBoxes) {
+    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
+  }
+}
+async function face(inCanvas2, result, drawOptions) {
+  opt = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.font = opt.font;
+  ctx.strokeStyle = opt.color;
+  ctx.fillStyle = opt.color;
+  for (const f of result) {
+    drawFaceBoxes(f, ctx);
+    drawLabels(f, ctx);
+    if (f.mesh && f.mesh.length > 0) {
+      drawFacePoints(f, ctx);
+      drawFacePolygons(f, ctx);
+      drawGazeSpheres(f, ctx);
+      drawGazeArrows(f, ctx);
+    }
+  }
+}
+
+// src/draw/body.ts
+async function body(inCanvas2, result, drawOptions) {
+  var _a;
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  for (let i2 = 0; i2 < result.length; i2++) {
+    ctx.strokeStyle = localOptions.color;
+    ctx.fillStyle = localOptions.color;
+    ctx.lineWidth = localOptions.lineWidth;
+    ctx.font = localOptions.font;
+    if (localOptions.drawBoxes && result[i2].box && ((_a = result[i2].box) == null ? void 0 : _a.length) === 4) {
+      rect(ctx, result[i2].box[0], result[i2].box[1], result[i2].box[2], result[i2].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`body ${100 * result[i2].score}%`, result[i2].box[0] + 3, 1 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`body ${100 * result[i2].score}%`, result[i2].box[0] + 2, 0 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+      }
+    }
+    if (localOptions.drawPoints && result[i2].keypoints) {
+      for (let pt = 0; pt < result[i2].keypoints.length; pt++) {
+        if (!result[i2].keypoints[pt].score || result[i2].keypoints[pt].score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(result[i2].keypoints[pt].position[2], localOptions);
+        point(ctx, result[i2].keypoints[pt].position[0], result[i2].keypoints[pt].position[1], 0, localOptions);
+      }
+    }
+    if (localOptions.drawLabels && result[i2].keypoints) {
+      ctx.font = localOptions.font;
+      for (const pt of result[i2].keypoints) {
+        if (!pt.score || pt.score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(pt.position[2], localOptions);
+        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
+      }
+    }
+    if (localOptions.drawPolygons && result[i2].keypoints && result[i2].annotations) {
+      for (const part of Object.values(result[i2].annotations)) {
+        for (const connected4 of part)
+          curves(ctx, connected4, localOptions);
+      }
+    }
+  }
+}
+
+// src/draw/hand.ts
+async function hand(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+    if (localOptions.drawPoints) {
+      if (h.keypoints && h.keypoints.length > 0) {
+        for (const pt of h.keypoints) {
+          ctx.fillStyle = colorDepth(pt[2], localOptions);
+          point(ctx, pt[0], pt[1], 0, localOptions);
+        }
+      }
+    }
+    if (localOptions.drawLabels && h.annotations) {
+      const addHandLabel = (part, title) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        const z = part[part.length - 1][2] || -256;
+        ctx.fillStyle = colorDepth(z, localOptions);
+        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
+      };
+      ctx.font = localOptions.font;
+      addHandLabel(h.annotations["index"], "index");
+      addHandLabel(h.annotations["middle"], "middle");
+      addHandLabel(h.annotations["ring"], "ring");
+      addHandLabel(h.annotations["pinky"], "pinky");
+      addHandLabel(h.annotations["thumb"], "thumb");
+      addHandLabel(h.annotations["palm"], "palm");
+    }
+    if (localOptions.drawPolygons && h.annotations) {
+      const addHandLine = (part) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        for (let i2 = 0; i2 < part.length; i2++) {
+          ctx.beginPath();
+          const z = part[i2][2] || 0;
+          ctx.strokeStyle = colorDepth(i2 * z, localOptions);
+          ctx.moveTo(part[i2 > 0 ? i2 - 1 : 0][0], part[i2 > 0 ? i2 - 1 : 0][1]);
+          ctx.lineTo(part[i2][0], part[i2][1]);
+          ctx.stroke();
+        }
+      };
+      ctx.lineWidth = localOptions.lineWidth;
+      addHandLine(h.annotations["index"]);
+      addHandLine(h.annotations["middle"]);
+      addHandLine(h.annotations["ring"]);
+      addHandLine(h.annotations["pinky"]);
+      addHandLine(h.annotations["thumb"]);
+    }
+  }
+}
+
+// src/draw/object.ts
+async function object(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `${h.label} ${Math.round(100 * h.score)}%`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+
+// src/draw/gesture.ts
+async function gesture(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  if (localOptions.drawGestures) {
+    const ctx = getCanvasContext(inCanvas2);
+    if (!ctx)
+      return;
+    ctx.font = localOptions.font;
+    ctx.fillStyle = localOptions.color;
+    let i2 = 1;
+    for (let j = 0; j < result.length; j++) {
+      let where2 = [];
+      let what = [];
+      [where2, what] = Object.entries(result[j]);
+      if (what.length > 1 && what[1].length > 0) {
+        const who = where2[1] > 0 ? `#${where2[1]}` : "";
+        const label = `${where2[0]} ${who}: ${what[1]}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, 8, 2 + i2 * localOptions.lineHeight);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, 6, 0 + i2 * localOptions.lineHeight);
+        i2 += 1;
+      }
+    }
+  }
+}
+
+// src/draw/draw.ts
+var drawTime = 0;
+async function person(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (let i2 = 0; i2 < result.length; i2++) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, result[i2].box[0], result[i2].box[1], result[i2].box[2], result[i2].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `person #${i2}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, result[i2].box[0] + 3, 1 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, result[i2].box[0] + 2, 0 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+async function canvas2(input2, output) {
+  if (!input2 || !output)
+    return;
+  const ctx = getCanvasContext(output);
+  if (!ctx)
+    return;
+  ctx.drawImage(input2, 0, 0);
+}
+async function all5(inCanvas2, result, drawOptions) {
+  if (!result || !result.performance || !result || !inCanvas2)
+    return null;
+  const timeStamp = now();
+  const localOptions = mergeDeep(options3, drawOptions);
+  const promise = Promise.all([
+    face(inCanvas2, result.face, localOptions),
+    body(inCanvas2, result.body, localOptions),
+    hand(inCanvas2, result.hand, localOptions),
+    object(inCanvas2, result.object, localOptions),
+    gesture(inCanvas2, result.gesture, localOptions)
+  ]);
+  drawTime = env2.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
+  result.performance.draw = drawTime;
+  return promise;
+}
+
+// src/face/mask.ts
+var expandFact = 0.1;
+var alpha = 0.5;
+function insidePoly(x, y, polygon) {
+  let inside = false;
+  let j = polygon.length - 1;
+  for (let i2 = 0; i2 < polygon.length; j = i2++) {
+    if (polygon[i2].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i2].x) * (y - polygon[i2].y) / (polygon[j].y - polygon[i2].y) + polygon[i2].x)
+      inside = !inside;
+  }
+  return inside;
+}
+async function mask(face4) {
+  if (!face4.tensor)
+    return face4.tensor;
+  if (!face4.mesh || face4.mesh.length < 100)
+    return face4.tensor;
+  const width = face4.tensor.shape[2] || 0;
+  const height = face4.tensor.shape[1] || 0;
+  const buffer2 = await face4.tensor.buffer();
+  let silhouette = [];
+  for (const pt of meshAnnotations.silhouette)
+    silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
+  if (expandFact && expandFact > 0)
+    silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
+  for (let x = 0; x < width; x++) {
+    for (let y = 0; y < height; y++) {
+      const inside = insidePoly(x / width, y / width, silhouette);
+      if (!inside) {
+        buffer2.set(alpha * buffer2.get(0, y, x, 0), 0, y, x, 0);
+        buffer2.set(alpha * buffer2.get(0, y, x, 1), 0, y, x, 1);
+        buffer2.set(alpha * buffer2.get(0, y, x, 2), 0, y, x, 2);
+      }
+    }
+  }
+  const output = buffer2.toTensor();
+  dispose(buffer2);
+  return output;
+}
+
+// src/face/angles.ts
+var calculateGaze = (face4) => {
+  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
+  if (!face4.annotations["rightEyeIris"] || !face4.annotations["leftEyeIris"])
+    return { bearing: 0, strength: 0 };
+  const offsetIris = [0, -0.1];
+  const eyeRatio = 1;
+  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
+  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
+  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
+  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
+  const eyeDiff = [
+    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
+    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
+  ];
+  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
+  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
+  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
+  return { bearing, strength };
+};
+var calculateFaceAngle = (face4, imageSize) => {
+  const normalize = (v) => {
+    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
+    v[0] /= length;
+    v[1] /= length;
+    v[2] /= length;
+    return v;
+  };
+  const subVectors = (a6, b) => {
+    const x = a6[0] - b[0];
+    const y = a6[1] - b[1];
+    const z = a6[2] - b[2];
+    return [x, y, z];
+  };
+  const crossVectors = (a6, b) => {
+    const x = a6[1] * b[2] - a6[2] * b[1];
+    const y = a6[2] * b[0] - a6[0] * b[2];
+    const z = a6[0] * b[1] - a6[1] * b[0];
+    return [x, y, z];
+  };
+  const rotationMatrixToEulerAngle = (r2) => {
+    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r2;
+    let thetaX;
+    let thetaY;
+    let thetaZ;
+    if (r10 < 1) {
+      if (r10 > -1) {
+        thetaZ = Math.asin(r10);
+        thetaY = Math.atan2(-r20, r00);
+        thetaX = Math.atan2(-r12, r11);
+      } else {
+        thetaZ = -Math.PI / 2;
+        thetaY = -Math.atan2(r21, r22);
+        thetaX = 0;
+      }
+    } else {
+      thetaZ = Math.PI / 2;
+      thetaY = Math.atan2(r21, r22);
+      thetaX = 0;
+    }
+    if (isNaN(thetaX))
+      thetaX = 0;
+    if (isNaN(thetaY))
+      thetaY = 0;
+    if (isNaN(thetaZ))
+      thetaZ = 0;
+    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
+  };
+  const mesh = face4.meshRaw;
+  if (!mesh || mesh.length < 300)
+    return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
+  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
+  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
+  const y_axis = normalize(subVectors(pts[1], pts[0]));
+  let x_axis = normalize(subVectors(pts[3], pts[2]));
+  const z_axis = normalize(crossVectors(x_axis, y_axis));
+  x_axis = crossVectors(y_axis, z_axis);
+  const matrix = [
+    x_axis[0],
+    x_axis[1],
+    x_axis[2],
+    y_axis[0],
+    y_axis[1],
+    y_axis[2],
+    z_axis[0],
+    z_axis[1],
+    z_axis[2]
+  ];
+  const angle = rotationMatrixToEulerAngle(matrix);
+  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
+  return { angle, matrix, gaze };
+};
+
+// src/face/face.ts
+var detectFace = async (instance, input2) => {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v;
+  let timeStamp = now();
+  let ageRes;
+  let gearRes;
+  let genderRes;
+  let emotionRes;
+  let mobilefacenetRes;
+  let antispoofRes;
+  let livenessRes;
+  let descRes;
+  const faceRes = [];
+  instance.state = "run:face";
+  const faces = await predict10(input2, instance.config);
+  instance.performance.face = env2.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+  if (!input2.shape || input2.shape.length !== 4)
+    return [];
+  if (!faces)
+    return [];
+  for (let i2 = 0; i2 < faces.length; i2++) {
+    instance.analyze("Get Face");
+    if (!faces[i2].tensor || faces[i2].tensor["isDisposedInternal"]) {
+      log("Face object is disposed:", faces[i2].tensor);
+      continue;
+    }
+    if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) {
+      const masked = await mask(faces[i2]);
+      dispose(faces[i2].tensor);
+      faces[i2].tensor = masked;
+    }
+    const rotation = faces[i2].mesh && faces[i2].mesh.length > 200 ? calculateFaceAngle(faces[i2], [input2.shape[2], input2.shape[1]]) : null;
+    instance.analyze("Start Emotion:");
+    if (instance.config.async) {
+      emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict8(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : [];
+    } else {
+      instance.state = "run:emotion";
+      timeStamp = now();
+      emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict8(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : [];
+      instance.performance.emotion = env2.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End Emotion:");
+    instance.analyze("Start AntiSpoof:");
+    if (instance.config.async) {
+      antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict4(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : 0;
+    } else {
+      instance.state = "run:antispoof";
+      timeStamp = now();
+      antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict4(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : 0;
+      instance.performance.antispoof = env2.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End AntiSpoof:");
+    instance.analyze("Start Liveness:");
+    if (instance.config.async) {
+      livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict14(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : 0;
+    } else {
+      instance.state = "run:liveness";
+      timeStamp = now();
+      livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict14(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : 0;
+      instance.performance.liveness = env2.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End Liveness:");
+    instance.analyze("Start GEAR:");
+    if (instance.config.async) {
+      gearRes = ((_h = instance.config.face["gear"]) == null ? void 0 : _h.enabled) ? predict(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+    } else {
+      instance.state = "run:gear";
+      timeStamp = now();
+      gearRes = ((_i = instance.config.face["gear"]) == null ? void 0 : _i.enabled) ? await predict(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+      instance.performance.gear = Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End GEAR:");
+    instance.analyze("Start SSRNet:");
+    if (instance.config.async) {
+      ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict2(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+      genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict3(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+    } else {
+      instance.state = "run:ssrnet";
+      timeStamp = now();
+      ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict2(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+      genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict3(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+      instance.performance.ssrnet = Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End SSRNet:");
+    instance.analyze("Start MobileFaceNet:");
+    if (instance.config.async) {
+      mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict9(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+    } else {
+      instance.state = "run:mobilefacenet";
+      timeStamp = now();
+      mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict9(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End MobileFaceNet:");
+    instance.analyze("Start Description:");
+    if (instance.config.async) {
+      descRes = ((_p = instance.config.face.description) == null ? void 0 : _p.enabled) ? predict11(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+    } else {
+      instance.state = "run:description";
+      timeStamp = now();
+      descRes = ((_q = instance.config.face.description) == null ? void 0 : _q.enabled) ? await predict11(faces[i2].tensor || tensor([]), instance.config, i2, faces.length) : null;
+      instance.performance.description = env2.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance.analyze("End Description:");
+    if (instance.config.async) {
+      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);
+    }
+    instance.analyze("Finish Face:");
+    if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
+      descRes = {
+        ...descRes,
+        age: ageRes.age,
+        gender: genderRes.gender,
+        genderScore: genderRes.genderScore
+      };
+    }
+    if (((_s = instance.config.face["gear"]) == null ? void 0 : _s.enabled) && gearRes) {
+      descRes = {
+        ...descRes,
+        age: gearRes.age,
+        gender: gearRes.gender,
+        genderScore: gearRes.genderScore,
+        race: gearRes.race
+      };
+    }
+    if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
+      descRes.descriptor = mobilefacenetRes;
+    }
+    if (!((_u = instance.config.face.iris) == null ? void 0 : _u.enabled)) {
+    }
+    const irisSize = faces[i2].annotations && faces[i2].annotations.leftEyeIris && faces[i2].annotations.leftEyeIris[0] && faces[i2].annotations.rightEyeIris && faces[i2].annotations.rightEyeIris[0] && faces[i2].annotations.leftEyeIris.length > 0 && faces[i2].annotations.rightEyeIris.length > 0 && faces[i2].annotations.leftEyeIris[0] !== null && faces[i2].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i2].annotations.leftEyeIris[3][0] - faces[i2].annotations.leftEyeIris[1][0]), Math.abs(faces[i2].annotations.rightEyeIris[4][1] - faces[i2].annotations.rightEyeIris[2][1])) / input2.shape[2] : 0;
+    const tensor2 = ((_v = instance.config.face.detector) == null ? void 0 : _v.return) ? squeeze(faces[i2].tensor) : null;
+    dispose(faces[i2].tensor);
+    if (faces[i2].tensor)
+      delete faces[i2].tensor;
+    const res = {
+      ...faces[i2],
+      id: i2
+    };
+    if (descRes == null ? void 0 : descRes.age)
+      res.age = descRes.age;
+    if (descRes == null ? void 0 : descRes.gender)
+      res.gender = descRes.gender;
+    if (descRes == null ? void 0 : descRes.genderScore)
+      res.genderScore = descRes == null ? void 0 : descRes.genderScore;
+    if (descRes == null ? void 0 : descRes.descriptor)
+      res.embedding = descRes == null ? void 0 : descRes.descriptor;
+    if (descRes == null ? void 0 : descRes.race)
+      res.race = descRes == null ? void 0 : descRes.race;
+    if (emotionRes)
+      res.emotion = emotionRes;
+    if (antispoofRes)
+      res.real = antispoofRes;
+    if (livenessRes)
+      res.live = livenessRes;
+    if (irisSize && irisSize !== 0)
+      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
+    if (rotation)
+      res.rotation = rotation;
+    if (tensor2)
+      res.tensor = tensor2;
+    faceRes.push(res);
+    instance.analyze("End Face");
+  }
+  instance.analyze("End FaceMesh:");
+  if (instance.config.async) {
+    if (instance.performance.face)
+      delete instance.performance.face;
+    if (instance.performance.age)
+      delete instance.performance.age;
+    if (instance.performance.gender)
+      delete instance.performance.gender;
+    if (instance.performance.emotion)
+      delete instance.performance.emotion;
+  }
+  return faceRes;
+};
+
+// src/gesture/gesture.ts
+var body2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i2 = 0; i2 < res.length; i2++) {
+    const leftWrist = res[i2].keypoints.find((a6) => a6.part === "leftWrist");
+    const rightWrist = res[i2].keypoints.find((a6) => a6.part === "rightWrist");
+    const nose = res[i2].keypoints.find((a6) => a6.part === "nose");
+    if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i2, gesture: "i give up" });
+    else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
+      gestures.push({ body: i2, gesture: "raise left hand" });
+    else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i2, gesture: "raise right hand" });
+    const leftShoulder = res[i2].keypoints.find((a6) => a6.part === "leftShoulder");
+    const rightShoulder = res[i2].keypoints.find((a6) => a6.part === "rightShoulder");
+    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
+      gestures.push({ body: i2, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
+    }
+  }
+  return gestures;
+};
+var face2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i2 = 0; i2 < res.length; i2++) {
+    if (res[i2].mesh && res[i2].mesh.length > 450) {
+      const zDiff = (res[i2].mesh[33][2] || 0) - (res[i2].mesh[263][2] || 0);
+      const xDiff = res[i2].mesh[33][0] - res[i2].mesh[263][0];
+      if (Math.abs(zDiff / xDiff) <= 0.15)
+        gestures.push({ face: i2, gesture: "facing center" });
+      else
+        gestures.push({ face: i2, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
+      const openLeft = Math.abs(res[i2].mesh[374][1] - res[i2].mesh[386][1]) / Math.abs(res[i2].mesh[443][1] - res[i2].mesh[450][1]);
+      if (openLeft < 0.2)
+        gestures.push({ face: i2, gesture: "blink left eye" });
+      const openRight = Math.abs(res[i2].mesh[145][1] - res[i2].mesh[159][1]) / Math.abs(res[i2].mesh[223][1] - res[i2].mesh[230][1]);
+      if (openRight < 0.2)
+        gestures.push({ face: i2, gesture: "blink right eye" });
+      const mouthOpen = Math.min(100, 500 * Math.abs(res[i2].mesh[13][1] - res[i2].mesh[14][1]) / Math.abs(res[i2].mesh[10][1] - res[i2].mesh[152][1]));
+      if (mouthOpen > 10)
+        gestures.push({ face: i2, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
+      const chinDepth = res[i2].mesh[152][2] || 0;
+      if (Math.abs(chinDepth) > 10)
+        gestures.push({ face: i2, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
+    }
+  }
+  return gestures;
+};
+var iris2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i2 = 0; i2 < res.length; i2++) {
+    if (!res[i2].annotations || !res[i2].annotations.leftEyeIris || !res[i2].annotations.leftEyeIris[0] || !res[i2].annotations.rightEyeIris || !res[i2].annotations.rightEyeIris[0])
+      continue;
+    const sizeXLeft = res[i2].annotations.leftEyeIris[3][0] - res[i2].annotations.leftEyeIris[1][0];
+    const sizeYLeft = res[i2].annotations.leftEyeIris[4][1] - res[i2].annotations.leftEyeIris[2][1];
+    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
+    const sizeXRight = res[i2].annotations.rightEyeIris[3][0] - res[i2].annotations.rightEyeIris[1][0];
+    const sizeYRight = res[i2].annotations.rightEyeIris[4][1] - res[i2].annotations.rightEyeIris[2][1];
+    const areaRight = Math.abs(sizeXRight * sizeYRight);
+    let center = false;
+    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
+    if (difference < 0.25) {
+      center = true;
+      gestures.push({ iris: i2, gesture: "facing center" });
+    }
+    const leftIrisCenterX = Math.abs(res[i2].mesh[263][0] - res[i2].annotations.leftEyeIris[0][0]) / res[i2].box[2];
+    const rightIrisCenterX = Math.abs(res[i2].mesh[33][0] - res[i2].annotations.rightEyeIris[0][0]) / res[i2].box[2];
+    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
+      center = false;
+    if (leftIrisCenterX > rightIrisCenterX) {
+      if (leftIrisCenterX > 0.05)
+        gestures.push({ iris: i2, gesture: "looking right" });
+    } else {
+      if (rightIrisCenterX > 0.05)
+        gestures.push({ iris: i2, gesture: "looking left" });
+    }
+    const rightIrisCenterY = Math.abs(res[i2].mesh[145][1] - res[i2].annotations.rightEyeIris[0][1]) / res[i2].box[3];
+    const leftIrisCenterY = Math.abs(res[i2].mesh[374][1] - res[i2].annotations.leftEyeIris[0][1]) / res[i2].box[3];
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      center = false;
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
+      gestures.push({ iris: i2, gesture: "looking down" });
+    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      gestures.push({ iris: i2, gesture: "looking up" });
+    if (center)
+      gestures.push({ iris: i2, gesture: "looking center" });
+  }
+  return gestures;
+};
+var hand2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i2 = 0; i2 < res.length; i2++) {
+    const fingers = [];
+    if (res[i2]["annotations"]) {
+      for (const [finger, pos] of Object.entries(res[i2]["annotations"])) {
+        if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
+          fingers.push({ name: finger.toLowerCase(), position: pos[0] });
+      }
+    }
+    if (fingers && fingers.length > 0) {
+      const closest = fingers.reduce((best, a6) => (best.position[2] || 0) < (a6.position[2] || 0) ? best : a6);
+      gestures.push({ hand: i2, gesture: `${closest.name} forward` });
+      const highest = fingers.reduce((best, a6) => best.position[1] < a6.position[1] ? best : a6);
+      gestures.push({ hand: i2, gesture: `${highest.name} up` });
+    }
+    if (res[i2]["keypoints"]) {
+      const poses = match(res[i2]["keypoints"]);
+      for (const pose of poses)
+        gestures.push({ hand: i2, gesture: pose.name });
+    }
+  }
+  return gestures;
+};
+
+// src/util/interpolate.ts
+var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+var interpolateTime = 0;
+function calc2(newResult, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A;
+  const t0 = now();
+  if (!newResult)
+    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+  const elapsed = Date.now() - newResult.timestamp;
+  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
+  if (newResult.canvas)
+    bufferedResult.canvas = newResult.canvas;
+  if (newResult.error)
+    bufferedResult.error = newResult.error;
+  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
+    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
+  } else {
+    for (let i2 = 0; i2 < newResult.body.length; i2++) {
+      const box = newResult.body[i2].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i2].box[j] + newBoxCoord) / bufferedFactor);
+      const boxRaw = newResult.body[i2].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i2].boxRaw[j] + newBoxCoord) / bufferedFactor);
+      const keypoints = newResult.body[i2].keypoints.map((newKpt, j) => {
+        var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
+        return {
+          score: newKpt.score,
+          part: newKpt.part,
+          position: [
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
+          ],
+          positionRaw: [
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
+          ],
+          distance: [
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i2].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i2].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i2].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+          ]
+        };
+      });
+      const annotations2 = {};
+      let coords3 = { connected: {} };
+      if ((_b = (_a = config3.body) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("efficientpose"))
+        coords3 = efficientposecoords_exports;
+      else if ((_d = (_c = config3.body) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("blazepose"))
+        coords3 = blazeposecoords_exports;
+      else if ((_f = (_e = config3.body) == null ? void 0 : _e.modelPath) == null ? void 0 : _f.includes("movenet"))
+        coords3 = movenetcoords_exports;
+      for (const [name, indexes] of Object.entries(coords3.connected)) {
+        const pt = [];
+        for (let j = 0; j < indexes.length - 1; j++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);
+          if (pt0 && pt1)
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      bufferedResult.body[i2] = { ...newResult.body[i2], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
+    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
+  } else {
+    for (let i2 = 0; i2 < newResult.hand.length; i2++) {
+      const box = newResult.hand[i2].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i2].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.hand[i2].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i2].boxRaw[j] + b) / bufferedFactor);
+      if (bufferedResult.hand[i2].keypoints.length !== newResult.hand[i2].keypoints.length)
+        bufferedResult.hand[i2].keypoints = newResult.hand[i2].keypoints;
+      const keypoints = newResult.hand[i2].keypoints && newResult.hand[i2].keypoints.length > 0 ? newResult.hand[i2].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i2].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
+      let annotations2 = {};
+      if (Object.keys(bufferedResult.hand[i2].annotations).length !== Object.keys(newResult.hand[i2].annotations).length) {
+        bufferedResult.hand[i2].annotations = newResult.hand[i2].annotations;
+        annotations2 = bufferedResult.hand[i2].annotations;
+      } else if (newResult.hand[i2].annotations) {
+        for (const key of Object.keys(newResult.hand[i2].annotations)) {
+          annotations2[key] = newResult.hand[i2].annotations[key] && newResult.hand[i2].annotations[key][0] ? newResult.hand[i2].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i2].annotations[key][j][k] + coord) / bufferedFactor)) : null;
+        }
+      }
+      bufferedResult.hand[i2] = { ...newResult.hand[i2], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
+    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
+  } else {
+    for (let i2 = 0; i2 < newResult.face.length; i2++) {
+      const box = newResult.face[i2].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i2].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.face[i2].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i2].boxRaw[j] + b) / bufferedFactor);
+      if (newResult.face[i2].rotation) {
+        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
+        rotation.matrix = (_g = newResult.face[i2].rotation) == null ? void 0 : _g.matrix;
+        rotation.angle = {
+          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i2].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i2].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i2].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i2].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
+          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i2].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i2].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
+        };
+        rotation.gaze = {
+          bearing: ((bufferedFactor - 1) * (((_u = (_t = bufferedResult.face[i2].rotation) == null ? void 0 : _t.gaze) == null ? void 0 : _u.bearing) || 0) + (((_w = (_v = newResult.face[i2].rotation) == null ? void 0 : _v.gaze) == null ? void 0 : _w.bearing) || 0)) / bufferedFactor,
+          strength: ((bufferedFactor - 1) * (((_y = (_x = bufferedResult.face[i2].rotation) == null ? void 0 : _x.gaze) == null ? void 0 : _y.strength) || 0) + (((_A = (_z = newResult.face[i2].rotation) == null ? void 0 : _z.gaze) == null ? void 0 : _A.strength) || 0)) / bufferedFactor
+        };
+        bufferedResult.face[i2] = { ...newResult.face[i2], rotation, box, boxRaw };
+      }
+      bufferedResult.face[i2] = { ...newResult.face[i2], box, boxRaw };
+    }
+  }
+  if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
+    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
+  } else {
+    for (let i2 = 0; i2 < newResult.object.length; i2++) {
+      const box = newResult.object[i2].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i2].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.object[i2].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i2].boxRaw[j] + b) / bufferedFactor);
+      bufferedResult.object[i2] = { ...newResult.object[i2], box, boxRaw };
+    }
+  }
+  if (newResult.persons) {
+    const newPersons = newResult.persons;
+    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
+      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
+    } else {
+      for (let i2 = 0; i2 < newPersons.length; i2++) {
+        bufferedResult.persons[i2].box = newPersons[i2].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i2].box[j] + box) / bufferedFactor);
+      }
+    }
+  }
+  if (newResult.gesture)
+    bufferedResult.gesture = newResult.gesture;
+  const t1 = now();
+  interpolateTime = env2.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);
+  if (newResult.performance)
+    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
+  return bufferedResult;
+}
+
+// src/face/match.ts
+var match_exports = {};
+__export(match_exports, {
+  distance: () => distance,
+  match: () => match2,
+  similarity: () => similarity
+});
+function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
+  let sum7 = 0;
+  for (let i2 = 0; i2 < descriptor1.length; i2++) {
+    const diff = !options4.order || options4.order === 2 ? descriptor1[i2] - descriptor2[i2] : Math.abs(descriptor1[i2] - descriptor2[i2]);
+    sum7 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
+  }
+  return (options4.multiplier || 20) * sum7;
+}
+var normalizeDistance = (dist, order, min7, max7) => {
+  if (dist === 0)
+    return 1;
+  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
+  const norm2 = (1 - root / 100 - min7) / (max7 - min7);
+  const clamp3 = Math.max(Math.min(norm2, 1), 0);
+  return clamp3;
+};
+function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
+  const dist = distance(descriptor1, descriptor2, options4);
+  return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
+}
+function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
+  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) {
+    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
+  }
+  let lowestDistance = Number.MAX_SAFE_INTEGER;
+  let index2 = -1;
+  for (let i2 = 0; i2 < descriptors.length; i2++) {
+    const res = distance(descriptor, descriptors[i2], options4);
+    if (res < lowestDistance) {
+      lowestDistance = res;
+      index2 = i2;
+    }
+    if (lowestDistance < (options4.threshold || 0))
+      break;
+  }
+  const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
+  return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
+}
+
+// src/util/persons.ts
+function join2(faces, bodies, hands, gestures, shape) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p;
+  let id = 0;
+  const persons = [];
+  for (const face4 of faces) {
+    const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
+    for (const body4 of bodies) {
+      if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
+        person2.body = body4;
+      }
+    }
+    if (person2.body) {
+      for (const hand3 of hands) {
+        if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.left = hand3;
+        }
+        if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.right = hand3;
+        }
+      }
+    }
+    for (const gesture2 of gestures) {
+      if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
+        (_a = person2.gestures) == null ? void 0 : _a.push(gesture2);
+      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
+        (_b = person2.gestures) == null ? void 0 : _b.push(gesture2);
+      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_c = person2.body) == null ? void 0 : _c.id))
+        (_d = person2.gestures) == null ? void 0 : _d.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_f = (_e = person2.hands) == null ? void 0 : _e.left) == null ? void 0 : _f.id))
+        (_g = person2.gestures) == null ? void 0 : _g.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_i = (_h = person2.hands) == null ? void 0 : _h.right) == null ? void 0 : _i.id))
+        (_j = person2.gestures) == null ? void 0 : _j.push(gesture2);
+    }
+    const x = [];
+    const y = [];
+    const extractXY = (box) => {
+      if (box && box.length === 4) {
+        x.push(box[0], box[0] + box[2]);
+        y.push(box[1], box[1] + box[3]);
+      }
+    };
+    extractXY((_k = person2.face) == null ? void 0 : _k.box);
+    extractXY((_l = person2.body) == null ? void 0 : _l.box);
+    extractXY((_n = (_m = person2.hands) == null ? void 0 : _m.left) == null ? void 0 : _n.box);
+    extractXY((_p = (_o = person2.hands) == null ? void 0 : _o.right) == null ? void 0 : _p.box);
+    const minX = Math.min(...x);
+    const minY = Math.min(...y);
+    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
+    if (shape && shape[1] && shape[2])
+      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
+    persons.push(person2);
+  }
+  return persons;
+}
+
+// src/sample.ts
+var face3 = `
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+
+// src/warmup.ts
+async function warmupBitmap(instance) {
+  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
+  let blob;
+  let res;
+  switch (instance.config.warmup) {
+    case "face":
+      blob = await b64toBlob(face3);
+      break;
+    case "body":
+    case "full":
+      blob = await b64toBlob(body3);
+      break;
+    default:
+      blob = null;
+  }
+  if (blob) {
+    const bitmap = await createImageBitmap(blob);
+    res = await instance.detect(bitmap, instance.config);
+    bitmap.close();
+  }
+  return res;
+}
+async function warmupCanvas(instance) {
+  return new Promise((resolve) => {
+    let src;
+    switch (instance.config.warmup) {
+      case "face":
+        src = "data:image/jpeg;base64," + face3;
+        break;
+      case "full":
+      case "body":
+        src = "data:image/jpeg;base64," + body3;
+        break;
+      default:
+        src = null;
+    }
+    let img;
+    if (typeof Image !== "undefined")
+      img = new Image();
+    else if (env2.Image)
+      img = new env2.Image();
+    else
+      return;
+    img.onload = async () => {
+      const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
+      if (!canvas3) {
+        log("Warmup: Canvas not found");
+        resolve(void 0);
+      } else {
+        const ctx = canvas3.getContext("2d");
+        if (ctx)
+          ctx.drawImage(img, 0, 0);
+        const tensor2 = await instance.image(canvas3);
+        const res = await instance.detect(tensor2.tensor, instance.config);
+        resolve(res);
+      }
+    };
+    if (src)
+      img.src = src;
+    else
+      resolve(void 0);
+  });
+}
+async function warmupNode(instance) {
+  const atob2 = (str) => Buffer.from(str, "base64");
+  let img;
+  if (instance.config.warmup === "face")
+    img = atob2(face3);
+  else
+    img = atob2(body3);
+  let res;
+  if ("node" in tfjs_esm_exports) {
+    const data = (void 0).decodeJpeg(img);
+    const expanded = data.expandDims(0);
+    instance.tf.dispose(data);
+    res = await instance.detect(expanded, instance.config);
+    instance.tf.dispose(expanded);
+  } else {
+    if (instance.config.debug)
+      log("Warmup tfjs-node not loaded");
+  }
+  return res;
+}
+async function runInference(instance) {
+  let res;
+  if (typeof createImageBitmap === "function")
+    res = await warmupBitmap(instance);
+  else if (typeof Image !== "undefined" || env2.Canvas !== void 0)
+    res = await warmupCanvas(instance);
+  else
+    res = await warmupNode(instance);
+  return res;
+}
+async function runCompile(allModels) {
+  const backendType = getBackend();
+  const webGLBackend = backend();
+  if (backendType !== "webgl" && backendType !== "humangl" || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {
+    return;
+  }
+  env().set("ENGINE_COMPILE_ONLY", true);
+  const numTensorsStart = engine().state.numTensors;
+  const compiledModels = [];
+  for (const [modelName, model19] of Object.entries(allModels).filter(([key, val]) => key !== null && val !== null)) {
+    const shape = model19.inputs && model19.inputs[0] && model19.inputs[0].shape ? [...model19.inputs[0].shape] : [1, 64, 64, 3];
+    const dtype = model19.inputs && model19.inputs[0] && model19.inputs[0].dtype ? model19.inputs[0].dtype : "float32";
+    for (let dim = 0; dim < shape.length; dim++) {
+      if (shape[dim] === -1)
+        shape[dim] = dim === 0 ? 1 : 64;
+    }
+    const tensor2 = zeros(shape, dtype);
+    try {
+      const res = model19.execute(tensor2);
+      compiledModels.push(modelName);
+      if (Array.isArray(res))
+        res.forEach((t2) => dispose(t2));
+      else
+        dispose(res);
+    } catch (e2) {
+      log("compile fail model:", modelName);
+    }
+    dispose(tensor2);
+  }
+  const kernels = await webGLBackend.checkCompileCompletionAsync();
+  webGLBackend.getUniformLocations();
+  log("compile pass models:", compiledModels);
+  log("compile pass kernels:", kernels.length);
+  env().set("ENGINE_COMPILE_ONLY", false);
+  const numTensorsEnd = engine().state.numTensors;
+  if (numTensorsEnd - numTensorsStart > 0)
+    log("tensor leak:", numTensorsEnd - numTensorsStart);
+}
+async function warmup(instance, userConfig) {
+  const t0 = now();
+  instance.state = "warmup";
+  if (userConfig)
+    instance.config = mergeDeep(instance.config, userConfig);
+  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") {
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };
+  }
+  return new Promise(async (resolve) => {
+    await runCompile(instance.models);
+    const res = await runInference(instance);
+    const t1 = now();
+    if (instance.config.debug)
+      log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms");
+    instance.emit("warmup");
+    resolve(res);
+  });
+}
+
+// src/human.ts
+var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity;
+var Human = class {
+  constructor(userConfig) {
+    __publicField(this, "version");
+    __publicField(this, "config");
+    __publicField(this, "result");
+    __publicField(this, "state");
+    __publicField(this, "process");
+    __publicField(this, "tf");
+    __publicField(this, "env");
+    __publicField(this, "draw");
+    __publicField(this, "models");
+    __publicField(this, "events");
+    __publicField(this, "faceTriangulation");
+    __publicField(this, "faceUVMap");
+    __publicField(this, "performance");
+    __privateAdd(this, _numTensors, void 0);
+    __privateAdd(this, _analyzeMemoryLeaks, void 0);
+    __privateAdd(this, _checkSanity, void 0);
+    __publicField(this, "gl");
+    __publicField(this, "analyze", (...msg) => {
+      if (!__privateGet(this, _analyzeMemoryLeaks))
+        return;
+      const currentTensors = this.tf.engine().state.numTensors;
+      const previousTensors = __privateGet(this, _numTensors);
+      __privateSet(this, _numTensors, currentTensors);
+      const leaked = currentTensors - previousTensors;
+      if (leaked !== 0)
+        log(...msg, leaked);
+    });
+    __privateAdd(this, _sanity, (input2) => {
+      if (!__privateGet(this, _checkSanity))
+        return null;
+      if (!input2)
+        return "input is not defined";
+      if (this.env.node && !(input2 instanceof Tensor))
+        return "input must be a tensor";
+      try {
+        this.tf.getBackend();
+      } catch (e2) {
+        return "backend not loaded";
+      }
+      return null;
+    });
+    __publicField(this, "similarity", similarity);
+    __publicField(this, "distance", distance);
+    __publicField(this, "match", match2);
+    __publicField(this, "emit", (event) => {
+      var _a;
+      if (this.events && this.events.dispatchEvent)
+        (_a = this.events) == null ? void 0 : _a.dispatchEvent(new Event(event));
+    });
+    var _a;
+    this.env = env2;
+    const tfVersion = (((_a = V) == null ? void 0 : _a.tfjs) || version).replace(/-(.*)/, "");
+    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
+    config.modelBasePath = env2.browser ? "../models/" : "file://models/";
+    config.backend = env2.browser ? "humangl" : "tensorflow";
+    this.version = version5;
+    Object.defineProperty(this, "version", { value: version5 });
+    this.config = JSON.parse(JSON.stringify(config));
+    Object.seal(this.config);
+    this.config.cacheModels = typeof indexedDB !== "undefined";
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    setModelLoadOptions(this.config);
+    this.tf = tfjs_esm_exports;
+    this.state = "idle";
+    __privateSet(this, _numTensors, 0);
+    __privateSet(this, _analyzeMemoryLeaks, false);
+    __privateSet(this, _checkSanity, false);
+    this.performance = {};
+    this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
+    this.models = new Models();
+    this.draw = {
+      options: options3,
+      canvas: (input2, output) => canvas2(input2, output),
+      face: (output, result, options4) => face(output, result, options4),
+      body: (output, result, options4) => body(output, result, options4),
+      hand: (output, result, options4) => hand(output, result, options4),
+      gesture: (output, result, options4) => gesture(output, result, options4),
+      object: (output, result, options4) => object(output, result, options4),
+      person: (output, result, options4) => person(output, result, options4),
+      all: (output, result, options4) => all5(output, result, options4)
+    };
+    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
+    this.process = { tensor: null, canvas: null };
+    this.faceTriangulation = triangulation;
+    this.faceUVMap = uvmap;
+    this.gl = config2;
+    this.emit("create");
+  }
+  reset() {
+    const currentBackend = this.config.backend;
+    this.config = JSON.parse(JSON.stringify(config));
+    this.config.backend = currentBackend;
+  }
+  validate(userConfig) {
+    return validate(config, userConfig || this.config);
+  }
+  now() {
+    return now();
+  }
+  image(input2, getTensor2 = true) {
+    return process2(input2, this.config, getTensor2);
+  }
+  async segmentation(input2, background) {
+    return process5(input2, background, this.config);
+  }
+  enhance(input2) {
+    return enhance(input2);
+  }
+  compare(firstImageTensor, secondImageTensor) {
+    return compare(this.config, firstImageTensor, secondImageTensor);
+  }
+  async init() {
+    await check(this, true);
+    await this.tf.ready();
+  }
+  async load(userConfig) {
+    this.state = "load";
+    const timeStamp = now();
+    const count3 = Object.values(this.models).filter((model19) => model19).length;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (this.env.initial) {
+      if (this.config.debug)
+        log(`version: ${this.version}`);
+      if (this.config.debug)
+        log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
+      if (!await check(this))
+        log("error: backend check failed");
+      await ready();
+      if (this.env.browser) {
+        if (this.config.debug)
+          log("configuration:", this.config);
+        if (this.config.debug)
+          log("environment:", this.env);
+        if (this.config.debug)
+          log("tf flags:", this.tf.ENV["flags"]);
+      }
+    }
+    await load19(this);
+    if (this.env.initial && this.config.debug)
+      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
+    this.env.initial = false;
+    const loaded = Object.values(this.models).filter((model19) => model19).length;
+    if (loaded !== count3) {
+      await validate2(this);
+      this.emit("load");
+    }
+    const current = Math.trunc(now() - timeStamp);
+    if (current > (this.performance.loadModels || 0))
+      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
+  }
+  next(result = this.result) {
+    return calc2(result, this.config);
+  }
+  getModelStats() {
+    return getModelStats(this);
+  }
+  async warmup(userConfig) {
+    const t0 = now();
+    const res = await warmup(this, userConfig);
+    const t1 = now();
+    this.performance.warmup = Math.trunc(t1 - t0);
+    return res;
+  }
+  async profile(input2, userConfig) {
+    const profile2 = await this.tf.profile(() => this.detect(input2, userConfig));
+    const kernels = {};
+    for (const kernel of profile2.kernels) {
+      if (kernels[kernel.name])
+        kernels[kernel.name] += kernel.kernelTimeMs;
+      else
+        kernels[kernel.name] = kernel.kernelTimeMs;
+    }
+    const kernelArr = [];
+    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] }));
+    kernelArr.sort((a6, b) => b.ms - a6.ms);
+    kernelArr.length = 20;
+    const res = {};
+    for (const kernel of kernelArr)
+      res[kernel.name] = kernel.ms;
+    return res;
+  }
+  async detect(input2, userConfig) {
+    this.state = "detect";
+    return new Promise(async (resolve) => {
+      var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v;
+      this.state = "config";
+      let timeStamp;
+      this.config = mergeDeep(this.config, userConfig);
+      this.state = "check";
+      const error = __privateGet(this, _sanity).call(this, input2);
+      if (error) {
+        log(error, input2);
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
+      }
+      const timeStart = now();
+      await check(this);
+      await this.load();
+      timeStamp = now();
+      this.state = "image";
+      const img = await process2(input2, this.config);
+      this.process = img;
+      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Get Image:");
+      if (!img.tensor) {
+        if (this.config.debug)
+          log("could not convert input to tensor");
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
+        return;
+      }
+      this.emit("image");
+      timeStamp = now();
+      this.config.skipAllowed = await skip(this.config, img.tensor);
+      if (!this.performance.totalFrames)
+        this.performance.totalFrames = 0;
+      if (!this.performance.cachedFrames)
+        this.performance.cachedFrames = 0;
+      this.performance.totalFrames++;
+      if (this.config.skipAllowed)
+        this.performance.cachedFrames++;
+      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Check Changed:");
+      let faceRes = [];
+      let bodyRes = [];
+      let handRes = [];
+      let objectRes = [];
+      this.state = "detect:face";
+      if (this.config.async) {
+        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
+        if (this.performance.face)
+          delete this.performance.face;
+      } else {
+        timeStamp = now();
+        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
+        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
+        faceRes = await faceRes;
+      this.analyze("Start Body:");
+      this.state = "detect:body";
+      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
+          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
+        else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? predict5(img.tensor, bodyConfig) : [];
+        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? predict7(img.tensor, bodyConfig) : [];
+        else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
+          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
+        if (this.performance.body)
+          delete this.performance.body;
+      } else {
+        timeStamp = now();
+        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
+          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
+        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? await predict5(img.tensor, bodyConfig) : [];
+        else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? await predict7(img.tensor, bodyConfig) : [];
+        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
+          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
+        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Body:");
+      this.analyze("Start Hand:");
+      this.state = "detect:hand";
+      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
+          handRes = this.config.hand.enabled ? predict12(img.tensor, handConfig) : [];
+        else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
+          handRes = this.config.hand.enabled ? predict13(img.tensor, handConfig) : [];
+        if (this.performance.hand)
+          delete this.performance.hand;
+      } else {
+        timeStamp = now();
+        if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
+          handRes = this.config.hand.enabled ? await predict12(img.tensor, handConfig) : [];
+        else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
+          handRes = this.config.hand.enabled ? await predict13(img.tensor, handConfig) : [];
+        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Hand:");
+      this.analyze("Start Object:");
+      this.state = "detect:object";
+      if (this.config.async) {
+        if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
+          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
+        else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
+          objectRes = this.config.object.enabled ? predict6(img.tensor, this.config) : [];
+        if (this.performance.object)
+          delete this.performance.object;
+      } else {
+        timeStamp = now();
+        if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
+          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
+        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
+          objectRes = this.config.object.enabled ? await predict6(img.tensor, this.config) : [];
+        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Object:");
+      this.state = "detect:await";
+      if (this.config.async)
+        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
+      this.state = "detect:gesture";
+      let gestureRes = [];
+      if (this.config.gesture.enabled) {
+        timeStamp = now();
+        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
+        if (!this.config.async)
+          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+        else if (this.performance.gesture)
+          delete this.performance.gesture;
+      }
+      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
+      const shape = ((_v = (_u = this.process) == null ? void 0 : _u.tensor) == null ? void 0 : _v.shape) || [];
+      this.result = {
+        face: faceRes,
+        body: bodyRes,
+        hand: handRes,
+        gesture: gestureRes,
+        object: objectRes,
+        performance: this.performance,
+        canvas: this.process.canvas,
+        timestamp: Date.now(),
+        error: null,
+        get persons() {
+          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
+        }
+      };
+      dispose(img.tensor);
+      this.emit("detect");
+      this.state = "idle";
+      resolve(this.result);
+    });
+  }
+};
+_numTensors = new WeakMap();
+_analyzeMemoryLeaks = new WeakMap();
+_checkSanity = new WeakMap();
+_sanity = new WeakMap();
+export {
+  Human,
+  Human as default,
+  config as defaults,
+  draw_exports as draw,
+  env2 as env,
+  match_exports as match,
+  models_exports2 as models
+};
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use backend file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the License);
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the License);
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the 'License');
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an 'AS IS' BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * Human main module
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
+/**
+* @license
+* Copyright 2018 Google LLC. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+* =============================================================================
+*/
+/** @license See the LICENSE file. */
+//# sourceMappingURL=human.esm.js.map
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
new file mode 100644
index 00000000..ff62249b
--- /dev/null
+++ b/dist/human.esm.js.map
@@ -0,0 +1,7 @@
+{
+  "version": 3,
+  "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/constants.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** Keep detected faces that cannot be verified using facemesh */\n  keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n  /** minimum confidence for a detected race before results are discarded */\n  minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n  gear: Partial<FaceGearConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: false,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      keepInvalid: false,\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar __create = Object.create;\nvar __defProp = Object.defineProperty;\nvar __getOwnPropDesc = Object.getOwnPropertyDescriptor;\nvar __getOwnPropNames = Object.getOwnPropertyNames;\nvar __getProtoOf = Object.getPrototypeOf;\nvar __hasOwnProp = Object.prototype.hasOwnProperty;\nvar __commonJS = (cb, mod4) => function __require() {\n  return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports;\n};\nvar __export = (target, all5) => {\n  for (var name in all5)\n    __defProp(target, name, { get: all5[name], enumerable: true });\n};\nvar __copyProps = (to, from, except, desc) => {\n  if (from && typeof from === \"object\" || typeof from === \"function\") {\n    for (let key of __getOwnPropNames(from))\n      if (!__hasOwnProp.call(to, key) && key !== except)\n        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });\n  }\n  return to;\n};\nvar __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps(isNodeMode || !mod4 || !mod4.__esModule ? __defProp(target, \"default\", { value: mod4, enumerable: true }) : target, mod4));\n\n// node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js\nvar require_long = __commonJS({\n  \"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js\"(exports, module) {\n    module.exports = Long2;\n    var wasm = null;\n    try {\n      wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([\n        0,\n        97,\n        115,\n        109,\n        1,\n        0,\n        0,\n        0,\n        1,\n        13,\n        2,\n        96,\n        0,\n        1,\n        127,\n        96,\n        4,\n        127,\n        127,\n        127,\n        127,\n        1,\n        127,\n        3,\n        7,\n        6,\n        0,\n        1,\n        1,\n        1,\n        1,\n        1,\n        6,\n        6,\n        1,\n        127,\n        1,\n        65,\n        0,\n        11,\n        7,\n        50,\n        6,\n        3,\n        109,\n        117,\n        108,\n        0,\n        1,\n        5,\n        100,\n        105,\n        118,\n        95,\n        115,\n        0,\n        2,\n        5,\n        100,\n        105,\n        118,\n        95,\n        117,\n        0,\n        3,\n        5,\n        114,\n        101,\n        109,\n        95,\n        115,\n        0,\n        4,\n        5,\n        114,\n        101,\n        109,\n        95,\n        117,\n        0,\n        5,\n        8,\n        103,\n        101,\n        116,\n        95,\n        104,\n        105,\n        103,\n        104,\n        0,\n        0,\n        10,\n        191,\n        1,\n        6,\n        4,\n        0,\n        35,\n        0,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        126,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        127,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        128,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        129,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        130,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11\n      ])), {}).exports;\n    } catch (e2) {\n    }\n    function Long2(low, high, unsigned) {\n      this.low = low | 0;\n      this.high = high | 0;\n      this.unsigned = !!unsigned;\n    }\n    Long2.prototype.__isLong__;\n    Object.defineProperty(Long2.prototype, \"__isLong__\", { value: true });\n    function isLong(obj) {\n      return (obj && obj[\"__isLong__\"]) === true;\n    }\n    Long2.isLong = isLong;\n    var INT_CACHE = {};\n    var UINT_CACHE = {};\n    function fromInt(value, unsigned) {\n      var obj, cachedObj, cache;\n      if (unsigned) {\n        value >>>= 0;\n        if (cache = 0 <= value && value < 256) {\n          cachedObj = UINT_CACHE[value];\n          if (cachedObj)\n            return cachedObj;\n        }\n        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\n        if (cache)\n          UINT_CACHE[value] = obj;\n        return obj;\n      } else {\n        value |= 0;\n        if (cache = -128 <= value && value < 128) {\n          cachedObj = INT_CACHE[value];\n          if (cachedObj)\n            return cachedObj;\n        }\n        obj = fromBits(value, value < 0 ? -1 : 0, false);\n        if (cache)\n          INT_CACHE[value] = obj;\n        return obj;\n      }\n    }\n    Long2.fromInt = fromInt;\n    function fromNumber(value, unsigned) {\n      if (isNaN(value))\n        return unsigned ? UZERO : ZERO;\n      if (unsigned) {\n        if (value < 0)\n          return UZERO;\n        if (value >= TWO_PWR_64_DBL)\n          return MAX_UNSIGNED_VALUE;\n      } else {\n        if (value <= -TWO_PWR_63_DBL)\n          return MIN_VALUE;\n        if (value + 1 >= TWO_PWR_63_DBL)\n          return MAX_VALUE;\n      }\n      if (value < 0)\n        return fromNumber(-value, unsigned).neg();\n      return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);\n    }\n    Long2.fromNumber = fromNumber;\n    function fromBits(lowBits, highBits, unsigned) {\n      return new Long2(lowBits, highBits, unsigned);\n    }\n    Long2.fromBits = fromBits;\n    var pow_dbl = Math.pow;\n    function fromString(str, unsigned, radix) {\n      if (str.length === 0)\n        throw Error(\"empty string\");\n      if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\n        return ZERO;\n      if (typeof unsigned === \"number\") {\n        radix = unsigned, unsigned = false;\n      } else {\n        unsigned = !!unsigned;\n      }\n      radix = radix || 10;\n      if (radix < 2 || 36 < radix)\n        throw RangeError(\"radix\");\n      var p2;\n      if ((p2 = str.indexOf(\"-\")) > 0)\n        throw Error(\"interior hyphen\");\n      else if (p2 === 0) {\n        return fromString(str.substring(1), unsigned, radix).neg();\n      }\n      var radixToPower = fromNumber(pow_dbl(radix, 8));\n      var result = ZERO;\n      for (var i2 = 0; i2 < str.length; i2 += 8) {\n        var size = Math.min(8, str.length - i2), value = parseInt(str.substring(i2, i2 + size), radix);\n        if (size < 8) {\n          var power = fromNumber(pow_dbl(radix, size));\n          result = result.mul(power).add(fromNumber(value));\n        } else {\n          result = result.mul(radixToPower);\n          result = result.add(fromNumber(value));\n        }\n      }\n      result.unsigned = unsigned;\n      return result;\n    }\n    Long2.fromString = fromString;\n    function fromValue(val, unsigned) {\n      if (typeof val === \"number\")\n        return fromNumber(val, unsigned);\n      if (typeof val === \"string\")\n        return fromString(val, unsigned);\n      return fromBits(val.low, val.high, typeof unsigned === \"boolean\" ? unsigned : val.unsigned);\n    }\n    Long2.fromValue = fromValue;\n    var TWO_PWR_16_DBL = 1 << 16;\n    var TWO_PWR_24_DBL = 1 << 24;\n    var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\n    var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\n    var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\n    var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\n    var ZERO = fromInt(0);\n    Long2.ZERO = ZERO;\n    var UZERO = fromInt(0, true);\n    Long2.UZERO = UZERO;\n    var ONE = fromInt(1);\n    Long2.ONE = ONE;\n    var UONE = fromInt(1, true);\n    Long2.UONE = UONE;\n    var NEG_ONE = fromInt(-1);\n    Long2.NEG_ONE = NEG_ONE;\n    var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);\n    Long2.MAX_VALUE = MAX_VALUE;\n    var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);\n    Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\n    var MIN_VALUE = fromBits(0, 2147483648 | 0, false);\n    Long2.MIN_VALUE = MIN_VALUE;\n    var LongPrototype = Long2.prototype;\n    LongPrototype.toInt = function toInt() {\n      return this.unsigned ? this.low >>> 0 : this.low;\n    };\n    LongPrototype.toNumber = function toNumber() {\n      if (this.unsigned)\n        return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);\n      return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\n    };\n    LongPrototype.toString = function toString(radix) {\n      radix = radix || 10;\n      if (radix < 2 || 36 < radix)\n        throw RangeError(\"radix\");\n      if (this.isZero())\n        return \"0\";\n      if (this.isNegative()) {\n        if (this.eq(MIN_VALUE)) {\n          var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);\n          return div3.toString(radix) + rem1.toInt().toString(radix);\n        } else\n          return \"-\" + this.neg().toString(radix);\n      }\n      var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;\n      var result = \"\";\n      while (true) {\n        var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);\n        rem = remDiv;\n        if (rem.isZero())\n          return digits + result;\n        else {\n          while (digits.length < 6)\n            digits = \"0\" + digits;\n          result = \"\" + digits + result;\n        }\n      }\n    };\n    LongPrototype.getHighBits = function getHighBits() {\n      return this.high;\n    };\n    LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\n      return this.high >>> 0;\n    };\n    LongPrototype.getLowBits = function getLowBits() {\n      return this.low;\n    };\n    LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\n      return this.low >>> 0;\n    };\n    LongPrototype.getNumBitsAbs = function getNumBitsAbs() {\n      if (this.isNegative())\n        return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\n      var val = this.high != 0 ? this.high : this.low;\n      for (var bit = 31; bit > 0; bit--)\n        if ((val & 1 << bit) != 0)\n          break;\n      return this.high != 0 ? bit + 33 : bit + 1;\n    };\n    LongPrototype.isZero = function isZero() {\n      return this.high === 0 && this.low === 0;\n    };\n    LongPrototype.eqz = LongPrototype.isZero;\n    LongPrototype.isNegative = function isNegative() {\n      return !this.unsigned && this.high < 0;\n    };\n    LongPrototype.isPositive = function isPositive() {\n      return this.unsigned || this.high >= 0;\n    };\n    LongPrototype.isOdd = function isOdd() {\n      return (this.low & 1) === 1;\n    };\n    LongPrototype.isEven = function isEven2() {\n      return (this.low & 1) === 0;\n    };\n    LongPrototype.equals = function equals(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)\n        return false;\n      return this.high === other.high && this.low === other.low;\n    };\n    LongPrototype.eq = LongPrototype.equals;\n    LongPrototype.notEquals = function notEquals(other) {\n      return !this.eq(other);\n    };\n    LongPrototype.neq = LongPrototype.notEquals;\n    LongPrototype.ne = LongPrototype.notEquals;\n    LongPrototype.lessThan = function lessThan(other) {\n      return this.comp(other) < 0;\n    };\n    LongPrototype.lt = LongPrototype.lessThan;\n    LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\n      return this.comp(other) <= 0;\n    };\n    LongPrototype.lte = LongPrototype.lessThanOrEqual;\n    LongPrototype.le = LongPrototype.lessThanOrEqual;\n    LongPrototype.greaterThan = function greaterThan(other) {\n      return this.comp(other) > 0;\n    };\n    LongPrototype.gt = LongPrototype.greaterThan;\n    LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\n      return this.comp(other) >= 0;\n    };\n    LongPrototype.gte = LongPrototype.greaterThanOrEqual;\n    LongPrototype.ge = LongPrototype.greaterThanOrEqual;\n    LongPrototype.compare = function compare(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      if (this.eq(other))\n        return 0;\n      var thisNeg = this.isNegative(), otherNeg = other.isNegative();\n      if (thisNeg && !otherNeg)\n        return -1;\n      if (!thisNeg && otherNeg)\n        return 1;\n      if (!this.unsigned)\n        return this.sub(other).isNegative() ? -1 : 1;\n      return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;\n    };\n    LongPrototype.comp = LongPrototype.compare;\n    LongPrototype.negate = function negate() {\n      if (!this.unsigned && this.eq(MIN_VALUE))\n        return MIN_VALUE;\n      return this.not().add(ONE);\n    };\n    LongPrototype.neg = LongPrototype.negate;\n    LongPrototype.add = function add5(addend) {\n      if (!isLong(addend))\n        addend = fromValue(addend);\n      var a48 = this.high >>> 16;\n      var a32 = this.high & 65535;\n      var a16 = this.low >>> 16;\n      var a00 = this.low & 65535;\n      var b48 = addend.high >>> 16;\n      var b32 = addend.high & 65535;\n      var b16 = addend.low >>> 16;\n      var b00 = addend.low & 65535;\n      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n      c00 += a00 + b00;\n      c16 += c00 >>> 16;\n      c00 &= 65535;\n      c16 += a16 + b16;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c32 += a32 + b32;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c48 += a48 + b48;\n      c48 &= 65535;\n      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n    };\n    LongPrototype.subtract = function subtract(subtrahend) {\n      if (!isLong(subtrahend))\n        subtrahend = fromValue(subtrahend);\n      return this.add(subtrahend.neg());\n    };\n    LongPrototype.sub = LongPrototype.subtract;\n    LongPrototype.multiply = function multiply4(multiplier) {\n      if (this.isZero())\n        return ZERO;\n      if (!isLong(multiplier))\n        multiplier = fromValue(multiplier);\n      if (wasm) {\n        var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high);\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      if (multiplier.isZero())\n        return ZERO;\n      if (this.eq(MIN_VALUE))\n        return multiplier.isOdd() ? MIN_VALUE : ZERO;\n      if (multiplier.eq(MIN_VALUE))\n        return this.isOdd() ? MIN_VALUE : ZERO;\n      if (this.isNegative()) {\n        if (multiplier.isNegative())\n          return this.neg().mul(multiplier.neg());\n        else\n          return this.neg().mul(multiplier).neg();\n      } else if (multiplier.isNegative())\n        return this.mul(multiplier.neg()).neg();\n      if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\n        return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\n      var a48 = this.high >>> 16;\n      var a32 = this.high & 65535;\n      var a16 = this.low >>> 16;\n      var a00 = this.low & 65535;\n      var b48 = multiplier.high >>> 16;\n      var b32 = multiplier.high & 65535;\n      var b16 = multiplier.low >>> 16;\n      var b00 = multiplier.low & 65535;\n      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n      c00 += a00 * b00;\n      c16 += c00 >>> 16;\n      c00 &= 65535;\n      c16 += a16 * b00;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c16 += a00 * b16;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c32 += a32 * b00;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c32 += a16 * b16;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c32 += a00 * b32;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\n      c48 &= 65535;\n      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n    };\n    LongPrototype.mul = LongPrototype.multiply;\n    LongPrototype.divide = function divide(divisor) {\n      if (!isLong(divisor))\n        divisor = fromValue(divisor);\n      if (divisor.isZero())\n        throw Error(\"division by zero\");\n      if (wasm) {\n        if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {\n          return this;\n        }\n        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(this.low, this.high, divisor.low, divisor.high);\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      if (this.isZero())\n        return this.unsigned ? UZERO : ZERO;\n      var approx, rem, res;\n      if (!this.unsigned) {\n        if (this.eq(MIN_VALUE)) {\n          if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\n            return MIN_VALUE;\n          else if (divisor.eq(MIN_VALUE))\n            return ONE;\n          else {\n            var halfThis = this.shr(1);\n            approx = halfThis.div(divisor).shl(1);\n            if (approx.eq(ZERO)) {\n              return divisor.isNegative() ? ONE : NEG_ONE;\n            } else {\n              rem = this.sub(divisor.mul(approx));\n              res = approx.add(rem.div(divisor));\n              return res;\n            }\n          }\n        } else if (divisor.eq(MIN_VALUE))\n          return this.unsigned ? UZERO : ZERO;\n        if (this.isNegative()) {\n          if (divisor.isNegative())\n            return this.neg().div(divisor.neg());\n          return this.neg().div(divisor).neg();\n        } else if (divisor.isNegative())\n          return this.div(divisor.neg()).neg();\n        res = ZERO;\n      } else {\n        if (!divisor.unsigned)\n          divisor = divisor.toUnsigned();\n        if (divisor.gt(this))\n          return UZERO;\n        if (divisor.gt(this.shru(1)))\n          return UONE;\n        res = UZERO;\n      }\n      rem = this;\n      while (rem.gte(divisor)) {\n        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\n        var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);\n        while (approxRem.isNegative() || approxRem.gt(rem)) {\n          approx -= delta;\n          approxRes = fromNumber(approx, this.unsigned);\n          approxRem = approxRes.mul(divisor);\n        }\n        if (approxRes.isZero())\n          approxRes = ONE;\n        res = res.add(approxRes);\n        rem = rem.sub(approxRem);\n      }\n      return res;\n    };\n    LongPrototype.div = LongPrototype.divide;\n    LongPrototype.modulo = function modulo(divisor) {\n      if (!isLong(divisor))\n        divisor = fromValue(divisor);\n      if (wasm) {\n        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(this.low, this.high, divisor.low, divisor.high);\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      return this.sub(this.div(divisor).mul(divisor));\n    };\n    LongPrototype.mod = LongPrototype.modulo;\n    LongPrototype.rem = LongPrototype.modulo;\n    LongPrototype.not = function not() {\n      return fromBits(~this.low, ~this.high, this.unsigned);\n    };\n    LongPrototype.and = function and(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\n    };\n    LongPrototype.or = function or(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\n    };\n    LongPrototype.xor = function xor(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\n    };\n    LongPrototype.shiftLeft = function shiftLeft(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      if ((numBits &= 63) === 0)\n        return this;\n      else if (numBits < 32)\n        return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);\n      else\n        return fromBits(0, this.low << numBits - 32, this.unsigned);\n    };\n    LongPrototype.shl = LongPrototype.shiftLeft;\n    LongPrototype.shiftRight = function shiftRight(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      if ((numBits &= 63) === 0)\n        return this;\n      else if (numBits < 32)\n        return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);\n      else\n        return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);\n    };\n    LongPrototype.shr = LongPrototype.shiftRight;\n    LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      numBits &= 63;\n      if (numBits === 0)\n        return this;\n      else {\n        var high = this.high;\n        if (numBits < 32) {\n          var low = this.low;\n          return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);\n        } else if (numBits === 32)\n          return fromBits(high, 0, this.unsigned);\n        else\n          return fromBits(high >>> numBits - 32, 0, this.unsigned);\n      }\n    };\n    LongPrototype.shru = LongPrototype.shiftRightUnsigned;\n    LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\n    LongPrototype.toSigned = function toSigned() {\n      if (!this.unsigned)\n        return this;\n      return fromBits(this.low, this.high, false);\n    };\n    LongPrototype.toUnsigned = function toUnsigned() {\n      if (this.unsigned)\n        return this;\n      return fromBits(this.low, this.high, true);\n    };\n    LongPrototype.toBytes = function toBytes(le) {\n      return le ? this.toBytesLE() : this.toBytesBE();\n    };\n    LongPrototype.toBytesLE = function toBytesLE() {\n      var hi = this.high, lo = this.low;\n      return [\n        lo & 255,\n        lo >>> 8 & 255,\n        lo >>> 16 & 255,\n        lo >>> 24,\n        hi & 255,\n        hi >>> 8 & 255,\n        hi >>> 16 & 255,\n        hi >>> 24\n      ];\n    };\n    LongPrototype.toBytesBE = function toBytesBE() {\n      var hi = this.high, lo = this.low;\n      return [\n        hi >>> 24,\n        hi >>> 16 & 255,\n        hi >>> 8 & 255,\n        hi & 255,\n        lo >>> 24,\n        lo >>> 16 & 255,\n        lo >>> 8 & 255,\n        lo & 255\n      ];\n    };\n    Long2.fromBytes = function fromBytes(bytes, unsigned, le) {\n      return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);\n    };\n    Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {\n      return new Long2(bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned);\n    };\n    Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {\n      return new Long2(bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned);\n    };\n  }\n});\n\n// (disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js\nvar require_browser = __commonJS({\n  \"(disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js\"() {\n  }\n});\n\n// (disabled):util\nvar require_util = __commonJS({\n  \"(disabled):util\"() {\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js\nvar require_alea = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function Alea(seed) {\n        var me = this, mash = Mash();\n        me.next = function() {\n          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;\n          me.s0 = me.s1;\n          me.s1 = me.s2;\n          return me.s2 = t2 - (me.c = t2 | 0);\n        };\n        me.c = 1;\n        me.s0 = mash(\" \");\n        me.s1 = mash(\" \");\n        me.s2 = mash(\" \");\n        me.s0 -= mash(seed);\n        if (me.s0 < 0) {\n          me.s0 += 1;\n        }\n        me.s1 -= mash(seed);\n        if (me.s1 < 0) {\n          me.s1 += 1;\n        }\n        me.s2 -= mash(seed);\n        if (me.s2 < 0) {\n          me.s2 += 1;\n        }\n        mash = null;\n      }\n      function copy(f, t2) {\n        t2.c = f.c;\n        t2.s0 = f.s0;\n        t2.s1 = f.s1;\n        t2.s2 = f.s2;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;\n        prng.int32 = function() {\n          return xg.next() * 4294967296 | 0;\n        };\n        prng.double = function() {\n          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;\n        };\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      function Mash() {\n        var n = 4022871197;\n        var mash = function(data) {\n          data = data.toString();\n          for (var i2 = 0; i2 < data.length; i2++) {\n            n += data.charCodeAt(i2);\n            var h = 0.02519603282416938 * n;\n            n = h >>> 0;\n            h -= n;\n            h *= n;\n            n = h >>> 0;\n            h -= n;\n            n += h * 4294967296;\n          }\n          return (n >>> 0) * 23283064365386963e-26;\n        };\n        return mash;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.alea = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js\nvar require_xor128 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.next = function() {\n          var t2 = me.x ^ me.x << 11;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;\n        };\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.x = f.x;\n        t2.y = f.y;\n        t2.z = f.z;\n        t2.w = f.w;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor128 = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js\nvar require_xorwow = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var t2 = me.x ^ me.x >>> 2;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          me.w = me.v;\n          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;\n        };\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.v = 0;\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          if (k == strseed.length) {\n            me.d = me.x << 10 ^ me.x >>> 4;\n          }\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.x = f.x;\n        t2.y = f.y;\n        t2.z = f.z;\n        t2.w = f.w;\n        t2.v = f.v;\n        t2.d = f.d;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorwow = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js\nvar require_xorshift7 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var X = me.x, i2 = me.i, t2, v, w;\n          t2 = X[i2];\n          t2 ^= t2 >>> 7;\n          v = t2 ^ t2 << 24;\n          t2 = X[i2 + 1 & 7];\n          v ^= t2 ^ t2 >>> 10;\n          t2 = X[i2 + 3 & 7];\n          v ^= t2 ^ t2 >>> 3;\n          t2 = X[i2 + 4 & 7];\n          v ^= t2 ^ t2 << 7;\n          t2 = X[i2 + 7 & 7];\n          t2 = t2 ^ t2 << 13;\n          v ^= t2 ^ t2 << 9;\n          X[i2] = v;\n          me.i = i2 + 1 & 7;\n          return v;\n        };\n        function init2(me2, seed2) {\n          var j, w, X = [];\n          if (seed2 === (seed2 | 0)) {\n            w = X[0] = seed2;\n          } else {\n            seed2 = \"\" + seed2;\n            for (j = 0; j < seed2.length; ++j) {\n              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;\n            }\n          }\n          while (X.length < 8)\n            X.push(0);\n          for (j = 0; j < 8 && X[j] === 0; ++j)\n            ;\n          if (j == 8)\n            w = X[7] = -1;\n          else\n            w = X[j];\n          me2.x = X;\n          me2.i = 0;\n          for (j = 256; j > 0; --j) {\n            me2.next();\n          }\n        }\n        init2(me, seed);\n      }\n      function copy(f, t2) {\n        t2.x = f.x.slice();\n        t2.i = f.i;\n        return t2;\n      }\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.x)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorshift7 = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js\nvar require_xor4096 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var w = me.w, X = me.X, i2 = me.i, t2, v;\n          me.w = w = w + 1640531527 | 0;\n          v = X[i2 + 34 & 127];\n          t2 = X[i2 = i2 + 1 & 127];\n          v ^= v << 13;\n          t2 ^= t2 << 17;\n          v ^= v >>> 15;\n          t2 ^= t2 >>> 12;\n          v = X[i2] = v ^ t2;\n          me.i = i2;\n          return v + (w ^ w >>> 16) | 0;\n        };\n        function init2(me2, seed2) {\n          var t2, v, i2, j, w, X = [], limit = 128;\n          if (seed2 === (seed2 | 0)) {\n            v = seed2;\n            seed2 = null;\n          } else {\n            seed2 = seed2 + \"\\0\";\n            v = 0;\n            limit = Math.max(limit, seed2.length);\n          }\n          for (i2 = 0, j = -32; j < limit; ++j) {\n            if (seed2)\n              v ^= seed2.charCodeAt((j + 32) % seed2.length);\n            if (j === 0)\n              w = v;\n            v ^= v << 10;\n            v ^= v >>> 15;\n            v ^= v << 4;\n            v ^= v >>> 13;\n            if (j >= 0) {\n              w = w + 1640531527 | 0;\n              t2 = X[j & 127] ^= v + w;\n              i2 = 0 == t2 ? i2 + 1 : 0;\n            }\n          }\n          if (i2 >= 128) {\n            X[(seed2 && seed2.length || 0) & 127] = -1;\n          }\n          i2 = 127;\n          for (j = 4 * 128; j > 0; --j) {\n            v = X[i2 + 34 & 127];\n            t2 = X[i2 = i2 + 1 & 127];\n            v ^= v << 13;\n            t2 ^= t2 << 17;\n            v ^= v >>> 15;\n            t2 ^= t2 >>> 12;\n            X[i2] = v ^ t2;\n          }\n          me2.w = w;\n          me2.X = X;\n          me2.i = i2;\n        }\n        init2(me, seed);\n      }\n      function copy(f, t2) {\n        t2.i = f.i;\n        t2.w = f.w;\n        t2.X = f.X.slice();\n        return t2;\n      }\n      ;\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.X)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor4096 = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js\nvar require_tychei = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var b = me.b, c = me.c, d = me.d, a6 = me.a;\n          b = b << 25 ^ b >>> 7 ^ c;\n          c = c - d | 0;\n          d = d << 24 ^ d >>> 8 ^ a6;\n          a6 = a6 - b | 0;\n          me.b = b = b << 20 ^ b >>> 12 ^ c;\n          me.c = c = c - d | 0;\n          me.d = d << 16 ^ c >>> 16 ^ a6;\n          return me.a = a6 - b | 0;\n        };\n        me.a = 0;\n        me.b = 0;\n        me.c = 2654435769 | 0;\n        me.d = 1367130551;\n        if (seed === Math.floor(seed)) {\n          me.a = seed / 4294967296 | 0;\n          me.b = seed | 0;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 20; k++) {\n          me.b ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.a = f.a;\n        t2.b = f.b;\n        t2.c = f.c;\n        t2.d = f.d;\n        return t2;\n      }\n      ;\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.tychei = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// (disabled):crypto\nvar require_crypto = __commonJS({\n  \"(disabled):crypto\"() {\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js\nvar require_seedrandom = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js\"(exports, module) {\n    (function(pool3, math) {\n      var global2 = this, width = 256, chunks = 6, digits = 52, rngname = \"random\", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;\n      function seedrandom5(seed, options, callback) {\n        var key = [];\n        options = options == true ? { entropy: true } : options || {};\n        var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);\n        var arc4 = new ARC4(key);\n        var prng = function() {\n          var n = arc4.g(chunks), d = startdenom, x = 0;\n          while (n < significance) {\n            n = (n + x) * width;\n            d *= width;\n            x = arc4.g(1);\n          }\n          while (n >= overflow) {\n            n /= 2;\n            d /= 2;\n            x >>>= 1;\n          }\n          return (n + x) / d;\n        };\n        prng.int32 = function() {\n          return arc4.g(4) | 0;\n        };\n        prng.quick = function() {\n          return arc4.g(4) / 4294967296;\n        };\n        prng.double = prng;\n        mixkey(tostring(arc4.S), pool3);\n        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {\n          if (state) {\n            if (state.S) {\n              copy(state, arc4);\n            }\n            prng2.state = function() {\n              return copy(arc4, {});\n            };\n          }\n          if (is_math_call) {\n            math[rngname] = prng2;\n            return seed2;\n          } else\n            return prng2;\n        })(prng, shortseed, \"global\" in options ? options.global : this == math, options.state);\n      }\n      math[\"seed\" + rngname] = seedrandom5;\n      function ARC4(key) {\n        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];\n        if (!keylen) {\n          key = [keylen++];\n        }\n        while (i2 < width) {\n          s2[i2] = i2++;\n        }\n        for (i2 = 0; i2 < width; i2++) {\n          s2[i2] = s2[j = mask & j + key[i2 % keylen] + (t2 = s2[i2])];\n          s2[j] = t2;\n        }\n        (me.g = function(count2) {\n          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;\n          while (count2--) {\n            t3 = s3[i3 = mask & i3 + 1];\n            r2 = r2 * width + s3[mask & (s3[i3] = s3[j2 = mask & j2 + t3]) + (s3[j2] = t3)];\n          }\n          me.i = i3;\n          me.j = j2;\n          return r2;\n        })(width);\n      }\n      function copy(f, t2) {\n        t2.i = f.i;\n        t2.j = f.j;\n        t2.S = f.S.slice();\n        return t2;\n      }\n      ;\n      function flatten4(obj, depth) {\n        var result = [], typ = typeof obj, prop;\n        if (depth && typ == \"object\") {\n          for (prop in obj) {\n            try {\n              result.push(flatten4(obj[prop], depth - 1));\n            } catch (e2) {\n            }\n          }\n        }\n        return result.length ? result : typ == \"string\" ? obj : obj + \"\\0\";\n      }\n      function mixkey(seed, key) {\n        var stringseed = seed + \"\", smear, j = 0;\n        while (j < stringseed.length) {\n          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);\n        }\n        return tostring(key);\n      }\n      function autoseed() {\n        try {\n          var out;\n          if (nodecrypto && (out = nodecrypto.randomBytes)) {\n            out = out(width);\n          } else {\n            out = new Uint8Array(width);\n            (global2.crypto || global2.msCrypto).getRandomValues(out);\n          }\n          return tostring(out);\n        } catch (e2) {\n          var browser = global2.navigator, plugins = browser && browser.plugins;\n          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];\n        }\n      }\n      function tostring(a6) {\n        return String.fromCharCode.apply(0, a6);\n      }\n      mixkey(math.random(), pool3);\n      if (typeof module == \"object\" && module.exports) {\n        module.exports = seedrandom5;\n        try {\n          nodecrypto = require_crypto();\n        } catch (ex) {\n        }\n      } else if (typeof define == \"function\" && define.amd) {\n        define(function() {\n          return seedrandom5;\n        });\n      }\n    })([], Math);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js\nvar require_seedrandom2 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js\"(exports, module) {\n    var alea5 = require_alea();\n    var xor128 = require_xor128();\n    var xorwow = require_xorwow();\n    var xorshift7 = require_xorshift7();\n    var xor4096 = require_xor4096();\n    var tychei = require_tychei();\n    var sr = require_seedrandom();\n    sr.alea = alea5;\n    sr.xor128 = xor128;\n    sr.xorwow = xorwow;\n    sr.xorshift7 = xorshift7;\n    sr.xor4096 = xor4096;\n    sr.tychei = tychei;\n    module.exports = sr;\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\nvar require_alea2 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function Alea(seed) {\n        var me = this, mash = Mash();\n        me.next = function() {\n          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;\n          me.s0 = me.s1;\n          me.s1 = me.s2;\n          return me.s2 = t2 - (me.c = t2 | 0);\n        };\n        me.c = 1;\n        me.s0 = mash(\" \");\n        me.s1 = mash(\" \");\n        me.s2 = mash(\" \");\n        me.s0 -= mash(seed);\n        if (me.s0 < 0) {\n          me.s0 += 1;\n        }\n        me.s1 -= mash(seed);\n        if (me.s1 < 0) {\n          me.s1 += 1;\n        }\n        me.s2 -= mash(seed);\n        if (me.s2 < 0) {\n          me.s2 += 1;\n        }\n        mash = null;\n      }\n      function copy(f, t2) {\n        t2.c = f.c;\n        t2.s0 = f.s0;\n        t2.s1 = f.s1;\n        t2.s2 = f.s2;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;\n        prng.int32 = function() {\n          return xg.next() * 4294967296 | 0;\n        };\n        prng.double = function() {\n          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;\n        };\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      function Mash() {\n        var n = 4022871197;\n        var mash = function(data) {\n          data = String(data);\n          for (var i2 = 0; i2 < data.length; i2++) {\n            n += data.charCodeAt(i2);\n            var h = 0.02519603282416938 * n;\n            n = h >>> 0;\n            h -= n;\n            h *= n;\n            n = h >>> 0;\n            h -= n;\n            n += h * 4294967296;\n          }\n          return (n >>> 0) * 23283064365386963e-26;\n        };\n        return mash;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.alea = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\nvar require_xor1282 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.next = function() {\n          var t2 = me.x ^ me.x << 11;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;\n        };\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.x = f.x;\n        t2.y = f.y;\n        t2.z = f.z;\n        t2.w = f.w;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor128 = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\nvar require_xorwow2 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var t2 = me.x ^ me.x >>> 2;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          me.w = me.v;\n          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;\n        };\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.v = 0;\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          if (k == strseed.length) {\n            me.d = me.x << 10 ^ me.x >>> 4;\n          }\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.x = f.x;\n        t2.y = f.y;\n        t2.z = f.z;\n        t2.w = f.w;\n        t2.v = f.v;\n        t2.d = f.d;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorwow = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\nvar require_xorshift72 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var X = me.x, i2 = me.i, t2, v, w;\n          t2 = X[i2];\n          t2 ^= t2 >>> 7;\n          v = t2 ^ t2 << 24;\n          t2 = X[i2 + 1 & 7];\n          v ^= t2 ^ t2 >>> 10;\n          t2 = X[i2 + 3 & 7];\n          v ^= t2 ^ t2 >>> 3;\n          t2 = X[i2 + 4 & 7];\n          v ^= t2 ^ t2 << 7;\n          t2 = X[i2 + 7 & 7];\n          t2 = t2 ^ t2 << 13;\n          v ^= t2 ^ t2 << 9;\n          X[i2] = v;\n          me.i = i2 + 1 & 7;\n          return v;\n        };\n        function init2(me2, seed2) {\n          var j, w, X = [];\n          if (seed2 === (seed2 | 0)) {\n            w = X[0] = seed2;\n          } else {\n            seed2 = \"\" + seed2;\n            for (j = 0; j < seed2.length; ++j) {\n              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;\n            }\n          }\n          while (X.length < 8)\n            X.push(0);\n          for (j = 0; j < 8 && X[j] === 0; ++j)\n            ;\n          if (j == 8)\n            w = X[7] = -1;\n          else\n            w = X[j];\n          me2.x = X;\n          me2.i = 0;\n          for (j = 256; j > 0; --j) {\n            me2.next();\n          }\n        }\n        init2(me, seed);\n      }\n      function copy(f, t2) {\n        t2.x = f.x.slice();\n        t2.i = f.i;\n        return t2;\n      }\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.x)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorshift7 = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\nvar require_xor40962 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var w = me.w, X = me.X, i2 = me.i, t2, v;\n          me.w = w = w + 1640531527 | 0;\n          v = X[i2 + 34 & 127];\n          t2 = X[i2 = i2 + 1 & 127];\n          v ^= v << 13;\n          t2 ^= t2 << 17;\n          v ^= v >>> 15;\n          t2 ^= t2 >>> 12;\n          v = X[i2] = v ^ t2;\n          me.i = i2;\n          return v + (w ^ w >>> 16) | 0;\n        };\n        function init2(me2, seed2) {\n          var t2, v, i2, j, w, X = [], limit = 128;\n          if (seed2 === (seed2 | 0)) {\n            v = seed2;\n            seed2 = null;\n          } else {\n            seed2 = seed2 + \"\\0\";\n            v = 0;\n            limit = Math.max(limit, seed2.length);\n          }\n          for (i2 = 0, j = -32; j < limit; ++j) {\n            if (seed2)\n              v ^= seed2.charCodeAt((j + 32) % seed2.length);\n            if (j === 0)\n              w = v;\n            v ^= v << 10;\n            v ^= v >>> 15;\n            v ^= v << 4;\n            v ^= v >>> 13;\n            if (j >= 0) {\n              w = w + 1640531527 | 0;\n              t2 = X[j & 127] ^= v + w;\n              i2 = 0 == t2 ? i2 + 1 : 0;\n            }\n          }\n          if (i2 >= 128) {\n            X[(seed2 && seed2.length || 0) & 127] = -1;\n          }\n          i2 = 127;\n          for (j = 4 * 128; j > 0; --j) {\n            v = X[i2 + 34 & 127];\n            t2 = X[i2 = i2 + 1 & 127];\n            v ^= v << 13;\n            t2 ^= t2 << 17;\n            v ^= v >>> 15;\n            t2 ^= t2 >>> 12;\n            X[i2] = v ^ t2;\n          }\n          me2.w = w;\n          me2.X = X;\n          me2.i = i2;\n        }\n        init2(me, seed);\n      }\n      function copy(f, t2) {\n        t2.i = f.i;\n        t2.w = f.w;\n        t2.X = f.X.slice();\n        return t2;\n      }\n      ;\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.X)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor4096 = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\nvar require_tychei2 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var b = me.b, c = me.c, d = me.d, a6 = me.a;\n          b = b << 25 ^ b >>> 7 ^ c;\n          c = c - d | 0;\n          d = d << 24 ^ d >>> 8 ^ a6;\n          a6 = a6 - b | 0;\n          me.b = b = b << 20 ^ b >>> 12 ^ c;\n          me.c = c = c - d | 0;\n          me.d = d << 16 ^ c >>> 16 ^ a6;\n          return me.a = a6 - b | 0;\n        };\n        me.a = 0;\n        me.b = 0;\n        me.c = 2654435769 | 0;\n        me.d = 1367130551;\n        if (seed === Math.floor(seed)) {\n          me.a = seed / 4294967296 | 0;\n          me.b = seed | 0;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 20; k++) {\n          me.b ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.a = f.a;\n        t2.b = f.b;\n        t2.c = f.c;\n        t2.d = f.d;\n        return t2;\n      }\n      ;\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.tychei = impl;\n      }\n    })(exports, typeof module == \"object\" && module, typeof define == \"function\" && define);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\nvar require_seedrandom3 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\"(exports, module) {\n    (function(global2, pool3, math) {\n      var width = 256, chunks = 6, digits = 52, rngname = \"random\", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;\n      function seedrandom5(seed, options, callback) {\n        var key = [];\n        options = options == true ? { entropy: true } : options || {};\n        var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);\n        var arc4 = new ARC4(key);\n        var prng = function() {\n          var n = arc4.g(chunks), d = startdenom, x = 0;\n          while (n < significance) {\n            n = (n + x) * width;\n            d *= width;\n            x = arc4.g(1);\n          }\n          while (n >= overflow) {\n            n /= 2;\n            d /= 2;\n            x >>>= 1;\n          }\n          return (n + x) / d;\n        };\n        prng.int32 = function() {\n          return arc4.g(4) | 0;\n        };\n        prng.quick = function() {\n          return arc4.g(4) / 4294967296;\n        };\n        prng.double = prng;\n        mixkey(tostring(arc4.S), pool3);\n        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {\n          if (state) {\n            if (state.S) {\n              copy(state, arc4);\n            }\n            prng2.state = function() {\n              return copy(arc4, {});\n            };\n          }\n          if (is_math_call) {\n            math[rngname] = prng2;\n            return seed2;\n          } else\n            return prng2;\n        })(prng, shortseed, \"global\" in options ? options.global : this == math, options.state);\n      }\n      function ARC4(key) {\n        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];\n        if (!keylen) {\n          key = [keylen++];\n        }\n        while (i2 < width) {\n          s2[i2] = i2++;\n        }\n        for (i2 = 0; i2 < width; i2++) {\n          s2[i2] = s2[j = mask & j + key[i2 % keylen] + (t2 = s2[i2])];\n          s2[j] = t2;\n        }\n        (me.g = function(count2) {\n          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;\n          while (count2--) {\n            t3 = s3[i3 = mask & i3 + 1];\n            r2 = r2 * width + s3[mask & (s3[i3] = s3[j2 = mask & j2 + t3]) + (s3[j2] = t3)];\n          }\n          me.i = i3;\n          me.j = j2;\n          return r2;\n        })(width);\n      }\n      function copy(f, t2) {\n        t2.i = f.i;\n        t2.j = f.j;\n        t2.S = f.S.slice();\n        return t2;\n      }\n      ;\n      function flatten4(obj, depth) {\n        var result = [], typ = typeof obj, prop;\n        if (depth && typ == \"object\") {\n          for (prop in obj) {\n            try {\n              result.push(flatten4(obj[prop], depth - 1));\n            } catch (e2) {\n            }\n          }\n        }\n        return result.length ? result : typ == \"string\" ? obj : obj + \"\\0\";\n      }\n      function mixkey(seed, key) {\n        var stringseed = seed + \"\", smear, j = 0;\n        while (j < stringseed.length) {\n          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);\n        }\n        return tostring(key);\n      }\n      function autoseed() {\n        try {\n          var out;\n          if (nodecrypto && (out = nodecrypto.randomBytes)) {\n            out = out(width);\n          } else {\n            out = new Uint8Array(width);\n            (global2.crypto || global2.msCrypto).getRandomValues(out);\n          }\n          return tostring(out);\n        } catch (e2) {\n          var browser = global2.navigator, plugins = browser && browser.plugins;\n          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];\n        }\n      }\n      function tostring(a6) {\n        return String.fromCharCode.apply(0, a6);\n      }\n      mixkey(math.random(), pool3);\n      if (typeof module == \"object\" && module.exports) {\n        module.exports = seedrandom5;\n        try {\n          nodecrypto = require_crypto();\n        } catch (ex) {\n        }\n      } else if (typeof define == \"function\" && define.amd) {\n        define(function() {\n          return seedrandom5;\n        });\n      } else {\n        math[\"seed\" + rngname] = seedrandom5;\n      }\n    })(typeof self !== \"undefined\" ? self : exports, [], Math);\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\nvar require_seedrandom4 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\"(exports, module) {\n    var alea5 = require_alea2();\n    var xor128 = require_xor1282();\n    var xorwow = require_xorwow2();\n    var xorshift7 = require_xorshift72();\n    var xor4096 = require_xor40962();\n    var tychei = require_tychei2();\n    var sr = require_seedrandom3();\n    sr.alea = alea5;\n    sr.xor128 = xor128;\n    sr.xorwow = xorwow;\n    sr.xorshift7 = xorshift7;\n    sr.xor4096 = xor4096;\n    sr.tychei = tychei;\n    module.exports = sr;\n  }\n});\n\n// (disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js\nvar require_string_decoder = __commonJS({\n  \"(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js\"() {\n  }\n});\n\n// (disabled):fs\nvar require_fs = __commonJS({\n  \"(disabled):fs\"() {\n  }\n});\n\n// (disabled):path\nvar require_path = __commonJS({\n  \"(disabled):path\"() {\n  }\n});\n\n// (disabled):worker_threads\nvar require_worker_threads = __commonJS({\n  \"(disabled):worker_threads\"() {\n  }\n});\n\n// (disabled):perf_hooks\nvar require_perf_hooks = __commonJS({\n  \"(disabled):perf_hooks\"() {\n  }\n});\n\n// (disabled):os\nvar require_os = __commonJS({\n  \"(disabled):os\"() {\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\nvar require_tfjs_backend_wasm_threaded_simd = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\"(exports, module) {\n    var WasmBackendModuleThreadedSimd2 = (() => {\n      var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n      if (typeof __filename !== \"undefined\")\n        _scriptDir = _scriptDir || __filename;\n      return function(WasmBackendModuleThreadedSimd3) {\n        WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {};\n        function GROWABLE_HEAP_I8() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP8;\n        }\n        function GROWABLE_HEAP_U8() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPU8;\n        }\n        function GROWABLE_HEAP_I16() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP16;\n        }\n        function GROWABLE_HEAP_U16() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPU16;\n        }\n        function GROWABLE_HEAP_I32() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP32;\n        }\n        function GROWABLE_HEAP_F32() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPF32;\n        }\n        function GROWABLE_HEAP_F64() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPF64;\n        }\n        var Module = typeof WasmBackendModuleThreadedSimd3 !== \"undefined\" ? WasmBackendModuleThreadedSimd3 : {};\n        var readyPromiseResolve, readyPromiseReject;\n        Module[\"ready\"] = new Promise(function(resolve, reject) {\n          readyPromiseResolve = resolve;\n          readyPromiseReject = reject;\n        });\n        var beforeListeners;\n        if (typeof process !== \"undefined\" && process.listeners) {\n          beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n        }\n        var moduleOverrides = Object.assign({}, Module);\n        var arguments_ = [];\n        var thisProgram = \"./this.program\";\n        var quit_ = (status, toThrow) => {\n          throw toThrow;\n        };\n        var ENVIRONMENT_IS_WEB = typeof window === \"object\";\n        var ENVIRONMENT_IS_WORKER = typeof importScripts === \"function\";\n        var ENVIRONMENT_IS_NODE = typeof process === \"object\" && typeof process.versions === \"object\" && typeof process.versions.node === \"string\";\n        var ENVIRONMENT_IS_PTHREAD = Module[\"ENVIRONMENT_IS_PTHREAD\"] || false;\n        var scriptDirectory = \"\";\n        function locateFile(path) {\n          if (Module[\"locateFile\"]) {\n            return Module[\"locateFile\"](path, scriptDirectory);\n          }\n          return scriptDirectory + path;\n        }\n        var read_, readAsync, readBinary, setWindowTitle;\n        function logExceptionOnExit(e2) {\n          if (e2 instanceof ExitStatus)\n            return;\n          let toLog = e2;\n          err(\"exiting due to exception: \" + toLog);\n        }\n        var fs;\n        var nodePath;\n        var requireNodeFS;\n        if (ENVIRONMENT_IS_NODE) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = require_path().dirname(scriptDirectory) + \"/\";\n          } else {\n            scriptDirectory = __dirname + \"/\";\n          }\n          requireNodeFS = () => {\n            if (!nodePath) {\n              fs = require_fs();\n              nodePath = require_path();\n            }\n          };\n          read_ = function shell_read(filename, binary) {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n          };\n          readBinary = (filename) => {\n            var ret = read_(filename, true);\n            if (!ret.buffer) {\n              ret = new Uint8Array(ret);\n            }\n            return ret;\n          };\n          readAsync = (filename, onload, onerror) => {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            fs.readFile(filename, function(err2, data) {\n              if (err2)\n                onerror(err2);\n              else\n                onload(data.buffer);\n            });\n          };\n          if (process[\"argv\"].length > 1) {\n            thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n          }\n          arguments_ = process[\"argv\"].slice(2);\n          process[\"on\"](\"uncaughtException\", function(ex) {\n            if (!(ex instanceof ExitStatus)) {\n              throw ex;\n            }\n          });\n          process[\"on\"](\"unhandledRejection\", function(reason) {\n            throw reason;\n          });\n          quit_ = (status, toThrow) => {\n            if (keepRuntimeAlive()) {\n              process[\"exitCode\"] = status;\n              throw toThrow;\n            }\n            logExceptionOnExit(toThrow);\n            process[\"exit\"](status);\n          };\n          Module[\"inspect\"] = function() {\n            return \"[Emscripten Module object]\";\n          };\n          let nodeWorkerThreads;\n          try {\n            nodeWorkerThreads = require_worker_threads();\n          } catch (e2) {\n            console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');\n            throw e2;\n          }\n          global.Worker = nodeWorkerThreads.Worker;\n        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = self.location.href;\n          } else if (typeof document !== \"undefined\" && document.currentScript) {\n            scriptDirectory = document.currentScript.src;\n          }\n          if (typeof _scriptDir !== \"undefined\" && _scriptDir) {\n            scriptDirectory = _scriptDir;\n          }\n          if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n          } else {\n            scriptDirectory = \"\";\n          }\n          if (!ENVIRONMENT_IS_NODE) {\n            read_ = (url) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, false);\n              xhr.send(null);\n              return xhr.responseText;\n            };\n            if (ENVIRONMENT_IS_WORKER) {\n              readBinary = (url) => {\n                var xhr = new XMLHttpRequest();\n                xhr.open(\"GET\", url, false);\n                xhr.responseType = \"arraybuffer\";\n                xhr.send(null);\n                return new Uint8Array(xhr.response);\n              };\n            }\n            readAsync = (url, onload, onerror) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, true);\n              xhr.responseType = \"arraybuffer\";\n              xhr.onload = () => {\n                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n                  onload(xhr.response);\n                  return;\n                }\n                onerror();\n              };\n              xhr.onerror = onerror;\n              xhr.send(null);\n            };\n          }\n          setWindowTitle = (title) => document.title = title;\n        } else {\n        }\n        if (ENVIRONMENT_IS_NODE) {\n          if (typeof performance === \"undefined\") {\n            global.performance = require_perf_hooks().performance;\n          }\n        }\n        var defaultPrint = console.log.bind(console);\n        var defaultPrintErr = console.warn.bind(console);\n        if (ENVIRONMENT_IS_NODE) {\n          requireNodeFS();\n          defaultPrint = (str) => fs.writeSync(1, str + \"\\n\");\n          defaultPrintErr = (str) => fs.writeSync(2, str + \"\\n\");\n        }\n        var out = Module[\"print\"] || defaultPrint;\n        var err = Module[\"printErr\"] || defaultPrintErr;\n        Object.assign(Module, moduleOverrides);\n        moduleOverrides = null;\n        if (Module[\"arguments\"])\n          arguments_ = Module[\"arguments\"];\n        if (Module[\"thisProgram\"])\n          thisProgram = Module[\"thisProgram\"];\n        if (Module[\"quit\"])\n          quit_ = Module[\"quit\"];\n        var POINTER_SIZE = 4;\n        function warnOnce(text) {\n          if (!warnOnce.shown)\n            warnOnce.shown = {};\n          if (!warnOnce.shown[text]) {\n            warnOnce.shown[text] = 1;\n            err(text);\n          }\n        }\n        function convertJsFunctionToWasm(func2, sig) {\n          if (typeof WebAssembly.Function === \"function\") {\n            var typeNames = { \"i\": \"i32\", \"j\": \"i64\", \"f\": \"f32\", \"d\": \"f64\" };\n            var type = { parameters: [], results: sig[0] == \"v\" ? [] : [typeNames[sig[0]]] };\n            for (var i2 = 1; i2 < sig.length; ++i2) {\n              type.parameters.push(typeNames[sig[i2]]);\n            }\n            return new WebAssembly.Function(type, func2);\n          }\n          var typeSection = [1, 0, 1, 96];\n          var sigRet = sig.slice(0, 1);\n          var sigParam = sig.slice(1);\n          var typeCodes = { \"i\": 127, \"j\": 126, \"f\": 125, \"d\": 124 };\n          typeSection.push(sigParam.length);\n          for (var i2 = 0; i2 < sigParam.length; ++i2) {\n            typeSection.push(typeCodes[sigParam[i2]]);\n          }\n          if (sigRet == \"v\") {\n            typeSection.push(0);\n          } else {\n            typeSection = typeSection.concat([1, typeCodes[sigRet]]);\n          }\n          typeSection[1] = typeSection.length - 2;\n          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));\n          var module2 = new WebAssembly.Module(bytes);\n          var instance = new WebAssembly.Instance(module2, { \"e\": { \"f\": func2 } });\n          var wrappedFunc = instance.exports[\"f\"];\n          return wrappedFunc;\n        }\n        var freeTableIndexes = [];\n        var functionsInTableMap;\n        function getEmptyTableSlot() {\n          if (freeTableIndexes.length) {\n            return freeTableIndexes.pop();\n          }\n          try {\n            wasmTable.grow(1);\n          } catch (err2) {\n            if (!(err2 instanceof RangeError)) {\n              throw err2;\n            }\n            throw \"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.\";\n          }\n          return wasmTable.length - 1;\n        }\n        function updateTableMap(offset, count2) {\n          for (var i2 = offset; i2 < offset + count2; i2++) {\n            var item = getWasmTableEntry(i2);\n            if (item) {\n              functionsInTableMap.set(item, i2);\n            }\n          }\n        }\n        var tempRet0 = 0;\n        var setTempRet0 = (value) => {\n          tempRet0 = value;\n        };\n        var Atomics_load = Atomics.load;\n        var Atomics_store = Atomics.store;\n        var Atomics_compareExchange = Atomics.compareExchange;\n        var wasmBinary;\n        if (Module[\"wasmBinary\"])\n          wasmBinary = Module[\"wasmBinary\"];\n        var noExitRuntime = Module[\"noExitRuntime\"] || true;\n        if (typeof WebAssembly !== \"object\") {\n          abort(\"no native wasm support detected\");\n        }\n        var wasmMemory;\n        var wasmModule;\n        var ABORT = false;\n        var EXITSTATUS;\n        function assert3(condition, text) {\n          if (!condition) {\n            abort(text);\n          }\n        }\n        function getCFunc(ident) {\n          var func2 = Module[\"_\" + ident];\n          return func2;\n        }\n        function ccall(ident, returnType, argTypes, args, opts) {\n          var toC = { \"string\": function(str) {\n            var ret2 = 0;\n            if (str !== null && str !== void 0 && str !== 0) {\n              var len = (str.length << 2) + 1;\n              ret2 = stackAlloc(len);\n              stringToUTF8(str, ret2, len);\n            }\n            return ret2;\n          }, \"array\": function(arr) {\n            var ret2 = stackAlloc(arr.length);\n            writeArrayToMemory(arr, ret2);\n            return ret2;\n          } };\n          function convertReturnValue(ret2) {\n            if (returnType === \"string\")\n              return UTF8ToString(ret2);\n            if (returnType === \"boolean\")\n              return Boolean(ret2);\n            return ret2;\n          }\n          var func2 = getCFunc(ident);\n          var cArgs = [];\n          var stack2 = 0;\n          if (args) {\n            for (var i2 = 0; i2 < args.length; i2++) {\n              var converter = toC[argTypes[i2]];\n              if (converter) {\n                if (stack2 === 0)\n                  stack2 = stackSave();\n                cArgs[i2] = converter(args[i2]);\n              } else {\n                cArgs[i2] = args[i2];\n              }\n            }\n          }\n          var ret = func2.apply(null, cArgs);\n          function onDone(ret2) {\n            if (stack2 !== 0)\n              stackRestore(stack2);\n            return convertReturnValue(ret2);\n          }\n          ret = onDone(ret);\n          return ret;\n        }\n        function cwrap(ident, returnType, argTypes, opts) {\n          argTypes = argTypes || [];\n          var numericArgs = argTypes.every(function(type) {\n            return type === \"number\";\n          });\n          var numericRet = returnType !== \"string\";\n          if (numericRet && numericArgs && !opts) {\n            return getCFunc(ident);\n          }\n          return function() {\n            return ccall(ident, returnType, argTypes, arguments, opts);\n          };\n        }\n        var ALLOC_STACK = 1;\n        function TextDecoderWrapper(encoding) {\n          var textDecoder = new TextDecoder(encoding);\n          this.decode = (data) => {\n            if (data.buffer instanceof SharedArrayBuffer) {\n              data = new Uint8Array(data);\n            }\n            return textDecoder.decode.call(textDecoder, data);\n          };\n        }\n        var UTF8Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoderWrapper(\"utf8\") : void 0;\n        function UTF8ArrayToString(heap, idx, maxBytesToRead) {\n          var endIdx = idx + maxBytesToRead;\n          var endPtr = idx;\n          while (heap[endPtr] && !(endPtr >= endIdx))\n            ++endPtr;\n          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {\n            return UTF8Decoder.decode(heap.subarray(idx, endPtr));\n          } else {\n            var str = \"\";\n            while (idx < endPtr) {\n              var u0 = heap[idx++];\n              if (!(u0 & 128)) {\n                str += String.fromCharCode(u0);\n                continue;\n              }\n              var u1 = heap[idx++] & 63;\n              if ((u0 & 224) == 192) {\n                str += String.fromCharCode((u0 & 31) << 6 | u1);\n                continue;\n              }\n              var u2 = heap[idx++] & 63;\n              if ((u0 & 240) == 224) {\n                u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n              } else {\n                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;\n              }\n              if (u0 < 65536) {\n                str += String.fromCharCode(u0);\n              } else {\n                var ch = u0 - 65536;\n                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n              }\n            }\n          }\n          return str;\n        }\n        function UTF8ToString(ptr, maxBytesToRead) {\n          return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : \"\";\n        }\n        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n          if (!(maxBytesToWrite > 0))\n            return 0;\n          var startIdx = outIdx;\n          var endIdx = outIdx + maxBytesToWrite - 1;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343) {\n              var u1 = str.charCodeAt(++i2);\n              u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n            }\n            if (u <= 127) {\n              if (outIdx >= endIdx)\n                break;\n              heap[outIdx++] = u;\n            } else if (u <= 2047) {\n              if (outIdx + 1 >= endIdx)\n                break;\n              heap[outIdx++] = 192 | u >> 6;\n              heap[outIdx++] = 128 | u & 63;\n            } else if (u <= 65535) {\n              if (outIdx + 2 >= endIdx)\n                break;\n              heap[outIdx++] = 224 | u >> 12;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            } else {\n              if (outIdx + 3 >= endIdx)\n                break;\n              heap[outIdx++] = 240 | u >> 18;\n              heap[outIdx++] = 128 | u >> 12 & 63;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            }\n          }\n          heap[outIdx] = 0;\n          return outIdx - startIdx;\n        }\n        function stringToUTF8(str, outPtr, maxBytesToWrite) {\n          return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);\n        }\n        function lengthBytesUTF8(str) {\n          var len = 0;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343)\n              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;\n            if (u <= 127)\n              ++len;\n            else if (u <= 2047)\n              len += 2;\n            else if (u <= 65535)\n              len += 3;\n            else\n              len += 4;\n          }\n          return len;\n        }\n        var UTF16Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoderWrapper(\"utf-16le\") : void 0;\n        function writeArrayToMemory(array2, buffer3) {\n          GROWABLE_HEAP_I8().set(array2, buffer3);\n        }\n        function writeAsciiToMemory(str, buffer3, dontAddNull) {\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i2);\n          }\n          if (!dontAddNull)\n            GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;\n        }\n        function alignUp(x, multiple) {\n          if (x % multiple > 0) {\n            x += multiple - x % multiple;\n          }\n          return x;\n        }\n        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n        if (ENVIRONMENT_IS_PTHREAD) {\n          buffer2 = Module[\"buffer\"];\n        }\n        function updateGlobalBufferAndViews(buf) {\n          buffer2 = buf;\n          Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n          Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n          Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n          Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n          Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n          Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n          Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n          Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n        }\n        var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n        if (ENVIRONMENT_IS_PTHREAD) {\n          wasmMemory = Module[\"wasmMemory\"];\n          buffer2 = Module[\"buffer\"];\n        } else {\n          if (Module[\"wasmMemory\"]) {\n            wasmMemory = Module[\"wasmMemory\"];\n          } else {\n            wasmMemory = new WebAssembly.Memory({ \"initial\": INITIAL_MEMORY / 65536, \"maximum\": 2147483648 / 65536, \"shared\": true });\n            if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {\n              err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");\n              if (ENVIRONMENT_IS_NODE) {\n                console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\");\n              }\n              throw Error(\"bad memory\");\n            }\n          }\n        }\n        if (wasmMemory) {\n          buffer2 = wasmMemory.buffer;\n        }\n        INITIAL_MEMORY = buffer2.byteLength;\n        updateGlobalBufferAndViews(buffer2);\n        var wasmTable;\n        var __ATPRERUN__ = [];\n        var __ATINIT__ = [];\n        var __ATEXIT__ = [];\n        var __ATPOSTRUN__ = [];\n        var runtimeInitialized = false;\n        var runtimeExited = false;\n        var runtimeKeepaliveCounter = 0;\n        function keepRuntimeAlive() {\n          return noExitRuntime || runtimeKeepaliveCounter > 0;\n        }\n        function preRun() {\n          if (Module[\"preRun\"]) {\n            if (typeof Module[\"preRun\"] == \"function\")\n              Module[\"preRun\"] = [Module[\"preRun\"]];\n            while (Module[\"preRun\"].length) {\n              addOnPreRun(Module[\"preRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPRERUN__);\n        }\n        function initRuntime() {\n          runtimeInitialized = true;\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          callRuntimeCallbacks(__ATINIT__);\n        }\n        function exitRuntime() {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          PThread.terminateAllThreads();\n          runtimeExited = true;\n        }\n        function postRun() {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          if (Module[\"postRun\"]) {\n            if (typeof Module[\"postRun\"] == \"function\")\n              Module[\"postRun\"] = [Module[\"postRun\"]];\n            while (Module[\"postRun\"].length) {\n              addOnPostRun(Module[\"postRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPOSTRUN__);\n        }\n        function addOnPreRun(cb) {\n          __ATPRERUN__.unshift(cb);\n        }\n        function addOnInit(cb) {\n          __ATINIT__.unshift(cb);\n        }\n        function addOnPostRun(cb) {\n          __ATPOSTRUN__.unshift(cb);\n        }\n        var runDependencies = 0;\n        var runDependencyWatcher = null;\n        var dependenciesFulfilled = null;\n        function addRunDependency(id) {\n          runDependencies++;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n        }\n        function removeRunDependency(id) {\n          runDependencies--;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n          if (runDependencies == 0) {\n            if (runDependencyWatcher !== null) {\n              clearInterval(runDependencyWatcher);\n              runDependencyWatcher = null;\n            }\n            if (dependenciesFulfilled) {\n              var callback = dependenciesFulfilled;\n              dependenciesFulfilled = null;\n              callback();\n            }\n          }\n        }\n        Module[\"preloadedImages\"] = {};\n        Module[\"preloadedAudios\"] = {};\n        function abort(what) {\n          if (ENVIRONMENT_IS_PTHREAD) {\n            postMessage({ \"cmd\": \"onAbort\", \"arg\": what });\n          } else {\n            if (Module[\"onAbort\"]) {\n              Module[\"onAbort\"](what);\n            }\n          }\n          what = \"Aborted(\" + what + \")\";\n          err(what);\n          ABORT = true;\n          EXITSTATUS = 1;\n          what += \". Build with -s ASSERTIONS=1 for more info.\";\n          var e2 = new WebAssembly.RuntimeError(what);\n          readyPromiseReject(e2);\n          throw e2;\n        }\n        var dataURIPrefix = \"data:application/octet-stream;base64,\";\n        function isDataURI(filename) {\n          return filename.startsWith(dataURIPrefix);\n        }\n        function isFileURI(filename) {\n          return filename.startsWith(\"file://\");\n        }\n        var wasmBinaryFile;\n        wasmBinaryFile = \"tfjs-backend-wasm-threaded-simd.wasm\";\n        if (!isDataURI(wasmBinaryFile)) {\n          wasmBinaryFile = locateFile(wasmBinaryFile);\n        }\n        function getBinary(file) {\n          try {\n            if (file == wasmBinaryFile && wasmBinary) {\n              return new Uint8Array(wasmBinary);\n            }\n            if (readBinary) {\n              return readBinary(file);\n            } else {\n              throw \"both async and sync fetching of the wasm failed\";\n            }\n          } catch (err2) {\n            abort(err2);\n          }\n        }\n        function getBinaryPromise() {\n          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n            if (typeof fetch === \"function\" && !isFileURI(wasmBinaryFile)) {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                if (!response[\"ok\"]) {\n                  throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n                }\n                return response[\"arrayBuffer\"]();\n              }).catch(function() {\n                return getBinary(wasmBinaryFile);\n              });\n            } else {\n              if (readAsync) {\n                return new Promise(function(resolve, reject) {\n                  readAsync(wasmBinaryFile, function(response) {\n                    resolve(new Uint8Array(response));\n                  }, reject);\n                });\n              }\n            }\n          }\n          return Promise.resolve().then(function() {\n            return getBinary(wasmBinaryFile);\n          });\n        }\n        function createWasm() {\n          var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n          function receiveInstance(instance, module2) {\n            var exports3 = instance.exports;\n            Module[\"asm\"] = exports3;\n            registerTlsInit(Module[\"asm\"][\"emscripten_tls_init\"]);\n            wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n            addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n            wasmModule = module2;\n            if (!ENVIRONMENT_IS_PTHREAD) {\n              var numWorkersToLoad = PThread.unusedWorkers.length;\n              PThread.unusedWorkers.forEach(function(w) {\n                PThread.loadWasmModuleToWorker(w, function() {\n                  if (!--numWorkersToLoad)\n                    removeRunDependency(\"wasm-instantiate\");\n                });\n              });\n            }\n          }\n          if (!ENVIRONMENT_IS_PTHREAD) {\n            addRunDependency(\"wasm-instantiate\");\n          }\n          function receiveInstantiationResult(result) {\n            receiveInstance(result[\"instance\"], result[\"module\"]);\n          }\n          function instantiateArrayBuffer(receiver) {\n            return getBinaryPromise().then(function(binary) {\n              return WebAssembly.instantiate(binary, info);\n            }).then(function(instance) {\n              return instance;\n            }).then(receiver, function(reason) {\n              err(\"failed to asynchronously prepare wasm: \" + reason);\n              abort(reason);\n            });\n          }\n          function instantiateAsync() {\n            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === \"function\") {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                var result = WebAssembly.instantiateStreaming(response, info);\n                return result.then(receiveInstantiationResult, function(reason) {\n                  err(\"wasm streaming compile failed: \" + reason);\n                  err(\"falling back to ArrayBuffer instantiation\");\n                  return instantiateArrayBuffer(receiveInstantiationResult);\n                });\n              });\n            } else {\n              return instantiateArrayBuffer(receiveInstantiationResult);\n            }\n          }\n          if (Module[\"instantiateWasm\"]) {\n            try {\n              var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n              return exports2;\n            } catch (e2) {\n              err(\"Module.instantiateWasm callback failed with error: \" + e2);\n              return false;\n            }\n          }\n          instantiateAsync().catch(readyPromiseReject);\n          return {};\n        }\n        var tempDouble;\n        var tempI64;\n        var ASM_CONSTS = {};\n        function callRuntimeCallbacks(callbacks2) {\n          while (callbacks2.length > 0) {\n            var callback = callbacks2.shift();\n            if (typeof callback == \"function\") {\n              callback(Module);\n              continue;\n            }\n            var func2 = callback.func;\n            if (typeof func2 === \"number\") {\n              if (callback.arg === void 0) {\n                getWasmTableEntry(func2)();\n              } else {\n                getWasmTableEntry(func2)(callback.arg);\n              }\n            } else {\n              func2(callback.arg === void 0 ? null : callback.arg);\n            }\n          }\n        }\n        function withStackSave(f) {\n          var stack2 = stackSave();\n          var ret = f();\n          stackRestore(stack2);\n          return ret;\n        }\n        function demangle(func2) {\n          return func2;\n        }\n        function demangleAll(text) {\n          var regex = /\\b_Z[\\w\\d_]+/g;\n          return text.replace(regex, function(x) {\n            var y = demangle(x);\n            return x === y ? x : y + \" [\" + x + \"]\";\n          });\n        }\n        function killThread(pthread_ptr) {\n          GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;\n          var pthread = PThread.pthreads[pthread_ptr];\n          delete PThread.pthreads[pthread_ptr];\n          pthread.worker.terminate();\n          __emscripten_thread_free_data(pthread_ptr);\n          PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);\n          pthread.worker.pthread = void 0;\n        }\n        function cancelThread(pthread_ptr) {\n          var pthread = PThread.pthreads[pthread_ptr];\n          pthread.worker.postMessage({ \"cmd\": \"cancel\" });\n        }\n        function cleanupThread(pthread_ptr) {\n          var pthread = PThread.pthreads[pthread_ptr];\n          if (pthread) {\n            GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;\n            var worker = pthread.worker;\n            PThread.returnWorkerToPool(worker);\n          }\n        }\n        function _exit(status) {\n          exit(status);\n        }\n        function handleException(e2) {\n          if (e2 instanceof ExitStatus || e2 == \"unwind\") {\n            return EXITSTATUS;\n          }\n          quit_(1, e2);\n        }\n        var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {\n          if (ENVIRONMENT_IS_PTHREAD) {\n            PThread.initWorker();\n          } else {\n            PThread.initMainThread();\n          }\n        }, initMainThread: function() {\n          var pthreadPoolSize = 8;\n          for (var i2 = 0; i2 < pthreadPoolSize; ++i2) {\n            PThread.allocateUnusedWorker();\n          }\n        }, initWorker: function() {\n          noExitRuntime = false;\n        }, pthreads: {}, setExitStatus: function(status) {\n          EXITSTATUS = status;\n        }, terminateAllThreads: function() {\n          for (var t2 in PThread.pthreads) {\n            var pthread = PThread.pthreads[t2];\n            if (pthread && pthread.worker) {\n              PThread.returnWorkerToPool(pthread.worker);\n            }\n          }\n          for (var i2 = 0; i2 < PThread.unusedWorkers.length; ++i2) {\n            var worker = PThread.unusedWorkers[i2];\n            worker.terminate();\n          }\n          PThread.unusedWorkers = [];\n        }, returnWorkerToPool: function(worker) {\n          PThread.runWithoutMainThreadQueuedCalls(function() {\n            delete PThread.pthreads[worker.pthread.threadInfoStruct];\n            PThread.unusedWorkers.push(worker);\n            PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);\n            __emscripten_thread_free_data(worker.pthread.threadInfoStruct);\n            worker.pthread = void 0;\n          });\n        }, runWithoutMainThreadQueuedCalls: function(func2) {\n          GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0;\n          try {\n            func2();\n          } finally {\n            GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1;\n          }\n        }, receiveObjectTransfer: function(data) {\n        }, threadInit: function() {\n          for (var i2 in PThread.tlsInitFunctions) {\n            PThread.tlsInitFunctions[i2]();\n          }\n        }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {\n          worker.onmessage = (e2) => {\n            var d = e2[\"data\"];\n            var cmd = d[\"cmd\"];\n            if (worker.pthread)\n              PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;\n            if (d[\"targetThread\"] && d[\"targetThread\"] != _pthread_self()) {\n              var thread = PThread.pthreads[d.targetThread];\n              if (thread) {\n                thread.worker.postMessage(d, d[\"transferList\"]);\n              } else {\n                err('Internal error! Worker sent a message \"' + cmd + '\" to target pthread ' + d[\"targetThread\"] + \", but that thread no longer exists!\");\n              }\n              PThread.currentProxiedOperationCallerThread = void 0;\n              return;\n            }\n            if (cmd === \"processQueuedMainThreadWork\") {\n              _emscripten_main_thread_process_queued_calls();\n            } else if (cmd === \"spawnThread\") {\n              spawnThread(d);\n            } else if (cmd === \"cleanupThread\") {\n              cleanupThread(d[\"thread\"]);\n            } else if (cmd === \"killThread\") {\n              killThread(d[\"thread\"]);\n            } else if (cmd === \"cancelThread\") {\n              cancelThread(d[\"thread\"]);\n            } else if (cmd === \"loaded\") {\n              worker.loaded = true;\n              if (onFinishedLoading)\n                onFinishedLoading(worker);\n              if (worker.runPthread) {\n                worker.runPthread();\n                delete worker.runPthread;\n              }\n            } else if (cmd === \"print\") {\n              out(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (cmd === \"printErr\") {\n              err(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (cmd === \"alert\") {\n              alert(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (d.target === \"setimmediate\") {\n              worker.postMessage(d);\n            } else if (cmd === \"onAbort\") {\n              if (Module[\"onAbort\"]) {\n                Module[\"onAbort\"](d[\"arg\"]);\n              }\n            } else {\n              err(\"worker sent an unknown command \" + cmd);\n            }\n            PThread.currentProxiedOperationCallerThread = void 0;\n          };\n          worker.onerror = (e2) => {\n            var message = \"worker sent an error!\";\n            err(message + \" \" + e2.filename + \":\" + e2.lineno + \": \" + e2.message);\n            throw e2;\n          };\n          if (ENVIRONMENT_IS_NODE) {\n            worker.on(\"message\", function(data) {\n              worker.onmessage({ data });\n            });\n            worker.on(\"error\", function(e2) {\n              worker.onerror(e2);\n            });\n            worker.on(\"detachedExit\", function() {\n            });\n          }\n          worker.postMessage({ \"cmd\": \"load\", \"urlOrBlob\": Module[\"mainScriptUrlOrBlob\"] || _scriptDir, \"wasmMemory\": wasmMemory, \"wasmModule\": wasmModule });\n        }, allocateUnusedWorker: function() {\n          var pthreadMainJs = locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");\n          PThread.unusedWorkers.push(new Worker(pthreadMainJs));\n        }, getNewWorker: function() {\n          if (PThread.unusedWorkers.length == 0) {\n            PThread.allocateUnusedWorker();\n            PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);\n          }\n          return PThread.unusedWorkers.pop();\n        } };\n        function establishStackSpace() {\n          var pthread_ptr = _pthread_self();\n          var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2];\n          var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2];\n          var stackMax = stackTop - stackSize;\n          _emscripten_stack_set_limits(stackTop, stackMax);\n          stackRestore(stackTop);\n        }\n        Module[\"establishStackSpace\"] = establishStackSpace;\n        function exitOnMainThread(returnCode) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);\n          try {\n            _exit(returnCode);\n          } catch (e2) {\n            handleException(e2);\n          }\n        }\n        var wasmTableMirror = [];\n        function getWasmTableEntry(funcPtr) {\n          var func2 = wasmTableMirror[funcPtr];\n          if (!func2) {\n            if (funcPtr >= wasmTableMirror.length)\n              wasmTableMirror.length = funcPtr + 1;\n            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n          }\n          return func2;\n        }\n        function invokeEntryPoint(ptr, arg) {\n          return getWasmTableEntry(ptr)(arg);\n        }\n        Module[\"invokeEntryPoint\"] = invokeEntryPoint;\n        function jsStackTrace() {\n          var error = new Error();\n          if (!error.stack) {\n            try {\n              throw new Error();\n            } catch (e2) {\n              error = e2;\n            }\n            if (!error.stack) {\n              return \"(no stack trace available)\";\n            }\n          }\n          return error.stack.toString();\n        }\n        function registerTlsInit(tlsInitFunc, moduleExports, metadata) {\n          PThread.tlsInitFunctions.push(tlsInitFunc);\n        }\n        function setWasmTableEntry(idx, func2) {\n          wasmTable.set(idx, func2);\n          wasmTableMirror[idx] = func2;\n        }\n        var _emscripten_get_now;\n        if (ENVIRONMENT_IS_NODE) {\n          _emscripten_get_now = () => {\n            var t2 = process[\"hrtime\"]();\n            return t2[0] * 1e3 + t2[1] / 1e6;\n          };\n        } else if (ENVIRONMENT_IS_PTHREAD) {\n          _emscripten_get_now = () => performance.now() - Module[\"__performance_now_clock_drift\"];\n        } else\n          _emscripten_get_now = () => performance.now();\n        var _emscripten_get_now_is_monotonic = true;\n        function setErrNo(value) {\n          GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;\n          return value;\n        }\n        function _clock_gettime(clk_id, tp) {\n          var now2;\n          if (clk_id === 0) {\n            now2 = Date.now();\n          } else if ((clk_id === 1 || clk_id === 4) && _emscripten_get_now_is_monotonic) {\n            now2 = _emscripten_get_now();\n          } else {\n            setErrNo(28);\n            return -1;\n          }\n          GROWABLE_HEAP_I32()[tp >> 2] = now2 / 1e3 | 0;\n          GROWABLE_HEAP_I32()[tp + 4 >> 2] = now2 % 1e3 * 1e3 * 1e3 | 0;\n          return 0;\n        }\n        function ___clock_gettime(a0, a12) {\n          return _clock_gettime(a0, a12);\n        }\n        function ___emscripten_init_main_thread_js(tb) {\n          __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);\n          PThread.threadInit();\n        }\n        function ___emscripten_thread_cleanup(thread) {\n          if (!ENVIRONMENT_IS_PTHREAD)\n            cleanupThread(thread);\n          else\n            postMessage({ \"cmd\": \"cleanupThread\", \"thread\": thread });\n        }\n        function spawnThread(threadParams) {\n          var worker = PThread.getNewWorker();\n          if (!worker) {\n            return 6;\n          }\n          PThread.runningWorkers.push(worker);\n          var pthread = PThread.pthreads[threadParams.pthread_ptr] = { worker, threadInfoStruct: threadParams.pthread_ptr };\n          worker.pthread = pthread;\n          var msg = { \"cmd\": \"run\", \"start_routine\": threadParams.startRoutine, \"arg\": threadParams.arg, \"threadInfoStruct\": threadParams.pthread_ptr };\n          worker.runPthread = () => {\n            msg.time = performance.now();\n            worker.postMessage(msg, threadParams.transferList);\n          };\n          if (worker.loaded) {\n            worker.runPthread();\n            delete worker.runPthread;\n          }\n          return 0;\n        }\n        function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) {\n          if (typeof SharedArrayBuffer === \"undefined\") {\n            err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");\n            return 6;\n          }\n          var transferList = [];\n          var error = 0;\n          if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {\n            return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);\n          }\n          if (error)\n            return error;\n          var threadParams = { startRoutine: start_routine, pthread_ptr, arg, transferList };\n          if (ENVIRONMENT_IS_PTHREAD) {\n            threadParams.cmd = \"spawnThread\";\n            postMessage(threadParams, transferList);\n            return 0;\n          }\n          return spawnThread(threadParams);\n        }\n        function __emscripten_default_pthread_stack_size() {\n          return 2097152;\n        }\n        function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {\n          if (targetThreadId == mainThreadId) {\n            postMessage({ \"cmd\": \"processQueuedMainThreadWork\" });\n          } else if (ENVIRONMENT_IS_PTHREAD) {\n            postMessage({ \"targetThread\": targetThreadId, \"cmd\": \"processThreadQueue\" });\n          } else {\n            var pthread = PThread.pthreads[targetThreadId];\n            var worker = pthread && pthread.worker;\n            if (!worker) {\n              return;\n            }\n            worker.postMessage({ \"cmd\": \"processThreadQueue\" });\n          }\n          return 1;\n        }\n        function _abort() {\n          abort(\"\");\n        }\n        function _emscripten_check_blocking_allowed() {\n          if (ENVIRONMENT_IS_NODE)\n            return;\n          if (ENVIRONMENT_IS_WORKER)\n            return;\n          warnOnce(\"Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread\");\n        }\n        function _emscripten_get_heap_max() {\n          return 2147483648;\n        }\n        function _emscripten_memcpy_big(dest, src, num) {\n          GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);\n        }\n        function _emscripten_num_logical_cores() {\n          if (ENVIRONMENT_IS_NODE)\n            return require_os().cpus().length;\n          return navigator[\"hardwareConcurrency\"];\n        }\n        function _emscripten_proxy_to_main_thread_js(index, sync) {\n          var numCallArgs = arguments.length - 2;\n          var outerArgs = arguments;\n          return withStackSave(function() {\n            var serializedNumCallArgs = numCallArgs;\n            var args = stackAlloc(serializedNumCallArgs * 8);\n            var b = args >> 3;\n            for (var i2 = 0; i2 < numCallArgs; i2++) {\n              var arg = outerArgs[2 + i2];\n              GROWABLE_HEAP_F64()[b + i2] = arg;\n            }\n            return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);\n          });\n        }\n        var _emscripten_receive_on_main_thread_js_callArgs = [];\n        function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {\n          _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;\n          var b = args >> 3;\n          for (var i2 = 0; i2 < numCallArgs; i2++) {\n            _emscripten_receive_on_main_thread_js_callArgs[i2] = GROWABLE_HEAP_F64()[b + i2];\n          }\n          var isEmAsmConst = index < 0;\n          var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];\n          return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);\n        }\n        function emscripten_realloc_buffer(size) {\n          try {\n            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            return 1;\n          } catch (e2) {\n          }\n        }\n        function _emscripten_resize_heap(requestedSize) {\n          var oldSize = GROWABLE_HEAP_U8().length;\n          requestedSize = requestedSize >>> 0;\n          if (requestedSize <= oldSize) {\n            return false;\n          }\n          var maxHeapSize = _emscripten_get_heap_max();\n          if (requestedSize > maxHeapSize) {\n            return false;\n          }\n          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n            var replacement = emscripten_realloc_buffer(newSize);\n            if (replacement) {\n              return true;\n            }\n          }\n          return false;\n        }\n        var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {\n          for (var i2 = JSEvents.eventHandlers.length - 1; i2 >= 0; --i2) {\n            JSEvents._removeHandler(i2);\n          }\n          JSEvents.eventHandlers = [];\n          JSEvents.deferredCalls = [];\n        }, registerRemoveEventListeners: function() {\n          if (!JSEvents.removeEventListenersRegistered) {\n            __ATEXIT__.push(JSEvents.removeAllEventListeners);\n            JSEvents.removeEventListenersRegistered = true;\n          }\n        }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {\n          function arraysHaveEqualContent(arrA, arrB) {\n            if (arrA.length != arrB.length)\n              return false;\n            for (var i3 in arrA) {\n              if (arrA[i3] != arrB[i3])\n                return false;\n            }\n            return true;\n          }\n          for (var i2 in JSEvents.deferredCalls) {\n            var call = JSEvents.deferredCalls[i2];\n            if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {\n              return;\n            }\n          }\n          JSEvents.deferredCalls.push({ targetFunction, precedence, argsList });\n          JSEvents.deferredCalls.sort(function(x, y) {\n            return x.precedence < y.precedence;\n          });\n        }, removeDeferredCalls: function(targetFunction) {\n          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {\n            if (JSEvents.deferredCalls[i2].targetFunction == targetFunction) {\n              JSEvents.deferredCalls.splice(i2, 1);\n              --i2;\n            }\n          }\n        }, canPerformEventHandlerRequests: function() {\n          return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;\n        }, runDeferredCalls: function() {\n          if (!JSEvents.canPerformEventHandlerRequests()) {\n            return;\n          }\n          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {\n            var call = JSEvents.deferredCalls[i2];\n            JSEvents.deferredCalls.splice(i2, 1);\n            --i2;\n            call.targetFunction.apply(null, call.argsList);\n          }\n        }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {\n          for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {\n            if (JSEvents.eventHandlers[i2].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i2].eventTypeString)) {\n              JSEvents._removeHandler(i2--);\n            }\n          }\n        }, _removeHandler: function(i2) {\n          var h = JSEvents.eventHandlers[i2];\n          h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);\n          JSEvents.eventHandlers.splice(i2, 1);\n        }, registerOrRemoveHandler: function(eventHandler) {\n          var jsEventHandler = function jsEventHandler2(event) {\n            ++JSEvents.inEventHandler;\n            JSEvents.currentEventHandler = eventHandler;\n            JSEvents.runDeferredCalls();\n            eventHandler.handlerFunc(event);\n            JSEvents.runDeferredCalls();\n            --JSEvents.inEventHandler;\n          };\n          if (eventHandler.callbackfunc) {\n            eventHandler.eventListenerFunc = jsEventHandler;\n            eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);\n            JSEvents.eventHandlers.push(eventHandler);\n            JSEvents.registerRemoveEventListeners();\n          } else {\n            for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {\n              if (JSEvents.eventHandlers[i2].target == eventHandler.target && JSEvents.eventHandlers[i2].eventTypeString == eventHandler.eventTypeString) {\n                JSEvents._removeHandler(i2--);\n              }\n            }\n          }\n        }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {\n          withStackSave(function() {\n            var varargs = stackAlloc(12);\n            GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;\n            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;\n            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;\n            _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);\n          });\n        }, getTargetThreadForEventCallback: function(targetThread) {\n          switch (targetThread) {\n            case 1:\n              return 0;\n            case 2:\n              return PThread.currentProxiedOperationCallerThread;\n            default:\n              return targetThread;\n          }\n        }, getNodeNameForTarget: function(target) {\n          if (!target)\n            return \"\";\n          if (target == window)\n            return \"#window\";\n          if (target == screen)\n            return \"#screen\";\n          return target && target.nodeName ? target.nodeName : \"\";\n        }, fullscreenEnabled: function() {\n          return document.fullscreenEnabled || document.webkitFullscreenEnabled;\n        } };\n        function stringToNewUTF8(jsString) {\n          var length = lengthBytesUTF8(jsString) + 1;\n          var cString = _malloc(length);\n          stringToUTF8(jsString, cString, length);\n          return cString;\n        }\n        function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {\n          withStackSave(function() {\n            var varargs = stackAlloc(12);\n            var targetCanvasPtr = 0;\n            if (targetCanvas) {\n              targetCanvasPtr = stringToNewUTF8(targetCanvas);\n            }\n            GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;\n            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;\n            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;\n            _emscripten_dispatch_to_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs);\n          });\n        }\n        function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {\n          targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : \"\";\n          _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);\n        }\n        function maybeCStringToJsString(cString) {\n          return cString > 2 ? UTF8ToString(cString) : cString;\n        }\n        var specialHTMLTargets = [0, typeof document !== \"undefined\" ? document : 0, typeof window !== \"undefined\" ? window : 0];\n        function findEventTarget(target) {\n          target = maybeCStringToJsString(target);\n          var domElement = specialHTMLTargets[target] || (typeof document !== \"undefined\" ? document.querySelector(target) : void 0);\n          return domElement;\n        }\n        function findCanvasEventTarget(target) {\n          return findEventTarget(target);\n        }\n        function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {\n          var canvas = findCanvasEventTarget(target);\n          if (!canvas)\n            return -4;\n          if (canvas.canvasSharedPtr) {\n            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width;\n            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height;\n          }\n          if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) {\n            if (canvas.offscreenCanvas)\n              canvas = canvas.offscreenCanvas;\n            var autoResizeViewport = false;\n            if (canvas.GLctxObject && canvas.GLctxObject.GLctx) {\n              var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978);\n              autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height;\n            }\n            canvas.width = width;\n            canvas.height = height;\n            if (autoResizeViewport) {\n              canvas.GLctxObject.GLctx.viewport(0, 0, width, height);\n            }\n          } else if (canvas.canvasSharedPtr) {\n            var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2];\n            _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);\n            return 1;\n          } else {\n            return -4;\n          }\n          return 0;\n        }\n        function _emscripten_set_canvas_element_size_main_thread(target, width, height) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);\n          return _emscripten_set_canvas_element_size_calling_thread(target, width, height);\n        }\n        function _emscripten_set_canvas_element_size(target, width, height) {\n          var canvas = findCanvasEventTarget(target);\n          if (canvas) {\n            return _emscripten_set_canvas_element_size_calling_thread(target, width, height);\n          } else {\n            return _emscripten_set_canvas_element_size_main_thread(target, width, height);\n          }\n        }\n        function _emscripten_unwind_to_js_event_loop() {\n          throw \"unwind\";\n        }\n        function __webgl_enable_ANGLE_instanced_arrays(ctx) {\n          var ext = ctx.getExtension(\"ANGLE_instanced_arrays\");\n          if (ext) {\n            ctx[\"vertexAttribDivisor\"] = function(index, divisor) {\n              ext[\"vertexAttribDivisorANGLE\"](index, divisor);\n            };\n            ctx[\"drawArraysInstanced\"] = function(mode, first, count2, primcount) {\n              ext[\"drawArraysInstancedANGLE\"](mode, first, count2, primcount);\n            };\n            ctx[\"drawElementsInstanced\"] = function(mode, count2, type, indices, primcount) {\n              ext[\"drawElementsInstancedANGLE\"](mode, count2, type, indices, primcount);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_OES_vertex_array_object(ctx) {\n          var ext = ctx.getExtension(\"OES_vertex_array_object\");\n          if (ext) {\n            ctx[\"createVertexArray\"] = function() {\n              return ext[\"createVertexArrayOES\"]();\n            };\n            ctx[\"deleteVertexArray\"] = function(vao) {\n              ext[\"deleteVertexArrayOES\"](vao);\n            };\n            ctx[\"bindVertexArray\"] = function(vao) {\n              ext[\"bindVertexArrayOES\"](vao);\n            };\n            ctx[\"isVertexArray\"] = function(vao) {\n              return ext[\"isVertexArrayOES\"](vao);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_WEBGL_draw_buffers(ctx) {\n          var ext = ctx.getExtension(\"WEBGL_draw_buffers\");\n          if (ext) {\n            ctx[\"drawBuffers\"] = function(n, bufs) {\n              ext[\"drawBuffersWEBGL\"](n, bufs);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_WEBGL_multi_draw(ctx) {\n          return !!(ctx.multiDrawWebgl = ctx.getExtension(\"WEBGL_multi_draw\"));\n        }\n        var GL = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) {\n          if (!GL.lastError) {\n            GL.lastError = errorCode;\n          }\n        }, getNewId: function(table) {\n          var ret = GL.counter++;\n          for (var i2 = table.length; i2 < ret; i2++) {\n            table[i2] = null;\n          }\n          return ret;\n        }, getSource: function(shader, count2, string2, length) {\n          var source = \"\";\n          for (var i2 = 0; i2 < count2; ++i2) {\n            var len = length ? GROWABLE_HEAP_I32()[length + i2 * 4 >> 2] : -1;\n            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i2 * 4 >> 2], len < 0 ? void 0 : len);\n          }\n          return source;\n        }, createContext: function(canvas, webGLContextAttributes) {\n          if (!canvas.getContextSafariWebGL2Fixed) {\n            canvas.getContextSafariWebGL2Fixed = canvas.getContext;\n            canvas.getContext = function(ver, attrs) {\n              var gl = canvas.getContextSafariWebGL2Fixed(ver, attrs);\n              return ver == \"webgl\" == gl instanceof WebGLRenderingContext ? gl : null;\n            };\n          }\n          var ctx = canvas.getContext(\"webgl\", webGLContextAttributes);\n          if (!ctx)\n            return 0;\n          var handle = GL.registerContext(ctx, webGLContextAttributes);\n          return handle;\n        }, registerContext: function(ctx, webGLContextAttributes) {\n          var handle = _malloc(8);\n          GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();\n          var context = { handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx };\n          if (ctx.canvas)\n            ctx.canvas.GLctxObject = context;\n          GL.contexts[handle] = context;\n          if (typeof webGLContextAttributes.enableExtensionsByDefault === \"undefined\" || webGLContextAttributes.enableExtensionsByDefault) {\n            GL.initExtensions(context);\n          }\n          return handle;\n        }, makeContextCurrent: function(contextHandle) {\n          GL.currentContext = GL.contexts[contextHandle];\n          Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;\n          return !(contextHandle && !GLctx);\n        }, getContext: function(contextHandle) {\n          return GL.contexts[contextHandle];\n        }, deleteContext: function(contextHandle) {\n          if (GL.currentContext === GL.contexts[contextHandle])\n            GL.currentContext = null;\n          if (typeof JSEvents === \"object\")\n            JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);\n          if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)\n            GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;\n          _free(GL.contexts[contextHandle].handle);\n          GL.contexts[contextHandle] = null;\n        }, initExtensions: function(context) {\n          if (!context)\n            context = GL.currentContext;\n          if (context.initExtensionsDone)\n            return;\n          context.initExtensionsDone = true;\n          var GLctx2 = context.GLctx;\n          __webgl_enable_ANGLE_instanced_arrays(GLctx2);\n          __webgl_enable_OES_vertex_array_object(GLctx2);\n          __webgl_enable_WEBGL_draw_buffers(GLctx2);\n          {\n            GLctx2.disjointTimerQueryExt = GLctx2.getExtension(\"EXT_disjoint_timer_query\");\n          }\n          __webgl_enable_WEBGL_multi_draw(GLctx2);\n          var exts = GLctx2.getSupportedExtensions() || [];\n          exts.forEach(function(ext) {\n            if (!ext.includes(\"lose_context\") && !ext.includes(\"debug\")) {\n              GLctx2.getExtension(ext);\n            }\n          });\n        } };\n        var __emscripten_webgl_power_preferences = [\"default\", \"low-power\", \"high-performance\"];\n        function _emscripten_webgl_do_create_context(target, attributes) {\n          var a6 = attributes >> 2;\n          var powerPreference = GROWABLE_HEAP_I32()[a6 + (24 >> 2)];\n          var contextAttributes = { \"alpha\": !!GROWABLE_HEAP_I32()[a6 + (0 >> 2)], \"depth\": !!GROWABLE_HEAP_I32()[a6 + (4 >> 2)], \"stencil\": !!GROWABLE_HEAP_I32()[a6 + (8 >> 2)], \"antialias\": !!GROWABLE_HEAP_I32()[a6 + (12 >> 2)], \"premultipliedAlpha\": !!GROWABLE_HEAP_I32()[a6 + (16 >> 2)], \"preserveDrawingBuffer\": !!GROWABLE_HEAP_I32()[a6 + (20 >> 2)], \"powerPreference\": __emscripten_webgl_power_preferences[powerPreference], \"failIfMajorPerformanceCaveat\": !!GROWABLE_HEAP_I32()[a6 + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a6 + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a6 + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a6 + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a6 + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a6 + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a6 + (52 >> 2)] };\n          var canvas = findCanvasEventTarget(target);\n          if (!canvas) {\n            return 0;\n          }\n          if (contextAttributes.explicitSwapControl) {\n            return 0;\n          }\n          var contextHandle = GL.createContext(canvas, contextAttributes);\n          return contextHandle;\n        }\n        function _emscripten_webgl_create_context(a0, a12) {\n          return _emscripten_webgl_do_create_context(a0, a12);\n        }\n        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {\n          var buffer3 = SYSCALLS.buffers[stream];\n          if (curr === 0 || curr === 10) {\n            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n            buffer3.length = 0;\n          } else {\n            buffer3.push(curr);\n          }\n        }, varargs: void 0, get: function() {\n          SYSCALLS.varargs += 4;\n          var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];\n          return ret;\n        }, getStr: function(ptr) {\n          var ret = UTF8ToString(ptr);\n          return ret;\n        }, get64: function(low, high) {\n          return low;\n        } };\n        function _fd_close(fd) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(3, 1, fd);\n          return 0;\n        }\n        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);\n        }\n        function _fd_write(fd, iov, iovcnt, pnum) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);\n          var num = 0;\n          for (var i2 = 0; i2 < iovcnt; i2++) {\n            var ptr = GROWABLE_HEAP_I32()[iov >> 2];\n            var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];\n            iov += 8;\n            for (var j = 0; j < len; j++) {\n              SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);\n            }\n            num += len;\n          }\n          GROWABLE_HEAP_I32()[pnum >> 2] = num;\n          return 0;\n        }\n        function _setTempRet0(val) {\n          setTempRet0(val);\n        }\n        PThread.init();\n        var GLctx;\n        var proxiedFunctionTable = [null, exitOnMainThread, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write];\n        var ASSERTIONS = false;\n        var asmLibraryArg = { \"__clock_gettime\": ___clock_gettime, \"__emscripten_init_main_thread_js\": ___emscripten_init_main_thread_js, \"__emscripten_thread_cleanup\": ___emscripten_thread_cleanup, \"__pthread_create_js\": ___pthread_create_js, \"_emscripten_default_pthread_stack_size\": __emscripten_default_pthread_stack_size, \"_emscripten_notify_thread_queue\": __emscripten_notify_thread_queue, \"abort\": _abort, \"emscripten_check_blocking_allowed\": _emscripten_check_blocking_allowed, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_get_now\": _emscripten_get_now, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_num_logical_cores\": _emscripten_num_logical_cores, \"emscripten_receive_on_main_thread_js\": _emscripten_receive_on_main_thread_js, \"emscripten_resize_heap\": _emscripten_resize_heap, \"emscripten_set_canvas_element_size\": _emscripten_set_canvas_element_size, \"emscripten_unwind_to_js_event_loop\": _emscripten_unwind_to_js_event_loop, \"emscripten_webgl_create_context\": _emscripten_webgl_create_context, \"exit\": _exit, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"memory\": wasmMemory || Module[\"wasmMemory\"], \"setTempRet0\": _setTempRet0 };\n        var asm = createWasm();\n        var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n          return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n        };\n        var _init = Module[\"_init\"] = function() {\n          return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n        };\n        var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n          return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n        };\n        var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n          return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n        };\n        var _register_tensor = Module[\"_register_tensor\"] = function() {\n          return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n        };\n        var _dispose_data = Module[\"_dispose_data\"] = function() {\n          return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n        };\n        var _dispose = Module[\"_dispose\"] = function() {\n          return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n        };\n        var _Abs = Module[\"_Abs\"] = function() {\n          return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n        };\n        var _Add = Module[\"_Add\"] = function() {\n          return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n        };\n        var _AddN = Module[\"_AddN\"] = function() {\n          return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n        };\n        var _All = Module[\"_All\"] = function() {\n          return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n        };\n        var _Any = Module[\"_Any\"] = function() {\n          return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n        };\n        var _ArgMax = Module[\"_ArgMax\"] = function() {\n          return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n        };\n        var _AvgPool = Module[\"_AvgPool\"] = function() {\n          return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n        };\n        var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n          return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n        };\n        var _Ceil = Module[\"_Ceil\"] = function() {\n          return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n        };\n        var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n          return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n        };\n        var _Conv2D = Module[\"_Conv2D\"] = function() {\n          return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n        };\n        var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n          return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n        };\n        var _Cos = Module[\"_Cos\"] = function() {\n          return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n        };\n        var _Cosh = Module[\"_Cosh\"] = function() {\n          return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n        };\n        var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n          return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n        };\n        var _Cumprod = Module[\"_Cumprod\"] = function() {\n          return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n        };\n        var _Cumsum = Module[\"_Cumsum\"] = function() {\n          return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n        };\n        var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n          return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n        };\n        var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n          return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n        };\n        var _Elu = Module[\"_Elu\"] = function() {\n          return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n        };\n        var _Equal = Module[\"_Equal\"] = function() {\n          return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n        };\n        var _Exp = Module[\"_Exp\"] = function() {\n          return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n        };\n        var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n          return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n        };\n        var _Floor = Module[\"_Floor\"] = function() {\n          return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n        };\n        var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n          return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n        };\n        var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n          return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n        };\n        var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n          return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n        };\n        var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n          return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n        };\n        var _Gather = Module[\"_Gather\"] = function() 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{\n          return (_Maximum = Module[\"_Maximum\"] = Module[\"asm\"][\"Maximum\"]).apply(null, arguments);\n        };\n        var _Mean = Module[\"_Mean\"] = function() {\n          return (_Mean = Module[\"_Mean\"] = Module[\"asm\"][\"Mean\"]).apply(null, arguments);\n        };\n        var _Min = Module[\"_Min\"] = function() {\n          return (_Min = Module[\"_Min\"] = Module[\"asm\"][\"Min\"]).apply(null, arguments);\n        };\n        var _Minimum = Module[\"_Minimum\"] = function() {\n          return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n        };\n        var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n          return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n        };\n        var _Multiply = Module[\"_Multiply\"] = function() {\n          return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n        };\n    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Module[\"asm\"][\"NotEqual\"]).apply(null, arguments);\n        };\n        var _OneHot = Module[\"_OneHot\"] = function() {\n          return (_OneHot = Module[\"_OneHot\"] = Module[\"asm\"][\"OneHot\"]).apply(null, arguments);\n        };\n        var _PadV2 = Module[\"_PadV2\"] = function() {\n          return (_PadV2 = Module[\"_PadV2\"] = Module[\"asm\"][\"PadV2\"]).apply(null, arguments);\n        };\n        var _Pow = Module[\"_Pow\"] = function() {\n          return (_Pow = Module[\"_Pow\"] = Module[\"asm\"][\"Pow\"]).apply(null, arguments);\n        };\n        var _Prelu = Module[\"_Prelu\"] = function() {\n          return (_Prelu = Module[\"_Prelu\"] = Module[\"asm\"][\"Prelu\"]).apply(null, arguments);\n        };\n        var _Prod = Module[\"_Prod\"] = function() {\n          return (_Prod = Module[\"_Prod\"] = Module[\"asm\"][\"Prod\"]).apply(null, arguments);\n        };\n        var _RealDiv = Module[\"_RealDiv\"] = function() {\n          return (_RealDiv = 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var _memalign = Module[\"_memalign\"] = function() {\n          return (_memalign = Module[\"_memalign\"] = Module[\"asm\"][\"memalign\"]).apply(null, arguments);\n        };\n        var _emscripten_stack_set_limits = Module[\"_emscripten_stack_set_limits\"] = function() {\n          return (_emscripten_stack_set_limits = Module[\"_emscripten_stack_set_limits\"] = Module[\"asm\"][\"emscripten_stack_set_limits\"]).apply(null, arguments);\n        };\n        var stackSave = Module[\"stackSave\"] = function() {\n          return (stackSave = Module[\"stackSave\"] = Module[\"asm\"][\"stackSave\"]).apply(null, arguments);\n        };\n        var stackRestore = Module[\"stackRestore\"] = function() {\n          return (stackRestore = Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n        };\n        var stackAlloc = Module[\"stackAlloc\"] = function() {\n          return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n        };\n        var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n          return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n        };\n        var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n          return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n        };\n        var __emscripten_allow_main_runtime_queued_calls = Module[\"__emscripten_allow_main_runtime_queued_calls\"] = 21464;\n        Module[\"cwrap\"] = cwrap;\n        Module[\"keepRuntimeAlive\"] = keepRuntimeAlive;\n        Module[\"PThread\"] = PThread;\n        Module[\"PThread\"] = PThread;\n        Module[\"wasmMemory\"] = wasmMemory;\n        Module[\"ExitStatus\"] = ExitStatus;\n        var calledRun;\n        function ExitStatus(status) {\n          this.name = \"ExitStatus\";\n          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readyPromiseResolve(Module);\n            if (Module[\"onRuntimeInitialized\"])\n              Module[\"onRuntimeInitialized\"]();\n            postRun();\n          }\n          if (Module[\"setStatus\"]) {\n            Module[\"setStatus\"](\"Running...\");\n            setTimeout(function() {\n              setTimeout(function() {\n                Module[\"setStatus\"](\"\");\n              }, 1);\n              doRun();\n            }, 1);\n          } else {\n            doRun();\n          }\n        }\n        Module[\"run\"] = run;\n        function exit(status, implicit) {\n          EXITSTATUS = status;\n          if (!implicit) {\n            if (ENVIRONMENT_IS_PTHREAD) {\n              exitOnMainThread(status);\n              throw \"unwind\";\n            } else {\n            }\n          }\n          if (keepRuntimeAlive()) {\n          } else {\n            exitRuntime();\n          }\n          procExit(status);\n        }\n        function procExit(code) {\n          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> -1;\n          }) };\n        }\n        var actualModule;\n        if (typeof WasmBackendModule !== \"undefined\") {\n          actualModule = WasmBackendModule;\n        } else if (typeof WasmBackendModuleThreadedSimd3 !== \"undefined\") {\n          actualModule = WasmBackendModuleThreadedSimd3;\n        } else {\n          throw new Error(\"Could not find wasm module in post.js\");\n        }\n        if (listenersAdded) {\n          var tmpDispose = actualModule[\"_dispose\"];\n          actualModule[\"_dispose\"] = function() {\n            tmpDispose();\n            listenersAdded.uncaughtException.forEach(function(listener) {\n              process.removeListener(\"uncaughtException\", listener);\n            });\n            listenersAdded.unhandledRejection.forEach(function(listener) {\n              process.removeListener(\"unhandledRejection\", listener);\n            });\n          };\n        }\n        return WasmBackendModuleThreadedSimd3.ready;\n      };\n    })();\n    if (typeof exports === \"object\" && typeof module === \"object\")\n      module.exports = WasmBackendModuleThreadedSimd2;\n    else if (typeof define === \"function\" && define[\"amd\"])\n      define([], function() {\n        return WasmBackendModuleThreadedSimd2;\n      });\n    else if (typeof exports === \"object\")\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd2;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\nvar require_tfjs_backend_wasm = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\"(exports, module) {\n    var WasmBackendModule2 = (() => {\n      var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n      if (typeof __filename !== \"undefined\")\n        _scriptDir = _scriptDir || __filename;\n      return function(WasmBackendModule3) {\n        WasmBackendModule3 = WasmBackendModule3 || {};\n        var Module = typeof WasmBackendModule3 !== \"undefined\" ? WasmBackendModule3 : {};\n        var readyPromiseResolve, readyPromiseReject;\n        Module[\"ready\"] = new Promise(function(resolve, reject) {\n          readyPromiseResolve = resolve;\n          readyPromiseReject = reject;\n        });\n        var beforeListeners;\n        if (typeof process !== \"undefined\" && process.listeners) {\n          beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n        }\n        var moduleOverrides = Object.assign({}, Module);\n        var arguments_ = [];\n        var thisProgram = \"./this.program\";\n        var quit_ = (status, toThrow) => {\n          throw toThrow;\n        };\n        var ENVIRONMENT_IS_WEB = typeof window === \"object\";\n        var ENVIRONMENT_IS_WORKER = typeof importScripts === \"function\";\n        var ENVIRONMENT_IS_NODE = typeof process === \"object\" && typeof process.versions === \"object\" && typeof process.versions.node === \"string\";\n        var scriptDirectory = \"\";\n        function locateFile(path) {\n          if (Module[\"locateFile\"]) {\n            return Module[\"locateFile\"](path, scriptDirectory);\n          }\n          return scriptDirectory + path;\n        }\n        var read_, readAsync, readBinary, setWindowTitle;\n        function logExceptionOnExit(e2) {\n          if (e2 instanceof ExitStatus)\n            return;\n          let toLog = e2;\n          err(\"exiting due to exception: \" + toLog);\n        }\n        var fs;\n        var nodePath;\n        var requireNodeFS;\n        if (ENVIRONMENT_IS_NODE) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = require_path().dirname(scriptDirectory) + \"/\";\n          } else {\n            scriptDirectory = __dirname + \"/\";\n          }\n          requireNodeFS = () => {\n            if (!nodePath) {\n              fs = require_fs();\n              nodePath = require_path();\n            }\n          };\n          read_ = function shell_read(filename, binary) {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n          };\n          readBinary = (filename) => {\n            var ret = read_(filename, true);\n            if (!ret.buffer) {\n              ret = new Uint8Array(ret);\n            }\n            return ret;\n          };\n          readAsync = (filename, onload, onerror) => {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            fs.readFile(filename, function(err2, data) {\n              if (err2)\n                onerror(err2);\n              else\n                onload(data.buffer);\n            });\n          };\n          if (process[\"argv\"].length > 1) {\n            thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n          }\n          arguments_ = process[\"argv\"].slice(2);\n          process[\"on\"](\"uncaughtException\", function(ex) {\n            if (!(ex instanceof ExitStatus)) {\n              throw ex;\n            }\n          });\n          process[\"on\"](\"unhandledRejection\", function(reason) {\n            throw reason;\n          });\n          quit_ = (status, toThrow) => {\n            if (keepRuntimeAlive()) {\n              process[\"exitCode\"] = status;\n              throw toThrow;\n            }\n            logExceptionOnExit(toThrow);\n            process[\"exit\"](status);\n          };\n          Module[\"inspect\"] = function() {\n            return \"[Emscripten Module object]\";\n          };\n        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = self.location.href;\n          } else if (typeof document !== \"undefined\" && document.currentScript) {\n            scriptDirectory = document.currentScript.src;\n          }\n          if (_scriptDir) {\n            scriptDirectory = _scriptDir;\n          }\n          if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n          } else {\n            scriptDirectory = \"\";\n          }\n          {\n            read_ = (url) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, false);\n              xhr.send(null);\n              return xhr.responseText;\n            };\n            if (ENVIRONMENT_IS_WORKER) {\n              readBinary = (url) => {\n                var xhr = new XMLHttpRequest();\n                xhr.open(\"GET\", url, false);\n                xhr.responseType = \"arraybuffer\";\n                xhr.send(null);\n                return new Uint8Array(xhr.response);\n              };\n            }\n            readAsync = (url, onload, onerror) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, true);\n              xhr.responseType = \"arraybuffer\";\n              xhr.onload = () => {\n                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n                  onload(xhr.response);\n                  return;\n                }\n                onerror();\n              };\n              xhr.onerror = onerror;\n              xhr.send(null);\n            };\n          }\n          setWindowTitle = (title) => document.title = title;\n        } else {\n        }\n        var out = Module[\"print\"] || console.log.bind(console);\n        var err = Module[\"printErr\"] || console.warn.bind(console);\n        Object.assign(Module, moduleOverrides);\n        moduleOverrides = null;\n        if (Module[\"arguments\"])\n          arguments_ = Module[\"arguments\"];\n        if (Module[\"thisProgram\"])\n          thisProgram = Module[\"thisProgram\"];\n        if (Module[\"quit\"])\n          quit_ = Module[\"quit\"];\n        var POINTER_SIZE = 4;\n        function warnOnce(text) {\n          if (!warnOnce.shown)\n            warnOnce.shown = {};\n          if (!warnOnce.shown[text]) {\n            warnOnce.shown[text] = 1;\n            err(text);\n          }\n        }\n        function convertJsFunctionToWasm(func2, sig) {\n          if (typeof WebAssembly.Function === \"function\") {\n            var typeNames = { \"i\": \"i32\", \"j\": \"i64\", \"f\": \"f32\", \"d\": \"f64\" };\n            var type = { parameters: [], results: sig[0] == \"v\" ? [] : [typeNames[sig[0]]] };\n            for (var i2 = 1; i2 < sig.length; ++i2) {\n              type.parameters.push(typeNames[sig[i2]]);\n            }\n            return new WebAssembly.Function(type, func2);\n          }\n          var typeSection = [1, 0, 1, 96];\n          var sigRet = sig.slice(0, 1);\n          var sigParam = sig.slice(1);\n          var typeCodes = { \"i\": 127, \"j\": 126, \"f\": 125, \"d\": 124 };\n          typeSection.push(sigParam.length);\n          for (var i2 = 0; i2 < sigParam.length; ++i2) {\n            typeSection.push(typeCodes[sigParam[i2]]);\n          }\n          if (sigRet == \"v\") {\n            typeSection.push(0);\n          } else {\n            typeSection = typeSection.concat([1, typeCodes[sigRet]]);\n          }\n          typeSection[1] = typeSection.length - 2;\n          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));\n          var module2 = new WebAssembly.Module(bytes);\n          var instance = new WebAssembly.Instance(module2, { \"e\": { \"f\": func2 } });\n          var wrappedFunc = instance.exports[\"f\"];\n          return wrappedFunc;\n        }\n        var freeTableIndexes = [];\n        var functionsInTableMap;\n        function getEmptyTableSlot() {\n          if (freeTableIndexes.length) {\n            return freeTableIndexes.pop();\n          }\n          try {\n            wasmTable.grow(1);\n          } catch (err2) {\n            if (!(err2 instanceof RangeError)) {\n              throw err2;\n            }\n            throw \"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.\";\n          }\n          return wasmTable.length - 1;\n        }\n        function updateTableMap(offset, count2) {\n          for (var i2 = offset; i2 < offset + count2; i2++) {\n            var item = getWasmTableEntry(i2);\n            if (item) {\n              functionsInTableMap.set(item, i2);\n            }\n          }\n        }\n        var tempRet0 = 0;\n        var setTempRet0 = (value) => {\n          tempRet0 = value;\n        };\n        var wasmBinary;\n        if (Module[\"wasmBinary\"])\n          wasmBinary = Module[\"wasmBinary\"];\n        var noExitRuntime = Module[\"noExitRuntime\"] || true;\n        if (typeof WebAssembly !== \"object\") {\n          abort(\"no native wasm support detected\");\n        }\n        var wasmMemory;\n        var ABORT = false;\n        var EXITSTATUS;\n        function assert3(condition, text) {\n          if (!condition) {\n            abort(text);\n          }\n        }\n        function getCFunc(ident) {\n          var func2 = Module[\"_\" + ident];\n          return func2;\n        }\n        function ccall(ident, returnType, argTypes, args, opts) {\n          var toC = { \"string\": function(str) {\n            var ret2 = 0;\n            if (str !== null && str !== void 0 && str !== 0) {\n              var len = (str.length << 2) + 1;\n              ret2 = stackAlloc(len);\n              stringToUTF8(str, ret2, len);\n            }\n            return ret2;\n          }, \"array\": function(arr) {\n            var ret2 = stackAlloc(arr.length);\n            writeArrayToMemory(arr, ret2);\n            return ret2;\n          } };\n          function convertReturnValue(ret2) {\n            if (returnType === \"string\")\n              return UTF8ToString(ret2);\n            if (returnType === \"boolean\")\n              return Boolean(ret2);\n            return ret2;\n          }\n          var func2 = getCFunc(ident);\n          var cArgs = [];\n          var stack2 = 0;\n          if (args) {\n            for (var i2 = 0; i2 < args.length; i2++) {\n              var converter = toC[argTypes[i2]];\n              if (converter) {\n                if (stack2 === 0)\n                  stack2 = stackSave();\n                cArgs[i2] = converter(args[i2]);\n              } else {\n                cArgs[i2] = args[i2];\n              }\n            }\n          }\n          var ret = func2.apply(null, cArgs);\n          function onDone(ret2) {\n            if (stack2 !== 0)\n              stackRestore(stack2);\n            return convertReturnValue(ret2);\n          }\n          ret = onDone(ret);\n          return ret;\n        }\n        function cwrap(ident, returnType, argTypes, opts) {\n          argTypes = argTypes || [];\n          var numericArgs = argTypes.every(function(type) {\n            return type === \"number\";\n          });\n          var numericRet = returnType !== \"string\";\n          if (numericRet && numericArgs && !opts) {\n            return getCFunc(ident);\n          }\n          return function() {\n            return ccall(ident, returnType, argTypes, arguments, opts);\n          };\n        }\n        var ALLOC_STACK = 1;\n        var UTF8Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoder(\"utf8\") : void 0;\n        function UTF8ArrayToString(heap, idx, maxBytesToRead) {\n          var endIdx = idx + maxBytesToRead;\n          var endPtr = idx;\n          while (heap[endPtr] && !(endPtr >= endIdx))\n            ++endPtr;\n          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {\n            return UTF8Decoder.decode(heap.subarray(idx, endPtr));\n          } else {\n            var str = \"\";\n            while (idx < endPtr) {\n              var u0 = heap[idx++];\n              if (!(u0 & 128)) {\n                str += String.fromCharCode(u0);\n                continue;\n              }\n              var u1 = heap[idx++] & 63;\n              if ((u0 & 224) == 192) {\n                str += String.fromCharCode((u0 & 31) << 6 | u1);\n                continue;\n              }\n              var u2 = heap[idx++] & 63;\n              if ((u0 & 240) == 224) {\n                u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n              } else {\n                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;\n              }\n              if (u0 < 65536) {\n                str += String.fromCharCode(u0);\n              } else {\n                var ch = u0 - 65536;\n                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n              }\n            }\n          }\n          return str;\n        }\n        function UTF8ToString(ptr, maxBytesToRead) {\n          return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : \"\";\n        }\n        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n          if (!(maxBytesToWrite > 0))\n            return 0;\n          var startIdx = outIdx;\n          var endIdx = outIdx + maxBytesToWrite - 1;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343) {\n              var u1 = str.charCodeAt(++i2);\n              u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n            }\n            if (u <= 127) {\n              if (outIdx >= endIdx)\n                break;\n              heap[outIdx++] = u;\n            } else if (u <= 2047) {\n              if (outIdx + 1 >= endIdx)\n                break;\n              heap[outIdx++] = 192 | u >> 6;\n              heap[outIdx++] = 128 | u & 63;\n            } else if (u <= 65535) {\n              if (outIdx + 2 >= endIdx)\n                break;\n              heap[outIdx++] = 224 | u >> 12;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            } else {\n              if (outIdx + 3 >= endIdx)\n                break;\n              heap[outIdx++] = 240 | u >> 18;\n              heap[outIdx++] = 128 | u >> 12 & 63;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            }\n          }\n          heap[outIdx] = 0;\n          return outIdx - startIdx;\n        }\n        function stringToUTF8(str, outPtr, maxBytesToWrite) {\n          return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);\n        }\n        function lengthBytesUTF8(str) {\n          var len = 0;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343)\n              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;\n            if (u <= 127)\n              ++len;\n            else if (u <= 2047)\n              len += 2;\n            else if (u <= 65535)\n              len += 3;\n            else\n              len += 4;\n          }\n          return len;\n        }\n        var UTF16Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoder(\"utf-16le\") : void 0;\n        function writeArrayToMemory(array2, buffer3) {\n          HEAP8.set(array2, buffer3);\n        }\n        function writeAsciiToMemory(str, buffer3, dontAddNull) {\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            HEAP8[buffer3++ >> 0] = str.charCodeAt(i2);\n          }\n          if (!dontAddNull)\n            HEAP8[buffer3 >> 0] = 0;\n        }\n        function alignUp(x, multiple) {\n          if (x % multiple > 0) {\n            x += multiple - x % multiple;\n          }\n          return x;\n        }\n        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n        function updateGlobalBufferAndViews(buf) {\n          buffer2 = buf;\n          Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n          Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n          Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n          Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n          Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n          Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n          Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n          Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n        }\n        var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n        var wasmTable;\n        var __ATPRERUN__ = [];\n        var __ATINIT__ = [];\n        var __ATPOSTRUN__ = [];\n        var runtimeInitialized = false;\n        var runtimeExited = false;\n        var runtimeKeepaliveCounter = 0;\n        function keepRuntimeAlive() {\n          return noExitRuntime || runtimeKeepaliveCounter > 0;\n        }\n        function preRun() {\n          if (Module[\"preRun\"]) {\n            if (typeof Module[\"preRun\"] == \"function\")\n              Module[\"preRun\"] = [Module[\"preRun\"]];\n            while (Module[\"preRun\"].length) {\n              addOnPreRun(Module[\"preRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPRERUN__);\n        }\n        function initRuntime() {\n          runtimeInitialized = true;\n          callRuntimeCallbacks(__ATINIT__);\n        }\n        function exitRuntime() {\n          runtimeExited = true;\n        }\n        function postRun() {\n          if (Module[\"postRun\"]) {\n            if (typeof Module[\"postRun\"] == \"function\")\n              Module[\"postRun\"] = [Module[\"postRun\"]];\n            while (Module[\"postRun\"].length) {\n              addOnPostRun(Module[\"postRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPOSTRUN__);\n        }\n        function addOnPreRun(cb) {\n          __ATPRERUN__.unshift(cb);\n        }\n        function addOnInit(cb) {\n          __ATINIT__.unshift(cb);\n        }\n        function addOnPostRun(cb) {\n          __ATPOSTRUN__.unshift(cb);\n        }\n        var runDependencies = 0;\n        var runDependencyWatcher = null;\n        var dependenciesFulfilled = null;\n        function addRunDependency(id) {\n          runDependencies++;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n        }\n        function removeRunDependency(id) {\n          runDependencies--;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n          if (runDependencies == 0) {\n            if (runDependencyWatcher !== null) {\n              clearInterval(runDependencyWatcher);\n              runDependencyWatcher = null;\n            }\n            if (dependenciesFulfilled) {\n              var callback = dependenciesFulfilled;\n              dependenciesFulfilled = null;\n              callback();\n            }\n          }\n        }\n        Module[\"preloadedImages\"] = {};\n        Module[\"preloadedAudios\"] = {};\n        function abort(what) {\n          {\n            if (Module[\"onAbort\"]) {\n              Module[\"onAbort\"](what);\n            }\n          }\n          what = \"Aborted(\" + what + \")\";\n          err(what);\n          ABORT = true;\n          EXITSTATUS = 1;\n          what += \". Build with -s ASSERTIONS=1 for more info.\";\n          var e2 = new WebAssembly.RuntimeError(what);\n          readyPromiseReject(e2);\n          throw e2;\n        }\n        var dataURIPrefix = \"data:application/octet-stream;base64,\";\n        function isDataURI(filename) {\n          return filename.startsWith(dataURIPrefix);\n        }\n        function isFileURI(filename) {\n          return filename.startsWith(\"file://\");\n        }\n        var wasmBinaryFile;\n        wasmBinaryFile = \"tfjs-backend-wasm.wasm\";\n        if (!isDataURI(wasmBinaryFile)) {\n          wasmBinaryFile = locateFile(wasmBinaryFile);\n        }\n        function getBinary(file) {\n          try {\n            if (file == wasmBinaryFile && wasmBinary) {\n              return new Uint8Array(wasmBinary);\n            }\n            if (readBinary) {\n              return readBinary(file);\n            } else {\n              throw \"both async and sync fetching of the wasm failed\";\n            }\n          } catch (err2) {\n            abort(err2);\n          }\n        }\n        function getBinaryPromise() {\n          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n            if (typeof fetch === \"function\" && !isFileURI(wasmBinaryFile)) {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                if (!response[\"ok\"]) {\n                  throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n                }\n                return response[\"arrayBuffer\"]();\n              }).catch(function() {\n                return getBinary(wasmBinaryFile);\n              });\n            } else {\n              if (readAsync) {\n                return new Promise(function(resolve, reject) {\n                  readAsync(wasmBinaryFile, function(response) {\n                    resolve(new Uint8Array(response));\n                  }, reject);\n                });\n              }\n            }\n          }\n          return Promise.resolve().then(function() {\n            return getBinary(wasmBinaryFile);\n          });\n        }\n        function createWasm() {\n          var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n          function receiveInstance(instance, module2) {\n            var exports3 = instance.exports;\n            Module[\"asm\"] = exports3;\n            wasmMemory = Module[\"asm\"][\"memory\"];\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n            addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n            removeRunDependency(\"wasm-instantiate\");\n          }\n          addRunDependency(\"wasm-instantiate\");\n          function receiveInstantiationResult(result) {\n            receiveInstance(result[\"instance\"]);\n          }\n          function instantiateArrayBuffer(receiver) {\n            return getBinaryPromise().then(function(binary) {\n              return WebAssembly.instantiate(binary, info);\n            }).then(function(instance) {\n              return instance;\n            }).then(receiver, function(reason) {\n              err(\"failed to asynchronously prepare wasm: \" + reason);\n              abort(reason);\n            });\n          }\n          function instantiateAsync() {\n            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === \"function\") {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                var result = WebAssembly.instantiateStreaming(response, info);\n                return result.then(receiveInstantiationResult, function(reason) {\n                  err(\"wasm streaming compile failed: \" + reason);\n                  err(\"falling back to ArrayBuffer instantiation\");\n                  return instantiateArrayBuffer(receiveInstantiationResult);\n                });\n              });\n            } else {\n              return instantiateArrayBuffer(receiveInstantiationResult);\n            }\n          }\n          if (Module[\"instantiateWasm\"]) {\n            try {\n              var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n              return exports2;\n            } catch (e2) {\n              err(\"Module.instantiateWasm callback failed with error: \" + e2);\n              return false;\n            }\n          }\n          instantiateAsync().catch(readyPromiseReject);\n          return {};\n        }\n        var tempDouble;\n        var tempI64;\n        function callRuntimeCallbacks(callbacks2) {\n          while (callbacks2.length > 0) {\n            var callback = callbacks2.shift();\n            if (typeof callback == \"function\") {\n              callback(Module);\n              continue;\n            }\n            var func2 = callback.func;\n            if (typeof func2 === \"number\") {\n              if (callback.arg === void 0) {\n                getWasmTableEntry(func2)();\n              } else {\n                getWasmTableEntry(func2)(callback.arg);\n              }\n            } else {\n              func2(callback.arg === void 0 ? null : callback.arg);\n            }\n          }\n        }\n        function demangle(func2) {\n          return func2;\n        }\n        function demangleAll(text) {\n          var regex = /\\b_Z[\\w\\d_]+/g;\n          return text.replace(regex, function(x) {\n            var y = demangle(x);\n            return x === y ? x : y + \" [\" + x + \"]\";\n          });\n        }\n        var wasmTableMirror = [];\n        function getWasmTableEntry(funcPtr) {\n          var func2 = wasmTableMirror[funcPtr];\n          if (!func2) {\n            if (funcPtr >= wasmTableMirror.length)\n              wasmTableMirror.length = funcPtr + 1;\n            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n          }\n          return func2;\n        }\n        function jsStackTrace() {\n          var error = new Error();\n          if (!error.stack) {\n            try {\n              throw new Error();\n            } catch (e2) {\n              error = e2;\n            }\n            if (!error.stack) {\n              return \"(no stack trace available)\";\n            }\n          }\n          return error.stack.toString();\n        }\n        function setWasmTableEntry(idx, func2) {\n          wasmTable.set(idx, func2);\n          wasmTableMirror[idx] = func2;\n        }\n        function _abort() {\n          abort(\"\");\n        }\n        function _emscripten_get_heap_max() {\n          return 2147483648;\n        }\n        function _emscripten_memcpy_big(dest, src, num) {\n          HEAPU8.copyWithin(dest, src, src + num);\n        }\n        function emscripten_realloc_buffer(size) {\n          try {\n            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            return 1;\n          } catch (e2) {\n          }\n        }\n        function _emscripten_resize_heap(requestedSize) {\n          var oldSize = HEAPU8.length;\n          requestedSize = requestedSize >>> 0;\n          var maxHeapSize = _emscripten_get_heap_max();\n          if (requestedSize > maxHeapSize) {\n            return false;\n          }\n          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n            var replacement = emscripten_realloc_buffer(newSize);\n            if (replacement) {\n              return true;\n            }\n          }\n          return false;\n        }\n        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {\n          var buffer3 = SYSCALLS.buffers[stream];\n          if (curr === 0 || curr === 10) {\n            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n            buffer3.length = 0;\n          } else {\n            buffer3.push(curr);\n          }\n        }, varargs: void 0, get: function() {\n          SYSCALLS.varargs += 4;\n          var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];\n          return ret;\n        }, getStr: function(ptr) {\n          var ret = UTF8ToString(ptr);\n          return ret;\n        }, get64: function(low, high) {\n          return low;\n        } };\n        function _fd_close(fd) {\n          return 0;\n        }\n        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n        }\n        function _fd_write(fd, iov, iovcnt, pnum) {\n          var num = 0;\n          for (var i2 = 0; i2 < iovcnt; i2++) {\n            var ptr = HEAP32[iov >> 2];\n            var len = HEAP32[iov + 4 >> 2];\n            iov += 8;\n            for (var j = 0; j < len; j++) {\n              SYSCALLS.printChar(fd, HEAPU8[ptr + j]);\n            }\n            num += len;\n          }\n          HEAP32[pnum >> 2] = num;\n          return 0;\n        }\n        function _setTempRet0(val) {\n          setTempRet0(val);\n        }\n        var ASSERTIONS = false;\n        var asmLibraryArg = { \"abort\": _abort, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_resize_heap\": _emscripten_resize_heap, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"setTempRet0\": _setTempRet0 };\n        var asm = createWasm();\n        var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n          return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n        };\n        var _init = Module[\"_init\"] = function() {\n          return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n        };\n        var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n          return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n        };\n        var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n          return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n        };\n        var _register_tensor = Module[\"_register_tensor\"] = function() {\n          return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n        };\n        var _dispose_data = Module[\"_dispose_data\"] = function() {\n          return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n        };\n        var _dispose = Module[\"_dispose\"] = function() {\n          return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n        };\n        var _Abs = Module[\"_Abs\"] = function() {\n          return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n        };\n        var _Add = Module[\"_Add\"] = function() {\n          return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n        };\n        var _AddN = Module[\"_AddN\"] = function() {\n          return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n        };\n        var _All = Module[\"_All\"] = function() {\n          return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n        };\n        var _Any = Module[\"_Any\"] = function() {\n          return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n        };\n        var _ArgMax = Module[\"_ArgMax\"] = function() {\n          return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n        };\n        var _AvgPool = Module[\"_AvgPool\"] = function() {\n          return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n        };\n        var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n          return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n        };\n        var _Ceil = Module[\"_Ceil\"] = function() {\n          return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n        };\n        var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n          return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n        };\n        var _Conv2D = Module[\"_Conv2D\"] = function() {\n          return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n        };\n        var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n          return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n        };\n        var _Cos = Module[\"_Cos\"] = function() {\n          return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n        };\n        var _Cosh = Module[\"_Cosh\"] = function() {\n          return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n        };\n        var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n          return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n        };\n        var _Cumprod = Module[\"_Cumprod\"] = function() {\n          return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n        };\n        var _Cumsum = Module[\"_Cumsum\"] = function() {\n          return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n        };\n        var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n          return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n        };\n        var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n          return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n        };\n        var _Elu = Module[\"_Elu\"] = function() {\n          return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n        };\n        var _Equal = Module[\"_Equal\"] = function() {\n          return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n        };\n        var _Exp = Module[\"_Exp\"] = function() {\n          return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n        };\n        var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n          return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n        };\n        var _Floor = Module[\"_Floor\"] = function() {\n          return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n        };\n        var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n          return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n        };\n        var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n          return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n        };\n        var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n          return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n        };\n        var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n          return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n        };\n        var _Gather = Module[\"_Gather\"] = function() {\n          return (_Gather = Module[\"_Gather\"] = Module[\"asm\"][\"Gather\"]).apply(null, arguments);\n        };\n        var _GatherNd = Module[\"_GatherNd\"] = function() {\n          return (_GatherNd = Module[\"_GatherNd\"] = Module[\"asm\"][\"GatherNd\"]).apply(null, arguments);\n        };\n        var _Greater = Module[\"_Greater\"] = function() {\n          return (_Greater = Module[\"_Greater\"] = Module[\"asm\"][\"Greater\"]).apply(null, arguments);\n        };\n        var _GreaterEqual = Module[\"_GreaterEqual\"] = function() {\n          return (_GreaterEqual = Module[\"_GreaterEqual\"] = Module[\"asm\"][\"GreaterEqual\"]).apply(null, arguments);\n        };\n        var _LeakyRelu = Module[\"_LeakyRelu\"] = function() {\n          return (_LeakyRelu = Module[\"_LeakyRelu\"] = Module[\"asm\"][\"LeakyRelu\"]).apply(null, arguments);\n        };\n        var _Less = Module[\"_Less\"] = function() {\n          return (_Less = Module[\"_Less\"] = Module[\"asm\"][\"Less\"]).apply(null, arguments);\n        };\n        var _LessEqual = Module[\"_LessEqual\"] = function() {\n          return (_LessEqual = Module[\"_LessEqual\"] = Module[\"asm\"][\"LessEqual\"]).apply(null, arguments);\n        };\n        var _Log = Module[\"_Log\"] = function() {\n          return (_Log = Module[\"_Log\"] = Module[\"asm\"][\"Log\"]).apply(null, arguments);\n        };\n        var _LogicalAnd = Module[\"_LogicalAnd\"] = function() {\n          return (_LogicalAnd = Module[\"_LogicalAnd\"] = Module[\"asm\"][\"LogicalAnd\"]).apply(null, arguments);\n        };\n        var _Max = Module[\"_Max\"] = function() {\n          return (_Max = Module[\"_Max\"] = Module[\"asm\"][\"Max\"]).apply(null, arguments);\n        };\n        var _MaxPool = Module[\"_MaxPool\"] = function() {\n          return (_MaxPool = Module[\"_MaxPool\"] = Module[\"asm\"][\"MaxPool\"]).apply(null, arguments);\n        };\n        var _Maximum = Module[\"_Maximum\"] = function() {\n          return (_Maximum = Module[\"_Maximum\"] = Module[\"asm\"][\"Maximum\"]).apply(null, arguments);\n        };\n        var _Mean = Module[\"_Mean\"] = function() {\n          return (_Mean = Module[\"_Mean\"] = Module[\"asm\"][\"Mean\"]).apply(null, arguments);\n        };\n        var _Min = Module[\"_Min\"] = function() {\n          return (_Min = Module[\"_Min\"] = Module[\"asm\"][\"Min\"]).apply(null, arguments);\n        };\n        var _Minimum = Module[\"_Minimum\"] = function() {\n          return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n        };\n        var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n          return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n        };\n        var _Multiply = Module[\"_Multiply\"] = function() {\n          return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n        };\n        var _Neg = Module[\"_Neg\"] = function() {\n          return (_Neg = Module[\"_Neg\"] = Module[\"asm\"][\"Neg\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = function() {\n          return (_NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = function() {\n          return (_NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = function() {\n          return (_NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null, arguments);\n        };\n        var _NotEqual = Module[\"_NotEqual\"] = function() {\n          return (_NotEqual = Module[\"_NotEqual\"] = Module[\"asm\"][\"NotEqual\"]).apply(null, arguments);\n        };\n        var _OneHot = Module[\"_OneHot\"] = function() {\n          return (_OneHot = Module[\"_OneHot\"] = Module[\"asm\"][\"OneHot\"]).apply(null, arguments);\n        };\n        var _PadV2 = Module[\"_PadV2\"] = function() {\n          return (_PadV2 = Module[\"_PadV2\"] = Module[\"asm\"][\"PadV2\"]).apply(null, arguments);\n        };\n        var _Pow = Module[\"_Pow\"] = function() {\n          return (_Pow = Module[\"_Pow\"] = Module[\"asm\"][\"Pow\"]).apply(null, arguments);\n        };\n        var _Prelu = Module[\"_Prelu\"] = function() {\n          return (_Prelu = Module[\"_Prelu\"] = Module[\"asm\"][\"Prelu\"]).apply(null, arguments);\n        };\n        var _Prod = Module[\"_Prod\"] = function() {\n          return (_Prod = Module[\"_Prod\"] = Module[\"asm\"][\"Prod\"]).apply(null, arguments);\n        };\n        var _RealDiv = Module[\"_RealDiv\"] = function() {\n          return (_RealDiv = Module[\"_RealDiv\"] = Module[\"asm\"][\"RealDiv\"]).apply(null, arguments);\n        };\n        var _Relu = Module[\"_Relu\"] = function() {\n          return (_Relu = Module[\"_Relu\"] = Module[\"asm\"][\"Relu\"]).apply(null, arguments);\n        };\n        var _Relu6 = Module[\"_Relu6\"] = function() {\n          return (_Relu6 = Module[\"_Relu6\"] = Module[\"asm\"][\"Relu6\"]).apply(null, arguments);\n        };\n        var _ResizeBilinear = Module[\"_ResizeBilinear\"] = function() {\n          return (_ResizeBilinear = Module[\"_ResizeBilinear\"] = Module[\"asm\"][\"ResizeBilinear\"]).apply(null, arguments);\n        };\n        var _Reverse = Module[\"_Reverse\"] = function() {\n          return (_Reverse = Module[\"_Reverse\"] = Module[\"asm\"][\"Reverse\"]).apply(null, arguments);\n        };\n        var _RotateWithOffset = Module[\"_RotateWithOffset\"] = function() {\n          return (_RotateWithOffset = Module[\"_RotateWithOffset\"] = Module[\"asm\"][\"RotateWithOffset\"]).apply(null, arguments);\n        };\n        var _Round = Module[\"_Round\"] = function() {\n          return (_Round = Module[\"_Round\"] = Module[\"asm\"][\"Round\"]).apply(null, arguments);\n        };\n        var _Rsqrt = Module[\"_Rsqrt\"] = function() {\n          return (_Rsqrt = Module[\"_Rsqrt\"] = Module[\"asm\"][\"Rsqrt\"]).apply(null, arguments);\n        };\n        var _ScatterNd = Module[\"_ScatterNd\"] = function() {\n          return (_ScatterNd = Module[\"_ScatterNd\"] = Module[\"asm\"][\"ScatterNd\"]).apply(null, arguments);\n        };\n        var _SelectV2 = Module[\"_SelectV2\"] = function() {\n          return (_SelectV2 = Module[\"_SelectV2\"] = Module[\"asm\"][\"SelectV2\"]).apply(null, arguments);\n        };\n        var _Sigmoid = Module[\"_Sigmoid\"] = function() {\n          return (_Sigmoid = Module[\"_Sigmoid\"] = Module[\"asm\"][\"Sigmoid\"]).apply(null, arguments);\n        };\n        var _Sin = Module[\"_Sin\"] = function() {\n          return (_Sin = Module[\"_Sin\"] = Module[\"asm\"][\"Sin\"]).apply(null, arguments);\n        };\n        var _Softmax = Module[\"_Softmax\"] = function() {\n          return (_Softmax = Module[\"_Softmax\"] = Module[\"asm\"][\"Softmax\"]).apply(null, arguments);\n        };\n        var _SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = function() {\n          return (_SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = Module[\"asm\"][\"SparseFillEmptyRows\"]).apply(null, arguments);\n        };\n        var _SparseReshape = Module[\"_SparseReshape\"] = function() {\n          return (_SparseReshape = Module[\"_SparseReshape\"] = Module[\"asm\"][\"SparseReshape\"]).apply(null, arguments);\n        };\n        var _SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = function() {\n          return (_SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = Module[\"asm\"][\"SparseSegmentReduction\"]).apply(null, arguments);\n        };\n        var _Sqrt = Module[\"_Sqrt\"] = function() {\n          return (_Sqrt = Module[\"_Sqrt\"] = Module[\"asm\"][\"Sqrt\"]).apply(null, arguments);\n        };\n        var _Square = Module[\"_Square\"] = function() {\n          return (_Square = Module[\"_Square\"] = Module[\"asm\"][\"Square\"]).apply(null, arguments);\n        };\n        var _SquaredDifference = Module[\"_SquaredDifference\"] = function() {\n          return (_SquaredDifference = Module[\"_SquaredDifference\"] = Module[\"asm\"][\"SquaredDifference\"]).apply(null, arguments);\n        };\n        var _Step = Module[\"_Step\"] = function() {\n          return (_Step = Module[\"_Step\"] = Module[\"asm\"][\"Step\"]).apply(null, arguments);\n        };\n        var _StridedSlice = Module[\"_StridedSlice\"] = function() {\n          return (_StridedSlice = Module[\"_StridedSlice\"] = Module[\"asm\"][\"StridedSlice\"]).apply(null, arguments);\n        };\n        var _Sub = Module[\"_Sub\"] = function() {\n          return (_Sub = Module[\"_Sub\"] = Module[\"asm\"][\"Sub\"]).apply(null, arguments);\n        };\n        var _Sum = Module[\"_Sum\"] = function() {\n          return (_Sum = Module[\"_Sum\"] = Module[\"asm\"][\"Sum\"]).apply(null, arguments);\n        };\n        var _Tan = Module[\"_Tan\"] = function() {\n          return (_Tan = Module[\"_Tan\"] = Module[\"asm\"][\"Tan\"]).apply(null, arguments);\n        };\n        var _Tanh = Module[\"_Tanh\"] = function() {\n          return (_Tanh = Module[\"_Tanh\"] = Module[\"asm\"][\"Tanh\"]).apply(null, arguments);\n        };\n        var _Tile = Module[\"_Tile\"] = function() {\n          return (_Tile = Module[\"_Tile\"] = Module[\"asm\"][\"Tile\"]).apply(null, arguments);\n        };\n        var _TopK = Module[\"_TopK\"] = function() {\n          return (_TopK = Module[\"_TopK\"] = Module[\"asm\"][\"TopK\"]).apply(null, arguments);\n        };\n        var _Transform = Module[\"_Transform\"] = function() {\n          return (_Transform = Module[\"_Transform\"] = Module[\"asm\"][\"Transform\"]).apply(null, arguments);\n        };\n        var _Transpose = Module[\"_Transpose\"] = function() {\n          return (_Transpose = Module[\"_Transpose\"] = Module[\"asm\"][\"Transpose\"]).apply(null, arguments);\n        };\n        var __FusedMatMul = Module[\"__FusedMatMul\"] = function() {\n          return (__FusedMatMul = Module[\"__FusedMatMul\"] = Module[\"asm\"][\"_FusedMatMul\"]).apply(null, arguments);\n        };\n        var _malloc = Module[\"_malloc\"] = function() {\n          return (_malloc = Module[\"_malloc\"] = Module[\"asm\"][\"malloc\"]).apply(null, arguments);\n        };\n        var _free = Module[\"_free\"] = function() {\n          return (_free = Module[\"_free\"] = Module[\"asm\"][\"free\"]).apply(null, arguments);\n        };\n        var ___errno_location = Module[\"___errno_location\"] = function() {\n          return (___errno_location = Module[\"___errno_location\"] = Module[\"asm\"][\"__errno_location\"]).apply(null, arguments);\n        };\n        var _emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = function() {\n          return (_emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = Module[\"asm\"][\"emscripten_main_thread_process_queued_calls\"]).apply(null, arguments);\n        };\n        var stackSave = Module[\"stackSave\"] = function() {\n          return (stackSave = Module[\"stackSave\"] = Module[\"asm\"][\"stackSave\"]).apply(null, arguments);\n        };\n        var stackRestore = Module[\"stackRestore\"] = function() {\n          return (stackRestore = Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n        };\n        var stackAlloc = Module[\"stackAlloc\"] = function() {\n          return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n        };\n        var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n          return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n        };\n        var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n          return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n        };\n        Module[\"cwrap\"] = cwrap;\n        var calledRun;\n        function ExitStatus(status) {\n          this.name = \"ExitStatus\";\n          this.message = \"Program terminated with exit(\" + status + \")\";\n          this.status = status;\n        }\n        dependenciesFulfilled = function runCaller() {\n          if (!calledRun)\n            run();\n          if (!calledRun)\n            dependenciesFulfilled = runCaller;\n        };\n        function run(args) {\n          args = args || arguments_;\n          if (runDependencies > 0) {\n            return;\n          }\n          preRun();\n          if (runDependencies > 0) {\n            return;\n          }\n          function doRun() {\n            if (calledRun)\n              return;\n            calledRun = true;\n            Module[\"calledRun\"] = true;\n            if (ABORT)\n              return;\n            initRuntime();\n            readyPromiseResolve(Module);\n            if (Module[\"onRuntimeInitialized\"])\n              Module[\"onRuntimeInitialized\"]();\n            postRun();\n          }\n          if (Module[\"setStatus\"]) {\n            Module[\"setStatus\"](\"Running...\");\n            setTimeout(function() {\n              setTimeout(function() {\n                Module[\"setStatus\"](\"\");\n              }, 1);\n              doRun();\n            }, 1);\n          } else {\n            doRun();\n          }\n        }\n        Module[\"run\"] = run;\n        function procExit(code) {\n          EXITSTATUS = code;\n          if (!keepRuntimeAlive()) {\n            if (Module[\"onExit\"])\n              Module[\"onExit\"](code);\n            ABORT = true;\n          }\n          quit_(code, new ExitStatus(code));\n        }\n        if (Module[\"preInit\"]) {\n          if (typeof Module[\"preInit\"] == \"function\")\n            Module[\"preInit\"] = [Module[\"preInit\"]];\n          while (Module[\"preInit\"].length > 0) {\n            Module[\"preInit\"].pop()();\n          }\n        }\n        run();\n        var listenersAdded;\n        if (beforeListeners) {\n          listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n            return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n          }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n          }) };\n        }\n        var actualModule;\n        if (typeof WasmBackendModule3 !== \"undefined\") {\n          actualModule = WasmBackendModule3;\n        } else if (typeof WasmBackendModuleThreadedSimd !== \"undefined\") {\n          actualModule = WasmBackendModuleThreadedSimd;\n        } else {\n          throw new Error(\"Could not find wasm module in post.js\");\n        }\n        if (listenersAdded) {\n          var tmpDispose = actualModule[\"_dispose\"];\n          actualModule[\"_dispose\"] = function() {\n            tmpDispose();\n            listenersAdded.uncaughtException.forEach(function(listener) {\n              process.removeListener(\"uncaughtException\", listener);\n            });\n            listenersAdded.unhandledRejection.forEach(function(listener) {\n              process.removeListener(\"unhandledRejection\", listener);\n            });\n          };\n        }\n        return WasmBackendModule3.ready;\n      };\n    })();\n    if (typeof exports === \"object\" && typeof module === \"object\")\n      module.exports = WasmBackendModule2;\n    else if (typeof define === \"function\" && define[\"amd\"])\n      define([], function() {\n        return WasmBackendModule2;\n      });\n    else if (typeof exports === \"object\")\n      exports[\"WasmBackendModule\"] = WasmBackendModule2;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js\nvar EPSILON_FLOAT32 = 1e-7;\nvar EPSILON_FLOAT16 = 1e-4;\nvar DataStorage = class {\n  constructor(backend2, dataMover) {\n    this.backend = backend2;\n    this.dataMover = dataMover;\n    this.data = /* @__PURE__ */ new WeakMap();\n    this.dataIdsCount = 0;\n  }\n  get(dataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n  set(dataId, value) {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n  has(dataId) {\n    return this.data.has(dataId);\n  }\n  delete(dataId) {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n  numDataIds() {\n    return this.dataIdsCount;\n  }\n};\nvar KernelBackend = class {\n  refCount(dataId) {\n    return notYetImplemented(\"refCount\");\n  }\n  incRef(dataId) {\n    return notYetImplemented(\"incRef\");\n  }\n  timerAvailable() {\n    return true;\n  }\n  time(f) {\n    return notYetImplemented(\"time\");\n  }\n  read(dataId) {\n    return notYetImplemented(\"read\");\n  }\n  readSync(dataId) {\n    return notYetImplemented(\"readSync\");\n  }\n  readToGPU(dataId, options) {\n    return notYetImplemented(\"readToGPU\");\n  }\n  numDataIds() {\n    return notYetImplemented(\"numDataIds\");\n  }\n  disposeData(dataId, force) {\n    return notYetImplemented(\"disposeData\");\n  }\n  write(values, shape, dtype) {\n    return notYetImplemented(\"write\");\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    return notYetImplemented(\"move\");\n  }\n  memory() {\n    return notYetImplemented(\"memory\");\n  }\n  floatPrecision() {\n    return notYetImplemented(\"floatPrecision\");\n  }\n  epsilon() {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n  dispose() {\n    return notYetImplemented(\"dispose\");\n  }\n};\nfunction notYetImplemented(kernelName) {\n  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js\nfunction shuffle(array2) {\n  let counter = array2.length;\n  let index = 0;\n  while (counter > 0) {\n    index = Math.random() * counter | 0;\n    counter--;\n    swap(array2, counter, index);\n  }\n}\nfunction shuffleCombo(array2, array22) {\n  if (array2.length !== array22.length) {\n    throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);\n  }\n  let counter = array2.length;\n  let index = 0;\n  while (counter > 0) {\n    index = Math.random() * counter | 0;\n    counter--;\n    swap(array2, counter, index);\n    swap(array22, counter, index);\n  }\n}\nfunction clamp(min7, x, max7) {\n  return Math.max(min7, Math.min(x, max7));\n}\nfunction nearestLargerEven(val) {\n  return val % 2 === 0 ? val : val + 1;\n}\nfunction swap(object, left, right) {\n  const temp = object[left];\n  object[left] = object[right];\n  object[right] = temp;\n}\nfunction sum(arr) {\n  let sum7 = 0;\n  for (let i2 = 0; i2 < arr.length; i2++) {\n    sum7 += arr[i2];\n  }\n  return sum7;\n}\nfunction randUniform(a6, b) {\n  const r2 = Math.random();\n  return b * r2 + (1 - r2) * a6;\n}\nfunction distSquared(a6, b) {\n  let result = 0;\n  for (let i2 = 0; i2 < a6.length; i2++) {\n    const diff = Number(a6[i2]) - Number(b[i2]);\n    result += diff * diff;\n  }\n  return result;\n}\nfunction assert(expr, msg) {\n  if (!expr) {\n    throw new Error(typeof msg === \"string\" ? msg : msg());\n  }\n}\nfunction assertShapesMatch(shapeA, shapeB, errorMessagePrefix = \"\") {\n  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nfunction assertNonNull(a6) {\n  assert(a6 != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\nfunction flatten(arr, result = [], skipTypedArray = false) {\n  if (result == null) {\n    result = [];\n  }\n  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n    for (let i2 = 0; i2 < arr.length; ++i2) {\n      flatten(arr[i2], result, skipTypedArray);\n    }\n  } else {\n    result.push(arr);\n  }\n  return result;\n}\nfunction sizeFromShape(shape) {\n  if (shape.length === 0) {\n    return 1;\n  }\n  let size = shape[0];\n  for (let i2 = 1; i2 < shape.length; i2++) {\n    size *= shape[i2];\n  }\n  return size;\n}\nfunction isScalarShape(shape) {\n  return shape.length === 0;\n}\nfunction arraysEqual(n1, n2) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i2 = 0; i2 < n1.length; i2++) {\n    if (n1[i2] !== n2[i2]) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction isInt(a6) {\n  return a6 % 1 === 0;\n}\nfunction tanh(x) {\n  if (Math.tanh != null) {\n    return Math.tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\nfunction sizeToSquarishShape(size) {\n  const width = Math.ceil(Math.sqrt(size));\n  return [width, Math.ceil(size / width)];\n}\nfunction createShuffledIndices(n) {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i2 = 0; i2 < n; ++i2) {\n    shuffledIndices[i2] = i2;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\nfunction rightPad(a6, size) {\n  if (size <= a6.length) {\n    return a6;\n  }\n  return a6 + \" \".repeat(size - a6.length);\n}\nfunction repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n  return new Promise((resolve, reject) => {\n    let tryCount = 0;\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n      tryCount++;\n      const nextBackoff = delayFn(tryCount);\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n    tryFn();\n  });\n}\nfunction inferFromImplicitShape(shape, size) {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n  for (let i2 = 0; i2 < shape.length; ++i2) {\n    if (shape[i2] >= 0) {\n      shapeProd *= shape[i2];\n    } else if (shape[i2] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i2}`);\n      }\n      implicitIdx = i2;\n    } else if (shape[i2] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i2]} at dim ${i2}`);\n    }\n  }\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n  if (shapeProd === 0) {\n    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);\n  }\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\nfunction parseAxisParam(axis, shape) {\n  const rank = shape.length;\n  axis = axis == null ? shape.map((s2, i2) => i2) : [].concat(axis);\n  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);\n  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);\n  return axis.map((a6) => a6 < 0 ? rank + a6 : a6);\n}\nfunction squeezeShape(shape, axis) {\n  const newShape = [];\n  const keptDims = [];\n  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();\n  let j = 0;\n  for (let i2 = 0; i2 < shape.length; ++i2) {\n    if (axes != null) {\n      if (axes[j] === i2 && shape[i2] !== 1) {\n        throw new Error(`Can't squeeze axis ${i2} since its dim '${shape[i2]}' is not 1`);\n      }\n      if ((axes[j] == null || axes[j] > i2) && shape[i2] === 1) {\n        newShape.push(shape[i2]);\n        keptDims.push(i2);\n      }\n      if (axes[j] <= i2) {\n        j++;\n      }\n    }\n    if (shape[i2] !== 1) {\n      newShape.push(shape[i2]);\n      keptDims.push(i2);\n    }\n  }\n  return { newShape, keptDims };\n}\nfunction getTypedArrayFromDType(dtype, size) {\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\nfunction getArrayFromDType(dtype, size) {\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else if (dtype === \"string\") {\n    values = new Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\nfunction checkConversionForErrors(vals, dtype) {\n  for (let i2 = 0; i2 < vals.length; i2++) {\n    const num = vals[i2];\n    if (isNaN(num) || !isFinite(num)) {\n      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n    }\n  }\n}\nfunction isValidDtype(dtype) {\n  return dtype === \"bool\" || dtype === \"complex64\" || dtype === \"float32\" || dtype === \"int32\" || dtype === \"string\";\n}\nfunction hasEncodingLoss(oldType, newType) {\n  if (newType === \"complex64\") {\n    return false;\n  }\n  if (newType === \"float32\" && oldType !== \"complex64\") {\n    return false;\n  }\n  if (newType === \"int32\" && oldType !== \"float32\" && oldType !== \"complex64\") {\n    return false;\n  }\n  if (newType === \"bool\" && oldType === \"bool\") {\n    return false;\n  }\n  return true;\n}\nfunction isTypedArray(a6) {\n  return a6 instanceof Float32Array || a6 instanceof Int32Array || a6 instanceof Uint8Array || a6 instanceof Uint8ClampedArray;\n}\nfunction bytesPerElement(dtype) {\n  if (dtype === \"float32\" || dtype === \"int32\") {\n    return 4;\n  } else if (dtype === \"complex64\") {\n    return 8;\n  } else if (dtype === \"bool\") {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction bytesFromStringArray(arr) {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach((x) => bytes += x.length);\n  return bytes;\n}\nfunction isString(value) {\n  return typeof value === \"string\" || value instanceof String;\n}\nfunction isBoolean(value) {\n  return typeof value === \"boolean\";\n}\nfunction isNumber(value) {\n  return typeof value === \"number\";\n}\nfunction inferDtype(values) {\n  if (Array.isArray(values)) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return \"float32\";\n  } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {\n    return \"int32\";\n  } else if (isNumber(values)) {\n    return \"float32\";\n  } else if (isString(values)) {\n    return \"string\";\n  } else if (isBoolean(values)) {\n    return \"bool\";\n  }\n  return \"float32\";\n}\nfunction isFunction(f) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\nfunction nearestDivisor(size, start) {\n  for (let i2 = start; i2 < size; ++i2) {\n    if (size % i2 === 0) {\n      return i2;\n    }\n  }\n  return size;\n}\nfunction computeStrides(shape) {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i2 = rank - 3; i2 >= 0; --i2) {\n    strides[i2] = strides[i2 + 1] * shape[i2 + 1];\n  }\n  return strides;\n}\nfunction createNestedArray(offset, shape, a6, isComplex = false) {\n  const ret = new Array();\n  if (shape.length === 1) {\n    const d = shape[0] * (isComplex ? 2 : 1);\n    for (let i2 = 0; i2 < d; i2++) {\n      ret[i2] = a6[offset + i2];\n    }\n  } else {\n    const d = shape[0];\n    const rest = shape.slice(1);\n    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n    for (let i2 = 0; i2 < d; i2++) {\n      ret[i2] = createNestedArray(offset + i2 * len, rest, a6, isComplex);\n    }\n  }\n  return ret;\n}\nfunction toNestedArray(shape, a6, isComplex = false) {\n  if (shape.length === 0) {\n    return a6[0];\n  }\n  const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n  if (size === 0) {\n    return [];\n  }\n  if (size !== a6.length) {\n    throw new Error(`[${shape}] does not match the input size ${a6.length}${isComplex ? \" for a complex tensor\" : \"\"}.`);\n  }\n  return createNestedArray(0, shape, a6, isComplex);\n}\nfunction makeOnesTypedArray(size, dtype) {\n  const array2 = makeZerosTypedArray(size, dtype);\n  for (let i2 = 0; i2 < array2.length; i2++) {\n    array2[i2] = 1;\n  }\n  return array2;\n}\nfunction makeZerosTypedArray(size, dtype) {\n  if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n    return new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    return new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction makeZerosNestedTypedArray(shape, dtype) {\n  const size = shape.reduce((prev, curr) => prev * curr, 1);\n  if (dtype == null || dtype === \"float32\") {\n    return toNestedArray(shape, new Float32Array(size));\n  } else if (dtype === \"int32\") {\n    return toNestedArray(shape, new Int32Array(size));\n  } else if (dtype === \"bool\") {\n    return toNestedArray(shape, new Uint8Array(size));\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction assertNonNegativeIntegerDimensions(shape) {\n  shape.forEach((dimSize) => {\n    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);\n  });\n}\nfunction locToIndex(locs, rank, strides) {\n  if (rank === 0) {\n    return 0;\n  } else if (rank === 1) {\n    return locs[0];\n  }\n  let index = locs[locs.length - 1];\n  for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n    index += strides[i2] * locs[i2];\n  }\n  return index;\n}\nfunction indexToLoc(index, rank, strides) {\n  if (rank === 0) {\n    return [];\n  } else if (rank === 1) {\n    return [index];\n  }\n  const locs = new Array(rank);\n  for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n    locs[i2] = Math.floor(index / strides[i2]);\n    index -= locs[i2] * strides[i2];\n  }\n  locs[locs.length - 1] = index;\n  return locs;\n}\nfunction isPromise(object) {\n  return object && object.then && typeof object.then === \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/environment.js\nvar TENSORFLOWJS_FLAGS_PREFIX = \"tfjsflags\";\nvar Environment = class {\n  constructor(global2) {\n    this.global = global2;\n    this.flags = {};\n    this.flagRegistry = {};\n    this.urlFlags = {};\n    this.getQueryParams = getQueryParams;\n    this.populateURLFlags();\n  }\n  setPlatform(platformName, platform) {\n    if (this.platform != null) {\n      if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);\n      }\n    }\n    this.platformName = platformName;\n    this.platform = platform;\n  }\n  registerFlag(flagName, evaluationFn, setHook) {\n    this.flagRegistry[flagName] = { evaluationFn, setHook };\n    if (this.urlFlags[flagName] != null) {\n      const flagValue = this.urlFlags[flagName];\n      if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n      }\n      this.set(flagName, flagValue);\n    }\n  }\n  async getAsync(flagName) {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n    this.flags[flagName] = await this.evaluateFlag(flagName);\n    return this.flags[flagName];\n  }\n  get(flagName) {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n    const flagValue = this.evaluateFlag(flagName);\n    if (isPromise(flagValue)) {\n      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);\n    }\n    this.flags[flagName] = flagValue;\n    return this.flags[flagName];\n  }\n  getNumber(flagName) {\n    return this.get(flagName);\n  }\n  getBool(flagName) {\n    return this.get(flagName);\n  }\n  getFlags() {\n    return this.flags;\n  }\n  get features() {\n    return this.flags;\n  }\n  set(flagName, value) {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n    }\n    this.flags[flagName] = value;\n    if (this.flagRegistry[flagName].setHook != null) {\n      this.flagRegistry[flagName].setHook(value);\n    }\n  }\n  evaluateFlag(flagName) {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n    }\n    return this.flagRegistry[flagName].evaluationFn();\n  }\n  setFlags(flags) {\n    this.flags = Object.assign({}, flags);\n  }\n  reset() {\n    this.flags = {};\n    this.urlFlags = {};\n    this.populateURLFlags();\n  }\n  populateURLFlags() {\n    if (typeof this.global === \"undefined\" || typeof this.global.location === \"undefined\" || typeof this.global.location.search === \"undefined\") {\n      return;\n    }\n    const urlParams = this.getQueryParams(this.global.location.search);\n    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(\",\");\n      keyValues.forEach((keyValue) => {\n        const [key, value] = keyValue.split(\":\");\n        this.urlFlags[key] = parseValue(key, value);\n      });\n    }\n  }\n};\nfunction getQueryParams(queryString) {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s2, ...t2) => {\n    decodeParam(params, t2[0], t2[1]);\n    return t2.join(\"=\");\n  });\n  return params;\n}\nfunction decodeParam(params, name, value) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || \"\");\n}\nfunction parseValue(flagName, value) {\n  value = value.toLowerCase();\n  if (value === \"true\" || value === \"false\") {\n    return value === \"true\";\n  } else if (`${+value}` === value) {\n    return +value;\n  }\n  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\nfunction env() {\n  return ENV;\n}\nvar ENV = null;\nfunction setEnvironmentGlobal(environment) {\n  ENV = environment;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js\nvar globalNameSpace;\nfunction getGlobalNamespace() {\n  if (globalNameSpace == null) {\n    let ns;\n    if (typeof window !== \"undefined\") {\n      ns = window;\n    } else if (typeof global !== \"undefined\") {\n      ns = global;\n    } else if (typeof process !== \"undefined\") {\n      ns = process;\n    } else if (typeof self !== \"undefined\") {\n      ns = self;\n    } else {\n      throw new Error(\"Could not find a global object\");\n    }\n    globalNameSpace = ns;\n  }\n  return globalNameSpace;\n}\nfunction getGlobalMap() {\n  const ns = getGlobalNamespace();\n  if (ns._tfGlobals == null) {\n    ns._tfGlobals = /* @__PURE__ */ new Map();\n  }\n  return ns._tfGlobals;\n}\nfunction getGlobal(key, init2) {\n  const globalMap = getGlobalMap();\n  if (globalMap.has(key)) {\n    return globalMap.get(key);\n  } else {\n    const singleton = init2();\n    globalMap.set(key, singleton);\n    return globalMap.get(key);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js\nvar Abs = \"Abs\";\nvar Acos = \"Acos\";\nvar Acosh = \"Acosh\";\nvar Add = \"Add\";\nvar AddN = \"AddN\";\nvar All = \"All\";\nvar Any = \"Any\";\nvar ArgMax = \"ArgMax\";\nvar ArgMin = \"ArgMin\";\nvar Asin = \"Asin\";\nvar Asinh = \"Asinh\";\nvar Atan = \"Atan\";\nvar Atanh = \"Atanh\";\nvar Atan2 = \"Atan2\";\nvar AvgPool = \"AvgPool\";\nvar AvgPoolGrad = \"AvgPoolGrad\";\nvar AvgPool3D = \"AvgPool3D\";\nvar AvgPool3DGrad = \"AvgPool3DGrad\";\nvar BatchMatMul = \"BatchMatMul\";\nvar BatchToSpaceND = \"BatchToSpaceND\";\nvar Bincount = \"Bincount\";\nvar BroadcastTo = \"BroadcastTo\";\nvar BroadcastArgs = \"BroadcastArgs\";\nvar Cast = \"Cast\";\nvar Ceil = \"Ceil\";\nvar ClipByValue = \"ClipByValue\";\nvar Complex = \"Complex\";\nvar ComplexAbs = \"ComplexAbs\";\nvar Concat = \"Concat\";\nvar Conv2D = \"Conv2D\";\nvar Conv2DBackpropFilter = \"Conv2DBackpropFilter\";\nvar Conv2DBackpropInput = \"Conv2DBackpropInput\";\nvar Conv3D = \"Conv3D\";\nvar Conv3DBackpropFilterV2 = \"Conv3DBackpropFilterV2\";\nvar Conv3DBackpropInputV2 = \"Conv3DBackpropInputV2\";\nvar Cos = \"Cos\";\nvar Cosh = \"Cosh\";\nvar Cumprod = \"Cumprod\";\nvar Cumsum = \"Cumsum\";\nvar CropAndResize = \"CropAndResize\";\nvar DenseBincount = \"DenseBincount\";\nvar DepthToSpace = \"DepthToSpace\";\nvar DepthwiseConv2dNative = \"DepthwiseConv2dNative\";\nvar DepthwiseConv2dNativeBackpropFilter = \"DepthwiseConv2dNativeBackpropFilter\";\nvar DepthwiseConv2dNativeBackpropInput = \"DepthwiseConv2dNativeBackpropInput\";\nvar Diag = \"Diag\";\nvar Dilation2D = \"Dilation2D\";\nvar Dilation2DBackpropInput = \"Dilation2DBackpropInput\";\nvar Dilation2DBackpropFilter = \"Dilation2DBackpropFilter\";\nvar RealDiv = \"RealDiv\";\nvar Einsum = \"Einsum\";\nvar Elu = \"Elu\";\nvar EluGrad = \"EluGrad\";\nvar Erf = \"Erf\";\nvar Equal = \"Equal\";\nvar Exp = \"Exp\";\nvar ExpandDims = \"ExpandDims\";\nvar Expm1 = \"Expm1\";\nvar FFT = \"FFT\";\nvar Fill = \"Fill\";\nvar FlipLeftRight = \"FlipLeftRight\";\nvar Floor = \"Floor\";\nvar FloorDiv = \"FloorDiv\";\nvar FusedBatchNorm = \"FusedBatchNorm\";\nvar GatherV2 = \"GatherV2\";\nvar GatherNd = \"GatherNd\";\nvar Greater = \"Greater\";\nvar GreaterEqual = \"GreaterEqual\";\nvar Identity = \"Identity\";\nvar IFFT = \"IFFT\";\nvar Imag = \"Imag\";\nvar IsFinite = \"IsFinite\";\nvar IsInf = \"IsInf\";\nvar IsNan = \"IsNan\";\nvar LeakyRelu = \"LeakyRelu\";\nvar Less = \"Less\";\nvar LessEqual = \"LessEqual\";\nvar LinSpace = \"LinSpace\";\nvar Log = \"Log\";\nvar Log1p = \"Log1p\";\nvar LogicalAnd = \"LogicalAnd\";\nvar LogicalNot = \"LogicalNot\";\nvar LogicalOr = \"LogicalOr\";\nvar LogSoftmax = \"LogSoftmax\";\nvar LowerBound = \"LowerBound\";\nvar LRN = \"LRN\";\nvar LRNGrad = \"LRNGrad\";\nvar Max = \"Max\";\nvar Maximum = \"Maximum\";\nvar MaxPool = \"MaxPool\";\nvar MaxPoolGrad = \"MaxPoolGrad\";\nvar MaxPool3D = \"MaxPool3D\";\nvar MaxPool3DGrad = \"MaxPool3DGrad\";\nvar MaxPoolWithArgmax = \"MaxPoolWithArgmax\";\nvar Mean = \"Mean\";\nvar Min = \"Min\";\nvar Minimum = \"Minimum\";\nvar MirrorPad = \"MirrorPad\";\nvar Mod = \"Mod\";\nvar Multinomial = \"Multinomial\";\nvar Multiply = \"Multiply\";\nvar Neg = \"Neg\";\nvar NotEqual = \"NotEqual\";\nvar NonMaxSuppressionV3 = \"NonMaxSuppressionV3\";\nvar NonMaxSuppressionV4 = \"NonMaxSuppressionV4\";\nvar NonMaxSuppressionV5 = \"NonMaxSuppressionV5\";\nvar OnesLike = \"OnesLike\";\nvar OneHot = \"OneHot\";\nvar Pack = \"Pack\";\nvar PadV2 = \"PadV2\";\nvar Pool = \"Pool\";\nvar Pow = \"Pow\";\nvar Prelu = \"Prelu\";\nvar Prod = \"Prod\";\nvar Range = \"Range\";\nvar Real = \"Real\";\nvar Reciprocal = \"Reciprocal\";\nvar Relu = \"Relu\";\nvar Reshape = \"Reshape\";\nvar ResizeNearestNeighbor = \"ResizeNearestNeighbor\";\nvar ResizeNearestNeighborGrad = \"ResizeNearestNeighborGrad\";\nvar ResizeBilinear = \"ResizeBilinear\";\nvar ResizeBilinearGrad = \"ResizeBilinearGrad\";\nvar Relu6 = \"Relu6\";\nvar Reverse = \"Reverse\";\nvar Round = \"Round\";\nvar Rsqrt = \"Rsqrt\";\nvar ScatterNd = \"ScatterNd\";\nvar SearchSorted = \"SearchSorted\";\nvar Select = \"Select\";\nvar Selu = \"Selu\";\nvar Slice = \"Slice\";\nvar Sin = \"Sin\";\nvar Sinh = \"Sinh\";\nvar Sign = \"Sign\";\nvar Sigmoid = \"Sigmoid\";\nvar Softplus = \"Softplus\";\nvar Sqrt = \"Sqrt\";\nvar Sum = \"Sum\";\nvar SpaceToBatchND = \"SpaceToBatchND\";\nvar SplitV = \"SplitV\";\nvar Softmax = \"Softmax\";\nvar SparseFillEmptyRows = \"SparseFillEmptyRows\";\nvar SparseReshape = \"SparseReshape\";\nvar SparseSegmentMean = \"SparseSegmentMean\";\nvar SparseSegmentSum = \"SparseSegmentSum\";\nvar SparseToDense = \"SparseToDense\";\nvar SquaredDifference = \"SquaredDifference\";\nvar Square = \"Square\";\nvar StridedSlice = \"StridedSlice\";\nvar StringNGrams = \"StringNGrams\";\nvar StringSplit = \"StringSplit\";\nvar StringToHashBucketFast = \"StringToHashBucketFast\";\nvar Sub = \"Sub\";\nvar Tan = \"Tan\";\nvar Tanh = \"Tanh\";\nvar Tile = \"Tile\";\nvar TopK = \"TopK\";\nvar Transform = \"Transform\";\nvar Transpose = \"Transpose\";\nvar Unique = \"Unique\";\nvar Unpack = \"Unpack\";\nvar UnsortedSegmentSum = \"UnsortedSegmentSum\";\nvar UpperBound = \"UpperBound\";\nvar ZerosLike = \"ZerosLike\";\nvar Step = \"Step\";\nvar FromPixels = \"FromPixels\";\nvar RotateWithOffset = \"RotateWithOffset\";\nvar _FusedMatMul = \"_FusedMatMul\";\nvar FusedConv2D = \"FusedConv2D\";\nvar FusedDepthwiseConv2D = \"FusedDepthwiseConv2D\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/log.js\nfunction warn(...msg) {\n  if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n    console.warn(...msg);\n  }\n}\nfunction log(...msg) {\n  if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n    console.log(...msg);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js\nvar kernelRegistry = getGlobal(\"kernelRegistry\", () => /* @__PURE__ */ new Map());\nvar gradRegistry = getGlobal(\"gradRegistry\", () => /* @__PURE__ */ new Map());\nfunction getKernel(kernelName, backendName) {\n  const key = makeKey(kernelName, backendName);\n  return kernelRegistry.get(key);\n}\nfunction getGradient(kernelName) {\n  return gradRegistry.get(kernelName);\n}\nfunction getKernelsForBackend(backendName) {\n  const it = kernelRegistry.entries();\n  const result = [];\n  while (true) {\n    const { done, value } = it.next();\n    if (done) {\n      break;\n    }\n    const [key, config] = value;\n    const [backend2] = key.split(\"_\");\n    if (backend2 === backendName) {\n      result.push(config);\n    }\n  }\n  return result;\n}\nfunction registerKernel(config) {\n  const { kernelName, backendName } = config;\n  const key = makeKey(kernelName, backendName);\n  if (kernelRegistry.has(key)) {\n    warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);\n  }\n  kernelRegistry.set(key, config);\n}\nfunction registerGradient(config) {\n  const { kernelName } = config;\n  if (gradRegistry.has(kernelName)) {\n    if (env().getBool(\"DEBUG\")) {\n      warn(`Overriding the gradient for '${kernelName}'`);\n    }\n  }\n  gradRegistry.set(kernelName, config);\n}\nfunction unregisterKernel(kernelName, backendName) {\n  const key = makeKey(kernelName, backendName);\n  if (!kernelRegistry.has(key)) {\n    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);\n  }\n  kernelRegistry.delete(key);\n}\nfunction unregisterGradient(kernelName) {\n  if (!gradRegistry.has(kernelName)) {\n    throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n  }\n  gradRegistry.delete(kernelName);\n}\nfunction copyRegisteredKernels(registeredBackendName, newBackendName) {\n  const kernels = getKernelsForBackend(registeredBackendName);\n  kernels.forEach((kernelConfig) => {\n    const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n    registerKernel(newKernelConfig);\n  });\n}\nfunction makeKey(kernelName, backendName) {\n  return `${backendName}_${kernelName}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nvar util_exports = {};\n__export(util_exports, {\n  arraysEqual: () => arraysEqual,\n  assert: () => assert,\n  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,\n  assertNonNull: () => assertNonNull,\n  assertShapesMatch: () => assertShapesMatch,\n  bytesFromStringArray: () => bytesFromStringArray,\n  bytesPerElement: () => bytesPerElement,\n  checkConversionForErrors: () => checkConversionForErrors,\n  clamp: () => clamp,\n  computeStrides: () => computeStrides,\n  createScalarValue: () => createScalarValue,\n  createShuffledIndices: () => createShuffledIndices,\n  decodeString: () => decodeString,\n  distSquared: () => distSquared,\n  encodeString: () => encodeString,\n  fetch: () => fetch3,\n  fingerPrint64: () => fingerPrint64,\n  flatten: () => flatten,\n  getArrayFromDType: () => getArrayFromDType,\n  getTypedArrayFromDType: () => getTypedArrayFromDType,\n  hasEncodingLoss: () => hasEncodingLoss,\n  hexToLong: () => hexToLong,\n  indexToLoc: () => indexToLoc,\n  inferDtype: () => inferDtype,\n  inferFromImplicitShape: () => inferFromImplicitShape,\n  isBoolean: () => isBoolean,\n  isFunction: () => isFunction,\n  isInt: () => isInt,\n  isNumber: () => isNumber,\n  isPromise: () => isPromise,\n  isScalarShape: () => isScalarShape,\n  isString: () => isString,\n  isTypedArray: () => isTypedArray,\n  isValidDtype: () => isValidDtype,\n  locToIndex: () => locToIndex,\n  makeOnesTypedArray: () => makeOnesTypedArray,\n  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,\n  makeZerosTypedArray: () => makeZerosTypedArray,\n  nearestDivisor: () => nearestDivisor,\n  nearestLargerEven: () => nearestLargerEven,\n  now: () => now,\n  parseAxisParam: () => parseAxisParam,\n  randUniform: () => randUniform,\n  repeatedTry: () => repeatedTry,\n  rightPad: () => rightPad,\n  shuffle: () => shuffle,\n  shuffleCombo: () => shuffleCombo,\n  sizeFromShape: () => sizeFromShape,\n  sizeToSquarishShape: () => sizeToSquarishShape,\n  squeezeShape: () => squeezeShape,\n  sum: () => sum,\n  swap: () => swap,\n  tanh: () => tanh,\n  toNestedArray: () => toNestedArray,\n  toTypedArray: () => toTypedArray\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/hash_util.js\nvar LongExports = __toESM(require_long());\nvar Long = LongExports.default || LongExports;\nfunction hexToLong(hex) {\n  return Long.fromString(hex, true, 16);\n}\nvar k0 = hexToLong(\"c3a5c85c97cb3127\");\nvar k1 = hexToLong(\"b492b66fbe98f273\");\nvar k2 = hexToLong(\"9ae16a3b2f90404f\");\nfunction shiftMix(val) {\n  return val.xor(val.shru(47));\n}\nfunction fetch2(s2, offset, numBytes) {\n  const bytes = s2.slice(offset, offset + numBytes);\n  return Long.fromBytes(Array.from(bytes), true, true);\n}\nfunction fetch64(s2, offset) {\n  return fetch2(s2, offset, 8);\n}\nfunction fetch32(s2, offset) {\n  return fetch2(s2, offset, 4);\n}\nfunction rotate64(val, shift) {\n  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\nfunction hashLen16(u, v, mul2 = hexToLong(\"9ddfea08eb382d69\")) {\n  let a6 = u.xor(v).mul(mul2);\n  a6 = a6.xor(a6.shru(47));\n  let b = v.xor(a6).mul(mul2);\n  b = b.xor(b.shru(47));\n  b = b.mul(mul2);\n  return b;\n}\nfunction weakHashLen32WithSeeds(w, x, y, z, a6, b) {\n  a6 = a6.add(w);\n  b = rotate64(b.add(a6).add(z), 21);\n  const c = a6;\n  a6 = a6.add(x);\n  a6 = a6.add(y);\n  b = b.add(rotate64(a6, 44));\n  return [a6.add(z), b.add(c)];\n}\nfunction weakHashLen32WithSeedsStr(s2, offset, a6, b) {\n  return weakHashLen32WithSeeds(fetch64(s2, offset), fetch64(s2, offset + 8), fetch64(s2, offset + 16), fetch64(s2, offset + 24), a6, b);\n}\nfunction hashLen0to16(s2, len = s2.length) {\n  if (len >= 8) {\n    const mul2 = k2.add(len * 2);\n    const a6 = fetch64(s2, 0).add(k2);\n    const b = fetch64(s2, len - 8);\n    const c = rotate64(b, 37).mul(mul2).add(a6);\n    const d = rotate64(a6, 25).add(b).mul(mul2);\n    return hashLen16(c, d, mul2);\n  }\n  if (len >= 4) {\n    const mul2 = k2.add(len * 2);\n    const a6 = fetch32(s2, 0);\n    return hashLen16(a6.shl(3).add(len), fetch32(s2, len - 4), mul2);\n  }\n  if (len > 0) {\n    const a6 = s2[0];\n    const b = s2[len >> 1];\n    const c = s2[len - 1];\n    const y = a6 + (b << 8);\n    const z = len + (c << 2);\n    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n  }\n  return k2;\n}\nfunction hashLen17to32(s2, len = s2.length) {\n  const mul2 = k2.add(len * 2);\n  const a6 = fetch64(s2, 0).mul(k1);\n  const b = fetch64(s2, 8);\n  const c = fetch64(s2, len - 8).mul(mul2);\n  const d = fetch64(s2, len - 16).mul(k2);\n  return hashLen16(rotate64(a6.add(b), 43).add(rotate64(c, 30)).add(d), a6.add(rotate64(b.add(k2), 18)).add(c), mul2);\n}\nfunction hashLen33to64(s2, len = s2.length) {\n  const mul2 = k2.add(len * 2);\n  const a6 = fetch64(s2, 0).mul(k2);\n  const b = fetch64(s2, 8);\n  const c = fetch64(s2, len - 8).mul(mul2);\n  const d = fetch64(s2, len - 16).mul(k2);\n  const y = rotate64(a6.add(b), 43).add(rotate64(c, 30)).add(d);\n  const z = hashLen16(y, a6.add(rotate64(b.add(k2), 18)).add(c), mul2);\n  const e2 = fetch64(s2, 16).mul(mul2);\n  const f = fetch64(s2, 24);\n  const g = y.add(fetch64(s2, len - 32)).mul(mul2);\n  const h = z.add(fetch64(s2, len - 24)).mul(mul2);\n  return hashLen16(rotate64(e2.add(f), 43).add(rotate64(g, 30)).add(h), e2.add(rotate64(f.add(a6), 18)).add(g), mul2);\n}\nfunction fingerPrint64(s2, len = s2.length) {\n  const seed = Long.fromNumber(81, true);\n  if (len <= 32) {\n    if (len <= 16) {\n      return hashLen0to16(s2, len);\n    } else {\n      return hashLen17to32(s2, len);\n    }\n  } else if (len <= 64) {\n    return hashLen33to64(s2, len);\n  }\n  let x = seed;\n  let y = seed.mul(k1).add(113);\n  let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n  let v = [Long.UZERO, Long.UZERO];\n  let w = [Long.UZERO, Long.UZERO];\n  x = x.mul(k2).add(fetch64(s2, 0));\n  let offset = 0;\n  const end = (len - 1 >> 6) * 64;\n  const last64 = end + (len - 1 & 63) - 63;\n  do {\n    x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(k1);\n    y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(k1);\n    x = x.xor(w[1]);\n    y = y.add(v[0]).add(fetch64(s2, offset + 40));\n    z = rotate64(z.add(w[0]), 33).mul(k1);\n    v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(k1), x.add(w[0]));\n    w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));\n    [z, x] = [x, z];\n    offset += 64;\n  } while (offset !== end);\n  const mul2 = k1.add(z.and(255).shl(1));\n  offset = last64;\n  w[0] = w[0].add(len - 1 & 63);\n  v[0] = v[0].add(w[0]);\n  w[0] = w[0].add(v[0]);\n  x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(mul2);\n  y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(mul2);\n  x = x.xor(w[1].mul(9));\n  y = y.add(v[0].mul(9).add(fetch64(s2, offset + 40)));\n  z = rotate64(z.add(w[0]), 33).mul(mul2);\n  v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(mul2), x.add(w[0]));\n  w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));\n  [z, x] = [x, z];\n  return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nfunction createScalarValue(value, dtype) {\n  if (dtype === \"string\") {\n    return encodeString(value);\n  }\n  return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a6, dtype) {\n  return a6 instanceof Float32Array && dtype === \"float32\" || a6 instanceof Int32Array && dtype === \"int32\" || a6 instanceof Uint8Array && dtype === \"bool\";\n}\nfunction toTypedArray(a6, dtype) {\n  if (dtype === \"string\") {\n    throw new Error(\"Cannot convert a string[] to a TypedArray\");\n  }\n  if (Array.isArray(a6)) {\n    a6 = flatten(a6);\n  }\n  if (env().getBool(\"DEBUG\")) {\n    checkConversionForErrors(a6, dtype);\n  }\n  if (noConversionNeeded(a6, dtype)) {\n    return a6;\n  }\n  if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n    return new Float32Array(a6);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(a6);\n  } else if (dtype === \"bool\") {\n    const bool = new Uint8Array(a6.length);\n    for (let i2 = 0; i2 < bool.length; ++i2) {\n      if (Math.round(a6[i2]) !== 0) {\n        bool[i2] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction now() {\n  return env().platform.now();\n}\nfunction fetch3(path, requestInits) {\n  return env().platform.fetch(path, requestInits);\n}\nfunction encodeString(s2, encoding = \"utf-8\") {\n  encoding = encoding || \"utf-8\";\n  return env().platform.encode(s2, encoding);\n}\nfunction decodeString(bytes, encoding = \"utf-8\") {\n  encoding = encoding || \"utf-8\";\n  return env().platform.decode(bytes, encoding);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js\nvar Profiler = class {\n  constructor(backendTimer, logger) {\n    this.backendTimer = backendTimer;\n    this.logger = logger;\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n  profileKernel(kernelName, inputs, f) {\n    let outputs;\n    const holdResultWrapperFn = () => {\n      outputs = f();\n    };\n    let timer;\n    const start = now();\n    if (this.backendTimer.timerAvailable()) {\n      timer = this.backendTimer.time(holdResultWrapperFn);\n    } else {\n      holdResultWrapperFn();\n      for (const output of outputs) {\n        output.dataSync();\n      }\n      timer = Promise.resolve({ kernelMs: now() - start });\n    }\n    if (env().getBool(\"CHECK_COMPUTATION_FOR_ERRORS\")) {\n      for (let i2 = 0; i2 < outputs.length; i2++) {\n        const output = outputs[i2];\n        output.data().then((tensorVals) => {\n          checkComputationForErrors(tensorVals, output.dtype, kernelName);\n        });\n      }\n    }\n    const kernelProfile = {\n      kernelName,\n      outputs,\n      inputs,\n      timeMs: timer.then((timing) => timing.kernelMs),\n      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : \"\")\n    };\n    return kernelProfile;\n  }\n  logKernelProfile(kernelProfile) {\n    const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n    outputs.forEach((result) => {\n      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {\n        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n      });\n    });\n  }\n};\nfunction checkComputationForErrors(vals, dtype, kernelName) {\n  if (dtype !== \"float32\") {\n    return false;\n  }\n  for (let i2 = 0; i2 < vals.length; i2++) {\n    const num = vals[i2];\n    if (isNaN(num) || !isFinite(num)) {\n      console.warn(`Found ${num} in the result of '${kernelName}'`);\n      return true;\n    }\n  }\n  return false;\n}\nvar Logger = class {\n  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n    const time2 = typeof timeMs === \"number\" ? rightPad(`${timeMs}ms`, 9) : timeMs[\"error\"];\n    const paddedName = rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = rightPad(result.shape.toString(), 14);\n    let inputShapesDescription = \"\";\n    for (const name2 in inputs) {\n      const input2 = inputs[name2];\n      if (input2 != null) {\n        const inputShape = input2.shape || result.shape;\n        const inputRank = inputShape.length;\n        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : \"\"} `;\n      }\n    }\n    console.log(`%c${paddedName}\t%c${time2}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, \"font-weight:bold\", \"color:red\", \"color:blue\", \"color: orange\", \"color: green\", \"color: steelblue\");\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tape.js\nfunction getFilteredNodesXToY(tape, xs, y) {\n  const tensorsFromX = {};\n  const nodesFromX = {};\n  for (let i2 = 0; i2 < xs.length; i2++) {\n    tensorsFromX[xs[i2].id] = true;\n  }\n  for (let i2 = 0; i2 < tape.length; i2++) {\n    const node = tape[i2];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input2 = nodeInputs[inputName];\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input2.id]) {\n          node.outputs.forEach((output) => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n  const tensorsLeadToY = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY = {};\n  for (let i2 = tape.length - 1; i2 >= 0; i2--) {\n    const node = tape[i2];\n    const nodeInputs = node.inputs;\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n  const filteredTape = [];\n  for (let i2 = 0; i2 < tape.length; i2++) {\n    const node = tape[i2];\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      const prunedInputs = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n      const prunedNode = Object.assign({}, node);\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n      filteredTape.push(prunedNode);\n    }\n  }\n  return filteredTape;\n}\nfunction backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {\n  for (let i2 = filteredTape.length - 1; i2 >= 0; i2--) {\n    const node = filteredTape[i2];\n    const dys = [];\n    node.outputs.forEach((o) => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        dys.push(null);\n      }\n    });\n    if (node.gradient == null) {\n      throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);\n    }\n    const inputGradients = node.gradient(dys);\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n      const dx = tidy2(() => inputGradients[inputName]());\n      if (dx.dtype !== \"float32\") {\n        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!arraysEqual(dx.shape, x.shape)) {\n        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);\n      }\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js\nvar FORMAT_LIMIT_NUM_VALS = 20;\nvar FORMAT_NUM_FIRST_LAST_VALS = 3;\nvar FORMAT_NUM_SIG_DIGITS = 7;\nfunction tensorToString(vals, shape, dtype, verbose) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = [\"Tensor\"];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map((l3) => \"    \" + l3).join(\"\\n\"));\n  return lines.join(\"\\n\");\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples = dtype === \"complex64\" ? createComplexTuples(vals) : vals;\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n      }\n    }\n  }\n  return padPerCol;\n}\nfunction valToString(val, pad3, dtype) {\n  let valStr;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else if (dtype === \"bool\") {\n    valStr = boolNumToString(val);\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n  return rightPad(valStr, pad3);\n}\nfunction boolNumToString(v) {\n  return v === 0 ? \"false\" : \"true\";\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n  const storagePerElement = dtype === \"complex64\" ? 2 : 1;\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === \"complex64\") {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0, dtype)];\n    }\n    if (dtype === \"bool\") {\n      return [boolNumToString(vals[0])];\n    }\n    return [vals[0].toString()];\n  }\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n      let firstVals = Array.from(vals.slice(0, firstValsSize));\n      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n      if (dtype === \"complex64\") {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        \"[\" + firstVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(\", \") + \", ..., \" + lastVals.map((x, i2) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i2], dtype)).join(\", \") + \"]\"\n      ];\n    }\n    const displayVals = dtype === \"complex64\" ? createComplexTuples(vals) : Array.from(vals);\n    return [\n      \"[\" + displayVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(\", \") + \"]\"\n    ];\n  }\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i2 = 0; i2 < FORMAT_NUM_FIRST_LAST_VALS; i2++) {\n      const start = i2 * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));\n    }\n    lines.push(\"...\");\n    for (let i2 = size - FORMAT_NUM_FIRST_LAST_VALS; i2 < size; i2++) {\n      const start = i2 * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));\n    }\n  } else {\n    for (let i2 = 0; i2 < size; i2++) {\n      const start = i2 * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));\n    }\n  }\n  const sep = rank === 2 ? \",\" : \"\";\n  lines[0] = \"[\" + lines[0] + sep;\n  for (let i2 = 1; i2 < lines.length - 1; i2++) {\n    lines[i2] = \" \" + lines[i2] + sep;\n  }\n  let newLineSep = \",\\n\";\n  for (let i2 = 2; i2 < rank; i2++) {\n    newLineSep += \"\\n\";\n  }\n  lines[lines.length - 1] = \" \" + lines[lines.length - 1] + \"]\" + (isLast ? \"\" : newLineSep);\n  return lines;\n}\nfunction createComplexTuples(vals) {\n  const complexTuples = [];\n  for (let i2 = 0; i2 < vals.length; i2 += 2) {\n    complexTuples.push([vals[i2], vals[i2 + 1]]);\n  }\n  return complexTuples;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js\nvar TensorBuffer = class {\n  constructor(shape, dtype, values) {\n    this.dtype = dtype;\n    this.shape = shape.slice();\n    this.size = sizeFromShape(shape);\n    if (values != null) {\n      const n = values.length;\n      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === \"complex64\") {\n      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);\n    }\n    this.values = values || getArrayFromDType(dtype, this.size);\n    this.strides = computeStrides(shape);\n  }\n  set(value, ...locs) {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);\n    const index = this.locToIndex(locs);\n    this.values[index] = value;\n  }\n  get(...locs) {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let i2 = 0;\n    for (const loc of locs) {\n      if (loc < 0 || loc >= this.shape[i2]) {\n        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;\n        throw new Error(msg);\n      }\n      i2++;\n    }\n    let index = locs[locs.length - 1];\n    for (let i3 = 0; i3 < locs.length - 1; ++i3) {\n      index += this.strides[i3] * locs[i3];\n    }\n    return this.values[index];\n  }\n  locToIndex(locs) {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n      index += this.strides[i2] * locs[i2];\n    }\n    return index;\n  }\n  indexToLoc(index) {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs = new Array(this.shape.length);\n    for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n      locs[i2] = Math.floor(index / this.strides[i2]);\n      index -= locs[i2] * this.strides[i2];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n  get rank() {\n    return this.shape.length;\n  }\n  toTensor() {\n    return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n  }\n};\nvar trackerFn = null;\nvar opHandler = null;\nvar deprecationWarningFn = null;\nfunction setTensorTracker(fn) {\n  trackerFn = fn;\n}\nfunction setOpHandler(handler) {\n  opHandler = handler;\n}\nfunction setDeprecationWarningFn(fn) {\n  deprecationWarningFn = fn;\n}\nvar Tensor = class {\n  constructor(shape, dtype, dataId, id) {\n    this.kept = false;\n    this.isDisposedInternal = false;\n    this.shape = shape.slice();\n    this.dtype = dtype || \"float32\";\n    this.size = sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId;\n    this.id = id;\n    this.rankType = this.rank < 5 ? this.rank.toString() : \"higher\";\n  }\n  get rank() {\n    return this.shape.length;\n  }\n  async buffer() {\n    const vals = await this.data();\n    return opHandler.buffer(this.shape, this.dtype, vals);\n  }\n  bufferSync() {\n    return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n  }\n  async array() {\n    const vals = await this.data();\n    return toNestedArray(this.shape, vals, this.dtype === \"complex64\");\n  }\n  arraySync() {\n    return toNestedArray(this.shape, this.dataSync(), this.dtype === \"complex64\");\n  }\n  async data() {\n    this.throwIfDisposed();\n    const data = trackerFn().read(this.dataId);\n    if (this.dtype === \"string\") {\n      const bytes = await data;\n      try {\n        return bytes.map((b) => decodeString(b));\n      } catch (_a) {\n        throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n      }\n    }\n    return data;\n  }\n  dataToGPU(options) {\n    this.throwIfDisposed();\n    return trackerFn().readToGPU(this.dataId, options);\n  }\n  dataSync() {\n    this.throwIfDisposed();\n    const data = trackerFn().readSync(this.dataId);\n    if (this.dtype === \"string\") {\n      try {\n        return data.map((b) => decodeString(b));\n      } catch (_a) {\n        throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n      }\n    }\n    return data;\n  }\n  async bytes() {\n    this.throwIfDisposed();\n    const data = await trackerFn().read(this.dataId);\n    if (this.dtype === \"string\") {\n      return data;\n    } else {\n      return new Uint8Array(data.buffer);\n    }\n  }\n  dispose() {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n  get isDisposed() {\n    return this.isDisposedInternal;\n  }\n  throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n  print(verbose = false) {\n    return opHandler.print(this, verbose);\n  }\n  clone() {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n  toString(verbose = false) {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n  cast(dtype) {\n    this.throwIfDisposed();\n    return opHandler.cast(this, dtype);\n  }\n  variable(trainable = true, name, dtype) {\n    this.throwIfDisposed();\n    return trackerFn().makeVariable(this, trainable, name, dtype);\n  }\n};\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance) => {\n    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;\n  }\n});\nfunction getGlobalTensorClass() {\n  return getGlobal(\"Tensor\", () => {\n    return Tensor;\n  });\n}\ngetGlobalTensorClass();\nvar Variable = class extends Tensor {\n  constructor(initialValue, trainable, name, tensorId) {\n    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n    this.trainable = trainable;\n    this.name = name;\n  }\n  assign(newValue) {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);\n    }\n    if (!arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().incRef(this, null);\n  }\n  dispose() {\n    trackerFn().disposeVariable(this);\n    this.isDisposedInternal = true;\n  }\n};\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance) => {\n    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nvar tensor_util_exports = {};\n__export(tensor_util_exports, {\n  assertTypesMatch: () => assertTypesMatch,\n  getTensorsInContainer: () => getTensorsInContainer,\n  isTensorInList: () => isTensorInList,\n  makeTypesMatch: () => makeTypesMatch\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.js\nvar Rank;\n(function(Rank2) {\n  Rank2[\"R0\"] = \"R0\";\n  Rank2[\"R1\"] = \"R1\";\n  Rank2[\"R2\"] = \"R2\";\n  Rank2[\"R3\"] = \"R3\";\n  Rank2[\"R4\"] = \"R4\";\n  Rank2[\"R5\"] = \"R5\";\n  Rank2[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\nvar UpcastInt32AndMap;\n(function(UpcastInt32AndMap2) {\n  UpcastInt32AndMap2[\"float32\"] = \"float32\";\n  UpcastInt32AndMap2[\"int32\"] = \"int32\";\n  UpcastInt32AndMap2[\"bool\"] = \"int32\";\n  UpcastInt32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function(UpcastBoolAndMap2) {\n  UpcastBoolAndMap2[\"float32\"] = \"float32\";\n  UpcastBoolAndMap2[\"int32\"] = \"int32\";\n  UpcastBoolAndMap2[\"bool\"] = \"bool\";\n  UpcastBoolAndMap2[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function(UpcastFloat32AndMap2) {\n  UpcastFloat32AndMap2[\"float32\"] = \"float32\";\n  UpcastFloat32AndMap2[\"int32\"] = \"float32\";\n  UpcastFloat32AndMap2[\"bool\"] = \"float32\";\n  UpcastFloat32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function(UpcastComplex64AndMap2) {\n  UpcastComplex64AndMap2[\"float32\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"int32\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"bool\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nvar upcastTypeMap = {\n  \"float32\": UpcastFloat32AndMap,\n  \"int32\": UpcastInt32AndMap,\n  \"bool\": UpcastBoolAndMap,\n  \"complex64\": UpcastComplex64AndMap\n};\nfunction upcastType(typeA, typeB) {\n  if (typeA === \"string\" || typeB === \"string\") {\n    if (typeA === \"string\" && typeB === \"string\") {\n      return \"string\";\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\nfunction sumOutType(type) {\n  return upcastType(type, \"int32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nfunction makeTypesMatch(a6, b) {\n  if (a6.dtype === b.dtype) {\n    return [a6, b];\n  }\n  const dtype = upcastType(a6.dtype, b.dtype);\n  return [a6.cast(dtype), b.cast(dtype)];\n}\nfunction assertTypesMatch(a6, b) {\n  assert(a6.dtype === b.dtype, () => `The dtypes of the first(${a6.dtype}) and second(${b.dtype}) input must match`);\n}\nfunction isTensorInList(tensor2, tensorList) {\n  return tensorList.some((x) => x.id === tensor2.id);\n}\nfunction getTensorsInContainer(result) {\n  const list = [];\n  const seen = /* @__PURE__ */ new Set();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  const iterable = container;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\nfunction isIterable(obj) {\n  return Array.isArray(obj) || typeof obj === \"object\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/engine.js\nfunction isRegisteredKernelInvocation(kernelInvocation) {\n  return kernelInvocation.kernelName != null;\n}\nvar EngineState = class {\n  constructor() {\n    this.registeredVariables = {};\n    this.nextTapeNodeId = 0;\n    this.numBytes = 0;\n    this.numTensors = 0;\n    this.numStringTensors = 0;\n    this.numDataBuffers = 0;\n    this.gradientDepth = 0;\n    this.kernelDepth = 0;\n    this.scopeStack = [];\n    this.numDataMovesStack = [];\n    this.nextScopeId = 0;\n    this.tensorInfo = /* @__PURE__ */ new WeakMap();\n    this.profiling = false;\n    this.activeProfile = {\n      newBytes: 0,\n      newTensors: 0,\n      peakBytes: 0,\n      kernels: [],\n      result: null,\n      get kernelNames() {\n        return Array.from(new Set(this.kernels.map((k) => k.name)));\n      }\n    };\n  }\n  dispose() {\n    for (const variableName in this.registeredVariables) {\n      this.registeredVariables[variableName].dispose();\n    }\n  }\n};\nvar Engine = class {\n  constructor(ENV8) {\n    this.ENV = ENV8;\n    this.registry = {};\n    this.registryFactory = {};\n    this.pendingBackendInitId = 0;\n    this.state = new EngineState();\n  }\n  async ready() {\n    if (this.pendingBackendInit != null) {\n      return this.pendingBackendInit.then(() => {\n      });\n    }\n    if (this.backendInstance != null) {\n      return;\n    }\n    const sortedBackends = this.getSortedBackends();\n    for (let i2 = 0; i2 < sortedBackends.length; i2++) {\n      const backendName = sortedBackends[i2];\n      const success = await this.initializeBackend(backendName).success;\n      if (success) {\n        await this.setBackend(backendName);\n        return;\n      }\n    }\n    throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n  }\n  get backend() {\n    if (this.pendingBackendInit != null) {\n      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n    }\n    if (this.backendInstance == null) {\n      const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n      if (asyncInit) {\n        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n      }\n      this.setBackend(name);\n    }\n    return this.backendInstance;\n  }\n  backendNames() {\n    return Object.keys(this.registryFactory);\n  }\n  findBackend(backendName) {\n    if (!(backendName in this.registry)) {\n      if (backendName in this.registryFactory) {\n        const { asyncInit } = this.initializeBackend(backendName);\n        if (asyncInit) {\n          return null;\n        }\n      } else {\n        return null;\n      }\n    }\n    return this.registry[backendName];\n  }\n  findBackendFactory(backendName) {\n    if (!(backendName in this.registryFactory)) {\n      return null;\n    }\n    return this.registryFactory[backendName].factory;\n  }\n  registerBackend(backendName, factory, priority = 1) {\n    if (backendName in this.registryFactory) {\n      warn(`${backendName} backend was already registered. Reusing existing backend factory.`);\n      return false;\n    }\n    this.registryFactory[backendName] = { factory, priority };\n    return true;\n  }\n  async setBackend(backendName) {\n    if (this.registryFactory[backendName] == null) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    this.backendName = backendName;\n    if (this.registry[backendName] == null) {\n      this.backendInstance = null;\n      const { success, asyncInit } = this.initializeBackend(backendName);\n      const result = asyncInit ? await success : success;\n      if (!result) {\n        return false;\n      }\n    }\n    this.backendInstance = this.registry[backendName];\n    this.setupRegisteredKernels();\n    this.profiler = new Profiler(this.backendInstance);\n    return true;\n  }\n  setupRegisteredKernels() {\n    const kernels = getKernelsForBackend(this.backendName);\n    kernels.forEach((kernel) => {\n      if (kernel.setupFunc != null) {\n        kernel.setupFunc(this.backendInstance);\n      }\n    });\n  }\n  disposeRegisteredKernels(backendName) {\n    const kernels = getKernelsForBackend(backendName);\n    kernels.forEach((kernel) => {\n      if (kernel.disposeFunc != null) {\n        kernel.disposeFunc(this.registry[backendName]);\n      }\n    });\n  }\n  initializeBackend(backendName) {\n    const registryFactoryEntry = this.registryFactory[backendName];\n    if (registryFactoryEntry == null) {\n      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n    }\n    try {\n      const backend2 = registryFactoryEntry.factory();\n      if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === \"function\") {\n        const promiseId = ++this.pendingBackendInitId;\n        const success = backend2.then((backendInstance) => {\n          if (promiseId < this.pendingBackendInitId) {\n            return false;\n          }\n          this.registry[backendName] = backendInstance;\n          this.pendingBackendInit = null;\n          return true;\n        }).catch((err) => {\n          if (promiseId < this.pendingBackendInitId) {\n            return false;\n          }\n          this.pendingBackendInit = null;\n          warn(`Initialization of backend ${backendName} failed`);\n          warn(err.stack || err.message);\n          return false;\n        });\n        this.pendingBackendInit = success;\n        return { success, asyncInit: true };\n      } else {\n        this.registry[backendName] = backend2;\n        return { success: true, asyncInit: false };\n      }\n    } catch (err) {\n      warn(`Initialization of backend ${backendName} failed`);\n      warn(err.stack || err.message);\n      return { success: false, asyncInit: false };\n    }\n  }\n  removeBackend(backendName) {\n    if (!(backendName in this.registryFactory)) {\n      throw new Error(`${backendName} backend not found in registry`);\n    }\n    if (this.backendName === backendName && this.pendingBackendInit != null) {\n      this.pendingBackendInitId++;\n    }\n    if (backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    delete this.registryFactory[backendName];\n    if (this.backendName === backendName) {\n      this.pendingBackendInit = null;\n      this.backendName = null;\n      this.backendInstance = null;\n    }\n  }\n  getSortedBackends() {\n    if (Object.keys(this.registryFactory).length === 0) {\n      throw new Error(\"No backend found in registry.\");\n    }\n    return Object.keys(this.registryFactory).sort((a6, b) => {\n      return this.registryFactory[b].priority - this.registryFactory[a6].priority;\n    });\n  }\n  initializeBackendsAndReturnBest() {\n    const sortedBackends = this.getSortedBackends();\n    for (let i2 = 0; i2 < sortedBackends.length; i2++) {\n      const backendName = sortedBackends[i2];\n      const { success, asyncInit } = this.initializeBackend(backendName);\n      if (asyncInit || success) {\n        return { name: backendName, asyncInit };\n      }\n    }\n    throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n  }\n  moveData(backend2, dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    const srcBackend = info.backend;\n    const values = this.readSync(dataId);\n    const refCount = srcBackend.refCount(dataId);\n    srcBackend.disposeData(dataId, true);\n    info.backend = backend2;\n    backend2.move(dataId, values, info.shape, info.dtype, refCount);\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n    }\n  }\n  tidy(nameOrFn, fn) {\n    let name = null;\n    if (fn == null) {\n      if (typeof nameOrFn !== \"function\") {\n        throw new Error(\"Please provide a function to tidy()\");\n      }\n      fn = nameOrFn;\n    } else {\n      if (typeof nameOrFn !== \"string\" && !(nameOrFn instanceof String)) {\n        throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");\n      }\n      if (typeof fn !== \"function\") {\n        throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");\n      }\n      name = nameOrFn;\n    }\n    let result;\n    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n      result = fn();\n      if (result instanceof Promise) {\n        console.error(\"Cannot return a Promise inside of tidy.\");\n      }\n      return result;\n    });\n  }\n  scopedRun(start, end, f) {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n  nextTensorId() {\n    return Engine.nextTensorId++;\n  }\n  nextVariableId() {\n    return Engine.nextVariableId++;\n  }\n  clone(x) {\n    const y = ENGINE.runKernel(Identity, { x });\n    const inputs = { x };\n    const grad2 = (dy) => ({\n      x: () => {\n        const dtype = \"float32\";\n        const gradInputs = { x: dy };\n        const attrs = { dtype };\n        return ENGINE.runKernel(Cast, gradInputs, attrs);\n      }\n    });\n    const saved = [];\n    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});\n    return y;\n  }\n  runKernel(kernelName, inputs, attrs) {\n    if (this.backendName == null) {\n      this.backend;\n    }\n    const hasKernel = getKernel(kernelName, this.backendName) != null;\n    if (!hasKernel) {\n      throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);\n    }\n    return this.runKernelFunc({ kernelName, inputs, attrs });\n  }\n  shouldCheckForMemLeaks() {\n    return this.ENV.getBool(\"IS_TEST\");\n  }\n  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n    const numDataIdsAfter = this.backend.numDataIds();\n    let numOutputDataIds = 0;\n    outInfos.forEach((info) => {\n      numOutputDataIds += info.dtype === \"complex64\" ? 3 : 1;\n    });\n    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n    if (dataIdsLeaked > 0) {\n      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);\n    }\n  }\n  runKernelFunc(kernelParams) {\n    let outputs;\n    let saved = [];\n    const isTapeOn = this.isTapeOn();\n    const startingBytecount = this.state.numBytes;\n    const startingNumTensors = this.state.numTensors;\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack.push(0);\n    }\n    let kernelFunc3;\n    if (this.backendName == null) {\n      this.backend;\n    }\n    let out;\n    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : \"\";\n    if (isRegisteredKernelInvocation(kernelParams)) {\n      const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;\n      if (this.backendName == null) {\n        this.backend;\n      }\n      const kernel = getKernel(kernelName, this.backendName);\n      assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);\n      kernelFunc3 = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });\n        const outInfos = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n        }\n        const outTensors = outInfos.map((outInfo) => {\n          if (outInfo.rank != null) {\n            return outInfo;\n          }\n          return this.makeTensorFromTensorInfo(outInfo);\n        });\n        if (isTapeOn) {\n          const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);\n          saved = this.saveTensorsForBackwardMode(tensorsToSave);\n        }\n        return outTensors;\n      };\n    } else {\n      const { forwardFunc } = kernelParams;\n      const saveFunc = (tensors) => {\n        if (!isTapeOn) {\n          return;\n        }\n        saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n      };\n      kernelFunc3 = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n        const outs = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n        }\n        return outs;\n      };\n    }\n    const { inputs, attrs } = kernelParams;\n    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;\n    let kernelProfile;\n    this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {\n      if (!this.ENV.getBool(\"DEBUG\") && !this.state.profiling) {\n        outputs = kernelFunc3();\n      } else {\n        kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());\n        if (this.ENV.getBool(\"DEBUG\")) {\n          this.profiler.logKernelProfile(kernelProfile);\n        }\n        outputs = kernelProfile.outputs;\n      }\n    });\n    if (isTapeOn) {\n      this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n    }\n    if (this.state.profiling) {\n      this.state.activeProfile.kernels.push({\n        name: kernelOrScopeName,\n        bytesAdded: this.state.numBytes - startingBytecount,\n        totalBytesSnapshot: this.state.numBytes,\n        tensorsAdded: this.state.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.state.numTensors,\n        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),\n        outputShapes: outputs.map((item) => item.shape),\n        kernelTimeMs: kernelProfile.timeMs,\n        extraInfo: kernelProfile.extraInfo\n      });\n    }\n    return Array.isArray(out) ? outputs : outputs[0];\n  }\n  saveTensorsForBackwardMode(tensors) {\n    const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n    return saved;\n  }\n  getTensorsForGradient(kernelName, inputs, outputs) {\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      const inputsToSave = gradConfig.inputsToSave || [];\n      const outputsToSave = gradConfig.outputsToSave || [];\n      let inputTensorsToSave;\n      if (gradConfig.saveAllInputs) {\n        assert(Array.isArray(inputs), () => \"saveAllInputs is true, expected inputs to be an array.\");\n        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n      } else {\n        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n      }\n      const outputTensorsToSave = outputs.filter((_, i2) => outputsToSave[i2]);\n      return inputTensorsToSave.concat(outputTensorsToSave);\n    }\n    return [];\n  }\n  makeTensor(values, shape, dtype, backend2) {\n    if (values == null) {\n      throw new Error(\"Values passed to engine.makeTensor() are null\");\n    }\n    dtype = dtype || \"float32\";\n    backend2 = backend2 || this.backend;\n    let backendVals = values;\n    if (dtype === \"string\" && isString(values[0])) {\n      backendVals = values.map((d) => encodeString(d));\n    }\n    const dataId = backend2.write(backendVals, shape, dtype);\n    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t2, backend2);\n    if (dtype === \"string\") {\n      const info = this.state.tensorInfo.get(dataId);\n      const newBytes = bytesFromStringArray(backendVals);\n      this.state.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n    return t2;\n  }\n  makeTensorFromDataId(dataId, shape, dtype, backend2) {\n    dtype = dtype || \"float32\";\n    const tensorInfo = { dataId, shape, dtype };\n    return this.makeTensorFromTensorInfo(tensorInfo, backend2);\n  }\n  makeTensorFromTensorInfo(tensorInfo, backend2) {\n    const { dataId, shape, dtype } = tensorInfo;\n    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t2, backend2);\n    return t2;\n  }\n  makeVariable(initialValue, trainable = true, name, dtype) {\n    name = name || this.nextVariableId().toString();\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.cast(dtype);\n    }\n    const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n    if (this.state.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.state.registeredVariables[v.name] = v;\n    this.incRef(v, this.backend);\n    return v;\n  }\n  trackTensor(a6, backend2) {\n    this.state.numTensors++;\n    if (a6.dtype === \"string\") {\n      this.state.numStringTensors++;\n    }\n    let bytes = 0;\n    if (a6.dtype !== \"complex64\" && a6.dtype !== \"string\") {\n      bytes = a6.size * bytesPerElement(a6.dtype);\n    }\n    this.state.numBytes += bytes;\n    if (!this.state.tensorInfo.has(a6.dataId)) {\n      this.state.numDataBuffers++;\n      this.state.tensorInfo.set(a6.dataId, {\n        backend: backend2 || this.backend,\n        dtype: a6.dtype,\n        shape: a6.shape,\n        bytes\n      });\n    }\n    if (!(a6 instanceof Variable)) {\n      this.track(a6);\n    }\n  }\n  incRef(a6, backend2) {\n    this.trackTensor(a6, backend2);\n    this.backend.incRef(a6.dataId);\n  }\n  removeDataId(dataId, backend2) {\n    if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {\n      this.state.tensorInfo.delete(dataId);\n      this.state.numDataBuffers--;\n    }\n  }\n  disposeTensor(a6) {\n    if (!this.state.tensorInfo.has(a6.dataId)) {\n      return;\n    }\n    const info = this.state.tensorInfo.get(a6.dataId);\n    this.state.numTensors--;\n    if (a6.dtype === \"string\") {\n      this.state.numStringTensors--;\n      this.state.numBytes -= info.bytes;\n    }\n    if (a6.dtype !== \"complex64\" && a6.dtype !== \"string\") {\n      const bytes = a6.size * bytesPerElement(a6.dtype);\n      this.state.numBytes -= bytes;\n    }\n    if (info.backend.disposeData(a6.dataId)) {\n      this.removeDataId(a6.dataId, info.backend);\n    }\n  }\n  disposeVariables() {\n    for (const varName in this.state.registeredVariables) {\n      const v = this.state.registeredVariables[varName];\n      this.disposeVariable(v);\n    }\n  }\n  disposeVariable(v) {\n    this.disposeTensor(v);\n    if (this.state.registeredVariables[v.name] != null) {\n      delete this.state.registeredVariables[v.name];\n    }\n  }\n  memory() {\n    const info = this.backend.memory();\n    info.numTensors = this.state.numTensors;\n    info.numDataBuffers = this.state.numDataBuffers;\n    info.numBytes = this.state.numBytes;\n    if (this.state.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\");\n    }\n    return info;\n  }\n  async profile(query) {\n    this.state.profiling = true;\n    const startBytes = this.state.numBytes;\n    const startNumTensors = this.state.numTensors;\n    this.state.activeProfile.kernels = [];\n    this.state.activeProfile.result = await query();\n    this.state.profiling = false;\n    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));\n    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;\n    for (const kernel of this.state.activeProfile.kernels) {\n      kernel.kernelTimeMs = await kernel.kernelTimeMs;\n      kernel.extraInfo = await kernel.extraInfo;\n    }\n    return this.state.activeProfile;\n  }\n  isTapeOn() {\n    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n  }\n  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n    const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      gradientsFunc = gradConfig.gradFunc;\n    }\n    if (gradientsFunc != null) {\n      tapeNode.gradient = (dys) => {\n        dys = dys.map((dy, i2) => {\n          if (dy == null) {\n            const output = outputs[i2];\n            const vals = makeZerosTypedArray(output.size, output.dtype);\n            return this.makeTensor(vals, output.shape, output.dtype);\n          }\n          return dy;\n        });\n        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n      };\n    }\n    this.state.activeTape.push(tapeNode);\n  }\n  keep(result) {\n    result.kept = true;\n    return result;\n  }\n  startTape() {\n    if (this.state.gradientDepth === 0) {\n      this.state.activeTape = [];\n    }\n    this.state.gradientDepth++;\n  }\n  endTape() {\n    this.state.gradientDepth--;\n  }\n  startScope(name) {\n    const scopeInfo = {\n      track: [],\n      name: \"unnamed scope\",\n      id: this.state.nextScopeId++\n    };\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.state.scopeStack.push(scopeInfo);\n    this.state.activeScope = scopeInfo;\n  }\n  endScope(result) {\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t2) => t2.id));\n    for (let i2 = 0; i2 < this.state.activeScope.track.length; i2++) {\n      const tensor2 = this.state.activeScope.track[i2];\n      if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {\n        tensor2.dispose();\n      }\n    }\n    const oldScope = this.state.scopeStack.pop();\n    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];\n    tensorsToTrackInParent.forEach((tensor2) => {\n      if (!tensor2.kept && tensor2.scopeId === oldScope.id) {\n        this.track(tensor2);\n      }\n    });\n  }\n  gradients(f, xs, dy, allowNoGradients = false) {\n    assert(xs.length > 0, () => \"gradients() received an empty list of xs.\");\n    if (dy != null && dy.dtype !== \"float32\") {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy(\"forward\", f));\n    assert(y instanceof Tensor, () => \"The result y returned by f() must be a tensor.\");\n    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n      throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");\n    }\n    return this.tidy(\"backward\", () => {\n      const accumulatedGradientMap = {};\n      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;\n      backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);\n      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);\n      if (this.state.gradientDepth === 0) {\n        this.state.activeTape.forEach((node) => {\n          for (const tensor2 of node.saved) {\n            tensor2.dispose();\n          }\n        });\n        this.state.activeTape = null;\n      }\n      return { value: y, grads: grads2 };\n    });\n  }\n  customGrad(f) {\n    assert(isFunction(f), () => \"The f passed in customGrad(f) must be a function.\");\n    return (...inputs) => {\n      assert(inputs.every((t2) => t2 instanceof Tensor), () => \"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");\n      let res;\n      const inputMap = {};\n      inputs.forEach((input2, i2) => {\n        inputMap[i2] = input2;\n      });\n      const forwardFunc = (_, save) => {\n        res = f(...[...inputs, save]);\n        assert(res.value instanceof Tensor, () => \"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\");\n        assert(isFunction(res.gradFunc), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\");\n        return res.value;\n      };\n      const backwardsFunc = (dy, saved) => {\n        const gradRes = res.gradFunc(dy, saved);\n        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];\n        assert(grads2.length === inputs.length, () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\");\n        assert(grads2.every((t2) => t2 instanceof Tensor), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");\n        const gradMap = {};\n        grads2.forEach((grad2, i2) => {\n          gradMap[i2] = () => grad2;\n        });\n        return gradMap;\n      };\n      return this.runKernelFunc({\n        forwardFunc,\n        backwardsFunc,\n        inputs: inputMap\n      });\n    };\n  }\n  readSync(dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n  readToGPU(dataId, options) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readToGPU(dataId, options);\n  }\n  async time(query) {\n    const start = now();\n    const timingInfo = await this.backend.time(query);\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n  track(result) {\n    if (this.state.activeScope != null) {\n      result.scopeId = this.state.activeScope.id;\n      this.state.activeScope.track.push(result);\n    }\n    return result;\n  }\n  get registeredVariables() {\n    return this.state.registeredVariables;\n  }\n  reset() {\n    this.pendingBackendInitId++;\n    this.state.dispose();\n    this.ENV.reset();\n    this.state = new EngineState();\n    for (const backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    this.backendName = null;\n    this.backendInstance = null;\n    this.pendingBackendInit = null;\n  }\n};\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n  const values = makeOnesTypedArray(sizeFromShape(shape), \"float32\");\n  return ENGINE.makeTensor(values, shape, \"float32\");\n}\nfunction getOrMakeEngine() {\n  const ns = getGlobalNamespace();\n  if (ns._tfengine == null) {\n    const environment = new Environment(ns);\n    ns._tfengine = new Engine(environment);\n  }\n  setEnvironmentGlobal(ns._tfengine.ENV);\n  setTensorTracker(() => ns._tfengine);\n  return ns._tfengine;\n}\nvar ENGINE = getOrMakeEngine();\nfunction add(a6, b) {\n  const inputs = { a: a6, b };\n  return ENGINE.runKernel(Add, inputs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js\nvar device_util_exports = {};\n__export(device_util_exports, {\n  isBrowser: () => isBrowser,\n  isMobile: () => isMobile,\n  mockIsMobile: () => mockIsMobile\n});\nfunction _isNavigatorDefined() {\n  return typeof navigator !== \"undefined\" && navigator != null;\n}\nvar isMobileMockValue;\nfunction mockIsMobile(value) {\n  isMobileMockValue = value;\n}\nfunction isMobile(nav) {\n  if (isMobileMockValue !== void 0) {\n    return isMobileMockValue;\n  }\n  if (nav || _isNavigatorDefined()) {\n    if (!nav) {\n      nav = navigator;\n    }\n    if (nav.product === \"ReactNative\") {\n      return true;\n    }\n    const a6 = nav.userAgent || nav.vendor || (typeof window !== \"undefined\" ? window.opera : \"\");\n    if (!a6) {\n      const navAny = nav;\n      return navAny.userAgentData && navAny.userAgentData.mobile;\n    }\n    return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a6) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i.test(a6.substr(0, 4));\n  }\n  return false;\n}\nfunction isBrowser() {\n  return typeof window !== \"undefined\" && window.document != null || typeof WorkerGlobalScope !== \"undefined\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/flags.js\nvar ENV2 = env();\nENV2.registerFlag(\"DEBUG\", () => false, (debugValue) => {\n  if (debugValue) {\n    console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\");\n  }\n});\nENV2.registerFlag(\"IS_BROWSER\", () => isBrowser());\nENV2.registerFlag(\"IS_NODE\", () => typeof process !== \"undefined\" && typeof process.versions !== \"undefined\" && typeof process.versions.node !== \"undefined\");\nENV2.registerFlag(\"IS_CHROME\", () => typeof navigator !== \"undefined\" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));\nENV2.registerFlag(\"PROD\", () => false);\nENV2.registerFlag(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\", () => ENV2.getBool(\"DEBUG\"));\nENV2.registerFlag(\"DEPRECATION_WARNINGS_ENABLED\", () => true);\nENV2.registerFlag(\"IS_TEST\", () => false);\nENV2.registerFlag(\"CHECK_COMPUTATION_FOR_ERRORS\", () => true);\nENV2.registerFlag(\"WRAP_TO_IMAGEBITMAP\", () => false);\nENV2.registerFlag(\"ENGINE_COMPILE_ONLY\", () => false);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js\nfunction inferShape(val, dtype) {\n  let firstElem = val;\n  if (isTypedArray(val)) {\n    return dtype === \"string\" ? [] : [val.length];\n  }\n  if (!Array.isArray(val)) {\n    return [];\n  }\n  const shape = [];\n  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== \"string\") {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (Array.isArray(val) && env().getBool(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n  return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n  indices = indices || [];\n  if (!Array.isArray(val) && !isTypedArray(val)) {\n    assert(shape.length === 0, () => `Element arr[${indices.join(\"][\")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(shape.length > 0, () => `Element arr[${indices.join(\"][\")}] should be a primitive, but is an array of ${val.length} elements`);\n  assert(val.length === shape[0], () => `Element arr[${indices.join(\"][\")}] should have ${shape[0]} elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i2 = 0; i2 < val.length; ++i2) {\n    deepAssertShapeConsistency(val[i2], subShape, indices.concat(i2));\n  }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n  if (expectedDtype === \"string_or_numeric\") {\n    return;\n  }\n  if (expectedDtype == null) {\n    throw new Error(`Expected dtype cannot be null.`);\n  }\n  if (expectedDtype !== \"numeric\" && expectedDtype !== actualDType || expectedDtype === \"numeric\" && actualDType === \"string\") {\n    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\nfunction convertToTensor(x, argName, functionName, parseAsDtype = \"numeric\") {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  if (inferredDtype !== \"string\" && [\"bool\", \"int32\", \"float32\"].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== \"number\" && typeof x !== \"boolean\" && typeof x !== \"string\") {\n    const type = x == null ? \"null\" : x.constructor.name;\n    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);\n  }\n  const inferredShape = inferShape(x, inferredDtype);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x];\n  }\n  const skipTypedArray = true;\n  const values = inferredDtype !== \"string\" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);\n  return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nfunction convertToTensorArray(arg, argName, functionName, parseAsDtype = \"numeric\") {\n  if (!Array.isArray(arg)) {\n    throw new Error(`Argument ${argName} passed to ${functionName} must be a \\`Tensor[]\\` or \\`TensorLike[]\\``);\n  }\n  const tensors = arg;\n  return tensors.map((t2, i2) => convertToTensor(t2, `${argName}[${i2}]`, functionName, parseAsDtype));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js\nvar OP_SCOPE_SUFFIX = \"__op\";\nfunction op(f) {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);\n  }\n  let opName = keys[0];\n  const fn = f[opName];\n  if (opName.endsWith(\"_\")) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n  opName = opName + OP_SCOPE_SUFFIX;\n  const f2 = (...args) => {\n    ENGINE.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (isPromise(result)) {\n        console.error(\"Cannot return a Promise inside of tidy.\");\n      }\n      ENGINE.endScope(result);\n      return result;\n    } catch (ex) {\n      ENGINE.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, \"name\", { value: opName, configurable: true });\n  return f2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js\nfunction complex_(real5, imag5) {\n  const $real = convertToTensor(real5, \"real\", \"complex\");\n  const $imag = convertToTensor(imag5, \"imag\", \"complex\");\n  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);\n  const inputs = { real: $real, imag: $imag };\n  return ENGINE.runKernel(Complex, inputs);\n}\nvar complex = op({ complex_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js\nfunction makeTensor(values, shape, inferredShape, dtype) {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === \"complex64\") {\n    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== \"number\" && typeof values !== \"boolean\" && typeof values !== \"string\") {\n    throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");\n  }\n  if (shape != null) {\n    assertNonNegativeIntegerDimensions(shape);\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);\n    for (let i2 = 0; i2 < inferredShape.length; ++i2) {\n      const inferred = inferredShape[i2];\n      const flatDimsDontMatch = i2 === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i2)) : true;\n      assert(inferredShape[i2] === shape[i2] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);\n    }\n  }\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values];\n  }\n  shape = shape || inferredShape;\n  values = dtype !== \"string\" ? toTypedArray(values, dtype) : flatten(values, [], true);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js\nfunction tensor(values, shape, dtype) {\n  const inferredShape = inferShape(values, dtype);\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js\nvar DTYPE_VALUE_SIZE_MAP = {\n  \"float32\": 4,\n  \"float16\": 2,\n  \"int32\": 4,\n  \"uint16\": 2,\n  \"uint8\": 1,\n  \"bool\": 1,\n  \"complex64\": 8\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js\nvar NUM_BYTES_STRING_LENGTH = 4;\nasync function encodeWeights(tensors, group) {\n  const specs = [];\n  const dataPromises = [];\n  const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);\n  for (let i2 = 0; i2 < names.length; ++i2) {\n    const name = names[i2];\n    const t2 = Array.isArray(tensors) ? tensors[i2].tensor : tensors[name];\n    if (t2.dtype !== \"float32\" && t2.dtype !== \"int32\" && t2.dtype !== \"bool\" && t2.dtype !== \"string\" && t2.dtype !== \"complex64\") {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t2.dtype}`);\n    }\n    const spec = { name, shape: t2.shape, dtype: t2.dtype };\n    if (t2.dtype === \"string\") {\n      const utf8bytes = new Promise(async (resolve) => {\n        const vals = await t2.bytes();\n        const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;\n        const bytes = new Uint8Array(totalNumBytes);\n        let offset = 0;\n        for (let i3 = 0; i3 < vals.length; i3++) {\n          const val = vals[i3];\n          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n          bytes.set(bytesOfLength, offset);\n          offset += NUM_BYTES_STRING_LENGTH;\n          bytes.set(val, offset);\n          offset += val.length;\n        }\n        resolve(bytes);\n      });\n      dataPromises.push(utf8bytes);\n    } else {\n      dataPromises.push(t2.data());\n    }\n    if (group != null) {\n      spec.group = group;\n    }\n    specs.push(spec);\n  }\n  const tensorValues = await Promise.all(dataPromises);\n  return { data: concatenateTypedArrays(tensorValues), specs };\n}\nfunction decodeWeights(buffer2, specs) {\n  const out = {};\n  let float16Decode;\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let values;\n    if (\"quantization\" in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n        if (!(\"min\" in quantization && \"scale\" in quantization)) {\n          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);\n        }\n      } else if (quantization.dtype === \"float16\") {\n        if (dtype !== \"float32\") {\n          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);\n        }\n      } else {\n        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = quantization.dtype === \"uint8\" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);\n      if (dtype === \"float32\") {\n        if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n          values = new Float32Array(quantizedArray.length);\n          for (let i2 = 0; i2 < quantizedArray.length; i2++) {\n            const v = quantizedArray[i2];\n            values[i2] = v * quantization.scale + quantization.min;\n          }\n        } else if (quantization.dtype === \"float16\") {\n          if (float16Decode === void 0) {\n            float16Decode = getFloat16Decoder();\n          }\n          values = float16Decode(quantizedArray);\n        } else {\n          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);\n        }\n      } else if (dtype === \"int32\") {\n        if (quantization.dtype !== \"uint8\" && quantization.dtype !== \"uint16\") {\n          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);\n        }\n        values = new Int32Array(quantizedArray.length);\n        for (let i2 = 0; i2 < quantizedArray.length; i2++) {\n          const v = quantizedArray[i2];\n          values[i2] = Math.round(v * quantization.scale + quantization.min);\n        }\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else if (dtype === \"string\") {\n      const size2 = sizeFromShape(spec.shape);\n      values = [];\n      for (let i2 = 0; i2 < size2; i2++) {\n        const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n        offset += NUM_BYTES_STRING_LENGTH;\n        const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));\n        values.push(bytes);\n        offset += byteLength;\n      }\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor);\n      if (dtype === \"float32\") {\n        values = new Float32Array(byteBuffer);\n      } else if (dtype === \"int32\") {\n        values = new Int32Array(byteBuffer);\n      } else if (dtype === \"bool\") {\n        values = new Uint8Array(byteBuffer);\n      } else if (dtype === \"complex64\") {\n        values = new Float32Array(byteBuffer);\n        const real5 = new Float32Array(values.length / 2);\n        const image2 = new Float32Array(values.length / 2);\n        for (let i2 = 0; i2 < real5.length; i2++) {\n          real5[i2] = values[i2 * 2];\n          image2[i2] = values[i2 * 2 + 1];\n        }\n        const realTensor = tensor(real5, shape, \"float32\");\n        const imageTensor = tensor(image2, shape, \"float32\");\n        out[name] = complex(realTensor, imageTensor);\n        realTensor.dispose();\n        imageTensor.dispose();\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n    if (dtype !== \"complex64\") {\n      out[name] = tensor(values, shape, dtype);\n    }\n  }\n  return out;\n}\nfunction concatenateTypedArrays(xs) {\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n  let totalByteLength = 0;\n  const normalizedXs = [];\n  xs.forEach((x) => {\n    totalByteLength += x.byteLength;\n    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));\n    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n  });\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n  return y.buffer;\n}\nvar useNodeBuffer = typeof Buffer !== \"undefined\" && (typeof Blob === \"undefined\" || typeof atob === \"undefined\" || typeof btoa === \"undefined\");\nfunction stringByteLength(str) {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\nfunction arrayBufferToBase64String(buffer2) {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer2).toString(\"base64\");\n  }\n  const buf = new Uint8Array(buffer2);\n  let s2 = \"\";\n  for (let i2 = 0, l3 = buf.length; i2 < l3; i2++) {\n    s2 += String.fromCharCode(buf[i2]);\n  }\n  return btoa(s2);\n}\nfunction base64StringToArrayBuffer(str) {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, \"base64\");\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s2 = atob(str);\n  const buffer2 = new Uint8Array(s2.length);\n  for (let i2 = 0; i2 < s2.length; ++i2) {\n    buffer2.set([s2.charCodeAt(i2)], i2);\n  }\n  return buffer2.buffer;\n}\nfunction concatenateArrayBuffers(buffers) {\n  if (buffers.length === 1) {\n    return buffers[0];\n  }\n  let totalByteLength = 0;\n  buffers.forEach((buffer2) => {\n    totalByteLength += buffer2.byteLength;\n  });\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer2) => {\n    temp.set(new Uint8Array(buffer2), offset);\n    offset += buffer2.byteLength;\n  });\n  return temp.buffer;\n}\nfunction basename(path) {\n  const SEPARATOR = \"/\";\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\nfunction getModelJSONForModelArtifacts(artifacts, manifest) {\n  const result = {\n    modelTopology: artifacts.modelTopology,\n    format: artifacts.format,\n    generatedBy: artifacts.generatedBy,\n    convertedBy: artifacts.convertedBy,\n    weightsManifest: manifest\n  };\n  if (artifacts.signature != null) {\n    result.signature = artifacts.signature;\n  }\n  if (artifacts.userDefinedMetadata != null) {\n    result.userDefinedMetadata = artifacts.userDefinedMetadata;\n  }\n  if (artifacts.modelInitializer != null) {\n    result.modelInitializer = artifacts.modelInitializer;\n  }\n  if (artifacts.trainingConfig != null) {\n    result.trainingConfig = artifacts.trainingConfig;\n  }\n  return result;\n}\nasync function getModelArtifactsForJSON(modelJSON, loadWeights2) {\n  const modelArtifacts = {\n    modelTopology: modelJSON.modelTopology,\n    format: modelJSON.format,\n    generatedBy: modelJSON.generatedBy,\n    convertedBy: modelJSON.convertedBy\n  };\n  if (modelJSON.trainingConfig != null) {\n    modelArtifacts.trainingConfig = modelJSON.trainingConfig;\n  }\n  if (modelJSON.weightsManifest != null) {\n    const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);\n    modelArtifacts.weightSpecs = weightSpecs;\n    modelArtifacts.weightData = weightData;\n  }\n  if (modelJSON.signature != null) {\n    modelArtifacts.signature = modelJSON.signature;\n  }\n  if (modelJSON.userDefinedMetadata != null) {\n    modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;\n  }\n  if (modelJSON.modelInitializer != null) {\n    modelArtifacts.modelInitializer = modelJSON.modelInitializer;\n  }\n  return modelArtifacts;\n}\nfunction getModelArtifactsInfoForJSON(modelArtifacts) {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");\n  }\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: \"JSON\",\n    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength\n  };\n}\nfunction computeFloat16MantisaTable() {\n  const convertMantissa = (i2) => {\n    let m = i2 << 13;\n    let e2 = 0;\n    while ((m & 8388608) === 0) {\n      e2 -= 8388608;\n      m <<= 1;\n    }\n    m &= ~8388608;\n    e2 += 947912704;\n    return m | e2;\n  };\n  const mantisaTable = new Uint32Array(2048);\n  mantisaTable[0] = 0;\n  for (let i2 = 1; i2 < 1024; i2++) {\n    mantisaTable[i2] = convertMantissa(i2);\n  }\n  for (let i2 = 1024; i2 < 2048; i2++) {\n    mantisaTable[i2] = 939524096 + (i2 - 1024 << 13);\n  }\n  return mantisaTable;\n}\nfunction computeFloat16ExponentTable() {\n  const exponentTable = new Uint32Array(64);\n  exponentTable[0] = 0;\n  exponentTable[31] = 1199570944;\n  exponentTable[32] = 2147483648;\n  exponentTable[63] = 3347054592;\n  for (let i2 = 1; i2 < 31; i2++) {\n    exponentTable[i2] = i2 << 23;\n  }\n  for (let i2 = 33; i2 < 63; i2++) {\n    exponentTable[i2] = 2147483648 + (i2 - 32 << 23);\n  }\n  return exponentTable;\n}\nfunction computeFloat16OffsetTable() {\n  const offsetTable = new Uint32Array(64);\n  for (let i2 = 0; i2 < 64; i2++) {\n    offsetTable[i2] = 1024;\n  }\n  offsetTable[0] = offsetTable[32] = 0;\n  return offsetTable;\n}\nfunction getFloat16Decoder() {\n  const mantisaTable = computeFloat16MantisaTable();\n  const exponentTable = computeFloat16ExponentTable();\n  const offsetTable = computeFloat16OffsetTable();\n  return (quantizedArray) => {\n    const buffer2 = new ArrayBuffer(4 * quantizedArray.length);\n    const bufferUint32View = new Uint32Array(buffer2);\n    for (let index = 0; index < quantizedArray.length; index++) {\n      const float16Bits = quantizedArray[index];\n      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];\n      bufferUint32View[index] = float32Bits;\n    }\n    return new Float32Array(buffer2);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js\nvar IORouterRegistry = class {\n  constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n  static getInstance() {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n  static registerSaveRouter(saveRouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n  static registerLoadRouter(loadRouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n  static getSaveHandlers(url) {\n    return IORouterRegistry.getHandlers(url, \"save\");\n  }\n  static getLoadHandlers(url, loadOptions) {\n    return IORouterRegistry.getHandlers(url, \"load\", loadOptions);\n  }\n  static getHandlers(url, handlerType, loadOptions) {\n    const validHandlers = [];\n    const routers = handlerType === \"load\" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;\n    routers.forEach((router) => {\n      const handler = router(url, loadOptions);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n};\nvar registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nvar registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nvar getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nvar getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js\nvar DATABASE_NAME = \"tensorflowjs\";\nvar DATABASE_VERSION = 1;\nvar MODEL_STORE_NAME = \"models_store\";\nvar INFO_STORE_NAME = \"model_info_store\";\nfunction getIndexedDBFactory() {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");\n  }\n  const theWindow = typeof window === \"undefined\" ? self : window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\"The current browser does not appear to support IndexedDB.\");\n  }\n  return factory;\n}\nfunction setUpDatabase(openRequest) {\n  const db = openRequest.result;\n  db.createObjectStore(MODEL_STORE_NAME, { keyPath: \"modelPath\" });\n  db.createObjectStore(INFO_STORE_NAME, { keyPath: \"modelPath\" });\n}\nvar BrowserIndexedDB = class {\n  constructor(modelPath) {\n    this.indexedDB = getIndexedDBFactory();\n    if (modelPath == null || !modelPath) {\n      throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");\n    }\n    this.modelPath = modelPath;\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n    }\n    return this.databaseAction(this.modelPath, modelArtifacts);\n  }\n  async load() {\n    return this.databaseAction(this.modelPath);\n  }\n  databaseAction(modelPath, modelArtifacts) {\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        if (modelArtifacts == null) {\n          const modelTx = db.transaction(MODEL_STORE_NAME, \"readonly\");\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = (error) => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n          const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n          let modelTx;\n          putInfoRequest.onsuccess = () => {\n            modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n            putModelRequest.onerror = (error) => {\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = (error2) => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = (error) => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n};\nBrowserIndexedDB.URL_SCHEME = \"indexeddb://\";\nvar indexedDBRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\nfunction browserIndexedDB(modelPath) {\n  return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;\n}\nvar BrowserIndexedDBManager = class {\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n  async listModels() {\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const tx = db.transaction(INFO_STORE_NAME, \"readonly\");\n        const store = tx.objectStore(INFO_STORE_NAME);\n        const getAllInfoRequest = store.getAll();\n        getAllInfoRequest.onsuccess = () => {\n          const out = {};\n          for (const item of getAllInfoRequest.result) {\n            out[item.modelPath] = item.modelArtifactsInfo;\n          }\n          resolve(out);\n        };\n        getAllInfoRequest.onerror = (error) => {\n          db.close();\n          return reject(getAllInfoRequest.error);\n        };\n        tx.oncomplete = () => db.close();\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n  async removeModel(path) {\n    path = maybeStripScheme(path);\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n        const getInfoRequest = infoStore.get(path);\n        let modelTx;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));\n          } else {\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);\n            };\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = (error) => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = (error) => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js\nvar PATH_SEPARATOR = \"/\";\nvar PATH_PREFIX = \"tensorflowjs_models\";\nvar INFO_SUFFIX = \"info\";\nvar MODEL_TOPOLOGY_SUFFIX = \"model_topology\";\nvar WEIGHT_SPECS_SUFFIX = \"weight_specs\";\nvar WEIGHT_DATA_SUFFIX = \"weight_data\";\nvar MODEL_METADATA_SUFFIX = \"model_metadata\";\nfunction getModelKeys(path) {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n    modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\nfunction removeItems(keys) {\n  for (const key of Object.values(keys)) {\n    window.localStorage.removeItem(key);\n  }\n}\nfunction getModelPathFromKey(key) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme2(key) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;\n}\nvar BrowserLocalStorage = class {\n  constructor(modelPath) {\n    if (!env().getBool(\"IS_BROWSER\") || typeof window === \"undefined\" || typeof window.localStorage === \"undefined\") {\n      throw new Error(\"The current environment does not support local storage.\");\n    }\n    this.LS = window.localStorage;\n    if (modelPath == null || !modelPath) {\n      throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n      const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n        const metadata = {\n          format: modelArtifacts.format,\n          generatedBy: modelArtifacts.generatedBy,\n          convertedBy: modelArtifacts.convertedBy,\n          signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,\n          userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,\n          modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,\n          trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0\n        };\n        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));\n        return { modelArtifactsInfo };\n      } catch (err) {\n        removeItems(this.keys);\n        throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n  async load() {\n    const info = JSON.parse(this.LS.getItem(this.keys.info));\n    if (info == null) {\n      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n    }\n    if (info.modelTopologyType !== \"JSON\") {\n      throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");\n    }\n    const out = {};\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);\n    }\n    out.modelTopology = topology;\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n    const metadataString = this.LS.getItem(this.keys.modelMetadata);\n    if (metadataString != null) {\n      const metadata = JSON.parse(metadataString);\n      out.format = metadata.format;\n      out.generatedBy = metadata.generatedBy;\n      out.convertedBy = metadata.convertedBy;\n      if (metadata.signature != null) {\n        out.signature = metadata.signature;\n      }\n      if (metadata.userDefinedMetadata != null) {\n        out.userDefinedMetadata = metadata.userDefinedMetadata;\n      }\n      if (metadata.modelInitializer != null) {\n        out.modelInitializer = metadata.modelInitializer;\n      }\n      if (metadata.trainingConfig != null) {\n        out.trainingConfig = metadata.trainingConfig;\n      }\n    }\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n    return out;\n  }\n};\nBrowserLocalStorage.URL_SCHEME = \"localstorage://\";\nvar localStorageRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\nfunction browserLocalStorage(modelPath) {\n  return new BrowserLocalStorage(modelPath);\n}\nvar BrowserLocalStorageManager = class {\n  constructor() {\n    assert(env().getBool(\"IS_BROWSER\"), () => \"Current environment is not a web browser\");\n    assert(typeof window === \"undefined\" || typeof window.localStorage !== \"undefined\", () => \"Current browser does not appear to support localStorage\");\n    this.LS = window.localStorage;\n  }\n  async listModels() {\n    const out = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i2 = 0; i2 < this.LS.length; ++i2) {\n      const key = this.LS.key(i2);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key));\n      }\n    }\n    return out;\n  }\n  async removeModel(path) {\n    path = maybeStripScheme2(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info));\n    removeItems(keys);\n    return info;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js\nvar URL_SCHEME_SUFFIX = \"://\";\nvar ModelStoreManagerRegistry = class {\n  constructor() {\n    this.managers = {};\n  }\n  static getInstance() {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n  static registerManager(scheme, manager) {\n    assert(scheme != null, () => \"scheme must not be undefined or null.\");\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, () => \"scheme must not be an empty string.\");\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n  static getManager(scheme) {\n    const manager = this.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n  static getSchemes() {\n    return Object.keys(this.getInstance().managers);\n  }\n};\nfunction parseURL(url) {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(\",\")}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1]\n  };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);\n  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n  const modelArtifacts = await loadHandler.load();\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n  }\n  const saveResult = await saveHandler.save(modelArtifacts);\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n  }\n  return saveResult.modelArtifactsInfo;\n}\nasync function listModels() {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out = {};\n  for (const scheme of schemes) {\n    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\nasync function removeModel(url) {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return manager.removeModel(schemeAndPath.path);\n}\nasync function copyModel(sourceURL, destURL) {\n  const deleteSource = false;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nasync function moveModel(sourceURL, destURL) {\n  const deleteSource = true;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js\nvar PlatformBrowser = class {\n  fetch(path, init2) {\n    return fetch(path, init2);\n  }\n  now() {\n    return performance.now();\n  }\n  encode(text, encoding) {\n    if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n      throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n    }\n    if (this.textEncoder == null) {\n      this.textEncoder = new TextEncoder();\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes, encoding) {\n    return new TextDecoder(encoding).decode(bytes);\n  }\n};\nif (env().get(\"IS_BROWSER\")) {\n  env().setPlatform(\"browser\", new PlatformBrowser());\n  try {\n    ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n  try {\n    ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js\nvar getNodeFetch = {\n  importFetch: () => require_browser()\n};\nvar systemFetch;\nvar PlatformNode = class {\n  constructor() {\n    this.util = require_util();\n    this.textEncoder = new this.util.TextEncoder();\n  }\n  fetch(path, requestInits) {\n    if (env().global.fetch != null) {\n      return env().global.fetch(path, requestInits);\n    }\n    if (systemFetch == null) {\n      systemFetch = getNodeFetch.importFetch();\n    }\n    return systemFetch(path, requestInits);\n  }\n  now() {\n    const time2 = process.hrtime();\n    return time2[0] * 1e3 + time2[1] / 1e6;\n  }\n  encode(text, encoding) {\n    if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n      throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes, encoding) {\n    if (bytes.length === 0) {\n      return \"\";\n    }\n    return new this.util.TextDecoder(encoding).decode(bytes);\n  }\n};\nif (env().get(\"IS_NODE\") && !env().get(\"IS_BROWSER\")) {\n  env().setPlatform(\"node\", new PlatformNode());\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js\nfunction buffer(shape, dtype = \"float32\", values) {\n  dtype = dtype || \"float32\";\n  assertNonNegativeIntegerDimensions(shape);\n  return new TensorBuffer(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js\nfunction cast_(x, dtype) {\n  const $x = convertToTensor(x, \"x\", \"cast\");\n  if (!isValidDtype(dtype)) {\n    throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n  }\n  if (dtype === \"string\" && $x.dtype !== \"string\" || dtype !== \"string\" && $x.dtype === \"string\") {\n    throw new Error(\"Only strings can be casted to strings\");\n  }\n  const inputs = { x: $x };\n  const attrs = { dtype };\n  return ENGINE.runKernel(Cast, inputs, attrs);\n}\nvar cast = op({ cast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js\nfunction clone_(x) {\n  const $x = convertToTensor(x, \"x\", \"clone\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Identity, inputs);\n}\nvar clone = op({ clone_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js\nfunction print(x, verbose = false) {\n  console.log(x.toString(verbose));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js\ngetOrMakeEngine();\nvar opHandler2 = {\n  buffer,\n  cast,\n  clone,\n  print\n};\nsetOpHandler(opHandler2);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js\nvar io_exports = {};\n__export(io_exports, {\n  browserFiles: () => browserFiles,\n  browserHTTPRequest: () => browserHTTPRequest,\n  concatenateArrayBuffers: () => concatenateArrayBuffers,\n  copyModel: () => copyModel,\n  decodeWeights: () => decodeWeights,\n  encodeWeights: () => encodeWeights,\n  fromMemory: () => fromMemory,\n  fromMemorySync: () => fromMemorySync,\n  getLoadHandlers: () => getLoadHandlers,\n  getModelArtifactsForJSON: () => getModelArtifactsForJSON,\n  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,\n  getSaveHandlers: () => getSaveHandlers,\n  http: () => http,\n  isHTTPScheme: () => isHTTPScheme,\n  listModels: () => listModels,\n  loadWeights: () => loadWeights,\n  moveModel: () => moveModel,\n  registerLoadRouter: () => registerLoadRouter,\n  registerSaveRouter: () => registerSaveRouter,\n  removeModel: () => removeModel,\n  weightsLoaderFactory: () => weightsLoaderFactory,\n  withSaveHandler: () => withSaveHandler,\n  withSaveHandlerSync: () => withSaveHandlerSync\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js\nvar DEFAULT_FILE_NAME_PREFIX = \"model\";\nvar DEFAULT_JSON_EXTENSION_NAME = \".json\";\nvar DEFAULT_WEIGHT_DATA_EXTENSION_NAME = \".weights.bin\";\nfunction defer(f) {\n  return new Promise((resolve) => setTimeout(resolve)).then(f);\n}\nvar BrowserDownloads = class {\n  constructor(fileNamePrefix) {\n    if (!env().getBool(\"IS_BROWSER\")) {\n      throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");\n    }\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n    this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n  async save(modelArtifacts) {\n    if (typeof document === \"undefined\") {\n      throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");\n    }\n    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: \"application/octet-stream\" }));\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");\n    } else {\n      const weightsManifest = [{\n        paths: [\"./\" + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n      const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: \"application/json\" }));\n      const jsonAnchor = this.modelJsonAnchor == null ? document.createElement(\"a\") : this.modelJsonAnchor;\n      jsonAnchor.download = this.modelJsonFileName;\n      jsonAnchor.href = modelJsonURL;\n      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent(\"click\")));\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement(\"a\") : this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent(\"click\")));\n      }\n      return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n    }\n  }\n};\nBrowserDownloads.URL_SCHEME = \"downloads://\";\nvar BrowserFiles = class {\n  constructor(files) {\n    if (files == null || files.length < 1) {\n      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);\n    }\n    this.jsonFile = files[0];\n    this.weightsFiles = files.slice(1);\n  }\n  async load() {\n    return new Promise((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event) => {\n        const modelJSON = JSON.parse(event.target.result);\n        const modelTopology = modelJSON.modelTopology;\n        if (modelTopology == null) {\n          reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));\n          return;\n        }\n        const weightsManifest = modelJSON.weightsManifest;\n        if (weightsManifest == null) {\n          reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));\n          return;\n        }\n        if (this.weightsFiles.length === 0) {\n          resolve({ modelTopology });\n          return;\n        }\n        const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n        resolve(modelArtifactsPromise);\n      };\n      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(this.jsonFile);\n    });\n  }\n  loadWeights(weightsManifest) {\n    const weightSpecs = [];\n    const paths = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n      paths.push(...entry.paths);\n    }\n    const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);\n    const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));\n    return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]);\n  }\n  loadWeightsFile(path, file) {\n    return new Promise((resolve, reject) => {\n      const weightFileReader = new FileReader();\n      weightFileReader.onload = (event) => {\n        const weightData = event.target.result;\n        resolve(weightData);\n      };\n      weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);\n      weightFileReader.readAsArrayBuffer(file);\n    });\n  }\n  checkManifestAndWeightFiles(manifest) {\n    const basenames = [];\n    const fileNames = this.weightsFiles.map((file) => basename(file.name));\n    const pathToFile = {};\n    for (const group of manifest) {\n      group.paths.forEach((path) => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n    if (basenames.length !== this.weightsFiles.length) {\n      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);\n    }\n    return pathToFile;\n  }\n};\nvar browserDownloadsRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\nfunction browserDownloads(fileNamePrefix = \"model\") {\n  return new BrowserDownloads(fileNamePrefix);\n}\nfunction browserFiles(files) {\n  return new BrowserFiles(files);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js\nfunction monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n  checkPromises(promises);\n  startFraction = startFraction == null ? 0 : startFraction;\n  endFraction = endFraction == null ? 1 : endFraction;\n  checkFraction(startFraction, endFraction);\n  let resolvedPromise = 0;\n  const registerMonitor = (promise) => {\n    promise.then((value) => {\n      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);\n      onProgress(fraction);\n      return value;\n    });\n    return promise;\n  };\n  function checkPromises(promises2) {\n    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => \"promises must be a none empty array\");\n  }\n  function checkFraction(startFraction2, endFraction2) {\n    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);\n    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);\n    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);\n  }\n  return Promise.all(promises.map(registerMonitor));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js\nasync function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n  if (loadOptions == null) {\n    loadOptions = {};\n  }\n  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;\n  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n  const fetchStartFraction = 0;\n  const fetchEndFraction = 0.5;\n  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n  const bufferPromises = responses.map((response) => response.arrayBuffer());\n  const bufferStartFraction = 0.5;\n  const bufferEndFraction = 1;\n  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n  return buffers;\n}\nasync function loadWeights(manifest, filePathPrefix = \"\", weightNames, requestInit) {\n  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n  const loadWeights2 = weightsLoaderFactory(fetchWeights);\n  return loadWeights2(manifest, filePathPrefix, weightNames);\n}\nfunction weightsLoaderFactory(fetchWeightsFunction) {\n  return async (manifest, filePathPrefix = \"\", weightNames) => {\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch = {};\n    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n    const allManifestWeightNames = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach((weightsEntry) => {\n        const rawDtype = \"quantization\" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n    if (!weightsFound.every((found) => found)) {\n      const weightsNotFound = weightNames.filter((_, i2) => !weightsFound[i2]);\n      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(\", \")}. \nManifest JSON has weights with names: ${allManifestWeightNames.join(\", \")}.`);\n    }\n    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i2) => {\n      if (shouldFetch) {\n        accumulator.push(i2);\n      }\n      return accumulator;\n    }, []);\n    const fetchUrls = [];\n    groupIndicesToFetch.forEach((i2) => {\n      manifest[i2].paths.forEach((filepath) => {\n        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith(\"/\") ? \"/\" : \"\") + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n    const weightsTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach((i2) => {\n      const numBuffers = manifest[i2].paths.length;\n      let groupBytes = 0;\n      for (let i3 = 0; i3 < numBuffers; i3++) {\n        groupBytes += buffers[bufferIndexOffset + i3].byteLength;\n      }\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i3 = 0; i3 < numBuffers; i3++) {\n        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i3]);\n        groupByteBuffer.set(buffer2, groupBufferOffset);\n        groupBufferOffset += buffer2.byteLength;\n      }\n      const weightsEntries = groupWeightsToFetch[i2];\n      weightsEntries.forEach((weightsEntry) => {\n        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n      bufferIndexOffset += numBuffers;\n    });\n    return weightsTensorMap;\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js\nvar OCTET_STREAM_MIME_TYPE = \"application/octet-stream\";\nvar JSON_TYPE = \"application/json\";\nvar HTTPRequest = class {\n  constructor(path, loadOptions) {\n    this.DEFAULT_METHOD = \"POST\";\n    if (loadOptions == null) {\n      loadOptions = {};\n    }\n    this.weightPathPrefix = loadOptions.weightPathPrefix;\n    this.onProgress = loadOptions.onProgress;\n    this.weightUrlConverter = loadOptions.weightUrlConverter;\n    if (loadOptions.fetchFunc != null) {\n      assert(typeof loadOptions.fetchFunc === \"function\", () => \"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\");\n      this.fetch = loadOptions.fetchFunc;\n    } else {\n      this.fetch = env().platform.fetch;\n    }\n    assert(path != null && path.length > 0, () => \"URL path for http must not be null, undefined or empty.\");\n    if (Array.isArray(path)) {\n      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);\n    }\n    this.path = path;\n    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {\n      throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");\n    }\n    this.requestInit = loadOptions.requestInit || {};\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");\n    }\n    const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n    init2.body = new FormData();\n    const weightsManifest = [{\n      paths: [\"./model.weights.bin\"],\n      weights: modelArtifacts.weightSpecs\n    }];\n    const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n    init2.body.append(\"model.json\", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), \"model.json\");\n    if (modelArtifacts.weightData != null) {\n      init2.body.append(\"model.weights.bin\", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), \"model.weights.bin\");\n    }\n    const response = await this.fetch(this.path, init2);\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response]\n      };\n    } else {\n      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);\n    }\n  }\n  async load() {\n    const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n    if (!modelConfigRequest.ok) {\n      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);\n    }\n    let modelJSON;\n    try {\n      modelJSON = await modelConfigRequest.json();\n    } catch (e2) {\n      let message = `Failed to parse model JSON of response from ${this.path}.`;\n      if (this.path.endsWith(\".pb\")) {\n        message += \" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.\";\n      } else {\n        message += \" Please make sure the server is serving valid JSON for this request.\";\n      }\n      throw new Error(message);\n    }\n    const modelTopology = modelJSON.modelTopology;\n    const weightsManifest = modelJSON.weightsManifest;\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);\n    }\n    return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n  }\n  async loadWeights(weightsManifest) {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n    const fetchURLs = [];\n    const urlPromises = [];\n    for (const weightsGroup of weightsManifest) {\n      for (const path of weightsGroup.paths) {\n        if (this.weightUrlConverter != null) {\n          urlPromises.push(this.weightUrlConverter(path));\n        } else {\n          fetchURLs.push(pathPrefix + path + suffix);\n        }\n      }\n    }\n    if (this.weightUrlConverter) {\n      fetchURLs.push(...await Promise.all(urlPromises));\n    }\n    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n      requestInit: this.requestInit,\n      fetchFunc: this.fetch,\n      onProgress: this.onProgress\n    });\n    return [weightSpecs, concatenateArrayBuffers(buffers)];\n  }\n};\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\nfunction parseUrl(url) {\n  const lastSlash = url.lastIndexOf(\"/\");\n  const lastSearchParam = url.lastIndexOf(\"?\");\n  const prefix = url.substring(0, lastSlash);\n  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : \"\";\n  return [prefix + \"/\", suffix];\n}\nfunction isHTTPScheme(url) {\n  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nvar httpRouter = (url, loadOptions) => {\n  if (typeof fetch === \"undefined\" && (loadOptions == null || loadOptions.fetchFunc == null)) {\n    return null;\n  } else {\n    let isHTTP = true;\n    if (Array.isArray(url)) {\n      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));\n    } else {\n      isHTTP = isHTTPScheme(url);\n    }\n    if (isHTTP) {\n      return http(url, loadOptions);\n    }\n  }\n  return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\nfunction http(path, loadOptions) {\n  return new HTTPRequest(path, loadOptions);\n}\nfunction browserHTTPRequest(path, loadOptions) {\n  return http(path, loadOptions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js\nvar PassthroughLoader = class {\n  constructor(modelArtifacts) {\n    this.modelArtifacts = modelArtifacts;\n  }\n  load() {\n    return this.modelArtifacts;\n  }\n};\nvar PassthroughSaver = class {\n  constructor(saveHandler) {\n    this.saveHandler = saveHandler;\n  }\n  save(modelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n};\nvar PassthroughAsync = class {\n  constructor(handler) {\n    if (handler.load) {\n      this.load = () => Promise.resolve(handler.load());\n    }\n    if (handler.save) {\n      this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));\n    }\n  }\n};\nfunction fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n  const args = arguments;\n  return new PassthroughAsync(fromMemorySync(...args));\n}\nfunction fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n  if (arguments.length === 1) {\n    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;\n    if (isModelArtifacts) {\n      return new PassthroughLoader(modelArtifacts);\n    } else {\n      console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n      return new PassthroughLoader({ modelTopology: modelArtifacts });\n    }\n  } else {\n    console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n    return new PassthroughLoader({\n      modelTopology: modelArtifacts,\n      weightSpecs,\n      weightData,\n      trainingConfig\n    });\n  }\n}\nfunction withSaveHandler(saveHandler) {\n  return new PassthroughSaver(saveHandler);\n}\nfunction withSaveHandlerSync(saveHandler) {\n  return new PassthroughSaver(saveHandler);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/math.js\nvar math_exports = {};\n__export(math_exports, {\n  confusionMatrix: () => confusionMatrix\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js\nfunction matMul_(a6, b, transposeA = false, transposeB = false) {\n  let $a = convertToTensor(a6, \"a\", \"matMul\");\n  let $b = convertToTensor(b, \"b\", \"matMul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  const attrs = { transposeA, transposeB };\n  return ENGINE.runKernel(BatchMatMul, inputs, attrs);\n}\nvar matMul = op({ matMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0) {\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const $indices = convertToTensor(indices, \"indices\", \"oneHot\", \"int32\");\n  const inputs = { indices: $indices };\n  const attrs = { depth, onValue, offValue };\n  return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nvar oneHot = op({ oneHot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/globals.js\nfunction enableProdMode() {\n  env().set(\"PROD\", true);\n}\nfunction enableDebugMode() {\n  env().set(\"DEBUG\", true);\n}\nfunction disableDeprecationWarnings() {\n  env().set(\"DEPRECATION_WARNINGS_ENABLED\", false);\n  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\nfunction deprecationWarn(msg) {\n  if (env().getBool(\"DEPRECATION_WARNINGS_ENABLED\")) {\n    console.warn(msg + \" You can disable deprecation warnings with tf.disableDeprecationWarnings().\");\n  }\n}\nsetDeprecationWarningFn(deprecationWarn);\nfunction disposeVariables() {\n  ENGINE.disposeVariables();\n}\nfunction engine() {\n  return ENGINE;\n}\nfunction memory() {\n  return ENGINE.memory();\n}\nfunction profile(f) {\n  return ENGINE.profile(f);\n}\nfunction tidy(nameOrFn, fn) {\n  return ENGINE.tidy(nameOrFn, fn);\n}\nfunction dispose(container) {\n  const tensors = getTensorsInContainer(container);\n  tensors.forEach((tensor2) => tensor2.dispose());\n}\nfunction keep(result) {\n  return ENGINE.keep(result);\n}\nfunction time(f) {\n  return ENGINE.time(f);\n}\nfunction setBackend(backendName) {\n  return ENGINE.setBackend(backendName);\n}\nfunction ready() {\n  return ENGINE.ready();\n}\nfunction getBackend() {\n  return ENGINE.backendName;\n}\nfunction removeBackend(name) {\n  ENGINE.removeBackend(name);\n}\nfunction findBackend(name) {\n  return ENGINE.findBackend(name);\n}\nfunction findBackendFactory(name) {\n  return ENGINE.findBackendFactory(name);\n}\nfunction registerBackend(name, factory, priority = 1) {\n  return ENGINE.registerBackend(name, factory, priority);\n}\nfunction backend() {\n  return ENGINE.backend;\n}\nfunction setPlatform(platformName, platform) {\n  env().setPlatform(platformName, platform);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js\nfunction imag_(input2) {\n  const $input = convertToTensor(input2, \"input\", \"imag\");\n  const inputs = { input: $input };\n  return ENGINE.runKernel(Imag, inputs);\n}\nvar imag = op({ imag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js\nfunction neg_(x) {\n  const $x = convertToTensor(x, \"x\", \"neg\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Neg, inputs);\n}\nvar neg = op({ neg_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js\nfunction real_(input2) {\n  const $input = convertToTensor(input2, \"input\", \"real\");\n  const inputs = { input: $input };\n  return ENGINE.runKernel(Real, inputs);\n}\nvar real = op({ real_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js\nfunction transpose_(x, perm, conjugate) {\n  const $x = convertToTensor(x, \"x\", \"transpose\");\n  if (perm == null) {\n    perm = $x.shape.map((s2, i2) => i2).reverse();\n  }\n  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);\n  perm.forEach((axis) => {\n    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);\n  });\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n  const inputs = { x: $x };\n  const attrs = { perm };\n  if ($x.dtype === \"complex64\") {\n    return tidy(() => {\n      let $real = real($x);\n      let $imag = imag($x);\n      $real = ENGINE.runKernel(Transpose, { x: $real }, attrs);\n      $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);\n      if (conjugate) {\n        $imag = neg($imag);\n      }\n      return complex($real, $imag);\n    });\n  }\n  return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nvar transpose = op({ transpose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js\nfunction confusionMatrix_(labels, predictions, numClasses) {\n  const $labels = convertToTensor(labels, \"labels\", \"confusionMatrix\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"confusionMatrix\");\n  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);\n  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);\n  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);\n  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);\n  const oneHotLabels = oneHot(cast($labels, \"int32\"), numClasses);\n  const oneHotPredictions = oneHot(cast($predictions, \"int32\"), numClasses);\n  const oneHotLabelsT = transpose(oneHotLabels);\n  const product = matMul(oneHotLabelsT, oneHotPredictions);\n  return cast(product, \"int32\");\n}\nvar confusionMatrix = op({ confusionMatrix_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js\nvar broadcast_util_exports = {};\n__export(broadcast_util_exports, {\n  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n  getBroadcastDims: () => getBroadcastDims,\n  getReductionAxes: () => getReductionAxes\n});\nfunction getBroadcastDims(inShape, outShape) {\n  const inRank = inShape.length;\n  const dims = [];\n  for (let i2 = 0; i2 < inRank; i2++) {\n    const dim = inRank - 1 - i2;\n    const a6 = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i2] || 1;\n    if (b > 1 && a6 === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\nfunction getReductionAxes(inShape, outShape) {\n  const result = [];\n  for (let i2 = 0; i2 < outShape.length; i2++) {\n    const inDim = inShape[inShape.length - i2 - 1];\n    const outAxis = outShape.length - i2 - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || inDim === 1 && outDim > 1) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\nfunction assertAndGetBroadcastShape(shapeA, shapeB) {\n  const result = [];\n  const l3 = Math.max(shapeA.length, shapeB.length);\n  for (let i2 = 0; i2 < l3; i2++) {\n    let a6 = shapeA[shapeA.length - i2 - 1];\n    if (a6 == null) {\n      a6 = 1;\n    }\n    let b = shapeB[shapeB.length - i2 - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a6 === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a6);\n    } else if (a6 !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a6);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar browser_exports = {};\n__export(browser_exports, {\n  fromPixels: () => fromPixels,\n  fromPixelsAsync: () => fromPixelsAsync,\n  toPixels: () => toPixels\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js\nfunction tensor3d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error(\"tensor3d() requires shape to have three numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar fromPixels2DContext;\nfunction fromPixels_(pixels, numChannels = 3) {\n  if (numChannels > 4) {\n    throw new Error(\"Cannot construct Tensor with more than 4 channels from pixels.\");\n  }\n  if (pixels == null) {\n    throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n  }\n  let isPixelData2 = false;\n  let isImageData = false;\n  let isVideo = false;\n  let isImage = false;\n  let isCanvasLike = false;\n  let isImageBitmap = false;\n  if (pixels.data instanceof Uint8Array) {\n    isPixelData2 = true;\n  } else if (typeof ImageData !== \"undefined\" && pixels instanceof ImageData) {\n    isImageData = true;\n  } else if (typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement) {\n    isVideo = true;\n  } else if (typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement) {\n    isImage = true;\n  } else if (pixels.getContext != null) {\n    isCanvasLike = true;\n  } else if (typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap) {\n    isImageBitmap = true;\n  } else {\n    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);\n  }\n  if (isVideo) {\n    const HAVE_CURRENT_DATA_READY_STATE = 2;\n    if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {\n      throw new Error(\"The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.\");\n    }\n  }\n  const kernel = getKernel(FromPixels, ENGINE.backendName);\n  if (kernel != null) {\n    const inputs = { pixels };\n    const attrs = { numChannels };\n    return ENGINE.runKernel(FromPixels, inputs, attrs);\n  }\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  let vals;\n  if (isCanvasLike) {\n    vals = pixels.getContext(\"2d\").getImageData(0, 0, width, height).data;\n  } else if (isImageData || isPixelData2) {\n    vals = pixels.data;\n  } else if (isImage || isVideo || isImageBitmap) {\n    if (fromPixels2DContext == null) {\n      if (typeof document === \"undefined\") {\n        if (typeof OffscreenCanvas !== \"undefined\" && typeof OffscreenCanvasRenderingContext2D !== \"undefined\") {\n          fromPixels2DContext = new OffscreenCanvas(1, 1).getContext(\"2d\");\n        } else {\n          throw new Error(\"Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.\");\n        }\n      } else {\n        fromPixels2DContext = document.createElement(\"canvas\").getContext(\"2d\");\n      }\n    }\n    fromPixels2DContext.canvas.width = width;\n    fromPixels2DContext.canvas.height = height;\n    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n  }\n  let values;\n  if (numChannels === 4) {\n    values = new Int32Array(vals);\n  } else {\n    const numPixels = width * height;\n    values = new Int32Array(numPixels * numChannels);\n    for (let i2 = 0; i2 < numPixels; i2++) {\n      for (let channel = 0; channel < numChannels; ++channel) {\n        values[i2 * numChannels + channel] = vals[i2 * 4 + channel];\n      }\n    }\n  }\n  const outShape = [height, width, numChannels];\n  return tensor3d(values, outShape, \"int32\");\n}\nfunction isPixelData(pixels) {\n  return pixels != null && pixels.data instanceof Uint8Array;\n}\nfunction isImageBitmapFullySupported() {\n  return typeof window !== \"undefined\" && typeof ImageBitmap !== \"undefined\" && window.hasOwnProperty(\"createImageBitmap\");\n}\nfunction isNonEmptyPixels(pixels) {\n  return pixels != null && pixels.width !== 0 && pixels.height !== 0;\n}\nfunction canWrapPixelsToImageBitmap(pixels) {\n  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);\n}\nasync function fromPixelsAsync(pixels, numChannels = 3) {\n  let inputs = null;\n  if (env().getBool(\"WRAP_TO_IMAGEBITMAP\") && canWrapPixelsToImageBitmap(pixels)) {\n    let imageBitmap;\n    try {\n      imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: \"none\" });\n    } catch (e2) {\n      imageBitmap = null;\n    }\n    if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {\n      inputs = imageBitmap;\n    } else {\n      inputs = pixels;\n    }\n  } else {\n    inputs = pixels;\n  }\n  return fromPixels_(inputs, numChannels);\n}\nasync function toPixels(img, canvas) {\n  let $img = convertToTensor(img, \"img\", \"toPixels\");\n  if (!(img instanceof Tensor)) {\n    const originalImgTensor = $img;\n    $img = cast(originalImgTensor, \"int32\");\n    originalImgTensor.dispose();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n  if (depth > 4 || depth === 2) {\n    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);\n  }\n  if ($img.dtype !== \"float32\" && $img.dtype !== \"int32\") {\n    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);\n  }\n  const data = await $img.data();\n  const multiplier = $img.dtype === \"float32\" ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n  for (let i2 = 0; i2 < height * width; ++i2) {\n    const rgba = [0, 0, 0, 255];\n    for (let d = 0; d < depth; d++) {\n      const value = data[i2 * depth + d];\n      if ($img.dtype === \"float32\") {\n        if (value < 0 || value > 1) {\n          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);\n        }\n      } else if ($img.dtype === \"int32\") {\n        if (value < 0 || value > 255) {\n          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);\n        }\n      }\n      if (depth === 1) {\n        rgba[0] = value * multiplier;\n        rgba[1] = value * multiplier;\n        rgba[2] = value * multiplier;\n      } else {\n        rgba[d] = value * multiplier;\n      }\n    }\n    const j = i2 * 4;\n    bytes[j + 0] = Math.round(rgba[0]);\n    bytes[j + 1] = Math.round(rgba[1]);\n    bytes[j + 2] = Math.round(rgba[2]);\n    bytes[j + 3] = Math.round(rgba[3]);\n  }\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext(\"2d\");\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\nvar fromPixels = op({ fromPixels_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js\nvar gather_nd_util_exports = {};\n__export(gather_nd_util_exports, {\n  prepareAndValidate: () => prepareAndValidate\n});\nfunction prepareAndValidate(tensor2, indices) {\n  const tensorRank = tensor2.shape.length;\n  const indicesRank = indices.shape.length;\n  if (tensorRank < 1) {\n    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);\n  }\n  if (indicesRank < 1) {\n    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);\n  }\n  if (indices.dtype !== \"int32\") {\n    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indicesRank - 1] > tensorRank) {\n    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);\n  }\n  if (sizeFromShape(tensor2.shape) === 0) {\n    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);\n  }\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  let nResult = 1;\n  for (let i2 = 0; i2 < indicesShape.length - 1; ++i2) {\n    nResult *= indicesShape[i2];\n  }\n  const inputShape = tensor2.shape;\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n  let sliceSize = 1;\n  for (let i2 = sliceRank; i2 < tensorRank; ++i2) {\n    sliceSize *= inputShape[i2];\n    resultShape.push(inputShape[i2]);\n  }\n  const strides = [\n    ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),\n    1\n  ].slice(0, sliceRank);\n  return [resultShape, nResult, sliceSize, strides];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js\nvar scatter_nd_util_exports = {};\n__export(scatter_nd_util_exports, {\n  calculateShapes: () => calculateShapes,\n  validateInput: () => validateInput,\n  validateUpdateShape: () => validateUpdateShape\n});\nfunction validateUpdateShape(shape, indices, updates) {\n  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;\n  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n    }\n  }\n}\nfunction validateInput(updates, indices, shape) {\n  if (indices.rank < 1) {\n    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== \"int32\") {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n    }\n  }\n  validateUpdateShape(shape, indices, updates);\n}\nfunction calculateShapes(updates, indices, shape) {\n  const indicesRank = indices.shape.length;\n  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;\n  const totalNd = shape.length;\n  let sliceSize = 1;\n  for (let i2 = sliceRank; i2 < totalNd; ++i2) {\n    sliceSize *= shape[i2];\n  }\n  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js\nvar slice_util_exports = {};\n__export(slice_util_exports, {\n  assertParamsValid: () => assertParamsValid,\n  computeFlatOffset: () => computeFlatOffset,\n  computeOutShape: () => computeOutShape,\n  getNormalizedAxes: () => getNormalizedAxes,\n  isSliceContinous: () => isSliceContinous,\n  maskToAxes: () => maskToAxes,\n  parseSliceParams: () => parseSliceParams,\n  sliceInfo: () => sliceInfo,\n  startForAxis: () => startForAxis,\n  startIndicesWithElidedDims: () => startIndicesWithElidedDims,\n  stopForAxis: () => stopForAxis,\n  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,\n  stridesForAxis: () => stridesForAxis,\n  stridesWithElidedDims: () => stridesWithElidedDims\n});\nvar NEW_AXIS = -2;\nvar SHRINK_AXIS = -1;\nfunction assertParamsValid(input2, begin, size) {\n  const inputRank = input2.shape.length;\n  assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);\n  assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);\n  for (let i2 = 0; i2 < inputRank; ++i2) {\n    assert(begin[i2] + size[i2] <= input2.shape[i2], () => `Error in slice${inputRank}D: begin[${i2}] + size[${i2}] (${begin[i2] + size[i2]}) would overflow input.shape[${i2}] (${input2.shape[i2]})`);\n  }\n}\nfunction maskToAxes(mask) {\n  const axes = [];\n  let axis = 0;\n  while (mask > 0) {\n    if (mask & 1) {\n      axes.push(axis);\n    }\n    mask /= 2;\n    axis++;\n  }\n  return axes;\n}\nfunction computeOutShape(begin, end, strides) {\n  const size = [];\n  for (let axis = 0; axis < begin.length; axis++) {\n    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n  }\n  return size;\n}\nfunction stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n  const newStrides = [...strides];\n  for (let i2 = newStrides.length; i2 < inputShape.length; i2++) {\n    newStrides.push(1);\n  }\n  for (let i2 = 0; i2 < numElidedAxes; i2++) {\n    if (i2 === 0) {\n      newStrides[ellipsisInsertionIndex] = 1;\n    } else {\n      newStrides.splice(ellipsisInsertionIndex, 0, 1);\n      newStrides.pop();\n    }\n  }\n  return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n  if (normalizedAxis <= ellipsisInsertionIndex) {\n    return normalizedAxis;\n  }\n  return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n  const elidedAxes = [];\n  for (let i2 = 0; i2 < numElidedAxes; i2++) {\n    elidedAxes.push(ellipsisInsertionIndex + i2);\n  }\n  return elidedAxes;\n}\nfunction getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n  const inputRank = inputShape.length;\n  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n  if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n    const fullIndex = ellipsisAxes[0];\n    const numElidedAxes = numInterpolatedAxes + 1;\n    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n  } else {\n    for (let axis = 0; axis < inputRank; axis++) {\n      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n    }\n  }\n  return {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  };\n}\nfunction startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = 0;\n    } else {\n      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalBegin[originalAxis];\n      if (beginMask & 1 << originalAxis) {\n        originalValue = 0;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n  return newIndices;\n}\nfunction stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = Number.MAX_SAFE_INTEGER;\n    } else {\n      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalEnd[originalAxis];\n      if (endMask & 1 << originalAxis) {\n        originalValue = Number.MAX_SAFE_INTEGER;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n  for (let i2 = 0; i2 < newIndices.length; i2++) {\n    const axisSize = inputShape[i2];\n    if (newIndices[i2] < 0) {\n      newIndices[i2] += axisSize;\n    }\n    newIndices[i2] = clamp(0, newIndices[i2], inputShape[i2]);\n  }\n  return newIndices;\n}\nfunction stridesForAxis(strides, axis, ellipsisMask) {\n  let stride = strides[axis];\n  if (ellipsisMask & 1 << axis || stride == null) {\n    stride = 1;\n  }\n  return stride;\n}\nfunction startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n  let start = startIndices[axis];\n  const stride = strides[axis] || 1;\n  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n    if (stride > 0) {\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n  start = clamp(0, start, axisSize - 1);\n  return start;\n}\nfunction stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n  let stop = stopIndices[axis];\n  const stride = strides[axis] || 1;\n  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {\n    if (stride > 0) {\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n  if (stride > 0) {\n    stop = clamp(0, stop, axisSize);\n  } else {\n    stop = clamp(-1, stop, axisSize - 1);\n  }\n  return stop;\n}\nfunction isSliceContinous(shape, begin, size) {\n  let firstNonOneAxis = size.length;\n  for (let i2 = 0; i2 < size.length; i2++) {\n    if (size[i2] > 1) {\n      firstNonOneAxis = i2;\n      break;\n    }\n  }\n  for (let i2 = firstNonOneAxis + 1; i2 < size.length; i2++) {\n    if (begin[i2] > 0 || size[i2] !== shape[i2]) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction computeFlatOffset(begin, strides) {\n  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n  for (let i2 = 0; i2 < begin.length - 1; i2++) {\n    flatOffset += begin[i2] * strides[i2];\n  }\n  return flatOffset;\n}\nfunction parseSliceParams(x, begin, size) {\n  let begin_;\n  const xRank = x.shape.length;\n  if (typeof begin === \"number\") {\n    begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n  } else if (begin.length < xRank) {\n    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  begin_.forEach((d) => {\n    assert(d !== -1, () => \"slice() does not support negative begin indexing.\");\n  });\n  let size_;\n  if (size == null) {\n    size_ = new Array(xRank).fill(-1);\n  } else if (typeof size === \"number\") {\n    size_ = [size, ...new Array(xRank - 1).fill(-1)];\n  } else if (size.length < xRank) {\n    size_ = size.concat(new Array(xRank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i2) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i2}.`);\n      return x.shape[i2] - begin_[i2];\n    }\n  });\n  return [begin_, size_];\n}\nfunction sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n  let stridesNonNull;\n  if (strides == null) {\n    stridesNonNull = new Array(begin.length);\n    stridesNonNull.fill(1);\n  } else {\n    stridesNonNull = strides;\n  }\n  if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {\n    throw new Error(\"Multiple ellipses in slice is not allowed.\");\n  }\n  let ellipsisSeen = false;\n  const sparseSpec = {\n    dims: stridesNonNull.length,\n    numAddAxisAfterEllipsis: 0,\n    begin: begin.slice(),\n    end: end.slice(),\n    strides: stridesNonNull.slice(),\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n  for (let i2 = 0; i2 < sparseSpec.dims; i2++) {\n    if (ellipsisSeen && (1 << i2 & newAxisMask) !== 0) {\n      sparseSpec.numAddAxisAfterEllipsis++;\n    }\n    if (1 << i2 & ellipsisMask) {\n      ellipsisSeen = true;\n    }\n  }\n  if (!ellipsisSeen) {\n    sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;\n    sparseSpec.dims++;\n  }\n  const denseSpec = {\n    dims: xShape.length,\n    beginMask: 0,\n    endMask: 0,\n    beginValid: false,\n    endValid: false\n  };\n  buildDenseSpec(sparseSpec, denseSpec);\n  let isIdentity = true;\n  let sliceDim0 = true;\n  let isSimpleSlice = true;\n  const processingShape = [];\n  const finalShape = [];\n  for (let i2 = 0; i2 < xShape.length; ++i2) {\n    if (denseSpec.strides[i2] === 0) {\n      throw Error(`strides[${i2}] must be non-zero`);\n    }\n    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i2);\n    const dimI = xShape[i2];\n    if (dimI === -1) {\n      processingShape.push(shrinkI ? 1 : -1);\n      continue;\n    }\n    const masks = [denseSpec.beginMask & 1 << i2, denseSpec.endMask & 1 << i2];\n    const validRange = [\n      denseSpec.strides[i2] > 0 ? 0 : -1,\n      denseSpec.strides[i2] > 0 ? dimI : dimI - 1\n    ];\n    if (shrinkI && denseSpec.strides[i2] <= 0) {\n      throw Error(\"only stride 1 allowed on non-range indexing.\");\n    }\n    isSimpleSlice = isSimpleSlice && denseSpec.strides[i2] === 1;\n    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i2 && denseSpec.endMask & 1 << i2);\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      if (shrinkI) {\n        const xFwd = denseSpec.begin[i2] < 0 ? dimI + denseSpec.begin[i2] : denseSpec.begin[i2];\n        denseSpec.begin[i2] = xFwd;\n        denseSpec.end[i2] = denseSpec.begin[i2] + 1;\n        if (xFwd < 0 || xFwd >= dimI) {\n          throw Error(`slice index ${denseSpec.begin[i2]} of dimension ${i2} out of bounds.`);\n        }\n      } else {\n        denseSpec.begin[i2] = canonical(denseSpec.begin[i2], 0, denseSpec.strides[i2], dimI, masks, validRange);\n        denseSpec.end[i2] = canonical(denseSpec.end[i2], 1, denseSpec.strides[i2], dimI, masks, validRange);\n      }\n      const takeAllInDimension = denseSpec.strides[i2] === 1 && denseSpec.begin[i2] === 0 && denseSpec.end[i2] === dimI;\n      isIdentity = isIdentity && takeAllInDimension;\n      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || takeAllInDimension);\n    } else {\n      isIdentity = isIdentity && (denseSpec.strides[i2] === 1 && beginAndEndMasked);\n      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || beginAndEndMasked);\n    }\n    let intervalLength;\n    let knownInterval = false;\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      intervalLength = denseSpec.end[i2] - denseSpec.begin[i2];\n      knownInterval = true;\n    } else if (shrinkI) {\n      intervalLength = 1;\n      knownInterval = true;\n    } else if (beginAndEndMasked) {\n      if (dimI >= 0) {\n        if (denseSpec.strides[i2] < 0) {\n          intervalLength = -dimI;\n        } else {\n          intervalLength = dimI;\n        }\n        knownInterval = true;\n      }\n    }\n    if (knownInterval) {\n      let sizeI;\n      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i2] < 0) {\n        sizeI = 0;\n      } else {\n        sizeI = Math.trunc(intervalLength / denseSpec.strides[i2]) + (intervalLength % denseSpec.strides[i2] !== 0 ? 1 : 0);\n      }\n      processingShape.push(sizeI);\n    } else {\n      processingShape.push(-1);\n    }\n  }\n  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {\n    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n    if (gatherIndex >= 0) {\n      finalShape.push(processingShape[gatherIndex]);\n    } else if (gatherIndex === NEW_AXIS) {\n      finalShape.push(1);\n    }\n  }\n  const finalShapeSparse = finalShape.filter((dim, i2) => denseSpec.finalShapeGatherIndices[i2] !== NEW_AXIS);\n  return {\n    finalShapeSparse,\n    finalShape,\n    isIdentity,\n    sliceDim0,\n    isSimpleSlice,\n    begin: denseSpec.begin,\n    end: denseSpec.end,\n    strides: denseSpec.strides\n  };\n}\nfunction buildDenseSpec(sparse2, dense2) {\n  dense2.beginMask = 0;\n  dense2.endMask = 0;\n  dense2.shrinkAxisMask = 0;\n  let fullIndex = 0;\n  dense2.beginValid = sparse2.begin != null;\n  dense2.endValid = sparse2.end != null;\n  dense2.begin = new Array(dense2.dims);\n  dense2.end = new Array(dense2.dims);\n  dense2.strides = new Array(dense2.dims);\n  dense2.finalShapeGatherIndices = [];\n  dense2.finalShapeGatherIndicesSparse = [];\n  dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);\n  for (let i2 = 0; i2 < sparse2.dims; i2++) {\n    if (1 << i2 & sparse2.ellipsisMask) {\n      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i2) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);\n      for (; fullIndex < nextIndex; fullIndex++) {\n        dense2.begin[fullIndex] = 0;\n        dense2.end[fullIndex] = 0;\n        dense2.strides[fullIndex] = 1;\n        dense2.beginMask |= 1 << fullIndex;\n        dense2.endMask |= 1 << fullIndex;\n        dense2.finalShapeGatherIndices.push(fullIndex);\n        dense2.finalShapeGatherIndicesSparse.push(-1);\n        dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;\n      }\n    } else if (1 << i2 & sparse2.newAxisMask) {\n      dense2.finalShapeGatherIndices.push(NEW_AXIS);\n      dense2.finalShapeGatherIndicesSparse.push(-1);\n    } else {\n      if (fullIndex === dense2.begin.length) {\n        throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);\n      }\n      if (sparse2.begin != null) {\n        dense2.begin[fullIndex] = sparse2.begin[i2];\n      }\n      if (sparse2.end != null) {\n        dense2.end[fullIndex] = sparse2.end[i2];\n      }\n      dense2.strides[fullIndex] = sparse2.strides[i2];\n      if (sparse2.beginMask & 1 << i2) {\n        dense2.beginMask |= 1 << fullIndex;\n      }\n      if (sparse2.endMask & 1 << i2) {\n        dense2.endMask |= 1 << fullIndex;\n      }\n      if (sparse2.shrinkAxisMask & 1 << i2) {\n        dense2.finalShapeGatherIndices.push(SHRINK_AXIS);\n        dense2.finalShapeGatherIndicesSparse.push(-1);\n        dense2.shrinkAxisMask |= 1 << fullIndex;\n      } else {\n        dense2.finalShapeGatherIndices.push(fullIndex);\n        dense2.finalShapeGatherIndicesSparse.push(i2);\n      }\n      dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;\n      fullIndex++;\n    }\n  }\n}\nfunction canonical(x, c, strideI, dimI, masks, validRange) {\n  if (masks[c]) {\n    return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];\n  } else {\n    const xFwd = x < 0 ? dimI + x : x;\n    return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js\nvar serialization_exports = {};\n__export(serialization_exports, {\n  Serializable: () => Serializable,\n  SerializationMap: () => SerializationMap,\n  registerClass: () => registerClass\n});\nvar Serializable = class {\n  getClassName() {\n    return this.constructor.className;\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nvar SerializationMap = class {\n  constructor() {\n    this.classNameMap = {};\n  }\n  static getMap() {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n  static register(cls) {\n    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];\n  }\n};\nfunction registerClass(cls) {\n  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);\n  assert(typeof cls.className === \"string\", () => `className is required to be a string, but got type ` + typeof cls.className);\n  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);\n  SerializationMap.register(cls);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js\nvar test_util_exports = {};\n__export(test_util_exports, {\n  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,\n  encodeStrings: () => encodeStrings,\n  expectArrayBuffersEqual: () => expectArrayBuffersEqual,\n  expectArraysClose: () => expectArraysClose,\n  expectArraysEqual: () => expectArraysEqual,\n  expectNumbersClose: () => expectNumbersClose,\n  expectPromiseToFail: () => expectPromiseToFail,\n  expectValuesInRange: () => expectValuesInRange,\n  testEpsilon: () => testEpsilon\n});\nvar TEST_EPSILON_FLOAT32 = 1e-3;\nvar TEST_EPSILON_FLOAT16 = 0.1;\nfunction expectArraysClose(actual, expected, epsilon3) {\n  if (epsilon3 == null) {\n    epsilon3 = testEpsilon();\n  }\n  return expectArraysPredicate(actual, expected, (a6, b) => areClose(a6, b, epsilon3));\n}\nfunction testEpsilon() {\n  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n  let checkClassType = true;\n  if (isTypedArray(actual) || isTypedArray(expected)) {\n    checkClassType = false;\n  }\n  if (isTypedArray(actual) && isTypedArray(expected)) {\n    checkClassType = true;\n  }\n  if (checkClassType) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n    if (aType !== bType) {\n      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);\n    }\n  }\n  if (Array.isArray(actual) && Array.isArray(expected)) {\n    const actualShape = inferShape(actual);\n    const expectedShape = inferShape(expected);\n    if (!arraysEqual(actualShape, expectedShape)) {\n      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n    }\n  }\n  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);\n  if (actualFlat.length !== expectedFlat.length) {\n    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.\nActual:   ${actualFlat}.\nExpected: ${expectedFlat}.`);\n  }\n  for (let i2 = 0; i2 < expectedFlat.length; ++i2) {\n    const a6 = actualFlat[i2];\n    const e2 = expectedFlat[i2];\n    if (!predicate(a6, e2)) {\n      throw new Error(`Arrays differ: actual[${i2}] = ${a6}, expected[${i2}] = ${e2}.\nActual:   ${actualFlat}.\nExpected: ${expectedFlat}.`);\n    }\n  }\n}\nfunction expectPromiseToFail(fn, done) {\n  fn().then(() => done.fail(), () => done());\n}\nfunction expectArraysEqual(actual, expected) {\n  const exp5 = typeof expected === \"string\" || typeof expected === \"number\" || typeof expected === \"boolean\" ? [expected] : expected;\n  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {\n    return expectArraysPredicate(actual, exp5, (a6, b) => a6 == b);\n  }\n  return expectArraysPredicate(actual, expected, (a6, b) => areClose(a6, b, 0));\n}\nfunction expectNumbersClose(a6, e2, epsilon3) {\n  if (epsilon3 == null) {\n    epsilon3 = testEpsilon();\n  }\n  if (!areClose(a6, e2, epsilon3)) {\n    throw new Error(`Numbers differ: actual === ${a6}, expected === ${e2}`);\n  }\n}\nfunction areClose(a6, e2, epsilon3) {\n  if (!isFinite(a6) && !isFinite(e2)) {\n    return true;\n  }\n  if (isNaN(a6) || isNaN(e2) || Math.abs(a6 - e2) > epsilon3) {\n    return false;\n  }\n  return true;\n}\nfunction expectValuesInRange(actual, low, high) {\n  for (let i2 = 0; i2 < actual.length; i2++) {\n    if (actual[i2] < low || actual[i2] > high) {\n      throw new Error(`Value out of range:${actual[i2]} low: ${low}, high: ${high}`);\n    }\n  }\n}\nfunction expectArrayBuffersEqual(actual, expected) {\n  const actualArray = new Float32Array(actual);\n  const expectedArray = new Float32Array(expected);\n  if (actualArray.length !== expectedArray.length) {\n    throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);\n  }\n  for (let i2 = 0; i2 < expectedArray.length; i2++) {\n    if (actualArray[i2] !== expectedArray[i2]) {\n      throw new Error(`Expected ArrayBuffer value at ${i2} to be ${expectedArray[i2]} but got ${actualArray[i2]} instead`);\n    }\n  }\n}\nfunction encodeStrings(a6) {\n  for (let i2 = 0; i2 < a6.length; i2++) {\n    const val = a6[i2];\n    if (Array.isArray(val)) {\n      encodeStrings(val);\n    } else {\n      a6[i2] = encodeString(val);\n    }\n  }\n  return a6;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/version.js\nvar version = \"3.18.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js\nfunction add_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"add\");\n  let $b = convertToTensor(b, \"b\", \"add\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Add, inputs);\n}\nvar add2 = op({ add_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js\nfunction floorDiv_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"floorDiv\");\n  let $b = convertToTensor(b, \"b\", \"floorDiv\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(FloorDiv, inputs);\n}\nvar floorDiv = op({ floorDiv_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js\nfunction div_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"div\");\n  let $b = convertToTensor(b, \"b\", \"div\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"int32\" && $b.dtype === \"int32\") {\n    return floorDiv($a, $b);\n  }\n  const inputs = { a: $a, b: $b };\n  const attrs = {};\n  return ENGINE.runKernel(RealDiv, inputs, attrs);\n}\nvar div = op({ div_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js\nfunction mul_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"mul\");\n  let $b = convertToTensor(b, \"b\", \"mul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Multiply, inputs);\n}\nvar mul = op({ mul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js\nfunction abs_(x) {\n  const $x = convertToTensor(x, \"x\", \"abs\");\n  if ($x.dtype === \"complex64\") {\n    const inputs = { x: $x };\n    return ENGINE.runKernel(ComplexAbs, inputs);\n  } else {\n    const inputs = { x: $x };\n    return ENGINE.runKernel(Abs, inputs);\n  }\n}\nvar abs = op({ abs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js\nfunction acos_(x) {\n  const $x = convertToTensor(x, \"x\", \"acos\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Acos, inputs);\n}\nvar acos = op({ acos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js\nfunction acosh_(x) {\n  const $x = convertToTensor(x, \"x\", \"acosh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Acosh, inputs);\n}\nvar acosh = op({ acosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js\nfunction addN_(tensors) {\n  assert(Array.isArray(tensors), () => \"The argument passed to tf.addN() must be a list of tensors\");\n  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);\n  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, \"addN\"));\n  const firstTensor = $tensors[0];\n  $tensors.forEach((t2) => {\n    if (t2.dtype !== firstTensor.dtype) {\n      throw new Error(\"All tensors passed to tf.addN() must have the same dtype\");\n    }\n  });\n  $tensors.forEach((t2) => {\n    if (!arraysEqual(t2.shape, firstTensor.shape)) {\n      throw new Error(\"All tensors passed to tf.addN() must have the same shape\");\n    }\n  });\n  const inputs = $tensors;\n  return ENGINE.runKernel(AddN, inputs);\n}\nvar addN = op({ addN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js\nfunction all_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"all\", \"bool\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(All, inputs, attrs);\n}\nvar all = op({ all_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js\nfunction any_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"any\", \"bool\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Any, inputs, attrs);\n}\nvar any = op({ any_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js\nfunction argMax_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"argMax\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(ArgMax, inputs, attrs);\n}\nvar argMax = op({ argMax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js\nfunction argMin_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"argMin\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(ArgMin, inputs, attrs);\n}\nvar argMin = op({ argMin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js\nfunction asin_(x) {\n  const $x = convertToTensor(x, \"x\", \"asin\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Asin, inputs);\n}\nvar asin = op({ asin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js\nfunction asinh_(x) {\n  const $x = convertToTensor(x, \"x\", \"asinh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Asinh, inputs);\n}\nvar asinh = op({ asinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js\nfunction atan_(x) {\n  const $x = convertToTensor(x, \"x\", \"atan\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Atan, inputs);\n}\nvar atan = op({ atan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js\nfunction atan2_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"atan2\");\n  let $b = convertToTensor(b, \"b\", \"atan2\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Atan2, inputs);\n}\nvar atan2 = op({ atan2_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js\nfunction atanh_(x) {\n  const $x = convertToTensor(x, \"x\", \"atanh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Atanh, inputs);\n}\nvar atanh = op({ atanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js\nfunction computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = \"NHWC\", dilations) {\n  const inputChannels = inputShape[3];\n  const $filterShape = [...filterShape, inputChannels];\n  const $dataFormat = convertConv2DDataFormat(dataFormat);\n  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat);\n}\nfunction computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"channelsLast\") {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n  let filterShape;\n  if (dataFormat === \"channelsLast\") {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === \"channelsFirst\") {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat);\n}\nfunction computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"NDHWC\") {\n  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n  let filterShape;\n  let $dataFormat;\n  if (dataFormat === \"NDHWC\") {\n    $dataFormat = \"channelsLast\";\n    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n  } else if (dataFormat === \"NCDHW\") {\n    $dataFormat = \"channelsFirst\";\n    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode);\n}\nfunction computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = \"channelsLast\") {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === \"channelsLast\") {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === \"channelsFirst\") {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n  const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n  let outShape;\n  if (dataFormat === \"channelsFirst\") {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === \"channelsLast\") {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\nfunction computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = \"channelsLast\", roundingMode) {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n  if (dataFormat === \"channelsLast\") {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === \"channelsFirst\") {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n  const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n  let outShape;\n  if (dataFormat === \"channelsFirst\") {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === \"channelsLast\") {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterDepth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputDepth = inShape[0];\n  const inputRows = inShape[1];\n  const inputCols = inShape[2];\n  const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  return [outputDepths, outputRows, outputCols, outChannels];\n}\nfunction computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n  if (typeof param === \"number\") {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param;\n}\nfunction parse3TupleParam(param) {\n  return typeof param === \"number\" ? [param, param, param] : param;\n}\nfunction getEffectiveFilterSize(filterSize, dilation) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n  let padInfo;\n  let outHeight;\n  let outWidth;\n  if (typeof pad3 === \"number\") {\n    const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n    padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType };\n    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad3 === \"same\") {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = { top, bottom, left, right, type: \"SAME\" };\n  } else if (pad3 === \"valid\") {\n    padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: \"VALID\" };\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else if (typeof pad3 === \"object\") {\n    const top = dataFormat === \"channelsLast\" ? pad3[1][0] : pad3[2][0];\n    const bottom = dataFormat === \"channelsLast\" ? pad3[1][1] : pad3[2][1];\n    const left = dataFormat === \"channelsLast\" ? pad3[2][0] : pad3[3][0];\n    const right = dataFormat === \"channelsLast\" ? pad3[2][1] : pad3[3][1];\n    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? \"VALID\" : \"EXPLICIT\";\n    padInfo = { top, bottom, left, right, type: padType };\n    outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n    outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad3}`);\n  }\n  return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n  let padInfo;\n  let outDepth;\n  let outHeight;\n  let outWidth;\n  if (typeof pad3 === \"number\") {\n    const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n    padInfo = {\n      top: pad3,\n      bottom: pad3,\n      left: pad3,\n      right: pad3,\n      front: pad3,\n      back: pad3,\n      type: padType\n    };\n    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad3, roundingMode);\n    outDepth = outShape[0];\n    outHeight = outShape[1];\n    outWidth = outShape[2];\n  } else if (pad3 === \"same\") {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = { top, bottom, left, right, front, back, type: \"SAME\" };\n  } else if (pad3 === \"valid\") {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: \"VALID\"\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad3}`);\n  }\n  return { padInfo, outDepth, outHeight, outWidth };\n}\nfunction round(value, roundingMode) {\n  if (!roundingMode) {\n    return Math.trunc(value);\n  }\n  switch (roundingMode) {\n    case \"round\":\n      return Math.round(value);\n    case \"ceil\":\n      return Math.ceil(value);\n    case \"floor\":\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\nfunction tupleValuesAreOne(param) {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\nfunction eitherStridesOrDilationsAreOne(strides, dilations) {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\nfunction convertConv2DDataFormat(dataFormat) {\n  if (dataFormat === \"NHWC\") {\n    return \"channelsLast\";\n  } else if (dataFormat === \"NCHW\") {\n    return \"channelsFirst\";\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n}\nfunction checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) {\n  if (dimRoundingMode != null) {\n    if (typeof pad3 === \"string\") {\n      throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n    } else if (typeof pad3 === \"number\") {\n      assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n    } else if (typeof pad3 === \"object\") {\n      pad3.forEach((p2) => {\n        p2.forEach((v) => {\n          assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);\n        });\n      });\n    } else {\n      throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`);\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js\nfunction reshape_(x, shape) {\n  const $x = convertToTensor(x, \"x\", \"reshape\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  const attrs = { shape };\n  return ENGINE.runKernel(Reshape, inputs, attrs);\n}\nvar reshape = op({ reshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js\nfunction avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"avgPool\", \"float32\");\n  const dilations = 1;\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n  checkPadOnDimRoundingMode(\"avgPool\", pad3, dimRoundingMode);\n  const inputs = { x: x4D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  let res = ENGINE.runKernel(AvgPool, inputs, attrs);\n  res = cast(res, $x.dtype);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar avgPool = op({ avgPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js\nfunction avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n  const $x = convertToTensor(x, \"x\", \"avgPool3d\", \"float32\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  assert(dataFormat === \"NDHWC\", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode(\"avgPool3d\", pad3, dimRoundingMode);\n  const inputs = { x: x5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n  let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);\n  res = cast(res, x5D.dtype);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar avgPool3d = op({ avgPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js\nfunction concat_(tensors, axis = 0) {\n  assert(tensors.length >= 1, () => \"Pass at least one tensor to concat\");\n  const $tensors = convertToTensorArray(tensors, \"tensors\", \"concat\", \"string_or_numeric\");\n  if ($tensors[0].dtype === \"complex64\") {\n    $tensors.forEach((tensor2) => {\n      if (tensor2.dtype !== \"complex64\") {\n        throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor2.dtype}. `);\n      }\n    });\n  }\n  if ($tensors.length === 1) {\n    return clone($tensors[0]);\n  }\n  const inputs = $tensors;\n  const attr = { axis };\n  return ENGINE.runKernel(Concat, inputs, attr);\n}\nvar concat = op({ concat_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js\nfunction sigmoid_(x) {\n  const $x = convertToTensor(x, \"x\", \"sigmoid\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sigmoid, inputs);\n}\nvar sigmoid = op({ sigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js\nfunction slice_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice\", \"string_or_numeric\");\n  if ($x.rank === 0) {\n    throw new Error(\"Slicing scalar is not possible\");\n  }\n  const inputs = { x: $x };\n  const attrs = { begin, size };\n  return ENGINE.runKernel(Slice, inputs, attrs);\n}\nvar slice = op({ slice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js\nfunction tanh_(x) {\n  const $x = convertToTensor(x, \"x\", \"tanh\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Tanh, inputs);\n}\nvar tanh2 = op({ tanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n  const $forgetBias = convertToTensor(forgetBias, \"forgetBias\", \"basicLSTMCell\");\n  const $lstmKernel = convertToTensor(lstmKernel, \"lstmKernel\", \"basicLSTMCell\");\n  const $lstmBias = convertToTensor(lstmBias, \"lstmBias\", \"basicLSTMCell\");\n  const $data = convertToTensor(data, \"data\", \"basicLSTMCell\");\n  const $c = convertToTensor(c, \"c\", \"basicLSTMCell\");\n  const $h = convertToTensor(h, \"h\", \"basicLSTMCell\");\n  const combined = concat([$data, $h], 1);\n  const weighted = matMul(combined, $lstmKernel);\n  const res = add2(weighted, $lstmBias);\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize = [batchSize, sliceCols];\n  const i2 = slice(res, [0, 0], sliceSize);\n  const j = slice(res, [0, sliceCols], sliceSize);\n  const f = slice(res, [0, sliceCols * 2], sliceSize);\n  const o = slice(res, [0, sliceCols * 3], sliceSize);\n  const newC = add2(mul(sigmoid(i2), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));\n  const newH = mul(tanh2(newC), sigmoid(o));\n  return [newC, newH];\n}\nvar basicLSTMCell = op({ basicLSTMCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js\nfunction batchToSpaceND_(x, blockShape, crops) {\n  const $x = convertToTensor(x, \"x\", \"batchToSpaceND\");\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n  assert($x.shape[0] % prod6 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(\" * \")} === ${prod6}`);\n  const inputs = { x: $x };\n  const attrs = { blockShape, crops };\n  return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);\n}\nvar batchToSpaceND = op({ batchToSpaceND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js\nfunction xAs4D(x) {\n  let x4D;\n  if (x.rank === 0 || x.rank === 1) {\n    x4D = reshape(x, [1, 1, 1, x.size]);\n  } else if (x.rank === 2) {\n    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n  } else if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  } else {\n    x4D = x;\n  }\n  return x4D;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js\nfunction batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($mean.rank === $variance.rank, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  assert($offset == null || $mean.rank === $offset.rank, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  assert($scale == null || $mean.rank === $scale.rank, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  const x4D = xAs4D($x);\n  const inputs = {\n    x: x4D,\n    scale: $scale,\n    offset: $offset,\n    mean: $mean,\n    variance: $variance\n  };\n  const attrs = { varianceEpsilon };\n  const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);\n  return reshape(res, $x.shape);\n}\nvar batchNorm = op({ batchNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js\nfunction batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);\n  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm2d = op({ batchNorm2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js\nfunction batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);\n  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm3d = op({ batchNorm3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js\nfunction batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);\n  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm4d = op({ batchNorm4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js\nfunction bincount_(x, weights, size) {\n  const $x = convertToTensor(x, \"x\", \"bincount\");\n  const $weights = convertToTensor(weights, \"weights\", \"bincount\");\n  assert($x.dtype === \"int32\", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);\n  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n  const inputs = { x: $x, weights: $weights };\n  const attrs = { size };\n  return ENGINE.runKernel(Bincount, inputs, attrs);\n}\nvar bincount = op({ bincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_args.js\nfunction broadcastArgs_(s0, s1) {\n  const shape1Input = convertToTensor(s0, \"s0\", \"broadcastArgs\", \"int32\");\n  const shape2Input = convertToTensor(s1, \"s1\", \"broadcastArgs\", \"int32\");\n  if (shape1Input.rank !== 1) {\n    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`);\n  }\n  if (shape2Input.rank !== 1) {\n    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`);\n  }\n  const inputs = { s0: shape1Input, s1: shape2Input };\n  return ENGINE.runKernel(BroadcastArgs, inputs);\n}\nvar broadcastArgs = op({ broadcastArgs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js\nfunction broadcastTo_(x, shape) {\n  let input2 = convertToTensor(x, \"broadcastTo\", \"x\");\n  const xShape = input2.shape;\n  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {\n    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n  }\n  if (shape.length < input2.rank) {\n    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);\n  }\n  if (shape.length > input2.rank) {\n    const newShape = input2.shape.slice();\n    while (newShape.length < shape.length) {\n      newShape.unshift(1);\n    }\n    input2 = reshape(input2, newShape);\n  }\n  const inputShape = input2.shape;\n  const reps = Array.from(shape);\n  for (let i2 = shape.length - 1; i2 >= 0; i2--) {\n    if (inputShape[i2] === shape[i2]) {\n      reps[i2] = 1;\n    } else if (input2.shape[i2] !== 1) {\n      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n    }\n  }\n  const axes = reps.map((n, i2) => n > 1 ? i2 : -1).filter((i2) => i2 >= 0);\n  if (axes.length === 0) {\n    return clone(input2);\n  }\n  const inputs = { x: input2 };\n  const attrs = { reps };\n  return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar broadcastTo = op({ broadcastTo_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js\nfunction ceil_(x) {\n  const $x = convertToTensor(x, \"x\", \"ceil\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Ceil, inputs);\n}\nvar ceil = op({ ceil_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n  const $x = convertToTensor(x, \"x\", \"clipByValue\");\n  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);\n  const inputs = { x: $x };\n  const attrs = { clipValueMin, clipValueMax };\n  return ENGINE.runKernel(ClipByValue, inputs, attrs);\n}\nvar clipByValue = op({ clipByValue_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js\nfunction concat1d_(tensors) {\n  return concat(tensors, 0);\n}\nvar concat1d = op({ concat1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js\nfunction concat2d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat2d = op({ concat2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js\nfunction concat3d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat3d = op({ concat3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js\nfunction concat4d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat4d = op({ concat4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js\nfunction conv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"conv2d\", pad3, dimRoundingMode);\n  const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n  const res = ENGINE.runKernel(Conv2D, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar conv2d = op({ conv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js\nfunction conv1d_(x, filter, stride, pad3, dataFormat = \"NWC\", dilation = 1, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv1d\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv1d\");\n  let x3D = $x;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n  }\n  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"conv1d\", pad3, dimRoundingMode);\n  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);\n  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);\n  assert(dataFormat === \"NWC\", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n  const strides = [1, stride];\n  const dilations = [1, dilation];\n  const conv2dDataFormat = \"NHWC\";\n  const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode);\n  if (reshapedTo3D) {\n    return reshape(res, [res.shape[2], res.shape[3]]);\n  }\n  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nvar conv1d = op({ conv1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n  let xShape4D = xShape;\n  let dy4D = dy;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);\n  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);\n  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);\n  const inDepth = dataFormat === \"NHWC\" ? xShape4D[3] : xShape4D[1];\n  const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);\n  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);\n  checkPadOnDimRoundingMode(\"conv2dDerInput\", pad3, dimRoundingMode);\n  const inputs = { dy: dy4D, filter };\n  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D };\n  const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar conv2DBackpropInput = op({ conv2DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv2dTranspose\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2dTranspose\");\n  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, \"NHWC\", dimRoundingMode);\n}\nvar conv2dTranspose = op({ conv2dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js\nfunction conv3d_(x, filter, strides, pad3, dataFormat = \"NDHWC\", dilations = [1, 1, 1]) {\n  const $x = convertToTensor(x, \"x\", \"conv3d\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv3d\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);\n  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  assert(dataFormat === \"NDHWC\", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n  const inputs = { x: x5D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations };\n  const res = ENGINE.runKernel(Conv3D, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar conv3d = op({ conv3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {\n  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n  let xShape5D = xShape;\n  let dy5D = dy;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);\n  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);\n  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);\n  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);\n  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);\n  const inputs = { dy: dy5D, filter };\n  const attrs = { pad: pad3, strides, inputShape: xShape5D };\n  const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar conv3DBackpropInput = op({ conv3DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad3) {\n  const $x = convertToTensor(x, \"x\", \"conv3dTranspose\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv3dTranspose\");\n  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3);\n}\nvar conv3dTranspose = op({ conv3dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js\nfunction cos_(x) {\n  const $x = convertToTensor(x, \"x\", \"cos\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Cos, inputs);\n}\nvar cos = op({ cos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js\nfunction cosh_(x) {\n  const $x = convertToTensor(x, \"x\", \"cosh\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Cosh, inputs);\n}\nvar cosh = op({ cosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js\nfunction cumprod_(x, axis = 0, exclusive = false, reverse5 = false) {\n  const $x = convertToTensor(x, \"x\", \"cumprod\");\n  const inputs = { x: $x };\n  const attrs = { axis, exclusive, reverse: reverse5 };\n  return ENGINE.runKernel(Cumprod, inputs, attrs);\n}\nvar cumprod = op({ cumprod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js\nfunction cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {\n  const $x = convertToTensor(x, \"x\", \"cumsum\");\n  const inputs = { x: $x };\n  const attrs = { axis, exclusive, reverse: reverse5 };\n  return ENGINE.runKernel(Cumsum, inputs, attrs);\n}\nvar cumsum = op({ cumsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js\nfunction denseBincount_(x, weights, size, binaryOutput = false) {\n  const $x = convertToTensor(x, \"x\", \"denseBincount\");\n  const $weights = convertToTensor(weights, \"weights\", \"denseBincount\");\n  assert($x.dtype === \"int32\", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);\n  assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);\n  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n  const inputs = { x: $x, weights: $weights };\n  const attrs = { size, binaryOutput };\n  return ENGINE.runKernel(DenseBincount, inputs, attrs);\n}\nvar denseBincount = op({ denseBincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js\nfunction depthToSpace_(x, blockSize, dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"depthToSpace\", \"float32\");\n  const inputHeight = dataFormat === \"NHWC\" ? $x.shape[1] : $x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? $x.shape[2] : $x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? $x.shape[3] : $x.shape[1];\n  assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n  const inputs = { x: $x };\n  const attrs = { blockSize, dataFormat };\n  return ENGINE.runKernel(DepthToSpace, inputs, attrs);\n}\nvar depthToSpace = op({ depthToSpace_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js\nfunction depthwiseConv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n  checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n  const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar depthwiseConv2d = op({ depthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js\nfunction diag_(x) {\n  const $x = convertToTensor(x, \"x\", \"diag\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Diag, inputs);\n}\nvar diag = op({ diag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js\nfunction dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"dilation2d\");\n  const $filter = convertToTensor(filter, \"filter\", \"dilation2d\");\n  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);\n  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);\n  assert(dataFormat === \"NHWC\", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    reshapedTo4D = true;\n  }\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dilations };\n  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar dilation2d = op({ dilation2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js\nfunction equal_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"equal\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"equal\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Equal, inputs);\n}\nvar equal = op({ equal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js\nfunction where_(condition, a6, b) {\n  const $a = convertToTensor(a6, \"a\", \"where\");\n  const $b = convertToTensor(b, \"b\", \"where\");\n  const $condition = convertToTensor(condition, \"condition\", \"where\", \"bool\");\n  const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);\n  const $broadcastedCondition = broadcastTo($condition, broadcastShape);\n  const $broadcastedA = broadcastTo($a, broadcastShape);\n  const $broadcastedB = broadcastTo($b, broadcastShape);\n  const inputs = {\n    condition: $broadcastedCondition,\n    t: $broadcastedA,\n    e: $broadcastedB\n  };\n  return ENGINE.runKernel(Select, inputs);\n}\nvar where = op({ where_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js\nfunction zerosLike_(x) {\n  const $x = convertToTensor(x, \"x\", \"zerosLike\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(ZerosLike, inputs);\n}\nvar zerosLike = op({ zerosLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js\nfunction divNoNan_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"div\");\n  let $b = convertToTensor(b, \"b\", \"div\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const divResult = div($a, $b);\n  const zeros4 = zerosLike(divResult);\n  const bEqualsZero = equal($b, zeros4);\n  return where(bEqualsZero, zeros4, divResult);\n}\nvar divNoNan = op({ divNoNan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js\nfunction dot_(t1, t2) {\n  const $t1 = convertToTensor(t1, \"t1\", \"dot\");\n  const $t2 = convertToTensor(t2, \"t2\", \"dot\");\n  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);\n  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];\n  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];\n  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, []);\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul($t1, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else {\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul($t1, t22D);\n    return t1t2;\n  }\n}\nvar dot = op({ dot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js\nfunction einsum_(equation, ...tensors) {\n  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, \"einsum\"));\n  const attrs = { equation };\n  return ENGINE.runKernel(Einsum, $tensors, attrs);\n}\nvar einsum = op({ einsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js\nfunction elu_(x) {\n  const $x = convertToTensor(x, \"x\", \"elu\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Elu, inputs);\n}\nvar elu = op({ elu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js\nfunction erf_(x) {\n  let $x = convertToTensor(x, \"x\", \"erf\");\n  assert($x.dtype === \"int32\" || $x.dtype === \"float32\", () => \"Input dtype must be `int32` or `float32`.\");\n  if ($x.dtype === \"int32\") {\n    $x = cast($x, \"float32\");\n  }\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Erf, inputs);\n}\nvar erf = op({ erf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js\nfunction axesAreInnerMostDims(axes, rank) {\n  for (let i2 = 0; i2 < axes.length; ++i2) {\n    if (axes[axes.length - i2 - 1] !== rank - 1 - i2) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction combineLocations(outputLoc, reduceLoc, axes) {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\nfunction computeOutAndReduceShapes(aShape, axes) {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map((dim) => aShape[dim]);\n  return [outShape, reduceShape];\n}\nfunction expandShapeToKeepDim(shape, axes) {\n  const reduceSubShape = axes.map((x) => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\nfunction assertAxesAreInnerMostDims(msg, axes, rank) {\n  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);\n}\nfunction getAxesPermutation(axes, rank) {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result = [];\n  for (let i2 = 0; i2 < rank; ++i2) {\n    if (axes.indexOf(i2) === -1) {\n      result.push(i2);\n    }\n  }\n  axes.forEach((axis) => result.push(axis));\n  return result;\n}\nfunction getUndoAxesPermutation(axes) {\n  return axes.map((axis, i2) => [i2, axis]).sort((a6, b) => a6[1] - b[1]).map((x) => x[0]);\n}\nfunction getInnerMostAxes(numAxes, rank) {\n  const res = [];\n  for (let i2 = rank - numAxes; i2 < rank; ++i2) {\n    res.push(i2);\n  }\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js\nfunction max_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"max\");\n  const inputs = { x: $x };\n  const attrs = { reductionIndices: axis, keepDims };\n  return ENGINE.runKernel(Max, inputs, attrs);\n}\nvar max = op({ max_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js\nfunction min_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"min\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Min, inputs, attrs);\n}\nvar min = op({ min_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js\nfunction pow_(base, exp5) {\n  let $base = convertToTensor(base, \"base\", \"pow\");\n  let $exp = convertToTensor(exp5, \"exp\", \"pow\");\n  [$base, $exp] = makeTypesMatch($base, $exp);\n  const inputs = { a: $base, b: $exp };\n  return ENGINE.runKernel(Pow, inputs);\n}\nvar pow = op({ pow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js\nfunction scalar(value, dtype) {\n  if ((isTypedArray(value) && dtype !== \"string\" || Array.isArray(value)) && dtype !== \"complex64\") {\n    throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");\n  }\n  if (dtype === \"string\" && isTypedArray(value) && !(value instanceof Uint8Array)) {\n    throw new Error(\"When making a scalar from encoded string, the value must be `Uint8Array`.\");\n  }\n  const shape = [];\n  const inferredShape = [];\n  return makeTensor(value, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js\nfunction sqrt_(x) {\n  const $x = convertToTensor(x, \"x\", \"sqrt\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sqrt, inputs);\n}\nvar sqrt = op({ sqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js\nfunction square_(x) {\n  const $x = convertToTensor(x, \"x\", \"square\");\n  const attrs = {};\n  return ENGINE.runKernel(\"Square\", { x: $x }, attrs);\n}\nvar square = op({ square_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js\nfunction sum_(x, axis = null, keepDims = false) {\n  let $x = convertToTensor(x, \"x\", \"sum\");\n  if ($x.dtype === \"bool\") {\n    $x = cast($x, \"int32\");\n  }\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Sum, inputs, attrs);\n}\nvar sum2 = op({ sum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js\nfunction norm_(x, ord = \"euclidean\", axis = null, keepDims = false) {\n  x = convertToTensor(x, \"x\", \"norm\");\n  const norm2 = normImpl(x, ord, axis);\n  let keepDimsShape = norm2.shape;\n  if (keepDims) {\n    const axes = parseAxisParam(axis, x.shape);\n    keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);\n  }\n  return reshape(norm2, keepDimsShape);\n}\nfunction normImpl(x, p2, axis = null) {\n  if (x.rank === 0) {\n    return abs(x);\n  }\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(reshape(x, [-1]), p2, axis);\n  }\n  if (x.rank === 1 || typeof axis === \"number\" || Array.isArray(axis) && axis.length === 1) {\n    if (p2 === 1) {\n      return sum2(abs(x), axis);\n    }\n    if (p2 === Infinity) {\n      return max(abs(x), axis);\n    }\n    if (p2 === -Infinity) {\n      return min(abs(x), axis);\n    }\n    if (p2 === \"euclidean\" || p2 === 2) {\n      return sqrt(sum2(pow(abs(x), scalar(2, \"int32\")), axis));\n    }\n    throw new Error(`Error in norm: invalid ord value: ${p2}`);\n  }\n  if (Array.isArray(axis) && axis.length === 2) {\n    if (p2 === 1) {\n      return max(sum2(abs(x), axis[0]), axis[1] - 1);\n    }\n    if (p2 === Infinity) {\n      return max(sum2(abs(x), axis[1]), axis[0]);\n    }\n    if (p2 === -Infinity) {\n      return min(sum2(abs(x), axis[1]), axis[0]);\n    }\n    if (p2 === \"fro\" || p2 === \"euclidean\") {\n      return sqrt(sum2(square(x), axis));\n    }\n    throw new Error(`Error in norm: invalid ord value: ${p2}`);\n  }\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nvar norm = op({ norm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js\nfunction euclideanNorm_(x, axis = null, keepDims = false) {\n  return norm(x, \"euclidean\", axis, keepDims);\n}\nvar euclideanNorm = op({ euclideanNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js\nfunction exp_(x) {\n  const $x = convertToTensor(x, \"x\", \"exp\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Exp, inputs);\n}\nvar exp = op({ exp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js\nfunction expandDims_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"expandDims\", \"string_or_numeric\");\n  assert(axis <= $x.rank, () => \"Axis must be <= rank of the tensor\");\n  const inputs = { input: $x };\n  const attrs = { dim: axis };\n  return ENGINE.runKernel(ExpandDims, inputs, attrs);\n}\nvar expandDims = op({ expandDims_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js\nfunction expm1_(x) {\n  const $x = convertToTensor(x, \"x\", \"expm1\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Expm1, inputs);\n}\nvar expm1 = op({ expm1_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js\nfunction tile_(x, reps) {\n  const $x = convertToTensor(x, \"x\", \"tile\", \"string_or_numeric\");\n  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);\n  const inputs = { x: $x };\n  const attrs = { reps };\n  return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar tile = op({ tile_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js\nfunction eye_(numRows, numColumns, batchShape, dtype = \"float32\") {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i2 = 0; i2 < n; ++i2) {\n    buff.set(1, i2, i2);\n  }\n  const out = reshape(buff.toTensor(), [numRows, numColumns]);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n    } else if (batchShape.length === 2) {\n      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n    } else if (batchShape.length === 3) {\n      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n        batchShape[0],\n        batchShape[1],\n        batchShape[2],\n        1,\n        1\n      ]);\n    } else {\n      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);\n    }\n  }\n}\nvar eye = op({ eye_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js\nfunction fill(shape, value, dtype) {\n  const attrs = { shape, value, dtype };\n  return ENGINE.runKernel(Fill, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js\nfunction floor_(x) {\n  const $x = convertToTensor(x, \"x\", \"floor\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Floor, inputs);\n}\nvar floor = op({ floor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js\nfunction gather_(x, indices, axis = 0, batchDims = 0) {\n  const $x = convertToTensor(x, \"x\", \"gather\");\n  const $indices = convertToTensor(indices, \"indices\", \"gather\", \"int32\");\n  const inputs = { x: $x, indices: $indices };\n  const attrs = { axis, batchDims };\n  return ENGINE.runKernel(GatherV2, inputs, attrs);\n}\nvar gather = op({ gather_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js\nfunction greater_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"greater\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"greater\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Greater, inputs);\n}\nvar greater = op({ greater_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js\nfunction greaterEqual_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"greaterEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"greaterEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(GreaterEqual, inputs);\n}\nvar greaterEqual = op({ greaterEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js\nfunction isFinite_(x) {\n  const $x = convertToTensor(x, \"x\", \"isFinite\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsFinite, inputs);\n}\nvar isFinite2 = op({ isFinite_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js\nfunction isInf_(x) {\n  const $x = convertToTensor(x, \"x\", \"isInf\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsInf, inputs);\n}\nvar isInf = op({ isInf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js\nfunction isNaN_(x) {\n  const $x = convertToTensor(x, \"x\", \"isNaN\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsNan, inputs);\n}\nvar isNaN2 = op({ isNaN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js\nfunction leakyRelu_(x, alpha = 0.2) {\n  const $x = convertToTensor(x, \"x\", \"leakyRelu\");\n  const inputs = { x: $x };\n  const attrs = { alpha };\n  return ENGINE.runKernel(LeakyRelu, inputs, attrs);\n}\nvar leakyRelu = op({ leakyRelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js\nfunction less_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"less\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"less\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Less, inputs);\n}\nvar less = op({ less_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js\nfunction lessEqual_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"lessEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"lessEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LessEqual, inputs);\n}\nvar lessEqual = op({ lessEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js\nfunction linspace(start, stop, num) {\n  if (num <= 0) {\n    throw new Error(\"The number of values should be positive.\");\n  }\n  const attrs = { start, stop, num };\n  return ENGINE.runKernel(LinSpace, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n  const $x = convertToTensor(x, \"x\", \"localResponseNormalization\");\n  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  const inputs = { x: x4D };\n  const attrs = { depthRadius, bias, alpha, beta };\n  const res = ENGINE.runKernel(LRN, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  } else {\n    return res;\n  }\n}\nvar localResponseNormalization = op({ localResponseNormalization_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js\nfunction log_(x) {\n  const $x = convertToTensor(x, \"x\", \"log\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Log, inputs);\n}\nvar log2 = op({ log_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js\nfunction log1p_(x) {\n  const $x = convertToTensor(x, \"x\", \"log1p\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Log1p, inputs);\n}\nvar log1p = op({ log1p_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js\nfunction grad(f) {\n  assert(isFunction(f), () => \"The f passed in grad(f) must be a function\");\n  return (x, dy) => {\n    const $x = convertToTensor(x, \"x\", \"tf.grad\", \"string_or_numeric\");\n    const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grad\") : null;\n    return ENGINE.tidy(() => {\n      const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy);\n      if ($dy != null) {\n        assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\");\n      }\n      checkGrads(grads2);\n      return grads2[0];\n    });\n  };\n}\nfunction grads(f) {\n  assert(isFunction(f), () => \"The f passed in grads(f) must be a function\");\n  return (args, dy) => {\n    assert(Array.isArray(args), () => \"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s\");\n    const $args = convertToTensorArray(args, \"args\", \"tf.grads\", \"string_or_numeric\");\n    const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grads\") : null;\n    return ENGINE.tidy(() => {\n      const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy);\n      if ($dy != null) {\n        assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n      }\n      checkGrads(grads2);\n      return grads2;\n    });\n  };\n}\nfunction valueAndGrad(f) {\n  assert(isFunction(f), () => \"The f passed in valueAndGrad(f) must be a function\");\n  return (x, dy) => {\n    assert(x instanceof Tensor, () => \"The x passed in valueAndGrad(f)(x) must be a tensor\");\n    assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");\n    const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy);\n    checkGrads(grads2);\n    return { grad: grads2[0], value };\n  };\n}\nfunction valueAndGrads(f) {\n  assert(isFunction(f), () => \"The f passed in valueAndGrads(f) must be a function\");\n  return (args, dy) => {\n    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => \"The args passed in valueAndGrads(f)(args) must be array of tensors\");\n    assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");\n    const res = ENGINE.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      assertShapesMatch(res.value.shape, dy.shape, \"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\nfunction variableGrads(f, varList) {\n  assert(isFunction(f), () => \"The f passed in variableGrads(f) must be a function\");\n  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => \"The varList passed in variableGrads(f, varList) must be an array of variables\");\n  const specifiedVarList = varList != null;\n  if (!specifiedVarList) {\n    varList = [];\n    for (const varName in ENGINE.registeredVariables) {\n      varList.push(ENGINE.registeredVariables[varName]);\n    }\n  }\n  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;\n  const originalVarCount = varList.length;\n  varList = varList.filter((variable2) => variable2.trainable);\n  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);\n  const allowNoGradients = true;\n  const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients);\n  assert(grads2.some((g) => g != null), () => \"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\");\n  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);\n  const namedGrads = {};\n  varList.forEach((v, i2) => {\n    if (grads2[i2] != null) {\n      namedGrads[v.name] = grads2[i2];\n    }\n  });\n  if (specifiedNonTrainable != null) {\n    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);\n  }\n  return { value, grads: namedGrads };\n}\nfunction customGrad(f) {\n  return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads2) {\n  const numNullGradients = grads2.filter((g) => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js\nfunction softplus_(x) {\n  const $x = convertToTensor(x, \"x\", \"softplus\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Softplus, inputs);\n}\nvar softplus = op({ softplus_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js\nfunction logSigmoid_(x) {\n  const $x = convertToTensor(x, \"x\", \"logSigmoid\");\n  const customOp = customGrad((x2) => {\n    const value = neg(softplus(neg(x2)));\n    const gradFunc = (dy) => {\n      const derX = mul(dy, sigmoid(neg(x2)));\n      return derX;\n    };\n    return { value, gradFunc };\n  });\n  return customOp($x);\n}\nvar logSigmoid = op({ logSigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js\nfunction sub_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"sub\");\n  let $b = convertToTensor(b, \"b\", \"sub\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Sub, inputs);\n}\nvar sub = op({ sub_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js\nfunction logSoftmax_(logits, axis = -1) {\n  const $logits = convertToTensor(logits, \"logits\", \"logSoftmax\");\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n  const customOp = customGrad((logits2, save) => {\n    const keepDims = true;\n    const xMax = max(logits2, axis, true);\n    const shifted = sub(logits2, xMax);\n    const value = sub(cast(shifted, \"float32\"), log2(sum2(exp(shifted), axis, keepDims)));\n    save([value]);\n    const gradFunc = (dy, saved) => {\n      const [value2] = saved;\n      const keepDims2 = true;\n      const softmax7 = exp(value2);\n      return sub(dy, mul(sum2(dy, axis, keepDims2), softmax7));\n    };\n    return { value, gradFunc };\n  });\n  return customOp($logits);\n}\nvar logSoftmax = op({ logSoftmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js\nfunction logSumExp_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"logSumExp\");\n  const axes = parseAxisParam(axis, $x.shape);\n  const xMax = max($x, axes, true);\n  const a6 = sub($x, xMax);\n  const b = exp(a6);\n  const c = sum2(b, axes);\n  const d = log2(c);\n  const res = add2(reshape(xMax, d.shape), d);\n  if (keepDims) {\n    const newShape = expandShapeToKeepDim(res.shape, axes);\n    return reshape(res, newShape);\n  }\n  return res;\n}\nvar logSumExp = op({ logSumExp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js\nfunction logicalAnd_(a6, b) {\n  const $a = convertToTensor(a6, \"a\", \"logicalAnd\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalAnd\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LogicalAnd, inputs);\n}\nvar logicalAnd = op({ logicalAnd_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js\nfunction logicalNot_(x) {\n  const $x = convertToTensor(x, \"x\", \"logicalNot\", \"bool\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(LogicalNot, inputs);\n}\nvar logicalNot = op({ logicalNot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js\nfunction logicalOr_(a6, b) {\n  const $a = convertToTensor(a6, \"a\", \"logicalOr\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalOr\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LogicalOr, inputs);\n}\nvar logicalOr = op({ logicalOr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js\nfunction logicalXor_(a6, b) {\n  const $a = convertToTensor(a6, \"a\", \"logicalXor\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalXor\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  return logicalAnd(logicalOr(a6, b), logicalNot(logicalAnd(a6, b)));\n}\nvar logicalXor = op({ logicalXor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/search_sorted.js\nvar INT32_MAX = 2147483648;\nfunction searchSorted_(sortedSequence, values, side = \"left\") {\n  const $sortedSequence = convertToTensor(sortedSequence, \"sortedSequence\", \"searchSorted\");\n  const $values = convertToTensor(values, \"values\", \"searchSorted\");\n  const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];\n  const valuesSize = $values.shape[$values.shape.length - 1];\n  const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);\n  const $values2D = reshape($values, [-1, valuesSize]);\n  if ($sortedSequence2D.rank < 2) {\n    throw new Error(`Sorted input argument must be at least 2-dimensional`);\n  }\n  if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {\n    throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);\n  }\n  if (sizeFromShape($values2D.shape) >= INT32_MAX) {\n    throw new Error(`values tensor size must less than ${INT32_MAX}`);\n  }\n  if ($sortedSequence2D.shape[1] >= INT32_MAX) {\n    throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);\n  }\n  const inputs = {\n    sortedSequence: $sortedSequence2D,\n    values: $values2D\n  };\n  const attrs = { side };\n  return ENGINE.runKernel(SearchSorted, inputs, attrs);\n}\nvar searchSorted = op({ searchSorted_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/lower_bound.js\nfunction lowerBound(sortedSequence, values) {\n  return searchSorted(sortedSequence, values, \"left\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js\nfunction maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"maxPool\");\n  const dilations = 1;\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  checkPadOnDimRoundingMode(\"maxPool\", pad3, dimRoundingMode);\n  const inputs = { x: x4D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(MaxPool, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar maxPool = op({ maxPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n  const $x = convertToTensor(x, \"x\", \"maxPool3d\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  assert(dataFormat === \"NDHWC\", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode(\"maxPool3d\", pad3, dimRoundingMode);\n  const inputs = { x: x5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n  const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar maxPool3d = op({ maxPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) {\n  const $x = convertToTensor(x, \"x\", \"maxPoolWithArgmax\");\n  const inputs = { x: $x };\n  const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex };\n  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n  return { result: result[0], indexes: result[1] };\n}\nvar maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js\nfunction maximum_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"maximum\");\n  let $b = convertToTensor(b, \"b\", \"maximum\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"bool\") {\n    $a = cast($a, \"int32\");\n    $b = cast($b, \"int32\");\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Maximum, inputs);\n}\nvar maximum = op({ maximum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js\nfunction mean_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"mean\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Mean, inputs, attrs);\n}\nvar mean = op({ mean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js\nfunction zeros(shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = zeros(shape, \"float32\");\n    const imag5 = zeros(shape, \"float32\");\n    return complex(real5, imag5);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js\nfunction ones2(shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = ones2(shape, \"float32\");\n    const imag5 = zeros(shape, \"float32\");\n    return complex(real5, imag5);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/meshgrid.js\nfunction meshgrid(x, y, { indexing = \"xy\" } = {}) {\n  if (indexing !== \"xy\" && indexing !== \"ij\") {\n    throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);\n  }\n  if (x === void 0) {\n    return [];\n  }\n  let $x = convertToTensor(x, \"x\", \"meshgrid\", x instanceof Tensor ? x.dtype : \"float32\");\n  if (y === void 0) {\n    return [$x];\n  }\n  let $y = convertToTensor(y, \"y\", \"meshgrid\", y instanceof Tensor ? y.dtype : \"float32\");\n  const w = sizeFromShape($x.shape);\n  const h = sizeFromShape($y.shape);\n  if (indexing === \"xy\") {\n    $x = reshape($x, [1, -1]);\n    $y = reshape($y, [-1, 1]);\n    return [\n      matMul(ones2([h, 1], $x.dtype), $x),\n      matMul($y, ones2([1, w], $y.dtype))\n    ];\n  }\n  $x = reshape($x, [-1, 1]);\n  $y = reshape($y, [1, -1]);\n  return [\n    matMul($x, ones2([1, h], $x.dtype)),\n    matMul(ones2([w, 1], $y.dtype), $y)\n  ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js\nfunction minimum_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"minimum\");\n  let $b = convertToTensor(b, \"b\", \"minimum\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"bool\") {\n    $a = cast($a, \"int32\");\n    $b = cast($b, \"int32\");\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Minimum, inputs);\n}\nvar minimum = op({ minimum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js\nfunction mirrorPad_(x, paddings, mode) {\n  assert(mode === \"reflect\" || mode === \"symmetric\", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);\n  const $x = convertToTensor(x, \"x\", \"mirrorPad\");\n  if ($x.rank === 0) {\n    throw new Error(\"mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad\");\n  }\n  assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);\n  const shapeOffset = mode === \"reflect\" ? 1 : 0;\n  for (let i2 = 0; i2 < $x.rank; i2++) {\n    assert(paddings[i2].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n    assert(paddings[i2][0] >= 0 && paddings[i2][0] <= $x.shape[i2] - shapeOffset && paddings[i2][1] >= 0 && paddings[i2][1] <= $x.shape[i2] - shapeOffset, () => `Padding in dimension ${i2} cannot be greater than or equal to ${$x.shape[i2] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);\n  }\n  const attrs = { paddings, mode };\n  const inputs = { x: $x };\n  return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nvar mirrorPad = op({ mirrorPad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js\nfunction mod_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"mod\");\n  let $b = convertToTensor(b, \"b\", \"mod\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Mod, inputs);\n}\nvar mod = op({ mod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js\nfunction moments_(x, axis = null, keepDims = false) {\n  x = convertToTensor(x, \"x\", \"moments\");\n  const axes = parseAxisParam(axis, x.shape);\n  const xMean = mean(x, axes, keepDims);\n  let keepDimsShape = xMean.shape;\n  if (!keepDims) {\n    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n  }\n  const devSquared = square(sub(cast(x, \"float32\"), reshape(xMean, keepDimsShape)));\n  const variance = mean(devSquared, axes, keepDims);\n  return { mean: xMean, variance };\n}\nvar moments = op({ moments_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js\nfunction multiRNNCell_(lstmCells, data, c, h) {\n  const $data = convertToTensor(data, \"data\", \"multiRNNCell\");\n  const $c = convertToTensorArray(c, \"c\", \"multiRNNCell\");\n  const $h = convertToTensorArray(h, \"h\", \"multiRNNCell\");\n  let input2 = $data;\n  const newStates = [];\n  for (let i2 = 0; i2 < lstmCells.length; i2++) {\n    const output = lstmCells[i2](input2, $c[i2], $h[i2]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input2 = output[1];\n  }\n  const newC = [];\n  const newH = [];\n  for (let i2 = 0; i2 < newStates.length; i2 += 2) {\n    newC.push(newStates[i2]);\n    newH.push(newStates[i2 + 1]);\n  }\n  return [newC, newH];\n}\nvar multiRNNCell = op({ multiRNNCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n  const $logits = convertToTensor(logits, \"logits\", \"multinomial\");\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n  const inputs = { logits: logits2D };\n  const attrs = { numSamples, seed, normalized };\n  const res = ENGINE.runKernel(Multinomial, inputs, attrs);\n  return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nvar multinomial = op({ multinomial_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js\nfunction notEqual_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"notEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"notEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(NotEqual, inputs);\n}\nvar notEqual = op({ notEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js\nfunction onesLike_(x) {\n  const $x = convertToTensor(x, \"x\", \"onesLike\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(OnesLike, inputs);\n}\nvar onesLike = op({ onesLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js\nfunction outerProduct_(v1, v2) {\n  const $v1 = convertToTensor(v1, \"v1\", \"outerProduct\");\n  const $v2 = convertToTensor(v2, \"v2\", \"outerProduct\");\n  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);\n  const v12D = reshape($v1, [-1, 1]);\n  const v22D = reshape($v2, [1, -1]);\n  return matMul(v12D, v22D);\n}\nvar outerProduct = op({ outerProduct_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js\nfunction pad_(x, paddings, constantValue = 0) {\n  const $x = convertToTensor(x, \"x\", \"pad\");\n  if ($x.rank === 0) {\n    throw new Error(\"pad(scalar) is not defined. Pass non-scalar to pad\");\n  }\n  const attrs = { paddings, constantValue };\n  const inputs = { x: $x };\n  return ENGINE.runKernel(PadV2, inputs, attrs);\n}\nvar pad = op({ pad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js\nfunction pad1d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 2, () => \"Invalid number of paddings. Must be length of 2.\");\n  return pad(x, [paddings], constantValue);\n}\nvar pad1d = op({ pad1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js\nfunction pad2d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad2d = op({ pad2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js\nfunction pad3d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad3d = op({ pad3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js\nfunction pad4d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad4d = op({ pad4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js\nfunction spaceToBatchND_(x, blockShape, paddings) {\n  const $x = convertToTensor(x, \"x\", \"spaceToBatchND\");\n  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n  assert($x.shape.reduce((a6, b, i2) => {\n    if (i2 > 0 && i2 <= blockShape.length) {\n      return a6 && (b + paddings[i2 - 1][0] + paddings[i2 - 1][1]) % blockShape[i2 - 1] === 0;\n    }\n    return a6;\n  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n  const inputs = { x: $x };\n  const attrs = { blockShape, paddings };\n  return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);\n}\nvar spaceToBatchND = op({ spaceToBatchND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js\nfunction pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad3 === 0) {\n    pad3 = \"valid\";\n  }\n  const $x = convertToTensor(input2, \"x\", \"maxPool\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3);\n  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n  let basePadding;\n  if (pad3 === \"same\") {\n    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad3 : \"valid\";\n  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n  const forwardOp = poolingType === \"avg\" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);\n  const y = forwardOp();\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n  const padStart = basePadding.map((b) => b[0]);\n  const origPadEnd = basePadding.map((b) => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i2) => (b - fullInputShape[i2] % b) % b);\n  const padEnd = origPadEnd.map((s2, i2) => s2 + padEndExtra[i2]);\n  const paddings = blockShape.map((_, i2) => [padStart[i2], padEnd[i2]]);\n  const crops = blockShape.map((_, i2) => [0, padEndExtra[i2]]);\n  return [paddings, crops];\n}\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n  const dilatedFilterShape = filterShape.map((s2, i2) => {\n    return s2 + (s2 - 1) * (dilation[i2] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map((s2) => s2 - 1);\n  const padExtraStart = padExtraShape.map((s2) => Math.floor(s2 / 2));\n  const padExtraEnd = padExtraShape.map((s2, i2) => s2 - padExtraStart[i2]);\n  return padExtraShape.map((_, i2) => {\n    return [padExtraStart[i2], padExtraEnd[i2]];\n  });\n}\nvar pool = op({ pool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js\nfunction prelu_(x, alpha) {\n  const $x = convertToTensor(x, \"x\", \"prelu\");\n  const $alpha = convertToTensor(alpha, \"alpha\", \"prelu\");\n  const inputs = { x: $x, alpha: $alpha };\n  return ENGINE.runKernel(Prelu, inputs);\n}\nvar prelu = op({ prelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js\nfunction prod_(x, axis = null, keepDims = false) {\n  let $x = convertToTensor(x, \"x\", \"prod\");\n  if ($x.dtype === \"bool\") {\n    $x = cast($x, \"int32\");\n  }\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Prod, inputs, attrs);\n}\nvar prod = op({ prod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js\nfunction rand_(shape, randFunction, dtype) {\n  const size = sizeFromShape(shape);\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  for (let i2 = 0; i2 < size; i2++) {\n    values[i2] = randFunction();\n  }\n  return ENGINE.makeTensor(values, shape, dtype);\n}\nvar rand = op({ rand_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js\nvar seedrandom = __toESM(require_seedrandom2());\nvar MPRandGauss = class {\n  constructor(mean5, stdDeviation, dtype, truncated, seed) {\n    this.mean = mean5;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n  nextValue() {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n    let resultX, resultY;\n    let isValid = false;\n    while (!isValid) {\n      let v1, v2, s2;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s2 = v1 * v1 + v2 * v2;\n      } while (s2 >= 1 || s2 === 0);\n      const mul2 = Math.sqrt(-2 * Math.log(s2) / s2);\n      resultX = this.mean + this.stdDev * v1 * mul2;\n      resultY = this.mean + this.stdDev * v2 * mul2;\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n  convertValue(value) {\n    if (this.dtype == null || this.dtype === \"float32\") {\n      return value;\n    }\n    return Math.round(value);\n  }\n  isValidTruncated(value) {\n    return value <= this.upper && value >= this.lower;\n  }\n};\nvar RandGamma = class {\n  constructor(alpha, beta, dtype, seed) {\n    this.alpha = alpha;\n    this.beta = 1 / beta;\n    this.dtype = dtype;\n    const seedValue = seed ? seed : Math.random();\n    this.randu = seedrandom.alea(seedValue.toString());\n    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n    if (alpha < 1) {\n      this.d = alpha + 2 / 3;\n    } else {\n      this.d = alpha - 1 / 3;\n    }\n    this.c = 1 / Math.sqrt(9 * this.d);\n  }\n  nextValue() {\n    let x2, v0, v1, x, u, v;\n    while (true) {\n      do {\n        x = this.randn.nextValue();\n        v = 1 + this.c * x;\n      } while (v <= 0);\n      v *= v * v;\n      x2 = x * x;\n      v0 = 1 - 0.331 * x2 * x2;\n      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));\n      u = this.randu();\n      if (u < v0 || Math.log(u) < v1) {\n        break;\n      }\n    }\n    v = 1 / this.beta * this.d * v;\n    if (this.alpha < 1) {\n      v *= Math.pow(this.randu(), 1 / this.alpha);\n    }\n    return this.convertValue(v);\n  }\n  convertValue(value) {\n    if (this.dtype === \"float32\") {\n      return value;\n    }\n    return Math.round(value);\n  }\n};\nvar UniformRandom = class {\n  constructor(min7 = 0, max7 = 1, dtype, seed) {\n    this.canReturnFloat = () => this.dtype == null || this.dtype === \"float32\";\n    this.min = min7;\n    this.range = max7 - min7;\n    this.dtype = dtype;\n    if (seed == null) {\n      seed = Math.random();\n    }\n    if (typeof seed === \"number\") {\n      seed = seed.toString();\n    }\n    if (!this.canReturnFloat() && this.range <= 1) {\n      throw new Error(`The difference between ${min7} - ${max7} <= 1 and dtype is not float`);\n    }\n    this.random = seedrandom.alea(seed);\n  }\n  convertValue(value) {\n    if (this.canReturnFloat()) {\n      return value;\n    }\n    return Math.round(value);\n  }\n  nextValue() {\n    return this.convertValue(this.min + this.range * this.random());\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js\nfunction randomGamma_(shape, alpha, beta = 1, dtype = \"float32\", seed) {\n  if (beta == null) {\n    beta = 1;\n  }\n  if (dtype == null) {\n    dtype = \"float32\";\n  }\n  if (dtype !== \"float32\" && dtype !== \"int32\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const rgamma = new RandGamma(alpha, beta, dtype, seed);\n  const res = buffer(shape, dtype);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = rgamma.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomGamma = op({ randomGamma_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js\nfunction randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);\n  const res = buffer(shape, dtype);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomNormal = op({ randomNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = \"float32\", seed) {\n  const res = buffer(shape, dtype);\n  const random = new UniformRandom(minval, maxval, null, seed);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = random.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomUniform = op({ randomUniform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js\nfunction range(start, stop, step5 = 1, dtype = \"float32\") {\n  if (step5 === 0) {\n    throw new Error(\"Cannot have a step of zero\");\n  }\n  const attrs = { start, stop, step: step5, dtype };\n  return ENGINE.runKernel(Range, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js\nfunction reciprocal_(x) {\n  const $x = convertToTensor(x, \"x\", \"reciprocal\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Reciprocal, inputs);\n}\nvar reciprocal = op({ reciprocal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js\nfunction relu_(x) {\n  const $x = convertToTensor(x, \"x\", \"relu\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Relu, inputs);\n}\nvar relu = op({ relu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js\nfunction relu6_(x) {\n  const $x = convertToTensor(x, \"x\", \"relu6\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Relu6, inputs);\n}\nvar relu6 = op({ relu6_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js\nfunction reverse_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  const inputs = { x: $x };\n  const attrs = { dims: axis };\n  return ENGINE.runKernel(Reverse, inputs, attrs);\n}\nvar reverse = op({ reverse_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js\nfunction reverse1d_(x) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\nvar reverse1d = op({ reverse1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js\nfunction reverse2d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse2d = op({ reverse2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js\nfunction reverse3d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse3d = op({ reverse3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js\nfunction reverse4d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse4d = op({ reverse4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js\nfunction round_(x) {\n  const $x = convertToTensor(x, \"x\", \"round\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Round, inputs);\n}\nvar round2 = op({ round_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js\nfunction rsqrt_(x) {\n  const $x = convertToTensor(x, \"x\", \"rsqrt\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Rsqrt, inputs);\n}\nvar rsqrt = op({ rsqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js\nfunction selu_(x) {\n  const $x = convertToTensor(x, \"x\", \"selu\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Selu, inputs);\n}\nvar selu = op({ selu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"separableConv2d\");\n  const $depthwiseFilter = convertToTensor(depthwiseFilter, \"depthwiseFilter\", \"separableConv2d\");\n  const $pointwiseFilter = convertToTensor(pointwiseFilter, \"pointwiseFilter\", \"separableConv2d\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  if (dataFormat === \"NCHW\") {\n    throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");\n  }\n  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);\n  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, \"valid\", dataFormat);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar separableConv2d = op({ separableConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js\nasync function setdiff1dAsync_(x, y) {\n  const $x = convertToTensor(x, \"x\", \"setdiff1d\");\n  const $y = convertToTensor(y, \"y\", \"setdiff1d\");\n  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n  let outputSize = 0;\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    if (!ySet.has(xVals[i2])) {\n      outputSize++;\n    }\n  }\n  const buffer2 = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], \"int32\");\n  for (let i2 = 0, p2 = 0; i2 < xVals.length; i2++) {\n    if (!ySet.has(xVals[i2])) {\n      buffer2.values[p2] = xVals[i2];\n      indices.values[p2] = i2;\n      p2++;\n    }\n  }\n  return [buffer2.toTensor(), indices.toTensor()];\n}\nvar setdiff1dAsync = setdiff1dAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js\nfunction sign_(x) {\n  const $x = convertToTensor(x, \"x\", \"sign\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sign, inputs);\n}\nvar sign = op({ sign_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js\nfunction sin_(x) {\n  const $x = convertToTensor(x, \"x\", \"sin\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sin, inputs);\n}\nvar sin = op({ sin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js\nfunction sinh_(x) {\n  const $x = convertToTensor(x, \"x\", \"sinh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sinh, inputs);\n}\nvar sinh = op({ sinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js\nfunction slice1d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice1d\");\n  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\nvar slice1d = op({ slice1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js\nfunction slice2d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice2d\");\n  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice2d = op({ slice2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js\nfunction slice3d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice3d\");\n  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice3d = op({ slice3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js\nfunction slice4d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice4d\");\n  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice4d = op({ slice4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js\nfunction softmax_(logits, dim = -1) {\n  const $logits = convertToTensor(logits, \"logits\", \"softmax\", \"float32\");\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n  const inputs = { logits: $logits };\n  const attrs = { dim };\n  return ENGINE.runKernel(Softmax, inputs, attrs);\n}\nvar softmax = op({ softmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js\nfunction fft_(input2) {\n  assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);\n  const inputs = { input: input2 };\n  return ENGINE.runKernel(FFT, inputs);\n}\nvar fft = op({ fft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js\nfunction ifft_(input2) {\n  assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);\n  const inputs = { input: input2 };\n  return ENGINE.runKernel(IFFT, inputs);\n}\nvar ifft = op({ ifft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js\nfunction irfft_(input2) {\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = input2.size / innerDimensionSize;\n  let ret;\n  if (innerDimensionSize <= 2) {\n    const complexInput = reshape(input2, [batch, innerDimensionSize]);\n    ret = ifft(complexInput);\n  } else {\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = reshape(real(input2), [batch, innerDimensionSize]);\n    const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);\n    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n    const r2 = concat([realInput, realConjugate], 1);\n    const i2 = concat([imagInput, imagConjugate], 1);\n    const complexInput = reshape(complex(r2, i2), [outputShape[0], outputShape[1]]);\n    ret = ifft(complexInput);\n  }\n  ret = real(ret);\n  if (input2.rank === 3 && input2.shape[0] !== 0) {\n    const temp = ret;\n    const batch2 = input2.shape[0];\n    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);\n    temp.dispose();\n  }\n  return ret;\n}\nvar irfft = op({ irfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js\nfunction split_(x, numOrSizeSplits, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"split\");\n  const inputs = { x: $x };\n  const attr = { numOrSizeSplits, axis };\n  return ENGINE.runKernel(SplitV, inputs, attr);\n}\nvar split = op({ split_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js\nfunction rfft_(input2, fftLength) {\n  assert(input2.dtype === \"float32\", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);\n  let innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = input2.size / innerDimensionSize;\n  let adjustedInput;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    const begin = input2.shape.map((v) => 0);\n    const size = input2.shape.map((v) => v);\n    size[input2.shape.length - 1] = fftLength;\n    adjustedInput = slice(input2, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    const zerosShape = input2.shape.map((v) => v);\n    zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input2;\n  }\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n  const ret = fft(complexInput);\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nvar rfft = op({ rfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js\nfunction squaredDifference_(a6, b) {\n  let $a = convertToTensor(a6, \"a\", \"squaredDifference\");\n  let $b = convertToTensor(b, \"b\", \"squaredDifference\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  const attrs = {};\n  return ENGINE.runKernel(SquaredDifference, inputs, attrs);\n}\nvar squaredDifference = op({ squaredDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js\nfunction squeeze_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"squeeze\");\n  return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nvar squeeze = op({ squeeze_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js\nfunction stack_(tensors, axis = 0) {\n  const $tensors = convertToTensorArray(tensors, \"tensors\", \"stack\", \"string_or_numeric\");\n  assert($tensors.length >= 1, () => \"Pass at least one tensor to tf.stack\");\n  if ($tensors.length > 0) {\n    assert(axis <= $tensors[0].rank, () => \"Axis must be <= rank of the tensor\");\n  }\n  const inputs = $tensors;\n  const attrs = { axis };\n  return ENGINE.runKernel(Pack, inputs, attrs);\n}\nvar stack = op({ stack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js\nfunction step_(x, alpha = 0) {\n  const $x = convertToTensor(x, \"x\", \"step\");\n  const inputs = { x: $x };\n  const attrs = { alpha };\n  return ENGINE.runKernel(Step, inputs, attrs);\n}\nvar step = op({ step_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n  const $x = convertToTensor(x, \"x\", \"stridedSlice\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  const attrs = {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n  return ENGINE.runKernel(StridedSlice, inputs, attrs);\n}\nvar stridedSlice = op({ stridedSlice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js\nfunction tan_(x) {\n  const $x = convertToTensor(x, \"x\", \"tan\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Tan, inputs);\n}\nvar tan = op({ tan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js\nfunction tensor1d(values, dtype) {\n  assertNonNull(values);\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 1) {\n    throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");\n  }\n  const shape = null;\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js\nfunction tensor2d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error(\"tensor2d() requires shape to have two numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js\nfunction tensor4d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error(\"tensor4d() requires shape to have four numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js\nfunction tensor5d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error(\"tensor5d() requires shape to have five numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js\nfunction tensor6d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error(\"tensor6d() requires shape to have six numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\"tensor6d() requires values to be number[][][][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");\n  }\n  shape = shape || inferredShape;\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js\nfunction topk_(x, k = 1, sorted = true) {\n  const $x = convertToTensor(x, \"x\", \"topk\");\n  if ($x.rank === 0) {\n    throw new Error(\"topk() expects the input to be of rank 1 or higher\");\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k < 0) {\n    throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);\n  }\n  if (k > lastDim) {\n    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);\n  }\n  const inputs = { x: $x };\n  const attrs = { k, sorted };\n  const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);\n  return { values, indices };\n}\nvar topk = op({ topk_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js\nfunction truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type $ { dtype }`);\n  }\n  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);\n  const res = buffer(shape, dtype);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\nvar truncatedNormal = op({ truncatedNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js\nfunction unique_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"unique\", \"string_or_numeric\");\n  assert($x.rank > 0, () => \"The input tensor must be at least 1D\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n  return { values, indices };\n}\nvar unique = op({ unique_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n  const $x = convertToTensor(x, \"x\", \"unsortedSegmentSum\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"unsortedSegmentSum\", \"int32\");\n  assert(isInt(numSegments), () => \"numSegments must be of dtype int\");\n  const inputs = { x: $x, segmentIds: $segmentIds };\n  const attrs = { numSegments };\n  return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);\n}\nvar unsortedSegmentSum = op({ unsortedSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js\nfunction unstack_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"unstack\", \"string_or_numeric\");\n  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n  const inputs = { value: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(Unpack, inputs, attrs);\n}\nvar unstack = op({ unstack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/upper_bound.js\nfunction upperBound(sortedSequence, values) {\n  return searchSorted(sortedSequence, values, \"right\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js\nfunction variable(initialValue, trainable = true, name, dtype) {\n  return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js\nfunction whereImpl(condShape, condVals) {\n  const indices = [];\n  for (let i2 = 0; i2 < condVals.length; i2++) {\n    if (condVals[i2]) {\n      indices.push(i2);\n    }\n  }\n  const inBuffer = buffer(condShape, \"int32\");\n  const out = buffer([indices.length, condShape.length], \"int32\");\n  for (let i2 = 0; i2 < indices.length; i2++) {\n    const loc = inBuffer.indexToLoc(indices[i2]);\n    const offset = i2 * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js\nasync function whereAsync_(condition) {\n  const $condition = convertToTensor(condition, \"condition\", \"whereAsync\", \"bool\");\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\nvar whereAsync = whereAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js\nasync function booleanMaskAsync_(tensor2, mask, axis) {\n  const $tensor = convertToTensor(tensor2, \"tensor\", \"boolMask\");\n  const $mask = convertToTensor(mask, \"mask\", \"boolMask\", \"bool\");\n  const axisFrom = axis == null ? 0 : axis;\n  const maskDim = $mask.rank;\n  const tensorShape = $tensor.shape;\n  assert(maskDim > 0, () => \"mask cannot be scalar\");\n  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n  let leadingSize = 1;\n  for (let i2 = axisFrom; i2 < axisFrom + maskDim; i2++) {\n    leadingSize *= tensorShape[i2];\n  }\n  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n  const reshapedTensor = reshape($tensor, targetTensorShape);\n  const reshapedMask = reshape($mask, [-1]);\n  const positivePositions = await whereAsync(reshapedMask);\n  const indices = squeeze(positivePositions, [1]);\n  const res = gather(reshapedTensor, indices, axisFrom);\n  if (tensor2 !== $tensor) {\n    $tensor.dispose();\n  }\n  if (mask !== $mask) {\n    $mask.dispose();\n  }\n  indices.dispose();\n  reshapedTensor.dispose();\n  reshapedMask.dispose();\n  positivePositions.dispose();\n  return res;\n}\nvar booleanMaskAsync = booleanMaskAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js\nfunction movingAverage_(v, x, decay, step5, zeroDebias = true) {\n  const $v = convertToTensor(v, \"v\", \"movingAverage\");\n  const $x = convertToTensor(x, \"x\", \"movingAverage\");\n  const $decay = convertToTensor(decay, \"decay\", \"movingAverage\");\n  assertTypesMatch($v, $x);\n  assert(arraysEqual($v.shape, $x.shape), () => \"Shape mismatch in v and x\");\n  const one = scalar(1);\n  const oneMinusDecay = sub(one, $decay);\n  let update = mul(sub($x, $v), oneMinusDecay);\n  if (zeroDebias) {\n    assert(step5 != null, () => \"When using zeroDebias: true, step is required.\");\n    const $step = convertToTensor(step5, \"step\", \"movingAverage\");\n    update = div(update, sub(one, pow($decay, $step)));\n  }\n  return add2($v, update);\n}\nvar movingAverage = op({ movingAverage_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js\nfunction scatterND_(indices, updates, shape) {\n  const $indices = convertToTensor(indices, \"indices\", \"scatterND\", \"int32\");\n  const $updates = convertToTensor(updates, \"updates\", \"scatterND\");\n  validateInput($updates, $indices, shape);\n  const inputs = { indices: $indices, updates: $updates };\n  const attrs = { shape };\n  return ENGINE.runKernel(ScatterNd, inputs, attrs);\n}\nvar scatterND = op({ scatterND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js\nfunction validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {\n  if (sparseIndices.dtype !== \"int32\") {\n    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);\n  }\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n  if (outputShape.length !== numDims) {\n    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);\n  }\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error(\"sparseValues.dtype must match defaultValues.dtype\");\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n  const $sparseIndices = convertToTensor(sparseIndices, \"sparseIndices\", \"sparseToDense\", \"int32\");\n  const $sparseValues = convertToTensor(sparseValues, \"sparseValues\", \"sparseToDense\", \"string_or_numeric\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseToDense\", $sparseValues.dtype);\n  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);\n  const inputs = {\n    sparseIndices: $sparseIndices,\n    sparseValues: $sparseValues,\n    defaultValue: $defaultValue\n  };\n  const attrs = { outputShape };\n  return ENGINE.runKernel(SparseToDense, inputs, attrs);\n}\nvar sparseToDense = op({ sparseToDense_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js\nfunction gatherND_(x, indices) {\n  const $indices = convertToTensor(indices, \"indices\", \"gatherND\", \"int32\");\n  const $x = convertToTensor(x, \"x\", \"gatherND\", \"string_or_numeric\");\n  const inputs = { params: $x, indices: $indices };\n  return ENGINE.runKernel(GatherNd, inputs);\n}\nvar gatherND = op({ gatherND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js\nfunction getNoiseShape(x, noiseShape) {\n  if (noiseShape == null) {\n    return x.shape.slice();\n  }\n  if (arraysEqual(x.shape, noiseShape)) {\n    return noiseShape;\n  }\n  if (x.shape.length === noiseShape.length) {\n    const newDimension = [];\n    for (let i2 = 0; i2 < x.shape.length; i2++) {\n      if (noiseShape[i2] == null && x.shape[i2] != null) {\n        newDimension.push(x.shape[i2]);\n      } else {\n        newDimension.push(noiseShape[i2]);\n      }\n    }\n    return newDimension;\n  }\n  return noiseShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js\nfunction dropout_(x, rate, noiseShape, seed) {\n  const $x = convertToTensor(x, \"x\", \"dropout\");\n  assert($x.dtype === \"float32\", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);\n  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n  if (rate === 0) {\n    return x instanceof Tensor ? $x.clone() : $x;\n  }\n  const $noiseShape = getNoiseShape($x, noiseShape);\n  const keepProb = 1 - rate;\n  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, \"float32\", seed), keepProb)), keepProb);\n  return mul($x, multiplier);\n}\nvar dropout = op({ dropout_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js\nfunction enclosingPowerOfTwo(value) {\n  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));\n}\nfunction cosineWindow(windowLength, a6, b) {\n  const even = 1 - windowLength % 2;\n  const newValues = new Float32Array(windowLength);\n  for (let i2 = 0; i2 < windowLength; ++i2) {\n    const cosArg = 2 * Math.PI * i2 / (windowLength + even - 1);\n    newValues[i2] = a6 - b * Math.cos(cosArg);\n  }\n  return tensor1d(newValues, \"float32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js\nasync function inTopKAsync_(predictions, targets, k = 1) {\n  const $predictions = convertToTensor(predictions, \"predictions\", \"inTopK\");\n  const $targets = convertToTensor(targets, \"targets\", \"inTopK\");\n  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);\n  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision3 = getTypedArrayFromDType(\"bool\", batch);\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd = [];\n    for (let i2 = 0; i2 < vals.length; i2++) {\n      valAndInd.push({ value: vals[i2], index: i2 });\n    }\n    valAndInd.sort((a6, b2) => b2.value - a6.value);\n    precision3[b] = 0;\n    for (let i2 = 0; i2 < k; i2++) {\n      if (valAndInd[i2].index === targetsVals[b]) {\n        precision3[b] = 1;\n        break;\n      }\n    }\n  }\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n  return tensor(precision3, $targets.shape, \"bool\");\n}\nvar inTopKAsync = inTopKAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js\nvar fused_ops_exports = {};\n__export(fused_ops_exports, {\n  conv2d: () => conv2d2,\n  depthwiseConv2d: () => depthwiseConv2d2,\n  matMul: () => matMul2\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n  let x4D = x;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);\n  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);\n  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);\n  const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);\n  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);\n  checkPadOnDimRoundingMode(\"conv2dDerFilter\", pad3, dimRoundingMode);\n  const inputs = { x: x4D, dy: dy4D };\n  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape };\n  return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);\n}\nvar conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js\nfunction getFusedDyActivation(dy, y, activation2) {\n  if (activation2 == null || activation2 === \"linear\") {\n    return dy;\n  }\n  if (activation2 === \"relu\") {\n    return mul(dy, step(y));\n  }\n  throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);\n}\nfunction getFusedBiasGradient(bias, dyActivation) {\n  let res = dyActivation;\n  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);\n  if (reduceAxes.length > 0) {\n    res = sum2(res, reduceAxes);\n  }\n  return reshape(res, bias.shape);\n}\nfunction applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {\n  if (activation2 === \"linear\") {\n    return x;\n  } else if (activation2 === \"relu\") {\n    return relu(x);\n  } else if (activation2 === \"elu\") {\n    return elu(x);\n  } else if (activation2 === \"relu6\") {\n    return relu6(x);\n  } else if (activation2 === \"prelu\") {\n    return prelu(x, preluActivationWeights);\n  } else if (activation2 === \"leakyrelu\") {\n    return leakyRelu(x, leakyreluAlpha);\n  } else if (activation2 === \"sigmoid\") {\n    return sigmoid(x);\n  }\n  throw new Error(`Unknown fused activation ${activation2}.`);\n}\nvar shouldFuse = (gradientDepth, activation2) => {\n  const gradientMode = gradientDepth > 0;\n  return !gradientMode || activation2 === \"linear\";\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js\nfunction fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  activation2 = activation2 || \"linear\";\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    assert(dataFormat === \"NHWC\", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);\n    let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"fused conv2d\", pad3, dimRoundingMode);\n  const inputChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n    [$bias] = makeTypesMatch($bias, $x);\n    if (dataFormat === \"NHWC\") {\n      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    } else {\n      assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);\n      assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);\n    }\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    const alphaShape = preluActivationWeights.shape;\n    assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);\n    if (alphaShape.length === 1) {\n      assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);\n    } else if (alphaShape.length === 3) {\n      try {\n        assertAndGetBroadcastShape(alphaShape, convInfo.outShape);\n      } catch (e2) {\n        const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;\n        throw Error(errMsg);\n      }\n    }\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused conv2d\");\n  }\n  const grad2 = (dy, saved) => {\n    assert(dataFormat === \"NHWC\", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const [$filter2, x4D2, y, $bias2] = saved;\n    const dyActivation = getFusedDyActivation(dy, y, activation2);\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3);\n    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3);\n    const der = [xDer, filterDer];\n    if ($bias2 != null) {\n      const biasDer = getFusedBiasGradient($bias2, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n  const inputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = {\n    strides,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation: activation2,\n    leakyreluAlpha\n  };\n  if (bias == null) {\n    const customOp = customGrad((x4D2, filter2, save) => {\n      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n      save([filter2, x4D2, res]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOp(x4D, $filter);\n  } else {\n    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n      save([filter2, x4D2, res, bias2]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOpWithBias(x4D, $filter, $bias);\n  }\n}\nvar conv2d2 = op({ fusedConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n  let x4D = x;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  const inputs = { x: x4D, dy: dy4D };\n  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape };\n  return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);\n}\nvar depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n  let dy4D = dy;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  const inputs = { dy: dy4D, filter };\n  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape };\n  const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  checkPadOnDimRoundingMode(\"fused depthwiseConv2d\", pad3, dimRoundingMode);\n  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n    [$bias] = makeTypesMatch($bias, $x);\n    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused depthwiseConv2d\");\n  }\n  const grad2 = (dy, saved) => {\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);\n    const [$filter2, x4D2, y, bias2] = saved;\n    const dyActivation = getFusedDyActivation(dy, y, activation2);\n    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode);\n    if (bias2 != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n  const inputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = {\n    strides,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation: activation2,\n    leakyreluAlpha\n  };\n  if (bias == null) {\n    const customOp = customGrad((x4D2, filter2, save) => {\n      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n      save([filter2, x4D2, res]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOp(x4D, $filter);\n  } else {\n    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n      save([filter2, x4D2, res, bias2]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOpWithBias(x4D, $filter, $bias);\n  }\n}\nvar depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js\nfunction fusedMatMul_({ a: a6, b, transposeA = false, transposeB = false, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    let result = matMul(a6, b, transposeA, transposeB);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  let $a = convertToTensor(a6, \"a\", \"fused matMul\");\n  let $b = convertToTensor(b, \"b\", \"fused matMul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = sizeFromShape(outerDimsA);\n  const batchDimB = sizeFromShape(outerDimsB);\n  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused matMul\");\n    [$bias] = makeTypesMatch($bias, $a);\n    assertAndGetBroadcastShape(outShape, $bias.shape);\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused matMul\");\n  }\n  const grad2 = (dy, saved) => {\n    const [a3D2, b3D2, y, $bias2] = saved;\n    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);\n    let aDer;\n    let bDer;\n    if (!transposeA && !transposeB) {\n      aDer = matMul(dyActivation, b3D2, false, true);\n      bDer = matMul(a3D2, dyActivation, true, false);\n    } else if (!transposeA && transposeB) {\n      aDer = matMul(dyActivation, b3D2, false, false);\n      bDer = matMul(dyActivation, a3D2, true, false);\n    } else if (transposeA && !transposeB) {\n      aDer = matMul(b3D2, dyActivation, false, true);\n      bDer = matMul(a3D2, dyActivation, false, false);\n    } else {\n      aDer = matMul(b3D2, dyActivation, true, true);\n      bDer = matMul(dyActivation, a3D2, true, true);\n    }\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias2, dyActivation);\n      return [aDer, bDer, biasDer];\n    } else {\n      return [aDer, bDer];\n    }\n  };\n  const inputs = {\n    a: a3D,\n    b: b3D,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha };\n  if (bias == null) {\n    const customOp = customGrad((a3D2, b3D2, save) => {\n      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n      save([a3D2, b3D2, res]);\n      return { value: reshape(res, outShape), gradFunc: grad2 };\n    });\n    return customOp(a3D, b3D);\n  } else {\n    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {\n      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n      save([a3D2, b3D2, res, $bias2]);\n      return { value: reshape(res, outShape), gradFunc: grad2 };\n    });\n    return customOpWithBias(a3D, b3D, $bias);\n  }\n}\nvar matMul2 = op({ fusedMatMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js\nfunction hammingWindow_(windowLength) {\n  return cosineWindow(windowLength, 0.54, 0.46);\n}\nvar hammingWindow = op({ hammingWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js\nfunction hannWindow_(windowLength) {\n  return cosineWindow(windowLength, 0.5, 0.5);\n}\nvar hannWindow = op({ hannWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js\nfunction frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {\n  let start = 0;\n  const output = [];\n  while (start + frameLength <= signal2.size) {\n    output.push(slice(signal2, start, frameLength));\n    start += frameStep;\n  }\n  if (padEnd) {\n    while (start < signal2.size) {\n      const padLen = start + frameLength - signal2.size;\n      const pad3 = concat([\n        slice(signal2, start, frameLength - padLen),\n        fill([padLen], padValue)\n      ]);\n      output.push(pad3);\n      start += frameStep;\n    }\n  }\n  if (output.length === 0) {\n    return tensor2d([], [0, frameLength]);\n  }\n  return reshape(concat(output), [output.length, frameLength]);\n}\nvar frame = op({ frame_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js\nfunction stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n  if (fftLength == null) {\n    fftLength = enclosingPowerOfTwo(frameLength);\n  }\n  const framedSignal = frame(signal2, frameLength, frameStep);\n  const windowedSignal = mul(framedSignal, windowFn(frameLength));\n  return rfft(windowedSignal, fftLength);\n}\nvar stft = op({ stft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js\nfunction cropAndResize_(image2, boxes, boxInd, cropSize, method = \"bilinear\", extrapolationValue = 0) {\n  const $image = convertToTensor(image2, \"image\", \"cropAndResize\");\n  const $boxes = convertToTensor(boxes, \"boxes\", \"cropAndResize\", \"float32\");\n  const $boxInd = convertToTensor(boxInd, \"boxInd\", \"cropAndResize\", \"int32\");\n  const numBoxes = $boxes.shape[0];\n  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);\n  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);\n  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);\n  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);\n  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  assert(method === \"bilinear\" || method === \"nearest\", () => `method must be bilinear or nearest, but was ${method}`);\n  const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n  const attrs = { method, extrapolationValue, cropSize };\n  const res = ENGINE.runKernel(CropAndResize, inputs, attrs);\n  return res;\n}\nvar cropAndResize = op({ cropAndResize_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js\nfunction flipLeftRight_(image2) {\n  const $image = convertToTensor(image2, \"image\", \"flipLeftRight\", \"float32\");\n  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);\n  const inputs = { image: $image };\n  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n  return res;\n}\nvar flipLeftRight = op({ flipLeftRight_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js\nfunction grayscaleToRGB_(image2) {\n  const $image = convertToTensor(image2, \"image\", \"grayscaleToRGB\");\n  const lastDimsIdx = $image.rank - 1;\n  const lastDims = $image.shape[lastDimsIdx];\n  assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);\n  assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);\n  const reps = new Array($image.rank);\n  reps.fill(1, 0, lastDimsIdx);\n  reps[lastDimsIdx] = 3;\n  return tile($image, reps);\n}\nvar grayscaleToRGB = op({ grayscaleToRGB_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js\nfunction rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {\n  const $image = convertToTensor(image2, \"image\", \"rotateWithOffset\", \"float32\");\n  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);\n  const inputs = { image: $image };\n  const attrs = { radians, fillValue, center };\n  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n  return res;\n}\nvar rotateWithOffset = op({ rotateWithOffset_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  if (softNmsSigma == null) {\n    softNmsSigma = 0;\n  }\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  assert(scores.rank === 1, () => \"scores must be a 1D tensor\");\n  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);\n  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n  return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\", \"float32\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\", \"float32\");\n  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n  const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n  return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);\n}\nvar nonMaxSuppression = op({ nonMaxSuppression_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js\nfunction binaryInsert(arr, element, comparator) {\n  const index = binarySearch(arr, element, comparator);\n  const insertionPoint = index < 0 ? -(index + 1) : index;\n  arr.splice(insertionPoint, 0, element);\n}\nfunction binarySearch(arr, target, comparator) {\n  return binarySearch_(arr, target, comparator || defaultComparator);\n}\nfunction defaultComparator(a6, b) {\n  return a6 > b ? 1 : a6 < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n  let left = 0;\n  let right = arr.length;\n  let middle = 0;\n  let found = false;\n  while (left < right) {\n    middle = left + (right - left >>> 1);\n    const compareResult = comparator(target, arr[middle]);\n    if (compareResult > 0) {\n      left = middle + 1;\n    } else {\n      right = middle;\n      found = !compareResult;\n    }\n  }\n  return found ? left : -left - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js\nfunction nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);\n}\nfunction nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);\n}\nfunction nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n  const candidates = [];\n  for (let i2 = 0; i2 < scores.length; i2++) {\n    if (scores[i2] > scoreThreshold) {\n      candidates.push({ score: scores[i2], boxIndex: i2, suppressBeginIndex: 0 });\n    }\n  }\n  candidates.sort(ascendingComparator);\n  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;\n  const selectedIndices = [];\n  const selectedScores = [];\n  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n    const candidate = candidates.pop();\n    const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n    if (originalScore < scoreThreshold) {\n      break;\n    }\n    let ignoreCandidate = false;\n    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou);\n      if (candidate.score <= scoreThreshold) {\n        break;\n      }\n    }\n    candidate.suppressBeginIndex = selectedIndices.length;\n    if (!ignoreCandidate) {\n      if (candidate.score === originalScore) {\n        selectedIndices.push(boxIndex);\n        selectedScores.push(candidate.score);\n      } else if (candidate.score > scoreThreshold) {\n        binaryInsert(candidates, candidate, ascendingComparator);\n      }\n    }\n  }\n  const validOutputs = selectedIndices.length;\n  const elemsToPad = maxOutputSize - validOutputs;\n  if (padToMaxOutputSize && elemsToPad > 0) {\n    selectedIndices.push(...new Array(elemsToPad).fill(0));\n    selectedScores.push(...new Array(elemsToPad).fill(0));\n  }\n  const result = { selectedIndices };\n  if (returnScoresTensor) {\n    result[\"selectedScores\"] = selectedScores;\n  }\n  if (returnValidOutputs) {\n    result[\"validOutputs\"] = validOutputs;\n  }\n  return result;\n}\nfunction intersectionOverUnion(boxes, i2, j) {\n  const iCoord = boxes.subarray(i2 * 4, i2 * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\nfunction suppressWeight(iouThreshold, scale2, iou) {\n  const weight = Math.exp(scale2 * iou * iou);\n  return iou <= iouThreshold ? weight : 0;\n}\nfunction ascendingComparator(c1, c2) {\n  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n  const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return tensor1d(selectedIndices, \"int32\");\n}\nvar nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n  const inputs = { boxes: $boxes, scores: $scores };\n  const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n  return { selectedIndices: result[0], selectedScores: result[1] };\n}\nvar nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return {\n    selectedIndices: tensor1d(selectedIndices, \"int32\"),\n    selectedScores: tensor1d(selectedScores)\n  };\n}\nvar nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n  const inputs = { boxes: $boxes, scores: $scores };\n  const attrs = {\n    maxOutputSize: $maxOutputSize,\n    iouThreshold: $iouThreshold,\n    scoreThreshold: $scoreThreshold,\n    padToMaxOutputSize\n  };\n  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n  return { selectedIndices: result[0], validOutputs: result[1] };\n}\nvar nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return {\n    selectedIndices: tensor1d(selectedIndices, \"int32\"),\n    validOutputs: scalar(validOutputs, \"int32\")\n  };\n}\nvar nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js\nfunction resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {\n  const $images = convertToTensor(images, \"images\", \"resizeBilinear\");\n  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);\n  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);\n  let batchImages = $images;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n  const inputs = { images: batchImages };\n  const attrs = { alignCorners, halfPixelCenters, size };\n  const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar resizeBilinear = op({ resizeBilinear_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js\nfunction resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {\n  const $images = convertToTensor(images, \"images\", \"resizeNearestNeighbor\");\n  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);\n  assert($images.dtype === \"float32\" || $images.dtype === \"int32\", () => \"`images` must have `int32` or `float32` as dtype\");\n  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);\n  let batchImages = $images;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n  const inputs = { images: batchImages };\n  const attrs = { alignCorners, halfPixelCenters, size };\n  const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js\nfunction threshold_(image2, method = \"binary\", inverted = false, threshValue = 0.5) {\n  const $image = convertToTensor(image2, \"image\", \"threshold\");\n  const RED_INTENCITY_COEF = 0.2989;\n  const GREEN_INTENCITY_COEF = 0.587;\n  const BLUE_INTENCITY_COEF = 0.114;\n  const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n  let $threshold = mul(tensor1d([threshValue]), 255);\n  let r2, g, b, grayscale;\n  assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);\n  assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);\n  assert($image.dtype === \"int32\" || $image.dtype === \"float32\", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);\n  assert(method === \"otsu\" || method === \"binary\", () => `Method must be binary or otsu, but was ${method}`);\n  if ($image.shape[2] === 3) {\n    [r2, g, b] = split($image, [1, 1, 1], -1);\n    const $r = mul(r2, RED_INTENCITY_COEF);\n    const $g = mul(g, GREEN_INTENCITY_COEF);\n    const $b = mul(b, BLUE_INTENCITY_COEF);\n    grayscale = add2(add2($r, $g), $b);\n  } else {\n    grayscale = image2;\n  }\n  if (method === \"otsu\") {\n    const $histogram = bincount(cast(round2(grayscale), \"int32\"), tensor([]), 256);\n    $threshold = otsu($histogram, totalPixelsInImage);\n  }\n  const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n  const result = cast(mul(invCondition, 255), \"int32\");\n  return result;\n}\nfunction otsu(histogram, total) {\n  let bestThresh = tensor1d([-1]);\n  let bestInBetVar = tensor1d([0]);\n  let cInBetVar = tensor1d([0]);\n  let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;\n  for (let index = 0; index < histogram.size - 1; index++) {\n    classFirst = slice(histogram, 0, index + 1);\n    classSecond = slice(histogram, index + 1);\n    weightForeground = div(sum2(classFirst), total);\n    weightBack = div(sum2(classSecond), total);\n    const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));\n    meanFirst = div(meanFirstDivA, sum2(classFirst));\n    const meanSecFill = fill(classSecond.shape, classFirst.size);\n    const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);\n    const meanSecMul = mul(classSecond, meanSecAdd);\n    meanSec = div(sum2(meanSecMul), sum2(classSecond));\n    const cInBetVarSubA = sub(meanFirst, meanSec);\n    const cInBetVarSubB = sub(meanFirst, meanSec);\n    const cInBetVarMul = mul(weightForeground, weightBack);\n    cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);\n    const condition = greater(cInBetVar, bestInBetVar);\n    bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n    bestThresh = where(condition, tensor1d([index]), bestThresh);\n  }\n  return bestThresh;\n}\nvar threshold = op({ threshold_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js\nfunction transform_(image2, transforms, interpolation = \"nearest\", fillMode = \"constant\", fillValue = 0, outputShape) {\n  const $image = convertToTensor(image2, \"image\", \"transform\", \"float32\");\n  const $transforms = convertToTensor(transforms, \"transforms\", \"transform\", \"float32\");\n  assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);\n  assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n  assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);\n  const inputs = { image: $image, transforms: $transforms };\n  const attrs = { interpolation, fillMode, fillValue, outputShape };\n  return ENGINE.runKernel(Transform, inputs, attrs);\n}\nvar transform = op({ transform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js\nfunction bandPart_(a6, numLower, numUpper) {\n  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n  const $a = convertToTensor(a6, \"a\", \"bandPart\");\n  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n  if (!(numLower <= M)) {\n    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);\n  }\n  if (!(numUpper <= N)) {\n    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);\n  }\n  if (numLower < 0) {\n    numLower = M;\n  }\n  if (numUpper < 0) {\n    numUpper = N;\n  }\n  const i2 = reshape(range(0, M, 1, \"int32\"), [-1, 1]);\n  const j = range(0, N, 1, \"int32\");\n  const ij = sub(i2, j);\n  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, \"int32\")), greaterEqual(ij, scalar(-numUpper, \"int32\")));\n  const zero = zeros([M, N], $a.dtype);\n  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);\n}\nvar bandPart = op({ bandPart_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js\nfunction gramSchmidt_(xs) {\n  let inputIsTensor2D;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(xs != null && xs.length > 0, () => \"Gram-Schmidt process: input must not be null, undefined, or empty\");\n    const dim = xs[0].shape[0];\n    for (let i2 = 1; i2 < xs.length; ++i2) {\n      assert(xs[i2].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i2].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));\n  }\n  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);\n  const ys = [];\n  const xs1d = xs;\n  for (let i2 = 0; i2 < xs.length; ++i2) {\n    ys.push(ENGINE.tidy(() => {\n      let x = xs1d[i2];\n      if (i2 > 0) {\n        for (let j = 0; j < i2; ++j) {\n          const proj = mul(sum2(mul(ys[j], x)), ys[j]);\n          x = sub(x, proj);\n        }\n      }\n      return div(x, norm(x, \"euclidean\"));\n    }));\n  }\n  if (inputIsTensor2D) {\n    return stack(ys, 0);\n  } else {\n    return ys;\n  }\n}\nvar gramSchmidt = op({ gramSchmidt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js\nfunction qr_(x, fullMatrices = false) {\n  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n  if (x.rank === 2) {\n    return qr2d(x, fullMatrices);\n  } else {\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);\n    const x2ds = unstack(reshape(x, [\n      outerDimsProd,\n      x.shape[x.shape.length - 2],\n      x.shape[x.shape.length - 1]\n    ]), 0);\n    const q2ds = [];\n    const r2ds = [];\n    x2ds.forEach((x2d) => {\n      const [q2d, r2d] = qr2d(x2d, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = reshape(stack(q2ds, 0), x.shape);\n    const r2 = reshape(stack(r2ds, 0), x.shape);\n    return [q, r2];\n  }\n}\nfunction qr2d(x, fullMatrices = false) {\n  return ENGINE.tidy(() => {\n    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n    const m = x.shape[0];\n    const n = x.shape[1];\n    let q = eye(m);\n    let r2 = clone(x);\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w = clone(one2D);\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      const rTemp = r2;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r2, q] = ENGINE.tidy(() => {\n        const rjEnd1 = slice(r2, [j, j], [m - j, 1]);\n        const normX = norm(rjEnd1);\n        const rjj = slice(r2, [j, j], [1, 1]);\n        const s2 = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n        const u1 = sub(rjj, mul(s2, normX));\n        const wPre = div(rjEnd1, u1);\n        if (wPre.shape[0] === 1) {\n          w = clone(one2D);\n        } else {\n          w = concat([\n            one2D,\n            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n          ], 0);\n        }\n        const tau = neg(div(matMul(s2, u1), normX));\n        const rjEndAll = slice(r2, [j, 0], [m - j, n]);\n        const tauTimesW = mul(tau, w);\n        const wT = transpose(w);\n        if (j === 0) {\n          r2 = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n        } else {\n          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n          r2 = concat([slice(r2, [0, 0], [j, n]), rTimesTau], 0);\n        }\n        const tawTimesWT = transpose(tauTimesW);\n        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n        } else {\n          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n        }\n        return [w, r2, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n    if (!fullMatrices && m > n) {\n      q = slice(q, [0, 0], [m, n]);\n      r2 = slice(r2, [0, 0], [n, n]);\n    }\n    return [q, r2];\n  });\n}\nvar qr = op({ qr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js\nvar Reduction;\n(function(Reduction2) {\n  Reduction2[Reduction2[\"NONE\"] = 0] = \"NONE\";\n  Reduction2[Reduction2[\"MEAN\"] = 1] = \"MEAN\";\n  Reduction2[Reduction2[\"SUM\"] = 2] = \"SUM\";\n  Reduction2[Reduction2[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js\nfunction computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $losses = convertToTensor(losses2, \"losses\", \"computeWeightedLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"computeWeightedLoss\");\n  }\n  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);\n  if (reduction === Reduction.NONE) {\n    return weightedLoss;\n  }\n  if (reduction === Reduction.SUM) {\n    return sum2(weightedLoss);\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return mean(weightedLoss);\n    } else {\n      const broadcastFactor = $losses.size / $weights.size;\n      const result = div(sum2(weightedLoss), sum2($weights));\n      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return div(sum2(weightedLoss), scalar($losses.size));\n    } else {\n      const broadcastedWeights = mul($weights, ones2($losses.shape));\n      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), \"float32\");\n      return div(sum2(weightedLoss), numNonZeros);\n    }\n  }\n  throw Error(`Unknown reduction: ${reduction}`);\n}\nvar computeWeightedLoss = op({ computeWeightedLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"absoluteDifference\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"absoluteDifference\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"absoluteDifference\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in absoluteDifference: \");\n  const losses2 = abs(sub($labels, $predictions));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar absoluteDifference = op({ absoluteDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"cosineDistance\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"cosineDistance\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"cosineDistance\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in cosineDistance: \");\n  const one = scalar(1);\n  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar cosineDistance = op({ cosineDistance_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $labels = convertToTensor(labels, \"labels\", \"hingeLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"hingeLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"hingeLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in hingeLoss: \");\n  const one = scalar(1);\n  $labels = sub(mul(scalar(2), $labels), one);\n  const losses2 = relu(sub(one, mul($labels, $predictions)));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar hingeLoss = op({ hingeLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js\nfunction huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"huberLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"huberLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"huberLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in huberLoss: \");\n  const deltaScalar = scalar(delta);\n  const error = abs(sub($predictions, $labels));\n  const quadratic = minimum(error, deltaScalar);\n  const linear = sub(error, quadratic);\n  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar huberLoss = op({ huberLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js\nfunction logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"logLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"logLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"logLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in logLoss: \");\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon3);\n  const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar))));\n  const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar)));\n  const losses2 = sub(l13, l23);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar logLoss = op({ logLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"meanSquaredError\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"meanSquaredError\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"meanSquaredError\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in meanSquaredError: \");\n  const losses2 = squaredDifference($labels, $predictions);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar meanSquaredError = op({ meanSquaredError_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n  const $labels = convertToTensor(labels, \"labels\", \"sigmoidCrossEntropyWithLogits\");\n  const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropyWithLogits\");\n  assertShapesMatch($labels.shape, $logits.shape, \"Error in sigmoidCrossEntropyWithLogits: \");\n  const maxOutput = relu($logits);\n  const outputXTarget = mul($logits, $labels);\n  const sigmoidOutput = log1p(exp(neg(abs($logits))));\n  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $multiClassLabels = convertToTensor(multiClassLabels, \"multiClassLabels\", \"sigmoidCrossEntropy\");\n  const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropy\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"sigmoidCrossEntropy\");\n  }\n  assertShapesMatch($multiClassLabels.shape, $logits.shape, \"Error in sigmoidCrossEntropy: \");\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n  }\n  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n  if (dim !== logits.rank - 1) {\n    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);\n  }\n  const customOp = customGrad((labels2, logits2, save) => {\n    const keepDims = true;\n    const lse = logSumExp(logits2, [dim], keepDims);\n    const logResult = sub(cast(logits2, \"float32\"), lse);\n    save([labels2, logResult]);\n    const costVector = neg(mul(logResult, labels2));\n    const value = sum2(costVector, [dim]);\n    const gradFunc = (dy, saved) => {\n      const [labels3, logResult2] = saved;\n      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n      return [\n        mul(reshape(dy, dyShape), sub(cast(labels3, \"float32\"), exp(logResult2))),\n        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, \"float32\")))\n      ];\n    };\n    return { value, gradFunc };\n  });\n  return customOp(labels, logits);\n}\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $onehotLabels = convertToTensor(onehotLabels, \"onehotLabels\", \"softmaxCrossEntropy\");\n  const $logits = convertToTensor(logits, \"logits\", \"softmaxCrossEntropy\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"softmaxCrossEntropy\");\n  }\n  assertShapesMatch($onehotLabels.shape, $logits.shape, \"Error in softmaxCrossEntropy: \");\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n  }\n  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows.js\nfunction sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {\n  const $indices = convertToTensor(indices, \"indices\", \"sparseFillEmptyRows\", \"int32\");\n  const $values = convertToTensor(values, \"values\", \"sparseFillEmptyRows\");\n  const $denseShape = convertToTensor(denseShape, \"denseShape\", \"sparseFillEmptyRows\", \"int32\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseFillEmptyRows\", $values.dtype);\n  if ($indices.rank !== 2) {\n    throw new Error(`Indices should be Tensor2D but received shape\n        ${$indices.shape}`);\n  }\n  if ($values.rank !== 1) {\n    throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);\n  }\n  if ($denseShape.rank !== 1) {\n    throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);\n  }\n  if ($defaultValue.rank !== 0) {\n    throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);\n  }\n  const inputs = {\n    indices: $indices,\n    values: $values,\n    denseShape: $denseShape,\n    defaultValue: $defaultValue\n  };\n  const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);\n  return {\n    outputIndices: result[0],\n    outputValues: result[1],\n    emptyRowIndicator: result[2],\n    reverseIndexMap: result[3]\n  };\n}\nvar sparseFillEmptyRows = op({ sparseFillEmptyRows_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape.js\nfunction sparseReshape_(inputIndices, inputShape, newShape) {\n  const $inputIndices = convertToTensor(inputIndices, \"inputIndices\", \"sparseReshape\", \"int32\");\n  const $inputShape = convertToTensor(inputShape, \"inputShape\", \"sparseReshape\", \"int32\");\n  const $newShape = convertToTensor(newShape, \"newShape\", \"sparseReshape\", \"int32\");\n  if ($inputIndices.rank !== 2) {\n    throw new Error(`Input indices should be Tensor2D but received shape\n        ${$inputIndices.shape}`);\n  }\n  if ($inputShape.rank !== 1) {\n    throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);\n  }\n  if ($newShape.rank !== 1) {\n    throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);\n  }\n  const inputs = {\n    inputIndices: $inputIndices,\n    inputShape: $inputShape,\n    newShape: $newShape\n  };\n  const result = ENGINE.runKernel(SparseReshape, inputs);\n  return { outputIndices: result[0], outputShape: result[1] };\n}\nvar sparseReshape = op({ sparseReshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_mean.js\nfunction sparseSegmentMean_(data, indices, segmentIds) {\n  const $data = convertToTensor(data, \"data\", \"sparseSegmentMean\");\n  const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentMean\", \"int32\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentMean\", \"int32\");\n  if ($data.rank < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n          ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n          ${$segmentIds.shape}`);\n  }\n  const inputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n  return ENGINE.runKernel(SparseSegmentMean, inputs);\n}\nvar sparseSegmentMean = op({ sparseSegmentMean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum.js\nfunction sparseSegmentSum_(data, indices, segmentIds) {\n  const $data = convertToTensor(data, \"data\", \"sparseSegmentSum\");\n  const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentSum\", \"int32\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentSum\", \"int32\");\n  if ($data.rank < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n         ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n         ${$segmentIds.shape}`);\n  }\n  const inputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n  return ENGINE.runKernel(SparseSegmentSum, inputs);\n}\nvar sparseSegmentSum = op({ sparseSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_n_grams.js\nfunction stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n  const $data = convertToTensor(data, \"data\", \"stringNGrams\", \"string\");\n  if ($data.dtype !== \"string\") {\n    throw new Error(\"Data must be of datatype string\");\n  }\n  if ($data.shape.length !== 1) {\n    throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n  }\n  const $dataSplits = convertToTensor(dataSplits, \"dataSplits\", \"stringNGrams\");\n  if ($dataSplits.dtype !== \"int32\") {\n    throw new Error(\"Data splits must be of datatype int32\");\n  }\n  const attrs = {\n    separator,\n    nGramWidths,\n    leftPad,\n    rightPad: rightPad2,\n    padWidth,\n    preserveShortSequences\n  };\n  const inputs = { data: $data, dataSplits: $dataSplits };\n  const result = ENGINE.runKernel(StringNGrams, inputs, attrs);\n  return { nGrams: result[0], nGramsSplits: result[1] };\n}\nvar stringNGrams = op({ stringNGrams_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_split.js\nfunction stringSplit_(input2, delimiter, skipEmpty = true) {\n  const $input = convertToTensor(input2, \"input\", \"stringSplit\", \"string\");\n  const $delimiter = convertToTensor(delimiter, \"delimiter\", \"stringSplit\", \"string\");\n  if ($input.rank !== 1) {\n    throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);\n  }\n  if ($delimiter.rank !== 0) {\n    throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n  }\n  const attrs = { skipEmpty };\n  const inputs = { input: $input, delimiter: $delimiter };\n  const result = ENGINE.runKernel(StringSplit, inputs, attrs);\n  return { indices: result[0], values: result[1], shape: result[2] };\n}\nvar stringSplit = op({ stringSplit_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_to_hash_bucket_fast.js\nfunction stringToHashBucketFast_(input2, numBuckets) {\n  const $input = convertToTensor(input2, \"input\", \"stringToHashBucketFast\", \"string\");\n  const attrs = { numBuckets };\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const inputs = { input: $input };\n  return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);\n}\nvar stringToHashBucketFast = op({ stringToHashBucketFast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js\nvar spectral = {\n  fft,\n  ifft,\n  rfft,\n  irfft\n};\nvar signal = {\n  hammingWindow,\n  hannWindow,\n  frame,\n  stft\n};\nvar image = {\n  flipLeftRight,\n  grayscaleToRGB,\n  resizeNearestNeighbor,\n  resizeBilinear,\n  rotateWithOffset,\n  cropAndResize,\n  nonMaxSuppression,\n  nonMaxSuppressionAsync,\n  nonMaxSuppressionWithScore,\n  nonMaxSuppressionWithScoreAsync,\n  nonMaxSuppressionPadded,\n  nonMaxSuppressionPaddedAsync,\n  threshold,\n  transform\n};\nvar linalg = {\n  bandPart,\n  gramSchmidt,\n  qr\n};\nvar losses = {\n  absoluteDifference,\n  computeWeightedLoss,\n  cosineDistance,\n  hingeLoss,\n  huberLoss,\n  logLoss,\n  meanSquaredError,\n  sigmoidCrossEntropy,\n  softmaxCrossEntropy\n};\nvar sparse = {\n  sparseFillEmptyRows,\n  sparseReshape,\n  sparseSegmentMean,\n  sparseSegmentSum\n};\nvar string = {\n  stringNGrams,\n  stringSplit,\n  stringToHashBucketFast\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js\nvar Optimizer = class extends Serializable {\n  minimize(f, returnCost = false, varList) {\n    const { value, grads: grads2 } = this.computeGradients(f, varList);\n    if (varList != null) {\n      const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] }));\n      this.applyGradients(gradArray);\n    } else {\n      this.applyGradients(grads2);\n    }\n    dispose(grads2);\n    if (returnCost) {\n      return value;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n  get iterations() {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return this.iterations_;\n  }\n  incrementIterations() {\n    this.iterations_ = this.iterations + 1;\n  }\n  computeGradients(f, varList) {\n    return variableGrads(f, varList);\n  }\n  dispose() {\n    if (this.iterations_ != null) {\n      dispose(this.iterations_);\n    }\n  }\n  async saveIterations() {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return {\n      name: \"iter\",\n      tensor: scalar(this.iterations_, \"int32\")\n    };\n  }\n  async getWeights() {\n    throw new Error(\"getWeights() is not implemented for this optimizer yet.\");\n  }\n  async setWeights(weightValues) {\n    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);\n  }\n  async extractIterations(weightValues) {\n    this.iterations_ = (await weightValues[0].tensor.data())[0];\n    return weightValues.slice(1);\n  }\n};\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n  value: (instance) => {\n    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js\nvar AdadeltaOptimizer = class extends Optimizer {\n  constructor(learningRate, rho, epsilon3 = null) {\n    super();\n    this.learningRate = learningRate;\n    this.rho = rho;\n    this.epsilon = epsilon3;\n    this.accumulatedGrads = [];\n    this.accumulatedUpdates = [];\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedGrads[i2] == null) {\n        this.accumulatedGrads[i2] = {\n          originalName: `${name}/accum_grad`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedUpdates[i2] == null) {\n        this.accumulatedUpdates[i2] = {\n          originalName: `${name}/accum_var`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedGrad = this.accumulatedGrads[i2].variable;\n      const accumulatedUpdate = this.accumulatedUpdates[i2].variable;\n      tidy(() => {\n        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);\n        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n        accumulatedGrad.assign(newAccumulatedGrad);\n        accumulatedUpdate.assign(newAccumulatedUpdate);\n        const newValue = add2(mul(updates, -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedUpdates != null) {\n      dispose(this.accumulatedGrads.map((v) => v.variable));\n      dispose(this.accumulatedUpdates.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"rho\": this.rho,\n      \"epsilon\": this.epsilon\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"rho\"], config[\"epsilon\"]);\n  }\n};\nAdadeltaOptimizer.className = \"Adadelta\";\nregisterClass(AdadeltaOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js\nvar AdagradOptimizer = class extends Optimizer {\n  constructor(learningRate, initialAccumulatorValue = 0.1) {\n    super();\n    this.learningRate = learningRate;\n    this.initialAccumulatorValue = initialAccumulatorValue;\n    this.accumulatedGrads = [];\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulatedGrads[i2] == null) {\n        const trainable = false;\n        this.accumulatedGrads[i2] = {\n          originalName: `${name}/accumulator`,\n          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedGrad = this.accumulatedGrads[i2].variable;\n      tidy(() => {\n        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));\n        accumulatedGrad.assign(newAccumulatedGrad);\n        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedGrads != null) {\n      dispose(this.accumulatedGrads.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"initialAccumulatorValue\": this.initialAccumulatorValue\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"initialAccumulatorValue\"]);\n  }\n};\nAdagradOptimizer.className = \"Adagrad\";\nregisterClass(AdagradOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js\nvar AdamOptimizer = class extends Optimizer {\n  constructor(learningRate, beta1, beta2, epsilon3 = null) {\n    super();\n    this.learningRate = learningRate;\n    this.beta1 = beta1;\n    this.beta2 = beta2;\n    this.epsilon = epsilon3;\n    this.accumulatedFirstMoment = [];\n    this.accumulatedSecondMoment = [];\n    tidy(() => {\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const oneMinusAccBeta2 = sub(1, this.accBeta2);\n      varNames.forEach((name, i2) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i2] == null) {\n          this.accumulatedFirstMoment[i2] = {\n            originalName: `${name}/m`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        if (this.accumulatedSecondMoment[i2] == null) {\n          this.accumulatedSecondMoment[i2] = {\n            originalName: `${name}/v`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n        const firstMoment = this.accumulatedFirstMoment[i2].variable;\n        const secondMoment = this.accumulatedSecondMoment[i2].variable;\n        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n        firstMoment.assign(newFirstMoment);\n        secondMoment.assign(newSecondMoment);\n        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n        value.assign(newValue);\n      });\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n      this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n    }\n    if (this.accumulatedSecondMoment != null) {\n      dispose(this.accumulatedSecondMoment.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    tidy(() => {\n      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n    });\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"beta1\": this.beta1,\n      \"beta2\": this.beta2,\n      \"epsilon\": this.epsilon\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"]);\n  }\n};\nAdamOptimizer.className = \"Adam\";\nregisterClass(AdamOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js\nvar AdamaxOptimizer = class extends Optimizer {\n  constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {\n    super();\n    this.learningRate = learningRate;\n    this.beta1 = beta1;\n    this.beta2 = beta2;\n    this.epsilon = epsilon3;\n    this.decay = decay;\n    this.accumulatedFirstMoment = [];\n    this.accumulatedWeightedInfNorm = [];\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));\n      variableNames.forEach((name, i2) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i2] == null) {\n          this.accumulatedFirstMoment[i2] = {\n            originalName: `${name}/m`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        if (this.accumulatedWeightedInfNorm[i2] == null) {\n          this.accumulatedWeightedInfNorm[i2] = {\n            originalName: `${name}/v`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n        const firstMoment = this.accumulatedFirstMoment[i2].variable;\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[i2].variable;\n        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const ut0 = mul(weightedInfNorm, this.beta2);\n        const ut1 = abs(gradient);\n        const newWeightedInfNorm = maximum(ut0, ut1);\n        firstMoment.assign(newFirstMoment);\n        weightedInfNorm.assign(newWeightedInfNorm);\n        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);\n        value.assign(newValue);\n      });\n      this.iteration.assign(add2(this.iteration, 1));\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.accBeta1.dispose();\n    this.iteration.dispose();\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n    }\n    if (this.accumulatedWeightedInfNorm != null) {\n      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    throw new Error(\"getWeights() is not implemented for Adamax yet.\");\n  }\n  async setWeights(weightValues) {\n    throw new Error(\"setWeights() is not implemented for Adamax yet.\");\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"beta1\": this.beta1,\n      \"beta2\": this.beta2,\n      \"epsilon\": this.epsilon,\n      \"decay\": this.decay\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"], config[\"decay\"]);\n  }\n};\nAdamaxOptimizer.className = \"Adamax\";\nregisterClass(AdamaxOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js\nvar SGDOptimizer = class extends Optimizer {\n  constructor(learningRate) {\n    super();\n    this.learningRate = learningRate;\n    this.setLearningRate(learningRate);\n  }\n  applyGradients(variableGradients) {\n    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n    varNames.forEach((name, i2) => {\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const value = ENGINE.registeredVariables[name];\n      tidy(() => {\n        const newValue = add2(mul(this.c, gradient), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  setLearningRate(learningRate) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n  dispose() {\n    this.c.dispose();\n  }\n  async getWeights() {\n    return [await this.saveIterations()];\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    if (weightValues.length !== 0) {\n      throw new Error(\"SGD optimizer does not have settable weights.\");\n    }\n  }\n  getConfig() {\n    return { \"learningRate\": this.learningRate };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"]);\n  }\n};\nSGDOptimizer.className = \"SGD\";\nregisterClass(SGDOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js\nvar MomentumOptimizer = class extends SGDOptimizer {\n  constructor(learningRate, momentum, useNesterov = false) {\n    super(learningRate);\n    this.learningRate = learningRate;\n    this.momentum = momentum;\n    this.useNesterov = useNesterov;\n    this.accumulations = [];\n    this.m = scalar(this.momentum);\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulations[i2] == null) {\n        const trainable = false;\n        this.accumulations[i2] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const accumulation = this.accumulations[i2].variable;\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      tidy(() => {\n        let newValue;\n        const newAccumulation = add2(mul(this.m, accumulation), gradient);\n        if (this.useNesterov) {\n          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);\n        } else {\n          newValue = add2(mul(this.c, newAccumulation), value);\n        }\n        accumulation.assign(newAccumulation);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.m.dispose();\n    if (this.accumulations != null) {\n      dispose(this.accumulations.map((v) => v.variable));\n    }\n  }\n  setMomentum(momentum) {\n    this.momentum = momentum;\n  }\n  async getWeights() {\n    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"momentum\": this.momentum,\n      \"useNesterov\": this.useNesterov\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"momentum\"], config[\"useNesterov\"]);\n  }\n};\nMomentumOptimizer.className = \"Momentum\";\nregisterClass(MomentumOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js\nvar RMSPropOptimizer = class extends Optimizer {\n  constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n    super();\n    this.learningRate = learningRate;\n    this.decay = decay;\n    this.momentum = momentum;\n    this.epsilon = epsilon3;\n    this.accumulatedMeanSquares = [];\n    this.accumulatedMoments = [];\n    this.accumulatedMeanGrads = [];\n    this.centered = centered;\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n    if (learningRate == null) {\n      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedMeanSquares[i2] == null) {\n        this.accumulatedMeanSquares[i2] = {\n          originalName: `${name}/rms`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMoments[i2] == null) {\n        this.accumulatedMoments[i2] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMeanGrads[i2] == null && this.centered) {\n        this.accumulatedMeanGrads[i2] = {\n          originalName: `${name}/mg`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[i2].variable;\n      const accumulatedMoments = this.accumulatedMoments[i2].variable;\n      tidy(() => {\n        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n        if (this.centered) {\n          const accumulatedMeanGrad = this.accumulatedMeanGrads[i2].variable;\n          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));\n          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n          accumulatedMoments.assign(newAccumulatedMoments);\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);\n          accumulatedMoments.assign(newAccumulatedMoments);\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedMeanSquares != null) {\n      dispose(this.accumulatedMeanSquares.map((v) => v.variable));\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      dispose(this.accumulatedMeanGrads.map((v) => v.variable));\n    }\n    if (this.accumulatedMoments != null) {\n      dispose(this.accumulatedMoments.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n    if (this.centered) {\n      variables.push(...this.accumulatedMeanGrads);\n    }\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    if (this.centered) {\n      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({\n        originalName: v.name,\n        variable: v.tensor.variable(trainable)\n      }));\n    }\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"decay\": this.decay,\n      \"momentum\": this.momentum,\n      \"epsilon\": this.epsilon,\n      \"centered\": this.centered\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"decay\"], config[\"momentum\"], config[\"epsilon\"], config[\"centered\"]);\n  }\n};\nRMSPropOptimizer.className = \"RMSProp\";\nregisterClass(RMSPropOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js\nvar OptimizerConstructors = class {\n  static sgd(learningRate) {\n    return new SGDOptimizer(learningRate);\n  }\n  static momentum(learningRate, momentum, useNesterov = false) {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);\n  }\n  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);\n  }\n  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon3);\n  }\n  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);\n  }\n  static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/train.js\nvar train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js\nvar delayCallback = (() => {\n  if (typeof requestAnimationFrame !== \"undefined\") {\n    return requestAnimationFrame;\n  } else if (typeof setImmediate !== \"undefined\") {\n    return setImmediate;\n  }\n  return (f) => f();\n})();\nfunction nextFrame() {\n  return new Promise((resolve) => delayCallback(() => resolve()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nvar backend_util_exports = {};\n__export(backend_util_exports, {\n  ERF_A1: () => ERF_A1,\n  ERF_A2: () => ERF_A2,\n  ERF_A3: () => ERF_A3,\n  ERF_A4: () => ERF_A4,\n  ERF_A5: () => ERF_A5,\n  ERF_P: () => ERF_P,\n  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,\n  SELU_SCALE: () => SELU_SCALE,\n  SELU_SCALEALPHA: () => SELU_SCALEALPHA,\n  applyActivation: () => applyActivation,\n  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,\n  assertParamsConsistent: () => assertParamsConsistent,\n  assignToTypedArray: () => assignToTypedArray,\n  axesAreInnerMostDims: () => axesAreInnerMostDims,\n  calculateShapes: () => calculateShapes,\n  checkEinsumDimSizes: () => checkEinsumDimSizes,\n  checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,\n  combineLocations: () => combineLocations,\n  complexWithEvenIndex: () => complexWithEvenIndex,\n  complexWithOddIndex: () => complexWithOddIndex,\n  computeConv2DInfo: () => computeConv2DInfo,\n  computeConv3DInfo: () => computeConv3DInfo,\n  computeDefaultPad: () => computeDefaultPad,\n  computeDilation2DInfo: () => computeDilation2DInfo,\n  computeOptimalWindowSize: () => computeOptimalWindowSize,\n  computeOutAndReduceShapes: () => computeOutAndReduceShapes,\n  computeOutShape: () => computeOutShape2,\n  computePool2DInfo: () => computePool2DInfo,\n  computePool3DInfo: () => computePool3DInfo,\n  convertConv2DDataFormat: () => convertConv2DDataFormat,\n  decodeEinsumEquation: () => decodeEinsumEquation,\n  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,\n  expandShapeToKeepDim: () => expandShapeToKeepDim,\n  exponent: () => exponent,\n  exponents: () => exponents,\n  fromStringArrayToUint8: () => fromStringArrayToUint8,\n  fromUint8ToStringArray: () => fromUint8ToStringArray,\n  getAxesPermutation: () => getAxesPermutation,\n  getBroadcastDims: () => getBroadcastDims,\n  getComplexWithIndex: () => getComplexWithIndex,\n  getEinsumComputePath: () => getEinsumComputePath,\n  getEinsumPermutation: () => getEinsumPermutation,\n  getFusedBiasGradient: () => getFusedBiasGradient,\n  getFusedDyActivation: () => getFusedDyActivation,\n  getImageCenter: () => getImageCenter,\n  getInnerMostAxes: () => getInnerMostAxes,\n  getPermuted: () => getPermuted,\n  getReductionAxes: () => getReductionAxes,\n  getReshaped: () => getReshaped,\n  getReshapedPermuted: () => getReshapedPermuted,\n  getSliceBeginCoords: () => getSliceBeginCoords,\n  getSliceSize: () => getSliceSize,\n  getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,\n  getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,\n  getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,\n  getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,\n  getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,\n  getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,\n  getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,\n  getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,\n  getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,\n  getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,\n  getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,\n  getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,\n  getUndoAxesPermutation: () => getUndoAxesPermutation,\n  isIdentityPermutation: () => isIdentityPermutation,\n  log: () => log,\n  mergeRealAndImagArrays: () => mergeRealAndImagArrays,\n  prepareAndValidate: () => prepareAndValidate,\n  prepareSplitSize: () => prepareSplitSize,\n  segment_util: () => segment_util_exports,\n  shouldFuse: () => shouldFuse,\n  slice_util: () => slice_util_exports,\n  splitRealAndImagArrays: () => splitRealAndImagArrays,\n  tupleValuesAreOne: () => tupleValuesAreOne,\n  upcastType: () => upcastType,\n  validateInput: () => validateInput,\n  validateUpdateShape: () => validateUpdateShape,\n  warn: () => warn\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js\nfunction assertParamsConsistent(shapes, axis) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i2) => {\n    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i2}] must be the same as the rank of the rest (${rank})`);\n  });\n  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i2) => {\n    for (let r2 = 0; r2 < rank; r2++) {\n      assert(r2 === axis || shape[r2] === firstShape[r2], () => `Error in concat${rank}D: Shape of tensors[${i2}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i2}.`);\n    }\n  });\n}\nfunction computeOutShape2(shapes, axis) {\n  const outputShape = shapes[0].slice();\n  for (let i2 = 1; i2 < shapes.length; i2++) {\n    outputShape[axis] += shapes[i2][axis];\n  }\n  return outputShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js\nvar PARALLELIZE_THRESHOLD = 30;\nfunction computeOptimalWindowSize(inSize) {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js\nfunction getImageCenter(center, imageHeight, imageWidth) {\n  const centerX = imageWidth * (typeof center === \"number\" ? center : center[0]);\n  const centerY = imageHeight * (typeof center === \"number\" ? center : center[1]);\n  return [centerX, centerY];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js\nfunction getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {\n  let reshaped = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod6);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i2 = 0; i2 < spatialLength; ++i2) {\n      reshaped = reshaped.concat([inputShape[i2 + 1] / blockShape[i2], blockShape[i2]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\nfunction getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i2 = blockShapeRank + 1; i2 < reshapedRank; ++i2) {\n      if (i2 <= 2 * blockShapeRank) {\n        permuted.push(i2);\n        permuted.push(i2 - (blockShapeRank + 1));\n      } else {\n        permuted.push(i2);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i2 = 1; i2 < reshapedRank; ++i2) {\n      if (i2 >= blockShapeRank * 2 + 1 || i2 % 2 === 1) {\n        permutedAfterBatch.push(i2);\n      } else {\n        permutedBeforeBatch.push(i2);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\nfunction getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true) {\n  const reshapedPermuted = [];\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod6);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod6);\n  }\n  for (let i2 = 1; i2 < inputShape.length; ++i2) {\n    if (i2 <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i2 - 1] * inputShape[i2]);\n      } else {\n        reshapedPermuted.push(inputShape[i2] / blockShape[i2 - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i2]);\n    }\n  }\n  return reshapedPermuted;\n}\nfunction getSliceBeginCoords(crops, blockShape) {\n  const sliceBeginCoords = [0];\n  for (let i2 = 0; i2 < blockShape; ++i2) {\n    sliceBeginCoords.push(crops[i2][0]);\n  }\n  return sliceBeginCoords;\n}\nfunction getSliceSize(uncroppedShape, crops, blockShape) {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i2 = 0; i2 < blockShape; ++i2) {\n    sliceSize.push(uncroppedShape[i2 + 1] - crops[i2][0] - crops[i2][1]);\n  }\n  return sliceSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js\nvar SELU_SCALEALPHA = 1.7580993408473768;\nvar SELU_SCALE = 1.0507009873554805;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js\nvar ERF_P = 0.3275911;\nvar ERF_A1 = 0.254829592;\nvar ERF_A2 = -0.284496736;\nvar ERF_A3 = 1.421413741;\nvar ERF_A4 = -1.453152027;\nvar ERF_A5 = 1.061405429;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js\nfunction mergeRealAndImagArrays(real5, imag5) {\n  if (real5.length !== imag5.length) {\n    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);\n  }\n  const result = new Float32Array(real5.length * 2);\n  for (let i2 = 0; i2 < result.length; i2 += 2) {\n    result[i2] = real5[i2 / 2];\n    result[i2 + 1] = imag5[i2 / 2];\n  }\n  return result;\n}\nfunction splitRealAndImagArrays(complex5) {\n  const real5 = new Float32Array(complex5.length / 2);\n  const imag5 = new Float32Array(complex5.length / 2);\n  for (let i2 = 0; i2 < complex5.length; i2 += 2) {\n    real5[i2 / 2] = complex5[i2];\n    imag5[i2 / 2] = complex5[i2 + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction complexWithEvenIndex(complex5) {\n  const len = Math.ceil(complex5.length / 4);\n  const real5 = new Float32Array(len);\n  const imag5 = new Float32Array(len);\n  for (let i2 = 0; i2 < complex5.length; i2 += 4) {\n    real5[Math.floor(i2 / 4)] = complex5[i2];\n    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction complexWithOddIndex(complex5) {\n  const len = Math.floor(complex5.length / 4);\n  const real5 = new Float32Array(len);\n  const imag5 = new Float32Array(len);\n  for (let i2 = 2; i2 < complex5.length; i2 += 4) {\n    real5[Math.floor(i2 / 4)] = complex5[i2];\n    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction getComplexWithIndex(complex5, index) {\n  const real5 = complex5[index * 2];\n  const imag5 = complex5[index * 2 + 1];\n  return { real: real5, imag: imag5 };\n}\nfunction assignToTypedArray(data, real5, imag5, index) {\n  data[index * 2] = real5;\n  data[index * 2 + 1] = imag5;\n}\nfunction exponents(n, inverse) {\n  const real5 = new Float32Array(n / 2);\n  const imag5 = new Float32Array(n / 2);\n  for (let i2 = 0; i2 < Math.ceil(n / 2); i2++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i2 / n);\n    real5[i2] = Math.cos(x);\n    imag5[i2] = Math.sin(x);\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction exponent(k, n, inverse) {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real5 = Math.cos(x);\n  const imag5 = Math.sin(x);\n  return { real: real5, imag: imag5 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js\nvar ARROW = \"->\";\nvar ARROW_REGEX = /->/g;\nvar COMMA = \",\";\nvar ELLIPSIS = \"...\";\nfunction decodeEinsumEquation(equation, numTensors) {\n  equation = equation.replace(/\\s/g, \"\");\n  const numArrows = (equation.length - equation.replace(ARROW_REGEX, \"\").length) / ARROW.length;\n  if (numArrows < 1) {\n    throw new Error(\"Equations without an arrow are not supported.\");\n  } else if (numArrows > 1) {\n    throw new Error(`Equation must contain exactly one arrow (\"${ARROW}\").`);\n  }\n  const [inputString, outputString] = equation.split(ARROW);\n  assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation (\"${ELLIPSIS}\") is not supported yet.`);\n  const inputTerms = inputString.split(COMMA);\n  const numInputs = inputTerms.length;\n  if (numTensors !== numInputs) {\n    throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);\n  }\n  if (numInputs > 2) {\n    throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");\n  }\n  const allDims = [];\n  for (let i2 = 0; i2 < outputString.length; ++i2) {\n    const dimName = outputString[i2];\n    if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {\n      throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);\n    }\n    if (allDims.indexOf(dimName) === -1) {\n      allDims.push(dimName);\n    }\n  }\n  for (let i2 = 0; i2 < inputString.length; ++i2) {\n    const dimName = inputString[i2];\n    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {\n      allDims.push(dimName);\n    }\n  }\n  const idDims = new Array(inputTerms.length);\n  for (let i2 = 0; i2 < numInputs; ++i2) {\n    if (new Set(inputTerms[i2].split(\"\")).size !== inputTerms[i2].length) {\n      throw new Error(`Found duplicate axes in input component ${inputTerms[i2]}. Support for duplicate axes in input is not implemented yet.`);\n    }\n    idDims[i2] = [];\n    for (let j = 0; j < inputTerms[i2].length; ++j) {\n      idDims[i2].push(allDims.indexOf(inputTerms[i2][j]));\n    }\n  }\n  const numDims = allDims.length;\n  const numOutDims = outputString.length;\n  const summedDims = [];\n  for (let i2 = numOutDims; i2 < numDims; ++i2) {\n    summedDims.push(i2);\n  }\n  return { allDims, summedDims, idDims };\n}\nfunction getEinsumPermutation(nDims, idDims) {\n  let permutationIndices = new Array(nDims);\n  permutationIndices.fill(-1);\n  for (let i2 = 0; i2 < idDims.length; ++i2) {\n    permutationIndices[idDims[i2]] = i2;\n  }\n  const expandDims7 = [];\n  for (let i2 = 0; i2 < nDims; ++i2) {\n    if (permutationIndices[i2] === -1) {\n      expandDims7.push(i2);\n    }\n  }\n  permutationIndices = permutationIndices.filter((d) => d !== -1);\n  return { permutationIndices, expandDims: expandDims7 };\n}\nfunction checkEinsumDimSizes(nDims, idDims, tensors) {\n  const dimSizes = new Array(nDims);\n  for (let i2 = 0; i2 < tensors.length; ++i2) {\n    const shape = tensors[i2].shape;\n    for (let j = 0; j < idDims[i2].length; ++j) {\n      if (dimSizes[idDims[i2][j]] === void 0) {\n        dimSizes[idDims[i2][j]] = shape[j];\n      } else {\n        assert(dimSizes[idDims[i2][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i2][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);\n      }\n    }\n  }\n}\nfunction getEinsumComputePath(summedDims, idDims) {\n  const path = summedDims;\n  const steps = [];\n  let nSteps = 0;\n  if (summedDims.length === 0) {\n    path.push(-1);\n  }\n  nSteps = summedDims.length + 1;\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    steps.push([]);\n  }\n  const computedTermIndices = [];\n  for (let i2 = 0; i2 < path.length; ++i2) {\n    const summedDim = path[i2];\n    const termIndices = findTermsWithDim(idDims, summedDim);\n    for (const termIndex of termIndices) {\n      if (computedTermIndices.indexOf(termIndex) === -1) {\n        steps[i2].push(termIndex);\n        computedTermIndices.push(termIndex);\n      }\n    }\n  }\n  return { path, steps };\n}\nfunction isIdentityPermutation(perm) {\n  return perm.every((dim, index) => dim === index);\n}\nfunction findTermsWithDim(idDims, dim) {\n  const termIndices = [];\n  for (let i2 = 0; i2 < idDims.length; ++i2) {\n    if (idDims[i2].length === 0 || idDims[i2].indexOf(dim) !== -1 || dim === -1) {\n      termIndices.push(i2);\n    }\n  }\n  return termIndices;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js\nfunction prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n  let splitSizes = [];\n  if (typeof numOrSizeSplits === \"number\") {\n    assert(x.shape[axis] % numOrSizeSplits === 0, () => \"Number of splits must evenly divide the axis.\");\n    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n  } else {\n    const numOfNegs = numOrSizeSplits.reduce((count2, value) => {\n      if (value === -1) {\n        count2 += 1;\n      }\n      return count2;\n    }, 0);\n    assert(numOfNegs <= 1, () => \"There should be only one negative value in split array.\");\n    const negIndex = numOrSizeSplits.indexOf(-1);\n    if (negIndex !== -1) {\n      const total = numOrSizeSplits.reduce((a6, b) => b > 0 ? a6 + b : a6);\n      numOrSizeSplits[negIndex] = x.shape[axis] - total;\n    }\n    assert(x.shape[axis] === numOrSizeSplits.reduce((a6, b) => a6 + b), () => \"The sum of sizes must match the size of the axis dimension.\");\n    splitSizes = numOrSizeSplits;\n  }\n  return splitSizes;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js\nfunction getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {\n  return `Received SparseTensor with denseShape[0] = 0 but\n  indices.shape[0] = ${indicesLength}`;\n}\nfunction getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {\n  return `indices(${index}, 0) is invalid: ${value} < 0`;\n}\nfunction getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {\n  return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js\nfunction getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {\n  return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;\n}\nfunction getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {\n  return `size ${dim} must be non-negative, not ${value}`;\n}\nfunction getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {\n  return \"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\";\n}\nfunction getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {\n  const inputSize = sizeFromShape(inputShape);\n  const outputSize = sizeFromShape(outputShape);\n  return `Input to reshape is a SparseTensor with ${inputSize}\n  dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;\n}\nfunction getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {\n  const inputSize = sizeFromShape(inputShape);\n  const outputSize = sizeFromShape(outputShape);\n  return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js\nfunction getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {\n  return `segment ids must be >= 0`;\n}\nfunction getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {\n  return `segment ids are not increasing`;\n}\nfunction getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {\n  return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;\n}\nfunction getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {\n  return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js\nvar segment_util_exports = {};\n__export(segment_util_exports, {\n  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,\n  computeOutShape: () => computeOutShape3,\n  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize\n});\nfunction segOpComputeOptimalWindowSize(inSize, numSegments) {\n  let done = false;\n  let res;\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\nfunction computeOutShape3(aShape, axis, numSegments) {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\nfunction collectGatherOpShapeInfo(x, indices, axis, batchDims) {\n  const indicesRank = indices.shape.length;\n  const xRank = x.shape.length;\n  if (batchDims !== 0) {\n    if (batchDims < -indicesRank || batchDims > indicesRank) {\n      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);\n    }\n  }\n  if (batchDims < 0) {\n    batchDims += indicesRank;\n  }\n  if (batchDims > xRank) {\n    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (\n    ${xRank}).`);\n  }\n  if (axis < batchDims) {\n    throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);\n  }\n  for (let i2 = 0; i2 < batchDims; ++i2) {\n    if (x.shape[i2] !== indices.shape[i2]) {\n      throw new Error(`x.shape[${i2}]: ${x.shape[i2]} should be equal to indices.shape[${i2}]: ${indices.shape[i2]}.`);\n    }\n  }\n  const dimSize = x.shape[axis];\n  const outputShape = [];\n  let batchSize = 1;\n  let outerSize = 1;\n  let sliceSize = 1;\n  for (let i2 = 0; i2 < batchDims; ++i2) {\n    outputShape.push(x.shape[i2]);\n    batchSize *= x.shape[i2];\n  }\n  for (let i2 = batchDims; i2 < axis; i2++) {\n    outputShape.push(x.shape[i2]);\n    outerSize *= x.shape[i2];\n  }\n  for (let i2 = batchDims; i2 < indicesRank; i2++) {\n    outputShape.push(indices.shape[i2]);\n  }\n  for (let i2 = axis + 1; i2 < xRank; i2++) {\n    outputShape.push(x.shape[i2]);\n    sliceSize *= x.shape[i2];\n  }\n  return { batchSize, sliceSize, outerSize, dimSize, outputShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nfunction fromUint8ToStringArray(vals) {\n  try {\n    return vals.map((val) => decodeString(val));\n  } catch (err) {\n    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);\n  }\n}\nfunction fromStringArrayToUint8(strings) {\n  return strings.map((s2) => encodeString(s2));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js\nvar kernel_impls_exports = {};\n__export(kernel_impls_exports, {\n  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,\n  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,\n  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,\n  whereImpl: () => whereImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js\nvar absGradConfig = {\n  kernelName: Abs,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, step(cast(x, \"float32\"), -1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js\nvar acosGradConfig = {\n  kernelName: Acos,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a6 = square(cast(x, \"float32\"));\n        const b = sqrt(sub(scalar(1), a6));\n        return neg(div(dy, b));\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js\nvar acoshGradConfig = {\n  kernelName: Acosh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a6 = sqrt(sub(square(cast(x, \"float32\")), 1));\n        return div(dy, a6);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js\nvar addGradConfig = {\n  kernelName: Add,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a6.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js\nvar addNGradConfig = {\n  kernelName: AddN,\n  saveAllInputs: true,\n  gradFunc: (dy, saved) => {\n    const ders = {};\n    saved.forEach((_, i2) => {\n      ders[i2] = () => dy.clone();\n    });\n    return ders;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js\nvar argMaxGradConfig = {\n  kernelName: ArgMax,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => zerosLike(x) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js\nvar argMinGradConfig = {\n  kernelName: ArgMin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => zerosLike(x) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js\nvar asinGradConfig = {\n  kernelName: Asin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, \"float32\"))))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js\nvar asinhGradConfig = {\n  kernelName: Asinh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a6 = sqrt(add2(scalar(1), square(cast(x, \"float32\"))));\n        return div(dy, a6);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js\nvar atan2GradConfig = {\n  kernelName: Atan2,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      const d = add2(square(a6), square(b));\n      let res = mul(dy, div(b, d));\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a6.shape);\n    };\n    const derB = () => {\n      const d = add2(square(a6), square(b));\n      let res = neg(mul(dy, div(a6, d)));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js\nvar atanGradConfig = {\n  kernelName: Atan,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, add2(square(cast(x, \"float32\")), 1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js\nvar atanhGradConfig = {\n  kernelName: Atanh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, sub(scalar(1), square(cast(x, \"float32\")))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js\nfunction avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"avgPool3dGrad\");\n  const $input = convertToTensor(input2, \"input\", \"avgPool3dGrad\");\n  let dy5D = $dy;\n  let input5D = $input;\n  let reshapedTo5D = false;\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1,\n      $input.shape[0],\n      $input.shape[1],\n      $input.shape[2],\n      $input.shape[3]\n    ]);\n  }\n  assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n  assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n  checkPadOnDimRoundingMode(\"avgPool3dGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: dy5D, input: input5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar avgPool3dGrad = op({ avgPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js\nvar avgPool3DGradConfig = {\n  kernelName: AvgPool3D,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n    return {\n      x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js\nfunction avgPoolGrad_(dy, input2, filterSize, strides, pad3) {\n  const $dy = convertToTensor(dy, \"dy\", \"avgPoolGrad\");\n  const $input = convertToTensor(input2, \"input\", \"avgPoolGrad\");\n  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  let input4D = $input;\n  let dy4D = $dy;\n  let reshapedTo4D = false;\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n  }\n  assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);\n  assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);\n  const inputs = { dy: dy4D, input: input4D };\n  const attrs = { filterSize, strides, pad: pad3 };\n  const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar avgPoolGrad = op({ avgPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js\nvar avgPoolGradConfig = {\n  kernelName: AvgPool,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { filterSize, strides, pad: pad3 } = attrs;\n    return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js\nvar batchMatMulGradConfig = {\n  kernelName: BatchMatMul,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [a6, b] = saved;\n    const { transposeA, transposeB } = attrs;\n    if (!transposeA && !transposeB) {\n      return {\n        a: () => matMul(dy, b, false, true),\n        b: () => matMul(a6, dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        a: () => matMul(dy, b, false, false),\n        b: () => matMul(dy, a6, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        a: () => matMul(b, dy, false, true),\n        b: () => matMul(a6, dy, false, false)\n      };\n    } else {\n      return {\n        a: () => matMul(b, dy, true, true),\n        b: () => matMul(dy, a6, true, true)\n      };\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js\nvar batchToSpaceNDGradConfig = {\n  kernelName: BatchToSpaceND,\n  gradFunc: (dy, saved, attrs) => {\n    const { blockShape, crops } = attrs;\n    return { x: () => spaceToBatchND(dy, blockShape, crops) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js\nvar broadcastToGradConfig = {\n  kernelName: BroadcastTo,\n  gradFunc: (dy, saved, attrs) => {\n    const broadCastToAttrs = attrs;\n    const inputShape = broadCastToAttrs.inputShape;\n    const outputShape = broadCastToAttrs.shape;\n    const reps = Array.from(outputShape);\n    for (let i2 = inputShape.length - 1; i2 >= 0; i2--) {\n      if (inputShape[i2] === outputShape[i2]) {\n        reps[i2] = 1;\n      } else if (inputShape[i2] !== 1) {\n        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n      }\n    }\n    const axes = [];\n    for (let i2 = 0; i2 < reps.length; i2++) {\n      if (reps[i2] > 1) {\n        axes.push(i2);\n      }\n    }\n    return { x: () => sum2(dy, axes, true) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js\nvar castGradConfig = {\n  kernelName: Cast,\n  gradFunc: (dy) => {\n    return { x: () => dy.clone() };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js\nvar ceilGradConfig = {\n  kernelName: Ceil,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js\nvar clipByValueGradConfig = {\n  kernelName: ClipByValue,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { clipValueMin, clipValueMax } = attrs;\n    return {\n      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js\nvar complexAbsGradConfig = {\n  kernelName: ComplexAbs,\n  inputsToSave: [\"x\"],\n  gradFunc: absGradConfig.gradFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js\nvar concatGradConfig = {\n  kernelName: Concat,\n  saveAllInputs: true,\n  gradFunc: (dy, saved, attrs) => {\n    const shapes = saved.map((t2) => t2.shape);\n    const { axis } = attrs;\n    const $axis = parseAxisParam(axis, saved[0].shape)[0];\n    const sizeSplits = shapes.map((s2) => s2[$axis]);\n    const derTensors = split(dy, sizeSplits, $axis);\n    return derTensors.map((t2) => () => t2);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js\nvar conv2DGradConfig = {\n  kernelName: Conv2D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x4D, $filter] = saved;\n    const { dilations, strides, pad: pad3, dataFormat } = attrs;\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    return {\n      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat),\n      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js\nvar conv2DBackpropInputGradConfig = {\n  kernelName: Conv2DBackpropInput,\n  inputsToSave: [\"dy\", \"filter\"],\n  gradFunc: (ddx, saved, attrs) => {\n    const [dy, filter] = saved;\n    const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n    return {\n      dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode),\n      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {\n  let x5D = x;\n  if (x.rank === 4) {\n    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n  }\n  let dy5D = dy;\n  if (dy5D.rank === 4) {\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);\n  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);\n  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);\n  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);\n  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);\n  const inputs = { x: x5D, dy: dy5D };\n  const attrs = { strides, pad: pad3, filterShape };\n  return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);\n}\nvar conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js\nvar conv3DGradConfig = {\n  kernelName: Conv3D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { dilations, strides, pad: pad3 } = attrs;\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    const [x5D, $filter] = saved;\n    return {\n      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3),\n      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js\nvar cosGradConfig = {\n  kernelName: Cos,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(neg(sin(cast(x, \"float32\"))), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js\nvar coshGradConfig = {\n  kernelName: Cosh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(sinh(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js\nvar cumsumGradConfig = {\n  kernelName: Cumsum,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis, exclusive, reverse: reverse5 } = attrs;\n    return {\n      x: () => {\n        const permutation = getAxesPermutation([axis], x.rank);\n        let out = cumsum(dy, axis, exclusive, !reverse5);\n        if (permutation != null) {\n          out = transpose(out, permutation);\n        }\n        return out;\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js\nvar depthwiseConv2dNativeGradConfig = {\n  kernelName: DepthwiseConv2dNative,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { dilations, strides, pad: pad3, dimRoundingMode } = attrs;\n    const $dilations = dilations == null ? [1, 1] : dilations;\n    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);\n    const [x, filter] = saved;\n    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);\n    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);\n    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);\n    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);\n    checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n    return {\n      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode),\n      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js\nvar dilation2dGradConfig = {\n  kernelName: Dilation2D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, filter] = saved;\n    const inputInputs = { x, filter, dy };\n    const filterInputs = { x, filter, dy };\n    return {\n      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js\nvar eluGradConfig = {\n  kernelName: Elu,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    const inputs = { dy, y };\n    return { x: () => ENGINE.runKernel(EluGrad, inputs) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js\nvar erfGradConfig = {\n  kernelName: Erf,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    const a6 = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n    return { x: () => mul(dy, a6) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js\nvar expGradConfig = {\n  kernelName: Exp,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(dy, y) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js\nvar expandDimsGradConfig = {\n  kernelName: ExpandDims,\n  inputsToSave: [\"input\"],\n  gradFunc: (dy, saved) => {\n    const [input2] = saved;\n    return { input: () => reshape(dy, input2.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js\nvar expm1GradConfig = {\n  kernelName: Expm1,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, exp(x)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js\nvar floorGradConfig = {\n  kernelName: Floor,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js\nvar floorDivGradConfig = {\n  kernelName: FloorDiv,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a6.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a6, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum2(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, \"float32\")));\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js\nvar fusedBatchNormGradConfig = {\n  kernelName: FusedBatchNorm,\n  inputsToSave: [\"x\", \"mean\", \"variance\", \"scale\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { varianceEpsilon } = attrs;\n    const [x, mean5, variance, scale2] = saved;\n    const scaleValue = scale2 == null ? scalar(1) : scale2;\n    const reductionAxes = getReductionAxes(mean5.shape, x.shape);\n    const tileShape = [];\n    if (mean5.rank === 1) {\n      for (let i2 = 0; i2 < x.shape.length - 1; ++i2) {\n        tileShape.push(x.shape[i2]);\n      }\n      tileShape.push(1);\n    }\n    const xMinusMean = sub(x, mean5);\n    const dyTimesScaleValue = mul(dy, scaleValue);\n    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));\n    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n    const derX = () => {\n      if (mean5.rank === 1) {\n        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);\n      } else {\n        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n      if (mean5.rank === 1) {\n        meanDer = sum2(meanDer, reductionAxes);\n      }\n      return reshape(meanDer, mean5.shape);\n    };\n    const derVariance = () => {\n      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n      if (mean5.rank === 1) {\n        varianceDer = sum2(varianceDer, reductionAxes);\n      }\n      return reshape(varianceDer, mean5.shape);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n      if (mean5.rank === 1) {\n        scaleDer = sum2(scaleDer, reductionAxes);\n      }\n      return reshape(scaleDer, mean5.shape);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if (mean5.rank === 1) {\n        offsetDer = sum2(offsetDer, reductionAxes);\n      }\n      return reshape(offsetDer, mean5.shape);\n    };\n    return {\n      x: derX,\n      mean: derMean,\n      variance: derVariance,\n      scale: derScale,\n      offset: derOffset\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js\nvar gatherGradConfig = {\n  kernelName: GatherV2,\n  inputsToSave: [\"x\", \"indices\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, indices] = saved;\n    const { axis } = attrs;\n    const parsedAxis = parseAxisParam(axis, x.shape)[0];\n    const derX = () => {\n      const paramsShape = x.shape;\n      const indicesSize = indices.size;\n      const outerShape = paramsShape.slice(0, parsedAxis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n      const values = reshape(dy, valuesShape);\n      const reshapedIndices = reshape(indices, [indicesSize]);\n      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = transpose(values, transposeDims);\n      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = transpose(paramsGrad, invertTransposeDims);\n      return paramsGrad;\n    };\n    return { x: derX, indices: () => indices };\n  }\n};\nfunction arrayRange(start, stop) {\n  const result = [];\n  for (let i2 = start; i2 < stop; ++i2) {\n    result.push(i2);\n  }\n  return result;\n}\nfunction arrayConcat(arrays) {\n  const result = [];\n  for (let i2 = 0; i2 < arrays.length; ++i2) {\n    for (let j = 0; j < arrays[i2].length; ++j) {\n      result.push(arrays[i2][j]);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js\nvar greaterEqualGradConfig = {\n  kernelName: GreaterEqual,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    return { a: () => zerosLike(a6), b: () => zerosLike(b) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js\nvar identityGradConfig = {\n  kernelName: Identity,\n  gradFunc: (dy) => {\n    return { x: () => cast(dy, \"float32\") };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js\nvar isFiniteGradConfig = {\n  kernelName: IsFinite,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js\nvar isInfGradConfig = {\n  kernelName: IsInf,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js\nvar isNanGradConfig = {\n  kernelName: IsNan,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js\nvar leakyReluGradConfig = {\n  kernelName: LeakyRelu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { alpha } = attrs;\n    const mask = greater(x, 0);\n    return { x: () => where(mask, dy, mul(dy, alpha)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js\nvar log1pGradConfig = {\n  kernelName: Log1p,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, add2(x, 1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js\nvar logGradConfig = {\n  kernelName: Log,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, cast(x, \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js\nvar logSoftmaxGradConfig = {\n  kernelName: LogSoftmax,\n  inputsToSave: [],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [value] = saved;\n    const { axis } = attrs;\n    return {\n      logits: () => {\n        const keepDims = true;\n        const softmax7 = exp(value);\n        return sub(dy, mul(sum2(dy, axis, keepDims), softmax7));\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n  const inputs = { x, y, dy };\n  const attrs = { depthRadius, bias, alpha, beta };\n  return ENGINE.runKernel(LRNGrad, inputs, attrs);\n}\nvar localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js\nvar lrnGradConfig = {\n  kernelName: LRN,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { depthRadius, bias, alpha, beta } = attrs;\n    return {\n      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js\nfunction gradForMinAndMax(dy, y, xOrig, origAxes) {\n  if (y.rank < xOrig.rank) {\n    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));\n  }\n  return {\n    x: () => {\n      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n      return dx;\n    }\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js\nvar maxGradConfig = {\n  kernelName: Max,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const maxAttrs = attrs;\n    const { reductionIndices } = maxAttrs;\n    const x = saved[0];\n    const y = saved[1];\n    const origAxes = parseAxisParam(reductionIndices, x.shape);\n    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return maxGrad[\"x\"]();\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js\nvar maximumGradConfig = {\n  kernelName: Maximum,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const derA = () => mul(dy, cast(greaterEqual(a6, b), \"float32\"));\n    const derB = () => mul(dy, cast(less(a6, b), \"float32\"));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js\nfunction maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"maxPool3dGrad\");\n  const $input = convertToTensor(input2, \"input\", \"maxPool3dGrad\");\n  const $output = convertToTensor(output, \"output\", \"maxPool3dGrad\");\n  let dy5D = $dy;\n  let input5D = $input;\n  let output5D = $output;\n  let reshapedTo5D = false;\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1,\n      $input.shape[0],\n      $input.shape[1],\n      $input.shape[2],\n      $input.shape[3]\n    ]);\n    output5D = reshape($output, [\n      1,\n      $output.shape[0],\n      $output.shape[1],\n      $output.shape[2],\n      $output.shape[3]\n    ]);\n  }\n  assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n  assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n  assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);\n  checkPadOnDimRoundingMode(\"maxPool3dGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: dy5D, input: input5D, output: output5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar maxPool3dGrad = op({ maxPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js\nvar maxPool3DGradConfig = {\n  kernelName: MaxPool3D,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n    return {\n      x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js\nfunction maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"maxPoolGrad\");\n  const $input = convertToTensor(input2, \"input\", \"maxPoolGrad\");\n  const $output = convertToTensor(output, \"output\", \"maxPoolGrad\");\n  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);\n  assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);\n  checkPadOnDimRoundingMode(\"maxPoolGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: $dy, input: $input, output: $output };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);\n}\nvar maxPoolGrad = op({ maxPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js\nvar maxPoolGradConfig = {\n  kernelName: MaxPool,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { filterSize, strides, pad: pad3 } = attrs;\n    return {\n      x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js\nvar meanGradConfig = {\n  kernelName: Mean,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis } = attrs;\n    const axes = parseAxisParam(axis, x.shape);\n    const shapes = computeOutAndReduceShapes(x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    const derX = () => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach((axis2) => {\n        expandedDyShape[axis2] = 1;\n      });\n      const expandedDy = reshape(dy, expandedDyShape);\n      const res = div(mul(expandedDy, ones2(x.shape, \"float32\")), reduceSize);\n      return res;\n    };\n    return { x: derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js\nvar minGradConfig = {\n  kernelName: Min,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const minAttrs = attrs;\n    const { axis } = minAttrs;\n    const [x, y] = saved;\n    const origAxes = parseAxisParam(axis, x.shape);\n    const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return minGrad[\"x\"]();\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js\nvar minimumGradConfig = {\n  kernelName: Minimum,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const derA = () => mul(dy, cast(lessEqual(a6, b), \"float32\"));\n    const derB = () => mul(dy, cast(greater(a6, b), \"float32\"));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js\nvar mirrorPadGradConfig = {\n  kernelName: MirrorPad,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const x = saved[0];\n    const { paddings } = attrs;\n    const begin = paddings.map((p2) => p2[0]);\n    return { x: () => slice(dy, begin, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js\nvar modGradConfig = {\n  kernelName: Mod,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(dy, reduceAxes), a6.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = mul(dy, neg(floor(div(a6, b))));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js\nvar multiplyGradConfig = {\n  kernelName: Multiply,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      const res = mul(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a6.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = mul(dy, cast(a6, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js\nvar negGradConfig = {\n  kernelName: Neg,\n  gradFunc: (dy) => {\n    return { x: () => neg(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js\nvar oneHotGradConfig = {\n  kernelName: OneHot,\n  inputsToSave: [\"indices\"],\n  gradFunc: (dy, saved) => {\n    const indices = saved[0];\n    return { indices: () => zeros(indices.shape, \"float32\") };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js\nvar onesLikeGradConfig = {\n  kernelName: OnesLike,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js\nvar packGradConfig = {\n  kernelName: Pack,\n  saveAllInputs: true,\n  gradFunc: (dy, saved, attrs) => {\n    const { axis } = attrs;\n    const derTensors = unstack(dy, axis);\n    return derTensors.map((t2) => () => t2);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js\nvar padV2GradConfig = {\n  kernelName: PadV2,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const x = saved[0];\n    const { paddings } = attrs;\n    const begin = paddings.map((p2) => p2[0]);\n    return { x: () => slice(dy, begin, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js\nvar powGradConfig = {\n  kernelName: Pow,\n  inputsToSave: [\"a\", \"b\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [a6, b, y] = saved;\n    const base = a6;\n    const exp5 = b;\n    const outShape = assertAndGetBroadcastShape(base.shape, exp5.shape);\n    const derBase = () => {\n      const expFloat = cast(exp5, \"float32\");\n      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n      const reduceAxes = getReductionAxes(base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, base.shape);\n    };\n    const derExp = () => {\n      const condition = greater(base, 0);\n      const logBase = where(condition, log2(base), zerosLike(base));\n      let res = mul(dy, mul(y, logBase));\n      const reduceAxes = getReductionAxes(exp5.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, exp5.shape);\n    };\n    return { a: derBase, b: derExp };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js\nvar preluGradConfig = {\n  kernelName: Prelu,\n  inputsToSave: [\"x\", \"alpha\"],\n  gradFunc: (dy, saved) => {\n    const [x, alpha] = saved;\n    const mask = greater(x, 0);\n    return {\n      x: () => where(mask, dy, mul(dy, alpha)),\n      alpha: () => {\n        let res = where(mask, zerosLike(dy), mul(dy, x));\n        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n        if (reduceAxes.length > 0) {\n          res = sum2(res, reduceAxes);\n        }\n        return reshape(res, alpha.shape);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js\nfunction prodGradFn_(x, dy, axis) {\n  const expandedYShape = x.shape.slice();\n  expandedYShape[axis] = 1;\n  const expandedDy = reshape(dy, expandedYShape);\n  const xCumProd = cumprod(x, axis, true, false);\n  const xCumRevProd = cumprod(x, axis, true, true);\n  const dx = mul(xCumProd, xCumRevProd);\n  return mul(expandedDy, dx);\n}\nfunction prodsGradFn_(x, dy, axis) {\n  const xRank = x.shape.length;\n  const finalProdAxis = xRank - axis.length;\n  const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);\n  let permutedX = x;\n  if (xPermutation != null) {\n    permutedX = transpose(x, xPermutation);\n  }\n  const newShape = permutedX.shape.slice();\n  const removedShape = newShape.splice(xRank - axis.length, axis.length);\n  const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);\n  newShape.push(endPartShape);\n  const reshapedPermutedX = permutedX.reshape(newShape);\n  let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);\n  prodGrad = prodGrad.reshape(permutedX.shape);\n  if (xPermutation != null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);\n    prodGrad = transpose(prodGrad, undoPermutation);\n  }\n  return prodGrad;\n}\nvar prodGradConfig = {\n  kernelName: Prod,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis } = attrs;\n    let axisArr = [];\n    if (axis === void 0 || axis === null) {\n      axisArr = x.shape.map((_, i2) => i2);\n    } else if (typeof axis === \"number\") {\n      axisArr = [axis];\n    } else {\n      axisArr = axis;\n    }\n    return { x: () => prodsGradFn_(x, dy, axisArr) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js\nvar divGradConfig = {\n  kernelName: RealDiv,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a6.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a6, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum2(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, \"float32\")));\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js\nvar reciprocalGradConfig = {\n  kernelName: Reciprocal,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, neg(square(x))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js\nvar relu6GradConfig = {\n  kernelName: Relu6,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    const mask = mul(lessEqual(x, 6), step(x));\n    return { x: () => mul(dy, cast(mask, \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js\nvar reluGradConfig = {\n  kernelName: Relu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, cast(step(x), \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js\nvar reshapeGradConfig = {\n  kernelName: Reshape,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => reshape(dy, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js\nvar resizeBilinearGradConfig = {\n  kernelName: ResizeBilinear,\n  inputsToSave: [\"images\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [images] = saved;\n    const inputs = { dy, images };\n    const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);\n    return { images: imagesDer };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js\nvar resizeNearestNeighborGradConfig = {\n  kernelName: ResizeNearestNeighbor,\n  inputsToSave: [\"images\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [images] = saved;\n    const inputs = { dy, images };\n    const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);\n    return { images: imagesDer };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js\nvar reverseGradConfig = {\n  kernelName: Reverse,\n  gradFunc: (dy, saved, attrs) => {\n    const { dims } = attrs;\n    const axes = parseAxisParam(dims, dy.shape);\n    return { x: () => reverse(dy, axes) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js\nvar roundGradConfig = {\n  kernelName: Round,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js\nvar rsqrtGradConfig = {\n  kernelName: Rsqrt,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js\nvar selectGradConfig = {\n  kernelName: Select,\n  inputsToSave: [\"condition\"],\n  gradFunc: (dy, saved) => {\n    const [condition] = saved;\n    return {\n      condition: () => cast(zerosLike(condition), \"float32\"),\n      t: () => mul(dy, cast(condition, dy.dtype)),\n      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js\nvar seluGradConfig = {\n  kernelName: Selu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const mask = greater(x, scalar(0));\n        const scaleAlpha2 = scalar(SELU_SCALEALPHA);\n        const scale2 = scalar(SELU_SCALE);\n        const greaterThanZeroDer = mul(dy, scale2);\n        const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, \"float32\")));\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js\nvar sigmoidGradConfig = {\n  kernelName: Sigmoid,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js\nvar signGradConfig = {\n  kernelName: Sign,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js\nvar sinGradConfig = {\n  kernelName: Sin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(cos(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js\nvar sinhGradConfig = {\n  kernelName: Sinh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(cosh(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js\nvar sliceGradConfig = {\n  kernelName: Slice,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { begin, size } = attrs;\n    const inputShape = x.shape;\n    const [begin_, size_] = parseSliceParams(x, begin, size);\n    const paddings = [];\n    for (let i2 = 0; i2 < dy.rank; i2++) {\n      paddings.push([begin_[i2], inputShape[i2] - begin_[i2] - size_[i2]]);\n    }\n    return { x: () => pad(dy, paddings) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js\nvar softmaxGradConfig = {\n  kernelName: Softmax,\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [y] = saved;\n    const { dim } = attrs;\n    const keepDims = true;\n    const dyTimesY = mul(dy, y);\n    return {\n      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js\nvar softplusGradConfig = {\n  kernelName: Softplus,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, sigmoid(x)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js\nvar spaceToBatchNDGradConfig = {\n  kernelName: SpaceToBatchND,\n  gradFunc: (dy, saved, attrs) => {\n    const { blockShape, paddings } = attrs;\n    return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js\nvar splitVGradConfig = {\n  kernelName: SplitV,\n  gradFunc: (dy, saved, attrs) => {\n    const { axis } = attrs;\n    return { x: () => concat(dy, axis) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js\nvar sqrtGradConfig = {\n  kernelName: Sqrt,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, mul(sqrt(cast(x, \"float32\")), 2)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js\nvar squareGradConfig = {\n  kernelName: Square,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, mul(cast(x, \"float32\"), 2)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js\nvar squaredDifferenceGradConfig = {\n  kernelName: SquaredDifference,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const two = scalar(2);\n    const derA = () => mul(dy, mul(two, sub(a6, b)));\n    const derB = () => mul(dy, mul(two, sub(b, a6)));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js\nvar stepGradConfig = {\n  kernelName: Step,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js\nvar subGradConfig = {\n  kernelName: Sub,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a6, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(a6.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a6.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(neg(res), b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js\nvar sumGradConfig = {\n  kernelName: Sum,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const expandedDyShape = x.shape.slice();\n    const { axis } = attrs;\n    const axes = parseAxisParam(axis, x.shape);\n    axes.forEach((axis2) => {\n      expandedDyShape[axis2] = 1;\n    });\n    const expandedDy = reshape(dy, expandedDyShape);\n    const derX = mul(expandedDy, ones2(x.shape, \"float32\"));\n    return { x: () => derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js\nvar tanGradConfig = {\n  kernelName: Tan,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, square(cos(x))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js\nvar tanhGradConfig = {\n  kernelName: Tanh,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(sub(scalar(1), square(y)), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js\nvar tileGradConfig = {\n  kernelName: Tile,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { reps } = attrs;\n    const derX = () => {\n      let xGrad = zerosLike(x);\n      if (x.rank === 1) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0]], [x.shape[0]]));\n        }\n      } else if (x.rank === 2) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1]], [\n              x.shape[0],\n              x.shape[1]\n            ]));\n          }\n        }\n      } else if (x.rank === 3) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n            }\n          }\n        }\n      } else if (x.rank === 4) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l3 = 0; l3 < reps[3]; ++l3) {\n                xGrad = add2(xGrad, slice(dy, [\n                  i2 * x.shape[0],\n                  j * x.shape[1],\n                  k * x.shape[2],\n                  l3 * x.shape[3]\n                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return { x: derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js\nvar transposeGradConfig = {\n  kernelName: Transpose,\n  gradFunc: (dy, saved, attrs) => {\n    const transposeAttrs = attrs;\n    const { perm } = transposeAttrs;\n    const undoPerm = getUndoAxesPermutation(perm);\n    return { x: () => transpose(dy, undoPerm) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js\nvar unpackGradConfig = {\n  kernelName: Unpack,\n  gradFunc: (dy, saved, attrs) => {\n    const unpackAttrs = attrs;\n    const { axis } = unpackAttrs;\n    return { value: () => stack(dy, axis) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js\nvar unsortedSegmentSumGradConfig = {\n  kernelName: UnsortedSegmentSum,\n  inputsToSave: [\"segmentIds\"],\n  gradFunc: (dy, saved) => {\n    const [segmentIds] = saved;\n    const derX = () => {\n      return gatherDropNegatives(dy, segmentIds);\n    };\n    return { x: derX };\n  }\n};\nfunction gatherDropNegatives(x, indices) {\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices);\n  let isPositive = greaterEqual(indices, scalar(0, \"int32\"));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i2 = 0; i2 < numIters; ++i2) {\n    isPositive = expandDims(isPositive, i2 + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones2(gathered.shape, \"bool\"));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js\nvar zerosLikeGradConfig = {\n  kernelName: ZerosLike,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js\nvar gradConfigs = [\n  absGradConfig,\n  acosGradConfig,\n  acoshGradConfig,\n  addGradConfig,\n  addNGradConfig,\n  argMaxGradConfig,\n  argMinGradConfig,\n  asinGradConfig,\n  asinhGradConfig,\n  atan2GradConfig,\n  atanGradConfig,\n  atanhGradConfig,\n  avgPool3DGradConfig,\n  avgPoolGradConfig,\n  batchMatMulGradConfig,\n  batchToSpaceNDGradConfig,\n  broadcastToGradConfig,\n  castGradConfig,\n  ceilGradConfig,\n  clipByValueGradConfig,\n  complexAbsGradConfig,\n  concatGradConfig,\n  conv2DBackpropInputGradConfig,\n  conv2DGradConfig,\n  conv3DGradConfig,\n  cosGradConfig,\n  coshGradConfig,\n  cumsumGradConfig,\n  depthwiseConv2dNativeGradConfig,\n  dilation2dGradConfig,\n  divGradConfig,\n  eluGradConfig,\n  erfGradConfig,\n  expGradConfig,\n  expandDimsGradConfig,\n  expm1GradConfig,\n  floorDivGradConfig,\n  floorGradConfig,\n  fusedBatchNormGradConfig,\n  gatherGradConfig,\n  greaterEqualGradConfig,\n  identityGradConfig,\n  isFiniteGradConfig,\n  isInfGradConfig,\n  isNanGradConfig,\n  leakyReluGradConfig,\n  log1pGradConfig,\n  logGradConfig,\n  logSoftmaxGradConfig,\n  lrnGradConfig,\n  maxGradConfig,\n  maxGradConfig,\n  maximumGradConfig,\n  maxPool3DGradConfig,\n  maxPoolGradConfig,\n  meanGradConfig,\n  minGradConfig,\n  minimumGradConfig,\n  mirrorPadGradConfig,\n  modGradConfig,\n  multiplyGradConfig,\n  negGradConfig,\n  oneHotGradConfig,\n  onesLikeGradConfig,\n  packGradConfig,\n  padV2GradConfig,\n  padV2GradConfig,\n  powGradConfig,\n  preluGradConfig,\n  prodGradConfig,\n  reciprocalGradConfig,\n  relu6GradConfig,\n  reluGradConfig,\n  reshapeGradConfig,\n  resizeBilinearGradConfig,\n  resizeNearestNeighborGradConfig,\n  reverseGradConfig,\n  roundGradConfig,\n  rsqrtGradConfig,\n  selectGradConfig,\n  seluGradConfig,\n  sigmoidGradConfig,\n  signGradConfig,\n  sinGradConfig,\n  sinhGradConfig,\n  sliceGradConfig,\n  softmaxGradConfig,\n  softplusGradConfig,\n  spaceToBatchNDGradConfig,\n  spaceToBatchNDGradConfig,\n  splitVGradConfig,\n  splitVGradConfig,\n  sqrtGradConfig,\n  squaredDifferenceGradConfig,\n  squareGradConfig,\n  stepGradConfig,\n  subGradConfig,\n  sumGradConfig,\n  tanGradConfig,\n  tanhGradConfig,\n  tileGradConfig,\n  transposeGradConfig,\n  unpackGradConfig,\n  unsortedSegmentSumGradConfig,\n  zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n  registerGradient(gradientConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js\ngetGlobalTensorClass().prototype.abs = function() {\n  this.throwIfDisposed();\n  return abs(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js\ngetGlobalTensorClass().prototype.acos = function() {\n  this.throwIfDisposed();\n  return acos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js\ngetGlobalTensorClass().prototype.acosh = function() {\n  this.throwIfDisposed();\n  return acosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js\ngetGlobalTensorClass().prototype.add = function(b) {\n  this.throwIfDisposed();\n  return add2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js\ngetGlobalTensorClass().prototype.all = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return all(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js\ngetGlobalTensorClass().prototype.any = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return any(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js\ngetGlobalTensorClass().prototype.argMax = function(axis) {\n  this.throwIfDisposed();\n  return argMax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js\ngetGlobalTensorClass().prototype.argMin = function(axis) {\n  this.throwIfDisposed();\n  return argMin(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js\ngetGlobalTensorClass().prototype.asScalar = function() {\n  this.throwIfDisposed();\n  assert(this.size === 1, () => \"The array must have only 1 element.\");\n  return reshape(this, []);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js\ngetGlobalTensorClass().prototype.asType = function(dtype) {\n  this.throwIfDisposed();\n  return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js\ngetGlobalTensorClass().prototype.as1D = function() {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js\ngetGlobalTensorClass().prototype.as2D = function(rows, columns) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js\ngetGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js\ngetGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js\ngetGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js\ngetGlobalTensorClass().prototype.asin = function() {\n  this.throwIfDisposed();\n  return asin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js\ngetGlobalTensorClass().prototype.asinh = function() {\n  this.throwIfDisposed();\n  return asinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js\ngetGlobalTensorClass().prototype.atan = function() {\n  this.throwIfDisposed();\n  return atan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js\ngetGlobalTensorClass().prototype.atan2 = function(b) {\n  this.throwIfDisposed();\n  return atan2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js\ngetGlobalTensorClass().prototype.atanh = function() {\n  this.throwIfDisposed();\n  return atanh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js\ngetGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return avgPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js\ngetGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {\n  this.throwIfDisposed();\n  return batchToSpaceND(this, blockShape, crops);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js\ngetGlobalTensorClass().prototype.batchNorm = function(mean5, variance, offset, scale2, varianceEpsilon) {\n  this.throwIfDisposed();\n  return batchNorm(this, mean5, variance, offset, scale2, varianceEpsilon);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js\ngetGlobalTensorClass().prototype.broadcastTo = function(shape) {\n  this.throwIfDisposed();\n  return broadcastTo(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js\ngetGlobalTensorClass().prototype.cast = function(dtype) {\n  this.throwIfDisposed();\n  return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js\ngetGlobalTensorClass().prototype.ceil = function() {\n  this.throwIfDisposed();\n  return ceil(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js\ngetGlobalTensorClass().prototype.clipByValue = function(min7, max7) {\n  this.throwIfDisposed();\n  return clipByValue(this, min7, max7);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js\ngetGlobalTensorClass().prototype.concat = function(x, axis) {\n  this.throwIfDisposed();\n  if (x instanceof Tensor) {\n    x = [x];\n  }\n  return concat([this, ...x], axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js\ngetGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js\ngetGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js\ngetGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js\ngetGlobalTensorClass().prototype.cos = function() {\n  this.throwIfDisposed();\n  return cos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js\ngetGlobalTensorClass().prototype.cosh = function() {\n  this.throwIfDisposed();\n  return cosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js\ngetGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) {\n  this.throwIfDisposed();\n  return cumprod(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js\ngetGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {\n  this.throwIfDisposed();\n  return cumsum(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js\ngetGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {\n  this.throwIfDisposed();\n  return depthToSpace(this, blockSize, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js\ngetGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n  this.throwIfDisposed();\n  return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js\ngetGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) {\n  this.throwIfDisposed();\n  return dilation2d(this, filter, strides, pad3, dilations, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js\ngetGlobalTensorClass().prototype.divNoNan = function(b) {\n  this.throwIfDisposed();\n  return divNoNan(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js\ngetGlobalTensorClass().prototype.div = function(b) {\n  this.throwIfDisposed();\n  return div(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js\ngetGlobalTensorClass().prototype.dot = function(b) {\n  this.throwIfDisposed();\n  return dot(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js\ngetGlobalTensorClass().prototype.elu = function() {\n  this.throwIfDisposed();\n  return elu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js\ngetGlobalTensorClass().prototype.equal = function(b) {\n  this.throwIfDisposed();\n  return equal(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js\ngetGlobalTensorClass().prototype.erf = function() {\n  this.throwIfDisposed();\n  return erf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js\ngetGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return euclideanNorm(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js\ngetGlobalTensorClass().prototype.exp = function() {\n  this.throwIfDisposed();\n  return exp(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js\ngetGlobalTensorClass().prototype.expandDims = function(axis) {\n  this.throwIfDisposed();\n  return expandDims(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js\ngetGlobalTensorClass().prototype.expm1 = function() {\n  this.throwIfDisposed();\n  return expm1(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js\ngetGlobalTensorClass().prototype.fft = function() {\n  this.throwIfDisposed();\n  return fft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js\ngetGlobalTensorClass().prototype.flatten = function() {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js\ngetGlobalTensorClass().prototype.floor = function() {\n  this.throwIfDisposed();\n  return floor(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js\ngetGlobalTensorClass().prototype.floorDiv = function(b) {\n  this.throwIfDisposed();\n  return floorDiv(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js\ngetGlobalTensorClass().prototype.gather = function(indices, axis) {\n  this.throwIfDisposed();\n  return gather(this, indices, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js\ngetGlobalTensorClass().prototype.greaterEqual = function(b) {\n  this.throwIfDisposed();\n  return greaterEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js\ngetGlobalTensorClass().prototype.greater = function(b) {\n  this.throwIfDisposed();\n  return greater(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js\ngetGlobalTensorClass().prototype.ifft = function() {\n  this.throwIfDisposed();\n  return ifft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js\ngetGlobalTensorClass().prototype.irfft = function() {\n  this.throwIfDisposed();\n  return irfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js\ngetGlobalTensorClass().prototype.isFinite = function() {\n  this.throwIfDisposed();\n  return isFinite2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js\ngetGlobalTensorClass().prototype.isInf = function() {\n  this.throwIfDisposed();\n  return isInf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js\ngetGlobalTensorClass().prototype.isNaN = function() {\n  this.throwIfDisposed();\n  return isNaN2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js\ngetGlobalTensorClass().prototype.leakyRelu = function(alpha) {\n  this.throwIfDisposed();\n  return leakyRelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js\ngetGlobalTensorClass().prototype.lessEqual = function(b) {\n  this.throwIfDisposed();\n  return lessEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js\ngetGlobalTensorClass().prototype.less = function(b) {\n  this.throwIfDisposed();\n  return less(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js\ngetGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {\n  this.throwIfDisposed();\n  return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js\ngetGlobalTensorClass().prototype.logSigmoid = function() {\n  this.throwIfDisposed();\n  return logSigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js\ngetGlobalTensorClass().prototype.logSoftmax = function(axis) {\n  this.throwIfDisposed();\n  return logSoftmax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js\ngetGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return logSumExp(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js\ngetGlobalTensorClass().prototype.log = function() {\n  this.throwIfDisposed();\n  return log2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js\ngetGlobalTensorClass().prototype.log1p = function() {\n  this.throwIfDisposed();\n  return log1p(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js\ngetGlobalTensorClass().prototype.logicalAnd = function(b) {\n  this.throwIfDisposed();\n  return logicalAnd(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js\ngetGlobalTensorClass().prototype.logicalNot = function() {\n  this.throwIfDisposed();\n  return logicalNot(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js\ngetGlobalTensorClass().prototype.logicalOr = function(b) {\n  this.throwIfDisposed();\n  return logicalOr(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js\ngetGlobalTensorClass().prototype.logicalXor = function(b) {\n  this.throwIfDisposed();\n  return logicalXor(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js\ngetGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {\n  this.throwIfDisposed();\n  return matMul(this, b, transposeA, transposeB);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js\ngetGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return maxPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js\ngetGlobalTensorClass().prototype.max = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return max(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js\ngetGlobalTensorClass().prototype.maximum = function(b) {\n  this.throwIfDisposed();\n  return maximum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js\ngetGlobalTensorClass().prototype.mean = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return mean(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js\ngetGlobalTensorClass().prototype.min = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return min(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js\ngetGlobalTensorClass().prototype.minimum = function(b) {\n  this.throwIfDisposed();\n  return minimum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js\ngetGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {\n  this.throwIfDisposed();\n  return mirrorPad(this, paddings, mode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js\ngetGlobalTensorClass().prototype.mod = function(b) {\n  this.throwIfDisposed();\n  return mod(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js\ngetGlobalTensorClass().prototype.mul = function(b) {\n  this.throwIfDisposed();\n  return mul(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js\ngetGlobalTensorClass().prototype.neg = function() {\n  this.throwIfDisposed();\n  return neg(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js\ngetGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {\n  this.throwIfDisposed();\n  return norm(this, ord, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js\ngetGlobalTensorClass().prototype.notEqual = function(b) {\n  this.throwIfDisposed();\n  return notEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js\ngetGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {\n  this.throwIfDisposed();\n  return oneHot(this, depth, onValue, offValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js\ngetGlobalTensorClass().prototype.onesLike = function() {\n  this.throwIfDisposed();\n  return onesLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js\ngetGlobalTensorClass().prototype.pad = function(paddings, constantValue) {\n  this.throwIfDisposed();\n  return pad(this, paddings, constantValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js\ngetGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {\n  this.throwIfDisposed();\n  return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js\ngetGlobalTensorClass().prototype.pow = function(exp5) {\n  this.throwIfDisposed();\n  return pow(this, exp5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js\ngetGlobalTensorClass().prototype.prelu = function(alpha) {\n  this.throwIfDisposed();\n  return prelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js\ngetGlobalTensorClass().prototype.prod = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return prod(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js\ngetGlobalTensorClass().prototype.reciprocal = function() {\n  this.throwIfDisposed();\n  return reciprocal(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js\ngetGlobalTensorClass().prototype.relu = function() {\n  this.throwIfDisposed();\n  return relu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js\ngetGlobalTensorClass().prototype.relu6 = function() {\n  this.throwIfDisposed();\n  return relu6(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js\ngetGlobalTensorClass().prototype.reshapeAs = function(x) {\n  this.throwIfDisposed();\n  return reshape(this, x.shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js\ngetGlobalTensorClass().prototype.reshape = function(shape) {\n  this.throwIfDisposed();\n  return reshape(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js\ngetGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {\n  this.throwIfDisposed();\n  return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js\ngetGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {\n  this.throwIfDisposed();\n  return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js\ngetGlobalTensorClass().prototype.reverse = function(axis) {\n  this.throwIfDisposed();\n  return reverse(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js\ngetGlobalTensorClass().prototype.rfft = function() {\n  this.throwIfDisposed();\n  return rfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js\ngetGlobalTensorClass().prototype.round = function() {\n  this.throwIfDisposed();\n  return round2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js\ngetGlobalTensorClass().prototype.rsqrt = function() {\n  this.throwIfDisposed();\n  return rsqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js\ngetGlobalTensorClass().prototype.selu = function() {\n  this.throwIfDisposed();\n  return selu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js\ngetGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) {\n  this.throwIfDisposed();\n  return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js\ngetGlobalTensorClass().prototype.sigmoid = function() {\n  this.throwIfDisposed();\n  return sigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js\ngetGlobalTensorClass().prototype.sign = function() {\n  this.throwIfDisposed();\n  return sign(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js\ngetGlobalTensorClass().prototype.sin = function() {\n  this.throwIfDisposed();\n  return sin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js\ngetGlobalTensorClass().prototype.sinh = function() {\n  this.throwIfDisposed();\n  return sinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js\ngetGlobalTensorClass().prototype.slice = function(begin, size) {\n  this.throwIfDisposed();\n  return slice(this, begin, size);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js\ngetGlobalTensorClass().prototype.softmax = function(dim) {\n  this.throwIfDisposed();\n  return softmax(this, dim);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js\ngetGlobalTensorClass().prototype.softplus = function() {\n  this.throwIfDisposed();\n  return softplus(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js\ngetGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {\n  this.throwIfDisposed();\n  return spaceToBatchND(this, blockShape, paddings);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js\ngetGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {\n  this.throwIfDisposed();\n  return split(this, numOrSizeSplits, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js\ngetGlobalTensorClass().prototype.sqrt = function() {\n  this.throwIfDisposed();\n  return sqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js\ngetGlobalTensorClass().prototype.square = function() {\n  this.throwIfDisposed();\n  return square(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js\ngetGlobalTensorClass().prototype.squaredDifference = function(b) {\n  this.throwIfDisposed();\n  return squaredDifference(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js\ngetGlobalTensorClass().prototype.squeeze = function(axis) {\n  this.throwIfDisposed();\n  return squeeze(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js\ngetGlobalTensorClass().prototype.stack = function(x, axis) {\n  this.throwIfDisposed();\n  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n  return stack(tensorsToBeStacked, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js\ngetGlobalTensorClass().prototype.step = function(alpha) {\n  this.throwIfDisposed();\n  return step(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js\ngetGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n  this.throwIfDisposed();\n  return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js\ngetGlobalTensorClass().prototype.sub = function(b) {\n  this.throwIfDisposed();\n  return sub(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js\ngetGlobalTensorClass().prototype.sum = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return sum2(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js\ngetGlobalTensorClass().prototype.tan = function() {\n  this.throwIfDisposed();\n  return tan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js\ngetGlobalTensorClass().prototype.tanh = function() {\n  this.throwIfDisposed();\n  return tanh2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js\ngetGlobalTensorClass().prototype.tile = function(reps) {\n  this.throwIfDisposed();\n  return tile(this, reps);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js\ngetGlobalTensorClass().prototype.toBool = function() {\n  this.throwIfDisposed();\n  return cast(this, \"bool\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js\ngetGlobalTensorClass().prototype.toFloat = function() {\n  this.throwIfDisposed();\n  return cast(this, \"float32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js\ngetGlobalTensorClass().prototype.toInt = function() {\n  this.throwIfDisposed();\n  return cast(this, \"int32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js\ngetGlobalTensorClass().prototype.topk = function(k, sorted) {\n  this.throwIfDisposed();\n  return topk(this, k, sorted);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js\ngetGlobalTensorClass().prototype.transpose = function(perm) {\n  this.throwIfDisposed();\n  return transpose(this, perm);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js\ngetGlobalTensorClass().prototype.unique = function(axis) {\n  this.throwIfDisposed();\n  return unique(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js\ngetGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {\n  this.throwIfDisposed();\n  return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js\ngetGlobalTensorClass().prototype.unstack = function(axis) {\n  this.throwIfDisposed();\n  return unstack(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js\ngetGlobalTensorClass().prototype.where = function(condition, x) {\n  this.throwIfDisposed();\n  return where(condition, this, x);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js\ngetGlobalTensorClass().prototype.zerosLike = function() {\n  this.throwIfDisposed();\n  return zerosLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/errors.js\nvar AttributeError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, AttributeError.prototype);\n  }\n};\nvar RuntimeError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, RuntimeError.prototype);\n  }\n};\nvar ValueError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, ValueError.prototype);\n  }\n};\nvar NotImplementedError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, NotImplementedError.prototype);\n  }\n};\nvar AssertionError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, AssertionError.prototype);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js\nvar LruCache = class {\n  constructor(maxEntries) {\n    this.maxEntries = maxEntries || 100;\n    this.cache = /* @__PURE__ */ new Map();\n  }\n  get(key) {\n    let entry;\n    if (this.cache.has(key)) {\n      entry = this.cache.get(key);\n      this.cache.delete(key);\n      this.cache.set(key, entry);\n    }\n    return entry;\n  }\n  put(key, value) {\n    if (this.cache.has(key)) {\n      this.cache.delete(key);\n    } else if (this.cache.size >= this.maxEntries) {\n      const keyToDelete = this.cache.keys().next().value;\n      this.cache.delete(keyToDelete);\n    }\n    this.cache.set(key, value);\n  }\n  getMaxEntries() {\n    return this.maxEntries;\n  }\n  setMaxEntries(maxEntries) {\n    if (maxEntries < 0) {\n      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);\n    }\n    if (this.maxEntries > maxEntries) {\n      for (let i2 = 0; i2 < this.maxEntries - maxEntries; i2++) {\n        const keyToDelete = this.cache.keys().next().value;\n        this.cache.delete(keyToDelete);\n      }\n    }\n    this.maxEntries = maxEntries;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js\nfunction pyListRepeat(value, numValues) {\n  if (Array.isArray(value)) {\n    let newArray = [];\n    for (let i2 = 0; i2 < numValues; i2++) {\n      newArray = newArray.concat(value);\n    }\n    return newArray;\n  } else {\n    const newArray = new Array(numValues);\n    newArray.fill(value);\n    return newArray;\n  }\n}\nfunction assert2(val, message) {\n  if (!val) {\n    throw new AssertionError(message);\n  }\n}\nfunction count(array2, refernce) {\n  let counter = 0;\n  for (const item of array2) {\n    if (item === refernce) {\n      counter++;\n    }\n  }\n  return counter;\n}\nfunction singletonOrArray(xs) {\n  if (xs.length === 1) {\n    return xs[0];\n  }\n  return xs;\n}\nfunction toList(x) {\n  if (Array.isArray(x)) {\n    return x;\n  }\n  return [x];\n}\nfunction toSnakeCase(name) {\n  const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, \"$1_$2\");\n  const insecure = intermediate.replace(/([a-z])([A-Z])/g, \"$1_$2\").toLowerCase();\n  if (insecure[0] !== \"_\") {\n    return insecure;\n  }\n  return \"private\" + insecure;\n}\nfunction toCamelCase(identifier) {\n  if (identifier.length <= 1) {\n    return identifier;\n  }\n  if (identifier.indexOf(\"_\") === -1) {\n    return identifier;\n  }\n  return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\nvar _GLOBAL_CUSTOM_OBJECTS = {};\nfunction serializeKerasObject(instance) {\n  if (instance === null || instance === void 0) {\n    return null;\n  }\n  const dict = {};\n  dict[\"className\"] = instance.getClassName();\n  dict[\"config\"] = instance.getConfig();\n  return dict;\n}\nfunction convertNDArrayScalarsInConfig(config) {\n  if (config == null || typeof config !== \"object\") {\n    return;\n  } else if (Array.isArray(config)) {\n    config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));\n  } else {\n    const fields = Object.keys(config);\n    for (const field of fields) {\n      const value = config[field];\n      if (value != null && typeof value === \"object\") {\n        if (!Array.isArray(value) && value[\"type\"] === \"ndarray\" && typeof value[\"value\"] === \"number\") {\n          config[field] = value[\"value\"];\n        } else {\n          convertNDArrayScalarsInConfig(value);\n        }\n      }\n    }\n  }\n}\nfunction deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = \"object\", fastWeightInit = false) {\n  if (typeof identifier === \"string\") {\n    const functionName = identifier;\n    let fn;\n    if (functionName in customObjects) {\n      fn = customObjects[functionName];\n    } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n      fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n    } else {\n      fn = moduleObjects[functionName];\n      if (fn == null) {\n        throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n      }\n    }\n    return fn;\n  } else {\n    const config = identifier;\n    if (config[\"className\"] == null || config[\"config\"] == null) {\n      throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.\n'className' and 'config' must set.`);\n    }\n    const className = config[\"className\"];\n    let cls, fromConfig;\n    if (className in customObjects) {\n      [cls, fromConfig] = customObjects[className];\n    } else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n      [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS[\"className\"];\n    } else if (className in moduleObjects) {\n      [cls, fromConfig] = moduleObjects[className];\n    }\n    if (cls == null) {\n      throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n    }\n    if (fromConfig != null) {\n      const customObjectsCombined = {};\n      for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n        customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n      }\n      for (const key of Object.keys(customObjects)) {\n        customObjectsCombined[key] = customObjects[key];\n      }\n      const nestedConfig = config[\"config\"];\n      nestedConfig[\"customObjects\"] = customObjectsCombined;\n      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n      for (const key of Object.keys(customObjects)) {\n        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n      }\n      convertNDArrayScalarsInConfig(config[\"config\"]);\n      const returnObj = fromConfig(cls, config[\"config\"], customObjects, fastWeightInit);\n      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n      return returnObj;\n    } else {\n      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n      for (const key of Object.keys(customObjects)) {\n        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n      }\n      const returnObj = new cls(config[\"config\"]);\n      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n      return returnObj;\n    }\n  }\n}\nfunction numberCompare(a6, b) {\n  return a6 < b ? -1 : a6 > b ? 1 : 0;\n}\nfunction reverseNumberCompare(a6, b) {\n  return -1 * numberCompare(a6, b);\n}\nfunction unique2(xs) {\n  if (xs == null) {\n    return xs;\n  }\n  const out = [];\n  for (const x of xs) {\n    if (out.indexOf(x) === -1) {\n      out.push(x);\n    }\n  }\n  return out;\n}\nfunction isObjectEmpty(obj) {\n  if (obj == null) {\n    throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n  }\n  for (const key in obj) {\n    if (obj.hasOwnProperty(key)) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction checkStringTypeUnionValue(values, label, value) {\n  if (value == null) {\n    return;\n  }\n  if (values.indexOf(value) < 0) {\n    throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n  }\n}\nfunction checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n  assert2(minLength >= 0);\n  assert2(maxLength >= minLength);\n  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e2) => typeof e2 === expectedType);\n}\nfunction assertPositiveInteger(value, name) {\n  if (Array.isArray(value)) {\n    util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n    value.forEach((v, i2) => assertPositiveInteger(v, `element ${i2 + 1} of ${name}`));\n  } else {\n    util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);\n  }\n}\nfunction formatAsFriendlyString(value) {\n  if (value === null) {\n    return \"null\";\n  } else if (Array.isArray(value)) {\n    return \"[\" + value.map((v) => formatAsFriendlyString(v)).join(\",\") + \"]\";\n  } else if (typeof value === \"string\") {\n    return `\"${value}\"`;\n  } else {\n    return `${value}`;\n  }\n}\nfunction debounce(f, waitMs, nowFunc) {\n  let lastTime = nowFunc != null ? nowFunc() : util_exports.now();\n  let lastResult;\n  const f2 = (...args) => {\n    const now2 = nowFunc != null ? nowFunc() : util_exports.now();\n    if (now2 - lastTime < waitMs) {\n      return lastResult;\n    }\n    lastTime = now2;\n    lastResult = f(...args);\n    return lastResult;\n  };\n  return f2;\n}\nfunction mapActivationToFusedKernel(activationName) {\n  if (activationName === \"relu\") {\n    return \"relu\";\n  }\n  if (activationName === \"linear\") {\n    return \"linear\";\n  }\n  if (activationName === \"elu\") {\n    return \"elu\";\n  }\n  return null;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js\nvar _nextUniqueTensorId = 0;\nfunction getNextUniqueTensorId() {\n  return _nextUniqueTensorId++;\n}\nvar _uidPrefixes = {};\nfunction getUid(prefix = \"\") {\n  if (!(prefix in _uidPrefixes)) {\n    _uidPrefixes[prefix] = 0;\n  }\n  _uidPrefixes[prefix] += 1;\n  return prefix + _uidPrefixes[prefix].toString();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js\nvar VALID_DATA_FORMAT_VALUES = [\"channelsFirst\", \"channelsLast\"];\nvar VALID_INTERPOLATION_FORMAT_VALUES = [\"nearest\", \"bilinear\"];\nvar VALID_PADDING_MODE_VALUES = [\"valid\", \"same\", \"causal\"];\nvar VALID_POOL_MODE_VALUES = [\"max\", \"avg\"];\nvar VALID_BIDIRECTIONAL_MERGE_MODES = [\"sum\", \"mul\", \"concat\", \"ave\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/common.js\nvar nameMap = /* @__PURE__ */ new Map();\nfunction checkDataFormat(value) {\n  checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, \"DataFormat\", value);\n}\nfunction checkInterpolationFormat(value) {\n  checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, \"InterpolationFormat\", value);\n}\nfunction checkPaddingMode(value) {\n  checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, \"PaddingMode\", value);\n}\nfunction checkPoolMode(value) {\n  checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, \"PoolMode\", value);\n}\nvar _nameScopeStack = [];\nvar _nameScopeDivider = \"/\";\nfunction nameScope(name, fn) {\n  _nameScopeStack.push(name);\n  try {\n    const val = fn();\n    _nameScopeStack.pop();\n    return val;\n  } catch (e2) {\n    _nameScopeStack.pop();\n    throw e2;\n  }\n}\nfunction currentNameScopePrefix() {\n  if (_nameScopeStack.length === 0) {\n    return \"\";\n  } else {\n    return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n  }\n}\nfunction getScopedTensorName(tensorName) {\n  if (!isValidTensorName(tensorName)) {\n    throw new Error(\"Not a valid tensor name: '\" + tensorName + \"'\");\n  }\n  return currentNameScopePrefix() + tensorName;\n}\nfunction getUniqueTensorName(scopedName) {\n  if (!isValidTensorName(scopedName)) {\n    throw new Error(\"Not a valid tensor name: '\" + scopedName + \"'\");\n  }\n  if (!nameMap.has(scopedName)) {\n    nameMap.set(scopedName, 0);\n  }\n  const index = nameMap.get(scopedName);\n  nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n  if (index > 0) {\n    const result = `${scopedName}_${index}`;\n    nameMap.set(result, 1);\n    return result;\n  } else {\n    return scopedName;\n  }\n}\nvar tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\nfunction isValidTensorName(name) {\n  return !!name.match(tensorNameRegex);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js\nfunction isInteger(x) {\n  return x === parseInt(x.toString(), 10);\n}\nfunction arrayProd(array2, begin, end) {\n  if (begin == null) {\n    begin = 0;\n  }\n  if (end == null) {\n    end = array2.length;\n  }\n  let prod6 = 1;\n  for (let i2 = begin; i2 < end; ++i2) {\n    prod6 *= array2[i2];\n  }\n  return prod6;\n}\nfunction min2(array2) {\n  if (array2.length === 0) {\n    return Number.NaN;\n  }\n  let min7 = Number.POSITIVE_INFINITY;\n  for (let i2 = 0; i2 < array2.length; i2++) {\n    const value = array2[i2];\n    if (value < min7) {\n      min7 = value;\n    }\n  }\n  return min7;\n}\nfunction max2(array2) {\n  if (array2.length === 0) {\n    return Number.NaN;\n  }\n  let max7 = Number.NEGATIVE_INFINITY;\n  for (let i2 = 0; i2 < array2.length; i2++) {\n    const value = array2[i2];\n    if (value > max7) {\n      max7 = value;\n    }\n  }\n  return max7;\n}\nfunction range2(begin, end) {\n  if (end < begin) {\n    throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n  }\n  const out = [];\n  for (let i2 = begin; i2 < end; ++i2) {\n    out.push(i2);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js\nvar _epsilon;\nfunction epsilon() {\n  if (_epsilon == null) {\n    _epsilon = backend().epsilon();\n  }\n  return _epsilon;\n}\nfunction imageDataFormat() {\n  return \"channelsLast\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js\nfunction cast2(x, dtype) {\n  return cast(x, dtype);\n}\nfunction expandDims2(x, axis = -1) {\n  const outShape = x.shape.slice();\n  if (axis < 0) {\n    axis = outShape.length + axis + 1;\n  }\n  outShape.splice(axis, 0, 1);\n  return reshape(x, outShape);\n}\nfunction repeat(x, n) {\n  return tidy(() => {\n    if (x.shape.length !== 2) {\n      throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);\n    }\n    const y = expandDims2(x, 1);\n    return tile2(y, [1, n, 1]);\n  });\n}\nfunction flatten2(x) {\n  const newShape = [arrayProd(x.shape)];\n  return reshape(x, newShape);\n}\nfunction batchFlatten(x) {\n  if (x.rank <= 1) {\n    throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n  }\n  const newShape = [x.shape[0], arrayProd(x.shape, 1)];\n  return reshape(x, newShape);\n}\nfunction sliceAlongFirstAxis(array2, start, size) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        return slice2d(array2, [start, 0], [size, array2.shape[1]]);\n      case 3:\n        return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);\n      case 4:\n        return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);\n      case 5:\n        return slice(array2, [start, 0, 0, 0, 0], [\n          size,\n          array2.shape[1],\n          array2.shape[2],\n          array2.shape[3],\n          array2.shape[4]\n        ]);\n      case 6:\n        return slice(array2, [start, 0, 0, 0, 0, 0], [\n          size,\n          array2.shape[1],\n          array2.shape[2],\n          array2.shape[3],\n          array2.shape[4],\n          array2.shape[5]\n        ]);\n      default:\n        throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction sliceAlongLastAxis(array2, start, size) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        return slice2d(array2, [0, start], [array2.shape[0], size]);\n      case 3:\n        return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);\n      case 4:\n        return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);\n      default:\n        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction sliceAlongAxis(array2, start, size, axis) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      case 3:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);\n          case 3:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      case 4:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);\n          case 3:\n            return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);\n          case 4:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      default:\n        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction concatenate(tensors, axis = -1) {\n  let rank;\n  if (axis < 0) {\n    rank = tensors[0].rank;\n    if (rank !== 0) {\n      axis = rank;\n    } else {\n      axis = 0;\n    }\n  }\n  if (axis === tensors[0].rank) {\n    axis = -1;\n  }\n  return concat(tensors, axis);\n}\nfunction concatAlongFirstAxis(a6, b) {\n  switch (a6.rank) {\n    case 1:\n      return concat1d([a6, b]);\n    case 2:\n      return concat2d([a6, b], 0);\n    case 3:\n      return concat3d([a6, b], 0);\n    case 4:\n      return concat4d([a6, b], 0);\n    default:\n      throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a6.rank}`);\n  }\n}\nfunction tile2(x, n) {\n  if (!Array.isArray(n)) {\n    n = [n];\n  }\n  if (x.rank !== n.length) {\n    throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);\n  }\n  return tile(x, n);\n}\nfunction randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {\n  return randomNormal(shape, mean5, stddev, dtype, seed);\n}\nfunction dot2(a6, b, activation2, bias) {\n  if (a6.rank < 2 || b.rank < 2) {\n    throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a6.shape} and y shape = ${b.shape}`);\n  }\n  if (b.rank >= 3) {\n    const xLastDim = a6.shape.slice(-1)[0];\n    const ySecondLastDim = b.shape.slice(-2)[0];\n    if (xLastDim !== ySecondLastDim) {\n      throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a6.shape} and  y shape = ${b.shape}`);\n    }\n  }\n  if (a6.rank === 2 && b.rank === 2) {\n    const transposeA = false;\n    const transposeB = false;\n    return fused_ops_exports.matMul({\n      a: a6,\n      b,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a6.rank, bias, imageDataFormat()) : null,\n      activation: activation2\n    });\n  } else {\n    const aFirstDims = a6.shape.slice();\n    const aLastDim = aFirstDims.pop();\n    a6 = reshape(a6, [-1, aLastDim]);\n    const bShape = b.shape.slice();\n    const bLastDim = bShape.pop();\n    const ySecondLastDim = bShape.pop();\n    const yOtherDims = [...bShape, bLastDim];\n    const perm = Array.from({ length: b.rank }, (_, i2) => {\n      if (i2 === 0) {\n        return b.rank - 2;\n      } else if (i2 <= b.rank - 2) {\n        return i2 - 1;\n      }\n      return i2;\n    });\n    b = reshape(transpose(b, perm), [ySecondLastDim, -1]);\n    const outputShape = [...aFirstDims, ...yOtherDims];\n    const transposeA = false;\n    const transposeB = false;\n    return reshape(fused_ops_exports.matMul({\n      a: a6,\n      b,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a6.rank, bias, imageDataFormat()) : null,\n      activation: activation2\n    }), outputShape);\n  }\n}\nfunction gather2(reference, indices, axis) {\n  return tidy(() => {\n    if (Array.isArray(indices)) {\n      indices = tensor1d(indices, \"int32\");\n    } else {\n      indices = cast(indices, \"int32\");\n    }\n    return gather(reference, indices, axis);\n  });\n}\nfunction square2(x) {\n  return mul(x, x);\n}\nfunction reshapeBias(xRank, bias, dataFormat) {\n  const biasShape = bias.shape;\n  if (bias.rank !== 1 && bias.rank !== xRank) {\n    throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);\n  }\n  if (xRank === 5) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1, 1, 1]);\n      } else {\n        return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, 1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 4) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1, 1]);\n      } else {\n        return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 3) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1]);\n      } else {\n        return reshape(bias, [1, biasShape[1], biasShape[0]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank < 3) {\n    return bias;\n  }\n  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\nfunction biasAdd(x, bias, dataFormat) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    return add2(x, reshapeBias(x.rank, bias, dataFormat));\n  });\n}\nfunction elu2(x, alpha = 1) {\n  if (alpha !== 1) {\n    throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);\n  }\n  return elu(x);\n}\nfunction softsign(x) {\n  return tidy(() => div(x, add2(abs(x), 1)));\n}\nfunction dropout2(x, level, noiseShape, seed) {\n  return tidy(() => dropout(x, level, noiseShape, seed));\n}\nfunction hardSigmoid(x) {\n  return tidy(() => {\n    const y = add2(0.5, mul(0.2, x));\n    return clipByValue(y, 0, 1);\n  });\n}\nfunction inTrainPhase(x, alt, training = false) {\n  return training ? x() : alt();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js\nvar VALID_FAN_MODE_VALUES = [\"fanIn\", \"fanOut\", \"fanAvg\"];\nvar VALID_DISTRIBUTION_VALUES = [\"normal\", \"uniform\", \"truncatedNormal\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/initializers.js\nfunction checkFanMode(value) {\n  checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, \"FanMode\", value);\n}\nfunction checkDistribution(value) {\n  checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, \"Distribution\", value);\n}\nvar Initializer = class extends serialization_exports.Serializable {\n  fromConfigUsesCustomObjects() {\n    return false;\n  }\n  getConfig() {\n    return {};\n  }\n};\nvar Zeros = class extends Initializer {\n  apply(shape, dtype) {\n    return zeros(shape, dtype);\n  }\n};\nZeros.className = \"Zeros\";\nserialization_exports.registerClass(Zeros);\nvar Ones = class extends Initializer {\n  apply(shape, dtype) {\n    return ones2(shape, dtype);\n  }\n};\nOnes.className = \"Ones\";\nserialization_exports.registerClass(Ones);\nvar Constant = class extends Initializer {\n  constructor(args) {\n    super();\n    if (typeof args !== \"object\") {\n      throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n    }\n    if (args.value === void 0) {\n      throw new ValueError(`config must have value set but got ${args}`);\n    }\n    this.value = args.value;\n  }\n  apply(shape, dtype) {\n    return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));\n  }\n  getConfig() {\n    return {\n      value: this.value\n    };\n  }\n};\nConstant.className = \"Constant\";\nserialization_exports.registerClass(Constant);\nvar RandomUniform = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MINVAL = -0.05;\n    this.DEFAULT_MAXVAL = 0.05;\n    this.minval = args.minval || this.DEFAULT_MINVAL;\n    this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    return randomUniform(shape, this.minval, this.maxval, dtype);\n  }\n  getConfig() {\n    return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n  }\n};\nRandomUniform.className = \"RandomUniform\";\nserialization_exports.registerClass(RandomUniform);\nvar RandomNormal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MEAN = 0;\n    this.DEFAULT_STDDEV = 0.05;\n    this.mean = args.mean || this.DEFAULT_MEAN;\n    this.stddev = args.stddev || this.DEFAULT_STDDEV;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    dtype = dtype || \"float32\";\n    if (dtype !== \"float32\" && dtype !== \"int32\") {\n      throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);\n  }\n  getConfig() {\n    return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n  }\n};\nRandomNormal.className = \"RandomNormal\";\nserialization_exports.registerClass(RandomNormal);\nvar TruncatedNormal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MEAN = 0;\n    this.DEFAULT_STDDEV = 0.05;\n    this.mean = args.mean || this.DEFAULT_MEAN;\n    this.stddev = args.stddev || this.DEFAULT_STDDEV;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    dtype = dtype || \"float32\";\n    if (dtype !== \"float32\" && dtype !== \"int32\") {\n      throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n    }\n    return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n  }\n  getConfig() {\n    return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n  }\n};\nTruncatedNormal.className = \"TruncatedNormal\";\nserialization_exports.registerClass(TruncatedNormal);\nvar Identity2 = class extends Initializer {\n  constructor(args) {\n    super();\n    this.gain = args.gain != null ? args.gain : 1;\n  }\n  apply(shape, dtype) {\n    return tidy(() => {\n      if (shape.length !== 2 || shape[0] !== shape[1]) {\n        throw new ValueError(\"Identity matrix initializer can only be used for 2D square matrices.\");\n      } else {\n        return mul(this.gain, eye(shape[0]));\n      }\n    });\n  }\n  getConfig() {\n    return { gain: this.gain };\n  }\n};\nIdentity2.className = \"Identity\";\nserialization_exports.registerClass(Identity2);\nfunction computeFans(shape, dataFormat = \"channelsLast\") {\n  let fanIn;\n  let fanOut;\n  checkDataFormat(dataFormat);\n  if (shape.length === 2) {\n    fanIn = shape[0];\n    fanOut = shape[1];\n  } else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n    if (dataFormat === \"channelsFirst\") {\n      const receptiveFieldSize = arrayProd(shape, 2);\n      fanIn = shape[1] * receptiveFieldSize;\n      fanOut = shape[0] * receptiveFieldSize;\n    } else if (dataFormat === \"channelsLast\") {\n      const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n      fanIn = shape[shape.length - 2] * receptiveFieldSize;\n      fanOut = shape[shape.length - 1] * receptiveFieldSize;\n    }\n  } else {\n    const shapeProd = arrayProd(shape);\n    fanIn = Math.sqrt(shapeProd);\n    fanOut = Math.sqrt(shapeProd);\n  }\n  return [fanIn, fanOut];\n}\nvar VarianceScaling = class extends Initializer {\n  constructor(args) {\n    super();\n    if (args.scale < 0) {\n      throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n    }\n    this.scale = args.scale == null ? 1 : args.scale;\n    this.mode = args.mode == null ? \"fanIn\" : args.mode;\n    checkFanMode(this.mode);\n    this.distribution = args.distribution == null ? \"normal\" : args.distribution;\n    checkDistribution(this.distribution);\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    const fans = computeFans(shape);\n    const fanIn = fans[0];\n    const fanOut = fans[1];\n    let scale2 = this.scale;\n    if (this.mode === \"fanIn\") {\n      scale2 /= Math.max(1, fanIn);\n    } else if (this.mode === \"fanOut\") {\n      scale2 /= Math.max(1, fanOut);\n    } else {\n      scale2 /= Math.max(1, (fanIn + fanOut) / 2);\n    }\n    if (this.distribution === \"normal\") {\n      const stddev = Math.sqrt(scale2);\n      dtype = dtype || \"float32\";\n      if (dtype !== \"float32\" && dtype !== \"int32\") {\n        throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n      }\n      return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n    } else {\n      const limit = Math.sqrt(3 * scale2);\n      return randomUniform(shape, -limit, limit, dtype);\n    }\n  }\n  getConfig() {\n    return {\n      scale: this.scale,\n      mode: this.mode,\n      distribution: this.distribution,\n      seed: this.seed\n    };\n  }\n};\nVarianceScaling.className = \"VarianceScaling\";\nserialization_exports.registerClass(VarianceScaling);\nvar GlorotUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanAvg\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nGlorotUniform.className = \"GlorotUniform\";\nserialization_exports.registerClass(GlorotUniform);\nvar GlorotNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanAvg\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nGlorotNormal.className = \"GlorotNormal\";\nserialization_exports.registerClass(GlorotNormal);\nvar HeNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 2,\n      mode: \"fanIn\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nHeNormal.className = \"HeNormal\";\nserialization_exports.registerClass(HeNormal);\nvar HeUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 2,\n      mode: \"fanIn\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nHeUniform.className = \"HeUniform\";\nserialization_exports.registerClass(HeUniform);\nvar LeCunNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanIn\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nLeCunNormal.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunNormal);\nvar LeCunUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanIn\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nLeCunUniform.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunUniform);\nvar Orthogonal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_GAIN = 1;\n    this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n    this.seed = args.seed;\n    if (this.seed != null) {\n      throw new NotImplementedError(\"Random seed is not implemented for Orthogonal Initializer yet.\");\n    }\n  }\n  apply(shape, dtype) {\n    return tidy(() => {\n      if (shape.length < 2) {\n        throw new NotImplementedError(\"Shape must be at least 2D.\");\n      }\n      if (shape[0] * shape[1] > 2e3) {\n        console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);\n      }\n      const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n      const a6 = randomNormal2(normalizedShape, 0, 1, \"float32\");\n      let q = linalg.gramSchmidt(a6);\n      if (shape[0] > shape[1]) {\n        q = transpose(q);\n      }\n      return mul(this.gain, q);\n    });\n  }\n  getConfig() {\n    return {\n      gain: this.gain,\n      seed: this.seed\n    };\n  }\n};\nOrthogonal.className = \"Orthogonal\";\nserialization_exports.registerClass(Orthogonal);\nvar INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"constant\": \"Constant\",\n  \"glorotNormal\": \"GlorotNormal\",\n  \"glorotUniform\": \"GlorotUniform\",\n  \"heNormal\": \"HeNormal\",\n  \"heUniform\": \"HeUniform\",\n  \"identity\": \"Identity\",\n  \"leCunNormal\": \"LeCunNormal\",\n  \"leCunUniform\": \"LeCunUniform\",\n  \"ones\": \"Ones\",\n  \"orthogonal\": \"Orthogonal\",\n  \"randomNormal\": \"RandomNormal\",\n  \"randomUniform\": \"RandomUniform\",\n  \"truncatedNormal\": \"TruncatedNormal\",\n  \"varianceScaling\": \"VarianceScaling\",\n  \"zeros\": \"Zeros\"\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"initializer\");\n}\nfunction serializeInitializer(initializer) {\n  return serializeKerasObject(initializer);\n}\nfunction getInitializer(identifier) {\n  if (typeof identifier === \"string\") {\n    const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    if (className === \"GlorotNormal\") {\n      return new GlorotNormal();\n    } else if (className === \"GlorotUniform\") {\n      return new GlorotUniform();\n    } else if (className === \"HeNormal\") {\n      return new HeNormal();\n    } else if (className === \"HeUniform\") {\n      return new HeUniform();\n    } else if (className === \"LeCunNormal\") {\n      return new LeCunNormal();\n    } else if (className === \"LeCunUniform\") {\n      return new LeCunUniform();\n    } else {\n      const config = {};\n      config[\"className\"] = className;\n      config[\"config\"] = {};\n      return deserializeInitializer(config);\n    }\n  } else if (identifier instanceof Initializer) {\n    return identifier;\n  } else {\n    return deserializeInitializer(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js\nfunction isArrayOfShapes(x) {\n  return Array.isArray(x) && Array.isArray(x[0]);\n}\nfunction normalizeShapeList(x) {\n  if (x.length === 0) {\n    return [];\n  }\n  if (!Array.isArray(x[0])) {\n    return [x];\n  }\n  return x;\n}\nfunction getExactlyOneTensor(xs) {\n  let x;\n  if (Array.isArray(xs)) {\n    if (xs.length !== 1) {\n      throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n    }\n    x = xs[0];\n  } else {\n    x = xs;\n  }\n  return x;\n}\nfunction getExactlyOneShape(shapes) {\n  if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n    if (shapes.length === 1) {\n      shapes = shapes;\n      return shapes[0];\n    } else {\n      throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n    }\n  } else {\n    return shapes;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js\nfunction countParamsInWeights(weights) {\n  let count2 = 0;\n  for (const weight of weights) {\n    if (weight.shape.length === 0) {\n      count2 += 1;\n    } else {\n      count2 += weight.shape.reduce((a6, b) => a6 * b);\n    }\n  }\n  return count2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/variables.js\nvar DEFAULT_VARIABLE_NAME_PREFIX = \"Variable\";\nvar LayerVariable = class {\n  constructor(val, dtype = \"float32\", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n    this.dtype = dtype == null ? \"float32\" : dtype;\n    this.shape = val.shape;\n    this.id = getNextUniqueTensorId();\n    name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n    this.originalName = getScopedTensorName(name);\n    this.name = getUniqueTensorName(this.originalName);\n    this.trainable_ = trainable;\n    this.constraint = constraint;\n    this.val = variable(val, this.trainable_, this.name, this.dtype);\n  }\n  read() {\n    this.assertNotDisposed();\n    return this.val;\n  }\n  write(newVal) {\n    this.assertNotDisposed();\n    checkShapesMatch(this.val, newVal);\n    if (this.val.id !== newVal.id) {\n      this.val.assign(newVal);\n      if (this.constraint != null) {\n        this.val.assign(this.constraint.apply(this.val));\n      }\n    }\n    return this;\n  }\n  dispose() {\n    this.assertNotDisposed();\n    this.val.dispose();\n  }\n  assertNotDisposed() {\n    if (this.val.isDisposed) {\n      throw new Error(`LayersVariable ${this.name} is already disposed.`);\n    }\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this.trainable_ = trainable;\n    this.val.trainable = trainable;\n  }\n};\nfunction checkShapesMatch(x, y) {\n  if (x.shape.toString() !== y.shape.toString()) {\n    throw new Error(\"Shape mismatch: \" + JSON.stringify(x.shape) + \" vs. \" + JSON.stringify(y.shape));\n  }\n}\nfunction batchGetValue(xs) {\n  return xs.map((x) => x.read());\n}\nfunction batchSetValue(variablesAndValues) {\n  variablesAndValues.forEach((variableAndValue) => {\n    const variable2 = variableAndValue[0];\n    variable2.write(variableAndValue[1]);\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js\nvar InputSpec = class {\n  constructor(args) {\n    this.dtype = args.dtype;\n    this.shape = args.shape;\n    if (args.shape != null) {\n      this.ndim = args.shape.length;\n    } else {\n      this.ndim = args.ndim;\n    }\n    this.maxNDim = args.maxNDim;\n    this.minNDim = args.minNDim;\n    this.axes = args.axes || {};\n  }\n};\nvar SymbolicTensor = class {\n  constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n    this.dtype = dtype;\n    this.shape = shape;\n    this.sourceLayer = sourceLayer;\n    this.inputs = inputs;\n    this.callArgs = callArgs;\n    this.outputTensorIndex = outputTensorIndex;\n    this.id = getNextUniqueTensorId();\n    if (name != null) {\n      this.originalName = getScopedTensorName(name);\n      this.name = getUniqueTensorName(this.originalName);\n    }\n    this.rank = shape.length;\n  }\n};\nvar _nextNodeID = 0;\nvar Node = class {\n  constructor(args, callArgs) {\n    this.callArgs = callArgs;\n    this.id = _nextNodeID++;\n    this.outboundLayer = args.outboundLayer;\n    this.inboundLayers = args.inboundLayers;\n    this.nodeIndices = args.nodeIndices;\n    this.tensorIndices = args.tensorIndices;\n    this.inputTensors = args.inputTensors;\n    this.outputTensors = args.outputTensors;\n    this.inputMasks = args.inputMasks;\n    this.outputMasks = args.outputMasks;\n    this.inputShapes = args.inputShapes;\n    this.outputShapes = args.outputShapes;\n    for (const layer of args.inboundLayers) {\n      if (layer != null) {\n        layer.outboundNodes.push(this);\n      }\n    }\n    args.outboundLayer.inboundNodes.push(this);\n  }\n  getConfig() {\n    const inboundNames = [];\n    for (const layer of this.inboundLayers) {\n      if (layer != null) {\n        inboundNames.push(layer.name);\n      } else {\n        inboundNames.push(null);\n      }\n    }\n    return {\n      outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n      inboundLayers: inboundNames,\n      nodeIndices: this.nodeIndices,\n      tensorIndices: this.tensorIndices\n    };\n  }\n};\nvar _nextLayerID = 0;\nvar Layer = class extends serialization_exports.Serializable {\n  constructor(args = {}) {\n    super();\n    this._callHook = null;\n    this._addedWeightNames = [];\n    this._stateful = false;\n    this.id = _nextLayerID++;\n    this.activityRegularizer = null;\n    this.inputSpec = null;\n    this.supportsMasking = false;\n    this._trainableWeights = [];\n    this._nonTrainableWeights = [];\n    this._losses = [];\n    this._updates = [];\n    this._built = false;\n    this.inboundNodes = [];\n    this.outboundNodes = [];\n    let name = args.name;\n    if (!name) {\n      const prefix = this.getClassName();\n      name = toSnakeCase(prefix) + \"_\" + getUid(prefix);\n    }\n    this.name = name;\n    this.trainable_ = args.trainable == null ? true : args.trainable;\n    if (args.inputShape != null || args.batchInputShape != null) {\n      let batchInputShape;\n      if (args.batchInputShape != null) {\n        batchInputShape = args.batchInputShape;\n      } else if (args.inputShape != null) {\n        let batchSize = null;\n        if (args.batchSize != null) {\n          batchSize = args.batchSize;\n        }\n        batchInputShape = [batchSize].concat(args.inputShape);\n      }\n      this.batchInputShape = batchInputShape;\n      let dtype = args.dtype;\n      if (dtype == null) {\n        dtype = args.inputDType;\n      }\n      if (dtype == null) {\n        dtype = \"float32\";\n      }\n      this.dtype = dtype;\n    }\n    if (args.weights != null) {\n      this.initialWeights = args.weights;\n    } else {\n      this.initialWeights = null;\n    }\n    this._refCount = null;\n    this.fastWeightInitDuringBuild = false;\n  }\n  static nodeKey(layer, nodeIndex) {\n    return layer.name + \"_ib-\" + nodeIndex.toString();\n  }\n  getNodeAtIndex(nodeIndex, attrName) {\n    if (this.inboundNodes.length === 0) {\n      throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);\n    }\n    if (this.inboundNodes.length <= nodeIndex) {\n      throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n    }\n    return this.inboundNodes[nodeIndex];\n  }\n  getInputAt(nodeIndex) {\n    return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"input\").inputTensors);\n  }\n  getOutputAt(nodeIndex) {\n    return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"output\").outputTensors);\n  }\n  get input() {\n    if (this.inboundNodes.length > 1) {\n      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. Use \\`getInputAt(nodeIndex)\\` instead.`);\n    } else if (this.inboundNodes.length === 0) {\n      throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);\n    }\n    return singletonOrArray(this.getNodeAtIndex(0, \"input\").inputTensors);\n  }\n  get output() {\n    if (this.inboundNodes.length === 0) {\n      throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);\n    }\n    if (this.inboundNodes.length > 1) {\n      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer output\" is ill-defined. Use \\`getOutputAt(nodeIndex)\\` instead.`);\n    }\n    return singletonOrArray(this.getNodeAtIndex(0, \"output\").outputTensors);\n  }\n  get losses() {\n    return this._losses;\n  }\n  calculateLosses() {\n    return this.losses.map((lossFn) => lossFn());\n  }\n  get updates() {\n    return this._updates;\n  }\n  get built() {\n    return this._built;\n  }\n  set built(built) {\n    this._built = built;\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this._trainableWeights.forEach((w) => w.trainable = trainable);\n    this.trainable_ = trainable;\n  }\n  get trainableWeights() {\n    if (this.trainable_) {\n      return this._trainableWeights.filter((w) => w.trainable);\n    } else {\n      return [];\n    }\n  }\n  set trainableWeights(weights) {\n    this._trainableWeights = weights;\n  }\n  get nonTrainableWeights() {\n    if (this.trainable) {\n      return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);\n    } else {\n      return this._trainableWeights.concat(this._nonTrainableWeights);\n    }\n  }\n  set nonTrainableWeights(weights) {\n    this._nonTrainableWeights = weights;\n  }\n  get weights() {\n    return this.trainableWeights.concat(this.nonTrainableWeights);\n  }\n  get stateful() {\n    return this._stateful;\n  }\n  resetStates() {\n    if (!this.stateful) {\n      throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\");\n    }\n  }\n  assertInputCompatibility(inputs) {\n    inputs = toList(inputs);\n    if (this.inputSpec == null || this.inputSpec.length === 0) {\n      return;\n    }\n    const inputSpec = toList(this.inputSpec);\n    if (inputs.length !== inputSpec.length) {\n      throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);\n    }\n    for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n      const x = inputs[inputIndex];\n      const spec = inputSpec[inputIndex];\n      if (spec == null) {\n        continue;\n      }\n      const ndim = x.rank;\n      if (spec.ndim != null) {\n        if (ndim !== spec.ndim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);\n        }\n      }\n      if (spec.maxNDim != null) {\n        if (ndim > spec.maxNDim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n        }\n      }\n      if (spec.minNDim != null) {\n        if (ndim < spec.minNDim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n        }\n      }\n      if (spec.dtype != null) {\n        if (x.dtype !== spec.dtype) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n        }\n      }\n      if (spec.axes) {\n        const xShape = x.shape;\n        for (const key in spec.axes) {\n          const axis = Number(key);\n          const value = spec.axes[key];\n          const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n          if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);\n          }\n        }\n      }\n      if (spec.shape != null) {\n        for (let i2 = 0; i2 < spec.shape.length; ++i2) {\n          const specDim = spec.shape[i2];\n          const dim = x.shape[i2];\n          if (specDim != null && dim != null) {\n            if (specDim !== dim) {\n              throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);\n            }\n          }\n        }\n      }\n    }\n  }\n  call(inputs, kwargs) {\n    return inputs;\n  }\n  invokeCallHook(inputs, kwargs) {\n    if (this._callHook != null) {\n      this._callHook(inputs, kwargs);\n    }\n  }\n  setCallHook(callHook) {\n    this._callHook = callHook;\n  }\n  clearCallHook() {\n    this._callHook = null;\n  }\n  apply(inputs, kwargs) {\n    kwargs = kwargs || {};\n    this.assertNotDisposed();\n    const inputsList = toList(inputs);\n    let allAreSymbolic = true;\n    for (const input2 of inputsList) {\n      if (!(input2 instanceof SymbolicTensor)) {\n        allAreSymbolic = false;\n        break;\n      }\n    }\n    let noneAreSymbolic = true;\n    for (const input2 of inputsList) {\n      if (input2 instanceof SymbolicTensor) {\n        noneAreSymbolic = false;\n        break;\n      }\n    }\n    if (allAreSymbolic === noneAreSymbolic) {\n      throw new ValueError(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");\n    }\n    return nameScope(this.name, () => {\n      if (!this.built) {\n        this.assertInputCompatibility(inputs);\n        const inputShapes = [];\n        for (const xElem of toList(inputs)) {\n          inputShapes.push(xElem.shape);\n        }\n        this.build(singletonOrArray(inputShapes));\n        this.built = true;\n        if (this.initialWeights) {\n          this.setWeights(this.initialWeights);\n        }\n        if (this._refCount === null && noneAreSymbolic) {\n          this._refCount = 1;\n        }\n      }\n      this.assertInputCompatibility(inputs);\n      if (noneAreSymbolic) {\n        let output = this.call(inputs, kwargs);\n        const outputList = toList(output);\n        const outputListCopy = [];\n        for (let x of outputList) {\n          if (inputsList.indexOf(x) !== -1) {\n            x = x.clone();\n          }\n          outputListCopy.push(x);\n        }\n        output = singletonOrArray(outputListCopy);\n        if (this.activityRegularizer != null) {\n          throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n        }\n        return output;\n      } else {\n        const inputShape = collectInputShape(inputs);\n        const outputShape = this.computeOutputShape(inputShape);\n        let output;\n        const outputDType = guessOutputDType(inputs);\n        this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);\n        if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {\n          output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));\n        } else {\n          output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);\n        }\n        this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n        this._refCount++;\n        if (this.activityRegularizer != null) {\n          throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n        }\n        return output;\n      }\n    });\n  }\n  warnOnIncompatibleInputShape(inputShape) {\n    if (this.batchInputShape == null) {\n      return;\n    } else if (inputShape.length !== this.batchInputShape.length) {\n      console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);\n    } else {\n      let dimMismatch = false;\n      this.batchInputShape.forEach((dimension, i2) => {\n        if (dimension != null && inputShape[i2] != null && inputShape[i2] !== dimension) {\n          dimMismatch = true;\n        }\n      });\n      if (dimMismatch) {\n        console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);\n      }\n    }\n  }\n  get outputShape() {\n    if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n      throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);\n    }\n    const allOutputShapes = [];\n    for (const node of this.inboundNodes) {\n      const shapeString = JSON.stringify(node.outputShapes);\n      if (allOutputShapes.indexOf(shapeString) === -1) {\n        allOutputShapes.push(shapeString);\n      }\n    }\n    if (allOutputShapes.length === 1) {\n      const outputShapes = this.inboundNodes[0].outputShapes;\n      if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {\n        return outputShapes[0];\n      } else {\n        return outputShapes;\n      }\n    } else {\n      throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of \"output shape\" is ill-defined for the layer.`);\n    }\n  }\n  countParams() {\n    if (!this.built) {\n      throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);\n    }\n    return countParamsInWeights(this.weights);\n  }\n  build(inputShape) {\n    this.built = true;\n  }\n  getWeights(trainableOnly = false) {\n    return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n  }\n  setWeights(weights) {\n    tidy(() => {\n      const params = this.weights;\n      if (params.length !== weights.length) {\n        throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);\n      }\n      if (params.length === 0) {\n        return;\n      }\n      const weightValueTuples = [];\n      const paramValues = batchGetValue(params);\n      for (let i2 = 0; i2 < paramValues.length; ++i2) {\n        const pv = paramValues[i2];\n        const p2 = params[i2];\n        const w = weights[i2];\n        if (!util_exports.arraysEqual(pv.shape, w.shape)) {\n          throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);\n        }\n        weightValueTuples.push([p2, w]);\n      }\n      batchSetValue(weightValueTuples);\n    });\n  }\n  addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {\n    if (this._addedWeightNames.indexOf(name) !== -1) {\n      throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n    }\n    this._addedWeightNames.push(name);\n    if (dtype == null) {\n      dtype = \"float32\";\n    }\n    if (this.fastWeightInitDuringBuild) {\n      initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer(\"zeros\");\n    }\n    const initValue = initializer.apply(shape, dtype);\n    const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n    initValue.dispose();\n    if (regularizer != null) {\n      this.addLoss(() => regularizer.apply(weight.read()));\n    }\n    if (trainable == null) {\n      trainable = true;\n    }\n    if (trainable) {\n      this._trainableWeights.push(weight);\n    } else {\n      this._nonTrainableWeights.push(weight);\n    }\n    return weight;\n  }\n  setFastWeightInitDuringBuild(value) {\n    this.fastWeightInitDuringBuild = value;\n  }\n  addLoss(losses2) {\n    if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) {\n      return;\n    }\n    losses2 = toList(losses2);\n    if (this._losses !== void 0 && this._losses !== null) {\n      this.losses.push(...losses2);\n    }\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  computeMask(inputs, mask) {\n    if (!this.supportsMasking) {\n      if (mask != null) {\n        if (Array.isArray(mask)) {\n          mask.forEach((maskElement) => {\n            if (maskElement != null) {\n              throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n            }\n          });\n        } else {\n          throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n        }\n      }\n      return null;\n    }\n    return mask;\n  }\n  addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n    const inputTensorList = toList(inputTensors);\n    outputTensors = toList(outputTensors);\n    inputMasks = toList(inputMasks);\n    outputMasks = toList(outputMasks);\n    inputShapes = normalizeShapeList(inputShapes);\n    outputShapes = normalizeShapeList(outputShapes);\n    const inboundLayers = [];\n    const nodeIndices = [];\n    const tensorIndices = [];\n    for (const x of inputTensorList) {\n      inboundLayers.push(x.sourceLayer);\n      nodeIndices.push(x.nodeIndex);\n      tensorIndices.push(x.tensorIndex);\n    }\n    new Node({\n      outboundLayer: this,\n      inboundLayers,\n      nodeIndices,\n      tensorIndices,\n      inputTensors: inputTensorList,\n      outputTensors,\n      inputMasks,\n      outputMasks,\n      inputShapes,\n      outputShapes\n    }, kwargs);\n    for (let i2 = 0; i2 < outputTensors.length; i2++) {\n      outputTensors[i2].sourceLayer = this;\n      outputTensors[i2].nodeIndex = this.inboundNodes.length - 1;\n      outputTensors[i2].tensorIndex = i2;\n    }\n  }\n  getConfig() {\n    const config = { name: this.name, trainable: this.trainable };\n    if (this.batchInputShape != null) {\n      config[\"batchInputShape\"] = this.batchInputShape;\n    }\n    if (this.dtype != null) {\n      config[\"dtype\"] = this.dtype;\n    }\n    return config;\n  }\n  disposeWeights() {\n    this.weights.forEach((weight) => weight.dispose());\n    return this.weights.length;\n  }\n  assertNotDisposed() {\n    if (this._refCount === 0) {\n      throw new Error(`Layer '${this.name}' is already disposed.`);\n    }\n  }\n  dispose() {\n    if (!this.built) {\n      throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);\n    }\n    if (this._refCount === null) {\n      throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);\n    }\n    this.assertNotDisposed();\n    let numDisposedVariables = 0;\n    if (--this._refCount === 0) {\n      numDisposedVariables = this.disposeWeights();\n    }\n    return { refCountAfterDispose: this._refCount, numDisposedVariables };\n  }\n};\nfunction collectInputShape(inputTensors) {\n  inputTensors = toList(inputTensors);\n  const shapes = [];\n  for (const x of inputTensors) {\n    shapes.push(x.shape);\n  }\n  return singletonOrArray(shapes);\n}\nfunction guessOutputDType(inputTensors) {\n  return \"float32\";\n}\nfunction getSourceInputs(tensor2, layer, nodeIndex) {\n  if (layer == null || nodeIndex != null && nodeIndex > 0) {\n    layer = tensor2.sourceLayer;\n    nodeIndex = tensor2.nodeIndex;\n  }\n  if (layer.inboundNodes.length === 0) {\n    return [tensor2];\n  } else {\n    const node = layer.inboundNodes[nodeIndex];\n    if (node.inboundLayers.length === 0) {\n      return node.inputTensors;\n    } else {\n      const sourceTensors = [];\n      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {\n        const x = node.inputTensors[i2];\n        const layer2 = node.inboundLayers[i2];\n        const nodeIndex2 = node.nodeIndices[i2];\n        const previousSources = getSourceInputs(x, layer2, nodeIndex2);\n        for (const x2 of previousSources) {\n          if (sourceTensors.indexOf(x2) === -1) {\n            sourceTensors.push(x2);\n          }\n        }\n      }\n      return sourceTensors;\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js\nvar InputLayer = class extends Layer {\n  constructor(args) {\n    super({\n      dtype: args.dtype,\n      name: args.name != null ? args.name : getUid(\"input\").toString()\n    });\n    if (args.batchSize == null) {\n      args.batchSize = null;\n    }\n    if (args.sparse == null) {\n      args.sparse = false;\n    }\n    this.trainable = false;\n    this.built = true;\n    this.sparse = args.sparse;\n    if (args.inputShape != null && args.batchInputShape != null) {\n      throw new ValueError(\"Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.\");\n    }\n    let batchInputShape = args.batchInputShape;\n    if (batchInputShape == null) {\n      if (args.inputShape == null) {\n        throw new ValueError(\"An InputLayer should be passed either a `batchInputShape` or an `inputShape`.\");\n      } else {\n        batchInputShape = [args.batchSize].concat(args.inputShape);\n      }\n    } else {\n      if (args.batchSize != null) {\n        throw new ValueError(\"Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.\");\n      }\n    }\n    const dtype = args.dtype || \"float32\";\n    this.batchInputShape = batchInputShape;\n    this.dtype = dtype;\n    this.inputSpec = [{ shape: batchInputShape }];\n    const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n    inputTensor.nodeIndex = 0;\n    inputTensor.tensorIndex = 0;\n    new Node({\n      outboundLayer: this,\n      inboundLayers: [],\n      nodeIndices: [],\n      tensorIndices: [],\n      inputTensors: [inputTensor],\n      outputTensors: [inputTensor],\n      inputMasks: [null],\n      outputMasks: [null],\n      inputShapes: [batchInputShape],\n      outputShapes: [batchInputShape]\n    });\n  }\n  apply(inputs, kwargs) {\n    throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);\n  }\n  dispose() {\n    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n  }\n  getConfig() {\n    return {\n      batchInputShape: this.batchInputShape,\n      dtype: this.dtype,\n      sparse: this.sparse,\n      name: this.name\n    };\n  }\n};\nInputLayer.className = \"InputLayer\";\nserialization_exports.registerClass(InputLayer);\nfunction Input(config) {\n  if (config.batchShape == null && config.shape == null) {\n    throw new Error(\"Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.\");\n  }\n  if (config.batchShape != null && config.shape != null) {\n    throw new ValueError(\"Please provide either a `shape` or `batchShape` argument to Input, but not both.\");\n  }\n  let batchShape = config.batchShape;\n  if (config.shape != null && batchShape == null) {\n    batchShape = [null].concat(config.shape);\n  }\n  let dtype = config.dtype;\n  if (dtype == null) {\n    dtype = \"float32\";\n  }\n  const inputLayer2 = new InputLayer({\n    batchInputShape: batchShape,\n    name: config.name,\n    dtype,\n    sparse: config.sparse\n  });\n  const outputs = inputLayer2.inboundNodes[0].outputTensors;\n  return outputs[0];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js\nfunction assertFeedCompatibility(key, val) {\n  if (key.dtype == null || key.dtype === val.dtype) {\n    return val;\n  }\n  try {\n    return cast(val, key.dtype);\n  } catch (err) {\n    throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);\n  }\n}\nvar FeedDict = class {\n  constructor(feeds) {\n    this.id2Value = {};\n    this.id2Mask = {};\n    this.name2Id = {};\n    if (feeds instanceof FeedDict) {\n      for (const id in feeds.id2Value) {\n        this.id2Value[id] = feeds.id2Value[id];\n        if (id in feeds.id2Mask) {\n          this.id2Mask[id] = feeds.id2Mask[id];\n        }\n      }\n    } else {\n      if (feeds == null) {\n        return;\n      }\n      for (const feed of feeds) {\n        this.add(feed.key, feed.value);\n      }\n    }\n  }\n  add(key, value, mask) {\n    if (this.id2Value[key.id] == null) {\n      this.id2Value[key.id] = assertFeedCompatibility(key, value);\n      this.name2Id[key.name] = key.id;\n      if (mask != null) {\n        this.id2Mask[key.id] = mask;\n      }\n    } else {\n      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n    }\n    return this;\n  }\n  addFeed(feed) {\n    this.add(feed.key, feed.value);\n  }\n  hasKey(key) {\n    return this.id2Value[key.id] != null;\n  }\n  names() {\n    return Object.keys(this.name2Id);\n  }\n  getValue(key) {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Value[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Value[id];\n    }\n  }\n  getMask(key) {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Mask[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Mask[id];\n    }\n  }\n  disposeMasks() {\n    if (this.id2Mask != null) {\n      dispose(this.id2Mask);\n    }\n  }\n};\nvar cachedSorted = new LruCache();\nvar cachedRecipientCounts = new LruCache();\nfunction updateCacheMaxEntries(maxEntries) {\n  if (cachedSorted != null) {\n    cachedSorted.setMaxEntries(maxEntries);\n  }\n  if (cachedRecipientCounts != null) {\n    cachedRecipientCounts.setMaxEntries(maxEntries);\n  }\n}\nfunction execute(fetches, feedDict, kwargs, probe) {\n  const training = kwargs == null ? false : kwargs[\"training\"];\n  const arrayFetches = Array.isArray(fetches);\n  const fetchArray = arrayFetches ? fetches : [fetches];\n  const outputNames = fetchArray.map((t2) => t2.name);\n  const finalOutputs = [];\n  const feedNames = feedDict.names();\n  for (const outputName of outputNames) {\n    if (feedNames.indexOf(outputName) !== -1) {\n      finalOutputs.push(feedDict.getValue(outputName));\n    } else {\n      finalOutputs.push(null);\n    }\n  }\n  if (probe != null) {\n    probe.maxNumTensors = -Infinity;\n    probe.minNumTensors = Infinity;\n  }\n  const fetchAndFeedKey = outputNames.join(\",\") + \"|\" + feedDict.names().sort().join(\",\");\n  let sorted = cachedSorted.get(fetchAndFeedKey);\n  let recipientCounts;\n  if (sorted == null) {\n    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n    sorted = out.sorted;\n    recipientCounts = out.recipientCounts;\n    cachedSorted.put(fetchAndFeedKey, sorted);\n    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);\n  }\n  recipientCounts = {};\n  if (!training) {\n    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));\n  }\n  const internalFeedDict = new FeedDict(feedDict);\n  for (let i2 = 0; i2 < sorted.length; ++i2) {\n    if (probe != null) {\n      const numTensors = memory().numTensors;\n      if (numTensors > probe.maxNumTensors) {\n        probe.maxNumTensors = numTensors;\n      }\n      if (numTensors < probe.minNumTensors) {\n        probe.minNumTensors = numTensors;\n      }\n    }\n    const symbolic = sorted[i2];\n    const srcLayer = symbolic.sourceLayer;\n    if (srcLayer instanceof InputLayer) {\n      continue;\n    }\n    const inputValues = [];\n    const inputMasks = [];\n    const tensorsToDispose = [];\n    let maskExists = false;\n    for (const input2 of symbolic.inputs) {\n      const value = internalFeedDict.getValue(input2);\n      const mask = internalFeedDict.getMask(input2);\n      inputValues.push(value);\n      inputMasks.push(mask);\n      if (mask != null) {\n        maskExists = true;\n      }\n      if (!training) {\n        recipientCounts[input2.name]--;\n        if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {\n          tensorsToDispose.push(value);\n        }\n      }\n    }\n    if (maskExists) {\n      kwargs = kwargs || {};\n      kwargs[\"mask\"] = inputMasks[0];\n    }\n    const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n    let outputMask = null;\n    if (srcLayer.supportsMasking) {\n      outputMask = srcLayer.computeMask(inputValues, inputMasks);\n    }\n    const layerOutputs = getNodeOutputs(symbolic);\n    const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n    for (let i3 = 0; i3 < outputSymbolicTensors.length; ++i3) {\n      if (!internalFeedDict.hasKey(outputSymbolicTensors[i3])) {\n        internalFeedDict.add(outputSymbolicTensors[i3], outputTensors[i3], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n      }\n      const index = outputNames.indexOf(outputSymbolicTensors[i3].name);\n      if (index !== -1) {\n        finalOutputs[index] = outputTensors[i3];\n      }\n    }\n    if (!training) {\n      dispose(tensorsToDispose);\n    }\n  }\n  internalFeedDict.disposeMasks();\n  return arrayFetches ? finalOutputs : finalOutputs[0];\n}\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n  util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n  let finalSorted = [];\n  let finalRecipientMap = {};\n  if (fetches.length === 1) {\n    const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n    finalSorted = out.sorted;\n    finalRecipientMap = out.recipientMap;\n  } else {\n    const visited = /* @__PURE__ */ new Set();\n    for (const fetch4 of fetches) {\n      const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict);\n      for (const symbolicTensor of sorted) {\n        if (!visited.has(symbolicTensor.name)) {\n          finalSorted.push(symbolicTensor);\n          visited.add(symbolicTensor.name);\n        }\n      }\n      for (const name in recipientMap) {\n        if (finalRecipientMap[name] == null) {\n          finalRecipientMap[name] = /* @__PURE__ */ new Set();\n        }\n        recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));\n      }\n    }\n  }\n  return {\n    sorted: finalSorted,\n    recipientCounts: recipientMap2Counts(finalRecipientMap)\n  };\n}\nfunction recipientMap2Counts(recipientMap) {\n  const recipientCounts = {};\n  for (const name in recipientMap) {\n    recipientCounts[name] = recipientMap[name].size;\n  }\n  return recipientCounts;\n}\nfunction getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) {\n  const visited = /* @__PURE__ */ new Set();\n  const sorted = [];\n  const recipientMap = {};\n  for (const key of feedDict.names()) {\n    visited.add(key);\n  }\n  const stack2 = [];\n  const marks = [];\n  stack2.push(fetch4);\n  while (stack2.length > 0) {\n    const top = stack2[stack2.length - 1];\n    if (visited.has(top.name)) {\n      stack2.pop();\n      continue;\n    }\n    const topIsMarked = marks[marks.length - 1] === stack2.length - 1;\n    if (top.inputs.length === 0 || topIsMarked) {\n      stack2.pop();\n      sorted.push(top);\n      visited.add(top.name);\n      if (topIsMarked) {\n        marks.pop();\n      }\n    } else {\n      marks.push(stack2.length - 1);\n      for (const input2 of top.inputs) {\n        if (recipientMap[input2.name] == null) {\n          recipientMap[input2.name] = /* @__PURE__ */ new Set();\n        }\n        recipientMap[input2.name].add(top.name);\n        if (visited.has(input2.name)) {\n          continue;\n        }\n        stack2.push(input2);\n      }\n    }\n  }\n  return { sorted, recipientMap };\n}\nfunction getNodeOutputs(fetch4) {\n  let layerOutputs;\n  if (fetch4.sourceLayer.inboundNodes.length === 1) {\n    layerOutputs = fetch4.sourceLayer.output;\n  } else {\n    let nodeIndex = null;\n    for (let i2 = 0; i2 < fetch4.sourceLayer.inboundNodes.length; ++i2) {\n      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i2].outputTensors) {\n        if (outputTensor.id === fetch4.id) {\n          nodeIndex = i2;\n          break;\n        }\n      }\n    }\n    layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);\n  }\n  return layerOutputs;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js\nvar ENV3 = env();\nENV3.registerFlag(\"TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES\", () => 100, updateCacheMaxEntries);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nvar exports_constraints_exports = {};\n__export(exports_constraints_exports, {\n  maxNorm: () => maxNorm,\n  minMaxNorm: () => minMaxNorm,\n  nonNeg: () => nonNeg,\n  unitNorm: () => unitNorm\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/constraints.js\nfunction calcL2Norms(w, axis) {\n  return tidy(() => sqrt(sum2(mul(w, w), axis, true)));\n}\nvar Constraint = class extends serialization_exports.Serializable {\n  getConfig() {\n    return {};\n  }\n};\nvar MaxNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultMaxValue = 2;\n    this.defaultAxis = 0;\n    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => {\n      const norms = calcL2Norms(w, this.axis);\n      const desired = clipByValue(norms, 0, this.maxValue);\n      return mul(w, div(desired, add2(epsilon(), norms)));\n    });\n  }\n  getConfig() {\n    return { maxValue: this.maxValue, axis: this.axis };\n  }\n};\nMaxNorm.className = \"MaxNorm\";\nserialization_exports.registerClass(MaxNorm);\nvar UnitNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultAxis = 0;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));\n  }\n  getConfig() {\n    return { axis: this.axis };\n  }\n};\nUnitNorm.className = \"UnitNorm\";\nserialization_exports.registerClass(UnitNorm);\nvar NonNeg = class extends Constraint {\n  apply(w) {\n    return relu(w);\n  }\n};\nNonNeg.className = \"NonNeg\";\nserialization_exports.registerClass(NonNeg);\nvar MinMaxNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultMinValue = 0;\n    this.defaultMaxValue = 1;\n    this.defaultRate = 1;\n    this.defaultAxis = 0;\n    this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;\n    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n    this.rate = args.rate != null ? args.rate : this.defaultRate;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => {\n      const norms = calcL2Norms(w, this.axis);\n      const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));\n      return mul(w, div(desired, add2(epsilon(), norms)));\n    });\n  }\n  getConfig() {\n    return {\n      minValue: this.minValue,\n      maxValue: this.maxValue,\n      rate: this.rate,\n      axis: this.axis\n    };\n  }\n};\nMinMaxNorm.className = \"MinMaxNorm\";\nserialization_exports.registerClass(MinMaxNorm);\nvar CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"maxNorm\": \"MaxNorm\",\n  \"minMaxNorm\": \"MinMaxNorm\",\n  \"nonNeg\": \"NonNeg\",\n  \"unitNorm\": \"UnitNorm\"\n};\nfunction serializeConstraint(constraint) {\n  return serializeKerasObject(constraint);\n}\nfunction deserializeConstraint(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"constraint\");\n}\nfunction getConstraint(identifier) {\n  if (identifier == null) {\n    return null;\n  }\n  if (typeof identifier === \"string\") {\n    const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    const config = { className, config: {} };\n    return deserializeConstraint(config);\n  } else if (identifier instanceof Constraint) {\n    return identifier;\n  } else {\n    return deserializeConstraint(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nfunction maxNorm(args) {\n  return new MaxNorm(args);\n}\nfunction unitNorm(args) {\n  return new UnitNorm(args);\n}\nfunction nonNeg() {\n  return new NonNeg();\n}\nfunction minMaxNorm(config) {\n  return new MinMaxNorm(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js\nvar exports_initializers_exports = {};\n__export(exports_initializers_exports, {\n  constant: () => constant,\n  glorotNormal: () => glorotNormal,\n  glorotUniform: () => glorotUniform,\n  heNormal: () => heNormal,\n  heUniform: () => heUniform,\n  identity: () => identity,\n  leCunNormal: () => leCunNormal,\n  leCunUniform: () => leCunUniform,\n  ones: () => ones3,\n  orthogonal: () => orthogonal,\n  randomNormal: () => randomNormal3,\n  randomUniform: () => randomUniform2,\n  truncatedNormal: () => truncatedNormal2,\n  varianceScaling: () => varianceScaling,\n  zeros: () => zeros2\n});\nfunction zeros2() {\n  return new Zeros();\n}\nfunction ones3() {\n  return new Ones();\n}\nfunction constant(args) {\n  return new Constant(args);\n}\nfunction randomUniform2(args) {\n  return new RandomUniform(args);\n}\nfunction randomNormal3(args) {\n  return new RandomNormal(args);\n}\nfunction truncatedNormal2(args) {\n  return new TruncatedNormal(args);\n}\nfunction identity(args) {\n  return new Identity2(args);\n}\nfunction varianceScaling(config) {\n  return new VarianceScaling(config);\n}\nfunction glorotUniform(args) {\n  return new GlorotUniform(args);\n}\nfunction glorotNormal(args) {\n  return new GlorotNormal(args);\n}\nfunction heNormal(args) {\n  return new HeNormal(args);\n}\nfunction heUniform(args) {\n  return new HeUniform(args);\n}\nfunction leCunNormal(args) {\n  return new LeCunNormal(args);\n}\nfunction leCunUniform(args) {\n  return new LeCunUniform(args);\n}\nfunction orthogonal(args) {\n  return new Orthogonal(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nvar exports_layers_exports = {};\n__export(exports_layers_exports, {\n  Layer: () => Layer,\n  RNN: () => RNN,\n  RNNCell: () => RNNCell,\n  activation: () => activation,\n  add: () => add3,\n  alphaDropout: () => alphaDropout,\n  average: () => average,\n  averagePooling1d: () => averagePooling1d,\n  averagePooling2d: () => averagePooling2d,\n  averagePooling3d: () => averagePooling3d,\n  avgPool1d: () => avgPool1d,\n  avgPool2d: () => avgPool2d,\n  avgPool3d: () => avgPool3d2,\n  avgPooling1d: () => avgPooling1d,\n  avgPooling2d: () => avgPooling2d,\n  avgPooling3d: () => avgPooling3d,\n  batchNormalization: () => batchNormalization2,\n  bidirectional: () => bidirectional,\n  concatenate: () => concatenate2,\n  conv1d: () => conv1d2,\n  conv2d: () => conv2d3,\n  conv2dTranspose: () => conv2dTranspose2,\n  conv3d: () => conv3d2,\n  conv3dTranspose: () => conv3dTranspose2,\n  convLstm2d: () => convLstm2d,\n  convLstm2dCell: () => convLstm2dCell,\n  cropping2D: () => cropping2D,\n  dense: () => dense,\n  depthwiseConv2d: () => depthwiseConv2d4,\n  dot: () => dot3,\n  dropout: () => dropout3,\n  elu: () => elu3,\n  embedding: () => embedding,\n  flatten: () => flatten3,\n  gaussianDropout: () => gaussianDropout,\n  gaussianNoise: () => gaussianNoise,\n  globalAveragePooling1d: () => globalAveragePooling1d,\n  globalAveragePooling2d: () => globalAveragePooling2d,\n  globalMaxPool1d: () => globalMaxPool1d,\n  globalMaxPool2d: () => globalMaxPool2d,\n  globalMaxPooling1d: () => globalMaxPooling1d,\n  globalMaxPooling2d: () => globalMaxPooling2d,\n  gru: () => gru,\n  gruCell: () => gruCell,\n  input: () => input,\n  inputLayer: () => inputLayer,\n  layerNormalization: () => layerNormalization,\n  leakyReLU: () => leakyReLU,\n  lstm: () => lstm,\n  lstmCell: () => lstmCell,\n  masking: () => masking,\n  maxPool1d: () => maxPool1d,\n  maxPool2d: () => maxPool2d,\n  maxPooling1d: () => maxPooling1d,\n  maxPooling2d: () => maxPooling2d,\n  maxPooling3d: () => maxPooling3d,\n  maximum: () => maximum2,\n  minimum: () => minimum2,\n  multiply: () => multiply,\n  permute: () => permute,\n  prelu: () => prelu2,\n  reLU: () => reLU,\n  repeatVector: () => repeatVector,\n  reshape: () => reshape2,\n  rnn: () => rnn2,\n  separableConv2d: () => separableConv2d2,\n  simpleRNN: () => simpleRNN,\n  simpleRNNCell: () => simpleRNNCell,\n  softmax: () => softmax2,\n  spatialDropout1d: () => spatialDropout1d,\n  stackedRNNCells: () => stackedRNNCells,\n  thresholdedReLU: () => thresholdedReLU,\n  timeDistributed: () => timeDistributed,\n  upSampling2d: () => upSampling2d,\n  zeroPadding2d: () => zeroPadding2d\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/logs.js\nasync function resolveScalarsInLogs(logs) {\n  if (logs == null) {\n    return;\n  }\n  const promises = [];\n  const keys = [];\n  const scalarsToDispose = [];\n  for (const key in logs) {\n    const value = logs[key];\n    if (typeof value !== \"number\") {\n      const valueScalar = value;\n      promises.push(valueScalar.data());\n      keys.push(key);\n      scalarsToDispose.push(valueScalar);\n    }\n  }\n  if (promises.length > 0) {\n    const values = await Promise.all(promises);\n    for (let i2 = 0; i2 < values.length; ++i2) {\n      logs[keys[i2]] = values[i2][0];\n    }\n    dispose(scalarsToDispose);\n  }\n}\nfunction disposeTensorsInLogs(logs) {\n  if (logs == null) {\n    return;\n  }\n  for (const key in logs) {\n    const value = logs[key];\n    if (typeof value !== \"number\") {\n      value.dispose();\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js\nvar ModelLoggingVerbosity;\n(function(ModelLoggingVerbosity2) {\n  ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"SILENT\"] = 0] = \"SILENT\";\n  ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\nvar DEFAULT_YIELD_EVERY_MS = 125;\nvar BaseCallback = class {\n  constructor() {\n    this.validationData = null;\n  }\n  setParams(params) {\n    this.params = params;\n  }\n  async onEpochBegin(epoch, logs) {\n  }\n  async onEpochEnd(epoch, logs) {\n  }\n  async onBatchBegin(batch, logs) {\n  }\n  async onBatchEnd(batch, logs) {\n  }\n  async onTrainBegin(logs) {\n  }\n  async onTrainEnd(logs) {\n  }\n  setModel(model2) {\n  }\n};\nvar CallbackList = class {\n  constructor(callbacks2, queueLength = 10) {\n    if (callbacks2 == null) {\n      callbacks2 = [];\n    }\n    this.callbacks = callbacks2;\n    this.queueLength = queueLength;\n  }\n  append(callback) {\n    this.callbacks.push(callback);\n  }\n  setParams(params) {\n    for (const callback of this.callbacks) {\n      callback.setParams(params);\n    }\n  }\n  setModel(model2) {\n    for (const callback of this.callbacks) {\n      callback.setModel(model2);\n    }\n  }\n  async onEpochBegin(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onEpochBegin(epoch, logs);\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onEpochEnd(epoch, logs);\n    }\n  }\n  async onBatchBegin(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onBatchBegin(batch, logs);\n    }\n  }\n  async onBatchEnd(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onBatchEnd(batch, logs);\n    }\n  }\n  async onTrainBegin(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onTrainBegin(logs);\n    }\n  }\n  async onTrainEnd(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onTrainEnd(logs);\n    }\n  }\n};\nvar BaseLogger = class extends BaseCallback {\n  constructor() {\n    super();\n  }\n  async onEpochBegin(epoch) {\n    this.seen = 0;\n    this.totals = {};\n  }\n  async onBatchEnd(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    const batchSize = logs[\"size\"] == null ? 0 : logs[\"size\"];\n    this.seen += batchSize;\n    for (const key in logs) {\n      const value = logs[key];\n      if (typeof value === \"number\") {\n        if (!this.totals.hasOwnProperty(key)) {\n          this.totals[key] = 0;\n        }\n        this.totals[key] = this.totals[key] + value * batchSize;\n      } else {\n        let oldTotalsToDispose;\n        if (key in this.totals) {\n          oldTotalsToDispose = this.totals[key];\n        } else {\n          this.totals[key] = 0;\n        }\n        const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));\n        this.totals[key] = total;\n        if (oldTotalsToDispose != null) {\n          oldTotalsToDispose.dispose();\n        }\n      }\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs != null) {\n      for (const key of this.params[\"metrics\"]) {\n        if (this.totals[key] == null) {\n          continue;\n        }\n        if (typeof this.totals[key] === \"number\") {\n          logs[key] = this.totals[key] / this.seen;\n        } else {\n          tidy(() => {\n            const log6 = mul(div(1, this.seen), this.totals[key]);\n            logs[key] = log6;\n            this.totals[key].dispose();\n            keep(logs[key]);\n          });\n        }\n      }\n    }\n  }\n};\nvar History = class extends BaseCallback {\n  async onTrainBegin(logs) {\n    this.epoch = [];\n    this.history = {};\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    this.epoch.push(epoch);\n    for (const key in logs) {\n      if (this.history[key] == null) {\n        this.history[key] = [];\n      }\n      this.history[key].push(logs[key]);\n    }\n  }\n  async syncData() {\n    const promises = [];\n    const keys = [];\n    const indices = [];\n    for (const key in this.history) {\n      const valueArray = this.history[key];\n      for (let i2 = 0; i2 < valueArray.length; ++i2) {\n        if (typeof valueArray[i2] !== \"number\") {\n          const valueScalar = valueArray[i2];\n          promises.push(valueScalar.data());\n          keys.push(key);\n          indices.push(i2);\n        }\n      }\n    }\n    const values = await Promise.all(promises);\n    for (let n = 0; n < values.length; ++n) {\n      const tensorToDispose = this.history[keys[n]][indices[n]];\n      tensorToDispose.dispose();\n      this.history[keys[n]][indices[n]] = values[n][0];\n    }\n  }\n};\nvar CustomCallback = class extends BaseCallback {\n  constructor(args, yieldEvery) {\n    super();\n    this.currentEpoch = 0;\n    this.nowFunc = args.nowFunc;\n    this.nextFrameFunc = args.nextFrameFunc || nextFrame;\n    this.yieldEvery = yieldEvery || \"auto\";\n    if (this.yieldEvery === \"auto\") {\n      this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n    }\n    if (this.yieldEvery === \"never\" && args.onYield != null) {\n      throw new Error(\"yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback\");\n    }\n    if (util_exports.isNumber(this.yieldEvery)) {\n      this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc);\n    }\n    this.trainBegin = args.onTrainBegin;\n    this.trainEnd = args.onTrainEnd;\n    this.epochBegin = args.onEpochBegin;\n    this.epochEnd = args.onEpochEnd;\n    this.batchBegin = args.onBatchBegin;\n    this.batchEnd = args.onBatchEnd;\n    this.yield = args.onYield;\n  }\n  async maybeWait(epoch, batch, logs) {\n    const ps = [];\n    if (this.yield != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.yield(epoch, batch, logs));\n    }\n    ps.push(this.nextFrameFunc());\n    await Promise.all(ps);\n  }\n  async onEpochBegin(epoch, logs) {\n    this.currentEpoch = epoch;\n    if (this.epochBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.epochBegin(epoch, logs);\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    const ps = [];\n    if (this.epochEnd != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.epochEnd(epoch, logs));\n    }\n    if (this.yieldEvery === \"epoch\") {\n      ps.push(this.nextFrameFunc());\n    }\n    await Promise.all(ps);\n  }\n  async onBatchBegin(batch, logs) {\n    if (this.batchBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.batchBegin(batch, logs);\n    }\n  }\n  async onBatchEnd(batch, logs) {\n    const ps = [];\n    if (this.batchEnd != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.batchEnd(batch, logs));\n    }\n    if (this.yieldEvery === \"batch\") {\n      ps.push(this.nextFrameFunc());\n    } else if (util_exports.isNumber(this.yieldEvery)) {\n      ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n    }\n    await Promise.all(ps);\n  }\n  async onTrainBegin(logs) {\n    if (this.trainBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.trainBegin(logs);\n    }\n  }\n  async onTrainEnd(logs) {\n    if (this.trainEnd != null) {\n      await resolveScalarsInLogs(logs);\n      await this.trainEnd(logs);\n    }\n  }\n};\nfunction standardizeCallbacks(callbacks2, yieldEvery) {\n  if (callbacks2 == null) {\n    callbacks2 = {};\n  }\n  if (callbacks2 instanceof BaseCallback) {\n    return [callbacks2];\n  }\n  if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {\n    return callbacks2;\n  }\n  const callbackConfigs = toList(callbacks2);\n  return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));\n}\nvar CallbackConstructorRegistry = class {\n  constructor() {\n  }\n  static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);\n    CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n    if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n      CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n    }\n    CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n  }\n  static checkForDuplicate(callbackConstructor) {\n    for (const levelName in CallbackConstructorRegistry.constructors) {\n      const constructors = CallbackConstructorRegistry.constructors[+levelName];\n      constructors.forEach((ctor) => {\n        if (ctor === callbackConstructor) {\n          throw new ValueError(\"Duplicate callback constructor.\");\n        }\n      });\n    }\n  }\n  static clear() {\n    CallbackConstructorRegistry.constructors = {};\n  }\n  static createCallbacks(verbosityLevel) {\n    const constructors = [];\n    for (const levelName in CallbackConstructorRegistry.constructors) {\n      const level = +levelName;\n      if (verbosityLevel >= level) {\n        constructors.push(...CallbackConstructorRegistry.constructors[level]);\n      }\n    }\n    return constructors.map((ctor) => new ctor());\n  }\n};\nCallbackConstructorRegistry.constructors = {};\nfunction configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n  const history = new History();\n  const actualCallbacks = [\n    new BaseLogger(),\n    ...CallbackConstructorRegistry.createCallbacks(verbose)\n  ];\n  if (callbacks2 != null) {\n    actualCallbacks.push(...callbacks2);\n  }\n  actualCallbacks.push(history);\n  const callbackList = new CallbackList(actualCallbacks);\n  callbackList.setParams({\n    epochs,\n    initialEpoch,\n    samples: numTrainSamples,\n    steps: stepsPerEpoch,\n    batchSize,\n    verbose,\n    doValidation,\n    metrics: callbackMetrics\n  });\n  return { callbackList, history };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js\nfunction deserialize(config, customObjects = {}, fastWeightInit = false) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"layer\", fastWeightInit);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/losses.js\nfunction l2Normalize(x, axis) {\n  return tidy(() => {\n    if (x.dtype !== \"float32\") {\n      x = cast(x, \"float32\");\n    }\n    const squareSum = sum2(square2(x), axis, true);\n    const epsilonTensor = fill(squareSum.shape, epsilon());\n    const norm2 = sqrt(maximum(squareSum, epsilonTensor));\n    return div(x, norm2);\n  });\n}\nfunction meanSquaredError2(yTrue, yPred) {\n  return tidy(() => mean(square2(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsoluteError(yTrue, yPred) {\n  return tidy(() => mean(abs(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsolutePercentageError(yTrue, yPred) {\n  return tidy(() => {\n    const diff = sub(yTrue, yPred);\n    const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);\n    const absResult = abs(div(diff, clippedTrue));\n    return mul(100, mean(absResult, -1));\n  });\n}\nfunction meanSquaredLogarithmicError(yTrue, yPred) {\n  return tidy(() => {\n    const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n    const firstLog = log2(add2(1, clippedPred));\n    const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n    const secondLog = log2(add2(1, clippedTrue));\n    return mean(square2(sub(firstLog, secondLog)), -1);\n  });\n}\nfunction squaredHinge(yTrue, yPred) {\n  return tidy(() => {\n    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n    return mean(square2(maxResult), -1);\n  });\n}\nfunction hinge(yTrue, yPred) {\n  return tidy(() => {\n    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n    return mean(maxResult, -1);\n  });\n}\nfunction categoricalHinge(yTrue, yPred) {\n  return tidy(() => {\n    const pos = sum2(mul(yTrue, yPred), -1);\n    const neg5 = max(mul(sub(1, yTrue), yPred), -1);\n    return maximum(0, add2(1, sub(neg5, pos)));\n  });\n}\nfunction logcosh(yTrue, yPred) {\n  return tidy(() => {\n    const log22 = Math.log(2);\n    const predictionDiff = sub(yPred, yTrue);\n    const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);\n    return mean(logcoshResult, -1);\n  });\n}\nfunction categoricalCrossentropy(target, output, fromLogits = false) {\n  return tidy(() => {\n    if (fromLogits) {\n      output = softmax(output);\n    } else {\n      const outputSum = sum2(output, output.shape.length - 1, true);\n      output = div(output, outputSum);\n    }\n    output = clipByValue(output, epsilon(), 1 - epsilon());\n    return neg(sum2(mul(cast(target, \"float32\"), log2(output)), output.shape.length - 1));\n  });\n}\nfunction sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n  return tidy(() => {\n    const flatTarget = cast(floor(flatten2(target)), \"int32\");\n    output = clipByValue(output, epsilon(), 1 - epsilon());\n    const outputShape = output.shape;\n    const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);\n    return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n  });\n}\nfunction sigmoidCrossEntropyWithLogits(labels, logits) {\n  if (!util_exports.arraysEqual(labels.shape, logits.shape)) {\n    throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n  }\n  return tidy(() => {\n    const reluLogits = relu(logits);\n    const negAbsLogits = neg(abs(logits));\n    return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits)));\n  });\n}\nfunction binaryCrossentropy(yTrue, yPred) {\n  return tidy(() => {\n    let y;\n    y = clipByValue(yPred, epsilon(), 1 - epsilon());\n    y = log2(div(y, sub(1, y)));\n    return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n  });\n}\nfunction kullbackLeiblerDivergence(yTrue, yPred) {\n  return tidy(() => {\n    const clippedTrue = clipByValue(yTrue, epsilon(), 1);\n    const clippedPred = clipByValue(yPred, epsilon(), 1);\n    return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1);\n  });\n}\nfunction poisson(yTrue, yPred) {\n  return tidy(() => {\n    const logPred = log2(add2(epsilon(), yPred));\n    return mean(sub(yPred, mul(yTrue, logPred)), -1);\n  });\n}\nfunction cosineProximity(yTrue, yPred) {\n  return tidy(() => {\n    const trueNormalized = l2Normalize(yTrue, -1);\n    const predNormalized = l2Normalize(yPred, -1);\n    const trueXPred = mul(trueNormalized, predNormalized);\n    return neg(sum2(trueXPred, -1));\n  });\n}\nvar lossesMap = {\n  meanSquaredError: meanSquaredError2,\n  meanAbsoluteError,\n  meanAbsolutePercentageError,\n  meanSquaredLogarithmicError,\n  squaredHinge,\n  hinge,\n  categoricalHinge,\n  logcosh,\n  categoricalCrossentropy,\n  sparseCategoricalCrossentropy,\n  binaryCrossentropy,\n  kullbackLeiblerDivergence,\n  poisson,\n  cosineProximity\n};\nfunction get(identifierOrFn) {\n  if (typeof identifierOrFn === \"string\") {\n    if (identifierOrFn in lossesMap) {\n      return lossesMap[identifierOrFn];\n    }\n    let errMsg = `Unknown loss ${identifierOrFn}`;\n    if (identifierOrFn.toLowerCase().includes(\"softmaxcrossentropy\")) {\n      errMsg = `Unknown loss ${identifierOrFn}. Use \"categoricalCrossentropy\" as the string name for tf.losses.softmaxCrossEntropy`;\n    }\n    throw new ValueError(errMsg);\n  } else {\n    return identifierOrFn;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/metrics.js\nfunction binaryAccuracy(yTrue, yPred) {\n  return tidy(() => {\n    const threshold3 = mul(0.5, onesLike(yPred));\n    const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);\n    return mean(equal(yTrue, yPredThresholded), -1);\n  });\n}\nfunction categoricalAccuracy(yTrue, yPred) {\n  return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), \"float32\"));\n}\nfunction truePositives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), \"float32\");\n  });\n}\nfunction falseNegatives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), \"float32\");\n  });\n}\nfunction falsePositives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), \"float32\");\n  });\n}\nfunction precision(yTrue, yPred) {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fp = falsePositives(yTrue, yPred);\n    const denominator = add2(tp, fp);\n    return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n  });\n}\nfunction recall(yTrue, yPred) {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fn = falseNegatives(yTrue, yPred);\n    const denominator = add2(tp, fn);\n    return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n  });\n}\nfunction binaryCrossentropy2(yTrue, yPred) {\n  return binaryCrossentropy(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy(yTrue, yPred) {\n  if (yTrue.rank === yPred.rank) {\n    yTrue = squeeze(yTrue, [yTrue.rank - 1]);\n  }\n  yPred = argMax(yPred, -1);\n  if (yPred.dtype !== yTrue.dtype) {\n    yPred = cast(yPred, yTrue.dtype);\n  }\n  return cast(equal(yTrue, yPred), \"float32\");\n}\nvar mse = meanSquaredError2;\nvar MSE = meanSquaredError2;\nvar mae = meanAbsoluteError;\nvar MAE = meanAbsoluteError;\nvar mape = meanAbsolutePercentageError;\nvar MAPE = meanAbsolutePercentageError;\nvar categoricalCrossentropy2 = categoricalCrossentropy;\nvar cosine = cosineProximity;\nvar sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;\nvar metricsMap = {\n  binaryAccuracy,\n  categoricalAccuracy,\n  precision,\n  categoricalCrossentropy: categoricalCrossentropy2,\n  sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,\n  mse,\n  MSE,\n  mae,\n  MAE,\n  mape,\n  MAPE,\n  cosine\n};\nfunction get2(identifier) {\n  if (typeof identifier === \"string\" && identifier in metricsMap) {\n    return metricsMap[identifier];\n  } else if (typeof identifier !== \"string\" && identifier != null) {\n    return identifier;\n  } else {\n    throw new ValueError(`Unknown metric ${identifier}`);\n  }\n}\nfunction getLossOrMetricName(fn) {\n  assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n  if (typeof fn === \"string\") {\n    return fn;\n  } else {\n    let fnName;\n    for (const key of Object.keys(lossesMap)) {\n      if (lossesMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== void 0) {\n      return fnName;\n    }\n    for (const key of Object.keys(metricsMap)) {\n      if (metricsMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== void 0) {\n      return fnName;\n    }\n    return fn.name;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js\nfunction getOptimizer(identifier) {\n  const optimizerMap = {\n    \"Adagrad\": () => train.adagrad(0.01),\n    \"Adadelta\": () => train.adadelta(1, 0.95, epsilon()),\n    \"Adam\": () => train.adam(1e-3, 0.9, 0.999, epsilon()),\n    \"Adamax\": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),\n    \"RMSProp\": () => train.rmsprop(1e-3, 0.9, 0, epsilon()),\n    \"SGD\": () => train.sgd(0.01)\n  };\n  optimizerMap[\"adagrad\"] = optimizerMap[\"Adagrad\"];\n  optimizerMap[\"adadelta\"] = optimizerMap[\"Adadelta\"];\n  optimizerMap[\"adam\"] = optimizerMap[\"Adam\"];\n  optimizerMap[\"adamax\"] = optimizerMap[\"Adamax\"];\n  optimizerMap[\"rmsprop\"] = optimizerMap[\"RMSProp\"];\n  optimizerMap[\"sgd\"] = optimizerMap[\"SGD\"];\n  if (identifier in optimizerMap) {\n    return optimizerMap[identifier]();\n  }\n  throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js\nvar MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\nfunction checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n  if (userDefinedMetadata == null || typeof userDefinedMetadata !== \"object\" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {\n    throw new Error(\"User-defined metadata is expected to be a JSON object, but is not.\");\n  }\n  if (checkSize) {\n    const out = JSON.stringify(userDefinedMetadata);\n    if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n      console.warn(`User-defined metadata of model \"${modelName}\" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n    }\n  }\n}\nfunction plainObjectCheck(x) {\n  if (x === null) {\n    return true;\n  } else if (typeof x === \"object\") {\n    if (Object.getPrototypeOf(x) === Object.prototype) {\n      const keys = Object.keys(x);\n      for (const key of keys) {\n        if (typeof key !== \"string\") {\n          return false;\n        }\n        if (!plainObjectCheck(x[key])) {\n          return false;\n        }\n      }\n      return true;\n    } else {\n      if (Array.isArray(x)) {\n        for (const item of x) {\n          if (!plainObjectCheck(item)) {\n            return false;\n          }\n        }\n        return true;\n      } else {\n        return false;\n      }\n    }\n  } else {\n    const xType = typeof x;\n    return xType === \"string\" || xType === \"number\" || xType === \"boolean\";\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js\nfunction printSummary(model2, lineLength, positions, printFn = console.log) {\n  const sequentialLike = isModelSequentialLike(model2);\n  const toDisplay = [\"Layer (type)\", \"Input Shape\", \"Output shape\", \"Param #\"];\n  if (sequentialLike) {\n    lineLength = lineLength || 90;\n    positions = positions || [0.32, 0.61, 0.89, 1];\n  } else {\n    lineLength = lineLength || 115;\n    positions = positions || [0.24, 0.48, 0.7, 0.8, 1];\n  }\n  if (positions[positions.length - 1] <= 1) {\n    positions = positions.map((p2) => Math.floor(lineLength * p2));\n  }\n  let relevantNodes;\n  if (!sequentialLike) {\n    toDisplay.push(\"Receives inputs\");\n    relevantNodes = [];\n    for (const depth in model2.nodesByDepth) {\n      relevantNodes.push(...model2.nodesByDepth[depth]);\n    }\n  }\n  printFn(\"_\".repeat(lineLength));\n  printRow(toDisplay, positions, printFn);\n  printFn(\"=\".repeat(lineLength));\n  const layers = model2.layers;\n  for (let i2 = 0; i2 < layers.length; ++i2) {\n    if (sequentialLike) {\n      printLayerSummary(layers[i2], positions, printFn);\n    } else {\n      printLayerSummaryWithConnections(layers[i2], positions, relevantNodes, printFn);\n    }\n    printFn((i2 === layers.length - 1 ? \"=\" : \"_\").repeat(lineLength));\n  }\n  model2.checkTrainableWeightsConsistency();\n  const trainableCount = countTrainableParams(model2);\n  const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);\n  printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n  printFn(`Trainable params: ${trainableCount}`);\n  printFn(`Non-trainable params: ${nonTrainableCount}`);\n  printFn(\"_\".repeat(lineLength));\n}\nfunction countTrainableParams(model2) {\n  let trainableCount;\n  if (model2.collectedTrainableWeights != null) {\n    trainableCount = countParamsInWeights(model2.collectedTrainableWeights);\n  } else {\n    trainableCount = countParamsInWeights(model2.trainableWeights);\n  }\n  return trainableCount;\n}\nfunction isModelSequentialLike(model2) {\n  let sequentialLike = true;\n  const nodesByDepth = [];\n  const nodes = [];\n  for (const depth in model2.nodesByDepth) {\n    nodesByDepth.push(model2.nodesByDepth[depth]);\n  }\n  for (const depthNodes of nodesByDepth) {\n    if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n      sequentialLike = false;\n      break;\n    }\n    nodes.push(...depthNodes);\n  }\n  if (sequentialLike) {\n    for (const layer of model2.layers) {\n      let flag = false;\n      for (const node of layer.inboundNodes) {\n        if (nodes.indexOf(node) !== -1) {\n          if (flag) {\n            sequentialLike = false;\n            break;\n          } else {\n            flag = true;\n          }\n        }\n      }\n      if (!sequentialLike) {\n        break;\n      }\n    }\n  }\n  return sequentialLike;\n}\nfunction printRow(fields, positions, printFn = console.log) {\n  let line = \"\";\n  for (let i2 = 0; i2 < fields.length; ++i2) {\n    if (i2 > 0) {\n      line = line.slice(0, line.length - 1) + \" \";\n    }\n    line += fields[i2];\n    line = line.slice(0, positions[i2]);\n    line += \" \".repeat(positions[i2] - line.length);\n  }\n  printFn(line);\n}\nfunction printLayerSummary(layer, positions, printFn) {\n  let outputShape;\n  let inputShape;\n  try {\n    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n  } catch (err) {\n    inputShape = \"multiple\";\n  }\n  try {\n    outputShape = JSON.stringify(layer.outputShape);\n  } catch (err) {\n    outputShape = \"multiple\";\n  }\n  const name = layer.name;\n  const className = layer.getClassName();\n  const fields = [\n    `${name} (${className})`,\n    inputShape,\n    outputShape,\n    layer.countParams().toString()\n  ];\n  printRow(fields, positions, printFn);\n}\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {\n  let outputShape;\n  let inputShape;\n  try {\n    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n  } catch (err) {\n    inputShape = \"multiple\";\n  }\n  try {\n    outputShape = JSON.stringify(layer.outputShape);\n  } catch (err) {\n    outputShape = \"multiple\";\n  }\n  const connections = [];\n  for (const node of layer.inboundNodes) {\n    if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {\n      continue;\n    }\n    for (let i2 = 0; i2 < node.inboundLayers.length; ++i2) {\n      const inboundLayer = node.inboundLayers[i2].name;\n      const inboundLayerIndex = node.nodeIndices[i2];\n      const inboundTensorIndex = node.tensorIndices[i2];\n      connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n    }\n  }\n  const name = layer.name;\n  const className = layer.getClassName();\n  const firstConnection = connections.length === 0 ? \"\" : connections[0];\n  const fields = [\n    `${name} (${className})`,\n    inputShape,\n    outputShape,\n    layer.countParams().toString(),\n    firstConnection\n  ];\n  printRow(fields, positions, printFn);\n  for (let i2 = 1; i2 < connections.length; ++i2) {\n    printRow([\"\", \"\", \"\", \"\", connections[i2]], positions, printFn);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js\nfunction isArrayItemInputOrOutputName(key, index, value) {\n  return (key === \"inboundNodes\" || key === \"outputLayers\" || key === \"inputLayers\") && index === 0 && typeof value === \"string\";\n}\nfunction convertPythonicToTs(pythonicConfig, key) {\n  if (pythonicConfig === null) {\n    return null;\n  } else if (typeof pythonicConfig === \"string\") {\n    return toCamelCase(pythonicConfig);\n  } else if (typeof pythonicConfig === \"number\" || typeof pythonicConfig === \"boolean\") {\n    return pythonicConfig;\n  } else if (pythonicConfig instanceof Array) {\n    const tsArray = [];\n    const arrayLength = pythonicConfig.length;\n    for (let i2 = 0; i2 < arrayLength; ++i2) {\n      const item = pythonicConfig[i2];\n      if (isArrayItemInputOrOutputName(key, i2, item)) {\n        tsArray.push(item);\n      } else {\n        tsArray.push(convertPythonicToTs(item, key));\n      }\n    }\n    return tsArray;\n  } else {\n    const tsDict = {};\n    for (const pythonicKey of Object.keys(pythonicConfig)) {\n      const pythonicValue = pythonicConfig[pythonicKey];\n      if (pythonicKey === \"name\" && typeof pythonicValue === \"string\") {\n        tsDict[pythonicKey] = pythonicValue;\n      } else {\n        const tsKey = toCamelCase(pythonicKey);\n        tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n      }\n    }\n    return tsDict;\n  }\n}\nfunction convertTsToPythonic(tsConfig, key) {\n  if (tsConfig === null || tsConfig === void 0) {\n    return null;\n  } else if (typeof tsConfig === \"string\") {\n    return toSnakeCase(tsConfig);\n  } else if (typeof tsConfig === \"number\" || typeof tsConfig === \"boolean\") {\n    return tsConfig;\n  } else if (tsConfig instanceof Array) {\n    const pyArray = [];\n    const arrayLength = tsConfig.length;\n    for (let i2 = 0; i2 < arrayLength; ++i2) {\n      const item = tsConfig[i2];\n      if (isArrayItemInputOrOutputName(key, i2, item)) {\n        pyArray.push(item);\n      } else {\n        pyArray.push(convertTsToPythonic(item, key));\n      }\n    }\n    return pyArray;\n  } else {\n    const pyDict = {};\n    for (const tsKey of Object.keys(tsConfig)) {\n      const tsValue = tsConfig[tsKey];\n      const pyKey = toSnakeCase(tsKey);\n      if ((tsKey === \"name\" || tsKey === \"className\") && typeof tsValue === \"string\") {\n        pyDict[pyKey] = tsValue;\n      } else {\n        pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n      }\n    }\n    return pyDict;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/version.js\nvar version2 = \"3.18.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js\nvar Container = class extends Layer {\n  constructor(args) {\n    super({});\n    this.containerNodes = /* @__PURE__ */ new Set();\n    this.name = args.name;\n    if (this.name == null) {\n      const prefix = this.getClassName().toLowerCase();\n      this.name = getUid(prefix);\n    }\n    this.supportsMasking = false;\n    this.trainable_ = true;\n    if (Array.isArray(args.inputs)) {\n      this.inputs = args.inputs.slice();\n    } else {\n      this.inputs = [args.inputs];\n    }\n    if (Array.isArray(args.outputs)) {\n      this.outputs = args.outputs.slice();\n    } else {\n      this.outputs = [args.outputs];\n    }\n    if (unique2(this.inputs).length !== this.inputs.length) {\n      throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);\n    }\n    if (unique2(this.outputs).length !== this.outputs.length) {\n      console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);\n    }\n    this.inputLayers = [];\n    this.inputLayersNodeIndices = [];\n    this.inputLayersTensorIndices = [];\n    this.outputLayers = [];\n    this.outputLayersNodeIndices = [];\n    this.outputLayersTensorIndices = [];\n    this.layers = [];\n    this.internalContainerRefs = [];\n    for (const x of this.outputs) {\n      const layer = x.sourceLayer;\n      const nodeIndex = x.nodeIndex;\n      const tensorIndex = x.tensorIndex;\n      this.outputLayers.push(layer);\n      this.outputLayersNodeIndices.push(nodeIndex);\n      this.outputLayersTensorIndices.push(tensorIndex);\n    }\n    for (const x of this.inputs) {\n      const layer = x.sourceLayer;\n      const nodeIndex = x.nodeIndex;\n      const tensorIndex = x.tensorIndex;\n      assert2(nodeIndex === 0, \"input layer has >1 nodes\");\n      assert2(tensorIndex === 0, \"input layer has >1 tensors\");\n      this.inputLayers.push(layer);\n      this.inputLayersNodeIndices.push(nodeIndex);\n      this.inputLayersTensorIndices.push(tensorIndex);\n    }\n    this.inputNames = [];\n    this.outputNames = [];\n    this.feedInputShapes = [];\n    this.feedInputNames = [];\n    this.feedOutputNames = [];\n    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {\n      const layer = this.inputLayers[i2];\n      if (!(layer instanceof InputLayer)) {\n        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i2} (0-based) originates from layer type ${layer.getClassName()}.`);\n      }\n      this.inputNames.push(layer.name);\n      this.feedInputShapes.push(layer.batchInputShape);\n      this.feedInputNames.push(layer.name);\n    }\n    for (const layer of this.outputLayers) {\n      this.outputNames.push(layer.name);\n    }\n    this.internalInputShapes = this.inputs.map((x) => x.shape);\n    this.internalOutputShapes = this.outputs.map((x) => x.shape);\n    const nodesDepths = {};\n    const nodeIDToNode = {};\n    const layersDepths = {};\n    const layerIDToLayer = {};\n    const layerIndices = {};\n    const nodesInDecreasingDepth = [];\n    const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {\n      if (layer == null || nodeIndex == null || tensorIndex == null) {\n        layer = tensor2.sourceLayer;\n        nodeIndex = tensor2.nodeIndex;\n        tensorIndex = tensor2.tensorIndex;\n      }\n      const node = layer.inboundNodes[nodeIndex];\n      if (nodesInProgress2.indexOf(node) !== -1) {\n        throw new RuntimeError(`The tensor ${tensor2.name} at layer \"${layer.name}\" is part of a cycle.`);\n      }\n      if (finishedNodes2.indexOf(node) !== -1) {\n        return;\n      }\n      this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n      if (!(layer.id in layerIndices)) {\n        layerIndices[layer.id] = Object.keys(layerIndices).length;\n      }\n      if (nodesInProgress2.indexOf(node) === -1) {\n        nodesInProgress2.push(node);\n      }\n      const numInboundLayers = node.inboundLayers.length;\n      for (let i2 = 0; i2 < numInboundLayers; i2++) {\n        const x = node.inputTensors[i2];\n        const layer2 = node.inboundLayers[i2];\n        const nodeIndex2 = node.nodeIndices[i2];\n        const tensorIndex2 = node.tensorIndices[i2];\n        buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);\n      }\n      finishedNodes2.push(node);\n      while (nodesInProgress2.indexOf(node) >= 0) {\n        nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1);\n      }\n      nodesInDecreasingDepth.push(node);\n    };\n    const finishedNodes = [];\n    const nodesInProgress = [];\n    for (const x of this.outputs) {\n      buildMapOfGraph(x, finishedNodes, nodesInProgress);\n    }\n    const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n    for (const node of reversedNodesInDecreasingDepth) {\n      nodeIDToNode[node.id] = node;\n      if (!(node.id in nodesDepths)) {\n        nodesDepths[node.id] = 0;\n      }\n      let depth = nodesDepths[node.id];\n      const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id];\n      depth = Math.max(depth, previousDepth);\n      layersDepths[node.outboundLayer.id] = depth;\n      layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n      nodesDepths[node.id] = depth;\n      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {\n        const inboundLayer = node.inboundLayers[i2];\n        const nodeIndex = node.nodeIndices[i2];\n        const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n        const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];\n        nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);\n        nodeIDToNode[inboundNode.id] = inboundNode;\n      }\n    }\n    const nodesByDepth = {};\n    for (const nodeID in nodesDepths) {\n      const depth = nodesDepths[nodeID];\n      if (!(depth in nodesByDepth)) {\n        nodesByDepth[depth] = [];\n      }\n      nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n    }\n    const layersByDepth = {};\n    for (const layerID in layersDepths) {\n      const depth = layersDepths[layerID];\n      if (!(depth in layersByDepth)) {\n        layersByDepth[depth] = [];\n      }\n      layersByDepth[depth].push(layerIDToLayer[layerID]);\n    }\n    let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    this.layers = [];\n    for (const depth of depthKeys) {\n      const layersForDepth = layersByDepth[depth];\n      layersForDepth.sort((a6, b) => {\n        const aIndex = layerIndices[a6.id];\n        const bIndex = layerIndices[b.id];\n        if (aIndex < bIndex) {\n          return -1;\n        }\n        if (aIndex > bIndex) {\n          return 1;\n        }\n        return 0;\n      });\n      for (const layer of layersForDepth) {\n        if (layer instanceof Container) {\n          this.internalContainerRefs.push(layer);\n        }\n        this.layers.push(layer);\n      }\n    }\n    this.layersByDepth = layersByDepth;\n    depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    const computableTensors = this.inputs.slice();\n    const layersWithCompleteInput = [];\n    for (const depth of depthKeys) {\n      for (const node of nodesByDepth[depth]) {\n        const layer = node.outboundLayer;\n        if (layer != null) {\n          for (const x of node.inputTensors) {\n            if (computableTensors.indexOf(x) === -1) {\n              throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${layer.name}\". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);\n            }\n          }\n          for (const x of node.outputTensors) {\n            computableTensors.push(x);\n          }\n          layersWithCompleteInput.push(layer.name);\n        }\n      }\n    }\n    this.nodesByDepth = nodesByDepth;\n    const allNames = this.layers.map((x) => x.name);\n    for (const name of allNames) {\n      const numOccurrences = allNames.filter((x) => x === name).length;\n      if (numOccurrences !== 1) {\n        throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));\n      }\n    }\n    this.outboundNodes = [];\n    this.inboundNodes = [];\n    new Node({\n      outboundLayer: this,\n      inboundLayers: [],\n      nodeIndices: [],\n      tensorIndices: [],\n      inputTensors: this.inputs,\n      outputTensors: this.outputs,\n      inputMasks: this.inputs.map((x) => null),\n      outputMasks: this.outputs.map((x) => null),\n      inputShapes: this.inputs.map((x) => x.shape),\n      outputShapes: this.outputs.map((x) => x.shape)\n    });\n    this.built = true;\n    this._refCount = 1;\n  }\n  assertNotDisposed() {\n    if (this._refCount === 0) {\n      throw new Error(`Container '${this.name}' is already disposed.`);\n    }\n  }\n  dispose() {\n    this.assertNotDisposed();\n    const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n    if (--this._refCount === 0) {\n      for (const layer of this.layers) {\n        result.numDisposedVariables += layer.dispose().numDisposedVariables;\n      }\n      for (const container of this.internalContainerRefs) {\n        result.numDisposedVariables += container.dispose().numDisposedVariables;\n      }\n    }\n    result.refCountAfterDispose = this._refCount;\n    return result;\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this.layers.forEach((layer) => {\n      layer._trainableWeights.forEach((w) => w.trainable = trainable);\n    });\n    this.trainable_ = trainable;\n  }\n  get trainableWeights() {\n    if (this._trainableWeights.length > 0) {\n      throw new ValueError(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");\n    }\n    if (!this.trainable) {\n      return [];\n    }\n    let weights = [];\n    for (const layer of this.layers) {\n      weights = weights.concat(layer.trainableWeights);\n    }\n    return weights;\n  }\n  get nonTrainableWeights() {\n    const weights = [];\n    for (const layer of this.layers) {\n      weights.push(...layer.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights = [];\n      for (const layer of this.layers) {\n        trainableWeights.push(...layer.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n  get weights() {\n    return this.trainableWeights.concat(this.nonTrainableWeights);\n  }\n  loadWeights(weights, strict = true) {\n    const nameToWeight = {};\n    let totalWeightsCount = 0;\n    for (const layer of this.layers) {\n      for (const weight of layer.weights) {\n        if (nameToWeight[weight.originalName] != null) {\n          throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n        }\n        nameToWeight[weight.originalName] = weight;\n        totalWeightsCount++;\n      }\n    }\n    const weightValueTuples = [];\n    for (const name in weights) {\n      let validatedName = name;\n      if (nameToWeight[name] == null) {\n        const tokens = name.split(\"/\");\n        const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n        validatedName = shortenNameArray.join(\"/\");\n      }\n      if (nameToWeight[validatedName] != null) {\n        weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n      } else if (strict) {\n        throw new ValueError(`Provided weight data has no target variable: ${name}`);\n      }\n      delete nameToWeight[validatedName];\n    }\n    if (strict) {\n      const unsetNames = [];\n      for (const name in nameToWeight) {\n        unsetNames.push(name);\n      }\n      if (unsetNames.length > 0) {\n        throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);\n      }\n    }\n    batchSetValue(weightValueTuples);\n  }\n  updatedConfig() {\n    const theConfig = this.getConfig();\n    const modelConfig = {};\n    modelConfig[\"className\"] = this.getClassName();\n    modelConfig[\"config\"] = theConfig;\n    modelConfig[\"kerasVersion\"] = `tfjs-layers ${version2}`;\n    modelConfig[\"backend\"] = \"TensorFlow.js\";\n    return modelConfig;\n  }\n  toJSON(unused, returnString = true) {\n    const modelConfig = convertTsToPythonic(this.updatedConfig());\n    return returnString ? JSON.stringify(modelConfig) : modelConfig;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = toList(inputs);\n      const feedDict = new FeedDict();\n      for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n        feedDict.add(this.inputs[i2], inputs[i2]);\n      }\n      return execute(this.outputs, feedDict, kwargs);\n    });\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      inputs = toList(inputs);\n      let masks;\n      if (mask == null) {\n        masks = pyListRepeat(null, inputs.length);\n      } else {\n        masks = toList(mask);\n      }\n      return this.runInternalGraph(inputs, masks)[1];\n    });\n  }\n  computeOutputShape(inputShape) {\n    const inputShapes = normalizeShapeList(inputShape);\n    if (inputShapes.length !== this.inputLayers.length) {\n      throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);\n    }\n    const layersToOutputShapes = {};\n    for (let i2 = 0; i2 < inputShapes.length; i2++) {\n      const layer = this.inputLayers[i2];\n      const inputShape2 = inputShapes[i2];\n      const shapeKey = layer.name + \"_0_0\";\n      layersToOutputShapes[shapeKey] = inputShape2;\n    }\n    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    if (depthKeys.length > 1) {\n      for (const depth of depthKeys) {\n        const nodes = this.nodesByDepth[depth];\n        for (const node of nodes) {\n          const layer = node.outboundLayer;\n          if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {\n            continue;\n          }\n          const inputShapes2 = [];\n          for (let j = 0; j < node.inboundLayers.length; j++) {\n            const inboundLayer = node.inboundLayers[j];\n            const nodeIndex2 = node.nodeIndices[j];\n            const tensorIndex = node.tensorIndices[j];\n            const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;\n            const inputShape2 = layersToOutputShapes[shapeKey];\n            inputShapes2.push(inputShape2);\n          }\n          const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));\n          const outputShapes2 = normalizeShapeList(outputShape);\n          const nodeIndex = layer.inboundNodes.indexOf(node);\n          for (let j = 0; j < outputShapes2.length; j++) {\n            const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n            layersToOutputShapes[shapeKey] = outputShapes2[j];\n          }\n        }\n      }\n    }\n    const outputShapes = [];\n    const outputShapeKeys = [];\n    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {\n      const layer = this.outputLayers[i2];\n      const nodeIndex = this.outputLayersNodeIndices[i2];\n      const tensorIndex = this.outputLayersTensorIndices[i2];\n      const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n      outputShapeKeys.push(shapeKey);\n    }\n    for (let i2 = 0; i2 < outputShapeKeys.length; i2++) {\n      const key = outputShapeKeys[i2];\n      assert2(key in layersToOutputShapes);\n      outputShapes.push(layersToOutputShapes[key]);\n    }\n    return singletonOrArray(outputShapes);\n  }\n  runInternalGraph(inputs, masks) {\n    if (masks == null) {\n      masks = pyListRepeat(null, inputs.length);\n    }\n    const tensorMap = {};\n    for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n      const x = this.inputs[i2];\n      const y = inputs[i2];\n      const mask = masks[i2];\n      tensorMap[x.id] = [y, mask];\n    }\n    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    for (const depth of depthKeys) {\n      const nodes = this.nodesByDepth[depth];\n      for (const node of nodes) {\n        const layer = node.outboundLayer;\n        const referenceInputTensors = node.inputTensors;\n        const referenceOutputTensors = node.outputTensors;\n        const computedData = new Array();\n        for (const x of referenceInputTensors) {\n          if (x.id in tensorMap) {\n            computedData.push(tensorMap[x.id]);\n          }\n        }\n        if (computedData.length === referenceInputTensors.length) {\n          let kwargs = {};\n          let computedTensors;\n          let computedMasks;\n          let outputTensors2;\n          let outputMasks2;\n          if (node.callArgs != null) {\n            kwargs = node.callArgs;\n          }\n          if (computedData.length === 1) {\n            const [computedTensor, computedMask] = computedData[0];\n            if (kwargs[\"mask\"] == null) {\n              kwargs[\"mask\"] = computedMask;\n            }\n            outputTensors2 = toList(layer.call(computedTensor, kwargs));\n            outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));\n            computedTensors = [computedTensor];\n            computedMasks = [computedMask];\n          } else {\n            computedTensors = computedData.map((x) => x[0]);\n            computedMasks = computedData.map((x) => x[1]);\n            if (kwargs[\"mask\"] == null) {\n              kwargs[\"mask\"] = computedMasks;\n            }\n            outputTensors2 = toList(layer.call(computedTensors, kwargs));\n            outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));\n          }\n          if (layer.activityRegularizer) {\n            throw new NotImplementedError(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");\n          }\n          for (let i2 = 0; i2 < referenceOutputTensors.length; ++i2) {\n            const x = referenceOutputTensors[i2];\n            const y = outputTensors2[i2];\n            const mask = outputMasks2[i2];\n            tensorMap[x.id] = [y, mask];\n          }\n        }\n      }\n    }\n    const outputTensors = [];\n    const outputMasks = [];\n    const outputShapes = [];\n    for (const x of this.outputs) {\n      assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n      const [tensor2, mask] = tensorMap[x.id];\n      outputShapes.push(tensor2.shape);\n      outputTensors.push(tensor2);\n      outputMasks.push(mask);\n    }\n    return [outputTensors, outputMasks, outputShapes];\n  }\n  buildNodeConversionMap(layers) {\n    const nodeConversionMap = {};\n    let keptNodes;\n    for (const layer of this.layers) {\n      keptNodes = layer instanceof Container ? 1 : 0;\n      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n        const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n        if (this.containerNodes.has(nodeKey)) {\n          nodeConversionMap[nodeKey] = keptNodes;\n          keptNodes += 1;\n        }\n      }\n    }\n    return nodeConversionMap;\n  }\n  getLayer(name, index) {\n    if (index != null) {\n      if (this.layers.length <= index) {\n        throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);\n      } else {\n        return this.layers[index];\n      }\n    } else {\n      if (name == null) {\n        throw new ValueError(\"Provide either a layer name or layer index\");\n      }\n    }\n    for (const layer of this.layers) {\n      if (layer.name === name) {\n        return layer;\n      }\n    }\n    throw new ValueError(`No such layer: ${name}`);\n  }\n  calculateLosses() {\n    return tidy(() => {\n      const losses2 = [];\n      for (const layer of this.layers) {\n        for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n          const nodeKey = Container.nodeKey(layer, nodeIndex);\n          if (this.containerNodes.has(nodeKey)) {\n            losses2.push(...layer.calculateLosses());\n          }\n        }\n      }\n      return losses2;\n    });\n  }\n  getConfig() {\n    const config = { name: this.name };\n    const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n    const layerConfigs = [];\n    for (const layer of this.layers) {\n      const layerClassName = layer.getClassName();\n      const layerConfig = layer.getConfig();\n      const filteredInboundNodes = [];\n      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n        const node = layer.inboundNodes[originalNodeIndex];\n        const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n        let kwargs = {};\n        if (this.containerNodes.has(nodeKey)) {\n          if (node.callArgs) {\n            try {\n              JSON.stringify(node.callArgs);\n              kwargs = node.callArgs;\n            } catch (err) {\n              console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);\n              kwargs = {};\n            }\n          }\n          if (node.inboundLayers.length > 0) {\n            const nodeData = [];\n            for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {\n              const inboundLayer = node.inboundLayers[i2];\n              const nodeIndex = node.nodeIndices[i2];\n              const tensorIndex = node.tensorIndices[i2];\n              const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);\n              let newNodeIndex = nodeConversionMap[nodeKey2];\n              if (newNodeIndex == null) {\n                newNodeIndex = 0;\n              }\n              nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n            }\n            filteredInboundNodes.push(nodeData);\n          }\n        }\n      }\n      const dict = {};\n      dict[\"name\"] = layer.name;\n      dict[\"className\"] = layerClassName;\n      dict[\"config\"] = layerConfig;\n      dict[\"inboundNodes\"] = filteredInboundNodes;\n      layerConfigs.push(dict);\n    }\n    config[\"layers\"] = layerConfigs;\n    const modelInputs = [];\n    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {\n      const layer = this.inputLayers[i2];\n      const nodeIndex = this.inputLayersNodeIndices[i2];\n      const nodeKey = Container.nodeKey(layer, nodeIndex);\n      if (!this.containerNodes.has(nodeKey)) {\n        continue;\n      }\n      let newNodeIndex = nodeConversionMap[nodeKey];\n      if (newNodeIndex === null || newNodeIndex === void 0) {\n        newNodeIndex = 0;\n      }\n      const tensorIndex = this.inputLayersTensorIndices[i2];\n      modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n    }\n    config[\"inputLayers\"] = modelInputs;\n    const modelOutputs = [];\n    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {\n      const layer = this.outputLayers[i2];\n      const nodeIndex = this.outputLayersNodeIndices[i2];\n      const nodeKey = Container.nodeKey(layer, nodeIndex);\n      if (!this.containerNodes.has(nodeKey)) {\n        continue;\n      }\n      let newNodeIndex = nodeConversionMap[nodeKey];\n      if (newNodeIndex === null || newNodeIndex === void 0) {\n        newNodeIndex = 0;\n      }\n      const tensorIndex = this.outputLayersTensorIndices[i2];\n      modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n    }\n    config[\"outputLayers\"] = modelOutputs;\n    return config;\n  }\n  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n    const createdLayers = {};\n    const unprocessedNodes = {};\n    function addUnprocessedNode(layer, nodeData) {\n      if (!(layer.name in unprocessedNodes)) {\n        unprocessedNodes[layer.name] = [nodeData];\n      } else {\n        unprocessedNodes[layer.name].push(nodeData);\n      }\n    }\n    function processNode(layer, nodeData) {\n      const inputTensors2 = [];\n      let kwargs;\n      for (const inputData of nodeData) {\n        const inboundLayerName = inputData[0];\n        const inboundNodeIndex = inputData[1];\n        const inboundTensorIndex = inputData[2];\n        kwargs = inputData[3] == null ? {} : inputData[3];\n        if (!(inboundLayerName in createdLayers)) {\n          addUnprocessedNode(layer, nodeData);\n          return;\n        }\n        const inboundLayer = createdLayers[inboundLayerName];\n        if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n          addUnprocessedNode(layer, nodeData);\n          return;\n        }\n        const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n        inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);\n      }\n      if (inputTensors2.length > 0) {\n        layer.apply(singletonOrArray(inputTensors2), kwargs);\n      }\n    }\n    function processLayer(layerData) {\n      const layerName = layerData[\"name\"];\n      const layer = deserialize(layerData, config[\"customObjects\"] != null ? config[\"customObjects\"] : {});\n      layer.setFastWeightInitDuringBuild(fastWeightInit);\n      createdLayers[layerName] = layer;\n      const inboundNodesData = layerData[\"inboundNodes\"];\n      inboundNodesData.forEach((nodeData) => {\n        if (!(nodeData instanceof Array)) {\n          throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n        }\n        addUnprocessedNode(layer, nodeData);\n      });\n    }\n    const name = config[\"name\"];\n    const layersFromConfig = config[\"layers\"];\n    for (const layerData of layersFromConfig) {\n      processLayer(layerData);\n    }\n    while (!isObjectEmpty(unprocessedNodes)) {\n      for (const layerData of layersFromConfig) {\n        const layer = createdLayers[layerData[\"name\"]];\n        if (layer.name in unprocessedNodes) {\n          const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n          delete unprocessedNodes[layer.name];\n          for (const nodeData of currentUnprocessedNodesForLayer) {\n            processNode(layer, nodeData);\n          }\n        }\n      }\n    }\n    const inputTensors = [];\n    const outputTensors = [];\n    const inputLayersFromConfig = config[\"inputLayers\"];\n    for (const layerData of inputLayersFromConfig) {\n      const layerName = layerData[0];\n      const nodeIndex = layerData[1];\n      const tensorIndex = layerData[2];\n      assert2(layerName in createdLayers);\n      const layer = createdLayers[layerName];\n      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n      inputTensors.push(layerOutputTensors[tensorIndex]);\n    }\n    const outputLayersFromConfig = config[\"outputLayers\"];\n    for (const layerData of outputLayersFromConfig) {\n      const layerName = layerData[0];\n      const nodeIndex = layerData[1];\n      const tensorIndex = layerData[2];\n      assert2(layerName in createdLayers);\n      const layer = createdLayers[layerName];\n      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n      outputTensors.push(layerOutputTensors[tensorIndex]);\n    }\n    return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n  }\n  get stateful() {\n    if (this._stateful) {\n      throw new ValueError(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");\n    }\n    for (const layer of this.layers) {\n      if (layer.stateful) {\n        return true;\n      }\n    }\n    return false;\n  }\n  resetStates() {\n    tidy(() => {\n      this.layers.forEach((layer) => {\n        if (layer.stateful) {\n          layer.resetStates();\n        }\n      });\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n  const numOutputs = outputNames.length;\n  if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {\n    return outputNames.map((name) => null);\n  }\n  if (numOutputs === 1) {\n    if (Array.isArray(xWeight) && xWeight.length === 1) {\n      return xWeight;\n    } else if (typeof xWeight === \"object\" && outputNames[0] in xWeight) {\n      return [xWeight[outputNames[0]]];\n    } else {\n      return [xWeight];\n    }\n  }\n  if (Array.isArray(xWeight)) {\n    if (xWeight.length !== numOutputs) {\n      throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);\n    }\n    return xWeight;\n  } else if (typeof xWeight === \"object\" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === \"object\") {\n    const output = [];\n    outputNames.forEach((outputName) => {\n      if (outputName in xWeight) {\n        output.push(xWeight[outputName]);\n      } else {\n        output.push(null);\n      }\n    });\n    return output;\n  } else {\n    throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n  }\n}\nfunction standardizeClassWeights(classWeight, outputNames) {\n  return standardizeSampleOrClassWeights(classWeight, outputNames, \"classWeight\");\n}\nasync function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n  if (sampleWeight != null || sampleWeightMode != null) {\n    throw new Error(\"Support sampleWeight is not implemented yet\");\n  }\n  if (classWeight != null) {\n    const yClasses = tidy(() => {\n      if (y.shape.length === 1) {\n        return clone(y);\n      } else if (y.shape.length === 2) {\n        if (y.shape[1] > 1) {\n          const axis = 1;\n          return argMax(y, axis);\n        } else if (y.shape[1] === 1) {\n          return reshape(y, [y.shape[0]]);\n        } else {\n          throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);\n        }\n      } else {\n        throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);\n      }\n    });\n    const yClassIndices = Array.from(await yClasses.data());\n    dispose(yClasses);\n    const classSampleWeight = [];\n    yClassIndices.forEach((classIndex) => {\n      if (classWeight[classIndex] == null) {\n        throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);\n      } else {\n        classSampleWeight.push(classWeight[classIndex]);\n      }\n    });\n    return tensor1d(classSampleWeight, \"float32\");\n  } else {\n    return null;\n  }\n}\nfunction computeWeightedLoss2(losses2, sampleWeights) {\n  return mul(losses2, sampleWeights);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js\nvar DEFAULT_VALIDATION_BATCH_SIZE = 32;\nfunction standardizeDataIteratorOutput(model2, iteratorOut) {\n  let xs;\n  let ys;\n  const iteratorOutObj = iteratorOut;\n  xs = iteratorOutObj[\"xs\"];\n  ys = iteratorOutObj[\"ys\"];\n  util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);\n  const flattenedXs = flattenTensorOrArrayOrMap(\"input\", model2.inputNames, xs);\n  const flattenedYs = flattenTensorOrArrayOrMap(\"output\", model2.outputNames, ys);\n  const batchSize = flattenedXs[0].shape[0];\n  util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`);\n  util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);\n  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n    util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);\n  }\n  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n    util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);\n  }\n  return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n  if (values instanceof Tensor) {\n    return [values];\n  } else if (Array.isArray(values)) {\n    util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n    return values;\n  } else {\n    const result = [];\n    for (const name of names) {\n      if (values[name] == null) {\n        throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);\n      }\n      result.push(values[name]);\n    }\n    return result;\n  }\n}\nfunction standardizeTensorValidationData(data) {\n  if (data.length === 3) {\n    throw new NotImplementedError(\"Validation with sample weights is not implemented yet.\");\n  }\n  return { xs: data[0], ys: data[1] };\n}\nasync function fitDataset(model2, dataset, args) {\n  const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n  util_exports.assert(model2.optimizer != null, () => \"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\");\n  util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);\n  util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);\n  util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);\n  util_exports.assert(args[\"validationSplit\"] == null, () => \"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.\");\n  if (model2.isTraining) {\n    throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n  }\n  model2.isTraining = true;\n  try {\n    const doValidation = args.validationData != null;\n    let valXs;\n    let valYs;\n    if (doValidation) {\n      if (isDatasetObject(args.validationData)) {\n        util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);\n      } else {\n        const validationData = standardizeTensorValidationData(args.validationData);\n        valXs = validationData.xs;\n        valYs = validationData.ys;\n      }\n    }\n    const trainFunction = model2.makeTrainFunction();\n    const outLabels = model2.getDedupedMetricsNames();\n    let callbackMetrics;\n    if (doValidation) {\n      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => \"val_\" + n));\n    } else {\n      callbackMetrics = outLabels.slice();\n    }\n    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n    const verbose = args.verbose == null ? 1 : args.verbose;\n    const { callbackList, history } = configureCallbacks(callbacks2, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, doValidation, callbackMetrics);\n    callbackList.setModel(model2);\n    model2.history = history;\n    await callbackList.onTrainBegin();\n    model2.stopTraining_ = false;\n    let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n    let dataIterator = await dataset.iterator();\n    while (epoch < args.epochs) {\n      const epochLogs = {};\n      await callbackList.onEpochBegin(epoch);\n      let stepsDone = 0;\n      let batchIndex = 0;\n      if (!hasBatchesPerEpoch) {\n        dataIterator = await dataset.iterator();\n      }\n      while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n        const iteratorOut = await dataIterator.next();\n        if (hasBatchesPerEpoch && iteratorOut.done) {\n          console.warn(`You provided \\`batchesPerEpoch\\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \\`batchesPerEpoch * epochs\\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);\n          break;\n        }\n        if (iteratorOut.value != null) {\n          const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n          const batchLogs = {};\n          batchLogs[\"batch\"] = batchIndex;\n          batchLogs[\"size\"] = xs[0].shape[0];\n          await callbackList.onBatchBegin(batchIndex, batchLogs);\n          const sampleWeights = [];\n          if (args.classWeight != null) {\n            const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);\n            for (let i2 = 0; i2 < standardClassWeights.length; ++i2) {\n              sampleWeights.push(await standardizeWeights(ys[i2], null, standardClassWeights[i2]));\n            }\n          }\n          const ins = xs.concat(ys).concat(sampleWeights);\n          const outs = trainFunction(ins);\n          dispose(ins);\n          for (let i2 = 0; i2 < outLabels.length; ++i2) {\n            const label = outLabels[i2];\n            const out = outs[i2];\n            batchLogs[label] = out;\n            keep(out);\n          }\n          await callbackList.onBatchEnd(batchIndex, batchLogs);\n          disposeTensorsInLogs(batchLogs);\n          batchIndex++;\n          stepsDone++;\n        }\n        if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {\n          if (doValidation) {\n            let valOuts;\n            if (isDatasetObject(args.validationData)) {\n              valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n            } else {\n              valOuts = toList(model2.evaluate(valXs, valYs, {\n                batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,\n                verbose: 0\n              }));\n            }\n            for (let i2 = 0; i2 < model2.metricsNames.length; ++i2) {\n              epochLogs[`val_${model2.metricsNames[i2]}`] = valOuts[i2];\n            }\n          }\n          break;\n        }\n        if (model2.stopTraining_) {\n          break;\n        }\n      }\n      await callbackList.onEpochEnd(epoch, epochLogs);\n      epoch++;\n      if (model2.stopTraining_) {\n        break;\n      }\n    }\n    await callbackList.onTrainEnd();\n    await model2.history.syncData();\n    return model2.history;\n  } finally {\n    model2.isTraining = false;\n  }\n}\nfunction getStepsPerEpoch(dataset, args) {\n  let stepsPerEpoch = null;\n  if (args.batchesPerEpoch != null) {\n    stepsPerEpoch = args.batchesPerEpoch;\n  } else if (Number.isFinite(dataset.size)) {\n    stepsPerEpoch = dataset.size;\n  }\n  return stepsPerEpoch;\n}\nfunction isDatasetObject(dataset) {\n  return typeof dataset.iterator === \"function\";\n}\nfunction isLazyIteratorObject(iterator) {\n  return typeof iterator.next === \"function\";\n}\nasync function evaluateDataset(model2, dataset, args) {\n  args = args || {};\n  const hasBatches = args.batches != null;\n  const f = model2.testFunction;\n  let outs = [];\n  if (args.verbose > 0) {\n    throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n  }\n  util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \\`batches\\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);\n  const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();\n  let numExamples = 0;\n  let batch = 0;\n  while (hasBatches ? batch < args.batches : true) {\n    const iteratorOut = await dataIterator.next();\n    outs = tidy(() => {\n      if (iteratorOut.value) {\n        const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n        const xsAndYs = xs.concat(ys);\n        const batchOuts = tidy(() => f(xsAndYs));\n        dispose(xsAndYs);\n        if (batch === 0) {\n          for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n            outs.push(scalar(0));\n          }\n        }\n        const batchSize = xsAndYs[0].shape[0];\n        for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n          const batchOut = batchOuts[i2];\n          const oldScalar = outs[i2];\n          outs[i2] = tidy(() => add2(outs[i2], mul(batchSize, batchOut)));\n          if (batch > 0) {\n            dispose(oldScalar);\n          }\n        }\n        dispose(batchOuts);\n        numExamples += batchSize;\n        ++batch;\n      }\n      return outs;\n    });\n    if (iteratorOut.done) {\n      if (hasBatches) {\n        console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \\`batches\\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);\n      }\n      break;\n    }\n  }\n  for (let i2 = 0; i2 < outs.length; ++i2) {\n    const oldScalar = outs[i2];\n    outs[i2] = div(outs[i2], numExamples);\n    dispose(oldScalar);\n  }\n  return singletonOrArray(outs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js\nfunction checkBatchSize(batchSize) {\n  util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\nfunction sliceArrays(arrays, start, stop) {\n  if (arrays == null) {\n    return [null];\n  } else if (Array.isArray(arrays)) {\n    return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));\n  } else {\n    return sliceAlongFirstAxis(arrays, start, stop - start);\n  }\n}\nfunction sliceArraysByIndices(arrays, indices) {\n  return tidy(() => {\n    if (arrays == null) {\n      return null;\n    } else if (Array.isArray(arrays)) {\n      return arrays.map((array2) => sliceArraysByIndices(array2, indices));\n    } else {\n      return gather2(arrays, indices.dtype === \"int32\" ? indices : cast(indices, \"int32\"));\n    }\n  });\n}\nfunction makeBatches(size, batchSize) {\n  const output = [];\n  let batchStart = 0;\n  let batchEnd = null;\n  while (batchStart < size) {\n    batchEnd = batchStart + batchSize;\n    if (batchEnd >= size) {\n      batchEnd = size;\n    }\n    output.push([batchStart, batchEnd]);\n    batchStart = batchEnd;\n  }\n  return output;\n}\nasync function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n  if (batchSize == null) {\n    batchSize = 32;\n  }\n  if (epochs == null) {\n    epochs = 1;\n  }\n  if (shuffle2 == null) {\n    shuffle2 = true;\n  }\n  if (initialEpoch == null) {\n    initialEpoch = 0;\n  }\n  let doValidation = false;\n  if (valF != null && valIns != null) {\n    doValidation = true;\n  }\n  if (validationSteps != null) {\n    doValidation = true;\n    if (stepsPerEpoch == null) {\n      throw new ValueError(\"Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.\");\n    }\n  }\n  const numTrainSamples = model2.checkNumSamples(ins, batchSize, stepsPerEpoch, \"steps_per_epoch\");\n  let indexArray;\n  if (numTrainSamples != null) {\n    indexArray = range2(0, numTrainSamples);\n  }\n  if (verbose == null) {\n    verbose = 1;\n  }\n  const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n  callbackList.setModel(model2);\n  model2.history = history;\n  await callbackList.onTrainBegin();\n  model2.stopTraining_ = false;\n  for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n    await callbackList.onEpochBegin(epoch);\n    const epochLogs = {};\n    if (stepsPerEpoch != null) {\n      throw new NotImplementedError(\"stepsPerEpoch mode is not implemented yet.\");\n    } else {\n      if (shuffle2 === \"batch\") {\n        throw new NotImplementedError(\"batch shuffling is not implemneted yet\");\n      } else if (shuffle2) {\n        util_exports.shuffle(indexArray);\n      }\n      const epochIndexArray1D = tensor1d(indexArray);\n      const batches = makeBatches(numTrainSamples, batchSize);\n      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n        const batchLogs = {};\n        await callbackList.onBatchBegin(batchIndex, batchLogs);\n        tidy(() => {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n          batchLogs[\"batch\"] = batchIndex;\n          batchLogs[\"size\"] = batchEnd - batchStart;\n          const insBatch = sliceArraysByIndices(ins, batchIds);\n          const outs = f(insBatch);\n          for (let i2 = 0; i2 < outLabels.length; ++i2) {\n            const label = outLabels[i2];\n            const out = outs[i2];\n            batchLogs[label] = out;\n            keep(out);\n          }\n          if (batchIndex === batches.length - 1) {\n            if (doValidation) {\n              const valOuts = model2.testLoop(valF, valIns, batchSize);\n              for (let i2 = 0; i2 < outLabels.length; ++i2) {\n                const label = outLabels[i2];\n                const out = valOuts[i2];\n                keep(out);\n                epochLogs[\"val_\" + label] = out;\n              }\n            }\n          }\n        });\n        await callbackList.onBatchEnd(batchIndex, batchLogs);\n        disposeTensorsInLogs(batchLogs);\n        if (model2.stopTraining_) {\n          break;\n        }\n      }\n      epochIndexArray1D.dispose();\n    }\n    await callbackList.onEpochEnd(epoch, epochLogs);\n    if (model2.stopTraining_) {\n      break;\n    }\n  }\n  await callbackList.onTrainEnd();\n  await model2.history.syncData();\n  return model2.history;\n}\nasync function fitTensors(model2, x, y, args = {}) {\n  if (model2.isTraining) {\n    throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n  }\n  model2.isTraining = true;\n  let inputs;\n  let targets;\n  let originalInputs;\n  let originalTargets;\n  let inputValX;\n  let inputValY;\n  let valX;\n  let valY;\n  let sampleWeights;\n  try {\n    const batchSize = args.batchSize == null ? 32 : args.batchSize;\n    checkBatchSize(batchSize);\n    const checkBatchAxis = false;\n    const standardizedOuts = await model2.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n    inputs = standardizedOuts[0];\n    targets = standardizedOuts[1];\n    sampleWeights = standardizedOuts[2];\n    let doValidation = false;\n    let valIns;\n    if (args.validationData != null && args.validationData.length > 0) {\n      doValidation = true;\n      if (args.validationData.length === 2) {\n        inputValX = args.validationData[0];\n        inputValY = args.validationData[1];\n      } else if (args.validationData.length === 3) {\n        throw new NotImplementedError(\"validationData including sample weights is not supported yet.\");\n      } else {\n        throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);\n      }\n      const checkBatchAxis2 = true;\n      const valStandardized = await model2.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);\n      valX = valStandardized[0];\n      valY = valStandardized[1];\n      valIns = valX.concat(valY);\n    } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {\n      doValidation = true;\n      const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n      const originalBatchSize = inputs[0].shape[0];\n      valX = sliceArrays(inputs, splitAt, originalBatchSize);\n      originalInputs = inputs;\n      inputs = sliceArrays(inputs, 0, splitAt);\n      valY = sliceArrays(targets, splitAt, originalBatchSize);\n      originalTargets = targets;\n      targets = sliceArrays(targets, 0, splitAt);\n      valIns = valX.concat(valY);\n    } else if (args.validationSteps != null) {\n      doValidation = true;\n    }\n    const ins = inputs.concat(targets).concat(sampleWeights);\n    model2.checkTrainableWeightsConsistency();\n    const trainFunction = model2.makeTrainFunction();\n    const outLabels = model2.getDedupedMetricsNames();\n    let valFunction;\n    let callbackMetrics;\n    if (doValidation) {\n      model2.makeTestFunction();\n      valFunction = model2.testFunction;\n      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => \"val_\" + n));\n    } else {\n      valFunction = null;\n      valIns = [];\n      callbackMetrics = outLabels.slice();\n    }\n    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n    const out = await fitLoop(model2, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n    return out;\n  } finally {\n    model2.isTraining = false;\n    disposeNewTensors(inputs, x);\n    disposeNewTensors(targets, y);\n    disposeNewTensors(originalInputs, x);\n    disposeNewTensors(originalTargets, y);\n    disposeNewTensors(valX, inputValX);\n    disposeNewTensors(valY, inputValY);\n    if (sampleWeights != null) {\n      dispose(sampleWeights);\n    }\n  }\n}\nfunction ensureTensorsRank2OrHigher(tensors) {\n  const outs = [];\n  if (tensors instanceof Tensor) {\n    tensors = [tensors];\n  }\n  for (let i2 = 0; i2 < tensors.length; ++i2) {\n    const tensor2 = tensors[i2];\n    if (tensor2.rank === 1) {\n      outs.push(expandDims2(tensor2, 1));\n    } else if (tensor2.rank === 0) {\n      throw new Error(\"Expected tensor to be at least 1D, but received a 0D tensor (scalar).\");\n    } else {\n      outs.push(tensor2);\n    }\n  }\n  return outs;\n}\nfunction disposeNewTensors(tensors, refTensors) {\n  if (tensors == null) {\n    return;\n  }\n  const oldTensorIds = [];\n  if (refTensors instanceof Tensor) {\n    oldTensorIds.push(refTensors.id);\n  } else if (Array.isArray(refTensors)) {\n    refTensors.forEach((t2) => oldTensorIds.push(t2.id));\n  } else if (refTensors != null) {\n    for (const name in refTensors) {\n      const oldTensor = refTensors[name];\n      oldTensorIds.push(oldTensor.id);\n    }\n  }\n  const tensorsToDispose = [];\n  if (tensors instanceof Tensor) {\n    if (oldTensorIds.indexOf(tensors.id) === -1) {\n      tensorsToDispose.push(tensors);\n    }\n  } else if (Array.isArray(tensors)) {\n    tensors.forEach((t2) => {\n      if (oldTensorIds.indexOf(t2.id) === -1) {\n        tensorsToDispose.push(t2);\n      }\n    });\n  } else if (tensors != null) {\n    for (const name in tensors) {\n      const tensor2 = tensors[name];\n      if (oldTensorIds.indexOf(tensor2.id) === -1) {\n        tensorsToDispose.push(tensor2);\n      }\n    }\n  }\n  tensorsToDispose.forEach((t2) => {\n    if (!t2.isDisposed) {\n      t2.dispose();\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js\nfunction isDataTensor(x) {\n  return x instanceof Tensor;\n}\nfunction isDataArray(x) {\n  return Array.isArray(x);\n}\nfunction isDataDict(x) {\n  return !isDataTensor(x) && !isDataArray(x);\n}\nfunction standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n  if (names == null || names.length === 0) {\n    if (data != null) {\n      let gotUnexpectedData = false;\n      if (isDataArray(data) && data.length > 0) {\n        gotUnexpectedData = true;\n      } else if (isDataDict(data)) {\n        for (const key in data) {\n          if (data.hasOwnProperty(key)) {\n            gotUnexpectedData = true;\n            break;\n          }\n        }\n      } else {\n        gotUnexpectedData = true;\n      }\n      if (gotUnexpectedData) {\n        throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);\n      }\n    }\n    return [];\n  }\n  if (data == null) {\n    return names.map((name) => null);\n  }\n  let arrays;\n  if (isDataDict(data)) {\n    data = data;\n    arrays = [];\n    for (const name of names) {\n      if (data[name] == null) {\n        throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ${names}`);\n      }\n      arrays.push(data[name]);\n    }\n  } else if (isDataArray(data)) {\n    data = data;\n    if (data.length !== names.length) {\n      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);\n    }\n    arrays = data;\n  } else {\n    data = data;\n    if (names.length > 1) {\n      throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n    }\n    arrays = [data];\n  }\n  arrays = ensureTensorsRank2OrHigher(arrays);\n  if (shapes != null) {\n    for (let i2 = 0; i2 < names.length; ++i2) {\n      if (shapes[i2] == null) {\n        continue;\n      }\n      const array2 = arrays[i2];\n      if (array2.shape.length !== shapes[i2].length) {\n        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s). but got array with shape ${array2.shape}`);\n      }\n      for (let j = 0; j < shapes[i2].length; ++j) {\n        if (j === 0 && !checkBatchAxis) {\n          continue;\n        }\n        const dim = array2.shape[j];\n        const refDim = shapes[i2][j];\n        if (refDim != null && refDim >= 0 && dim !== refDim) {\n          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i2].slice(1, shapes[i2].length)}] (i.e.,tensor shape [*,${shapes[i2].slice(1, shapes[i2].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);\n        }\n      }\n    }\n  }\n  return arrays;\n}\nfunction checkArrayLengths(inputs, targets, weights) {\n  const setX = unique2(inputs.map((input2) => input2.shape[0]));\n  setX.sort();\n  const setY = unique2(targets.map((target) => target.shape[0]));\n  setY.sort();\n  if (setX.length > 1) {\n    throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);\n  }\n  if (setY.length > 1) {\n    throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);\n  }\n  if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {\n    throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);\n  }\n}\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n  const keyLosses = [\n    meanSquaredError2,\n    binaryCrossentropy,\n    categoricalCrossentropy\n  ];\n  for (let i2 = 0; i2 < targets.length; ++i2) {\n    const y = targets[i2];\n    const loss = lossFns[i2];\n    const shape = outputShapes[i2];\n    if (loss == null) {\n      continue;\n    }\n    if (loss === categoricalCrossentropy) {\n      if (y.shape[y.shape.length - 1] === 1) {\n        throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);\n      }\n    }\n    if (keyLosses.indexOf(loss) !== -1) {\n      const slicedYShape = y.shape.slice(1);\n      const slicedShape = shape.slice(1);\n      for (let j = 0; j < slicedYShape.length; ++j) {\n        const targetDim = slicedYShape[j];\n        const outDim = slicedShape[j];\n        if (outDim != null && targetDim !== outDim) {\n          throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);\n        }\n      }\n    }\n  }\n}\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n  let arrays;\n  if (Array.isArray(data)) {\n    if (data.length !== names.length) {\n      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);\n    }\n    arrays = data;\n  } else {\n    if (names.length > 1) {\n      throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);\n    }\n    arrays = [data];\n  }\n  if (shapes != null) {\n    for (let i2 = 0; i2 < names.length; ++i2) {\n      if (shapes[i2] == null) {\n        continue;\n      }\n      const array2 = arrays[i2];\n      if (array2.shape.length !== shapes[i2].length) {\n        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);\n      }\n      for (let j = 0; j < shapes[i2].length; ++j) {\n        if (j === 0 && !checkBatchAxis) {\n          continue;\n        }\n        const dim = array2.shape[j];\n        const refDim = shapes[i2][j];\n        if (refDim != null) {\n          if (refDim !== dim) {\n            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have shape ${JSON.stringify(shapes[i2])} but got array with shape ${JSON.stringify(array2.shape)}.`);\n          }\n        }\n      }\n    }\n  }\n}\nfunction collectMetrics(metrics, outputNames) {\n  if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n    return outputNames.map((name) => []);\n  }\n  let wrappedMetrics;\n  if (typeof metrics === \"string\" || typeof metrics === \"function\") {\n    wrappedMetrics = [metrics];\n  } else if (Array.isArray(metrics) || typeof metrics === \"object\") {\n    wrappedMetrics = metrics;\n  } else {\n    throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);\n  }\n  if (Array.isArray(wrappedMetrics)) {\n    return outputNames.map((name) => wrappedMetrics);\n  } else {\n    const nestedMetrics = [];\n    for (const name of outputNames) {\n      let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n      if (!Array.isArray(outputMetrics)) {\n        outputMetrics = [outputMetrics];\n      }\n      nestedMetrics.push(outputMetrics);\n    }\n    return nestedMetrics;\n  }\n}\nvar LAYERS_MODEL_FORMAT_NAME = \"layers-model\";\nvar LayersModel = class extends Container {\n  constructor(args) {\n    super(args);\n    this.isTraining = false;\n  }\n  summary(lineLength, positions, printFn = console.log) {\n    if (!this.built) {\n      throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);\n    }\n    printSummary(this, lineLength, positions, printFn);\n  }\n  compile(args) {\n    if (args.loss == null) {\n      args.loss = [];\n    }\n    this.loss = args.loss;\n    if (typeof args.optimizer === \"string\") {\n      this.optimizer_ = getOptimizer(args.optimizer);\n      this.isOptimizerOwned = true;\n    } else {\n      if (!(args.optimizer instanceof Optimizer)) {\n        throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n      }\n      this.optimizer_ = args.optimizer;\n      this.isOptimizerOwned = false;\n    }\n    let lossFunctions = [];\n    if (!Array.isArray(args.loss) && typeof args.loss !== \"string\" && typeof args.loss !== \"function\") {\n      args.loss = args.loss;\n      for (const name in args.loss) {\n        if (this.outputNames.indexOf(name) === -1) {\n          throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". Only expected the following keys: ${this.outputNames}`);\n        }\n      }\n      for (const name of this.outputNames) {\n        if (args.loss[name] == null) {\n          console.warn(`Output \"${name}\" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);\n        }\n        lossFunctions.push(get(args.loss[name]));\n      }\n    } else if (Array.isArray(args.loss)) {\n      if (args.loss.length !== this.outputs.length) {\n        throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);\n      }\n      const theLosses = args.loss;\n      lossFunctions = theLosses.map((l3) => get(l3));\n    } else {\n      const lossFunction = get(args.loss);\n      this.outputs.forEach((_) => {\n        lossFunctions.push(lossFunction);\n      });\n    }\n    this.lossFunctions = lossFunctions;\n    this.feedOutputNames = [];\n    this.feedOutputShapes = [];\n    this.feedLossFns = [];\n    for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n      const shape = this.internalOutputShapes[i2];\n      const name = this.outputNames[i2];\n      this.feedOutputNames.push(name);\n      this.feedOutputShapes.push(shape);\n      this.feedLossFns.push(this.lossFunctions[i2]);\n    }\n    const skipTargetIndices = [];\n    this.metrics = args.metrics;\n    this.metricsNames = [\"loss\"];\n    this.metricsTensors = [];\n    nameScope(\"loss\", () => {\n      for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n        if (skipTargetIndices.indexOf(i2) !== -1) {\n          continue;\n        }\n        const weightedLoss = this.lossFunctions[i2];\n        if (this.outputs.length > 1) {\n          this.metricsTensors.push([weightedLoss, i2]);\n          this.metricsNames.push(this.outputNames[i2] + \"_loss\");\n        }\n      }\n    });\n    const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n    const appendMetric = (outputIndex, metricName, metricTensor) => {\n      if (this.outputNames.length > 1) {\n        metricName = this.outputNames[outputIndex] + \"_\" + metricName;\n      }\n      this.metricsNames.push(metricName);\n      this.metricsTensors.push([metricTensor, outputIndex]);\n    };\n    nameScope(\"metric\", () => {\n      for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n        if (skipTargetIndices.indexOf(i2) !== -1) {\n          continue;\n        }\n        const outputMetrics = nestedMetrics[i2];\n        const handleMetrics = (metrics) => {\n          const metricNamePrefix = \"\";\n          let metricName;\n          let accFn;\n          let weightedMetricFn;\n          for (const metric of metrics) {\n            if (typeof metric === \"string\" && [\"accuracy\", \"acc\", \"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n              const outputShape = this.internalOutputShapes[i2];\n              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i2] === binaryCrossentropy) {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = binaryAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = binaryCrossentropy2;\n                }\n              } else if (this.lossFunctions[i2] === sparseCategoricalCrossentropy) {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = sparseCategoricalAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = sparseCategoricalCrossentropy2;\n                }\n              } else {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = categoricalAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = categoricalCrossentropy2;\n                }\n              }\n              let suffix;\n              if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                suffix = \"acc\";\n              } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                suffix = \"ce\";\n              }\n              weightedMetricFn = accFn;\n              metricName = metricNamePrefix + suffix;\n            } else {\n              const metricFn = get2(metric);\n              weightedMetricFn = metricFn;\n              metricName = metricNamePrefix + getLossOrMetricName(metric);\n            }\n            let metricResult;\n            nameScope(metricName, () => {\n              metricResult = weightedMetricFn;\n            });\n            appendMetric(i2, metricName, metricResult);\n          }\n        };\n        handleMetrics(outputMetrics);\n      }\n    });\n    this.collectedTrainableWeights = this.trainableWeights;\n  }\n  checkTrainableWeightsConsistency() {\n    if (this.collectedTrainableWeights == null) {\n      return;\n    }\n    if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {\n      console.warn(\"Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?\");\n    }\n  }\n  evaluate(x, y, args = {}) {\n    const batchSize = args.batchSize == null ? 32 : args.batchSize;\n    checkBatchSize(batchSize);\n    const checkBatchAxis = true;\n    const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n    try {\n      const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n      this.makeTestFunction();\n      const f = this.testFunction;\n      const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n      return singletonOrArray(testOuts);\n    } finally {\n      disposeNewTensors(standardizedOuts[0], x);\n      disposeNewTensors(standardizedOuts[1], y);\n    }\n  }\n  async evaluateDataset(dataset, args) {\n    this.makeTestFunction();\n    return evaluateDataset(this, dataset, args);\n  }\n  checkNumSamples(ins, batchSize, steps, stepsName = \"steps\") {\n    let numSamples;\n    if (steps != null) {\n      numSamples = null;\n      if (batchSize != null) {\n        throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);\n      }\n    } else if (ins != null) {\n      if (Array.isArray(ins)) {\n        numSamples = ins[0].shape[0];\n      } else {\n        numSamples = ins.shape[0];\n      }\n    } else {\n      throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);\n    }\n    return numSamples;\n  }\n  execute(inputs, outputs) {\n    if (Array.isArray(outputs) && outputs.length === 0) {\n      throw new ValueError(\"`outputs` is an empty Array, which is not allowed.\");\n    }\n    const outputsIsArray = Array.isArray(outputs);\n    const outputNames = outputsIsArray ? outputs : [outputs];\n    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n    const feedDict = new FeedDict();\n    if (inputs instanceof Tensor) {\n      inputs = [inputs];\n    }\n    if (Array.isArray(inputs)) {\n      if (inputs.length !== this.inputs.length) {\n        throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);\n      }\n      for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n        feedDict.add(this.inputs[i2], inputs[i2]);\n      }\n    } else {\n      for (const input2 of this.inputs) {\n        const tensorValue = inputs[input2.name];\n        if (tensorValue == null) {\n          throw new ValueError(`No value is provided for the model's input ${input2.name}`);\n        }\n        feedDict.add(input2, tensorValue);\n      }\n    }\n    const executeOutputs = execute(outputSymbolicTensors, feedDict);\n    return outputsIsArray ? executeOutputs : executeOutputs[0];\n  }\n  retrieveSymbolicTensors(symbolicTensorNames) {\n    const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n    let outputsRemaining = symbolicTensorNames.length;\n    for (const layer of this.layers) {\n      const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n      const layerOutputNames = layerOutputs.map((output) => output.name);\n      for (let i2 = 0; i2 < symbolicTensorNames.length; ++i2) {\n        const index = layerOutputNames.indexOf(symbolicTensorNames[i2]);\n        if (index !== -1) {\n          outputSymbolicTensors[i2] = layerOutputs[index];\n          outputsRemaining--;\n        }\n        if (outputsRemaining === 0) {\n          break;\n        }\n      }\n      if (outputsRemaining === 0) {\n        break;\n      }\n    }\n    if (outputsRemaining > 0) {\n      const remainingNames = [];\n      outputSymbolicTensors.forEach((tensor2, i2) => {\n        if (tensor2 == null) {\n          remainingNames.push(symbolicTensorNames[i2]);\n        }\n      });\n      throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);\n    }\n    return outputSymbolicTensors;\n  }\n  predictLoop(ins, batchSize = 32, verbose = false) {\n    return tidy(() => {\n      const numSamples = this.checkNumSamples(ins);\n      if (verbose) {\n        throw new NotImplementedError(\"Verbose predictLoop() is not implemented yet.\");\n      }\n      const batches = makeBatches(numSamples, batchSize);\n      const outsBatches = this.outputs.map((output) => []);\n      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n        const batchOuts = tidy(() => {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const insBatch = sliceArrays(ins, batchStart, batchEnd);\n          const feeds = [];\n          if (Array.isArray(insBatch)) {\n            for (let i2 = 0; i2 < insBatch.length; ++i2) {\n              feeds.push({ key: this.inputs[i2], value: insBatch[i2] });\n            }\n          } else {\n            feeds.push({ key: this.inputs[0], value: insBatch });\n          }\n          const feedDict = new FeedDict(feeds);\n          return execute(this.outputs, feedDict);\n        });\n        batchOuts.forEach((batchOut, i2) => outsBatches[i2].push(batchOut));\n      }\n      return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));\n    });\n  }\n  predict(x, args = {}) {\n    const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n    checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n    try {\n      const batchSize = args.batchSize == null ? 32 : args.batchSize;\n      checkBatchSize(batchSize);\n      return this.predictLoop(xsRank2OrHigher, batchSize);\n    } finally {\n      disposeNewTensors(xsRank2OrHigher, x);\n    }\n  }\n  predictOnBatch(x) {\n    checkInputData(x, this.inputNames, this.feedInputShapes, true);\n    const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n    return this.predictLoop(x, batchSize);\n  }\n  standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n    if (this.optimizer_ == null) {\n      throw new RuntimeError(\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).\");\n    }\n    const outputShapes = [];\n    for (let i2 = 0; i2 < this.feedOutputShapes.length; ++i2) {\n      const outputShape = this.feedOutputShapes[i2];\n      const lossFn = this.feedLossFns[i2];\n      if (lossFn === sparseCategoricalCrossentropy) {\n        outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n      } else {\n        outputShapes.push(outputShape);\n      }\n    }\n    x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, \"input\");\n    y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, \"target\");\n    checkArrayLengths(x, y, null);\n    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n    if (this.stateful && batchSize != null && batchSize > 0) {\n      if (x[0].shape[0] % batchSize !== 0) {\n        throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n      }\n    }\n    return [x, y];\n  }\n  async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n    const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n    if (sampleWeight != null) {\n      throw new Error(\"sample weight is not supported yet.\");\n    }\n    let standardSampleWeights = null;\n    if (classWeight != null) {\n      const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n      standardSampleWeights = [];\n      for (let i2 = 0; i2 < classWeights.length; ++i2) {\n        standardSampleWeights.push(await standardizeWeights(standardYs[i2], null, classWeights[i2]));\n      }\n    }\n    return [standardXs, standardYs, standardSampleWeights];\n  }\n  testLoop(f, ins, batchSize, verbose = 0, steps) {\n    return tidy(() => {\n      const numSamples = this.checkNumSamples(ins, batchSize, steps, \"steps\");\n      const outs = [];\n      if (verbose > 0) {\n        throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n      }\n      if (steps != null) {\n        throw new NotImplementedError(\"steps mode in testLoop() is not implemented yet\");\n      } else {\n        const batches = makeBatches(numSamples, batchSize);\n        const indexArray = tensor1d(range2(0, numSamples));\n        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n          const insBatch = sliceArraysByIndices(ins, batchIds);\n          const batchOuts = f(insBatch);\n          if (batchIndex === 0) {\n            for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n              outs.push(scalar(0));\n            }\n          }\n          for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n            const batchOut = batchOuts[i2];\n            outs[i2] = add2(outs[i2], mul(batchEnd - batchStart, batchOut));\n          }\n        }\n        for (let i2 = 0; i2 < outs.length; ++i2) {\n          outs[i2] = div(outs[i2], numSamples);\n        }\n      }\n      return outs;\n    });\n  }\n  getDedupedMetricsNames() {\n    const outLabels = this.metricsNames;\n    const dedupedOutLabels = [];\n    for (let i2 = 0; i2 < outLabels.length; ++i2) {\n      const label = outLabels[i2];\n      let newLabel = label;\n      if (count(outLabels, label) > 1) {\n        const dupIndex = count(outLabels.slice(0, i2), label);\n        newLabel += `_${dupIndex}`;\n      }\n      dedupedOutLabels.push(newLabel);\n    }\n    return dedupedOutLabels;\n  }\n  makeTrainFunction() {\n    return (data) => {\n      const lossValues = [];\n      const inputs = data.slice(0, this.inputs.length);\n      const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n      const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n      const metricsValues = [];\n      const totalLossFunction = () => {\n        const feeds = [];\n        for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n          feeds.push({ key: this.inputs[i2], value: inputs[i2] });\n        }\n        const feedDict = new FeedDict(feeds);\n        const outputs = execute(this.outputs, feedDict, { \"training\": true });\n        let totalLoss;\n        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {\n          const lossFunction = this.lossFunctions[i2];\n          let loss = lossFunction(targets[i2], outputs[i2]);\n          if (sampleWeights[i2] != null) {\n            loss = computeWeightedLoss2(loss, sampleWeights[i2]);\n          }\n          const meanLoss = mean(loss);\n          lossValues.push(meanLoss);\n          if (i2 === 0) {\n            totalLoss = loss;\n          } else {\n            totalLoss = add2(totalLoss, loss);\n          }\n        }\n        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {\n          let weightedMetric;\n          if (this.outputs.length > 1 && i2 < this.outputs.length) {\n            weightedMetric = lossValues[i2];\n          } else {\n            const metric = this.metricsTensors[i2][0];\n            const outputIndex = this.metricsTensors[i2][1];\n            weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n          }\n          keep(weightedMetric);\n          metricsValues.push(weightedMetric);\n        }\n        totalLoss = mean(totalLoss);\n        this.calculateLosses().forEach((regularizerLoss) => {\n          totalLoss = add2(totalLoss, regularizerLoss);\n        });\n        return totalLoss;\n      };\n      const variables = this.collectedTrainableWeights.map((param) => param.read());\n      const returnCost = true;\n      const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n      return [totalLossValue].concat(metricsValues);\n    };\n  }\n  makeTestFunction() {\n    this.testFunction = (data) => {\n      return tidy(() => {\n        const valOutputs = [];\n        let totalLoss;\n        const inputs = data.slice(0, this.inputs.length);\n        const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n        const feeds = [];\n        for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n          feeds.push({ key: this.inputs[i2], value: inputs[i2] });\n        }\n        const feedDict = new FeedDict(feeds);\n        const outputs = execute(this.outputs, feedDict);\n        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {\n          const lossFunction = this.lossFunctions[i2];\n          const loss = mean(lossFunction(targets[i2], outputs[i2]));\n          if (i2 === 0) {\n            totalLoss = loss;\n          } else {\n            totalLoss = add2(totalLoss, loss);\n          }\n          valOutputs.push(totalLoss);\n        }\n        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {\n          const metric = this.metricsTensors[i2][0];\n          const outputIndex = this.metricsTensors[i2][1];\n          const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n          valOutputs.push(meanMetric);\n        }\n        return valOutputs;\n      });\n    };\n  }\n  async fit(x, y, args = {}) {\n    return fitTensors(this, x, y, args);\n  }\n  async fitDataset(dataset, args) {\n    return fitDataset(this, dataset, args);\n  }\n  async trainOnBatch(x, y) {\n    const standardizeOut = await this.standardizeUserData(x, y);\n    const inputs = standardizeOut[0];\n    const targets = standardizeOut[1];\n    const trainFunction = this.makeTrainFunction();\n    const losses2 = trainFunction(inputs.concat(targets));\n    const lossValues = [];\n    for (const loss of losses2) {\n      const v = await loss.data();\n      lossValues.push(v[0]);\n    }\n    dispose(losses2);\n    disposeNewTensors(standardizeOut[0], x);\n    disposeNewTensors(standardizeOut[1], y);\n    return singletonOrArray(lossValues);\n  }\n  getNamedWeights(config) {\n    const namedWeights = [];\n    const trainableOnly = config != null && config.trainableOnly;\n    const weights = trainableOnly ? this.trainableWeights : this.weights;\n    const weightValues = this.getWeights(trainableOnly);\n    for (let i2 = 0; i2 < weights.length; ++i2) {\n      if (trainableOnly && !weights[i2].trainable) {\n        continue;\n      }\n      namedWeights.push({ name: weights[i2].originalName, tensor: weightValues[i2] });\n    }\n    return namedWeights;\n  }\n  set stopTraining(stop) {\n    this.stopTraining_ = stop;\n  }\n  get stopTraining() {\n    return this.stopTraining_;\n  }\n  get optimizer() {\n    return this.optimizer_;\n  }\n  set optimizer(optimizer) {\n    if (this.optimizer_ !== optimizer) {\n      this.optimizer_ = optimizer;\n      this.isOptimizerOwned = false;\n    }\n  }\n  dispose() {\n    const result = super.dispose();\n    if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {\n      const numTensorsBeforeOptmizerDisposal = memory().numTensors;\n      this.optimizer_.dispose();\n      result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;\n    }\n    return result;\n  }\n  getLossIdentifiers() {\n    let lossNames;\n    if (typeof this.loss === \"string\") {\n      lossNames = toSnakeCase(this.loss);\n    } else if (Array.isArray(this.loss)) {\n      for (const loss of this.loss) {\n        if (typeof loss !== \"string\") {\n          throw new Error(\"Serialization of non-string loss is not supported.\");\n        }\n      }\n      lossNames = this.loss.map((name) => toSnakeCase(name));\n    } else {\n      const outputNames = Object.keys(this.loss);\n      lossNames = {};\n      const losses2 = this.loss;\n      for (const outputName of outputNames) {\n        if (typeof losses2[outputName] === \"string\") {\n          lossNames[outputName] = toSnakeCase(losses2[outputName]);\n        } else {\n          throw new Error(\"Serialization of non-string loss is not supported.\");\n        }\n      }\n    }\n    return lossNames;\n  }\n  getMetricIdentifiers() {\n    if (typeof this.metrics === \"string\" || typeof this.metrics === \"function\") {\n      return [toSnakeCase(getLossOrMetricName(this.metrics))];\n    } else if (Array.isArray(this.metrics)) {\n      return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));\n    } else {\n      const metricsIdentifiers = {};\n      for (const key in this.metrics) {\n        metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));\n      }\n      return metricsIdentifiers;\n    }\n  }\n  getTrainingConfig() {\n    return {\n      loss: this.getLossIdentifiers(),\n      metrics: this.getMetricIdentifiers(),\n      optimizer_config: {\n        class_name: this.optimizer.getClassName(),\n        config: this.optimizer.getConfig()\n      }\n    };\n  }\n  loadTrainingConfig(trainingConfig) {\n    if (trainingConfig.weighted_metrics != null) {\n      throw new Error(\"Loading weight_metrics is not supported yet.\");\n    }\n    if (trainingConfig.loss_weights != null) {\n      throw new Error(\"Loading loss_weights is not supported yet.\");\n    }\n    if (trainingConfig.sample_weight_mode != null) {\n      throw new Error(\"Loading sample_weight_mode is not supported yet.\");\n    }\n    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n    const optimizer = deserialize(tsConfig);\n    let loss;\n    if (typeof trainingConfig.loss === \"string\") {\n      loss = toCamelCase(trainingConfig.loss);\n    } else if (Array.isArray(trainingConfig.loss)) {\n      loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));\n    } else if (trainingConfig.loss != null) {\n      loss = {};\n      for (const key in trainingConfig.loss) {\n        loss[key] = toCamelCase(trainingConfig.loss[key]);\n      }\n    }\n    let metrics;\n    if (Array.isArray(trainingConfig.metrics)) {\n      metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));\n    } else if (trainingConfig.metrics != null) {\n      metrics = {};\n      for (const key in trainingConfig.metrics) {\n        metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n      }\n    }\n    this.compile({ loss, metrics, optimizer });\n  }\n  async save(handlerOrURL, config) {\n    if (typeof handlerOrURL === \"string\") {\n      const handlers = io_exports.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new ValueError(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n    }\n    const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));\n    const returnString = false;\n    const unusedArg = null;\n    const modelConfig = this.toJSON(unusedArg, returnString);\n    const modelArtifacts = {\n      modelTopology: modelConfig,\n      format: LAYERS_MODEL_FORMAT_NAME,\n      generatedBy: `TensorFlow.js tfjs-layers v${version2}`,\n      convertedBy: null\n    };\n    const includeOptimizer = config == null ? false : config.includeOptimizer;\n    if (includeOptimizer && this.optimizer != null) {\n      modelArtifacts.trainingConfig = this.getTrainingConfig();\n      const weightType = \"optimizer\";\n      const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);\n      weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n      weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n    }\n    if (this.userDefinedMetadata != null) {\n      const checkSize = true;\n      checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n      modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n    }\n    modelArtifacts.weightData = weightDataAndSpecs.data;\n    modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n    return handlerOrURL.save(modelArtifacts);\n  }\n  setUserDefinedMetadata(userDefinedMetadata) {\n    checkUserDefinedMetadata(userDefinedMetadata, this.name);\n    this.userDefinedMetadata = userDefinedMetadata;\n  }\n  getUserDefinedMetadata() {\n    return this.userDefinedMetadata;\n  }\n};\nLayersModel.className = \"Model\";\nserialization_exports.registerClass(LayersModel);\nvar Functional = class extends LayersModel {\n};\nFunctional.className = \"Functional\";\nserialization_exports.registerClass(Functional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/models.js\nasync function modelFromJSON(modelAndWeightsConfig, customObjects) {\n  if (!(\"modelTopology\" in modelAndWeightsConfig)) {\n    modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n  }\n  modelAndWeightsConfig = modelAndWeightsConfig;\n  let modelTopology = modelAndWeightsConfig.modelTopology;\n  if (modelTopology[\"model_config\"] != null) {\n    modelTopology = modelTopology[\"model_config\"];\n  }\n  const tsConfig = convertPythonicToTs(modelTopology);\n  const model2 = deserialize(tsConfig, customObjects);\n  if (modelAndWeightsConfig.weightsManifest != null) {\n    const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));\n    const uniqueWeightValues = {};\n    for (const weight of model2.weights) {\n      uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];\n    }\n    model2.loadWeights(uniqueWeightValues);\n    dispose(weightValues);\n  }\n  return model2;\n}\nasync function loadLayersModelInternal(pathOrIOHandler, options) {\n  if (options == null) {\n    options = {};\n  }\n  if (typeof pathOrIOHandler === \"string\") {\n    const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);\n    if (handlers.length === 0) {\n      handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));\n    } else if (handlers.length > 1) {\n      throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);\n    }\n    pathOrIOHandler = handlers[0];\n  }\n  return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);\n}\nasync function loadLayersModelFromIOHandler(handler, customObjects, options) {\n  if (options == null) {\n    options = {};\n  }\n  if (handler.load == null) {\n    throw new ValueError(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n  }\n  const artifacts = await handler.load();\n  let modelTopology = artifacts.modelTopology;\n  if (modelTopology[\"model_config\"] != null) {\n    modelTopology = modelTopology[\"model_config\"];\n  }\n  const strict = options.strict == null ? true : options.strict;\n  const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n  const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n  const trainingConfig = artifacts.trainingConfig;\n  if (trainingConfig != null) {\n    model2.loadTrainingConfig(trainingConfig);\n  }\n  if (artifacts.userDefinedMetadata != null) {\n    model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n  }\n  if (artifacts.weightData != null) {\n    if (artifacts.weightSpecs == null) {\n      throw new ValueError(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");\n    }\n    const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n    model2.loadWeights(modelWeights, strict);\n    if (model2.optimizer != null && optimizerWeights.length > 0) {\n      await model2.optimizer.setWeights(optimizerWeights);\n    }\n    dispose(modelWeights);\n    dispose(optimizerWeights.map((w) => w.tensor));\n  }\n  return model2;\n}\nfunction decodeModelAndOptimizerWeights(buffer2, specs) {\n  const name2Tensor = io_exports.decodeWeights(buffer2, specs);\n  const modelWeights = {};\n  const optimizerWeights = [];\n  specs.forEach((spec) => {\n    if (spec.group === \"optimizer\") {\n      optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n    } else {\n      modelWeights[spec.name] = name2Tensor[spec.name];\n    }\n  });\n  return { modelWeights, optimizerWeights };\n}\nvar Sequential = class extends LayersModel {\n  constructor(args) {\n    super({ inputs: [], outputs: [] });\n    args = args || {};\n    this.trainable = true;\n    this.built = false;\n    this.name = args.name != null ? args.name : getUid(\"sequential_\");\n    if (args.layers != null) {\n      for (const layer of args.layers) {\n        this.add(layer);\n      }\n    }\n  }\n  checkShape(layer) {\n    const shape = layer.inboundNodes[0].outputTensors[0].shape;\n    if (shape.some((x) => x < 0)) {\n      throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);\n    }\n  }\n  add(layer) {\n    const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n    let modelLayer;\n    if (isLayerModelInstance) {\n      modelLayer = layer;\n      if (modelLayer.outputs.length !== 1) {\n        throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n      }\n      if (modelLayer.inputs.length !== 1) {\n        throw new ValueError(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\");\n      }\n    }\n    if (this.outputs.length === 0) {\n      if (layer.inboundNodes.length === 0) {\n        if (layer.batchInputShape == null) {\n          throw new ValueError(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");\n        }\n        const x = Input({\n          batchShape: layer.batchInputShape,\n          dtype: layer.dtype,\n          name: layer.name + \"_input\"\n        });\n        layer.apply(x);\n      }\n      if (isLayerModelInstance) {\n        this.outputs = modelLayer.outputs;\n        this.inputs = modelLayer.inputs;\n      } else {\n        if (layer.inboundNodes.length !== 1) {\n          throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);\n        }\n        if (layer.inboundNodes[0].outputTensors.length !== 1) {\n          throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n        }\n        this.checkShape(layer);\n        this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n        this.inputs = getSourceInputs(this.outputs[0]);\n      }\n      this.inboundNodes = [];\n      new Node({\n        outboundLayer: this,\n        inboundLayers: [],\n        nodeIndices: [],\n        tensorIndices: [],\n        inputTensors: this.inputs,\n        outputTensors: this.outputs,\n        inputMasks: pyListRepeat(null, this.inputs.length),\n        outputMasks: [null],\n        inputShapes: this.inputs.map((x) => x.shape),\n        outputShapes: this.outputs[0].shape\n      });\n    } else {\n      const outputTensor = layer.apply(this.outputs[0]);\n      if (Array.isArray(outputTensor)) {\n        throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n      }\n      this.checkShape(layer);\n      this.outputs = [outputTensor];\n      this.inboundNodes[0].outputTensors = this.outputs;\n      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n    }\n    this.layers.push(layer);\n    this.built = false;\n  }\n  pop() {\n    if (this.layers.length === 0) {\n      throw new TypeError(\"There are no layers in the model.\");\n    }\n    this.layers.pop();\n    if (this.layers.length === 0) {\n      this.outputs = [];\n      this.inboundNodes = [];\n      this.outboundNodes = [];\n    } else {\n      const lastLayerIndex = this.layers.length - 1;\n      this.layers[lastLayerIndex].outboundNodes = [];\n      this.outputs = [this.layers[lastLayerIndex].output];\n      this.inboundNodes[0].outputTensors = this.outputs;\n      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n    }\n  }\n  call(inputs, kwargs) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.call(inputs, kwargs);\n  }\n  build(inputShape) {\n    getExactlyOneShape(inputShape);\n    if (this.inputs.length === 0 || this.outputs.length === 0) {\n      throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");\n    }\n    this.model = new LayersModel({\n      inputs: this.inputs,\n      outputs: this.outputs[0],\n      name: this.name + \"_model\"\n    });\n    this.model.trainable = this.trainable;\n    this.supportsMasking = this.model.supportsMasking;\n    this.inputLayers = this.model.inputLayers;\n    this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n    this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n    this.outputLayers = this.model.outputLayers;\n    this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n    this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n    this.nodesByDepth = this.model.nodesByDepth;\n    this.containerNodes = this.model.containerNodes;\n    this.outputNames = this.model.outputNames;\n    this.inputNames = this.model.inputNames;\n    this.built = true;\n  }\n  countParams() {\n    if (!this.built) {\n      this.build();\n    }\n    return super.countParams();\n  }\n  summary(lineLength, positions, printFn = console.log) {\n    if (!this.built) {\n      this.build();\n    }\n    super.summary(lineLength, positions, printFn);\n  }\n  setWeights(weights) {\n    if (this.model == null) {\n      this.build();\n    }\n    this.model.setWeights(weights);\n  }\n  evaluate(x, y, args = {}) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.evaluate(x, y, args);\n  }\n  async evaluateDataset(dataset, args) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.evaluateDataset(dataset, args);\n  }\n  predict(x, args = {}) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.predict(x, args);\n  }\n  predictOnBatch(x) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.predictOnBatch(x);\n  }\n  compile(args) {\n    this.build();\n    this.model.compile(args);\n    this.optimizer_ = this.model.optimizer;\n    this.isOptimizerOwned = this.model.isOptimizerOwned;\n    this.loss = this.model.loss;\n    this.metrics = this.model.metrics;\n    this.metricsTensors = this.model.metricsTensors;\n    this.metricsNames = this.model.metricsNames;\n  }\n  get optimizer() {\n    return this.model == null ? void 0 : this.model.optimizer;\n  }\n  set optimizer(optimizer) {\n    this.model.optimizer = optimizer;\n  }\n  async fit(x, y, args = {}) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.fit(x, y, args);\n  }\n  async fitDataset(dataset, args) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.fitDataset(dataset, args);\n  }\n  async trainOnBatch(x, y) {\n    return this.model.trainOnBatch(x, y);\n  }\n  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n    let configArray;\n    let extraModelConfig = {};\n    if (config instanceof Array) {\n      if (!(config[0].className != null) || config[0][\"className\"] === \"Merge\") {\n        throw new ValueError(\"Legacy serialization format not supported yet.\");\n      }\n      configArray = config;\n    } else {\n      util_exports.assert(config[\"layers\"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);\n      configArray = config[\"layers\"];\n      delete config[\"layers\"];\n      extraModelConfig = config;\n    }\n    const model2 = new cls(extraModelConfig);\n    if (!(model2 instanceof Sequential)) {\n      throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);\n    }\n    for (const conf of configArray) {\n      const customObjects2 = void 0;\n      const layer = deserialize(conf, customObjects2, fastWeightInit);\n      if (fastWeightInit) {\n        layer.setFastWeightInitDuringBuild(true);\n      }\n      model2.add(layer);\n    }\n    return model2;\n  }\n  set stopTraining(stop) {\n    if (this.model == null) {\n      throw new ValueError(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");\n    }\n    this.model.stopTraining = stop;\n  }\n  get stopTraining() {\n    if (this.model == null) {\n      throw new ValueError(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");\n    }\n    return this.model.stopTraining;\n  }\n  getConfig() {\n    const layers = [];\n    for (const layer of this.layers) {\n      const dict = {};\n      dict[\"className\"] = layer.getClassName();\n      dict[\"config\"] = layer.getConfig();\n      layers.push(dict);\n    }\n    return { name: this.name, layers };\n  }\n};\nSequential.className = \"Sequential\";\nserialization_exports.registerClass(Sequential);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports.js\nfunction model(args) {\n  return new LayersModel(args);\n}\nfunction sequential(config) {\n  return new Sequential(config);\n}\nfunction loadLayersModel(pathOrIOHandler, options) {\n  if (options == null) {\n    options = {};\n  }\n  return loadLayersModelInternal(pathOrIOHandler, options);\n}\nfunction input(config) {\n  return Input(config);\n}\nfunction registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n  CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/activations.js\nvar Activation = class extends serialization_exports.Serializable {\n  getConfig() {\n    return {};\n  }\n};\nvar Elu2 = class extends Activation {\n  apply(x, alpha = 1) {\n    return elu2(x, alpha);\n  }\n};\nElu2.className = \"elu\";\nserialization_exports.registerClass(Elu2);\nvar Selu2 = class extends Activation {\n  apply(x) {\n    return selu(x);\n  }\n};\nSelu2.className = \"selu\";\nserialization_exports.registerClass(Selu2);\nvar Relu2 = class extends Activation {\n  apply(x) {\n    return relu(x);\n  }\n};\nRelu2.className = \"relu\";\nserialization_exports.registerClass(Relu2);\nvar Relu62 = class extends Activation {\n  apply(x) {\n    return tidy(() => minimum(6, relu(x)));\n  }\n};\nRelu62.className = \"relu6\";\nserialization_exports.registerClass(Relu62);\nvar Linear = class extends Activation {\n  apply(x) {\n    return x;\n  }\n};\nLinear.className = \"linear\";\nserialization_exports.registerClass(Linear);\nvar Sigmoid2 = class extends Activation {\n  apply(x) {\n    return sigmoid(x);\n  }\n};\nSigmoid2.className = \"sigmoid\";\nserialization_exports.registerClass(Sigmoid2);\nvar HardSigmoid = class extends Activation {\n  apply(x) {\n    return hardSigmoid(x);\n  }\n};\nHardSigmoid.className = \"hardSigmoid\";\nserialization_exports.registerClass(HardSigmoid);\nvar Softplus2 = class extends Activation {\n  apply(x) {\n    return softplus(x);\n  }\n};\nSoftplus2.className = \"softplus\";\nserialization_exports.registerClass(Softplus2);\nvar Softsign = class extends Activation {\n  apply(x) {\n    return softsign(x);\n  }\n};\nSoftsign.className = \"softsign\";\nserialization_exports.registerClass(Softsign);\nvar Tanh2 = class extends Activation {\n  apply(x) {\n    return tanh2(x);\n  }\n};\nTanh2.className = \"tanh\";\nserialization_exports.registerClass(Tanh2);\nvar Softmax2 = class extends Activation {\n  apply(x, axis = -1) {\n    return softmax(x, axis);\n  }\n};\nSoftmax2.className = \"softmax\";\nserialization_exports.registerClass(Softmax2);\nvar LogSoftmax2 = class extends Activation {\n  apply(x, axis = -1) {\n    return logSoftmax(x, axis);\n  }\n};\nLogSoftmax2.className = \"logSoftmax\";\nserialization_exports.registerClass(LogSoftmax2);\nvar Swish = class extends Activation {\n  apply(x, alpha = 1) {\n    return tidy(() => mul(sigmoid(mul(x, alpha)), x));\n  }\n};\nSwish.className = \"swish\";\nserialization_exports.registerClass(Swish);\nvar Mish = class extends Activation {\n  apply(x) {\n    return tidy(() => mul(x, tanh2(softplus(x))));\n  }\n};\nMish.className = \"mish\";\nserialization_exports.registerClass(Mish);\nfunction serializeActivation(activation2) {\n  return activation2.getClassName();\n}\nfunction deserializeActivation(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"activation\");\n}\nfunction getActivation(identifier) {\n  if (identifier == null) {\n    const config = {};\n    config[\"className\"] = \"linear\";\n    config[\"config\"] = {};\n    return deserializeActivation(config);\n  }\n  if (typeof identifier === \"string\") {\n    const config = {};\n    config[\"className\"] = identifier;\n    config[\"config\"] = {};\n    return deserializeActivation(config);\n  } else if (identifier instanceof Activation) {\n    return identifier;\n  } else {\n    return deserializeActivation(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js\nfunction assertObjectArgs(args) {\n  if (args != null && typeof args !== \"object\") {\n    throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);\n  }\n}\nvar Regularizer = class extends serialization_exports.Serializable {\n};\nvar L1L2 = class extends Regularizer {\n  constructor(args) {\n    super();\n    assertObjectArgs(args);\n    this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n    this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n    this.hasL1 = this.l1 !== 0;\n    this.hasL2 = this.l2 !== 0;\n  }\n  apply(x) {\n    return tidy(() => {\n      let regularization = zeros([1]);\n      if (this.hasL1) {\n        regularization = add2(regularization, sum2(mul(this.l1, abs(x))));\n      }\n      if (this.hasL2) {\n        regularization = add2(regularization, sum2(mul(this.l2, square2(x))));\n      }\n      return reshape(regularization, []);\n    });\n  }\n  getConfig() {\n    return { \"l1\": this.l1, \"l2\": this.l2 };\n  }\n  static fromConfig(cls, config) {\n    return new cls({ l1: config[\"l1\"], l2: config[\"l2\"] });\n  }\n};\nL1L2.className = \"L1L2\";\nserialization_exports.registerClass(L1L2);\nfunction l1(args) {\n  assertObjectArgs(args);\n  return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nfunction l2(args) {\n  assertObjectArgs(args);\n  return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\nvar REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"l1l2\": \"L1L2\"\n};\nfunction serializeRegularizer(constraint) {\n  return serializeKerasObject(constraint);\n}\nfunction deserializeRegularizer(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"regularizer\");\n}\nfunction getRegularizer(identifier) {\n  if (identifier == null) {\n    return null;\n  }\n  if (typeof identifier === \"string\") {\n    const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    const config = { className, config: {} };\n    return deserializeRegularizer(config);\n  } else if (identifier instanceof Regularizer) {\n    return identifier;\n  } else {\n    return deserializeRegularizer(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js\nvar ReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.supportsMasking = true;\n    if (args != null) {\n      this.maxValue = args.maxValue;\n    }\n  }\n  call(inputs, kwargs) {\n    inputs = getExactlyOneTensor(inputs);\n    let output = relu(inputs);\n    if (this.maxValue != null) {\n      output = clipByValue(output, 0, this.maxValue);\n    }\n    return output;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { maxValue: this.maxValue };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nReLU.className = \"ReLU\";\nserialization_exports.registerClass(ReLU);\nvar LeakyReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA = 0.3;\n    if (args == null) {\n      args = {};\n    }\n    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return leakyRelu(x, this.alpha);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { alpha: this.alpha };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nLeakyReLU.className = \"LeakyReLU\";\nserialization_exports.registerClass(LeakyReLU);\nvar PReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA_INITIALIZER = \"zeros\";\n    if (args == null) {\n      args = {};\n    }\n    this.supportsMasking = true;\n    this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n    this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n    this.alphaConstraint = getConstraint(args.alphaConstraint);\n    if (args.sharedAxes == null) {\n      this.sharedAxes = null;\n    } else if (Array.isArray(args.sharedAxes)) {\n      this.sharedAxes = args.sharedAxes;\n    } else if (typeof args.sharedAxes === \"number\") {\n      this.sharedAxes = [args.sharedAxes];\n    } else {\n      throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const paramShape = inputShape.slice(1);\n    if (this.sharedAxes != null) {\n      for (const i2 of this.sharedAxes) {\n        paramShape[i2 - 1] = 1;\n      }\n    }\n    this.alpha = this.addWeight(\"alpha\", paramShape, \"float32\", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n    const axes = {};\n    if (this.sharedAxes != null) {\n      for (let i2 = 1; i2 < inputShape.length; ++i2) {\n        axes[i2] = inputShape[i2];\n      }\n    }\n    this.inputSpec = [new InputSpec({\n      ndim: inputShape.length,\n      axes\n    })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    inputs = getExactlyOneTensor(inputs);\n    return prelu(inputs, this.alpha.read());\n  }\n  getConfig() {\n    const config = {\n      alphaInitializer: serializeInitializer(this.alphaInitializer),\n      alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n      alphaConstraint: serializeConstraint(this.alphaConstraint),\n      sharedAxes: this.sharedAxes\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nPReLU.className = \"PReLU\";\nserialization_exports.registerClass(PReLU);\nvar ELU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA = 1;\n    if (args == null) {\n      args = {};\n    }\n    if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n      throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);\n    }\n    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return elu(x);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { alpha: this.alpha };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nELU.className = \"ELU\";\nserialization_exports.registerClass(ELU);\nvar ThresholdedReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_THETA = 1;\n    if (args == null) {\n      args = {};\n    }\n    this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return mul(x, cast(greater(x, this.theta), \"float32\"));\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { theta: this.theta };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nThresholdedReLU.className = \"ThresholdedReLU\";\nserialization_exports.registerClass(ThresholdedReLU);\nvar Softmax3 = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_AXIS = 1;\n    if (args == null) {\n      args = {};\n    }\n    this.softmax = new Softmax2().apply;\n    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return this.softmax(x, this.axis);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { axis: this.axis };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nSoftmax3.className = \"Softmax\";\nserialization_exports.registerClass(Softmax3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js\nfunction normalizeArray(value, n, name) {\n  if (typeof value === \"number\") {\n    return pyListRepeat(value, n);\n  } else {\n    if (value.length !== n) {\n      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);\n    }\n    for (let i2 = 0; i2 < n; ++i2) {\n      const singleValue = value[i2];\n      if (!isInteger(singleValue)) {\n        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);\n      }\n    }\n    return value;\n  }\n}\nfunction convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n  if (inputLength == null) {\n    return inputLength;\n  }\n  const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n  let outputLength;\n  if (padding === \"same\") {\n    outputLength = inputLength;\n  } else {\n    outputLength = inputLength - dilatedFilterSize + 1;\n  }\n  return Math.floor((outputLength + stride - 1) / stride);\n}\nfunction deconvLength(dimSize, strideSize, kernelSize, padding) {\n  if (dimSize == null) {\n    return null;\n  }\n  if (padding === \"valid\") {\n    dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);\n  } else if (padding === \"same\") {\n    dimSize = dimSize * strideSize;\n  } else {\n    throw new ValueError(`Unsupport padding mode: ${padding}.`);\n  }\n  return dimSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js\nfunction preprocessConv2DInput(x, dataFormat) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    if (dataFormat === \"channelsFirst\") {\n      return transpose(x, [0, 2, 3, 1]);\n    } else {\n      return x;\n    }\n  });\n}\nfunction preprocessConv3DInput(x, dataFormat) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    if (dataFormat === \"channelsFirst\") {\n      return transpose(x, [0, 2, 3, 4, 1]);\n    } else {\n      return x;\n    }\n  });\n}\nfunction conv1dWithBias(x, kernel, bias, strides = 1, padding = \"valid\", dataFormat, dilationRate = 1) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.shape.length !== 3) {\n      throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);\n    }\n    if (kernel.shape.length !== 3) {\n      throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);\n    }\n    if (bias != null && bias.shape.length !== 1) {\n      throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      x = transpose(x, [0, 2, 1]);\n    }\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n    }\n    let y = conv1d(x, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NWC\", dilationRate);\n    if (bias != null) {\n      y = biasAdd(y, bias);\n    }\n    return y;\n  });\n}\nfunction conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate, activation2 = null) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.rank !== 3 && x.rank !== 4) {\n      throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);\n    }\n    if (kernel.rank !== 3 && kernel.rank !== 4) {\n      throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);\n    }\n    let y = preprocessConv2DInput(x, dataFormat);\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n    }\n    y = fused_ops_exports.conv2d({\n      x: y,\n      filter: kernel,\n      strides,\n      pad: padding === \"same\" ? \"same\" : \"valid\",\n      dilations: dilationRate,\n      dataFormat: \"NHWC\",\n      bias,\n      activation: activation2\n    });\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nfunction conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = \"valid\", dataFormat, dilationRate) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.rank !== 4 && x.rank !== 5) {\n      throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);\n    }\n    if (kernel.rank !== 4 && kernel.rank !== 5) {\n      throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);\n    }\n    let y = preprocessConv3DInput(x, dataFormat);\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");\n    }\n    y = conv3d(y, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NDHWC\", dilationRate);\n    if (bias != null) {\n      y = biasAdd(y, bias);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 4, 1, 2, 3]);\n    }\n    return y;\n  });\n}\nvar BaseConv = class extends Layer {\n  constructor(rank, args) {\n    super(args);\n    this.bias = null;\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    BaseConv.verifyArgs(args);\n    this.rank = rank;\n    assertPositiveInteger(this.rank, \"rank\");\n    if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n      throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);\n    }\n    this.kernelSize = normalizeArray(args.kernelSize, rank, \"kernelSize\");\n    this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    checkPaddingMode(this.padding);\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.activation = getActivation(args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, \"dilationRate\");\n    if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n      throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n    } else if (this.rank === 2) {\n      if (typeof this.dilationRate === \"number\") {\n        this.dilationRate = [this.dilationRate, this.dilationRate];\n      } else if (this.dilationRate.length !== 2) {\n        throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n      }\n    } else if (this.rank === 3) {\n      if (typeof this.dilationRate === \"number\") {\n        this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];\n      } else if (this.dilationRate.length !== 3) {\n        throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n      }\n    }\n  }\n  static verifyArgs(args) {\n    assert2(\"kernelSize\" in args, `required key 'kernelSize' not in config`);\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 3)) {\n      throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n  getConfig() {\n    const config = {\n      kernelSize: this.kernelSize,\n      strides: this.strides,\n      padding: this.padding,\n      dataFormat: this.dataFormat,\n      dilationRate: this.dilationRate,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      biasConstraint: serializeConstraint(this.biasConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar Conv = class extends BaseConv {\n  constructor(rank, args) {\n    super(rank, args);\n    this.kernel = null;\n    Conv.verifyArgs(args);\n    this.filters = args.filters;\n    assertPositiveInteger(this.filters, \"filters\");\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let outputs;\n      const biasValue = this.bias == null ? null : this.bias.read();\n      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n      if (fusedActivationName != null && this.rank === 2) {\n        outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n      } else {\n        if (this.rank === 1) {\n          outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n        } else if (this.rank === 2) {\n          outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n        } else if (this.rank === 3) {\n          outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n        } else {\n          throw new NotImplementedError(\"convolutions greater than 3D are not implemented yet.\");\n        }\n        if (this.activation != null) {\n          outputs = this.activation.apply(outputs);\n        }\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const newSpace = [];\n    const space = this.dataFormat === \"channelsLast\" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);\n    for (let i2 = 0; i2 < space.length; ++i2) {\n      const newDim = convOutputLength(space[i2], this.kernelSize[i2], this.padding, this.strides[i2], typeof this.dilationRate === \"number\" ? this.dilationRate : this.dilationRate[i2]);\n      newSpace.push(newDim);\n    }\n    let outputShape = [inputShape[0]];\n    if (this.dataFormat === \"channelsLast\") {\n      outputShape = outputShape.concat(newSpace);\n      outputShape.push(this.filters);\n    } else {\n      outputShape.push(this.filters);\n      outputShape = outputShape.concat(newSpace);\n    }\n    return outputShape;\n  }\n  getConfig() {\n    const config = {\n      filters: this.filters,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  static verifyArgs(args) {\n    if (!(\"filters\" in args) || typeof args.filters !== \"number\" || args.filters < 1) {\n      throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);\n    }\n  }\n};\nvar Conv2D2 = class extends Conv {\n  constructor(args) {\n    super(2, args);\n    Conv2D2.verifyArgs(args);\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 2)) {\n      throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n};\nConv2D2.className = \"Conv2D\";\nserialization_exports.registerClass(Conv2D2);\nvar Conv3D2 = class extends Conv {\n  constructor(args) {\n    super(3, args);\n    Conv3D2.verifyArgs(args);\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\") {\n      if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n        throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n      }\n    }\n  }\n};\nConv3D2.className = \"Conv3D\";\nserialization_exports.registerClass(Conv3D2);\nvar Conv2DTranspose = class extends Conv2D2 {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n    if (this.padding !== \"same\" && this.padding !== \"valid\") {\n      throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length !== 4) {\n      throw new ValueError(\"Input should have rank 4; Received input shape: \" + JSON.stringify(inputShape));\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.shape.length !== 4) {\n        throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n      }\n      const inputShape = input2.shape;\n      const batchSize = inputShape[0];\n      let hAxis;\n      let wAxis;\n      if (this.dataFormat === \"channelsFirst\") {\n        hAxis = 2;\n        wAxis = 3;\n      } else {\n        hAxis = 1;\n        wAxis = 2;\n      }\n      const height = inputShape[hAxis];\n      const width = inputShape[wAxis];\n      const kernelH = this.kernelSize[0];\n      const kernelW = this.kernelSize[1];\n      const strideH = this.strides[0];\n      const strideW = this.strides[1];\n      const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n      const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n      const outputShape = [batchSize, outHeight, outWidth, this.filters];\n      if (this.dataFormat !== \"channelsLast\") {\n        input2 = transpose(input2, [0, 2, 3, 1]);\n      }\n      let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n      if (this.dataFormat !== \"channelsLast\") {\n        outputs = transpose(outputs, [0, 3, 1, 2]);\n      }\n      if (this.bias != null) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    let channelAxis;\n    let heightAxis;\n    let widthAxis;\n    if (this.dataFormat === \"channelsFirst\") {\n      channelAxis = 1;\n      heightAxis = 2;\n      widthAxis = 3;\n    } else {\n      channelAxis = 3;\n      heightAxis = 1;\n      widthAxis = 2;\n    }\n    const kernelH = this.kernelSize[0];\n    const kernelW = this.kernelSize[1];\n    const strideH = this.strides[0];\n    const strideW = this.strides[1];\n    outputShape[channelAxis] = this.filters;\n    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n    return outputShape;\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"dilationRate\"];\n    return config;\n  }\n};\nConv2DTranspose.className = \"Conv2DTranspose\";\nserialization_exports.registerClass(Conv2DTranspose);\nvar Conv3DTranspose = class extends Conv3D2 {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n    if (this.padding !== \"same\" && this.padding !== \"valid\") {\n      throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length !== 5) {\n      throw new ValueError(\"Input should have rank 5; Received input shape: \" + JSON.stringify(inputShape));\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.shape.length !== 5) {\n        throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n      }\n      const inputShape = input2.shape;\n      const batchSize = inputShape[0];\n      let hAxis;\n      let wAxis;\n      let dAxis;\n      if (this.dataFormat === \"channelsFirst\") {\n        dAxis = 2;\n        hAxis = 3;\n        wAxis = 4;\n      } else {\n        dAxis = 1;\n        hAxis = 2;\n        wAxis = 3;\n      }\n      const depth = inputShape[dAxis];\n      const height = inputShape[hAxis];\n      const width = inputShape[wAxis];\n      const kernelD = this.kernelSize[0];\n      const kernelH = this.kernelSize[1];\n      const kernelW = this.kernelSize[2];\n      const strideD = this.strides[0];\n      const strideH = this.strides[1];\n      const strideW = this.strides[2];\n      const outDepth = deconvLength(depth, strideD, kernelD, this.padding);\n      const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n      const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n      const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];\n      if (this.dataFormat !== \"channelsLast\") {\n        input2 = transpose(input2, [0, 2, 3, 4, 1]);\n      }\n      let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n      if (this.dataFormat !== \"channelsLast\") {\n        outputs = transpose(outputs, [0, 4, 1, 2, 3]);\n      }\n      if (this.bias !== null) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation !== null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    let channelAxis;\n    let depthAxis;\n    let heightAxis;\n    let widthAxis;\n    if (this.dataFormat === \"channelsFirst\") {\n      channelAxis = 1;\n      depthAxis = 2;\n      heightAxis = 3;\n      widthAxis = 4;\n    } else {\n      channelAxis = 4;\n      depthAxis = 1;\n      heightAxis = 2;\n      widthAxis = 3;\n    }\n    const kernelD = this.kernelSize[0];\n    const kernelH = this.kernelSize[1];\n    const kernelW = this.kernelSize[2];\n    const strideD = this.strides[0];\n    const strideH = this.strides[1];\n    const strideW = this.strides[2];\n    outputShape[channelAxis] = this.filters;\n    outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);\n    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n    return outputShape;\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"dilationRate\"];\n    return config;\n  }\n};\nConv3DTranspose.className = \"Conv3DTranspose\";\nserialization_exports.registerClass(Conv3DTranspose);\nvar SeparableConv = class extends Conv {\n  constructor(rank, config) {\n    super(rank, config);\n    this.DEFAULT_DEPTHWISE_INITIALIZER = \"glorotUniform\";\n    this.DEFAULT_POINTWISE_INITIALIZER = \"glorotUniform\";\n    this.depthwiseKernel = null;\n    this.pointwiseKernel = null;\n    if (config.filters == null) {\n      throw new ValueError(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");\n    }\n    if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {\n      throw new ValueError(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");\n    }\n    if (config.padding != null && config.padding !== \"same\" && config.padding !== \"valid\") {\n      throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n    }\n    this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;\n    this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n    this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n    this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n    this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n    this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n    this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < this.rank + 2) {\n      throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n      throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n    const pointwiseKernelShape = [];\n    for (let i2 = 0; i2 < this.rank; ++i2) {\n      pointwiseKernelShape.push(1);\n    }\n    pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n    const trainable = true;\n    this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, \"float32\", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n    this.pointwiseKernel = this.addWeight(\"pointwise_kernel\", pointwiseKernelShape, \"float32\", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let output;\n      if (this.rank === 1) {\n        throw new NotImplementedError(\"1D separable convolution is not implemented yet.\");\n      } else if (this.rank === 2) {\n        if (this.dataFormat === \"channelsFirst\") {\n          inputs = transpose(inputs, [0, 2, 3, 1]);\n        }\n        output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, \"NHWC\");\n      }\n      if (this.useBias) {\n        output = biasAdd(output, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n      if (this.dataFormat === \"channelsFirst\") {\n        output = transpose(output, [0, 3, 1, 2]);\n      }\n      return output;\n    });\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    delete config[\"kernelInitializer\"];\n    delete config[\"kernelRegularizer\"];\n    delete config[\"kernelConstraint\"];\n    config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n    config[\"pointwiseInitializer\"] = serializeInitializer(this.pointwiseInitializer);\n    config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n    config[\"pointwiseRegularizer\"] = serializeRegularizer(this.pointwiseRegularizer);\n    config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseConstraint);\n    config[\"pointwiseConstraint\"] = serializeConstraint(this.pointwiseConstraint);\n    return config;\n  }\n};\nSeparableConv.className = \"SeparableConv\";\nvar SeparableConv2D = class extends SeparableConv {\n  constructor(args) {\n    super(2, args);\n  }\n};\nSeparableConv2D.className = \"SeparableConv2D\";\nserialization_exports.registerClass(SeparableConv2D);\nvar Conv1D = class extends Conv {\n  constructor(args) {\n    super(1, args);\n    Conv1D.verifyArgs(args);\n    this.inputSpec = [{ ndim: 3 }];\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    delete config[\"dataFormat\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 1)) {\n      throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n};\nConv1D.className = \"Conv1D\";\nserialization_exports.registerClass(Conv1D);\nvar Cropping2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    if (typeof args.cropping === \"number\") {\n      this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n    } else if (typeof args.cropping[0] === \"number\") {\n      this.cropping = [\n        [args.cropping[0], args.cropping[0]],\n        [args.cropping[1], args.cropping[1]]\n      ];\n    } else {\n      this.cropping = args.cropping;\n    }\n    this.dataFormat = args.dataFormat === void 0 ? \"channelsLast\" : args.dataFormat;\n    this.inputSpec = [{ ndim: 4 }];\n  }\n  computeOutputShape(inputShape) {\n    if (this.dataFormat === \"channelsFirst\") {\n      return [\n        inputShape[0],\n        inputShape[1],\n        inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n        inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n      ];\n    } else {\n      return [\n        inputShape[0],\n        inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n        inputShape[2] - this.cropping[1][0] - this.cropping[1][1],\n        inputShape[3]\n      ];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n      } else {\n        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n      }\n    });\n  }\n  getConfig() {\n    const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nCropping2D.className = \"Cropping2D\";\nserialization_exports.registerClass(Cropping2D);\nvar UpSampling2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_SIZE = [2, 2];\n    this.inputSpec = [{ ndim: 4 }];\n    this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.interpolation = args.interpolation == null ? \"nearest\" : args.interpolation;\n    checkInterpolationFormat(this.interpolation);\n  }\n  computeOutputShape(inputShape) {\n    if (this.dataFormat === \"channelsFirst\") {\n      const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n      const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n      return [inputShape[0], inputShape[1], height, width];\n    } else {\n      const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n      const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n      return [inputShape[0], height, width, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      if (this.dataFormat === \"channelsFirst\") {\n        input2 = transpose(input2, [0, 2, 3, 1]);\n        const height = this.size[0] * inputShape[2];\n        const width = this.size[1] * inputShape[3];\n        const resized = this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n        return transpose(resized, [0, 3, 1, 2]);\n      } else {\n        const height = this.size[0] * inputShape[1];\n        const width = this.size[1] * inputShape[2];\n        return this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n      }\n    });\n  }\n  getConfig() {\n    const config = {\n      size: this.size,\n      dataFormat: this.dataFormat,\n      interpolation: this.interpolation\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nUpSampling2D.className = \"UpSampling2D\";\nserialization_exports.registerClass(UpSampling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js\nfunction depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    let y = preprocessConv2DInput(x, dataFormat);\n    if (x.rank !== 4) {\n      throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);\n    }\n    if (depthwiseKernel.rank !== 4) {\n      throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);\n    }\n    y = depthwiseConv2d(y, depthwiseKernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NHWC\", dilationRate);\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nvar DepthwiseConv2D = class extends BaseConv {\n  constructor(args) {\n    super(2, args);\n    this.depthwiseKernel = null;\n    this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;\n    this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n    this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < 4) {\n      throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : 3;\n    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n      throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const depthwiseKernelShape = [\n      this.kernelSize[0],\n      this.kernelSize[1],\n      inputDim,\n      this.depthMultiplier\n    ];\n    this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n      if (this.useBias) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    const cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    const outFilters = this.dataFormat === \"channelsFirst\" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;\n    const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n    const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], outFilters, outRows, outCols];\n    } else {\n      return [inputShape[0], outRows, outCols, outFilters];\n    }\n  }\n  getConfig() {\n    const config = super.getConfig();\n    config[\"depthMultiplier\"] = this.depthMultiplier;\n    config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n    config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n    config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseRegularizer);\n    return config;\n  }\n};\nDepthwiseConv2D.className = \"DepthwiseConv2D\";\nserialization_exports.registerClass(DepthwiseConv2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js\nfunction standardizeArgs(inputs, initialState, constants, numConstants) {\n  if (Array.isArray(inputs)) {\n    if (initialState != null || constants != null) {\n      throw new ValueError(\"When inputs is an array, neither initialState or constants should be provided\");\n    }\n    if (numConstants != null) {\n      constants = inputs.slice(inputs.length - numConstants, inputs.length);\n      inputs = inputs.slice(0, inputs.length - numConstants);\n    }\n    if (inputs.length > 1) {\n      initialState = inputs.slice(1, inputs.length);\n    }\n    inputs = inputs[0];\n  }\n  function toListOrNull(x) {\n    if (x == null || Array.isArray(x)) {\n      return x;\n    } else {\n      return [x];\n    }\n  }\n  initialState = toListOrNull(initialState);\n  constants = toListOrNull(constants);\n  return { inputs, initialState, constants };\n}\nfunction rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n  return tidy(() => {\n    const ndim = inputs.shape.length;\n    if (ndim < 3) {\n      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n    }\n    const axes = [1, 0].concat(range2(2, ndim));\n    inputs = transpose(inputs, axes);\n    if (constants != null) {\n      throw new NotImplementedError(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");\n    }\n    if (unroll) {\n      console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\");\n    }\n    if (mask != null) {\n      mask = cast(cast(mask, \"bool\"), \"float32\");\n      if (mask.rank === ndim - 1) {\n        mask = expandDims(mask, -1);\n      }\n      mask = transpose(mask, axes);\n    }\n    if (goBackwards) {\n      inputs = reverse(inputs, 0);\n      if (mask != null) {\n        mask = reverse(mask, 0);\n      }\n    }\n    const perStepOutputs = [];\n    let lastOutput;\n    let states = initialStates;\n    const timeSteps = inputs.shape[0];\n    const perStepInputs = unstack(inputs);\n    let perStepMasks;\n    if (mask != null) {\n      perStepMasks = unstack(mask);\n    }\n    for (let t2 = 0; t2 < timeSteps; ++t2) {\n      const currentInput = perStepInputs[t2];\n      const stepOutputs = tidy(() => stepFunction(currentInput, states));\n      if (mask == null) {\n        lastOutput = stepOutputs[0];\n        states = stepOutputs[1];\n      } else {\n        const maskedOutputs = tidy(() => {\n          const stepMask = perStepMasks[t2];\n          const negStepMask = sub(onesLike(stepMask), stepMask);\n          const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));\n          const newStates = states.map((state, i2) => {\n            return add2(mul(stepOutputs[1][i2], stepMask), mul(state, negStepMask));\n          });\n          return { output, newStates };\n        });\n        lastOutput = maskedOutputs.output;\n        states = maskedOutputs.newStates;\n      }\n      if (needPerStepOutputs) {\n        perStepOutputs.push(lastOutput);\n      }\n    }\n    let outputs;\n    if (needPerStepOutputs) {\n      const axis = 1;\n      outputs = stack(perStepOutputs, axis);\n    }\n    return [lastOutput, outputs, states];\n  });\n}\nvar RNN = class extends Layer {\n  constructor(args) {\n    super(args);\n    let cell;\n    if (args.cell == null) {\n      throw new ValueError(\"cell property is missing for the constructor of RNN.\");\n    } else if (Array.isArray(args.cell)) {\n      cell = new StackedRNNCells({ cells: args.cell });\n    } else {\n      cell = args.cell;\n    }\n    if (cell.stateSize == null) {\n      throw new ValueError(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");\n    }\n    this.cell = cell;\n    this.returnSequences = args.returnSequences == null ? false : args.returnSequences;\n    this.returnState = args.returnState == null ? false : args.returnState;\n    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n    this._stateful = args.stateful == null ? false : args.stateful;\n    this.unroll = args.unroll == null ? false : args.unroll;\n    this.supportsMasking = true;\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n    this.stateSpec = null;\n    this.states_ = null;\n    this.numConstants = null;\n    this.keptStates = [];\n  }\n  getStates() {\n    if (this.states_ == null) {\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      return range2(0, numStates).map((x) => null);\n    } else {\n      return this.states_;\n    }\n  }\n  setStates(states) {\n    this.states_ = states;\n  }\n  computeOutputShape(inputShape) {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    let stateSize = this.cell.stateSize;\n    if (!Array.isArray(stateSize)) {\n      stateSize = [stateSize];\n    }\n    const outputDim = stateSize[0];\n    let outputShape;\n    if (this.returnSequences) {\n      outputShape = [inputShape[0], inputShape[1], outputDim];\n    } else {\n      outputShape = [inputShape[0], outputDim];\n    }\n    if (this.returnState) {\n      const stateShape = [];\n      for (const dim of stateSize) {\n        stateShape.push([inputShape[0], dim]);\n      }\n      return [outputShape].concat(stateShape);\n    } else {\n      return outputShape;\n    }\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (Array.isArray(mask)) {\n        mask = mask[0];\n      }\n      const outputMask = this.returnSequences ? mask : null;\n      if (this.returnState) {\n        const stateMask = this.states.map((s2) => null);\n        return [outputMask].concat(stateMask);\n      } else {\n        return outputMask;\n      }\n    });\n  }\n  get states() {\n    if (this.states_ == null) {\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      const output = [];\n      for (let i2 = 0; i2 < numStates; ++i2) {\n        output.push(null);\n      }\n      return output;\n    } else {\n      return this.states_;\n    }\n  }\n  set states(s2) {\n    this.states_ = s2;\n  }\n  build(inputShape) {\n    const constantShape = null;\n    if (this.numConstants != null) {\n      throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n    }\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    const batchSize = this.stateful ? inputShape[0] : null;\n    const inputDim = inputShape.slice(2);\n    this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (constantShape != null) {\n      throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n    } else {\n      this.cell.build(stepInputShape);\n    }\n    let stateSize;\n    if (Array.isArray(this.cell.stateSize)) {\n      stateSize = this.cell.stateSize;\n    } else {\n      stateSize = [this.cell.stateSize];\n    }\n    if (this.stateSpec != null) {\n      if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {\n        throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);\n      }\n    } else {\n      this.stateSpec = stateSize.map((dim) => new InputSpec({ shape: [null, dim] }));\n    }\n    if (this.stateful) {\n      this.resetStates();\n    }\n  }\n  resetStates(states, training = false) {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n      }\n      const batchSize = this.inputSpec[0].shape[0];\n      if (batchSize == null) {\n        throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");\n      }\n      if (this.states_ == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n        } else {\n          this.states_ = [zeros([batchSize, this.cell.stateSize])];\n        }\n      } else if (states == null) {\n        dispose(this.states_);\n        if (this.keptStates != null) {\n          dispose(this.keptStates);\n          this.keptStates = [];\n        }\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n        } else {\n          this.states_[0] = zeros([batchSize, this.cell.stateSize]);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n        }\n        if (training === true) {\n          this.keptStates.push(this.states_.slice());\n        } else {\n          dispose(this.states_);\n        }\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;\n          const expectedShape = [batchSize, dim];\n          if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map((state) => keep(state.clone()));\n    });\n  }\n  apply(inputs, kwargs) {\n    let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n    let constants = kwargs == null ? null : kwargs[\"constants\"];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n    let additionalInputs = [];\n    let additionalSpecs = [];\n    if (initialState != null) {\n      kwargs[\"initialState\"] = initialState;\n      additionalInputs = additionalInputs.concat(initialState);\n      this.stateSpec = [];\n      for (const state of initialState) {\n        this.stateSpec.push(new InputSpec({ shape: state.shape }));\n      }\n      additionalSpecs = additionalSpecs.concat(this.stateSpec);\n    }\n    if (constants != null) {\n      kwargs[\"constants\"] = constants;\n      additionalInputs = additionalInputs.concat(constants);\n      this.numConstants = constants.length;\n    }\n    const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n    if (isTensor) {\n      const fullInput = [inputs].concat(additionalInputs);\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      inputs = getExactlyOneTensor(inputs);\n      if (initialState == null) {\n        if (this.stateful) {\n          initialState = this.states_;\n        } else {\n          initialState = this.getInitialState(inputs);\n        }\n      }\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      if (initialState.length !== numStates) {\n        throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);\n      }\n      if (this.unroll) {\n        console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");\n      }\n      const cellCallKwargs = { training };\n      const step5 = (inputs2, states2) => {\n        const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);\n        return [outputs2[0], outputs2.slice(1)];\n      };\n      const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n      const lastOutput = rnnOutputs[0];\n      const outputs = rnnOutputs[1];\n      const states = rnnOutputs[2];\n      if (this.stateful) {\n        this.resetStates(states, training);\n      }\n      const output = this.returnSequences ? outputs : lastOutput;\n      if (this.returnState) {\n        return [output].concat(states);\n      } else {\n        return output;\n      }\n    });\n  }\n  getInitialState(inputs) {\n    return tidy(() => {\n      let initialState = zeros(inputs.shape);\n      initialState = sum2(initialState, [1, 2]);\n      initialState = expandDims2(initialState);\n      if (Array.isArray(this.cell.stateSize)) {\n        return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);\n      } else {\n        return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];\n      }\n    });\n  }\n  get trainableWeights() {\n    if (!this.trainable) {\n      return [];\n    }\n    return this.cell.trainableWeights;\n  }\n  get nonTrainableWeights() {\n    if (!this.trainable) {\n      return this.cell.weights;\n    }\n    return this.cell.nonTrainableWeights;\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.cell != null) {\n      this.cell.setFastWeightInitDuringBuild(value);\n    }\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      returnSequences: this.returnSequences,\n      returnState: this.returnState,\n      goBackwards: this.goBackwards,\n      stateful: this.stateful,\n      unroll: this.unroll\n    };\n    if (this.numConstants != null) {\n      config[\"numConstants\"] = this.numConstants;\n    }\n    const cellConfig = this.cell.getConfig();\n    if (this.getClassName() === RNN.className) {\n      config[\"cell\"] = {\n        \"className\": this.cell.getClassName(),\n        \"config\": cellConfig\n      };\n    }\n    return Object.assign({}, cellConfig, baseConfig, config);\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const cellConfig = config[\"cell\"];\n    const cell = deserialize(cellConfig, customObjects);\n    return new cls(Object.assign(config, { cell }));\n  }\n};\nRNN.className = \"RNN\";\nserialization_exports.registerClass(RNN);\nvar RNNCell = class extends Layer {\n};\nvar SimpleRNNCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    this.units = args.units;\n    assertPositiveInteger(this.units, `units`);\n    this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    this.kernel = this.addWeight(\"kernel\", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (inputs.length !== 2) {\n        throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n      }\n      let prevOutput = inputs[1];\n      inputs = inputs[0];\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(prevOutput),\n          rate: this.recurrentDropout,\n          training,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      let h;\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      if (dpMask != null) {\n        h = dot2(mul(inputs, dpMask), this.kernel.read());\n      } else {\n        h = dot2(inputs, this.kernel.read());\n      }\n      if (this.bias != null) {\n        h = biasAdd(h, this.bias.read());\n      }\n      if (recDpMask != null) {\n        prevOutput = mul(prevOutput, recDpMask);\n      }\n      let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n      return [output, output];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nSimpleRNNCell.className = \"SimpleRNNCell\";\nserialization_exports.registerClass(SimpleRNNCell);\nvar SimpleRNN = class extends RNN {\n  constructor(args) {\n    args.cell = new SimpleRNNCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nSimpleRNN.className = \"SimpleRNN\";\nserialization_exports.registerClass(SimpleRNN);\nvar GRUCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    if (args.resetAfter) {\n      throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (inputs.length !== 2) {\n        throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      let hTMinus1 = inputs[1];\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          count: 3,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: 3,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      let z;\n      let r2;\n      let hh;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = mul(inputs, dpMask[0]);\n      }\n      let matrixX = dot2(inputs, this.kernel.read());\n      if (this.useBias) {\n        matrixX = biasAdd(matrixX, this.bias.read());\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = mul(hTMinus1, recDpMask[0]);\n      }\n      const recurrentKernelValue = this.recurrentKernel.read();\n      const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n      const matrixInner = dot2(hTMinus1, rk1);\n      const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);\n      const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);\n      z = this.recurrentActivation.apply(add2(xZ, recurrentZ));\n      r2 = this.recurrentActivation.apply(add2(xR, recurrentR));\n      const recurrentH = dot2(mul(r2, hTMinus1), rk2);\n      hh = this.activation.apply(add2(xH, recurrentH));\n      const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));\n      return [h, h];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation,\n      resetAfter: false\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nGRUCell.className = \"GRUCell\";\nserialization_exports.registerClass(GRUCell);\nvar GRU = class extends RNN {\n  constructor(args) {\n    if (args.implementation === 0) {\n      console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n    }\n    args.cell = new GRUCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    if (config[\"implmentation\"] === 0) {\n      config[\"implementation\"] = 1;\n    }\n    return new cls(config);\n  }\n};\nGRU.className = \"GRU\";\nserialization_exports.registerClass(GRU);\nvar LSTMCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.unitForgetBias = args.unitForgetBias;\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = [this.units, this.units];\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    var _a;\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    let biasInitializer;\n    if (this.useBias) {\n      if (this.unitForgetBias) {\n        const capturedBiasInit = this.biasInitializer;\n        const capturedUnits = this.units;\n        biasInitializer = new (_a = class CustomInit extends Initializer {\n          apply(shape, dtype) {\n            const bI = capturedBiasInit.apply([capturedUnits]);\n            const bF = new Ones().apply([capturedUnits]);\n            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n            return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);\n          }\n        }, _a.className = \"CustomInit\", _a)();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\"bias\", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      inputs = inputs;\n      if (inputs.length !== 3) {\n        throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      let hTMinus1 = inputs[1];\n      const cTMinus1 = inputs[2];\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          count: 4,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: 4,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      let i2;\n      let f;\n      let c;\n      let o;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = mul(inputs, dpMask[0]);\n      }\n      let z = dot2(inputs, this.kernel.read());\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = mul(hTMinus1, recDpMask[0]);\n      }\n      z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));\n      if (this.useBias) {\n        z = biasAdd(z, this.bias.read());\n      }\n      const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);\n      i2 = this.recurrentActivation.apply(z0);\n      f = this.recurrentActivation.apply(z1);\n      c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(z2)));\n      o = this.recurrentActivation.apply(z3);\n      const h = mul(o, this.activation.apply(c));\n      return [h, h, c];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      unitForgetBias: this.unitForgetBias,\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nLSTMCell.className = \"LSTMCell\";\nserialization_exports.registerClass(LSTMCell);\nvar LSTM = class extends RNN {\n  constructor(args) {\n    if (args.implementation === 0) {\n      console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n    }\n    args.cell = new LSTMCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    if (config[\"implmentation\"] === 0) {\n      config[\"implementation\"] = 1;\n    }\n    return new cls(config);\n  }\n};\nLSTM.className = \"LSTM\";\nserialization_exports.registerClass(LSTM);\nvar StackedRNNCells = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.cells = args.cells;\n  }\n  get stateSize() {\n    const stateSize = [];\n    for (const cell of this.cells.slice().reverse()) {\n      if (Array.isArray(cell.stateSize)) {\n        stateSize.push(...cell.stateSize);\n      } else {\n        stateSize.push(cell.stateSize);\n      }\n    }\n    return stateSize;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      let states = inputs.slice(1);\n      const nestedStates = [];\n      for (const cell of this.cells.slice().reverse()) {\n        if (Array.isArray(cell.stateSize)) {\n          nestedStates.push(states.splice(0, cell.stateSize.length));\n        } else {\n          nestedStates.push(states.splice(0, 1));\n        }\n      }\n      nestedStates.reverse();\n      const newNestedStates = [];\n      let callInputs;\n      for (let i2 = 0; i2 < this.cells.length; ++i2) {\n        const cell = this.cells[i2];\n        states = nestedStates[i2];\n        if (i2 === 0) {\n          callInputs = [inputs[0]].concat(states);\n        } else {\n          callInputs = [callInputs[0]].concat(states);\n        }\n        callInputs = cell.call(callInputs, kwargs);\n        newNestedStates.push(callInputs.slice(1));\n      }\n      states = [];\n      for (const cellStates of newNestedStates.slice().reverse()) {\n        states.push(...cellStates);\n      }\n      return [callInputs[0]].concat(states);\n    });\n  }\n  build(inputShape) {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    let outputDim;\n    this.cells.forEach((cell, i2) => {\n      nameScope(`RNNCell_${i2}`, () => {\n        cell.build(inputShape);\n        if (Array.isArray(cell.stateSize)) {\n          outputDim = cell.stateSize[0];\n        } else {\n          outputDim = cell.stateSize;\n        }\n        inputShape = [inputShape[0], outputDim];\n      });\n    });\n    this.built = true;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const getCellConfig = (cell) => {\n      return {\n        \"className\": cell.getClassName(),\n        \"config\": cell.getConfig()\n      };\n    };\n    const cellConfigs = this.cells.map(getCellConfig);\n    const config = { \"cells\": cellConfigs };\n    return Object.assign({}, baseConfig, config);\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const cells = [];\n    for (const cellConfig of config[\"cells\"]) {\n      cells.push(deserialize(cellConfig, customObjects));\n    }\n    return new cls({ cells });\n  }\n  get trainableWeights() {\n    if (!this.trainable) {\n      return [];\n    }\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.trainableWeights);\n    }\n    return weights;\n  }\n  get nonTrainableWeights() {\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights = [];\n      for (const cell of this.cells) {\n        trainableWeights.push(...cell.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n  getWeights() {\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.weights);\n    }\n    return batchGetValue(weights);\n  }\n  setWeights(weights) {\n    const tuples = [];\n    for (const cell of this.cells) {\n      const numParams = cell.weights.length;\n      const inputWeights = weights.splice(numParams);\n      for (let i2 = 0; i2 < cell.weights.length; ++i2) {\n        tuples.push([cell.weights[i2], inputWeights[i2]]);\n      }\n    }\n    batchSetValue(tuples);\n  }\n};\nStackedRNNCells.className = \"StackedRNNCells\";\nserialization_exports.registerClass(StackedRNNCells);\nfunction generateDropoutMask(args) {\n  const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args;\n  const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate);\n  const createMask = () => inTrainPhase(droppedInputs, ones4, training);\n  if (!count2 || count2 <= 1) {\n    return keep(createMask().clone());\n  }\n  const masks = Array(count2).fill(void 0).map(createMask);\n  return masks.map((m) => keep(m.clone()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js\nvar __rest = function(s2, e2) {\n  var t2 = {};\n  for (var p2 in s2)\n    if (Object.prototype.hasOwnProperty.call(s2, p2) && e2.indexOf(p2) < 0)\n      t2[p2] = s2[p2];\n  if (s2 != null && typeof Object.getOwnPropertySymbols === \"function\")\n    for (var i2 = 0, p2 = Object.getOwnPropertySymbols(s2); i2 < p2.length; i2++) {\n      if (e2.indexOf(p2[i2]) < 0 && Object.prototype.propertyIsEnumerable.call(s2, p2[i2]))\n        t2[p2[i2]] = s2[p2[i2]];\n    }\n  return t2;\n};\nvar ConvRNN2D = class extends RNN {\n  constructor(args) {\n    if (args.unroll) {\n      throw new NotImplementedError(\"Unrolling is not possible with convolutional RNNs.\");\n    }\n    if (Array.isArray(args.cell)) {\n      throw new NotImplementedError(\"It is not possible at the moment to stack convolutional cells.\");\n    }\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      if (kwargs && kwargs[\"constants\"]) {\n        throw new ValueError(\"ConvRNN2D cell does not support constants\");\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  computeOutputShape(inputShape) {\n    let outShape = this.computeSingleOutputShape(inputShape);\n    if (!this.returnSequences) {\n      outShape = [outShape[0], ...outShape.slice(2)];\n    }\n    if (this.returnState) {\n      outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n    }\n    return outShape;\n  }\n  getInitialState(inputs) {\n    return tidy(() => {\n      const { stateSize } = this.cell;\n      const inputShape = inputs.shape;\n      const outputShape = this.computeSingleOutputShape(inputShape);\n      const stateShape = [outputShape[0], ...outputShape.slice(2)];\n      const initialState = zeros(stateShape);\n      if (Array.isArray(stateSize)) {\n        return Array(stateSize.length).fill(initialState);\n      }\n      return [initialState];\n    });\n  }\n  resetStates(states, training = false) {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n      }\n      const inputShape = this.inputSpec[0].shape;\n      const outputShape = this.computeSingleOutputShape(inputShape);\n      const stateShape = [outputShape[0], ...outputShape.slice(2)];\n      const batchSize = inputShape[0];\n      if (batchSize == null) {\n        throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");\n      }\n      if (this.getStates() == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n        } else {\n          this.states_ = [zeros(stateShape)];\n        }\n      } else if (states == null) {\n        dispose(this.states_);\n        if (this.keptStates != null) {\n          dispose(this.keptStates);\n          this.keptStates = [];\n        }\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n        } else {\n          this.states_[0] = zeros(stateShape);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n        }\n        if (training) {\n          this.keptStates.push(this.states_.slice());\n        } else {\n          dispose(this.states_);\n        }\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const expectedShape = stateShape;\n          if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map((state) => keep(state.clone()));\n    });\n  }\n  computeSingleOutputShape(inputShape) {\n    const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n    const isChannelsFirst = dataFormat === \"channelsFirst\";\n    const h = inputShape[isChannelsFirst ? 3 : 2];\n    const w = inputShape[isChannelsFirst ? 4 : 3];\n    const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n    const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n    const outShape = [\n      ...inputShape.slice(0, 2),\n      ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]\n    ];\n    return outShape;\n  }\n};\nConvRNN2D.className = \"ConvRNN2D\";\nvar ConvLSTM2DCell = class extends LSTMCell {\n  constructor(args) {\n    const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args;\n    super(Object.assign({}, args, { units: filters }));\n    this.filters = filters;\n    assertPositiveInteger(this.filters, \"filters\");\n    this.kernelSize = normalizeArray(kernelSize, 2, \"kernelSize\");\n    this.kernelSize.forEach((size) => assertPositiveInteger(size, \"kernelSize\"));\n    this.strides = normalizeArray(strides || 1, 2, \"strides\");\n    this.strides.forEach((stride) => assertPositiveInteger(stride, \"strides\"));\n    this.padding = padding || \"valid\";\n    checkPaddingMode(this.padding);\n    this.dataFormat = dataFormat || \"channelsLast\";\n    checkDataFormat(this.dataFormat);\n    this.dilationRate = normalizeArray(dilationRate || 1, 2, \"dilationRate\");\n    this.dilationRate.forEach((rate) => assertPositiveInteger(rate, \"dilationRate\"));\n  }\n  build(inputShape) {\n    var _a;\n    inputShape = getExactlyOneShape(inputShape);\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const numOfKernels = 4;\n    const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      let biasInitializer;\n      if (this.unitForgetBias) {\n        const init2 = this.biasInitializer;\n        const filters = this.filters;\n        biasInitializer = new (_a = class CustomInit extends Initializer {\n          apply(shape, dtype) {\n            const biasI = init2.apply([filters]);\n            const biasF = ones2([filters]);\n            const biasCAndO = init2.apply([filters * 2]);\n            return concatenate([biasI, biasF, biasCAndO]);\n          }\n        }, _a.className = \"CustomInit\", _a)();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\"bias\", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (inputs.length !== 3) {\n        throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      const training = kwargs[\"training\"] || false;\n      const x = inputs[0];\n      const hTMinus1 = inputs[1];\n      const cTMinus1 = inputs[2];\n      const numOfKernels = 4;\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(x),\n          rate: this.dropout,\n          training,\n          count: numOfKernels,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dropoutMask = this.dropoutMask;\n      const applyDropout = (x2, mask, index) => {\n        if (!mask || !mask[index]) {\n          return x2;\n        }\n        return mul(mask[index], x2);\n      };\n      let xI = applyDropout(x, dropoutMask, 0);\n      let xF = applyDropout(x, dropoutMask, 1);\n      let xC = applyDropout(x, dropoutMask, 2);\n      let xO = applyDropout(x, dropoutMask, 3);\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: numOfKernels,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const recDropoutMask = this.recurrentDropoutMask;\n      let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n      let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n      let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n      let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n      const kernelChannelAxis = 3;\n      const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n      const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];\n      xI = this.inputConv(xI, kernelI, biasI, this.padding);\n      xF = this.inputConv(xF, kernelF, biasF, this.padding);\n      xC = this.inputConv(xC, kernelC, biasC, this.padding);\n      xO = this.inputConv(xO, kernelO, biasO, this.padding);\n      const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n      hI = this.recurrentConv(hI, recKernelI);\n      hF = this.recurrentConv(hF, recKernelF);\n      hC = this.recurrentConv(hC, recKernelC);\n      hO = this.recurrentConv(hO, recKernelO);\n      const i2 = this.recurrentActivation.apply(add2(xI, hI));\n      const f = this.recurrentActivation.apply(add2(xF, hF));\n      const c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(add2(xC, hC))));\n      const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));\n      return [h, h, c];\n    });\n  }\n  getConfig() {\n    const _a = super.getConfig(), { \"units\": _ } = _a, baseConfig = __rest(_a, [\"units\"]);\n    const config = {\n      filters: this.filters,\n      kernelSize: this.kernelSize,\n      padding: this.padding,\n      dataFormat: this.dataFormat,\n      dilationRate: this.dilationRate,\n      strides: this.strides\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n  inputConv(x, w, b, padding) {\n    const out = conv2d(x, w, this.strides, padding || \"valid\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\", this.dilationRate);\n    if (b) {\n      return biasAdd(out, b, this.dataFormat);\n    }\n    return out;\n  }\n  recurrentConv(x, w) {\n    const strides = 1;\n    return conv2d(x, w, strides, \"same\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\");\n  }\n};\nConvLSTM2DCell.className = \"ConvLSTM2DCell\";\nserialization_exports.registerClass(ConvLSTM2DCell);\nvar ConvLSTM2D = class extends ConvRNN2D {\n  constructor(args) {\n    const cell = new ConvLSTM2DCell(args);\n    super(Object.assign({}, args, { cell }));\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nConvLSTM2D.className = \"ConvLSTM2D\";\nserialization_exports.registerClass(ConvLSTM2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js\nvar Dropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.rate = Math.max(Math.min(args.rate, 1), 0);\n    this.noiseShape = args.noiseShape;\n    this.seed = args.seed;\n    this.supportsMasking = true;\n  }\n  getNoiseShape(input2) {\n    if (this.noiseShape == null) {\n      return this.noiseShape;\n    }\n    const inputShape = input2.shape;\n    const noiseShape = [];\n    for (let i2 = 0; i2 < this.noiseShape.length; ++i2) {\n      noiseShape.push(this.noiseShape[i2] == null ? inputShape[i2] : this.noiseShape[i2]);\n    }\n    return noiseShape;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      if (0 < this.rate && this.rate < 1) {\n        const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n        const noiseShape = this.getNoiseShape(input2);\n        const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);\n        return output;\n      }\n      return inputs;\n    });\n  }\n  getConfig() {\n    const config = {\n      rate: this.rate,\n      noiseShape: this.noiseShape,\n      seed: this.seed\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  dispose() {\n    return super.dispose();\n  }\n};\nDropout.className = \"Dropout\";\nserialization_exports.registerClass(Dropout);\nvar SpatialDropout1D = class extends Dropout {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [{ ndim: 3 }];\n  }\n  getNoiseShape(input2) {\n    const inputShape = input2.shape;\n    return [inputShape[0], 1, inputShape[2]];\n  }\n};\nSpatialDropout1D.className = \"SpatialDropout1D\";\nserialization_exports.registerClass(SpatialDropout1D);\nvar Dense = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.activation = null;\n    this.useBias = true;\n    this.kernel = null;\n    this.bias = null;\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {\n      let batchSize = null;\n      if (args.batchSize != null) {\n        batchSize = args.batchSize;\n      }\n      this.batchInputShape = [batchSize, args.inputDim];\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation);\n    if (args.useBias != null) {\n      this.useBias = args.useBias;\n    }\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.supportsMasking = true;\n    this.inputSpec = [{ minNDim: 2 }];\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputLastDim = inputShape[inputShape.length - 1];\n    if (this.kernel == null) {\n      this.kernel = this.addWeight(\"kernel\", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n      if (this.useBias) {\n        this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n      }\n    }\n    this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n    this.built = true;\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    outputShape[outputShape.length - 1] = this.units;\n    return outputShape;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n      let output;\n      if (fusedActivationName != null) {\n        output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n      } else {\n        output = dot2(input2, this.kernel.read());\n        if (this.bias != null) {\n          output = biasAdd(output, this.bias.read());\n        }\n        if (this.activation != null) {\n          output = this.activation.apply(output);\n        }\n      }\n      return output;\n    });\n  }\n  getConfig() {\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nDense.className = \"Dense\";\nserialization_exports.registerClass(Dense);\nvar Flatten = class extends Layer {\n  constructor(args) {\n    args = args || {};\n    super(args);\n    this.inputSpec = [{ minNDim: 3 }];\n    this.dataFormat = args.dataFormat;\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    for (const dim of inputShape.slice(1)) {\n      if (dim == null) {\n        throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);\n      }\n    }\n    return [inputShape[0], arrayProd(inputShape, 1)];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      let input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsFirst\" && input2.rank > 1) {\n        const permutation = [0];\n        for (let i2 = 2; i2 < input2.rank; ++i2) {\n          permutation.push(i2);\n        }\n        permutation.push(1);\n        input2 = transpose(input2, permutation);\n      }\n      return batchFlatten(input2);\n    });\n  }\n  getConfig() {\n    const config = {};\n    if (this.dataFormat != null) {\n      config[\"dataFormat\"] = this.dataFormat;\n    }\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nFlatten.className = \"Flatten\";\nserialization_exports.registerClass(Flatten);\nvar Activation2 = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.activation = getActivation(args.activation);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      return this.activation.apply(input2);\n    });\n  }\n  getConfig() {\n    const config = { activation: serializeActivation(this.activation) };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nActivation2.className = \"Activation\";\nserialization_exports.registerClass(Activation2);\nvar RepeatVector = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.n = args.n;\n    this.inputSpec = [{ ndim: 2 }];\n  }\n  computeOutputShape(inputShape) {\n    return [inputShape[0], this.n, inputShape[1]];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      return repeat(inputs, this.n);\n    });\n  }\n  getConfig() {\n    const config = {\n      n: this.n\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nRepeatVector.className = \"RepeatVector\";\nserialization_exports.registerClass(RepeatVector);\nvar Reshape2 = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.targetShape = args.targetShape;\n    for (let i2 = 0; i2 < this.targetShape.length; ++i2) {\n      if (this.isUnknown(this.targetShape[i2])) {\n        this.targetShape[i2] = null;\n      }\n    }\n  }\n  isUnknown(dim) {\n    return dim < 0 || dim == null;\n  }\n  fixUnknownDimension(inputShape, outputShape) {\n    const errorMsg = \"Total size of new array must be unchanged.\";\n    const finalShape = outputShape.slice();\n    let known = 1;\n    let unknown = null;\n    for (let i2 = 0; i2 < finalShape.length; ++i2) {\n      const dim = finalShape[i2];\n      if (this.isUnknown(dim)) {\n        if (unknown === null) {\n          unknown = i2;\n        } else {\n          throw new ValueError(\"Can only specifiy one unknown dimension.\");\n        }\n      } else {\n        known *= dim;\n      }\n    }\n    const originalSize = arrayProd(inputShape);\n    if (unknown !== null) {\n      if (known === 0 || originalSize % known !== 0) {\n        throw new ValueError(errorMsg);\n      }\n      finalShape[unknown] = originalSize / known;\n    } else if (originalSize !== known) {\n      throw new ValueError(errorMsg);\n    }\n    return finalShape;\n  }\n  computeOutputShape(inputShape) {\n    let anyUnknownDims = false;\n    for (let i2 = 0; i2 < inputShape.length; ++i2) {\n      if (this.isUnknown(inputShape[i2])) {\n        anyUnknownDims = true;\n        break;\n      }\n    }\n    if (anyUnknownDims) {\n      return inputShape.slice(0, 1).concat(this.targetShape);\n    } else {\n      return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n      return reshape(input2, outputShape);\n    });\n  }\n  getConfig() {\n    const config = {\n      targetShape: this.targetShape\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nReshape2.className = \"Reshape\";\nserialization_exports.registerClass(Reshape2);\nvar Permute = class extends Layer {\n  constructor(args) {\n    super(args);\n    if (args.dims == null) {\n      throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");\n    }\n    if (!Array.isArray(args.dims)) {\n      throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${args.dims} instead.`);\n    }\n    const expectedSortedIndices = range2(1, args.dims.length + 1);\n    if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n      throw new Error(\"Invalid permutation `dims`: \" + JSON.stringify(args.dims) + \" `dims` must contain consecutive integers starting from 1.\");\n    }\n    this.dims = args.dims;\n    this.dimsIncludingBatch = [0].concat(this.dims);\n    this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    this.dims.forEach((dim, i2) => {\n      outputShape[i2 + 1] = inputShape[dim];\n    });\n    return outputShape;\n  }\n  call(inputs, kwargs) {\n    return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n  }\n  getConfig() {\n    const config = {\n      dims: this.dims\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nPermute.className = \"Permute\";\nserialization_exports.registerClass(Permute);\nvar Masking = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.supportsMasking = true;\n    if (args != null) {\n      this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n    } else {\n      this.maskValue = 0;\n    }\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { maskValue: this.maskValue };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  computeMask(inputs, mask) {\n    const input2 = getExactlyOneTensor(inputs);\n    const axis = -1;\n    return any(notEqual(input2, this.maskValue), axis);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const axis = -1;\n      const keepDims = true;\n      const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);\n      const output = mul(input2, cast(booleanMask, input2.dtype));\n      return output;\n    });\n  }\n};\nMasking.className = \"Masking\";\nserialization_exports.registerClass(Masking);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js\nvar Embedding = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.embeddings = null;\n    this.DEFAULT_EMBEDDINGS_INITIALIZER = \"randomUniform\";\n    if (args.batchInputShape == null && args.inputShape == null) {\n      let batchSize = null;\n      if (args.batchSize != null) {\n        batchSize = args.batchSize;\n      }\n      if (args.inputLength == null) {\n        this.batchInputShape = [batchSize, null];\n      } else {\n        this.batchInputShape = [batchSize].concat(toList(args.inputLength));\n      }\n    }\n    this.inputDim = args.inputDim;\n    assertPositiveInteger(this.inputDim, \"inputDim\");\n    this.outputDim = args.outputDim;\n    assertPositiveInteger(this.outputDim, \"outputDim\");\n    this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n    this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n    this.maskZero = args.maskZero;\n    this.supportsMasking = args.maskZero;\n    this.inputLength = args.inputLength;\n  }\n  build(inputShape) {\n    this.embeddings = this.addWeight(\"embeddings\", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n    this.built = true;\n  }\n  warnOnIncompatibleInputShape(inputShape) {\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (!this.maskZero) {\n        return null;\n      } else {\n        inputs = getExactlyOneTensor(inputs);\n        return notEqual(inputs, zerosLike(inputs));\n      }\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (this.inputLength == null) {\n      return [...inputShape, this.outputDim];\n    }\n    const inLens = toList(this.inputLength);\n    if (inLens.length !== inputShape.length - 1) {\n      throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n    } else {\n      let i2 = 0;\n      for (let k = 0; k < inLens.length; ++k) {\n        const s1 = inLens[k];\n        const s2 = inputShape[k + 1];\n        if (s1 != null && s2 != null && s1 !== s2) {\n          throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n        } else if (s1 == null) {\n          inLens[i2] = s2;\n        }\n        i2++;\n      }\n    }\n    return [inputShape[0], ...inLens, this.outputDim];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.dtype !== \"int32\") {\n        input2 = cast2(input2, \"int32\");\n      }\n      const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));\n      return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));\n    });\n  }\n  getConfig() {\n    const config = {\n      inputDim: this.inputDim,\n      outputDim: this.outputDim,\n      embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n      embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n      maskZero: this.maskZero,\n      inputLength: this.inputLength\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nEmbedding.className = \"Embedding\";\nserialization_exports.registerClass(Embedding);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js\nvar Merge = class extends Layer {\n  constructor(args) {\n    super(args || {});\n    this.supportsMasking = true;\n  }\n  mergeFunction(inputs) {\n    throw new NotImplementedError();\n  }\n  computeElementwiseOpOutputShape(shape1, shape2) {\n    if (shape1 == null || shape2 == null) {\n      return null;\n    } else if (shape1.length < shape2.length) {\n      return this.computeElementwiseOpOutputShape(shape2, shape1);\n    } else if (shape2.length === 0) {\n      return shape1;\n    }\n    const outputShape = shape1.slice(0, shape1.length - shape2.length);\n    for (let k = 0; k < shape2.length; ++k) {\n      const i2 = shape1[shape1.length - shape2.length + k];\n      const j = shape2[k];\n      if (i2 == null || j == null || i2 < 0 || j < 0) {\n        outputShape.push(null);\n      } else if (i2 === 1) {\n        outputShape.push(j);\n      } else if (j === 1) {\n        outputShape.push(i2);\n      } else {\n        if (i2 !== j) {\n          throw new ValueError(\"Operands could not be broadcast together with shapes \" + JSON.stringify(shape1) + \" \" + JSON.stringify(shape2));\n        }\n        outputShape.push(i2);\n      }\n    }\n    return outputShape;\n  }\n  build(inputShape) {\n    if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n      inputShape = [getExactlyOneShape(inputShape)];\n    }\n    inputShape = inputShape;\n    if (inputShape.length < 2) {\n      throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);\n    }\n    let batchSizes = [];\n    for (const shape of inputShape) {\n      if (shape != null && shape[0] !== null) {\n        batchSizes.push(shape[0]);\n      }\n    }\n    batchSizes = unique2(batchSizes);\n    if (batchSizes.length > 1) {\n      throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n    }\n    let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n    for (let i2 = 1; i2 < inputShape.length; ++i2) {\n      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);\n      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n    }\n    const allRanks = inputShape.map((shape) => shape.length);\n    if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {\n      this.reshapeRequired = false;\n    } else {\n      this.reshapeRequired = true;\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (this.reshapeRequired) {\n        const reshapedInputs = [];\n        const inputDims = inputs.map((input2) => input2.rank);\n        if (inputDims.indexOf(null) === -1) {\n          const maxNDim = max2(inputDims);\n          for (let x of inputs) {\n            const xNDim = x.rank;\n            for (let k = 0; k < maxNDim - xNDim; ++k) {\n              x = expandDims2(x, 1);\n            }\n            reshapedInputs.push(x);\n          }\n          return this.mergeFunction(reshapedInputs);\n        } else {\n          let transposed = false;\n          for (const x of inputs) {\n            const xNDim = x.rank;\n            if (xNDim == null) {\n              const xShape = x.shape;\n              const batchSize = xShape[0];\n              const newShape = xShape.slice(1).concat([batchSize]);\n              let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));\n              xTransposed = transpose(xTransposed, [1, 0]);\n              xTransposed = reshape(xTransposed, newShape);\n              reshapedInputs.push(xTransposed);\n              transposed = true;\n            } else if (xNDim > 1) {\n              const dims = range2(1, xNDim).concat([0]);\n              reshapedInputs.push(transpose(x, dims));\n              transposed = true;\n            } else {\n              reshapedInputs.push(x);\n            }\n          }\n          let y = this.mergeFunction(reshapedInputs);\n          const yNDim = y.rank;\n          if (transposed) {\n            if (yNDim == null) {\n              const yShape = y.shape;\n              const yNDim2 = yShape.length;\n              const batchSize = yShape[yNDim2 - 1];\n              const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n              y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);\n            } else if (yNDim > 1) {\n              const dims = [yNDim - 1].concat(range2(0, yNDim - 1));\n              y = transpose(y, dims);\n            }\n          }\n          return y;\n        }\n      } else {\n        return this.mergeFunction(inputs);\n      }\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = inputShape;\n    let outputShape;\n    if (inputShape[0] == null) {\n      outputShape = null;\n    } else {\n      outputShape = inputShape[0].slice(1);\n    }\n    for (let i2 = 1; i2 < inputShape.length; ++i2) {\n      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);\n      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n    }\n    let batchSizes = [];\n    for (const shape of inputShape) {\n      if (shape != null && shape[0] !== null) {\n        batchSizes.push(shape[0]);\n      }\n    }\n    batchSizes = unique2(batchSizes);\n    if (batchSizes.length === 1) {\n      outputShape = batchSizes.concat(outputShape);\n    } else {\n      outputShape = [null].concat(outputShape);\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (mask == null) {\n        return null;\n      }\n      if (!Array.isArray(mask)) {\n        throw new ValueError(\"`mask` should be an Array\");\n      }\n      if (!Array.isArray(inputs)) {\n        throw new ValueError(\"`inputs` should be an Array\");\n      }\n      if (mask.length !== inputs.length) {\n        throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);\n      }\n      if (mask.every((m) => m == null)) {\n        return null;\n      }\n      mask = mask.map((m) => m == null ? m : expandDims(m, 0));\n      let output = mask[0];\n      for (let i2 = 1; i2 < mask.length - 1; ++i2) {\n        output = logicalAnd(output, mask[i2]);\n      }\n      return output;\n    });\n  }\n};\nvar Add2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = add2(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nAdd2.className = \"Add\";\nserialization_exports.registerClass(Add2);\nvar Multiply2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = mul(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nMultiply2.className = \"Multiply\";\nserialization_exports.registerClass(Multiply2);\nvar Average = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = add2(output, inputs[i2]);\n      }\n      return mul(1 / inputs.length, output);\n    });\n  }\n};\nAverage.className = \"Average\";\nserialization_exports.registerClass(Average);\nvar Maximum2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0];\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = maximum(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nMaximum2.className = \"Maximum\";\nserialization_exports.registerClass(Maximum2);\nvar Minimum2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0];\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = minimum(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nMinimum2.className = \"Minimum\";\nserialization_exports.registerClass(Minimum2);\nvar Concatenate = class extends Merge {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_AXIS = -1;\n    if (args == null) {\n      args = {};\n    }\n    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    this.supportsMasking = true;\n    this.reshapeRequired = false;\n  }\n  build(inputShape) {\n    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {\n      throw new ValueError(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");\n    }\n    inputShape = inputShape;\n    let allNoneShape = true;\n    for (const shape of inputShape) {\n      if (shape != null) {\n        allNoneShape = false;\n        break;\n      }\n    }\n    if (allNoneShape) {\n      return;\n    }\n    const shapeSet = [];\n    for (let i2 = 0; i2 < inputShape.length; ++i2) {\n      const shapeWithoutConcatAxis = inputShape[i2].slice();\n      shapeWithoutConcatAxis.splice(this.axis, 1);\n      let exists = false;\n      for (const shape of shapeSet) {\n        if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {\n          exists = true;\n          break;\n        }\n      }\n      if (!exists) {\n        shapeSet.push(shapeWithoutConcatAxis);\n      }\n    }\n    if (shapeSet.length > 1) {\n      throw new ValueError(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \" + JSON.stringify(inputShape));\n    }\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      return concatenate(inputs, this.axis);\n    });\n  }\n  computeOutputShape(inputShape) {\n    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n      throw new ValueError(\"A `Concatenate` layer should be called on a list of inputs.\");\n    }\n    const inputShapes = inputShape;\n    const outputShape = inputShapes[0].slice();\n    const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n    for (const shape of inputShapes.slice(1)) {\n      if (outputShape[axis] == null || shape[axis] == null) {\n        outputShape[axis] = null;\n        break;\n      }\n      outputShape[axis] += shape[axis];\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    if (mask == null) {\n      return null;\n    }\n    if (!Array.isArray(mask)) {\n      throw new ValueError(\"`mask` should be an array for Concatenate\");\n    }\n    if (!Array.isArray(inputs)) {\n      throw new ValueError(\"`inputs` should be an array for Concatenate\");\n    }\n    if (mask.length !== inputs.length) {\n      throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);\n    }\n    return tidy(() => {\n      let allNullMasks = true;\n      mask.forEach((m) => {\n        if (m != null) {\n          allNullMasks = false;\n          return;\n        }\n      });\n      if (allNullMasks) {\n        return null;\n      }\n      const outputMasks = [];\n      for (let i2 = 0; i2 < inputs.length; ++i2) {\n        if (mask[i2] == null) {\n          outputMasks.push(cast(onesLike(inputs[i2]), \"bool\"));\n        } else if (mask[i2].rank < inputs[i2].rank) {\n          outputMasks.push(expandDims(mask[i2], -1));\n        } else {\n          outputMasks.push(mask[i2]);\n        }\n      }\n      const concatenatedMasks = concat(outputMasks, this.axis);\n      return all(concatenatedMasks, -1, false);\n    });\n  }\n  getConfig() {\n    const config = {\n      \"axis\": this.axis\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nConcatenate.className = \"Concatenate\";\nserialization_exports.registerClass(Concatenate);\nfunction interpretAxis(axis, dim) {\n  while (axis < 0) {\n    axis += dim;\n  }\n  return axis;\n}\nfunction batchDot(x, y, axes) {\n  if (x.shape.length > 3 || y.shape.length > 3) {\n    throw new NotImplementedError(\"batchDot is not implemented for tensors of 4D or higher rank yet\");\n  }\n  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);\n  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);\n  if (typeof axes === \"number\") {\n    axes = [axes, axes];\n  }\n  if (x.dtype === \"complex64\" || y.dtype === \"complex64\") {\n    throw new NotImplementedError(\"batchDot is not implemented for complex64-type Tensors yet.\");\n  }\n  const xNDim = x.shape.length;\n  const yNDim = y.shape.length;\n  if (axes == null) {\n    axes = [xNDim - 1, yNDim - 2];\n  }\n  const axesArray = axes;\n  return tidy(() => {\n    let diff;\n    if (xNDim > yNDim) {\n      diff = xNDim - yNDim;\n      const diffShape = [];\n      for (let i2 = 0; i2 < diff; ++i2) {\n        diffShape.push(1);\n      }\n      y = reshape(y, y.shape.concat(diffShape));\n    } else if (yNDim > xNDim) {\n      diff = yNDim - xNDim;\n      const diffShape = [];\n      for (let i2 = 0; i2 < diff; ++i2) {\n        diffShape.push(1);\n      }\n      x = reshape(x, x.shape.concat(diffShape));\n    } else {\n      diff = 0;\n    }\n    let out;\n    if (x.shape.length === 2 && y.shape.length === 2) {\n      if (axesArray[0] === axesArray[1]) {\n        out = sum2(mul(x, y), axesArray[0]);\n      } else {\n        out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);\n      }\n    } else {\n      const adjX = axesArray[0] !== x.shape.length - 1;\n      const adjY = axesArray[1] === y.shape.length - 1;\n      out = matMul(x, y, adjX, adjY);\n    }\n    if (diff > 0) {\n      let idx;\n      if (xNDim > yNDim) {\n        idx = xNDim + yNDim - 3;\n      } else {\n        idx = xNDim - 1;\n      }\n      const squeezeAxes = [];\n      for (let i2 = idx; i2 < idx + diff; ++i2) {\n        squeezeAxes.push(i2);\n      }\n      out = squeeze(out, squeezeAxes);\n    }\n    if (out.shape.length === 1) {\n      out = expandDims(out, 1);\n    }\n    return out;\n  });\n}\nvar Dot = class extends Merge {\n  constructor(args) {\n    super(args);\n    this.axes = args.axes;\n    this.normalize = args.normalize == null ? false : args.normalize;\n    this.supportsMasking = true;\n    this.reshapeRequired = false;\n  }\n  build(inputShape) {\n    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n    const shape1 = inputShape[0];\n    const shape2 = inputShape[1];\n    if (shape1.length > 3 || shape2.length > 3) {\n      throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n    }\n    const axes = this.interpretAxes(shape1, shape2);\n    if (shape1[axes[0]] !== shape2[axes[1]]) {\n      throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n    }\n  }\n  mergeFunction(inputs) {\n    if (inputs.length !== 2) {\n      throw new ValueError(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);\n    }\n    let x1 = inputs[0];\n    let x2 = inputs[1];\n    let axes;\n    if (!Array.isArray(this.axes)) {\n      axes = [\n        interpretAxis(this.axes, x1.shape.length),\n        interpretAxis(this.axes, x2.shape.length)\n      ];\n    } else {\n      axes = this.axes.map((axis, i2) => interpretAxis(axis, inputs[i2].shape.length));\n    }\n    if (this.normalize) {\n      x1 = l2Normalize(x1, axes[0]);\n      x2 = l2Normalize(x2, axes[1]);\n    }\n    return batchDot(x1, x2, axes);\n  }\n  interpretAxes(shape1, shape2) {\n    let axes;\n    if (!Array.isArray(this.axes)) {\n      axes = [\n        interpretAxis(this.axes, shape1.length),\n        interpretAxis(this.axes, shape2.length)\n      ];\n    } else {\n      axes = this.axes;\n    }\n    return axes;\n  }\n  computeOutputShape(inputShape) {\n    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n    const shape1 = inputShape[0].slice();\n    const shape2 = inputShape[1].slice();\n    if (shape1.length > 3 || shape2.length > 3) {\n      throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n    }\n    const axes = this.interpretAxes(shape1, shape2);\n    shape1.splice(axes[0], 1);\n    shape2.splice(axes[1], 1);\n    shape2.splice(0, 1);\n    const outputShape = shape1.concat(shape2);\n    if (outputShape.length === 1) {\n      outputShape.push(1);\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    return null;\n  }\n  getConfig() {\n    const config = {\n      \"axes\": this.axes,\n      \"normalize\": this.normalize\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nDot.className = \"Dot\";\nserialization_exports.registerClass(Dot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js\nvar GaussianNoise = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.stddev = args.stddev;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { stddev: this.stddev };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);\n      const output = inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n      return output;\n    });\n  }\n};\nGaussianNoise.className = \"GaussianNoise\";\nserialization_exports.registerClass(GaussianNoise);\nvar GaussianDropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.rate = args.rate;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { rate: this.rate };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.rate > 0 && this.rate < 1) {\n        const noised = () => {\n          const stddev = Math.sqrt(this.rate / (1 - this.rate));\n          return mul(input2, randomNormal2(input2.shape, 1, stddev));\n        };\n        return inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n      }\n      return input2;\n    });\n  }\n};\nGaussianDropout.className = \"GaussianDropout\";\nserialization_exports.registerClass(GaussianDropout);\nvar AlphaDropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.rate = args.rate;\n    this.noiseShape = args.noiseShape;\n  }\n  _getNoiseShape(inputs) {\n    return this.noiseShape || getExactlyOneTensor(inputs).shape;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { rate: this.rate };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.rate < 1 && this.rate > 0) {\n        const noiseShape = this._getNoiseShape(inputs);\n        const droppedInputs = () => {\n          const input2 = getExactlyOneTensor(inputs);\n          const alpha = 1.6732632423543772;\n          const scale2 = 1.0507009873554805;\n          const alphaP = -alpha * scale2;\n          let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n          keptIdx = cast2(keptIdx, \"float32\");\n          const a6 = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n          const b = -a6 * alphaP * this.rate;\n          const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));\n          return add2(mul(x, a6), b);\n        };\n        return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs[\"training\"] || false);\n      }\n      return inputs;\n    });\n  }\n};\nAlphaDropout.className = \"AlphaDropout\";\nserialization_exports.registerClass(AlphaDropout);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js\nfunction batchNormalization(x, mean5, variance, beta, gamma, epsilon3 = 1e-3) {\n  let out;\n  if (x.rank === 2) {\n    out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon3);\n  } else if (x.rank === 3) {\n    out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon3);\n  } else if (x.rank === 4) {\n    out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon3);\n  } else {\n    throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);\n  }\n  return out;\n}\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  return tidy(() => {\n    const meanAndVariance = moments(x, reductionAxes);\n    const mean5 = meanAndVariance.mean;\n    const variance = meanAndVariance.variance;\n    const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon3);\n    return [normed, mean5, variance];\n  });\n}\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  return tidy(() => {\n    const meanAndVariance = moments(x, reductionAxes);\n    const mean5 = meanAndVariance.mean;\n    const variance = meanAndVariance.variance;\n    const targetShape = [];\n    for (const axis of range2(0, x.rank)) {\n      if (reductionAxes.indexOf(axis) !== -1) {\n        targetShape.push(1);\n      } else {\n        targetShape.push(x.shape[axis]);\n      }\n    }\n    const broadcastMean = reshape(mean5, targetShape);\n    const broadcastVariance = reshape(variance, targetShape);\n    const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);\n    const broadcastBeta = beta == null ? null : reshape(beta, targetShape);\n    const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);\n    return [normed, mean5, variance];\n  });\n}\nfunction normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {\n    return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n  } else {\n    return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n  }\n}\nvar BatchNormalization = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.supportsMasking = true;\n    this.axis = args.axis == null ? -1 : args.axis;\n    this.momentum = args.momentum == null ? 0.99 : args.momentum;\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n    this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n    this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || \"zeros\");\n    this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || \"ones\");\n    this.betaConstraint = getConstraint(args.betaConstraint);\n    this.gammaConstraint = getConstraint(args.gammaConstraint);\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;\n    const dim = inputShape[axis];\n    if (dim == null) {\n      throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);\n    }\n    this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n    const shape = [dim];\n    if (this.scale) {\n      this.gamma = this.addWeight(\"gamma\", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\"beta\", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n    }\n    this.movingMean = this.addWeight(\"moving_mean\", shape, null, this.movingMeanInitializer, null, false);\n    this.movingVariance = this.addWeight(\"moving_variance\", shape, null, this.movingVarianceInitializer, null, false);\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      const input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      const ndim = inputShape.length;\n      const reductionAxes = range2(0, ndim);\n      const axis = this.axis >= 0 ? this.axis : this.axis + ndim;\n      reductionAxes.splice(axis, 1);\n      const broadcastShape = pyListRepeat(1, ndim);\n      broadcastShape[axis] = inputShape[axis];\n      const sortedReductionAxes = reductionAxes.slice();\n      sortedReductionAxes.sort();\n      const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));\n      const normalizeInference = () => {\n        if (needsBroadcasting) {\n          const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);\n          const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);\n          const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;\n          const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;\n          return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n        } else {\n          return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n        }\n      };\n      if (!training) {\n        return normalizeInference();\n      }\n      const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n      const doMovingAverage = (variable2, value, momentum) => {\n        tidy(() => {\n          const decay = 1 - momentum;\n          const origValue = variable2.read();\n          const updateDelta = mul(sub(origValue, value), decay);\n          variable2.write(sub(origValue, updateDelta));\n        });\n      };\n      const updateMovingMeanAndVariance = () => {\n        doMovingAverage(this.movingMean, mean5, this.momentum);\n        doMovingAverage(this.movingVariance, variance, this.momentum);\n      };\n      updateMovingMeanAndVariance();\n      return normedTraining;\n    });\n  }\n  getConfig() {\n    const config = {\n      axis: this.axis,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n      movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n      betaConstraint: serializeConstraint(this.betaConstraint),\n      gammaConstraint: serializeConstraint(this.gammaConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nBatchNormalization.className = \"BatchNormalization\";\nserialization_exports.registerClass(BatchNormalization);\nvar LayerNormalization = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.axis = args.axis == null ? -1 : args.axis;\n    if (typeof this.axis === \"number\") {\n      if (!Number.isInteger(this.axis)) {\n        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n      }\n    } else if (Array.isArray(this.axis)) {\n      for (const axis of this.axis) {\n        if (!Number.isInteger(axis)) {\n          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);\n        }\n      }\n    } else {\n      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);\n    }\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n    this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    this.supportsMasking = true;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const nDims = inputShape.length;\n    if (typeof this.axis === \"number\") {\n      this.axis = [this.axis];\n    }\n    for (let i2 = 0; i2 < this.axis.length; ++i2) {\n      if (this.axis[i2] < 0) {\n        this.axis[i2] += nDims;\n      }\n    }\n    for (const axis of this.axis) {\n      if (axis < 0 || axis >= nDims) {\n        throw new Error(`Invalid axis: ${axis}`);\n      }\n    }\n    if (this.axis.length !== unique2(this.axis).length) {\n      throw new Error(`Found duplicate axes in: ${this.axis}`);\n    }\n    const paramShape = this.axis.map((axis) => inputShape[axis]);\n    const trainable = true;\n    if (this.scale) {\n      this.gamma = this.addWeight(\"gamma\", paramShape, \"float32\", this.gammaInitializer, this.gammaRegularizer, trainable);\n    } else {\n      this.gamma = null;\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\"beta\", paramShape, \"float32\", this.betaInitializer, this.betaRegularizer, trainable);\n    } else {\n      this.beta = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    const input2 = getExactlyOneTensor(inputs);\n    const inputShape = input2.shape;\n    const nDims = inputShape.length;\n    return tidy(() => {\n      const keepDims = true;\n      let { mean: mean5, variance } = moments(input2, this.axis, keepDims);\n      const broadcastShape = pyListRepeat(1, nDims);\n      for (const dim of this.axis) {\n        broadcastShape[dim] = inputShape[dim];\n      }\n      const broadcast = (v) => {\n        if (v != null && v.shape.length !== nDims) {\n          return reshape(v, broadcastShape);\n        } else {\n          return v;\n        }\n      };\n      let scale2 = this.scale ? broadcast(this.gamma.read()) : null;\n      let offset = this.center ? broadcast(this.beta.read()) : null;\n      const momentsTiling = [];\n      const scaleOffsetTiling = [];\n      for (let i2 = 0; i2 < nDims; ++i2) {\n        if (this.axis.indexOf(i2) !== -1) {\n          momentsTiling.push(inputShape[i2]);\n          scaleOffsetTiling.push(1);\n        } else {\n          momentsTiling.push(1);\n          scaleOffsetTiling.push(inputShape[i2]);\n        }\n      }\n      mean5 = tile(mean5, momentsTiling);\n      variance = tile(variance, momentsTiling);\n      if (scale2 != null) {\n        scale2 = tile(scale2, scaleOffsetTiling);\n      }\n      if (offset != null) {\n        offset = tile(offset, scaleOffsetTiling);\n      }\n      return batchNormalization(input2, mean5, variance, offset, scale2, this.epsilon);\n    });\n  }\n  getConfig() {\n    const config = {\n      axis: this.axis,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nLayerNormalization.className = \"LayerNormalization\";\nserialization_exports.registerClass(LayerNormalization);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js\nfunction spatial2dPadding(x, padding, dataFormat) {\n  return tidy(() => {\n    if (x.rank !== 4) {\n      throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);\n    }\n    if (padding == null) {\n      padding = [[1, 1], [1, 1]];\n    }\n    if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) {\n      throw new ValueError(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (dataFormat !== \"channelsLast\" && dataFormat !== \"channelsFirst\") {\n      throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);\n    }\n    let pattern;\n    if (dataFormat === \"channelsFirst\") {\n      pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n    } else {\n      pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n    }\n    return pad(x, pattern);\n  });\n}\nvar ZeroPadding2D = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n    if (args.padding == null) {\n      this.padding = [[1, 1], [1, 1]];\n    } else if (typeof args.padding === \"number\") {\n      this.padding = [[args.padding, args.padding], [args.padding, args.padding]];\n    } else {\n      args.padding = args.padding;\n      if (args.padding.length !== 2) {\n        throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);\n      }\n      let heightPadding;\n      let widthPadding;\n      if (typeof args.padding[0] === \"number\") {\n        heightPadding = [args.padding[0], args.padding[0]];\n        widthPadding = [args.padding[1], args.padding[1]];\n      } else {\n        args.padding = args.padding;\n        if (args.padding[0].length !== 2) {\n          throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);\n        }\n        heightPadding = args.padding[0];\n        if (args.padding[1].length !== 2) {\n          throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);\n        }\n        widthPadding = args.padding[1];\n      }\n      this.padding = [heightPadding, widthPadding];\n    }\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let rows;\n    let cols;\n    if (this.dataFormat === \"channelsFirst\") {\n      if (inputShape[2] != null && inputShape[2] >= 0) {\n        rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n      } else {\n        rows = null;\n      }\n      if (inputShape[3] != null && inputShape[3] >= 0) {\n        cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n      } else {\n        cols = null;\n      }\n      return [inputShape[0], inputShape[1], rows, cols];\n    } else {\n      if (inputShape[1] != null && inputShape[1] >= 0) {\n        rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n      } else {\n        rows = null;\n      }\n      if (inputShape[2] != null && inputShape[2] >= 0) {\n        cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n      } else {\n        cols = null;\n      }\n      return [inputShape[0], rows, cols, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n  }\n  getConfig() {\n    const config = {\n      padding: this.padding,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nZeroPadding2D.className = \"ZeroPadding2D\";\nserialization_exports.registerClass(ZeroPadding2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js\nfunction pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    checkPoolMode(poolMode);\n    checkPaddingMode(padding);\n    if (strides == null) {\n      strides = [1, 1];\n    }\n    if (padding == null) {\n      padding = \"valid\";\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (poolMode == null) {\n      poolMode = \"max\";\n    }\n    x = preprocessConv2DInput(x, dataFormat);\n    let y;\n    const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n    if (poolMode === \"max\") {\n      y = maxPool(x, poolSize, strides, paddingString);\n    } else {\n      y = avgPool(x, poolSize, strides, paddingString);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nfunction pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    checkPoolMode(poolMode);\n    checkPaddingMode(padding);\n    if (strides == null) {\n      strides = [1, 1, 1];\n    }\n    if (padding == null) {\n      padding = \"valid\";\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (poolMode == null) {\n      poolMode = \"max\";\n    }\n    x = preprocessConv3DInput(x, dataFormat);\n    let y;\n    const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n    if (poolMode === \"max\") {\n      y = maxPool3d(x, poolSize, strides, paddingString);\n    } else {\n      y = avgPool3d(x, poolSize, strides, paddingString);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 4, 1, 2, 3]);\n    }\n    return y;\n  });\n}\nvar Pooling1D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = 2;\n    }\n    super(args);\n    if (typeof args.poolSize === \"number\") {\n      this.poolSize = [args.poolSize];\n    } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === \"number\") {\n      this.poolSize = args.poolSize;\n    } else {\n      throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else {\n      if (typeof args.strides === \"number\") {\n        this.strides = [args.strides];\n      } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === \"number\") {\n        this.strides = args.strides;\n      } else {\n        throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);\n      }\n    }\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n    return [inputShape[0], length, inputShape[2]];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      inputs = expandDims2(getExactlyOneTensor(inputs), 2);\n      const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, \"channelsLast\");\n      return squeeze(output, [2]);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling1D = class extends Pooling1D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling1D.className = \"MaxPooling1D\";\nserialization_exports.registerClass(MaxPooling1D);\nvar AveragePooling1D = class extends Pooling1D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling1D.className = \"AveragePooling1D\";\nserialization_exports.registerClass(AveragePooling1D);\nvar Pooling2D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = [2, 2];\n    }\n    super(args);\n    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else if (Array.isArray(args.strides)) {\n      if (args.strides.length !== 2) {\n        throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);\n      }\n      this.strides = args.strides;\n    } else {\n      this.strides = [args.strides, args.strides];\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    let cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n    cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], inputShape[1], rows, cols];\n    } else {\n      return [inputShape[0], rows, cols, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling2D = class extends Pooling2D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling2D.className = \"MaxPooling2D\";\nserialization_exports.registerClass(MaxPooling2D);\nvar AveragePooling2D = class extends Pooling2D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling2D.className = \"AveragePooling2D\";\nserialization_exports.registerClass(AveragePooling2D);\nvar Pooling3D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = [2, 2, 2];\n    }\n    super(args);\n    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else if (Array.isArray(args.strides)) {\n      if (args.strides.length !== 3) {\n        throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);\n      }\n      this.strides = args.strides;\n    } else {\n      this.strides = [args.strides, args.strides, args.strides];\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let depths = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    let rows = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    let cols = this.dataFormat === \"channelsFirst\" ? inputShape[4] : inputShape[3];\n    depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n    rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n    cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], inputShape[1], depths, rows, cols];\n    } else {\n      return [inputShape[0], depths, rows, cols, inputShape[4]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling3D = class extends Pooling3D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool3d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling3D.className = \"MaxPooling3D\";\nserialization_exports.registerClass(MaxPooling3D);\nvar AveragePooling3D = class extends Pooling3D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool3d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling3D.className = \"AveragePooling3D\";\nserialization_exports.registerClass(AveragePooling3D);\nvar GlobalPooling1D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n  }\n  computeOutputShape(inputShape) {\n    return [inputShape[0], inputShape[2]];\n  }\n  call(inputs, kwargs) {\n    throw new NotImplementedError();\n  }\n};\nvar GlobalAveragePooling1D = class extends GlobalPooling1D {\n  constructor(args) {\n    super(args || {});\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      return mean(input2, 1);\n    });\n  }\n};\nGlobalAveragePooling1D.className = \"GlobalAveragePooling1D\";\nserialization_exports.registerClass(GlobalAveragePooling1D);\nvar GlobalMaxPooling1D = class extends GlobalPooling1D {\n  constructor(args) {\n    super(args || {});\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      return max(input2, 1);\n    });\n  }\n};\nGlobalMaxPooling1D.className = \"GlobalMaxPooling1D\";\nserialization_exports.registerClass(GlobalMaxPooling1D);\nvar GlobalPooling2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = inputShape;\n    if (this.dataFormat === \"channelsLast\") {\n      return [inputShape[0], inputShape[3]];\n    } else {\n      return [inputShape[0], inputShape[1]];\n    }\n  }\n  call(inputs, kwargs) {\n    throw new NotImplementedError();\n  }\n  getConfig() {\n    const config = { dataFormat: this.dataFormat };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar GlobalAveragePooling2D = class extends GlobalPooling2D {\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        return mean(input2, [1, 2]);\n      } else {\n        return mean(input2, [2, 3]);\n      }\n    });\n  }\n};\nGlobalAveragePooling2D.className = \"GlobalAveragePooling2D\";\nserialization_exports.registerClass(GlobalAveragePooling2D);\nvar GlobalMaxPooling2D = class extends GlobalPooling2D {\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        return max(input2, [1, 2]);\n      } else {\n        return max(input2, [2, 3]);\n      }\n    });\n  }\n};\nGlobalMaxPooling2D.className = \"GlobalMaxPooling2D\";\nserialization_exports.registerClass(GlobalMaxPooling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js\nvar Wrapper = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.layer = args.layer;\n  }\n  build(inputShape) {\n    this.built = true;\n  }\n  get trainable() {\n    if (this.layer != null) {\n      return this.layer.trainable;\n    } else {\n      return false;\n    }\n  }\n  set trainable(value) {\n    if (this.layer != null) {\n      this.layer.trainable = value;\n    }\n  }\n  get trainableWeights() {\n    return this.layer.trainableWeights;\n  }\n  get nonTrainableWeights() {\n    return this.layer.nonTrainableWeights;\n  }\n  get updates() {\n    return this.layer._updates;\n  }\n  get losses() {\n    return this.layer.losses;\n  }\n  getWeights() {\n    return this.layer.getWeights();\n  }\n  setWeights(weights) {\n    this.layer.setWeights(weights);\n  }\n  getConfig() {\n    const config = {\n      \"layer\": {\n        \"className\": this.layer.getClassName(),\n        \"config\": this.layer.getConfig()\n      }\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.layer != null) {\n      this.layer.setFastWeightInitDuringBuild(value);\n    }\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const layerConfig = config[\"layer\"];\n    const layer = deserialize(layerConfig, customObjects);\n    delete config[\"layer\"];\n    const newConfig = { layer };\n    Object.assign(newConfig, config);\n    return new cls(newConfig);\n  }\n};\nvar TimeDistributed = class extends Wrapper {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < 3) {\n      throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);\n    }\n    this.inputSpec = [{ shape: inputShape }];\n    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (!this.layer.built) {\n      this.layer.build(childInputShape);\n      this.layer.built = true;\n    }\n    super.build(inputShape);\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    const childOutputShape = this.layer.computeOutputShape(childInputShape);\n    const timesteps = inputShape[1];\n    return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      const step5 = (inputs2, states) => {\n        const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));\n        return [output, []];\n      };\n      const rnnOutputs = rnn(step5, inputs, [], false, null, null, false, true);\n      const y = rnnOutputs[1];\n      return y;\n    });\n  }\n};\nTimeDistributed.className = \"TimeDistributed\";\nserialization_exports.registerClass(TimeDistributed);\nfunction checkBidirectionalMergeMode(value) {\n  checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, \"BidirectionalMergeMode\", value);\n}\nvar DEFAULT_BIDIRECTIONAL_MERGE_MODE = \"concat\";\nvar Bidirectional = class extends Wrapper {\n  constructor(args) {\n    super(args);\n    const layerConfig = args.layer.getConfig();\n    const forwDict = {};\n    forwDict[\"className\"] = args.layer.getClassName();\n    forwDict[\"config\"] = layerConfig;\n    this.forwardLayer = deserialize(forwDict);\n    layerConfig[\"goBackwards\"] = layerConfig[\"goBackwards\"] === true ? false : true;\n    const backDict = {};\n    backDict[\"className\"] = args.layer.getClassName();\n    backDict[\"config\"] = layerConfig;\n    this.backwardLayer = deserialize(backDict);\n    this.forwardLayer.name = \"forward_\" + this.forwardLayer.name;\n    this.backwardLayer.name = \"backward_\" + this.backwardLayer.name;\n    this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;\n    checkBidirectionalMergeMode(this.mergeMode);\n    if (args.weights) {\n      throw new NotImplementedError(\"weights support is not implemented for Bidirectional layer yet.\");\n    }\n    this._stateful = args.layer.stateful;\n    this.returnSequences = args.layer.returnSequences;\n    this.returnState = args.layer.returnState;\n    this.supportsMasking = true;\n    this._trainable = true;\n    this.inputSpec = args.layer.inputSpec;\n    this.numConstants = null;\n  }\n  get trainable() {\n    return this._trainable;\n  }\n  set trainable(value) {\n    this._trainable = value;\n    if (this.forwardLayer != null) {\n      this.forwardLayer.trainable = value;\n    }\n    if (this.backwardLayer != null) {\n      this.backwardLayer.trainable = value;\n    }\n  }\n  getWeights() {\n    return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n  }\n  setWeights(weights) {\n    const numWeights = weights.length;\n    const numeightsOver2 = Math.floor(numWeights / 2);\n    this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n    this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n  }\n  computeOutputShape(inputShape) {\n    let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n    if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n      layerShapes = [layerShapes];\n    }\n    layerShapes = layerShapes;\n    let outputShape;\n    let outputShapes;\n    let stateShape;\n    if (this.returnState) {\n      stateShape = layerShapes.slice(1);\n      outputShape = layerShapes[0];\n    } else {\n      outputShape = layerShapes[0];\n    }\n    outputShape = outputShape;\n    if (this.mergeMode === \"concat\") {\n      outputShape[outputShape.length - 1] *= 2;\n      outputShapes = [outputShape];\n    } else if (this.mergeMode == null) {\n      outputShapes = [outputShape, outputShape.slice()];\n    } else {\n      outputShapes = [outputShape];\n    }\n    if (this.returnState) {\n      if (this.mergeMode == null) {\n        return outputShapes.concat(stateShape).concat(stateShape.slice());\n      }\n      return [outputShape].concat(stateShape).concat(stateShape.slice());\n    }\n    return singletonOrArray(outputShapes);\n  }\n  apply(inputs, kwargs) {\n    let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n    let constants = kwargs == null ? null : kwargs[\"constants\"];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n    if (Array.isArray(inputs)) {\n      initialState = inputs.slice(1);\n      inputs = inputs[0];\n    }\n    if ((initialState == null || initialState.length === 0) && constants == null) {\n      return super.apply(inputs, kwargs);\n    }\n    const additionalInputs = [];\n    const additionalSpecs = [];\n    if (initialState != null) {\n      const numStates = initialState.length;\n      if (numStates % 2 > 0) {\n        throw new ValueError(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");\n      }\n      kwargs[\"initialState\"] = initialState;\n      additionalInputs.push(...initialState);\n      const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape }));\n      this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n      this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n      additionalSpecs.push(...stateSpecs);\n    }\n    if (constants != null) {\n      throw new NotImplementedError(\"Support for constants in Bidirectional layers is not implemented yet.\");\n    }\n    const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n    for (const tensor2 of additionalInputs) {\n      if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {\n        throw new ValueError(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");\n      }\n    }\n    if (isSymbolicTensor) {\n      const fullInput = [inputs].concat(additionalInputs);\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const initialState = kwargs[\"initialState\"];\n      let y;\n      let yRev;\n      if (initialState == null) {\n        y = this.forwardLayer.call(inputs, kwargs);\n        yRev = this.backwardLayer.call(inputs, kwargs);\n      } else {\n        const forwardState = initialState.slice(0, initialState.length / 2);\n        const backwardState = initialState.slice(initialState.length / 2);\n        y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n        yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n      }\n      let states;\n      if (this.returnState) {\n        if (Array.isArray(y)) {\n          states = y.slice(1).concat(yRev.slice(1));\n        } else {\n        }\n        y = y[0];\n        yRev = yRev[0];\n      }\n      if (this.returnSequences) {\n        yRev = reverse(yRev, 1);\n      }\n      let output;\n      if (this.mergeMode === \"concat\") {\n        output = concatenate([y, yRev]);\n      } else if (this.mergeMode === \"sum\") {\n        output = add2(y, yRev);\n      } else if (this.mergeMode === \"ave\") {\n        output = mul(0.5, add2(y, yRev));\n      } else if (this.mergeMode === \"mul\") {\n        output = mul(y, yRev);\n      } else if (this.mergeMode == null) {\n        output = [y, yRev];\n      }\n      if (this.returnState) {\n        if (this.mergeMode == null) {\n          return output.concat(states);\n        }\n        return [output].concat(states);\n      }\n      return output;\n    });\n  }\n  resetStates(states) {\n    this.forwardLayer.resetStates();\n    this.backwardLayer.resetStates();\n  }\n  build(inputShape) {\n    nameScope(this.forwardLayer.name, () => {\n      this.forwardLayer.build(inputShape);\n    });\n    nameScope(this.backwardLayer.name, () => {\n      this.backwardLayer.build(inputShape);\n    });\n    this.built = true;\n  }\n  computeMask(inputs, mask) {\n    if (Array.isArray(mask)) {\n      mask = mask[0];\n    }\n    let outputMask;\n    if (this.returnSequences) {\n      if (this.mergeMode == null) {\n        outputMask = [mask, mask];\n      } else {\n        outputMask = mask;\n      }\n    } else {\n      if (this.mergeMode == null) {\n        outputMask = [null, null];\n      } else {\n        outputMask = null;\n      }\n    }\n    if (this.returnState) {\n      const states = this.forwardLayer.states;\n      const stateMask = states.map((state) => null);\n      if (Array.isArray(outputMask)) {\n        return outputMask.concat(stateMask).concat(stateMask);\n      } else {\n        return [outputMask].concat(stateMask).concat(stateMask);\n      }\n    } else {\n      return outputMask;\n    }\n  }\n  get trainableWeights() {\n    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n  }\n  get nonTrainableWeights() {\n    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.forwardLayer != null) {\n      this.forwardLayer.setFastWeightInitDuringBuild(value);\n    }\n    if (this.backwardLayer != null) {\n      this.backwardLayer.setFastWeightInitDuringBuild(value);\n    }\n  }\n  getConfig() {\n    const config = {\n      \"mergeMode\": this.mergeMode\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  static fromConfig(cls, config) {\n    const rnnLayer = deserialize(config[\"layer\"]);\n    delete config[\"layer\"];\n    if (config[\"numConstants\"] != null) {\n      throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);\n    }\n    const newConfig = config;\n    newConfig[\"layer\"] = rnnLayer;\n    return new cls(newConfig);\n  }\n};\nBidirectional.className = \"Bidirectional\";\nserialization_exports.registerClass(Bidirectional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nfunction inputLayer(args) {\n  return new InputLayer(args);\n}\nfunction elu3(args) {\n  return new ELU(args);\n}\nfunction reLU(args) {\n  return new ReLU(args);\n}\nfunction leakyReLU(args) {\n  return new LeakyReLU(args);\n}\nfunction prelu2(args) {\n  return new PReLU(args);\n}\nfunction softmax2(args) {\n  return new Softmax3(args);\n}\nfunction thresholdedReLU(args) {\n  return new ThresholdedReLU(args);\n}\nfunction conv1d2(args) {\n  return new Conv1D(args);\n}\nfunction conv2d3(args) {\n  return new Conv2D2(args);\n}\nfunction conv2dTranspose2(args) {\n  return new Conv2DTranspose(args);\n}\nfunction conv3d2(args) {\n  return new Conv3D2(args);\n}\nfunction conv3dTranspose2(args) {\n  return new Conv3DTranspose(args);\n}\nfunction separableConv2d2(args) {\n  return new SeparableConv2D(args);\n}\nfunction cropping2D(args) {\n  return new Cropping2D(args);\n}\nfunction upSampling2d(args) {\n  return new UpSampling2D(args);\n}\nfunction depthwiseConv2d4(args) {\n  return new DepthwiseConv2D(args);\n}\nfunction activation(args) {\n  return new Activation2(args);\n}\nfunction dense(args) {\n  return new Dense(args);\n}\nfunction dropout3(args) {\n  return new Dropout(args);\n}\nfunction spatialDropout1d(args) {\n  return new SpatialDropout1D(args);\n}\nfunction flatten3(args) {\n  return new Flatten(args);\n}\nfunction repeatVector(args) {\n  return new RepeatVector(args);\n}\nfunction reshape2(args) {\n  return new Reshape2(args);\n}\nfunction permute(args) {\n  return new Permute(args);\n}\nfunction embedding(args) {\n  return new Embedding(args);\n}\nfunction add3(args) {\n  return new Add2(args);\n}\nfunction average(args) {\n  return new Average(args);\n}\nfunction concatenate2(args) {\n  return new Concatenate(args);\n}\nfunction maximum2(args) {\n  return new Maximum2(args);\n}\nfunction minimum2(args) {\n  return new Minimum2(args);\n}\nfunction multiply(args) {\n  return new Multiply2(args);\n}\nfunction dot3(args) {\n  return new Dot(args);\n}\nfunction batchNormalization2(args) {\n  return new BatchNormalization(args);\n}\nfunction layerNormalization(args) {\n  return new LayerNormalization(args);\n}\nfunction zeroPadding2d(args) {\n  return new ZeroPadding2D(args);\n}\nfunction averagePooling1d(args) {\n  return new AveragePooling1D(args);\n}\nfunction avgPool1d(args) {\n  return averagePooling1d(args);\n}\nfunction avgPooling1d(args) {\n  return averagePooling1d(args);\n}\nfunction averagePooling2d(args) {\n  return new AveragePooling2D(args);\n}\nfunction avgPool2d(args) {\n  return averagePooling2d(args);\n}\nfunction avgPooling2d(args) {\n  return averagePooling2d(args);\n}\nfunction averagePooling3d(args) {\n  return new AveragePooling3D(args);\n}\nfunction avgPool3d2(args) {\n  return averagePooling3d(args);\n}\nfunction avgPooling3d(args) {\n  return averagePooling3d(args);\n}\nfunction globalAveragePooling1d(args) {\n  return new GlobalAveragePooling1D(args);\n}\nfunction globalAveragePooling2d(args) {\n  return new GlobalAveragePooling2D(args);\n}\nfunction globalMaxPooling1d(args) {\n  return new GlobalMaxPooling1D(args);\n}\nfunction globalMaxPooling2d(args) {\n  return new GlobalMaxPooling2D(args);\n}\nfunction maxPooling1d(args) {\n  return new MaxPooling1D(args);\n}\nfunction maxPooling2d(args) {\n  return new MaxPooling2D(args);\n}\nfunction maxPooling3d(args) {\n  return new MaxPooling3D(args);\n}\nfunction gru(args) {\n  return new GRU(args);\n}\nfunction gruCell(args) {\n  return new GRUCell(args);\n}\nfunction lstm(args) {\n  return new LSTM(args);\n}\nfunction lstmCell(args) {\n  return new LSTMCell(args);\n}\nfunction simpleRNN(args) {\n  return new SimpleRNN(args);\n}\nfunction simpleRNNCell(args) {\n  return new SimpleRNNCell(args);\n}\nfunction convLstm2d(args) {\n  return new ConvLSTM2D(args);\n}\nfunction convLstm2dCell(args) {\n  return new ConvLSTM2DCell(args);\n}\nfunction rnn2(args) {\n  return new RNN(args);\n}\nfunction stackedRNNCells(args) {\n  return new StackedRNNCells(args);\n}\nfunction bidirectional(args) {\n  return new Bidirectional(args);\n}\nfunction timeDistributed(args) {\n  return new TimeDistributed(args);\n}\nvar globalMaxPool1d = globalMaxPooling1d;\nvar globalMaxPool2d = globalMaxPooling2d;\nvar maxPool1d = maxPooling1d;\nvar maxPool2d = maxPooling2d;\nfunction gaussianNoise(args) {\n  return new GaussianNoise(args);\n}\nfunction gaussianDropout(args) {\n  return new GaussianDropout(args);\n}\nfunction alphaDropout(args) {\n  return new AlphaDropout(args);\n}\nfunction masking(args) {\n  return new Masking(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js\nvar exports_metrics_exports = {};\n__export(exports_metrics_exports, {\n  MAPE: () => MAPE2,\n  MSE: () => MSE2,\n  binaryAccuracy: () => binaryAccuracy2,\n  binaryCrossentropy: () => binaryCrossentropy3,\n  categoricalAccuracy: () => categoricalAccuracy2,\n  categoricalCrossentropy: () => categoricalCrossentropy3,\n  cosineProximity: () => cosineProximity2,\n  mape: () => mape2,\n  meanAbsoluteError: () => meanAbsoluteError2,\n  meanAbsolutePercentageError: () => meanAbsolutePercentageError2,\n  meanSquaredError: () => meanSquaredError3,\n  mse: () => mse2,\n  precision: () => precision2,\n  recall: () => recall2,\n  sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2\n});\nfunction binaryAccuracy2(yTrue, yPred) {\n  return binaryAccuracy(yTrue, yPred);\n}\nfunction binaryCrossentropy3(yTrue, yPred) {\n  return binaryCrossentropy2(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy2(yTrue, yPred) {\n  return sparseCategoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalAccuracy2(yTrue, yPred) {\n  return categoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalCrossentropy3(yTrue, yPred) {\n  return categoricalCrossentropy2(yTrue, yPred);\n}\nfunction precision2(yTrue, yPred) {\n  return precision(yTrue, yPred);\n}\nfunction recall2(yTrue, yPred) {\n  return recall(yTrue, yPred);\n}\nfunction cosineProximity2(yTrue, yPred) {\n  return cosineProximity(yTrue, yPred);\n}\nfunction meanAbsoluteError2(yTrue, yPred) {\n  return meanAbsoluteError(yTrue, yPred);\n}\nfunction meanAbsolutePercentageError2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction MAPE2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction mape2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction meanSquaredError3(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\nfunction MSE2(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\nfunction mse2(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js\nvar exports_models_exports = {};\n__export(exports_models_exports, {\n  modelFromJSON: () => modelFromJSON\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js\nvar exports_regularizers_exports = {};\n__export(exports_regularizers_exports, {\n  l1: () => l12,\n  l1l2: () => l1l2,\n  l2: () => l22\n});\nfunction l1l2(config) {\n  return new L1L2(config);\n}\nfunction l12(config) {\n  return l1(config);\n}\nfunction l22(config) {\n  return l2(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js\nvar Callback = class extends BaseCallback {\n  constructor() {\n    super(...arguments);\n    this.model = null;\n  }\n  setModel(model2) {\n    if (!(model2 instanceof LayersModel)) {\n      throw new Error(\"model must be a LayersModel, not some other Container\");\n    }\n    this.model = model2;\n  }\n};\nfunction less2(currVal, prevVal) {\n  return currVal < prevVal;\n}\nfunction greater2(currVal, prevVal) {\n  return currVal > prevVal;\n}\nvar EarlyStopping = class extends Callback {\n  constructor(args) {\n    super();\n    if (args == null) {\n      args = {};\n    }\n    if (args.restoreBestWeights) {\n      throw new NotImplementedError(\"restoreBestWeights = True is not implemented in EarlyStopping yet.\");\n    }\n    this.monitor = args.monitor || \"val_loss\";\n    this.minDelta = Math.abs(args.minDelta || 0);\n    this.patience = args.patience || 0;\n    this.verbose = args.verbose || 0;\n    this.mode = args.mode || \"auto\";\n    this.baseline = args.baseline;\n    if ([\"auto\", \"min\", \"max\"].indexOf(this.mode) === -1) {\n      console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);\n      this.mode = \"auto\";\n    }\n    if (this.mode === \"min\") {\n      this.monitorFunc = less2;\n    } else if (this.mode === \"max\") {\n      this.monitorFunc = greater2;\n    } else {\n      if (this.monitor.indexOf(\"acc\") !== -1) {\n        this.monitorFunc = greater2;\n      } else {\n        this.monitorFunc = less2;\n      }\n    }\n    if (this.monitorFunc === less2) {\n      this.minDelta *= -1;\n    }\n  }\n  async onTrainBegin(logs) {\n    this.wait = 0;\n    this.stoppedEpoch = 0;\n    if (this.baseline != null) {\n      this.best = this.baseline;\n    } else {\n      this.best = this.monitorFunc === less2 ? Infinity : -Infinity;\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    await resolveScalarsInLogs(logs);\n    const current = this.getMonitorValue(logs);\n    if (current == null) {\n      return;\n    }\n    if (this.monitorFunc(current - this.minDelta, this.best)) {\n      this.best = current;\n      this.wait = 0;\n    } else {\n      this.wait++;\n      if (this.wait >= this.patience) {\n        this.stoppedEpoch = epoch;\n        this.model.stopTraining = true;\n      }\n    }\n  }\n  async onTrainEnd(logs) {\n    if (this.stoppedEpoch > 0 && this.verbose) {\n      console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n    }\n  }\n  getMonitorValue(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    const monitorValue = logs[this.monitor];\n    if (monitorValue == null) {\n      console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);\n    }\n    return monitorValue;\n  }\n};\nfunction earlyStopping(args) {\n  return new EarlyStopping(args);\n}\nvar callbacks = { earlyStopping };\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/flags.js\nvar ENV4 = env();\nENV4.registerFlag(\"KEEP_INTERMEDIATE_TENSORS\", () => false, (debugValue) => {\n  if (debugValue) {\n    console.warn(\"Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.\");\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js\nvar DataType;\n(function(DataType2) {\n  DataType2[DataType2[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n  DataType2[DataType2[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n  DataType2[DataType2[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n  DataType2[DataType2[\"DT_INT32\"] = 3] = \"DT_INT32\";\n  DataType2[DataType2[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n  DataType2[DataType2[\"DT_INT16\"] = 5] = \"DT_INT16\";\n  DataType2[DataType2[\"DT_INT8\"] = 6] = \"DT_INT8\";\n  DataType2[DataType2[\"DT_STRING\"] = 7] = \"DT_STRING\";\n  DataType2[DataType2[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n  DataType2[DataType2[\"DT_INT64\"] = 9] = \"DT_INT64\";\n  DataType2[DataType2[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n  DataType2[DataType2[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n  DataType2[DataType2[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n  DataType2[DataType2[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n  DataType2[DataType2[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n  DataType2[DataType2[\"DT_QINT16\"] = 15] = \"DT_QINT16\";\n  DataType2[DataType2[\"DT_QUINT16\"] = 16] = \"DT_QUINT16\";\n  DataType2[DataType2[\"DT_UINT16\"] = 17] = \"DT_UINT16\";\n  DataType2[DataType2[\"DT_COMPLEX128\"] = 18] = \"DT_COMPLEX128\";\n  DataType2[DataType2[\"DT_HALF\"] = 19] = \"DT_HALF\";\n  DataType2[DataType2[\"DT_RESOURCE\"] = 20] = \"DT_RESOURCE\";\n  DataType2[DataType2[\"DT_VARIANT\"] = 21] = \"DT_VARIANT\";\n  DataType2[DataType2[\"DT_UINT32\"] = 22] = \"DT_UINT32\";\n  DataType2[DataType2[\"DT_UINT64\"] = 23] = \"DT_UINT64\";\n  DataType2[DataType2[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n  DataType2[DataType2[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n  DataType2[DataType2[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n  DataType2[DataType2[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n  DataType2[DataType2[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n  DataType2[DataType2[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n  DataType2[DataType2[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n  DataType2[DataType2[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n  DataType2[DataType2[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n  DataType2[DataType2[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n  DataType2[DataType2[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n  DataType2[DataType2[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n  DataType2[DataType2[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n  DataType2[DataType2[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n  DataType2[DataType2[\"DT_QINT16_REF\"] = 115] = \"DT_QINT16_REF\";\n  DataType2[DataType2[\"DT_QUINT16_REF\"] = 116] = \"DT_QUINT16_REF\";\n  DataType2[DataType2[\"DT_UINT16_REF\"] = 117] = \"DT_UINT16_REF\";\n  DataType2[DataType2[\"DT_COMPLEX128_REF\"] = 118] = \"DT_COMPLEX128_REF\";\n  DataType2[DataType2[\"DT_HALF_REF\"] = 119] = \"DT_HALF_REF\";\n  DataType2[DataType2[\"DT_RESOURCE_REF\"] = 120] = \"DT_RESOURCE_REF\";\n  DataType2[DataType2[\"DT_VARIANT_REF\"] = 121] = \"DT_VARIANT_REF\";\n  DataType2[DataType2[\"DT_UINT32_REF\"] = 122] = \"DT_UINT32_REF\";\n  DataType2[DataType2[\"DT_UINT64_REF\"] = 123] = \"DT_UINT64_REF\";\n})(DataType || (DataType = {}));\nvar SaverDef;\n(function(SaverDef2) {\n  let CheckpointFormatVersion;\n  (function(CheckpointFormatVersion2) {\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"LEGACY\"] = 0] = \"LEGACY\";\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"V1\"] = 1] = \"V1\";\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"V2\"] = 2] = \"V2\";\n  })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js\nvar CUSTOM_OPS = {};\nfunction registerOp(name, opFunc) {\n  const opMapper = {\n    tfOpName: name,\n    category: \"custom\",\n    inputs: [],\n    attrs: [],\n    customExecutor: opFunc\n  };\n  CUSTOM_OPS[name] = opMapper;\n}\nfunction getRegisteredOp(name) {\n  return CUSTOM_OPS[name];\n}\nfunction deregisterOp(name) {\n  delete CUSTOM_OPS[name];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js\nfunction getParamValue(paramName, node, tensorMap, context, resourceManager) {\n  const inputParam = node.inputParams[paramName];\n  if (inputParam && inputParam.inputIndexStart !== void 0) {\n    const start = inputParam.inputIndexStart;\n    const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;\n    if (inputParam.type === \"tensor\") {\n      return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);\n    }\n    if (inputParam.type === \"tensors\") {\n      const inputs = node.inputNames.slice(start, end);\n      return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));\n    }\n    const tensor2 = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n    const data = tensor2.dataSync();\n    return inputParam.type === \"number\" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);\n  }\n  const attrParam = node.attrParams[paramName];\n  return attrParam && attrParam.value;\n}\nfunction getTensor(name, tensorsMap, context, resourceManager) {\n  const [nodeName, index] = parseNodeName(name);\n  if (resourceManager != null) {\n    const tensor2 = resourceManager.getHashTableHandleByName(nodeName);\n    if (tensor2 != null) {\n      return tensor2;\n    }\n  }\n  const contextId = context.currentContextIds.find((contextId2) => {\n    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];\n  });\n  return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;\n}\nfunction getTensorsForCurrentContenxt(name, tensorsMap, context) {\n  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\nfunction getNodeNameAndIndex(inputName, context) {\n  const [nodeName, index, outputName] = parseNodeName(inputName);\n  return [\n    getNodeNameWithContextId(nodeName, context && context.currentContextId),\n    index,\n    outputName\n  ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n  return !!contextId ? `${name}-${contextId}` : name;\n}\nfunction parseNodeName(name) {\n  const parts = name.split(\":\");\n  if (parts.length === 1) {\n    return [name, 0, void 0];\n  }\n  const nodeName = parts[0];\n  const outputName = parts.length === 3 ? parts[1] : void 0;\n  const index = Number(parts[parts.length - 1]);\n  return [nodeName, index, outputName];\n}\nfunction getPadding(node, tensorMap, context) {\n  let pad3 = getParamValue(\"pad\", node, tensorMap, context);\n  if (pad3 === \"explicit\") {\n    pad3 = getParamValue(\"explicitPaddings\", node, tensorMap, context);\n    const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n    for (let i2 = 0; i2 < 4; i2++) {\n      explicitPadding[i2][0] = pad3[i2 * 2];\n      explicitPadding[i2][1] = pad3[i2 * 2 + 1];\n    }\n    return explicitPadding;\n  }\n  return pad3;\n}\nfunction cloneTensor(tensor2) {\n  return tensor2.kept ? tensor2 : clone(tensor2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js\nvar arithmetic_exports = {};\n__export(arithmetic_exports, {\n  json: () => json\n});\nvar json = [\n  {\n    \"tfOpName\": \"Add\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AddV2\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AddN\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BiasAdd\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sub\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RealDiv\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Div\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DivNoNan\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FloorDiv\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mul\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Maximum\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Minimum\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pow\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SquaredDifference\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mod\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FloorMod\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js\nvar basic_math_exports = {};\n__export(basic_math_exports, {\n  json: () => json2\n});\nvar json2 = [\n  {\n    \"tfOpName\": \"Abs\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Acos\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Asin\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atan2\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"y\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Ceil\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ClipByValue\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"clipValueMin\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"clipValueMax\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Complex\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"real\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"imag\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ComplexAbs\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cos\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cosh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Elu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Exp\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Floor\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Log\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Imag\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"outputType\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Neg\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Real\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"outputType\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prelu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"alpha\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Relu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Relu6\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Selu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sigmoid\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sin\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sinh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sqrt\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Rsqrt\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Square\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tanh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sign\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Round\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Expm1\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Log1p\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reciprocal\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Softplus\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Asinh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Acosh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atanh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Erf\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prod\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axes\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LeakyRelu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"alpha\",\n        \"name\": \"alpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IsNan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js\nvar control_exports = {};\n__export(control_exports, {\n  json: () => json3\n});\nvar json3 = [\n  {\n    \"tfOpName\": \"EmptyTensorList\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"maxNumElements\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LoopCond\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"pred\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Switch\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"pred\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Merge\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Enter\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"frame_name\",\n        \"name\": \"frameName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"is_constant\",\n        \"name\": \"isConstant\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Exit\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NextIteration\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorArrayV3\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dynamic_size\",\n        \"name\": \"dynamicSize\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"clear_after_read\",\n        \"name\": \"clearAfterRead\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"identical_element_shapes\",\n        \"name\": \"identicalElementShapes\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"tensor_array_name\",\n        \"name\": \"name\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorArrayWriteV3\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorArrayId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"index\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"flowIn\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorArrayReadV3\",\n    \"category\": \"control\",\n    \"inputs\": 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\"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListConcatV2\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListPopBack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListPushBack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListLength\",\n    \"category\": \"control\",\n    \"inputs\": [\n     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     \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPool\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [],\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPoolWithArgmax\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"include_batch_in_index\",\n        \"name\": \"includeBatchInIndex\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AvgPool3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPool3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv1D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"stride\",\n        \"name\": \"stride\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NWC\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dilation\",\n        \"name\": \"dilation\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"useCudnnOnGpu\",\n        \"name\": \"useCudnnOnGpu\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"_FusedConv2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"use_cudnn_on_gpu\",\n        \"name\": \"useCudnnOnGpu\",\n        \"type\": \"bool\",\n        \"defaultValue\": true\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-4\n      },\n      {\n        \"tfName\": \"leakyrelu_alpha\",\n        \"name\": \"leakyreluAlpha\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv2DBackpropInput\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 2,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 0,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthwiseConv2d\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthwiseConv2dNative\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedDepthwiseConv2dNative\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Dilation2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"rates\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js\nvar creation_exports = {};\n__export(creation_exports, {\n  json: () => json5\n});\nvar json5 = [\n  {\n    \"tfOpName\": \"Fill\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"value\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LinSpace\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"start\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"stop\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"num\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"OneHot\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"depth\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"onValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"start\": 3,\n        \"name\": \"offValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Ones\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"OnesLike\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RandomUniform\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"minval\",\n        \"name\": \"minval\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"maxval\",\n        \"name\": \"maxval\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Range\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"start\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"stop\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"step\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tidx\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TruncatedNormal\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"means\",\n        \"name\": \"mean\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"stddev\",\n        \"name\": \"stdDev\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Zeros\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ZerosLike\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Multinomial\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"logits\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numSamples\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"output_dtype\",\n        \"name\": \"output_dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js\nvar dynamic_exports = {};\n__export(dynamic_exports, {\n  json: () => json6\n});\nvar json6 = [\n  {\n    \"tfOpName\": \"NonMaxSuppressionV2\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV3\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV4\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T_threshold\",\n        \"name\": \"threshold\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"pad_to_max_output_size\",\n        \"name\": \"padToMaxOutputSize\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV5\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 5,\n        \"name\": \"softNmsSigma\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Where\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ListDiff\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"y\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js\nvar evaluation_exports = {};\n__export(evaluation_exports, {\n  json: () => json7\n});\nvar json7 = [\n  {\n    \"tfOpName\": \"LowerBound\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sortedSequence\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TopKV2\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"k\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"sorted\",\n        \"name\": \"sorted\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"UpperBound\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sortedSequence\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Unique\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"UniqueV2\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js\nvar graph_exports = {};\n__export(graph_exports, {\n  json: () => json8\n});\nvar json8 = [\n  {\n    \"tfOpName\": \"PlaceholderWithDefault\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"default\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"shape\",\n        \"name\": \"shape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Placeholder\",\n    \"category\": \"graph\",\n    \"attrs\": [\n      {\n        \"tfName\": \"shape\",\n        \"name\": \"shape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Const\",\n    \"category\": \"graph\"\n  },\n  {\n    \"tfOpName\": \"Identity\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IdentityN\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Snapshot\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Rank\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Size\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Shape\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ShapeN\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Print\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"data\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"message\",\n        \"name\": \"message\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"first_n\",\n        \"name\": \"firstN\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"summarize\",\n        \"name\": \"summarize\",\n        \"type\": \"number\",\n        \"defaultValue\": 3\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NoOp\",\n    \"category\": \"graph\",\n    \"inputs\": []\n  },\n  {\n    \"tfOpName\": \"StopGradient\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FakeQuantWithMinMaxVars\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"min\",\n        \"name\": \"min\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"max\",\n        \"name\": \"max\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js\nvar hash_table_exports = {};\n__export(hash_table_exports, {\n  json: () => json9\n});\nvar json9 = [\n  {\n    \"tfOpName\": \"HashTable\",\n    \"category\": \"hash_table\",\n    \"inputs\": [],\n    \"attrs\": [\n      {\n        \"tfName\": \"shared_name\",\n        \"name\": \"sharedName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"use_node_name_sharing\",\n        \"name\": \"useNodeNameSharing\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"key_dtype\",\n        \"name\": \"keyDType\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"value_dtype\",\n        \"name\": \"valueDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"HashTableV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [],\n    \"attrs\": [\n      {\n        \"tfName\": \"shared_name\",\n        \"name\": \"sharedName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"use_node_name_sharing\",\n        \"name\": \"useNodeNameSharing\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"key_dtype\",\n        \"name\": \"keyDType\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"value_dtype\",\n        \"name\": \"valueDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableImport\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableImportV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableFind\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableFindV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableSize\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableSizeV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js\nvar image_exports = {};\n__export(image_exports, {\n  json: () => json10\n});\nvar json10 = [\n  {\n    \"tfOpName\": \"ResizeBilinear\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"align_corners\",\n        \"name\": \"alignCorners\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"half_pixel_centers\",\n        \"name\": \"halfPixelCenters\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ResizeNearestNeighbor\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"align_corners\",\n        \"name\": \"alignCorners\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"half_pixel_centers\",\n        \"name\": \"halfPixelCenters\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"CropAndResize\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"image\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"boxInd\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"cropSize\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"method\",\n        \"name\": \"method\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"extrapolation_value\",\n        \"name\": \"extrapolationValue\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ImageProjectiveTransformV3\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"transforms\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"fillValue\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"interpolation\",\n        \"name\": \"interpolation\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"fill_mode\",\n        \"name\": \"fillMode\",\n        \"type\": \"string\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js\nvar logical_exports = {};\n__export(logical_exports, {\n  json: () => json11\n});\nvar json11 = [\n  {\n    \"tfOpName\": \"Equal\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NotEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Greater\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GreaterEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Less\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LessEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalAnd\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalNot\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalOr\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Select\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SelectV2\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js\nvar matrices_exports = {};\n__export(matrices_exports, {\n  json: () => json12\n});\nvar json12 = [\n  {\n    \"tfOpName\": \"_FusedMatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-4\n      },\n      {\n        \"tfName\": \"transpose_a\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"transpose_b\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"transpose_a\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"transpose_b\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchMatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"adj_x\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"adj_y\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchMatMulV2\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"adj_x\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"adj_y\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Transpose\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"perm\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Einsum\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"equation\",\n        \"name\": \"equation\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js\nvar normalization_exports = {};\n__export(normalization_exports, {\n  json: () => json13\n});\nvar json13 = [\n  {\n    \"tfOpName\": \"EuclideanNorm\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNorm\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNormV2\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNormV3\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LRN\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"depth_radius\",\n        \"name\": \"radius\",\n        \"type\": \"number\",\n        \"defaultValue\": 5\n      },\n      {\n        \"tfName\": \"bias\",\n        \"name\": \"bias\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"alpha\",\n        \"name\": \"alpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"beta\",\n        \"name\": \"beta\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.5\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Softmax\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogSoftmax\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseToDense\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sparseIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"sparseValues\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"defaultValue\": true,\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js\nvar reduction_exports = {};\n__export(reduction_exports, {\n  json: () => json14\n});\nvar json14 = [\n  {\n    \"tfOpName\": \"Bincount\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"weights\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DenseBincount\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"weights\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"binary_output\",\n        \"name\": \"binaryOutput\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Max\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mean\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Min\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sum\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"All\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Any\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ArgMax\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ArgMin\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prod\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cumprod\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"exclusive\",\n        \"name\": \"exclusive\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"reverse\",\n        \"name\": \"reverse\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cumsum\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"exclusive\",\n        \"name\": \"exclusive\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"reverse\",\n        \"name\": \"reverse\",\n        \"type\": \"bool\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js\nvar slice_join_exports = {};\n__export(slice_join_exports, {\n  json: () => json15\n});\nvar json15 = [\n  {\n    \"tfOpName\": \"ConcatV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": -1,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      },\n      {\n        \"start\": -1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Concat\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 1,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      },\n      {\n        \"start\": 0,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GatherV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"batch_dims\",\n        \"name\": \"batchDims\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Gather\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reverse\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"dims\",\n        \"type\": \"bool[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ReverseV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Slice\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"begin\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"StridedSlice\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"begin\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"end\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"begin_mask\",\n        \"name\": \"beginMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"end_mask\",\n        \"name\": \"endMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"new_axis_mask\",\n        \"name\": \"newAxisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"ellipsis_mask\",\n        \"name\": \"ellipsisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"shrink_axis_mask\",\n        \"name\": \"shrinkAxisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pack\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Unpack\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"num\",\n        \"name\": \"num\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tile\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"reps\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Split\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"start\": 1,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_split\",\n        \"name\": \"numOrSizeSplits\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SplitV\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numOrSizeSplits\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ScatterNd\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GatherNd\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseToDense\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sparseIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"sparseValues\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"defaultValue\": false,\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js\nvar sparse_exports = {};\n__export(sparse_exports, {\n  json: () => json16\n});\nvar json16 = [\n  {\n    \"tfOpName\": \"SparseFillEmptyRows\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"denseShape\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseReshape\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"inputIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"inputShape\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"newShape\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseSegmentMean\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"segmentIds\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseSegmentSum\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"segmentIds\",\n        \"type\": \"tensor\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js\nvar spectral_exports = {};\n__export(spectral_exports, {\n  json: () => json17\n});\nvar json17 = [\n  {\n    \"tfOpName\": \"FFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"fft_length\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IRFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"fft_length\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js\nvar string_exports = {};\n__export(string_exports, {\n  json: () => json18\n});\nvar json18 = [\n  {\n    \"tfOpName\": \"StringNGrams\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"dataSplits\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"separator\",\n        \"name\": \"separator\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"ngram_widths\",\n        \"name\": \"nGramWidths\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"left_pad\",\n        \"name\": \"leftPad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"right_pad\",\n        \"name\": \"rightPad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"pad_width\",\n        \"name\": \"padWidth\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"preserve_short_sequences\",\n        \"name\": \"preserveShortSequences\",\n        \"type\": \"bool\"\n      }\n    ],\n    \"outputs\": [\n      \"ngrams\",\n      \"ngrams_splits\"\n    ]\n  },\n  {\n    \"tfOpName\": \"StringSplit\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"delimiter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"skip_empty\",\n        \"name\": \"skipEmpty\",\n        \"type\": \"bool\"\n      }\n    ],\n    \"outputs\": [\n      \"indices\",\n      \"values\",\n      \"shape\"\n    ]\n  },\n  {\n    \"tfOpName\": \"StringToHashBucketFast\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_buckets\",\n        \"name\": \"numBuckets\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js\nvar transformation_exports = {};\n__export(transformation_exports, {\n  json: () => json19\n});\nvar json19 = [\n  {\n    \"tfOpName\": \"Cast\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"SrcT\",\n        \"name\": \"sdtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"DstT\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ExpandDims\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MirrorPad\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"mode\",\n        \"name\": \"mode\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pad\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"constant_value\",\n        \"name\": \"constantValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"PadV2\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"constantValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reshape\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Squeeze\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"tfDeprecatedName\": \"squeeze_dims\",\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SpaceToBatchND\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"blockShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"paddings\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchToSpaceND\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"blockShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"crops\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthToSpace\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"block_size\",\n        \"name\": \"blockSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BroadcastTo\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": []\n  },\n  {\n    \"tfOpName\": \"BroadcastArgs\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"s0\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"s1\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": []\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js\nvar OperationMapper = class {\n  static get Instance() {\n    return this._instance || (this._instance = new this());\n  }\n  constructor() {\n    const ops = [\n      arithmetic_exports,\n      basic_math_exports,\n      control_exports,\n      convolution_exports,\n      creation_exports,\n      dynamic_exports,\n      evaluation_exports,\n      graph_exports,\n      hash_table_exports,\n      image_exports,\n      logical_exports,\n      matrices_exports,\n      normalization_exports,\n      reduction_exports,\n      slice_join_exports,\n      sparse_exports,\n      spectral_exports,\n      string_exports,\n      transformation_exports\n    ];\n    const mappersJson = [].concat(...ops.map((op2) => op2.json));\n    this.opMappers = mappersJson.reduce((map, mapper) => {\n      map[mapper.tfOpName] = mapper;\n      return map;\n    }, {});\n  }\n  transformGraph(graph, signature = {}) {\n    const tfNodes = graph.node;\n    const placeholders = [];\n    const weights = [];\n    const initNodes = [];\n    const nodes = tfNodes.reduce((map, node) => {\n      map[node.name] = this.mapNode(node);\n      if (node.op.startsWith(\"Placeholder\")) {\n        placeholders.push(map[node.name]);\n      } else if (node.op === \"Const\") {\n        weights.push(map[node.name]);\n      } else if (node.input == null || node.input.length === 0) {\n        initNodes.push(map[node.name]);\n      }\n      return map;\n    }, {});\n    let inputs = [];\n    const outputs = [];\n    let inputNodeNameToKey = {};\n    let outputNodeNameToKey = {};\n    if (signature != null) {\n      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n    }\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach((key) => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n    if (Object.keys(outputNodeNameToKey).length === 0) {\n      allNodes.forEach((key) => {\n        const node = nodes[key];\n        if (node.children.length === 0) {\n          outputs.push(node);\n        }\n      });\n    } else {\n      Object.keys(outputNodeNameToKey).forEach((name) => {\n        const [nodeName] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node != null) {\n          node.signatureKey = outputNodeNameToKey[name];\n          outputs.push(node);\n        }\n      });\n    }\n    if (Object.keys(inputNodeNameToKey).length > 0) {\n      Object.keys(inputNodeNameToKey).forEach((name) => {\n        const [nodeName] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node) {\n          node.signatureKey = inputNodeNameToKey[name];\n          inputs.push(node);\n        }\n      });\n    } else {\n      inputs = placeholders;\n    }\n    let functions = {};\n    if (graph.library != null && graph.library.function != null) {\n      functions = graph.library.function.reduce((functions2, func2) => {\n        functions2[func2.signature.name] = this.mapFunction(func2);\n        return functions2;\n      }, {});\n    }\n    const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n    if (initNodes.length > 0) {\n      result.initNodes = initNodes;\n    }\n    return result;\n  }\n  mapSignatureEntries(entries) {\n    return Object.keys(entries || {}).reduce((prev, curr) => {\n      prev[entries[curr].name] = curr;\n      return prev;\n    }, {});\n  }\n  mapNode(node) {\n    const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n    if (node.attr == null) {\n      node.attr = {};\n    }\n    const newNode = {\n      name: node.name,\n      op: node.op,\n      category: mapper.category,\n      inputNames: (node.input || []).map((input2) => input2.startsWith(\"^\") ? input2.slice(1) : input2),\n      inputs: [],\n      children: [],\n      inputParams: {},\n      attrParams: {},\n      rawAttrs: node.attr,\n      outputs: mapper.outputs\n    };\n    if (mapper.inputs != null) {\n      newNode.inputParams = mapper.inputs.reduce((map, param) => {\n        map[param.name] = {\n          type: param.type,\n          inputIndexStart: param.start,\n          inputIndexEnd: param.end\n        };\n        return map;\n      }, {});\n    }\n    if (mapper.attrs != null) {\n      newNode.attrParams = mapper.attrs.reduce((map, param) => {\n        const type = param.type;\n        let value = void 0;\n        switch (param.type) {\n          case \"string\":\n            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"string[]\":\n            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"number\":\n            value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"number[]\":\n            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"bool\":\n            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"bool[]\":\n            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"shape\":\n            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"shape[]\":\n            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"dtype\":\n            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"dtype[]\":\n            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"func\":\n            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"tensor\":\n          case \"tensors\":\n            break;\n          default:\n            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n        }\n        map[param.name] = { value, type };\n        return map;\n      }, {});\n    }\n    return newNode;\n  }\n  mapFunction(functionDef) {\n    const tfNodes = functionDef.nodeDef;\n    const placeholders = [];\n    const weights = [];\n    let nodes = {};\n    if (tfNodes != null) {\n      nodes = tfNodes.reduce((map, node) => {\n        map[node.name] = this.mapNode(node);\n        if (node.op === \"Const\") {\n          weights.push(map[node.name]);\n        }\n        return map;\n      }, {});\n    }\n    const inputs = [];\n    const outputs = [];\n    functionDef.signature.inputArg.forEach((arg) => {\n      const [nodeName] = getNodeNameAndIndex(arg.name);\n      const node = {\n        name: nodeName,\n        op: \"Placeholder\",\n        inputs: [],\n        inputNames: [],\n        category: \"graph\",\n        inputParams: {},\n        attrParams: { dtype: { value: parseDtypeParam(arg.type), type: \"dtype\" } },\n        children: []\n      };\n      node.signatureKey = arg.name;\n      inputs.push(node);\n      nodes[nodeName] = node;\n    });\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach((key) => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n    const returnNodeMap = functionDef.ret;\n    functionDef.signature.outputArg.forEach((output) => {\n      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n      const node = nodes[nodeName];\n      if (node != null) {\n        node.defaultOutput = index;\n        outputs.push(node);\n      }\n    });\n    const signature = this.mapArgsToSignature(functionDef);\n    return { nodes, inputs, outputs, weights, placeholders, signature };\n  }\n  mapArgsToSignature(functionDef) {\n    return {\n      methodName: functionDef.signature.name,\n      inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n        map[arg.name] = this.mapArgToTensorInfo(arg);\n        return map;\n      }, {}),\n      outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n        map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n        return map;\n      }, {})\n    };\n  }\n  mapArgToTensorInfo(arg, nameMap2) {\n    let name = arg.name;\n    if (nameMap2 != null) {\n      name = nameMap2[name];\n    }\n    return { name, dtype: arg.type };\n  }\n};\nfunction decodeBase64(text) {\n  const global2 = env().global;\n  if (typeof global2.atob !== \"undefined\") {\n    return global2.atob(text);\n  } else if (typeof Buffer !== \"undefined\") {\n    return new Buffer(text, \"base64\").toString();\n  } else {\n    throw new Error(\"Unable to decode base64 in this environment. Missing built-in atob() or Buffer()\");\n  }\n}\nfunction parseStringParam(s2, keepCase) {\n  const value = Array.isArray(s2) ? String.fromCharCode.apply(null, s2) : decodeBase64(s2);\n  return keepCase ? value : value.toLowerCase();\n}\nfunction getStringParam(attrs, name, def, keepCase = false) {\n  const param = attrs[name];\n  if (param != null) {\n    return parseStringParam(param.s, keepCase);\n  }\n  return def;\n}\nfunction getBoolParam(attrs, name, def) {\n  const param = attrs[name];\n  return param ? param.b : def;\n}\nfunction getNumberParam(attrs, name, def) {\n  const param = attrs[name] || {};\n  const value = param[\"i\"] != null ? param[\"i\"] : param[\"f\"] != null ? param[\"f\"] : def;\n  return typeof value === \"number\" ? value : parseInt(value, 10);\n}\nfunction parseDtypeParam(value) {\n  if (typeof value === \"string\") {\n    value = DataType[value];\n  }\n  switch (value) {\n    case DataType.DT_FLOAT:\n    case DataType.DT_HALF:\n      return \"float32\";\n    case DataType.DT_INT32:\n    case DataType.DT_INT64:\n    case DataType.DT_INT8:\n    case DataType.DT_UINT8:\n      return \"int32\";\n    case DataType.DT_BOOL:\n      return \"bool\";\n    case DataType.DT_DOUBLE:\n      return \"float32\";\n    case DataType.DT_STRING:\n      return \"string\";\n    default:\n      return null;\n  }\n}\nfunction getFuncParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.func) {\n    return param.func.name;\n  }\n  return def;\n}\nfunction getDtypeParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.type) {\n    return parseDtypeParam(param.type);\n  }\n  return def;\n}\nfunction getDtypeArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.type) {\n    return param.list.type.map((v) => parseDtypeParam(v));\n  }\n  return def;\n}\nfunction parseTensorShapeParam(shape) {\n  if (shape.unknownRank) {\n    return void 0;\n  }\n  if (shape.dim != null) {\n    return shape.dim.map((dim) => typeof dim.size === \"number\" ? dim.size : parseInt(dim.size, 10));\n  }\n  return [];\n}\nfunction getTensorShapeParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.shape) {\n    return parseTensorShapeParam(param.shape);\n  }\n  return def;\n}\nfunction getNumericArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param) {\n    return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === \"number\" ? v : parseInt(v, 10));\n  }\n  return def;\n}\nfunction getStringArrayParam(attrs, name, def, keepCase = false) {\n  const param = attrs[name];\n  if (param && param.list && param.list.s) {\n    return param.list.s.map((v) => {\n      return parseStringParam(v, keepCase);\n    });\n  }\n  return def;\n}\nfunction getTensorShapeArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.shape) {\n    return param.list.shape.map((v) => {\n      return parseTensorShapeParam(v);\n    });\n  }\n  return def;\n}\nfunction getBoolArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.b) {\n    return param.list.b;\n  }\n  return def;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js\nvar NodeValueImpl = class {\n  constructor(node, tensorMap, context) {\n    this.node = node;\n    this.tensorMap = tensorMap;\n    this.context = context;\n    this.inputs = [];\n    this.attrs = {};\n    this.inputs = node.inputNames.map((name) => this.getInput(name));\n    if (node.rawAttrs != null) {\n      this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => {\n        attrs[key] = this.getAttr(key);\n        return attrs;\n      }, {});\n    }\n  }\n  getInput(name) {\n    return getTensor(name, this.tensorMap, this.context);\n  }\n  getAttr(name, defaultValue) {\n    const value = this.node.rawAttrs[name];\n    if (value.tensor != null) {\n      return getTensor(name, this.tensorMap, this.context);\n    }\n    if (value.i != null || value.f != null) {\n      return getNumberParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.s != null) {\n      return getStringParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.b != null) {\n      return getBoolParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.shape != null) {\n      return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.type != null) {\n      return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.list != null) {\n      if (value.list.i != null || value.list.f != null) {\n        return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.s != null) {\n        return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.shape != null) {\n        return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.b != null) {\n        return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.type != null) {\n        return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n    }\n    return defaultValue;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js\nvar executeOp = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"BiasAdd\":\n    case \"AddV2\":\n    case \"Add\": {\n      return [add2(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"AddN\": {\n      return [addN(getParamValue(\"tensors\", node, tensorMap, context))];\n    }\n    case \"FloorMod\":\n    case \"Mod\":\n      return [mod(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    case \"Mul\":\n      return [mul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    case \"RealDiv\":\n    case \"Div\": {\n      return [div(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"DivNoNan\": {\n      return [divNoNan(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"FloorDiv\": {\n      return [floorDiv(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Sub\": {\n      return [sub(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Minimum\": {\n      return [minimum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Maximum\": {\n      return [maximum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Pow\": {\n      return [pow(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"SquaredDifference\": {\n      return [squaredDifference(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js\nvar executeOp2 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Abs\":\n    case \"ComplexAbs\":\n      return [abs(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Acos\":\n      return [acos(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Acosh\":\n      return [acosh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Asin\":\n      return [asin(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Asinh\":\n      return [asinh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Atan\":\n      return [atan(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Atan2\":\n      return [atan2(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context))];\n    case \"Atanh\":\n      return [atanh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Ceil\":\n      return [ceil(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Complex\":\n      return [complex(getParamValue(\"real\", node, tensorMap, context), getParamValue(\"imag\", node, tensorMap, context))];\n    case \"Cos\":\n      return [cos(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Cosh\":\n      return [cosh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Elu\":\n      return [elu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Erf\":\n      return [erf(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Exp\":\n      return [exp(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Expm1\": {\n      return [expm1(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Floor\":\n      return [floor(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Log\":\n      return [log2(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Log1p\": {\n      return [log1p(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Imag\":\n      return [imag(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Neg\":\n      return [neg(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Reciprocal\": {\n      return [reciprocal(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Real\":\n      return [real(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Relu\":\n      return [relu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Round\": {\n      return [round2(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Selu\":\n      return [selu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sigmoid\":\n      return [sigmoid(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sin\":\n      return [sin(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sign\": {\n      return [sign(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Sinh\": {\n      return [sinh(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Softplus\": {\n      return [softplus(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Sqrt\": {\n      return [sqrt(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Square\": {\n      return [square(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Tanh\": {\n      return [tanh2(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Tan\":\n      return [tan(getParamValue(\"x\", node, tensorMap, context))];\n    case \"ClipByValue\":\n      return [clipByValue(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"clipValueMin\", node, tensorMap, context), getParamValue(\"clipValueMax\", node, tensorMap, context))];\n    case \"Relu6\":\n      return [relu6(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Rsqrt\":\n      return [rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n    case \"Prod\":\n      return [prod(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axes\", node, tensorMap, context))];\n    case \"LeakyRelu\":\n      return [leakyRelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n    case \"Prelu\":\n      return [prelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n    case \"IsNan\":\n      return [isNaN2(getTensor(node.inputNames[0], tensorMap, context))];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js\nfunction assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = \"\") {\n  if (typeof shapeA === \"number\" || typeof shapeB === \"number\") {\n    return;\n  }\n  util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  for (let i2 = 0; i2 < shapeA.length; i2++) {\n    const dim0 = shapeA[i2];\n    const dim1 = shapeB[i2];\n    util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  }\n}\nfunction fullDefinedShape(elementShape) {\n  if (typeof elementShape === \"number\" || elementShape.some((dim) => dim < 0)) {\n    return false;\n  }\n  return true;\n}\nfunction inferElementShape(listElementShape, tensors, elementShape) {\n  let partialShape = mergeElementShape(listElementShape, elementShape);\n  const notfullDefinedShape = !fullDefinedShape(partialShape);\n  if (notfullDefinedShape && tensors.length === 0) {\n    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);\n  }\n  if (notfullDefinedShape) {\n    tensors.forEach((tensor2) => {\n      partialShape = mergeElementShape(tensor2.shape, partialShape);\n    });\n  }\n  if (!fullDefinedShape(partialShape)) {\n    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n  }\n  return partialShape;\n}\nfunction mergeElementShape(elementShapeA, elementShapeB) {\n  if (typeof elementShapeA === \"number\") {\n    return elementShapeB;\n  }\n  if (typeof elementShapeB === \"number\") {\n    return elementShapeA;\n  }\n  if (elementShapeA.length !== elementShapeB.length) {\n    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);\n  }\n  const result = [];\n  for (let i2 = 0; i2 < elementShapeA.length; ++i2) {\n    const dim0 = elementShapeA[i2];\n    const dim1 = elementShapeB[i2];\n    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);\n    }\n    result[i2] = dim0 >= 0 ? dim0 : dim1;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js\nvar TensorArray = class {\n  constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n    this.name = name;\n    this.dtype = dtype;\n    this.maxSize = maxSize;\n    this.elementShape = elementShape;\n    this.identicalElementShapes = identicalElementShapes;\n    this.dynamicSize = dynamicSize;\n    this.clearAfterRead = clearAfterRead;\n    this.tensors = [];\n    this.closed_ = false;\n    this.idTensor = scalar(0);\n    keep(this.idTensor);\n  }\n  get id() {\n    return this.idTensor.id;\n  }\n  get closed() {\n    return this.closed_;\n  }\n  clearAndClose(keepIds) {\n    this.tensors.forEach((tensor2) => {\n      if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {\n        tensor2.tensor.dispose();\n      }\n    });\n    this.tensors = [];\n    this.closed_ = true;\n    this.idTensor.dispose();\n  }\n  size() {\n    return this.tensors.length;\n  }\n  read(index) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n    if (index < 0 || index >= this.size()) {\n      throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n    }\n    const tensorWithState = this.tensors[index];\n    if (tensorWithState.cleared) {\n      throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);\n    }\n    if (this.clearAfterRead) {\n      tensorWithState.cleared = true;\n    }\n    tensorWithState.read = true;\n    return tensorWithState.tensor;\n  }\n  readMany(indices) {\n    return indices.map((index) => this.read(index));\n  }\n  write(index, tensor2) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n      throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n    }\n    const t2 = this.tensors[index] || {};\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);\n    }\n    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {\n      this.elementShape = tensor2.shape;\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n    if (t2.read) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n    }\n    if (t2.written) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n    }\n    t2.tensor = tensor2;\n    keep(tensor2);\n    t2.written = true;\n    this.tensors[index] = t2;\n  }\n  writeMany(indices, tensors) {\n    if (indices.length !== tensors.length) {\n      throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n    }\n    indices.forEach((i2, index) => this.write(i2, tensors[index]));\n  }\n  gather(indices, dtype) {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n    }\n    if (!indices) {\n      indices = [];\n      for (let i2 = 0; i2 < this.size(); i2++) {\n        indices.push(i2);\n      }\n    } else {\n      indices = indices.slice(0, this.size());\n    }\n    if (indices.length === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n    const tensors = this.readMany(indices);\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, \"TensorArray shape mismatch: \");\n    return stack(tensors, 0);\n  }\n  concat(dtype) {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n    }\n    if (this.size() === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n    const indices = [];\n    for (let i2 = 0; i2 < this.size(); i2++) {\n      indices.push(i2);\n    }\n    const tensors = this.readMany(indices);\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n    return concat(tensors, 0);\n  }\n  scatter(indices, tensor2) {\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n    }\n    if (indices.length !== tensor2.shape[0]) {\n      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (!this.dynamicSize && maxIndex >= this.maxSize) {\n      throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n    }\n    this.writeMany(indices, unstack(tensor2, 0));\n  }\n  split(length, tensor2) {\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n    }\n    let totalLength = 0;\n    const cumulativeLengths = length.map((len) => {\n      totalLength += len;\n      return totalLength;\n    });\n    if (totalLength !== tensor2.shape[0]) {\n      throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n    }\n    if (!this.dynamicSize && length.length !== this.maxSize) {\n      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n    const tensors = [];\n    tidy(() => {\n      tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n      for (let i2 = 0; i2 < length.length; ++i2) {\n        const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];\n        const indices2 = [0, previousLength, 0];\n        const sizes = [1, length[i2], elementPerRow];\n        tensors[i2] = reshape(slice(tensor2, indices2, sizes), this.elementShape);\n      }\n      return tensors;\n    });\n    const indices = [];\n    for (let i2 = 0; i2 < length.length; i2++) {\n      indices[i2] = i2;\n    }\n    this.writeMany(indices, tensors);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js\nvar TensorList = class {\n  constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n    this.tensors = tensors;\n    this.elementShape = elementShape;\n    this.elementDtype = elementDtype;\n    if (tensors != null) {\n      tensors.forEach((tensor2) => {\n        if (elementDtype !== tensor2.dtype) {\n          throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n        keep(tensor2);\n      });\n    }\n    this.idTensor = scalar(0);\n    this.maxNumElements = maxNumElements;\n    keep(this.idTensor);\n  }\n  get id() {\n    return this.idTensor.id;\n  }\n  copy() {\n    return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n  }\n  clearAndClose(keepIds) {\n    this.tensors.forEach((tensor2) => {\n      if (keepIds == null || !keepIds.has(tensor2.id)) {\n        tensor2.dispose();\n      }\n    });\n    this.tensors.length = 0;\n    this.idTensor.dispose();\n  }\n  size() {\n    return this.tensors.length;\n  }\n  stack(elementShape, elementDtype, numElements = -1) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (numElements !== -1 && this.tensors.length !== numElements) {\n      throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    return tidy(() => {\n      const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));\n      return stack(reshapedTensors, 0);\n    });\n  }\n  popBack(elementShape, elementDtype) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (this.size() === 0) {\n      throw new Error(\"Trying to pop from an empty list.\");\n    }\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    const tensor2 = this.tensors.pop();\n    assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, \"TensorList shape mismatch: \");\n    return reshape(tensor2, outputElementShape);\n  }\n  pushBack(tensor2) {\n    if (tensor2.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, \"TensorList shape mismatch: \");\n    if (this.maxNumElements === this.size()) {\n      throw new Error(`Trying to push element into a full list.`);\n    }\n    keep(tensor2);\n    this.tensors.push(tensor2);\n  }\n  resize(size) {\n    if (size < 0) {\n      throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n    }\n    if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n      throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n    }\n    const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);\n    destTensorList.tensors.length = size;\n    for (let i2 = 0; i2 < Math.min(this.tensors.length, size); ++i2) {\n      destTensorList.tensors[i2] = this.tensors[i2];\n    }\n    return destTensorList;\n  }\n  getItem(elementIndex, elementShape, elementDtype) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || elementIndex > this.tensors.length) {\n      throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n    }\n    if (this.tensors[elementIndex] == null) {\n      throw new Error(`element at index ${elementIndex} is null.`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    return reshape(this.tensors[elementIndex], outputElementShape);\n  }\n  setItem(elementIndex, tensor2) {\n    if (tensor2.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n      throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n    keep(tensor2);\n    this.tensors[elementIndex] = tensor2;\n  }\n  gather(indices, elementDtype, elementShape) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n    indices = indices.slice(0, this.size());\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (indices.length === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = indices.map((i2) => reshape(this.tensors[i2], outputElementShape));\n      return stack(tensors, 0);\n    });\n  }\n  concat(elementDtype, elementShape) {\n    if (!!elementDtype && elementDtype !== this.elementDtype) {\n      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (this.size() === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = this.tensors.map((t2) => reshape(t2, outputElementShape));\n      return concat(tensors, 0);\n    });\n  }\n};\nfunction fromTensor(tensor2, elementShape, elementDtype) {\n  const dtype = tensor2.dtype;\n  if (tensor2.shape.length < 1) {\n    throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);\n  }\n  if (tensor2.dtype !== elementDtype) {\n    throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);\n  }\n  const tensorElementShape = tensor2.shape.slice(1);\n  assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, \"TensorList shape mismatch: \");\n  const tensorList = unstack(tensor2);\n  return new TensorList(tensorList, elementShape, dtype);\n}\nfunction reserve(elementShape, elementDtype, numElements) {\n  return new TensorList([], elementShape, elementDtype, numElements);\n}\nfunction scatter(tensor2, indices, elementShape, numElements) {\n  if (indices.length !== tensor2.shape[0]) {\n    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n  }\n  const maxIndex = Math.max(...indices);\n  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n    throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n  }\n  const list = new TensorList([], elementShape, tensor2.dtype, numElements);\n  const tensors = unstack(tensor2, 0);\n  indices.forEach((value, index) => {\n    list.setItem(value, tensors[index]);\n  });\n  return list;\n}\nfunction split2(tensor2, length, elementShape) {\n  let totalLength = 0;\n  const cumulativeLengths = length.map((len) => {\n    totalLength += len;\n    return totalLength;\n  });\n  if (totalLength !== tensor2.shape[0]) {\n    throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n  }\n  const shapeWithoutFirstDim = tensor2.shape.slice(1);\n  const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);\n  const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n  const tensors = tidy(() => {\n    const tensors2 = [];\n    tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n    for (let i2 = 0; i2 < length.length; ++i2) {\n      const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];\n      const indices = [0, previousLength, 0];\n      const sizes = [1, length[i2], elementPerRow];\n      tensors2[i2] = reshape(slice(tensor2, indices, sizes), outputElementShape);\n    }\n    tensor2.dispose();\n    return tensors2;\n  });\n  const list = new TensorList([], elementShape, tensor2.dtype, length.length);\n  for (let i2 = 0; i2 < tensors.length; i2++) {\n    list.setItem(i2, tensors[i2]);\n  }\n  return list;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js\nvar executeOp3 = async (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"If\":\n    case \"StatelessIf\": {\n      const thenFunc = getParamValue(\"thenBranch\", node, tensorMap, context);\n      const elseFunc = getParamValue(\"elseBranch\", node, tensorMap, context);\n      const cond = getParamValue(\"cond\", node, tensorMap, context);\n      const args = getParamValue(\"args\", node, tensorMap, context);\n      const condValue = await cond.data();\n      if (condValue[0]) {\n        return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      } else {\n        return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      }\n    }\n    case \"While\":\n    case \"StatelessWhile\": {\n      const bodyFunc = getParamValue(\"body\", node, tensorMap, context);\n      const condFunc = getParamValue(\"cond\", node, tensorMap, context);\n      const args = getParamValue(\"args\", node, tensorMap, context);\n      const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      const argIds = args.map((tensor2) => tensor2.id);\n      let condValue = await condResult[0].data();\n      condResult.forEach((tensor2) => {\n        if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {\n          tensor2.dispose();\n        }\n      });\n      let result = args;\n      while (condValue[0]) {\n        const origResult = result;\n        result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n        const resultIds = result.map((tensor2) => tensor2.id);\n        origResult.forEach((tensor2) => {\n          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n            tensor2.dispose();\n          }\n        });\n        const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n        condValue = await condResult2[0].data();\n        condResult2.forEach((tensor2) => {\n          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n            tensor2.dispose();\n          }\n        });\n      }\n      return result;\n    }\n    case \"LoopCond\": {\n      const pred = getParamValue(\"pred\", node, tensorMap, context);\n      return [cloneTensor(pred)];\n    }\n    case \"Switch\": {\n      const pred = getParamValue(\"pred\", node, tensorMap, context);\n      let data = getParamValue(\"data\", node, tensorMap, context);\n      if (!data.kept) {\n        data = cloneTensor(data);\n      }\n      return (await pred.data())[0] ? [void 0, data] : [data, void 0];\n    }\n    case \"Merge\": {\n      const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);\n      if (inputName) {\n        const data = getTensor(inputName, tensorMap, context);\n        return [cloneTensor(data)];\n      }\n      return void 0;\n    }\n    case \"Enter\": {\n      const frameId = getParamValue(\"frameName\", node, tensorMap, context);\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.enterFrame(frameId);\n      return [cloneTensor(data)];\n    }\n    case \"Exit\": {\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.exitFrame();\n      return [cloneTensor(data)];\n    }\n    case \"NextIteration\": {\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.nextIteration();\n      return [cloneTensor(data)];\n    }\n    case \"TensorArrayV3\": {\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const dynamicSize = getParamValue(\"dynamicSize\", node, tensorMap, context);\n      const clearAfterRead = getParamValue(\"clearAfterRead\", node, tensorMap, context);\n      const identicalElementShapes = getParamValue(\"identicalElementShapes\", node, tensorMap, context);\n      const name = getParamValue(\"name\", node, tensorMap, context);\n      const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n      context.addTensorArray(tensorArray);\n      return [tensorArray.idTensor, scalar(1)];\n    }\n    case \"TensorArrayWriteV3\": {\n      const id = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const index = getParamValue(\"index\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const writeTensorArray = context.getTensorArray(id.id);\n      writeTensorArray.write(index, writeTensor);\n      return [writeTensorArray.idTensor];\n    }\n    case \"TensorArrayReadV3\": {\n      const readId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const readIndex = getParamValue(\"index\", node, tensorMap, context);\n      const readTensorArray = context.getTensorArray(readId.id);\n      return [readTensorArray.read(readIndex)];\n    }\n    case \"TensorArrayGatherV3\": {\n      const gatherId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const gatherDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const gatherTensorArray = context.getTensorArray(gatherId.id);\n      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n    }\n    case \"TensorArrayScatterV3\": {\n      const scatterId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const scatterTensorArray = context.getTensorArray(scatterId.id);\n      scatterTensorArray.scatter(scatterIndices, scatterTensor);\n      return [scatterTensorArray.idTensor];\n    }\n    case \"TensorArrayConcatV3\": {\n      const concatId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const concatTensorArray = context.getTensorArray(concatId.id);\n      const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      return [concatTensorArray.concat(concatDtype)];\n    }\n    case \"TensorArraySplitV3\": {\n      const splitId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n      const splitTensorArray = context.getTensorArray(splitId.id);\n      splitTensorArray.split(lengths, splitTensor);\n      return [splitTensorArray.idTensor];\n    }\n    case \"TensorArraySizeV3\": {\n      const sizeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const sizeTensorArray = context.getTensorArray(sizeId.id);\n      return [scalar(sizeTensorArray.size(), \"int32\")];\n    }\n    case \"TensorArrayCloseV3\": {\n      const closeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const closeTensorArray = context.getTensorArray(closeId.id);\n      closeTensorArray.clearAndClose();\n      return [closeTensorArray.idTensor];\n    }\n    case \"TensorListSetItem\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const index = getParamValue(\"index\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.setItem(index, writeTensor);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListGetItem\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const readIndex = getParamValue(\"index\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.getItem(readIndex, elementShape, elementDType)];\n    }\n    case \"TensorListScatterV2\":\n    case \"TensorListScatter\": {\n      const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n      const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListReserve\":\n    case \"EmptyTensorList\": {\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      let numElementsParam;\n      if (node.op === \"TensorListReserve\") {\n        numElementsParam = \"numElements\";\n      } else {\n        numElementsParam = \"maxNumElements\";\n      }\n      const numElements = getParamValue(numElementsParam, node, tensorMap, context);\n      const tensorList = reserve(elementShape, elementDtype, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListGather\": {\n      const gatherId = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(gatherId.id);\n      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n    }\n    case \"TensorListStack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.stack(elementShape, elementDtype, numElements)];\n    }\n    case \"TensorListFromTensor\": {\n      const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = fromTensor(tensor2, elementShape, elementDtype);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListConcat\":\n    case \"TensorListConcatV2\": {\n      const concatId = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const tensorList = context.getTensorList(concatId.id);\n      const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      return [tensorList.concat(concatDtype, elementShape)];\n    }\n    case \"TensorListPushBack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.pushBack(writeTensor);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListPopBack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.popBack(elementShape, elementDType)];\n    }\n    case \"TensorListSplit\": {\n      const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n      const tensorList = split2(splitTensor, lengths, elementShape);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListLength\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [scalar(tensorList.size(), \"int32\")];\n    }\n    case \"TensorListResize\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const srcTensorList = context.getTensorList(idTensor.id);\n      const destTensorList = srcTensorList.resize(size);\n      context.addTensorList(destTensorList);\n      return [destTensorList.idTensor];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n  const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n  const isBiasAdd = extraOp === \"biasadd\";\n  const noBiasAdd = !isBiasAdd;\n  const isPrelu = activationFunc === \"prelu\";\n  const isBatchNorm = extraOp === \"fusedbatchnorm\";\n  const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n  if (isBiasAdd) {\n    if (isPrelu && numArgs !== 2) {\n      throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n    }\n    if (!isPrelu && isBiasAdd && numArgs !== 1) {\n      throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\");\n    }\n  }\n  if (isBatchNorm) {\n    throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");\n  }\n  const stride = getParamValue(\"strides\", node, tensorMap, context);\n  const pad3 = getPadding(node, tensorMap, context);\n  const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n  const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n  let [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n  if (noBiasAdd) {\n    preluArg = biasArg;\n    biasArg = void 0;\n  }\n  const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n  return {\n    stride,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    biasArg,\n    preluArg,\n    activationFunc,\n    leakyreluAlpha\n  };\n}\nvar executeOp4 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Conv1D\": {\n      const stride = getParamValue(\"stride\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilation = getParamValue(\"dilation\", node, tensorMap, context);\n      return [conv1d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), stride, pad3, dataFormat, dilation)];\n    }\n    case \"Conv2D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      return [conv2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n    }\n    case \"_FusedConv2D\": {\n      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n      return [fused_ops_exports.conv2d({\n        x: getParamValue(\"x\", node, tensorMap, context),\n        filter: getParamValue(\"filter\", node, tensorMap, context),\n        strides: [stride[1], stride[2]],\n        pad: pad3,\n        dataFormat,\n        dilations: [dilations[1], dilations[2]],\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    }\n    case \"FusedDepthwiseConv2dNative\": {\n      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n      return [fused_ops_exports.depthwiseConv2d({\n        x: getParamValue(\"x\", node, tensorMap, context),\n        filter: getParamValue(\"filter\", node, tensorMap, context),\n        strides: [stride[1], stride[2]],\n        pad: pad3,\n        dataFormat,\n        dilations: [dilations[1], dilations[2]],\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    }\n    case \"Conv2DBackpropInput\":\n    case \"Conv2dTranspose\": {\n      const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      return [conv2dTranspose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)];\n    }\n    case \"DepthwiseConv2dNative\":\n    case \"DepthwiseConv2d\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      return [depthwiseConv2d(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n    }\n    case \"Conv3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      return [conv3d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n    }\n    case \"AvgPool\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [avgPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n    }\n    case \"MaxPool\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [maxPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n    }\n    case \"MaxPoolWithArgmax\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      const includeBatchInIndex = getParamValue(\"includeBatchInIndex\", node, tensorMap, context);\n      const { result, indexes } = maxPoolWithArgmax(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);\n      return [result, indexes];\n    }\n    case \"AvgPool3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [avgPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n    }\n    case \"MaxPool3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [maxPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n    }\n    case \"Dilation2D\": {\n      const strides = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      const strideHeight = strides[1];\n      const strideWidth = strides[2];\n      const dilationHeight = dilations[1];\n      const dilationWidth = dilations[2];\n      return [dilation2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [strideHeight, strideWidth], pad3, [dilationHeight, dilationWidth], \"NHWC\")];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js\nvar executeOp5 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Fill\": {\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const value = getParamValue(\"value\", node, tensorMap, context);\n      return [fill(shape, value, dtype)];\n    }\n    case \"LinSpace\": {\n      const start = getParamValue(\"start\", node, tensorMap, context);\n      const stop = getParamValue(\"stop\", node, tensorMap, context);\n      const num = getParamValue(\"num\", node, tensorMap, context);\n      return [linspace(start, stop, num)];\n    }\n    case \"Multinomial\": {\n      const logits = getParamValue(\"logits\", node, tensorMap, context);\n      const numSamples = getParamValue(\"numSamples\", node, tensorMap, context);\n      const seed = getParamValue(\"seed\", node, tensorMap, context);\n      return [multinomial(logits, numSamples, seed)];\n    }\n    case \"OneHot\": {\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      const depth = getParamValue(\"depth\", node, tensorMap, context);\n      const onValue = getParamValue(\"onValue\", node, tensorMap, context);\n      const offValue = getParamValue(\"offValue\", node, tensorMap, context);\n      return [oneHot(indices, depth, onValue, offValue)];\n    }\n    case \"Ones\": {\n      return [ones2(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"OnesLike\": {\n      return [onesLike(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"RandomUniform\": {\n      return [randomUniform(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"minval\", node, tensorMap, context), getParamValue(\"maxval\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"Range\": {\n      const start = getParamValue(\"start\", node, tensorMap, context);\n      const stop = getParamValue(\"stop\", node, tensorMap, context);\n      const step5 = getParamValue(\"step\", node, tensorMap, context);\n      return [range(start, stop, step5, getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"TruncatedNormal\": {\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      const mean5 = getParamValue(\"mean\", node, tensorMap, context);\n      const stdDev = getParamValue(\"stdDev\", node, tensorMap, context);\n      const seed = getParamValue(\"seed\", node, tensorMap, context);\n      return [truncatedNormal(shape, mean5, stdDev, getParamValue(\"dtype\", node, tensorMap, context), seed)];\n    }\n    case \"Zeros\": {\n      return [zeros(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"ZerosLike\": {\n      return [zerosLike(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js\nfunction nmsParams(node, tensorMap, context) {\n  const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n  const scores = getParamValue(\"scores\", node, tensorMap, context);\n  const maxOutputSize = getParamValue(\"maxOutputSize\", node, tensorMap, context);\n  const iouThreshold = getParamValue(\"iouThreshold\", node, tensorMap, context);\n  const scoreThreshold = getParamValue(\"scoreThreshold\", node, tensorMap, context);\n  const softNmsSigma = getParamValue(\"softNmsSigma\", node, tensorMap, context);\n  return {\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    softNmsSigma\n  };\n}\nvar executeOp6 = async (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"NonMaxSuppressionV5\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n      const result = await image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n      return [result.selectedIndices, result.selectedScores];\n    }\n    case \"NonMaxSuppressionV4\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n      const padToMaxOutputSize = getParamValue(\"padToMaxOutputSize\", node, tensorMap, context);\n      const result = await image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n      return [result.selectedIndices, result.validOutputs];\n    }\n    case \"NonMaxSuppressionV3\":\n    case \"NonMaxSuppressionV2\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n      return [await image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n    }\n    case \"Where\": {\n      const condition = cast(getParamValue(\"condition\", node, tensorMap, context), \"bool\");\n      const result = [await whereAsync(condition)];\n      condition.dispose();\n      return result;\n    }\n    case \"ListDiff\": {\n      return setdiff1dAsync(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context));\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js\nvar executeOp7 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"LowerBound\": {\n      const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      return [lowerBound(sortedSequence, values)];\n    }\n    case \"TopKV2\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const k = getParamValue(\"k\", node, tensorMap, context);\n      const sorted = getParamValue(\"sorted\", node, tensorMap, context);\n      const result = topk(x, k, sorted);\n      return [result.values, result.indices];\n    }\n    case \"UpperBound\": {\n      const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      return [upperBound(sortedSequence, values)];\n    }\n    case \"Unique\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const result = unique(x);\n      return [result.values, result.indices];\n    }\n    case \"UniqueV2\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const result = unique(x, axis);\n      return [result.values, result.indices];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js\nvar executeOp8 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Const\": {\n      return tensorMap[node.name];\n    }\n    case \"PlaceholderWithDefault\":\n      const def = getParamValue(\"default\", node, tensorMap, context);\n      return [getTensor(node.name, tensorMap, context) || def];\n    case \"Placeholder\":\n      return [getTensor(node.name, tensorMap, context)];\n    case \"Identity\":\n    case \"StopGradient\":\n    case \"FakeQuantWithMinMaxVars\": {\n      const data2 = getParamValue(\"x\", node, tensorMap, context);\n      return [cloneTensor(data2)];\n    }\n    case \"IdentityN\":\n      return getParamValue(\"x\", node, tensorMap, context).map((t2) => cloneTensor(t2));\n    case \"Snapshot\":\n      const snapshot = getParamValue(\"x\", node, tensorMap, context);\n      return [cloneTensor(snapshot)];\n    case \"Shape\":\n      return [tensor1d(getParamValue(\"x\", node, tensorMap, context).shape, \"int32\")];\n    case \"ShapeN\":\n      return getParamValue(\"x\", node, tensorMap, context).map((t2) => tensor1d(t2.shape));\n    case \"Size\":\n      return [scalar(getParamValue(\"x\", node, tensorMap, context).size, \"int32\")];\n    case \"Rank\":\n      return [scalar(getParamValue(\"x\", node, tensorMap, context).rank, \"int32\")];\n    case \"NoOp\":\n      return [scalar(1)];\n    case \"Print\":\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const data = getParamValue(\"data\", node, tensorMap, context);\n      const message = getParamValue(\"message\", node, tensorMap, context);\n      const summarize = getParamValue(\"summarize\", node, tensorMap, context);\n      console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\");\n      console.log(message);\n      for (let i2 = 0; i2 < data.length; i2++) {\n        console.log(Array.prototype.slice.call(data[i2].dataSync()).slice(0, summarize));\n      }\n      return [input2];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js\nvar HashTable = class {\n  constructor(keyDType, valueDType) {\n    this.keyDType = keyDType;\n    this.valueDType = valueDType;\n    this.handle = scalar(0);\n    this.tensorMap = /* @__PURE__ */ new Map();\n    keep(this.handle);\n  }\n  get id() {\n    return this.handle.id;\n  }\n  clearAndClose() {\n    this.tensorMap.forEach((value) => value.dispose());\n    this.tensorMap.clear();\n    this.handle.dispose();\n  }\n  size() {\n    return this.tensorMap.size;\n  }\n  tensorSize() {\n    return scalar(this.size(), \"int32\");\n  }\n  async import(keys, values) {\n    this.checkKeyAndValueTensor(keys, values);\n    const $keys = await keys.data();\n    this.tensorMap.forEach((value) => value.dispose());\n    this.tensorMap.clear();\n    return tidy(() => {\n      const $values = unstack(values);\n      const keysLength = $keys.length;\n      const valuesLength = $values.length;\n      util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);\n      for (let i2 = 0; i2 < keysLength; i2++) {\n        const key = $keys[i2];\n        const value = $values[i2];\n        keep(value);\n        this.tensorMap.set(key, value);\n      }\n      return this.handle;\n    });\n  }\n  async find(keys, defaultValue) {\n    this.checkKeyAndValueTensor(keys, defaultValue);\n    const $keys = await keys.data();\n    return tidy(() => {\n      const result = [];\n      for (let i2 = 0; i2 < $keys.length; i2++) {\n        const key = $keys[i2];\n        const value = this.findWithDefault(key, defaultValue);\n        result.push(value);\n      }\n      return stack(result);\n    });\n  }\n  findWithDefault(key, defaultValue) {\n    const result = this.tensorMap.get(key);\n    return result != null ? result : defaultValue;\n  }\n  checkKeyAndValueTensor(key, value) {\n    if (key.dtype !== this.keyDType) {\n      throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);\n    }\n    if (value.dtype !== this.valueDType) {\n      throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js\nvar executeOp9 = async (node, tensorMap, context, resourceManager) => {\n  switch (node.op) {\n    case \"HashTable\":\n    case \"HashTableV2\": {\n      const keyDType = getParamValue(\"keyDType\", node, tensorMap, context);\n      const valueDType = getParamValue(\"valueDType\", node, tensorMap, context);\n      const hashTable = new HashTable(keyDType, valueDType);\n      resourceManager.addHashTable(node.name, hashTable);\n      return [hashTable.handle];\n    }\n    case \"LookupTableImport\":\n    case \"LookupTableImportV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const keys = getParamValue(\"keys\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.import(keys, values)];\n    }\n    case \"LookupTableFind\":\n    case \"LookupTableFindV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const keys = getParamValue(\"keys\", node, tensorMap, context);\n      const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.find(keys, defaultValue)];\n    }\n    case \"LookupTableSize\":\n    case \"LookupTableSizeV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [hashTable.tensorSize()];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js\nvar executeOp10 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"ResizeBilinear\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n      const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n      return [image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n    }\n    case \"ResizeNearestNeighbor\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n      const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n      return [image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n    }\n    case \"CropAndResize\": {\n      const image2 = getParamValue(\"image\", node, tensorMap, context);\n      const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n      const boxInd = getParamValue(\"boxInd\", node, tensorMap, context);\n      const cropSize = getParamValue(\"cropSize\", node, tensorMap, context);\n      const method = getParamValue(\"method\", node, tensorMap, context);\n      const extrapolationValue = getParamValue(\"extrapolationValue\", node, tensorMap, context);\n      return [image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)];\n    }\n    case \"ImageProjectiveTransformV3\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const transforms = getParamValue(\"transforms\", node, tensorMap, context);\n      const outputShape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const fillValue = getParamValue(\"fillValue\", node, tensorMap, context);\n      const interpolation = getParamValue(\"interpolation\", node, tensorMap, context);\n      const fillMode = getParamValue(\"fillMode\", node, tensorMap, context);\n      return [image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js\nvar executeOp11 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Equal\": {\n      return [equal(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"NotEqual\": {\n      return [notEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Greater\": {\n      return [greater(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"GreaterEqual\": {\n      return [greaterEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Less\": {\n      return [less(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LessEqual\": {\n      return [lessEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LogicalAnd\": {\n      return [logicalAnd(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LogicalNot\": {\n      return [logicalNot(getParamValue(\"a\", node, tensorMap, context))];\n    }\n    case \"LogicalOr\": {\n      return [logicalOr(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Select\":\n    case \"SelectV2\": {\n      return [where(getParamValue(\"condition\", node, tensorMap, context), getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js\nvar executeOp12 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"BatchMatMul\":\n    case \"BatchMatMulV2\":\n    case \"MatMul\":\n      return [matMul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context), getParamValue(\"transposeA\", node, tensorMap, context), getParamValue(\"transposeB\", node, tensorMap, context))];\n    case \"Einsum\":\n      return [einsum(getParamValue(\"equation\", node, tensorMap, context), ...getParamValue(\"tensors\", node, tensorMap, context))];\n    case \"Transpose\":\n      return [transpose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"perm\", node, tensorMap, context))];\n    case \"_FusedMatMul\":\n      const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n      const isBiasAdd = extraOp === \"biasadd\";\n      const isPrelu = activationFunc === \"prelu\";\n      const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n      const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n      if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n          throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n        }\n        if (!isPrelu && numArgs !== 1) {\n          throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\");\n        }\n      }\n      const [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n      return [fused_ops_exports.matMul({\n        a: getParamValue(\"a\", node, tensorMap, context),\n        b: getParamValue(\"b\", node, tensorMap, context),\n        transposeA: getParamValue(\"transposeA\", node, tensorMap, context),\n        transposeB: getParamValue(\"transposeB\", node, tensorMap, context),\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js\nvar executeOp13 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"EuclideanNorm\":\n      return [euclideanNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axis\", node, tensorMap, context), getParamValue(\"keepDims\", node, tensorMap, context))];\n    case \"FusedBatchNorm\":\n    case \"FusedBatchNormV2\": {\n      return [batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n    }\n    case \"FusedBatchNormV3\": {\n      return [batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n    }\n    case \"LRN\": {\n      return [localResponseNormalization(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"radius\", node, tensorMap, context), getParamValue(\"bias\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context), getParamValue(\"beta\", node, tensorMap, context))];\n    }\n    case \"Softmax\": {\n      return [softmax(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"LogSoftmax\": {\n      return [logSoftmax(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"SparseToDense\": {\n      return [sparseToDense(getParamValue(\"sparseIndices\", node, tensorMap, context), getParamValue(\"outputShape\", node, tensorMap, context), getParamValue(\"sparseValues\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js\nvar executeOp14 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Max\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [max(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Mean\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [mean(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Min\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [min(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Sum\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [sum2(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"All\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [all(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Any\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [any(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"ArgMax\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [argMax(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"ArgMin\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [argMin(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Prod\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [prod(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Cumprod\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n      const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n      return [cumprod(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n    }\n    case \"Cumsum\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n      const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n      return [cumsum(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n    }\n    case \"Bincount\":\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const weights = getParamValue(\"weights\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      return [bincount(x, weights, size)];\n    case \"DenseBincount\": {\n      const x2 = getParamValue(\"x\", node, tensorMap, context);\n      const weights2 = getParamValue(\"weights\", node, tensorMap, context);\n      const size2 = getParamValue(\"size\", node, tensorMap, context);\n      const binaryOutput = getParamValue(\"binaryOutput\", node, tensorMap, context);\n      return [denseBincount(x2, weights2, size2, binaryOutput)];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js\nvar executeOp15 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"ConcatV2\":\n    case \"Concat\": {\n      const n = getParamValue(\"n\", node, tensorMap, context);\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      let inputs = getParamValue(\"tensors\", node, tensorMap, context);\n      inputs = inputs.slice(0, n);\n      return [concat(inputs, axis)];\n    }\n    case \"Gather\": {\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [gather(input2, cast(indices, \"int32\"), 0)];\n    }\n    case \"GatherV2\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const batchDims = getParamValue(\"batchDims\", node, tensorMap, context);\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [gather(input2, cast(indices, \"int32\"), axis, batchDims)];\n    }\n    case \"Reverse\": {\n      const dims = getParamValue(\"dims\", node, tensorMap, context);\n      const axis = [];\n      for (let i2 = 0; i2 < dims.length; i2++) {\n        if (dims[i2]) {\n          axis.push(i2);\n        }\n      }\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      return [reverse(input2, axis)];\n    }\n    case \"ReverseV2\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      return [reverse(input2, axis)];\n    }\n    case \"Slice\": {\n      const begin = getParamValue(\"begin\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      return [slice(getParamValue(\"x\", node, tensorMap, context), begin, size)];\n    }\n    case \"StridedSlice\": {\n      const begin = getParamValue(\"begin\", node, tensorMap, context);\n      const end = getParamValue(\"end\", node, tensorMap, context);\n      const strides = getParamValue(\"strides\", node, tensorMap, context);\n      const beginMask = getParamValue(\"beginMask\", node, tensorMap, context);\n      const endMask = getParamValue(\"endMask\", node, tensorMap, context);\n      const ellipsisMask = getParamValue(\"ellipsisMask\", node, tensorMap, context);\n      const newAxisMask = getParamValue(\"newAxisMask\", node, tensorMap, context);\n      const shrinkAxisMask = getParamValue(\"shrinkAxisMask\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n      return [stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n    }\n    case \"Pack\": {\n      return tidy(() => {\n        const axis = getParamValue(\"axis\", node, tensorMap, context);\n        const tensors = getParamValue(\"tensors\", node, tensorMap, context);\n        const shape = tensors[0].shape;\n        const squeezedShape = squeeze(tensors[0]).shape;\n        const mapped = tensors.map((tensor2) => {\n          const sameShape = util_exports.arraysEqual(tensor2.shape, shape);\n          if (!sameShape && !util_exports.arraysEqual(squeeze(tensor2).shape, squeezedShape)) {\n            throw new Error(\"the input tensors shape does not match\");\n          }\n          return sameShape ? tensor2 : reshape(tensor2, shape);\n        });\n        return [stack(mapped, axis)];\n      });\n    }\n    case \"Unpack\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n      return unstack(tensor2, axis);\n    }\n    case \"Tile\": {\n      const reps = getParamValue(\"reps\", node, tensorMap, context);\n      return [tile(getParamValue(\"x\", node, tensorMap, context), reps)];\n    }\n    case \"Split\":\n    case \"SplitV\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const numOrSizeSplits = getParamValue(\"numOrSizeSplits\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n      return split(tensor2, numOrSizeSplits, axis);\n    }\n    case \"ScatterNd\": {\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      return [scatterND(indices, values, shape)];\n    }\n    case \"GatherNd\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [gatherND(x, indices)];\n    }\n    case \"SparseToDense\": {\n      const indices = getParamValue(\"sparseIndices\", node, tensorMap, context);\n      const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const sparseValues = getParamValue(\"sparseValues\", node, tensorMap, context);\n      const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n      return [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : cast(defaultValue, sparseValues.dtype))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js\nvar executeOp16 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"SparseFillEmptyRows\": {\n      const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = sparse.sparseFillEmptyRows(getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"values\", node, tensorMap, context), getParamValue(\"denseShape\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context));\n      return [\n        outputIndices,\n        outputValues,\n        emptyRowIndicator,\n        reverseIndexMap\n      ];\n    }\n    case \"SparseReshape\": {\n      const { outputIndices, outputShape } = sparse.sparseReshape(getParamValue(\"inputIndices\", node, tensorMap, context), getParamValue(\"inputShape\", node, tensorMap, context), getParamValue(\"newShape\", node, tensorMap, context));\n      return [outputIndices, outputShape];\n    }\n    case \"SparseSegmentMean\": {\n      const outputData = sparse.sparseSegmentMean(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n      return [outputData];\n    }\n    case \"SparseSegmentSum\": {\n      const outputData = sparse.sparseSegmentSum(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n      return [outputData];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js\nvar executeOp17 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"FFT\": {\n      return [fft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"IFFT\": {\n      return [ifft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"RFFT\": {\n      return [rfft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"IRFFT\": {\n      return [irfft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js\nvar executeOp18 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"StringNGrams\": {\n      const { nGrams, nGramsSplits } = string.stringNGrams(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"dataSplits\", node, tensorMap, context), getParamValue(\"separator\", node, tensorMap, context), getParamValue(\"nGramWidths\", node, tensorMap, context), getParamValue(\"leftPad\", node, tensorMap, context), getParamValue(\"rightPad\", node, tensorMap, context), getParamValue(\"padWidth\", node, tensorMap, context), getParamValue(\"preserveShortSequences\", node, tensorMap, context));\n      return [nGrams, nGramsSplits];\n    }\n    case \"StringSplit\": {\n      const { indices, values, shape } = string.stringSplit(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"delimiter\", node, tensorMap, context), getParamValue(\"skipEmpty\", node, tensorMap, context));\n      return [indices, values, shape];\n    }\n    case \"StringToHashBucketFast\": {\n      const output = string.stringToHashBucketFast(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"numBuckets\", node, tensorMap, context));\n      return [output];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js\nvar executeOp19 = (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"Cast\": {\n      return [cast(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"ExpandDims\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [expandDims(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Squeeze\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [squeeze(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Reshape\": {\n      return [reshape(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n    }\n    case \"MirrorPad\": {\n      return [mirrorPad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"mode\", node, tensorMap, context))];\n    }\n    case \"PadV2\":\n    case \"Pad\": {\n      return [pad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"constantValue\", node, tensorMap, context))];\n    }\n    case \"SpaceToBatchND\": {\n      const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n      const paddings = getParamValue(\"paddings\", node, tensorMap, context);\n      return [spaceToBatchND(getParamValue(\"x\", node, tensorMap, context), blockShape, paddings)];\n    }\n    case \"BatchToSpaceND\": {\n      const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n      const crops = getParamValue(\"crops\", node, tensorMap, context);\n      return [batchToSpaceND(getParamValue(\"x\", node, tensorMap, context), blockShape, crops)];\n    }\n    case \"DepthToSpace\": {\n      const blockSize = getParamValue(\"blockSize\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      return [depthToSpace(getParamValue(\"x\", node, tensorMap, context), blockSize, dataFormat)];\n    }\n    case \"BroadcastTo\": {\n      return [broadcastTo(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n    }\n    case \"BroadcastArgs\": {\n      return [broadcastArgs(getParamValue(\"s0\", node, tensorMap, context), getParamValue(\"s1\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js\nfunction executeOp20(node, tensorMap, context, resourceManager) {\n  const value = ((node2, tensorMap2, context2) => {\n    switch (node2.category) {\n      case \"arithmetic\":\n        return tidy(() => executeOp(node2, tensorMap2, context2));\n      case \"basic_math\":\n        return tidy(() => executeOp2(node2, tensorMap2, context2));\n      case \"control\":\n        return executeOp3(node2, tensorMap2, context2);\n      case \"convolution\":\n        return tidy(() => executeOp4(node2, tensorMap2, context2));\n      case \"creation\":\n        return tidy(() => executeOp5(node2, tensorMap2, context2));\n      case \"dynamic\":\n        return executeOp6(node2, tensorMap2, context2);\n      case \"evaluation\":\n        return tidy(() => executeOp7(node2, tensorMap2, context2));\n      case \"image\":\n        return tidy(() => executeOp10(node2, tensorMap2, context2));\n      case \"graph\":\n        return tidy(() => executeOp8(node2, tensorMap2, context2));\n      case \"logical\":\n        return tidy(() => executeOp11(node2, tensorMap2, context2));\n      case \"matrices\":\n        return tidy(() => executeOp12(node2, tensorMap2, context2));\n      case \"normalization\":\n        return tidy(() => executeOp13(node2, tensorMap2, context2));\n      case \"reduction\":\n        return tidy(() => executeOp14(node2, tensorMap2, context2));\n      case \"slice_join\":\n        return tidy(() => executeOp15(node2, tensorMap2, context2));\n      case \"sparse\":\n        return tidy(() => executeOp16(node2, tensorMap2, context2));\n      case \"spectral\":\n        return tidy(() => executeOp17(node2, tensorMap2, context2));\n      case \"string\":\n        return tidy(() => executeOp18(node2, tensorMap2, context2));\n      case \"transformation\":\n        return tidy(() => executeOp19(node2, tensorMap2, context2));\n      case \"hash_table\":\n        return executeOp9(node2, tensorMap2, context2, resourceManager);\n      case \"custom\":\n        const opMapper = getRegisteredOp(node2.op);\n        if (opMapper && opMapper.customExecutor) {\n          return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2));\n        } else {\n          throw TypeError(`Custom op ${node2.op} is not registered.`);\n        }\n      default:\n        throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);\n    }\n  })(node, tensorMap, context);\n  if (util_exports.isPromise(value)) {\n    return value.then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js\nvar ExecutionContext = class {\n  constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n    this.weightMap = weightMap;\n    this.tensorArrayMap = tensorArrayMap;\n    this.tensorListMap = tensorListMap;\n    this.functionMap = functionMap;\n    this.rootContext = { id: 0, frameName: \"\", iterationId: 0 };\n    this.contexts = [this.rootContext];\n    this.lastId = 0;\n    this.generateCurrentContextIds();\n  }\n  newFrame(id, frameName) {\n    return { id, frameName, iterationId: 0 };\n  }\n  set currentContext(contexts2) {\n    if (this.contexts !== contexts2) {\n      this.contexts = contexts2;\n      this.generateCurrentContextIds();\n    }\n  }\n  get currentContext() {\n    return this.contexts;\n  }\n  get currentContextId() {\n    return this._currentContextIds[0];\n  }\n  get currentContextIds() {\n    return this._currentContextIds;\n  }\n  generateCurrentContextIds() {\n    const names = [];\n    for (let i2 = 0; i2 < this.contexts.length - 1; i2++) {\n      const contexts2 = this.contexts.slice(0, this.contexts.length - i2);\n      names.push(this.contextIdforContexts(contexts2));\n    }\n    names.push(\"\");\n    this._currentContextIds = names;\n  }\n  contextIdforContexts(contexts2) {\n    return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? \"\" : `${context.frameName}-${context.iterationId}`).join(\"/\") : \"\";\n  }\n  enterFrame(frameId) {\n    if (this.contexts) {\n      this.lastId++;\n      this.contexts = this.contexts.slice();\n      this.contexts.push(this.newFrame(this.lastId, frameId));\n      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n    }\n  }\n  exitFrame() {\n    if (this.contexts && this.contexts.length > 1) {\n      this.contexts = this.contexts.slice();\n      this.contexts.splice(-1);\n      this.currentContextIds.shift();\n    } else {\n      throw new Error(\"Cannot exit frame, the context is empty\");\n    }\n  }\n  nextIteration() {\n    if (this.contexts && this.contexts.length > 0) {\n      this.contexts = this.contexts.slice();\n      this.lastId++;\n      const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n      context.iterationId += 1;\n      context.id = this.lastId;\n      this.contexts.splice(-1, 1, context);\n      this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n    } else {\n      throw new Error(\"Cannot increase frame iteration, the context is empty\");\n    }\n  }\n  getWeight(name) {\n    return this.weightMap[name];\n  }\n  addTensorArray(tensorArray) {\n    this.tensorArrayMap[tensorArray.id] = tensorArray;\n  }\n  getTensorArray(id) {\n    return this.tensorArrayMap[id];\n  }\n  addTensorList(tensorList) {\n    this.tensorListMap[tensorList.id] = tensorList;\n  }\n  getTensorList(id) {\n    return this.tensorListMap[id];\n  }\n  dispose(keepIds) {\n    for (const key in this.tensorArrayMap) {\n      this.tensorArrayMap[key].clearAndClose(keepIds);\n    }\n    for (const key in this.tensorListMap) {\n      this.tensorListMap[key].clearAndClose(keepIds);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js\nfunction getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n  const usedNodes = /* @__PURE__ */ new Set();\n  const missingInputs = [];\n  let dynamicNode = null;\n  let syncInputs = null;\n  const seen = /* @__PURE__ */ new Set();\n  const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);\n  let initNodeNames = [];\n  if (initNodes != null) {\n    initNodeNames = initNodes.map((node) => parseNodeName(node.name)[0]);\n  }\n  const frontier = [...outputs];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n      if (dynamicNode == null) {\n        dynamicNode = node;\n        syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));\n      }\n    }\n    usedNodes.add(node.name);\n    if (weightMap[node.name] != null) {\n      continue;\n    }\n    if (inputNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (initNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (node.inputs.length === 0) {\n      missingInputs.push(node.name);\n      continue;\n    }\n    node.inputs.forEach((input2) => {\n      if (seen.has(input2.name)) {\n        return;\n      }\n      seen.add(input2.name);\n      frontier.push(input2);\n    });\n  }\n  return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\nfunction getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n  const { usedNodes, inputs } = executionInfo;\n  const frontier = [];\n  const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]);\n  const initNodes = graph.initNodes;\n  inputNodes.forEach((input2) => {\n    if (usedNodes.has(input2.name)) {\n      frontier.push(input2);\n    }\n  });\n  graph.weights.forEach((weight) => {\n    if (usedNodes.has(weight.name)) {\n      frontier.push(weight);\n    }\n  });\n  if (initNodes != null) {\n    initNodes.forEach((node) => {\n      if (usedNodes.has(node.name)) {\n        frontier.push(node);\n      }\n    });\n  }\n  const seen = /* @__PURE__ */ new Set();\n  const orderedNodes = [];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    seen.add(node.name);\n    if (!weightMap[node.name]) {\n      orderedNodes.push(node);\n    }\n    node.children.forEach((child) => {\n      if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {\n        frontier.push(child);\n      }\n    });\n  }\n  return orderedNodes;\n}\nvar CONTROL_FLOW_OPS = [\n  \"Switch\",\n  \"Merge\",\n  \"Enter\",\n  \"Exit\",\n  \"NextIteration\",\n  \"StatelessIf\",\n  \"StatelessWhile\",\n  \"if\",\n  \"While\"\n];\nvar DYNAMIC_SHAPE_OPS = [\n  \"NonMaxSuppressionV2\",\n  \"NonMaxSuppressionV3\",\n  \"NonMaxSuppressionV5\",\n  \"Where\"\n];\nvar HASH_TABLE_OPS = [\n  \"HashTable\",\n  \"HashTableV2\",\n  \"LookupTableImport\",\n  \"LookupTableImportV2\",\n  \"LookupTableFind\",\n  \"LookupTableFindV2\",\n  \"LookupTableSize\",\n  \"LookupTableSizeV2\"\n];\nfunction isControlFlow(node) {\n  return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nfunction isDynamicShape(node) {\n  return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nfunction isHashTable(node) {\n  return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js\nvar GraphExecutor = class {\n  constructor(graph, parent) {\n    this.graph = graph;\n    this.parent = parent;\n    this.compiledMap = /* @__PURE__ */ new Map();\n    this._weightMap = {};\n    this.SEPERATOR = \",\";\n    this._functions = {};\n    this._functionExecutorMap = {};\n    this.intermediateTensors = {};\n    this.keepTensorForDebug = false;\n    this._outputs = graph.outputs;\n    this._inputs = graph.inputs;\n    this._initNodes = graph.initNodes;\n    this._signature = graph.signature;\n    this._functions = graph.functions;\n    if (graph.functions != null) {\n      Object.keys(graph.functions).forEach((name) => {\n        this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this);\n      });\n    }\n  }\n  get weightIds() {\n    return this.parent ? this.parent.weightIds : this._weightIds;\n  }\n  get functionExecutorMap() {\n    return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;\n  }\n  get weightMap() {\n    return this.parent ? this.parent.weightMap : this._weightMap;\n  }\n  set weightMap(weightMap) {\n    const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));\n    this._weightIds = [].concat(...weightIds);\n    this._weightMap = weightMap;\n  }\n  set resourceManager(resourceManager) {\n    this._resourceManager = resourceManager;\n  }\n  get inputs() {\n    return this._inputs.map((node) => {\n      return {\n        name: node.name,\n        shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n        dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n      };\n    });\n  }\n  get outputs() {\n    return this._outputs.map((node) => {\n      return {\n        name: node.name,\n        shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n        dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n      };\n    });\n  }\n  get inputNodes() {\n    return this._inputs.map((node) => node.signatureKey || node.name);\n  }\n  get outputNodes() {\n    return this._outputs.map((node) => {\n      const name = node.signatureKey || node.name;\n      return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;\n    });\n  }\n  get functions() {\n    return Object.keys(this._functions).reduce((map, key) => {\n      map[key] = this._functions[key].signature;\n      return map;\n    }, {});\n  }\n  getCompilationKey(inputs, outputs) {\n    const sortedInputs = inputs.map((node) => node.name).sort();\n    const sortedOutputs = outputs.map((node) => node.name).sort();\n    return sortedInputs.join(this.SEPERATOR) + \"--\" + sortedOutputs.join(this.SEPERATOR);\n  }\n  compile(inputs, outputs) {\n    const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n    const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n    if (dynamicNode != null) {\n      throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);\n    }\n    if (missingInputs.length > 0) {\n      const outNames = outputs.map((n) => n.name);\n      const inNames = Object.keys(inputs);\n      throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);\n    }\n    return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n  }\n  execute(inputs, outputs) {\n    inputs = this.mapInputs(inputs);\n    const names = Object.keys(inputs).sort();\n    this.checkInputs(inputs);\n    this.checkInputShapeAndType(inputs);\n    outputs = this.mapOutputs(outputs);\n    this.checkOutputs(outputs);\n    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n    this.resetIntermediateTensors();\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n    let orderedNodes = this.compiledMap.get(compilationKey);\n    if (orderedNodes == null) {\n      orderedNodes = this.compile(inputs, outputNodes);\n      this.compiledMap.set(compilationKey, orderedNodes);\n    }\n    const tensorArrayMap = {};\n    const tensorListMap = {};\n    return tidy(() => {\n      const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n      const tensorsMap = Object.assign({}, this.weightMap);\n      Object.keys(inputs).forEach((name) => {\n        const [nodeName, index] = parseNodeName(name);\n        const tensors = [];\n        tensors[index] = inputs[name];\n        tensorsMap[nodeName] = tensors;\n      });\n      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n      const intermediateTensorConsumerCount = {};\n      for (let i2 = 0; i2 < orderedNodes.length; i2++) {\n        const node = orderedNodes[i2];\n        if (!tensorsMap[node.name]) {\n          const tensors = executeOp20(node, tensorsMap, context, this._resourceManager);\n          if (util_exports.isPromise(tensors)) {\n            throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);\n          }\n          tensorsMap[node.name] = tensors;\n          this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n        }\n      }\n      if (this.parent == null) {\n        context.dispose(tensorsToKeep);\n      }\n      return outputs.map((name) => getTensor(name, tensorsMap, context));\n    });\n  }\n  getFrozenTensorIds(tensorMap) {\n    const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));\n    return new Set(ids);\n  }\n  checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n    if (node.category === \"control\" || outputNames.indexOf(nodeName) !== -1) {\n      return;\n    }\n    tensorMap[nodeName].forEach((tensor2) => {\n      if (tensor2 != null) {\n        intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length;\n      }\n    });\n    node.inputs.forEach((input2) => {\n      if (input2.category !== \"control\") {\n        const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);\n        if (tensors != null) {\n          tensors.forEach((tensor2) => {\n            if (tensor2 && !tensor2.kept && !tensorsToKeep.has(tensor2.id)) {\n              const count2 = intermediateTensorConsumerCount[tensor2.id];\n              if (count2 === 1) {\n                if (!this.keepTensorForDebug) {\n                  tensor2.dispose();\n                } else {\n                  const [nodeName2, index] = getNodeNameAndIndex(node.name, context);\n                  if (this.intermediateTensors[nodeName2]) {\n                    this.intermediateTensors[nodeName2][index] = tensor2;\n                  } else {\n                    this.intermediateTensors[nodeName2] = [];\n                    this.intermediateTensors[nodeName2][index] = tensor2;\n                  }\n                }\n                delete intermediateTensorConsumerCount[tensor2.id];\n              } else if (count2 != null) {\n                intermediateTensorConsumerCount[tensor2.id]--;\n              }\n            }\n          });\n        }\n      }\n    });\n  }\n  async executeAsync(inputs, outputs) {\n    return this._executeAsync(inputs, outputs);\n  }\n  disposeIntermediateTensors() {\n    if (!this.intermediateTensors) {\n      return;\n    }\n    Object.keys(this.intermediateTensors).forEach((key) => this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose()));\n    this.disposeTensorsMap();\n  }\n  disposeTensorsMap() {\n    if (!this.tensorsMap) {\n      return;\n    }\n    Object.keys(this.tensorsMap).forEach((key) => {\n      const tensorArray = this.tensorsMap[key];\n      tensorArray.forEach((tensor2) => {\n        if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !this.keepIds.has(tensor2.id)) {\n          tensor2.dispose();\n        }\n      });\n    });\n  }\n  getIntermediateTensors() {\n    return this.tensorsMap;\n  }\n  resetIntermediateTensors() {\n    for (const key in this.intermediateTensors) {\n      this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose());\n      delete this.intermediateTensors[key];\n    }\n  }\n  async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n    if (!isFunctionExecution) {\n      inputs = this.mapInputs(inputs);\n      this.checkInputs(inputs);\n      this.checkInputShapeAndType(inputs);\n      outputs = this.mapOutputs(outputs);\n      this.checkOutputs(outputs);\n    }\n    try {\n      this.keepTensorForDebug = env().getBool(\"KEEP_INTERMEDIATE_TENSORS\");\n    } catch (e2) {\n      console.warn(e2.message);\n    }\n    this.resetIntermediateTensors();\n    const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n    this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n    const results = outputs.map((name) => getTensor(name, this.tensorsMap, context));\n    const outputIds = results.map((t2) => t2.id);\n    const inputIds = Object.keys(inputs).map((name) => inputs[name].id);\n    this.keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);\n    if (!this.keepTensorForDebug) {\n      this.disposeTensorsMap();\n    }\n    if (this.parent == null) {\n      context.dispose(this.keepIds);\n    }\n    return results;\n  }\n  async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n    const mappedInputs = inputs.reduce((map, tensor2, index) => {\n      map[this.inputs[index].name] = tensor2;\n      return map;\n    }, {});\n    return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n  }\n  async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n    const names = Object.keys(inputs);\n    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n    const stack2 = [\n      ...inputNodes,\n      ...this.graph.weights,\n      ...this._initNodes || []\n    ].map((node) => {\n      return { node, contexts: context.currentContext };\n    });\n    const tensorsMap = Object.assign({}, this.weightMap);\n    Object.keys(inputs).forEach((name) => {\n      const [nodeName, index] = parseNodeName(name);\n      const tensors = [];\n      tensors[index] = inputs[name];\n      tensorsMap[nodeName] = tensors;\n    });\n    const intermediateTensorConsumerCount = {};\n    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n    const added = {};\n    while (stack2.length > 0) {\n      const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n      await Promise.all(promises);\n    }\n    if (dynamicNode == null && !isFunctionExecution) {\n      console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);\n    }\n    const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name);\n    if (missingOutputs.length > 0) {\n      let alternativeMsg = \"\";\n      if (dynamicNode != null) {\n        alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;\n      }\n      throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);\n    }\n    return tensorsMap;\n  }\n  processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n    const promises = [];\n    while (stack2.length > 0) {\n      const item = stack2.pop();\n      context.currentContext = item.contexts;\n      let nodeName = \"\";\n      if (item.node.op === \"Enter\" && getParamValue(\"isConstant\", item.node, tensorMap, context)) {\n        [nodeName] = getNodeNameAndIndex(item.node.name, context);\n      }\n      if (tensorMap[item.node.name] == null) {\n        const tensors = executeOp20(item.node, tensorMap, context, this._resourceManager);\n        if (!nodeName) {\n          [nodeName] = getNodeNameAndIndex(item.node.name, context);\n        }\n        const currentContext = context.currentContext;\n        if (util_exports.isPromise(tensors)) {\n          promises.push(tensors.then((t2) => {\n            tensorMap[nodeName] = t2;\n            context.currentContext = currentContext;\n            this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n            this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n            return t2;\n          }));\n        } else {\n          tensorMap[nodeName] = tensors;\n          this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n          this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n        }\n      } else {\n        this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n      }\n    }\n    return promises;\n  }\n  processChildNodes(node, stack2, context, tensorMap, added, usedNodes) {\n    node.children.forEach((childNode) => {\n      const [nodeName] = getNodeNameAndIndex(childNode.name, context);\n      if (added[nodeName] || !usedNodes.has(childNode.name)) {\n        return;\n      }\n      if (childNode.op === \"Merge\") {\n        if (childNode.inputNames.some((name) => {\n          return !!getTensor(name, tensorMap, context);\n        })) {\n          added[nodeName] = true;\n          stack2.push({ contexts: context.currentContext, node: childNode });\n        }\n      } else if (childNode.inputNames.every((name) => {\n        return !!getTensor(name, tensorMap, context);\n      })) {\n        added[nodeName] = true;\n        stack2.push({ contexts: context.currentContext, node: childNode });\n      }\n    });\n  }\n  dispose() {\n    Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));\n  }\n  checkInputShapeAndType(inputs) {\n    Object.keys(inputs).forEach((name) => {\n      const input2 = inputs[name];\n      const [nodeName] = parseNodeName(name);\n      const node = this.graph.nodes[nodeName];\n      if (node.attrParams[\"shape\"] && node.attrParams[\"shape\"].value) {\n        const shape = node.attrParams[\"shape\"].value;\n        const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n        util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);\n      }\n      if (node.attrParams[\"dtype\"] && node.attrParams[\"dtype\"].value) {\n        util_exports.assert(input2.dtype === node.attrParams[\"dtype\"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams[\"dtype\"].value}, but was ${input2.dtype}`);\n      }\n    });\n  }\n  mapInputs(inputs) {\n    const result = {};\n    for (const inputName in inputs) {\n      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {\n        const tensor2 = this._signature.inputs[inputName];\n        result[tensor2.name] = inputs[inputName];\n      } else {\n        result[inputName] = inputs[inputName];\n      }\n    }\n    return result;\n  }\n  checkInputs(inputs) {\n    const notInGraph = Object.keys(inputs).filter((name) => {\n      const [nodeName] = parseNodeName(name);\n      return this.graph.nodes[nodeName] == null;\n    });\n    if (notInGraph.length > 0) {\n      throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);\n    }\n  }\n  mapOutputs(outputs) {\n    return outputs.map((name) => {\n      if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {\n        const tensor2 = this._signature.outputs[name];\n        return tensor2.name;\n      }\n      return name;\n    }, {});\n  }\n  checkOutputs(outputs) {\n    outputs.forEach((name) => {\n      const [normalizedName] = parseNodeName(name);\n      if (!this.graph.nodes[normalizedName]) {\n        throw new Error(`The output '${name}' is not found in the graph`);\n      }\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js\nvar ResourceManager = class {\n  constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n    this.hashTableNameToHandle = hashTableNameToHandle;\n    this.hashTableMap = hashTableMap;\n  }\n  addHashTable(name, hashTable) {\n    this.hashTableNameToHandle[name] = hashTable.handle;\n    this.hashTableMap[hashTable.id] = hashTable;\n  }\n  getHashTableHandleByName(name) {\n    return this.hashTableNameToHandle[name];\n  }\n  getHashTableById(id) {\n    return this.hashTableMap[id];\n  }\n  dispose() {\n    for (const key in this.hashTableMap) {\n      this.hashTableMap[key].clearAndClose();\n      delete this.hashTableMap[key];\n    }\n    for (const name in this.hashTableNameToHandle) {\n      this.hashTableNameToHandle[name].dispose();\n      delete this.hashTableNameToHandle[name];\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js\nvar TFHUB_SEARCH_PARAM = \"?tfjs-format=file\";\nvar DEFAULT_MODEL_NAME = \"model.json\";\nvar GraphModel = class {\n  constructor(modelUrl, loadOptions = {}) {\n    this.modelUrl = modelUrl;\n    this.loadOptions = loadOptions;\n    this.version = \"n/a\";\n    if (loadOptions == null) {\n      this.loadOptions = {};\n    }\n    this.resourceManager = new ResourceManager();\n  }\n  get modelVersion() {\n    return this.version;\n  }\n  get inputNodes() {\n    return this.executor.inputNodes;\n  }\n  get outputNodes() {\n    return this.executor.outputNodes;\n  }\n  get inputs() {\n    return this.executor.inputs;\n  }\n  get outputs() {\n    return this.executor.outputs;\n  }\n  get weights() {\n    return this.executor.weightMap;\n  }\n  get metadata() {\n    return this.artifacts.userDefinedMetadata;\n  }\n  get modelSignature() {\n    return this.signature;\n  }\n  findIOHandler() {\n    const path = this.modelUrl;\n    if (path.load != null) {\n      this.handler = path;\n    } else if (this.loadOptions.requestInit != null) {\n      this.handler = io_exports.browserHTTPRequest(path, this.loadOptions);\n    } else {\n      const handlers = io_exports.getLoadHandlers(path, this.loadOptions);\n      if (handlers.length === 0) {\n        handlers.push(io_exports.browserHTTPRequest(path, this.loadOptions));\n      } else if (handlers.length > 1) {\n        throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);\n      }\n      this.handler = handlers[0];\n    }\n  }\n  load() {\n    this.findIOHandler();\n    if (this.handler.load == null) {\n      throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n    }\n    const loadResult = this.handler.load();\n    if (util_exports.isPromise(loadResult)) {\n      return loadResult.then((artifacts) => this.loadSync(artifacts));\n    }\n    return this.loadSync(loadResult);\n  }\n  loadSync(artifacts) {\n    this.artifacts = artifacts;\n    const graph = this.artifacts.modelTopology;\n    let signature;\n    if (this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null) {\n      signature = this.artifacts.userDefinedMetadata.signature;\n    } else {\n      signature = this.artifacts.signature;\n    }\n    this.signature = signature;\n    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n    const weightMap = io_exports.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n    this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));\n    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n    this.executor.resourceManager = this.resourceManager;\n    if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {\n      const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n      this.initializer = new GraphExecutor(initializer);\n      this.initializer.weightMap = this.executor.weightMap;\n      this.initializer.resourceManager = this.resourceManager;\n      this.initializer.executeAsync({}, []);\n    }\n    return true;\n  }\n  async save(handlerOrURL, config) {\n    if (typeof handlerOrURL === \"string\") {\n      const handlers = io_exports.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n    }\n    return handlerOrURL.save(this.artifacts);\n  }\n  predict(inputs, config) {\n    return this.execute(inputs, this.outputNodes);\n  }\n  normalizeInputs(inputs) {\n    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n      return inputs;\n    }\n    inputs = Array.isArray(inputs) ? inputs : [inputs];\n    if (inputs.length !== this.inputNodes.length) {\n      throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);\n    }\n    return this.inputNodes.reduce((map, inputName, i2) => {\n      map[inputName] = inputs[i2];\n      return map;\n    }, {});\n  }\n  normalizeOutputs(outputs) {\n    outputs = outputs || this.outputNodes;\n    return !Array.isArray(outputs) ? [outputs] : outputs;\n  }\n  execute(inputs, outputs) {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = this.executor.execute(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  async executeAsync(inputs, outputs) {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = await this.executor.executeAsync(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  getIntermediateTensors() {\n    return this.executor.getIntermediateTensors();\n  }\n  disposeIntermediateTensors() {\n    this.executor.disposeIntermediateTensors();\n  }\n  convertTensorMapToTensorsMap(map) {\n    return Object.keys(map).reduce((newMap, key) => {\n      newMap[key] = [map[key]];\n      return newMap;\n    }, {});\n  }\n  dispose() {\n    this.executor.dispose();\n    if (this.initializer) {\n      this.initializer.dispose();\n    }\n    this.resourceManager.dispose();\n  }\n};\nasync function loadGraphModel(modelUrl, options = {}) {\n  if (modelUrl == null) {\n    throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");\n  }\n  if (options == null) {\n    options = {};\n  }\n  if (options.fromTFHub && typeof modelUrl === \"string\") {\n    modelUrl = getTFHubUrl(modelUrl);\n  }\n  const model2 = new GraphModel(modelUrl, options);\n  await model2.load();\n  return model2;\n}\nfunction loadGraphModelSync(modelSource) {\n  if (modelSource == null) {\n    throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model\");\n  }\n  if (!modelSource.load) {\n    throw new Error(`modelUrl IO Handler ${modelSource} has no load function`);\n  }\n  const model2 = new GraphModel(modelSource);\n  model2.load();\n  return model2;\n}\nfunction getTFHubUrl(modelUrl) {\n  if (!modelUrl.endsWith(\"/\")) {\n    modelUrl = modelUrl + \"/\";\n  }\n  return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/version.js\nvar version3 = \"3.18.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/index.js\nvar dist_exports2 = {};\n__export(dist_exports2, {\n  CSVDataset: () => CSVDataset,\n  Dataset: () => Dataset,\n  FileDataSource: () => FileDataSource,\n  TextLineDataset: () => TextLineDataset,\n  URLDataSource: () => URLDataSource,\n  array: () => array,\n  csv: () => csv,\n  func: () => func,\n  generator: () => generator,\n  microphone: () => microphone,\n  version_data: () => version4,\n  webcam: () => webcam,\n  zip: () => zip\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar seedrandom3 = __toESM(require_seedrandom4());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nvar seedrandom2 = __toESM(require_seedrandom4());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js\nfunction deepMap(input2, mapFn) {\n  return deepMapInternal(input2, mapFn);\n}\nfunction deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {\n  if (input2 == null) {\n    return null;\n  }\n  if (typeof Blob === \"function\" && input2 instanceof Blob) {\n    return input2.slice();\n  }\n  if (containedIn.has(input2)) {\n    throw new Error(\"Circular references are not supported.\");\n  }\n  if (seen.has(input2)) {\n    return seen.get(input2);\n  }\n  const result = mapFn(input2);\n  if (result.recurse && result.value !== null) {\n    throw new Error(\"A deep map function may not return both a value and recurse=true.\");\n  }\n  if (!result.recurse) {\n    seen.set(input2, result.value);\n    return result.value;\n  } else if (isIterable2(input2)) {\n    const mappedIterable = Array.isArray(input2) ? [] : {};\n    containedIn.add(input2);\n    for (const k in input2) {\n      const child = input2[k];\n      const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input2);\n    if (input2.__proto__) {\n      mappedIterable.__proto__ = input2.__proto__;\n    }\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n  }\n}\nfunction deepZip(inputs, zipFn = zipToList) {\n  return deepZipInternal(inputs, zipFn);\n}\nfunction deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) {\n  const input2 = inputs[0];\n  if (containedIn.has(input2)) {\n    throw new Error(\"Circular references are not supported.\");\n  }\n  const result = zipFn(inputs);\n  if (result.recurse && result.value !== null) {\n    throw new Error(\"A deep zip function may not return both a value and recurse=true.\");\n  }\n  if (!result.recurse) {\n    return result.value;\n  } else if (isIterable2(input2)) {\n    const mappedIterable = Array.isArray(input2) ? [] : {};\n    containedIn.add(input2);\n    for (const k in input2) {\n      const children = inputs.map((x) => x[k]);\n      const childResult = deepZipInternal(children, zipFn, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input2);\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n  }\n}\nfunction zipToList(x) {\n  if (x === null) {\n    return null;\n  }\n  if (isIterable2(x[0])) {\n    return { value: null, recurse: true };\n  } else {\n    return { value: x, recurse: false };\n  }\n}\nasync function deepMapAndAwaitAll(input2, mapFn) {\n  const seen = /* @__PURE__ */ new Map();\n  deepMapInternal(input2, mapFn, seen);\n  for (const key of Array.from(seen.keys())) {\n    const value = seen.get(key);\n    if (util_exports.isPromise(value)) {\n      const mappedValue = await value;\n      seen.set(key, mappedValue);\n    }\n  }\n  const result = deepMapInternal(input2, mapFn, seen);\n  return result;\n}\nfunction isIterable2(obj) {\n  let isTextDecoder = false;\n  if (env().get(\"IS_BROWSER\")) {\n    isTextDecoder = obj instanceof TextDecoder;\n  } else {\n    const { StringDecoder } = require_string_decoder();\n    isTextDecoder = obj instanceof StringDecoder;\n  }\n  return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === \"object\" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);\n}\nfunction canTensorify(obj) {\n  return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === \"object\" && obj instanceof Tensor || util_exports.isTypedArray(obj);\n}\nfunction isPrimitive(value) {\n  return value === null || typeof value !== \"object\" && typeof value !== \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js\nfunction deepClone(container) {\n  return deepMap(container, cloneIfTensor);\n}\nfunction cloneIfTensor(item) {\n  if (item instanceof Tensor) {\n    return { value: item.clone(), recurse: false };\n  } else if (isIterable2(item)) {\n    return { value: null, recurse: true };\n  } else {\n    return { value: item, recurse: false };\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js\nvar RingBuffer = class {\n  constructor(capacity) {\n    this.capacity = capacity;\n    this.begin = 0;\n    this.end = 0;\n    if (capacity == null) {\n      throw new RangeError(\"Can't create a ring buffer of unknown capacity.\");\n    }\n    if (capacity < 1) {\n      throw new RangeError(\"Can't create ring buffer of capacity < 1.\");\n    }\n    this.data = new Array(capacity);\n    this.doubledCapacity = 2 * capacity;\n  }\n  wrap(index) {\n    while (index < 0) {\n      index += this.doubledCapacity;\n    }\n    return index % this.doubledCapacity;\n  }\n  get(index) {\n    if (index < 0) {\n      throw new RangeError(\"Can't get item at a negative index.\");\n    }\n    return this.data[index % this.capacity];\n  }\n  set(index, value) {\n    if (index < 0) {\n      throw new RangeError(\"Can't set item at a negative index.\");\n    }\n    this.data[index % this.capacity] = value;\n  }\n  length() {\n    let length = this.end - this.begin;\n    if (length < 0) {\n      length = this.doubledCapacity + length;\n    }\n    return length;\n  }\n  isFull() {\n    return this.length() === this.capacity;\n  }\n  isEmpty() {\n    return this.length() === 0;\n  }\n  push(value) {\n    if (this.isFull()) {\n      throw new RangeError(\"Ring buffer is full.\");\n    }\n    this.set(this.end, value);\n    this.end = this.wrap(this.end + 1);\n  }\n  pushAll(values) {\n    for (const value of values) {\n      this.push(value);\n    }\n  }\n  pop() {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    this.end = this.wrap(this.end - 1);\n    const result = this.get(this.end);\n    this.set(this.end, void 0);\n    return result;\n  }\n  unshift(value) {\n    if (this.isFull()) {\n      throw new RangeError(\"Ring buffer is full.\");\n    }\n    this.begin = this.wrap(this.begin - 1);\n    this.set(this.begin, value);\n  }\n  shift() {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    const result = this.get(this.begin);\n    this.set(this.begin, void 0);\n    this.begin = this.wrap(this.begin + 1);\n    return result;\n  }\n  shuffleExcise(relativeIndex) {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    const index = this.wrap(this.begin + relativeIndex);\n    const result = this.get(index);\n    this.set(index, this.pop());\n    return result;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js\nvar GrowingRingBuffer = class extends RingBuffer {\n  constructor() {\n    super(GrowingRingBuffer.INITIAL_CAPACITY);\n  }\n  isFull() {\n    return false;\n  }\n  push(value) {\n    if (super.isFull()) {\n      this.expand();\n    }\n    super.push(value);\n  }\n  unshift(value) {\n    if (super.isFull()) {\n      this.expand();\n    }\n    super.unshift(value);\n  }\n  expand() {\n    const newCapacity = this.capacity * 2;\n    const newData = new Array(newCapacity);\n    const len = this.length();\n    for (let i2 = 0; i2 < len; i2++) {\n      newData[i2] = this.get(this.wrap(this.begin + i2));\n    }\n    this.data = newData;\n    this.capacity = newCapacity;\n    this.doubledCapacity = 2 * this.capacity;\n    this.begin = 0;\n    this.end = len;\n  }\n};\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nfunction iteratorFromItems(items) {\n  return new ArrayIterator(items);\n}\nfunction iteratorFromFunction(func2) {\n  return new FunctionCallIterator(func2);\n}\nfunction iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n  return new ChainedIterator(baseIterators, baseErrorHandler);\n}\nfunction iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n  return new ZipIterator(iterators, mismatchMode);\n}\nvar LazyIterator = class {\n  async toArray() {\n    const result = [];\n    let x = await this.next();\n    while (!x.done) {\n      result.push(x.value);\n      x = await this.next();\n    }\n    return result;\n  }\n  async toArrayForTest() {\n    const stream = this.prefetch(100);\n    const result = [];\n    let x = await stream.next();\n    while (!x.done) {\n      result.push(x.value);\n      x = await stream.next();\n    }\n    return result;\n  }\n  async resolveFully() {\n    let x = await this.next();\n    while (!x.done) {\n      x = await this.next();\n    }\n  }\n  async resolveWhile(predicate) {\n    let x = await this.next();\n    let shouldContinue = predicate(x.value);\n    while (!x.done && shouldContinue) {\n      x = await this.next();\n      shouldContinue = predicate(x.value);\n    }\n  }\n  handleErrors(handler) {\n    return new ErrorHandlingLazyIterator(this, handler);\n  }\n  filter(predicate) {\n    return new FilterIterator(this, predicate);\n  }\n  map(transform6) {\n    return new MapIterator(this, transform6);\n  }\n  mapAsync(transform6) {\n    return new AsyncMapIterator(this, transform6);\n  }\n  serialMapAsync(transform6) {\n    return new AsyncMapIterator(this, transform6).serial();\n  }\n  flatmap(transform6) {\n    return new FlatmapIterator(this, transform6);\n  }\n  async forEachAsync(f) {\n    return this.map(f).resolveFully();\n  }\n  async serialForEach(f) {\n    return this.serialMapAsync(f).resolveWhile((x) => x === true);\n  }\n  rowMajorBatch(batchSize, smallLastBatch = true) {\n    return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n  }\n  columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {\n    const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n    return rowBatches.map((x) => deepZip(x, zipFn));\n  }\n  concatenate(iterator, baseErrorHandler) {\n    return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n  }\n  take(count2) {\n    if (count2 < 0 || count2 == null) {\n      return this;\n    }\n    return new TakeIterator(this, count2);\n  }\n  skip(count2) {\n    if (count2 < 0 || count2 == null) {\n      return this;\n    }\n    return new SkipIterator(this, count2);\n  }\n  prefetch(bufferSize) {\n    return new PrefetchIterator(this, bufferSize);\n  }\n  shuffle(windowSize, seed) {\n    return new ShuffleIterator(this, windowSize, seed);\n  }\n  serial() {\n    return new SerialIterator(this);\n  }\n};\nvar ArrayIterator = class extends LazyIterator {\n  constructor(items) {\n    super();\n    this.items = items;\n    this.trav = 0;\n  }\n  summary() {\n    return `Array of ${this.items.length} items`;\n  }\n  async next() {\n    if (this.trav >= this.items.length) {\n      return { value: null, done: true };\n    }\n    const item = this.items[this.trav];\n    this.trav++;\n    return { value: deepClone(item), done: false };\n  }\n};\nvar FunctionCallIterator = class extends LazyIterator {\n  constructor(nextFn) {\n    super();\n    this.nextFn = nextFn;\n  }\n  summary() {\n    return `Function call`;\n  }\n  async next() {\n    try {\n      return this.nextFn();\n    } catch (e2) {\n      e2.message = `Error thrown while iterating through a dataset: ${e2.message}`;\n      throw e2;\n    }\n  }\n};\nvar SerialIterator = class extends LazyIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Serial`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    return this.upstream.next();\n  }\n};\nvar SkipIterator = class extends LazyIterator {\n  constructor(upstream, maxCount) {\n    super();\n    this.upstream = upstream;\n    this.maxCount = maxCount;\n    this.count = 0;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Skip`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (this.count++ < this.maxCount) {\n      const skipped = await this.upstream.next();\n      if (skipped.done) {\n        return skipped;\n      }\n      dispose(skipped.value);\n    }\n    return this.upstream.next();\n  }\n};\nvar TakeIterator = class extends LazyIterator {\n  constructor(upstream, maxCount) {\n    super();\n    this.upstream = upstream;\n    this.maxCount = maxCount;\n    this.count = 0;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Take`;\n  }\n  async next() {\n    if (this.count++ >= this.maxCount) {\n      return { value: null, done: true };\n    }\n    return this.upstream.next();\n  }\n};\nvar RowMajorBatchIterator = class extends LazyIterator {\n  constructor(upstream, batchSize, enableSmallLastBatch = true) {\n    super();\n    this.upstream = upstream;\n    this.batchSize = batchSize;\n    this.enableSmallLastBatch = enableSmallLastBatch;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> RowMajorBatch`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    const batch = [];\n    while (batch.length < this.batchSize) {\n      const item = await this.upstream.next();\n      if (item.done) {\n        if (this.enableSmallLastBatch && batch.length > 0) {\n          return { value: batch, done: false };\n        }\n        return { value: null, done: true };\n      }\n      batch.push(item.value);\n    }\n    return { value: batch, done: false };\n  }\n};\nvar FilterIterator = class extends LazyIterator {\n  constructor(upstream, predicate) {\n    super();\n    this.upstream = upstream;\n    this.predicate = predicate;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Filter`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (true) {\n      const item = await this.upstream.next();\n      if (item.done || this.predicate(item.value)) {\n        return item;\n      }\n      dispose(item.value);\n    }\n  }\n};\nvar MapIterator = class extends LazyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Map`;\n  }\n  async next() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return { value: null, done: true };\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mapped = this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n    for (const t2 of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {\n        t2.dispose();\n      }\n    }\n    return { value: mapped, done: false };\n  }\n};\nvar ErrorHandlingLazyIterator = class extends LazyIterator {\n  constructor(upstream, handler) {\n    super();\n    this.upstream = upstream;\n    this.handler = handler;\n    this.count = 0;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> handleErrors`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (true) {\n      try {\n        return await this.upstream.next();\n      } catch (e2) {\n        if (!this.handler(e2)) {\n          return { value: null, done: true };\n        }\n      }\n    }\n  }\n};\nvar AsyncMapIterator = class extends LazyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> AsyncMap`;\n  }\n  async next() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return { value: null, done: true };\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mapped = await this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n    for (const t2 of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {\n        t2.dispose();\n      }\n    }\n    return { value: mapped, done: false };\n  }\n};\nvar OneToManyIterator = class extends LazyIterator {\n  constructor() {\n    super();\n    this.outputQueue = new GrowingRingBuffer();\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (this.outputQueue.length() === 0) {\n      if (!await this.pump()) {\n        return { value: null, done: true };\n      }\n    }\n    return { value: this.outputQueue.shift(), done: false };\n  }\n};\nvar FlatmapIterator = class extends OneToManyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Flatmap`;\n  }\n  async pump() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return false;\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mappedArray = this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);\n    this.outputQueue.pushAll(mappedArray);\n    for (const t2 of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {\n        t2.dispose();\n      }\n    }\n    return true;\n  }\n};\nvar ChainedIterator = class extends LazyIterator {\n  constructor(iterators, baseErrorHandler) {\n    super();\n    this.baseErrorHandler = baseErrorHandler;\n    this.lastRead = null;\n    this.iterator = null;\n    this.moreIterators = iterators;\n  }\n  summary() {\n    const upstreamSummaries = \"TODO: fill in upstream of chained summaries\";\n    return `${upstreamSummaries} -> Chained`;\n  }\n  async next() {\n    this.lastRead = this.readFromChain(this.lastRead);\n    return this.lastRead;\n  }\n  async readFromChain(lastRead) {\n    await lastRead;\n    if (this.iterator == null) {\n      const iteratorResult = await this.moreIterators.next();\n      if (iteratorResult.done) {\n        return { value: null, done: true };\n      }\n      this.iterator = iteratorResult.value;\n      if (this.baseErrorHandler != null) {\n        this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n      }\n    }\n    const itemResult = await this.iterator.next();\n    if (itemResult.done) {\n      this.iterator = null;\n      return this.readFromChain(lastRead);\n    }\n    return itemResult;\n  }\n};\nvar ZipMismatchMode;\n(function(ZipMismatchMode2) {\n  ZipMismatchMode2[ZipMismatchMode2[\"FAIL\"] = 0] = \"FAIL\";\n  ZipMismatchMode2[ZipMismatchMode2[\"SHORTEST\"] = 1] = \"SHORTEST\";\n  ZipMismatchMode2[ZipMismatchMode2[\"LONGEST\"] = 2] = \"LONGEST\";\n})(ZipMismatchMode || (ZipMismatchMode = {}));\nvar ZipIterator = class extends LazyIterator {\n  constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    super();\n    this.iterators = iterators;\n    this.mismatchMode = mismatchMode;\n    this.count = 0;\n    this.currentPromise = null;\n  }\n  summary() {\n    const upstreamSummaries = \"TODO: fill in upstream of zip summaries\";\n    return `{${upstreamSummaries}} -> Zip`;\n  }\n  async nextState(afterState) {\n    await afterState;\n    let numIterators = 0;\n    let iteratorsDone = 0;\n    function getNext(container) {\n      if (container instanceof LazyIterator) {\n        const result = container.next();\n        return {\n          value: result.then((x) => {\n            numIterators++;\n            if (x.done) {\n              iteratorsDone++;\n            }\n            return x.value;\n          }),\n          recurse: false\n        };\n      } else {\n        return { value: null, recurse: true };\n      }\n    }\n    const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n    if (numIterators === iteratorsDone) {\n      return { value: null, done: true };\n    }\n    if (iteratorsDone > 0) {\n      switch (this.mismatchMode) {\n        case ZipMismatchMode.FAIL:\n          throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);\n        case ZipMismatchMode.SHORTEST:\n          return { value: null, done: true };\n        case ZipMismatchMode.LONGEST:\n        default:\n      }\n    }\n    this.count++;\n    return { value: mapped, done: false };\n  }\n  async next() {\n    this.currentPromise = this.nextState(this.currentPromise);\n    return this.currentPromise;\n  }\n};\nvar PrefetchIterator = class extends LazyIterator {\n  constructor(upstream, bufferSize) {\n    super();\n    this.upstream = upstream;\n    this.bufferSize = bufferSize;\n    this.buffer = new RingBuffer(bufferSize);\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Prefetch`;\n  }\n  refill() {\n    while (!this.buffer.isFull()) {\n      const v = this.upstream.next();\n      this.buffer.push(v);\n    }\n  }\n  next() {\n    this.refill();\n    return this.buffer.shift();\n  }\n};\nvar ShuffleIterator = class extends PrefetchIterator {\n  constructor(upstream, windowSize, seed) {\n    super(upstream, windowSize);\n    this.upstream = upstream;\n    this.windowSize = windowSize;\n    this.upstreamExhausted = false;\n    this.random = seedrandom2.alea(seed || util_exports.now().toString());\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  randomInt(max7) {\n    return Math.floor(this.random() * max7);\n  }\n  chooseIndex() {\n    return this.randomInt(this.buffer.length());\n  }\n  async serialNext() {\n    if (!this.upstreamExhausted) {\n      this.refill();\n    }\n    while (!this.buffer.isEmpty()) {\n      const chosenIndex = this.chooseIndex();\n      const result = await this.buffer.shuffleExcise(chosenIndex);\n      if (result.done) {\n        this.upstreamExhausted = true;\n      } else {\n        this.refill();\n        return result;\n      }\n    }\n    return { value: null, done: true };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar Dataset = class {\n  constructor() {\n    this.size = null;\n  }\n  batch(batchSize, smallLastBatch = true) {\n    const base = this;\n    util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n    let size;\n    if (this.size === Infinity || this.size == null) {\n      size = this.size;\n    } else if (smallLastBatch) {\n      size = Math.ceil(this.size / batchSize);\n    } else {\n      size = Math.floor(this.size / batchSize);\n    }\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n    }, size);\n  }\n  concatenate(dataset) {\n    const base = this;\n    let size;\n    if (this.size === Infinity || dataset.size === Infinity) {\n      size = Infinity;\n    } else if (this.size != null && dataset.size != null) {\n      size = this.size + dataset.size;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n  }\n  filter(predicate) {\n    const base = this;\n    let size;\n    if (this.size === Infinity) {\n      size = Infinity;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).filter((x) => tidy(() => predicate(x)));\n    }, size);\n  }\n  async forEachAsync(f) {\n    return (await this.iterator()).forEachAsync(f);\n  }\n  map(transform6) {\n    const base = this;\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).map((x) => tidy(() => transform6(x)));\n    }, this.size);\n  }\n  mapAsync(transform6) {\n    const base = this;\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).mapAsync(transform6);\n    }, this.size);\n  }\n  prefetch(bufferSize) {\n    if (bufferSize == null) {\n      throw new RangeError(\"`Dataset.prefetch()` requires bufferSize to be specified.\");\n    }\n    const base = this;\n    return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n  }\n  repeat(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && count2 > 0) {\n      size = this.size * count2;\n    } else if (count2 === 0) {\n      size = 0;\n    } else if (this.size != null && (count2 === void 0 || count2 < 0)) {\n      size = Infinity;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => {\n      const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n      return iteratorFromConcatenated(iteratorIterator.take(count2));\n    }, size);\n  }\n  skip(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && count2 >= 0 && this.size >= count2) {\n      size = this.size - count2;\n    } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {\n      size = 0;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size);\n  }\n  shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n    if (bufferSize == null || bufferSize < 0) {\n      if (this.size == null) {\n        throw new RangeError(\"`Dataset.shuffle()` requires bufferSize to be specified.\");\n      } else {\n        throw new RangeError(`\\`Dataset.shuffle()\\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \\`tf.Tensor\\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);\n      }\n    }\n    const base = this;\n    const random = seedrandom3.alea(seed || util_exports.now().toString());\n    return datasetFromIteratorFn(async () => {\n      let seed2 = random.int32();\n      if (reshuffleEachIteration) {\n        seed2 += random.int32();\n      }\n      return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n    }, this.size);\n  }\n  take(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && this.size > count2) {\n      size = count2;\n    } else if (this.size != null && this.size <= count2) {\n      size = this.size;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size);\n  }\n  async toArray() {\n    if (this.size === Infinity) {\n      throw new Error(\"Can not convert infinite data stream to array.\");\n    }\n    return (await this.iterator()).toArray();\n  }\n  async toArrayForTest() {\n    if (this.size === Infinity) {\n      throw new Error(\"Can not convert infinite data stream to array.\");\n    }\n    return (await this.iterator()).toArrayForTest();\n  }\n};\nDataset.MAX_BUFFER_SIZE = 1e4;\nfunction datasetFromIteratorFn(iteratorFn, size = null) {\n  return new class extends Dataset {\n    constructor() {\n      super(...arguments);\n      this.size = size;\n    }\n    async iterator() {\n      return iteratorFn();\n    }\n  }();\n}\nfunction array(items) {\n  return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\nfunction zip(datasets) {\n  if (!isIterable2(datasets)) {\n    throw new Error(\"The argument to zip() must be an object or array.\");\n  }\n  let size;\n  if (Array.isArray(datasets)) {\n    for (let i2 = 0; i2 < datasets.length; i2++) {\n      size = size == null ? datasets[i2].size : Math.min(size, datasets[i2].size);\n    }\n  } else if (datasets instanceof Object) {\n    for (const ds in datasets) {\n      size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);\n    }\n  }\n  return datasetFromIteratorFn(async () => {\n    const streams = await deepMapAndAwaitAll(datasets, (d) => {\n      if (d instanceof Dataset) {\n        return { value: d.iterator(), recurse: false };\n      } else if (isIterable2(d)) {\n        return { value: null, recurse: true };\n      } else {\n        throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\");\n      }\n    });\n    return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n  }, size);\n}\nfunction deepBatchConcat(rows) {\n  if (rows === null) {\n    return null;\n  }\n  const exampleRow = rows[0];\n  if (canTensorify(exampleRow)) {\n    const value = batchConcat(rows);\n    return { value, recurse: false };\n  }\n  return { value: null, recurse: true };\n}\nfunction batchConcat(arrays) {\n  if (arrays.length === 0) {\n    throw new Error(\"Can't make a batch of zero elements.\");\n  }\n  if (arrays[0] instanceof Tensor) {\n    return stack(arrays);\n  } else {\n    return tensor(arrays);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js\nvar TextLineDataset = class extends Dataset {\n  constructor(input2) {\n    super();\n    this.input = input2;\n  }\n  async iterator() {\n    const inputIterator = await this.input.iterator();\n    const utf8Iterator = inputIterator.decodeUTF8();\n    const lineIterator = utf8Iterator.split(\"\\n\").map((line) => {\n      if (line.endsWith(\"\\r\")) {\n        line = line.slice(0, -1);\n      }\n      return line;\n    });\n    return lineIterator;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js\nvar CODE_QUOTE = '\"';\nvar STATE_OUT = Symbol(\"out\");\nvar STATE_FIELD = Symbol(\"field\");\nvar STATE_QUOTE = Symbol(\"quote\");\nvar STATE_QUOTE_AFTER_QUOTE = Symbol(\"quoteafterquote\");\nvar STATE_WITHIN_QUOTE_IN_QUOTE = Symbol(\"quoteinquote\");\nvar CSVDataset = class extends Dataset {\n  constructor(input2, csvConfig) {\n    super();\n    this.input = input2;\n    this.hasHeader = true;\n    this.fullColumnNames = null;\n    this.columnNamesValidated = false;\n    this.columnConfigs = null;\n    this.configuredColumnsOnly = false;\n    this.delimiter = \",\";\n    this.delimWhitespace = false;\n    this.base = new TextLineDataset(input2);\n    if (!csvConfig) {\n      csvConfig = {};\n    }\n    this.hasHeader = csvConfig.hasHeader === false ? false : true;\n    this.fullColumnNames = csvConfig.columnNames;\n    this.columnConfigs = csvConfig.columnConfigs;\n    this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n    if (csvConfig.delimWhitespace) {\n      util_exports.assert(csvConfig.delimiter == null, () => \"Delimiter should not be provided when delimWhitespace is true.\");\n      this.delimWhitespace = true;\n      this.delimiter = \" \";\n    } else {\n      this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : \",\";\n    }\n  }\n  async columnNames() {\n    if (!this.columnNamesValidated) {\n      await this.setColumnNames();\n    }\n    return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;\n  }\n  async setColumnNames() {\n    const columnNamesFromFile = await this.maybeReadHeaderLine();\n    if (!this.fullColumnNames && !columnNamesFromFile) {\n      throw new Error(\"Column names must be provided if there is no header line.\");\n    } else if (this.fullColumnNames && columnNamesFromFile) {\n      util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => \"The length of provided columnNames (\" + this.fullColumnNames.length.toString() + \") does not match the length of the header line read from file (\" + columnNamesFromFile.length.toString() + \").\");\n    }\n    if (!this.fullColumnNames) {\n      this.fullColumnNames = columnNamesFromFile;\n    }\n    const counts = this.fullColumnNames.reduce((countAcc, name) => {\n      countAcc[name] = countAcc[name] + 1 || 1;\n      return countAcc;\n    }, {});\n    const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);\n    util_exports.assert(duplicateNames.length === 0, () => \"Duplicate column names found: \" + duplicateNames.toString());\n    if (this.columnConfigs) {\n      for (const key of Object.keys(this.columnConfigs)) {\n        const index = this.fullColumnNames.indexOf(key);\n        if (index === -1) {\n          throw new Error('The key \"' + key + '\" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + \").\");\n        }\n      }\n    }\n    this.columnNamesValidated = true;\n  }\n  async maybeReadHeaderLine() {\n    if (this.hasHeader) {\n      const iter = await this.base.iterator();\n      const firstElement = await iter.next();\n      if (firstElement.done) {\n        throw new Error(\"No data was found for CSV parsing.\");\n      }\n      const firstLine = firstElement.value;\n      const headers = this.parseRow(firstLine, false);\n      return headers;\n    } else {\n      return null;\n    }\n  }\n  async iterator() {\n    if (!this.columnNamesValidated) {\n      await this.setColumnNames();\n    }\n    let lines = await this.base.iterator();\n    if (this.hasHeader) {\n      lines = lines.skip(1);\n    }\n    return lines.map((x) => this.makeDataElement(x));\n  }\n  makeDataElement(line) {\n    const values = this.parseRow(line);\n    const features = {};\n    const labels = {};\n    for (let i2 = 0; i2 < this.fullColumnNames.length; i2++) {\n      const key = this.fullColumnNames[i2];\n      const config = this.columnConfigs ? this.columnConfigs[key] : null;\n      if (this.configuredColumnsOnly && !config) {\n        continue;\n      } else {\n        const value = values[i2];\n        let parsedValue = null;\n        if (value === \"\") {\n          if (config && config.default !== void 0) {\n            parsedValue = config.default;\n          } else if (config && (config.required || config.isLabel)) {\n            throw new Error(`Required column ${key} is empty in this line: ${line}`);\n          } else {\n            parsedValue = void 0;\n          }\n        } else {\n          const valueAsNum = Number(value);\n          if (isNaN(valueAsNum)) {\n            if (config && config.dtype === \"bool\") {\n              parsedValue = this.getBoolean(value);\n            } else {\n              parsedValue = value;\n            }\n          } else if (!config || !config.dtype) {\n            parsedValue = valueAsNum;\n          } else {\n            switch (config.dtype) {\n              case \"float32\":\n                parsedValue = valueAsNum;\n                break;\n              case \"int32\":\n                parsedValue = Math.floor(valueAsNum);\n                break;\n              case \"bool\":\n                parsedValue = this.getBoolean(value);\n                break;\n              default:\n                parsedValue = valueAsNum;\n            }\n          }\n        }\n        config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;\n      }\n    }\n    if (Object.keys(labels).length === 0) {\n      return features;\n    } else {\n      return { xs: features, ys: labels };\n    }\n  }\n  getBoolean(value) {\n    if (value === \"1\" || value.toLowerCase() === \"true\") {\n      return 1;\n    } else {\n      return 0;\n    }\n  }\n  parseRow(line, validateElementCount = true) {\n    const result = [];\n    let readOffset = 0;\n    const readLength = line.length;\n    let currentState = STATE_OUT;\n    for (let i2 = 0; i2 < readLength; i2++) {\n      switch (currentState) {\n        case STATE_OUT:\n          switch (line.charAt(i2)) {\n            case CODE_QUOTE:\n              readOffset = i2 + 1;\n              currentState = STATE_QUOTE;\n              break;\n            case this.delimiter:\n              readOffset = i2 + 1;\n              if (this.delimiter === \" \" && this.delimWhitespace) {\n                break;\n              }\n              result.push(\"\");\n              currentState = STATE_OUT;\n              break;\n            default:\n              currentState = STATE_FIELD;\n              readOffset = i2;\n              break;\n          }\n          break;\n        case STATE_FIELD:\n          switch (line.charAt(i2)) {\n            case this.delimiter:\n              result.push(line.substring(readOffset, i2));\n              currentState = STATE_OUT;\n              readOffset = i2 + 1;\n              break;\n            default:\n          }\n          break;\n        case STATE_QUOTE:\n          switch (line.charAt(i2)) {\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE_AFTER_QUOTE;\n              break;\n            default:\n          }\n          break;\n        case STATE_QUOTE_AFTER_QUOTE:\n          switch (line.charAt(i2)) {\n            case this.delimiter:\n              result.push(line.substring(readOffset, i2 - 1));\n              currentState = STATE_OUT;\n              readOffset = i2 + 1;\n              break;\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE;\n              break;\n            default:\n              currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n              break;\n          }\n          break;\n        case STATE_WITHIN_QUOTE_IN_QUOTE:\n          switch (line.charAt(i2)) {\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE;\n              break;\n            default:\n          }\n          break;\n        default:\n      }\n    }\n    if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n      result.push(line.substring(readOffset, readLength - 1));\n    } else {\n      result.push(line.substring(readOffset));\n    }\n    if (validateElementCount && result.length !== this.fullColumnNames.length) {\n      throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n    }\n    return result;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js\nvar MicrophoneIterator = class extends LazyIterator {\n  constructor(microphoneConfig) {\n    super();\n    this.microphoneConfig = microphoneConfig;\n    this.isClosed = false;\n    this.fftSize = microphoneConfig.fftSize || 1024;\n    const fftSizeLog2 = Math.log2(this.fftSize);\n    if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {\n      throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);\n    }\n    this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n    this.sampleRateHz = microphoneConfig.sampleRateHz;\n    this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;\n    this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n    this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n    this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;\n    this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;\n    if (!this.includeSpectrogram && !this.includeWaveform) {\n      throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\");\n    }\n  }\n  summary() {\n    return `microphone`;\n  }\n  static async create(microphoneConfig = {}) {\n    if (!env().get(\"IS_BROWSER\")) {\n      throw new Error(\"microphone API is only supported in browser environment.\");\n    }\n    const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n    await microphoneIterator.start();\n    return microphoneIterator;\n  }\n  async start() {\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia({\n        audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,\n        video: false\n      });\n    } catch (e2) {\n      throw new Error(`Error thrown while initializing video stream: ${e2.message}`);\n    }\n    if (!this.stream) {\n      throw new Error(\"Could not obtain audio from microphone.\");\n    }\n    const ctxConstructor = window.AudioContext || window.webkitAudioContext;\n    this.audioContext = new ctxConstructor();\n    if (!this.sampleRateHz) {\n      this.sampleRateHz = this.audioContext.sampleRate;\n    } else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n      throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);\n    }\n    const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n    this.analyser = this.audioContext.createAnalyser();\n    this.analyser.fftSize = this.fftSize * 2;\n    this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n    streamSource.connect(this.analyser);\n    this.freqData = new Float32Array(this.fftSize);\n    this.timeData = new Float32Array(this.fftSize);\n    return;\n  }\n  async next() {\n    if (this.isClosed) {\n      return { value: null, done: true };\n    }\n    let spectrogramTensor;\n    let waveformTensor;\n    const audioDataQueue = await this.getAudioData();\n    if (this.includeSpectrogram) {\n      const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n      spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n    }\n    if (this.includeWaveform) {\n      const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n      waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n    }\n    return {\n      value: { \"spectrogram\": spectrogramTensor, \"waveform\": waveformTensor },\n      done: false\n    };\n  }\n  async capture() {\n    return (await this.next()).value;\n  }\n  async getAudioData() {\n    const freqDataQueue = [];\n    const timeDataQueue = [];\n    let currentFrames = 0;\n    return new Promise((resolve) => {\n      const intervalID = setInterval(() => {\n        if (this.includeSpectrogram) {\n          this.analyser.getFloatFrequencyData(this.freqData);\n          if (this.freqData[0] === -Infinity) {\n            resolve({ freqDataQueue, timeDataQueue });\n          }\n          freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n        }\n        if (this.includeWaveform) {\n          this.analyser.getFloatTimeDomainData(this.timeData);\n          timeDataQueue.push(this.timeData.slice());\n        }\n        if (++currentFrames === this.numFrames) {\n          clearInterval(intervalID);\n          resolve({ freqDataQueue, timeDataQueue });\n        }\n      }, this.fftSize / this.sampleRateHz * 1e3);\n    });\n  }\n  stop() {\n    if (!this.isClosed) {\n      this.isClosed = true;\n      this.analyser.disconnect();\n      this.audioContext.close();\n      if (this.stream != null && this.stream.getTracks().length > 0) {\n        this.stream.getTracks()[0].stop();\n      }\n    }\n  }\n  toArray() {\n    throw new Error(\"Can not convert infinite audio stream to array.\");\n  }\n  getSampleRate() {\n    return this.sampleRateHz;\n  }\n  flattenQueue(queue) {\n    const frameSize = queue[0].length;\n    const freqData = new Float32Array(queue.length * frameSize);\n    queue.forEach((data, i2) => freqData.set(data, i2 * frameSize));\n    return freqData;\n  }\n  getTensorFromAudioDataArray(freqData, shape) {\n    const vals = new Float32Array(util_exports.sizeFromShape(shape));\n    vals.set(freqData, vals.length - freqData.length);\n    return tensor(vals, shape);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js\nvar WebcamIterator = class extends LazyIterator {\n  constructor(webcamVideoElement, webcamConfig) {\n    super();\n    this.webcamVideoElement = webcamVideoElement;\n    this.webcamConfig = webcamConfig;\n    this.isClosed = true;\n    this.resize = false;\n    if (this.needToResize()) {\n      this.resize = true;\n      this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n      this.cropBoxInd = tensor1d([0], \"int32\");\n      if (this.webcamConfig.centerCrop) {\n        const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;\n        const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;\n        const widthCropStart = (1 - widthCroppingRatio) / 2;\n        const heightCropStart = (1 - heightCroppingRatio) / 2;\n        const widthCropEnd = widthCropStart + widthCroppingRatio;\n        const heightCropEnd = heightCroppingRatio + heightCropStart;\n        this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n      } else {\n        this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n      }\n    }\n  }\n  summary() {\n    return `webcam`;\n  }\n  static async create(webcamVideoElement, webcamConfig = {}) {\n    if (!env().get(\"IS_BROWSER\")) {\n      throw new Error(\"tf.data.webcam is only supported in browser environment.\");\n    }\n    if (!webcamVideoElement) {\n      webcamVideoElement = document.createElement(\"video\");\n      if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n        throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");\n      }\n      webcamVideoElement.width = webcamConfig.resizeWidth;\n      webcamVideoElement.height = webcamConfig.resizeHeight;\n    }\n    const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n    await webcamIterator.start();\n    return webcamIterator;\n  }\n  async start() {\n    if (this.webcamConfig.facingMode) {\n      util_exports.assert(this.webcamConfig.facingMode === \"user\" || this.webcamConfig.facingMode === \"environment\", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia({\n        video: {\n          deviceId: this.webcamConfig.deviceId,\n          facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : \"user\",\n          width: this.webcamVideoElement.width,\n          height: this.webcamVideoElement.height\n        }\n      });\n    } catch (e2) {\n      e2.message = `Error thrown while initializing video stream: ${e2.message}`;\n      throw e2;\n    }\n    if (!this.stream) {\n      throw new Error(\"Could not obtain video from webcam.\");\n    }\n    try {\n      this.webcamVideoElement.srcObject = this.stream;\n    } catch (error) {\n      console.log(error);\n      this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n    }\n    this.webcamVideoElement.play();\n    this.isClosed = false;\n    return new Promise((resolve) => {\n      this.webcamVideoElement.onloadedmetadata = () => {\n        resolve();\n      };\n    });\n  }\n  async next() {\n    if (this.isClosed) {\n      return { value: null, done: true };\n    }\n    let img;\n    try {\n      img = browser_exports.fromPixels(this.webcamVideoElement);\n    } catch (e2) {\n      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e2)}`);\n    }\n    if (this.resize) {\n      try {\n        return { value: this.cropAndResizeFrame(img), done: false };\n      } catch (e2) {\n        throw new Error(`Error thrown cropping the video: ${e2.message}`);\n      } finally {\n        img.dispose();\n      }\n    } else {\n      return { value: img, done: false };\n    }\n  }\n  needToResize() {\n    if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n      return true;\n    }\n    return false;\n  }\n  cropAndResizeFrame(img) {\n    return tidy(() => {\n      const expandedImage = expandDims(cast(img, \"float32\"), 0);\n      let resizedImage;\n      resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, \"bilinear\");\n      const shape = resizedImage.shape;\n      return reshape(resizedImage, shape.slice(1));\n    });\n  }\n  async capture() {\n    return (await this.next()).value;\n  }\n  stop() {\n    const tracks = this.stream.getTracks();\n    tracks.forEach((track) => track.stop());\n    try {\n      this.webcamVideoElement.srcObject = null;\n    } catch (error) {\n      console.log(error);\n      this.webcamVideoElement.src = null;\n    }\n    this.isClosed = true;\n  }\n  toArray() {\n    throw new Error(\"Can not convert infinite video stream to array.\");\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/datasource.js\nvar DataSource = class {\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js\nvar StringIterator = class extends LazyIterator {\n  split(separator) {\n    return new SplitIterator(this, separator);\n  }\n};\nvar SplitIterator = class extends StringIterator {\n  constructor(upstream, separator) {\n    super();\n    this.upstream = upstream;\n    this.impl = new SplitIteratorImpl(upstream, separator);\n  }\n  summary() {\n    return this.impl.summary();\n  }\n  async next() {\n    return this.impl.next();\n  }\n};\nvar SplitIteratorImpl = class extends OneToManyIterator {\n  constructor(upstream, separator) {\n    super();\n    this.upstream = upstream;\n    this.separator = separator;\n    this.carryover = \"\";\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Split('${this.separator}')`;\n  }\n  async pump() {\n    const chunkResult = await this.upstream.next();\n    if (chunkResult.done) {\n      if (this.carryover === \"\") {\n        return false;\n      }\n      this.outputQueue.push(this.carryover);\n      this.carryover = \"\";\n      return true;\n    }\n    const lines = chunkResult.value.split(this.separator);\n    lines[0] = this.carryover + lines[0];\n    for (const line of lines.slice(0, -1)) {\n      this.outputQueue.push(line);\n    }\n    this.carryover = lines[lines.length - 1];\n    return true;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js\nvar ByteChunkIterator = class extends LazyIterator {\n  decodeUTF8() {\n    return new Utf8Iterator(this);\n  }\n};\nvar Utf8Iterator = class extends StringIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    this.impl = new Utf8IteratorImpl(upstream);\n  }\n  summary() {\n    return this.impl.summary();\n  }\n  async next() {\n    return this.impl.next();\n  }\n};\nvar Utf8IteratorImpl = class extends OneToManyIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    if (env().get(\"IS_BROWSER\")) {\n      this.decoder = new TextDecoder(\"utf-8\");\n    } else {\n      const { StringDecoder } = require_string_decoder();\n      this.decoder = new StringDecoder(\"utf8\");\n    }\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Utf8`;\n  }\n  async pump() {\n    const chunkResult = await this.upstream.next();\n    let chunk;\n    if (chunkResult.done) {\n      return false;\n    } else {\n      chunk = chunkResult.value;\n    }\n    let text;\n    if (env().get(\"IS_BROWSER\")) {\n      text = this.decoder.decode(chunk, { stream: true });\n    } else {\n      text = this.decoder.write(Buffer.from(chunk.buffer));\n    }\n    this.outputQueue.push(text);\n    return true;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js\nvar FileChunkIterator = class extends ByteChunkIterator {\n  constructor(file, options = {}) {\n    super();\n    this.file = file;\n    this.options = options;\n    util_exports.assert(file instanceof Uint8Array || (env().get(\"IS_BROWSER\") ? file instanceof File || file instanceof Blob : false), () => \"FileChunkIterator only supports File, Blob and Uint8Array right now.\");\n    this.offset = options.offset || 0;\n    this.chunkSize = options.chunkSize || 1024 * 1024;\n  }\n  summary() {\n    return `FileChunks ${this.file}`;\n  }\n  async next() {\n    if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {\n      return { value: null, done: true };\n    }\n    const chunk = new Promise((resolve, reject) => {\n      const end = this.offset + this.chunkSize;\n      if (this.file instanceof Uint8Array) {\n        resolve(new Uint8Array(this.file.slice(this.offset, end)));\n      } else {\n        const fileReader = new FileReader();\n        fileReader.onload = (event) => {\n          let data = fileReader.result;\n          if (data instanceof ArrayBuffer) {\n            data = new Uint8Array(data);\n          }\n          if (!(data instanceof Uint8Array)) {\n            return reject(new TypeError(\"FileReader returned unknown type.\"));\n          }\n          resolve(data);\n        };\n        fileReader.onabort = (event) => {\n          return reject(new Error(\"Aborted\"));\n        };\n        fileReader.onerror = (event) => {\n          return reject(new Error(event.type));\n        };\n        const slice6 = this.file.slice(this.offset, end);\n        fileReader.readAsArrayBuffer(slice6);\n      }\n      this.offset = end;\n    });\n    return { value: await chunk, done: false };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js\nasync function urlChunkIterator(url, options = {}, fetchFunc) {\n  let urlString;\n  let requestInit;\n  if (typeof url === \"string\") {\n    urlString = url;\n  } else {\n    urlString = url.url;\n    requestInit = getRequestInitFromRequest(url);\n  }\n  const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit);\n  if (response.ok) {\n    const uint8Array = new Uint8Array(await response.arrayBuffer());\n    return new FileChunkIterator(uint8Array, options);\n  } else {\n    throw new Error(response.statusText);\n  }\n}\nvar getRequestInitFromRequest = (request) => {\n  const init2 = {\n    method: request.method,\n    headers: request.headers,\n    body: request.body,\n    mode: request.mode,\n    credentials: request.credentials,\n    cache: request.cache,\n    redirect: request.redirect,\n    referrer: request.referrer,\n    integrity: request.integrity\n  };\n  return init2;\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js\nfunction isLocalPath(source) {\n  return typeof source === \"string\" && source.slice(0, 7) === \"file://\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js\nvar FileDataSource = class extends DataSource {\n  constructor(input2, options = {}) {\n    super();\n    this.input = input2;\n    this.options = options;\n  }\n  async iterator() {\n    if (isLocalPath(this.input) && env().get(\"IS_NODE\")) {\n      const fs = require_fs();\n      this.input = fs.readFileSync(this.input.slice(7));\n    }\n    return new FileChunkIterator(this.input, this.options);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js\nvar URLDataSource = class extends DataSource {\n  constructor(url, fileOptions = {}) {\n    super();\n    this.url = url;\n    this.fileOptions = fileOptions;\n  }\n  async iterator() {\n    if (isLocalPath(this.url)) {\n      return new FileDataSource(this.url, this.fileOptions).iterator();\n    } else {\n      return urlChunkIterator(this.url, this.fileOptions);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/readers.js\nfunction csv(source, csvConfig = {}) {\n  return new CSVDataset(new URLDataSource(source), csvConfig);\n}\nfunction func(f) {\n  const iter = iteratorFromFunction(f);\n  return datasetFromIteratorFn(async () => iter);\n}\nfunction generator(generator2) {\n  return datasetFromIteratorFn(async () => {\n    const gen = await generator2();\n    return iteratorFromFunction(() => gen.next());\n  });\n}\nasync function webcam(webcamVideoElement, webcamConfig) {\n  return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\nasync function microphone(microphoneConfig) {\n  return MicrophoneIterator.create(microphoneConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/version.js\nvar version4 = \"3.18.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js\nfunction assertNotComplex(tensor2, opName) {\n  if (!Array.isArray(tensor2)) {\n    tensor2 = [tensor2];\n  }\n  tensor2.forEach((t2) => {\n    if (t2 != null) {\n      util_exports.assert(t2.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the CPU backend.`);\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js\nvar whereImpl2 = kernel_impls_exports.whereImpl;\nvar MathBackendCPU = class extends KernelBackend {\n  constructor() {\n    super();\n    this.blockSize = 48;\n    this.firstUse = true;\n    this.data = new DataStorage(this, engine());\n  }\n  nextDataId() {\n    return MathBackendCPU.nextDataId++;\n  }\n  write(values, shape, dtype) {\n    if (this.firstUse) {\n      this.firstUse = false;\n      if (env().get(\"IS_NODE\")) {\n        backend_util_exports.warn(\"\\n============================\\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \\n============================\");\n      }\n    }\n    const dataId = { id: this.nextDataId() };\n    this.data.set(dataId, { values, dtype, refCount: 1 });\n    return dataId;\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let outId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      outId = this.write(encodedValues, shape, dtype);\n    } else {\n      outId = this.write(values, shape, dtype);\n    }\n    return { dataId: outId, shape, dtype };\n  }\n  refCount(dataId) {\n    if (this.data.has(dataId)) {\n      const tensorData = this.data.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const tensorData = this.data.get(dataId);\n    tensorData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.data.has(dataId)) {\n      const tensorData = this.data.get(dataId);\n      tensorData.refCount--;\n    }\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    this.data.set(dataId, { values, dtype, refCount });\n  }\n  numDataIds() {\n    return this.data.numDataIds();\n  }\n  async read(dataId) {\n    return this.readSync(dataId);\n  }\n  readSync(dataId) {\n    const { dtype, complexTensorInfos } = this.data.get(dataId);\n    if (dtype === \"complex64\") {\n      const realValues = this.readSync(complexTensorInfos.real.dataId);\n      const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n      return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    }\n    return this.data.get(dataId).values;\n  }\n  bufferSync(t2) {\n    const data = this.readSync(t2.dataId);\n    if (t2.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t2.shape, t2.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t2.shape, t2.dtype, data);\n  }\n  makeOutput(values, shape, dtype) {\n    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n  }\n  disposeData(dataId, force = false) {\n    if (this.data.has(dataId)) {\n      this.data.get(dataId).refCount--;\n      if (!force && this.data.get(dataId).refCount > 0) {\n        return false;\n      }\n      const { complexTensorInfos } = this.data.get(dataId);\n      if (complexTensorInfos != null) {\n        this.disposeData(complexTensorInfos.real.dataId, true);\n        this.disposeData(complexTensorInfos.imag.dataId, true);\n      }\n      this.data.delete(dataId);\n    }\n    return true;\n  }\n  disposeIntermediateTensorInfo(tensorInfo) {\n    this.disposeData(tensorInfo.dataId);\n  }\n  async time(f) {\n    const start = util_exports.now();\n    f();\n    const kernelMs = util_exports.now() - start;\n    return { kernelMs };\n  }\n  memory() {\n    return {\n      unreliable: true,\n      reasons: [\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]\n    };\n  }\n  where(condition) {\n    assertNotComplex([condition], \"where\");\n    const condVals = this.readSync(condition.dataId);\n    return whereImpl2(condition.shape, condVals);\n  }\n  dispose() {\n  }\n  floatPrecision() {\n    return 32;\n  }\n  epsilon() {\n    return super.epsilon();\n  }\n};\nMathBackendCPU.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js\nvar shared_exports = {};\n__export(shared_exports, {\n  addImpl: () => addImpl,\n  bincountImpl: () => bincountImpl,\n  bincountReduceImpl: () => bincountReduceImpl,\n  ceilImpl: () => ceilImpl,\n  concatImpl: () => concatImpl,\n  equalImpl: () => equalImpl,\n  expImpl: () => expImpl,\n  expm1Impl: () => expm1Impl,\n  floorImpl: () => floorImpl,\n  gatherNdImpl: () => gatherNdImpl,\n  gatherV2Impl: () => gatherV2Impl,\n  greaterEqualImpl: () => greaterEqualImpl,\n  greaterImpl: () => greaterImpl,\n  lessEqualImpl: () => lessEqualImpl,\n  lessImpl: () => lessImpl,\n  linSpaceImpl: () => linSpaceImpl,\n  logImpl: () => logImpl,\n  maxImpl: () => maxImpl,\n  maximumImpl: () => maximumImpl,\n  minimumImpl: () => minimumImpl,\n  multiplyImpl: () => multiplyImpl,\n  negImpl: () => negImpl,\n  notEqualImpl: () => notEqualImpl,\n  prodImpl: () => prodImpl,\n  rangeImpl: () => rangeImpl,\n  rsqrtImpl: () => rsqrtImpl,\n  scatterImpl: () => scatterImpl,\n  sigmoidImpl: () => sigmoidImpl,\n  simpleAbsImpl: () => simpleAbsImpl,\n  sliceImpl: () => sliceImpl,\n  sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,\n  sparseReshapeImpl: () => sparseReshapeImpl,\n  sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,\n  sqrtImpl: () => sqrtImpl,\n  squaredDifferenceImpl: () => squaredDifferenceImpl,\n  stridedSliceImpl: () => stridedSliceImpl,\n  stringNGramsImpl: () => stringNGramsImpl,\n  stringSplitImpl: () => stringSplitImpl,\n  stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,\n  subImpl: () => subImpl,\n  tileImpl: () => tileImpl,\n  topKImpl: () => topKImpl,\n  transposeImpl: () => transposeImpl,\n  uniqueImpl: () => uniqueImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js\nfunction simpleAbsImpl(vals) {\n  const resultValues = new Float32Array(vals.length);\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    resultValues[i2] = Math.abs(vals[i2]);\n  }\n  return resultValues;\n}\nvar abs2 = (args) => {\n  const { x } = args.inputs;\n  const cpuBackend = args.backend;\n  assertNotComplex(x, \"abs\");\n  let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n  const values = cpuBackend.data.get(x.dataId).values;\n  resultValues = simpleAbsImpl(values);\n  return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);\n};\nvar absConfig = {\n  kernelName: Abs,\n  backendName: \"cpu\",\n  kernelFunc: abs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js\nfunction createSimpleBinaryKernelImpl(op2) {\n  return (aShape, bShape, aVals, bVals, dtype) => {\n    const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const resultRank = newShape.length;\n    const resultStrides = util_exports.computeStrides(newShape);\n    const resultSize = util_exports.sizeFromShape(newShape);\n    const result = util_exports.getTypedArrayFromDType(dtype, resultSize);\n    const aRank = aShape.length;\n    const bRank = bShape.length;\n    const aStrides = util_exports.computeStrides(aShape);\n    const bStrides = util_exports.computeStrides(bShape);\n    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);\n    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i2 = 0; i2 < result.length; ++i2) {\n        result[i2] = op2(aVals[i2 % aVals.length], bVals[i2 % bVals.length]);\n      }\n    } else {\n      for (let i2 = 0; i2 < result.length; ++i2) {\n        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);\n        const aLoc = loc.slice(-aRank);\n        aBroadcastDims.forEach((d) => aLoc[d] = 0);\n        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n        const bLoc = loc.slice(-bRank);\n        bBroadcastDims.forEach((d) => bLoc[d] = 0);\n        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n        result[i2] = op2(aVals[aIndex], bVals[bIndex]);\n      }\n    }\n    return [result, newShape];\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js\nfunction complex2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const realVals = backend2.data.get(real5.dataId).values;\n  const imagVals = backend2.data.get(imag5.dataId).values;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.data.get(complexInfo.dataId);\n  complex5.complexTensorInfos = {\n    real: backend2.makeTensorInfo(real5.shape, \"float32\", realVals),\n    imag: backend2.makeTensorInfo(imag5.shape, \"float32\", imagVals)\n  };\n  return complexInfo;\n}\nvar complexConfig = {\n  kernelName: Complex,\n  backendName: \"cpu\",\n  kernelFunc: complex2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js\nfunction zeros3(backend2, shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = zeros3(backend2, shape, \"float32\");\n    const imag5 = zeros3(backend2, shape, \"float32\");\n    return complex2({ inputs: { real: real5, imag: imag5 }, backend: backend2 });\n  }\n  const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);\n  return backend2.makeTensorInfo(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js\nfunction identity2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  backend2.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig = {\n  kernelName: Identity,\n  backendName: \"cpu\",\n  kernelFunc: identity2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js\nfunction real2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const real5 = backend2.data.get(input2.dataId).complexTensorInfos.real;\n  const realVal = backend2.data.get(real5.dataId).values;\n  return backend2.makeTensorInfo(real5.shape, real5.dtype, realVal);\n}\nvar realConfig = {\n  kernelName: Real,\n  backendName: \"cpu\",\n  kernelFunc: real2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js\nfunction cast3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity2({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);\n    const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n    backend2.disposeIntermediateTensorInfo(floatX);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity2({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (dtype === \"int32\") {\n    const values = backend2.data.get(x.dataId).values;\n    const resultValues = Int32Array.from(values);\n    return backend2.makeTensorInfo(x.shape, \"int32\", resultValues);\n  }\n  if (dtype === \"bool\") {\n    const xVals = backend2.data.get(x.dataId).values;\n    const zero = util_exports.toTypedArray([0], x.dtype);\n    const [resultData, resultShape] = createSimpleBinaryKernelImpl((a6, b) => a6 !== b ? 1 : 0)(x.shape, [], xVals, zero, \"bool\");\n    return backend2.makeTensorInfo(resultShape, \"bool\", resultData);\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig = {\n  kernelName: Cast,\n  backendName: \"cpu\",\n  kernelFunc: cast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js\nfunction binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n  if (complexImpl == null) {\n    return ({ inputs, backend: backend2 }) => {\n      const { a: a6, b } = inputs;\n      const cpuBackend = backend2;\n      assertNotComplex([a6, b], name);\n      const aVals = cpuBackend.data.get(a6.dataId).values;\n      const bVals = cpuBackend.data.get(b.dataId).values;\n      const decodedAVals = a6.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;\n      const decodedBVals = a6.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;\n      const $dtype = dtype || a6.dtype;\n      const [resultData, resultShape] = simpleImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n    };\n  }\n  return ({ inputs, backend: backend2 }) => {\n    const { a: a6, b } = inputs;\n    const cpuBackend = backend2;\n    if (a6.dtype === \"complex64\" || b.dtype === \"complex64\") {\n      const $aComplex = cast3({ inputs: { x: a6 }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n      const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n      const aReal = $aComplexVals.complexTensorInfos.real;\n      const aImag = $aComplexVals.complexTensorInfos.imag;\n      const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n      const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n      const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n      const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n      const bReal = $bComplexVals.complexTensorInfos.real;\n      const bImag = $bComplexVals.complexTensorInfos.imag;\n      const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n      const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n      const [resultRealData, resultImagData, resultShape] = complexImpl(a6.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n      const resultReal = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultRealData);\n      const resultImag = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImagData);\n      const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n      cpuBackend.disposeIntermediateTensorInfo($aComplex);\n      cpuBackend.disposeIntermediateTensorInfo($bComplex);\n      cpuBackend.disposeIntermediateTensorInfo(resultReal);\n      cpuBackend.disposeIntermediateTensorInfo(resultImag);\n      return result;\n    } else {\n      const aVals = cpuBackend.data.get(a6.dataId).values;\n      const bVals = cpuBackend.data.get(b.dataId).values;\n      const $dtype = dtype || a6.dtype;\n      const [resultData, resultShape] = simpleImpl(a6.shape, b.shape, aVals, bVals, $dtype);\n      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n    }\n  };\n}\nfunction createComplexBinaryKernelImpl(op2) {\n  return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n    const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const resultSize = util_exports.sizeFromShape(resultShape);\n    const resultRank = resultShape.length;\n    const resultStrides = util_exports.computeStrides(resultShape);\n    const resultRealVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n    const resultImagVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);\n    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);\n    const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);\n    const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);\n    const aRank = aShape.length;\n    const aStrides = util_exports.computeStrides(aShape);\n    const bRank = bShape.length;\n    const bStrides = util_exports.computeStrides(bShape);\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i2 = 0; i2 < resultRealVals.length; i2++) {\n        const aIdx = i2 % aVals.length;\n        const bIdx = i2 % bVals.length;\n        const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n        resultRealVals[i2] = result.real;\n        resultImagVals[i2] = result.imag;\n      }\n    } else {\n      for (let i2 = 0; i2 < resultRealVals.length; i2++) {\n        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);\n        const aLoc = loc.slice(-aRank);\n        aBroadcastDims.forEach((d) => aLoc[d] = 0);\n        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n        const bLoc = loc.slice(-bRank);\n        bBroadcastDims.forEach((d) => bLoc[d] = 0);\n        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n        const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n        resultRealVals[i2] = opResult.real;\n        resultImagVals[i2] = opResult.imag;\n      }\n    }\n    return [resultRealVals, resultImagVals, resultShape];\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js\nvar addImpl = createSimpleBinaryKernelImpl((a6, b) => a6 + b);\nvar addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return { real: aReal + bReal, imag: aImag + bImag };\n});\nvar add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);\nvar addConfig = {\n  kernelName: Add,\n  backendName: \"cpu\",\n  kernelFunc: add4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js\nfunction bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {\n  const weightsSize = util_exports.sizeFromShape(weightsShape);\n  const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    const value = xVals[i2];\n    if (value < 0) {\n      throw new Error(\"Input x must be non-negative!\");\n    }\n    if (value >= size) {\n      continue;\n    }\n    if (weightsSize > 0) {\n      outVals[value] += weightsVals[i2];\n    } else {\n      outVals[value] += 1;\n    }\n  }\n  return outVals;\n}\nfunction bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {\n  const numRows = xBuf.shape[0];\n  const numCols = xBuf.shape[1];\n  const outBuf = buffer([numRows, size], weightsBuf.dtype);\n  for (let i2 = 0; i2 < numRows; i2++) {\n    for (let j = 0; j < numCols; j++) {\n      const value = xBuf.get(i2, j);\n      if (value < 0) {\n        throw new Error(\"Input x must be non-negative!\");\n      }\n      if (value >= size) {\n        continue;\n      }\n      if (binaryOutput) {\n        outBuf.set(1, i2, value);\n      } else {\n        if (weightsBuf.size > 0) {\n          outBuf.set(outBuf.get(i2, value) + weightsBuf.get(i2, j), i2, value);\n        } else {\n          outBuf.set(outBuf.get(i2, value) + 1, i2, value);\n        }\n      }\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js\nfunction createSimpleUnaryImpl(op2) {\n  return (values, dtype, attrs) => {\n    const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);\n    for (let i2 = 0; i2 < values.length; ++i2) {\n      newValues[i2] = op2(values[i2], attrs);\n    }\n    return newValues;\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js\nfunction unaryKernelFunc(name, op2, dtype) {\n  return ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    assertNotComplex(x, name);\n    if (x.dtype === \"string\" || dtype === \"string\") {\n      throw new Error(\"unaryKernelFunc does not support string input/output\");\n    }\n    const cpuBackend = backend2;\n    const values = cpuBackend.data.get(x.dataId).values;\n    const xSize = util_exports.sizeFromShape(x.shape);\n    const $dtype = dtype || x.dtype;\n    const newValues = util_exports.getArrayFromDType($dtype, xSize);\n    for (let i2 = 0; i2 < xSize; ++i2) {\n      newValues[i2] = op2(values[i2], attrs);\n    }\n    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n  };\n}\nfunction unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n  return ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    assertNotComplex(x, name);\n    if (x.dtype === \"string\" || dtype === \"string\") {\n      throw new Error(\"unaryKernelFunc does not support string input/output\");\n    }\n    const cpuBackend = backend2;\n    const values = cpuBackend.data.get(x.dataId).values;\n    const $dtype = dtype || x.dtype;\n    const newValues = unaryImpl(values, $dtype, attrs);\n    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js\nvar ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nvar ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nvar ceilConfig = {\n  kernelName: Ceil,\n  backendName: \"cpu\",\n  kernelFunc: ceil2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js\nfunction concatImpl(inputs, outShape, dtype, simplyConcat) {\n  const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n  if (simplyConcat && dtype !== \"string\") {\n    let offset = 0;\n    inputs.forEach((input2) => {\n      const size = util_exports.sizeFromShape(input2.shape);\n      outVals.set(input2.vals, offset);\n      offset += size;\n    });\n  } else {\n    let colOffset = 0;\n    inputs.forEach((input2) => {\n      const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;\n      let tIdx = 0;\n      for (let row = 0; row < input2.shape[0]; ++row) {\n        const resIdx = row * outShape[1] + colOffset;\n        for (let col = 0; col < input2.shape[1]; ++col) {\n          outVals[resIdx + col] = decodedData[tIdx++];\n        }\n      }\n      colOffset += input2.shape[1];\n    });\n  }\n  return outVals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js\nvar equalImpl = createSimpleBinaryKernelImpl((a6, b) => a6 === b ? 1 : 0);\nvar equal2 = binaryKernelFunc(Equal, equalImpl, null, \"bool\");\nvar equalConfig = {\n  kernelName: Equal,\n  backendName: \"cpu\",\n  kernelFunc: equal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js\nvar expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nvar exp2 = unaryKernelFuncFromImpl(Exp, expImpl, \"float32\");\nvar expConfig = {\n  kernelName: Exp,\n  backendName: \"cpu\",\n  kernelFunc: exp2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js\nvar expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nvar expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nvar expm1Config = {\n  kernelName: Expm1,\n  backendName: \"cpu\",\n  kernelFunc: expm12\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js\nvar floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nvar floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);\nvar floorConfig = {\n  kernelName: Floor,\n  backendName: \"cpu\",\n  kernelFunc: floor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js\nfunction gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {\n  const outBuf = buffer([numSlices, sliceSize], dtype);\n  for (let i2 = 0; i2 < numSlices; i2++) {\n    const index = [];\n    let flattenIndex = 0;\n    for (let j = 0; j < sliceRank; j++) {\n      const dim = indicesData[i2 * sliceRank + j];\n      flattenIndex += dim * strides[j];\n      index.push(dim);\n    }\n    if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n      throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);\n    }\n    for (let k = 0; k < sliceSize; k++) {\n      outBuf.values[i2 * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js\nfunction gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {\n  const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n  for (let i2 = 0; i2 < outBuf.size; ++i2) {\n    const newLoc = outBuf.indexToLoc(i2);\n    const originalLoc = newLoc.slice();\n    const batchIdx = originalLoc[0];\n    const indicesIdx = originalLoc[2];\n    const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n    originalLoc[2] = indicesBuf.values[indicesIndex];\n    const originalIndex = xBuf.locToIndex(originalLoc);\n    if (0 <= originalIndex && originalIndex < xBuf.values.length) {\n      outBuf.values[i2] = xBuf.values[originalIndex];\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js\nvar greaterImpl = createSimpleBinaryKernelImpl((a6, b) => a6 > b ? 1 : 0);\nvar greater3 = binaryKernelFunc(Greater, greaterImpl, null, \"bool\");\nvar greaterConfig = {\n  kernelName: Greater,\n  backendName: \"cpu\",\n  kernelFunc: greater3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js\nvar greaterEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 >= b ? 1 : 0);\nvar greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, \"bool\");\nvar greaterEqualConfig = {\n  kernelName: GreaterEqual,\n  backendName: \"cpu\",\n  kernelFunc: greaterEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js\nvar lessImpl = createSimpleBinaryKernelImpl((a6, b) => a6 < b ? 1 : 0);\nvar less3 = binaryKernelFunc(Less, lessImpl, null, \"bool\");\nvar lessConfig = {\n  kernelName: Less,\n  backendName: \"cpu\",\n  kernelFunc: less3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js\nvar lessEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 <= b ? 1 : 0);\nvar lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, \"bool\");\nvar lessEqualConfig = {\n  kernelName: LessEqual,\n  backendName: \"cpu\",\n  kernelFunc: lessEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js\nfunction linSpaceImpl(start, stop, num) {\n  const step5 = (stop - start) / (num - 1);\n  const values = util_exports.makeZerosTypedArray(num, \"float32\");\n  values[0] = start;\n  for (let i2 = 1; i2 < values.length; i2++) {\n    values[i2] = values[i2 - 1] + step5;\n  }\n  return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js\nvar logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nvar log3 = unaryKernelFuncFromImpl(Log, logImpl);\nvar logConfig = {\n  kernelName: Log,\n  backendName: \"cpu\",\n  kernelFunc: log3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js\nfunction maxImpl(aVals, reduceSize, outShape, dtype) {\n  const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let max7 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (Number.isNaN(value) || value > max7) {\n        max7 = value;\n      }\n    }\n    vals[i2] = max7;\n  }\n  return vals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js\nvar maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));\nvar maximum3 = binaryKernelFunc(Maximum, maximumImpl);\nvar maximumConfig = {\n  kernelName: Maximum,\n  backendName: \"cpu\",\n  kernelFunc: maximum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js\nvar minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));\nvar minimum3 = binaryKernelFunc(Minimum, minimumImpl);\nvar minimumConfig = {\n  kernelName: Minimum,\n  backendName: \"cpu\",\n  kernelFunc: minimum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js\nvar multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);\nvar multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return {\n    real: aReal * bReal - aImag * bImag,\n    imag: aReal * bImag + aImag * bReal\n  };\n});\nvar multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nvar multiplyConfig = {\n  kernelName: Multiply,\n  backendName: \"cpu\",\n  kernelFunc: multiply2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js\nfunction negImpl(xVals, xShape, xDtype) {\n  const minusOne = util_exports.createScalarValue(-1, xDtype);\n  return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\nfunction neg2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"neg\");\n  const xVals = backend2.data.get(x.dataId).values;\n  const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n  return backend2.makeTensorInfo(newShape, x.dtype, res);\n}\nvar negConfig = {\n  kernelName: Neg,\n  backendName: \"cpu\",\n  kernelFunc: neg2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js\nvar notEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 !== b ? 1 : 0);\nvar notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, \"bool\");\nvar notEqualConfig = {\n  kernelName: NotEqual,\n  backendName: \"cpu\",\n  kernelFunc: notEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js\nfunction transposeImpl(xVals, xShape, dtype, perm, newShape) {\n  const xRank = xShape.length;\n  const xSize = util_exports.sizeFromShape(xShape);\n  const xStrides = util_exports.computeStrides(xShape);\n  const newStrides = util_exports.computeStrides(newShape);\n  const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));\n  for (let i2 = 0; i2 < xSize; ++i2) {\n    const loc = util_exports.indexToLoc(i2, xRank, xStrides);\n    const newLoc = new Array(loc.length);\n    for (let i3 = 0; i3 < newLoc.length; i3++) {\n      newLoc[i3] = loc[perm[i3]];\n    }\n    const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);\n    result[newIndex] = xVals[i2];\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js\nfunction transpose2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  assertNotComplex(x, \"transpose\");\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[perm[i2]];\n  }\n  const values = backend2.data.get(x.dataId).values;\n  const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n  const dataId = backend2.write(result, newShape, x.dtype);\n  return { dataId, shape: newShape, dtype: x.dtype };\n}\nvar transposeConfig = {\n  kernelName: Transpose,\n  backendName: \"cpu\",\n  kernelFunc: transpose2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js\nfunction prodImpl(xShape, xDtype, xVals, reductionAxes) {\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);\n  const outDtype = upcastType(xDtype, \"int32\");\n  const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  for (let i2 = 0; i2 < outVals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let prod6 = 1;\n    for (let j = 0; j < reduceSize; ++j) {\n      prod6 *= xVals[offset + j];\n    }\n    outVals[i2] = prod6;\n  }\n  return { outVals, outShape, outDtype };\n}\nfunction prod2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"prod\");\n  const xRank = x.shape.length;\n  const axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n  let reductionAxes = axes;\n  let permutedX = x;\n  const intermediateTensorInfos = [];\n  if (permutation != null) {\n    permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n    intermediateTensorInfos.push(permutedX);\n    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n  }\n  const xVals = backend2.data.get(permutedX.dataId).values;\n  const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n  let resultShape = outShape;\n  if (keepDims) {\n    resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);\n  }\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return backend2.makeTensorInfo(resultShape, outDtype, outVals);\n}\nvar prodConfig = {\n  kernelName: Prod,\n  backendName: \"cpu\",\n  kernelFunc: prod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js\nfunction rangeImpl(start, stop, step5, dtype) {\n  const sameStartStop = start === stop;\n  const increasingRangeNegativeStep = start < stop && step5 < 0;\n  const decreasingRangePositiveStep = stop < start && step5 > 1;\n  if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {\n    return util_exports.makeZerosTypedArray(0, dtype);\n  }\n  const numElements = Math.abs(Math.ceil((stop - start) / step5));\n  const values = util_exports.makeZerosTypedArray(numElements, dtype);\n  if (stop < start && step5 === 1) {\n    step5 = -1;\n  }\n  values[0] = start;\n  for (let i2 = 1; i2 < values.length; i2++) {\n    values[i2] = values[i2 - 1] + step5;\n  }\n  return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js\nvar rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nvar rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nvar rsqrtConfig = {\n  kernelName: Rsqrt,\n  backendName: \"cpu\",\n  kernelFunc: rsqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js\nfunction scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  const indicesData = indices.values;\n  const updatesData = updates.values;\n  if (outputSize === 0) {\n    return buffer(shape, updates.dtype);\n  }\n  const outBuf = buffer(flattenShape, updates.dtype);\n  if (typeof defaultValue === \"string\") {\n    outBuf.values.fill(defaultValue);\n  } else if (typeof defaultValue === \"number\") {\n    outBuf.values.fill(defaultValue);\n  } else if (typeof defaultValue === \"boolean\") {\n    outBuf.values.fill(+defaultValue);\n  }\n  for (let i2 = 0; i2 < numUpdates; i2++) {\n    const index = [];\n    let flattenIndex = 0;\n    for (let j = 0; j < sliceRank; j++) {\n      const dim = indicesData[i2 * sliceRank + j];\n      index.push(dim);\n      flattenIndex += dim * strides[j];\n    }\n    if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n      throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n    }\n    for (let k = 0; k < sliceSize; k++) {\n      if (sumDupeIndices) {\n        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i2 * sliceSize + k];\n      } else {\n        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i2 * sliceSize + k];\n      }\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js\nvar sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoidConfig = {\n  kernelName: Sigmoid,\n  backendName: \"cpu\",\n  kernelFunc: sigmoid2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js\nfunction sliceImpl(vals, begin, size, shape, dtype) {\n  const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);\n  const length = util_exports.sizeFromShape(size);\n  const xStrides = util_exports.computeStrides(shape);\n  if (isContinous) {\n    const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);\n    if (dtype === \"string\") {\n      return vals.slice(flatOffset, flatOffset + length);\n    }\n    return vals.subarray(flatOffset, flatOffset + length);\n  }\n  const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;\n  const inBuf = buffer(shape, dtype, decodedData);\n  const outBuf = buffer(size, dtype);\n  for (let i2 = 0; i2 < outBuf.size; ++i2) {\n    const outLoc = outBuf.indexToLoc(i2);\n    const inLoc = outLoc.map((idx, j) => idx + begin[j]);\n    outBuf.set(inBuf.get(...inLoc), ...outLoc);\n  }\n  if (dtype === \"string\") {\n    return backend_util_exports.fromStringArrayToUint8(outBuf.values);\n  }\n  return outBuf.values;\n}\nfunction slice2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  assertNotComplex(x, \"slice\");\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  const vals = backend2.data.get(x.dataId).values;\n  const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n  return backend2.makeTensorInfo($size, x.dtype, outVals);\n}\nvar sliceConfig = {\n  kernelName: Slice,\n  backendName: \"cpu\",\n  kernelFunc: slice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js\nfunction sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {\n  const indicesCount = indicesShape[0];\n  const denseRows = denseShape[0];\n  const emptyRowIndicator = new Array(denseRows);\n  const reverseIndexMap = new Array(indicesCount);\n  const rank = indicesShape[1];\n  if (denseRows === 0) {\n    if (indicesCount !== 0) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));\n    }\n    const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);\n    const outputValues = util_exports.getArrayFromDType(valuesDType, 0);\n    return [\n      outputIndices,\n      [0, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n  let rowsAreOrdered = true;\n  let lastIndicesRow = 0;\n  const csrOffset = new Array(denseRows).fill(0);\n  for (let i2 = 0; i2 < indicesCount; ++i2) {\n    const row = indices[i2 * rank];\n    if (row < 0) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i2, row));\n    }\n    if (row >= denseRows) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i2, row, denseRows));\n    }\n    ++csrOffset[row];\n    rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;\n    lastIndicesRow = row;\n  }\n  let allRowsFull = true;\n  for (let row = 0; row < denseRows; ++row) {\n    const rowEmpty = csrOffset[row] === 0;\n    emptyRowIndicator[row] = rowEmpty;\n    allRowsFull = allRowsFull && !rowEmpty;\n    csrOffset[row] = Math.max(csrOffset[row], 1);\n    if (row > 0) {\n      csrOffset[row] += csrOffset[row - 1];\n    }\n  }\n  if (allRowsFull && rowsAreOrdered) {\n    const outputIndices = indices;\n    const outputValues = values;\n    for (let i2 = 0; i2 < indicesCount; ++i2) {\n      reverseIndexMap[i2] = i2;\n    }\n    return [\n      outputIndices,\n      [indicesCount, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  } else {\n    const fullIndicesCount = csrOffset[denseRows - 1];\n    const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);\n    const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);\n    const filledCount = new Array(denseRows).fill(0);\n    for (let i2 = 0; i2 < indicesCount; ++i2) {\n      const row = indices[i2 * rank];\n      const offset = filledCount[row];\n      const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;\n      filledCount[row]++;\n      for (let j = 0; j < rank; ++j) {\n        outputIndices[outputI * rank + j] = indices[i2 * rank + j];\n      }\n      outputValues[outputI] = values[i2];\n      reverseIndexMap[i2] = outputI;\n    }\n    for (let row = 0; row < denseRows; ++row) {\n      const rowCount = filledCount[row];\n      if (rowCount === 0) {\n        const startingIndex = row === 0 ? 0 : csrOffset[row - 1];\n        outputIndices[startingIndex * rank + 0] = row;\n        for (let col = 1; col < rank; ++col) {\n          outputIndices[startingIndex * rank + col] = 0;\n        }\n        outputValues[startingIndex] = defaultValue;\n      }\n    }\n    return [\n      outputIndices,\n      [fullIndicesCount, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js\nfunction sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {\n  const denseSize = util_exports.sizeFromShape(inputShape);\n  const nnz = inputIndicesShape[0];\n  const outputRank = targetShape.length;\n  const outputShape = [];\n  let product = 1;\n  let unknownIndex = -1;\n  for (let d = 0; d < outputRank; ++d) {\n    const size = targetShape[d];\n    if (size === -1) {\n      if (unknownIndex !== -1) {\n        throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));\n      }\n      unknownIndex = d;\n      outputShape.push(1);\n    } else {\n      if (size < 0) {\n        throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size));\n      }\n      product *= size;\n      outputShape.push(size);\n    }\n  }\n  if (unknownIndex !== -1) {\n    if (product <= 0) {\n      throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());\n    }\n    const missing = Math.trunc(denseSize / product);\n    if (product * missing !== denseSize) {\n      throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));\n    }\n    outputShape[unknownIndex] = missing;\n  }\n  const outputSize = util_exports.sizeFromShape(outputShape);\n  if (outputSize !== denseSize) {\n    throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));\n  }\n  const inputRank = inputShape.length;\n  const inputStrides = [];\n  if (inputRank > 0) {\n    inputStrides[inputRank - 1] = 1;\n    for (let d = inputRank - 2; d >= 0; --d) {\n      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n    }\n  }\n  const outputStrides = [];\n  if (outputRank > 0) {\n    outputStrides[outputRank - 1] = 1;\n    for (let d = outputRank - 2; d >= 0; --d) {\n      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n    }\n  }\n  const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);\n  for (let i2 = 0; i2 < nnz; ++i2) {\n    let id = 0;\n    for (let j = 0; j < inputRank; ++j) {\n      id += inputIndices[i2 * inputRank + j] * inputStrides[j];\n    }\n    for (let j = 0; j < outputRank; ++j) {\n      newIndices[i2 * outputRank + j] = Math.trunc(id / outputStrides[j]);\n      id %= outputStrides[j];\n    }\n  }\n  return [newIndices, [nnz, outputRank], outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js\nfunction sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {\n  const numIndices = indices.length;\n  const inputFlat = [inputShape[0], input2.length / inputShape[0]];\n  const numCol = inputFlat[1];\n  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n  if (outputRows < 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  const outputShape = inputShape.slice();\n  outputShape[0] = outputRows;\n  const outputLength = outputShape.reduce((product, value) => product * value, 1);\n  const output = util_exports.getArrayFromDType(inputDType, outputLength);\n  if (numIndices === 0) {\n    if (outputRows > 0) {\n      output.fill(defaultValue);\n    }\n    return [output, outputShape];\n  }\n  if (outputRows <= 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  let start = 0, end = 1;\n  let uninitializedIndex = 0;\n  let outIndex = segmentIds[start];\n  while (true) {\n    let nextIndex = 0;\n    if (end < numIndices) {\n      nextIndex = segmentIds[end];\n      if (outIndex === nextIndex) {\n        ++end;\n        continue;\n      }\n      if (outIndex >= nextIndex) {\n        throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());\n      }\n    }\n    if (outIndex < 0 || outIndex >= outputRows) {\n      throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));\n    }\n    if (outIndex > uninitializedIndex) {\n      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n    }\n    for (let i2 = start; i2 < end; ++i2) {\n      const index = indices[i2];\n      if (index < 0 || index >= inputFlat[0]) {\n        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i2, indices[i2], inputFlat[0]));\n      }\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] += input2[index * numCol + j];\n      }\n    }\n    if (isMean) {\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] /= end - start;\n      }\n    }\n    start = end;\n    ++end;\n    uninitializedIndex = outIndex + 1;\n    outIndex = nextIndex;\n    if (end > numIndices) {\n      break;\n    }\n  }\n  if (uninitializedIndex < outputRows) {\n    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n  }\n  return [output, outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js\nvar sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));\nvar sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nvar sqrtConfig = {\n  kernelName: Sqrt,\n  backendName: \"cpu\",\n  kernelFunc: sqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js\nvar squaredDifferenceImpl = createSimpleBinaryKernelImpl((a6, b) => {\n  const diff = a6 - b;\n  return diff * diff;\n});\nvar squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nvar squaredDifferenceConfig = {\n  kernelName: SquaredDifference,\n  backendName: \"cpu\",\n  kernelFunc: squaredDifference2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js\nfunction stridedSliceImpl(outShape, xBuf, strides, begin) {\n  const outBuf = buffer(outShape, xBuf.dtype);\n  for (let i2 = 0; i2 < outBuf.size; i2++) {\n    const loc = outBuf.indexToLoc(i2);\n    const newLoc = new Array(loc.length);\n    for (let j = 0; j < newLoc.length; j++) {\n      newLoc[j] = loc[j] * strides[j] + begin[j];\n    }\n    outBuf.set(xBuf.get(...newLoc), ...loc);\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js\nvar StringNGramsOp = class {\n  constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n    this.separator = util_exports.encodeString(separator);\n    this.nGramWidths = nGramWidths;\n    this.leftPad = util_exports.encodeString(leftPad);\n    this.rightPad = util_exports.encodeString(rightPad2);\n    this.padWidth = padWidth;\n    this.preserveShort = preserveShortSequences;\n  }\n  getPadWidth(nGramWidth) {\n    return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n  }\n  getNumNGrams(length, nGramWidth) {\n    const padWidth = this.getPadWidth(nGramWidth);\n    return Math.max(0, length + 2 * padWidth - nGramWidth + 1);\n  }\n  createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {\n    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n      const padWidth = this.getPadWidth(nGramWidth);\n      const leftPadding = Math.max(0, padWidth - nGramIndex);\n      const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n      const numTokens = nGramWidth - (leftPadding + rightPadding);\n      const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n      let nGramSize = 0;\n      nGramSize += leftPadding * this.leftPad.length;\n      for (let n = 0; n < numTokens; ++n) {\n        nGramSize += data[dataStartIndex + n].length;\n      }\n      nGramSize += rightPadding * this.rightPad.length;\n      const numSeparators = leftPadding + rightPadding + numTokens - 1;\n      nGramSize += numSeparators * this.separator.length;\n      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n      const nGram = output[outputStartIndex + nGramIndex];\n      let nextNGramIndex = 0;\n      const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);\n      for (let n = 0; n < leftPadding; ++n) {\n        appendToNGram(this.leftPad);\n        appendToNGram(this.separator);\n      }\n      for (let n = 0; n < numTokens - 1; ++n) {\n        appendToNGram(data[dataStartIndex + n]);\n        appendToNGram(this.separator);\n      }\n      if (numTokens > 0) {\n        appendToNGram(data[dataStartIndex + numTokens - 1]);\n        for (let n = 0; n < rightPadding; ++n) {\n          appendToNGram(this.separator);\n          appendToNGram(this.rightPad);\n        }\n      } else {\n        for (let n = 0; n < rightPadding - 1; ++n) {\n          appendToNGram(this.rightPad);\n          appendToNGram(this.separator);\n        }\n        appendToNGram(this.rightPad);\n      }\n    }\n  }\n  compute(data, splits) {\n    const inputDataSize = data.length;\n    const splitsSize = splits.length;\n    if (splitsSize > 0) {\n      let prevSplit = splits[0];\n      if (prevSplit !== 0) {\n        throw new Error(`First split value must be 0, got ${prevSplit}`);\n      }\n      for (let i2 = 1; i2 < splitsSize; ++i2) {\n        let validSplits = splits[i2] >= prevSplit;\n        validSplits = validSplits && splits[i2] <= inputDataSize;\n        if (!validSplits) {\n          throw new Error(`Invalid split value ${splits[i2]}, must be in [${prevSplit}, ${inputDataSize}]`);\n        }\n        prevSplit = splits[i2];\n      }\n      if (prevSplit !== inputDataSize) {\n        throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);\n      }\n    }\n    const numBatchItems = splitsSize - 1;\n    const nGramsSplits = util_exports.getArrayFromDType(\"int32\", splitsSize);\n    if (inputDataSize === 0 || splitsSize === 0) {\n      const empty = new Array(inputDataSize);\n      for (let i2 = 0; i2 <= numBatchItems; ++i2) {\n        nGramsSplits[i2] = 0;\n      }\n      return [empty, nGramsSplits];\n    }\n    nGramsSplits[0] = 0;\n    for (let i2 = 1; i2 <= numBatchItems; ++i2) {\n      const length = splits[i2] - splits[i2 - 1];\n      let numNGrams = 0;\n      this.nGramWidths.forEach((nGramWidth) => {\n        numNGrams += this.getNumNGrams(length, nGramWidth);\n      });\n      if (this.preserveShort && length > 0 && numNGrams === 0) {\n        numNGrams = 1;\n      }\n      nGramsSplits[i2] = nGramsSplits[i2 - 1] + numNGrams;\n    }\n    const nGrams = new Array(nGramsSplits[numBatchItems]);\n    for (let i2 = 0; i2 < numBatchItems; ++i2) {\n      const splitIndex = splits[i2];\n      let outputStartIdx = nGramsSplits[i2];\n      this.nGramWidths.forEach((nGramWidth) => {\n        const length = splits[i2 + 1] - splits[i2];\n        const numNGrams = this.getNumNGrams(length, nGramWidth);\n        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n        outputStartIdx += numNGrams;\n      });\n      if (this.preserveShort && outputStartIdx === nGramsSplits[i2]) {\n        const dataLength = splits[i2 + 1] - splits[i2];\n        if (dataLength === 0) {\n          continue;\n        }\n        const nGramWidth = dataLength + 2 * this.padWidth;\n        const numNGrams = 1;\n        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n      }\n    }\n    return [nGrams, nGramsSplits];\n  }\n};\nfunction stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n  return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js\nfunction split3(str, delimiters, skipEmpty, result) {\n  if (!str.length) {\n    return;\n  }\n  if (delimiters.length === 0) {\n    for (let i2 = 0; i2 < str.length; ++i2) {\n      result.push(str.subarray(i2, i2 + 1));\n    }\n    return;\n  }\n  if (delimiters.length === 1) {\n    const delimiter = delimiters[0];\n    let f = str.indexOf(delimiter);\n    while (f !== -1) {\n      const token = str.subarray(0, f);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      str = str.subarray(f + 1);\n      f = str.indexOf(delimiter);\n    }\n    if (!skipEmpty || str.length !== 0) {\n      result.push(str);\n    }\n    return;\n  }\n  let tokenStart = 0;\n  for (let i2 = 0; i2 < str.length + 1; i2++) {\n    if (i2 === str.length || delimiters.indexOf(str[i2]) !== -1) {\n      const token = str.subarray(tokenStart, i2);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      tokenStart = i2 + 1;\n    }\n  }\n}\nfunction stringSplitImpl(input2, delimiter, skipEmpty) {\n  const batchSize = input2.length;\n  const tokens = [];\n  let outputSize = 0;\n  let maxNumEntries = 0;\n  const numIndices = new Array(batchSize);\n  for (let i2 = 0; i2 < batchSize; ++i2) {\n    const prevTokensLength = tokens.length;\n    split3(input2[i2], delimiter, skipEmpty, tokens);\n    const nEntries = tokens.length - prevTokensLength;\n    numIndices[i2] = nEntries;\n    outputSize += nEntries;\n    maxNumEntries = Math.max(maxNumEntries, nEntries);\n  }\n  const indices = util_exports.getArrayFromDType(\"int32\", outputSize * 2);\n  const values = new Array(outputSize);\n  const shape = [batchSize, maxNumEntries];\n  let c = 0;\n  for (let i2 = 0; i2 < batchSize; ++i2) {\n    for (let j = 0; j < numIndices[i2]; ++j) {\n      indices[c * 2] = i2;\n      indices[c * 2 + 1] = j;\n      values[c] = tokens[c];\n      ++c;\n    }\n  }\n  return [indices, values, shape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js\nfunction stringToHashBucketFastImpl(input2, numBuckets) {\n  const output = util_exports.getArrayFromDType(\"int32\", input2.length);\n  for (let i2 = 0; i2 < input2.length; ++i2) {\n    output[i2] = util_exports.fingerPrint64(input2[i2]).modulo(numBuckets).getLowBitsUnsigned();\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js\nvar subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);\nvar subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return { real: aReal - bReal, imag: aImag - bImag };\n});\nvar sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nvar subConfig = {\n  kernelName: Sub,\n  backendName: \"cpu\",\n  kernelFunc: sub2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js\nfunction tileImpl(xBuf, reps) {\n  const newShape = new Array(xBuf.rank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = xBuf.shape[i2] * reps[i2];\n  }\n  const result = buffer(newShape, xBuf.dtype);\n  for (let i2 = 0; i2 < result.values.length; ++i2) {\n    const newLoc = result.indexToLoc(i2);\n    const originalLoc = new Array(xBuf.rank);\n    for (let j = 0; j < originalLoc.length; j++) {\n      originalLoc[j] = newLoc[j] % xBuf.shape[j];\n    }\n    const originalIndex = xBuf.locToIndex(originalLoc);\n    result.values[i2] = xBuf.values[originalIndex];\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js\nvar comparePair = (a6, b) => {\n  const valueDiff = b.value - a6.value;\n  return valueDiff === 0 ? a6.index - b.index : valueDiff;\n};\nfunction select(array2, k, left = 0, right = array2.length - 1) {\n  while (right > left) {\n    if (right - left > 600) {\n      const n = right - left + 1;\n      const i3 = k - left + 1;\n      const z = Math.log(n);\n      const s2 = 0.5 * Math.exp(2 * z / 3);\n      const sd = 0.5 * Math.sqrt(z * s2 * (n - s2) / n) * Math.sign(i3 - n / 2);\n      const newLeft = Math.max(left, Math.floor(k - i3 * s2 / n + sd));\n      const newRight = Math.min(right, Math.floor(k + (n - i3) * s2 / n + sd));\n      select(array2, k, newLeft, newRight);\n    }\n    const t2 = array2[k];\n    let i2 = left;\n    let j = right;\n    util_exports.swap(array2, left, k);\n    if (comparePair(array2[right], t2) > 0) {\n      util_exports.swap(array2, left, right);\n    }\n    while (i2 < j) {\n      util_exports.swap(array2, i2, j);\n      i2++;\n      j--;\n      while (comparePair(array2[i2], t2) < 0) {\n        i2 = i2 + 1;\n      }\n      while (comparePair(array2[j], t2) > 0) {\n        j = j - 1;\n      }\n    }\n    if (comparePair(array2[left], t2) === 0) {\n      util_exports.swap(array2, left, j);\n    } else {\n      j = j + 1;\n      util_exports.swap(array2, j, right);\n    }\n    if (j <= k) {\n      left = j + 1;\n    }\n    if (k <= j) {\n      right = j - 1;\n    }\n  }\n}\nfunction topKImpl(x, xShape, xDtype, k, sorted) {\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = util_exports.getTypedArrayFromDType(\"int32\", batch * k);\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    let valAndInd = new Array(vals.length);\n    vals.forEach((value, index) => valAndInd[index] = { value, index });\n    if (k < valAndInd.length) {\n      select(valAndInd, k);\n      valAndInd = valAndInd.slice(0, k);\n    }\n    if (sorted) {\n      valAndInd.sort(comparePair);\n    }\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i2 = 0; i2 < k; i2++) {\n      topKVals[i2] = valAndInd[i2].value;\n      topKIndices[i2] = valAndInd[i2].index;\n    }\n  }\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    buffer(outputShape, xDtype, allTopKVals),\n    buffer(outputShape, \"int32\", allTopKIndices)\n  ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js\nfunction uniqueImpl(values, axis, shape, dtype) {\n  const $axis = util_exports.parseAxisParam(axis, shape)[0];\n  const newShape = [1, shape[0], 1];\n  for (let i2 = 0; i2 < $axis; i2++) {\n    newShape[0] *= shape[i2];\n  }\n  newShape[1] = shape[$axis];\n  for (let i2 = $axis + 1; i2 < shape.length; i2++) {\n    newShape[2] *= shape[i2];\n  }\n  const uniqueElements = {};\n  const indices = new Int32Array(shape[$axis]);\n  const inputBuffer = new TensorBuffer(newShape, dtype, values);\n  const uniqueIndices = [];\n  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n  for (let i2 = 0; i2 < shape[$axis]; i2++) {\n    let element;\n    if (is1DTensor) {\n      element = values[i2].toString();\n    } else {\n      const axisValues = [];\n      for (let m = 0; m < newShape[0]; m++) {\n        for (let n = 0; n < newShape[2]; n++) {\n          axisValues.push(inputBuffer.get(m, i2, n));\n        }\n      }\n      element = axisValues.join(\",\");\n    }\n    if (uniqueElements[element] !== void 0) {\n      indices[i2] = uniqueElements[element];\n    } else {\n      const uniqueIndex = Object.keys(uniqueElements).length;\n      uniqueElements[element] = uniqueIndex;\n      indices[i2] = uniqueIndex;\n      uniqueIndices.push(i2);\n    }\n  }\n  const outputTmpShape = newShape.slice();\n  outputTmpShape[1] = Object.keys(uniqueElements).length;\n  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n  uniqueIndices.forEach((uniqueElementIndex, i2) => {\n    for (let m = 0; m < newShape[0]; m++) {\n      for (let n = 0; n < newShape[2]; n++) {\n        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i2, n);\n      }\n    }\n  });\n  const outputShape = shape.slice();\n  outputShape[$axis] = outputTmpShape[1];\n  return {\n    outputValues: outputBuffer.values,\n    outputShape,\n    indices\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js\nregisterBackend(\"cpu\", () => new MathBackendCPU(), 1);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js\nvar elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);\nvar eluConfig = {\n  kernelName: Elu,\n  backendName: \"cpu\",\n  kernelFunc: elu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  assertNotComplex([x], \"leakyRelu\");\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xVals = backend2.data.get(x.dataId).values;\n  const outVals = util_exports.getTypedArrayFromDType(\"float32\", xSize);\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    outVals[i2] = xVals[i2] < 0 ? alpha * xVals[i2] : xVals[i2];\n  }\n  return backend2.makeTensorInfo(x.shape, \"float32\", outVals);\n}\nvar leakyReluConfig = {\n  kernelName: LeakyRelu,\n  backendName: \"cpu\",\n  kernelFunc: leakyRelu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js\nvar preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nfunction prelu3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  assertNotComplex([x, alpha], \"prelu\");\n  const aVals = backend2.data.get(x.dataId).values;\n  const bVals = backend2.data.get(alpha.dataId).values;\n  const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, \"float32\");\n  return backend2.makeTensorInfo(resultShape, \"float32\", resultData);\n}\nvar preluConfig = {\n  kernelName: Prelu,\n  backendName: \"cpu\",\n  kernelFunc: prelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js\nvar relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nvar reluConfig = {\n  kernelName: Relu,\n  backendName: \"cpu\",\n  kernelFunc: relu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js\nvar relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nvar relu6Config = {\n  kernelName: Relu6,\n  backendName: \"cpu\",\n  kernelFunc: relu62\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js\nfunction applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {\n  if (activation2 === \"linear\") {\n    return identity2({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"relu\") {\n    return relu2({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"elu\") {\n    return elu4({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"relu6\") {\n    return relu62({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"prelu\") {\n    return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });\n  } else if (activation2 === \"leakyrelu\") {\n    return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });\n  } else if (activation2 === \"sigmoid\") {\n    return sigmoid2({ inputs: { x }, backend: backend2 });\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js\nfunction reshape3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  backend2.incRef(x.dataId);\n  const xData = backend2.data.get(x.dataId);\n  if (xData.complexTensorInfos != null) {\n    const real5 = xData.complexTensorInfos.real;\n    const imag5 = xData.complexTensorInfos.imag;\n    real5.shape = $shape;\n    imag5.shape = $shape;\n  }\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig = {\n  kernelName: Reshape,\n  backendName: \"cpu\",\n  kernelFunc: reshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  assertNotComplex([a6, b], \"matMul\");\n  const aRank = a6.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a6.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape3({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const a3dValues = backend2.data.get(a3d.dataId).values;\n  const b3dValues = backend2.data.get(b3d.dataId).values;\n  const a3dStrides = util_exports.computeStrides(a3d.shape);\n  const b3dStrides = util_exports.computeStrides(b3d.shape);\n  const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];\n  const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];\n  const size = leftDim * rightDim;\n  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n  const resVals = result.values;\n  const blockSize = backend2.blockSize;\n  for (let bi = 0; bi < batchDim; bi++) {\n    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n        for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {\n          const iBlock = Math.min(i0 + blockSize, leftDim);\n          const jBlock = Math.min(j0 + blockSize, rightDim);\n          const kBlock = Math.min(k02 + blockSize, sharedDim);\n          for (let i2 = i0; i2 < iBlock; i2++) {\n            for (let j = j0; j < jBlock; j++) {\n              let sum7 = 0;\n              for (let k = k02; k < kBlock; k++) {\n                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n                const aVal = a3dValues[batchOffsetA + i2 * aOuterStep + k * aInnerStep];\n                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n                sum7 += aVal * bVal;\n              }\n              resVals[bi * size + (i2 * rightDim + j)] += sum7;\n            }\n          }\n        }\n      }\n    }\n  }\n  backend2.disposeIntermediateTensorInfo(a3d);\n  backend2.disposeIntermediateTensorInfo(b3d);\n  return backend2.makeTensorInfo(outShape, result.dtype, result.values);\n}\nvar batchMatMulConfig = {\n  kernelName: BatchMatMul,\n  backendName: \"cpu\",\n  kernelFunc: batchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  let current;\n  let addRes;\n  let activationRes;\n  const intermediates = [];\n  const matMulRes = batchMatMul({ inputs: { a: a6, b }, attrs: { transposeA, transposeB }, backend: backend2 });\n  current = matMulRes;\n  if (bias) {\n    addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 });\n    intermediates.push(current);\n    current = addRes;\n  }\n  if (activation2) {\n    activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);\n    intermediates.push(current);\n    current = activationRes;\n  }\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return current;\n}\nvar _fusedMatMulConfig = {\n  kernelName: _FusedMatMul,\n  backendName: \"cpu\",\n  kernelFunc: _fusedMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js\nvar acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nvar acosConfig = {\n  kernelName: Acos,\n  backendName: \"cpu\",\n  kernelFunc: acos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js\nvar acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nvar acoshConfig = {\n  kernelName: Acosh,\n  backendName: \"cpu\",\n  kernelFunc: acosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js\nfunction addN2(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  assertNotComplex(inputs, \"addN\");\n  const vals = tensors.map((t2) => backend2.data.get(t2.dataId).values);\n  const outBuf = buffer(tensors[0].shape, tensors[0].dtype);\n  const outVals = outBuf.values;\n  for (let i2 = 0; i2 < tensors.length; i2++) {\n    const currVals = vals[i2];\n    for (let j = 0; j < outVals.length; j++) {\n      outVals[j] += currVals[j];\n    }\n  }\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar addNConfig = {\n  kernelName: AddN,\n  backendName: \"cpu\",\n  kernelFunc: addN2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js\nfunction all2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"all\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let all5 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      all5 = all5 && value;\n    }\n    vals[i2] = all5;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar allConfig = {\n  kernelName: All,\n  backendName: \"cpu\",\n  kernelFunc: all2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js\nfunction any2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"any\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let anyVal = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      anyVal = anyVal || value;\n    }\n    vals[i2] = anyVal;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar anyConfig = {\n  kernelName: Any,\n  backendName: \"cpu\",\n  kernelFunc: any2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js\nfunction argMax2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  assertNotComplex(x, \"argMax\");\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  axes = [axes[0]];\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const outSize = util_exports.sizeFromShape(outShape);\n  const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let max7 = aVals[offset];\n    let maxIndex = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (value > max7) {\n        max7 = value;\n        maxIndex = j;\n      }\n    }\n    vals[i2] = maxIndex;\n  }\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMaxConfig = {\n  kernelName: ArgMax,\n  backendName: \"cpu\",\n  kernelFunc: argMax2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js\nfunction argMin2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  assertNotComplex(x, \"argMin\");\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  axes = [axes[0]];\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const outSize = util_exports.sizeFromShape(outShape);\n  const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let min7 = aVals[offset];\n    let minIndex = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (value < min7) {\n        min7 = value;\n        minIndex = j;\n      }\n    }\n    vals[i2] = minIndex;\n  }\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMinConfig = {\n  kernelName: ArgMin,\n  backendName: \"cpu\",\n  kernelFunc: argMin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js\nvar asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nvar asinConfig = {\n  kernelName: Asin,\n  backendName: \"cpu\",\n  kernelFunc: asin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js\nvar asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nvar asinhConfig = {\n  kernelName: Asinh,\n  backendName: \"cpu\",\n  kernelFunc: asinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js\nvar atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nvar atanConfig = {\n  kernelName: Atan,\n  backendName: \"cpu\",\n  kernelFunc: atan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js\nvar atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));\nvar atan22 = binaryKernelFunc(Atan2, atan2Impl);\nvar atan2Config = {\n  kernelName: Atan2,\n  backendName: \"cpu\",\n  kernelFunc: atan22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js\nvar atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nvar atanhConfig = {\n  kernelName: Atanh,\n  backendName: \"cpu\",\n  kernelFunc: atanh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js\nfunction pool2(xValues, xShape, dtype, strides, convInfo, poolType) {\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n  const output = buffer(convInfo.outShape, dtype);\n  const outputVals = output.values;\n  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n  const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n  const outputColStrides = convInfo.outShape[3];\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const outputBatchOffset = b * outputBatchStrides;\n    const inputBatchOffset = b * strides[0];\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const xRCorner = yR * strideHeight - padTop;\n        const xRMin = Math.max(0, xRCorner);\n        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n        const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const xCCorner = yC * strideWidth - padLeft;\n          const xCMin = Math.max(0, xCCorner);\n          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n          let minMaxValue = initialValue;\n          let avgValue = 0;\n          let count2 = 0;\n          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n            const xROffset = inputBatchOffset + xR * strides[1];\n            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n              const xCOffset = xROffset + xC * strides[2];\n              const pixel = xValues[xCOffset + d];\n              if (poolType === \"max\" && pixel > minMaxValue) {\n                minMaxValue = pixel;\n              } else if (poolType === \"avg\") {\n                avgValue += pixel;\n                count2++;\n              }\n            }\n            if (isNaN(minMaxValue)) {\n              break;\n            }\n          }\n          const outputOffset = outputRowOffset + yC * outputColStrides + d;\n          outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n        }\n      }\n    }\n  }\n  return output;\n}\nfunction maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n  const maxPositions = buffer(convInfo.outShape, \"int32\");\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const xBuf = buffer(xShape, dtype, xValues);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const xRCorner = yR * strideHeight - padTop;\n        let xRMin = xRCorner;\n        while (xRMin < 0) {\n          xRMin += dilationHeight;\n        }\n        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const xCCorner = yC * strideWidth - padLeft;\n          let xCMin = xCCorner;\n          while (xCMin < 0) {\n            xCMin += dilationWidth;\n          }\n          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n          let maxValue = Number.NEGATIVE_INFINITY;\n          let maxPosition = -1;\n          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n            const wR = xR - xRCorner;\n            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n              const wC = xC - xCCorner;\n              const pixel = xBuf.get(b, xR, xC, d);\n              if (pixel > maxValue) {\n                maxValue = pixel;\n                if (flattenPositions) {\n                  maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                } else {\n                  maxPosition = wR * effectiveFilterWidth + wC;\n                }\n              }\n            }\n          }\n          maxPositions.set(maxPosition, b, yR, yC, d);\n        }\n      }\n    }\n  }\n  return maxPositions;\n}\nfunction pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = convInfo.padInfo.front;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n  const output = buffer(convInfo.outShape, dtype);\n  const outputVals = output.values;\n  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n  const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n  const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n  const outputColStrides = convInfo.outShape[4];\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    const outputBatchOffset = batch * outputBatchStrides;\n    const inputBatchOffset = batch * strides[0];\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n        const xDepthCorner = yDepth * strideDepth - padFront;\n        let xDepthMin = xDepthCorner;\n        while (xDepthMin < 0) {\n          xDepthMin += dilationDepth;\n        }\n        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n        const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n          const xRowCorner = yRow * strideHeight - padTop;\n          let xRowMin = xRowCorner;\n          while (xRowMin < 0) {\n            xRowMin += dilationHeight;\n          }\n          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n          const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n            const xColCorner = yCol * strideWidth - padLeft;\n            let xColMin = xColCorner;\n            while (xColMin < 0) {\n              xColMin += dilationWidth;\n            }\n            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n            let minMaxValue = initialValue;\n            let avgValue = 0;\n            let count2 = 0;\n            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n              const xDepthOffset = inputBatchOffset + xDepth * strides[1];\n              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                const xRowOffset = xDepthOffset + xRow * strides[2];\n                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                  const xColOffset = xRowOffset + xCol * strides[3];\n                  const pixel = xValues[xColOffset + channel];\n                  if (poolType === \"max\" && pixel > minMaxValue) {\n                    minMaxValue = pixel;\n                  } else if (poolType === \"avg\") {\n                    avgValue += pixel;\n                    count2++;\n                  }\n                  if (isNaN(minMaxValue)) {\n                    break;\n                  }\n                }\n                if (isNaN(minMaxValue)) {\n                  break;\n                }\n              }\n              if (isNaN(minMaxValue)) {\n                break;\n              }\n            }\n            const outputOffset = outputColOffset + channel;\n            outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n          }\n        }\n      }\n    }\n  }\n  return output;\n}\nfunction maxPool3dPositions(xBuf, convInfo) {\n  const maxPositions = buffer(convInfo.outShape, \"int32\");\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = convInfo.padInfo.front;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n        const xDepthCorner = yDepth * strideDepth - padFront;\n        let xDepthMin = xDepthCorner;\n        while (xDepthMin < 0) {\n          xDepthMin += dilationDepth;\n        }\n        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n          const xRowCorner = yRow * strideHeight - padTop;\n          let xRowMin = xRowCorner;\n          while (xRowMin < 0) {\n            xRowMin += dilationHeight;\n          }\n          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n            const xColCorner = yCol * strideWidth - padLeft;\n            let xColMin = xColCorner;\n            while (xColMin < 0) {\n              xColMin += dilationWidth;\n            }\n            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n            let maxValue = Number.NEGATIVE_INFINITY;\n            let maxPosition = -1;\n            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n              const wDepth = xDepth - xDepthCorner;\n              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                const wRow = xRow - xRowCorner;\n                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                  const wCol = xCol - xColCorner;\n                  const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                  if (pixel >= maxValue) {\n                    maxValue = pixel;\n                    maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;\n                  }\n                }\n              }\n            }\n            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return maxPositions;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js\nfunction avgPool2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"avgPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let res;\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    res = identity2({ inputs: { x }, backend: backend2 });\n  } else {\n    const xValues = backend2.data.get(x.dataId).values;\n    const strides2 = util_exports.computeStrides(x.shape);\n    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"avg\");\n    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n  }\n  return res;\n}\nvar avgPoolConfig = {\n  kernelName: AvgPool,\n  backendName: \"cpu\",\n  kernelFunc: avgPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js\nfunction avgPool3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  assertNotComplex(x, \"avgPool3d\");\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n  const xValues = backend2.data.get(x.dataId).values;\n  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"avg\");\n  return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar avgPool3DConfig = {\n  kernelName: AvgPool3D,\n  backendName: \"cpu\",\n  kernelFunc: avgPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  assertNotComplex([dy, input2], \"avgPool3DGrad\");\n  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(input2.shape, \"float32\");\n  const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n  const dyBuf = backend2.bufferSync(dy);\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n            const dyDepthCorner = dxDepth - padFront;\n            const dyRowCorner = dxRow - padTop;\n            const dyColCorner = dxCol - padLeft;\n            let dotProd = 0;\n            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n                continue;\n              }\n              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                const dyRow = (dyRowCorner + wRow) / strideHeight;\n                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n                  continue;\n                }\n                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                  const dyCol = (dyColCorner + wCol) / strideWidth;\n                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n                    continue;\n                  }\n                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  dotProd += pixel;\n                }\n              }\n            }\n            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPool3DGradConfig2 = {\n  kernelName: AvgPool3DGrad,\n  backendName: \"cpu\",\n  kernelFunc: avgPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  assertNotComplex([dy, input2], \"avgPoolGrad\");\n  const { filterSize, strides, pad: pad3 } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(x.shape, \"float32\");\n  const avgMultiplier = 1 / (filterHeight * filterWidth);\n  const dyData = backend2.data.get(dy.dataId).values;\n  const dyBuf = buffer(dy.shape, \"float32\", dyData);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel;\n            }\n          }\n          dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPoolGradConfig2 = {\n  kernelName: AvgPoolGrad,\n  backendName: \"cpu\",\n  kernelFunc: avgPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js\nfunction batchNorm2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, scale: scale2, offset, mean: mean5, variance } = inputs;\n  util_exports.assert(mean5.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  assertNotComplex([x, mean5, variance, scale2, offset], \"batchNorm\");\n  let { varianceEpsilon } = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const xVals = backend2.data.get(x.dataId).values;\n  const mVals = backend2.data.get(mean5.dataId).values;\n  const varVals = backend2.data.get(variance.dataId).values;\n  const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]);\n  const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);\n  const outVals = new Float32Array(xVals.length);\n  const offValsLength = offVals.length;\n  const sValsLength = sVals.length;\n  const varValsLength = varVals.length;\n  const mValsLength = mVals.length;\n  let offi = 0;\n  let mi = 0;\n  let si = 0;\n  let vi = 0;\n  for (let i2 = 0; i2 < xVals.length; ++i2) {\n    outVals[i2] = offVals[offi++] + (xVals[i2] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);\n    if (offi >= offValsLength) {\n      offi = 0;\n    }\n    if (mi >= mValsLength) {\n      mi = 0;\n    }\n    if (si >= sValsLength) {\n      si = 0;\n    }\n    if (vi >= varValsLength) {\n      vi = 0;\n    }\n  }\n  return backend2.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nvar batchNormConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: \"cpu\",\n  kernelFunc: batchNorm2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  assertNotComplex([x], \"batchToSpaceND\");\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n  const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n  const result = slice2({\n    inputs: { x: xTransposedReshaped },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  backend2.disposeIntermediateTensorInfo(xReshaped);\n  backend2.disposeIntermediateTensorInfo(xTransposed);\n  backend2.disposeIntermediateTensorInfo(xTransposedReshaped);\n  return result;\n}\nvar batchToSpaceNDConfig = {\n  kernelName: BatchToSpaceND,\n  backendName: \"cpu\",\n  kernelFunc: batchToSpaceND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js\nfunction bincount2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size } = attrs;\n  const xVals = backend2.data.get(x.dataId).values;\n  const weightsVals = backend2.data.get(weights.dataId).values;\n  const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n  return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig = {\n  kernelName: Bincount,\n  backendName: \"cpu\",\n  kernelFunc: bincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { s0, s1 } = inputs;\n  const s0Vals = backend2.data.get(s0.dataId).values;\n  const s1Vals = backend2.data.get(s1.dataId).values;\n  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n  return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig = {\n  kernelName: BroadcastArgs,\n  backendName: \"cpu\",\n  kernelFunc: broadcastArgs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js\nvar clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n  const clipAttrs = attrs;\n  if (xi > clipAttrs.clipValueMax) {\n    return clipAttrs.clipValueMax;\n  }\n  return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nvar clipByValueConfig = {\n  kernelName: ClipByValue,\n  backendName: \"cpu\",\n  kernelFunc: clipByValue2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js\nvar complexAbs = (args) => {\n  const { x } = args.inputs;\n  const cpuBackend = args.backend;\n  const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n  const complexVals = cpuBackend.data.get(x.dataId);\n  const real5 = complexVals.complexTensorInfos.real;\n  const imag5 = complexVals.complexTensorInfos.imag;\n  const realVals = cpuBackend.data.get(real5.dataId).values;\n  const imagVals = cpuBackend.data.get(imag5.dataId).values;\n  for (let i2 = 0; i2 < realVals.length; i2++) {\n    const real6 = realVals[i2];\n    const imag6 = imagVals[i2];\n    resultValues[i2] = Math.hypot(real6, imag6);\n  }\n  return cpuBackend.makeOutput(resultValues, x.shape, \"float32\");\n};\nvar complexAbsConfig = {\n  kernelName: ComplexAbs,\n  backendName: \"cpu\",\n  kernelFunc: complexAbs\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js\nfunction imag2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const imag5 = backend2.data.get(input2.dataId).complexTensorInfos.imag;\n  const imagVal = backend2.data.get(imag5.dataId).values;\n  return backend2.makeTensorInfo(imag5.shape, imag5.dtype, imagVal);\n}\nvar imagConfig = {\n  kernelName: Imag,\n  backendName: \"cpu\",\n  kernelFunc: imag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js\nfunction concat2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  if ($inputs[0].dtype === \"complex64\") {\n    const reals = $inputs.map((t2) => real2({ inputs: { input: t2 }, backend: backend2 }));\n    const imags = $inputs.map((t2) => imag2({ inputs: { input: t2 }, backend: backend2 }));\n    const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });\n    const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });\n    const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));\n    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));\n    backend2.disposeIntermediateTensorInfo(realConcated);\n    backend2.disposeIntermediateTensorInfo(imagConcated);\n    return result;\n  }\n  const inputs2D = $inputs.map((t2) => {\n    const innerSize = util_exports.sizeFromShape(t2.shape.slice($axis));\n    const shape = [-1, innerSize];\n    return reshape3({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n  });\n  const inputsValShapes = inputs2D.map((t2) => {\n    return { vals: backend2.data.get(t2.dataId).values, shape: t2.shape };\n  });\n  outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);\n  const simplyConcat = inputs2D[0].shape[0] === 1;\n  const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), $axis);\n  const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n  inputs2D.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return outInfo;\n}\nvar concatConfig = {\n  kernelName: Concat,\n  backendName: \"cpu\",\n  kernelFunc: concat2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js\nfunction conv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  assertNotComplex([x, filter], \"conv2d\");\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const padLeft = convInfo.padInfo.left;\n  const padTop = convInfo.padInfo.top;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const xBatchStride = xStrides[0];\n  const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n  const xColStride = isChannelsLast ? xStrides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n  const yBatchStride = y.strides[0];\n  const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n  const yColStride = isChannelsLast ? y.strides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xBatchStride;\n    const yOffset1 = b * yBatchStride;\n    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n      const yOffset2 = yOffset1 + yR * yRowStride;\n      const xRCorner = yR * convInfo.strideHeight - padTop;\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const xR = xRCorner + wR * dilationHeight;\n        if (xR < 0 || xR >= convInfo.inHeight) {\n          continue;\n        }\n        const wOffset1 = wR * filterStrides[0];\n        const xOffset2 = xOffset1 + xR * xRowStride;\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const yOffset3 = yOffset2 + yC * yColStride;\n          const xCCorner = yC * convInfo.strideWidth - padLeft;\n          for (let wC = 0; wC < filterWidth; ++wC) {\n            const xC = xCCorner + wC * dilationWidth;\n            if (xC < 0 || xC >= convInfo.inWidth) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wC * filterStrides[1];\n            const xOffset3 = xOffset2 + xC * xColStride;\n            let wOffset3 = wOffset2;\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n              const xVal = xVals[xOffset3 + d1 * xChannelStride];\n              for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];\n              }\n              wOffset3 += convInfo.outChannels;\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nvar conv2DConfig = {\n  kernelName: Conv2D,\n  backendName: \"cpu\",\n  kernelFunc: conv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n  assertNotComplex([x, dy], \"conv2dBackpropFilter\");\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  const xVals = backend2.data.get(x.dataId).values;\n  const dyVals = backend2.data.get(dy.dataId).values;\n  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n  for (let wR = 0; wR < filterHeight; ++wR) {\n    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n    for (let wC = 0; wC < filterWidth; ++wC) {\n      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n      for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n          let dotProd = 0;\n          for (let b = 0; b < convInfo.batchSize; ++b) {\n            for (let yR = yRMin; yR < yRMax; ++yR) {\n              const xR = wR + yR * strideHeight - topPad;\n              for (let yC = yCMin; yC < yCMax; ++yC) {\n                const xC = wC + yC * strideWidth - leftPad;\n                if (isChannelsLast) {\n                  dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                } else {\n                  dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                }\n              }\n            }\n          }\n          dW.set(dotProd, wR, wC, d1, d2);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar conv2DBackpropFilterConfig = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: conv2DBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  assertNotComplex([dy, filter], \"conv2dBackpropInput\");\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  $dataFormat = convInfo.dataFormat;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const isChannelsLast = $dataFormat === \"channelsLast\";\n  const xBatchStride = dx.strides[0];\n  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n  const xColStride = isChannelsLast ? dx.strides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n              for (let d2 = 0; d2 < outChannels; ++d2) {\n                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                const weight = fltValues[fltOffset + d2];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;\n          dxValues[dxOffset] = dotProd;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv2DBackpropInputConfig = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: conv2DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js\nfunction conv3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  assertNotComplex([x, filter], \"conv3d\");\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n  const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n  const padFront = padInfo.front;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n      const yOffset2 = yOffset1 + yF * y.strides[1];\n      const xFCorner = yF * convInfo.strideDepth - padFront;\n      for (let wF = 0; wF < filterDepth; ++wF) {\n        const xF = xFCorner + wF * dilationDepth;\n        if (xF < 0 || xF >= convInfo.inDepth) {\n          continue;\n        }\n        const wOffset1 = wF * filterStrides[0];\n        const xOffset2 = xOffset1 + xF * xStrides[1];\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const yOffset3 = yOffset2 + yR * y.strides[2];\n          const xRCorner = yR * convInfo.strideHeight - padTop;\n          for (let wR = 0; wR < filterHeight; ++wR) {\n            const xR = xRCorner + wR * dilationHeight;\n            if (xR < 0 || xR >= convInfo.inHeight) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wR * filterStrides[1];\n            const xOffset3 = xOffset2 + xR * xStrides[2];\n            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n              const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n              const xCCorner = yC * convInfo.strideWidth - padLeft;\n              for (let wC = 0; wC < filterWidth; ++wC) {\n                const xC = xCCorner + wC * dilationWidth;\n                if (xC < 0 || xC >= convInfo.inWidth) {\n                  continue;\n                }\n                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                let wOffset4 = wOffset3;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                  const xVal = xVals[xOffset4 + d1];\n                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                  }\n                  wOffset4 += convInfo.outChannels;\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar conv3DConfig = {\n  kernelName: Conv3D,\n  backendName: \"cpu\",\n  kernelFunc: conv3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, filterShape } = attrs;\n  assertNotComplex([x, dy], \"conv3dBackpropFilterV2\");\n  const xStrides = util_exports.computeStrides(x.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dw = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const dwValues = dw.values;\n  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const xValues = backend2.data.get(x.dataId).values;\n  const [xS0, xS1, xS2, xS3] = xStrides;\n  const frontPad = convInfo.padInfo.front;\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  for (let wF = 0; wF < filterDepth; ++wF) {\n    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n    const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n    const wOffset1 = wF * dwS0;\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n      const wOffset2 = wR * dwS1 + wOffset1;\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n        const wOffset3 = wC * dwS2 + wOffset2;\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          const wOffset4 = d1 * dwS3 + wOffset3;\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              const xOffset1 = b * xS0;\n              const yOffset1 = b * dyS0;\n              for (let yF = yFMin; yF < yFMax; ++yF) {\n                const xF = wF + yF * strideDepth - frontPad;\n                const xOffset2 = xF * xS1 + xOffset1;\n                const yOffset2 = yF * dyS1 + yOffset1;\n                for (let yR = yRMin; yR < yRMax; ++yR) {\n                  const xR = wR + yR * strideHeight - topPad;\n                  const xOffset3 = xR * xS2 + xOffset2;\n                  const yOffset3 = yR * dyS2 + yOffset2;\n                  for (let yC = yCMin; yC < yCMax; ++yC) {\n                    const xC = wC + yC * strideWidth - leftPad;\n                    const xOffset4 = xC * xS3 + xOffset3;\n                    const yOffset4 = yC * dyS3 + yOffset3;\n                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                  }\n                }\n              }\n            }\n            dwValues[wOffset4 + d2] = dotProd;\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nvar conv3DBackpropFilterV2Config = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: \"cpu\",\n  kernelFunc: conv3DBackpropFilterV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInputV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { pad: pad3, strides, inputShape } = attrs;\n  assertNotComplex([dy], \"conv3dBackpropInputV2\");\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n  const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n  const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xF = 0; xF < inDepth; ++xF) {\n        const xFCorner = xF - frontPad;\n        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n        const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n            let dotProd = 0;\n            for (let yF = xFMin; yF < yFMax; ++yF) {\n              const wF = yF * strideDepth - xFCorner;\n              for (let yR = xRMin; yR < yRMax; ++yR) {\n                const wR = yR * strideHeight - xRCorner;\n                for (let yC = xCMin; yC < yCMax; ++yC) {\n                  const wC = yC * strideWidth - xCCorner;\n                  const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                  const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                  for (let d2 = 0; d2 < outChannels; ++d2) {\n                    const pixel = dyValues[dyOffset + d2];\n                    const weight = fltValues[fltOffset + d2];\n                    dotProd += pixel * weight;\n                  }\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv3DBackpropInputV2Config = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: \"cpu\",\n  kernelFunc: conv3DBackpropInputV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js\nvar cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nvar cosConfig = {\n  kernelName: Cos,\n  backendName: \"cpu\",\n  kernelFunc: cos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js\nvar cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nvar coshConfig = {\n  kernelName: Cosh,\n  backendName: \"cpu\",\n  kernelFunc: cosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js\nfunction cropAndResize2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const numBoxes = boxes.shape[0];\n  const [cropHeight, cropWidth] = cropSize;\n  const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], \"float32\");\n  const boxVals = backend2.data.get(boxes.dataId).values;\n  const boxIndVals = backend2.data.get(boxInd.dataId).values;\n  const imageVals = backend2.data.get(image2.dataId).values;\n  const inStride = util_exports.computeStrides(image2.shape);\n  const outStride = util_exports.computeStrides(output.shape);\n  for (let b = 0; b < numBoxes; b++) {\n    const startInd = b * 4;\n    const y1 = boxVals[startInd];\n    const x1 = boxVals[startInd + 1];\n    const y2 = boxVals[startInd + 2];\n    const x2 = boxVals[startInd + 3];\n    const bInd = boxIndVals[b];\n    if (bInd >= batch) {\n      continue;\n    }\n    const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;\n    const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n    for (let y = 0; y < cropHeight; y++) {\n      const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);\n      if (yInd < 0 || yInd > imageHeight - 1) {\n        for (let x = 0; x < cropWidth; x++) {\n          for (let c = 0; c < numChannels; c++) {\n            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = extrapolationValue;\n          }\n        }\n        continue;\n      }\n      if (method === \"bilinear\") {\n        const topInd = Math.floor(yInd);\n        const bottomInd = Math.ceil(yInd);\n        const yLerp = yInd - topInd;\n        for (let x = 0; x < cropWidth; x++) {\n          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n          const leftInd = Math.floor(xInd);\n          const rightInd = Math.ceil(xInd);\n          const xLerp = xInd - leftInd;\n          for (let c = 0; c < numChannels; c++) {\n            let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n            const topLeft = imageVals[ind];\n            ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n            const topRight = imageVals[ind];\n            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n            const bottomLeft = imageVals[ind];\n            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n            const bottomRight = imageVals[ind];\n            const top = topLeft + (topRight - topLeft) * xLerp;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = top + (bottom - top) * yLerp;\n          }\n        }\n      } else {\n        for (let x = 0; x < cropWidth; ++x) {\n          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n          const closestX = Math.round(xInd);\n          const closestY = Math.round(yInd);\n          for (let c = 0; c < numChannels; c++) {\n            const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];\n            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[outInd] = imageVals[inInd];\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(output.shape, output.dtype, output.values);\n}\nvar cropAndResizeConfig = {\n  kernelName: CropAndResize,\n  backendName: \"cpu\",\n  kernelFunc: cropAndResize2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js\nfunction cumprod2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  assertNotComplex(x, \"cumprod\");\n  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n  const resultDtype = upcastType($x.dtype, \"int32\");\n  const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;\n  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i2, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 1 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i2, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];\n      }\n    }\n  }\n  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo($x);\n    return reverseTransposedResult;\n  }\n  return result;\n}\nvar cumprodConfig = {\n  kernelName: Cumprod,\n  backendName: \"cpu\",\n  kernelFunc: cumprod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js\nfunction cumsum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  assertNotComplex(x, \"cumsum\");\n  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n  const resultDtype = upcastType($x.dtype, \"int32\");\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;\n  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i2, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 0 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i2, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];\n      }\n    }\n  }\n  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo($x);\n    return reverseTransposedResult;\n  }\n  return result;\n}\nvar cumsumConfig = {\n  kernelName: Cumsum,\n  backendName: \"cpu\",\n  kernelFunc: cumsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js\nfunction denseBincount2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size, binaryOutput } = attrs;\n  if (x.shape.length === 1) {\n    const xVals = backend2.data.get(x.dataId).values;\n    const weightsVals = backend2.data.get(weights.dataId).values;\n    const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n    return backend2.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend2.bufferSync(x);\n    const weightsBuf = backend2.bufferSync(weights);\n    const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);\n    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig = {\n  kernelName: DenseBincount,\n  backendName: \"cpu\",\n  kernelFunc: denseBincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  util_exports.assert(dataFormat === \"NHWC\", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n  const batchSize = x.shape[0];\n  const inputHeight = x.shape[1];\n  const inputWidth = x.shape[2];\n  const inputDepth = x.shape[3];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const xValues = backend2.data.get(x.dataId).values;\n  const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n  let outputIdx = 0;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let h = 0; h < outputHeight; ++h) {\n      const inH = Math.floor(h / blockSize);\n      const offsetH = h % blockSize;\n      for (let w = 0; w < outputWidth; ++w) {\n        const inW = Math.floor(w / blockSize);\n        const offsetW = w % blockSize;\n        const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n        for (let d = 0; d < outputDepth; ++d) {\n          const inD = d + offsetD;\n          const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n          result[outputIdx++] = xValues[inputIdx];\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);\n}\nvar depthToSpaceConfig = {\n  kernelName: DepthToSpace,\n  backendName: \"cpu\",\n  kernelFunc: depthToSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  assertNotComplex([x, filter], \"depthwiseConv2DNative\");\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const chMul = convInfo.outChannels / convInfo.inChannels;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n      const yOffset2 = yOffset1 + yR * y.strides[1];\n      const xRCorner = yR * convInfo.strideHeight - padTop;\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const xR = xRCorner + wR * dilationHeight;\n        if (xR < 0 || xR >= convInfo.inHeight) {\n          continue;\n        }\n        const wOffset1 = wR * filterStrides[0];\n        const xOffset2 = xOffset1 + xR * xStrides[1];\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const yOffset3 = yOffset2 + yC * y.strides[2];\n          const xCCorner = yC * convInfo.strideWidth - padLeft;\n          for (let wC = 0; wC < filterWidth; ++wC) {\n            const xC = xCCorner + wC * dilationWidth;\n            if (xC < 0 || xC >= convInfo.inWidth) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wC * filterStrides[1];\n            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n            let yOffset4 = yOffset3;\n            let wOffset3 = wOffset2;\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n              const xVal = xVals[xOffset3 + d1];\n              for (let q = 0; q < chMul; ++q) {\n                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n              }\n              yOffset4 += chMul;\n              wOffset3 += chMul;\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar depthwiseConv2dNativeConfig = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNative\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n  assertNotComplex([x, dy], \"depthwiseConv2dNativeBackpropFilter\");\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);\n  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n  const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  const chMul = convInfo.outChannels / convInfo.inChannels;\n  const xVals = backend2.data.get(x.dataId).values;\n  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n  const dyVals = backend2.data.get(dy.dataId).values;\n  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n  for (let wR = 0; wR < filterHeight; ++wR) {\n    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n    for (let wC = 0; wC < filterWidth; ++wC) {\n      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n      for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n        const d1 = Math.trunc(d2 / chMul);\n        const dm = d2 % chMul;\n        let dotProd = 0;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n          for (let yR = yRMin; yR < yRMax; ++yR) {\n            const xR = wR + yR * strideHeight - topPad;\n            for (let yC = yCMin; yC < yCMax; ++yC) {\n              const xC = wC + yC * strideWidth - leftPad;\n              dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n            }\n          }\n        }\n        dW.set(dotProd, wR, wC, d1, dm);\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar depthwiseConv2dNativeBackpropFilterConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNativeBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n  assertNotComplex([dy, filter], \"depthwiseConv2DNativeBackpropInput\");\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2] = dx.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2] = dyStrides;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const chMul = outChannels / inChannels;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n              for (let dm = 0; dm < chMul; ++dm) {\n                const d2 = d1 * chMul + dm;\n                const pixel = dyValues[dyOffset + d2];\n                const weight = fltValues[fltOffset + dm];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar depthwiseConv2dNativeBackpropInputConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNativeBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js\nfunction diag2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xVals = backend2.data.get(x.dataId).values;\n  const outBuf = buffer([xSize, xSize], x.dtype);\n  const vals = outBuf.values;\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    vals[i2 * xSize + i2] = xVals[i2];\n  }\n  const outShape = [...x.shape, ...x.shape];\n  return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);\n}\nvar diagConfig = {\n  kernelName: Diag,\n  backendName: \"cpu\",\n  kernelFunc: diag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js\nvar dilation2DConfig = {\n  kernelName: Dilation2D,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const xVals = cpuBackend.data.get(x.dataId).values;\n    const xRank = x.shape.length;\n    const filterVals = cpuBackend.data.get(filter.dataId).values;\n    const filterRank = filter.shape.length;\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    const outSize = util_exports.sizeFromShape(outShape);\n    const outRank = outShape.length;\n    const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));\n                    const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));\n                    const val = xVals[xIndex] + filterVals[filterIndex];\n                    if (val > curVal) {\n                      curVal = val;\n                    }\n                  }\n                }\n              }\n            }\n            const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));\n            outputVals[outputIndex] = curVal;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n    return { dataId, shape: outShape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js\nvar dilation2DBackpropFilterConfig = {\n  kernelName: Dilation2DBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter, dy } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n    const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hMax = 0;\n            let wMax = 0;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hMax = h;\n                      wMax = w;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n    return { dataId, shape: filter.shape, dtype: filter.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js\nvar dilation2DBackpropInputConfig = {\n  kernelName: Dilation2DBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter, dy } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n    const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hInMax = hBeg < 0 ? 0 : hBeg;\n            let wInMax = wBeg < 0 ? 0 : wBeg;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hInMax = hIn;\n                      wInMax = wIn;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n    return { dataId, shape: x.shape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js\nfunction sum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"sum\");\n  let $x;\n  if (x.dtype === \"bool\") {\n    $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"int32\" } });\n  } else {\n    $x = identity2({ inputs: { x }, backend: backend2 });\n  }\n  const xRank = $x.shape.length;\n  const axes = util_exports.parseAxisParam(axis, $x.shape);\n  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n  let reductionAxes = axes;\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });\n    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, permutedX.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);\n  const resultDtype = backend_util_exports.upcastType(permutedX.dtype, \"int32\");\n  let result = zeros3(backend2, outShape, resultDtype);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = backend2.data.get(result.dataId).values;\n  const aVals = backend2.data.get(permutedX.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let sum7 = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      sum7 += aVals[offset + j];\n    }\n    vals[i2] = sum7;\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);\n    const oldResult = result;\n    result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  backend2.disposeIntermediateTensorInfo($x);\n  if (permutation != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return result;\n}\nvar sumConfig = {\n  kernelName: Sum,\n  backendName: \"cpu\",\n  kernelFunc: sum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js\nfunction einsum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    for (const idTerm of steps[i2]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i2 < nSteps - 1) {\n      if (path[i2] >= 0) {\n        out = sum3({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i2] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n  return out;\n}\nvar einsumConfig = {\n  kernelName: Einsum,\n  backendName: \"cpu\",\n  kernelFunc: einsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js\nfunction eluGrad(args) {\n  const { inputs, backend: backend2 } = args;\n  const { dy, y } = inputs;\n  assertNotComplex([dy, y], \"eluGrad\");\n  const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));\n  const values = backend2.data.get(y.dataId).values;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  for (let i2 = 0; i2 < values.length; ++i2) {\n    const v = values[i2];\n    if (v >= 1) {\n      resultValues[i2] = dyValues[i2];\n    } else {\n      resultValues[i2] = dyValues[i2] * (v + 1);\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, \"float32\", resultValues);\n}\nvar eluGradConfig2 = {\n  kernelName: EluGrad,\n  backendName: \"cpu\",\n  kernelFunc: eluGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js\nvar p = backend_util_exports.ERF_P;\nvar a1 = backend_util_exports.ERF_A1;\nvar a2 = backend_util_exports.ERF_A2;\nvar a3 = backend_util_exports.ERF_A3;\nvar a4 = backend_util_exports.ERF_A4;\nvar a5 = backend_util_exports.ERF_A5;\nvar erf2 = unaryKernelFunc(Erf, (xi) => {\n  const sign4 = Math.sign(xi);\n  const v = Math.abs(xi);\n  const t2 = 1 / (1 + p * v);\n  return sign4 * (1 - ((((a5 * t2 + a4) * t2 + a3) * t2 + a2) * t2 + a1) * t2 * Math.exp(-v * v));\n});\nvar erfConfig = {\n  kernelName: Erf,\n  backendName: \"cpu\",\n  kernelFunc: erf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js\nfunction expandDims3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { input: input2 } = inputs;\n  const { dim } = attrs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig = {\n  kernelName: ExpandDims,\n  backendName: \"cpu\",\n  kernelFunc: expandDims3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js\nvar realDivImpl = createSimpleBinaryKernelImpl((a6, b) => a6 / b);\nvar div2 = binaryKernelFunc(RealDiv, realDivImpl);\nvar realDivConfig = {\n  kernelName: RealDiv,\n  backendName: \"cpu\",\n  kernelFunc: div2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js\nfunction fftBatch(input2, inverse, cpuBackend) {\n  const inputShape = input2.shape;\n  const batch = inputShape[0];\n  const innerDim = inputShape[1];\n  const inputVals = cpuBackend.data.get(input2.dataId);\n  const real2D = inputVals.complexTensorInfos.real;\n  const imag2D = inputVals.complexTensorInfos.imag;\n  const resultShape = [batch, innerDim];\n  const resultSize = util_exports.sizeFromShape(resultShape);\n  const resultReal = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n  const resultImag = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n  for (let b = 0; b < batch; b++) {\n    const r2 = slice2({\n      inputs: { x: real2D },\n      backend: cpuBackend,\n      attrs: { begin: [b, 0], size: [1, innerDim] }\n    });\n    const i2 = slice2({\n      inputs: { x: imag2D },\n      backend: cpuBackend,\n      attrs: { begin: [b, 0], size: [1, innerDim] }\n    });\n    const input3 = complex2({ inputs: { real: r2, imag: i2 }, backend: cpuBackend });\n    const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);\n    const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);\n    for (let d = 0; d < innerDim; d++) {\n      const c = backend_util_exports.getComplexWithIndex(res, d);\n      resultReal[b * innerDim + d] = c.real;\n      resultImag[b * innerDim + d] = c.imag;\n    }\n    cpuBackend.disposeIntermediateTensorInfo(r2);\n    cpuBackend.disposeIntermediateTensorInfo(i2);\n    cpuBackend.disposeIntermediateTensorInfo(input3);\n  }\n  const $realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultReal);\n  const $imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImag);\n  const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n  cpuBackend.disposeIntermediateTensorInfo($realInfo);\n  cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n  return result;\n}\nfunction fftImpl(input2, inverse, cpuBackend) {\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const inputVals = cpuBackend.data.get(input2.dataId);\n  const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n  const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n  if (isExponentOf2(inputSize)) {\n    const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n    const resultShape = [input2.shape[0], input2.shape[1]];\n    if (inverse) {\n      const realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.real);\n      const imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.imag);\n      const sizeInfo = cpuBackend.makeTensorInfo([], \"float32\", util_exports.createScalarValue(inputSize, \"float32\"));\n      const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend });\n      const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n      const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n      const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n      const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n      cpuBackend.disposeIntermediateTensorInfo(realInfo);\n      cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n      cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n      cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n      cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n      cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n      return { real: divRealVals, imag: divImagVals };\n    }\n    return result;\n  } else {\n    const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n    const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n    return backend_util_exports.splitRealAndImagArrays(rawOutput);\n  }\n}\nfunction isExponentOf2(size) {\n  return (size & size - 1) === 0;\n}\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n  if (size === 1) {\n    return { real: realVals, imag: imagVals };\n  }\n  const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n  const half = size / 2;\n  const evenComplex = backend_util_exports.complexWithEvenIndex(data);\n  const evenRealVals = evenComplex.real;\n  const evenImagVals = evenComplex.imag;\n  const evenShape = [evenRealVals.length];\n  const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenRealVals);\n  const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenImagVals);\n  const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n  const oddComplex = backend_util_exports.complexWithOddIndex(data);\n  const oddRealVals = oddComplex.real;\n  const oddImagVals = oddComplex.imag;\n  const oddShape = [oddRealVals.length];\n  const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddRealVals);\n  const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddImagVals);\n  const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n  const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n  const $evenRealVals = $evenComplex.real;\n  const $evenImagVals = $evenComplex.imag;\n  const $evenShape = [$evenRealVals.length];\n  const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenRealVals);\n  const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenImagVals);\n  const $evenTensorInfo = complex2({\n    inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n    backend: cpuBackend\n  });\n  const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n  const $oddRealVals = $oddComplex.real;\n  const $oddImagVals = $oddComplex.imag;\n  const $oddShape = [$oddRealVals.length];\n  const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddRealVals);\n  const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddImagVals);\n  const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n  const e2 = backend_util_exports.exponents(size, inverse);\n  const eShape = [e2.real.length];\n  const eRealInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e2.real);\n  const eImagInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e2.imag);\n  const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n  const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n  const addPart = add4({\n    inputs: { a: $evenTensorInfo, b: exponentInfo },\n    backend: cpuBackend\n  });\n  const subPart = sub2({\n    inputs: { a: $evenTensorInfo, b: exponentInfo },\n    backend: cpuBackend\n  });\n  const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });\n  const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });\n  const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });\n  const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });\n  const $real = concat2({\n    inputs: [addPartReal, subPartReal],\n    backend: cpuBackend,\n    attrs: { axis: 0 }\n  });\n  const $imag = concat2({\n    inputs: [addPartImag, subPartImag],\n    backend: cpuBackend,\n    attrs: { axis: 0 }\n  });\n  const $realVals = cpuBackend.data.get($real.dataId).values;\n  const $imagVals = cpuBackend.data.get($imag.dataId).values;\n  cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n  cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n  cpuBackend.disposeIntermediateTensorInfo(addPart);\n  cpuBackend.disposeIntermediateTensorInfo(subPart);\n  cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n  cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n  cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n  cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n  cpuBackend.disposeIntermediateTensorInfo($real);\n  cpuBackend.disposeIntermediateTensorInfo($imag);\n  return { real: $realVals, imag: $imagVals };\n}\nfunction fourierTransformByMatmul(data, size, inverse) {\n  const ret = new Float32Array(size * 2);\n  for (let r2 = 0; r2 < size; r2++) {\n    let real5 = 0;\n    let imag5 = 0;\n    for (let c = 0; c < size; c++) {\n      const e2 = backend_util_exports.exponent(r2 * c, size, inverse);\n      const term = backend_util_exports.getComplexWithIndex(data, c);\n      real5 += term.real * e2.real - term.imag * e2.imag;\n      imag5 += term.real * e2.imag + term.imag * e2.real;\n    }\n    if (inverse) {\n      real5 /= size;\n      imag5 /= size;\n    }\n    backend_util_exports.assignToTypedArray(ret, real5, imag5, r2);\n  }\n  return ret;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js\nfunction fft2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape3({\n    inputs: { x: input2 },\n    backend: backend2,\n    attrs: { shape: [batch, innerDimensionSize] }\n  });\n  const result = fftBatch(input2D, false, backend2);\n  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar fftConfig = {\n  kernelName: FFT,\n  backendName: \"cpu\",\n  kernelFunc: fft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js\nfunction fill2(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value, dtype } = attrs;\n  const $dtype = dtype || util_exports.inferDtype(value);\n  const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));\n  fillValues(values, value, $dtype);\n  return backend2.makeTensorInfo(shape, $dtype, values);\n}\nvar fillConfig = {\n  kernelName: Fill,\n  backendName: \"cpu\",\n  kernelFunc: fill2\n};\nfunction fillValues(values, value, dtype) {\n  if (dtype === \"string\") {\n    values.fill(value);\n  } else {\n    values.fill(value);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig = {\n  kernelName: FlipLeftRight,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const cpuBackend = backend2;\n    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n    const imageVals = cpuBackend.data.get(image2.dataId).values;\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coordX = Math.round(imageWidth - col - 1);\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            let outputValue = imageVals[outIdx];\n            if (coordX >= 0 && coordX < imageWidth) {\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n            output[outIdx] = outputValue;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n    return { dataId, shape: image2.shape, dtype: image2.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js\nvar floorDivImpl = createSimpleBinaryKernelImpl((a6, b) => Math.floor(a6 / b));\nvar floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, \"int32\");\nvar floorDivConfig = {\n  kernelName: FloorDiv,\n  backendName: \"cpu\",\n  kernelFunc: floorDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let result = conv2D({\n    inputs: { x, filter },\n    backend: backend2,\n    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n  });\n  if (bias) {\n    const resultOld = result;\n    if (dataFormat === \"NCHW\" && bias.shape.length === 1 && bias.shape[0] !== 1) {\n      const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });\n      result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 });\n      backend2.disposeIntermediateTensorInfo(reshapedBias);\n    } else {\n      result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n    }\n    backend2.disposeIntermediateTensorInfo(resultOld);\n  }\n  if (activation2) {\n    const resultOld = result;\n    if (dataFormat === \"NCHW\" && activation2 === \"prelu\" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {\n      const reshapedAlpha = reshape3({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }\n      });\n      result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha);\n      backend2.disposeIntermediateTensorInfo(reshapedAlpha);\n    } else {\n      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n    }\n    backend2.disposeIntermediateTensorInfo(resultOld);\n  }\n  return result;\n}\nvar fusedConv2DConfig = {\n  kernelName: FusedConv2D,\n  backendName: \"cpu\",\n  kernelFunc: fusedConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let result = depthwiseConv2dNative({\n    inputs: { x, filter },\n    backend: backend2,\n    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n  });\n  if (bias) {\n    const oldResult = result;\n    result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  if (activation2) {\n    const oldResult = result;\n    result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  return result;\n}\nvar fusedDepthwiseConv2DConfig = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"cpu\",\n  kernelFunc: fusedDepthwiseConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js\nfunction gatherNd(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  if (numSlices === 0) {\n    return backend2.makeTensorInfo(resultShape, params.dtype, []);\n  }\n  const indicesData = backend2.data.get(indices.dataId).values;\n  const paramsBuf = backend2.bufferSync(params);\n  const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n  return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);\n}\nvar gatherNdConfig = {\n  kernelName: GatherNd,\n  backendName: \"cpu\",\n  kernelFunc: gatherNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js\nfunction gatherV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  assertNotComplex([x, indices], \"gatherV2\");\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const indicesVals = backend2.data.get(indices.dataId).values;\n  const axisDim = x.shape[parsedAxis];\n  for (let i2 = 0; i2 < indicesVals.length; ++i2) {\n    const index = indicesVals[i2];\n    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n  }\n  let $batchDims = batchDims;\n  if (batchDims == null) {\n    $batchDims = 0;\n  }\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);\n  const flattenX = reshape3({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape3({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  const indicesBuf = backend2.bufferSync(flattenIndex);\n  const xBuf = backend2.bufferSync(flattenX);\n  const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(flattenIndex);\n  return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n}\nvar gatherV2Config = {\n  kernelName: GatherV2,\n  backendName: \"cpu\",\n  kernelFunc: gatherV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js\nfunction ifft2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape3({\n    inputs: { x: input2 },\n    backend: backend2,\n    attrs: { shape: [batch, innerDimensionSize] }\n  });\n  const result = fftBatch(input2D, true, backend2);\n  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar ifftConfig = {\n  kernelName: IFFT,\n  backendName: \"cpu\",\n  kernelFunc: ifft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js\nvar isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, \"bool\");\nvar isFiniteConfig = {\n  kernelName: IsFinite,\n  backendName: \"cpu\",\n  kernelFunc: isFinite3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js\nvar isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, \"bool\");\nvar isInfConfig = {\n  kernelName: IsInf,\n  backendName: \"cpu\",\n  kernelFunc: isInf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js\nvar isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, \"bool\");\nvar isNaNConfig = {\n  kernelName: IsNan,\n  backendName: \"cpu\",\n  kernelFunc: isNaN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js\nfunction linSpace(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, num } = attrs;\n  const outVals = linSpaceImpl(start, stop, num);\n  return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig = {\n  kernelName: LinSpace,\n  backendName: \"cpu\",\n  kernelFunc: linSpace\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js\nvar log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nvar log1pConfig = {\n  kernelName: Log1p,\n  backendName: \"cpu\",\n  kernelFunc: log1p2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js\nvar logicalAndImpl = createSimpleBinaryKernelImpl((a6, b) => a6 && b);\nvar logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, \"bool\");\nvar logicalAndConfig = {\n  kernelName: LogicalAnd,\n  backendName: \"cpu\",\n  kernelFunc: logicalAnd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js\nvar logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, \"bool\");\nvar logicalNotConfig = {\n  kernelName: LogicalNot,\n  backendName: \"cpu\",\n  kernelFunc: logicalNot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js\nvar logicalOrImpl = createSimpleBinaryKernelImpl((a6, b) => a6 || b);\nvar logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, \"bool\");\nvar logicalOrConfig = {\n  kernelName: LogicalOr,\n  backendName: \"cpu\",\n  kernelFunc: logicalOr2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js\nfunction lRN(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  assertNotComplex(x, \"LRN\");\n  const channels = x.shape[3];\n  const maxD = channels - 1;\n  const xValues = backend2.data.get(x.dataId).values;\n  const size = util_exports.sizeFromShape(x.shape);\n  const result = new Float32Array(size);\n  function sumAcrossChannels(offset) {\n    const currentChannel = offset % channels;\n    let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n    const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);\n    let sum7 = 0;\n    for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n      const z = xValues[beginSumOffset];\n      sum7 += z * z;\n    }\n    return sum7;\n  }\n  for (let offset = 0; offset < size; offset++) {\n    const sum7 = sumAcrossChannels(offset);\n    const val = xValues[offset] * Math.pow(bias + alpha * sum7, -beta);\n    result[offset] = val;\n  }\n  return backend2.makeTensorInfo(x.shape, x.dtype, result);\n}\nvar LRNConfig = {\n  kernelName: LRN,\n  backendName: \"cpu\",\n  kernelFunc: lRN\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js\nfunction lRNGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, y, dy } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  assertNotComplex(dy, \"LRNGrad\");\n  const dySize = util_exports.sizeFromShape(dy.shape);\n  const channels = dy.shape[3];\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const xValues = backend2.data.get(x.dataId).values;\n  const yValues = backend2.data.get(y.dataId).values;\n  const result = new Float32Array(dySize);\n  const size = dySize;\n  for (let offset = 0; offset < size; offset++) {\n    const currentChannel = offset % channels;\n    const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n    const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);\n    let norm2 = 0;\n    for (let k = depthBegin; k < depthEnd; k++) {\n      norm2 += Math.pow(xValues[k], 2);\n    }\n    norm2 = alpha * norm2 + bias;\n    for (let k = depthBegin; k < depthEnd; k++) {\n      let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;\n      if (offset === k) {\n        dyi += Math.pow(norm2, -beta);\n      }\n      dyi *= dyValues[offset];\n      result[k] += dyi;\n    }\n  }\n  return backend2.makeTensorInfo(dy.shape, x.dtype, result);\n}\nvar LRNGradConfig = {\n  kernelName: LRNGrad,\n  backendName: \"cpu\",\n  kernelFunc: lRNGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js\nfunction max3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  const cpuBackend = backend2;\n  let xShape = x.shape;\n  const xRank = xShape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let xVals = cpuBackend.data.get(x.dataId).values;\n  if (permutedAxes != null) {\n    const newShape = new Array(xRank);\n    for (let i2 = 0; i2 < newShape.length; i2++) {\n      newShape[i2] = xShape[permutedAxes[i2]];\n    }\n    xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    xShape = newShape;\n  }\n  assertNotComplex(x, \"max\");\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n  const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n  let outShape = maxOutShape;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n    outShape = newShape;\n  }\n  return { dataId, shape: outShape, dtype: x.dtype };\n}\nvar maxConfig = {\n  kernelName: Max,\n  backendName: \"cpu\",\n  kernelFunc: max3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js\nfunction maxPool2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"maxPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let res;\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    res = identity2({ inputs: { x }, backend: backend2 });\n  } else {\n    const xValues = backend2.data.get(x.dataId).values;\n    const strides2 = util_exports.computeStrides(x.shape);\n    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"max\");\n    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n  }\n  return res;\n}\nvar maxPoolConfig = {\n  kernelName: MaxPool,\n  backendName: \"cpu\",\n  kernelFunc: maxPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js\nfunction maxPool3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  assertNotComplex(x, \"maxPool3d\");\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n  const xValues = backend2.data.get(x.dataId).values;\n  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"max\");\n  return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar maxPool3DConfig = {\n  kernelName: MaxPool3D,\n  backendName: \"cpu\",\n  kernelFunc: maxPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  assertNotComplex([dy, input2], \"maxPool3DGrad\");\n  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const inputBuf = backend2.bufferSync(input2);\n  const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(input2.shape, \"float32\");\n  const dyBuf = backend2.bufferSync(dy);\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n            const dyDepthCorner = dxDepth - padFront;\n            const dyRowCorner = dxRow - padTop;\n            const dyColCorner = dxCol - padLeft;\n            let dotProd = 0;\n            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n                continue;\n              }\n              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                const dyRow = (dyRowCorner + wRow) / strideHeight;\n                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n                  continue;\n                }\n                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                  const dyCol = (dyColCorner + wCol) / strideWidth;\n                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n                    continue;\n                  }\n                  const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;\n                  const mask = maxPos === curPos ? 1 : 0;\n                  if (mask === 0) {\n                    continue;\n                  }\n                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  dotProd += pixel * mask;\n                }\n              }\n            }\n            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPool3DGradConfig2 = {\n  kernelName: MaxPool3DGrad,\n  backendName: \"cpu\",\n  kernelFunc: maxPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2, output } = inputs;\n  const x = input2;\n  assertNotComplex([input2, output], \"maxPoolGrad\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const xValues = backend2.data.get(x.dataId).values;\n  const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(x.shape, \"float32\");\n  const dyData = backend2.data.get(dy.dataId).values;\n  const dyBuf = buffer(dy.shape, \"float32\", dyData);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);\n              const curPos = wR * effectiveFilterWidth + wC;\n              const mask = maxPos === curPos ? 1 : 0;\n              if (mask === 0) {\n                continue;\n              }\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel * mask;\n            }\n          }\n          dx.set(dotProd, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPoolGradConfig2 = {\n  kernelName: MaxPoolGrad,\n  backendName: \"cpu\",\n  kernelFunc: maxPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n  const strides = util_exports.computeStrides(xShape);\n  const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, \"max\");\n  const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n  return [maxPools.values, maxPositions.values];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n    const cpuBackend = backend2;\n    assertNotComplex(x, \"MaxPoolWithArgmax\");\n    const values = cpuBackend.data.get(x.dataId).values;\n    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);\n    const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n    const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n    const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n    return [\n      { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n      { dataId: indexesDataId, shape: convInfo.outShape, dtype: \"int32\" }\n    ];\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js\nfunction mean2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const axes = util_exports.parseAxisParam(axis, x.shape);\n  const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const toDispose = [];\n  const reduceSizeScalar = backend2.makeTensorInfo([], \"float32\", new Float32Array([reduceSize]));\n  toDispose.push(reduceSizeScalar);\n  const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n  toDispose.push($x);\n  const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });\n  toDispose.push(res);\n  const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar meanConfig = {\n  kernelName: Mean,\n  backendName: \"cpu\",\n  kernelFunc: mean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js\nfunction min3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"min\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let min7 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (Number.isNaN(value) || value < min7) {\n        min7 = value;\n      }\n    }\n    vals[i2] = min7;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar minConfig = {\n  kernelName: Min,\n  backendName: \"cpu\",\n  kernelFunc: min3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js\nfunction mirrorPad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, mode } = attrs;\n  assertNotComplex(x, \"mirrorPad\");\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  const start = paddings.map((p2) => p2[0]);\n  const end = paddings.map((p2, i2) => p2[0] + x.shape[i2]);\n  const offset = mode === \"reflect\" ? 0 : 1;\n  const xVals = backend2.data.get(x.dataId).values;\n  const xRank = x.shape.length;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const resultSize = util_exports.sizeFromShape(outShape);\n  const resultRank = outShape.length;\n  const resultStrides = util_exports.computeStrides(outShape);\n  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n  for (let i2 = 0; i2 < resultSize; i2++) {\n    let coords3 = util_exports.indexToLoc(i2, resultRank, resultStrides);\n    for (let i3 = 0; i3 < resultRank; i3++) {\n      if (coords3[i3] < start[i3]) {\n        coords3[i3] = start[i3] * 2 - coords3[i3] - offset;\n      } else if (coords3[i3] >= end[i3]) {\n        coords3[i3] = (end[i3] - 1) * 2 - coords3[i3] + offset;\n      }\n    }\n    coords3 = coords3.map((c, i3) => c - start[i3]);\n    const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);\n    resVals[i2] = xVals[inIndex];\n  }\n  const outId = backend2.write(resVals, outShape, x.dtype);\n  return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar mirrorPadConfig = {\n  kernelName: MirrorPad,\n  backendName: \"cpu\",\n  kernelFunc: mirrorPad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js\nvar modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {\n  const rem = aValue % bValue;\n  if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {\n    return rem;\n  } else {\n    return (rem + bValue) % bValue;\n  }\n});\nvar mod2 = binaryKernelFunc(Mod, modImpl);\nvar modConfig = {\n  kernelName: Mod,\n  backendName: \"cpu\",\n  kernelFunc: mod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nvar seedrandom4 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js\nfunction softmax3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const logitsRank = logits.shape.length;\n  let $dim = dim;\n  if ($dim === -1) {\n    $dim = logitsRank - 1;\n  }\n  if ($dim !== logitsRank - 1) {\n    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);\n  }\n  const axes = util_exports.parseAxisParam([$dim], logits.shape);\n  const maxLogit = max3({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a6 = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });\n  const b = exp2({ inputs: { x: a6 }, backend: backend2 });\n  const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(maxLogit);\n  backend2.disposeIntermediateTensorInfo(maxLogitReshaped);\n  backend2.disposeIntermediateTensorInfo(a6);\n  backend2.disposeIntermediateTensorInfo(b);\n  backend2.disposeIntermediateTensorInfo(sumExp);\n  backend2.disposeIntermediateTensorInfo(sumReshaped);\n  return result;\n}\nvar softmaxConfig = {\n  kernelName: Softmax,\n  backendName: \"cpu\",\n  kernelFunc: softmax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nfunction multinomial2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { numSamples, seed, normalized } = attrs;\n  assertNotComplex(logits, \"multinomial\");\n  const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });\n  const batchSize = probabilities.shape[0];\n  const numEvents = probabilities.shape[1];\n  const probVals = backend2.data.get(probabilities.dataId).values;\n  const resShape = [batchSize, numSamples];\n  const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), \"int32\");\n  for (let b = 0; b < batchSize; ++b) {\n    const offset = b * numEvents;\n    const cdf = new Float32Array(numEvents - 1);\n    cdf[0] = probVals[offset];\n    for (let event = 1; event < cdf.length; ++event) {\n      cdf[event] = cdf[event - 1] + probVals[offset + event];\n    }\n    const random = seedrandom4.alea(seed.toString());\n    const outOffset = b * numSamples;\n    for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n      const r2 = random();\n      resVals[outOffset + sampleId] = cdf.length;\n      for (let event = 0; event < cdf.length; event++) {\n        if (r2 < cdf[event]) {\n          resVals[outOffset + sampleId] = event;\n          break;\n        }\n      }\n    }\n  }\n  if (!normalized) {\n    backend2.disposeIntermediateTensorInfo(probabilities);\n  }\n  return backend2.makeTensorInfo(resShape, \"int32\", resVals);\n}\nvar multinomialConfig = {\n  kernelName: Multinomial,\n  backendName: \"cpu\",\n  kernelFunc: multinomial2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppression\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppressionPadded\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n  ];\n}\nvar nonMaxSuppressionV4Config = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppressionWithScore\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js\nfunction oneHot2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { depth, onValue, offValue } = attrs;\n  assertNotComplex(indices, \"oneHot\");\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const res = new Float32Array(indicesSize * depth);\n  res.fill(offValue);\n  const indicesVal = backend2.data.get(indices.dataId).values;\n  for (let event = 0; event < indicesSize; ++event) {\n    if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n      res[event * depth + indicesVal[event]] = onValue;\n    }\n  }\n  return backend2.makeTensorInfo([...indices.shape, depth], \"int32\", res);\n}\nvar oneHotConfig = {\n  kernelName: OneHot,\n  backendName: \"cpu\",\n  kernelFunc: oneHot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js\nfunction zerosLike2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"zerosLike is not supported for string tensors\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const r2 = zerosLike2({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });\n  }\n}\nvar zerosLikeConfig = {\n  kernelName: ZerosLike,\n  backendName: \"cpu\",\n  kernelFunc: zerosLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js\nfunction onesLike2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported for string tensors\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const r2 = onesLike2({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });\n  }\n}\nvar onesLikeConfig = {\n  kernelName: OnesLike,\n  backendName: \"cpu\",\n  kernelFunc: onesLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js\nfunction pack(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims3({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar packConfig = {\n  kernelName: Pack,\n  backendName: \"cpu\",\n  kernelFunc: pack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js\nfunction padV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  assertNotComplex(x, \"pad\");\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  const start = paddings.map((p2) => p2[0]);\n  const xVals = backend2.data.get(x.dataId).values;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xRank = x.shape.length;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const resultSize = util_exports.sizeFromShape(outShape);\n  const resultRank = outShape.length;\n  const resultStrides = util_exports.computeStrides(outShape);\n  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n  if (constantValue !== 0) {\n    resVals.fill(constantValue);\n  }\n  for (let i2 = 0; i2 < xSize; i2++) {\n    const coords3 = util_exports.indexToLoc(i2, xRank, xStrides);\n    const outCoords = coords3.map((c, i3) => c + start[i3]);\n    const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);\n    resVals[outIndex] = xVals[i2];\n  }\n  const outId = backend2.write(resVals, outShape, x.dtype);\n  return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar padV2Config = {\n  kernelName: PadV2,\n  backendName: \"cpu\",\n  kernelFunc: padV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js\nvar powImpl = createSimpleBinaryKernelImpl((a6, b) => Math.pow(a6, b));\nvar pow2 = binaryKernelFunc(Pow, powImpl);\nvar powConfig = {\n  kernelName: Pow,\n  backendName: \"cpu\",\n  kernelFunc: pow2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js\nfunction range3(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, dtype, step: step5 } = attrs;\n  const values = rangeImpl(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n}\nvar rangeConfig = {\n  kernelName: Range,\n  backendName: \"cpu\",\n  kernelFunc: range3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js\nvar reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nvar reciprocalConfig = {\n  kernelName: Reciprocal,\n  backendName: \"cpu\",\n  kernelFunc: reciprocal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  assertNotComplex(images, \"resizeBilinear\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend2.data.get(images.dataId).values;\n  const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n  const effectiveInputSize = [\n    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n  ];\n  const effectiveOutputSize = [\n    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n  ];\n  let outputIdx = 0;\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n  for (let b = 0; b < batch; b++) {\n    for (let r2 = 0; r2 < newHeight; r2++) {\n      let sourceFracRow;\n      if (halfPixelCenters) {\n        sourceFracRow = effectiveRowSizeRatio * (r2 + 0.5) - 0.5;\n      } else {\n        sourceFracRow = effectiveRowSizeRatio * r2;\n      }\n      const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));\n      const rowFrac = sourceFracRow - sourceRowFloor;\n      const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n      const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];\n      const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        let sourceFracCol;\n        if (halfPixelCenters) {\n          sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;\n        } else {\n          sourceFracCol = effectiveColSizeRatio * c;\n        }\n        const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));\n        const colFrac = sourceFracCol - sourceColFloor;\n        const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n        const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];\n        const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];\n        const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];\n        const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          const topLeft = xValues[topLeftOffest + d];\n          const bottomLeft = xValues[botLeftOffset + d];\n          const topRight = xValues[topRightOffset + d];\n          const bottomRight = xValues[botRightOffest + d];\n          const top = topLeft + (topRight - topLeft) * colFrac;\n          const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n          const newValue = top + (bottom - top) * rowFrac;\n          result[outputIdx++] = newValue;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], \"float32\", result);\n}\nvar resizeBilinearConfig = {\n  kernelName: ResizeBilinear,\n  backendName: \"cpu\",\n  kernelFunc: resizeBilinear2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  assertNotComplex([dy, images], \"resizeBilinearGrad\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const effectiveXSize = [\n    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n  ];\n  const effectiveYSize = [\n    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n  ];\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n  const dyValues = backend2.data.get(dy.dataId).values;\n  let offset = 0;\n  for (let b = 0; b < batch; b++) {\n    const bOffset = b * imagesStrides[0];\n    for (let r2 = 0; r2 < yHeight; r2++) {\n      const dxR = r2 * heightScale;\n      const topDxRIndex = Math.floor(dxR);\n      const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n      const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];\n      const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];\n      const dxRLerp = dxR - topDxRIndex;\n      const inverseDxRLerp = 1 - dxRLerp;\n      for (let c = 0; c < yWidth; c++) {\n        const dxC = c * widthScale;\n        const leftDxCIndex = Math.floor(dxC);\n        const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n        const dxCLerp = dxC - leftDxCIndex;\n        const inverseDxCLerp = 1 - dxCLerp;\n        const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];\n        const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];\n        const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];\n        const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];\n        const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n        const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n        const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n        const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n        for (let d = 0; d < depth; d++) {\n          const dyVal = dyValues[offset++];\n          output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;\n          output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n          output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;\n          output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], \"float32\", output);\n}\nvar resizeBilinearGradConfig2 = {\n  kernelName: ResizeBilinearGrad,\n  backendName: \"cpu\",\n  kernelFunc: resizeBilinearGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  assertNotComplex(images, \"resizeNearestNeighbor\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend2.data.get(images.dataId).values;\n  const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n  const effectiveInputSize = [\n    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n  ];\n  const effectiveOutputSize = [\n    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n  ];\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n  let outputOffset = 0;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r2 = 0; r2 < newHeight; r2++) {\n      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r2 + 0.5) : effectiveRowSizeRatio * r2;\n      let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n      if (halfPixelCenters) {\n        sourceNearestRow = Math.max(0, sourceNearestRow);\n      }\n      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;\n        let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n        if (halfPixelCenters) {\n          sourceNearestCol = Math.max(0, sourceNearestCol);\n        }\n        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          const newVal = xValues[colOffset + d];\n          output[outputOffset++] = newVal;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);\n}\nvar resizeNearestNeighborConfig = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"cpu\",\n  kernelFunc: resizeNearestNeighbor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  assertNotComplex([dy, images], \"resizeNearestNeighborGrad\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const effectiveXSize = [\n    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n  ];\n  const effectiveYSize = [\n    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n  ];\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n  const invHeightScale = 1 / heightScale;\n  const invWidthScale = 1 / widthScale;\n  const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n  const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r2 = 0; r2 < xHeight; r2++) {\n      const rowOffset = batchOffset + r2 * imagesStrides[1];\n      const startRLerp = Math.floor(r2 * invHeightScale);\n      const startDyR = Math.floor(startRLerp - winHeight / 2);\n      for (let c = 0; c < xWidth; c++) {\n        const colOffset = rowOffset + c * imagesStrides[2];\n        const startCLerp = Math.floor(c * invWidthScale);\n        const startDyC = Math.floor(startCLerp - winWidth / 2);\n        for (let d = 0; d < depth; d++) {\n          let accum = 0;\n          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n            const dyR = dyRIndex + startDyR;\n            if (dyR < 0 || dyR >= yHeight) {\n              continue;\n            }\n            const dyROffset = batchOffset + dyR * dyStrides[1];\n            const sourceFracRow = dyR * heightScale;\n            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n            if (r2 !== sourceNearestRow) {\n              continue;\n            }\n            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n              const dyC = dyCIndex + startDyC;\n              if (dyC < 0 || dyC >= yWidth) {\n                continue;\n              }\n              const dyCOffset = dyROffset + dyC * dyStrides[2];\n              const sourceFracCol = dyC * widthScale;\n              const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n              if (c === sourceNearestCol) {\n                accum += dyValues[dyCOffset + d];\n              }\n            }\n          }\n          output[colOffset + d] = accum;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(images.shape, images.dtype, output);\n}\nvar resizeNearestNeighborGradConfig2 = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: \"cpu\",\n  kernelFunc: resizeNearestNeighborGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js\nfunction reverse2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  assertNotComplex(x, \"reverse\");\n  const xRank = x.shape.length;\n  const $dims = util_exports.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity2({ inputs: { x }, backend: backend2 });\n  }\n  const outBuf = new TensorBuffer(x.shape, x.dtype);\n  const xBuf = backend2.bufferSync(x);\n  for (let i2 = 0; i2 < outBuf.size; i2++) {\n    const outLoc = outBuf.indexToLoc(i2);\n    const inLoc = outLoc.slice();\n    $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);\n    outBuf.set(xBuf.get(...inLoc), ...outLoc);\n  }\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar reverseConfig = {\n  kernelName: Reverse,\n  backendName: \"cpu\",\n  kernelFunc: reverse2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig = {\n  kernelName: RotateWithOffset,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const cpuBackend = backend2;\n    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n    const fullOpacityValue = 255;\n    const sinFactor = Math.sin(radians);\n    const cosFactor = Math.cos(radians);\n    const imageVals = cpuBackend.data.get(image2.dataId).values;\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coords3 = [batch, row, col, channel];\n            const x = coords3[2];\n            const y = coords3[1];\n            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n            coordX = Math.round(coordX + centerX);\n            coordY = Math.round(coordY + centerY);\n            let outputValue = fillValue;\n            if (typeof fillValue !== \"number\") {\n              if (channel === 3) {\n                outputValue = fullOpacityValue;\n              } else {\n                outputValue = fillValue[channel];\n              }\n            }\n            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {\n              const rotatedRowOffset = coordY * (imageWidth * numChannels);\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            output[outIdx] = outputValue;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n    return { dataId, shape: image2.shape, dtype: image2.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js\nvar round3 = unaryKernelFunc(Round, (xi) => {\n  const base = Math.floor(xi);\n  if (xi - base < 0.5) {\n    return Math.floor(xi);\n  } else if (xi - base > 0.5) {\n    return Math.ceil(xi);\n  } else {\n    if (base % 2 === 0) {\n      return base;\n    } else {\n      return base + 1;\n    }\n  }\n});\nvar roundConfig = {\n  kernelName: Round,\n  backendName: \"cpu\",\n  kernelFunc: round3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js\nfunction scatterNd(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const sumDupeIndices = true;\n  const indicesBuf = backend2.bufferSync(indices);\n  const updatesBuf = backend2.bufferSync(updates);\n  const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);\n  return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);\n}\nvar scatterNdConfig = {\n  kernelName: ScatterNd,\n  backendName: \"cpu\",\n  kernelFunc: scatterNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js\nfunction lowerBound2(array2, value) {\n  let left = 0;\n  let right = array2.length;\n  let mid = 0;\n  while (left < right) {\n    mid = Math.floor((left + right) / 2);\n    if (array2[mid] < value) {\n      left = mid + 1;\n    } else {\n      right = mid;\n    }\n  }\n  return right;\n}\nfunction upperBound2(array2, value) {\n  let left = 0;\n  let right = array2.length;\n  let mid = 0;\n  while (left < right) {\n    mid = Math.floor((left + right) / 2);\n    if (array2[mid] <= value) {\n      left = mid + 1;\n    } else {\n      right = mid;\n    }\n  }\n  return right;\n}\nfunction searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {\n  const output = util_exports.getArrayFromDType(\"int32\", batchSize * numValues);\n  for (let b = 0; b < batchSize; ++b) {\n    const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);\n    const outputOffset = b * numValues;\n    for (let i2 = 0; i2 < numValues; ++i2) {\n      output[outputOffset + i2] = side === \"left\" ? lowerBound2(sortedInputsSlice, values[i2 + outputOffset]) : upperBound2(sortedInputsSlice, values[i2 + outputOffset]);\n    }\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js\nfunction searchSorted2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sortedSequence, values } = inputs;\n  const { side } = attrs;\n  const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n  return backend2.makeTensorInfo(values.shape, \"int32\", output);\n}\nvar searchSortedConfig = {\n  kernelName: SearchSorted,\n  backendName: \"cpu\",\n  kernelFunc: searchSorted2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js\nfunction select2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  assertNotComplex([condition, t2, e2], \"select\");\n  const conditionRank = condition.shape.length;\n  const values = backend2.data.get(condition.dataId).values;\n  const tValues = backend2.data.get(t2.dataId).values;\n  const eValues = backend2.data.get(e2.dataId).values;\n  const resultDtype = upcastType(t2.dtype, e2.dtype);\n  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t2.shape), resultDtype);\n  let index = 0;\n  const offset = conditionRank === 0 || conditionRank > 1 || t2.shape.length === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));\n  for (let i2 = 0; i2 < values.length; i2++) {\n    for (let j = 0; j < offset; j++) {\n      if (values[i2] === 1) {\n        newValues[index++] = tValues[i2];\n      } else {\n        newValues[index++] = eValues[i2];\n      }\n    }\n  }\n  return backend2.makeTensorInfo(t2.shape, resultDtype, newValues);\n}\nvar selectConfig = {\n  kernelName: Select,\n  backendName: \"cpu\",\n  kernelFunc: select2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js\nvar scaleAlpha = backend_util_exports.SELU_SCALEALPHA;\nvar scale = backend_util_exports.SELU_SCALE;\nvar selu2 = unaryKernelFunc(Selu, (xi) => {\n  if (xi >= 0) {\n    return scale * xi;\n  } else {\n    return scaleAlpha * (Math.exp(xi) - 1);\n  }\n});\nvar seluConfig = {\n  kernelName: Selu,\n  backendName: \"cpu\",\n  kernelFunc: selu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js\nvar sign2 = unaryKernelFunc(Sign, (xi) => {\n  if (xi < 0) {\n    return -1;\n  } else if (xi > 0) {\n    return 1;\n  } else {\n    return 0;\n  }\n});\nvar signConfig = {\n  kernelName: Sign,\n  backendName: \"cpu\",\n  kernelFunc: sign2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js\nvar sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nvar sinConfig = {\n  kernelName: Sin,\n  backendName: \"cpu\",\n  kernelFunc: sin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js\nvar sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nvar sinhConfig = {\n  kernelName: Sinh,\n  backendName: \"cpu\",\n  kernelFunc: sinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js\nvar epsilon2 = 11920928955078125e-23;\nvar threshold2 = Math.log(epsilon2) + 2;\nvar softplus2 = unaryKernelFunc(Softplus, (xi) => {\n  const tooLarge = xi > -threshold2;\n  const tooSmall = xi < threshold2;\n  const expX = Math.exp(xi);\n  let result;\n  if (tooSmall) {\n    result = expX;\n  } else if (tooLarge) {\n    result = xi;\n  } else {\n    result = Math.log(1 + expX);\n  }\n  return result;\n});\nvar softplusConfig = {\n  kernelName: Softplus,\n  backendName: \"cpu\",\n  kernelFunc: softplus2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  assertNotComplex([x], \"spaceToBatchND\");\n  const prod6 = util_exports.sizeFromShape(blockShape);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const paddedX = padV2Config.kernelFunc({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapeInputs = { x: paddedX };\n  const reshapeAttrs = { shape: reshapedPaddedShape };\n  const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n  const transposeInputs = { x: paddedXReshaped };\n  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n  const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n  const resultReshapeInputs = { x: paddedXT };\n  const resultReshapeAttrs = { shape: flattenShape };\n  const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n  backend2.disposeIntermediateTensorInfo(paddedX);\n  backend2.disposeIntermediateTensorInfo(paddedXReshaped);\n  backend2.disposeIntermediateTensorInfo(paddedXT);\n  return result;\n}\nvar spaceToBatchNDConfig = {\n  kernelName: SpaceToBatchND,\n  backendName: \"cpu\",\n  kernelFunc: spaceToBatchND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n        ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n        ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n        ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const $denseShape = backend2.data.get(denseShape.dataId).values;\n  const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n  return [\n    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n    backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n  ];\n}\nvar sparseFillEmptyRowsConfig = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"cpu\",\n  kernelFunc: sparseFillEmptyRows2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js\nfunction sparseReshape2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape\n        ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape\n        ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);\n  const $inputIndices = backend2.data.get(inputIndices.dataId).values;\n  const targetShape = Array.from(backend2.data.get(newShape.dataId).values);\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n  return [\n    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n  ];\n}\nvar sparseReshapeConfig = {\n  kernelName: SparseReshape,\n  backendName: \"cpu\",\n  kernelFunc: sparseReshape2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n          ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n          ${segmentIds.shape}`);\n  }\n  if (indices.shape[0] !== segmentIds.shape[0]) {\n    throw new Error(`segmentIds and indices should have same size.`);\n  }\n  const $data = backend2.data.get(data.dataId).values;\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig = {\n  kernelName: SparseSegmentMean,\n  backendName: \"cpu\",\n  kernelFunc: sparseSegmentMean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n         ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n         ${segmentIds.shape}`);\n  }\n  if (indices.shape[0] !== segmentIds.shape[0]) {\n    throw new Error(`segmentIds and indices should have same size.`);\n  }\n  const $data = backend2.data.get(data.dataId).values;\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig = {\n  kernelName: SparseSegmentSum,\n  backendName: \"cpu\",\n  kernelFunc: sparseSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js\nfunction sparseToDense2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  const indicesBuf = backend2.bufferSync(sparseIndices);\n  let outBuf;\n  switch (sparseValues.dtype) {\n    case \"bool\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"float32\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"int32\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"string\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    default:\n      throw new Error(`Unsupported type ${sparseValues.dtype}`);\n  }\n  return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n}\nvar sparseToDenseConfig = {\n  kernelName: SparseToDense,\n  backendName: \"cpu\",\n  kernelFunc: sparseToDense2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js\nfunction splitV(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const begin = new Array(x.shape.length).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s2;\n    const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s2;\n    return sliceT;\n  });\n}\nvar splitVConfig = {\n  kernelName: SplitV,\n  backendName: \"cpu\",\n  kernelFunc: splitV\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js\nvar squareConfig = {\n  kernelName: Square,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const cpuBackend = backend2;\n    assertNotComplex(x, \"square\");\n    const values = cpuBackend.data.get(x.dataId).values;\n    const newValues = new Float32Array(values.length);\n    for (let i2 = 0; i2 < values.length; ++i2) {\n      const value = values[i2];\n      newValues[i2] = value * value;\n    }\n    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n    return { dataId, shape: x.shape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js\nvar step2 = unaryKernelFunc(Step, (xi, attrs) => {\n  const stepAttrs = attrs;\n  if (isNaN(xi)) {\n    return NaN;\n  } else {\n    return xi > 0 ? 1 : stepAttrs.alpha;\n  }\n});\nvar stepConfig = {\n  kernelName: Step,\n  backendName: \"cpu\",\n  kernelFunc: step2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js\nfunction stridedSlice2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  assertNotComplex(x, \"stridedSlice\");\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const xBuf = backend2.bufferSync(x);\n    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);\n    result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);\n  }\n  return result;\n}\nvar stridedSliceConfig = {\n  kernelName: StridedSlice,\n  backendName: \"cpu\",\n  kernelFunc: stridedSlice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js\nfunction stringNGrams2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.data.get(data.dataId).values;\n  const $dataSplits = backend2.data.get(dataSplits.dataId).values;\n  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig = {\n  kernelName: StringNGrams,\n  backendName: \"cpu\",\n  kernelFunc: stringNGrams2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js\nfunction stringSplit2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { skipEmpty } = attrs;\n  const { input: input2, delimiter } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (input2.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n  const $input = backend2.data.get(input2.dataId).values;\n  const $delimiter = backend2.data.get(delimiter.dataId).values[0];\n  const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n    backend2.makeTensorInfo([outputSize], \"string\", values),\n    backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n  ];\n}\nvar stringSplitConfig = {\n  kernelName: StringSplit,\n  backendName: \"cpu\",\n  kernelFunc: stringSplit2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { numBuckets } = attrs;\n  const { input: input2 } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const $input = backend2.data.get(input2.dataId).values;\n  const output = stringToHashBucketFastImpl($input, numBuckets);\n  return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"cpu\",\n  kernelFunc: stringToHashBucketFast2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js\nvar tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nvar tanConfig = {\n  kernelName: Tan,\n  backendName: \"cpu\",\n  kernelFunc: tan2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js\nvar tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nvar tanhConfig = {\n  kernelName: Tanh,\n  backendName: \"cpu\",\n  kernelFunc: tanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js\nfunction tile3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reps } = attrs;\n  assertNotComplex(x, \"tile\");\n  const outBuf = tileImpl(backend2.bufferSync(x), reps);\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar tileConfig = {\n  kernelName: Tile,\n  backendName: \"cpu\",\n  kernelFunc: tile3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js\nfunction topK(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  assertNotComplex(x, \"topk\");\n  const xVals = backend2.data.get(x.dataId).values;\n  const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);\n  return [\n    backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n    backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n  ];\n}\nvar topKConfig = {\n  kernelName: TopK,\n  backendName: \"cpu\",\n  kernelFunc: topK\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js\nfunction transform2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [batch, outHeight, outWidth, numChannels];\n  const strides = util_exports.computeStrides(image2.shape);\n  const batchStride = strides[0];\n  const rowStride = strides[1];\n  const colStride = strides[2];\n  const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape));\n  outVals.fill(fillValue);\n  const imageVals = backend2.data.get(image2.dataId).values;\n  const transformVals = backend2.data.get(transforms.dataId).values;\n  for (let b = 0; b < batch; ++b) {\n    const transform6 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);\n    for (let outY = 0; outY < outHeight; ++outY) {\n      for (let outX = 0; outX < outWidth; ++outX) {\n        for (let channel = 0; channel < numChannels; ++channel) {\n          let val;\n          const projection = transform6[6] * outX + transform6[7] * outY + 1;\n          if (projection === 0) {\n            continue;\n          }\n          const inX = (transform6[0] * outX + transform6[1] * outY + transform6[2]) / projection;\n          const inY = (transform6[3] * outX + transform6[4] * outY + transform6[5]) / projection;\n          const x = mapCoord(inX, imageWidth, fillMode);\n          const y = mapCoord(inY, imageHeight, fillMode);\n          switch (interpolation) {\n            case \"nearest\":\n              val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);\n              break;\n            case \"bilinear\":\n              val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);\n              break;\n            default:\n              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);\n          }\n          const ind = b * batchStride + outY * rowStride + outX * colStride + channel;\n          outVals[ind] = val;\n        }\n      }\n    }\n    return backend2.makeTensorInfo(outShape, image2.dtype, outVals);\n  }\n  const dataId = backend2.write(outVals, outShape, image2.dtype);\n  return { dataId, shape: image2.shape, dtype: image2.dtype };\n}\nvar transformConfig = {\n  kernelName: Transform,\n  backendName: \"cpu\",\n  kernelFunc: transform2\n};\nfunction mapCoord(outCoord, len, mode) {\n  switch (mode) {\n    case \"reflect\":\n      return mapCoordReflect(outCoord, len);\n    case \"wrap\":\n      return mapCoordWrap(outCoord, len);\n    case \"nearest\":\n      return mapCoordNearest(outCoord, len);\n    case \"constant\":\n    default:\n      return mapCoordConstant(outCoord, len);\n  }\n}\nfunction mapCoordReflect(outCoord, len) {\n  let inCoord = outCoord;\n  if (inCoord < 0) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz2 = 2 * len;\n      if (inCoord < sz2) {\n        inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;\n      }\n      inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;\n    }\n  } else if (inCoord > len - 1) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz2 = 2 * len;\n      inCoord -= sz2 * Math.trunc(inCoord / sz2);\n      if (inCoord >= len) {\n        inCoord = sz2 - inCoord - 1;\n      }\n    }\n  }\n  return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordWrap(outCoord, len) {\n  let inCoord = outCoord;\n  if (inCoord < 0) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz = len - 1;\n      inCoord += len * (Math.trunc(-inCoord / sz) + 1);\n    }\n  } else if (inCoord > len - 1) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz = len - 1;\n      inCoord -= len * Math.trunc(inCoord / sz);\n    }\n  }\n  return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordConstant(outCoord, len) {\n  return outCoord;\n}\nfunction mapCoordNearest(outCoord, len) {\n  return util_exports.clamp(0, outCoord, len - 1);\n}\nfunction readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const ind = batch * batchStride + y * rowStride + x * colStride + channel;\n  if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {\n    return imageVals[ind];\n  } else {\n    return fillValue;\n  }\n}\nfunction nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const $y = Math.round(y);\n  const $x = Math.round(x);\n  return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);\n}\nfunction bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const yFloor = Math.floor(y);\n  const xFloor = Math.floor(x);\n  const yCeil = yFloor + 1;\n  const xCeil = xFloor + 1;\n  const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);\n  const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);\n  return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js\nfunction unique3(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  assertNotComplex(x, \"unique\");\n  const values = backend2.data.get(x.dataId).values;\n  const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n  return [\n    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend2.makeTensorInfo([indices.length], \"int32\", indices)\n  ];\n}\nvar uniqueConfig = {\n  kernelName: Unique,\n  backendName: \"cpu\",\n  kernelFunc: unique3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js\nfunction unpack(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const valueRank = value.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(valueRank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < valueRank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = value.shape[i2];\n    }\n  }\n  const begin = new Array(valueRank).fill(0);\n  const size = value.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i2 = 0; i2 < res.length; i2++) {\n    begin[axis] = i2;\n    const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });\n    res[i2] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });\n    backend2.disposeIntermediateTensorInfo(tempRes);\n  }\n  return res;\n}\nvar unpackConfig = {\n  kernelName: Unpack,\n  backendName: \"cpu\",\n  kernelFunc: unpack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, segmentIds } = inputs;\n  const { numSegments } = attrs;\n  assertNotComplex(x, \"unsortedSegmentSum\");\n  const xRank = x.shape.length;\n  const segmentIdsRank = segmentIds.shape.length;\n  const res = [];\n  const intermediates = [];\n  const numIters = xRank - segmentIdsRank;\n  let $segmentIds = segmentIds;\n  for (let i2 = 0; i2 < numIters; ++i2) {\n    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i2 + 1 } });\n    $segmentIds = expanded;\n    intermediates.push(expanded);\n  }\n  for (let i2 = 0; i2 < numSegments; ++i2) {\n    const scalarValue = util_exports.createScalarValue(i2, \"int32\");\n    const segmentId = backend2.makeTensorInfo([], \"int32\", scalarValue);\n    const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });\n    const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 });\n    const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });\n    res.push(sumTensorInfo);\n    intermediates.push(segmentId);\n    intermediates.push(mask);\n    intermediates.push(maskCasted);\n    intermediates.push(mul2);\n    intermediates.push(sumTensorInfo);\n  }\n  const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });\n  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar unsortedSegmentSumConfig = {\n  kernelName: UnsortedSegmentSum,\n  backendName: \"cpu\",\n  kernelFunc: unsortedSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js\nvar kernelConfigs = [\n  _fusedMatMulConfig,\n  absConfig,\n  acosConfig,\n  acoshConfig,\n  addConfig,\n  addNConfig,\n  allConfig,\n  anyConfig,\n  argMaxConfig,\n  argMinConfig,\n  asinConfig,\n  asinhConfig,\n  atanConfig,\n  atan2Config,\n  atanhConfig,\n  avgPoolConfig,\n  avgPool3DConfig,\n  avgPool3DGradConfig2,\n  avgPoolGradConfig2,\n  batchMatMulConfig,\n  batchNormConfig,\n  batchToSpaceNDConfig,\n  bincountConfig,\n  broadcastArgsConfig,\n  castConfig,\n  ceilConfig,\n  clipByValueConfig,\n  complexConfig,\n  complexAbsConfig,\n  concatConfig,\n  conv2DConfig,\n  conv2DBackpropFilterConfig,\n  conv2DBackpropInputConfig,\n  conv3DConfig,\n  conv3DBackpropFilterV2Config,\n  conv3DBackpropInputV2Config,\n  cosConfig,\n  coshConfig,\n  cropAndResizeConfig,\n  cumprodConfig,\n  cumsumConfig,\n  denseBincountConfig,\n  depthToSpaceConfig,\n  depthwiseConv2dNativeConfig,\n  depthwiseConv2dNativeBackpropFilterConfig,\n  depthwiseConv2dNativeBackpropInputConfig,\n  diagConfig,\n  dilation2DConfig,\n  dilation2DBackpropFilterConfig,\n  dilation2DBackpropInputConfig,\n  einsumConfig,\n  eluConfig,\n  eluGradConfig2,\n  equalConfig,\n  erfConfig,\n  expConfig,\n  expandDimsConfig,\n  expm1Config,\n  fftConfig,\n  fillConfig,\n  flipLeftRightConfig,\n  floorConfig,\n  floorDivConfig,\n  fusedConv2DConfig,\n  fusedDepthwiseConv2DConfig,\n  gatherNdConfig,\n  gatherV2Config,\n  greaterConfig,\n  greaterEqualConfig,\n  identityConfig,\n  ifftConfig,\n  imagConfig,\n  isFiniteConfig,\n  isInfConfig,\n  isNaNConfig,\n  leakyReluConfig,\n  lessConfig,\n  lessEqualConfig,\n  linSpaceConfig,\n  logConfig,\n  log1pConfig,\n  logicalAndConfig,\n  logicalNotConfig,\n  logicalOrConfig,\n  LRNConfig,\n  LRNGradConfig,\n  maxConfig,\n  maximumConfig,\n  maxPoolConfig,\n  maxPool3DConfig,\n  maxPool3DGradConfig2,\n  maxPoolGradConfig2,\n  maxPoolWithArgmaxConfig,\n  meanConfig,\n  minConfig,\n  minimumConfig,\n  mirrorPadConfig,\n  modConfig,\n  multinomialConfig,\n  multiplyConfig,\n  negConfig,\n  nonMaxSuppressionV3Config,\n  nonMaxSuppressionV4Config,\n  nonMaxSuppressionV5Config,\n  notEqualConfig,\n  oneHotConfig,\n  onesLikeConfig,\n  packConfig,\n  padV2Config,\n  powConfig,\n  preluConfig,\n  prodConfig,\n  rangeConfig,\n  realConfig,\n  realDivConfig,\n  reciprocalConfig,\n  reluConfig,\n  relu6Config,\n  reshapeConfig,\n  resizeBilinearConfig,\n  resizeBilinearGradConfig2,\n  resizeNearestNeighborConfig,\n  resizeNearestNeighborGradConfig2,\n  reverseConfig,\n  rotateWithOffsetConfig,\n  roundConfig,\n  rsqrtConfig,\n  scatterNdConfig,\n  searchSortedConfig,\n  selectConfig,\n  seluConfig,\n  sigmoidConfig,\n  signConfig,\n  sinConfig,\n  sinhConfig,\n  sliceConfig,\n  softmaxConfig,\n  softplusConfig,\n  spaceToBatchNDConfig,\n  sparseFillEmptyRowsConfig,\n  sparseReshapeConfig,\n  sparseSegmentMeanConfig,\n  sparseSegmentSumConfig,\n  sparseToDenseConfig,\n  splitVConfig,\n  sqrtConfig,\n  squareConfig,\n  squaredDifferenceConfig,\n  stepConfig,\n  stridedSliceConfig,\n  stringNGramsConfig,\n  stringSplitConfig,\n  stringToHashBucketFastConfig,\n  subConfig,\n  sumConfig,\n  tanConfig,\n  tanhConfig,\n  tileConfig,\n  topKConfig,\n  transformConfig,\n  transposeConfig,\n  uniqueConfig,\n  unpackConfig,\n  unsortedSegmentSumConfig,\n  zerosLikeConfig\n];\nfor (const kernelConfig of kernelConfigs) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nvar webgl_util_exports = {};\n__export(webgl_util_exports, {\n  assertNotComplex: () => assertNotComplex2,\n  bindCanvasToFramebuffer: () => bindCanvasToFramebuffer,\n  bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer,\n  bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler,\n  bindTextureUnit: () => bindTextureUnit,\n  bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute,\n  callAndCheck: () => callAndCheck,\n  canBeRepresented: () => canBeRepresented,\n  createFragmentShader: () => createFragmentShader,\n  createFramebuffer: () => createFramebuffer,\n  createProgram: () => createProgram,\n  createStaticIndexBuffer: () => createStaticIndexBuffer,\n  createStaticVertexBuffer: () => createStaticVertexBuffer,\n  createTexture: () => createTexture,\n  createVertexShader: () => createVertexShader,\n  getBatchDim: () => getBatchDim,\n  getExtensionOrThrow: () => getExtensionOrThrow,\n  getFramebufferErrorMessage: () => getFramebufferErrorMessage,\n  getMaxTexturesInShader: () => getMaxTexturesInShader,\n  getNumChannels: () => getNumChannels,\n  getProgramUniformLocation: () => getProgramUniformLocation,\n  getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow,\n  getRowsCols: () => getRowsCols,\n  getShapeAs3D: () => getShapeAs3D,\n  getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape,\n  getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion,\n  getWebGLErrorMessage: () => getWebGLErrorMessage,\n  getWebGLMaxTextureSize: () => getWebGLMaxTextureSize,\n  hasExtension: () => hasExtension,\n  isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture,\n  isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled,\n  isReshapeFree: () => isReshapeFree,\n  isWebGLFenceEnabled: () => isWebGLFenceEnabled,\n  isWebGLVersionEnabled: () => isWebGLVersionEnabled,\n  linkProgram: () => linkProgram,\n  logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog,\n  resetMaxTextureSize: () => resetMaxTextureSize,\n  resetMaxTexturesInShader: () => resetMaxTexturesInShader,\n  unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer,\n  unbindTextureUnit: () => unbindTextureUnit,\n  validateFramebuffer: () => validateFramebuffer,\n  validateProgram: () => validateProgram,\n  validateTextureSize: () => validateTextureSize\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js\nvar contexts = {};\nvar WEBGL_ATTRIBUTES = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\nfunction setWebGLContext(webGLVersion, gl) {\n  contexts[webGLVersion] = gl;\n}\nfunction getWebGLContext(webGLVersion, customCanvas) {\n  if (!(webGLVersion in contexts) || customCanvas != null) {\n    const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);\n    if (newCtx !== null) {\n      contexts[webGLVersion] = newCtx;\n    } else {\n      console.log(\"Could not get context for WebGL version\", webGLVersion);\n      return null;\n    }\n  }\n  const gl = contexts[webGLVersion];\n  if (gl == null || gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n  return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n  if (typeof OffscreenCanvas !== \"undefined\" && webGLVersion === 2) {\n    return new OffscreenCanvas(300, 150);\n  } else if (typeof document !== \"undefined\") {\n    return document.createElement(\"canvas\");\n  } else {\n    throw new Error(\"Cannot create a canvas in this context\");\n  }\n}\nfunction getWebGLRenderingContext(webGLVersion, customCanvas) {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error(\"Cannot get WebGL rendering context, WebGL is disabled.\");\n  }\n  const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;\n  canvas.addEventListener(\"webglcontextlost\", (ev) => {\n    ev.preventDefault();\n    delete contexts[webGLVersion];\n  }, false);\n  if (webGLVersion === 1) {\n    return canvas.getContext(\"webgl\", WEBGL_ATTRIBUTES) || canvas.getContext(\"experimental-webgl\", WEBGL_ATTRIBUTES);\n  }\n  return canvas.getContext(\"webgl2\", WEBGL_ATTRIBUTES);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js\nvar PackingScheme;\n(function(PackingScheme2) {\n  PackingScheme2[PackingScheme2[\"DENSE\"] = 0] = \"DENSE\";\n  PackingScheme2[PackingScheme2[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nvar TextureUsage;\n(function(TextureUsage2) {\n  TextureUsage2[TextureUsage2[\"RENDER\"] = 0] = \"RENDER\";\n  TextureUsage2[TextureUsage2[\"UPLOAD\"] = 1] = \"UPLOAD\";\n  TextureUsage2[TextureUsage2[\"PIXELS\"] = 2] = \"PIXELS\";\n  TextureUsage2[TextureUsage2[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nvar PhysicalTextureType;\n(function(PhysicalTextureType2) {\n  PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n  PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nfunction getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n  return [columns, rows];\n}\nfunction getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n  return matrixSize * channelsPerTexture;\n}\nfunction getDenseTexShape(shape) {\n  const size = util_exports.sizeFromShape(shape);\n  const texelsNeeded = Math.ceil(size / 4);\n  return util_exports.sizeToSquarishShape(texelsNeeded);\n}\nfunction getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n  return [\n    Math.max(1, Math.ceil(columns / 2)),\n    Math.max(1, Math.ceil(rows / 2))\n  ];\n}\nfunction getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\nfunction getTextureConfig(gl, textureHalfFloatExtension) {\n  const glany = gl;\n  let internalFormatFloat;\n  let internalFormatHalfFloat;\n  let internalFormatPackedHalfFloat;\n  let internalFormatPackedFloat;\n  let textureFormatFloat;\n  let downloadTextureFormat;\n  let downloadUnpackNumChannels;\n  let defaultNumChannels;\n  let textureTypeHalfFloat;\n  let textureTypeFloat;\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedHalfFloat = glany.RGBA16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n    textureTypeFloat = glany.FLOAT;\n    downloadTextureFormat = glany.RGBA8;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;\n    textureTypeFloat = gl.FLOAT;\n    downloadTextureFormat = gl.RGBA;\n  }\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat,\n    textureTypeFloat\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nfunction callAndCheck(gl, func2) {\n  const returnValue = func2();\n  if (env().getBool(\"DEBUG\")) {\n    checkWebGLError(gl);\n  }\n  return returnValue;\n}\nfunction checkWebGLError(gl) {\n  const error = gl.getError();\n  if (error !== gl.NO_ERROR) {\n    throw new Error(\"WebGL Error: \" + getWebGLErrorMessage(gl, error));\n  }\n}\nvar MIN_FLOAT16 = 596e-10;\nvar MAX_FLOAT16 = 65504;\nfunction canBeRepresented(num) {\n  if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {\n    return true;\n  }\n  return false;\n}\nfunction getWebGLErrorMessage(gl, status) {\n  switch (status) {\n    case gl.NO_ERROR:\n      return \"NO_ERROR\";\n    case gl.INVALID_ENUM:\n      return \"INVALID_ENUM\";\n    case gl.INVALID_VALUE:\n      return \"INVALID_VALUE\";\n    case gl.INVALID_OPERATION:\n      return \"INVALID_OPERATION\";\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return \"INVALID_FRAMEBUFFER_OPERATION\";\n    case gl.OUT_OF_MEMORY:\n      return \"OUT_OF_MEMORY\";\n    case gl.CONTEXT_LOST_WEBGL:\n      return \"CONTEXT_LOST_WEBGL\";\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\nfunction getExtensionOrThrow(gl, extensionName) {\n  return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nfunction createVertexShader(gl, vertexShaderSource) {\n  const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), \"Unable to create vertex WebGLShader.\");\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error(\"Failed to compile vertex shader.\");\n  }\n  return vertexShader;\n}\nfunction createFragmentShader(gl, fragmentShaderSource) {\n  const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), \"Unable to create fragment WebGLShader.\");\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return fragmentShader;\n  }\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error(\"Failed to compile fragment shader.\");\n  }\n  return fragmentShader;\n}\nvar lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n  const lineNumber = +lineNumberRegexResult[1];\n  const shaderLines = shaderSource.split(\"\\n\");\n  const pad3 = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);\n  let maxLineLength = 0;\n  for (let i2 = 0; i2 < linesWithLineNumbers.length; i2++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i2].length, maxLineLength);\n  }\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n  console.log(beforeErrorLines.join(\"\\n\"));\n  console.log(shaderInfoLog.split(\"\\n\")[0]);\n  console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, \"border:1px solid red; background-color:#e3d2d2; color:#a61717\");\n  console.log(afterErrorLines.join(\"\\n\"));\n}\nfunction createProgram(gl) {\n  return throwIfNull(gl, () => gl.createProgram(), \"Unable to create WebGLProgram.\");\n}\nfunction linkProgram(gl, program) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return;\n  }\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error(\"Failed to link vertex and fragment shaders.\");\n  }\n}\nfunction validateProgram(gl, program) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error(\"Shader program validation failed.\");\n  }\n}\nfunction createStaticVertexBuffer(gl, data) {\n  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer2;\n}\nfunction createStaticIndexBuffer(gl, data) {\n  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer2;\n}\nfunction getNumChannels() {\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    return 1;\n  }\n  return 4;\n}\nfunction createTexture(gl) {\n  return throwIfNull(gl, () => gl.createTexture(), \"Unable to create WebGLTexture.\");\n}\nfunction validateTextureSize(width, height) {\n  const maxTextureSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n  if (width <= 0 || height <= 0) {\n    const requested = `[${width}x${height}]`;\n    throw new Error(\"Requested texture size \" + requested + \" is invalid.\");\n  }\n  if (width > maxTextureSize || height > maxTextureSize) {\n    const requested = `[${width}x${height}]`;\n    const max7 = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\"Requested texture size \" + requested + \" greater than WebGL maximum on this browser / GPU \" + max7 + \".\");\n  }\n}\nfunction createFramebuffer(gl) {\n  return throwIfNull(gl, () => gl.createFramebuffer(), \"Unable to create WebGLFramebuffer.\");\n}\nfunction bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\nfunction bindTextureUnit(gl, texture, textureUnit) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nfunction unbindTextureUnit(gl, textureUnit) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction getProgramUniformLocationOrThrow(gl, program, uniformName) {\n  return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nfunction getProgramUniformLocation(gl, program, uniformName) {\n  return gl.getUniformLocation(program, uniformName);\n}\nfunction bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nfunction bindCanvasToFramebuffer(gl) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nfunction bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nfunction unbindColorTextureFromFramebuffer(gl, framebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nfunction validateFramebuffer(gl) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\"Error binding framebuffer: \" + getFramebufferErrorMessage(gl, status));\n  }\n}\nfunction getFramebufferErrorMessage(gl, status) {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return \"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return \"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return \"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return \"FRAMEBUFFER_UNSUPPORTED\";\n    default:\n      return `unknown error ${status}`;\n  }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n  const tOrNull = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\nfunction getBatchDim(shape, dimsToSkip = 2) {\n  return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nfunction getRowsCols(shape) {\n  if (shape.length === 0) {\n    throw Error(\"Cannot get rows and columns of an empty shape array.\");\n  }\n  return [\n    shape.length > 1 ? shape[shape.length - 2] : 1,\n    shape[shape.length - 1]\n  ];\n}\nfunction getShapeAs3D(shape) {\n  let shapeAs3D = [1, 1, 1];\n  const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;\n  if (!isScalar) {\n    shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];\n  }\n  return shapeAs3D;\n}\nfunction getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n  let maxTexSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n    logShape = logShape.map((d, i2) => i2 >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i2]) : logShape[i2]);\n    if (logShape.length === 1) {\n      logShape = [2, logShape[0]];\n    }\n  }\n  if (logShape.length !== 2) {\n    const squeezeResult = util_exports.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n  let size = util_exports.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {\n    return logShape;\n  } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    if (isPacked) {\n      const batchDim = getBatchDim(logShape);\n      let rows = 2, cols = 2;\n      if (logShape.length) {\n        [rows, cols] = getRowsCols(logShape);\n      }\n      size = batchDim * (rows / 2) * (cols / 2);\n      return util_exports.sizeToSquarishShape(size).map((d) => d * 2);\n    }\n    return util_exports.sizeToSquarishShape(size);\n  }\n}\nfunction isEven(n) {\n  return n % 2 === 0;\n}\nfunction isReshapeFree(shape1, shape2) {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n  if (util_exports.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n  if (!shape1.length || !shape2.length) {\n    return true;\n  }\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {\n    return true;\n  }\n  if (shape1.length !== shape2.length) {\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n    if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  }\n  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\nvar MAX_TEXTURE_SIZE;\nvar MAX_TEXTURES_IN_SHADER;\nfunction getWebGLMaxTextureSize(webGLVersion) {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\nfunction resetMaxTextureSize() {\n  MAX_TEXTURE_SIZE = null;\n}\nfunction resetMaxTexturesInShader() {\n  MAX_TEXTURES_IN_SHADER = null;\n}\nfunction getMaxTexturesInShader(webGLVersion) {\n  if (MAX_TEXTURES_IN_SHADER == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n  }\n  return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nfunction getWebGLDisjointQueryTimerVersion(webGLVersion) {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  let queryTimerVersion;\n  const gl = getWebGLContext(webGLVersion);\n  if (hasExtension(gl, \"EXT_disjoint_timer_query_webgl2\") && webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, \"EXT_disjoint_timer_query\")) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\nfunction hasExtension(gl, extensionName) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\nfunction isWebGLVersionEnabled(webGLVersion) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e2) {\n    console.log(\"Error when getting WebGL context: \", e2);\n    return false;\n  }\n  return false;\n}\nfunction isCapableOfRenderingToFloatTexture(webGLVersion) {\n  if (webGLVersion === 0) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, \"OES_texture_float\")) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, \"EXT_color_buffer_float\")) {\n      return false;\n    }\n  }\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\nfunction isDownloadFloatTextureEnabled(webGLVersion) {\n  if (webGLVersion === 0) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, \"OES_texture_float\")) {\n      return false;\n    }\n    if (!hasExtension(gl, \"WEBGL_color_buffer_float\")) {\n      return false;\n    }\n  } else {\n    if (hasExtension(gl, \"EXT_color_buffer_float\")) {\n      return createFloatTextureAndBindToFramebuffer(gl);\n    }\n    const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n      const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n    }\n    return false;\n  }\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n  const texConfig = getTextureConfig(gl);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n  return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {\n  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n  return isFrameBufferComplete;\n}\nfunction isWebGLFenceEnabled(webGLVersion) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  const isEnabled = gl.fenceSync != null;\n  return isEnabled;\n}\nfunction assertNotComplex2(tensor2, opName) {\n  if (!Array.isArray(tensor2)) {\n    tensor2 = [tensor2];\n  }\n  tensor2.forEach((t2) => {\n    if (t2 != null) {\n      util_exports.assert(t2.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the WebGL backend.`);\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js\nvar ENV5 = env();\nENV5.registerFlag(\"HAS_WEBGL\", () => ENV5.getNumber(\"WEBGL_VERSION\") > 0);\nENV5.registerFlag(\"WEBGL_VERSION\", () => {\n  if (isWebGLVersionEnabled(2)) {\n    return 2;\n  } else if (isWebGLVersionEnabled(1)) {\n    return 1;\n  }\n  return 0;\n});\nENV5.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\", () => false);\nENV5.registerFlag(\"WEBGL_BUFFER_SUPPORTED\", () => ENV5.get(\"WEBGL_VERSION\") === 2);\nENV5.registerFlag(\"WEBGL_CPU_FORWARD\", () => true);\nENV5.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\", () => false);\nENV5.registerFlag(\"WEBGL_PACK\", () => ENV5.getBool(\"HAS_WEBGL\"));\nENV5.registerFlag(\"WEBGL_PACK_NORMALIZATION\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_CLIP\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_REDUCE\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_LAZILY_UNPACK\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_CONV_IM2COL\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\", () => getWebGLMaxTextureSize(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\", () => getMaxTexturesInShader(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\", () => {\n  const webGLVersion = ENV5.getNumber(\"WEBGL_VERSION\");\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\", () => ENV5.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 && !device_util_exports.isMobile());\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\", () => {\n  return ENV5.getBool(\"WEBGL_FORCE_F16_TEXTURES\") ? false : ENV5.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\");\n});\nENV5.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\", () => isDownloadFloatTextureEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_FENCE_API_ENABLED\", () => isWebGLFenceEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\", () => {\n  const useUniforms = ENV5.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\");\n  return useUniforms ? 4 : 0;\n});\nENV5.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\", () => {\n  return -1;\n}, (threshold3) => {\n  if (threshold3 < 0 && threshold3 !== -1) {\n    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);\n  }\n});\nENV5.registerFlag(\"WEBGL_FLUSH_THRESHOLD\", () => {\n  return device_util_exports.isMobile() ? 1 : -1;\n}, (threshold3) => {\n  if (threshold3 < 0 && threshold3 !== -1) {\n    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);\n  }\n});\nENV5.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\", () => false);\nENV5.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e5);\nENV5.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\", () => 128);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js\nfunction getGlslDifferences() {\n  let version9;\n  let attribute;\n  let varyingVs;\n  let varyingFs;\n  let texture2D;\n  let output;\n  let defineOutput;\n  let defineSpecialNaN;\n  let defineSpecialInf;\n  let defineRound;\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    version9 = \"#version 300 es\";\n    attribute = \"in\";\n    varyingVs = \"out\";\n    varyingFs = \"in\";\n    texture2D = \"texture\";\n    output = \"outputColor\";\n    defineOutput = \"out vec4 outputColor;\";\n    defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n    defineSpecialInf = ``;\n    defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  } else {\n    version9 = \"\";\n    attribute = \"attribute\";\n    varyingVs = \"varying\";\n    varyingFs = \"varying\";\n    texture2D = \"texture2D\";\n    output = \"gl_FragColor\";\n    defineOutput = \"\";\n    defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n    defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n    defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  }\n  return {\n    version: version9,\n    attribute,\n    varyingVs,\n    varyingFs,\n    texture2D,\n    output,\n    defineOutput,\n    defineSpecialNaN,\n    defineSpecialInf,\n    defineRound\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js\nfunction getLogicalCoordinatesFromFlatIndex(coords3, shape, index = \"index\") {\n  const strides = util_exports.computeStrides(shape);\n  return strides.map((stride, i2) => {\n    const line1 = `int ${coords3[i2]} = ${index} / ${stride}`;\n    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${stride}` : `index -= ${coords3[i2]} * ${stride}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index = \"index\") {\n  const strides = util_exports.computeStrides(shape);\n  return strides.map((_, i2) => {\n    const line1 = `int ${coords3[i2]} = ${index} / outShapeStrides[${i2}]`;\n    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * outShapeStrides[${i2}]` : `index -= ${coords3[i2]} * outShapeStrides[${i2}]`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction symbolicallyComputeStrides(indicesArr, variableName) {\n  const numCoords = indicesArr.length;\n  const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n  const strides = new Array(numCoords - 1);\n  strides[numCoords - 2] = shape[numCoords - 1];\n  for (let i2 = numCoords - 3; i2 >= 0; --i2) {\n    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;\n  }\n  return strides;\n}\nfunction getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index = \"index\") {\n  const indicesArray = coords3.map((_, i2) => i2);\n  const strides = symbolicallyComputeStrides(indicesArray, variableName);\n  return strides.map((_, i2) => {\n    const line1 = `int ${coords3[i2]} = ${index} / ${strides[i2]}`;\n    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${strides[i2]}` : `index -= ${coords3[i2]} * ${strides[i2]}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction getFlatIndexFrom3D(shape) {\n  const strides = util_exports.computeStrides(shape).map((d) => d.toString());\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nfunction getFlatIndexFrom3DOutput() {\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`;\n}\nvar ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js\nvar { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;\nfunction makeShader(inputsInfo, outputShape, program) {\n  const prefixSnippets = [];\n  inputsInfo.forEach((x) => {\n    const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);\n    if (x.shapeInfo.isUniform) {\n      prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : \"\"};`);\n    } else {\n      prefixSnippets.push(`uniform sampler2D ${x.name};`);\n      prefixSnippets.push(`uniform int offset${x.name};`);\n    }\n    if (program.enableShapeUniforms) {\n      const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);\n      switch (uniformShape.length) {\n        case 1:\n          prefixSnippets.push(`uniform int ${x.name}Shape;`);\n          break;\n        case 2:\n          prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);\n          break;\n        case 3:\n          prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);\n          break;\n        case 4:\n          prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);\n          break;\n        default:\n          break;\n      }\n      prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);\n    }\n  });\n  if (program.enableShapeUniforms) {\n    switch (outputShape.logicalShape.length) {\n      case 1:\n        prefixSnippets.push(`uniform int outShape;`);\n        break;\n      case 2:\n        prefixSnippets.push(`uniform ivec2 outShape;`);\n        prefixSnippets.push(`uniform int outShapeStrides;`);\n        break;\n      case 3:\n        prefixSnippets.push(`uniform ivec3 outShape;`);\n        prefixSnippets.push(`uniform ivec2 outShapeStrides;`);\n        break;\n      case 4:\n        prefixSnippets.push(`uniform ivec4 outShape;`);\n        prefixSnippets.push(`uniform ivec3 outShapeStrides;`);\n        break;\n      default:\n        break;\n    }\n    prefixSnippets.push(`uniform ivec2 outTexShape;`);\n  }\n  if (program.customUniforms) {\n    program.customUniforms.forEach((d) => {\n      prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : \"\"};`);\n    });\n  }\n  const inputPrefixSnippet = prefixSnippets.join(\"\\n\");\n  const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join(\"\\n\");\n  const outTexShape = outputShape.texShape;\n  const glsl = getGlslDifferences();\n  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n  let outputSamplingSnippet;\n  let floatTextureSetOutputSnippet;\n  let shaderPrefix = getShaderPrefix(glsl);\n  if (outputShape.isPacked) {\n    outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n  } else {\n    outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n  }\n  if (program.packedInputs) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n  const source = [\n    shaderPrefix,\n    floatTextureSampleSnippet,\n    floatTextureSetOutputSnippet,\n    inputPrefixSnippet,\n    outputSamplingSnippet,\n    inputSamplingSnippet,\n    program.userCode\n  ].join(\"\\n\");\n  return source;\n}\nfunction getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo, enableShapeUniforms);\n    case 1:\n      return getSampler1D(inInfo, enableShapeUniforms);\n    case 2:\n      return getSampler2D(inInfo, enableShapeUniforms);\n    case 3:\n      return getSampler3D(inInfo, enableShapeUniforms);\n    case 4:\n      return getSampler4D(inInfo, enableShapeUniforms);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(`${shape.length}-D input sampling is not yet supported`);\n  }\n}\nfunction getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo, enableShapeUniforms);\n    case 2:\n      return getPackedSampler2D(inInfo, enableShapeUniforms);\n    case 3:\n      return getPackedSampler3D(inInfo, enableShapeUniforms);\n    default:\n      return getPackedSamplerND(inInfo, enableShapeUniforms);\n  }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {\n  let res = \"\";\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);\n  } else {\n    res += getSamplerFromInInfo(inInfo, enableShapeUniforms);\n  }\n  const inShape = inInfo.shapeInfo.logicalShape;\n  const outShape = outShapeInfo.logicalShape;\n  if (inShape.length <= outShape.length) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n    }\n  }\n  return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 2:\n      return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 3:\n      return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);\n    default:\n      return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);\n  }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 2:\n      return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 3:\n      return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 4:\n      return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 5:\n      return getOutput5DCoords(outShape, outTexShape);\n    case 6:\n      return getOutput6DCoords(outShape, outTexShape);\n    default:\n      throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n  }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n  return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n  return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n  return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n  const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n  return SHADER_PREFIX;\n}\nvar SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (packedTexShape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n  if (packedTexShape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape, enableShapeUniforms) {\n  if (texShape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], shape);\n    return `\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${coordsFromIndexSnippet2}\n    return ivec3(r, c, d);\n  }\n`;\n  }\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n  let texelsInBatchN = texelsInBatch;\n  let batches = ``;\n  let coords3 = \"b, r, c\";\n  for (let b = 2; b < shape.length - 1; b++) {\n    texelsInBatchN *= shape[shape.length - b - 1];\n    batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n    coords3 = `b${b}, ` + coords3;\n  }\n  return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords3});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\", \"d2\"], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${coordsFromIndexSnippet2}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n  }\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\"], shape);\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\"], shape);\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\", \"d4\"], shape);\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n  }\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n  if (enableShapeUniforms) {\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape, enableShapeUniforms) {\n  if (util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n  return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n  if (texNumR === 1 && texNumC === 1) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n  return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (tNumC === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const glsl = getGlslDifferences();\n  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const texNumR2 = texShape[0];\n    const texNumC2 = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"row\", \"col\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const offset = getFlatOffsetUniformName(texName);\n  if (texNumC === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (enableShapeUniforms) {\n    return `\n      float ${funcName}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${texName}Shape[1] + col + ${offset};\n        vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"b\", \"row\", \"col\"];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"row\", \"col\", \"depth\"];\n    return `\n        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  if (texNumC === stride0 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth) {\n        int stride1 = ${texName}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n  if (texNumC === stride1 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];\n      int stride1 = ${texName}Shape[2];\n      int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n      return sampleTexture(${texName}, uv);\n    }\n    `;\n  }\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${texName}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const rank = shape.length;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n  let params = `int b, int row, int col`;\n  let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n  for (let b = 2; b < rank - 1; b++) {\n    params = `int b${b}, ` + params;\n    texelsInBatch *= shape[rank - b - 1];\n    index = `b${b} * ${texelsInBatch} + ` + index;\n  }\n  return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const stride2Str = `int stride2 = ${texName}Shape[3];`;\n  const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;\n  const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;\n  if (texNumC === stride0 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        ${stride2Str}\n        ${stride1Str}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${stride2Str}\n      ${stride1Str}\n      ${stride0Str}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride3 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\", \"depth4\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n  const texName = inputInfo.name;\n  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  if (inSize < 2) {\n    return `return ${texName};`;\n  }\n  return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const type = getCoordsDataType(outRank);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet;\n  const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n  if (inRank === 0) {\n    coordsSnippet = \"\";\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = \"coords = 0;\";\n  } else {\n    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(\", \");\n  }\n  let output = `return outputValue;`;\n  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  const isInputScalar = inSize === 1;\n  const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);\n  const isOutputScalar = outSize === 1;\n  if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n    output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n  } else if (isInputScalar && !isOutputScalar) {\n    if (outRank === 1) {\n      output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        return vec4(outputValue.x);\n      `;\n    }\n  } else if (broadcastDims.length) {\n    const rows = inRank - 2;\n    const cols = inRank - 1;\n    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.x);`;\n    } else if (broadcastDims.indexOf(rows) > -1) {\n      output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;\n    } else if (broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.xx, outputValue.zz);`;\n    }\n  }\n  return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n  const outTexShape = outShapeInfo.texShape;\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n  const type = getCoordsDataType(outRank);\n  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet;\n  const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n  if (inRank === 0) {\n    coordsSnippet = \"\";\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = \"coords = 0;\";\n  } else {\n    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(\", \");\n  }\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nfunction getCoordsDataType(rank) {\n  if (rank <= 1) {\n    return \"int\";\n  } else if (rank === 2) {\n    return \"ivec2\";\n  } else if (rank === 3) {\n    return \"ivec3\";\n  } else if (rank === 4) {\n    return \"ivec4\";\n  } else if (rank === 5) {\n    return \"ivec5\";\n  } else if (rank === 6) {\n    return \"ivec6\";\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\nfunction getUniformInfoFromShape(isPacked, shape, texShape) {\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const rank = shape.length;\n  const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;\n  const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;\n  const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;\n  const uniformShape = useSqueezeShape ? squeezeShape2 : shape;\n  return { useSqueezeShape, uniformShape, keptDims };\n}\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n  const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n  return keptDims.map((d) => params[d]).join(\", \");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js\nfunction compileProgram(gpgpu, program, inputs, output) {\n  const inputInfos = inputs.map((input2, i2) => {\n    const shapeInfo = {\n      logicalShape: input2.shape,\n      texShape: input2.isUniform ? null : input2.texData.texShape,\n      isUniform: input2.isUniform,\n      isPacked: input2.isUniform ? false : input2.texData.isPacked,\n      flatOffset: null\n    };\n    if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {\n      shapeInfo.flatOffset = input2.texData.slice.flatOffset;\n    }\n    return { name: program.variableNames[i2], shapeInfo };\n  });\n  const inShapeInfos = inputInfos.map((x) => x.shapeInfo);\n  const outShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked,\n    flatOffset: null\n  };\n  const source = makeShader(inputInfos, outShapeInfo, program);\n  const fragmentShader = createFragmentShader(gpgpu.gl, source);\n  const webGLProgram = gpgpu.createProgram(fragmentShader);\n  if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return Object.assign({\n      program,\n      fragmentShader,\n      source,\n      webGLProgram,\n      inShapeInfos,\n      outShapeInfo\n    }, getUniformLocations(gpgpu, program, webGLProgram));\n  } else {\n    return {\n      program,\n      fragmentShader,\n      source,\n      webGLProgram,\n      inShapeInfos,\n      outShapeInfo,\n      uniformLocations: null,\n      customUniformLocations: null,\n      infLoc: null,\n      nanLoc: null,\n      inShapesLocations: null,\n      inTexShapesLocations: null,\n      outShapeLocation: null,\n      outShapeStridesLocation: null,\n      outTexShapeLocation: null\n    };\n  }\n}\nfunction getUniformLocations(gpgpu, program, webGLProgram) {\n  const uniformLocations = {};\n  const inShapesLocations = {};\n  const inTexShapesLocations = {};\n  const customUniformLocations = [];\n  let outShapeLocation;\n  let outTexShapeLocation;\n  let outShapeStridesLocation;\n  let infLoc = null;\n  let nanLoc = null;\n  nanLoc = gpgpu.getUniformLocation(webGLProgram, \"NAN\", false);\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    infLoc = gpgpu.getUniformLocation(webGLProgram, \"INFINITY\", false);\n  }\n  const shouldThrow = false;\n  for (let i2 = 0; i2 < program.variableNames.length; i2++) {\n    const varName = program.variableNames[i2];\n    uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n    uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    if (program.enableShapeUniforms) {\n      inShapesLocations[`${varName}Shape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);\n      inTexShapesLocations[`${varName}TexShape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);\n    }\n  }\n  if (program.enableShapeUniforms) {\n    outShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outShape\", shouldThrow);\n    outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, \"outShapeStrides\", shouldThrow);\n    outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outTexShape\", shouldThrow);\n  }\n  if (program.customUniforms) {\n    program.customUniforms.forEach((d, i2) => {\n      customUniformLocations[i2] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);\n    });\n  }\n  return {\n    uniformLocations,\n    customUniformLocations,\n    infLoc,\n    nanLoc,\n    inShapesLocations,\n    inTexShapesLocations,\n    outShapeLocation,\n    outShapeStridesLocation,\n    outTexShapeLocation\n  };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);\n  }\n  shapeInfos.forEach((s2, i2) => {\n    const shapeA = s2.logicalShape;\n    const input2 = inputs[i2];\n    const shapeB = input2.shape;\n    if (!util_exports.arraysEqual(shapeA, shapeB)) {\n      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    if (s2.isUniform && input2.isUniform) {\n      return;\n    }\n    const texShapeA = s2.texShape;\n    const texShapeB = input2.isUniform ? null : input2.texData.texShape;\n    if (!util_exports.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\nfunction runProgram(gpgpu, binary, inputs, output, customUniformValues) {\n  if (!binary.program.enableShapeUniforms) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n  }\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    if (binary.infLoc !== null) {\n      gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n    }\n  }\n  if (binary.nanLoc !== null) {\n    gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n  }\n  inputs.forEach((input2, i2) => {\n    const varName = binary.program.variableNames[i2];\n    const varLoc = binary.uniformLocations[varName];\n    const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n    const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];\n    const varTexShapeLoc = binary.inTexShapesLocations[`${varName}TexShape`];\n    if (varShapeLoc) {\n      const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);\n      switch (uniformShape.length) {\n        case 1:\n          gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 2:\n          gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 3:\n          gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 4:\n          gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        default:\n          break;\n      }\n    }\n    if (varTexShapeLoc) {\n      gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);\n    }\n    if (varLoc == null) {\n      return;\n    }\n    if (input2.isUniform) {\n      if (util_exports.sizeFromShape(input2.shape) < 2) {\n        gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);\n      } else {\n        let vals = input2.uniformValues;\n        if (!(vals instanceof Float32Array)) {\n          vals = new Float32Array(vals);\n        }\n        gpgpu.gl.uniform1fv(varLoc, vals);\n      }\n      return;\n    }\n    if (input2.texData.slice != null && varOffsetLoc != null) {\n      gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);\n    }\n    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i2);\n  });\n  const outShapeLoc = binary.outShapeLocation;\n  if (outShapeLoc) {\n    switch (output.shape.length) {\n      case 1:\n        gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 2:\n        gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 3:\n        gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 4:\n        gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      default:\n        break;\n    }\n  }\n  if (binary.outShapeStridesLocation) {\n    const strides = util_exports.computeStrides(output.shape);\n    switch (output.shape.length) {\n      case 2:\n        gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      case 3:\n        gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      case 4:\n        gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      default:\n        break;\n    }\n  }\n  if (binary.outTexShapeLocation) {\n    gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);\n  }\n  if (binary.program.customUniforms && customUniformValues) {\n    binary.program.customUniforms.forEach((d, i2) => {\n      const customLoc = binary.customUniformLocations[i2];\n      const customValue = customUniformValues[i2];\n      if (d.type === \"float\") {\n        gpgpu.gl.uniform1fv(customLoc, customValue);\n      } else if (d.type === \"vec2\") {\n        gpgpu.gl.uniform2fv(customLoc, customValue);\n      } else if (d.type === \"vec3\") {\n        gpgpu.gl.uniform3fv(customLoc, customValue);\n      } else if (d.type === \"vec4\") {\n        gpgpu.gl.uniform4fv(customLoc, customValue);\n      } else if (d.type === \"int\") {\n        gpgpu.gl.uniform1iv(customLoc, customValue);\n      } else if (d.type === \"ivec2\") {\n        gpgpu.gl.uniform2iv(customLoc, customValue);\n      } else if (d.type === \"ivec3\") {\n        gpgpu.gl.uniform3iv(customLoc, customValue);\n      } else if (d.type === \"ivec4\") {\n        gpgpu.gl.uniform4iv(customLoc, customValue);\n      } else {\n        throw Error(`uniform type ${d.type} is not supported yet.`);\n      }\n    });\n  }\n  gpgpu.executeProgram();\n}\nfunction makeShaderKey(program, inputs, output) {\n  let keyInputs = \"\";\n  inputs.concat(output).forEach((x) => {\n    const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;\n    if (program.enableShapeUniforms && !x.isUniform) {\n      const xTexShape = x.texData.texShape;\n      const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);\n      let rank1 = \"\", rank2 = \"\", rank34 = \"\";\n      if (uniformShape.length === 1 && program.packedInputs) {\n        const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];\n        rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;\n      } else if (uniformShape.length === 2 && !program.packedInputs) {\n        rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;\n      } else if (uniformShape.length > 2 && !program.packedInputs) {\n        const strides = util_exports.computeStrides(uniformShape);\n        rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;\n      }\n      const xRank = x.shape.length;\n      const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);\n      const isScalar = util_exports.sizeFromShape(x.shape) === 1;\n      const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);\n      const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);\n      const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? \"\" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;\n      keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : \"\"}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;\n    } else {\n      const texShape = x.isUniform ? \"uniform\" : x.texData.texShape;\n      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    }\n  });\n  const keyUserCode = program.userCode;\n  let key = program.constructor.name;\n  key += \"_\" + keyInputs + \"_\" + keyUserCode + `${env().getNumber(\"WEBGL_VERSION\")}`;\n  return key;\n}\nfunction useShapeUniforms(rank) {\n  return env().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\") && rank <= 4;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js\nvar DecodeMatrixProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.outPackingScheme = PackingScheme.DENSE;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js\nvar DecodeMatrixPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outPackingScheme = PackingScheme.DENSE;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js\nvar EncodeFloatProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.outTexUsage = TextureUsage.DOWNLOAD;\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js\nvar EncodeFloatPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = false;\n    this.outTexUsage = TextureUsage.DOWNLOAD;\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js\nvar EncodeMatrixProgram = class {\n  constructor(outputShape, inputIsUnsignedByte = false) {\n    this.variableNames = [\"A\"];\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let output = `result`;\n    if (inputIsUnsignedByte) {\n      output = `floor(result * 255. + 0.5)`;\n    }\n    this.userCode = `\n      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js\nvar EncodeMatrixPackedProgram = class {\n  constructor(outputShape, inputIsUnsignedByte = false) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let mainLoop = \"\";\n    let output = \"result\";\n    if (inputIsUnsignedByte) {\n      output = \"floor(result * 255. + 0.5)\";\n    }\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        const channel = row * 2 + col;\n        mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? \"outShape[2]\" : `${outputShape[2]}`}) {\n          localCoords[2] += ${col};\n          if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? \"outShape[1]\" : `${outputShape[1]}`}) {\n            localCoords[1] += ${row};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${glsl.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${channel}] = values[0];\n            } else if (offset == 1) {\n              result[${channel}] = values[1];\n            } else if (offset == 2) {\n              result[${channel}] = values[2];\n            } else {\n              result[${channel}] = values[3];\n            }\n          }\n        }\n        `;\n      }\n    }\n    this.userCode = `\n        ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${mainLoop}\n\n          ${glsl.output} = ${output};\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js\nvar gpgpu_util_exports = {};\n__export(gpgpu_util_exports, {\n  bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams,\n  createBufferFromOutputTexture: () => createBufferFromOutputTexture,\n  createFloat16MatrixTexture: () => createFloat16MatrixTexture,\n  createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture,\n  createFloat32MatrixTexture: () => createFloat32MatrixTexture,\n  createIndexBuffer: () => createIndexBuffer,\n  createPackedMatrixTexture: () => createPackedMatrixTexture,\n  createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture,\n  createVertexBuffer: () => createVertexBuffer,\n  createVertexShader: () => createVertexShader2,\n  downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture,\n  downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer,\n  downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture,\n  downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer,\n  getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture,\n  getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture,\n  getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture,\n  getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture,\n  getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture,\n  uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture,\n  uploadPixelDataToTexture: () => uploadPixelDataToTexture\n});\nfunction createVertexShader2(gl) {\n  const glsl = getGlslDifferences();\n  const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return createVertexShader(gl, vertexShaderSource);\n}\nfunction createVertexBuffer(gl) {\n  const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return createStaticVertexBuffer(gl, vertexArray);\n}\nfunction createIndexBuffer(gl) {\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n  validateTextureSize(width, height);\n  const texture = createTexture(gl);\n  const tex2d = gl.TEXTURE_2D;\n  callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n  } else {\n    callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return { texture, texShape: [height, width] };\n}\nfunction getInternalFormatForFloat32MatrixTexture(textureConfig) {\n  return textureConfig.internalFormatFloat;\n}\nfunction createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16MatrixTexture(textureConfig) {\n  return textureConfig.internalFormatHalfFloat;\n}\nfunction createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nfunction getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n  return textureConfig.downloadTextureFormat;\n}\nfunction createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nfunction getInternalFormatForPackedMatrixTexture(textureConfig) {\n  return textureConfig.internalFormatPackedFloat;\n}\nfunction createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n  return textureConfig.internalFormatPackedHalfFloat;\n}\nfunction createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nfunction bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n  const posOffset = 0;\n  const uvOffset = 3 * 4;\n  const stride = 3 * 4 + 2 * 4;\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = bindVertexBufferToProgramAttribute(gl, program, \"clipSpacePos\", vertexBuffer, 3, stride, posOffset);\n  return success && bindVertexBufferToProgramAttribute(gl, program, \"uv\", vertexBuffer, 2, stride, uvOffset);\n}\nfunction uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  let dataForUpload, texelDataType, internalFormat;\n  if (data instanceof Uint8Array) {\n    dataForUpload = new Uint8Array(width * height * 4);\n    texelDataType = gl.UNSIGNED_BYTE;\n    internalFormat = gl.RGBA;\n  } else {\n    dataForUpload = new Float32Array(width * height * 4);\n    texelDataType = gl.FLOAT;\n    internalFormat = textureConfig.internalFormatPackedFloat;\n  }\n  dataForUpload.set(data);\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));\n  } else {\n    callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction uploadPixelDataToTexture(gl, texture, pixels) {\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  if (pixels.data instanceof Uint8Array) {\n    if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    } else {\n      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n  } else {\n    if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    } else {\n      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n  const buffer2 = gl2.createBuffer();\n  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));\n  const bytesPerFloat = 4;\n  const valuesPerTexel = 4;\n  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n  callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n  callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n  return buffer2;\n}\nfunction downloadFloat32MatrixFromBuffer(gl, buffer2, size) {\n  const gl2 = gl;\n  const downloadTarget = new Float32Array(size);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n  return downloadTarget;\n}\nfunction downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n  const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n  callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n  return new Float32Array(downloadTarget.buffer);\n}\nfunction downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n  const gl2 = gl;\n  const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n  return downloadTarget;\n}\nfunction downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n  callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n  return packedRGBA;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js\nvar GPGPUContext = class {\n  constructor(gl) {\n    this.outputTexture = null;\n    this.program = null;\n    this.disposed = false;\n    this.vertexAttrsAreBound = false;\n    this.itemsToPoll = [];\n    const glVersion = env().getNumber(\"WEBGL_VERSION\");\n    if (gl != null) {\n      this.gl = gl;\n      setWebGLContext(glVersion, gl);\n    } else {\n      this.gl = getWebGLContext(glVersion);\n    }\n    let COLOR_BUFFER_FLOAT = \"WEBGL_color_buffer_float\";\n    const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n    this.parallelCompilationExtension = this.gl.getExtension(\"KHR_parallel_shader_compile\");\n    if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n      const TEXTURE_FLOAT = \"OES_texture_float\";\n      const TEXTURE_HALF_FLOAT = \"OES_texture_half_float\";\n      this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n      if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n        this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n      } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n        throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n      }\n      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n      } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n        throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n      }\n    } else {\n      COLOR_BUFFER_FLOAT = \"EXT_color_buffer_float\";\n      if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n        this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      } else {\n        throw new Error(\"GL context does not support color renderable floats\");\n      }\n    }\n    this.vertexBuffer = createVertexBuffer(this.gl);\n    this.indexBuffer = createIndexBuffer(this.gl);\n    this.framebuffer = createFramebuffer(this.gl);\n    this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n  get debug() {\n    return env().getBool(\"DEBUG\");\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\");\n    }\n    if (this.outputTexture != null) {\n      console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");\n    }\n    const gl = this.gl;\n    callAndCheck(gl, () => gl.finish());\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n  createFloat32MatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createFloat16MatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createUnsignedBytesMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  uploadPixelDataToTexture(texture, pixels) {\n    this.throwIfDisposed();\n    uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n  uploadDenseMatrixToTexture(texture, width, height, data) {\n    this.throwIfDisposed();\n    uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n  }\n  createFloat16PackedMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createPackedMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  deleteMatrixTexture(texture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n  downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n    return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n  }\n  downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {\n    return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n  }\n  downloadFloat32MatrixFromBuffer(buffer2, size) {\n    return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);\n  }\n  createBufferFromTexture(texture, rows, columns) {\n    this.bindTextureToFrameBuffer(texture);\n    const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n  createAndWaitForFence() {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n  createFence(gl) {\n    let query;\n    let isFencePassed;\n    if (env().getBool(\"WEBGL_FENCE_API_ENABLED\")) {\n      const gl2 = gl;\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;\n      };\n      query = sync;\n    } else if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n    } else {\n      isFencePassed = () => true;\n    }\n    return { query, isFencePassed };\n  }\n  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n  }\n  createProgram(fragmentShader) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    if (this.vertexShader == null) {\n      this.vertexShader = createVertexShader2(gl);\n    }\n    const program = createProgram(gl);\n    callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));\n    callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    linkProgram(gl, program);\n    if (this.debug) {\n      validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n  deleteProgram(program) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n  setProgram(program) {\n    this.throwIfDisposed();\n    this.program = program;\n    if (this.program != null && this.debug) {\n      validateProgram(this.gl, this.program);\n    }\n    callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n  getUniformLocation(program, uniformName, shouldThrow = true) {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n    } else {\n      return getProgramUniformLocation(this.gl, program, uniformName);\n    }\n  }\n  getAttributeLocation(program, attribute) {\n    this.throwIfDisposed();\n    return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n  getUniformLocationNoThrow(program, uniformName) {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n  setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n  }\n  setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n  setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n    this.throwIfDisposed();\n    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n  setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n    this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n  }\n  setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n    throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\");\n  }\n  debugValidate() {\n    if (this.program != null) {\n      validateProgram(this.gl, this.program);\n    }\n    validateFramebuffer(this.gl);\n  }\n  executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.debug) {\n      this.debugValidate();\n    }\n    callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n  blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    callAndCheck(this.gl, () => this.gl.finish());\n  }\n  getQueryTimerExtension() {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2 ? \"EXT_disjoint_timer_query_webgl2\" : \"EXT_disjoint_timer_query\");\n    }\n    return this.disjointQueryTimerExtension;\n  }\n  getQueryTimerExtensionWebGL2() {\n    return this.getQueryTimerExtension();\n  }\n  getQueryTimerExtensionWebGL1() {\n    return this.getQueryTimerExtension();\n  }\n  beginQuery() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n      const gl2 = this.gl;\n      const ext2 = this.getQueryTimerExtensionWebGL2();\n      const query2 = gl2.createQuery();\n      gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);\n      return query2;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT();\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n  endQuery() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n      const gl2 = this.gl;\n      const ext2 = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext2.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n  async waitForQueryAndGetTime(query) {\n    await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")));\n    return this.getQueryTime(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n  }\n  getQueryTime(query, queryTimerVersion) {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl;\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      return timeElapsedNanos / 1e6;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n      const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      return timeElapsedNanos / 1e6;\n    }\n  }\n  isQueryAvailable(query, queryTimerVersion) {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n      const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n      return available && !this.disjoint;\n    }\n  }\n  pollFence(fenceContext) {\n    return new Promise((resolve) => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n  pollItems() {\n    const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));\n    for (let i2 = 0; i2 <= index; ++i2) {\n      const { resolveFn } = this.itemsToPoll[i2];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n  addItemToPoll(isDoneFn, resolveFn) {\n    this.itemsToPoll.push({ isDoneFn, resolveFn });\n    if (this.itemsToPoll.length > 1) {\n      return;\n    }\n    util_exports.repeatedTry(() => {\n      this.pollItems();\n      return this.itemsToPoll.length === 0;\n    });\n  }\n  bindTextureToFrameBuffer(texture) {\n    this.throwIfDisposed();\n    bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n    if (this.debug) {\n      validateFramebuffer(this.gl);\n    }\n  }\n  unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n      if (this.debug) {\n        validateFramebuffer(this.gl);\n      }\n    } else {\n      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n  downloadMatrixDriver(texture, downloadAndDecode) {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n  setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.debug) {\n      validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n  setOutputMatrixWriteRegionDriver(x, y, width, height) {\n    this.throwIfDisposed();\n    callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n  throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error(\"Attempted to use disposed GPGPUContext.\");\n    }\n  }\n  throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error(\"No GPU program is currently set.\");\n    }\n  }\n};\nfunction linearSearchLastTrue(arr) {\n  let i2 = 0;\n  for (; i2 < arr.length; ++i2) {\n    const isDone = arr[i2]();\n    if (!isDone) {\n      break;\n    }\n  }\n  return i2 - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js\nfunction getVecChannels(name, rank) {\n  return [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank).map((d) => `${name}.${d}`);\n}\nfunction getChannels(name, rank) {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\nfunction getSourceCoords(rank, dims) {\n  if (rank === 1) {\n    return \"rc\";\n  }\n  let coords3 = \"\";\n  for (let i2 = 0; i2 < rank; i2++) {\n    coords3 += dims[i2];\n    if (i2 < rank - 1) {\n      coords3 += \",\";\n    }\n  }\n  return coords3;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js\nvar PackProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    if (this.rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels(\"rc\", this.rank);\n      const dtype = getCoordsDataType(this.rank);\n      const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);\n      const setup50 = this.getSetup(channels);\n      const output = this.getOutput(channels);\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup50}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n  getSourceCoordsArr(dims) {\n    const coords3 = [];\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        let coord = `${row === 0 ? \"r\" : \"rp1\"}, ${col === 0 ? \"c\" : \"cp1\"}`;\n        for (let d = 2; d < this.rank; d++) {\n          coord = `${dims[dims.length - 1 - d]},` + coord;\n        }\n        coords3.push(coord);\n      }\n    }\n    return coords3;\n  }\n  getOutOfBoundsCondition(dims) {\n    if (this.rank === 1) {\n      return `rc > ${this.enableShapeUniforms ? \"outShape\" : this.outputShape[0]}`;\n    }\n    let cond = \"\";\n    for (let i2 = this.rank - 2; i2 < this.rank; i2++) {\n      cond += `${dims[i2]} >= ${this.enableShapeUniforms ? `outShape[${i2}]` : this.outputShape[i2]}`;\n      if (i2 < this.rank - 1) {\n        cond += \"||\";\n      }\n    }\n    return cond;\n  }\n  getSetup(dims) {\n    if (this.rank === 1) {\n      return \"\";\n    }\n    const innerDims = dims.slice(-2);\n    const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];\n    const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];\n    return `\n      int r = ${innerDims[0]};\n      int c = ${innerDims[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${col};\n      bool rEdge = rp1 >= ${row};\n    `;\n  }\n  getOutput(dims) {\n    const sourceCoords = this.getSourceCoordsArr(dims);\n    if (this.rank === 1) {\n      const outShape = this.enableShapeUniforms ? \"outShape\" : this.outputShape[0];\n      return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n            cEdge ? 0. : getA(${sourceCoords[1]}),\n            rEdge ? 0. : getA(${sourceCoords[2]}),\n            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js\nvar ReshapePackedProgram = class {\n  constructor(outputShape, inputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"inputShape\", type: \"ivec3\" }];\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let mainLoop = ``;\n    for (let i2 = 0; i2 < 4; i2++) {\n      let thisRC = `thisRC = rc;`;\n      if (i2 % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i2 > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n      mainLoop += `\n        ${thisRC}\n        ${i2 > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : \"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i2}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i2 > 0 ? \"}\" : \"\"}\n      `;\n    }\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}\n      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms ? \"outShape[1]\" : outputShape[1]};\n        int cols = ${this.enableShapeUniforms ? \"outShape[2]\" : outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\nfunction getReshapedInputCoords(shape, enableShapeUniforms) {\n  const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], \"inputShape\") : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js\nvar TextureManager = class {\n  constructor(gpgpu) {\n    this.gpgpu = gpgpu;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n    this.freeTextures = {};\n    this.logEnabled = false;\n    this.usedTextures = {};\n  }\n  acquireTexture(shapeRC, usage, isPacked) {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n    const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this._numBytesFree -= texBytes;\n      this.log();\n      const newTexture2 = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture2);\n      return newTexture2;\n    }\n    let newTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n    this.numUsedTextures++;\n    this._numBytesAllocated += texBytes;\n    this.log();\n    return newTexture;\n  }\n  releaseTexture(texture, shape, logicalTexType, isPacked) {\n    if (this.freeTextures == null) {\n      return;\n    }\n    const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n    const deleteTexThreshold = env().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");\n    if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {\n      this.gpgpu.deleteMatrixTexture(texture.texture);\n      this._numBytesAllocated -= texBytes;\n    } else {\n      this.freeTextures[shapeKey].push(texture);\n      this.numFreeTextures++;\n      this._numBytesFree += texBytes;\n    }\n    this.numUsedTextures--;\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n  log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\"Free/Used\", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n    const freeRatio = this._numBytesFree / this._numBytesAllocated;\n    console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n    console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n  }\n  get numBytesAllocated() {\n    return this._numBytesAllocated;\n  }\n  get numBytesFree() {\n    return this._numBytesFree;\n  }\n  getNumUsedTextures() {\n    return this.numUsedTextures;\n  }\n  getNumFreeTextures() {\n    return this.numFreeTextures;\n  }\n  dispose() {\n    if (this.freeTextures == null) {\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach((tex) => {\n        this.gpgpu.deleteMatrixTexture(tex.texture);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach((tex) => {\n        this.gpgpu.deleteMatrixTexture(tex.texture);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n  }\n};\nfunction numBytesForInternalFormat(gl, internalFormat) {\n  const glany = gl;\n  if (internalFormat === glany.R32F) {\n    return 4;\n  } else if (internalFormat === glany.R16F) {\n    return 2;\n  } else if (internalFormat === glany.RGBA32F) {\n    return 16;\n  } else if (internalFormat === gl.RGBA) {\n    return 16;\n  } else if (internalFormat === glany.RGBA16F) {\n    return 8;\n  } else if (internalFormat === glany.RGBA8) {\n    return 4;\n  }\n  throw new Error(`Unknown internal format ${internalFormat}`);\n}\nfunction computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n  const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n  let numElements;\n  if (isPacked) {\n    const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = packedWidth * packedHeight;\n  } else {\n    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = width * height;\n  }\n  const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);\n  return numElements * bytesPerElement2;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n  switch (physicalTexType) {\n    case PhysicalTextureType.PACKED_2X2_FLOAT32:\n      return getInternalFormatForPackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_2X2_FLOAT16:\n      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT32:\n      return getInternalFormatForFloat32MatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT16:\n      return getInternalFormatForFloat16MatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n    default:\n      throw new Error(`Unknown physical texture type ${physicalTexType}`);\n  }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n  if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n    if (isPacked) {\n      return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT32;\n  }\n  if (isPacked) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT16;\n  }\n  return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    return getPhysicalTextureForRendering(isPacked);\n  } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js\nvar UnaryOpProgram = class {\n  constructor(aShape, opSnippet) {\n    this.variableNames = [\"A\"];\n    this.outputShape = aShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n};\nvar CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nvar LINEAR = `return x;`;\nvar ABS = `return abs(x);`;\nvar ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar CLONE = \"return x;\";\nvar SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js\nvar LINEAR2 = `return x;`;\nvar ELU3 = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nvar RELU2 = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar RELU62 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;\nvar UnaryOpPackedProgram = class {\n  constructor(aShape, opSnippet) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = aShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js\nvar UnpackProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = false;\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const rank = outputShape.length;\n    const channels = getChannels(\"rc\", rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords3 = rank <= 1 ? \"rc\" : `vec2(${innerDims.join(\",\")})`;\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords3}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js\nvar whereImpl3 = kernel_impls_exports.whereImpl;\nvar EPSILON_FLOAT322 = 1e-7;\nvar EPSILON_FLOAT162 = 1e-4;\nvar binaryCaches = {};\nfunction getBinaryCache(webGLVersion) {\n  if (webGLVersion in binaryCaches) {\n    return binaryCaches[webGLVersion];\n  }\n  binaryCaches[webGLVersion] = {};\n  return binaryCaches[webGLVersion];\n}\nvar CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\");\nvar BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n  if (env().global.screen == null) {\n    return 1024;\n  }\n  return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\nvar MathBackendWebGL = class extends KernelBackend {\n  constructor(gpuResource) {\n    super();\n    this.pendingRead = /* @__PURE__ */ new WeakMap();\n    this.pendingDisposal = /* @__PURE__ */ new WeakSet();\n    this.dataRefCount = /* @__PURE__ */ new WeakMap();\n    this.numBytesInGPU = 0;\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    this.lastGlFlushTime = 0;\n    this.warnedAboutMemory = false;\n    this.pendingDeletes = 0;\n    this.disposed = false;\n    if (!env().getBool(\"HAS_WEBGL\")) {\n      throw new Error(\"WebGL is not supported on this device\");\n    }\n    let newGPGPU;\n    if (gpuResource != null) {\n      if (gpuResource instanceof GPGPUContext) {\n        newGPGPU = gpuResource;\n      } else {\n        const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"), gpuResource);\n        newGPGPU = new GPGPUContext(gl);\n      }\n      this.binaryCache = {};\n      this.gpgpuCreatedLocally = false;\n    } else {\n      const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"));\n      newGPGPU = new GPGPUContext(gl);\n      this.binaryCache = getBinaryCache(env().getNumber(\"WEBGL_VERSION\"));\n      this.gpgpuCreatedLocally = true;\n    }\n    this.gpgpu = newGPGPU;\n    this.canvas = this.gpgpu.gl.canvas;\n    this.textureManager = new TextureManager(this.gpgpu);\n    this.numMBBeforeWarning = numMBBeforeWarning();\n    this.texData = new DataStorage(this, engine());\n  }\n  nextDataId() {\n    return MathBackendWebGL.nextDataId++;\n  }\n  numDataIds() {\n    return this.texData.numDataIds() - this.pendingDeletes;\n  }\n  write(values, shape, dtype) {\n    if (env().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\") || env().getBool(\"DEBUG\")) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === \"complex64\" && values != null) {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const dataId = { id: this.nextDataId() };\n    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    return dataId;\n  }\n  refCount(dataId) {\n    if (this.texData.has(dataId)) {\n      const tensorData = this.texData.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const texData = this.texData.get(dataId);\n    texData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.texData.has(dataId)) {\n      const texData = this.texData.get(dataId);\n      texData.refCount--;\n    }\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    if (env().getBool(\"DEBUG\")) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === \"complex64\") {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });\n  }\n  disposeIntermediateTensorInfo(tensorInfo) {\n    this.disposeData(tensorInfo.dataId);\n  }\n  readSync(dataId) {\n    const texData = this.texData.get(dataId);\n    const { values, dtype, complexTensorInfos, slice: slice6, shape, isPacked } = texData;\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const data = this.readSync(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === \"string\") {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start;\n    if (shouldTimeProgram) {\n      start = util_exports.now();\n    }\n    let result;\n    if (dtype === \"complex64\") {\n      const realValues = this.readSync(complexTensorInfos.real.dataId);\n      const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n      result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += util_exports.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n  async read(dataId) {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers2 = this.pendingRead.get(dataId);\n      return new Promise((resolve) => subscribers2.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const { values, shape, slice: slice6, dtype, complexTensorInfos, isPacked } = texData;\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const data = this.read(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (env().getBool(\"DEBUG\")) {\n      if (!env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\") && env().getNumber(\"WEBGL_VERSION\") === 2) {\n        throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);\n      }\n    }\n    let buffer2 = null;\n    let tmpDownloadTarget;\n    if (dtype !== \"complex64\" && env().get(\"WEBGL_BUFFER_SUPPORTED\")) {\n      tmpDownloadTarget = this.decode(dataId);\n      const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n      buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));\n    }\n    this.pendingRead.set(dataId, []);\n    if (dtype !== \"complex64\") {\n      await this.gpgpu.createAndWaitForFence();\n    }\n    let vals;\n    if (dtype === \"complex64\") {\n      const ps = await Promise.all([\n        this.read(complexTensorInfos.real.dataId),\n        this.read(complexTensorInfos.imag.dataId)\n      ]);\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else if (buffer2 == null) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      const size = util_exports.sizeFromShape(shape);\n      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);\n    }\n    if (tmpDownloadTarget != null) {\n      this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n    }\n    if (buffer2 != null) {\n      const gl = this.gpgpu.gl;\n      callAndCheck(gl, () => gl.deleteBuffer(buffer2));\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n    subscribers.forEach((resolve) => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      if (this.disposeData(dataId)) {\n        engine().removeDataId(dataId, this);\n      }\n      this.pendingDeletes--;\n    }\n    return dTypeVals;\n  }\n  readToGPU(dataId, options = {}) {\n    const texData = this.texData.get(dataId);\n    const { values, shape, slice: slice6, dtype, isPacked, texture } = texData;\n    if (dtype === \"complex64\") {\n      throw new Error(\"Does not support reading texture for complex64 dtype.\");\n    }\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const gpuResouorce = this.readToGPU(res, options);\n      this.disposeIntermediateTensorInfo(res);\n      return gpuResouorce;\n    }\n    if (texture == null) {\n      if (values != null) {\n        throw new Error(\"Data is not on GPU but on CPU.\");\n      } else {\n        throw new Error(\"There is no data on GPU or CPU.\");\n      }\n    }\n    const tmpTarget = this.decode(dataId, options.customTexShape);\n    const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);\n    const tmpData = this.texData.get(tmpTarget.dataId);\n    return Object.assign({ tensorRef }, tmpData.texture);\n  }\n  bufferSync(t2) {\n    const data = this.readSync(t2.dataId);\n    if (t2.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t2.shape, t2.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t2.shape, t2.dtype, data);\n  }\n  checkNumericalProblems(values) {\n    if (values == null) {\n      return;\n    }\n    for (let i2 = 0; i2 < values.length; i2++) {\n      const num = values[i2];\n      if (!canBeRepresented(num)) {\n        if (env().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")) {\n          throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n        }\n        throw Error(`The value ${num} cannot be represented on this device.`);\n      }\n    }\n  }\n  getValuesFromTexture(dataId) {\n    const { shape, dtype, isPacked } = this.texData.get(dataId);\n    const size = util_exports.sizeFromShape(shape);\n    if (env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")) {\n      const tmpTarget = this.decode(dataId);\n      const tmpData2 = this.texData.get(tmpTarget.dataId);\n      const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size);\n      this.disposeIntermediateTensorInfo(tmpTarget);\n      return vals2;\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK\") && isPacked === true;\n    const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;\n    const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);\n    const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], \"float32\");\n    const tmpData = this.texData.get(output.dataId);\n    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);\n    this.disposeIntermediateTensorInfo(output);\n    return vals;\n  }\n  timerAvailable() {\n    return env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0;\n  }\n  time(f) {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers = [];\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n    f();\n    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n    this.activeTimers = oldActiveTimers;\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n    const res = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null\n    };\n    return (async () => {\n      if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n        const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n        res[\"kernelMs\"] = util_exports.sum(kernelMs);\n        res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n      } else {\n        res[\"kernelMs\"] = {\n          error: \"WebGL query timers are not supported in this environment.\"\n        };\n      }\n      this.uploadWaitMs = 0;\n      this.downloadWaitMs = 0;\n      return res;\n    })();\n  }\n  memory() {\n    return {\n      unreliable: false,\n      numBytesInGPU: this.numBytesInGPU,\n      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n      numBytesInGPUFree: this.textureManager.numBytesFree\n    };\n  }\n  startTimer() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return { startMs: util_exports.now(), endMs: null };\n  }\n  endTimer(query) {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    query.endMs = util_exports.now();\n    return query;\n  }\n  async getQueryTime(query) {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query);\n    }\n    const timerQuery = query;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n  disposeData(dataId, force = false) {\n    if (this.pendingDisposal.has(dataId)) {\n      return false;\n    }\n    if (!this.texData.has(dataId)) {\n      return true;\n    }\n    if (force) {\n      this.texData.get(dataId).refCount = 0;\n    } else {\n      this.texData.get(dataId).refCount--;\n    }\n    if (!force && this.texData.get(dataId).refCount > 0) {\n      return false;\n    }\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      this.pendingDeletes++;\n      return false;\n    }\n    this.releaseGPUData(dataId);\n    const { complexTensorInfos } = this.texData.get(dataId);\n    if (complexTensorInfos != null) {\n      this.disposeData(complexTensorInfos.real.dataId, force);\n      this.disposeData(complexTensorInfos.imag.dataId, force);\n    }\n    this.texData.delete(dataId);\n    return true;\n  }\n  releaseGPUData(dataId) {\n    const { texture, dtype, texShape, usage, isPacked, slice: slice6 } = this.texData.get(dataId);\n    const key = slice6 && slice6.origDataId || dataId;\n    const refCount = this.dataRefCount.get(key);\n    if (refCount > 1) {\n      this.dataRefCount.set(key, refCount - 1);\n    } else {\n      this.dataRefCount.delete(key);\n      if (texture != null) {\n        this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n      }\n    }\n    const texData = this.texData.get(dataId);\n    texData.texture = null;\n    texData.texShape = null;\n    texData.isPacked = false;\n    texData.slice = null;\n  }\n  getTexture(dataId) {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture.texture;\n  }\n  getDataInfo(dataId) {\n    return this.texData.get(dataId);\n  }\n  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n    return env().getBool(\"WEBGL_CPU_FORWARD\") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n  }\n  getGPGPUContext() {\n    return this.gpgpu;\n  }\n  where(condition) {\n    backend_util_exports.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");\n    const condVals = condition.dataSync();\n    return whereImpl3(condition.shape, condVals);\n  }\n  packedUnaryOp(x, op2, dtype) {\n    const program = new UnaryOpPackedProgram(x.shape, op2);\n    const outInfo = this.compileAndRun(program, [x], dtype);\n    return engine().makeTensorFromTensorInfo(outInfo);\n  }\n  abs(x) {\n    if (this.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n      const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n      return this.makeOutput(x.shape, x.dtype, outValues);\n    }\n    if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n      return this.packedUnaryOp(x, ABS, x.dtype);\n    }\n    const program = new UnaryOpProgram(x.shape, ABS);\n    const outInfo = this.compileAndRun(program, [x]);\n    return engine().makeTensorFromTensorInfo(outInfo);\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let dataId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      dataId = this.write(encodedValues, shape, dtype);\n    } else {\n      dataId = this.write(values, shape, dtype);\n    }\n    this.texData.get(dataId).usage = null;\n    return { dataId, shape, dtype };\n  }\n  makeOutput(shape, dtype, values) {\n    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n  }\n  unpackTensor(input2) {\n    const program = new UnpackProgram(input2.shape);\n    return this.runWebGLProgram(program, [input2], input2.dtype);\n  }\n  packTensor(input2) {\n    const program = new PackProgram(input2.shape);\n    const preventEagerUnpackingOutput = true;\n    return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);\n  }\n  packedReshape(input2, afterShape) {\n    const input3DShape = [\n      getBatchDim(input2.shape),\n      ...getRowsCols(input2.shape)\n    ];\n    const input3D = {\n      dtype: input2.dtype,\n      shape: input3DShape,\n      dataId: input2.dataId\n    };\n    const afterShapeAs3D = [\n      getBatchDim(afterShape),\n      ...getRowsCols(afterShape)\n    ];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const customValues = [input3DShape];\n    const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n  }\n  decode(dataId, customTexShape) {\n    const texData = this.texData.get(dataId);\n    const { isPacked, shape, dtype } = texData;\n    if (customTexShape != null) {\n      const size = util_exports.sizeFromShape(shape);\n      const texSize = customTexShape[0] * customTexShape[1] * 4;\n      util_exports.assert(size <= texSize, () => \"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\");\n    }\n    const shapeAs3D = getShapeAs3D(shape);\n    let program;\n    if (isPacked) {\n      program = new DecodeMatrixPackedProgram(shapeAs3D);\n    } else {\n      program = new DecodeMatrixProgram(shapeAs3D);\n    }\n    const preventEagerUnpackingOfOutput = true;\n    const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];\n    const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);\n    return { dtype, shape, dataId: out.dataId };\n  }\n  runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {\n    const output = this.makeTensorInfo(program.outputShape, outputDtype);\n    const outData = this.texData.get(output.dataId);\n    if (program.packedOutput) {\n      outData.isPacked = true;\n    }\n    if (program.outPackingScheme === PackingScheme.DENSE) {\n      const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);\n      outData.texShape = texelShape.map((d) => d * 2);\n    }\n    if (program.outTexUsage != null) {\n      outData.usage = program.outTexUsage;\n    }\n    if (util_exports.sizeFromShape(output.shape) === 0) {\n      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n      return output;\n    }\n    const dataToDispose = [];\n    const inputsData = inputs.map((input2) => {\n      if (input2.dtype === \"complex64\") {\n        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n      }\n      let texData = this.texData.get(input2.dataId);\n      if (texData.texture == null) {\n        if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\")) {\n          return {\n            shape: input2.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: texData.values\n          };\n        }\n        if (program.packedInputs) {\n          texData.isPacked = true;\n          texData.shape = input2.shape;\n        }\n      }\n      this.uploadToGPU(input2.dataId);\n      if (!!texData.isPacked !== !!program.packedInputs) {\n        input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);\n        dataToDispose.push(input2);\n        texData = this.texData.get(input2.dataId);\n      } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {\n        const savedInput = input2;\n        const targetShape = input2.shape;\n        input2.shape = texData.shape;\n        input2 = this.packedReshape(input2, targetShape);\n        dataToDispose.push(input2);\n        texData = this.texData.get(input2.dataId);\n        savedInput.shape = targetShape;\n      }\n      return { shape: input2.shape, texData, isUniform: false };\n    });\n    this.uploadToGPU(output.dataId);\n    const outputData = { shape: output.shape, texData: outData, isUniform: false };\n    const key = makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return compileProgram(this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n    if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n      runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);\n    }\n    dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n    }\n    const glFlushThreshold = env().get(\"WEBGL_FLUSH_THRESHOLD\");\n    if (glFlushThreshold > 0) {\n      const time2 = util_exports.now();\n      if (time2 - this.lastGlFlushTime > glFlushThreshold) {\n        this.gpgpu.gl.flush();\n        this.lastGlFlushTime = time2;\n      }\n    }\n    if (!env().getBool(\"WEBGL_LAZILY_UNPACK\") && outData.isPacked && preventEagerUnpackingOfOutput === false) {\n      const unpacked = this.unpackTensor(output);\n      this.disposeIntermediateTensorInfo(output);\n      return unpacked;\n    }\n    return output;\n  }\n  compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {\n    outputDtype = outputDtype || inputs[0].dtype;\n    const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);\n    return outInfo;\n  }\n  getAndSaveBinary(key, getBinary) {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n  getTextureManager() {\n    return this.textureManager;\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (!env().getBool(\"IS_TEST\")) {\n      const allKeys = Object.keys(this.binaryCache);\n      allKeys.forEach((key) => {\n        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n        delete this.binaryCache[key];\n      });\n    }\n    this.textureManager.dispose();\n    if (this.canvas != null && (typeof HTMLCanvasElement !== \"undefined\" && this.canvas instanceof HTMLCanvasElement)) {\n      this.canvas.remove();\n    } else {\n      this.canvas = null;\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.program = null;\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n  floatPrecision() {\n    if (this.floatPrecisionValue == null) {\n      this.floatPrecisionValue = tidy(() => {\n        if (!env().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n          const debugFlag = env().getBool(\"DEBUG\");\n          env().set(\"DEBUG\", false);\n          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n          env().set(\"DEBUG\", debugFlag);\n          if (underflowCheckValue > 0) {\n            return 32;\n          }\n        }\n        return 16;\n      });\n    }\n    return this.floatPrecisionValue;\n  }\n  epsilon() {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;\n  }\n  uploadToGPU(dataId) {\n    const texData = this.texData.get(dataId);\n    const { shape, dtype, values, texture, usage, isPacked } = texData;\n    if (texture != null) {\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start;\n    if (shouldTimeProgram) {\n      start = util_exports.now();\n    }\n    let texShape = texData.texShape;\n    if (texShape == null) {\n      texShape = getTextureShapeFromLogicalShape(shape, isPacked);\n      texData.texShape = texShape;\n    }\n    if (values != null) {\n      const shapeAs3D = getShapeAs3D(shape);\n      let program;\n      let width = texShape[1], height = texShape[0];\n      const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;\n      if (isPacked || !isByteArray) {\n        [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n      }\n      if (isPacked) {\n        program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);\n      } else {\n        program = new EncodeMatrixProgram(shapeAs3D, isByteArray);\n      }\n      const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;\n      const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);\n      const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);\n      if (isByteArray) {\n        tempDenseInputTexData.usage = TextureUsage.PIXELS;\n      } else {\n        tempDenseInputTexData.usage = TextureUsage.UPLOAD;\n      }\n      tempDenseInputTexData.texShape = tempDenseInputTexShape;\n      this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n      const customValues = [[height, width]];\n      const preventEagerUnpacking = true;\n      const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);\n      const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n      texData.texShape = outputTexData.texShape;\n      texData.isPacked = outputTexData.isPacked;\n      texData.usage = outputTexData.usage;\n      if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n        texData.texture = outputTexData.texture;\n        texData.values = null;\n        this.texData.delete(encodedOutputTarget.dataId);\n      } else {\n        this.disposeData(encodedOutputTarget.dataId);\n      }\n      this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += util_exports.now() - start;\n      }\n    } else {\n      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n      texData.texture = newTexture;\n    }\n  }\n  convertAndCacheOnCPU(dataId, float32Values) {\n    const texData = this.texData.get(dataId);\n    const { dtype } = texData;\n    this.releaseGPUData(dataId);\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype);\n    }\n    return texData.values;\n  }\n  acquireTexture(texShape, texType, dtype, isPacked) {\n    this.numBytesInGPU += this.computeBytes(texShape, dtype);\n    if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n      this.warnedAboutMemory = true;\n      console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);\n    }\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n  computeBytes(shape, dtype) {\n    return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);\n  }\n  checkCompileCompletion() {\n    for (const [, binary] of Object.entries(this.binaryCache)) {\n      this.checkCompletion_(binary);\n    }\n  }\n  async checkCompileCompletionAsync() {\n    const ps = [];\n    if (this.gpgpu.parallelCompilationExtension) {\n      for (const [, binary] of Object.entries(this.binaryCache)) {\n        ps.push(this.checkCompletionAsync_(binary));\n      }\n      return Promise.all(ps);\n    } else {\n      for (const [, binary] of Object.entries(this.binaryCache)) {\n        const p2 = new Promise((resolve) => {\n          try {\n            this.checkCompletion_(binary);\n            resolve(true);\n          } catch (error) {\n            throw error;\n          }\n        });\n        ps.push(p2);\n      }\n      return Promise.all(ps);\n    }\n  }\n  async checkCompletionAsync_(binary) {\n    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {\n      return this.checkCompletion_(binary);\n    } else {\n      await nextFrame();\n      return this.checkCompletionAsync_(binary);\n    }\n  }\n  checkCompletion_(binary) {\n    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {\n      console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));\n      if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));\n        throw new Error(\"Failed to compile fragment shader.\");\n      }\n      throw new Error(\"Failed to link vertex and fragment shaders.\");\n    }\n    return true;\n  }\n  getUniformLocations() {\n    for (const [, binary] of Object.entries(this.binaryCache)) {\n      const { uniformLocations, customUniformLocations, infLoc, nanLoc, inShapesLocations, inTexShapesLocations, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);\n      binary.uniformLocations = uniformLocations;\n      binary.customUniformLocations = customUniformLocations;\n      binary.infLoc = infLoc;\n      binary.nanLoc = nanLoc;\n      binary.inShapesLocations = inShapesLocations;\n      binary.inTexShapesLocations = inTexShapesLocations;\n      binary.outShapeLocation = outShapeLocation;\n      binary.outShapeStridesLocation = outShapeStridesLocation;\n      binary.outTexShapeLocation = outTexShapeLocation;\n    }\n  }\n};\nMathBackendWebGL.nextDataId = 0;\nfunction float32ToTypedArray(a6, dtype) {\n  if (dtype === \"float32\" || dtype === \"complex64\") {\n    return a6;\n  } else if (dtype === \"int32\" || dtype === \"bool\") {\n    const result = dtype === \"int32\" ? new Int32Array(a6.length) : new Uint8Array(a6.length);\n    for (let i2 = 0; i2 < result.length; ++i2) {\n      result[i2] = Math.round(a6[i2]);\n    }\n    return result;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js\nvar version6 = \"3.18.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl.js\nfunction forceHalfFloat() {\n  env().set(\"WEBGL_FORCE_F16_TEXTURES\", true);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js\nif (device_util_exports.isBrowser()) {\n  registerBackend(\"webgl\", () => new MathBackendWebGL(), 2);\n}\nvar webgl = { forceHalfFloat };\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js\nvar CHECK_NAN_SNIPPET2 = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nvar BinaryOpProgram = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"A\", \"B\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op2}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js\nvar CHECK_NAN_SNIPPET3 = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nvar BinaryOpPackedProgram = class {\n  constructor(op2, aShape, bShape, checkOutOfBounds = false) {\n    this.variableNames = [\"A\", \"B\"];\n    this.supportsBroadcasting = true;\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const rank = this.outputShape.length;\n    this.enableShapeUniforms = useShapeUniforms(rank);\n    let checkOutOfBoundsString = \"\";\n    if (checkOutOfBounds) {\n      if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {\n        checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n      } else {\n        const dtype = getCoordsDataType(rank);\n        checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n        if (rank === 1) {\n          if (this.enableShapeUniforms) {\n            checkOutOfBoundsString += `\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n          } else {\n            checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n          }\n        } else {\n          const channels = getChannels(\"coords\", rank);\n          if (this.enableShapeUniforms) {\n            checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n          } else {\n            checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n          }\n        }\n      }\n    }\n    this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op2}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js\nfunction identity3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  backend2.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig2 = {\n  kernelName: Identity,\n  backendName: \"webgl\",\n  kernelFunc: identity3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js\nfunction complex3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.texData.get(complexInfo.dataId);\n  const realTensorInfo = identity3({ inputs: { x: real5 }, backend: backend2 });\n  const imagTensorInfo = identity3({ inputs: { x: imag5 }, backend: backend2 });\n  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n  return complexInfo;\n}\nvar complexConfig2 = {\n  kernelName: Complex,\n  backendName: \"webgl\",\n  kernelFunc: complex3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js\nvar LEAKYRELU = `return (a < 0.) ? b * a : a;`;\nvar LEAKYRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction leakyRelu3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  const $alpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(alpha, \"float32\"));\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);\n  const result = backend2.runWebGLProgram(program, [x, $alpha], \"float32\");\n  backend2.disposeIntermediateTensorInfo($alpha);\n  return result;\n}\nvar leakyReluConfig2 = {\n  kernelName: LeakyRelu,\n  backendName: \"webgl\",\n  kernelFunc: leakyRelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js\nvar PRELU = `return (a < 0.) ? b * a : a;`;\nvar PRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction prelu4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);\n  return backend2.runWebGLProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig2 = {\n  kernelName: Prelu,\n  backendName: \"webgl\",\n  kernelFunc: prelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js\nvar CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nvar CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nvar CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nfunction unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webglBackend = backend2;\n    const $dtype = dtype || x.dtype;\n    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webglBackend.texData.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values, $dtype);\n      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\") && packedOpSnippet != null;\n    let program;\n    if (shouldUsePackedProgram) {\n      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);\n    } else {\n      program = new UnaryOpProgram(x.shape, opSnippet);\n    }\n    return webglBackend.runWebGLProgram(program, [x], $dtype);\n  };\n}\nfunction binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { a: a6, b } = inputs;\n    const webglBackend = backend2;\n    if (supportsComplex && a6.dtype === \"complex64\") {\n      const aData = webglBackend.texData.get(a6.dataId);\n      const bData = webglBackend.texData.get(b.dataId);\n      const [real5, imag5] = [\n        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n      ].map((complexParts) => {\n        const [aPart, bPart] = complexParts;\n        const aHandle = {\n          dataId: aPart.dataId,\n          dtype: aPart.dtype,\n          shape: a6.shape\n        };\n        const bHandle = {\n          dataId: bPart.dataId,\n          dtype: bPart.dtype,\n          shape: b.shape\n        };\n        const program2 = new BinaryOpProgram(opSnippet, a6.shape, b.shape);\n        return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n      });\n      const complexOutput = complex3({ inputs: { real: real5, imag: imag5 }, backend: webglBackend });\n      webglBackend.disposeIntermediateTensorInfo(real5);\n      webglBackend.disposeIntermediateTensorInfo(imag5);\n      return complexOutput;\n    }\n    const $dtype = dtype || upcastType(a6.dtype, b.dtype);\n    if ((a6.dtype === \"string\" || b.dtype === \"string\" || webglBackend.shouldExecuteOnCPU([a6, b])) && cpuKernelImpl != null) {\n      const aVals = webglBackend.texData.get(a6.dataId).values;\n      const bVals = webglBackend.texData.get(b.dataId).values;\n      const decodedAVals = a6.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;\n      const decodedBVals = a6.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;\n      const [outValues, outShape] = cpuKernelImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      const out = webglBackend.makeTensorInfo(outShape, $dtype);\n      const outData = webglBackend.texData.get(out.dataId);\n      outData.values = outValues;\n      return out;\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") && packedOpSnippet != null;\n    let program;\n    if (shouldUsePackedProgram) {\n      program = new BinaryOpPackedProgram(packedOpSnippet, a6.shape, b.shape, checkOutOfBounds);\n    } else {\n      program = new BinaryOpProgram(opSnippet, a6.shape, b.shape);\n    }\n    return webglBackend.runWebGLProgram(program, [a6, b], $dtype);\n  };\n}\nfunction mapActivationToShaderProgram(activation2, packed = false) {\n  if (activation2 === \"linear\") {\n    if (packed) {\n      return LINEAR2;\n    }\n    return LINEAR;\n  } else if (activation2 === \"relu\") {\n    if (packed) {\n      return RELU2;\n    }\n    return RELU;\n  } else if (activation2 === \"elu\") {\n    if (packed) {\n      return ELU3;\n    }\n    return ELU2;\n  } else if (activation2 === \"relu6\") {\n    if (packed) {\n      return RELU62;\n    }\n    return RELU6;\n  } else if (activation2 === \"prelu\") {\n    if (packed) {\n      return PRELU_PACKED;\n    }\n    return PRELU;\n  } else if (activation2 === \"leakyrelu\") {\n    if (packed) {\n      return LEAKYRELU_PACKED;\n    }\n    return LEAKYRELU;\n  } else if (activation2 === \"sigmoid\") {\n    if (packed) {\n      return SIGMOID2;\n    }\n    return SIGMOID;\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js\nvar MatMulPackedProgram = class {\n  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {\n    this.variableNames = [\"matrixA\", \"matrixB\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n    const aSample = transposeA ? \"i * 2, rc.y\" : \"rc.y, i * 2\";\n    const bSample = transposeB ? \"rc.z, i * 2\" : \"i * 2, rc.z\";\n    const aSwizzle = transposeA ? [\"a.xxyy\", \"a.zzww\"] : [\"a.xxzz\", \"a.yyww\"];\n    const bSwizzle = transposeB ? [\"b.xzxz\", \"b.ywyw\"] : [\"b.xyxy\", \"b.zwzw\"];\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation2}\n        }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyreluActivation) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    let batchASnippet = \"rc.x\";\n    let batchBSnippet = \"rc.x\";\n    if (aShape[0] < bShape[0]) {\n      batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n    } else if (bShape[0] < aShape[0]) {\n      batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n    }\n    this.userCode = `\n      ${activationSnippet}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js\nvar COMPLEX_MULTIPLY = {\n  REAL: \"return areal * breal - aimag * bimag;\",\n  IMAG: \"return areal * bimag + aimag * breal;\"\n};\nvar BinaryOpComplexProgram = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op2}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js\nvar MUL = \"return a * b;\";\nfunction multiply3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { a: a6, b } = inputs;\n  const dtype = backend_util_exports.upcastType(a6.dtype, b.dtype);\n  if (a6.dtype === \"complex64\") {\n    const aData = backend2.texData.get(a6.dataId);\n    const bData = backend2.texData.get(b.dataId);\n    const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a6.shape, b.shape);\n    const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a6.shape, b.shape);\n    const inputs2 = [\n      {\n        dataId: aData.complexTensorInfos.real.dataId,\n        dtype: aData.complexTensorInfos.real.dtype,\n        shape: a6.shape\n      },\n      {\n        dataId: aData.complexTensorInfos.imag.dataId,\n        dtype: aData.complexTensorInfos.imag.dtype,\n        shape: a6.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.real.dataId,\n        dtype: bData.complexTensorInfos.real.dtype,\n        shape: b.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.imag.dataId,\n        dtype: bData.complexTensorInfos.imag.dtype,\n        shape: b.shape\n      }\n    ];\n    const realPart = backend2.runWebGLProgram(realProgram, inputs2, \"float32\");\n    const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, \"float32\");\n    const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    return complexOutput;\n  }\n  if (backend2.shouldExecuteOnCPU([a6, b])) {\n    const aData = backend2.texData.get(a6.dataId);\n    const bData = backend2.texData.get(b.dataId);\n    const [outValues, outShape] = multiplyImplCPU(a6.shape, b.shape, aData.values, bData.values, dtype);\n    const out = backend2.makeTensorInfo(outShape, dtype);\n    const outData = backend2.texData.get(out.dataId);\n    outData.values = outValues;\n    return out;\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")) {\n    program = new BinaryOpPackedProgram(MUL, a6.shape, b.shape);\n  } else {\n    program = new BinaryOpProgram(MUL, a6.shape, b.shape);\n  }\n  return backend2.runWebGLProgram(program, [a6, b], dtype);\n}\nvar multiplyConfig2 = {\n  kernelName: Multiply,\n  backendName: \"webgl\",\n  kernelFunc: multiply3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js\nfunction packedReshape(input2, afterShape, backend2) {\n  const input3DShape = [\n    getBatchDim(input2.shape),\n    ...getRowsCols(input2.shape)\n  ];\n  const input3D = {\n    dtype: input2.dtype,\n    shape: input3DShape,\n    dataId: input2.dataId\n  };\n  const afterShapeAs3D = [\n    getBatchDim(afterShape),\n    ...getRowsCols(afterShape)\n  ];\n  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n  const preventEagerUnpackingOfOutput = true;\n  const customValues = [input3DShape];\n  const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n  return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js\nfunction reshape4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const webglBackend = backend2;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  const xTexData = webglBackend.texData.get(x.dataId);\n  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n    return packedReshape(x, $shape, webglBackend);\n  }\n  webglBackend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig2 = {\n  kernelName: Reshape,\n  backendName: \"webgl\",\n  kernelFunc: reshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js\nvar MeanProgram = class {\n  constructor(reduceInfo, divisor) {\n    this.variableNames = [\"x\"];\n    const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    let updateSnippet = `sumValue += dot(values, ones);`;\n    if (divisor != null) {\n      const denominator = 1 / divisor;\n      updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;\n    }\n    let checkOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n    }\n    this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js\nvar ReduceProgram = class {\n  constructor(reduceInfo, reduceType) {\n    this.variableNames = [\"x\"];\n    const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n    let initializationValue = \"0.0\";\n    let compareOp = ``;\n    if (reduceType === \"prod\") {\n      initializationValue = \"1.0\";\n    } else if (reduceType === \"min\") {\n      initializationValue = \"1.0 / 1e-20\";\n      compareOp = `min`;\n    } else if (reduceType === \"max\") {\n      initializationValue = \"-1.0 / 1e-20\";\n      compareOp = `max`;\n    }\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (reduceType === \"sum\") {\n      returnValue = `sumValue`;\n    } else if (reduceType === \"prod\") {\n      returnValue = `prodValue`;\n    } else if (reduceType === \"all\") {\n      returnValue = `allValue`;\n    } else if (reduceType === \"any\") {\n      returnValue = `anyValue`;\n    }\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    let updateSnippet = `\n      if (${reduceType === \"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === \"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n        if (${reduceType === \"min\"} || ${reduceType === \"max\"}) {\n          minMaxValue = ${compareOp}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `;\n    let vecType = `vec4`;\n    if (reduceType === \"all\") {\n      initializationValue = \"1.0\";\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === \"any\") {\n      initializationValue = \"0.0\";\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n    let checkOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js\nfunction getReductionStages(inShape) {\n  const stages = [];\n  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n    const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n    const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);\n    stages.push({\n      inSize: outSize,\n      windowSize,\n      outSize: Math.ceil(outSize / windowSize)\n    });\n  }\n  return stages;\n}\nfunction reduce(x, dtype, reductionType, backend2) {\n  const reductionStages = getReductionStages(x.shape);\n  let result = x;\n  for (let i2 = 0; i2 < reductionStages.length; i2++) {\n    const { inSize, windowSize, outSize } = reductionStages[i2];\n    let program;\n    let previousResult;\n    if (reductionType === \"mean\") {\n      program = i2 === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n    } else {\n      program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n    }\n    previousResult = result;\n    result = backend2.runWebGLProgram(program, [result], dtype);\n    if (previousResult.dataId !== x.dataId) {\n      backend2.disposeIntermediateTensorInfo(previousResult);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js\nvar TransposeProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n};\nfunction getSwitchedCoords(newDim) {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\", \"resRC.v\"];\n  const switchedCoords = new Array(rank);\n  for (let i2 = 0; i2 < newDim.length; i2++) {\n    switchedCoords[newDim[i2]] = originalOrder[i2];\n  }\n  return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js\nvar TransposePackedProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    if (this.rank > 6) {\n      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n    }\n    const dtype = getCoordsDataType(this.rank);\n    const outputOrder = getVecChannels(\"rc\", this.rank);\n    const switchedOrder = new Array(this.rank);\n    for (let i2 = 0; i2 < newDim.length; i2++) {\n      switchedOrder[newDim[i2]] = outputOrder[i2];\n    }\n    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n    const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n    this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js\nfunction transposeImpl2(x, perm, backend2) {\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js\nfunction sumImpl(x, axis, keepDims, backend2) {\n  const reductionIndices = axis;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  const sumInputIsTransposed = permutedAxes != null;\n  let sumInput = x;\n  if (sumInputIsTransposed) {\n    sumInput = transposeImpl2(x, permutedAxes, backend2);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n  const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);\n  let outShape = sumOutShape;\n  if (keepDims) {\n    outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);\n  }\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const outType = sumOutType(x.dtype);\n  const reduced = reduce(reshapedInput, outType, \"sum\", backend2);\n  const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (sumInputIsTransposed) {\n    backend2.disposeIntermediateTensorInfo(sumInput);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js\nfunction sum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return sumImpl(x, axis, keepDims, backend2);\n}\nvar sumConfig2 = {\n  kernelName: Sum,\n  backendName: \"webgl\",\n  kernelFunc: sum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js\nfunction transpose3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  const webglBackend = backend2;\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[perm[i2]];\n  }\n  let out;\n  if (webglBackend.shouldExecuteOnCPU([x])) {\n    const xTexData = webglBackend.texData.get(x.dataId);\n    const values = xTexData.values;\n    const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);\n    out = webglBackend.makeTensorInfo(newShape, x.dtype);\n    const outData = webglBackend.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = transposeImpl2(x, perm, webglBackend);\n  }\n  return out;\n}\nvar transposeConfig2 = {\n  kernelName: Transpose,\n  backendName: \"webgl\",\n  kernelFunc: transpose3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js\nvar MATMUL_SHARED_DIM_THRESHOLD = 1e3;\nfunction batchMatMulImpl({ a: a6, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const aRank = a6.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a6.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape4({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const intermediates = [a3d, b3d];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;\n  const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;\n  let out;\n  if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {\n    let aVec = a3d;\n    let bVec = b3d;\n    if (transposeA) {\n      aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n      intermediates.push(aVec);\n    }\n    if (transposeB) {\n      bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n      intermediates.push(bVec);\n    }\n    const shouldReshapeA = outerShapeB !== 1;\n    const shouldReshapeB = outerShapeB === 1;\n    let aVec3d = aVec;\n    if (shouldReshapeA) {\n      aVec3d = reshape4({\n        inputs: { x: aVec },\n        backend: backend2,\n        attrs: { shape: [batchDim, sharedDim, 1] }\n      });\n      intermediates.push(aVec3d);\n    }\n    const axis = outerShapeB === 1 ? 2 : 1;\n    let bVec3d = bVec;\n    if (shouldReshapeB) {\n      bVec3d = reshape4({\n        inputs: { x: bVec },\n        backend: backend2,\n        attrs: { shape: [batchDim, 1, sharedDim] }\n      });\n      intermediates.push(bVec3d);\n    }\n    const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });\n    out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });\n    intermediates.push(product);\n  } else {\n    const dtype = upcastType(a6.dtype, b.dtype);\n    const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const inputs = [a3d, b3d];\n    if (bias != null) {\n      inputs.push(bias);\n    }\n    if (hasPreluActivationWeights) {\n      inputs.push(preluActivationWeights);\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n      inputs.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    out = backend2.runWebGLProgram(program, inputs, dtype);\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n  intermediates.push(out);\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  return batchMatMulImpl({\n    a: a6,\n    b,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar _fusedMatMulConfig2 = {\n  kernelName: _FusedMatMul,\n  backendName: \"webgl\",\n  kernelFunc: _fusedMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js\nvar ABS2 = `return abs(x);`;\nfunction abs3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n    const xData = backend2.texData.get(x.dataId);\n    const outValues = simpleAbsImplCPU(xData.values);\n    return backend2.makeTensorInfo(x.shape, x.dtype, outValues);\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n    program = new UnaryOpPackedProgram(x.shape, ABS2);\n  } else {\n    program = new UnaryOpProgram(x.shape, ABS2);\n  }\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar absConfig2 = {\n  kernelName: Abs,\n  backendName: \"webgl\",\n  kernelFunc: abs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js\nvar ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nvar acos3 = unaryKernelFunc2({ opSnippet: ACOS });\nvar acosConfig2 = {\n  kernelName: Acos,\n  backendName: \"webgl\",\n  kernelFunc: acos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js\nvar ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`;\nvar acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });\nvar acoshConfig2 = {\n  kernelName: Acosh,\n  backendName: \"webgl\",\n  kernelFunc: acosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js\nvar ADD = \"return a + b;\";\nvar addKernelFunc = binaryKernelFunc2({\n  opSnippet: ADD,\n  packedOpSnippet: ADD,\n  supportsComplex: true,\n  cpuKernelImpl: addImplCPU\n});\nvar addConfig2 = {\n  kernelName: Add,\n  backendName: \"webgl\",\n  kernelFunc: addKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js\nvar AddNProgram = class {\n  constructor(outputShape, shapes) {\n    this.outputShape = [];\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    this.userCode = `\n      void main() {\n        ${snippets.join(\"\\n        \")}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js\nvar AddNPackedProgram = class {\n  constructor(outputShape, shapes) {\n    this.outputShape = [];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    this.userCode = `\n      void main() {\n        ${snippets.join(\"\\n        \")}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js\nfunction addN3(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity3({ inputs: { x: tensors[0] }, backend: backend2 });\n  }\n  if (tensors.length > env().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")) {\n    const midIndex = Math.floor(tensors.length / 2);\n    const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 });\n    const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });\n    return addN3({ inputs: [leftSide, rightSide], backend: backend2 });\n  }\n  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map((t2) => t2.shape);\n  const usePackedOp = env().getBool(\"WEBGL_PACK\");\n  const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);\n  return backend2.runWebGLProgram(program, tensors, dtype);\n}\nvar addNConfig2 = {\n  kernelName: AddN,\n  backendName: \"webgl\",\n  kernelFunc: addN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js\nfunction all3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"all\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar allConfig2 = {\n  kernelName: All,\n  backendName: \"webgl\",\n  kernelFunc: all3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js\nfunction any3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"any\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar anyConfig2 = {\n  kernelName: Any,\n  backendName: \"webgl\",\n  kernelFunc: any3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js\nvar ArgMinMaxProgram = class {\n  constructor(reduceInfo, op2, firstPass) {\n    this.variableNames = [\"A\"];\n    const { windowSize, batchSize, outSize } = reduceInfo;\n    if (!firstPass) {\n      this.variableNames.push(\"bestIndicesA\");\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = op2 === \"max\" ? \">\" : \"<\";\n    const indexSnippet = firstPass ? \"inOffset + i;\" : \"round(getBestIndicesA(batch, inOffset + i));\";\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js\nvar ArgMinMaxPackedProgram = class {\n  constructor(shape, windowSize, op2, firstPass) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);\n    const inSize = shape[shape.length - 1];\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = shape.slice(0, -1);\n    if (outSize > 1) {\n      this.outputShape.push(outSize);\n    }\n    if (!firstPass) {\n      this.variableNames.push(\"bestIndicesA\");\n    }\n    const outShape = this.outputShape;\n    const rank = outShape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords3 = getChannels(\"coords\", rank);\n    let sourceLocSetup;\n    let sourceRank;\n    if (outSize === 1) {\n      sourceRank = rank + 1;\n      const sourceLocDType = getCoordsDataType(sourceRank);\n      sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords3.join()}, 0);\n        ++${coords3[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords3.join()}, 0);\n        ++${coords3[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords3.join()}, 0);\n        --${coords3[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords3.join()}, 0);\n        --${coords3[rank - 2]};`;\n    } else {\n      sourceRank = rank;\n      sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords3[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords3[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords3[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords3[rank - 2]};`;\n    }\n    const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, sourceRank);\n    const inChannel = \".\" + channels[sourceRank - 1];\n    const intChannels = channels.map((x) => \"int \" + x);\n    const srcRCoords = getChannels(\"sourceLocR\", sourceRank - 1).concat(\"inIdx.r\");\n    const srcGCoords = getChannels(\"sourceLocG\", sourceRank - 1).concat(\"inIdx.g\");\n    const srcBCoords = getChannels(\"sourceLocB\", sourceRank - 1).concat(\"inIdx.b\");\n    const srcACoords = getChannels(\"sourceLocA\", sourceRank - 1).concat(\"inIdx.a\");\n    const compOp = op2 === \"max\" ? \"greaterThan\" : \"lessThan\";\n    const fetchCandidateIdx = firstPass ? \"\" : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n    const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n    const getBestIndicesAChannelSnippet = firstPass ? \"\" : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n    this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords3[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords3[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js\nfunction argReduce(backend2, x, reduceType, bestIndicesA = null) {\n  let batchSize = x.shape[0];\n  let inSize = x.shape[1];\n  if (bestIndicesA != null) {\n    batchSize = bestIndicesA.shape[0];\n    inSize = bestIndicesA.shape[1];\n  }\n  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n  const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };\n  const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n  const inputs = [x];\n  if (bestIndicesA != null) {\n    inputs.push(bestIndicesA);\n  }\n  const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n  if (output.shape[1] === 1) {\n    return output;\n  }\n  const result = argReduce(backend2, x, reduceType, output);\n  backend2.disposeIntermediateTensorInfo(output);\n  return result;\n}\nfunction argReducePacked(backend2, x, reduceType, bestIndicesA = null) {\n  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n  const inSize = inShape[inShape.length - 1];\n  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n  const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n  const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n  if (output.shape.length === x.shape.length) {\n    const result = argReducePacked(backend2, x, reduceType, output);\n    backend2.disposeIntermediateTensorInfo(output);\n    return result;\n  }\n  return output;\n}\nfunction argMinMaxReduce(backend2, x, axis, reduceType) {\n  const axes = [axis];\n  backend_util_exports.assertAxesAreInnerMostDims(\"arg\" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);\n  if (!env().getBool(\"WEBGL_PACK_REDUCE\") || x.shape.length <= 2) {\n    const intermediateTensorInfos = [];\n    const xtexData = backend2.texData.get(x.dataId);\n    const xIsPacked = xtexData !== null && xtexData.isPacked;\n    let xUnPacked = x;\n    if (xIsPacked) {\n      xUnPacked = backend2.unpackTensor(x);\n      intermediateTensorInfos.push(xUnPacked);\n    }\n    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });\n    intermediateTensorInfos.push(a2D);\n    const reduced = argReduce(backend2, a2D, reduceType);\n    intermediateTensorInfos.push(reduced);\n    const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n    intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n    return reshaped;\n  }\n  return argReducePacked(backend2, x, reduceType);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js\nfunction argMax3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend2, $x, axes[0], \"max\");\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return out;\n}\nvar argMaxConfig2 = {\n  kernelName: ArgMax,\n  backendName: \"webgl\",\n  kernelFunc: argMax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js\nfunction argMin3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend2, $x, axes[0], \"min\");\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return out;\n}\nvar argMinConfig2 = {\n  kernelName: ArgMin,\n  backendName: \"webgl\",\n  kernelFunc: argMin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js\nvar ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nvar asin3 = unaryKernelFunc2({ opSnippet: ASIN });\nvar asinConfig2 = {\n  kernelName: Asin,\n  backendName: \"webgl\",\n  kernelFunc: asin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js\nvar ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nvar asinh3 = unaryKernelFunc2({ opSnippet: ASINH });\nvar asinhConfig2 = {\n  kernelName: Asinh,\n  backendName: \"webgl\",\n  kernelFunc: asinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js\nvar ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nvar atan4 = unaryKernelFunc2({ opSnippet: ATAN });\nvar atanConfig2 = {\n  kernelName: Atan,\n  backendName: \"webgl\",\n  kernelFunc: atan4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js\nvar ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nvar ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nvar atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nvar atan2Config2 = {\n  kernelName: Atan2,\n  backendName: \"webgl\",\n  kernelFunc: atan23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js\nvar ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nvar atanh3 = unaryKernelFunc2({ opSnippet: ATANH });\nvar atanhConfig2 = {\n  kernelName: Atanh,\n  backendName: \"webgl\",\n  kernelFunc: atanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js\nvar Pool2DProgram = class {\n  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n    this.variableNames = [\"x\"];\n    if (poolType === \"avg\" && computePositions) {\n      throw new Error(\"Cannot compute positions for average pool.\");\n    }\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n    const isAvgPool = poolType === \"avg\";\n    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    let initializationValue = \"0.0\";\n    if (!isAvgPool) {\n      initializationValue = \"-1.0 / 1e-20\";\n    }\n    if (computePositions) {\n      const compareOp2 = \">=\";\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp2} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n    const compareOp = \"max\";\n    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (poolType === \"avg\") {\n      returnValue = `avgValue / count`;\n    }\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\nvar Pool3DProgram = class {\n  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n    this.variableNames = [\"x\"];\n    if (poolType === \"avg\" && computePositions) {\n      throw new Error(\"Cannot compute positions for average pool.\");\n    }\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n    const isAvgPool = poolType === \"avg\";\n    let initializationValue = \"0.0\";\n    if (!isAvgPool) {\n      initializationValue = \"-1.0 / 1e-20\";\n    }\n    if (computePositions) {\n      const compareOp2 = \">=\";\n      this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp2} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n    const compareOp = \"max\";\n    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (poolType === \"avg\") {\n      returnValue = `avgValue / count`;\n    }\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n    this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js\nfunction avgPool3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex2(x, \"avgPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const avgPoolProgram = new Pool2DProgram(convInfo, \"avg\", false);\n  return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPoolConfig2 = {\n  kernelName: AvgPool,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js\nfunction avgPool3D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n  const avgPoolProgram = new Pool3DProgram(convInfo, \"avg\", false);\n  return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPool3DConfig2 = {\n  kernelName: AvgPool3D,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js\nvar AvgPool2DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar AvgPool3DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPool3DGradConfig3 = {\n  kernelName: AvgPool3DGrad,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  assertNotComplex2([dy, input2], \"avgPoolGrad\");\n  const { filterSize, strides, pad: pad3 } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPoolGradConfig3 = {\n  kernelName: AvgPoolGrad,\n  backendName: \"webgl\",\n  kernelFunc: avgPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js\nfunction batchMatMul2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  return batchMatMulImpl({ a: a6, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig2 = {\n  kernelName: BatchMatMul,\n  backendName: \"webgl\",\n  kernelFunc: batchMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js\nvar BatchNormProgram = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n    this.outputShape = [];\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    let offsetSnippet = \"0.0\";\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n      offsetSnippet = \"getOffsetAtOutCoords()\";\n    }\n    let scaleSnippet = \"1.0\";\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n      scaleSnippet = \"getScaleAtOutCoords()\";\n    }\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js\nvar BatchNormPackedProgram = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    let offsetSnippet = \"vec4(0.0)\";\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n      offsetSnippet = \"getOffsetAtOutCoords()\";\n    }\n    let scaleSnippet = \"vec4(1.0)\";\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n      scaleSnippet = \"getScaleAtOutCoords()\";\n    }\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js\nvar batchNorm3 = ({ inputs, backend: backend2, attrs }) => {\n  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;\n  util_exports.assert(mean5.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  let { varianceEpsilon } = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const finalInputs = [x, mean5, variance];\n  let offsetShape = null;\n  if (offset != null) {\n    offsetShape = offset.shape;\n    finalInputs.push(offset);\n  }\n  let scaleShape = null;\n  if (scale2 != null) {\n    scaleShape = scale2.shape;\n    finalInputs.push(scale2);\n  }\n  const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new BatchNormPackedProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n  const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n  return output;\n};\nvar batchNormConfig2 = {\n  kernelName: FusedBatchNorm,\n  backendName: \"webgl\",\n  kernelFunc: batchNorm3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js\nvar SliceProgram = class {\n  constructor(destSize) {\n    this.variableNames = [\"source\"];\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    const dtype = getCoordsDataType(this.rank);\n    this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n    const sourceCoords = getCoords(this.rank);\n    let body;\n    const coordSum = destSize.map((_, i2) => {\n      return `sourceLoc.${coords[i2]} = start[${i2}] + coords.${coords[i2]};`;\n    });\n    body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join(\"\\n\")}\n      `;\n    this.userCode = `\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n};\nvar coords = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords(rank) {\n  if (rank === 1) {\n    return \"sourceLoc\";\n  } else if (rank <= 6) {\n    return coords.slice(0, rank).map((x) => \"sourceLoc.\" + x).join(\",\");\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js\nvar SlicePackedProgram = class {\n  constructor(destSize) {\n    this.variableNames = [\"source\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n    const dtype = getCoordsDataType(this.rank);\n    const coords3 = getChannels(\"coords\", this.rank);\n    const sourceLoc = getChannels(\"sourceLoc\", this.rank);\n    const innerDims = this.rank === 1 ? \"sourceLoc\" : `vec2(${sourceLoc.slice(-2).join()})`;\n    const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n    const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n    const lowerRow = this.rank === 1 ? \"\" : `\n      --${coords3[this.rank - 1]};\n      if (++${coords3[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n    const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i2) => `start[${i2}]`).join()});` : destSize.map((_, i2) => `${sourceLoc[i2]} = ${coords3[i2]} + start[${i2}];`).join(\"\\n\");\n    this.userCode = `\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js\nfunction shallowSlice(x, begin, size, backend2) {\n  const xTexData = backend2.texData.get(x.dataId);\n  const t2 = backend2.makeTensorInfo(size, x.dtype);\n  const newTexData = backend2.texData.get(t2.dataId);\n  Object.assign(newTexData, xTexData);\n  newTexData.refCount = 1;\n  newTexData.shape = size;\n  newTexData.dtype = x.dtype;\n  let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));\n  if (xTexData.slice) {\n    flatOffset += xTexData.slice.flatOffset;\n  }\n  newTexData.slice = {\n    flatOffset,\n    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n  };\n  const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;\n  backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n  return t2;\n}\nfunction slice3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  if (util_exports.sizeFromShape($size) === 0) {\n    return backend2.makeTensorInfo($size, x.dtype, []);\n  }\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n    const xTexData = backend2.texData.get(x.dataId);\n    const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);\n    return backend2.makeTensorInfo($size, x.dtype, outValues);\n  }\n  const { isPacked } = backend2.texData.get(x.dataId);\n  const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);\n  if (isPacked || !isContinous) {\n    const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new SlicePackedProgram($size) : new SliceProgram($size);\n    const customValues = [$begin];\n    return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n  }\n  backend2.uploadToGPU(x.dataId);\n  return shallowSlice(x, $begin, $size, backend2);\n}\nvar sliceConfig2 = {\n  kernelName: Slice,\n  backendName: \"webgl\",\n  kernelFunc: slice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const toDispose = [];\n  const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n  const reshapedIntermediate2 = reshape4({\n    inputs: { x: transposedIntermediate },\n    backend: backend2,\n    attrs: { shape: reshapedPermuted }\n  });\n  const sliced = slice3({\n    inputs: { x: reshapedIntermediate2 },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return sliced;\n};\nvar batchToSpaceNDConfig2 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"webgl\",\n  kernelFunc: batchToSpaceND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js\nfunction bincount3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size } = attrs;\n  const xVals = backend2.readSync(x.dataId);\n  const weightsVals = backend2.readSync(weights.dataId);\n  const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n  return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig2 = {\n  kernelName: Bincount,\n  backendName: \"webgl\",\n  kernelFunc: bincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { s0, s1 } = inputs;\n  const s0Vals = backend2.readSync(s0.dataId);\n  const s1Vals = backend2.readSync(s1.dataId);\n  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n  return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig2 = {\n  kernelName: BroadcastArgs,\n  backendName: \"webgl\",\n  kernelFunc: broadcastArgs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js\nvar NOT_EQUAL = `return float(a != b);`;\nvar notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: \"bool\" });\nvar notEqualConfig2 = {\n  kernelName: NotEqual,\n  backendName: \"webgl\",\n  kernelFunc: notEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js\nfunction real3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.texData.get(input2.dataId);\n  return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig2 = {\n  kernelName: Real,\n  backendName: \"webgl\",\n  kernelFunc: real3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js\nvar TO_INT = `return float(int(x));`;\nfunction int(input2, backend2) {\n  const program = new UnaryOpProgram(input2.shape, TO_INT);\n  const output = backend2.runWebGLProgram(program, [input2], \"int32\");\n  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js\nfunction cast4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity3({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n    zerosTensor.dispose();\n    backend2.disposeIntermediateTensorInfo(floatX);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity3({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (dtype === \"int32\") {\n    return int(x, backend2);\n  }\n  if (dtype === \"bool\") {\n    const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n    const binaryInputs = { a: x, b: zerosTensorInfo };\n    const result = notEqual3({ inputs: binaryInputs, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n    return result;\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig2 = {\n  kernelName: Cast,\n  backendName: \"webgl\",\n  kernelFunc: cast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js\nvar CEIL = `return ceil(x);`;\nvar ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });\nvar ceilConfig2 = {\n  kernelName: Ceil,\n  backendName: \"webgl\",\n  kernelFunc: ceil3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js\nvar ClipProgram = class {\n  constructor(aShape) {\n    this.variableNames = [\"A\"];\n    this.customUniforms = [\n      { name: \"minVal\", type: \"float\" },\n      { name: \"maxVal\", type: \"float\" }\n    ];\n    this.outputShape = aShape;\n    this.userCode = `\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js\nvar ClipPackedProgram = class {\n  constructor(aShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"minVal\", type: \"float\" },\n      { name: \"maxVal\", type: \"float\" }\n    ];\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js\nfunction clipByValue3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  let program;\n  if (env().getBool(\"WEBGL_PACK_CLIP\")) {\n    program = new ClipPackedProgram(x.shape);\n  } else {\n    program = new ClipProgram(x.shape);\n  }\n  const customValues = [[clipValueMin], [clipValueMax]];\n  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n}\nvar clipByValueConfig2 = {\n  kernelName: ClipByValue,\n  backendName: \"webgl\",\n  kernelFunc: clipByValue3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js\nvar ComplexAbsProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"real\", \"imag\"];\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js\nfunction makeComplexComponentTensorInfo(complexTensor, complexPart) {\n  return {\n    dataId: complexPart.dataId,\n    dtype: complexPart.dtype,\n    shape: complexTensor.shape\n  };\n}\nfunction complexAbs2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const xData = backend2.texData.get(x.dataId);\n  const program = new ComplexAbsProgram(x.shape);\n  const programInputs = [\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)\n  ];\n  return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);\n}\nvar complexAbsConfig2 = {\n  kernelName: ComplexAbs,\n  backendName: \"webgl\",\n  kernelFunc: complexAbs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js\nvar ConcatProgram = class {\n  constructor(shapes) {\n    this.outputShape = [];\n    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const offsets = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][1];\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];\n    }\n    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      const shift = offsets[i2 - 1];\n      snippets.push(`else if (yC < ${offsets[i2]}) setOutput(getT${i2}(yR, yC-${shift}));`);\n    }\n    const lastIndex = offsets.length;\n    const lastShift = offsets[offsets.length - 1];\n    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join(\"\\n        \")}\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js\nvar ConcatPackedProgram = class {\n  constructor(shapes, axis) {\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    this.outputShape = backend_util_exports.computeOutShape(shapes, axis);\n    const shape = this.outputShape;\n    const rank = shape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords3 = getChannels(\"coords\", rank);\n    const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank);\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const offsets = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][axis];\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      offsets[i2] = offsets[i2 - 1] + shapes[i2][axis];\n    }\n    const channel = channels[axis];\n    const lastChannels = channels.slice(-2);\n    const allChannels = channels.join();\n    let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      const shift2 = offsets[i2 - 1];\n      getValueSnippet += `\n        if (${channel} < ${offsets[i2]}  && ${channel} >= ${offsets[i2 - 1]}) {\n          return getChannel(\n            getT${i2}(${shiftedChannels(channels, channel, shift2)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift2)}));\n        }`;\n    }\n    const lastIndex = offsets.length;\n    const shift = offsets[offsets.length - 1];\n    getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n    this.userCode = `\n      float getValue(${channels.map((x) => \"int \" + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords3}), 0., 0., 0.);\n\n        ${coords3[rank - 1]} = ${coords3[rank - 1]} + 1;\n        if (${coords3[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords3});\n        }\n\n        ${coords3[rank - 2]} = ${coords3[rank - 2]} + 1;\n        if (${coords3[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords3});\n        }\n\n        ${coords3[rank - 1]} = ${coords3[rank - 1]} - 1;\n        if (${coords3[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords3[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords3});\n        }\n        setOutput(result);\n      }\n    `;\n  }\n};\nfunction shiftedChannels(channels, channel, shift) {\n  const channelIdx = channels.indexOf(channel);\n  const res = channels.map((c, idx) => {\n    if (idx === channelIdx) {\n      return `${c} - ${shift}`;\n    } else {\n      return c;\n    }\n  });\n  return res.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js\nfunction imag3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.texData.get(input2.dataId);\n  return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig2 = {\n  kernelName: Imag,\n  backendName: \"webgl\",\n  kernelFunc: imag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js\nfunction concatImpl2(inputs, axis, backend2) {\n  const dtype = inputs[0].dtype;\n  if (dtype === \"complex64\") {\n    const reals = inputs.map((t2) => real3({ inputs: { input: t2 }, backend: backend2 }));\n    const imags = inputs.map((t2) => imag3({ inputs: { input: t2 }, backend: backend2 }));\n    const realConcated = concatImpl2(reals, axis, backend2);\n    const imagConcated = concatImpl2(imags, axis, backend2);\n    const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));\n    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));\n    backend2.disposeIntermediateTensorInfo(realConcated);\n    backend2.disposeIntermediateTensorInfo(imagConcated);\n    return result2;\n  }\n  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n  if (dtype === \"string\") {\n    runOnCpu = true;\n  }\n  if (runOnCpu) {\n    const tensors2D2 = inputs.map((t2) => {\n      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape4({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = tensors2D2.map((t2) => {\n      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };\n    });\n    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);\n    const simplyConcat = tensors2D2[0].shape[0] === 1;\n    const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n    tensors2D2.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n    return outInfo;\n  }\n  if (inputs.length > env().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")) {\n    const midIndex = Math.floor(inputs.length / 2);\n    const leftSide = concatImpl2(inputs.slice(0, midIndex), axis, backend2);\n    const rightSide = concatImpl2(inputs.slice(midIndex), axis, backend2);\n    const result2 = concatImpl2([leftSide, rightSide], axis, backend2);\n    backend2.disposeIntermediateTensorInfo(leftSide);\n    backend2.disposeIntermediateTensorInfo(rightSide);\n    return result2;\n  }\n  if (env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") && inputs[0].shape.length > 1) {\n    const program2 = new ConcatPackedProgram(inputs.map((t2) => t2.shape), axis);\n    return backend2.runWebGLProgram(program2, inputs, dtype);\n  }\n  const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);\n  const program = new ConcatProgram(tensors2D.map((t2) => t2.shape));\n  const result = backend2.runWebGLProgram(program, tensors2D, dtype);\n  tensors2D.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));\n  const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(result);\n  return reshapedResult;\n}\nfunction computeTensors2D(inputs, axis, backend2) {\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n  const tensors2D = inputs.map((x) => reshape4({\n    inputs: { x },\n    attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },\n    backend: backend2\n  }));\n  return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js\nfunction concat3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  return concatImpl2($inputs, $axis, backend2);\n}\nvar concatConfig2 = {\n  kernelName: Concat,\n  backendName: \"webgl\",\n  kernelFunc: concat3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js\nvar Conv2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivationWeights) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyreluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation2}\n          }\n        `;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyreluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\nvar Conv3DProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js\nvar Im2ColPackedProgram = class {\n  constructor(outputShape, convInfo) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"inputShape\", type: \"ivec3\" },\n      { name: \"pad\", type: \"ivec2\" },\n      { name: \"stride\", type: \"ivec2\" },\n      { name: \"dilation\", type: \"ivec2\" },\n      { name: \"inChannels\", type: \"int\" },\n      { name: \"itemsPerBlockRow\", type: \"int\" },\n      { name: \"outWidth\", type: \"int\" }\n    ];\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const { dataFormat } = convInfo;\n    const glsl = getGlslDifferences();\n    const isChannelsLast = dataFormat === \"channelsLast\";\n    const rowDim = isChannelsLast ? 0 : 1;\n    const colDim = isChannelsLast ? 1 : 2;\n    const boundsCheckingSnippet = this.enableShapeUniforms ? \"if(blockIndex < outShape[1] && pos < outShape[0]) {\" : `if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {`;\n    let unrolled = ``;\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        unrolled += `\n          blockIndex = rc.y + ${col};\n          pos = rc.x + ${row};\n\n          ${boundsCheckingSnippet}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${rowDim}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${colDim}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n      }\n    }\n    this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js\nfunction conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const xShape = x.shape;\n  const xTexData = backend2.texData.get(x.dataId);\n  const sharedMatMulDim = convInfo.inChannels;\n  const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n  const outerShapeFilter = convInfo.outChannels;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const transposeA = false;\n  const transposeB = false;\n  let out;\n  const intermediates = [];\n  if (preluActivationWeights != null && !isChannelsLast && preluActivationWeights.shape.length === 3) {\n    const preluActivationWeightsInNhwcFormat = transpose3({\n      inputs: { x: preluActivationWeights },\n      backend: backend2,\n      attrs: { perm: [1, 2, 0] }\n    });\n    intermediates.push(preluActivationWeightsInNhwcFormat);\n    preluActivationWeights = preluActivationWeightsInNhwcFormat;\n  }\n  const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));\n  if (canOptimize) {\n    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);\n    const xReshaped = {\n      dataId: x.dataId,\n      shape: [1, targetShape, convInfo.inChannels],\n      dtype: x.dtype\n    };\n    const originalXTexDataShape = xTexData.shape;\n    xTexData.shape = xTexData.shape.slice();\n    xTexData.shape[xTexData.shape.length - 2]++;\n    util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n    const filterReshaped = reshape4({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n    intermediates.push(filterReshaped);\n    const pointwiseConv = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      backend: backend2,\n      transposeA,\n      transposeB,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n    const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);\n    util_exports.assert(pointwiseConvTexData.isPacked, () => \"batchMatMul result is expected to be packed\");\n    xTexData.shape = originalXTexDataShape;\n    pointwiseConvTexData.shape = convInfo.outShape;\n    out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 });\n    out.shape = convInfo.outShape;\n    intermediates.push(pointwiseConv);\n  } else {\n    const xInNhwcFormat = isChannelsLast ? x : transpose3({ inputs: { x }, backend: backend2, attrs: { perm: [0, 2, 3, 1] } });\n    const xInNhwcFormatShape = xInNhwcFormat.shape;\n    const targetShape = xInNhwcFormatShape[0] * xInNhwcFormatShape[1] * xInNhwcFormatShape[2];\n    const xReshaped = reshape4({\n      inputs: { x: xInNhwcFormat },\n      backend: backend2,\n      attrs: { shape: [1, targetShape, convInfo.inChannels] }\n    });\n    const filterReshaped = reshape4({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n    const result = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      transposeA,\n      transposeB,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n    const outInNHWCFormatShape = [\n      convInfo.batchSize,\n      convInfo.outHeight,\n      convInfo.outWidth,\n      convInfo.outChannels\n    ];\n    const outInNHWCFormat = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outInNHWCFormatShape } });\n    out = isChannelsLast ? outInNHWCFormat : transpose3({\n      inputs: { x: outInNHWCFormat },\n      backend: backend2,\n      attrs: { perm: [0, 3, 1, 2] }\n    });\n    if (!isChannelsLast) {\n      intermediates.push(xInNhwcFormat);\n      intermediates.push(outInNHWCFormat);\n    }\n    intermediates.push(xReshaped);\n    intermediates.push(filterReshaped);\n    intermediates.push(result);\n  }\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return out;\n}\nfunction conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n  const isChannelsLast = dataFormat === \"channelsLast\";\n  const sharedDim = filterWidth * filterHeight * inChannels;\n  const numCols = outHeight * outWidth;\n  const x2ColShape = [sharedDim, numCols];\n  const transposeA = true;\n  const transposeB = false;\n  const intermediates = [];\n  if (preluActivationWeights != null && !isChannelsLast && preluActivationWeights.shape.length === 3) {\n    const preluActivationWeightsInNhwcFormat = transpose3({\n      inputs: { x: preluActivationWeights },\n      backend: backend2,\n      attrs: { perm: [1, 2, 0] }\n    });\n    intermediates.push(preluActivationWeightsInNhwcFormat);\n    preluActivationWeights = preluActivationWeightsInNhwcFormat;\n  }\n  const xSqueezed = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: x.shape.slice(1) } });\n  const w2Row = reshape4({\n    inputs: { x: filter },\n    backend: backend2,\n    attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }\n  });\n  intermediates.push(xSqueezed);\n  intermediates.push(w2Row);\n  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);\n  const customValues = [\n    xSqueezed.shape,\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inChannels],\n    [convInfo.filterWidth * convInfo.inChannels],\n    [convInfo.outWidth]\n  ];\n  const im2Col = backend2.runWebGLProgram(im2ColProgram, [xSqueezed], \"float32\", customValues);\n  const im2ColReshaped = reshape4({\n    inputs: { x: im2Col },\n    backend: backend2,\n    attrs: { shape: [1, x2ColShape[0], x2ColShape[1]] }\n  });\n  intermediates.push(im2Col);\n  intermediates.push(im2ColReshaped);\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n  const matmulProgram = new MatMulPackedProgram(im2ColReshaped.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  const inputs = [im2ColReshaped, w2Row];\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n    inputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  const product = backend2.runWebGLProgram(matmulProgram, inputs, \"float32\");\n  const outInNHWCFormatShape = [1, outHeight, outWidth, convInfo.outChannels];\n  const outInNHWCFormat = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: outInNHWCFormatShape } });\n  const out = isChannelsLast ? outInNHWCFormat : transpose3({ inputs: { x: outInNHWCFormat }, backend: backend2, attrs: { perm: [0, 3, 1, 2] } });\n  if (!isChannelsLast) {\n    intermediates.push(outInNHWCFormat);\n  }\n  intermediates.push(product);\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js\nfunction conv2d4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  let out;\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });\n  } else if (env().getBool(\"WEBGL_CONV_IM2COL\") && x.shape[0] === 1) {\n    out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });\n  } else {\n    const program = new Conv2DProgram(convInfo);\n    out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeIntermediateTensorInfo(out);\n  return outReshaped;\n}\nvar conv2DConfig2 = {\n  kernelName: Conv2D,\n  backendName: \"webgl\",\n  kernelFunc: conv2d4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js\nvar Conv2DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv2DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv3DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv3DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const program = new Conv2DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv2DBackpropFilterConfig2 = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: \"webgl\",\n  kernelFunc: conv2DBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const program = new Conv2DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv2DBackpropInputConfig2 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"webgl\",\n  kernelFunc: conv2DBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js\nfunction conv3D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n  const program = new Conv3DProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, filter], \"float32\");\n}\nvar conv3DConfig2 = {\n  kernelName: Conv3D,\n  backendName: \"webgl\",\n  kernelFunc: conv3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV22(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, filterShape } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n  const program = new Conv3DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv3DBackpropFilterV2Config2 = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: \"webgl\",\n  kernelFunc: conv3DBackpropFilterV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { pad: pad3, strides, inputShape } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n  const program = new Conv3DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv3DBackpropInputConfig = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: \"webgl\",\n  kernelFunc: conv3DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js\nvar COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nvar cos3 = unaryKernelFunc2({ opSnippet: COS });\nvar cosConfig2 = {\n  kernelName: Cos,\n  backendName: \"webgl\",\n  kernelFunc: cos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js\nvar COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar cosh3 = unaryKernelFunc2({ opSnippet: COSH });\nvar coshConfig2 = {\n  kernelName: Cosh,\n  backendName: \"webgl\",\n  kernelFunc: cosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js\nvar CropAndResizeProgram = class {\n  constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n    this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n    this.outputShape = [];\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === \"bilinear\" ? 1 : 0;\n    const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ? [\n      `${(imageHeight - 1) / (cropHeight - 1)}`,\n      \"(y2-y1) * height_ratio\",\n      `y1*${inputHeightFloat} + float(y)*(height_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (y1+y2) * ${inputHeightFloat}`\n    ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ? [\n      `${(imageWidth - 1) / (cropWidth - 1)}`,\n      \"(x2-x1) * width_ratio\",\n      `x1*${inputWidthFloat} + float(x)*(width_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (x1+x2) * ${inputWidthFloat}`\n    ];\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js\nvar cropAndResize3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue);\n  return backend2.runWebGLProgram(program, [image2, boxes, boxInd], \"float32\");\n};\nvar cropAndResizeConfig2 = {\n  kernelName: CropAndResize,\n  backendName: \"webgl\",\n  kernelFunc: cropAndResize3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js\nvar CumOpType;\n(function(CumOpType3) {\n  CumOpType3[\"Prod\"] = \"*\";\n  CumOpType3[\"Sum\"] = \"+\";\n})(CumOpType || (CumOpType = {}));\nvar CumProgram = class {\n  constructor(op2, outputShape, exclusive, reverse5) {\n    this.op = op2;\n    this.outputShape = outputShape;\n    this.variableNames = [\"x\"];\n    this.customUniforms = [{ name: \"index\", type: \"float\" }];\n    const rank = this.outputShape.length;\n    const initVal = this.op === CumOpType.Prod ? \"1.0\" : \"0.0\";\n    const val = exclusive ? initVal : `getX(${getCoords2(rank, \"coords\", this.op)})`;\n    const length = this.outputShape[this.outputShape.length - 1];\n    let condition = \"\";\n    let idxString = \"\";\n    if (exclusive) {\n      condition = reverse5 ? `end != ${length - 1}` : \"end != 0\";\n      idxString = reverse5 ? \"end + 1\" : \"end - 1\";\n    } else {\n      condition = reverse5 ? `end + pow2 < ${length}` : \"end >= pow2\";\n      idxString = reverse5 ? \"end + pow2\" : \"end - pow2\";\n    }\n    this.userCode = `\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, \"coords\", this.op)};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, \"coords\", this.op)} = idx;\n          val ${this.op}= getX(${getCoords2(rank, \"coords\", this.op)});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n};\nfunction getCoords2(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\nfunction getFinalCoord(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js\nfunction cumImpl(op2, x, backend2, axis, exclusive, reverse5) {\n  const xRank = x.shape.length;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity3({ inputs: { x: permutedX }, backend: backend2 });\n  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {\n    const program = new CumProgram(op2, permutedX.shape, false, reverse5);\n    const customValues = [[i2]];\n    const prevResult = result;\n    result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);\n    backend2.disposeIntermediateTensorInfo(prevResult);\n  }\n  if (exclusive) {\n    const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5);\n    const prevResult = result;\n    result = backend2.runWebGLProgram(program, [result], result.dtype);\n    backend2.disposeIntermediateTensorInfo(prevResult);\n  }\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo(permutedX);\n    return reverseTransposedResult;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js\nfunction cumprod3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig2 = {\n  kernelName: Cumprod,\n  backendName: \"webgl\",\n  kernelFunc: cumprod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js\nfunction cumsum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig2 = {\n  kernelName: Cumsum,\n  backendName: \"webgl\",\n  kernelFunc: cumsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js\nfunction denseBincount3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size, binaryOutput } = attrs;\n  if (x.shape.length === 1) {\n    const xVals = backend2.readSync(x.dataId);\n    const weightsVals = backend2.readSync(weights.dataId);\n    const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n    return backend2.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend2.bufferSync(x);\n    const weightsBuf = backend2.bufferSync(weights);\n    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);\n    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig2 = {\n  kernelName: DenseBincount,\n  backendName: \"webgl\",\n  kernelFunc: denseBincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js\nvar DepthToSpaceProgram = class {\n  constructor(outputShape, blockSize, dataFormat) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n  getHeightCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n  getWidthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n  getDepthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n  getOutputDepthSize() {\n    if (this.dataFormat === \"NHWC\") {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n  getInputSamplingString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js\nfunction depthToSpace3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar depthToSpaceConfig2 = {\n  kernelName: DepthToSpace,\n  backendName: \"webgl\",\n  kernelFunc: depthToSpace3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js\nvar DepthwiseConv2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation2}\n          }\n        `;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js\nvar DepthwiseConvPacked2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    const padLeft = convInfo.padInfo.left;\n    const strideWidth = convInfo.strideWidth;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = filterWidth;\n    let mainLoop = `\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          vec4 xTexelC${c * 2};\n          int xTexelC${c * 2}Ready;\n          vec4 xTexelC${c * 2 + 1};\n          int xTexelC${c * 2 + 1}Ready;\n          vec4 xC${c};`;\n    }\n    mainLoop += `\n    for (int r = 0; r < ${filterHeight}; r++) {\n      `;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          xTexelC${c * 2} = vec4(0.0);\n          xTexelC${c * 2}Ready = 0;\n          xTexelC${c * 2 + 1} = vec4(0.0);\n          xTexelC${c * 2 + 1}Ready = 0;\n          xC${c} = vec4(0.0);`;\n    }\n    mainLoop += `\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;\n    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n      const colIndex = texelC * 2;\n      mainLoop += `\n          xC = xCCorner + ${colIndex * dilationWidth};\n          `;\n      if (strideWidth === 1) {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n              `;\n            if (dilationWidth === 1 && colIndex > 0) {\n              mainLoop += `\n                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n                `;\n            } else {\n              mainLoop += `\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n                  } else {\n                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n                  }\n                  `;\n            }\n          } else {\n            mainLoop += `\n                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                xC${colIndex} = xTexelC${colIndex};\n                `;\n          }\n          if (colIndex + 1 < filterWidth) {\n            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n              mainLoop += `\n                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${colIndex + 1}.zw = vec2(0.0);\n                    }\n                    xTexelC${colIndex + 1}Ready = 1;\n                  }\n                  `;\n              if (dilationWidth > 1) {\n                mainLoop += `\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                      xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${colIndex}Ready = 1;\n                    }\n                    `;\n              }\n              mainLoop += `\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n                  `;\n            } else {\n              if (nextTexelOffset === 1) {\n                mainLoop += `\n                    xC${colIndex + 1} = xTexelC${colIndex};\n                    `;\n              } else {\n                mainLoop += `\n                    xCOffset = xC + ${nextTexelOffset};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${colIndex + 1}.zw = vec2(0.0);\n                      }\n                      xTexelC${colIndex + 1}Ready = 1;\n                    }\n\n                    xC${colIndex + 1} = xTexelC${colIndex + 1};\n                    `;\n              }\n            }\n          }\n        }\n      } else {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${colIndex + 1}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex + 1}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n              `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n                `;\n            }\n          } else {\n            mainLoop += `\n                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex + 1}.zw = vec2(0.);\n                  }\n                  xTexelC${colIndex + 1}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(\n                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n              `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n                `;\n            }\n          }\n        }\n      }\n      if (colIndex < filterWidth) {\n        mainLoop += `\n            wTexel = getW(r, ${colIndex}, d1, q);\n            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);\n          `;\n        if (colIndex + 1 < filterWidth) {\n          mainLoop += `\n              wTexel = getW(r, ${colIndex + 1}, d1, q);\n              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);\n            `;\n        }\n      }\n    }\n    mainLoop += `\n    }\n  `;\n    mainLoop += `\n      }\n    `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation2}\n        }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${mainLoop}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  let program;\n  if (env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {\n    program = new DepthwiseConvPacked2DProgram(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo);\n  }\n  const customValues = [\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inHeight, convInfo.inWidth]\n  ];\n  return backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n}\nvar depthwiseConv2dNativeConfig2 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNative2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js\nvar DepthwiseConv2DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar DepthwiseConv2DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);\n  const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropFilterConfig2 = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNativeBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const program = new DepthwiseConv2DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropInputConfig2 = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNativeBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js\nvar DiagProgram = class {\n  constructor(size) {\n    this.variableNames = [\"X\"];\n    this.outputShape = [size, size];\n    this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js\nfunction diag3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const outShape = [...x.shape, ...x.shape];\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });\n  const program = new DiagProgram(xSize);\n  const res = backend2.runWebGLProgram(program, [flat], flat.dtype);\n  const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeIntermediateTensorInfo(flat);\n  backend2.disposeIntermediateTensorInfo(res);\n  return out;\n}\nvar diagConfig2 = {\n  kernelName: Diag,\n  backendName: \"webgl\",\n  kernelFunc: diag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js\nvar Dilation2DProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;\n    const { top: padTop, left: padLeft } = padInfo;\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${filterHeight}; h++) {\n          int hIn = hBeg + h * ${dilationHeight};\n\n          if (hIn >= 0 && hIn < ${inHeight}) {\n            for (int w = 0; w < ${filterWidth}; w++) {\n              int wIn = wBeg + w * ${dilationWidth};\n\n              if (wIn >= 0 && wIn < ${inWidth}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js\nfunction dilation2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n  let out;\n  const program = new Dilation2DProgram(convInfo);\n  out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeIntermediateTensorInfo(out);\n  return outReshaped;\n}\nvar dilation2DConfig2 = {\n  kernelName: Dilation2D,\n  backendName: \"webgl\",\n  kernelFunc: dilation2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js\nfunction einsum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    for (const idTerm of steps[i2]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiply3({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i2 < nSteps - 1) {\n      if (path[i2] >= 0) {\n        out = sum4({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i2] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n  return out;\n}\nvar einsumConfig2 = {\n  kernelName: Einsum,\n  backendName: \"webgl\",\n  kernelFunc: einsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js\nvar ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar ELU_PACKED = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nvar elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });\nvar eluConfig2 = {\n  kernelName: Elu,\n  backendName: \"webgl\",\n  kernelFunc: elu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js\nvar ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nvar ELU_DER_PACKED = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nvar eluGrad2 = (args) => {\n  const { inputs, backend: backend2 } = args;\n  const { dy, y } = inputs;\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);\n  return backend2.runWebGLProgram(program, [dy, y], dy.dtype);\n};\nvar eluGradConfig3 = {\n  kernelName: EluGrad,\n  backendName: \"webgl\",\n  kernelFunc: eluGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js\nvar PACKED_EQUAL = `\n  return vec4(equal(a, b));\n`;\nvar EQUAL = `return float(a == b);`;\nvar equal3 = binaryKernelFunc2({\n  opSnippet: EQUAL,\n  packedOpSnippet: PACKED_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: equalImplCPU\n});\nvar equalConfig2 = {\n  kernelName: Equal,\n  backendName: \"webgl\",\n  kernelFunc: equal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js\nvar ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util_exports.ERF_P};\n  float a1 = ${backend_util_exports.ERF_A1};\n  float a2 = ${backend_util_exports.ERF_A2};\n  float a3 = ${backend_util_exports.ERF_A3};\n  float a4 = ${backend_util_exports.ERF_A4};\n  float a5 = ${backend_util_exports.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nvar erf3 = unaryKernelFunc2({ opSnippet: ERF });\nvar erfConfig2 = {\n  kernelName: Erf,\n  backendName: \"webgl\",\n  kernelFunc: erf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js\nvar EXP = CHECK_NAN_SNIPPET_UNARY + `\n  return exp(x);\n`;\nvar EXP_PACKED = `\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar exp3 = unaryKernelFunc2({\n  opSnippet: EXP,\n  packedOpSnippet: EXP_PACKED,\n  cpuKernelImpl: expImplCPU,\n  dtype: \"float32\"\n});\nvar expConfig2 = {\n  kernelName: Exp,\n  backendName: \"webgl\",\n  kernelFunc: exp3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js\nfunction expandDims4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { dim } = attrs;\n  const { input: input2 } = inputs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig2 = {\n  kernelName: ExpandDims,\n  backendName: \"webgl\",\n  kernelFunc: expandDims4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js\nvar EXPM1 = `return exp(x) - 1.0;`;\nvar expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });\nvar expm1Config2 = {\n  kernelName: Expm1,\n  backendName: \"webgl\",\n  kernelFunc: expm13\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js\nvar FFTProgram = class {\n  constructor(component, inputShape, inverse) {\n    this.variableNames = [\"real\", \"imag\"];\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n    const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : \"1.0\";\n    let opString;\n    if (component === \"real\") {\n      opString = \"return real * expR - imag * expI;\";\n    } else if (component === \"imag\") {\n      opString = \"return real * expI + imag * expR;\";\n    } else {\n      throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n    }\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js\nfunction fftImpl2(x, inverse, backend2) {\n  const xData = backend2.texData.get(x.dataId);\n  const inputSize = util_exports.sizeFromShape(x.shape);\n  const innerDimensionSize = x.shape[x.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });\n  const xShape = input2D.shape;\n  const realProgram = new FFTProgram(\"real\", xShape, inverse);\n  const imagProgram = new FFTProgram(\"imag\", xShape, inverse);\n  const inputs = [\n    {\n      dataId: xData.complexTensorInfos.real.dataId,\n      dtype: xData.complexTensorInfos.real.dtype,\n      shape: xShape\n    },\n    {\n      dataId: xData.complexTensorInfos.imag.dataId,\n      dtype: xData.complexTensorInfos.imag.dtype,\n      shape: xShape\n    }\n  ];\n  const realPart = backend2.runWebGLProgram(realProgram, inputs, \"float32\");\n  const imagPart = backend2.runWebGLProgram(imagProgram, inputs, \"float32\");\n  const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(realPart);\n  backend2.disposeIntermediateTensorInfo(imagPart);\n  const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(complexOutput);\n  return complexOutputReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js\nfunction fft3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  return fftImpl2(input2, false, backend2);\n}\nvar fftConfig2 = {\n  kernelName: FFT,\n  backendName: \"webgl\",\n  kernelFunc: fft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js\nvar FillProgram = class {\n  constructor(shape, value) {\n    this.outputShape = [];\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.variableNames = [\"x\"];\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js\nfunction fill3(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value } = attrs;\n  let { dtype } = attrs;\n  dtype = dtype || util_exports.inferDtype(value);\n  if (dtype === \"string\") {\n    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n    values.fill(value);\n    return backend2.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram(shape, value);\n    const customValues = [[value]];\n    return backend2.runWebGLProgram(program, [], dtype, customValues);\n  }\n}\nvar fillConfig2 = {\n  kernelName: Fill,\n  backendName: \"webgl\",\n  kernelFunc: fill3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js\nvar FlipLeftRightProgram = class {\n  constructor(imageShape) {\n    this.variableNames = [\"Image\"];\n    this.outputShape = [];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig2 = {\n  kernelName: FlipLeftRight,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const webglBackend = backend2;\n    const program = new FlipLeftRightProgram(image2.shape);\n    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js\nvar FLOOR = `return floor(x);`;\nvar floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });\nvar floorConfig2 = {\n  kernelName: Floor,\n  backendName: \"webgl\",\n  kernelFunc: floor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js\nvar INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nvar INT_DIV_PACKED = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nvar floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: \"int32\" });\nvar floorDivConfig2 = {\n  kernelName: FloorDiv,\n  backendName: \"webgl\",\n  kernelFunc: floorDiv3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js\nvar FromPixelsProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    const glsl = getGlslDifferences();\n    const [height, width] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js\nvar FromPixelsPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    const glsl = getGlslDifferences();\n    const [height, width] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js\nvar fromPixelsConfig = {\n  kernelName: FromPixels,\n  backendName: \"webgl\",\n  kernelFunc: fromPixels2\n};\nvar fromPixels2DContext2;\nfunction fromPixels2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  let { pixels } = inputs;\n  const { numChannels } = attrs;\n  const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n  const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  const texShape = [height, width];\n  const outShape = [height, width, numChannels];\n  if (isImage || isVideo) {\n    if (fromPixels2DContext2 == null) {\n      fromPixels2DContext2 = document.createElement(\"canvas\").getContext(\"2d\");\n    }\n    fromPixels2DContext2.canvas.width = width;\n    fromPixels2DContext2.canvas.height = height;\n    fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);\n    pixels = fromPixels2DContext2.canvas;\n  }\n  const tempPixelHandle = backend2.makeTensorInfo(texShape, \"int32\");\n  backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n  backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);\n  const program = env().getBool(\"WEBGL_PACK\") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);\n  const res = backend2.runWebGLProgram(program, [tempPixelHandle], \"int32\");\n  backend2.disposeData(tempPixelHandle.dataId);\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js\nfunction fusedConv2d(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  let out;\n  const intermediates = [];\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    out = conv2dByMatMul({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else if (env().getBool(\"WEBGL_CONV_IM2COL\") && x.shape[0] === 1) {\n    out = conv2dWithIm2Row({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else {\n    const hasBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;\n    const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const inputs2 = [x, filter];\n    const alignInputWithDataFormat = (input2, dataFormat2) => {\n      if (dataFormat2 === \"NCHW\" && input2.shape.length === 1 && input2.shape[0] !== 1) {\n        const alignedInput = reshape4({\n          inputs: { x: input2 },\n          backend: backend2,\n          attrs: { shape: [input2.shape[0], 1, 1] }\n        });\n        intermediates.push(alignedInput);\n        return alignedInput;\n      }\n      return input2;\n    };\n    if (hasBias) {\n      inputs2.push(alignInputWithDataFormat(bias, dataFormat));\n    }\n    if (hasPreluActivationWeights) {\n      inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n      inputs2.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    out = backend2.runWebGLProgram(program, inputs2, \"float32\");\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(out);\n  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return outReshaped;\n}\nvar fusedConv2DConfig2 = {\n  kernelName: FusedConv2D,\n  backendName: \"webgl\",\n  kernelFunc: fusedConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const intermediates = [];\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const shouldPackDepthwiseConv = env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;\n  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;\n  const programInputs = [x, filter];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n    programInputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  let program;\n  if (shouldPackDepthwiseConv) {\n    program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  }\n  const customValues = [\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inHeight, convInfo.inWidth]\n  ];\n  const result = backend2.runWebGLProgram(program, programInputs, \"float32\", customValues);\n  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar fusedDepthwiseConv2DConfig2 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"webgl\",\n  kernelFunc: fusedDepthwiseConv2D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js\nvar GatherNDProgram = class {\n  constructor(sliceDim, strides, shape) {\n    this.sliceDim = sliceDim;\n    this.strides = strides;\n    this.variableNames = [\"x\", \"indices\"];\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? \"strides[j]\" : \"strides\";\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js\nfunction gatherNd2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n  const flattenX = reshape4({\n    inputs: { x: params },\n    backend: backend2,\n    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n  });\n  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n    const indicesData = backend2.readSync(indices.dataId);\n    const paramsBuf = backend2.bufferSync(params);\n    const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n  backend2.disposeIntermediateTensorInfo(flattenIndices);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\nvar gatherNdConfig2 = {\n  kernelName: GatherNd,\n  backendName: \"webgl\",\n  kernelFunc: gatherNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js\nvar GatherProgram = class {\n  constructor(aShape, outputShape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords2(aShape, 2);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${sourceCoords}));\n      }\n    `;\n  }\n};\nfunction getSourceCoords2(aShape, axis) {\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < aShape.length; i2++) {\n    if (i2 === 2) {\n      sourceCoords.push(\"index\");\n    } else {\n      sourceCoords.push(`${currentCoords[i2]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js\nfunction gatherV22(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  if (env().get(\"DEBUG\")) {\n    const indicesVals = backend2.readSync(indices.dataId);\n    const axisDim = x.shape[parsedAxis];\n    for (let i2 = 0; i2 < indicesVals.length; ++i2) {\n      const index = indicesVals[i2];\n      util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n    }\n  }\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const toDispose = [];\n  const flattenX = reshape4({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape4({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(flattenIndex);\n    const xBuf = backend2.bufferSync(flattenX);\n    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);\n    toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new GatherProgram(flattenX.shape, flattenOutputShape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return reshaped;\n}\nvar gatherV2Config2 = {\n  kernelName: GatherV2,\n  backendName: \"webgl\",\n  kernelFunc: gatherV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js\nvar GREATER = `return float(a > b);`;\nvar GREATER_PACKED = `\n  return vec4(greaterThan(a, b));\n`;\nvar greater4 = binaryKernelFunc2({\n  opSnippet: GREATER,\n  packedOpSnippet: GREATER_PACKED,\n  cpuKernelImpl: greaterImplCPU,\n  dtype: \"bool\"\n});\nvar greaterConfig2 = {\n  kernelName: Greater,\n  backendName: \"webgl\",\n  kernelFunc: greater4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js\nvar GREATER_EQUAL = `return float(a >= b);`;\nvar GREATER_EQUAL_PACKED = `\n  return vec4(greaterThanEqual(a, b));\n`;\nvar greaterEqual3 = binaryKernelFunc2({\n  opSnippet: GREATER_EQUAL,\n  packedOpSnippet: GREATER_EQUAL_PACKED,\n  dtype: \"bool\",\n  cpuKernelImpl: greaterEqualImplCPU\n});\nvar greaterEqualConfig2 = {\n  kernelName: GreaterEqual,\n  backendName: \"webgl\",\n  kernelFunc: greaterEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js\nfunction ifft3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  return fftImpl2(input2, true, backend2);\n}\nvar ifftConfig2 = {\n  kernelName: IFFT,\n  backendName: \"webgl\",\n  kernelFunc: ifft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js\nvar IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nvar isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: \"bool\" });\nvar isFiniteConfig2 = {\n  kernelName: IsFinite,\n  backendName: \"webgl\",\n  kernelFunc: isFinite4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js\nvar IS_INF = `return float(isinf(x));`;\nvar isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: \"bool\" });\nvar isInfConfig2 = {\n  kernelName: IsInf,\n  backendName: \"webgl\",\n  kernelFunc: isInf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js\nvar IS_NAN = `return float(isnan(x));`;\nvar isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: \"bool\" });\nvar isNaNConfig2 = {\n  kernelName: IsNan,\n  backendName: \"webgl\",\n  kernelFunc: isNaN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js\nvar LESS = `return float(a < b);`;\nvar LESS_PACKED = `\n  return vec4(lessThan(a, b));\n`;\nvar less4 = binaryKernelFunc2({\n  opSnippet: LESS,\n  packedOpSnippet: LESS_PACKED,\n  cpuKernelImpl: lessImplCPU,\n  dtype: \"bool\"\n});\nvar lessConfig2 = {\n  kernelName: Less,\n  backendName: \"webgl\",\n  kernelFunc: less4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js\nvar LESS_EQUAL = `return float(a <= b);`;\nvar LESS_EQUAL_PACKED = `\n  return vec4(lessThanEqual(a, b));\n`;\nvar lessEqual3 = binaryKernelFunc2({\n  opSnippet: LESS_EQUAL,\n  packedOpSnippet: LESS_EQUAL_PACKED,\n  cpuKernelImpl: lessEqualImplCPU,\n  dtype: \"bool\"\n});\nvar lessEqualConfig2 = {\n  kernelName: LessEqual,\n  backendName: \"webgl\",\n  kernelFunc: lessEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js\nfunction linSpace2(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, num } = attrs;\n  const outVals = linSpaceImplCPU(start, stop, num);\n  return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig2 = {\n  kernelName: LinSpace,\n  backendName: \"webgl\",\n  kernelFunc: linSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js\nvar LOG = CHECK_NAN_SNIPPET_UNARY + `\n  return x < 0.0 ? 0./0. : log(x);\n`;\nvar LOG_PACKED = `\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`;\nvar log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });\nvar logConfig2 = {\n  kernelName: Log,\n  backendName: \"webgl\",\n  kernelFunc: log4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js\nvar LOG1P = CHECK_NAN_SNIPPET_UNARY + `\n  return log(1.0 + x);\n`;\nvar log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });\nvar log1pConfig2 = {\n  kernelName: Log1p,\n  backendName: \"webgl\",\n  kernelFunc: log1p3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js\nvar LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nvar LOGICAL_AND_PACKED = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nvar logicalAnd3 = binaryKernelFunc2({\n  opSnippet: LOGICAL_AND,\n  packedOpSnippet: LOGICAL_AND_PACKED,\n  dtype: \"bool\"\n});\nvar logicalAndConfig2 = {\n  kernelName: LogicalAnd,\n  backendName: \"webgl\",\n  kernelFunc: logicalAnd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js\nvar LOGICAL_NOT = `return float(!(x >= 1.0));`;\nvar logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });\nvar logicalNotConfig2 = {\n  kernelName: LogicalNot,\n  backendName: \"webgl\",\n  kernelFunc: logicalNot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js\nvar LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nvar LOGICAL_OR_PACKED = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nvar logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: \"bool\" });\nvar logicalOrConfig2 = {\n  kernelName: LogicalOr,\n  backendName: \"webgl\",\n  kernelFunc: logicalOr3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js\nvar LRNProgram = class {\n  constructor(xShape, radius, bias, alpha, beta) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js\nvar LRNPackedProgram = class {\n  constructor(xShape, radius, bias, alpha, beta) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js\nvar lrn = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n};\nvar LRNConfig2 = {\n  kernelName: LRN,\n  backendName: \"webgl\",\n  kernelFunc: lrn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js\nvar LRNGradProgram = class {\n  constructor(inputShape, depthRadius, bias, alpha, beta) {\n    this.variableNames = [\"inputImage\", \"outputImage\", \"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js\nvar lrnGrad = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, y, dy } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);\n};\nvar LRNGradConfig2 = {\n  kernelName: LRNGrad,\n  backendName: \"webgl\",\n  kernelFunc: lrnGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js\nfunction maxImpl2(x, reduceShape, outShape, backend2) {\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const reduced = reduce(reshapedInput, x.dtype, \"max\", backend2);\n  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js\nfunction max4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  const maxInputIsTransposed = permutedAxes != null;\n  const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n  let maxInput = x;\n  if (maxInputIsTransposed) {\n    if (shouldExecuteOnCPU) {\n      const xTexData = backend2.texData.get(maxInput.dataId);\n      const values = xTexData.values;\n      const newShape = new Array(xRank);\n      for (let i2 = 0; i2 < newShape.length; i2++) {\n        newShape[i2] = x.shape[permutedAxes[i2]];\n      }\n      const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n      maxInput = backend2.makeTensorInfo(newShape, x.dtype);\n      const maxInputData = backend2.texData.get(maxInput.dataId);\n      maxInputData.values = maxInputValues;\n    } else {\n      maxInput = transposeImpl2(x, permutedAxes, backend2);\n    }\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);\n  let outShape = maxOutShape;\n  if (keepDims) {\n    outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n  }\n  let out;\n  if (shouldExecuteOnCPU) {\n    const xTexData = backend2.texData.get(maxInput.dataId);\n    const values = xTexData.values;\n    const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);\n    out = backend2.makeTensorInfo(outShape, x.dtype);\n    const outData = backend2.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = maxImpl2(maxInput, reduceShape, outShape, backend2);\n  }\n  if (maxInputIsTransposed) {\n    backend2.disposeIntermediateTensorInfo(maxInput);\n  }\n  return out;\n}\nvar maxConfig2 = {\n  kernelName: Max,\n  backendName: \"webgl\",\n  kernelFunc: max4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js\nvar MAXIMUM = CHECK_NAN_SNIPPET2 + `\n  return max(a, b);\n`;\nvar MAXIMUM_PACKED = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar maximum4 = binaryKernelFunc2({\n  opSnippet: MAXIMUM,\n  packedOpSnippet: MAXIMUM_PACKED,\n  cpuKernelImpl: maximumImplCPU\n});\nvar maximumConfig2 = {\n  kernelName: Maximum,\n  backendName: \"webgl\",\n  kernelFunc: maximum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js\nfunction maxPool3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex2(x, \"maxPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const maxPoolProgram = new Pool2DProgram(convInfo, \"max\", false);\n  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPoolConfig2 = {\n  kernelName: MaxPool,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js\nfunction maxPool3d2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n  const maxPoolProgram = new Pool3DProgram(convInfo, \"max\", false);\n  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPool3DConfig2 = {\n  kernelName: MaxPool3D,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js\nvar MaxPool2DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"maxPos\"];\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar MaxPool3DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"maxPos\"];\n    this.outputShape = convInfo.inShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, \"max\", true);\n  const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);\n  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);\n  const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);\n  backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);\n  return result;\n}\nvar maxPool3DGradConfig3 = {\n  kernelName: MaxPool3DGrad,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2, output } = inputs;\n  const x = input2;\n  assertNotComplex2([input2, output], \"maxPoolGrad\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const getPositions = true;\n  const maxPoolPositionsProgram = new Pool2DProgram(convInfo, \"max\", getPositions);\n  const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n  const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);\n  backend2.disposeIntermediateTensorInfo(maxPoolPositions2);\n  return result;\n}\nvar maxPoolGradConfig3 = {\n  kernelName: MaxPoolGrad,\n  backendName: \"webgl\",\n  kernelFunc: maxPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {\n  let program = new Pool2DProgram(convInfo, \"max\", false);\n  const poolOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n  program = new Pool2DProgram(convInfo, \"max\", true, true, includeBatchInIndex);\n  const indexOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n  return [poolOutput, indexOutput];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig2 = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n    const webglBackend = backend2;\n    util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n    const dilations = [1, 1];\n    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3);\n    const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);\n    return [result, indexes];\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js\nfunction meanImpl(x, reduceShape, outShape, backend2) {\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const reduced = reduce(reshapedInput, \"float32\", \"mean\", backend2);\n  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js\nvar meanConfig2 = {\n  kernelName: Mean,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { keepDims, axis } = attrs;\n    const webglBackend = backend2;\n    const xRank = x.shape.length;\n    const origAxes = util_exports.parseAxisParam(axis, x.shape);\n    let axes = origAxes;\n    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n    const meanInputIsTransposed = permutedAxes != null;\n    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n    const intermediates = [];\n    let meanInput = x;\n    if (meanInputIsTransposed) {\n      if (shouldExecuteOnCPU) {\n        const xTexData = webglBackend.texData.get(meanInput.dataId);\n        const values = xTexData.values;\n        const newShape = new Array(xRank);\n        for (let i2 = 0; i2 < newShape.length; i2++) {\n          newShape[i2] = x.shape[permutedAxes[i2]];\n        }\n        const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n        const meanInputData = webglBackend.texData.get(meanInput.dataId);\n        meanInputData.values = meanInputValues;\n      } else {\n        meanInput = transposeImpl2(x, permutedAxes, webglBackend);\n      }\n      intermediates.push(meanInput);\n      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    }\n    backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n    const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);\n    let outShape = meanOutShape;\n    if (keepDims) {\n      outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);\n    }\n    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n    for (const i2 of intermediates) {\n      webglBackend.disposeIntermediateTensorInfo(i2);\n    }\n    return out;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js\nfunction min4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"min\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar minConfig2 = {\n  kernelName: Min,\n  backendName: \"webgl\",\n  kernelFunc: min4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js\nvar MINIMUM = CHECK_NAN_SNIPPET2 + `\n  return min(a, b);\n`;\nvar MINIMUM_PACKED = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar minimum4 = binaryKernelFunc2({\n  opSnippet: MINIMUM,\n  packedOpSnippet: MINIMUM_PACKED,\n  cpuKernelImpl: minimumImplCPU\n});\nvar minimumConfig2 = {\n  kernelName: Minimum,\n  backendName: \"webgl\",\n  kernelFunc: minimum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js\nvar MirrorPadProgram = class {\n  constructor(xShape, paddings, mode) {\n    this.variableNames = [\"x\"];\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n    const offset = mode === \"reflect\" ? 0 : 1;\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js\nvar MirrorPadPackedProgram = class {\n  constructor(xShape, paddings, mode) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const coords3 = getChannels(\"rc\", rank);\n    const source = getChannels(\"source\", rank);\n    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n    const offset = mode === \"reflect\" ? 0 : 1;\n    let mainLoop = \"\";\n    if (rank === 1) {\n      const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords3[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    } else {\n      const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords3[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords3[rank - 2]} += 1;\n        if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords3[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n    }\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js\nvar mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {\n  const { x } = inputs;\n  const { paddings, mode } = attrs;\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);\n  const output = backend2.runWebGLProgram(program, [x], x.dtype);\n  return output;\n};\nvar mirrorPadConfig2 = {\n  kernelName: MirrorPad,\n  backendName: \"webgl\",\n  kernelFunc: mirrorPadKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js\nvar MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nvar MOD_PACKED = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar mod3 = binaryKernelFunc2({\n  opSnippet: MOD,\n  packedOpSnippet: MOD_PACKED\n});\nvar modConfig2 = {\n  kernelName: Mod,\n  backendName: \"webgl\",\n  kernelFunc: mod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js\nvar MultinomialProgram = class {\n  constructor(batchSize, numOutcomes, numSamples) {\n    this.variableNames = [\"probs\"];\n    this.customUniforms = [{ name: \"seed\", type: \"float\" }];\n    this.outputShape = [batchSize, numSamples];\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js\nvar DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\nvar DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nvar realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nvar realDivConfig2 = {\n  kernelName: RealDiv,\n  backendName: \"webgl\",\n  kernelFunc: realDiv\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js\nvar SUB = \"return a - b;\";\nvar sub3 = binaryKernelFunc2({\n  opSnippet: SUB,\n  packedOpSnippet: SUB,\n  supportsComplex: true,\n  cpuKernelImpl: subImplCPU\n});\nvar subConfig2 = {\n  kernelName: Sub,\n  backendName: \"webgl\",\n  kernelFunc: sub3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js\nfunction softmax4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const axes = util_exports.parseAxisParam([dim], logits.shape);\n  const maxLogit = max4({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a6 = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n  const b = exp3({ inputs: { x: a6 }, backend: backend2 });\n  const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(maxLogit);\n  backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);\n  backend2.disposeIntermediateTensorInfo(a6);\n  backend2.disposeIntermediateTensorInfo(b);\n  backend2.disposeIntermediateTensorInfo(sumExp);\n  backend2.disposeIntermediateTensorInfo(sumExpReshaped);\n  return res;\n}\nvar softmaxConfig2 = {\n  kernelName: Softmax,\n  backendName: \"webgl\",\n  kernelFunc: softmax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js\nfunction multinomial3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { numSamples, seed, normalized } = attrs;\n  const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });\n  const batchSize = probs.shape[0];\n  const numOutcomes = probs.shape[1];\n  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n  const customValues = [[seed]];\n  const res = backend2.runWebGLProgram(program, [probs], \"int32\", customValues);\n  if (!normalized) {\n    backend2.disposeIntermediateTensorInfo(probs);\n  }\n  return res;\n}\nvar multinomialConfig2 = {\n  kernelName: Multinomial,\n  backendName: \"webgl\",\n  kernelFunc: multinomial3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js\nvar NEG = CHECK_NAN_SNIPPET + `\n  return -x;\n`;\nvar NEG_PACKED = `\n  vec4 result = -x;\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nfunction neg3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = backend2.texData.get(x.dataId);\n    const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n    program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);\n  } else {\n    program = new UnaryOpProgram(x.shape, NEG);\n  }\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar negConfig2 = {\n  kernelName: Neg,\n  backendName: \"webgl\",\n  kernelFunc: neg3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV32(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config2 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV32\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV42(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n  ];\n}\nvar nonMaxSuppressionV4Config2 = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV42\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV52(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config2 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV52\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js\nvar OneHotProgram = class {\n  constructor(numIndices, depth, onValue, offValue) {\n    this.variableNames = [\"indices\"];\n    this.outputShape = [numIndices, depth];\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js\nvar oneHot3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { depth, onValue, offValue } = attrs;\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);\n  const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });\n  const result = backend2.runWebGLProgram(program, [reshaped], indices.dtype);\n  backend2.disposeIntermediateTensorInfo(reshaped);\n  const outShape = [...indices.shape, depth];\n  const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeIntermediateTensorInfo(result);\n  return out;\n};\nvar oneHotConfig2 = {\n  kernelName: OneHot,\n  backendName: \"webgl\",\n  kernelFunc: oneHot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js\nfunction zerosLike3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const r2 = zerosLike3({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill3({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === \"string\" ? \"\" : 0\n      },\n      backend: backend2\n    });\n  }\n}\nvar zerosLikeConfig2 = {\n  kernelName: ZerosLike,\n  backendName: \"webgl\",\n  kernelFunc: zerosLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js\nfunction onesLike3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported under string dtype\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const r2 = onesLike3({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n  }\n}\nvar onesLikeConfig2 = {\n  kernelName: OnesLike,\n  backendName: \"webgl\",\n  kernelFunc: onesLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js\nfunction pack2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims4({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar packConfig2 = {\n  kernelName: Pack,\n  backendName: \"webgl\",\n  kernelFunc: pack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js\nvar PadProgram = class {\n  constructor(xShape, paddings, constantValue) {\n    this.variableNames = [\"x\"];\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js\nvar PadPackedProgram = class {\n  constructor(xShape, paddings, constantValue) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const coords3 = getChannels(\"rc\", rank);\n    const source = getChannels(\"source\", rank);\n    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n    const componentSetup = [\n      `${dtype} rc = outputLoc;`,\n      `${coords3[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n      rank === 1 ? \"\" : `}\n       rc = outputLoc;\n       ${coords3[rank - 2]} += 1;\n       if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n      rank === 1 ? \"\" : `  ${coords3[rank - 1]} += 1;\n         if(${cLimit}) {`\n    ];\n    const paddingArea = rank === 1 ? \"rc < start || rc >= end\" : \"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\";\n    let mainLoop = \"\";\n    for (let i2 = 0, j = rank === 1 ? 2 : 4; i2 < j; i2++) {\n      mainLoop += `\n        ${componentSetup[i2]}\n        if (${paddingArea}) {\n          result[${i2}] = float(value);\n        } else {\n          ${dtype} source = rc - start;\n          result[${i2}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    }\n    mainLoop += rank === 1 ? `} ` : `}}`;\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js\nvar padV22 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n    return fill3({\n      backend: backend2,\n      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);\n  const customValues = [[constantValue]];\n  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n};\nvar padV2Config2 = {\n  kernelName: PadV2,\n  backendName: \"webgl\",\n  kernelFunc: padV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js\nvar POW = `\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nvar POW_PACKED = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });\nvar powConfig2 = {\n  kernelName: Pow,\n  backendName: \"webgl\",\n  kernelFunc: pow3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js\nfunction prod3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const toDispose = [];\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(permutedX);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"prod\", axes, xRank);\n  let res;\n  if (backend2.shouldExecuteOnCPU([permutedX])) {\n    const xVals = backend2.texData.get(permutedX.dataId).values;\n    const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);\n    res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n  } else {\n    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n    const outputDType = sumOutType(x.dtype);\n    const reduced = reduce(a2D, outputDType, \"prod\", backend2);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n    toDispose.push(a2D);\n    toDispose.push(reduced);\n  }\n  if (keepDims) {\n    toDispose.push(res);\n    const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);\n    res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });\n  }\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return res;\n}\nvar prodConfig2 = {\n  kernelName: Prod,\n  backendName: \"webgl\",\n  kernelFunc: prod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js\nvar range4 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImplCPU(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig2 = {\n  kernelName: Range,\n  backendName: \"webgl\",\n  kernelFunc: range4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js\nvar RECIPROCAL = `return 1.0 / x;`;\nvar reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });\nvar reciprocalConfig2 = {\n  kernelName: Reciprocal,\n  backendName: \"webgl\",\n  kernelFunc: reciprocal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js\nvar RELU3 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU_PACKED = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });\nvar reluConfig2 = {\n  kernelName: Relu,\n  backendName: \"webgl\",\n  kernelFunc: relu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js\nvar RELU63 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar RELU6_PACKED = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });\nvar relu6Config2 = {\n  kernelName: Relu6,\n  backendName: \"webgl\",\n  kernelFunc: relu63\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js\nvar ResizeBilinearProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js\nvar ResizeBilinearPackedProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n  return backend2.runWebGLProgram(program, [images], \"float32\");\n}\nvar resizeBilinearConfig2 = {\n  kernelName: ResizeBilinear,\n  backendName: \"webgl\",\n  kernelFunc: resizeBilinear3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js\nvar ResizeBilinearBackpropProgram = class {\n  constructor(dyShape, inputShape, alignCorners) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n    const effectiveXSize = [\n      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n    ];\n    const effectiveYSize = [\n      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n    ];\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n    const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n    const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);\n  return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeBilinearGradConfig3 = {\n  kernelName: ResizeBilinearGrad,\n  backendName: \"webgl\",\n  kernelFunc: resizeBilinearGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js\nvar ResizeNearestNeighborProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js\nvar ResizeNearestNeighborPackedProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n  return backend2.runWebGLProgram(program, [images], images.dtype);\n}\nvar resizeNearestNeighborConfig2 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"webgl\",\n  kernelFunc: resizeNearestNeighbor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js\nvar ResizeNearestNeigborBackpropProgram = class {\n  constructor(dyShape, inputShape, alignCorners) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n    const effectiveXSize = [\n      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n    ];\n    const effectiveYSize = [\n      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n    ];\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n    const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n    const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);\n  return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeNearestNeighborGradConfig3 = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: \"webgl\",\n  kernelFunc: resizeNearestNeighborGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js\nvar ReverseProgram = class {\n  constructor(xShape, axis) {\n    this.variableNames = [\"x\"];\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i2) => {\n      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {\n        return `${xShape[i2]} - coords[${i2}] - 1`;\n      }\n      return `coords[${i2}]`;\n    };\n    const inCoords = xShape.map((_, i2) => getInCoord(i2)).join(\",\");\n    const type = getCoordsDataType(rank);\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js\nvar ReversePackedProgram = class {\n  constructor(xShape, axis) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    const channels = getChannels(\"rc\", rank);\n    const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n    const type = getCoordsDataType(rank);\n    if (rank === 1) {\n      this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n    } else {\n      this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n    }\n    function getR(channels2) {\n      return getChannel(channels2);\n    }\n    function getG(channels2) {\n      channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getB(channels2) {\n      channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getA(channels2) {\n      channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n      channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getChannel(channels2) {\n      const inCoordsArray = xShape.map((_, i2) => getInCoord(i2, channels2));\n      const inCoords = inCoordsArray.join(\",\");\n      const innerDims = inCoordsArray.slice(-2).join(\",\");\n      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n    }\n    function getInCoord(i2, channels1) {\n      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {\n        return `${xShape[i2]} - ${channels1[i2]} - 1`;\n      } else {\n        return `${channels1[i2]}`;\n      }\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js\nfunction reverse3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  const xRank = x.shape.length;\n  const $dims = util_exports.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar reverseConfig2 = {\n  kernelName: Reverse,\n  backendName: \"webgl\",\n  kernelFunc: reverse3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js\nvar RotateProgram = class {\n  constructor(imageShape, fillValue) {\n    this.variableNames = [\"Image\"];\n    this.outputShape = [];\n    this.customUniforms = [{ name: \"params\", type: \"vec4\" }];\n    const imageHeight = imageShape[1];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n    let fillSnippet = \"\";\n    if (typeof fillValue === \"number\") {\n      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n    } else {\n      fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(\",\")});\n        float outputValue = fill[coords[3]];`;\n    }\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig2 = {\n  kernelName: RotateWithOffset,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const webglBackend = backend2;\n    const program = new RotateProgram(image2.shape, fillValue);\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n    const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];\n    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js\nvar ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nvar round4 = unaryKernelFunc2({ opSnippet: ROUND });\nvar roundConfig2 = {\n  kernelName: Round,\n  backendName: \"webgl\",\n  kernelFunc: round4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js\nvar RSQRT = `return inversesqrt(x);`;\nvar rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });\nvar rsqrtConfig2 = {\n  kernelName: Rsqrt,\n  backendName: \"webgl\",\n  kernelFunc: rsqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js\nvar ScatterProgram = class {\n  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n    this.variableNames = [\"updates\", \"indices\", \"defaultValue\"];\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = \"\";\n    if (indicesRank === 1) {\n      indicesString = \"i\";\n    } else if (indicesRank === 2) {\n      indicesString = \"i, j\";\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n    let updatesString = \"\";\n    if (updatesRank === 1) {\n      updatesString = \"i\";\n    } else if (updatesRank === 2) {\n      updatesString = \"i, coords[1]\";\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n    const strideString = sliceDim > 1 ? \"strides[j]\" : \"strides\";\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js\nfunction scatterNd2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  if (outputSize === 0) {\n    return backend2.makeTensorInfo(shape, indices.dtype);\n  }\n  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n  const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n  const defaultValue = backend2.makeTensorInfo([], \"float32\", new Float32Array([0]));\n  const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n  backend2.disposeIntermediateTensorInfo(flattenIndices);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(res);\n  backend2.disposeIntermediateTensorInfo(defaultValue);\n  return reshaped;\n}\nvar scatterNdConfig2 = {\n  kernelName: ScatterNd,\n  backendName: \"webgl\",\n  kernelFunc: scatterNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js\nvar SearchSortedProgram = class {\n  constructor(batchSize, numInputs, numValues, side) {\n    this.variableNames = [\"sortedSequence\", \"values\"];\n    this.customUniforms = [{ name: \"numInputs\", type: \"int\" }];\n    this.outputShape = [batchSize, numValues];\n    const webGL2LoopHead = \"while (left < right) {\";\n    const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;\n    const loopHead = env().getNumber(\"WEBGL_VERSION\") === 2 ? webGL2LoopHead : webGL1LoopHead;\n    const boundComparator = side === \"left\" ? \"<\" : \"<=\";\n    this.userCode = `\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${loopHead}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${boundComparator} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js\nfunction searchSorted3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sortedSequence, values } = inputs;\n  const { side } = attrs;\n  const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n  const customValues = [[sortedSequence.shape[1]]];\n  return backend2.runWebGLProgram(program, [sortedSequence, values], \"int32\", customValues);\n}\nvar searchSortedConfig2 = {\n  kernelName: SearchSorted,\n  backendName: \"webgl\",\n  kernelFunc: searchSorted3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js\nvar SelectProgram = class {\n  constructor(cRank, shape, rank) {\n    this.variableNames = [\"c\", \"a\", \"b\"];\n    this.outputShape = shape;\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i2 = 0; i2 < shape.length; i2++) {\n        abCoordVars.push(`${currentCoords[i2]}`);\n        if (i2 < cRank) {\n          cCoordVars.push(`${currentCoords[i2]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n    const dtype = getCoordsDataType(rank);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js\nfunction select3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  const program = new SelectProgram(condition.shape.length, t2.shape, t2.shape.length);\n  return backend2.runWebGLProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));\n}\nvar selectConfig2 = {\n  kernelName: Select,\n  backendName: \"webgl\",\n  kernelFunc: select3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js\nvar SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};\n  float scale = ${backend_util_exports.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nvar selu3 = unaryKernelFunc2({ opSnippet: SELU });\nvar seluConfig2 = {\n  kernelName: Selu,\n  backendName: \"webgl\",\n  kernelFunc: selu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js\nvar SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`;\nvar SIGMOID_PACKED = `\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar sigmoid3 = unaryKernelFunc2({\n  opSnippet: SIGMOID3,\n  packedOpSnippet: SIGMOID_PACKED,\n  cpuKernelImpl: sigmoidImplCPU\n});\nvar sigmoidConfig2 = {\n  kernelName: Sigmoid,\n  backendName: \"webgl\",\n  kernelFunc: sigmoid3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js\nvar SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nvar sign3 = unaryKernelFunc2({ opSnippet: SIGN });\nvar signConfig2 = {\n  kernelName: Sign,\n  backendName: \"webgl\",\n  kernelFunc: sign3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js\nvar SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nvar sin3 = unaryKernelFunc2({ opSnippet: SIN });\nvar sinConfig2 = {\n  kernelName: Sin,\n  backendName: \"webgl\",\n  kernelFunc: sin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js\nvar SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar sinh3 = unaryKernelFunc2({ opSnippet: SINH });\nvar sinhConfig2 = {\n  kernelName: Sinh,\n  backendName: \"webgl\",\n  kernelFunc: sinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js\nvar SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nvar softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });\nvar softplusConfig2 = {\n  kernelName: Softplus,\n  backendName: \"webgl\",\n  kernelFunc: softplus3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const toDispose = [];\n  const paddedX = padV22({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n  const paddedXT = transpose3({\n    inputs: { x: reshapedPaddedX },\n    backend: backend2,\n    attrs: { perm: permutedReshapedPaddedPermutation }\n  });\n  const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n};\nvar spaceToBatchNDConfig2 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"webgl\",\n  kernelFunc: spaceToBatchND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n         ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n         ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n         ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n  const $indices = backend2.readSync(indices.dataId);\n  const $values = backend2.readSync(values.dataId);\n  const $denseShape = backend2.readSync(denseShape.dataId);\n  const $defaultValue = backend2.readSync(defaultValue.dataId)[0];\n  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n  return [\n    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n    backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n  ];\n}\nvar sparseFillEmptyRowsConfig2 = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"webgl\",\n  kernelFunc: sparseFillEmptyRows3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js\nfunction sparseReshape3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const $inputShape = Array.from(backend2.readSync(inputShape.dataId));\n  const $inputIndices = backend2.readSync(inputIndices.dataId);\n  const targetShape = Array.from(backend2.readSync(newShape.dataId));\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n  return [\n    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n  ];\n}\nvar sparseReshapeConfig2 = {\n  kernelName: SparseReshape,\n  backendName: \"webgl\",\n  kernelFunc: sparseReshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n              ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n              ${segmentIds.shape}`);\n  }\n  const $data = backend2.readSync(data.dataId);\n  const $indices = backend2.readSync(indices.dataId);\n  const $segmentIds = backend2.readSync(segmentIds.dataId);\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig2 = {\n  kernelName: SparseSegmentMean,\n  backendName: \"webgl\",\n  kernelFunc: sparseSegmentMean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n             ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n             ${segmentIds.shape}`);\n  }\n  const $data = backend2.readSync(data.dataId);\n  const $indices = backend2.readSync(indices.dataId);\n  const $segmentIds = backend2.readSync(segmentIds.dataId);\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig2 = {\n  kernelName: SparseSegmentSum,\n  backendName: \"webgl\",\n  kernelFunc: sparseSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js\nfunction sparseToDense3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  if (sparseValues.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(sparseIndices);\n    const updatesBuf = backend2.bufferSync(sparseValues);\n    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n    const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);\n  const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });\n  backend2.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\nvar sparseToDenseConfig2 = {\n  kernelName: SparseToDense,\n  backendName: \"webgl\",\n  kernelFunc: sparseToDense3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js\nfunction splitV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s2;\n    const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s2;\n    return sliceT;\n  });\n}\nvar splitVConfig2 = {\n  kernelName: SplitV,\n  backendName: \"webgl\",\n  kernelFunc: splitV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js\nvar SQRT = `return sqrt(x);`;\nvar sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });\nvar sqrtConfig2 = {\n  kernelName: Sqrt,\n  backendName: \"webgl\",\n  kernelFunc: sqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js\nvar SQUARE = `return x * x;`;\nvar square3 = unaryKernelFunc2({ opSnippet: SQUARE });\nvar squareConfig2 = {\n  kernelName: Square,\n  backendName: \"webgl\",\n  kernelFunc: square3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js\nvar SQUARED_DIFFERENCE = \"return (a - b) * (a - b);\";\nvar squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nvar squaredDifferenceConfig2 = {\n  kernelName: SquaredDifference,\n  backendName: \"webgl\",\n  kernelFunc: squaredDifference3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js\nfunction step3({ inputs, attrs, backend: backend2 }) {\n  const { x } = inputs;\n  const opSnippet = CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${attrs.alpha});\n  `;\n  const program = new UnaryOpProgram(x.shape, opSnippet);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar stepConfig2 = {\n  kernelName: Step,\n  backendName: \"webgl\",\n  kernelFunc: step3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js\nvar StridedSliceProgram = class {\n  constructor(begin, strides, size) {\n    this.variableNames = [\"x\"];\n    this.outputShape = size;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(size.length);\n    let newCoords = \"\";\n    if (rank === 1) {\n      newCoords = \"coords * strides + begin\";\n    } else {\n      let outputAxis = 0;\n      newCoords = size.map((_, i2) => {\n        outputAxis++;\n        return size.length === 1 ? `coords * strides[${i2}] + begin[${i2}]` : `coords[${outputAxis - 1}] * strides[${i2}] + begin[${i2}]`;\n      }).join(\",\");\n    }\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js\nfunction stridedSlice3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      const values = backend2.readSync(x.dataId);\n      const xBuf = buffer(x.shape, x.dtype, values);\n      const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);\n      result = backend2.runWebGLProgram(program, [x], x.dtype);\n    }\n  }\n  const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar stridedSliceConfig2 = {\n  kernelName: StridedSlice,\n  backendName: \"webgl\",\n  kernelFunc: stridedSlice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js\nfunction stringNGrams3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig2 = {\n  kernelName: StringNGrams,\n  backendName: \"webgl\",\n  kernelFunc: stringNGrams3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js\nfunction stringSplit3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { skipEmpty } = attrs;\n  const { input: input2, delimiter } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (input2.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n  const $input = backend2.readSync(input2.dataId);\n  const $delimiter = backend2.readSync(delimiter.dataId)[0];\n  const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n    backend2.makeTensorInfo([outputSize], \"string\", values),\n    backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n  ];\n}\nvar stringSplitConfig2 = {\n  kernelName: StringSplit,\n  backendName: \"webgl\",\n  kernelFunc: stringSplit3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { numBuckets } = attrs;\n  const { input: input2 } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const $input = backend2.readSync(input2.dataId);\n  const output = stringToHashBucketFastImplCPU($input, numBuckets);\n  return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig2 = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"webgl\",\n  kernelFunc: stringToHashBucketFast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js\nvar TAN = `return tan(x);`;\nvar tan3 = unaryKernelFunc2({ opSnippet: TAN });\nvar tanConfig2 = {\n  kernelName: Tan,\n  backendName: \"webgl\",\n  kernelFunc: tan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js\nvar TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar tanh4 = unaryKernelFunc2({ opSnippet: TANH });\nvar tanhConfig2 = {\n  kernelName: Tanh,\n  backendName: \"webgl\",\n  kernelFunc: tanh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js\nvar TileProgram = class {\n  constructor(aShape, reps) {\n    this.variableNames = [\"A\"];\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[i2] * reps[i2];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords3(aShape);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n};\nfunction getSourceCoords3(aShape) {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < aShape.length; i2++) {\n    sourceCoords.push(`imod(${currentCoords[i2]}, ${aShape[i2]})`);\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js\nfunction tile4(params) {\n  const { inputs, backend: backend2, attrs } = params;\n  const { x } = inputs;\n  const { reps } = attrs;\n  if (x.dtype === \"string\" || x.shape.length > 5) {\n    const data = backend2.readSync(x.dataId);\n    const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU(buf, reps);\n    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n  const program = new TileProgram(x.shape, reps);\n  const output = backend2.runWebGLProgram(program, [x], x.dtype);\n  return output;\n}\nvar tileConfig2 = {\n  kernelName: Tile,\n  backendName: \"webgl\",\n  kernelFunc: tile4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js\nvar SwapProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.customUniforms = [\n      { name: \"n\", type: \"int\" },\n      { name: \"firstPass\", type: \"int\" },\n      { name: \"negativeInf\", type: \"float\" },\n      { name: \"dir\", type: \"int\" },\n      { name: \"inc\", type: \"int\" }\n    ];\n    this.outputShape = shape;\n    this.userCode = `\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `;\n  }\n};\nvar MergeProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.customUniforms = [\n      { name: \"n\", type: \"int\" },\n      { name: \"firstPass\", type: \"int\" },\n      { name: \"k\", type: \"int\" }\n    ];\n    this.outputShape = shape;\n    this.userCode = `\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {\n  if (tensorInfo !== null) {\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n}\nfunction roundUpToPow2(num) {\n  let pow22 = 1;\n  while (pow22 < num) {\n    pow22 *= 2;\n  }\n  return pow22;\n}\nfunction topK2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\");\n  const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\");\n  const xShape = x.shape;\n  const lastDim = xShape[xShape.length - 1];\n  if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {\n    const xVals = backend2.readSync(x.dataId);\n    const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);\n    return [\n      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n    ];\n  }\n  if (k === 0) {\n    xShape[xShape.length - 1] = 0;\n    return [\n      backend2.makeTensorInfo(xShape, x.dtype, []),\n      backend2.makeTensorInfo(xShape, \"int32\", [])\n    ];\n  }\n  if (lastDim === 1) {\n    return [\n      x,\n      fill3({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n    ];\n  }\n  const xtexData = backend2.texData.get(x.dataId);\n  const xIsPacked = xtexData !== null && xtexData.isPacked;\n  const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;\n  const xSize = util_exports.sizeFromShape(xShape);\n  const batch = xSize / lastDim;\n  const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n  if (xIsPacked) {\n    disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);\n  }\n  const kPow2 = roundUpToPow2(k);\n  const lastDimPow2 = roundUpToPow2(lastDim);\n  let indices = null;\n  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n  const runSwap = (dir, inc, shape) => {\n    const inputs2 = getInputs();\n    const program = new SwapProgram(shape);\n    const fistPass = indices === null ? 1 : 0;\n    const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGLProgram(program, inputs2, \"int32\", customValues);\n    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n  };\n  for (let len = 1; len < kPow2; len *= 2) {\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, [batch, lastDimPow2]);\n    }\n  }\n  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n    const inputs2 = getInputs();\n    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);\n    const firstPass = indices === null ? 1 : 0;\n    const customValues = [[lastDim], [firstPass], [kPow2]];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGLProgram(mergeProgram, inputs2, \"int32\", customValues);\n    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n    const len = kPow2 / 2;\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, indices.shape);\n    }\n  }\n  let prevIndices = indices;\n  indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n  let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n  disposeIntermediateTensorInfoOrNull(backend2, x2D);\n  const newShape = xShape.slice(0, -1);\n  newShape.push(k);\n  prevIndices = indices;\n  indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n  const prevValues = values;\n  values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull(backend2, prevValues);\n  return [values, indices];\n}\nvar topKConfig2 = {\n  kernelName: TopK,\n  backendName: \"webgl\",\n  kernelFunc: topK2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js\nvar TransformProgram = class {\n  constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {\n    this.variableNames = [\"Image\", \"Transforms\"];\n    this.outputShape = outShape;\n    const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n    let fillModeId;\n    switch (fillMode) {\n      case \"constant\":\n        fillModeId = 1;\n        break;\n      case \"reflect\":\n        fillModeId = 2;\n        break;\n      case \"wrap\":\n        fillModeId = 3;\n        break;\n      case \"nearest\":\n        fillModeId = 4;\n        break;\n      default:\n        fillModeId = 1;\n        break;\n    }\n    this.userCode = `\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${fillModeId} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${fillValue});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${fillValue});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${imageWidth}));\n                float mapY = mapCoord(inY, float(${imageHeight}));\n\n                if (${interpolationModeId} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js\nfunction transform3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);\n  return backend2.runWebGLProgram(program, [image2, transforms], \"float32\");\n}\nvar transformConfig2 = {\n  kernelName: Transform,\n  backendName: \"webgl\",\n  kernelFunc: transform3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js\nfunction unique4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  assertNotComplex2(x, \"unique\");\n  console.warn(\"WARNING: \", \"UI might be locked temporarily as data is being downloaded\");\n  const values = backend2.readSync(x.dataId);\n  const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n  return [\n    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend2.makeTensorInfo([indices.length], \"int32\", indices)\n  ];\n}\nvar uniqueConfig2 = {\n  kernelName: Unique,\n  backendName: \"webgl\",\n  kernelFunc: unique4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js\nfunction unpack2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const x = value;\n  const xRank = x.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < xRank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = x.shape[i2];\n    }\n  }\n  const toDispose = [];\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i2 = 0; i2 < res.length; i2++) {\n    begin[axis] = i2;\n    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n    const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n    res[i2] = reshaped;\n    toDispose.push(sliced);\n  }\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return res;\n}\nvar unpackConfig2 = {\n  kernelName: Unpack,\n  backendName: \"webgl\",\n  kernelFunc: unpack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js\nvar SegmentOpProgram = class {\n  constructor(segOpInfo, segOpType) {\n    this.variableNames = [\"x\", \"segmentIds\"];\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n    const initializationValue = \"0.0\";\n    const returnValue = `sumValue`;\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n    let checkValueOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    let checkSegmentIdOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, segmentIds } = inputs;\n  const { numSegments } = attrs;\n  const xRank = x.shape.length;\n  const toDispose = [];\n  let axis = 0;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n    toDispose.push(permutedX);\n    axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  }\n  const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n  const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  toDispose.push(a2D);\n  const outputDType = sumOutType(x.dtype);\n  const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {\n    const batchSize = x2.shape[0];\n    const inSize2 = x2.shape[1];\n    const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);\n    const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };\n    const program = new SegmentOpProgram(segOpInfo, segOpType);\n    const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);\n    toDispose.push(output);\n    if (output.shape[1] === numSegments2) {\n      return output;\n    }\n    const rangeInfo = range4({\n      backend: backend2,\n      attrs: { start: 0, stop: numSegments2, step: 1, dtype: \"float32\" }\n    });\n    const tileInfo = tile4({\n      inputs: { x: rangeInfo },\n      backend: backend2,\n      attrs: { reps: [inSize2 / windowSize] }\n    });\n    toDispose.push(rangeInfo);\n    toDispose.push(tileInfo);\n    const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);\n    return result2;\n  };\n  const segOpResult = segOpCompute(a2D, \"unsortedSegmentSum\", segmentIds, outputDType, numSegments);\n  const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });\n  let result = reshaped;\n  if (permutation != null) {\n    toDispose.push(reshaped);\n    const perm = backend_util_exports.getUndoAxesPermutation(permutation);\n    result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });\n  }\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar unsortedSegmentSumConfig2 = {\n  kernelName: UnsortedSegmentSum,\n  backendName: \"webgl\",\n  kernelFunc: unsortedSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js\nvar kernelConfigs2 = [\n  _fusedMatMulConfig2,\n  absConfig2,\n  acosConfig2,\n  acoshConfig2,\n  addConfig2,\n  addNConfig2,\n  allConfig2,\n  anyConfig2,\n  argMaxConfig2,\n  argMinConfig2,\n  asinConfig2,\n  asinhConfig2,\n  atanConfig2,\n  atan2Config2,\n  atanhConfig2,\n  avgPoolConfig2,\n  avgPool3DConfig2,\n  avgPool3DGradConfig3,\n  avgPoolGradConfig3,\n  batchMatMulConfig2,\n  batchNormConfig2,\n  batchToSpaceNDConfig2,\n  bincountConfig2,\n  broadcastArgsConfig2,\n  castConfig2,\n  ceilConfig2,\n  clipByValueConfig2,\n  complexConfig2,\n  complexAbsConfig2,\n  concatConfig2,\n  conv2DConfig2,\n  conv2DBackpropFilterConfig2,\n  conv2DBackpropInputConfig2,\n  conv3DConfig2,\n  conv3DBackpropFilterV2Config2,\n  conv3DBackpropInputConfig,\n  cosConfig2,\n  coshConfig2,\n  cropAndResizeConfig2,\n  cumprodConfig2,\n  cumsumConfig2,\n  denseBincountConfig2,\n  depthToSpaceConfig2,\n  depthwiseConv2dNativeConfig2,\n  depthwiseConv2dNativeBackpropFilterConfig2,\n  depthwiseConv2dNativeBackpropInputConfig2,\n  diagConfig2,\n  dilation2DConfig2,\n  einsumConfig2,\n  eluConfig2,\n  eluGradConfig3,\n  equalConfig2,\n  erfConfig2,\n  expConfig2,\n  expandDimsConfig2,\n  expm1Config2,\n  fftConfig2,\n  fillConfig2,\n  flipLeftRightConfig2,\n  floorConfig2,\n  floorDivConfig2,\n  fromPixelsConfig,\n  fusedConv2DConfig2,\n  fusedDepthwiseConv2DConfig2,\n  gatherNdConfig2,\n  gatherV2Config2,\n  greaterConfig2,\n  greaterEqualConfig2,\n  identityConfig2,\n  ifftConfig2,\n  imagConfig2,\n  isFiniteConfig2,\n  isInfConfig2,\n  isNaNConfig2,\n  leakyReluConfig2,\n  lessConfig2,\n  lessEqualConfig2,\n  linSpaceConfig2,\n  logConfig2,\n  log1pConfig2,\n  logicalAndConfig2,\n  logicalNotConfig2,\n  logicalOrConfig2,\n  LRNConfig2,\n  LRNGradConfig2,\n  maxConfig2,\n  maximumConfig2,\n  maxPoolConfig2,\n  maxPool3DConfig2,\n  maxPool3DGradConfig3,\n  maxPoolGradConfig3,\n  maxPoolWithArgmaxConfig2,\n  meanConfig2,\n  minConfig2,\n  minimumConfig2,\n  mirrorPadConfig2,\n  modConfig2,\n  multinomialConfig2,\n  multiplyConfig2,\n  negConfig2,\n  nonMaxSuppressionV3Config2,\n  nonMaxSuppressionV4Config2,\n  nonMaxSuppressionV5Config2,\n  notEqualConfig2,\n  oneHotConfig2,\n  onesLikeConfig2,\n  packConfig2,\n  padV2Config2,\n  powConfig2,\n  preluConfig2,\n  prodConfig2,\n  rangeConfig2,\n  realConfig2,\n  realDivConfig2,\n  reciprocalConfig2,\n  reluConfig2,\n  relu6Config2,\n  reshapeConfig2,\n  resizeBilinearConfig2,\n  resizeBilinearGradConfig3,\n  resizeNearestNeighborConfig2,\n  resizeNearestNeighborGradConfig3,\n  reverseConfig2,\n  rotateWithOffsetConfig2,\n  roundConfig2,\n  rsqrtConfig2,\n  scatterNdConfig2,\n  searchSortedConfig2,\n  selectConfig2,\n  seluConfig2,\n  sigmoidConfig2,\n  signConfig2,\n  sinConfig2,\n  sinhConfig2,\n  sliceConfig2,\n  softmaxConfig2,\n  softplusConfig2,\n  spaceToBatchNDConfig2,\n  sparseFillEmptyRowsConfig2,\n  sparseReshapeConfig2,\n  sparseSegmentMeanConfig2,\n  sparseSegmentSumConfig2,\n  sparseToDenseConfig2,\n  splitVConfig2,\n  sqrtConfig2,\n  squareConfig2,\n  squaredDifferenceConfig2,\n  stepConfig2,\n  stridedSliceConfig2,\n  stringNGramsConfig2,\n  stringSplitConfig2,\n  stringToHashBucketFastConfig2,\n  subConfig2,\n  sumConfig2,\n  tanConfig2,\n  tanhConfig2,\n  tileConfig2,\n  topKConfig2,\n  transformConfig2,\n  transposeConfig2,\n  uniqueConfig2,\n  unpackConfig2,\n  unsortedSegmentSumConfig2,\n  zerosLikeConfig2\n];\nfor (const kernelConfig of kernelConfigs2) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js\nvar CppDType;\n(function(CppDType2) {\n  CppDType2[CppDType2[\"float32\"] = 0] = \"float32\";\n  CppDType2[CppDType2[\"int32\"] = 1] = \"int32\";\n  CppDType2[CppDType2[\"bool\"] = 2] = \"bool\";\n  CppDType2[CppDType2[\"string\"] = 3] = \"string\";\n  CppDType2[CppDType2[\"complex64\"] = 4] = \"complex64\";\n})(CppDType || (CppDType = {}));\nvar FusableActivation;\n(function(FusableActivation2) {\n  FusableActivation2[FusableActivation2[\"linear\"] = 0] = \"linear\";\n  FusableActivation2[FusableActivation2[\"relu\"] = 1] = \"relu\";\n  FusableActivation2[FusableActivation2[\"relu6\"] = 2] = \"relu6\";\n  FusableActivation2[FusableActivation2[\"prelu\"] = 3] = \"prelu\";\n  FusableActivation2[FusableActivation2[\"leakyrelu\"] = 4] = \"leakyrelu\";\n  FusableActivation2[FusableActivation2[\"sigmoid\"] = 5] = \"sigmoid\";\n  FusableActivation2[FusableActivation2[\"elu\"] = 6] = \"elu\";\n})(FusableActivation || (FusableActivation = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js\nvar wasmFusedMatMul;\nfunction setup(backend2) {\n  wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedBatchMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b, bias, preluActivationWeights } = inputs;\n  if (a6.dtype !== \"float32\" || b.dtype !== \"float32\") {\n    throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);\n  }\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  const aId = backend2.dataIdMap.get(a6.dataId).id;\n  const bId = backend2.dataIdMap.get(b.dataId).id;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    biasId = biasData.id;\n  }\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n  }\n  const leftDim = transposeA ? a6.shape[2] : a6.shape[1];\n  const rightDim = transposeB ? b.shape[1] : b.shape[2];\n  const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));\n  const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a6.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const aShapeBytes = new Uint8Array(new Int32Array(a6.shape).buffer);\n  const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n  wasmFusedMatMul(aId, aShapeBytes, a6.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar _fusedMatMulConfig3 = {\n  kernelName: _FusedMatMul,\n  backendName: \"wasm\",\n  setupFunc: setup,\n  kernelFunc: fusedBatchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js\nfunction createUnaryKernelConfig(kernelName, outType) {\n  let wasmFunc9;\n  function setupFunc3(backend2) {\n    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [\n      \"number\",\n      \"number\",\n      \"number\"\n    ]);\n  }\n  function kernelFunc3(args) {\n    const { backend: backend2, inputs: { x } } = args;\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const out = backend2.makeOutput(x.shape, outType || x.dtype);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    if (util_exports.sizeFromShape(out.shape) === 0) {\n      return out;\n    }\n    wasmFunc9(xId, CppDType[x.dtype], outId);\n    return out;\n  }\n  return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js\nvar absConfig3 = createUnaryKernelConfig(Abs);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js\nfunction createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {\n  let wasmFunc9;\n  function setupFunc3(backend2) {\n    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [\n      \"number\",\n      \"array\",\n      \"number\",\n      \"number\",\n      \"array\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]);\n  }\n  function kernelFunc3(args) {\n    const { backend: backend2, inputs } = args;\n    const { a: a6, b } = inputs;\n    const aId = backend2.dataIdMap.get(a6.dataId).id;\n    const bId = backend2.dataIdMap.get(b.dataId).id;\n    const outputType = dtype != null ? dtype : a6.dtype;\n    const newShape = backend_util_exports.assertAndGetBroadcastShape(a6.shape, b.shape);\n    const out = backend2.makeOutput(newShape, outputType);\n    if (util_exports.sizeFromShape(newShape) === 0) {\n      return out;\n    }\n    const aShapeBytes = new Uint8Array(new Int32Array(a6.shape).buffer);\n    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a6.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a6.dtype], outId);\n    kernelFunc4();\n    return out;\n  }\n  return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js\nvar supportsFullBroadcast = true;\nvar addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js\nvar wasmFunc;\nfunction setupFunc(backend2) {\n  wasmFunc = backend2.wasm.cwrap(AddN, null, [\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction addn(args) {\n  const { inputs, backend: backend2 } = args;\n  const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);\n  const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);\n  return out;\n}\nvar addNConfig3 = {\n  kernelName: AddN,\n  backendName: \"wasm\",\n  setupFunc,\n  kernelFunc: addn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js\nfunction identity4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const inVals = backend2.typedArrayFromHeap(x);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(inVals);\n  return out;\n}\nvar identityConfig3 = {\n  kernelName: Identity,\n  backendName: \"wasm\",\n  kernelFunc: identity4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js\nvar wasmTranspose;\nfunction setup2(backend2) {\n  wasmTranspose = backend2.wasm.cwrap(Transpose, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction transpose4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);\n  let permIsNoOp = true;\n  for (let i2 = 0; i2 < perm.length; i2++) {\n    if (perm[i2] !== i2) {\n      permIsNoOp = false;\n    }\n  }\n  const outShape = computeOutShape4(inputs.x.shape, attrs.perm);\n  const x = {\n    dataId: inputs.x.dataId,\n    shape: reducedShape,\n    dtype: inputs.x.dtype\n  };\n  if (permIsNoOp) {\n    const cloned = identity4({ inputs, backend: backend2 });\n    cloned.shape = outShape;\n    return cloned;\n  }\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const permBytes = new Uint8Array(new Int32Array(perm).buffer);\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);\n  return out;\n}\nfunction computeOutShape4(inShape, perm) {\n  const outShape = new Array(inShape.length);\n  for (let i2 = 0; i2 < outShape.length; i2++) {\n    outShape[i2] = inShape[perm[i2]];\n  }\n  return outShape;\n}\nfunction removeOneSizeDims(shape, perm) {\n  const newShape = [];\n  const newPerm = [];\n  for (let i2 = 0; i2 < shape.length; ++i2) {\n    if (shape[i2] !== 1) {\n      newShape.push(shape[i2]);\n    }\n    if (shape[perm[i2]] !== 1) {\n      newPerm.push(perm[i2]);\n    }\n  }\n  for (let i2 = 0; i2 < newPerm.length; ++i2) {\n    let minValIdx = -1;\n    for (let j = 0; j < newPerm.length; ++j) {\n      if (newPerm[j] >= i2 && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {\n        minValIdx = j;\n      }\n    }\n    newPerm[minValIdx] = i2;\n  }\n  return [newShape, newPerm];\n}\nvar transposeConfig3 = {\n  kernelName: Transpose,\n  backendName: \"wasm\",\n  kernelFunc: transpose4,\n  setupFunc: setup2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js\nfunction permuteAxesAndTranspose(x, axis, backend2) {\n  const xShape = x.shape;\n  const xRank = x.shape.length;\n  const originalAxes = util_exports.parseAxisParam(axis, xShape);\n  let axes = originalAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let xTransposed = null;\n  let inputWasTransposed = false;\n  if (permutedAxes != null) {\n    const newShape = new Array(xRank);\n    for (let i2 = 0; i2 < newShape.length; i2++) {\n      newShape[i2] = xShape[permutedAxes[i2]];\n    }\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;\n    if (transposedId !== xId) {\n      inputWasTransposed = true;\n    }\n  }\n  return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/All.js\nvar wasmAll;\nfunction setup3(backend2) {\n  wasmAll = backend2.wasm.cwrap(All, null, [\"number, number, number\"]);\n}\nfunction all4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmAll(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar allConfig3 = {\n  kernelName: All,\n  backendName: \"wasm\",\n  setupFunc: setup3,\n  kernelFunc: all4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Any.js\nvar wasmAny;\nfunction setup4(backend2) {\n  wasmAny = backend2.wasm.cwrap(Any, null, [\"number, number, number\"]);\n}\nfunction any4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmAny(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar anyConfig3 = {\n  kernelName: Any,\n  backendName: \"wasm\",\n  setupFunc: setup4,\n  kernelFunc: any4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js\nvar wasmFunc2;\nfunction setup5(backend2) {\n  wasmFunc2 = backend2.wasm.cwrap(ArgMax, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction argmax(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n    }\n  }\n  const outShape = input2.shape.slice(0, -1);\n  const out = backend2.makeOutput(outShape, \"int32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const outerSize = util_exports.sizeFromShape(out.shape);\n  const innerSize = input2.shape[axes[0]];\n  wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  return out;\n}\nvar argMaxConfig3 = {\n  kernelName: ArgMax,\n  backendName: \"wasm\",\n  kernelFunc: argmax,\n  setupFunc: setup5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js\nvar wasmAvgPool;\nfunction setup6(backend2) {\n  wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction avgPool4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const x = inputs.x;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const channels = convInfo.inChannels;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {\n    throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);\n  return out;\n}\nvar avgPoolConfig3 = {\n  kernelName: AvgPool,\n  backendName: \"wasm\",\n  setupFunc: setup6,\n  kernelFunc: avgPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js\nfunction reshape5(args) {\n  const { inputs, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig3 = {\n  kernelName: Reshape,\n  backendName: \"wasm\",\n  kernelFunc: reshape5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js\nvar wasmBatchMatMul;\nfunction setup7(backend2) {\n  wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction batchMatMul3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  if (a6.dtype !== \"float32\" || b.dtype !== \"float32\") {\n    throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);\n  }\n  const aRank = a6.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a6.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape5({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const a3dId = backend2.dataIdMap.get(a3d.dataId).id;\n  const b3dId = backend2.dataIdMap.get(b3d.dataId).id;\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);\n  const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);\n  wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);\n  backend2.disposeData(a3d.dataId);\n  backend2.disposeData(b3d.dataId);\n  out.shape = outShape;\n  return out;\n}\nvar batchMatMulConfig3 = {\n  kernelName: BatchMatMul,\n  backendName: \"wasm\",\n  setupFunc: setup7,\n  kernelFunc: batchMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js\nfunction slice4(args) {\n  const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args;\n  const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);\n  const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);\n  const xVals = backend2.readSync(x.dataId);\n  const out = backend2.makeOutput(size_, x.dtype);\n  const xStrides = util_exports.computeStrides(x.shape);\n  const outData = backend2.dataIdMap.get(out.dataId);\n  if (isContinous) {\n    const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);\n    if (x.dtype === \"string\") {\n      outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));\n    } else {\n      const outVals2 = backend2.typedArrayFromHeap(out);\n      outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));\n    }\n    return out;\n  }\n  if (x.dtype === \"string\") {\n    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n    outData.stringBytes = res;\n    return out;\n  }\n  const outVals = backend2.typedArrayFromHeap(out);\n  const rank = x.shape.length;\n  if (rank === 2) {\n    slice2d2(xVals, xStrides[0], outVals, begin_, size_);\n  } else if (rank === 3) {\n    slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);\n  } else if (rank === 4) {\n    slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);\n  } else {\n    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n    outVals.set(res);\n  }\n  return out;\n}\nfunction slice2d2(xVals, xStride, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const endI = beginI + size[0];\n  for (let i2 = beginI; i2 < endI; i2++) {\n    const xOffset = i2 * xStride + beginJ;\n    outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);\n    outOffset += size[1];\n  }\n}\nfunction slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const beginK = begin[2];\n  const endI = beginI + size[0];\n  const endJ = beginJ + size[1];\n  for (let i2 = beginI; i2 < endI; i2++) {\n    for (let j = beginJ; j < endJ; j++) {\n      const xOffset = i2 * xStride1 + j * xStride2 + beginK;\n      outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);\n      outOffset += size[2];\n    }\n  }\n}\nfunction slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const beginK = begin[2];\n  const endI = beginI + size[0];\n  const endJ = beginJ + size[1];\n  const endK = beginK + size[2];\n  const beginL = begin[3];\n  for (let i2 = beginI; i2 < endI; i2++) {\n    for (let j = beginJ; j < endJ; j++) {\n      for (let k = beginK; k < endK; k++) {\n        const xOffset = i2 * xStride1 + j * xStride2 + k * xStride3 + beginL;\n        outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);\n        outOffset += size[3];\n      }\n    }\n  }\n}\nvar sliceConfig3 = {\n  kernelName: Slice,\n  backendName: \"wasm\",\n  kernelFunc: slice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n  const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n  const result = slice4({\n    inputs: { x: xTransposedReshaped },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  backend2.disposeData(xReshaped.dataId);\n  backend2.disposeData(xTransposed.dataId);\n  backend2.disposeData(xReshaped.dataId);\n  return result;\n}\nvar batchToSpaceNDConfig3 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"wasm\",\n  kernelFunc: batchToSpaceND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js\nfunction cast5(args) {\n  const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, dtype);\n  const inVals = backend2.typedArrayFromHeap(x);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(inVals);\n  return out;\n}\nvar castConfig3 = {\n  kernelName: Cast,\n  backendName: \"wasm\",\n  kernelFunc: cast5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js\nvar ceilConfig3 = createUnaryKernelConfig(Ceil);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js\nvar wasmClip;\nfunction setup8(backend2) {\n  wasmClip = backend2.wasm.cwrap(ClipByValue, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction clip(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmClip(xId, clipValueMin, clipValueMax, outId);\n  return out;\n}\nvar clipByValueConfig3 = {\n  kernelName: ClipByValue,\n  backendName: \"wasm\",\n  setupFunc: setup8,\n  kernelFunc: clip\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js\nfunction concat4(args) {\n  const { inputs, backend: backend2 } = args;\n  const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];\n  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const out = backend2.makeOutput(outShape, inputs[0].dtype);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return out;\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, axis);\n  if ($inputs[0].dtype === \"string\") {\n    const inputs2D = $inputs.map((t2) => {\n      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape5({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = inputs2D.map((t2) => {\n      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };\n    });\n    outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);\n    const simplyConcat = inputs2D[0].shape[0] === 1;\n    const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), axis);\n    out.shape = finalOutShape;\n    const outData = backend2.dataIdMap.get(out.dataId);\n    outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);\n    inputs2D.forEach((t2) => backend2.disposeData(t2.dataId));\n    return out;\n  }\n  const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));\n  let sumInnerDims = 0;\n  const innerDims = $inputs.map((input2) => {\n    const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));\n    sumInnerDims += innerDim;\n    return innerDim;\n  });\n  const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));\n  const outVals = backend2.typedArrayFromHeap(out);\n  for (let b = 0; b < batchDim; b++) {\n    let outOffset = b * sumInnerDims;\n    for (let i2 = 0; i2 < inVals.length; i2++) {\n      const innerDim = innerDims[i2];\n      const inOffset = b * innerDim;\n      const vals = inVals[i2].subarray(inOffset, inOffset + innerDim);\n      outVals.set(vals, outOffset);\n      outOffset += innerDim;\n    }\n  }\n  return out;\n}\nvar concatConfig3 = {\n  kernelName: Concat,\n  backendName: \"wasm\",\n  kernelFunc: concat4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js\nvar wasmConv2d;\nfunction setup9(backend2) {\n  wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction conv2d5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar conv2DConfig3 = {\n  kernelName: Conv2D,\n  backendName: \"wasm\",\n  setupFunc: setup9,\n  kernelFunc: conv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js\nvar wasmConv2DBackpropInput;\nfunction setup10(backend2) {\n  wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction conv2DBackpropInput4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs;\n  const dilations = 1;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const dxStrides = util_exports.computeStrides(convInfo.inShape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);\n  const xBatchStride = dxStrides[0];\n  const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];\n  const xColStride = isChannelsLast ? dxStrides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dxStrides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n  const out = backend2.makeOutput(convInfo.inShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const dyId = backend2.dataIdMap.get(dy.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);\n  return out;\n}\nvar conv2DBackpropInputConfig3 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"wasm\",\n  setupFunc: setup10,\n  kernelFunc: conv2DBackpropInput4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js\nvar cosConfig3 = createUnaryKernelConfig(Cos);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cosh.js\nvar coshConfig3 = createUnaryKernelConfig(Cosh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js\nvar InterpolationMethod;\n(function(InterpolationMethod2) {\n  InterpolationMethod2[InterpolationMethod2[\"bilinear\"] = 0] = \"bilinear\";\n  InterpolationMethod2[InterpolationMethod2[\"nearest\"] = 1] = \"nearest\";\n})(InterpolationMethod || (InterpolationMethod = {}));\nvar wasmCropAndResize;\nfunction setup11(backend2) {\n  wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cropAndResize4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { method, extrapolationValue, cropSize } = attrs;\n  const { image: image2, boxes, boxInd } = inputs;\n  const numBoxes = boxes.shape[0];\n  const [cropHeight, cropWidth] = cropSize;\n  const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]];\n  let imagesData = backend2.dataIdMap.get(image2.dataId);\n  let castedData;\n  if (image2.dtype !== \"float32\") {\n    castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: \"float32\" } });\n    imagesData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const imagesId = imagesData.id;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;\n  const out = backend2.makeOutput(outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer);\n  wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar cropAndResizeConfig3 = {\n  kernelName: CropAndResize,\n  backendName: \"wasm\",\n  setupFunc: setup11,\n  kernelFunc: cropAndResize4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumprod.js\nvar wasmCumprod;\nfunction setup12(backend2) {\n  wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cumprod4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  const xRank = x.shape.length;\n  util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation !== null) {\n    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  backend_util_exports.assertAxesAreInnerMostDims(\"cumprod\", [permutedAxis], xRank);\n  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n  const finalDim = permutedX.shape[permutedAxis];\n  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n  wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n  let out = permutedOut;\n  if (permutation !== null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n    backend2.disposeData(permutedX.dataId);\n    backend2.disposeData(permutedOut.dataId);\n  }\n  return out;\n}\nvar cumprodConfig3 = {\n  kernelName: Cumprod,\n  backendName: \"wasm\",\n  setupFunc: setup12,\n  kernelFunc: cumprod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js\nvar wasmCumsum;\nfunction setup13(backend2) {\n  wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cumsum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  const xRank = x.shape.length;\n  util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation !== null) {\n    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  backend_util_exports.assertAxesAreInnerMostDims(\"cumsum\", [permutedAxis], xRank);\n  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n  const finalDim = permutedX.shape[permutedAxis];\n  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n  wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n  let out = permutedOut;\n  if (permutation !== null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n    backend2.disposeData(permutedX.dataId);\n    backend2.disposeData(permutedOut.dataId);\n  }\n  return out;\n}\nvar cumsumConfig3 = {\n  kernelName: Cumsum,\n  backendName: \"wasm\",\n  setupFunc: setup13,\n  kernelFunc: cumsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js\nvar wasmDepthToSpace;\nfunction setup14(backend2) {\n  wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction depthToSpace4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const out = backend2.makeOutput(outputShape, \"float32\");\n  const xData = backend2.dataIdMap.get(x.dataId);\n  const xId = xData.id;\n  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n  const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);\n  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const channelsLast = dataFormat === \"NHWC\" ? 1 : 0;\n  wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);\n  return out;\n}\nvar depthToSpaceConfig3 = {\n  kernelName: DepthToSpace,\n  backendName: \"wasm\",\n  setupFunc: setup14,\n  kernelFunc: depthToSpace4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js\nvar wasmDepthwiseConv2d;\nfunction setup15(backend2) {\n  wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction depthwiseConv2d5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const { strides, dilations, pad: pad3, dimRoundingMode } = attrs;\n  const $dilations = dilations == null ? [1, 1] : dilations;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar depthwiseConv2dNativeConfig3 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"wasm\",\n  setupFunc: setup15,\n  kernelFunc: depthwiseConv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Elu.js\nvar eluConfig3 = createUnaryKernelConfig(Elu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js\nvar supportsFullBroadcast2 = false;\nvar equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js\nvar expConfig3 = createUnaryKernelConfig(Exp, \"float32\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js\nfunction expandDims5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const { dim } = attrs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig3 = {\n  kernelName: ExpandDims,\n  backendName: \"wasm\",\n  kernelFunc: expandDims5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js\nfunction fill4(args) {\n  const { attrs: { shape, value, dtype }, backend: backend2 } = args;\n  const out = backend2.makeOutput(shape, dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(value);\n  return out;\n}\nvar fillConfig3 = {\n  kernelName: Fill,\n  backendName: \"wasm\",\n  kernelFunc: fill4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js\nvar wasmFlipLeftRight;\nfunction setup16(backend2) {\n  wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction flipLeftRight2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { image: image2 } = inputs;\n  const out = backend2.makeOutput(image2.shape, image2.dtype);\n  const imageId = backend2.dataIdMap.get(image2.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);\n  return out;\n}\nvar flipLeftRightConfig3 = {\n  kernelName: FlipLeftRight,\n  backendName: \"wasm\",\n  kernelFunc: flipLeftRight2,\n  setupFunc: setup16\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js\nvar floorConfig3 = createUnaryKernelConfig(Floor);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js\nvar supportsFullBroadcast3 = false;\nvar floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js\nvar wasmBatchNorm;\nfunction setup17(backend2) {\n  wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, [\"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\"]);\n}\nfunction fusedBatchNorm(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { varianceEpsilon } = attrs;\n  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const meanId = backend2.dataIdMap.get(mean5.dataId).id;\n  const varianceId = backend2.dataIdMap.get(variance.dataId).id;\n  const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;\n  const scaleId = scale2 != null ? backend2.dataIdMap.get(scale2.dataId).id : 0;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return out;\n  }\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);\n  return out;\n}\nvar fusedBatchNormConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: \"wasm\",\n  setupFunc: setup17,\n  kernelFunc: fusedBatchNorm\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js\nvar wasmFusedConv2d;\nfunction setup18(backend2) {\n  wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedConv2d2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode);\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const outputChannels = convInfo.outChannels;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    if (biasData.shape[0] !== outputChannels) {\n      throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n    }\n    biasId = biasData.id;\n  }\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  const batchSize = convInfo.batchSize;\n  const inHeight = convInfo.inHeight;\n  const inWidth = convInfo.inWidth;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar fusedConv2DConfig3 = {\n  kernelName: FusedConv2D,\n  backendName: \"wasm\",\n  setupFunc: setup18,\n  kernelFunc: fusedConv2d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js\nvar wasmFusedDepthwiseConv2d;\nfunction setup19(backend2) {\n  wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedDepthwiseConv2d(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const outputChannels = convInfo.outChannels;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    if (biasData.shape[0] !== outputChannels) {\n      throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n    }\n    biasId = biasData.id;\n  }\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  const batchSize = convInfo.batchSize;\n  const inHeight = convInfo.inHeight;\n  const inWidth = convInfo.inWidth;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar fusedDepthwiseConv2DConfig3 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"wasm\",\n  setupFunc: setup19,\n  kernelFunc: fusedDepthwiseConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js\nvar wasmGatherNd;\nfunction setup20(backend2) {\n  wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction gatherNd3(args) {\n  const { backend: backend2, inputs } = args;\n  const { params, indices } = inputs;\n  const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);\n  const out = backend2.makeOutput(resultShape, params.dtype);\n  if (numSlices === 0) {\n    return out;\n  }\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const xData = backend2.dataIdMap.get(params.dataId);\n  const xId = xData.id;\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);\n  return out;\n}\nvar gatherNdConfig3 = {\n  kernelName: GatherNd,\n  backendName: \"wasm\",\n  setupFunc: setup20,\n  kernelFunc: gatherNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js\nvar wasmGather;\nfunction setup21(backend2) {\n  wasmGather = backend2.wasm.cwrap(\"Gather\", null, [\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction gatherV23(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const indicesVals = backend2.readSync(indices.dataId);\n  const axisDim = x.shape[parsedAxis];\n  for (let i2 = 0; i2 < indicesVals.length; ++i2) {\n    const index = indicesVals[i2];\n    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n  }\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const flattenX = reshape5({\n    inputs: { x },\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    },\n    backend: backend2\n  });\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const flattenIndex = reshape5({\n    inputs: { x: indices },\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] },\n    backend: backend2\n  });\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  const out = backend2.makeOutput(flattenOutputShape, x.dtype);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return out;\n  }\n  const stridesSize = flattenX.shape.length - 1;\n  const xData = backend2.dataIdMap.get(flattenX.dataId);\n  const xId = xData.id;\n  const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);\n  const indicesId = indicesData.id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);\n  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);\n  wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(flattenIndex.dataId);\n  out.shape = shapeInfo.outputShape;\n  return out;\n}\nvar gatherV2Config3 = {\n  kernelName: GatherV2,\n  backendName: \"wasm\",\n  setupFunc: setup21,\n  kernelFunc: gatherV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js\nvar supportsFullBroadcast4 = false;\nvar greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js\nvar supportsFullBroadcast5 = false;\nvar greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js\nvar wasmFunc3;\nfunction setupFunc2(backend2) {\n  wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction leakyRelu4(args) {\n  const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, \"float32\");\n  if (util_exports.sizeFromShape(x.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmFunc3(xId, CppDType[x.dtype], alpha, outId);\n  }\n  return out;\n}\nvar leakyReluConfig3 = {\n  kernelName: LeakyRelu,\n  backendName: \"wasm\",\n  setupFunc: setupFunc2,\n  kernelFunc: leakyRelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js\nvar supportsFullBroadcast6 = false;\nvar lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js\nvar supportsFullBroadcast7 = false;\nvar lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js\nvar logConfig3 = createUnaryKernelConfig(Log);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js\nvar supportsFullBroadcast8 = false;\nvar logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js\nvar wasmMax;\nfunction setup22(backend2) {\n  wasmMax = backend2.wasm.cwrap(Max, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction max5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { reductionIndices: axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar maxConfig3 = {\n  kernelName: Max,\n  backendName: \"wasm\",\n  setupFunc: setup22,\n  kernelFunc: max5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js\nvar supportsFullBroadcast9 = false;\nvar maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js\nvar wasmMaxPool;\nfunction setup23(backend2) {\n  wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction maxPool4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const x = inputs.x;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  util_exports.assert(x.dtype === \"float32\", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar maxPoolConfig3 = {\n  kernelName: MaxPool,\n  backendName: \"wasm\",\n  setupFunc: setup23,\n  kernelFunc: maxPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js\nvar wasmMean;\nfunction setup24(backend2) {\n  wasmMean = backend2.wasm.cwrap(Mean, null, [\"number, number, number\"]);\n}\nfunction mean3(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"mean\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  let castedInput = input2;\n  if (input2.dtype !== \"float32\") {\n    castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: \"float32\" } });\n    inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n  }\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMean(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  if (input2.dtype !== \"float32\") {\n    backend2.disposeData(castedInput.dataId);\n  }\n  return out;\n}\nvar meanConfig3 = {\n  kernelName: Mean,\n  backendName: \"wasm\",\n  setupFunc: setup24,\n  kernelFunc: mean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js\nvar wasmMin;\nfunction setup25(backend2) {\n  wasmMin = backend2.wasm.cwrap(Min, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction min5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n    }\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar minConfig3 = {\n  kernelName: Min,\n  backendName: \"wasm\",\n  setupFunc: setup25,\n  kernelFunc: min5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js\nvar supportsFullBroadcast10 = false;\nvar minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MirrorPad.js\nvar MirrorPaddingMode;\n(function(MirrorPaddingMode2) {\n  MirrorPaddingMode2[MirrorPaddingMode2[\"reflect\"] = 0] = \"reflect\";\n  MirrorPaddingMode2[MirrorPaddingMode2[\"symmetric\"] = 1] = \"symmetric\";\n})(MirrorPaddingMode || (MirrorPaddingMode = {}));\nvar wasmMirrorPad;\nfunction setup26(backend2) {\n  wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction mirrorPad3(args) {\n  const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n  wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId);\n  return out;\n}\nvar mirrorPadConfig3 = {\n  kernelName: MirrorPad,\n  backendName: \"wasm\",\n  kernelFunc: mirrorPad3,\n  setupFunc: setup26\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js\nvar supportsFullBroadcast11 = true;\nvar multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js\nvar negConfig3 = createUnaryKernelConfig(Neg);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js\nfunction parseResultStruct(backend2, resOffset) {\n  const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);\n  const pSelectedIndices = result[0];\n  const selectedSize = result[1];\n  const pSelectedScores = result[2];\n  const pValidOutputs = result[3];\n  backend2.wasm._free(resOffset);\n  return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js\nvar wasmFunc4;\nfunction setup27(backend2) {\n  wasmFunc4 = backend2.wasm.cwrap(NonMaxSuppressionV3, \"number\", [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction kernelFunc(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pSelectedScores);\n  backend2.wasm._free(pValidOutputs);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  return selectedIndicesTensor;\n}\nvar nonMaxSuppressionV3Config3 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"wasm\",\n  setupFunc: setup27,\n  kernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js\nvar wasmFunc5;\nfunction setup28(backend2) {\n  wasmFunc5 = backend2.wasm.cwrap(NonMaxSuppressionV4, \"number\", [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"bool\"\n  ]);\n}\nfunction nonMaxSuppressionV43(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pSelectedScores);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  const validOutputsTensor = backend2.makeOutput([], \"int32\", pValidOutputs);\n  return [selectedIndicesTensor, validOutputsTensor];\n}\nvar nonMaxSuppressionV4Config3 = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"wasm\",\n  setupFunc: setup28,\n  kernelFunc: nonMaxSuppressionV43\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js\nvar wasmFunc6;\nfunction setup29(backend2) {\n  wasmFunc6 = backend2.wasm.cwrap(NonMaxSuppressionV5, \"number\", [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction kernelFunc2(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pValidOutputs);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  const selectedScoresTensor = backend2.makeOutput([selectedSize], \"float32\", pSelectedScores);\n  return [selectedIndicesTensor, selectedScoresTensor];\n}\nvar nonMaxSuppressionV5Config3 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"wasm\",\n  setupFunc: setup29,\n  kernelFunc: kernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js\nvar supportsFullBroadcast12 = false;\nvar notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js\nvar wasmOneHot;\nfunction setup30(backend2) {\n  wasmOneHot = backend2.wasm.cwrap(OneHot, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction oneHot4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { depth, onValue, offValue } = attrs;\n  const out = backend2.makeOutput([...indices.shape, depth], \"int32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  wasmOneHot(indicesId, depth, onValue, offValue, outId);\n  return out;\n}\nvar oneHotConfig3 = {\n  kernelName: OneHot,\n  backendName: \"wasm\",\n  setupFunc: setup30,\n  kernelFunc: oneHot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js\nfunction onesLike4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(1);\n  return out;\n}\nvar onesLikeConfig3 = {\n  kernelName: OnesLike,\n  backendName: \"wasm\",\n  kernelFunc: onesLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js\nfunction pack3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims5({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return result;\n}\nvar packConfig3 = {\n  kernelName: Pack,\n  backendName: \"wasm\",\n  kernelFunc: pack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js\nvar wasmPadV2;\nfunction setup31(backend2) {\n  wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction pad2(args) {\n  const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return fill4({\n      backend: backend2,\n      attrs: { shape: outShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const outTensorData = backend2.dataIdMap.get(out.dataId);\n  const outId = outTensorData.id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n  wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);\n  return out;\n}\nvar padV2Config3 = {\n  kernelName: PadV2,\n  backendName: \"wasm\",\n  kernelFunc: pad2,\n  setupFunc: setup31\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js\nvar supportsFullBroadcast13 = false;\nvar powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast13);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js\nvar wasmPrelu;\nfunction setup32(backend2) {\n  wasmPrelu = backend2.wasm.cwrap(Prelu, null, [\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction prelu5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const weightsId = backend2.dataIdMap.get(alpha.dataId).id;\n  let inputId = xId;\n  const input2 = x;\n  let castedInput = input2;\n  if (input2.dtype !== \"float32\") {\n    castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: \"float32\" } });\n    inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n  }\n  const out = backend2.makeOutput(x.shape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmPrelu(inputId, weightsId, outId);\n  if (input2.dtype !== \"float32\") {\n    backend2.disposeData(castedInput.dataId);\n  }\n  return out;\n}\nvar preluConfig3 = {\n  kernelName: Prelu,\n  backendName: \"wasm\",\n  setupFunc: setup32,\n  kernelFunc: prelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js\nvar wasmProd;\nfunction setup33(backend2) {\n  wasmProd = backend2.wasm.cwrap(Prod, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction prod4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"prod\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar prodConfig3 = {\n  kernelName: Prod,\n  backendName: \"wasm\",\n  setupFunc: setup33,\n  kernelFunc: prod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js\nvar range5 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImpl(start, stop, step5, dtype);\n  const out = backend2.makeOutput([values.length], dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(values);\n  return out;\n};\nvar rangeConfig3 = {\n  kernelName: Range,\n  backendName: \"wasm\",\n  kernelFunc: range5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js\nvar supportsFullBroadcast14 = true;\nvar realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js\nvar reluConfig3 = createUnaryKernelConfig(Relu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js\nvar relu6Config3 = createUnaryKernelConfig(Relu6);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js\nvar wasmResizeBilinear;\nfunction setup34(backend2) {\n  wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction resizeBilinear4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const outShape = [batch, newHeight, newWidth, numChannels];\n  let xData = backend2.dataIdMap.get(images.dataId);\n  let castedData;\n  if (xData.dtype !== \"float32\") {\n    castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: \"float32\" } });\n    xData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const xId = xData.id;\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(images.shape) === 0) {\n    return out;\n  }\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar resizeBilinearConfig3 = {\n  kernelName: ResizeBilinear,\n  backendName: \"wasm\",\n  setupFunc: setup34,\n  kernelFunc: resizeBilinear4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js\nvar wasmReverse;\nfunction setup35(backend2) {\n  wasmReverse = backend2.wasm.cwrap(Reverse, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction reverse4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  const axes = util_exports.parseAxisParam(dims, x.shape);\n  if (x.shape.length === 0) {\n    return identity4({ inputs: { x }, backend: backend2 });\n  }\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const axesBytes = new Uint8Array(new Int32Array(axes).buffer);\n  const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);\n  const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 });\n  backend2.disposeData(out.dataId);\n  return reshaped;\n}\nvar reverseConfig3 = {\n  kernelName: Reverse,\n  backendName: \"wasm\",\n  kernelFunc: reverse4,\n  setupFunc: setup35\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js\nvar wasmRotate;\nfunction setup36(backend2) {\n  wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction rotateWithOffset2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2 } = inputs;\n  const { radians, fillValue, center } = attrs;\n  const out = backend2.makeOutput(image2.shape, image2.dtype);\n  const imageId = backend2.dataIdMap.get(image2.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n  const fillIsBlack = fillValue === 0;\n  const fullOpacityValue = 255;\n  const fillValues2 = typeof fillValue === \"number\" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];\n  const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);\n  wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);\n  return out;\n}\nvar rotateWithOffsetConfig3 = {\n  kernelName: RotateWithOffset,\n  backendName: \"wasm\",\n  kernelFunc: rotateWithOffset2,\n  setupFunc: setup36\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js\nvar roundConfig3 = createUnaryKernelConfig(Round);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js\nvar rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js\nvar wasmScatterNd;\nfunction setup37(backend2) {\n  wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction scatterNd3(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const out = backend2.makeOutput(shape, updates.dtype);\n  if (util_exports.sizeFromShape(shape) === 0) {\n    return out;\n  }\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape);\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  const updatesData = backend2.dataIdMap.get(updates.dataId);\n  const updatesId = updatesData.id;\n  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);\n  return out;\n}\nvar scatterNdConfig3 = {\n  kernelName: ScatterNd,\n  backendName: \"wasm\",\n  setupFunc: setup37,\n  kernelFunc: scatterNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js\nvar wasmSelect;\nfunction setup38(backend2) {\n  wasmSelect = backend2.wasm.cwrap(\"SelectV2\", null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction select4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  const conditionId = backend2.dataIdMap.get(condition.dataId).id;\n  const tId = backend2.dataIdMap.get(t2.dataId).id;\n  const eId = backend2.dataIdMap.get(e2.dataId).id;\n  const out = backend2.makeOutput(t2.shape, t2.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const cRank = condition.shape.length;\n  const tRank = t2.shape.length;\n  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));\n  wasmSelect(conditionId, tId, eId, offset, outId);\n  return out;\n}\nvar selectConfig3 = {\n  kernelName: Select,\n  backendName: \"wasm\",\n  kernelFunc: select4,\n  setupFunc: setup38\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js\nvar wasmFunc7;\nfunction setup39(backend2) {\n  wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, [\"number\", \"number\"]);\n}\nfunction sigmoid4(args) {\n  const { backend: backend2, inputs: { x } } = args;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  wasmFunc7(xId, outId);\n  return out;\n}\nvar sigmoidConfig3 = {\n  kernelName: \"Sigmoid\",\n  backendName: \"wasm\",\n  setupFunc: setup39,\n  kernelFunc: sigmoid4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js\nvar sinConfig3 = createUnaryKernelConfig(Sin);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js\nvar wasmFunc8;\nfunction setup40(backend2) {\n  wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction softmax5(args) {\n  const { backend: backend2, inputs: { logits }, attrs: { dim } } = args;\n  const xId = backend2.dataIdMap.get(logits.dataId).id;\n  const out = backend2.makeOutput(logits.shape, logits.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const channels = logits.shape[dim];\n  const batch = util_exports.sizeFromShape(logits.shape) / channels;\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  wasmFunc8(xId, outId, channels, batch);\n  return out;\n}\nvar softmaxConfig3 = {\n  kernelName: Softmax,\n  backendName: \"wasm\",\n  setupFunc: setup40,\n  kernelFunc: softmax5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  const prod6 = util_exports.sizeFromShape(blockShape);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const paddedX = padV2Config3.kernelFunc({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapeInputs = { x: paddedX };\n  const reshapeAttrs = { shape: reshapedPaddedShape };\n  const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n  const transposeInputs = { x: paddedXReshaped };\n  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n  const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n  const resultReshapeInputs = { x: paddedXT };\n  const resultReshapeAttrs = { shape: flattenShape };\n  const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n  backend2.disposeData(paddedX.dataId);\n  backend2.disposeData(paddedXReshaped.dataId);\n  backend2.disposeData(paddedXT.dataId);\n  return result;\n}\nvar spaceToBatchNDConfig3 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"wasm\",\n  kernelFunc: spaceToBatchND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseFillEmptyRows.js\nvar wasmSparseFillEmptyRows;\nfunction setup41(backend2) {\n  wasmSparseFillEmptyRows = backend2.wasm.cwrap(\"SparseFillEmptyRows\", \"number\", [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseFillEmptyRows4(args) {\n  const { backend: backend2, inputs } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  const indicesCount = indices.shape[0];\n  const rank = indices.shape[1];\n  const denseRows = backend2.readSync(denseShape.dataId)[0];\n  const maxOutputIndicesShape = [indicesCount + denseRows, rank];\n  const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n  const valuesId = backend2.dataIdMap.get(values.dataId).id;\n  const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;\n  const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype);\n  const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id;\n  const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);\n  const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id;\n  const emptyRowIndicator = backend2.makeOutput([denseRows], \"bool\");\n  const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id;\n  const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype);\n  const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id;\n  const exceptionValues = backend2.makeOutput([4], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]);\n      break;\n    }\n    case 2: {\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 3:\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n      break;\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(outputIndices.dataId);\n    backend2.disposeData(outputValues.dataId);\n    backend2.disposeData(emptyRowIndicator.dataId);\n    backend2.disposeData(reverseIndexMap.dataId);\n    throw new Error(exceptionMessage);\n  }\n  let resizedIndices = outputIndices;\n  let resizedValues = outputValues;\n  if (outputRows !== maxOutputIndicesShape[0]) {\n    resizedIndices = slice4({\n      inputs: { x: outputIndices },\n      attrs: { begin: 0, size: [outputRows, rank] },\n      backend: backend2\n    });\n    resizedValues = slice4({\n      inputs: { x: outputValues },\n      attrs: { begin: 0, size: outputRows },\n      backend: backend2\n    });\n    backend2.disposeData(outputIndices.dataId);\n    backend2.disposeData(outputValues.dataId);\n  }\n  return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];\n}\nvar sparseFillEmptyRowsConfig3 = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"wasm\",\n  setupFunc: setup41,\n  kernelFunc: sparseFillEmptyRows4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseReshape.js\nvar wasmSparseReshape;\nfunction setup42(backend2) {\n  wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseReshape4(args) {\n  const { backend: backend2, inputs } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape\n        ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape\n        ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id;\n  const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id;\n  const newShapeId = backend2.dataIdMap.get(newShape.dataId).id;\n  const nnz = inputIndices.shape[0];\n  const outputRank = util_exports.sizeFromShape(newShape.shape);\n  const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype);\n  const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id;\n  const outputShape = backend2.makeOutput([outputRank], newShape.dtype);\n  const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id;\n  const exceptionValues = backend2.makeOutput([3], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 0: {\n      exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 2:\n      exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();\n      break;\n    case 3: {\n      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues);\n      break;\n    }\n    case 4: {\n      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues);\n      break;\n    }\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(newIndices.dataId);\n    backend2.disposeData(outputShape.dataId);\n    throw new Error(exceptionMessage);\n  }\n  return [newIndices, outputShape];\n}\nvar sparseReshapeConfig3 = {\n  kernelName: SparseReshape,\n  backendName: \"wasm\",\n  setupFunc: setup42,\n  kernelFunc: sparseReshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentReduction.js\nvar wasmSparseSegmentReduction;\nfunction setup43(backend2) {\n  wasmSparseSegmentReduction = backend2.wasm.cwrap(\"SparseSegmentReduction\", null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseSegmentReduction(args, isMean) {\n  const { backend: backend2, inputs } = args;\n  const { data, indices, segmentIds } = inputs;\n  const numIndices = indices.shape[0];\n  const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0];\n  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n  if (outputRows < 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  const outputShape = data.shape.slice();\n  outputShape[0] = outputRows;\n  const dataId = backend2.dataIdMap.get(data.dataId).id;\n  const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n  const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id;\n  const output = backend2.makeOutput(outputShape, data.dtype);\n  const outputId = backend2.dataIdMap.get(output.dataId).id;\n  const exceptionValues = backend2.makeOutput([4], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 0: {\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();\n      break;\n    }\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();\n      break;\n    }\n    case 2:\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    case 3:\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n      break;\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(output.dataId);\n    throw new Error(exceptionMessage);\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean4(args) {\n  return sparseSegmentReduction(args, true);\n}\nvar sparseSegmentMeanConfig3 = {\n  kernelName: SparseSegmentMean,\n  backendName: \"wasm\",\n  setupFunc: setup43,\n  kernelFunc: sparseSegmentMean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum4(args) {\n  return sparseSegmentReduction(args, false);\n}\nvar sparseSegmentSumConfig3 = {\n  kernelName: SparseSegmentSum,\n  backendName: \"wasm\",\n  setupFunc: setup43,\n  kernelFunc: sparseSegmentSum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js\nfunction splitV3(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const begin = new Array(x.shape.length).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const xSliceSize = [...size];\n    xSliceSize[$axis] = s2;\n    const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });\n    begin[$axis] += s2;\n    return xSlice;\n  });\n}\nvar splitVConfig3 = {\n  kernelName: SplitV,\n  backendName: \"wasm\",\n  kernelFunc: splitV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js\nvar sqrtConfig3 = createUnaryKernelConfig(Sqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js\nvar squareConfig3 = createUnaryKernelConfig(Square);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js\nvar supportsFullBroadcast15 = true;\nvar squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js\nvar wasmStep;\nfunction setup44(backend2) {\n  wasmStep = backend2.wasm.cwrap(Step, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction step4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { alpha } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmStep(xId, alpha, CppDType[x.dtype], outId);\n  return out;\n}\nvar stepConfig3 = {\n  kernelName: Step,\n  backendName: \"wasm\",\n  setupFunc: setup44,\n  kernelFunc: step4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js\nvar wasmStridedSlice;\nfunction setup45(backend2) {\n  wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction stridedSlice4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(sliced.dataId);\n  } else {\n    const out = backend2.makeOutput(finalShapeSparse, \"float32\");\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n    const beginBytes = new Uint8Array(new Int32Array($begin).buffer);\n    const endBytes = new Uint8Array(new Int32Array($end).buffer);\n    const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);\n    const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer);\n    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);\n    result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(out.dataId);\n  }\n  return result;\n}\nvar stridedSliceConfig3 = {\n  kernelName: StridedSlice,\n  backendName: \"wasm\",\n  setupFunc: setup45,\n  kernelFunc: stridedSlice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js\nvar supportsFullBroadcast16 = true;\nvar subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast16);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js\nvar wasmSum;\nfunction setup46(backend2) {\n  wasmSum = backend2.wasm.cwrap(Sum, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sum5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar sumConfig3 = {\n  kernelName: Sum,\n  backendName: \"wasm\",\n  setupFunc: setup46,\n  kernelFunc: sum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tan.js\nvar tanConfig3 = createUnaryKernelConfig(Tan);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js\nvar tanhConfig3 = createUnaryKernelConfig(Tanh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js\nvar wasmTile;\nfunction setup47(backend2) {\n  wasmTile = backend2.wasm.cwrap(Tile, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction tile5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const { reps } = attrs;\n  const newShape = new Array(x.shape.length);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[i2] * reps[i2];\n  }\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);\n  const out = backend2.makeOutput(newShape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);\n  return out;\n}\nvar tileConfig3 = {\n  kernelName: Tile,\n  backendName: \"wasm\",\n  setupFunc: setup47,\n  kernelFunc: tile5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js\nvar wasmTopK;\nfunction setup48(backend2) {\n  wasmTopK = backend2.wasm.cwrap(TopK, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"bool\",\n    \"number\",\n    \"number\"\n  ]);\n}\nvar topk2 = ({ inputs, backend: backend2, attrs }) => {\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const outputShape = x.shape.slice();\n  outputShape[outputShape.length - 1] = k;\n  const outValues = backend2.makeOutput(outputShape, x.dtype);\n  const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;\n  const outIndices = backend2.makeOutput(outputShape, \"int32\");\n  const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;\n  wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);\n  return [outValues, outIndices];\n};\nvar topKConfig3 = {\n  kernelName: TopK,\n  backendName: \"wasm\",\n  setupFunc: setup48,\n  kernelFunc: topk2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transform.js\nvar wasmTransform;\nfunction setup49(backend2) {\n  wasmTransform = backend2.wasm.cwrap(Transform, null, [\n    \"number\",\n    \"number\",\n    \"bool\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction transform4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const strides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer);\n  const out = backend2.makeOutput(outShape, image2.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const imageData = backend2.dataIdMap.get(image2.dataId);\n  const imageId = imageData.id;\n  const transformsData = backend2.dataIdMap.get(transforms.dataId);\n  const transformsId = transformsData.id;\n  const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n  let fillModeId;\n  switch (fillMode) {\n    case \"constant\":\n      fillModeId = 1;\n      break;\n    case \"reflect\":\n      fillModeId = 2;\n      break;\n    case \"wrap\":\n      fillModeId = 3;\n      break;\n    case \"nearest\":\n      fillModeId = 4;\n      break;\n    default:\n      fillModeId = 1;\n      break;\n  }\n  wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, strides, image2.shape.length - 1, interpolationModeId, fillModeId, fillValue, outId);\n  return out;\n}\nvar transformConfig3 = {\n  kernelName: Transform,\n  backendName: \"wasm\",\n  setupFunc: setup49,\n  kernelFunc: transform4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js\nfunction unpack3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const numOutputs = value.shape[axis];\n  const rank = value.shape.length;\n  const outShape = new Array(rank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < rank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = value.shape[i2];\n    }\n  }\n  const outs = new Array(numOutputs);\n  const begin = new Array(rank).fill(0);\n  const size = value.shape.slice();\n  size[axis] = 1;\n  for (let i2 = 0; i2 < outs.length; i2++) {\n    begin[axis] = i2;\n    outs[i2] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });\n  }\n  return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));\n}\nvar unpackConfig3 = {\n  kernelName: Unpack,\n  backendName: \"wasm\",\n  kernelFunc: unpack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js\nfunction zerosLike4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(0);\n  return out;\n}\nvar zerosLikeConfig3 = {\n  kernelName: ZerosLike,\n  backendName: \"wasm\",\n  kernelFunc: zerosLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js\nvar kernelConfigs3 = [\n  _fusedMatMulConfig3,\n  absConfig3,\n  addConfig3,\n  addNConfig3,\n  allConfig3,\n  anyConfig3,\n  argMaxConfig3,\n  avgPoolConfig3,\n  batchMatMulConfig3,\n  batchToSpaceNDConfig3,\n  castConfig3,\n  ceilConfig3,\n  clipByValueConfig3,\n  concatConfig3,\n  conv2DConfig3,\n  conv2DBackpropInputConfig3,\n  cosConfig3,\n  coshConfig3,\n  cropAndResizeConfig3,\n  cumprodConfig3,\n  cumsumConfig3,\n  depthToSpaceConfig3,\n  depthwiseConv2dNativeConfig3,\n  eluConfig3,\n  equalConfig3,\n  expConfig3,\n  expandDimsConfig3,\n  fillConfig3,\n  flipLeftRightConfig3,\n  floorConfig3,\n  floorDivConfig3,\n  fusedBatchNormConfig,\n  fusedConv2DConfig3,\n  fusedDepthwiseConv2DConfig3,\n  gatherNdConfig3,\n  gatherV2Config3,\n  greaterConfig3,\n  greaterEqualConfig3,\n  identityConfig3,\n  leakyReluConfig3,\n  lessConfig3,\n  lessEqualConfig3,\n  logConfig3,\n  logicalAndConfig3,\n  maxConfig3,\n  maximumConfig3,\n  maxPoolConfig3,\n  meanConfig3,\n  minConfig3,\n  minimumConfig3,\n  mirrorPadConfig3,\n  multiplyConfig3,\n  negConfig3,\n  nonMaxSuppressionV3Config3,\n  nonMaxSuppressionV4Config3,\n  nonMaxSuppressionV5Config3,\n  notEqualConfig3,\n  oneHotConfig3,\n  onesLikeConfig3,\n  packConfig3,\n  padV2Config3,\n  powConfig3,\n  preluConfig3,\n  prodConfig3,\n  rangeConfig3,\n  realDivConfig3,\n  reluConfig3,\n  relu6Config3,\n  reshapeConfig3,\n  resizeBilinearConfig3,\n  reverseConfig3,\n  rotateWithOffsetConfig3,\n  roundConfig3,\n  rsqrtConfig3,\n  scatterNdConfig3,\n  selectConfig3,\n  sigmoidConfig3,\n  sinConfig3,\n  sliceConfig3,\n  softmaxConfig3,\n  spaceToBatchNDConfig3,\n  sparseFillEmptyRowsConfig3,\n  sparseReshapeConfig3,\n  sparseSegmentMeanConfig3,\n  sparseSegmentSumConfig3,\n  splitVConfig3,\n  sqrtConfig3,\n  squareConfig3,\n  squaredDifferenceConfig3,\n  stepConfig3,\n  stridedSliceConfig3,\n  subConfig3,\n  sumConfig3,\n  tanConfig3,\n  tanhConfig3,\n  tileConfig3,\n  topKConfig3,\n  transformConfig3,\n  transposeConfig3,\n  unpackConfig3,\n  zerosLikeConfig3\n];\nfor (const kernelConfig of kernelConfigs3) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js\nvar ENV6 = env();\nENV6.registerFlag(\"WASM_HAS_SIMD_SUPPORT\", async () => WebAssembly.validate(new Uint8Array([\n  0,\n  97,\n  115,\n  109,\n  1,\n  0,\n  0,\n  0,\n  1,\n  4,\n  1,\n  96,\n  0,\n  0,\n  3,\n  2,\n  1,\n  0,\n  10,\n  9,\n  1,\n  7,\n  0,\n  65,\n  0,\n  253,\n  15,\n  26,\n  11\n])));\nENV6.registerFlag(\"WASM_HAS_MULTITHREAD_SUPPORT\", async () => {\n  if (ENV6.get(\"IS_NODE\")) {\n    return false;\n  }\n  try {\n    new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));\n    return WebAssembly.validate(new Uint8Array([\n      0,\n      97,\n      115,\n      109,\n      1,\n      0,\n      0,\n      0,\n      1,\n      4,\n      1,\n      96,\n      0,\n      0,\n      3,\n      2,\n      1,\n      0,\n      5,\n      4,\n      1,\n      3,\n      1,\n      1,\n      10,\n      11,\n      1,\n      9,\n      0,\n      65,\n      0,\n      254,\n      16,\n      2,\n      0,\n      26,\n      11\n    ]));\n  } catch (e2) {\n    return false;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js\nvar import_tfjs_backend_wasm_threaded_simd = __toESM(require_tfjs_backend_wasm_threaded_simd());\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\nvar wasmWorkerContents = '\"use strict\";var Module={};var ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require(\"worker_threads\");var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var fs=require(\"fs\");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,\"utf8\"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+\"\\n\");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;self.alert=threadAlert;Module[\"instantiateWasm\"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);receiveInstance(instance);Module[\"wasmModule\"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0,1);Module[\"establishStackSpace\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].threadInit();try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(Module[\"keepRuntimeAlive\"]()){Module[\"PThread\"].setExitStatus(result)}else{Module[\"__emscripten_thread_exit\"](result)}}catch(ex){if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"keepRuntimeAlive\"]()){}else{Module[\"__emscripten_thread_exit\"](ex.status)}}else{throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"__emscripten_thread_exit\"](-1)}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else if(e.data.cmd===\"processProxyingQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_proxy_execute_queue\"](e.data.queue)}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);if(Module[\"__emscripten_thread_crashed\"]){Module[\"__emscripten_thread_crashed\"]()}throw ex}});';\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js\nvar import_tfjs_backend_wasm = __toESM(require_tfjs_backend_wasm());\nvar BackendWasm = class extends KernelBackend {\n  constructor(wasm) {\n    super();\n    this.wasm = wasm;\n    this.dataIdNextNumber = 1;\n    this.wasm.tfjs.initWithThreadsCount(threadsCount);\n    actualThreadsCount = this.wasm.tfjs.getThreadsCount();\n    this.dataIdMap = new DataStorage(this, engine());\n  }\n  write(values, shape, dtype) {\n    const dataId = { id: this.dataIdNextNumber++ };\n    this.move(dataId, values, shape, dtype, 1);\n    return dataId;\n  }\n  numDataIds() {\n    return this.dataIdMap.numDataIds();\n  }\n  async time(f) {\n    const start = util_exports.now();\n    f();\n    const kernelMs = util_exports.now() - start;\n    return { kernelMs };\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    const id = this.dataIdNextNumber++;\n    if (dtype === \"string\") {\n      const stringBytes = values;\n      this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount });\n      return;\n    }\n    const size = util_exports.sizeFromShape(shape);\n    const numBytes = size * util_exports.bytesPerElement(dtype);\n    const memoryOffset = this.wasm._malloc(numBytes);\n    this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount });\n    this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n    if (values != null) {\n      this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);\n    }\n  }\n  async read(dataId) {\n    return this.readSync(dataId);\n  }\n  readSync(dataId, start, end) {\n    const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId);\n    if (dtype === \"string\") {\n      if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) {\n        return stringBytes;\n      }\n      return stringBytes.slice(start, end);\n    }\n    start = start || 0;\n    end = end || util_exports.sizeFromShape(shape);\n    const bytesPerElement2 = util_exports.bytesPerElement(dtype);\n    const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2);\n    return typedArrayFromBuffer(bytes.buffer, dtype);\n  }\n  disposeData(dataId, force = false) {\n    if (this.dataIdMap.has(dataId)) {\n      const data = this.dataIdMap.get(dataId);\n      data.refCount--;\n      if (!force && data.refCount > 0) {\n        return false;\n      }\n      this.wasm._free(data.memoryOffset);\n      this.wasm.tfjs.disposeData(data.id);\n      this.dataIdMap.delete(dataId);\n    }\n    return true;\n  }\n  refCount(dataId) {\n    if (this.dataIdMap.has(dataId)) {\n      const tensorData = this.dataIdMap.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const data = this.dataIdMap.get(dataId);\n    if (data != null) {\n      data.refCount++;\n    }\n  }\n  floatPrecision() {\n    return 32;\n  }\n  getMemoryOffset(dataId) {\n    return this.dataIdMap.get(dataId).memoryOffset;\n  }\n  dispose() {\n    this.wasm.tfjs.dispose();\n    if (\"PThread\" in this.wasm) {\n      this.wasm.PThread.terminateAllThreads();\n    }\n    this.wasm = null;\n  }\n  memory() {\n    return { unreliable: false };\n  }\n  makeOutput(shape, dtype, memoryOffset) {\n    let dataId;\n    if (memoryOffset == null) {\n      dataId = this.write(null, shape, dtype);\n    } else {\n      const id = this.dataIdNextNumber++;\n      dataId = { id };\n      this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 });\n      const size = util_exports.sizeFromShape(shape);\n      this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n    }\n    return { dataId, shape, dtype };\n  }\n  typedArrayFromHeap({ shape, dtype, dataId }) {\n    const buffer2 = this.wasm.HEAPU8.buffer;\n    const { memoryOffset } = this.dataIdMap.get(dataId);\n    const size = util_exports.sizeFromShape(shape);\n    switch (dtype) {\n      case \"float32\":\n        return new Float32Array(buffer2, memoryOffset, size);\n      case \"int32\":\n        return new Int32Array(buffer2, memoryOffset, size);\n      case \"bool\":\n        return new Uint8Array(buffer2, memoryOffset, size);\n      default:\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n  }\n};\nfunction createInstantiateWasmFunc(path) {\n  return (imports, callback) => {\n    util_exports.fetch(path, { credentials: \"same-origin\" }).then((response) => {\n      if (!response[\"ok\"]) {\n        imports.env.a(`failed to load wasm binary file at '${path}'`);\n      }\n      response.arrayBuffer().then((binary) => {\n        WebAssembly.instantiate(binary, imports).then((output) => {\n          callback(output.instance, output.module);\n        });\n      });\n    });\n    return {};\n  };\n}\nfunction getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {\n  if (wasmPath != null) {\n    return wasmPath;\n  }\n  let path = \"tfjs-backend-wasm.wasm\";\n  if (simdSupported && threadsSupported) {\n    path = \"tfjs-backend-wasm-threaded-simd.wasm\";\n  } else if (simdSupported) {\n    path = \"tfjs-backend-wasm-simd.wasm\";\n  }\n  if (wasmFileMap != null) {\n    if (wasmFileMap[path] != null) {\n      return wasmFileMap[path];\n    }\n  }\n  return wasmModuleFolder + path;\n}\nasync function init() {\n  const [simdSupported, threadsSupported] = await Promise.all([\n    env().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),\n    env().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")\n  ]);\n  return new Promise((resolve, reject) => {\n    const factoryConfig = {};\n    factoryConfig.locateFile = (path, prefix) => {\n      if (path.endsWith(\".worker.js\")) {\n        const response = wasmWorkerContents.replace(/\\n/g, \"\\\\n\");\n        const blob = new Blob([response], { type: \"application/javascript\" });\n        return URL.createObjectURL(blob);\n      }\n      if (path.endsWith(\".wasm\")) {\n        return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);\n      }\n      return prefix + path;\n    };\n    if (customFetch) {\n      factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : \"\"));\n    }\n    let initialized = false;\n    factoryConfig.onAbort = () => {\n      if (initialized) {\n        return;\n      }\n      if (initAborted) {\n        return;\n      }\n      initAborted = true;\n      const rejectMsg = \"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers\";\n      reject({ message: rejectMsg });\n    };\n    let wasm;\n    if (threadsSupported && simdSupported && wasmPath == null) {\n      factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + import_tfjs_backend_wasm_threaded_simd.default.toString()], { type: \"text/javascript\" });\n      wasm = (0, import_tfjs_backend_wasm_threaded_simd.default)(factoryConfig);\n    } else {\n      wasm = (0, import_tfjs_backend_wasm.default)(factoryConfig);\n    }\n    wasm.then((module) => {\n      initialized = true;\n      initAborted = false;\n      const voidReturnType = null;\n      module.tfjs = {\n        init: module.cwrap(\"init\", null, []),\n        initWithThreadsCount: module.cwrap(\"init_with_threads_count\", null, [\"number\"]),\n        getThreadsCount: module.cwrap(\"get_threads_count\", \"number\", []),\n        registerTensor: module.cwrap(\"register_tensor\", null, [\n          \"number\",\n          \"number\",\n          \"number\"\n        ]),\n        disposeData: module.cwrap(\"dispose_data\", voidReturnType, [\"number\"]),\n        dispose: module.cwrap(\"dispose\", voidReturnType, [])\n      };\n      resolve({ wasm: module });\n    });\n  });\n}\nfunction typedArrayFromBuffer(buffer2, dtype) {\n  switch (dtype) {\n    case \"float32\":\n      return new Float32Array(buffer2);\n    case \"int32\":\n      return new Int32Array(buffer2);\n    case \"bool\":\n      return new Uint8Array(buffer2);\n    default:\n      throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nvar wasmBinaryNames = [\n  \"tfjs-backend-wasm.wasm\",\n  \"tfjs-backend-wasm-simd.wasm\",\n  \"tfjs-backend-wasm-threaded-simd.wasm\"\n];\nvar wasmPath = null;\nvar wasmPathPrefix = null;\nvar wasmFileMap = {};\nvar initAborted = false;\nvar customFetch = false;\nfunction setWasmPath(path, usePlatformFetch = false) {\n  deprecationWarn(\"setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.\");\n  if (initAborted) {\n    throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");\n  }\n  wasmPath = path;\n  customFetch = usePlatformFetch;\n}\nfunction setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {\n  if (initAborted) {\n    throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");\n  }\n  if (typeof prefixOrFileMap === \"string\") {\n    wasmPathPrefix = prefixOrFileMap;\n  } else {\n    wasmFileMap = prefixOrFileMap;\n    const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);\n    if (missingPaths.length > 0) {\n      throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);\n    }\n  }\n  customFetch = usePlatformFetch;\n}\nvar threadsCount = -1;\nvar actualThreadsCount = -1;\nfunction setThreadsCount(numThreads) {\n  threadsCount = numThreads;\n}\nfunction getThreadsCount() {\n  if (actualThreadsCount === -1) {\n    throw new Error(`WASM backend not initialized.`);\n  }\n  return actualThreadsCount;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js\nvar version8 = \"3.18.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js\nvar WASM_PRIORITY = 2;\nregisterBackend(\"wasm\", async () => {\n  const { wasm } = await init();\n  return new BackendWasm(wasm);\n}, WASM_PRIORITY);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flags_webgpu.js\nvar ENV7 = env();\nENV7.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\", () => 15);\nENV7.registerFlag(\"WEBGPU_CPU_FORWARD\", () => true);\nENV7.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\", () => 4);\nENV7.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\", () => false);\nENV7.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\", () => false);\nENV7.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e3);\nENV7.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\", () => false);\nENV7.registerFlag(\"WEBGPU_USE_IMPORT\", () => false);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_util.js\nvar BinaryOpType;\n(function(BinaryOpType2) {\n  BinaryOpType2[BinaryOpType2[\"MUL\"] = 0] = \"MUL\";\n  BinaryOpType2[BinaryOpType2[\"ADD\"] = 1] = \"ADD\";\n  BinaryOpType2[BinaryOpType2[\"SUB\"] = 2] = \"SUB\";\n  BinaryOpType2[BinaryOpType2[\"DIV\"] = 3] = \"DIV\";\n  BinaryOpType2[BinaryOpType2[\"EQUAL\"] = 4] = \"EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"GREATER\"] = 5] = \"GREATER\";\n  BinaryOpType2[BinaryOpType2[\"GREATER_EQUAL\"] = 6] = \"GREATER_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"LESS\"] = 7] = \"LESS\";\n  BinaryOpType2[BinaryOpType2[\"LESS_EQUAL\"] = 8] = \"LESS_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"LOGICAL_AND\"] = 9] = \"LOGICAL_AND\";\n  BinaryOpType2[BinaryOpType2[\"NOT_EQUAL\"] = 10] = \"NOT_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"SQUARED_DIFFERENCE\"] = 11] = \"SQUARED_DIFFERENCE\";\n  BinaryOpType2[BinaryOpType2[\"INT_DIV\"] = 12] = \"INT_DIV\";\n  BinaryOpType2[BinaryOpType2[\"POW\"] = 13] = \"POW\";\n  BinaryOpType2[BinaryOpType2[\"PRELU\"] = 14] = \"PRELU\";\n  BinaryOpType2[BinaryOpType2[\"MAX\"] = 15] = \"MAX\";\n  BinaryOpType2[BinaryOpType2[\"MIN\"] = 16] = \"MIN\";\n  BinaryOpType2[BinaryOpType2[\"COMPLEX_MULTIPLY_REAL\"] = 17] = \"COMPLEX_MULTIPLY_REAL\";\n  BinaryOpType2[BinaryOpType2[\"COMPLEX_MULTIPLY_IMAG\"] = 18] = \"COMPLEX_MULTIPLY_IMAG\";\n})(BinaryOpType || (BinaryOpType = {}));\nvar ADD2 = \"return a + b;\";\nvar COMPLEX_MULTIPLY_REAL = \"return areal * breal - aimag * bimag;\";\nvar COMPLEX_MULTIPLY_IMAG = \"return areal * bimag + aimag * breal;\";\nvar DIV2 = \"return a / b;\";\nvar MUL2 = \"return a * b;\";\nvar SQUARED_DIFFERENCE2 = \"return (a - b) * (a - b);\";\nvar SUB2 = \"return a - b;\";\nvar EQUAL2 = \"return f32(a == b);\";\nvar EQUAL_VEC4 = \"return vec4<f32>(a == b);\";\nvar GREATER2 = \"return f32(a > b);\";\nvar GREATER_VEC4 = \"return vec4<f32>(a > b);\";\nvar GREATER_EQUAL2 = \"return f32(a >= b);\";\nvar GREATER_EQUAL_VEC4 = \"return vec4<f32>(a >= b);\";\nvar LESS2 = \"return f32(a < b);\";\nvar LESS_VEC4 = \"return vec4<f32>(a < b);\";\nvar LESS_EQUAL2 = \"return f32(a <= b);\";\nvar LESS_EQUAL_VEC4 = \"return vec4<f32>(a <= b);\";\nvar LOGICAL_AND2 = \"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\";\nvar LOGICAL_AND_VEC4 = `return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`;\nvar CHECK_NAN_SNIPPET4 = `\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `;\nvar CHECK_NAN_SNIPPET_VEC4 = `\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `;\nvar INT_DIV2 = `\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `;\nvar INT_DIV_VEC4 = `\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `;\nvar NOT_EQUAL2 = \"return f32(a != b);\";\nvar NOT_EQUAL_VEC4 = \"return vec4<f32>(a != b);\";\nvar POW2 = `\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `;\nvar POW_VEC4 = `\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${CHECK_NAN_SNIPPET_VEC4}\n  return resultTemp;\n  `;\nvar PRELU2 = `if (a < 0.0) { return b * a; }  return a;`;\nvar PRELU_VEC4 = `\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;\nfunction getMinMaxString(op2, useVec4) {\n  const checkNanSnippet = useVec4 ? CHECK_NAN_SNIPPET_VEC4 : CHECK_NAN_SNIPPET4;\n  return useVec4 ? `\n    var resultTemp = vec4<f32>(${op2}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    ` + checkNanSnippet + `\n    return resultTemp;\n  ` : checkNanSnippet + `\n    return ${op2}(a, b);\n  `;\n}\nfunction getBinaryOpString(type, useVec4) {\n  switch (type) {\n    case BinaryOpType.MUL:\n      return MUL2;\n    case BinaryOpType.ADD:\n      return ADD2;\n    case BinaryOpType.SUB:\n      return SUB2;\n    case BinaryOpType.DIV:\n      return DIV2;\n    case BinaryOpType.EQUAL:\n      return useVec4 ? EQUAL_VEC4 : EQUAL2;\n    case BinaryOpType.GREATER:\n      return useVec4 ? GREATER_VEC4 : GREATER2;\n    case BinaryOpType.GREATER_EQUAL:\n      return useVec4 ? GREATER_EQUAL_VEC4 : GREATER_EQUAL2;\n    case BinaryOpType.LESS:\n      return useVec4 ? LESS_VEC4 : LESS2;\n    case BinaryOpType.LESS_EQUAL:\n      return useVec4 ? LESS_EQUAL_VEC4 : LESS_EQUAL2;\n    case BinaryOpType.LOGICAL_AND:\n      return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND2;\n    case BinaryOpType.NOT_EQUAL:\n      return useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL2;\n    case BinaryOpType.SQUARED_DIFFERENCE:\n      return SQUARED_DIFFERENCE2;\n    case BinaryOpType.INT_DIV:\n      return useVec4 ? INT_DIV_VEC4 : INT_DIV2;\n    case BinaryOpType.PRELU:\n      return useVec4 ? PRELU_VEC4 : PRELU2;\n    case BinaryOpType.MAX:\n      return getMinMaxString(\"max\", useVec4);\n    case BinaryOpType.MIN:\n      return getMinMaxString(\"min\", useVec4);\n    case BinaryOpType.POW:\n      return useVec4 ? POW_VEC4 : POW2;\n    case BinaryOpType.COMPLEX_MULTIPLY_REAL:\n      return COMPLEX_MULTIPLY_REAL;\n    case BinaryOpType.COMPLEX_MULTIPLY_IMAG:\n      return COMPLEX_MULTIPLY_IMAG;\n    default:\n      throw new Error(`BinaryType ${type} is not implemented!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_util.js\nvar UnaryOpType;\n(function(UnaryOpType2) {\n  UnaryOpType2[UnaryOpType2[\"ABS\"] = 0] = \"ABS\";\n  UnaryOpType2[UnaryOpType2[\"CEIL\"] = 1] = \"CEIL\";\n  UnaryOpType2[UnaryOpType2[\"COS\"] = 2] = \"COS\";\n  UnaryOpType2[UnaryOpType2[\"COSH\"] = 3] = \"COSH\";\n  UnaryOpType2[UnaryOpType2[\"ELU\"] = 4] = \"ELU\";\n  UnaryOpType2[UnaryOpType2[\"EXP\"] = 5] = \"EXP\";\n  UnaryOpType2[UnaryOpType2[\"EXPM1\"] = 6] = \"EXPM1\";\n  UnaryOpType2[UnaryOpType2[\"FLOOR\"] = 7] = \"FLOOR\";\n  UnaryOpType2[UnaryOpType2[\"LINEAR\"] = 8] = \"LINEAR\";\n  UnaryOpType2[UnaryOpType2[\"LOG\"] = 9] = \"LOG\";\n  UnaryOpType2[UnaryOpType2[\"LOGICAL_NOT\"] = 10] = \"LOGICAL_NOT\";\n  UnaryOpType2[UnaryOpType2[\"NEG\"] = 11] = \"NEG\";\n  UnaryOpType2[UnaryOpType2[\"RELU\"] = 12] = \"RELU\";\n  UnaryOpType2[UnaryOpType2[\"RELU6\"] = 13] = \"RELU6\";\n  UnaryOpType2[UnaryOpType2[\"LEAKYRELU\"] = 14] = \"LEAKYRELU\";\n  UnaryOpType2[UnaryOpType2[\"RSQRT\"] = 15] = \"RSQRT\";\n  UnaryOpType2[UnaryOpType2[\"SIN\"] = 16] = \"SIN\";\n  UnaryOpType2[UnaryOpType2[\"SINH\"] = 17] = \"SINH\";\n  UnaryOpType2[UnaryOpType2[\"SIGMOID\"] = 18] = \"SIGMOID\";\n  UnaryOpType2[UnaryOpType2[\"SQRT\"] = 19] = \"SQRT\";\n  UnaryOpType2[UnaryOpType2[\"SQUARE\"] = 20] = \"SQUARE\";\n  UnaryOpType2[UnaryOpType2[\"TANH\"] = 21] = \"TANH\";\n  UnaryOpType2[UnaryOpType2[\"TO_INT\"] = 22] = \"TO_INT\";\n})(UnaryOpType || (UnaryOpType = {}));\nvar ABS3 = `return abs(a);`;\nvar CEIL2 = `return ceil(a);`;\nvar COS2 = `return cos(a);`;\nvar COSH2 = `\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar EXPM12 = `return exp(a) - 1.0;`;\nvar ELU5 = `if (a >= 0.0) { return a; }  return (exp(a) - 1.0);`;\nvar ELU_VEC4 = `\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`;\nvar EXP2 = `return exp(a);`;\nvar FLOOR2 = `return floor(a);`;\nvar LINEAR3 = `return a;`;\nvar LOG2 = `if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`;\nvar LOGICAL_NOT2 = `return f32(!(a >= 1.0));`;\nvar NEG2 = `return -a;`;\nvar LEAKYRELU2 = `if (a < 0.0) { return uniforms.alpha * a; } return a;`;\nvar LEAKYRELU_VEC4 = `\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`;\nvar RELU4 = `return select(a, 0.0, a < 0.0);`;\nvar RELU64 = \"return clamp(a, 0.0, 6.0);\";\nvar RELU6_VEC4 = \"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\";\nvar RELU_VEC4 = `\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`;\nvar RSQRT2 = `return 1.0/sqrt(a);`;\nvar SIGMOID4 = `return 1.0 / (1.0 + exp(-1.0 * a));`;\nvar SIN2 = `return sin(a);`;\nvar SINH2 = `\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar SQRT2 = `return sqrt(a);`;\nvar SQUARE2 = `return a * a;`;\nvar TANH2 = `\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar TO_INT2 = `return f32(i32((a)));`;\nfunction getUnaryOpString(type, useVec4) {\n  switch (type) {\n    case UnaryOpType.ABS:\n      return ABS3;\n    case UnaryOpType.COS:\n      return COS2;\n    case UnaryOpType.COSH:\n      return COSH2;\n    case UnaryOpType.CEIL:\n      return CEIL2;\n    case UnaryOpType.ELU:\n      return useVec4 ? ELU_VEC4 : ELU5;\n    case UnaryOpType.EXP:\n      return EXP2;\n    case UnaryOpType.EXPM1:\n      return EXPM12;\n    case UnaryOpType.FLOOR:\n      return FLOOR2;\n    case UnaryOpType.LINEAR:\n      return LINEAR3;\n    case UnaryOpType.LOG:\n      return LOG2;\n    case UnaryOpType.LOGICAL_NOT:\n      return LOGICAL_NOT2;\n    case UnaryOpType.NEG:\n      return NEG2;\n    case UnaryOpType.LEAKYRELU:\n      return useVec4 ? LEAKYRELU_VEC4 : LEAKYRELU2;\n    case UnaryOpType.RELU:\n      return useVec4 ? RELU_VEC4 : RELU4;\n    case UnaryOpType.RELU6:\n      return useVec4 ? RELU6_VEC4 : RELU64;\n    case UnaryOpType.RSQRT:\n      return RSQRT2;\n    case UnaryOpType.SIGMOID:\n      return SIGMOID4;\n    case UnaryOpType.SIN:\n      return SIN2;\n    case UnaryOpType.SINH:\n      return SINH2;\n    case UnaryOpType.SQRT:\n      return SQRT2;\n    case UnaryOpType.SQUARE:\n      return SQUARE2;\n    case UnaryOpType.TANH:\n      return TANH2;\n    case UnaryOpType.TO_INT:\n      return TO_INT2;\n    default:\n      throw new Error(`BinaryType ${type} is not implemented!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/activation_util.js\nfunction mapActivationToShaderProgram2(activation2, packed = false) {\n  if (activation2 === null) {\n    return null;\n  } else if (activation2 === \"linear\") {\n    return getUnaryOpString(UnaryOpType.LINEAR);\n  } else if (activation2 === \"relu\") {\n    return getUnaryOpString(UnaryOpType.RELU, packed);\n  } else if (activation2 === \"elu\") {\n    return getUnaryOpString(UnaryOpType.ELU, packed);\n  } else if (activation2 === \"relu6\") {\n    return getUnaryOpString(UnaryOpType.RELU6, packed);\n  } else if (activation2 === \"prelu\") {\n    return getBinaryOpString(BinaryOpType.PRELU, packed);\n  } else if (activation2 === \"sigmoid\") {\n    return getUnaryOpString(UnaryOpType.SIGMOID, packed);\n  } else if (activation2 === \"leakyrelu\") {\n    return getUnaryOpString(UnaryOpType.LEAKYRELU, packed);\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the WebGPU backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/shader_util.js\nfunction symbolicallyComputeStrides2(indicesArr, variableName) {\n  if (Math.max(...indicesArr) > 3) {\n    throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");\n  }\n  const numCoords = indicesArr.length;\n  const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n  const strides = new Array(numCoords - 1);\n  strides[numCoords - 2] = shape[numCoords - 1];\n  for (let i2 = numCoords - 3; i2 >= 0; --i2) {\n    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;\n  }\n  return strides;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/shader_preprocessor.js\nfunction getCoordsDataType2(rank) {\n  if (rank <= 1) {\n    return \"i32\";\n  } else if (rank === 2) {\n    return `vec2<i32>`;\n  } else if (rank === 3) {\n    return `vec3<i32>`;\n  } else if (rank === 4) {\n    return `vec4<i32>`;\n  } else if (rank === 5) {\n    return `vec5`;\n  } else if (rank === 6) {\n    return `vec6`;\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\nfunction getCoordsXYZ(index) {\n  if (index === 0) {\n    return \"x\";\n  } else if (index === 1) {\n    return \"y\";\n  } else if (index === 2) {\n    return \"z\";\n  } else if (index === 3) {\n    return \"w\";\n  } else if (index === 4) {\n    return \"u\";\n  } else if (index === 5) {\n    return \"v\";\n  } else {\n    throw Error(`Index ${index} is not yet supported`);\n  }\n}\nfunction mapToWgslTypes(type, isVec4) {\n  if (type === \"float32\") {\n    return isVec4 ? \"vec4<f32>\" : \"f32\";\n  } else if (type === \"int32\") {\n    return isVec4 ? \"vec4<i32>\" : \"i32\";\n  } else if (type === \"bool\") {\n    return isVec4 ? \"vec4<i32>\" : \"i32\";\n  }\n  return type;\n}\nfunction getWorkGroupSizeString() {\n  return `\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`;\n}\nfunction getMainHeaderString() {\n  return `\n  ${getWorkGroupSizeString()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`;\n}\nfunction getMainHeaderAndGlobalIndexString() {\n  return `\n    ${getMainHeaderString()}\n      let index = getGlobalIndex();\n`;\n}\nfunction makeShader2(inputInfo, outputData, program, isFromPixel = false) {\n  const prefixSnippets = [];\n  prefixSnippets.push(`\n    let workGroupSizeX = ${program.workGroupSize[0]}u;\n    let workGroupSizeY = ${program.workGroupSize[1]}u;\n    let workGroupSizeZ = ${program.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `);\n  if (isFromPixel === true) {\n    prefixSnippets.push(`\n      struct Uniform {\n        size            : i32,\n        numChannels     : i32,\n        outShapeStrides : vec2<i32>,\n        dispatchSize    : vec3<u32>,\n      };\n\n      @group(0) @binding(0) var<storage, write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `);\n    return [\n      commonSnippet,\n      prefixSnippets.join(\"\\n\"),\n      getCoordsFromIndexSnippet(outputData.shape),\n      program.getUserCode()\n    ].join(\"\\n\");\n  }\n  let preMemberIsStruct = false;\n  let currentMemberIsStruct = false;\n  let uniformDeclaration = \"struct Uniforms { NAN : f32, \";\n  program.variableNames.forEach((x, i2) => {\n    const perDataType = getCoordsDataType2(inputInfo[i2].shape.length);\n    if (perDataType === \"vec5\" || perDataType === \"vec6\") {\n      currentMemberIsStruct = true;\n    }\n    if (preMemberIsStruct || currentMemberIsStruct) {\n      uniformDeclaration += `@align(16) `;\n    }\n    preMemberIsStruct = currentMemberIsStruct;\n    uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;\n  });\n  const outputDataType = getCoordsDataType2(outputData.shape.length);\n  currentMemberIsStruct = outputDataType === \"vec5\" || outputDataType === \"vec6\";\n  if (preMemberIsStruct || currentMemberIsStruct) {\n    uniformDeclaration += `@align(16) `;\n  }\n  preMemberIsStruct = currentMemberIsStruct;\n  uniformDeclaration += `outShape : ${outputDataType}, `;\n  const stridesLength = outputData.shape.length - 1;\n  const stridesDataType = getCoordsDataType2(stridesLength);\n  currentMemberIsStruct = stridesDataType === \"vec5\" || stridesDataType === \"vec6\";\n  if (preMemberIsStruct || currentMemberIsStruct) {\n    uniformDeclaration += `@align(16) `;\n  }\n  preMemberIsStruct = currentMemberIsStruct;\n  uniformDeclaration += `\n       outShapeStrides: ${stridesDataType}, `;\n  if (program.size) {\n    if (preMemberIsStruct) {\n      uniformDeclaration += `@align(16) `;\n    }\n    preMemberIsStruct = false;\n    uniformDeclaration += \"size : i32, \";\n  }\n  if (program.uniforms) {\n    if (preMemberIsStruct) {\n      uniformDeclaration += `@align(16) `;\n    }\n    uniformDeclaration += program.uniforms;\n  }\n  uniformDeclaration += \"};\";\n  prefixSnippets.push(uniformDeclaration);\n  if (program.atomic) {\n    prefixSnippets.push(`\n    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n  `);\n  } else {\n    prefixSnippets.push(`\n    @group(0) @binding(0) var<storage, write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;\n  `);\n  }\n  program.variableNames.forEach((x, i2) => {\n    prefixSnippets.push(`\n    @group(0) @binding(${1 + i2}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i2] : mapToWgslTypes(inputInfo[i2].dtype, program.isVec4)}>;\n      `);\n  });\n  if (uniformDeclaration !== \"\") {\n    prefixSnippets.push(`\n    @group(0) @binding(${1 + program.variableNames.length}) var<uniform> uniforms: Uniforms;\n    `);\n  }\n  const [coordsSnippet, dispatchLayoutRank] = getOutputCoordsSnippet(outputData.shape, program.dispatchLayout);\n  const sources = [\n    commonSnippet,\n    prefixSnippets.join(\"\\n\"),\n    getCoordsFromIndexSnippet(outputData.shape),\n    coordsSnippet,\n    getOutputIndexFromCoordsSnippet(outputData.shape.length)\n  ];\n  if (!program.atomic) {\n    sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));\n  }\n  if (dispatchLayoutRank === outputData.shape.length) {\n    const inputSnippet = inputInfo.map((x, i2) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i2] === \"vec4<f32>\" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join(\"\\n\");\n    sources.push(inputSnippet);\n  }\n  sources.push(program.getUserCode());\n  const source = sources.join(\"\\n\");\n  return source;\n}\nvar commonSnippet = `\n  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n  }\n  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;\nfunction getOutputIndexFromCoordsSnippet(outRank) {\n  let snippet = \"\";\n  switch (outRank) {\n    case 0:\n    case 1:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;\n      break;\n    case 2:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;\n      break;\n    case 3:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;\n      break;\n    case 4:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;\n      break;\n    case 5:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u;\n        }\n        `;\n      break;\n    case 6:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u * uniforms.outShapeStrides.u +\n              coords.v;\n        }\n        `;\n      break;\n    default:\n      util_exports.assert(false, () => `Unsupported ${outRank}D shape`);\n      break;\n  }\n  return snippet;\n}\nfunction setOutputSnippet(outShape, outBufferType, isVec4) {\n  const outRank = outShape.length;\n  const wgslType = mapToWgslTypes(outBufferType, isVec4);\n  let snippet;\n  if (isVec4) {\n    snippet = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${wgslType}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${wgslType}(value);\n    }`;\n  } else {\n    snippet = `fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${wgslType}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${wgslType}(value);\n    }`;\n  }\n  if (outRank >= 2) {\n    const dims = [\"d0\", \"d1\", \"d2\", \"d3\", \"d4\", \"d5\"].slice(0, outRank);\n    const type = getCoordsDataType2(outRank);\n    if (isVec4) {\n      snippet += `\n      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `;\n    } else {\n      snippet += `\n      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `;\n    }\n  }\n  return snippet;\n}\nfunction getInputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {\n  let res = getInputAtCoordsSnippet(inputInfo, isVec4);\n  const inShape = inputInfo.shape;\n  if (inShape.length <= outShape.length) {\n    res += getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout);\n  }\n  return res;\n}\nfunction getInputAtCoordsSnippet(inputInfo, isVec4) {\n  const texName = inputInfo.name;\n  const rank = inputInfo.shape.length;\n  const type = getCoordsDataType2(rank);\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const dims = [\"d0\", \"d1\", \"d2\", \"d3\", \"d4\", \"d5\"].slice(0, rank);\n  const inputs = dims.map((d) => `${d} : i32`).join(\", \");\n  if (rank < 1) {\n    if (isVec4) {\n      return `\n        fn ${funcName}() -> vec4<f32> {\n          return vec4<f32>(${texName}[0]);\n        }\n      `;\n    }\n    return `\n      fn ${funcName}() ->f32 {\n        return f32(${texName}[0]);\n      }\n    `;\n  }\n  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;\n  let rankStr = `${rank}D`;\n  if (rank === 0) {\n    rankStr = \"1D\";\n  }\n  if (isVec4) {\n    return `\n      fn ${funcName}(${inputs}) -> vec4<f32> {\n        return vec4<f32>(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(\",\")}),\n          ${shapeStr}) / 4]);\n      }\n      `;\n  }\n  return `\n    fn ${funcName}(${inputs}) -> f32 {\n      return f32(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(\",\")}),\n        ${shapeStr})]);\n    }\n   `;\n}\nfunction getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"ByOutput\";\n  const inRank = inputInfo.shape.length;\n  const outRank = outShape.length;\n  const type = getCoordsDataType2(outRank);\n  if (util_exports.arraysEqual(inputInfo.shape, outShape) && isFlatDispatchLayout) {\n    if (isVec4) {\n      return `\n        fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${texName}[globalIndex]);\n        }\n\n        fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {\n          return vec4<f32>(${texName}[${outRank > 1 ? \"getOutputIndexFromCoords(coords)\" : \"coords\"} / 4]);\n        }\n        `;\n    } else {\n      return `\n      fn ${funcName}Index(globalIndex : i32) -> f32 {\n        return f32(${texName}[globalIndex]);\n      }\n\n      fn ${funcName}Coords(coords : ${type}) -> f32 {\n        return f32(${texName}[${outRank > 1 ? \"getOutputIndexFromCoords(coords)\" : \"coords\"}]);\n      }\n      `;\n    }\n  }\n  const broadcastDims = backend_util_exports.getBroadcastDims(inputInfo.shape, outShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet = \"\";\n  if (inRank === 0) {\n    if (isVec4) {\n      return `\n      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n        return get${texFuncSnippet}();\n      }\n\n      fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {\n        return get${texFuncSnippet}();\n      }\n    `;\n    }\n    return `\n      fn ${funcName}Index(globalIndex : i32) -> f32{\n        return get${texFuncSnippet}();\n      }\n\n      fn ${funcName}Coords(coords : ${type}) -> f32{\n        return get${texFuncSnippet}();\n      }\n    `;\n  } else {\n    if (outRank < 2 && broadcastDims.length >= 1) {\n      coordsSnippet = \"coords = 0;\";\n    } else {\n      coordsSnippet = broadcastDims.map((d) => `coords.${getCoordsXYZ(d + rankDiff)} = 0;`).join(\"\\n\");\n    }\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    if (outRank > 1) {\n      const coordsType = getCoordsDataType2(inRank);\n      const coordsValues = inputInfo.shape.map((s2, i2) => `coords.${getCoordsXYZ(i2 + rankDiff)}`).join(\", \");\n      unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;\n    } else {\n      unpackedCoordsSnippet = \"coords\";\n    }\n  }\n  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;\n  const rankStr = `${inRank}D`;\n  if (isVec4) {\n    return `\n      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${coordsSnippet}\n        return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];\n      }\n\n      fn ${funcName}Coords(coordsIn : ${type}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${coordsSnippet}\n        return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];\n      }\n    `;\n  }\n  return `\n    fn ${funcName}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${coordsSnippet}\n      return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);\n    }\n\n    fn ${funcName}Coords(coordsIn : ${type}) -> f32 {\n      var coords = coordsIn;\n      ${coordsSnippet}\n      return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);\n    }\n  `;\n}\nfunction getOutputCoordsSnippet(outShape, dispatchLayout) {\n  const { x, y = [], z = [] } = dispatchLayout;\n  const outRank = outShape.length;\n  if (x.length === outRank) {\n    const dtype2 = getCoordsDataType2(outRank);\n    const snippet2 = `fn getOutputCoords() -> ${dtype2}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `;\n    return [snippet2, outRank];\n  }\n  let gatherDimensionsStr = \"\";\n  const dims = [x, y, z];\n  let rank = 0;\n  for (let i2 = 0; i2 < dims.length; i2++) {\n    const arr = dims[i2];\n    if (arr.length === 0) {\n      continue;\n    }\n    rank += arr.length;\n    if (arr.length === 1) {\n      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i2}]);`;\n    } else {\n      const strides = symbolicallyComputeStrides2(arr, \"uniforms.outShape\");\n      gatherDimensionsStr += `var index${i2} = i32(globalId[${i2}]);`;\n      for (let j = 0; j < strides.length; j++) {\n        gatherDimensionsStr += `let d${arr[j]} = index${i2} / ${strides[j]};`;\n        if (j === strides.length - 1) {\n          gatherDimensionsStr += `let d${arr[j + 1]} = index${i2} - d${arr[j]} * ${strides[j]};`;\n        } else {\n          gatherDimensionsStr += `index${i2} = index${i2} - d${arr[j]} * ${strides[j]};`;\n        }\n      }\n    }\n  }\n  const dimensions = [];\n  for (let i2 = 0; i2 < rank; i2++) {\n    dimensions.push(`d${i2}`);\n  }\n  const dtype = getCoordsDataType2(rank);\n  let snippet = `fn getOutputCoords() -> ${dtype} {\n    ${gatherDimensionsStr}\n  `;\n  if (dimensions.length === 0) {\n    snippet += `return ${dtype}(0); }`;\n  } else {\n    snippet += `return ${dtype}(${dimensions.join(\",\")}); }`;\n  }\n  return [snippet, rank];\n}\nfunction getCoordsFromIndexSnippet(shape) {\n  const rank = shape.length;\n  if (rank <= 1) {\n    return `fn getCoordsFromIndex(index : i32) -> i32 { return index; }`;\n  }\n  const strides = util_exports.computeStrides(shape);\n  const dtype = getCoordsDataType2(rank);\n  const coords3 = [];\n  for (let i2 = 0; i2 < rank; i2++) {\n    coords3.push(`d${i2}`);\n  }\n  if (strides.length === 1) {\n    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;\n  }\n  let snippet;\n  snippet = \"var index2 = index;\" + strides.map((_, i2) => {\n    const line1 = `let ${coords3[i2]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;\n    const line2 = i2 === strides.length - 1 ? `let ${coords3[i2 + 1]} = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}` : `index2 = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n  return `\n    fn getCoordsFromIndex(index : i32) -> ${dtype} {\n      ${snippet}\n      return ${dtype}(${coords3.join(\",\")});\n    }\n  `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_util.js\nvar webgpu_util_exports = {};\n__export(webgpu_util_exports, {\n  ArrayBufferToTypedArray: () => ArrayBufferToTypedArray,\n  GPUBytesPerElement: () => GPUBytesPerElement,\n  computeDispatch: () => computeDispatch,\n  computeWorkGroupSizeForConv2d: () => computeWorkGroupSizeForConv2d,\n  computeWorkGroupSizeForMatMul: () => computeWorkGroupSizeForMatMul,\n  computeWorkPerThreadForConv2d: () => computeWorkPerThreadForConv2d,\n  flatDispatchLayout: () => flatDispatchLayout,\n  isWebGPUSupported: () => isWebGPUSupported,\n  tilesFitEvenlyIntoShape: () => tilesFitEvenlyIntoShape\n});\nvar arrayProduct = (arr) => {\n  let product = 1;\n  for (let i2 = 0; i2 < arr.length; i2++) {\n    product *= arr[i2];\n  }\n  return product;\n};\nfunction tilesFitEvenlyIntoShape(tileSize, shape) {\n  if (tileSize.length !== shape.length) {\n    throw new Error(`Cannot compute whether rank ${tileSize.length} tiles fit evenly into rank ${shape.length} shape - ranks must match.`);\n  }\n  return shape.every((dim, dimIdx) => dim % tileSize[dimIdx] === 0);\n}\nfunction computeDispatch(layout, outputShape, workGroupSize = [1, 1, 1], elementsPerThread = [1, 1, 1]) {\n  const [dispatchX, dispatchY, dispatchZ] = [\n    Math.ceil(arrayProduct(layout.x.map((d) => outputShape[d])) / (workGroupSize[0] * elementsPerThread[0])),\n    layout.y ? Math.ceil(arrayProduct(layout.y.map((d) => outputShape[d])) / (workGroupSize[1] * elementsPerThread[1])) : 1,\n    layout.z ? Math.ceil(arrayProduct(layout.z.map((d) => outputShape[d])) / (workGroupSize[2] * elementsPerThread[2])) : 1\n  ];\n  return [dispatchX, dispatchY, dispatchZ];\n}\nfunction computeWorkGroupSizeForConv2d(layout, outputShape) {\n  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));\n  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));\n  if (dim0 <= 4) {\n    return [4, 16, 1];\n  }\n  if (dim1 <= 4) {\n    return [16, 4, 1];\n  }\n  return [16, 16, 1];\n}\nfunction computeWorkGroupSizeForMatMul(dimAOuter, dimInner, dimBOuter) {\n  if (dimAOuter === 1) {\n    return [32, 1, 1];\n  } else if (dimBOuter === 1) {\n    return [1, 32, 1];\n  }\n  return [8, 8, 1];\n}\nfunction computeWorkPerThreadForConv2d(layout, outputShape) {\n  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));\n  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));\n  if (dim0 <= 4) {\n    return [1, 2, 1];\n  }\n  if (dim1 <= 4) {\n    return [2, 1, 1];\n  }\n  return [2, 2, 1];\n}\nfunction flatDispatchLayout(shape) {\n  return { x: shape.map((d, i2) => i2) };\n}\nfunction GPUBytesPerElement(dtype) {\n  if (dtype === \"float32\" || dtype === \"int32\" || dtype === \"bool\" || dtype === \"string\") {\n    return 4;\n  } else if (dtype === \"complex64\") {\n    return 8;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction ArrayBufferToTypedArray(data, dtype) {\n  if (dtype === \"float32\") {\n    return new Float32Array(data);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(data);\n  } else if (dtype === \"bool\" || dtype === \"string\") {\n    return Uint8Array.from(new Int32Array(data));\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction isWebGPUSupported() {\n  return (typeof window !== \"undefined\" || typeof WorkerGlobalScope !== \"undefined\") && !!navigator.gpu;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_packed_vec4_webgpu.js\nfunction makeMatMulPackedVec4Source(workPerThread, tileAOuter, tileBOuter, tileInner, innerElementSize = 4) {\n  util_exports.assert((tileInner % 4 === 0 || tileInner % 3 === 0) && workPerThread[0] === 4 && (innerElementSize === 3 || innerElementSize === 4), () => `tileInner must be divisible by 4|3. ColPerThread must be 4.\n           innerElementSize must be 3|4.`);\n  return `\n  var<workgroup> mm_Asub : array<array<vec${innerElementSize}<f32>, ${tileInner / innerElementSize}>, ${tileAOuter}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${tileBOuter / workPerThread[0]}>, ${tileInner}>;\n\n  let RowPerThread = ${workPerThread[1]};\n  let ColPerThread = ${workPerThread[0]};\n  let InnerElementSize = ${innerElementSize};\n  let TileInner = ${tileInner};\n\n  ${getMainHeaderString()}\n\n    let tileRow = ${tileAOuter === 1 ? \"0\" : \"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${tileAOuter === 1 ? \"0\" : \"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / InnerElementSize;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {\n            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];\n            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];\n            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];\n            ${innerElementSize === 3 ? \"\" : \"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];\"}\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                let ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                ${innerElementSize === 3 ? \"\" : \"acc[i] = BCached[3] * ACached.w + acc[i];\"}\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`;\n}\nvar MatMulPackedVec4Program = class {\n  constructor(aShape, outputShape, rowPerThread, batchAEqualOne, batchBEqualOne, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [8, 8, 1];\n    this.isVec4 = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    if (outputShape[1] === 1) {\n      this.elementsPerThread = [4, 1, 1];\n    } else {\n      this.elementsPerThread = [4, 4, 1];\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.tileAOuter = outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1];\n    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    this.tileInner = this.tileBOuter;\n    this.aShape = aShape;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    [this.fitA, this.fitB] = this.getShapeFit();\n    this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getShapeFit() {\n    const dimInner = this.aShape[2];\n    const dimBOuter = this.outputShape[2];\n    const bShape = [this.outputShape[0], dimInner, dimBOuter];\n    const tileSizeA = [this.tileAOuter, this.tileInner];\n    const tileSizeB = [this.tileInner, this.tileBOuter];\n    return [\n      tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)),\n      tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1))\n    ];\n  }\n  getUserCode() {\n    const sampleA = this.fitA ? `return A[batch * batchASize + row * uniforms.dimInner / 4 + col]` : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`;\n    const sampleB = this.fitB ? `return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]` : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);\n      if (this.hasPreluActivationWeights) {\n        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${activationOp}\n                }`;\n      } else {\n        activationSnippet = `\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${activationOp}\n            }`;\n      }\n      applyActivationSnippet = \"value = activation(value, outCoord);\";\n    }\n    const addBiasSnippet = this.addBias ? \"value = value + getBiasByOutputCoords(outCoord);\" : \"\";\n    const userCode = `\n      ${activationSnippet}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        ${this.batchAEqualOne ? `\n          let batchASize = 0;\n          let batch = 0;\n        ` : `\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n          let batch = i32(globalId.z);\n        `}\n\n        ${sampleA};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        ${this.batchBEqualOne ? `\n          let batchBSize = 0;\n          let batch = 0;\n          ` : `\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n          let batch = i32(globalId.z);\n       `}\n        ${sampleB};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${addBiasSnippet}\n          ${applyActivationSnippet}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${makeMatMulPackedVec4Source(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_packed_webgpu.js\nfunction makeMatMulPackedSource(workPerThread, workGroupSize) {\n  const tileAOuter = workGroupSize[1] * workPerThread[1];\n  const tileBOuter = workGroupSize[0] * workPerThread[0];\n  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;\n  return `\n    var<workgroup> mm_Asub : array<array<f32, ${tileInner}>, ${tileAOuter}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n    ${getMainHeaderString()}\n      let tileRow = i32(localId.y) * ${workPerThread[1]};\n      let tileCol = i32(localId.x) * ${workPerThread[0]};\n\n      let globalRow = i32(globalId.y) * ${workPerThread[1]};\n      let globalCol = i32(globalId.x) * ${workPerThread[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;\n\n      var acc : array<array<f32, ${workPerThread[0]}>, ${workPerThread[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${workPerThread[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${tileInner} / ${workGroupSize[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${tileInner} / ${workGroupSize[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${tileInner} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${tileInner} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${tileInner}; k = k + 1) {\n          for (var inner = 0; inner < ${workPerThread[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `;\n}\nfunction makeMatMulVectorSource(workGroupSize) {\n  return `\n    let TileSize = ${workGroupSize[0] * 4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${workGroupSize[0]}>;\n\n    ${getMainHeaderString()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `;\n}\nvar MatMulPackedProgram2 = class {\n  constructor(aShape, outputShape, workPerThread, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [16, 16, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    const dimInner = transposeA ? aShape[1] : aShape[2];\n    this.workGroupSize = computeWorkGroupSizeForMatMul(outputShape[1], dimInner, outputShape[2]);\n    if (outputShape[1] === 1 || outputShape[2] === 1) {\n      workPerThread = 1;\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]);\n    if (util_exports.arraysEqual(this.dispatch, [1, 1, 1])) {\n      workPerThread = 1;\n      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]);\n    }\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.workPerThread = workPerThread;\n    this.aShape = aShape;\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    const dimBOuter = this.outputShape[2];\n    const bShape = this.transposeB ? [this.outputShape[0], dimBOuter, dimInner] : [this.outputShape[0], dimInner, dimBOuter];\n    [this.fitA, this.fitB] = this.getShapeFit(bShape);\n    this.shaderKey = `matMulPacked_${this.workPerThread}_${transposeA}_${transposeB}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getShapeFit(bShape) {\n    const tileAOuter = this.workGroupSize[1] * this.workPerThread;\n    const tileBOuter = this.workGroupSize[0] * this.workPerThread;\n    let tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;\n    if (this.outputShape[1] === 1) {\n      tileInner *= 4;\n    }\n    util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => `tileInner must be multiple of workgroupsize.x and workgroupsize.y`);\n    const tileSizeA = [tileAOuter, tileInner];\n    const tileSizeB = [tileInner, tileBOuter];\n    return [\n      tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)),\n      tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1))\n    ];\n  }\n  getUserCode() {\n    let sampleA;\n    if (this.transposeA === false) {\n      sampleA = this.fitA ? `return A[batch * batchASize + row * uniforms.dimInner + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`;\n    } else {\n      sampleA = this.fitA ? `return A[batch * batchASize + col * uniforms.dimAOuter + row];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;\n    }\n    let sampleB;\n    if (this.transposeB === false) {\n      sampleB = this.fitB ? `return B[batch * batchBSize + row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`;\n    } else {\n      sampleB = this.fitB ? `return B[batch * batchBSize + col * uniforms.dimInner + row];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;\n    }\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, false);\n      if (this.hasPreluActivationWeights) {\n        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${activationOp}\n            }`;\n      } else {\n        activationSnippet = `\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${activationOp}\n              }\n            `;\n      }\n      applyActivationSnippet = \"value = activation(value, outCoord);\";\n    }\n    const addBiasSnippet = this.addBias ? \"value = value + getBiasByOutputCoords(outCoord);\" : \"\";\n    const userCode = `\n      ${activationSnippet}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchAEqualOne ? `\n        let batch = 0;\n        let batchASize = 0;\n        ` : `\n        let batch = i32(globalId.z);\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        `}\n        ${sampleA}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchBEqualOne ? `\n        let batch = 0;\n        let batchBSize = 0;\n        ` : `\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        `}\n        ${sampleB}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1] > 1 ? makeMatMulPackedSource([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : makeMatMulVectorSource(this.workGroupSize)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_reduce.js\nfunction makeMatMulReduceSource() {\n  return `\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${getMainHeaderString()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `;\n}\nvar MatMulReduceProgram = class {\n  constructor(outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [], y: [1, 2], z: [0] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    this.shaderKey = `matMulReduce_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getUserCode() {\n    let sampleA;\n    if (this.transposeA === false) {\n      sampleA = `return f32(A[batch * batchASize + row * uniforms.dimInner + col]);`;\n    } else {\n      sampleA = `return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);`;\n    }\n    let sampleB;\n    if (this.transposeB === false) {\n      sampleB = `return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);`;\n    } else {\n      sampleB = `return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);`;\n    }\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, false);\n      if (this.hasPreluActivationWeights) {\n        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${activationOp}\n            }`;\n      } else {\n        activationSnippet = `\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${activationOp}\n              }\n            `;\n      }\n      applyActivationSnippet = \"value = activation(value, outCoord);\";\n    }\n    const addBiasSnippet = this.addBias ? \"value = value + getBiasByOutputCoords(outCoord);\" : \"\";\n    const userCode = `\n      ${activationSnippet}\n\n      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {\n        ${this.batchAEqualOne ? `\n          let batchASize = 0;\n          let batch = 0;\n          ` : `\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n          let batch = batchIn;\n          `}\n        ${sampleA}\n      }\n\n      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {\n        ${this.batchBEqualOne ? `\n          let batch = 0;\n          let batchBSize = 0;\n          ` : `\n          let batch = batchIn;\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n          `}\n        ${sampleB}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${makeMatMulReduceSource()}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_small_output_size_webgpu.js\nfunction makeMatMulSmallOutputSizeSource(workGroupSize) {\n  const tileAOuter = workGroupSize[1] / 2;\n  const tileBOuter = workGroupSize[0];\n  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;\n  return `\n  var<workgroup> mm_Asub1 : array<array<f32, ${tileInner}>, ${tileAOuter}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${tileInner}>, ${tileAOuter}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${getMainHeaderString()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${tileAOuter}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${tileInner};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${tileInner};\n        }\n      } else {\n        if (tileRow < ${tileAOuter}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${tileInner};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${tileInner};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${tileInner}; k = k + 1) {\n            let subRow = tileRow - ${tileAOuter};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${tileAOuter}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${tileInner};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${tileInner};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${tileInner}; k = k + 1) {\n            let subRow = tileRow - ${tileAOuter};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${tileAOuter};\n    if (tileRow >= ${tileAOuter} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `;\n}\nvar MatMulSmallOutputSizeProgram = class {\n  constructor(aShape, bShape, outputShape, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [8, 16, 1];\n    util_exports.assert(aShape[1] <= 16 || bShape[2] <= 16, () => \"This program can be only used when A width or B Height are small\");\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    this.dispatch = [\n      Math.ceil(outputShape[2] / this.workGroupSize[0]),\n      Math.ceil(outputShape[1] * 2 / this.workGroupSize[1]),\n      outputShape[0]\n    ];\n    const addBias = bias != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = aShape[0] === 1;\n    this.batchBEqualOne = bShape[0] === 1;\n    this.shaderKey = `matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getUserCode() {\n    const sampleA = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`;\n    const sampleB = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, false);\n      if (this.hasPreluActivationWeights) {\n        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${activationOp}\n            }`;\n      } else {\n        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${activationOp}\n        }`;\n      }\n      applyActivationSnippet = \"value = activation(value, outCoord);\";\n    }\n    const addBiasSnippet = this.addBias ? \"value = value + getBiasByOutputCoords(outCoord);\" : \"\";\n    const userCode = `\n      ${activationSnippet}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchAEqualOne ? `\n          let batch = 0;\n          let batchASize = 0;\n          ` : `\n          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n          let batch = i32(globalId.z);\n          `}\n        ${sampleA}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        ${this.batchBEqualOne ? `\n          let batch = 0;\n          let batchBSize = 0;\n          ` : `\n          let batch = i32(globalId.z);\n          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n          `}\n        ${sampleB}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${addBiasSnippet}\n          ${applyActivationSnippet}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${makeMatMulSmallOutputSizeSource(this.workGroupSize)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Reshape.js\nfunction reshape6(args) {\n  const { inputs, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig4 = {\n  kernelName: Reshape,\n  backendName: \"webgpu\",\n  kernelFunc: reshape6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul_impl.js\nfunction batchMatMulImpl2({ a: a6, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const aRank = a6.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a6.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape6({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape6({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const intermediates = [a3d, b3d];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const batchAEqualOne = batchDimA === 1;\n  const batchBEqualOne = batchDimB === 1;\n  const useVec4 = innerShapeA % 4 === 0 && outerShapeB % 4 === 0 && !transposeA && !transposeB;\n  let program;\n  if (outerShapeA * outerShapeB <= 32) {\n    program = new MatMulReduceProgram([batchDim, outerShapeA, outerShapeB], batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);\n  } else if (!transposeA && !transposeB && (outerShapeA <= 16 && (outerShapeB <= 512 || innerShapeB >= 2 * outerShapeB) || outerShapeB <= 16 && (outerShapeA <= 512 || innerShapeA >= 2 * outerShapeA))) {\n    program = new MatMulSmallOutputSizeProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], bias, activation2, preluActivationWeights);\n  } else if (useVec4) {\n    program = new MatMulPackedVec4Program(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"), batchAEqualOne, batchBEqualOne, bias, activation2, preluActivationWeights);\n  } else {\n    program = new MatMulPackedProgram2(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"), batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);\n  }\n  const inputs = [a3d, b3d];\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (preluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  const dimensions = [\n    { type: \"int32\", data: [outerShapeA] },\n    { type: \"int32\", data: [outerShapeB] },\n    { type: \"int32\", data: [innerShapeA] }\n  ];\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  const out = backend2.runWebGPUProgram(program, inputs, a6.dtype, dimensions);\n  const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n  intermediates.push(out);\n  for (const i2 of intermediates) {\n    backend2.disposeData(i2.dataId);\n  }\n  return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  return batchMatMulImpl2({\n    a: a6,\n    b,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar _fusedMatMulConfig4 = {\n  kernelName: _FusedMatMul,\n  backendName: \"webgpu\",\n  kernelFunc: _fusedMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_complex_webgpu.js\nvar BinaryOpComplexProgram2 = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `binaryOpComplex_${op2}`;\n    this.op = op2;\n  }\n  getUserCode() {\n    const opStr = getBinaryOpString(this.op, false);\n    const userCode = `\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${opStr}\n      }\n\n      ${getMainHeaderAndGlobalIndexString()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_shared_webgpu.js\nvar BinaryOpSharedProgram = class {\n  constructor(op2, aShape, bShape, useSharedMemoryWithB) {\n    this.variableNames = [\"A\", \"B\"];\n    this.size = true;\n    const workGroupSizeX = 256;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.lastDimensionSize = useSharedMemoryWithB ? bShape[0] : aShape[0];\n    if (this.lastDimensionSize < 256) {\n      this.workPerThread = 1;\n    } else if (this.lastDimensionSize < 512) {\n      this.workPerThread = 2;\n    } else {\n      this.workPerThread = 4;\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.useSharedMemoryWithB = useSharedMemoryWithB;\n    this.op = op2;\n    this.shaderKey = `binaryShared_${op2}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;\n  }\n  getUserCode() {\n    const sharedIndexSnippet = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : \"0\";\n    const accessDataSnippet = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${sharedIndexSnippet}];` : `let a = sharedBuf[${sharedIndexSnippet}];\n         let b = getBByOutputCoords(coords);`;\n    const opStr = getBinaryOpString(this.op, false);\n    const userCode = `\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${opStr}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${getMainHeaderAndGlobalIndexString()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? \"B\" : \"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${accessDataSnippet}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_vec4_webgpu.js\nvar BinaryOpVec4Program = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"A\", \"B\"];\n    this.workPerThread = 4;\n    this.isVec4 = true;\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.op = op2;\n    this.shaderKey = `binaryVec4_${op2}`;\n  }\n  getUserCode() {\n    const opStr = getBinaryOpString(this.op, this.isVec4);\n    const userCode = `\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${opStr}\n      }\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_webgpu.js\nvar BinaryOpProgram2 = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"A\", \"B\"];\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `binary_${op2}`;\n    this.op = op2;\n  }\n  getUserCode() {\n    const opStr = getBinaryOpString(this.op, false);\n    const userCode = `\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${opStr}\n      }\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_ops.js\nfunction getBinaryProgram(op2, aShape, bShape) {\n  const useVec4 = util_exports.arraysEqual(aShape, bShape) && util_exports.sizeFromShape(aShape) % 4 === 0;\n  if (useVec4) {\n    return new BinaryOpVec4Program(op2, aShape, bShape);\n  }\n  const useSharedMemoryWithA = aShape.length === 1 && bShape.length > 1 && aShape[0] < 1024;\n  const useSharedMemoryWithB = bShape.length === 1 && aShape.length > 1 && bShape[0] < 1024;\n  if (useSharedMemoryWithA || useSharedMemoryWithB) {\n    return new BinaryOpSharedProgram(op2, aShape, bShape, useSharedMemoryWithB);\n  } else {\n    return new BinaryOpProgram2(op2, aShape, bShape);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Identity.js\nfunction identity5(args) {\n  const { inputs } = args;\n  const { x } = inputs;\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig4 = {\n  kernelName: Identity,\n  backendName: \"webgpu\",\n  kernelFunc: identity5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Complex.js\nfunction complex4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.tensorMap.get(complexInfo.dataId);\n  const realTensorInfo = identity5({ inputs: { x: real5 }, backend: backend2 });\n  const imagTensorInfo = identity5({ inputs: { x: imag5 }, backend: backend2 });\n  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n  return complexInfo;\n}\nvar complexConfig3 = {\n  kernelName: Complex,\n  backendName: \"webgpu\",\n  kernelFunc: complex4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_webgpu.js\nvar UnaryOpProgram2 = class {\n  constructor(outputShape, op2) {\n    this.variableNames = [\"A\"];\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.op = op2;\n    this.shaderKey = `unary_${op2}`;\n  }\n  getUserCode() {\n    return `\n      fn unaryOperation(a : f32) -> f32 {\n        ${getUnaryOpString(this.op, false)}\n      }\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/kernel_funcs_utils.js\nfunction unaryKernelFunc3({ opType, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webgpuBackend = backend2;\n    const $dtype = dtype || x.dtype;\n    if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webgpuBackend.tensorMap.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values, $dtype);\n      return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n    const program = new UnaryOpProgram2(x.shape, opType);\n    return webgpuBackend.runWebGPUProgram(program, [x], $dtype);\n  };\n}\nfunction binaryKernelFunc3({ opSnippet, cpuKernelImpl, supportsComplex = false, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { a: a6, b } = inputs;\n    const webgpuBackend = backend2;\n    if (supportsComplex && a6.dtype === \"complex64\") {\n      const aData = webgpuBackend.tensorMap.get(a6.dataId);\n      const bData = webgpuBackend.tensorMap.get(b.dataId);\n      let real5, imag5;\n      if (opSnippet !== BinaryOpType.MUL) {\n        [real5, imag5] = [\n          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n        ].map((complexParts) => {\n          const [aPart, bPart] = complexParts;\n          const aHandle = {\n            dataId: aPart.dataId,\n            dtype: aPart.dtype,\n            shape: a6.shape\n          };\n          const bHandle = {\n            dataId: bPart.dataId,\n            dtype: bPart.dtype,\n            shape: b.shape\n          };\n          const program2 = getBinaryProgram(opSnippet, a6.shape, b.shape);\n          return webgpuBackend.runWebGPUProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n      } else {\n        const realProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_REAL, a6.shape, b.shape);\n        const imagProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_IMAG, a6.shape, b.shape);\n        const inputs2 = [\n          {\n            dataId: aData.complexTensorInfos.real.dataId,\n            dtype: aData.complexTensorInfos.real.dtype,\n            shape: a6.shape\n          },\n          {\n            dataId: aData.complexTensorInfos.imag.dataId,\n            dtype: aData.complexTensorInfos.imag.dtype,\n            shape: a6.shape\n          },\n          {\n            dataId: bData.complexTensorInfos.real.dataId,\n            dtype: bData.complexTensorInfos.real.dtype,\n            shape: b.shape\n          },\n          {\n            dataId: bData.complexTensorInfos.imag.dataId,\n            dtype: bData.complexTensorInfos.imag.dtype,\n            shape: b.shape\n          }\n        ];\n        real5 = webgpuBackend.runWebGPUProgram(realProgram, inputs2, \"float32\");\n        imag5 = webgpuBackend.runWebGPUProgram(imagProgram, inputs2, \"float32\");\n      }\n      const complexOutput = complex4({ inputs: { real: real5, imag: imag5 }, backend: webgpuBackend });\n      webgpuBackend.disposeData(real5.dataId);\n      webgpuBackend.disposeData(imag5.dataId);\n      return complexOutput;\n    }\n    const $dtype = dtype || upcastType(a6.dtype, b.dtype);\n    if ((a6.dtype === \"string\" || b.dtype === \"string\" || webgpuBackend.shouldExecuteOnCPU([a6, b])) && cpuKernelImpl != null) {\n      const aData = webgpuBackend.tensorMap.get(a6.dataId).values;\n      const bData = webgpuBackend.tensorMap.get(b.dataId).values;\n      const decodedAVals = a6.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aData) : aData;\n      const decodedBVals = a6.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bData) : bData;\n      const [outValues, outShape] = cpuKernelImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues);\n    }\n    const program = getBinaryProgram(opSnippet, a6.shape, b.shape);\n    return webgpuBackend.runWebGPUProgram(program, [a6, b], $dtype);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU2, ceilImpl: ceilImplCPU2, concatImpl: concatImplCPU2, equalImpl: equalImplCPU2, expImpl: expImplCPU2, expm1Impl: expm1ImplCPU2, floorImpl: floorImplCPU2, gatherNdImpl: gatherNdImplCPU2, gatherV2Impl: gatherV2ImplCPU2, greaterEqualImpl: greaterEqualImplCPU2, greaterImpl: greaterImplCPU2, lessEqualImpl: lessEqualImplCPU2, lessImpl: lessImplCPU2, logImpl: logImplCPU2, maxImpl: maxImplCPU2, maximumImpl: maximumImplCPU2, minimumImpl: minimumImplCPU2, multiplyImpl: multiplyImplCPU2, negImpl: negImplCPU2, notEqualImpl: notEqualImplCPU2, prodImpl: prodImplCPU2, rangeImpl: rangeImplCPU2, rsqrtImpl: rsqrtImplCPU2, scatterImpl: scatterImplCPU2, simpleAbsImpl: simpleAbsImplCPU2, sliceImpl: sliceImplCPU2, stridedSliceImpl: stridedSliceImplCPU2, stringNGramsImpl: stringNGramsImplCPU2, subImpl: subImplCPU2, tileImpl: tileImplCPU2, topKImpl: topKImplCPU2, transposeImpl: transposeImplCPU2, uniqueImpl: uniqueImplCPU2 } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Abs.js\nvar abs4 = unaryKernelFunc3({ opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU2 });\nvar absConfig4 = {\n  kernelName: Abs,\n  backendName: \"webgpu\",\n  kernelFunc: abs4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Add.js\nvar addKernelFunc2 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.ADD,\n  cpuKernelImpl: addImplCPU2,\n  supportsComplex: true\n});\nvar addConfig4 = {\n  kernelName: Add,\n  backendName: \"webgpu\",\n  kernelFunc: addKernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/addn_packed_webgpu.js\nvar AddNPackedProgram2 = class {\n  constructor(shapes) {\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shapes[0];\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.shaderKey = \"addN\";\n  }\n  getUserCode() {\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`let v${variable2} = get${variable2}ByOutputCoords(coords);`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${snippets.join(\"\\n        \")}\n            setOutputAtIndex(flatIndex, ${operation});\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AddN.js\nfunction addN4(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity5({ inputs: { x: tensors[0] }, backend: backend2 });\n  }\n  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map((t2) => t2.shape);\n  const program = new AddNPackedProgram2(shapes);\n  return backend2.runWebGPUProgram(program, tensors, dtype);\n}\nvar addNConfig4 = {\n  kernelName: AddN,\n  backendName: \"webgpu\",\n  kernelFunc: addN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/argminmax_webgpu.js\nvar ArgMinMaxProgram2 = class {\n  constructor(inputShape, axis, reduceType) {\n    this.workGroupSize = [64, 1, 1];\n    this.variableNames = [\"x\"];\n    this.uniforms = \"infinityValue : f32,\";\n    this.size = true;\n    const axes = [axis];\n    backend_util_exports.assertAxesAreInnerMostDims(\"arg\" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, inputShape.length);\n    this.op = reduceType === \"min\" ? \"<\" : \">\";\n    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(inputShape, axes);\n    this.outputShape = outputShape.length === 0 ? [1] : outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);\n    this.inputShape = inputShape;\n    this.shaderKey = `argMinMax${this.op}`;\n  }\n  getUserCode() {\n    const sharedMemorySnippet = `\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `;\n    const getInputShapeLastDim = () => {\n      if (this.inputShape.length === 1) {\n        return \"uniforms.xShape\";\n      } else {\n        return `uniforms.xShape.${getCoordsXYZ(this.inputShape.length - 1)}`;\n      }\n    };\n    const splitOutputCoords = () => {\n      let snippet = \"\";\n      if (this.outputShape.length === 1) {\n        if (this.inputShape.length !== 1) {\n          snippet += \"outputCoords,\";\n        }\n      } else {\n        for (let i2 = 0; i2 < this.outputShape.length; i2++) {\n          snippet += `outputCoords.${getCoordsXYZ(i2)},`;\n        }\n      }\n      return snippet;\n    };\n    const userCode = `\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${sharedMemorySnippet}\n\n      ${getMainHeaderAndGlobalIndexString()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let reduceLength = ${getInputShapeLastDim()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = getX(${splitOutputCoords()} k);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(reduceLength), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_shared_webgpu.js\nvar TransposeSharedProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.workGroupSize = [16, 16, 1];\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [0], y: [1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]);\n    this.shaderKey = \"transposeShared\";\n  }\n  getUserCode() {\n    const userCode = `\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;\n      ${getWorkGroupSizeString()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_webgpu.js\nvar TransposeProgram2 = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.newDim = newDim;\n    this.shaderKey = `transpose_${newDim}`;\n  }\n  getUserCode() {\n    const dtype = getCoordsDataType2(this.outputShape.length);\n    const switched = getSwitchedCoords2(this.newDim);\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${dtype}(${switched}), uniforms.aShape)]);\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSwitchedCoords2(newDim) {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const switchedCoords = new Array(rank);\n  for (let i2 = 0; i2 < newDim.length; i2++) {\n    switchedCoords[newDim[i2]] = `resRC.${getCoordsXYZ(i2)}`;\n  }\n  return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transpose.js\nfunction transpose5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  const webgpuBackend = backend2;\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[perm[i2]];\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = webgpuBackend.tensorMap.get(x.dataId);\n    const values = xData.values;\n    const outValues = transposeImplCPU2(values, x.shape, x.dtype, perm, newShape);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  if (x.shape.length === 2 && util_exports.arraysEqual(perm, [1, 0])) {\n    const program2 = new TransposeSharedProgram(x.shape, perm);\n    return webgpuBackend.runWebGPUProgram(program2, [x], x.dtype);\n  }\n  const program = new TransposeProgram2(x.shape, perm);\n  return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);\n}\nvar transposeConfig4 = {\n  kernelName: Transpose,\n  backendName: \"webgpu\",\n  kernelFunc: transpose5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMax.js\nfunction argMax4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n  const program = new ArgMinMaxProgram2($x.shape, axes[0], \"max\");\n  const uniformData = [{ type: \"float32\", data: [Number.NEGATIVE_INFINITY] }];\n  const out = backend2.runWebGPUProgram(program, [$x], \"int32\", uniformData);\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return out;\n}\nvar argMaxConfig4 = {\n  kernelName: ArgMax,\n  backendName: \"webgpu\",\n  kernelFunc: argMax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMin.js\nfunction argMin4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n  const program = new ArgMinMaxProgram2($x.shape, axes[0], \"min\");\n  const uniformData = [{ type: \"float32\", data: [Number.POSITIVE_INFINITY] }];\n  const out = backend2.runWebGPUProgram(program, [$x], \"int32\", uniformData);\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return out;\n}\nvar argMinConfig3 = {\n  kernelName: ArgMin,\n  backendName: \"webgpu\",\n  kernelFunc: argMin4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool2d_webgpu.js\nvar Pool2DProgram2 = class {\n  constructor(convInfo, poolType) {\n    this.variableNames = [\"x\"];\n    this.uniforms = `stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,`;\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `pool2D_${poolType}`;\n    this.poolType = poolType;\n  }\n  getUserCode() {\n    let updateSnippet = `resultValue = max(value, resultValue);`;\n    if (this.poolType === \"avg\") {\n      updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`;\n    }\n    let returnValue = `resultValue`;\n    if (this.poolType === \"avg\") {\n      returnValue = `resultValue / count`;\n    }\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType === \"avg\" ? \"0.0\" : \"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${updateSnippet}\n            }\n          }\n\n          setOutputAtIndex(index, ${returnValue});\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool_filtersizeone_webgpu.js\nvar PoolWithFilterSizeEqualsOneProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\"];\n    this.uniforms = `stride : vec2<i32>,`;\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"poolWithFilterSizeEqualsOne\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AvgPool.js\nfunction avgPool5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity5({ inputs: { x }, backend: backend2 });\n  }\n  let program;\n  const dimensions = [{ type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }];\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {\n    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);\n  } else {\n    program = new Pool2DProgram2(convInfo, \"avg\");\n    dimensions.push({ type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    }, { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }, {\n      type: \"int32\",\n      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]\n    });\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);\n}\nvar avgPoolConfig4 = {\n  kernelName: AvgPool,\n  backendName: \"webgpu\",\n  kernelFunc: avgPool5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a: a6, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  return batchMatMulImpl2({ a: a6, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig4 = {\n  kernelName: BatchMatMul,\n  backendName: \"webgpu\",\n  kernelFunc: batchMatMul4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/slice_webgpu.js\nvar SliceProgram2 = class {\n  constructor(start, destSize) {\n    this.variableNames = [\"source\"];\n    this.workPerThread = 1;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.start = start;\n    this.uniforms = `start : ${getCoordsDataType2(start.length)}, `;\n    this.shaderKey = \"slice\";\n  }\n  getUserCode() {\n    const dtype = getCoordsDataType2(this.rank);\n    const sourceCoords = getCoords3(this.rank);\n    let coordSum;\n    if (this.start.length === 1) {\n      coordSum = this.outputShape.map((_, i2) => {\n        return `sourceLoc = uniforms.start + coords;`;\n      });\n    } else {\n      coordSum = this.outputShape.map((_, i2) => {\n        return `sourceLoc.${coords2[i2]} = uniforms.start[${i2}] + coords.${coords2[i2]};`;\n      });\n    }\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${dtype};\n          let coords = getCoordsFromIndex(index);\n          ${coordSum.join(\"\\n\")}\n          setOutputAtIndex(index, getSource(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nvar coords2 = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords3(rank) {\n  if (rank === 1) {\n    return \"sourceLoc\";\n  } else if (rank <= 6) {\n    return coords2.slice(0, rank).map((coord) => `sourceLoc.${coord}`).join(\",\");\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Slice.js\nfunction slice5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n    const xBufferInfo = backend2.tensorMap.get(x.dataId);\n    const outValues = sliceImplCPU2(xBufferInfo.values, $begin, $size, x.shape, x.dtype);\n    return backend2.makeTensorInfo($size, x.dtype, outValues);\n  }\n  if (util_exports.sizeFromShape($size) === 0) {\n    return backend2.makeTensorInfo($size, x.dtype, []);\n  }\n  const program = new SliceProgram2($begin, $size);\n  const uniformData = [{ type: \"int32\", data: $begin }];\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar sliceConfig4 = {\n  kernelName: Slice,\n  backendName: \"webgpu\",\n  kernelFunc: slice5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const toDispose = [];\n  const reshapedIntermediate = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const transposedIntermediate = transpose5({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n  const reshapedIntermediate2 = reshape6({\n    inputs: { x: transposedIntermediate },\n    backend: backend2,\n    attrs: { shape: reshapedPermuted }\n  });\n  const sliced = slice5({\n    inputs: { x: reshapedIntermediate2 },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return sliced;\n};\nvar batchToSpaceNDConfig4 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"webgpu\",\n  kernelFunc: batchToSpaceND5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NotEqual.js\nvar notEqual4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.NOT_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: notEqualImplCPU2\n});\nvar notEqualConfig4 = {\n  kernelName: NotEqual,\n  backendName: \"webgpu\",\n  kernelFunc: notEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Real.js\nfunction real4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.tensorMap.get(input2.dataId);\n  return identity5({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig3 = {\n  kernelName: Real,\n  backendName: \"webgpu\",\n  kernelFunc: real4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/int.js\nfunction int2(input2, backend2) {\n  const program = new UnaryOpProgram2(input2.shape, UnaryOpType.TO_INT);\n  const output = backend2.runWebGPUProgram(program, [input2], \"int32\");\n  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cast.js\nfunction cast6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity5({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast6({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex4({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n    zerosTensor.dispose();\n    backend2.disposeData(floatX.dataId);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const result = cast6({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeData(realPart.dataId);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity5({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (dtype === \"int32\") {\n    return int2(x, backend2);\n  }\n  if (dtype === \"bool\") {\n    const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n    const binaryInputs = { a: x, b: zerosTensorInfo };\n    const result = notEqual4({ inputs: binaryInputs, backend: backend2 });\n    backend2.disposeData(zerosTensorInfo.dataId);\n    return result;\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig4 = {\n  kernelName: Cast,\n  backendName: \"webgpu\",\n  kernelFunc: cast6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Ceil.js\nvar ceil4 = unaryKernelFunc3({ opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU2 });\nvar ceilConfig4 = {\n  kernelName: Ceil,\n  backendName: \"webgpu\",\n  kernelFunc: ceil4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_vec4_webgpu.js\nvar ClipVec4Program = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.uniforms = \"minVal : f32, maxVal : f32,\";\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.isVec4 = true;\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.shaderKey = \"clipVec4\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_webgpu.js\nvar ClipProgram2 = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.uniforms = \"minVal : f32, maxVal : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"clip\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ClipByValue.js\nfunction clipByValue4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  let program;\n  const uniformData = [\n    { type: \"float32\", data: [clipValueMin] },\n    { type: \"float32\", data: [clipValueMax] }\n  ];\n  if (util_exports.sizeFromShape(x.shape) % 4 === 0) {\n    program = new ClipVec4Program(x.shape);\n  } else {\n    program = new ClipProgram2(x.shape);\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar clipByValueConfig4 = {\n  kernelName: ClipByValue,\n  backendName: \"webgpu\",\n  kernelFunc: clipByValue4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/concat_webgpu.js\nvar ConcatProgram2 = class {\n  constructor(shapes) {\n    this.uniforms = \"\";\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.offsetLength = shapes.length - 1;\n    for (let i2 = 0; i2 < this.offsetLength; i2++) {\n      this.uniforms += `offset${i2} : i32,`;\n    }\n    this.shaderKey = \"concat\";\n  }\n  getUserCode() {\n    const snippets = [];\n    if (this.offsetLength > 0) {\n      snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);\n      for (let i2 = 1; i2 < this.offsetLength; i2++) {\n        snippets.push(`else if (yC < uniforms.offset${[i2]}){ setOutputAtCoords(coords.x, coords.y, getT${i2}(yR, yC - uniforms.offset${i2 - 1})); }`);\n      }\n      const lastIndex = this.offsetLength;\n      const lastShiftIndex = this.offsetLength - 1;\n      snippets.push(`else { setOutputAtCoords(coords.x, coords.y, getT${lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`);\n    } else {\n      snippets.push(`setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));`);\n    }\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${snippets.join(\"\\n        \")}\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Imag.js\nfunction imag4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.tensorMap.get(input2.dataId);\n  return identity5({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig3 = {\n  kernelName: Imag,\n  backendName: \"webgpu\",\n  kernelFunc: imag4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat_impl.js\nfunction concatImpl3(inputs, axis, backend2) {\n  const dtype = inputs[0].dtype;\n  if (dtype === \"complex64\") {\n    const reals = inputs.map((t2) => real4({ inputs: { input: t2 }, backend: backend2 }));\n    const imags = inputs.map((t2) => imag4({ inputs: { input: t2 }, backend: backend2 }));\n    const realConcated = concatImpl3(reals, axis, backend2);\n    const imagConcated = concatImpl3(imags, axis, backend2);\n    const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r2) => backend2.disposeData(r2.dataId));\n    imags.forEach((i2) => backend2.disposeData(i2.dataId));\n    backend2.disposeData(realConcated.dataId);\n    backend2.disposeData(imagConcated.dataId);\n    return result;\n  }\n  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n  if (dtype === \"string\") {\n    runOnCpu = true;\n  }\n  if (runOnCpu) {\n    const tensors2D2 = inputs.map((t2) => {\n      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape6({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = tensors2D2.map((t2) => {\n      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };\n    });\n    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);\n    const simplyConcat = tensors2D2[0].shape[0] === 1;\n    const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n    tensors2D2.forEach((t2) => backend2.disposeData(t2.dataId));\n    return outInfo;\n  }\n  const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2);\n  const shapes = tensors2D.map((t2) => t2.shape);\n  const program = new ConcatProgram2(shapes);\n  const uniformData = [];\n  const offsets = new Array(shapes.length - 1);\n  if (offsets.length > 0) {\n    offsets[0] = shapes[0][1];\n    uniformData.push({ type: \"int32\", data: [offsets[0]] });\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];\n      uniformData.push({ type: \"int32\", data: [offsets[i2]] });\n    }\n  }\n  const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);\n  tensors2D.forEach((r2) => backend2.disposeData(r2.dataId));\n  const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeData(res.dataId);\n  return reshapedResult;\n}\nfunction computeTensors2D2(inputs, axis, backend2) {\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n  const tensors2D = inputs.map((t2) => reshape6({\n    inputs: { x: t2 },\n    backend: backend2,\n    attrs: {\n      shape: [\n        util_exports.sizeFromShape(t2.shape.slice(0, axis)),\n        util_exports.sizeFromShape(t2.shape.slice(axis))\n      ]\n    }\n  }));\n  return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat.js\nfunction concat5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity5({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  return concatImpl3($inputs, $axis, backend2);\n}\nvar concatConfig4 = {\n  kernelName: Concat,\n  backendName: \"webgpu\",\n  kernelFunc: concat5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv2d_mm_vec4_webgpu.js\nvar Conv2DMMVec4Program = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [8, 8, 1];\n    this.isVec4 = true;\n    this.outputShape = convInfo.outShape;\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };\n    if (this.outputShape[1] === 1) {\n      this.elementsPerThread = [4, 1, 1];\n    } else {\n      this.elementsPerThread = [4, 4, 1];\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.innerElementSize = this.convInfo.inChannels % 4 === 0 ? 4 : 3;\n    if (this.innerElementSize === 3) {\n      this.variableTypes = [\"f32\", \"vec4<f32>\"];\n    } else {\n      this.variableTypes = [\"vec4<f32>\", \"vec4<f32>\"];\n    }\n    if (this.addBias) {\n      this.variableNames.push(\"bias\");\n      this.variableTypes.push(\"vec4<f32>\");\n    }\n    if (this.hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n      this.variableTypes.push(\"vec4<f32>\");\n    }\n    this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1];\n    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    this.tileInner = this.workGroupSize[0] * this.innerElementSize;\n    [this.fitA, this.fitB] = this.getShapeFit();\n    this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`;\n  }\n  getShapeFit() {\n    const tileSizeA = [this.tileAOuter, this.tileInner];\n    const tileSizeB = [this.tileInner, this.tileBOuter];\n    const dimAOuter = this.outputShape[1] * this.outputShape[2];\n    const dimBOuter = this.outputShape[3];\n    const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;\n    return [\n      tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]),\n      tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter])\n    ];\n  }\n  getUserCode() {\n    const matMulSource = makeMatMulPackedVec4Source(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, this.innerElementSize);\n    const readASnippet = `let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n\n        var resData = vec${this.innerElementSize}<f32>(0.0);\n        // The bounds checking is always needed since we use it to pad zero for\n        // the 'same' padding type.\n        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {\n          var coord = vec4<i32>(\n            batch,\n            xRow,\n            xCol,\n            inChCoord);\n          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n          ${this.innerElementSize === 3 ? \"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\" : \"resData = x[xIndex / 4];\"}\n        }\n        return resData;`;\n    const sampleA = this.fitA ? `${readASnippet}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${readASnippet}\n         }\n         return vec${this.innerElementSize}<f32>(0.0);\n        `;\n    const sampleB = this.fitB ? `return W[row * uniforms.dimBOuter / 4 + col];` : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);\n      if (this.hasPreluActivationWeights) {\n        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${activationOp}\n        }`;\n      } else {\n        activationSnippet = `\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${activationOp}\n        }`;\n      }\n      applyActivationSnippet = `value = activation(value, outCoord);`;\n    }\n    const addBiasSnippet = this.addBias ? \"value = value + getBiasByOutputCoords(outCoord);\" : \"\";\n    const userCode = `\n        ${activationSnippet}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {\n          let r = row;\n          let c = col * ${this.innerElementSize};\n          var batch = i32(globalId.z);\n          ${sampleA}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${sampleB}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${addBiasSnippet}\n            ${applyActivationSnippet}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${matMulSource}\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv2d_mm_webgpu.js\nvar Conv2DMMProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.outputShape = convInfo.outShape;\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [1], y: [2, 3], z: [0] };\n    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape);\n    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    [this.fitA, this.fitB] = this.getShapeFit();\n    this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`;\n  }\n  getShapeFit() {\n    const tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];\n    const tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;\n    util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => \"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");\n    const tileSizeA = [tileAOuter, tileInner];\n    const tileSizeB = [tileInner, tileBOuter];\n    const dimAOuter = this.convInfo.outHeight * this.convInfo.outWidth;\n    const dimBOuter = this.convInfo.outChannels;\n    const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;\n    return [\n      tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]),\n      tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter])\n    ];\n  }\n  getUserCode() {\n    const coordASnippet = this.isChannelsLast ? `\n    let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);\n    ` : `\n    let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);\n    `;\n    const coordResSnippet = this.isChannelsLast ? `\n    let outCoord = vec4<i32>(\n      batch,\n      row / outWidth,\n      row % outWidth,\n      col);\n    ` : `\n    let outCoord = vec4<i32>(\n      batch,\n      col,\n      row / outWidth,\n      row % outWidth);\n    `;\n    const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);\n    const readASnippet = `\n    let inChannels = uniforms.wShape[2];\n    let outWidth = ${this.isChannelsLast ? \"uniforms.outShape[2]\" : \"uniforms.outShape[3]\"};\n    let outRow = row / outWidth;\n    let outCol = row % outWidth;\n\n    let WRow = col / (uniforms.filterDims[1] * inChannels);\n    let WCol = col / inChannels % uniforms.filterDims[1];\n    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n    ${coordASnippet}\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`;\n    const sampleA = this.fitA ? `${readASnippet}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${readASnippet}\n    }\n    return 0.0;\n    `;\n    const sampleB = this.fitB ? `return W[row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, false);\n      if (this.hasPreluActivationWeights) {\n        activationSnippet = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${activationOp}\n                }`;\n      } else {\n        activationSnippet = `\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${activationOp}\n                  }\n                `;\n      }\n      applyActivationSnippet = `value = activation(value, outCoord);`;\n    }\n    const addBiasSnippet = this.addBias ? \"value = value + getBiasByOutputCoords(outCoord);\" : \"\";\n    const userCode = `\n    ${activationSnippet}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${sampleA}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${sampleB}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outWidth = ${this.isChannelsLast ? \"uniforms.outShape[2]\" : \"uniforms.outShape[3]\"};\n      ${coordResSnippet}\n      ${addBiasSnippet}\n      ${applyActivationSnippet}\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${matMulSource}\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D_impl.js\nfunction conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const transposeA = isChannelsLast ? false : true;\n  const transposeB = false;\n  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === \"VALID\";\n  let xReshaped;\n  let filterReshaped;\n  if (sameSize) {\n    const sharedDim = convInfo.inHeight * convInfo.inWidth * convInfo.inChannels;\n    xReshaped = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.batchSize, sharedDim] }\n    });\n    filterReshaped = reshape6({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, sharedDim, convInfo.outChannels] }\n    });\n  } else {\n    xReshaped = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: isChannelsLast ? [\n          convInfo.batchSize,\n          convInfo.inHeight * convInfo.inWidth,\n          convInfo.inChannels\n        ] : [\n          convInfo.batchSize,\n          convInfo.inChannels,\n          convInfo.inHeight * convInfo.inWidth\n        ]\n      }\n    });\n    filterReshaped = reshape6({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n  }\n  const result = batchMatMulImpl2({\n    a: isChannelsLast ? xReshaped : filterReshaped,\n    b: isChannelsLast ? filterReshaped : xReshaped,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    activation: activation2,\n    preluActivationWeights,\n    leakyreluAlpha\n  });\n  const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeData(xReshaped.dataId);\n  backend2.disposeData(filterReshaped.dataId);\n  backend2.disposeData(result.dataId);\n  return out;\n}\nfunction conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  let program;\n  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === \"VALID\";\n  if (sameSize || convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    return conv2dByMatMul2({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  }\n  const useVec4 = (convInfo.inChannels % 4 === 0 || convInfo.inChannels % 3 === 0) && convInfo.outChannels % 4 === 0 && isChannelsLast;\n  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.filterHeight, convInfo.filterWidth] },\n    { type: \"int32\", data: [...padInfo] },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    { type: \"int32\", data: [convInfo.dilationHeight, convInfo.dilationWidth] }\n  ];\n  if (useVec4) {\n    program = new Conv2DMMVec4Program(convInfo, hasBias, activation2, hasPreluActivationWeights);\n  } else {\n    program = new Conv2DMMProgram(convInfo, hasBias, activation2, hasPreluActivationWeights);\n  }\n  const dimAOuter = convInfo.outHeight * convInfo.outWidth;\n  const dimBOuter = convInfo.outChannels;\n  const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inChannels;\n  dimensions.push({ type: \"int32\", data: [dimAOuter] }, { type: \"int32\", data: [dimBOuter] }, { type: \"int32\", data: [dimInner] });\n  const inputVar = [x, filter];\n  if (hasBias) {\n    inputVar.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    inputVar.push(preluActivationWeights);\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  return backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D.js\nfunction conv2d6(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  return conv2DImpl({ x, filter, convInfo, backend: backend2 });\n}\nvar conv2DConfig4 = {\n  kernelName: Conv2D,\n  backendName: \"webgpu\",\n  kernelFunc: conv2d6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_mm_webgpu.js\nvar Conv2DDerInputMMProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = \"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\";\n    this.outputShape = convInfo.inShape;\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };\n    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape);\n    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;\n  }\n  getUserCode() {\n    const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);\n    const readASnippet = `\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x[getIndexFromCoords4D(coord, uniforms.xShape)];`;\n    const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${readASnippet}\n    }\n    return 0.0;`;\n    const userCode = `\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${sampleA}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${matMulSource}\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_webgpu.js\nvar Conv2DDerInputProgram2 = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.uniforms = \"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.inShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const rowDim = this.isChannelsLast ? 1 : 2;\n    const colDim = this.isChannelsLast ? 2 : 3;\n    const channelDim = this.isChannelsLast ? 3 : 1;\n    return `\n    ${getMainHeaderAndGlobalIndexString()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${channelDim}];\n\n        let dyCorner = vec2<i32>(coords[${rowDim}]), coords[${colDim}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.filterHeight, convInfo.filterWidth] },\n    {\n      type: \"int32\",\n      data: [\n        convInfo.filterHeight - 1 - convInfo.padInfo.top,\n        convInfo.filterWidth - 1 - convInfo.padInfo.left\n      ]\n    },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    {\n      type: \"int32\",\n      data: [\n        convInfo.batchSize,\n        convInfo.outHeight,\n        convInfo.outWidth,\n        convInfo.outChannels\n      ]\n    }\n  ];\n  let program;\n  if (env().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")) {\n    program = new Conv2DDerInputProgram2(convInfo);\n  } else {\n    program = new Conv2DDerInputMMProgram(convInfo);\n    const dimAOuter = convInfo.inShape[1] * convInfo.inShape[2];\n    const dimBOuter = convInfo.inShape[3];\n    const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels;\n    dimensions.push({ type: \"uint32\", data: [dimAOuter] }, { type: \"uint32\", data: [dimBOuter] }, { type: \"uint32\", data: [dimInner] });\n  }\n  return backend2.runWebGPUProgram(program, [dy, filter], \"float32\", dimensions);\n}\nvar conv2DBackpropInputConfig4 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"webgpu\",\n  kernelFunc: conv2DBackpropInput5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cos.js\nvar cos4 = unaryKernelFunc3({ opType: UnaryOpType.COS });\nvar cosConfig4 = {\n  kernelName: Cos,\n  backendName: \"webgpu\",\n  kernelFunc: cos4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cosh.js\nvar cosh4 = unaryKernelFunc3({ opType: UnaryOpType.COSH });\nvar coshConfig4 = {\n  kernelName: Cosh,\n  backendName: \"webgpu\",\n  kernelFunc: cosh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/crop_and_resize_webgpu.js\nvar CropAndResizeProgram2 = class {\n  constructor(channnel, boxShape, cropSize, method) {\n    this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n    this.uniforms = \"extrapolationValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const [numBoxes] = boxShape;\n    this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.methodId = method === \"bilinear\" ? 1 : 0;\n    this.cropHeightBiggerThan1 = this.outputShape[1] > 1;\n    this.cropWidthBiggerThan1 = this.outputShape[2] > 1;\n    this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;\n  }\n  getUserCode() {\n    const [inputHeightFloat, inputWidthFloat] = [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`];\n    const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ? [\n      `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`,\n      \"(y2-y1) * height_ratio\",\n      `y1*${inputHeightFloat} + f32(y)*(height_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (y1+y2) * ${inputHeightFloat}`\n    ];\n    const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ? [\n      `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`,\n      \"(x2-x1) * width_ratio\",\n      `x1*${inputWidthFloat} + f32(x)*(width_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (x1+x2) * ${inputWidthFloat}`\n    ];\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${heightRatio});\n        let width_ratio = f32(${widthRatio});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${heightScale};\n        let width_scale = ${widthScale};\n        let in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/CropAndResize.js\nvar cropAndResize5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const program = new CropAndResizeProgram2(image2.shape[3], boxes.shape, cropSize, method);\n  const uniformData = [{ type: \"float32\", data: [extrapolationValue] }];\n  return backend2.runWebGPUProgram(program, [image2, boxes, boxInd], \"float32\", uniformData);\n};\nvar cropAndResizeConfig4 = {\n  kernelName: CropAndResize,\n  backendName: \"webgpu\",\n  kernelFunc: cropAndResize5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/cum_webgpu.js\nvar CumOpType2;\n(function(CumOpType3) {\n  CumOpType3[\"Prod\"] = \"*\";\n  CumOpType3[\"Sum\"] = \"+\";\n})(CumOpType2 || (CumOpType2 = {}));\nvar CumProgram2 = class {\n  constructor(op2, shape, exclusive, reverse5) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"index : f32,\";\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.exclusive = exclusive;\n    this.reverse = reverse5;\n    this.op = op2;\n    this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;\n  }\n  getUserCode() {\n    const rank = this.outputShape.length;\n    const initVal = this.op === CumOpType2.Prod ? \"1.0\" : \"0.0\";\n    const val = this.exclusive ? initVal : `getX(${getCoords4(rank, \"coords\", this.op)})`;\n    const length = this.outputShape[this.outputShape.length - 1];\n    let condition = \"\";\n    let idxString = \"\";\n    if (this.exclusive) {\n      condition = this.reverse ? `end != ${length - 1}` : \"end != 0\";\n      idxString = this.reverse ? \"end + 1\" : \"end - 1\";\n    } else {\n      condition = this.reverse ? `end + pow2 < ${length}` : \"end >= pow2\";\n      idxString = this.reverse ? \"end + pow2\" : \"end - pow2\";\n    }\n    return `\n      ${getMainHeaderAndGlobalIndexString()}\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${getFinalCoord2(rank, \"coords\", this.op)};\n         var val = ${val};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${condition}) {\n           let idx = ${idxString};\n           ${getFinalCoord2(rank, \"coords\", this.op)} = idx;\n           val ${this.op}= getX(${getCoords4(rank, \"coords\", this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `;\n  }\n};\nfunction getCoords4(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\nfunction getFinalCoord2(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cum_impl.js\nfunction cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {\n  const xRank = x.shape.length;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity5({ inputs: { x: permutedX }, backend: backend2 });\n  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {\n    const program = new CumProgram2(op2, permutedX.shape, false, reverse5);\n    const prevResult = result;\n    const uniformData = [{ type: \"float32\", data: [i2] }];\n    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);\n    backend2.disposeData(prevResult.dataId);\n  }\n  if (exclusive) {\n    const program = new CumProgram2(op2, permutedX.shape, exclusive, reverse5);\n    const prevResult = result;\n    const uniformData = [{ type: \"float32\", data: [0] }];\n    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);\n    backend2.disposeData(prevResult.dataId);\n  }\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose5({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeData(result.dataId);\n    backend2.disposeData(permutedX.dataId);\n    return reverseTransposedResult;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumprod.js\nfunction cumprod5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl2(CumOpType2.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig4 = {\n  kernelName: Cumprod,\n  backendName: \"webgpu\",\n  kernelFunc: cumprod5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumsum.js\nfunction cumsum5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl2(CumOpType2.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig4 = {\n  kernelName: Cumsum,\n  backendName: \"webgpu\",\n  kernelFunc: cumsum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depth_to_space_webgpu.js\nvar DepthToSpaceProgram2 = class {\n  constructor(outputShape, dataFormat) {\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.uniforms = \"blockSize : i32,\";\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `depthToSpace_${dataFormat}`;\n    this.dataFormat = dataFormat;\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`;\n    return userCode;\n  }\n  getHeightCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n  getWidthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n  getDepthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n  getOutputDepthSize() {\n    if (this.dataFormat === \"NHWC\") {\n      return `uniforms.outShape[3]`;\n    } else {\n      return `uniforms.outShape[1]`;\n    }\n  }\n  getInputSamplingString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const uniformData = [\n    { type: \"int32\", data: [blockSize] }\n  ];\n  const program = new DepthToSpaceProgram2(outputShape, dataFormat);\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar depthToSpaceConfig4 = {\n  kernelName: DepthToSpace,\n  backendName: \"webgpu\",\n  kernelFunc: depthToSpace5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_3x3_webgpu.js\nvar DepthwiseConv2D3x3Program = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = \"pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,\";\n    this.workGroupSize = [4, 4, 4];\n    this.isVec4 = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = { x: [0, 1], y: [2], z: [3] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]);\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.shaderKey = `depthwise3x3_${activation2}`;\n  }\n  getUserCode() {\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);\n      if (this.hasPreluActivation) {\n        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${activationOp}\n        }`;\n      } else {\n        activationSnippet = `\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${activationOp}\n          }\n        `;\n      }\n      applyActivationSnippet = `dotProd[i] = activation(dotProd[i], coords);`;\n    }\n    const addBiasSnippet = this.addBias ? \"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\" : \"\";\n    const userCode = `\n      ${activationSnippet}\n\n      ${getWorkGroupSizeString()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${addBiasSnippet}\n            ${applyActivationSnippet}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_webgpu.js\nvar DepthwiseConv2DProgram2 = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,\n      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,\n      channelMul : i32,`;\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.shaderKey = `depthwise_${this.activation}`;\n  }\n  getUserCode() {\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (this.activation) {\n      const activationOp = mapActivationToShaderProgram2(this.activation, false);\n      if (this.hasPreluActivation) {\n        activationSnippet = `fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${activationOp}\n        }`;\n      } else {\n        activationSnippet = `\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${activationOp}\n          }\n        `;\n      }\n      applyActivationSnippet = `dotProd = activation(dotProd, coords);`;\n    }\n    const addBiasSnippet = this.addBias ? \"dotProd = dotProd + getBiasByOutputCoords(coords);\" : \"\";\n    const userCode = `\n      ${activationSnippet}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${getMainHeaderString()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    { type: \"int32\", data: [convInfo.dilationHeight, convInfo.dilationWidth] },\n    { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }\n  ];\n  let program;\n  if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) {\n    program = new DepthwiseConv2D3x3Program(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram2(convInfo);\n    dimensions.push({ type: \"int32\", data: [convInfo.filterHeight] }, { type: \"int32\", data: [convInfo.filterWidth] }, { type: \"int32\", data: [convInfo.outChannels / convInfo.inChannels] });\n  }\n  return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions);\n}\nvar depthwiseConv2dNativeConfig4 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"webgpu\",\n  kernelFunc: depthwiseConv2dNative3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Multiply.js\nvar multiplyKernelFunc = binaryKernelFunc3({\n  opSnippet: BinaryOpType.MUL,\n  cpuKernelImpl: multiplyImplCPU2,\n  supportsComplex: true\n});\nvar multiplyConfig4 = {\n  kernelName: Multiply,\n  backendName: \"webgpu\",\n  kernelFunc: multiplyKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/reduce_webgpu.js\nvar ReduceProgram2 = class {\n  constructor(reduceInfo, reduceType) {\n    this.workGroupSize = [64, 1, 1];\n    this.variableNames = [\"x\"];\n    this.uniforms = \"reduceSize : i32,\";\n    this.size = true;\n    this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize];\n    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(this.inputShape, [1]);\n    this.outputShape = outputShape.length === 0 ? [1] : outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);\n    this.reduceType = reduceType;\n    this.shaderKey = `reduce_${reduceType}`;\n  }\n  getUserCode() {\n    let reduceOp = ``;\n    let initValue = \"0.0\";\n    if (this.reduceType === \"min\" || this.reduceType === \"max\") {\n      reduceOp = `\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType === \"min\" ? \"<\" : \">\"} bestValue)\n           {  bestValue = candidate; }`;\n      initValue = \"f32(x[offset])\";\n    } else if (this.reduceType === \"sum\" || this.reduceType === \"mean\") {\n      reduceOp = \" bestValue = bestValue + candidate; \";\n    } else if (this.reduceType === \"prod\") {\n      reduceOp = \" bestValue = bestValue * candidate; \";\n      initValue = \"1.0\";\n    }\n    const outputSnippet = this.reduceType === \"mean\" ? `setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));` : `setOutputAtIndex(outputIndex, bestValue);`;\n    const sharedMemorySnippet = `\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `;\n    const userCode = `\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${sharedMemorySnippet}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length === 1 ? \"outputCoords\" : \"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${getMainHeaderAndGlobalIndexString()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${initValue};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${reduceOp}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${reduceOp}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${outputSnippet}\n        }\n       }\n     `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/reduce.js\nfunction reduce2(x, axis, keepDims, reduceType, backend2) {\n  const xRank = x.shape.length;\n  const toDispose = [];\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let input2 = x;\n  if (permutedAxes != null) {\n    input2 = transpose5({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(input2);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(reduceType, axes, xRank);\n  const [reduceOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  let resOutShape = reduceOutShape;\n  if (keepDims) {\n    resOutShape = backend_util_exports.expandShapeToKeepDim(reduceOutShape, origAxes);\n  }\n  let res;\n  if ((reduceType === \"max\" || reduceType === \"prod\") && backend2.shouldExecuteOnCPU([input2])) {\n    const xVals = backend2.tensorMap.get(input2.dataId).values;\n    switch (reduceType) {\n      case \"max\":\n        const outValues = maxImplCPU2(xVals, util_exports.sizeFromShape(reduceShape), resOutShape, x.dtype);\n        res = backend2.makeTensorInfo(resOutShape, x.dtype, outValues);\n        break;\n      case \"prod\":\n        const { outVals, outShape, outDtype } = prodImplCPU2(input2.shape, input2.dtype, xVals, axes);\n        res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n        break;\n      default:\n        throw new Error(`${reduceType} CPU implementation is not yet supported.`);\n    }\n  } else {\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const xSize = util_exports.sizeFromShape(input2.shape);\n    const batchSize = xSize / inSize;\n    const reduceInfo = { windowSize: inSize, inSize, batchSize, outSize: 1 };\n    const dtype = reduceType === \"mean\" ? \"float32\" : sumOutType(x.dtype);\n    const uniformData = [\n      { type: \"int32\", data: [inSize] }\n    ];\n    const program = new ReduceProgram2(reduceInfo, reduceType);\n    const reduced = backend2.runWebGPUProgram(program, [input2], dtype, uniformData);\n    toDispose.push(reduced);\n    res = reshape6({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 });\n  }\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sum.js\nfunction sum6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"sum\", backend2);\n}\nvar sumConfig4 = {\n  kernelName: Sum,\n  backendName: \"webgpu\",\n  kernelFunc: sum6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Einsum.js\nfunction einsum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    for (const idTerm of steps[i2]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose5({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiplyKernelFunc({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i2 < nSteps - 1) {\n      if (path[i2] >= 0) {\n        out = sum6({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i2] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeData(tensorInfo.dataId);\n  }\n  return out;\n}\nvar einsumConfig3 = {\n  kernelName: Einsum,\n  backendName: \"webgpu\",\n  kernelFunc: einsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Elu.js\nvar elu6 = unaryKernelFunc3({ opType: UnaryOpType.ELU });\nvar eluConfig4 = {\n  kernelName: Elu,\n  backendName: \"webgpu\",\n  kernelFunc: elu6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Equal.js\nvar equal4 = binaryKernelFunc3({ opSnippet: BinaryOpType.EQUAL, dtype: \"bool\", cpuKernelImpl: equalImplCPU2 });\nvar equalConfig4 = {\n  kernelName: Equal,\n  backendName: \"webgpu\",\n  kernelFunc: equal4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Exp.js\nvar exp4 = unaryKernelFunc3({\n  opType: UnaryOpType.EXP,\n  cpuKernelImpl: expImplCPU2,\n  dtype: \"float32\"\n});\nvar expConfig4 = {\n  kernelName: Exp,\n  backendName: \"webgpu\",\n  kernelFunc: exp4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ExpandDims.js\nfunction expandDims6(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { dim } = attrs;\n  const { input: input2 } = inputs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape6({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig4 = {\n  kernelName: ExpandDims,\n  backendName: \"webgpu\",\n  kernelFunc: expandDims6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Expm1.js\nvar expm14 = unaryKernelFunc3({ opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU2 });\nvar expm1Config3 = {\n  kernelName: Expm1,\n  backendName: \"webgpu\",\n  kernelFunc: expm14\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/fill_webgpu.js\nvar FillProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [];\n    this.outputShape = [];\n    this.uniforms = \"value : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"fill\";\n  }\n  getUserCode() {\n    const userCode = `\n    ${getMainHeaderAndGlobalIndexString()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Fill.js\nfunction fill5(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value } = attrs;\n  let { dtype } = attrs;\n  dtype = dtype || util_exports.inferDtype(value);\n  if (dtype === \"string\") {\n    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n    values.fill(value);\n    return backend2.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram2(shape);\n    const uniformData = [{ type: \"float32\", data: [value] }];\n    return backend2.runWebGPUProgram(program, [], dtype, uniformData);\n  }\n}\nvar fillConfig4 = {\n  kernelName: Fill,\n  backendName: \"webgpu\",\n  kernelFunc: fill5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flip_left_right_webgpu.js\nvar FlipLeftRightProgram2 = class {\n  constructor(imageShape) {\n    this.outputShape = [];\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = imageShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"flipLeftRight\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig4 = {\n  kernelName: FlipLeftRight,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const webgpuBackend = backend2;\n    const program = new FlipLeftRightProgram2(image2.shape);\n    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Floor.js\nvar floor4 = unaryKernelFunc3({ opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU2 });\nvar floorConfig4 = {\n  kernelName: Floor,\n  backendName: \"webgpu\",\n  kernelFunc: floor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FloorDiv.js\nvar floorDiv4 = binaryKernelFunc3({ opSnippet: BinaryOpType.INT_DIV, dtype: \"int32\" });\nvar floorDivConfig4 = {\n  kernelName: FloorDiv,\n  backendName: \"webgpu\",\n  kernelFunc: floorDiv4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/from_pixels_webgpu.js\nvar FromPixelsProgram2 = class {\n  constructor(outputShape, useImport = false) {\n    this.outputShape = [0];\n    this.variableNames = [];\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.useImport = useImport;\n    this.shaderKey = `fromPixels_${this.useImport}`;\n  }\n  getUserCode() {\n    const textureLoad = this.useImport ? \"textureLoad(src, vec2<i32>(coords.yx));\" : \"textureLoad(src, vec2<i32>(coords.yx), 0)\";\n    const textureType = this.useImport ? \"texture_external\" : \"texture_2d<f32>\";\n    return `\n      @binding(1) @group(0) var src: ${textureType};\n\n      ${getMainHeaderAndGlobalIndexString()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${textureLoad};\n            result[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FromPixels.js\nvar fromPixelsConfig2 = {\n  kernelName: FromPixels,\n  backendName: \"webgpu\",\n  kernelFunc: fromPixels3\n};\nvar fromPixels2DContext3;\nfunction fromPixels3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  let { pixels } = inputs;\n  const { numChannels } = attrs;\n  if (pixels == null) {\n    throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n  }\n  const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n  const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n  const isCanvas = typeof HTMLCanvasElement !== \"undefined\" && pixels instanceof HTMLCanvasElement || typeof OffscreenCanvas !== \"undefined\" && pixels instanceof OffscreenCanvas;\n  const isImageBitmap = typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap;\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  const outShape = [height, width, numChannels];\n  if (env().getBool(\"WEBGPU_USE_IMPORT\")) {\n    if (isVideo) {\n      return fromPixelsExternalImage({\n        externalImage: pixels,\n        backend: backend2,\n        attrs,\n        outShape,\n        useImport: true\n      });\n    }\n  }\n  if (isVideo || isImage) {\n    if (fromPixels2DContext3 == null) {\n      fromPixels2DContext3 = document.createElement(\"canvas\").getContext(\"2d\");\n    }\n    fromPixels2DContext3.canvas.width = width;\n    fromPixels2DContext3.canvas.height = height;\n    fromPixels2DContext3.drawImage(pixels, 0, 0, width, height);\n    pixels = fromPixels2DContext3.canvas;\n  }\n  if (isImageBitmap || isCanvas || isVideo || isImage) {\n    return fromPixelsExternalImage({\n      externalImage: pixels,\n      backend: backend2,\n      attrs,\n      outShape,\n      useImport: false\n    });\n  }\n  const imageData = pixels.data;\n  let pixelArray = imageData;\n  if (numChannels != null && numChannels !== 4) {\n    pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);\n    const dataLength = imageData.length;\n    let j = 0;\n    for (let i2 = 0; i2 < dataLength; i2++) {\n      if (i2 % 4 < numChannels) {\n        pixelArray[j++] = imageData[i2];\n      }\n    }\n  }\n  const output = backend2.makeTensorInfo(outShape, \"int32\");\n  const info = backend2.tensorMap.get(output.dataId);\n  info.values = new Int32Array(pixelArray);\n  backend2.maybeReleaseBuffer(output.dataId);\n  backend2.uploadToGPU(output.dataId);\n  return output;\n}\nfunction fromPixelsExternalImage(args) {\n  const { externalImage, backend: backend2, attrs, outShape, useImport } = args;\n  const { numChannels } = attrs;\n  const size = util_exports.sizeFromShape(outShape);\n  const strides = util_exports.computeStrides(outShape);\n  const program = new FromPixelsProgram2(outShape, useImport);\n  const uniformData = [\n    { type: \"uint32\", data: [size] },\n    { type: \"uint32\", data: [numChannels] },\n    { type: \"uint32\", data: [...strides] },\n    { type: \"uint32\", data: [...program.dispatch] }\n  ];\n  const output = backend2.runFromPixelsProgram(program, outShape, uniformData, useImport, externalImage);\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/batchnorm_webgpu.js\nvar BatchNormProgram2 = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape) {\n    this.uniforms = \"varianceEpsilon : f32,\";\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    this.outputShape = xShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n    }\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n    }\n    this.offsetShape = offsetShape;\n    this.scaleShape = scaleShape;\n    this.shaderKey = \"batchNorm\";\n  }\n  getUserCode() {\n    let offsetSnippet = \"0.0\";\n    if (this.offsetShape != null) {\n      offsetSnippet = \"getOffsetByOutputIndex(index)\";\n    }\n    let scaleSnippet = \"1.0\";\n    if (this.scaleShape != null) {\n      scaleSnippet = \"getScaleByOutputIndex(index)\";\n    }\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${offsetSnippet};\n          let scaleValue = ${scaleSnippet};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedBatchNorm.js\nvar fusedBatchNormConfig2 = {\n  kernelName: FusedBatchNorm,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x, scale: scale2, offset, mean: mean5, variance } = inputs;\n    const { varianceEpsilon } = attrs;\n    const webGPUBackend = backend2;\n    const batchNormInputs = [x, mean5, variance];\n    let offsetShape = null;\n    if (offset != null) {\n      offsetShape = offset.shape;\n      batchNormInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale2 != null) {\n      scaleShape = scale2.shape;\n      batchNormInputs.push(scale2);\n    }\n    const program = new BatchNormProgram2(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape);\n    const uniformData = [{ type: \"float32\", data: [varianceEpsilon] }];\n    return webGPUBackend.runWebGPUProgram(program, batchNormInputs, x.dtype, uniformData);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2d3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`WebGPU backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  return conv2DImpl({\n    x,\n    filter,\n    convInfo,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar fusedConv2DConfig4 = {\n  kernelName: FusedConv2D,\n  backendName: \"webgpu\",\n  kernelFunc: fusedConv2d3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const programInputs = [x, filter];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    { type: \"int32\", data: [convInfo.dilationHeight, convInfo.dilationWidth] },\n    { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }\n  ];\n  let program;\n  if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) {\n    program = new DepthwiseConv2D3x3Program(convInfo, hasBias, activation2, hasPreluActivationWeights);\n  } else {\n    program = new DepthwiseConv2DProgram2(convInfo, hasBias, activation2, hasPreluActivationWeights);\n    dimensions.push({ type: \"int32\", data: [convInfo.filterHeight] }, { type: \"int32\", data: [convInfo.filterWidth] }, { type: \"int32\", data: [convInfo.outChannels / convInfo.inChannels] });\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  const result = backend2.runWebGPUProgram(program, programInputs, \"float32\", dimensions);\n  return result;\n}\nvar fusedDepthwiseConv2DConfig4 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"webgpu\",\n  kernelFunc: fusedDepthwiseConv2D3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_nd_webgpu.js\nvar GatherNDProgram2 = class {\n  constructor(sliceDim, shape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `gathernd_${sliceDim}`;\n    this.sliceDim = sliceDim;\n    this.uniforms = `sliceDim : i32, strides : ${getCoordsDataType2(sliceDim)},`;\n  }\n  getUserCode() {\n    let strideString;\n    if (this.sliceDim > 1) {\n      strideString = \"uniforms.strides[j]\";\n    } else {\n      strideString = \"uniforms.strides\";\n    }\n    const userCode = `\n        ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${strideString};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherNd.js\nfunction gatherNd4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n  const flattenX = reshape6({\n    inputs: { x: params },\n    backend: backend2,\n    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n  });\n  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n    const indicesData = backend2.readSync(indices.dataId);\n    const paramsBuf = backend2.bufferSync(params);\n    const outValue = gatherNdImplCPU2(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program = new GatherNDProgram2(sliceRank, [numSlices, sliceSize]);\n  const uniformData = [{ type: \"int32\", data: [sliceRank] }, { type: \"int32\", data: strides }];\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], flattenX.dtype, uniformData);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n  backend2.disposeData(flattenIndices.dataId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar gatherNdConfig4 = {\n  kernelName: GatherNd,\n  backendName: \"webgpu\",\n  kernelFunc: gatherNd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_webgpu.js\nvar GatherProgram2 = class {\n  constructor(aShape, outputShape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = aShape.slice();\n    this.aShape = aShape;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `gather`;\n  }\n  getUserCode() {\n    const sourceCoords = getSourceCoords4(this.aShape);\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSourceCoords4(aShape) {\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < aShape.length; i2++) {\n    if (i2 === 2) {\n      sourceCoords.push(\"indexZ\");\n    } else {\n      sourceCoords.push(`${currentCoords[i2]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherV2.js\nfunction gatherV24(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const toDispose = [];\n  const flattenX = reshape6({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape6({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  if (backend2.shouldExecuteOnCPU([x, indices])) {\n    const indicesBufferInfo = backend2.tensorMap.get(flattenIndex.dataId);\n    const indicesValues = indicesBufferInfo.values;\n    const indicesBuf = buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues);\n    const xBufferInfo = backend2.tensorMap.get(flattenX.dataId);\n    const xValues = xBufferInfo.values;\n    const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues);\n    const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape);\n    toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new GatherProgram2(flattenX.shape, flattenOutputShape);\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return reshaped;\n}\nvar gatherV2Config4 = {\n  kernelName: GatherV2,\n  backendName: \"webgpu\",\n  kernelFunc: gatherV24\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Greater.js\nvar greater5 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.GREATER,\n  cpuKernelImpl: greaterImplCPU2,\n  dtype: \"bool\"\n});\nvar greaterConfig4 = {\n  kernelName: Greater,\n  backendName: \"webgpu\",\n  kernelFunc: greater5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GreaterEqual.js\nvar greaterEqual4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.GREATER_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: greaterEqualImplCPU2\n});\nvar greaterEqualConfig4 = {\n  kernelName: GreaterEqual,\n  backendName: \"webgpu\",\n  kernelFunc: greaterEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  const uniformData = [{ type: \"float32\", data: [alpha] }];\n  const program = new UnaryOpProgram2(x.shape, UnaryOpType.LEAKYRELU);\n  program.uniforms = \"alpha : f32,\";\n  return backend2.runWebGPUProgram(program, [x], \"float32\", uniformData);\n}\nvar leakyReluConfig4 = {\n  kernelName: LeakyRelu,\n  backendName: \"webgpu\",\n  kernelFunc: leakyRelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Less.js\nvar less5 = binaryKernelFunc3({ opSnippet: BinaryOpType.LESS, dtype: \"bool\", cpuKernelImpl: lessImplCPU2 });\nvar lessConfig4 = {\n  kernelName: Less,\n  backendName: \"webgpu\",\n  kernelFunc: less5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LessEqual.js\nvar lessEqual4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.LESS_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: lessEqualImplCPU2\n});\nvar lessEqualConfig4 = {\n  kernelName: LessEqual,\n  backendName: \"webgpu\",\n  kernelFunc: lessEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Log.js\nvar log5 = unaryKernelFunc3({ opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU2 });\nvar logConfig4 = {\n  kernelName: Log,\n  backendName: \"webgpu\",\n  kernelFunc: log5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalAnd.js\nvar logicalAnd4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.LOGICAL_AND,\n  dtype: \"bool\"\n});\nvar logicalAndConfig4 = {\n  kernelName: LogicalAnd,\n  backendName: \"webgpu\",\n  kernelFunc: logicalAnd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalNot.js\nvar logicalNot4 = unaryKernelFunc3({ opType: UnaryOpType.LOGICAL_NOT });\nvar logicalNotConfig3 = {\n  kernelName: LogicalNot,\n  backendName: \"webgpu\",\n  kernelFunc: logicalNot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Max.js\nfunction max6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  return reduce2(x, reductionIndices, keepDims, \"max\", backend2);\n}\nvar maxConfig4 = {\n  kernelName: Max,\n  backendName: \"webgpu\",\n  kernelFunc: max6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Maximum.js\nvar maximum5 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.MAX,\n  cpuKernelImpl: maximumImplCPU2\n});\nvar maximumConfig4 = {\n  kernelName: Maximum,\n  backendName: \"webgpu\",\n  kernelFunc: maximum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MaxPool.js\nfunction maxPool5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let program;\n  const dimensions = [];\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {\n    if (util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n      return identity5({ inputs: { x }, backend: backend2 });\n    }\n    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);\n    dimensions.push({ type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] });\n  } else {\n    program = new Pool2DProgram2(convInfo, \"max\");\n    dimensions.push({ type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }, { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    }, { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }, {\n      type: \"int32\",\n      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]\n    });\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);\n}\nvar maxPoolConfig4 = {\n  kernelName: MaxPool,\n  backendName: \"webgpu\",\n  kernelFunc: maxPool5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Mean.js\nfunction mean4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { keepDims, axis } = attrs;\n  return reduce2(x, axis, keepDims, \"mean\", backend2);\n}\nvar meanConfig4 = {\n  kernelName: Mean,\n  backendName: \"webgpu\",\n  kernelFunc: mean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Min.js\nfunction min6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"min\", backend2);\n}\nvar minConfig4 = {\n  kernelName: Min,\n  backendName: \"webgpu\",\n  kernelFunc: min6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Minimum.js\nvar minimum5 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.MIN,\n  cpuKernelImpl: minimumImplCPU2\n});\nvar minimumConfig4 = {\n  kernelName: Minimum,\n  backendName: \"webgpu\",\n  kernelFunc: minimum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/mirror_pad_webgpu.js\nvar MirrorPadProgram2 = class {\n  constructor(xShape, paddings, mode) {\n    this.uniforms = \"\";\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.xShape = xShape;\n    paddings.map((_, i2) => {\n      this.uniforms += ` pad${i2} : vec2<i32>,`;\n    });\n    this.offset = mode === \"reflect\" ? 0 : 1;\n    this.shaderKey = `mirrorPad_${mode}`;\n  }\n  getUserCode() {\n    const rank = this.xShape.length;\n    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(\",\");\n    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : \"\"}`).join(\",\");\n    const shaderStart = rank === 1 ? \"start\" : \"start[i]\";\n    const shaderEnd = rank === 1 ? \"end\" : \"end[i]\";\n    const shaderOutC = rank === 1 ? \"outC\" : \"outC[i]\";\n    const dtype = getCoordsDataType2(rank);\n    const unpackedCoords = rank > 1 ? [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank) : \"coords\";\n    return `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let start = ${dtype}(${start});\n          let end = ${dtype}(${end});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${rank}; i = i + 1) {\n            if (${shaderOutC} < ${shaderStart}) {\n              ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${this.offset};\n            } else if(${shaderOutC} >= ${shaderEnd}) {\n              ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MirrorPad.js\nvar mirrorPadConfig4 = {\n  kernelName: MirrorPad,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    const webGPUBackend = backend2;\n    const uniformData = paddings.map((p2) => {\n      return { type: \"int32\", data: [p2[0], p2[1]] };\n    });\n    const program = new MirrorPadProgram2(x.shape, paddings, mode);\n    const output = webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Neg.js\nfunction neg4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = backend2.tensorMap.get(x.dataId);\n    const [outValues, newShape] = negImplCPU2(xData.values, x.shape, x.dtype);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  const program = new UnaryOpProgram2(x.shape, UnaryOpType.NEG);\n  return backend2.runWebGPUProgram(program, [x], x.dtype);\n}\nvar negConfig4 = {\n  kernelName: Neg,\n  backendName: \"webgpu\",\n  kernelFunc: neg4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV3.js\nfunction nonMaxSuppressionV33(args) {\n  console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices } = kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config4 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"webgpu\",\n  kernelFunc: nonMaxSuppressionV33\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV5.js\nfunction nonMaxSuppressionV53(args) {\n  console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = kernel_impls_exports.nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config4 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"webgpu\",\n  kernelFunc: nonMaxSuppressionV53\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ZerosLike.js\nfunction zerosLike5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const r2 = zerosLike5({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeData(realPart.dataId);\n    backend2.disposeData(r2.dataId);\n    backend2.disposeData(imagPart.dataId);\n    backend2.disposeData(i2.dataId);\n    return result;\n  } else {\n    return fill5({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === \"string\" ? \"\" : 0\n      },\n      backend: backend2\n    });\n  }\n}\nvar zerosLikeConfig4 = {\n  kernelName: ZerosLike,\n  backendName: \"webgpu\",\n  kernelFunc: zerosLike5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/OnesLike.js\nfunction onesLike5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported under string dtype\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const r2 = onesLike5({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeData(realPart.dataId);\n    backend2.disposeData(r2.dataId);\n    backend2.disposeData(imagPart.dataId);\n    backend2.disposeData(i2.dataId);\n    return result;\n  } else {\n    return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n  }\n}\nvar onesLikeConfig4 = {\n  kernelName: OnesLike,\n  backendName: \"webgpu\",\n  kernelFunc: onesLike5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pack.js\nfunction pack4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims6({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims6({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return result;\n}\nvar packConfig4 = {\n  kernelName: Pack,\n  backendName: \"webgpu\",\n  kernelFunc: pack4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pad_webgpu.js\nvar PadProgram2 = class {\n  constructor(xShape, paddings) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"constantValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    paddings.map((_, i2) => {\n      this.uniforms += ` pad${i2} : vec2<i32>,`;\n    });\n    this.xShape = xShape;\n    this.shaderKey = \"pad\";\n  }\n  getUserCode() {\n    const rank = this.xShape.length;\n    const type = getCoordsDataType2(rank);\n    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(\",\");\n    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : \"\"}`).join(\",\");\n    const startValue = rank > 1 ? `${type}(${start})` : `${start}`;\n    const endValue = rank > 1 ? `${type}(${end})` : `${end}`;\n    const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;\n    const rightPadCondition = rank > 1 ? `any(outC >= end)` : `outC >= end`;\n    const unpackedCoords = rank > 1 ? [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank) : \"coords\";\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let start = ${startValue};\n          let end = ${endValue};\n          let outC = getCoordsFromIndex(index);\n\n          if (${leftPadCondition} || ${rightPadCondition}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${unpackedCoords}));\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/PadV2.js\nvar padV23 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  if (paddings.every((p2) => util_exports.arraysEqual(p2, [0, 0]))) {\n    return identity5({ inputs: { x }, backend: backend2 });\n  }\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n    return fill5({\n      backend: backend2,\n      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const uniformData = [{ type: \"float32\", data: [constantValue] }];\n  paddings.map((p2) => uniformData.push({ type: \"int32\", data: [p2[0], p2[1]] }));\n  const program = new PadProgram2(x.shape, paddings);\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n};\nvar padV2Config4 = {\n  kernelName: PadV2,\n  backendName: \"webgpu\",\n  kernelFunc: padV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pow.js\nvar pow4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.POW\n});\nvar powConfig4 = {\n  kernelName: Pow,\n  backendName: \"webgpu\",\n  kernelFunc: pow4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prelu.js\nfunction prelu6(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const program = new BinaryOpProgram2(BinaryOpType.PRELU, x.shape, alpha.shape);\n  return backend2.runWebGPUProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig4 = {\n  kernelName: Prelu,\n  backendName: \"webgpu\",\n  kernelFunc: prelu6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prod.js\nfunction prod5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"prod\", backend2);\n}\nvar prodConfig4 = {\n  kernelName: Prod,\n  backendName: \"webgpu\",\n  kernelFunc: prod5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Range.js\nvar range6 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImplCPU2(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig4 = {\n  kernelName: Range,\n  backendName: \"webgpu\",\n  kernelFunc: range6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RealDiv.js\nvar realDiv2 = binaryKernelFunc3({ opSnippet: BinaryOpType.DIV });\nvar realDivConfig4 = {\n  kernelName: RealDiv,\n  backendName: \"webgpu\",\n  kernelFunc: realDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu.js\nvar relu4 = unaryKernelFunc3({ opType: UnaryOpType.RELU });\nvar reluConfig4 = {\n  kernelName: Relu,\n  backendName: \"webgpu\",\n  kernelFunc: relu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu6.js\nvar relu64 = unaryKernelFunc3({ opType: UnaryOpType.RELU6 });\nvar relu6Config4 = {\n  kernelName: Relu6,\n  backendName: \"webgpu\",\n  kernelFunc: relu64\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_bilinear_webgpu.js\nvar ResizeBilinearProgram2 = class {\n  constructor(inputShape, newHeight, newWidth) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `resizeBilinear`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, size, halfPixelCenters } = attrs;\n  const [newHeight, newWidth] = size;\n  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;\n  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;\n  const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0;\n  const uniformData = [\n    { type: \"float32\", data: [adjustHeight, adjustWidth] },\n    { type: \"float32\", data: [halfPixelCentersValue] }\n  ];\n  const program = new ResizeBilinearProgram2(images.shape, newHeight, newWidth);\n  return backend2.runWebGPUProgram(program, [images], \"float32\", uniformData);\n}\nvar resizeBilinearConfig4 = {\n  kernelName: ResizeBilinear,\n  backendName: \"webgpu\",\n  kernelFunc: resizeBilinear5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_nearest_neighbor_webgpu.js\nvar ResizeNearestNeighborProgram2 = class {\n  constructor(inputShape, newHeight, newWidth, halfPixelCenters) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"adjustHeightWidth : vec2<f32>, roundBase : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.halfPixelCenters = halfPixelCenters;\n    this.shaderKey = `resizeNearest_${halfPixelCenters}`;\n  }\n  getUserCode() {\n    let sourceFracIndexRC;\n    if (this.halfPixelCenters) {\n      sourceFracIndexRC = `max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2<f32>(rc) * effectiveInputOverOutputRatioRC`;\n    }\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${sourceFracIndexRC};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;\n  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;\n  const roundBase = alignCorners ? 0.5 : 0;\n  const uniformData = [\n    { type: \"float32\", data: [adjustHeight, adjustWidth] },\n    { type: \"float32\", data: [roundBase] }\n  ];\n  const program = new ResizeNearestNeighborProgram2(images.shape, newHeight, newWidth, halfPixelCenters);\n  return backend2.runWebGPUProgram(program, [images], images.dtype, uniformData);\n}\nvar resizeNearestNeighborConfig3 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"webgpu\",\n  kernelFunc: resizeNearestNeighbor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/rotate_webgpu.js\nvar RotateProgram2 = class {\n  constructor(imageShape, fillValue) {\n    this.outputShape = [];\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = imageShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`;\n    this.shaderKey = \"rotate\";\n    this.outputShape = imageShape;\n    if (typeof fillValue === \"number\") {\n      this.uniforms += ` fillValue : f32,`;\n      this.fillSnippet = `var outputValue = uniforms.fillValue;`;\n      this.shaderKey += \"_float\";\n    } else {\n      this.uniforms += ` fillValue : vec3<f32>,`;\n      this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`;\n      this.shaderKey += \"_vec3\";\n    }\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderAndGlobalIndexString()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig4 = {\n  kernelName: RotateWithOffset,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const webgpuBackend = backend2;\n    const program = new RotateProgram2(image2.shape, fillValue);\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n    const uniformData = [\n      { type: \"float32\", data: [centerX] },\n      { type: \"float32\", data: [centerY] },\n      { type: \"float32\", data: [Math.sin(radians)] },\n      { type: \"float32\", data: [Math.cos(radians)] }\n    ];\n    if (typeof fillValue === \"number\") {\n      uniformData.push({ type: \"float32\", data: [Number.parseFloat(fillValue.toFixed(2))] });\n    } else {\n      uniformData.push({ type: \"float32\", data: fillValue });\n    }\n    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype, uniformData);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Rsqrt.js\nvar rsqrt4 = unaryKernelFunc3({ opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU2 });\nvar rsqrtConfig4 = {\n  kernelName: Rsqrt,\n  backendName: \"webgpu\",\n  kernelFunc: rsqrt4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/scatter_optimized_webgpu.js\nvar ScatterOptimizedProgram = class {\n  constructor(flattenXShape, sliceDim, indicesRank, updatesRank, strides, shape, outputDtype) {\n    this.variableNames = [\"updates\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.atomic = true;\n    this.outputShape = shape;\n    this.type = outputDtype;\n    this.dispatchLayout = flatDispatchLayout(flattenXShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, flattenXShape, this.workGroupSize);\n    this.sliceDimGreaterThanOne = sliceDim > 1;\n    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${outputDtype}`;\n    const stridesType = getCoordsDataType2(strides.length);\n    this.uniforms = `sliceDim : i32, strides: ${stridesType}, size: i32,`;\n    this.updatesRank = updatesRank;\n    this.indicesRank = indicesRank;\n  }\n  getUserCode() {\n    let indicesString = \"\";\n    if (this.indicesRank === 1) {\n      indicesString = \"coords[0]\";\n    } else if (this.indicesRank === 2) {\n      indicesString = \"coords[0], j\";\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n    const strideString = this.sliceDimGreaterThanOne ? \"uniforms.strides[j]\" : \"uniforms.strides\";\n    let updatesString = \"\";\n    let outCoordsString = \"\";\n    let getUpdatesCoordsFromFlatIndex = \"\";\n    if (this.updatesRank === 1) {\n      updatesString = \"coords[0]\";\n      outCoordsString = \"flattenedIndex\";\n      getUpdatesCoordsFromFlatIndex = `\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `;\n    } else if (this.updatesRank === 2) {\n      updatesString = \"coords[0], coords[1]\";\n      outCoordsString = \"vec2<i32>(flattenedIndex, coords[1])\";\n      getUpdatesCoordsFromFlatIndex = `\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `;\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n    const atomicAddSnippet = this.type === \"int32\" ? `atomicAdd(&(result[flatIndex]), i32(updateValue));` : `\n     var assumed = atomicLoad(&(result[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;\n    const userCode = `\n    ${getUpdatesCoordsFromFlatIndex}\n\n      ${getMainHeaderAndGlobalIndexString()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${indicesSnippet}));\n            flattenedIndex = flattenedIndex + indexInside * ${strideString};\n          }\n          let updateValue = ${updatesSnippet};\n          let flatIndex = getOutputIndexFromCoords(${outCoordsString});\n\n         ${atomicAddSnippet}\n        }\n      }`;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ScatterNd.js\nfunction scatterNd4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  if (outputSize === 0) {\n    return backend2.makeTensorInfo(shape, indices.dtype);\n  }\n  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n  const flattenX = reshape6({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n  const type = flattenX.dtype;\n  const output = fill5({ backend: backend2, attrs: { shape: flattenShape, value: 0, dtype: type } });\n  const size = util_exports.sizeFromShape(flattenX.shape);\n  const uniformData = [\n    { type: \"int32\", data: [sliceRank] },\n    { type: \"int32\", data: strides },\n    { type: \"int32\", data: [size] }\n  ];\n  const program = new ScatterOptimizedProgram(flattenX.shape, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, type);\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], type, uniformData, output);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n  backend2.disposeData(flattenIndices.dataId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar scatterNdConfig4 = {\n  kernelName: ScatterNd,\n  backendName: \"webgpu\",\n  kernelFunc: scatterNd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/select_webgpu.js\nvar SelectProgram2 = class {\n  constructor(cRank, shape, rank) {\n    this.variableNames = [\"c\", \"a\", \"b\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.cRank = cRank;\n    this.rank = rank;\n    this.shaderKey = \"select\";\n  }\n  getUserCode() {\n    let cCoords;\n    let abCoords;\n    if (this.rank > 4) {\n      throw Error(`Where for rank ${this.rank} is not yet supported`);\n    }\n    if (this.rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i2 = 0; i2 < this.outputShape.length; i2++) {\n        abCoordVars.push(`${currentCoords[i2]}`);\n        if (i2 < this.cRank) {\n          cCoordVars.push(`${currentCoords[i2]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${cCoords});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${abCoords}));\n          } else {\n            setOutputAtIndex(index, getB(${abCoords}));\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Select.js\nfunction select5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  const program = new SelectProgram2(condition.shape.length, t2.shape, t2.shape.length);\n  return backend2.runWebGPUProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));\n}\nvar selectConfig4 = {\n  kernelName: Select,\n  backendName: \"webgpu\",\n  kernelFunc: select5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sigmoid.js\nvar sigmoid5 = unaryKernelFunc3({ opType: UnaryOpType.SIGMOID });\nvar sigmoidConfig4 = {\n  kernelName: Sigmoid,\n  backendName: \"webgpu\",\n  kernelFunc: sigmoid5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sin.js\nvar sin4 = unaryKernelFunc3({ opType: UnaryOpType.SIN });\nvar sinConfig4 = {\n  kernelName: Sin,\n  backendName: \"webgpu\",\n  kernelFunc: sin4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sinh.js\nvar sinh4 = unaryKernelFunc3({ opType: UnaryOpType.SINH });\nvar sinhConfig3 = {\n  kernelName: Sinh,\n  backendName: \"webgpu\",\n  kernelFunc: sinh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sub.js\nvar sub4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.SUB,\n  cpuKernelImpl: subImplCPU2,\n  supportsComplex: true\n});\nvar subConfig4 = {\n  kernelName: Sub,\n  backendName: \"webgpu\",\n  kernelFunc: sub4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Softmax.js\nfunction softmax6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const axes = util_exports.parseAxisParam([dim], logits.shape);\n  const maxLogit = max6({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitsReshaped = reshape6({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a6 = sub4({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n  const b = exp4({ inputs: { x: a6 }, backend: backend2 });\n  const sumExp = sum6({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumExpReshaped = reshape6({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const res = realDiv2({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n  backend2.disposeData(maxLogit.dataId);\n  backend2.disposeData(maxLogitsReshaped.dataId);\n  backend2.disposeData(a6.dataId);\n  backend2.disposeData(b.dataId);\n  backend2.disposeData(sumExp.dataId);\n  backend2.disposeData(sumExpReshaped.dataId);\n  return res;\n}\nvar softmaxConfig4 = {\n  kernelName: Softmax,\n  backendName: \"webgpu\",\n  kernelFunc: softmax6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");\n  const prod6 = blockShape.reduce((a6, b) => a6 * b);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const toDispose = [];\n  const paddedX = padV23({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapedPaddedX = reshape6({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n  const paddedXT = transpose5({\n    inputs: { x: reshapedPaddedX },\n    backend: backend2,\n    attrs: { perm: permutedReshapedPaddedPermutation }\n  });\n  const result = reshape6({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return result;\n};\nvar spaceToBatchNDConfig4 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"webgpu\",\n  kernelFunc: spaceToBatchND5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/scatter_webgpu.js\nvar ScatterProgram2 = class {\n  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n    this.variableNames = [\"updates\", \"indices\", \"defaultValue\"];\n    this.workGroupSize = [64, 1, 1];\n    this.workPerThread = 4;\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    const sliceDimGreaterThanOne = sliceDim > 1;\n    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${sliceDimGreaterThanOne}`;\n    const stridesType = getCoordsDataType2(strides.length);\n    this.uniforms = `updateSize : i32, sliceDim : i32, strides: ${stridesType},`;\n    let indicesString = \"\";\n    if (indicesRank === 1) {\n      indicesString = \"i\";\n    } else if (indicesRank === 2) {\n      indicesString = \"i, j\";\n    }\n    this.indicesSnippet = `getIndices(${indicesString})`;\n    let updatesString = \"\";\n    if (updatesRank === 1) {\n      updatesString = \"i\";\n    } else if (updatesRank === 2) {\n      updatesString = \"i, coords[1]\";\n    }\n    this.updatesSnippet = `getUpdates(${updatesString})`;\n    this.strideString = sliceDimGreaterThanOne ? \"uniforms.strides[j]\" : \"uniforms.strides\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SparseToDense.js\nfunction sparseToDense4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  if (sparseValues.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(sparseIndices);\n    const updatesBuf = backend2.bufferSync(sparseValues);\n    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n    const outBuf = scatterImplCPU2(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n  }\n  const uniformData = [\n    { type: \"int32\", data: [numUpdates] },\n    { type: \"int32\", data: [sliceRank] },\n    { type: \"int32\", data: strides }\n  ];\n  const program = new ScatterProgram2(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);\n  const res = backend2.runWebGPUProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype, uniformData);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar sparseToDenseConfig3 = {\n  kernelName: SparseToDense,\n  backendName: \"webgpu\",\n  kernelFunc: sparseToDense4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SplitV.js\nfunction splitV4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s2;\n    const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s2;\n    return sliceT;\n  });\n}\nvar splitVConfig4 = {\n  kernelName: SplitV,\n  backendName: \"webgpu\",\n  kernelFunc: splitV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sqrt.js\nvar sqrt4 = unaryKernelFunc3({ opType: UnaryOpType.SQRT });\nvar sqrtConfig4 = {\n  kernelName: Sqrt,\n  backendName: \"webgpu\",\n  kernelFunc: sqrt4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Square.js\nvar squareConfig4 = {\n  kernelName: Square,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webGPUBackend = backend2;\n    const program = new UnaryOpProgram2(x.shape, UnaryOpType.SQUARE);\n    return webGPUBackend.runWebGPUProgram(program, [x], x.dtype);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SquaredDifference.js\nvar squaredDifference4 = binaryKernelFunc3({\n  opSnippet: BinaryOpType.SQUARED_DIFFERENCE\n});\nvar squaredDifferenceConfig4 = {\n  kernelName: SquaredDifference,\n  backendName: \"webgpu\",\n  kernelFunc: squaredDifference4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/strided_slice_webgpu.js\nvar StridedSliceProgram2 = class {\n  constructor(destSize) {\n    this.variableNames = [\"x\"];\n    this.workPerThread = 1;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = destSize;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    const dtype = getCoordsDataType2(this.outputShape.length);\n    this.uniforms = `begin : ${dtype},  strides : ${dtype}, `;\n    this.shaderKey = \"stridedSlice\";\n  }\n  getUserCode() {\n    const rank = this.outputShape.length;\n    let newCoords = \"\";\n    if (rank === 1) {\n      newCoords = \"coords * uniforms.strides + uniforms.begin\";\n    } else {\n      let outputAxis = 0;\n      newCoords = this.outputShape.map((_, i2) => {\n        outputAxis++;\n        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i2}] + uniforms.begin[${i2}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i2}] + uniforms.begin[${i2}]`;\n      }).join(\",\");\n    }\n    const userCode = `\n       ${getMainHeaderAndGlobalIndexString()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${newCoords}));\n         }\n       }\n     `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StridedSlice.js\nfunction stridedSlice5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(sliced.dataId);\n  } else {\n    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      const values = backend2.readSync(x.dataId);\n      const xBuf = buffer(x.shape, x.dtype, values);\n      const resultValues = stridedSliceImplCPU2(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram2(finalShapeSparse);\n      const uniformData = [{ type: \"int32\", data: $begin }, { type: \"int32\", data: $strides }];\n      const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n      result = reshape6({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } });\n      backend2.disposeData(resultValues.dataId);\n    }\n  }\n  return result;\n}\nvar stridedSliceConfig4 = {\n  kernelName: StridedSlice,\n  backendName: \"webgpu\",\n  kernelFunc: stridedSlice5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StringNGrams.js\nfunction stringNGrams4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU2($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig3 = {\n  kernelName: StringNGrams,\n  backendName: \"webgpu\",\n  kernelFunc: stringNGrams4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tanh.js\nvar tanh5 = unaryKernelFunc3({ opType: UnaryOpType.TANH });\nvar tanhConfig4 = {\n  kernelName: Tanh,\n  backendName: \"webgpu\",\n  kernelFunc: tanh5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/tile_webgpu.js\nvar TileProgram2 = class {\n  constructor(aShape, reps) {\n    this.variableNames = [\"A\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[i2] * reps[i2];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.rank = this.outputShape.length;\n    this.shaderKey = \"tile\";\n  }\n  getUserCode() {\n    const sourceCoords = getSourceCoords5(this.rank, \"uniforms.\");\n    const userCode = `\n      ${getMainHeaderAndGlobalIndexString()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSourceCoords5(rank, uniformPrefix = \"\") {\n  if (rank >= 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `(resRC % ${uniformPrefix}aShape)`;\n  }\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < rank; i2++) {\n    sourceCoords.push(`(${currentCoords[i2]} % ${uniformPrefix}aShape[${i2}])`);\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tile.js\nfunction tile6(params) {\n  const { inputs, backend: backend2, attrs } = params;\n  const { x } = inputs;\n  const { reps } = attrs;\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\" || x.shape.length >= 5) {\n    const data = backend2.readSync(x.dataId);\n    const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU2(buf, reps);\n    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n  const program = new TileProgram2(x.shape, reps);\n  const output = backend2.runWebGPUProgram(program, [x], x.dtype);\n  return output;\n}\nvar tileConfig4 = {\n  kernelName: Tile,\n  backendName: \"webgpu\",\n  kernelFunc: tile6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/top_k_webgpu.js\nvar SwapProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`;\n    this.shaderKey = \"swap\";\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderAndGlobalIndexString()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `;\n    return userCode;\n  }\n};\nvar MergeProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `inputSize : i32, firstPass : i32, k : i32,`;\n    this.shaderKey = \"merge\";\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderAndGlobalIndexString()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull2(backend2, tensorInfo) {\n  if (tensorInfo !== null) {\n    backend2.disposeData(tensorInfo.dataId);\n  }\n}\nfunction roundUpToPow22(num) {\n  let pow22 = 1;\n  while (pow22 < num) {\n    pow22 *= 2;\n  }\n  return pow22;\n}\nfunction topK3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const xShape = x.shape;\n  const lastDim = xShape[xShape.length - 1];\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xVals = backend2.readSync(x.dataId);\n    const [allTopKVals, allTopKIndices] = topKImplCPU2(xVals, xShape, x.dtype, k, sorted);\n    return [\n      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n    ];\n  }\n  if (k === 0) {\n    xShape[xShape.length - 1] = 0;\n    return [\n      backend2.makeTensorInfo(xShape, x.dtype, []),\n      backend2.makeTensorInfo(xShape, \"int32\", [])\n    ];\n  }\n  if (lastDim === 1) {\n    return [\n      x,\n      fill5({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n    ];\n  }\n  const xSize = util_exports.sizeFromShape(xShape);\n  const batch = xSize / lastDim;\n  const x2D = reshape6({ inputs: { x }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n  const kPow2 = roundUpToPow22(k);\n  const lastDimPow2 = roundUpToPow22(lastDim);\n  let indices = null;\n  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n  const runSwap = (dir, inc, shape) => {\n    const inputs2 = getInputs();\n    const program = new SwapProgram2(shape);\n    const firstPass = indices === null ? 1 : 0;\n    const uniformDataSwap = [\n      { type: \"int32\", data: [lastDim] },\n      { type: \"int32\", data: [firstPass] },\n      { type: \"float32\", data: [Number.NEGATIVE_INFINITY] },\n      { type: \"int32\", data: [dir] },\n      { type: \"int32\", data: [inc] }\n    ];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGPUProgram(program, inputs2, \"int32\", uniformDataSwap);\n    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);\n  };\n  for (let len = 1; len < kPow2; len *= 2) {\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, [batch, lastDimPow2]);\n    }\n  }\n  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n    const inputs2 = getInputs();\n    const mergeProgram = new MergeProgram2([batch, indicesSize / 2]);\n    const firstPass = indices === null ? 1 : 0;\n    const uniformDataMerge = [\n      { type: \"int32\", data: [lastDim] },\n      { type: \"int32\", data: [firstPass] },\n      { type: \"int32\", data: [kPow2] }\n    ];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGPUProgram(mergeProgram, inputs2, \"int32\", uniformDataMerge);\n    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);\n    const len = kPow2 / 2;\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, indices.shape);\n    }\n  }\n  let prevIndices = indices;\n  indices = slice5({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);\n  let values = gatherV24({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n  disposeIntermediateTensorInfoOrNull2(backend2, x2D);\n  const newShape = xShape.slice(0, -1);\n  newShape.push(k);\n  prevIndices = indices;\n  indices = reshape6({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);\n  const prevValues = values;\n  values = reshape6({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevValues);\n  return [values, indices];\n}\nvar topKConfig4 = {\n  kernelName: TopK,\n  backendName: \"webgpu\",\n  kernelFunc: topK3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transform_webgpu.js\nvar TransformProgram2 = class {\n  constructor(outShape) {\n    this.variableNames = [\"Image\", \"Transforms\"];\n    this.uniforms = \"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"transform\";\n  }\n  getUserCode() {\n    const userCode = `\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${getMainHeaderAndGlobalIndexString()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transform.js\nfunction transform5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const program = new TransformProgram2(outShape);\n  const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n  let fillModeId;\n  switch (fillMode) {\n    case \"constant\":\n      fillModeId = 1;\n      break;\n    case \"reflect\":\n      fillModeId = 2;\n      break;\n    case \"wrap\":\n      fillModeId = 3;\n      break;\n    case \"nearest\":\n      fillModeId = 4;\n      break;\n    default:\n      fillModeId = 1;\n      break;\n  }\n  const uniformData = [\n    { type: \"int32\", data: [interpolationModeId] },\n    { type: \"int32\", data: [fillModeId] },\n    { type: \"float32\", data: [fillValue] }\n  ];\n  return backend2.runWebGPUProgram(program, [image2, transforms], \"float32\", uniformData);\n}\nvar transformConfig4 = {\n  kernelName: Transform,\n  backendName: \"webgpu\",\n  kernelFunc: transform5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Unpack.js\nfunction unpack4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const x = value;\n  const xRank = x.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < xRank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = x.shape[i2];\n    }\n  }\n  const toDispose = [];\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i2 = 0; i2 < res.length; i2++) {\n    begin[axis] = i2;\n    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n    const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n    res[i2] = reshaped;\n    toDispose.push(sliced);\n  }\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return res;\n}\nvar unpackConfig4 = {\n  kernelName: Unpack,\n  backendName: \"webgpu\",\n  kernelFunc: unpack4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/register_all_kernels.js\nvar kernelConfigs4 = [\n  _fusedMatMulConfig4,\n  absConfig4,\n  addConfig4,\n  addNConfig4,\n  argMaxConfig4,\n  argMinConfig3,\n  avgPoolConfig4,\n  batchMatMulConfig4,\n  batchToSpaceNDConfig4,\n  castConfig4,\n  ceilConfig4,\n  clipByValueConfig4,\n  complexConfig3,\n  concatConfig4,\n  conv2DConfig4,\n  conv2DBackpropInputConfig4,\n  cosConfig4,\n  coshConfig4,\n  cropAndResizeConfig4,\n  cumprodConfig4,\n  cumsumConfig4,\n  depthToSpaceConfig4,\n  depthwiseConv2dNativeConfig4,\n  einsumConfig3,\n  eluConfig4,\n  equalConfig4,\n  expConfig4,\n  expandDimsConfig4,\n  expm1Config3,\n  fillConfig4,\n  flipLeftRightConfig4,\n  fromPixelsConfig2,\n  floorConfig4,\n  floorDivConfig4,\n  fusedBatchNormConfig2,\n  fusedConv2DConfig4,\n  fusedDepthwiseConv2DConfig4,\n  gatherNdConfig4,\n  gatherV2Config4,\n  greaterConfig4,\n  greaterEqualConfig4,\n  identityConfig4,\n  imagConfig3,\n  leakyReluConfig4,\n  lessConfig4,\n  lessEqualConfig4,\n  logConfig4,\n  logicalAndConfig4,\n  logicalNotConfig3,\n  maxConfig4,\n  maximumConfig4,\n  maxPoolConfig4,\n  meanConfig4,\n  minConfig4,\n  minimumConfig4,\n  mirrorPadConfig4,\n  multiplyConfig4,\n  negConfig4,\n  nonMaxSuppressionV3Config4,\n  nonMaxSuppressionV5Config4,\n  notEqualConfig4,\n  onesLikeConfig4,\n  packConfig4,\n  padV2Config4,\n  powConfig4,\n  preluConfig4,\n  prodConfig4,\n  rangeConfig4,\n  realConfig3,\n  realDivConfig4,\n  reluConfig4,\n  relu6Config4,\n  reshapeConfig4,\n  resizeBilinearConfig4,\n  resizeNearestNeighborConfig3,\n  rotateWithOffsetConfig4,\n  rsqrtConfig4,\n  scatterNdConfig4,\n  selectConfig4,\n  sigmoidConfig4,\n  sinConfig4,\n  sinhConfig3,\n  sliceConfig4,\n  stridedSliceConfig4,\n  stringNGramsConfig3,\n  softmaxConfig4,\n  spaceToBatchNDConfig4,\n  sparseToDenseConfig3,\n  splitVConfig4,\n  sqrtConfig4,\n  squareConfig4,\n  squaredDifferenceConfig4,\n  subConfig4,\n  sumConfig4,\n  tanhConfig4,\n  tileConfig4,\n  topKConfig4,\n  transformConfig4,\n  transposeConfig4,\n  unpackConfig4,\n  zerosLikeConfig4\n];\nfor (const kernelConfig of kernelConfigs4) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/buffer_manager.js\nvar BufferManager = class {\n  constructor(device) {\n    this.device = device;\n    this.numUsedBuffers = 0;\n    this.numFreeBuffers = 0;\n    this.freeBuffers = /* @__PURE__ */ new Map();\n    this.usedBuffers = /* @__PURE__ */ new Map();\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n  acquireUploadBuffer(byteSize, usage) {\n    return this.acquireBuffer(byteSize, usage, true);\n  }\n  acquireBuffer(byteSize, usage, mappedAtCreation = false) {\n    const key = getBufferKey(byteSize, usage);\n    if (!this.freeBuffers.has(key)) {\n      this.freeBuffers.set(key, []);\n    }\n    if (!this.usedBuffers.has(key)) {\n      this.usedBuffers.set(key, []);\n    }\n    this.numBytesUsed += byteSize;\n    this.numUsedBuffers++;\n    if (this.freeBuffers.get(key).length > 0) {\n      this.numFreeBuffers--;\n      const newBuffer2 = this.freeBuffers.get(key).shift();\n      this.usedBuffers.get(key).push(newBuffer2);\n      return newBuffer2;\n    }\n    this.numBytesAllocated += byteSize;\n    const newBuffer = this.device.createBuffer({ mappedAtCreation, size: byteSize, usage });\n    this.usedBuffers.get(key).push(newBuffer);\n    return newBuffer;\n  }\n  releaseBuffer(buffer2, byteSize, usage) {\n    if (this.freeBuffers.size === 0) {\n      return;\n    }\n    const key = getBufferKey(byteSize, usage);\n    if (!this.freeBuffers.has(key)) {\n      this.freeBuffers.set(key, []);\n    }\n    this.freeBuffers.get(key).push(buffer2);\n    this.numFreeBuffers++;\n    this.numUsedBuffers--;\n    const bufferList = this.usedBuffers.get(key);\n    const bufferIndex = bufferList.indexOf(buffer2);\n    if (bufferIndex < 0) {\n      throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");\n    }\n    bufferList.splice(bufferIndex, 1);\n    this.numBytesUsed -= byteSize;\n  }\n  releaseUploadBuffer(buffer2, byteSize, usage) {\n    buffer2.mapAsync(GPUMapMode.WRITE).then(() => {\n      this.releaseBuffer(buffer2, byteSize, usage);\n    }, (err) => {\n    });\n  }\n  getNumUsedBuffers() {\n    return this.numUsedBuffers;\n  }\n  getNumFreeBuffers() {\n    return this.numFreeBuffers;\n  }\n  dispose() {\n    this.freeBuffers.forEach((buffers, key) => {\n      buffers.forEach((buff) => {\n        buff.destroy();\n      });\n    });\n    this.usedBuffers.forEach((buffers, key) => {\n      buffers.forEach((buff) => {\n        buff.destroy();\n      });\n    });\n    this.freeBuffers = /* @__PURE__ */ new Map();\n    this.usedBuffers = /* @__PURE__ */ new Map();\n    this.numUsedBuffers = 0;\n    this.numFreeBuffers = 0;\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n};\nfunction getBufferKey(byteSize, usage) {\n  return `${byteSize}_${usage}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/texture_manager.js\nvar TextureManager2 = class {\n  constructor(device) {\n    this.device = device;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this.freeTextures = /* @__PURE__ */ new Map();\n    this.usedTextures = /* @__PURE__ */ new Map();\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n  acquireTexture(width, height, format, usage) {\n    const bytesPerElement2 = getBytesPerElement(format);\n    const byteSize = width * height * bytesPerElement2;\n    const key = getTextureKey(width, height, format, usage);\n    if (!this.freeTextures.has(key)) {\n      this.freeTextures.set(key, []);\n    }\n    if (!this.usedTextures.has(key)) {\n      this.usedTextures.set(key, []);\n    }\n    this.numBytesUsed += byteSize;\n    this.numUsedTextures++;\n    if (this.freeTextures.get(key).length > 0) {\n      this.numFreeTextures--;\n      const newTexture2 = this.freeTextures.get(key).shift();\n      this.usedTextures.get(key).push(newTexture2);\n      return newTexture2;\n    }\n    this.numBytesAllocated += byteSize;\n    const newTexture = this.device.createTexture({\n      size: [width, height],\n      format,\n      usage\n    });\n    this.usedTextures.get(key).push(newTexture);\n    return newTexture;\n  }\n  releaseTexture(texture, width, height, format, usage) {\n    if (this.freeTextures.size === 0) {\n      return;\n    }\n    const key = getTextureKey(width, height, format, usage);\n    if (!this.freeTextures.has(key)) {\n      this.freeTextures.set(key, []);\n    }\n    this.freeTextures.get(key).push(texture);\n    this.numFreeTextures++;\n    this.numUsedTextures--;\n    const textureList = this.usedTextures.get(key);\n    const textureIndex = textureList.indexOf(texture);\n    if (textureIndex < 0) {\n      throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n    }\n    textureList.splice(textureIndex, 1);\n    const bytesPerElement2 = getBytesPerElement(format);\n    const byteSize = width * height * bytesPerElement2;\n    this.numBytesUsed -= byteSize;\n  }\n  getNumUsedTextures() {\n    return this.numUsedTextures;\n  }\n  getNumFreeTextures() {\n    return this.numFreeTextures;\n  }\n  dispose() {\n    this.freeTextures.forEach((textures, key) => {\n      textures.forEach((texture) => {\n        texture.destroy();\n      });\n    });\n    this.usedTextures.forEach((textures, key) => {\n      textures.forEach((texture) => {\n        texture.destroy();\n      });\n    });\n    this.freeTextures = /* @__PURE__ */ new Map();\n    this.usedTextures = /* @__PURE__ */ new Map();\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n};\nfunction getTextureKey(width, height, format, usage) {\n  return `${width}_${height}_${format}_${usage}`;\n}\nfunction getBytesPerElement(format) {\n  if (format === \"rgba8unorm\") {\n    return 16;\n  } else {\n    throw new Error(`${format} is not supported!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_program.js\nvar makeBindGroup = (device, bindGroupLayout, inputs, output, uniforms) => {\n  const bindings = [output, ...inputs];\n  if (uniforms) {\n    bindings.push(uniforms);\n  }\n  return device.createBindGroup({\n    layout: bindGroupLayout,\n    entries: bindings.map((b, i2) => ({ binding: i2, resource: b }))\n  });\n};\nvar compileProgram2 = (device, program, pipelineLayout, inputsData, output, isFromPixel = false) => {\n  const outputData = { dtype: output.dtype, shape: output.shape };\n  const source = makeShader2(inputsData, outputData, program, isFromPixel);\n  const module = device.createShaderModule({ code: source, label: program.constructor.name });\n  const pipeline = device.createComputePipeline({\n    layout: pipelineLayout,\n    compute: { module, entryPoint: \"main\" },\n    label: program.constructor.name\n  });\n  return pipeline;\n};\nfunction makeShaderKey2(program, shapes, types = [], broadcastDimsKey = \"\", inputShapesEqualsOutShape = \"\") {\n  const key = program.shaderKey + \"_\" + (program.workGroupSize ? program.workGroupSize.join(\",\") : \"\") + shapes.map((shape) => shape.length).join(\",\") + types.join(\",\") + program.variableNames.join(\",\") + broadcastDimsKey + inputShapesEqualsOutShape;\n  return key;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/backend_webgpu.js\nvar CPU_HANDOFF_SIZE_THRESHOLD2 = env().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\");\nvar reshapeDispatch = (device, program) => {\n  const MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE = device.limits.maxComputeWorkgroupsPerDimension;\n  const layout = program[\"dispatchLayout\"];\n  const dispatch = program[\"dispatch\"];\n  if (dispatch.every((d) => d <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE)) {\n    return dispatch;\n  }\n  util_exports.assert(dispatch[0] > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && layout.y === void 0 && layout.z === void 0, () => \"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");\n  let dispatchAverage = Math.ceil(Math.sqrt(dispatch[0]));\n  if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) {\n    dispatchAverage = Math.ceil(Math.cbrt(dispatch[0]));\n    util_exports.assert(dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE, () => \"Total dispatch size exceeds WebGPU maximum.\");\n    return [dispatchAverage, dispatchAverage, dispatchAverage];\n  } else {\n    return [dispatchAverage, dispatchAverage, 1];\n  }\n};\nvar WebGPUBackend = class extends KernelBackend {\n  constructor(device, supportTimeQuery = false) {\n    super();\n    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();\n    this.tensorDisposalQueue = [];\n    this.uniformDisposalQueue = [];\n    this.stagingDisposalQueue = [];\n    this.textureDisposalQueue = [];\n    this.disposed = false;\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    this.dispatchNumberInEncoder = 0;\n    this.fromPixelTextureLayout = null;\n    this.fromPixelImportTextureLayout = null;\n    if (!isWebGPUSupported()) {\n      throw new Error(\"WebGPU is not supported on this device\");\n    }\n    this.layoutCache = {};\n    this.pipelineCache = {};\n    this.device = device;\n    this.queue = device.queue;\n    this.currentCommandEncoder = null;\n    this.currentComputePass = null;\n    this.supportTimeQuery = supportTimeQuery;\n    this.bufferManager = new BufferManager(this.device);\n    this.textureManager = new TextureManager2(this.device);\n    this.tensorMap = new DataStorage(this, engine());\n    if (this.supportTimeQuery) {\n      this.querySet = this.device.createQuerySet({\n        type: \"timestamp\",\n        count: 2\n      });\n    }\n    if (env().getBool(\"WEBGPU_USE_PROFILE_TOOL\")) {\n      this.dummyCanvas = document.createElement(\"canvas\");\n      this.dummyCanvas.width = 1;\n      this.dummyCanvas.height = 1;\n      this.dummyContext = this.dummyCanvas.getContext(\"webgpu\");\n      this.dummyContext.configure({\n        device,\n        format: \"bgra8unorm\"\n      });\n      document.body.appendChild(this.dummyCanvas);\n    }\n  }\n  nextDataId() {\n    return WebGPUBackend.nextDataId++;\n  }\n  floatPrecision() {\n    return 32;\n  }\n  defaultGpuBufferUsage() {\n    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;\n  }\n  flushDisposalQueue() {\n    this.tensorDisposalQueue.forEach((d) => {\n      this.maybeReleaseBuffer(d);\n      this.tensorMap.delete(d);\n    });\n    this.uniformDisposalQueue.forEach((d) => this.bufferManager.releaseBuffer(d.buffer, d.byteSize, d.usage));\n    this.stagingDisposalQueue.forEach((d) => this.bufferManager.releaseUploadBuffer(d.buffer, d.byteSize, d.usage));\n    this.textureDisposalQueue.forEach((d) => this.textureManager.releaseTexture(d.texture, d.width, d.height, d.format, d.usage));\n    this.tensorDisposalQueue = [];\n    this.uniformDisposalQueue = [];\n    this.stagingDisposalQueue = [];\n    this.textureDisposalQueue = [];\n  }\n  disposeData(dataId, force = false) {\n    if (this.tensorMap.has(dataId)) {\n      const data = this.tensorMap.get(dataId);\n      data.refCount--;\n      if (!force && data.refCount > 0) {\n        return false;\n      }\n      if (this.commandQueueOwnedIds.has(dataId)) {\n        this.tensorDisposalQueue.push(dataId);\n        return false;\n      } else {\n        this.maybeReleaseBuffer(dataId);\n      }\n      const { complexTensorInfos } = this.tensorMap.get(dataId);\n      if (complexTensorInfos != null) {\n        this.disposeData(complexTensorInfos.real.dataId, true);\n        this.disposeData(complexTensorInfos.imag.dataId, true);\n      }\n      this.tensorMap.delete(dataId);\n    }\n    return true;\n  }\n  memory() {\n    return {\n      numBytesInGPU: this.bufferManager.numBytesUsed,\n      numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated,\n      unreliable: false\n    };\n  }\n  getBufferManager() {\n    return this.bufferManager;\n  }\n  getTextureManager() {\n    return this.textureManager;\n  }\n  acquireBuffer(byteSize, usage = this.defaultGpuBufferUsage()) {\n    return this.bufferManager.acquireBuffer(byteSize, usage);\n  }\n  maybeReleaseBuffer(dataId) {\n    const info = this.tensorMap.get(dataId);\n    if (info != null && info.bufferInfo.buffer != null) {\n      this.bufferManager.releaseBuffer(info.bufferInfo.buffer, info.bufferInfo.byteSize, info.bufferInfo.usage);\n      info.bufferInfo.buffer = null;\n    }\n  }\n  refCount(dataId) {\n    if (this.tensorMap.has(dataId)) {\n      const tensorData = this.tensorMap.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    tensorData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.tensorMap.has(dataId)) {\n      const tensorData = this.tensorMap.get(dataId);\n      tensorData.refCount--;\n    }\n  }\n  write(values, shape, dtype) {\n    if (dtype === \"complex64\" && values != null) {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const dataId = { id: this.nextDataId() };\n    const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);\n    this.tensorMap.set(dataId, {\n      dtype,\n      shape,\n      values,\n      bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() },\n      refCount: 1\n    });\n    return dataId;\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    if (dtype === \"complex64\") {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);\n    this.tensorMap.set(dataId, {\n      dtype,\n      shape,\n      values,\n      bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() },\n      refCount\n    });\n  }\n  submitQueue() {\n    this.ensureComputePassEnded();\n    this.queue.submit([this.currentCommandEncoder.finish()]);\n    this.currentCommandEncoder = null;\n    this.dispatchNumberInEncoder = 0;\n    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();\n    this.flushDisposalQueue();\n  }\n  getBuffer(dataId) {\n    this.uploadToGPU(dataId);\n    return this.tensorMap.get(dataId).bufferInfo.buffer;\n  }\n  ensureCommandEncoderReady() {\n    if (!this.currentCommandEncoder) {\n      this.currentCommandEncoder = this.device.createCommandEncoder();\n    }\n  }\n  ensureComputePassEnded() {\n    if (this.currentComputePass) {\n      this.currentComputePass.end();\n      this.currentComputePass = null;\n    }\n  }\n  getComputePass() {\n    if (!this.currentComputePass) {\n      this.currentComputePass = this.currentCommandEncoder.beginComputePass();\n    }\n    return this.currentComputePass;\n  }\n  async getBufferData(buffer2, size) {\n    const staging = this.acquireBuffer(size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.copyBufferToBuffer(buffer2, 0, staging, 0, size);\n    this.submitQueue();\n    await staging.mapAsync(GPUMapMode.READ);\n    const values = staging.getMappedRange().slice(0);\n    staging.unmap();\n    if (staging != null) {\n      this.bufferManager.releaseBuffer(staging, size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);\n    }\n    if (env().getBool(\"WEBGPU_USE_PROFILE_TOOL\")) {\n      util_exports.assert(this.dummyContext !== void 0, () => `Fail to get context for profiling tool`);\n      this.dummyContext.getCurrentTexture();\n    }\n    return values;\n  }\n  convertAndCacheOnCPU(dataId, data) {\n    const info = this.tensorMap.get(dataId);\n    this.maybeReleaseBuffer(dataId);\n    info.values = data;\n    return info.values;\n  }\n  readSync(dataId) {\n    const texData = this.tensorMap.get(dataId);\n    const { values } = texData;\n    if (values == null) {\n      throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");\n    }\n    return values;\n  }\n  async read(dataId) {\n    if (!this.tensorMap.has(dataId)) {\n      throw new Error(`Tensor ${dataId} was not registered!`);\n    }\n    const info = this.tensorMap.get(dataId);\n    const { values } = info;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId, values);\n    }\n    let vals;\n    if (info.dtype === \"complex64\") {\n      const ps = await Promise.all([\n        this.read(info.complexTensorInfos.real.dataId),\n        this.read(info.complexTensorInfos.imag.dataId)\n      ]);\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      const data = info.values != null ? info.values : await this.getBufferData(info.bufferInfo.buffer, info.bufferInfo.byteSize);\n      vals = ArrayBufferToTypedArray(data, info.dtype);\n    }\n    this.convertAndCacheOnCPU(dataId, vals);\n    return vals;\n  }\n  readToGPU(dataId) {\n    const srcData = this.tensorMap.get(dataId);\n    const { values, dtype, shape, bufferInfo } = srcData;\n    if (dtype === \"complex64\") {\n      throw new Error(\"Does not support reading buffer for complex64 dtype.\");\n    }\n    if (bufferInfo.buffer == null) {\n      if (values != null) {\n        throw new Error(\"Data is not on GPU but on CPU.\");\n      } else {\n        throw new Error(\"There is no data on GPU or CPU.\");\n      }\n    }\n    const size = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);\n    const resBuffer = this.acquireBuffer(size);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.copyBufferToBuffer(bufferInfo.buffer, 0, resBuffer, 0, size);\n    this.submitQueue();\n    const tensorInfo = this.makeTensorInfo(shape, dtype);\n    const tensorRef = engine().makeTensorFromTensorInfo(tensorInfo);\n    const info = this.tensorMap.get(tensorInfo.dataId);\n    info.bufferInfo.buffer = resBuffer;\n    return { tensorRef, buffer: resBuffer, bufSize: size };\n  }\n  bufferSync(t2) {\n    const data = this.readSync(t2.dataId);\n    if (t2.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t2.shape, t2.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t2.shape, t2.dtype, data);\n  }\n  async time(f) {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers = [];\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n    f();\n    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n    this.activeTimers = oldActiveTimers;\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n    const res = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null\n    };\n    const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n    res[\"kernelMs\"] = util_exports.sum(kernelMs);\n    res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  getAndSavePipeline(key, getPipeline) {\n    if (!(key in this.pipelineCache)) {\n      this.pipelineCache[key] = getPipeline();\n    }\n    return this.pipelineCache[key];\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let dataId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      dataId = this.write(encodedValues, shape, dtype);\n    } else {\n      dataId = this.write(values, shape, dtype);\n    }\n    return { dataId, shape, dtype };\n  }\n  tensorToBinding(tensor2) {\n    if (!tensor2) {\n      return null;\n    }\n    const tensorData = this.tensorMap.get(tensor2.dataId);\n    return {\n      offset: 0,\n      size: tensorData.bufferInfo.byteSize,\n      buffer: tensorData.bufferInfo.buffer\n    };\n  }\n  async getQueryTime(query) {\n    if (this.supportTimeQuery) {\n      return this.getTimeFromQuerySet(query);\n    } else {\n      return 0;\n    }\n  }\n  uploadToGPU(dataId) {\n    const info = this.tensorMap.get(dataId);\n    if (info.bufferInfo.buffer != null) {\n      return;\n    }\n    info.bufferInfo.buffer = this.acquireBuffer(info.bufferInfo.byteSize);\n    if (info.values) {\n      const stagingBuffer = this.bufferManager.acquireUploadBuffer(info.bufferInfo.byteSize, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC);\n      const arrayBuffer = stagingBuffer.getMappedRange();\n      if (info.dtype === \"int32\" || info.dtype === \"bool\") {\n        new Int32Array(arrayBuffer).set(info.values);\n      } else {\n        new Float32Array(arrayBuffer).set(info.values);\n      }\n      stagingBuffer.unmap();\n      this.ensureCommandEncoderReady();\n      this.ensureComputePassEnded();\n      this.currentCommandEncoder.copyBufferToBuffer(stagingBuffer, 0, info.bufferInfo.buffer, 0, info.bufferInfo.byteSize);\n      const stagingInfo = {\n        byteSize: info.bufferInfo.byteSize,\n        usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC,\n        buffer: stagingBuffer\n      };\n      this.stagingDisposalQueue.push(stagingInfo);\n    }\n  }\n  makeUniforms(uniformsWithType) {\n    let currentOffset = 0;\n    let preLength = 0;\n    const offsets = [];\n    uniformsWithType.forEach((d) => {\n      if (d.data.length === 0) {\n        d.data = [1];\n      }\n      let baseAlignment;\n      switch (d.data.length) {\n        case 1:\n          baseAlignment = 4;\n          break;\n        case 2:\n          baseAlignment = 8;\n          break;\n        case 3:\n          baseAlignment = 16;\n          break;\n        case 4:\n          baseAlignment = 16;\n          break;\n        case 5:\n          baseAlignment = 16;\n          break;\n        case 6:\n          baseAlignment = 16;\n          break;\n        default:\n          util_exports.assert(false, () => `Unsupported ${d.data.length}D shape`);\n      }\n      if (preLength === 5 || preLength === 6) {\n        baseAlignment = 16;\n      }\n      currentOffset = Math.ceil(currentOffset / baseAlignment) * baseAlignment;\n      preLength = d.data.length;\n      offsets.push(currentOffset);\n      currentOffset += d.data.length * 4;\n    });\n    const arrayBuffer = new ArrayBuffer(currentOffset);\n    uniformsWithType.forEach((d, i2) => {\n      const offset = offsets[i2];\n      if (d.type === \"int32\") {\n        new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      } else if (d.type === \"uint32\") {\n        new Uint32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      } else {\n        new Float32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      }\n    });\n    const uniformBuffer = this.acquireBuffer(currentOffset, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);\n    this.queue.writeBuffer(uniformBuffer, 0, arrayBuffer, 0, currentOffset);\n    const uniformInfo = {\n      byteSize: currentOffset,\n      usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM,\n      buffer: uniformBuffer\n    };\n    this.uniformDisposalQueue.push(uniformInfo);\n    return { offset: 0, size: currentOffset, buffer: uniformBuffer };\n  }\n  createLayout(inputEntrySize) {\n    const bindGroupLayoutEntries = [];\n    bindGroupLayoutEntries.push({\n      binding: 0,\n      visibility: GPUShaderStage.COMPUTE,\n      buffer: { type: \"storage\" }\n    });\n    for (let i2 = 0; i2 < inputEntrySize; i2++) {\n      bindGroupLayoutEntries.push({\n        binding: i2 + 1,\n        visibility: GPUShaderStage.COMPUTE,\n        buffer: { type: \"read-only-storage\" }\n      });\n    }\n    bindGroupLayoutEntries.push({\n      binding: inputEntrySize + 1,\n      visibility: GPUShaderStage.COMPUTE,\n      buffer: { type: \"uniform\" }\n    });\n    const bindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries });\n    const pipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [bindGroupLayout] });\n    return { bindGroupLayout, pipelineLayout };\n  }\n  getCachedOrCreateLayout(inputEntrySize) {\n    if (!(inputEntrySize in this.layoutCache)) {\n      this.layoutCache[inputEntrySize] = this.createLayout(inputEntrySize);\n    }\n    return this.layoutCache[inputEntrySize];\n  }\n  runWebGPUProgram(program, inputs, outputDtype, programUniforms, output) {\n    if (!output) {\n      output = this.makeTensorInfo(program.outputShape, outputDtype);\n      if (util_exports.sizeFromShape(output.shape) === 0) {\n        const outData = this.tensorMap.get(output.dataId);\n        outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n        return output;\n      }\n      this.uploadToGPU(output.dataId);\n    }\n    program.dispatch = reshapeDispatch(this.device, program);\n    let uniformsWithType = [{ type: \"float32\", data: [NaN] }];\n    const bufferShapes = inputs.concat(output).map((d) => d.shape);\n    const uniformsType = \"int32\";\n    bufferShapes.map((d) => {\n      uniformsWithType.push({ type: uniformsType, data: d });\n    });\n    const strides = util_exports.computeStrides(output.shape);\n    uniformsWithType.push({ type: uniformsType, data: strides });\n    if (program.size) {\n      const size = util_exports.sizeFromShape(program.outputShape);\n      uniformsWithType.push({ type: uniformsType, data: [program.isVec4 ? size / 4 : size] });\n    }\n    if (programUniforms) {\n      uniformsWithType = [...uniformsWithType, ...programUniforms];\n    }\n    const uniforms = this.makeUniforms(uniformsWithType);\n    const inputsData = inputs.map((input2, i2) => {\n      if (input2.dtype === \"complex64\") {\n        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n      }\n      this.uploadToGPU(input2.dataId);\n      return {\n        dtype: this.tensorMap.get(input2.dataId).dtype,\n        shape: input2.shape,\n        name: program.variableNames[i2]\n      };\n    });\n    const bufferTypes = inputsData.map((d) => d.dtype).concat(output.dtype);\n    const broadcastDims = inputsData.map((d) => backend_util_exports.getBroadcastDims(d.shape, output.shape));\n    const inputShapesEqualsOutShape = inputsData.map((d) => util_exports.arraysEqual(d.shape, output.shape)).join(\"_\");\n    const broadcastDimsKey = broadcastDims.map((d) => d.join(\"_\")).join(\";\");\n    const key = makeShaderKey2(program, bufferShapes, bufferTypes, broadcastDimsKey, inputShapesEqualsOutShape);\n    const { bindGroupLayout, pipelineLayout } = this.getCachedOrCreateLayout(program.variableNames.length);\n    const pipeline = this.getAndSavePipeline(key, () => {\n      return compileProgram2(this.device, program, pipelineLayout, inputsData, output);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    const bg = makeBindGroup(this.device, bindGroupLayout, inputs.map((t2) => this.tensorToBinding(t2)), this.tensorToBinding(output), uniforms);\n    this.ensureCommandEncoderReady();\n    const pass = this.getComputePass();\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 0);\n      }\n    }\n    pass.setPipeline(pipeline);\n    pass.setBindGroup(0, bg);\n    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 1);\n      }\n    }\n    this.dispatchNumberInEncoder++;\n    inputs.forEach((input2) => {\n      this.commandQueueOwnedIds.add(input2.dataId);\n    });\n    this.commandQueueOwnedIds.add(output.dataId);\n    if (env().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\") <= this.dispatchNumberInEncoder) {\n      this.submitQueue();\n    }\n    if (shouldTimeProgram) {\n      this.activeTimers.push({\n        name: program.constructor.name,\n        query: this.getQueryTime(this.querySet)\n      });\n    }\n    return output;\n  }\n  getFromPixelTextureLayout(useImport) {\n    if (useImport) {\n      if (this.fromPixelImportTextureLayout === null) {\n        this.fromPixelImportTextureLayout = this.createFromPixelTextureLayout(true);\n      }\n      return this.fromPixelImportTextureLayout;\n    }\n    if (this.fromPixelTextureLayout === null) {\n      this.fromPixelTextureLayout = this.createFromPixelTextureLayout(false);\n    }\n    return this.fromPixelTextureLayout;\n  }\n  createFromPixelTextureLayout(useImport) {\n    const bindGroupLayoutEntries = [];\n    bindGroupLayoutEntries.push({\n      binding: 0,\n      visibility: GPUShaderStage.COMPUTE,\n      buffer: { type: \"storage\" }\n    });\n    if (useImport) {\n      bindGroupLayoutEntries.push({\n        binding: 1,\n        visibility: GPUShaderStage.COMPUTE,\n        externalTexture: {}\n      });\n    } else {\n      bindGroupLayoutEntries.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, texture: {} });\n    }\n    bindGroupLayoutEntries.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} });\n    const fromPixelBindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries });\n    const fromPixelPipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [fromPixelBindGroupLayout] });\n    return {\n      bindGroupLayout: fromPixelBindGroupLayout,\n      pipelineLayout: fromPixelPipelineLayout\n    };\n  }\n  copyExternalImageToTexture(externalImage, outShape) {\n    const textureUsage = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING;\n    const textureFormat = \"rgba8unorm\";\n    const texture = this.textureManager.acquireTexture(outShape[1], outShape[0], textureFormat, textureUsage);\n    const externalResource = texture.createView();\n    this.queue.copyExternalImageToTexture({ source: externalImage }, { texture }, [outShape[1], outShape[0]]);\n    const textureInfo = {\n      width: outShape[1],\n      height: outShape[0],\n      format: textureFormat,\n      usage: textureUsage,\n      texture\n    };\n    this.textureDisposalQueue.push(textureInfo);\n    return externalResource;\n  }\n  runFromPixelsProgram(program, outShape, programUniforms, useImport, externalImage) {\n    program.dispatch = reshapeDispatch(this.device, program);\n    const output = this.makeTensorInfo(outShape, \"int32\");\n    if (util_exports.sizeFromShape(output.shape) === 0) {\n      const outData = this.tensorMap.get(output.dataId);\n      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n      return output;\n    }\n    this.uploadToGPU(output.dataId);\n    const key = makeShaderKey2(program, [output.shape]);\n    const layout = this.getFromPixelTextureLayout(useImport);\n    const pipeline = this.getAndSavePipeline(key, () => {\n      return compileProgram2(this.device, program, layout.pipelineLayout, [], output, true);\n    });\n    let externalResource;\n    if (useImport) {\n      const externalTextureDescriptor = {\n        source: externalImage\n      };\n      externalResource = this.device.importExternalTexture(externalTextureDescriptor);\n    } else {\n      externalResource = this.copyExternalImageToTexture(externalImage, output.shape);\n    }\n    const binding = this.tensorToBinding(output);\n    const uniforms = this.makeUniforms(programUniforms);\n    const bindGroup = this.device.createBindGroup({\n      layout: layout.bindGroupLayout,\n      entries: [\n        {\n          binding: 0,\n          resource: {\n            buffer: binding.buffer\n          }\n        },\n        {\n          binding: 1,\n          resource: externalResource\n        },\n        {\n          binding: 2,\n          resource: {\n            buffer: uniforms.buffer\n          }\n        }\n      ]\n    });\n    this.ensureCommandEncoderReady();\n    const pass = this.getComputePass();\n    const shouldTimeProgram = this.activeTimers != null;\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 0);\n      }\n    }\n    pass.setPipeline(pipeline);\n    pass.setBindGroup(0, bindGroup);\n    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 1);\n      }\n    }\n    this.commandQueueOwnedIds.add(output.dataId);\n    this.dispatchNumberInEncoder++;\n    if (env().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\") <= this.dispatchNumberInEncoder) {\n      this.submitQueue();\n    }\n    if (shouldTimeProgram) {\n      this.activeTimers.push({\n        name: program.constructor.name,\n        query: this.getQueryTime(this.querySet)\n      });\n    }\n    return output;\n  }\n  async getTimeFromQuerySet(querySet) {\n    const queryBuffer = this.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);\n    const dst = this.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0);\n    this.currentCommandEncoder.copyBufferToBuffer(queryBuffer, 0, dst, 0, 16);\n    this.submitQueue();\n    await dst.mapAsync(GPUMapMode.READ);\n    const arrayBuf = new BigUint64Array(dst.getMappedRange());\n    const timeElapsedNanos = Number(arrayBuf[1] - arrayBuf[0]);\n    dst.unmap();\n    this.bufferManager.releaseBuffer(dst, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);\n    this.bufferManager.releaseBuffer(queryBuffer, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);\n    return timeElapsedNanos / 1e6;\n  }\n  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD2) {\n    return env().getBool(\"WEBGPU_CPU_FORWARD\") && inputs.every((input2) => this.tensorMap.get(input2.dataId).bufferInfo.buffer == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n  }\n  numDataIds() {\n    return this.tensorMap.numDataIds() - this.tensorDisposalQueue.length;\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    this.bufferManager.dispose();\n    this.textureManager.dispose();\n    this.disposed = true;\n  }\n};\nWebGPUBackend.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu.js\nvar webgpu_exports = {};\n__export(webgpu_exports, {\n  WebGPUBackend: () => WebGPUBackend,\n  webgpu_util: () => webgpu_util_exports\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/index.js\nif (isWebGPUSupported()) {\n  registerBackend(\"webgpu\", async () => {\n    env().set(\"CHECK_COMPUTATION_FOR_ERRORS\", false);\n    const gpuDescriptor = {\n      powerPreference: env().get(\"WEBGPU_USE_LOW_POWER_GPU\") ? \"low-power\" : \"high-performance\"\n    };\n    const adapter = await navigator.gpu.requestAdapter(gpuDescriptor);\n    const adapterLimits = adapter.limits;\n    const deviceDescriptor = {};\n    const supportTimeQuery = adapter.features.has(\"timestamp-query\");\n    deviceDescriptor.requiredLimits = {\n      \"maxComputeWorkgroupStorageSize\": adapterLimits.maxComputeWorkgroupStorageSize,\n      \"maxComputeWorkgroupsPerDimension\": adapterLimits.maxComputeWorkgroupsPerDimension\n    };\n    if (supportTimeQuery) {\n      deviceDescriptor.requiredFeatures = [\"timestamp-query\"];\n    } else {\n      console.warn(`This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.`);\n    }\n    const device = await adapter.requestDevice(deviceDescriptor);\n    return new WebGPUBackend(device, supportTimeQuery);\n  }, 3);\n}\n\n// dist/tfjs.version.js\nvar e = \"3.18.0\";\nvar s = \"3.18.0\";\nvar t = \"3.18.0\";\nvar r = \"3.18.0\";\nvar l = \"3.18.0\";\nvar i = \"3.18.0\";\nvar a = \"3.18.0\";\nvar V = { tfjs: e, \"tfjs-core\": s, \"tfjs-data\": t, \"tfjs-layers\": r, \"tfjs-converter\": l, \"tfjs-backend-webgl\": i, \"tfjs-backend-wasm\": a };\nexport {\n  Abs,\n  Acos,\n  Acosh,\n  AdadeltaOptimizer,\n  AdagradOptimizer,\n  AdamOptimizer,\n  AdamaxOptimizer,\n  Add,\n  AddN,\n  All,\n  Any,\n  ArgMax,\n  ArgMin,\n  Asin,\n  Asinh,\n  Atan,\n  Atan2,\n  Atanh,\n  AvgPool,\n  AvgPool3D,\n  AvgPool3DGrad,\n  AvgPoolGrad,\n  BackendWasm,\n  BatchMatMul,\n  BatchToSpaceND,\n  Bincount,\n  BroadcastArgs,\n  BroadcastTo,\n  Callback,\n  CallbackList,\n  Cast,\n  Ceil,\n  ClipByValue,\n  Complex,\n  ComplexAbs,\n  Concat,\n  Conv2D,\n  Conv2DBackpropFilter,\n  Conv2DBackpropInput,\n  Conv3D,\n  Conv3DBackpropFilterV2,\n  Conv3DBackpropInputV2,\n  Cos,\n  Cosh,\n  CropAndResize,\n  Cumprod,\n  Cumsum,\n  CustomCallback,\n  DataStorage,\n  DenseBincount,\n  DepthToSpace,\n  DepthwiseConv2dNative,\n  DepthwiseConv2dNativeBackpropFilter,\n  DepthwiseConv2dNativeBackpropInput,\n  Diag,\n  Dilation2D,\n  Dilation2DBackpropFilter,\n  Dilation2DBackpropInput,\n  ENV,\n  EarlyStopping,\n  Einsum,\n  Elu,\n  EluGrad,\n  Environment,\n  Equal,\n  Erf,\n  Exp,\n  ExpandDims,\n  Expm1,\n  FFT,\n  Fill,\n  FlipLeftRight,\n  Floor,\n  FloorDiv,\n  FromPixels,\n  FusedBatchNorm,\n  FusedConv2D,\n  FusedDepthwiseConv2D,\n  GPGPUContext,\n  GatherNd,\n  GatherV2,\n  GraphModel,\n  Greater,\n  GreaterEqual,\n  History,\n  IFFT,\n  Identity,\n  Imag,\n  InputSpec,\n  IsFinite,\n  IsInf,\n  IsNan,\n  KernelBackend,\n  LRN,\n  LRNGrad,\n  LayerVariable,\n  LayersModel,\n  LeakyRelu,\n  Less,\n  LessEqual,\n  LinSpace,\n  Log,\n  Log1p,\n  LogSoftmax,\n  LogicalAnd,\n  LogicalNot,\n  LogicalOr,\n  LowerBound,\n  MathBackendWebGL,\n  Max,\n  MaxPool,\n  MaxPool3D,\n  MaxPool3DGrad,\n  MaxPoolGrad,\n  MaxPoolWithArgmax,\n  Maximum,\n  Mean,\n  Min,\n  Minimum,\n  MirrorPad,\n  Mod,\n  MomentumOptimizer,\n  Multinomial,\n  Multiply,\n  Neg,\n  NonMaxSuppressionV3,\n  NonMaxSuppressionV4,\n  NonMaxSuppressionV5,\n  NotEqual,\n  OP_SCOPE_SUFFIX,\n  OneHot,\n  OnesLike,\n  Optimizer,\n  OptimizerConstructors,\n  Pack,\n  PadV2,\n  Pool,\n  Pow,\n  Prelu,\n  Prod,\n  RMSPropOptimizer,\n  RNN,\n  Range,\n  Rank,\n  Real,\n  RealDiv,\n  Reciprocal,\n  Reduction,\n  Relu,\n  Relu6,\n  Reshape,\n  ResizeBilinear,\n  ResizeBilinearGrad,\n  ResizeNearestNeighbor,\n  ResizeNearestNeighborGrad,\n  Reverse,\n  RotateWithOffset,\n  Round,\n  Rsqrt,\n  SGDOptimizer,\n  ScatterNd,\n  SearchSorted,\n  Select,\n  Selu,\n  Sequential,\n  Sigmoid,\n  Sign,\n  Sin,\n  Sinh,\n  Slice,\n  Softmax,\n  Softplus,\n  SpaceToBatchND,\n  SparseFillEmptyRows,\n  SparseReshape,\n  SparseSegmentMean,\n  SparseSegmentSum,\n  SparseToDense,\n  SplitV,\n  Sqrt,\n  Square,\n  SquaredDifference,\n  Step,\n  StridedSlice,\n  StringNGrams,\n  StringSplit,\n  StringToHashBucketFast,\n  Sub,\n  Sum,\n  SymbolicTensor,\n  Tan,\n  Tanh,\n  Tensor,\n  TensorBuffer,\n  Tile,\n  TopK,\n  Transform,\n  Transpose,\n  Unique,\n  Unpack,\n  UnsortedSegmentSum,\n  UpperBound,\n  Variable,\n  ZerosLike,\n  _FusedMatMul,\n  abs,\n  acos,\n  acosh,\n  add2 as add,\n  addN,\n  all,\n  any,\n  argMax,\n  argMin,\n  asin,\n  asinh,\n  atan,\n  atan2,\n  atanh,\n  avgPool,\n  avgPool3d,\n  backend,\n  backend_util_exports as backend_util,\n  basicLSTMCell,\n  batchNorm,\n  batchNorm2d,\n  batchNorm3d,\n  batchNorm4d,\n  batchToSpaceND,\n  bincount,\n  booleanMaskAsync,\n  broadcastArgs,\n  broadcastTo,\n  broadcast_util_exports as broadcast_util,\n  browser_exports as browser,\n  buffer,\n  callbacks,\n  cast,\n  ceil,\n  clipByValue,\n  clone,\n  complex,\n  concat,\n  concat1d,\n  concat2d,\n  concat3d,\n  concat4d,\n  exports_constraints_exports as constraints,\n  conv1d,\n  conv2d,\n  conv2dTranspose,\n  conv3d,\n  conv3dTranspose,\n  copyRegisteredKernels,\n  cos,\n  cosh,\n  cosineWindow,\n  cumprod,\n  cumsum,\n  customGrad,\n  dist_exports2 as data,\n  denseBincount,\n  deprecationWarn,\n  depthToSpace,\n  depthwiseConv2d,\n  deregisterOp,\n  device_util_exports as device_util,\n  diag,\n  dilation2d,\n  disableDeprecationWarnings,\n  dispose,\n  disposeVariables,\n  div,\n  divNoNan,\n  dot,\n  dropout,\n  einsum,\n  elu,\n  enableDebugMode,\n  enableProdMode,\n  enclosingPowerOfTwo,\n  engine,\n  env,\n  equal,\n  erf,\n  euclideanNorm,\n  exp,\n  expandDims,\n  expm1,\n  eye,\n  fft,\n  fill,\n  findBackend,\n  findBackendFactory,\n  floor,\n  floorDiv,\n  forceHalfFloat,\n  fused_ops_exports as fused,\n  gather,\n  gatherND,\n  gather_nd_util_exports as gather_util,\n  getBackend,\n  getGradient,\n  getKernel,\n  getKernelsForBackend,\n  getThreadsCount,\n  gpgpu_util_exports as gpgpu_util,\n  grad,\n  grads,\n  greater,\n  greaterEqual,\n  ifft,\n  imag,\n  image,\n  inTopKAsync,\n  exports_initializers_exports as initializers,\n  input,\n  io_exports as io,\n  irfft,\n  isFinite2 as isFinite,\n  isInf,\n  isNaN2 as isNaN,\n  keep,\n  kernel_impls_exports as kernel_impls,\n  exports_layers_exports as layers,\n  leakyRelu,\n  less,\n  lessEqual,\n  linalg,\n  linspace,\n  loadGraphModel,\n  loadGraphModelSync,\n  loadLayersModel,\n  localResponseNormalization,\n  log2 as log,\n  log1p,\n  logSigmoid,\n  logSoftmax,\n  logSumExp,\n  logicalAnd,\n  logicalNot,\n  logicalOr,\n  logicalXor,\n  losses,\n  lowerBound,\n  matMul,\n  math_exports as math,\n  max,\n  maxPool,\n  maxPool3d,\n  maxPoolWithArgmax,\n  maximum,\n  mean,\n  memory,\n  meshgrid,\n  exports_metrics_exports as metrics,\n  min,\n  minimum,\n  mirrorPad,\n  mod,\n  model,\n  exports_models_exports as models,\n  moments,\n  movingAverage,\n  mul,\n  multiRNNCell,\n  multinomial,\n  neg,\n  nextFrame,\n  norm,\n  notEqual,\n  oneHot,\n  ones2 as ones,\n  onesLike,\n  op,\n  outerProduct,\n  pad,\n  pad1d,\n  pad2d,\n  pad3d,\n  pad4d,\n  pool,\n  pow,\n  prelu,\n  print,\n  prod,\n  profile,\n  rand,\n  randomGamma,\n  randomNormal,\n  randomUniform,\n  range,\n  ready,\n  real,\n  reciprocal,\n  registerBackend,\n  registerCallbackConstructor,\n  registerGradient,\n  registerKernel,\n  registerOp,\n  exports_regularizers_exports as regularizers,\n  relu,\n  relu6,\n  removeBackend,\n  reshape,\n  reverse,\n  reverse1d,\n  reverse2d,\n  reverse3d,\n  reverse4d,\n  rfft,\n  round2 as round,\n  rsqrt,\n  scalar,\n  scatterND,\n  scatter_nd_util_exports as scatter_util,\n  searchSorted,\n  selu,\n  separableConv2d,\n  sequential,\n  serialization_exports as serialization,\n  setBackend,\n  setPlatform,\n  setThreadsCount,\n  setWasmPath,\n  setWasmPaths,\n  setWebGLContext,\n  setdiff1dAsync,\n  sigmoid,\n  sign,\n  signal,\n  sin,\n  sinh,\n  slice,\n  slice1d,\n  slice2d,\n  slice3d,\n  slice4d,\n  slice_util_exports as slice_util,\n  softmax,\n  softplus,\n  spaceToBatchND,\n  sparse,\n  sparseToDense,\n  spectral,\n  split,\n  sqrt,\n  square,\n  squaredDifference,\n  squeeze,\n  stack,\n  step,\n  stridedSlice,\n  string,\n  sub,\n  sum2 as sum,\n  sumOutType,\n  tan,\n  tanh2 as tanh,\n  tensor,\n  tensor1d,\n  tensor2d,\n  tensor3d,\n  tensor4d,\n  tensor5d,\n  tensor6d,\n  tensor_util_exports as tensor_util,\n  test_util_exports as test_util,\n  tidy,\n  tile,\n  time,\n  topk,\n  train,\n  transpose,\n  truncatedNormal,\n  unique,\n  unregisterGradient,\n  unregisterKernel,\n  unsortedSegmentSum,\n  unstack,\n  upcastType,\n  upperBound,\n  util_exports as util,\n  valueAndGrad,\n  valueAndGrads,\n  variable,\n  variableGrads,\n  V as version,\n  version3 as version_converter,\n  version as version_core,\n  version2 as version_layers,\n  version8 as version_wasm,\n  version6 as version_webgl,\n  webgl,\n  webgl_util_exports as webgl_util,\n  webgpu_exports as webgpu,\n  where,\n  whereAsync,\n  zeros,\n  zerosLike\n};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2022 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the 'License');\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an 'AS IS' BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        env.webgl.supported = false;\n        config.filter.enabled = false;\n        copy(inCanvas, outCanvas); // filter failed to initialize\n        // return { tensor: null, canvas: inCanvas };\n      } else {\n        fx.reset();\n        if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n        if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n        if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n        if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n        if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n        if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n        if (config.filter.negative) fx.add('negative');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.vintage) fx.add('brownie');\n        if (config.filter.sepia) fx.add('sepia');\n        if (config.filter.kodachrome) fx.add('kodachrome');\n        if (config.filter.technicolor) fx.add('technicolor');\n        if (config.filter.polaroid) fx.add('polaroid');\n        if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n        if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n        else outCanvas = fx.draw(inCanvas);\n      }\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n  cacheModels: true,\n  cacheSupported: true,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nexport type ModelInfo = {\n  name: string,\n  inCache: boolean,\n  sizeDesired: number,\n  sizeFromManifest: number,\n  sizeLoadedWeights: number,\n}\n\nexport const modelStats: Record<string, ModelInfo> = {};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n  const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n  modelStats[shortModelName] = {\n    name: shortModelName,\n    sizeFromManifest: 0,\n    sizeLoadedWeights: 0,\n    sizeDesired: modelsDefs[shortModelName],\n    inCache: false,\n  };\n  options.cacheSupported = (typeof window !== 'undefined') && (typeof window.localStorage !== 'undefined') && (typeof window.indexedDB !== 'undefined'); // check if running in browser and if indexedb is available\n  let cachedModels = {};\n  try {\n    cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n  } catch {\n    options.cacheSupported = false;\n  }\n  modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    if (options.debug) log('model load handler:', model['handler']);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n    model.loadSync(artifacts); // load weights\n    // @ts-ignore private property\n    modelStats[shortModelName].sizeLoadedWeights = model?.artifacts?.weightData?.byteLength || 0;\n    if (options.verbose) log('load model:', model['modelUrl'], { bytes: modelStats[shortModelName].sizeLoadedWeights }, options);\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport type ModelStats = {\n  numLoadedModels: number,\n  numEnabledModels: undefined,\n  numDefinedModels: number,\n  percentageLoaded: number,\n  totalSizeFromManifest: number,\n  totalSizeWeights: number,\n  totalSizeLoading: number,\n  totalSizeEnabled: undefined,\n  modelStats: ModelInfo[],\n}\n\nexport const getModelStats = (instance: Human): ModelStats => {\n  let totalSizeFromManifest = 0;\n  let totalSizeWeights = 0;\n  let totalSizeLoading = 0;\n  for (const m of Object.values(modelStats)) {\n    totalSizeFromManifest += m.sizeFromManifest;\n    totalSizeWeights += m.sizeLoadedWeights;\n    totalSizeLoading += m.sizeDesired;\n  }\n  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n  return {\n    numLoadedModels: Object.values(modelStats).length,\n    numEnabledModels: undefined,\n    numDefinedModels: Object.keys(instance.models).length,\n    percentageLoaded,\n    totalSizeFromManifest,\n    totalSizeWeights,\n    totalSizeLoading,\n    totalSizeEnabled: undefined,\n    modelStats: Object.values(modelStats),\n  };\n};\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') {\n      instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n    }\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const irisIndices: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = Array<[number, number]>;\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const pairsLips: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n  [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n  [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n  [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n  [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n  lips: connectionsToIndices(pairsLips),\n  leftEye: connectionsToIndices(pairsLeftEye),\n  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n  leftIris: connectionsToIndices(pairsLeftIris),\n  rightEye: connectionsToIndices(pairsRightEye),\n  rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n  rightIris: connectionsToIndices(pairsRightIris),\n  faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n  [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n  [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n  [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n  [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n  [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n  [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n  [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n  [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n  [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n  [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n  [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n  [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n  [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n  [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n  [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n  [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n  [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n  [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n  [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n  [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n  [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n  [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n  [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n  [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n  [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n  [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n  [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n  [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n  [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n  [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n  [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n  [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n  [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n  [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n  [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n  [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n  [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n  [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n  [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n  [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n  [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n  [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n  [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n  [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n  [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n  [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n  [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n  [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n  [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n  [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n  [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n  [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n  [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n  [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n  [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n  [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n  [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n  [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n  [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n  [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n  [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n  [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n  [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n  [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n  [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n  [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n  [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n  [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n  [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n  [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n  [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n  [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n  [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n  [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n  [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n  [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n  [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n  [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n  [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n  [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n  [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n  [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n  [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n  [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n  [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n  [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n  [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n  [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n  [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n  [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n  [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n  [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n  [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n  [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n  [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 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[394, 395], [395, 379], [379, 394],\n  [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n  [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n  [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n  [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n  [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n  [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n  [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n  [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n  [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n  [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n  [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n  [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n  [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n  [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n  [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n  [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n  [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n  [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n  [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n  [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n  [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n  [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n  [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n  [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n    t.batch = tf.squeeze(res[0]);\n  } else { // original blazeface tfhub model\n    t.batch = tf.squeeze(res);\n  }\n  tf.dispose(res);\n  t.boxes = decodeBoxes(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.irisIndices.length; i++) {\n    const { key, indices } = coords.irisIndices[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = Array<[number, number]>;\n\nconst LIPS_CONNECTIONS: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n  lips: connectionsToIndices(LIPS_CONNECTIONS),\n  leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n  leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n  leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n  rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n  rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n  rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n  faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: Array<[number, string]> = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n  .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n  .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n  indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n  zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n  61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n  185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n  78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n  191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n  76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n  184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n  62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n  183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n  130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n  247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n  226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n  113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n  35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n  143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n  156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n  359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n  467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n  446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n  342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n  265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n  372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n  383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n  468, // Center.\n  469, // Iris right edge.\n  470, // Iris top edge.\n  471, // Iris left edge.\n  472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n  473, // Center.\n  474, // Iris right edge.\n  475, // Iris top edge.\n  476, // Iris left edge.\n  477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n    // flag: results[1], // already processed in parent // conv_faceflag\n    lips: await results.filter((r) => r.size === 160)[0].data() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: await results.filter((r) => r.size === 10)[0].data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: await results.filter((r) => r.size === 142)[0].data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: await results.filter((r) => r.size === 10)[1].data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: await results.filter((r) => r.size === 142)[1].data() as Float32Array, // 71 x 2d = 142// output_left_eye\n  };\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n  const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n  return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      // contoursRaw: [],\n      // contours: [],\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const results = model.execute(face.tensor as Tensor) as Array<Tensor>;\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n        if (config.face.mesh?.keepInvalid) {\n          face.box = util.clampBox(box, input);\n          face.boxRaw = util.getRawBox(box, input);\n          face.score = face.boxScore;\n          face.mesh = box.landmarks.map((pt) => [\n            ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n            ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n          ]);\n          face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / inputSize]);\n          for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n            face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n          }\n        }\n      } else {\n        const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n        const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n        let rawCoords = await coordsReshaped.array();\n        tf.dispose(coordsReshaped);\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // run iris model and augment results\n        }\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        /*\n        const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n        const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n        face.contoursRaw = [];\n        for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n        face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n        */\n        newCache.push(calculatedBox);\n      }\n      tf.dispose(results);\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  // @ts-ignore private property\n  if (config?.face?.attention?.enabled && model?.signature) {\n    // @ts-ignore private property\n    if (Object.keys(model?.signature?.outputs || {}).length < 6) model = null;\n  }\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention?.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 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0.3125 },\n  { x: 0.3125, y: 0.3125 },\n  { x: 0.3125, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'humangl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            // eslint-disable-next-line no-undef\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).requestAdapterInfo() : undefined;\n            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n            log('webgpu adapter info:', adapterInfo);\n          }\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        try {\n          tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        } catch { /**/ }\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => {\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  const color = `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n  return color;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  ctx.fillStyle = colorDepth(z, localOptions);\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should face attention keypoints be highlighted */\n  drawAttention: boolean;\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  alpha: 0.5,\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawAttention: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n    const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n    ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n    const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n    ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n    ctx.strokeStyle = 'pink';\n    const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n    const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n    const pathV = new Path2D(`\n      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n      C\n        ${valX} ${f.box[1]},\n        ${valX} ${f.box[1] + f.box[3]},\n        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n    `);\n    const pathH = new Path2D(`\n      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n      C \n        ${f.box[0]} ${valY},\n        ${f.box[0] + f.box[2]} ${valY},\n        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n    `);\n    ctx.stroke(pathH);\n    ctx.stroke(pathV);\n  }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n    ctx.strokeStyle = 'pink';\n    ctx.fillStyle = 'pink';\n    const leftGaze = [\n      f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n    const rightGaze = [\n      f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n  }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPolygons && f.mesh.length >= 468) {\n    ctx.lineWidth = 1;\n    for (let i = 0; i < triangulation.length / 3; i++) {\n      const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n      lines(ctx, points, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n  /*\n  if (opt.drawPolygons && f.contours.length > 1) {\n    ctx.lineWidth = 5;\n    lines(ctx, f.contours, opt);\n  }\n  ctx.lineWidth = 1;\n  */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && f.mesh.length >= 468) {\n    for (let i = 0; i < f.mesh.length; i++) {\n      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);\n      if (opt.drawAttention) {\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.color;\n  for (const f of result) {\n    drawFaceBoxes(f, ctx);\n    drawLabels(f, ctx);\n    if (f.mesh && f.mesh.length > 0) {\n      drawFacePoints(f, ctx);\n      drawFacePolygons(f, ctx);\n      drawGazeSpheres(f, ctx);\n      drawGazeArrows(f, ctx);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = colorDepth(i * z, localOptions);\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human, Models } from './human';\nimport type { Tensor } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(allModels: Models) {\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || (!webGLBackend || !webGLBackend.checkCompileCompletion)) {\n    // log('compile pass: skip');\n    return;\n  }\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  const compiledModels: string[] = [];\n  for (const [modelName, model] of Object.entries(allModels).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs && model.inputs[0] && model.inputs[0].shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype = (model.inputs && model.inputs[0] && model.inputs[0].dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    // const res = await model.executeAsync(tensor); // fails with current tfjs\n    try {\n      const res = model.execute(tensor);\n      compiledModels.push(modelName);\n      if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n      else tf.dispose(res);\n    } catch {\n      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  log('compile pass models:', compiledModels);\n  log('compile pass kernels:', kernels.length);\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await runCompile(instance.models);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas, ModelStats } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API](https://js.tensorflow.org/api/latest/)\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    /*\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    */\n    const tfVersion = (tf.version?.tfjs || tf.version_core).replace(/-(.*)/, '');\n    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** get model loading/loaded stats */\n  getModelStats(): ModelStats { return models.getModelStats(this); }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "names": []
+}
diff --git a/dist/human.js b/dist/human.js
new file mode 100644
index 00000000..f14e9597
--- /dev/null
+++ b/dist/human.js
@@ -0,0 +1,8026 @@
+"use strict";/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var Human=(()=>{var _f=Object.defineProperty;var iR=Object.getOwnPropertyDescriptor;var lR=Object.getOwnPropertyNames;var uR=Object.prototype.hasOwnProperty;var cR=(e,t,n)=>t in e?_f(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var ra=(e,t)=>{for(var n in t)_f(e,n,{get:t[n],enumerable:!0})},dR=(e,t,n,s)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of lR(t))!uR.call(e,r)&&r!==n&&_f(e,r,{get:()=>t[r],enumerable:!(s=iR(t,r))||s.enumerable});return e};var pR=e=>dR(_f({},"__esModule",{value:!0}),e);var ge=(e,t,n)=>(cR(e,typeof t!="symbol"?t+"":t,n),n),N4=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var Gd=(e,t,n)=>(N4(e,t,"read from private field"),n?n.call(e):t.get(e)),Hd=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},jd=(e,t,n,s)=>(N4(e,t,"write to private field"),s?s.call(e,n):t.set(e,n),n);var Q5e={};ra(Q5e,{Human:()=>o4,default:()=>o4,defaults:()=>Oa,draw:()=>Qb,env:()=>me,match:()=>a4,models:()=>n1});function oe(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function E4(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var ue=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function zg(e,t,n="config",s=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")zg(e[r],t[r],r,s);else{let a=e&&typeof e[r]!="undefined";a||s.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let 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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,La(`Initialization of backend ${e} failed`),La(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return La(`Initialization of backend ${e} failed`),La(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=Wg(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Wg(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=lm(h,this.backendName);M(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,x);let A=x.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(s){let b=this.getTensorsForGradient(h,f,A);n=this.saveTensorsForBackwardMode(b)}return A}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,p=Wg(e)?null:e.backwardsFunc,d;return 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va{constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=B.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:K(()=>lt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:K(()=>lt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;K(()=>{let u=ce(L(i,this.rho),L(vt(o),1-this.rho)),c=L(pe(Tn(ce(l,this.epsilon)),Tn(ce(i,this.epsilon))),o),p=ce(L(l,this.rho),L(vt(c),1-this.rho));i.assign(u),l.assign(p);let d=ce(L(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(ne(this.accumulatedGrads.map(e=>e.variable)),ne(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};P0.className="Adadelta";ti(P0);var O0=class extends va{constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=B.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:K(()=>Wc(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;K(()=>{let i=ce(o,vt(a));o.assign(i);let l=ce(L(pe(a,Tn(ce(i,B.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&ne(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};O0.className="Adagrad";ti(O0);var M0=class extends va{constructor(e,t,n,s=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(n).variable()}),s==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);K(()=>{let n=fe(1,this.accBeta1),s=fe(1,this.accBeta2);t.forEach((r,a)=>{let o=B.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:K(()=>lt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:K(()=>lt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ce(L(u,this.beta1),L(l,1-this.beta1)),d=ce(L(c,this.beta2),L(vt(l),1-this.beta2)),h=pe(p,n),f=pe(d,s);u.assign(p),c.assign(d);let m=ce(L(pe(h,ce(Tn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ne(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(fa(this.beta1,this.iterations_+1)),this.accBeta2.assign(fa(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};M0.className="Adam";ti(M0);var z0=class extends va{constructor(e,t,n,s=null,r=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),s==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);K(()=>{let n=fe(1,this.accBeta1),s=pe(-this.learningRate,ce(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=B.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:lt(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:lt(o).variable(i)});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let u=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ce(L(u,this.beta1),L(l,1-this.beta1)),d=L(c,this.beta2),h=nn(l),f=Kr(d,h);u.assign(p),c.assign(f);let m=ce(L(pe(s,n),pe(p,ce(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(ce(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ne(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};z0.className="Adamax";ti(z0);var ph=class extends va{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=B.registeredVariables[n];K(()=>{let o=ce(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=bn(Ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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r=B.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:K(()=>lt(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:K(()=>lt(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:K(()=>lt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;K(()=>{let u=ce(L(i,this.decay),L(vt(o),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ce(L(c,this.decay),L(o,1-this.decay)),d=pe(L(o,this.learningRate),Tn(fe(u,ce(vt(p),this.epsilon)))),h=ce(L(l,this.momentum),d);i.assign(u),c.assign(p),l.assign(h);let f=fe(r,h);r.assign(f)}else{let c=ce(L(i,this.decay),L(vt(o),1-this.decay)),p=ce(L(l,this.momentum),pe(L(o,this.learningRate),Tn(ce(c,this.epsilon))));i.assign(c),l.assign(p);let d=fe(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ne(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new q(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),u=r.axes[i],c=l>=0?o[l]:o[o.length+l];if(u!=null&&[u,null].indexOf(c)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=Dt(e),s=!0;for(let a of n)if(!(a instanceof wr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof wr){r=!1;break}if(s===r)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Bi(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of Dt(e))a.push(o.shape);this.build(cs(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=Dt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=cs(i),this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=CV(e),o=this.computeOutputShape(a),i,l=TV(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((u,c)=>new wr(l,u,this,Dt(e),t,this.name,c)):i=new wr(l,o,this,Dt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Ge("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new aa(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new aa(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new vr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return mm(this.weights)}build(e){this.built=!0}getWeights(e=!1){return x3(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Input ${y} (0-based) originates from layer type ${x.getClassName()}.`);this.inputNames.push(x.name),this.feedInputShapes.push(x.batchInputShape),this.feedInputNames.push(x.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},s={},r={},a={},o=[],i=(y,x,A,b,w,k)=>{(b==null||w==null||k==null)&&(b=y.sourceLayer,w=y.nodeIndex,k=y.tensorIndex);let S=b.inboundNodes[w];if(A.indexOf(S)!==-1)throw new vr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(x.indexOf(S)!==-1)return;this.containerNodes.add(zr.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),A.indexOf(S)===-1&&A.push(S);let E=S.inboundLayers.length;for(let R=0;R<E;R++){let $=S.inputTensors[R],_=S.inboundLayers[R],D=S.nodeIndices[R],C=S.tensorIndices[R];i($,x,A,_,D,C)}for(x.push(S);A.indexOf(S)>=0;)A.splice(A.indexOf(S),1);o.push(S)},l=[],u=[];for(let y of this.outputs)i(y,l,u);let c=o.slice().reverse();for(let y of c){n[y.id]=y,y.id in t||(t[y.id]=0);let x=t[y.id],A=s[y.outboundLayer.id]==null?0:s[y.outboundLayer.id];x=Math.max(x,A),s[y.outboundLayer.id]=x,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=x;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],k=y.nodeIndices[b],S=w.inboundNodes[k],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(x+1,E),n[S.id]=S}}let p={};for(let y in t){let x=t[y];x in p||(p[x]=[]),p[x].push(n[y])}let d={};for(let y in s){let x=s[y];x in d||(d[x]=[]),d[x].push(r[y])}let h=Object.keys(d).map(y=>parseInt(y,10)).sort(Ff);this.layers=[];for(let y of h){let x=d[y];x.sort((A,b)=>{let w=a[A.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let A of x)A instanceof zr&&this.internalContainerRefs.push(A),this.layers.push(A)}this.layersByDepth=d,h=Object.keys(p).map(y=>parseInt(y,10)).sort(Ff);let f=this.inputs.slice(),m=[];for(let y of h)for(let x of p[y]){let A=x.outboundLayer;if(A!=null){for(let b of x.inputTensors)if(f.indexOf(b)===-1)throw new vr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${A.name}". The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(A.name)}}this.nodesByDepth=p;let g=this.layers.map(y=>y.name);for(let y of g){let x=g.filter(A=>A===y).length;if(x!==1)throw new vr(`The name "${y}" is used ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Y0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new q(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new q(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new q(`${a.length} of ${s} weights are not set: ${a}`)}TA(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${MA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=v3(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return K(()=>{e=Dt(e);let n=new Mi;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return ep(this.outputs,n,t)})}computeMask(e,t){return K(()=>{e=Dt(e);let n;return t==null?n=Ji(null,e.length):n=Dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=fm(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],u=i.name+"_0_0";n[u]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Ff);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let c=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],x=`${m.name}_${g}_${y}`,A=n[x];c.push(A)}let p=u.computeOutputShape(cs(c)),d=fm(p),h=u.inboundNodes.indexOf(l);for(let f=0;f<d.length;f++){let m=`${u.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],u=this.outputLayersTensorIndices[o],c=`${i.name}_${l}_${u}`;a.push(c)}for(let o=0;o<a.length;o++){let i=a[o];Lr(i in n),r.push(n[i])}return cs(r)}runInternalGraph(e,t){t==null&&(t=Ji(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],u=e[i],c=t[i];n[l.id]=[u,c]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Ff);for(let i of s){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer,p=u.inputTensors,d=u.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,y,x;if(u.callArgs!=null&&(f=u.callArgs),h.length===1){let[A,b]=h[0];f.mask==null&&(f.mask=b),y=Dt(c.call(A,f)),x=Dt(c.computeMask(A,b)),m=[A],g=[b]}else m=h.map(A=>A[0]),g=h.map(A=>A[1]),f.mask==null&&(f.mask=g),y=Dt(c.call(m,f)),x=Dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Ge("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let A=0;A<d.length;++A){let b=d[A],w=y[A],k=x[A];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Lr(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,u]=n[i.id];o.push(l.shape),r.push(l),a.push(u)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof zr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=zr.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=zr.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=zr.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],y=p.nodeIndices[m],x=p.tensorIndices[m],A=zr.nodeKey(g,y),b=t[A];b==null&&(b=0),f.push([g.name,b,x,h])}l.push(f)}}}let u={};u.name=a.name,u.className=o,u.config=i,u.inboundNodes=l,n.push(u)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=zr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.inputLayersTensorIndices[a];s.push([o.name,u,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=zr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let c=this.outputLayersTensorIndices[a];r.push([o.name,u,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let y=[],x;for(let A of g){let b=A[0],w=A[1],k=A[2];if(x=A[3]==null?{}:A[3],!(b in r)){o(m,g);return}let S=r[b];if(S.inboundNodes.length<=w){o(m,g);return}let E=S.inboundNodes[w];y.push(E.outputTensors[k])}y.length>0&&m.apply(cs(y),x)}function l(m){let g=m.name,y=Sr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(s),r[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);o(y,A)})}let u=t.name,c=t.layers;for(let m of c)l(m);for(;!eV(a);)for(let m of c){let g=r[m.name];if(g.name in a){let y=a[g.name];delete a[g.name];for(let x of y)i(g,x)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],y=m[1],x=m[2];Lr(g in r);let b=r[g].inboundNodes[y].outputTensors;p.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],x=m[2];Lr(g in r);let b=r[g].inboundNodes[y].outputTensors;d.push(b[x])}return new e({inputs:p,outputs:d,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function CU(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function t8(e,t){return CU(e,t,"classWeight")}async function n8(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=K(()=>{if(e.shape.length===1)return Jn(e);if(e.shape.length===2){if(e.shape[1]>1)return Cs(e,1);if(e.shape[1]===1)return U(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());ne(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),$t(o,"float32")}else return null}function TU(e,t){return L(e,t)}var NU=32;function s8(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=uv("input",e.inputNames,n),o=uv("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)v.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected  ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function uv(e,t,n){if(n instanceof st)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function EU(e){if(e.length===3)throw new Ge("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function RU(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(cv(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=EU(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;r?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=qw(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:d,history:h}=Xw(c,p,n.epochs,null,null,_U(t,n),null,r,u);d.setModel(e),e.history=h,await d.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await d.onEpochBegin(f);let y=0,x=0;for(s||(m=await t.iterator());!s||y<n.batchesPerEpoch;){let A=await m.next();if(s&&A.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(A.value!=null){let{xs:b,ys:w}=s8(e,A.value),k={};k.batch=x,k.size=b[0].shape[0],await d.onBatchBegin(x,k);let S=[];if(n.classWeight!=null){let $=t8(n.classWeight,e.outputNames);for(let _=0;_<$.length;++_)S.push(await n8(w[_],null,$[_]))}let E=b.concat(w).concat(S),R=i(E);ne(E);for(let $=0;$<l.length;++$){let _=l[$],D=R[$];k[_]=D,bn(D)}await d.onBatchEnd(x,k),Uw(k),x++,y++}if(s?y>=n.batchesPerEpoch:A.done){if(r){let b;cv(n.validationData)?b=Dt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=Dt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?NU:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await d.onEpochEnd(f,g),f++,e.stopTraining_)break}return await d.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function _U(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function cv(e){return typeof e.iterator=="function"}function DU(e){return typeof e.next=="function"}async function $U(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new Ge("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=DU(t)?t:await t.iterator(),i=0,l=0;for(;!s||l<n.batches;){let u=await o.next();if(a=K(()=>{if(u.value){let{xs:c,ys:p}=s8(e,u.value),d=c.concat(p),h=K(()=>r(d));if(ne(d),l===0)for(let m=0;m<h.length;++m)a.push(Ce(0));let f=d[0].shape[0];for(let m=0;m<h.length;++m){let g=h[m],y=a[m];a[m]=K(()=>ce(a[m],L(f,g))),l>0&&ne(y)}ne(h),i+=f,++l}return a}),u.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new Mi(a),i=ep(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],c=Wt(u(r[l],i[l]));l===0?n=c:n=ce(n,c),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],c=this.metricsTensors[l][1],p=Wt(u(r[c],i[c]));t.push(p)}return t})}async fit(e,t,n={}){return PU(this,e,t,n)}async fitDataset(e,t){return RU(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let u=await l.data();i.push(u[0])}return ne(o),br(n[0],e),br(n[1],t),cs(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=dm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-dm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=oa(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>oa(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=oa(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[oa(Mf(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>oa(Mf(e)));{let e={};for(let t in this.metrics)e[t]=oa(Mf(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=vp(e.optimizer_config),n=Sr(t),s;if(typeof e.loss=="string")s=Fi(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>Fi(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=Fi(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>Fi(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=Fi(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=$n.getSaveHandlers(e);if(l.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new q(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await $n.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:BU,generatedBy:`TensorFlow.js tfjs-layers v${MA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:u,specs:c}=await $n.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...c),n.data=$n.concatenateArrayBuffers([n.data,u])}return this.userDefinedMetadata!=null&&(lv(this.userDefinedMetadata,this.name,!0),o.userDefinedMetadata=this.userDefinedMetadata),o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){lv(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ca.className="Model";de.registerClass(ca);var a8=class extends ca{};a8.className="Functional";de.registerClass(a8);async function WU(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=vp(n),r=Sr(s,t);if(e.weightsManifest!=null){let a=await $n.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),ne(a)}return r}async function VU(e,t){if(t==null&&(t={}),typeof e=="string"){let n=$n.getLoadHandlers(e,t);if(n.length===0)n.push($n.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return UU(e,void 0,t)}async function UU(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=Sr(vp(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:c}=GU(s.weightData,s.weightSpecs);i.loadWeights(u,a),i.optimizer!=null&&c.length>0&&await i.optimizer.setWeights(c),ne(u),ne(c.map(p=>p.tensor))}return i}function GU(e,t){let n=$n.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var ec=class extends ca{constructor(e){if(super({inputs:[],outputs:[]}),e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:W0("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof ec||e instanceof ca,n;if(t){if(n=e,n.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=Lw({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new ca({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new vr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new vr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new vr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new vr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof ec))throw new Ge(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let u=Sr(i,void 0,s);s&&u.setFastWeightInitDuringBuild(!0),o.add(u)}return o}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};ec.className="Sequential";de.registerClass(ec);function HU(e){return new ca(e)}function jU(e){return new ec(e)}function qU(e,t){return t==null&&(t={}),VU(e,t)}function o8(e){return Lw(e)}function XU(e,t){tr.registerCallbackConstructor(e,t)}var gs=class extends de.Serializable{getConfig(){return{}}},i8=class extends gs{apply(e,t=1){return mV(e,t)}};i8.className="elu";de.registerClass(i8);var l8=class extends gs{apply(e){return I0(e)}};l8.className="selu";de.registerClass(l8);var u8=class extends gs{apply(e){return _r(e)}};u8.className="relu";de.registerClass(u8);var c8=class extends gs{apply(e){return K(()=>Uc(6,_r(e)))}};c8.className="relu6";de.registerClass(c8);var d8=class extends gs{apply(e){return e}};d8.className="linear";de.registerClass(d8);var p8=class extends gs{apply(e){return Cn(e)}};p8.className="sigmoid";de.registerClass(p8);var h8=class extends gs{apply(e){return yV(e)}};h8.className="hardSigmoid";de.registerClass(h8);var f8=class extends gs{apply(e){return ql(e)}};f8.className="softplus";de.registerClass(f8);var m8=class extends gs{apply(e){return gV(e)}};m8.className="softsign";de.registerClass(m8);var g8=class extends gs{apply(e){return ji(e)}};g8.className="tanh";de.registerClass(g8);var LA=class extends gs{apply(e,t=-1){return Zl(e,t)}};LA.className="softmax";de.registerClass(LA);var y8=class extends gs{apply(e,t=-1){return y0(e,t)}};y8.className="logSoftmax";de.registerClass(y8);var A8=class extends gs{apply(e,t=1){return K(()=>L(Cn(L(e,t)),e))}};A8.className="swish";de.registerClass(A8);var x8=class extends gs{apply(e){return K(()=>L(e,ji(ql(e))))}};x8.className="mish";de.registerClass(x8);function eo(e){return e.getClassName()}function Xg(e,t={}){return hh(e,de.SerializationMap.getMap().classNameMap,t,"activation")}function to(e){if(e==null){let t={};return t.className="linear",t.config={},Xg(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Xg(t)}else return e instanceof gs?e:Xg(e)}function BA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var b8=class extends de.Serializable{},Ah=class extends b8{constructor(e){super(),BA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Vt([1]);return this.hasL1&&(t=ce(t,ke(L(this.l1,nn(e))))),this.hasL2&&(t=ce(t,ke(L(this.l2,mh(e))))),U(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ah.className="L1L2";de.registerClass(Ah);function KU(e){return BA(e),new Ah({l1:e!=null?e.l1:null,l2:0})}function ZU(e){return BA(e),new Ah({l2:e!=null?e.l2:null,l1:0})}var fv={l1l2:"L1L2"};function It(e){return yA(e)}function mv(e,t={}){return hh(e,de.SerializationMap.getMap().classNameMap,t,"regularizer")}function zt(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in fv?fv[e]:e,config:{}};return mv(n)}else return e instanceof b8?e:mv(e)}var WA=class extends ut{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=qe(e);let n=_r(e);return this.maxValue!=null&&(n=ps(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};WA.className="ReLU";de.registerClass(WA);var VA=class extends ut{constructor(e){super(e==null?{}:e),this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=qe(e);return rh(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};VA.className="LeakyReLU";de.registerClass(VA);var UA=class extends ut{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Mt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=zt(e.alphaRegularizer),this.alphaConstraint=yn(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=xt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new rn({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=qe(e),uh(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Ut(this.alphaInitializer),alphaRegularizer:It(this.alphaRegularizer),alphaConstraint:gn(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};UA.className="PReLU";de.registerClass(UA);var GA=class extends ut{constructor(e){if(super(e==null?{}:e),this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ge(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=qe(e);return Bc(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};GA.className="ELU";de.registerClass(GA);var HA=class extends ut{constructor(e){super(e==null?{}:e),this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=qe(e);return L(n,he(ms(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};HA.className="ThresholdedReLU";de.registerClass(HA);var jA=class extends ut{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new LA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=qe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};jA.className="Softmax";de.registerClass(jA);function Uu(e,t,n){if(typeof e=="number")return Ji(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!dV(r))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Cr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Br(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Qa([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function qA(e,t){return K(()=>(Jt(t),t==="channelsFirst"?rt(e,[0,2,3,1]):e))}function v8(e,t){return K(()=>(Jt(t),t==="channelsFirst"?rt(e,[0,2,3,4,1]):e))}function YU(e,t,n,s=1,r="valid",a,o=1){return K(()=>{if(a==null&&(a=Nr()),Jt(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=rt(e,[0,2,1])),r==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=d0(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Dr(i,n)),i})}function gv(e,t,n,s=[1,1],r="valid",a,o,i=null){return K(()=>{if(a==null&&(a=Nr()),Jt(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=qA(e,a);if(r==="causal")throw new Ge("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Ja.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=rt(l,[0,3,1,2])),l})}function JU(e,t,n,s=[1,1,1],r="valid",a,o){return K(()=>{if(a==null&&(a=Nr()),Jt(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=v8(e,a);if(r==="causal")throw new Ge("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=zy(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Dr(i,n)),a==="channelsFirst"&&(i=rt(i,[0,4,1,2,3])),i})}var XA=class extends ut{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",XA.verifyArgs(t),this.rank=e,vn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ge(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Uu(t.kernelSize,e,"kernelSize"),this.strides=Uu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Zs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Jt(this.dataFormat),this.activation=to(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Mt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=yn(t.biasConstraint),this.biasRegularizer=zt(t.biasRegularizer),this.activityRegularizer=zt(t.activityRegularizer),this.dilationRate=Uu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Lr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!AA(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:eo(this.activation),useBias:this.useBias,biasInitializer:Ut(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:gn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},xh=class extends XA{constructor(e,t){super(e,t),this.kernel=null,xh.verifyArgs(t),this.filters=t.filters,vn(this.filters,"filters"),this.kernelInitializer=Mt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=yn(t.kernelConstraint),this.kernelRegularizer=zt(t.kernelRegularizer)}build(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return K(()=>{e=qe(e);let n,s=this.bias==null?null:this.bias.read(),r=Ew(this.activation.getClassName());if(r!=null&&this.rank===2)n=gv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=YU(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=gv(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=JU(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ge("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=xt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=Cr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Ut(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:gn(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},bh=class extends xh{constructor(e){super(2,e),bh.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!AA(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};bh.className="Conv2D";de.registerClass(bh);var vh=class extends xh{constructor(e){super(3,e),vh.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};vh.className="Conv3D";de.registerClass(vh);var KA=class extends bh{constructor(e){if(super(e),this.inputSpec=[new rn({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=xt(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new rn({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{let n=qe(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Br(i,p,u,this.padding),f=Br(l,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=rt(n,[0,2,3,1]));let g=p0(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=rt(g,[0,3,1,2])),this.bias!=null&&(g=Dr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=xt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Br(t[s],i,a,this.padding),t[r]=Br(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};KA.className="Conv2DTranspose";de.registerClass(KA);var ZA=class extends vh{constructor(e){if(super(e),this.inputSpec=[new rn({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=xt(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new rn({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{let n=qe(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Br(l,f,p,this.padding),x=Br(u,m,d,this.padding),A=Br(c,g,h,this.padding),b=[r,y,x,A,this.filters];this.dataFormat!=="channelsLast"&&(n=rt(n,[0,2,3,4,1]));let w=z6(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=rt(w,[0,4,1,2,3])),this.bias!==null&&(w=Dr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=xt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Br(t[s],u,o,this.padding),t[r]=Br(t[r],c,i,this.padding),t[a]=Br(t[a],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ZA.className="Conv3DTranspose";de.registerClass(ZA);var w8=class extends xh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=zt(t.depthwiseRegularizer),this.depthwiseConstraint=yn(t.depthwiseConstraint),this.pointwiseInitializer=Mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=zt(t.pointwiseRegularizer),this.pointwiseConstraint=yn(t.pointwiseConstraint)}build(e){if(e=xt(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new rn({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{e=qe(e);let n;if(this.rank===1)throw new Ge("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=rt(e,[0,2,3,1])),n=sA(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Dr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=rt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ut(this.depthwiseInitializer),e.pointwiseInitializer=Ut(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=gn(this.depthwiseConstraint),e.pointwiseConstraint=gn(this.pointwiseConstraint),e}};w8.className="SeparableConv";var YA=class extends w8{constructor(e){super(2,e)}};YA.className="SeparableConv2D";de.registerClass(YA);var e2=class extends xh{constructor(e){super(1,e),e2.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!AA(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};e2.className="Conv1D";de.registerClass(e2);var JA=class extends ut{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=qe(e),this.dataFormat==="channelsLast"){let n=Of(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Of(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Of(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Of(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};JA.className="Cropping2D";de.registerClass(JA);var QA=class extends ut{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Jt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,lV(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return K(()=>{let n=qe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=rt(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Se.resizeNearestNeighbor(n,[r,a]):Se.resizeBilinear(n,[r,a]);return rt(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Se.resizeNearestNeighbor(n,[r,a]):Se.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};QA.className="UpSampling2D";de.registerClass(QA);function QU(e,t,n=[1,1],s="valid",r,a){return K(()=>{r==null&&(r=Nr()),Jt(r);let o=qA(e,r);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Lc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=rt(o,[0,3,1,2])),o})}var e5=class extends XA{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=yn(e.depthwiseConstraint),this.depthwiseRegularizer=zt(e.depthwiseRegularizer)}build(e){if(e=xt(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=qe(e);let n=QU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Dr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Cr(t,this.kernelSize[0],this.padding,this.strides[0]),a=Cr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ut(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=gn(this.depthwiseRegularizer),e}};e5.className="DepthwiseConv2D";de.registerClass(e5);function k8(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function I8(e,t,n,s=!1,r,a,o=!1,i=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Tr(2,l));if(t=rt(t,u),a!=null)throw new Ge("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=he(he(r,"bool"),"float32"),r.rank===l-1&&(r=Zt(r,-1)),r=rt(r,u)),s&&(t=_s(t,0),r!=null&&(r=_s(r,0)));let c=[],p,d=n,h=t.shape[0],f=Qn(t),m;r!=null&&(m=Qn(r));for(let y=0;y<h;++y){let x=f[y],A=K(()=>e(x,d));if(r==null)p=A[0],d=A[1];else{let b=K(()=>{let w=m[y],k=fe(Rs(w),w),S=ce(L(A[0],w),L(d[0],k)),E=d.map((R,$)=>ce(L(A[1][$],w),L(R,k)));return{output:S,newStates:E}});p=b.output,d=b.newStates}i&&c.push(p)}let g;return i&&(g=ln(c,1)),[p,g,d]})}var Zr=class extends ut{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new s2({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new rn({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Tr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){A3(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ge("Constants support is not implemented in RNN yet.");A3(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new rn({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new rn({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Vt([n,s])):this.states_=[Vt([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Vt([n,s])):this.states_[0]=Vt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>bn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=k8(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new rn({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof wr){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return K(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=qe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new q(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=I8((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],p=l[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:u;return this.returnState?[d].concat(p):d})}getInitialState(e){return K(()=>{let t=Vt(e.shape);return t=ke(t,[1,2]),t=fh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?g3(t,[1,n]):t):this.cell.stateSize>1?[g3(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Zr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Sr(s,n);return new e(Object.assign(t,{cell:r}))}};Zr.className="RNN";de.registerClass(Zr);var wh=class extends ut{},t2=class extends wh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,vn(this.units,"units"),this.activation=to(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=yn(e.kernelConstraint),this.recurrentConstraint=yn(e.recurrentConstraint),this.biasConstraint=yn(e.biasConstraint),this.dropout=Ju([1,Qa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Qa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=xt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=no({ones:()=>Rs(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=no({ones:()=>Rs(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Ur(L(e,a),this.kernel.read()):r=Ur(e,this.kernel.read()),this.bias!=null&&(r=Dr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ce(r,Ur(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:eo(this.activation),useBias:this.useBias,kernelInitializer:Ut(this.kernelInitializer),recurrentInitializer:Ut(this.recurrentInitializer),biasInitializer:Ut(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:gn(this.kernelConstraint),recurrentConstraint:gn(this.recurrentConstraint),biasConstraint:gn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};t2.className="SimpleRNNCell";de.registerClass(t2);var t5=class extends Zr{constructor(e){e.cell=new t2(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};t5.className="SimpleRNN";de.registerClass(t5);var n2=class extends wh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,vn(this.units,"units"),this.activation=to(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=to(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=yn(e.kernelConstraint),this.recurrentConstraint=yn(e.recurrentConstraint),this.biasConstraint=yn(e.biasConstraint),this.dropout=Ju([1,Qa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Qa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=xt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=no({ones:()=>Rs(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=no({ones:()=>Rs(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let u=Ur(e,this.kernel.read());this.useBias&&(u=Dr(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Yt(c,[2*this.units,this.units],c.rank-1),h=Ur(s,p),[f,m,g]=Yt(u,3,u.rank-1),[y,x]=Yt(h,2,h.rank-1);o=this.recurrentActivation.apply(ce(f,y)),i=this.recurrentActivation.apply(ce(m,x));let A=Ur(L(i,s),d);l=this.activation.apply(ce(g,A));let b=ce(L(o,s),L(ce(1,Ft(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:eo(this.activation),recurrentActivation:eo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ut(this.kernelInitializer),recurrentInitializer:Ut(this.recurrentInitializer),biasInitializer:Ut(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:gn(this.kernelConstraint),recurrentConstraint:gn(this.recurrentConstraint),biasConstraint:gn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};n2.className="GRUCell";de.registerClass(n2);var n5=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new n2(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};n5.className="GRU";de.registerClass(n5);var kh=class extends wh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,vn(this.units,"units"),this.activation=to(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=to(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=yn(e.kernelConstraint),this.recurrentConstraint=yn(e.recurrentConstraint),this.biasConstraint=yn(e.biasConstraint),this.dropout=Ju([1,Qa([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Qa([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=xt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends lr{apply(i,l){let u=r.apply([a]),c=new G0().apply([a]),p=r.apply([a*2]);return Q4(Q4(u,c),p)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=no({ones:()=>Rs(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=no({ones:()=>Rs(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,u,c;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let p=Ur(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),p=ce(p,Ur(s,this.recurrentKernel.read())),this.useBias&&(p=Dr(p,this.bias.read()));let[d,h,f,m]=Yt(p,4,p.rank-1);i=this.recurrentActivation.apply(d),l=this.recurrentActivation.apply(h),u=ce(L(l,r),L(i,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=L(c,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:eo(this.activation),recurrentActivation:eo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ut(this.kernelInitializer),recurrentInitializer:Ut(this.recurrentInitializer),biasInitializer:Ut(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:gn(this.kernelConstraint),recurrentConstraint:gn(this.recurrentConstraint),biasConstraint:gn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};kh.className="LSTMCell";de.registerClass(kh);var s5=class extends Zr{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new kh(e),super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};s5.className="LSTM";de.registerClass(s5);var s2=class extends wh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){A3(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Bi(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Sr(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return x3(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}TA(t)}};s2.className="StackedRNNCells";de.registerClass(s2);function no(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):Ow(t(),n),i=()=>gh(o,t,s);return!r||r<=1?bn(i().clone()):Array(r).fill(void 0).map(i).map(u=>bn(u.clone()))}var eG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},S8=class extends Zr{constructor(e){if(e.unroll)throw new Ge("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ge("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new rn({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Vt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new aa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Vt(r)):this.states_=[Vt(r)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Vt(r)):this.states_[0]=Vt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new q(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>bn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=Cr(l,s[0],r,a[0],o[0]),p=Cr(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};S8.className="ConvRNN2D";var r2=class extends kh{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t})),this.filters=t,vn(this.filters,"filters"),this.kernelSize=Uu(n,2,"kernelSize"),this.kernelSize.forEach(i=>vn(i,"kernelSize")),this.strides=Uu(s||1,2,"strides"),this.strides.forEach(i=>vn(i,"strides")),this.padding=r||"valid",Zs(this.padding),this.dataFormat=a||"channelsLast",Jt(this.dataFormat),this.dilationRate=Uu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>vn(i,"dilationRate"))}build(e){var t;e=xt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends lr{apply(p,d){let h=l.apply([u]),f=Ss([u]),m=l.apply([u*2]);return xA([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=no({ones:()=>Rs(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(W,ee,Q)=>!ee||!ee[Q]?W:L(ee[Q],W),u=l(s,i,0),c=l(s,i,1),p=l(s,i,2),d=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=no({ones:()=>Rs(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),x=3,[A,b,w,k]=Yt(this.kernel.read(),o,x),[S,E,R,$]=this.useBias?Yt(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,A,S,this.padding),c=this.inputConv(c,b,E,this.padding),p=this.inputConv(p,w,R,this.padding),d=this.inputConv(d,k,$,this.padding);let[_,D,C,P]=Yt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,_),m=this.recurrentConv(m,D),g=this.recurrentConv(g,C),y=this.recurrentConv(y,P);let V=this.recurrentActivation.apply(ce(u,f)),j=this.recurrentActivation.apply(ce(c,m)),z=ce(L(j,a),L(V,this.activation.apply(ce(p,g)))),Z=L(this.recurrentActivation.apply(ce(d,y)),this.activation.apply(z));return[Z,Z,z]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=eG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=pa(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Dr(r,n,this.dataFormat):r}recurrentConv(e,t){return pa(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};r2.className="ConvLSTM2DCell";de.registerClass(r2);var r5=class extends S8{constructor(e){let t=new r2(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};r5.className="ConvLSTM2D";de.registerClass(r5);var a2=class extends ut{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return gh(()=>Ow(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};a2.className="Dropout";de.registerClass(a2);var a5=class extends a2{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};a5.className="SpatialDropout1D";de.registerClass(a5);var o5=class extends ut{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,vn(this.units,"units"),this.activation=to(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=yn(e.kernelConstraint),this.biasConstraint=yn(e.biasConstraint),this.kernelRegularizer=zt(e.kernelRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.activityRegularizer=zt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=xt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=xt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e),s=Ew(this.activation.getClassName()),r;return s!=null?r=Ur(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Ur(n,this.kernel.read()),this.bias!=null&&(r=Dr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:eo(this.activation),useBias:this.useBias,kernelInitializer:Ut(this.kernelInitializer),biasInitializer:Ut(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:gn(this.kernelConstraint),biasConstraint:gn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};o5.className="Dense";de.registerClass(o5);var i5=class extends ut{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=xt(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ha(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=rt(n,s)}return fV(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};i5.className="Flatten";de.registerClass(i5);var l5=class extends ut{constructor(e){super(e),this.supportsMasking=!0,this.activation=to(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e);return this.activation.apply(n)})}getConfig(){let e={activation:eo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};l5.className="Activation";de.registerClass(l5);var u5=class extends ut{constructor(e){super(e),this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=qe(e),pV(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};u5.className="RepeatVector";de.registerClass(u5);var c5=class extends ut{constructor(e){super(e),this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new q("Can only specifiy one unknown dimension.");else r*=l}let o=Ha(e);if(a!==null){if(r===0||o%r!==0)throw new q(n);s[a]=o/r}else if(o!==r)throw new q(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return U(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};c5.className="Reshape";de.registerClass(c5);var d5=class extends ut{constructor(e){if(super(e),e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Tr(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new rn({ndim:this.dims.length+1})]}computeOutputShape(e){e=xt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return rt(qe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};d5.className="Permute";de.registerClass(d5);var p5=class extends ut{constructor(e){super(e==null?{}:e),this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=qe(e),s=-1;return Ap(Zi(n,this.maskValue),s)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e),s=-1,r=!0,a=Ap(Zi(n,this.maskValue),s,r);return L(n,he(a,n.dtype))})}};p5.className="Masking";de.registerClass(p5);var h5=class extends ut{constructor(e){if(super(e),this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Dt(e.inputLength))}this.inputDim=e.inputDim,vn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,vn(this.outputDim,"outputDim"),this.embeddingsInitializer=Mt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=zt(e.embeddingsRegularizer),this.activityRegularizer=zt(e.activityRegularizer),this.embeddingsConstraint=yn(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=qe(e),Zi(e,lt(e))):null)}computeOutputShape(e){if(e=xt(e),this.inputLength==null)return[...e,this.outputDim];let t=Dt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e);n.dtype!=="int32"&&(n=V0(n,"int32"));let s=Pw(this.embeddings.read(),U(n,[n.size]));return U(s,xt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Ut(this.embeddingsInitializer),embeddingsRegularizer:It(this.embeddingsRegularizer),activityRegularizer:It(this.activityRegularizer),embeddingsConstraint:gn(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};h5.className="Embedding";de.registerClass(h5);var Ql=class extends ut{constructor(e){super(e||{}),this.supportsMasking=!0}mergeFunction(e){throw new Ge}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[xt(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Ga(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Ga(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Qa(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=fh(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let u=i.shape,c=u[0],p=u.slice(1).concat([c]),d=U(i,[c].concat(Ha(u.slice(1))));d=rt(d,[1,0]),d=U(d,p),n.push(d),r=!0}else if(l>1){let u=Tr(1,l).concat([0]);n.push(rt(i,u)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,u=i[l-1],c=[u].concat(i.slice(0,i.length-1));a=U(rt(U(a,[-1,u]),[1,0]),c)}else if(o>1){let i=[o-1].concat(Tr(0,o-1));a=rt(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Ga(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Zt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=rr(n,t[s]);return n})}},f5=class extends Ql{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ce(t,e[n]);return t})}};f5.className="Add";de.registerClass(f5);var m5=class extends Ql{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};m5.className="Multiply";de.registerClass(m5);var g5=class extends Ql{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ce(t,e[n]);return L(1/e.length,t)})}};g5.className="Average";de.registerClass(g5);var y5=class extends Ql{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Kr(t,e[n]);return t})}};y5.className="Maximum";de.registerClass(y5);var A5=class extends Ql{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Uc(t,e[n]);return t})}};A5.className="Minimum";de.registerClass(A5);var x5=class extends Ql{constructor(e){super(e),this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>xA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(he(Rs(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Zt(t[a],-1)):s.push(t[a]);let r=St(s,this.axis);return c0(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};x5.className="Concatenate";de.registerClass(x5);function Xd(e,t){for(;e<0;)e+=t;return e}function tG(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Ge("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ge("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return K(()=>{let o;if(s>r){o=s-r;let l=[];for(let u=0;u<o;++u)l.push(1);t=U(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let u=0;u<o;++u)l.push(1);e=U(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=ke(L(e,t),a[0]):i=ke(L(rt(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,u=a[1]===t.shape.length-1;i=Je(e,t,l,u)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let u=[];for(let c=l;c<l+o;++c)u.push(c);i=et(i,u)}return i.shape.length===1&&(i=Zt(i,1)),i})}var b5=class extends Ql{constructor(e){super(e),this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Xd(r,e[a].shape.length)):s=[Xd(this.axes,t.shape.length),Xd(this.axes,n.shape.length)],this.normalize&&(t=Am(t,s[0]),n=Am(n,s[1])),tG(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Xd(this.axes,e.length),Xd(this.axes,t.length)],n}computeOutputShape(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Ge("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};b5.className="Dot";de.registerClass(b5);var v5=class extends ut{constructor(e){super(e),this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e);return gh(()=>ce(U0(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};v5.className="GaussianNoise";de.registerClass(v5);var w5=class extends ut{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=qe(e);return this.rate>0&&this.rate<1?gh(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,U0(n.shape,1,r))},()=>n,t.training||!1):n})}};w5.className="GaussianDropout";de.registerClass(w5);var k5=class extends ut{constructor(e){super(e),this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||qe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return gh(()=>{let r=qe(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=ni(Gc(n),this.rate);l=V0(l,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,c=-u*i*this.rate,p=ce(L(r,l),L(ce(l,-1),i));return ce(L(p,u),c)},()=>qe(e),t.training||!1)}return e})}};k5.className="AlphaDropout";de.registerClass(k5);function wp(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=R6(e,t,n,s,r,a);else if(e.rank===3)o=_6(e,t,n,s,r,a);else if(e.rank===4)o=D6(e,t,n,s,r,a);else throw new Ge(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function nG(e,t,n,s,r=.001){return K(()=>{let a=x0(e,s),o=a.mean,i=a.variance;return[wp(e,o,i,n,t,r),o,i]})}function sG(e,t,n,s,r=.001){return K(()=>{let a=x0(e,s),o=a.mean,i=a.variance,l=[];for(let f of Tr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let u=U(o,l),c=U(i,l),p=t==null?null:U(t,l),d=n==null?null:U(n,l);return[wp(e,u,c,d,p,r),o,i]})}function rG(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),Tr(0,e.rank-1))?nG(e,t,n,s,r):sG(e,t,n,s,r)}var I5=class extends ut{constructor(e){e==null&&(e={}),super(e),this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Mt(e.betaInitializer||"zeros"),this.gammaInitializer=Mt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Mt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Mt(e.movingVarianceInitializer||"ones"),this.betaConstraint=yn(e.betaConstraint),this.gammaConstraint=yn(e.gammaConstraint),this.betaRegularizer=zt(e.betaRegularizer),this.gammaRegularizer=zt(e.gammaRegularizer)}build(e){e=xt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new rn({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let n=t.training==null?!1:t.training,s=qe(e),r=s.shape,a=r.length,o=Tr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=Ji(1,a);l[i]=r[i];let u=o.slice();u.sort();let c=!v.arraysEqual(u,Tr(0,a).slice(0,a-1)),p=()=>{if(c){let y=U(this.movingMean.read(),l),x=U(this.movingVariance.read(),l),A=this.center?U(this.beta.read(),l):null,b=this.scale?U(this.gamma.read(),l):null;return wp(s,y,x,A,b,this.epsilon)}else return wp(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=rG(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,x,A)=>{K(()=>{let b=1-A,w=y.read(),k=L(fe(w,x),b);y.write(fe(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ut(this.betaInitializer),gammaInitializer:Ut(this.gammaInitializer),movingMeanInitializer:Ut(this.movingMeanInitializer),movingVarianceInitializer:Ut(this.movingVarianceInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer),betaConstraint:gn(this.betaConstraint),gammaConstraint:gn(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};I5.className="BatchNormalization";de.registerClass(I5);var S5=class extends ut{constructor(e){if(e==null&&(e={}),super(e),this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Mt(e.betaInitializer||"zeros"),this.gammaInitializer=Mt(e.gammaInitializer||"ones"),this.betaRegularizer=zt(e.betaRegularizer),this.gammaRegularizer=zt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=xt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Ga(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=qe(e),s=n.shape,r=s.length;return K(()=>{let{mean:o,variance:i}=x0(n,this.axis,!0),l=Ji(1,r);for(let f of this.axis)l[f]=s[f];let u=f=>f!=null&&f.shape.length!==r?U(f,l):f,c=this.scale?u(this.gamma.read()):null,p=this.center?u(this.beta.read()):null,d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return o=Us(o,d),i=Us(i,d),c!=null&&(c=Us(c,h)),p!=null&&(p=Us(p,h)),wp(n,o,i,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ut(this.betaInitializer),gammaInitializer:Ut(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};S5.className="LayerNormalization";de.registerClass(S5);function aG(e,t,n){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Nr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Ks(e,s)})}var C5=class extends ut{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Nr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new rn({ndim:4})]}computeOutputShape(e){e=xt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return K(()=>aG(qe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};C5.className="ZeroPadding2D";de.registerClass(C5);function o2(e,t,n,s,r,a){return K(()=>{Jt(r),_w(a),Zs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=Nr()),a==null&&(a="max"),e=qA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=ih(e,t,n,i):o=eh(e,t,n,i),r==="channelsFirst"&&(o=rt(o,[0,3,1,2])),o})}function C8(e,t,n,s,r,a){return K(()=>{Jt(r),_w(a),Zs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Nr()),a==null&&(a="max"),e=v8(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Jy(e,t,n,i):o=Fy(e,t,n,i),r==="channelsFirst"&&(o=rt(o,[0,4,1,2,3])),o})}var T8=class extends ut{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(vn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);vn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Zs(this.padding),this.inputSpec=[new rn({ndim:3})]}computeOutputShape(e){e=xt(e);let t=Cr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=fh(qe(e),2);let n=this.poolingFunction(qe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},T5=class extends T8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Jt(r),Zs(s),o2(e,t,n,s,r,"max")}};T5.className="MaxPooling1D";de.registerClass(T5);var N5=class extends T8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Jt(r),Zs(s),o2(e,t,n,s,r,"avg")}};N5.className="AveragePooling1D";de.registerClass(N5);var N8=class extends ut{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];vn(this.poolSize,"poolSize"),vn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Jt(this.dataFormat),Zs(this.padding),this.inputSpec=[new rn({ndim:4})]}computeOutputShape(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Cr(t,this.poolSize[0],this.padding,this.strides[0]),n=Cr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(qe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},E5=class extends N8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Jt(r),Zs(s),o2(e,t,n,s,r,"max")}};E5.className="MaxPooling2D";de.registerClass(E5);var R5=class extends N8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Jt(r),Zs(s),o2(e,t,n,s,r,"avg")}};R5.className="AveragePooling2D";de.registerClass(R5);var E8=class extends ut{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];vn(this.poolSize,"poolSize"),vn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Jt(this.dataFormat),Zs(this.padding),this.inputSpec=[new rn({ndim:5})]}computeOutputShape(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Cr(t,this.poolSize[0],this.padding,this.strides[0]),n=Cr(n,this.poolSize[1],this.padding,this.strides[1]),s=Cr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(qe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_5=class extends E8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Jt(r),Zs(s),C8(e,t,n,s,r,"max")}};_5.className="MaxPooling3D";de.registerClass(_5);var D5=class extends E8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Jt(r),Zs(s),C8(e,t,n,s,r,"avg")}};D5.className="AveragePooling3D";de.registerClass(D5);var R8=class extends ut{constructor(e){super(e),this.inputSpec=[new rn({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ge}},$5=class extends R8{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=qe(e);return Wt(n,1)})}};$5.className="GlobalAveragePooling1D";de.registerClass($5);var F5=class extends R8{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=qe(e);return mn(n,1)})}};F5.className="GlobalMaxPooling1D";de.registerClass(F5);var _8=class extends ut{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Jt(this.dataFormat),this.inputSpec=[new rn({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ge}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},P5=class extends _8{call(e,t){return K(()=>{let n=qe(e);return this.dataFormat==="channelsLast"?Wt(n,[1,2]):Wt(n,[2,3])})}};P5.className="GlobalAveragePooling2D";de.registerClass(P5);var O5=class extends _8{call(e,t){return K(()=>{let n=qe(e);return this.dataFormat==="channelsLast"?mn(n,[1,2]):mn(n,[2,3])})}};O5.className="GlobalMaxPooling2D";de.registerClass(O5);var D8=class extends ut{constructor(e){super(e),this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=Sr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},M5=class extends D8{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=xt(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=xt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return K(()=>(e=qe(e),I8((a,o)=>[qe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};M5.className="TimeDistributed";de.registerClass(M5);function oG(e){Yl(iV,"BidirectionalMergeMode",e)}var iG="concat",z5=class extends D8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Sr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Sr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?iG:e.mergeMode,oG(this.mergeMode),e.weights)throw new Ge("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):cs(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=k8(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new rn({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(s!=null)throw new Ge("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof wr;for(let l of a)if(l instanceof wr!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return K(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=_s(r,1));let 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this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=Sr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Ge("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};z5.className="Bidirectional";de.registerClass(z5);function lG(e){return new qc(e)}function uG(e){return new GA(e)}function cG(e){return new WA(e)}function dG(e){return new VA(e)}function pG(e){return new UA(e)}function hG(e){return new jA(e)}function fG(e){return new HA(e)}function mG(e){return new e2(e)}function gG(e){return new bh(e)}function yG(e){return new KA(e)}function 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dj=class{constructor(e,t,n,s,r,a,o){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=s,this.identicalElementShapes=r,this.dynamicSize=a,this.clearAfterRead=o,this.tensors=[],this.closed_=!1,this.idTensor=Ce(0),bn(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return 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+          tensor.shape[0], but sum of lengths is
+        ${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];K(()=>{t=U(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],u=[0,l,0],c=[1,e[i],r];a[i]=U(Oe(t,u,c),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},tc=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);nr(t,r.shape,"TensorList shape mismatch: "),bn(r)}),this.idTensor=Ce(0),this.maxNumElements=s,bn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new tc([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);nr(e,this.elementShape,"TensorList shape mismatch: ");let s=Kd(this.elementShape,this.tensors,e);return K(()=>{let r=this.tensors.map(a=>U(a,s));return ln(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=Kd(this.elementShape,this.tensors,e),s=this.tensors.pop();return nr(s.shape,e,"TensorList shape mismatch: "),U(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(nr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");bn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. 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+          tensor.shape[0], but sum of lengths is
+        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=P3(a,n),i=s===0?0:e.size/s,l=K(()=>{let c=[];e=U(e,[1,s,i]);for(let p=0;p<t.length;++p){let d=p===0?0:r[p-1],h=[0,d,0],f=[1,t[p],i];c[p]=U(Oe(e,h,f),o)}return e.dispose(),c}),u=new tc([],n,e.dtype,t.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var gj=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),a=I("cond",e,t,n),o=I("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=I("body",e,t,n),r=I("cond",e,t,n),a=I("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(c=>c.id),l=await o[0].data();o.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=a;for(;l[0];){let c=u;u=await 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implemented`)}},Cj=(e,t,n)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[W6(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[rt(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[s,r]=I("fusedOps",e,t,n),a=s==="biasadd",o=r==="prelu",i=I("numArgs",e,t,n),l=I("leakyreluAlpha",e,t,n);if(a){if(o&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!o&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=I("args",e,t,n);return[Ja.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:u,activation:r,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Tj=(e,t,n)=>{switch(e.op){case"EuclideanNorm":return[Hy(I("x",e,t,n),I("axis",e,t,n),I("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[qi(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[qi(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[Ky(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[Zl(I("x",e,t,n))];case"LogSoftmax":return[y0(I("x",e,t,n))];case"SparseToDense":return[dA(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nj=(e,t,n)=>{switch(e.op){case"Max":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[mn(I("x",e,t,n),o,i)]}case"Mean":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Wt(I("x",e,t,n),o,i)]}case"Min":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[ha(I("x",e,t,n),o,i)]}case"Sum":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[ke(I("x",e,t,n),o,i)]}case"All":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[c0(I("x",e,t,n),o,i)]}case"Any":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[Ap(I("x",e,t,n),o,i)]}case"ArgMax":{let o=I("axis",e,t,n);return[Cs(I("x",e,t,n),o)]}case"ArgMin":{let o=I("axis",e,t,n);return[Ty(I("x",e,t,n),o)]}case"Prod":{let o=I("axis",e,t,n),i=I("keepDims",e,t,n);return[b0(I("x",e,t,n),o,i)]}case"Cumprod":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[xp(I("x",e,t,n),o,i,l)]}case"Cumsum":{let o=I("axis",e,t,n),i=I("exclusive",e,t,n),l=I("reverse",e,t,n);return[f0(I("x",e,t,n),o,i,l)]}case"Bincount":let s=I("x",e,t,n),r=I("weights",e,t,n),a=I("size",e,t,n);return[Py(s,r,a)];case"DenseBincount":{let o=I("x",e,t,n),i=I("weights",e,t,n),l=I("size",e,t,n),u=I("binaryOutput",e,t,n);return[L6(o,i,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ej=(e,t,n)=>{switch(e.op){case"ConcatV2":case"Concat":{let s=I("n",e,t,n),r=I("axis",e,t,n),a=I("tensors",e,t,n);return a=a.slice(0,s),[St(a,r)]}case"Gather":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[Ki(s,he(r,"int32"),0)]}case"GatherV2":{let s=I("axis",e,t,n),r=I("batchDims",e,t,n),a=I("x",e,t,n),o=I("indices",e,t,n);return[Ki(a,he(o,"int32"),s,r)]}case"Reverse":{let s=I("dims",e,t,n),r=[];for(let o=0;o<s.length;o++)s[o]&&r.push(o);let a=I("x",e,t,n);return[_s(a,r)]}case"ReverseV2":{let s=I("axis",e,t,n),r=I("x",e,t,n);return[_s(r,s)]}case"Slice":{let s=I("begin",e,t,n),r=I("size",e,t,n);return[Oe(I("x",e,t,n),s,r)]}case"StridedSlice":{let s=I("begin",e,t,n),r=I("end",e,t,n),a=I("strides",e,t,n),o=I("beginMask",e,t,n),i=I("endMask",e,t,n),l=I("ellipsisMask",e,t,n),u=I("newAxisMask",e,t,n),c=I("shrinkAxisMask",e,t,n),p=I("x",e,t,n);return[oA(p,s,r,a,o,i,l,u,c)]}case"Pack":return K(()=>{let s=I("axis",e,t,n),r=I("tensors",e,t,n),a=r[0].shape,o=et(r[0]).shape,i=r.map(l=>{let u=v.arraysEqual(l.shape,a);if(!u&&!v.arraysEqual(et(l).shape,o))throw new Error("the input tensors shape does not match");return u?l:U(l,a)});return[ln(i,s)]});case"Unpack":{let s=I("axis",e,t,n),r=I("tensor",e,t,n);return Qn(r,s)}case"Tile":{let s=I("reps",e,t,n);return[Us(I("x",e,t,n),s)]}case"Split":case"SplitV":{let s=I("axis",e,t,n),r=I("numOrSizeSplits",e,t,n),a=I("x",e,t,n);return Yt(a,r,s)}case"ScatterNd":{let s=I("indices",e,t,n),r=I("values",e,t,n),a=I("shape",e,t,n);return[iw(s,r,a)]}case"GatherNd":{let s=I("x",e,t,n),r=I("indices",e,t,n);return[lw(s,r)]}case"SparseToDense":{let s=I("sparseIndices",e,t,n),r=I("outputShape",e,t,n),a=I("sparseValues",e,t,n),o=I("defaultValue",e,t,n);return[dA(s,a,r,a.dtype===o.dtype?o:he(o,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Rj=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:o}=Qd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,o]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Qd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Qd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Qd.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_j=(e,t,n)=>{switch(e.op){case"FFT":return[ch(I("x",e,t,n))];case"IFFT":return[Yu(I("x",e,t,n))];case"RFFT":return[dh(I("x",e,t,n))];case"IRFFT":return[N0(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Dj=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=qf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=qf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[qf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},$j=(e,t,n)=>{switch(e.op){case"Cast":return[he(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[Zt(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[et(I("x",e,t,n),s)]}case"Reshape":return[U(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[Qy(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[Ks(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[lh(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[th(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[Ly(I("x",e,t,n),s,r)]}case"BroadcastTo":return[Vu(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[$6(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function kv(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return K(()=>uj(a,o,i));case"basic_math":return K(()=>cj(a,o,i));case"control":return gj(a,o,i);case"convolution":return K(()=>yj(a,o,i));case"creation":return K(()=>Aj(a,o,i));case"dynamic":return xj(a,o,i);case"evaluation":return K(()=>bj(a,o,i));case"image":return K(()=>Ij(a,o,i));case"graph":return K(()=>vj(a,o,i));case"logical":return K(()=>Sj(a,o,i));case"matrices":return K(()=>Cj(a,o,i));case"normalization":return K(()=>Tj(a,o,i));case"reduction":return K(()=>Nj(a,o,i));case"slice_join":return K(()=>Ej(a,o,i));case"sparse":return K(()=>Rj(a,o,i));case"spectral":return K(()=>_j(a,o,i));case"string":return K(()=>Dj(a,o,i));case"transformation":return K(()=>$j(a,o,i));case"hash_table":return kj(a,o,i,s);case"custom":let l=V8(a.op);if(l&&l.customExecutor)return l.customExecutor(new lj(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Iv=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Sv(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(d=>ks(d)[0]),c=[];s!=null&&(c=s.map(d=>ks(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((ck(d)||zj(d)||Lj(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function Fj(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>ks(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&a.push(p)})}return u}var Pj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Oj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Mj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function ck(e){return Pj.indexOf(e.op)>=0}function zj(e){return Oj.indexOf(e.op)>=0}function Lj(e){return Mj.indexOf(e.op)>=0}var O3=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new O3(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Sv(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return Fj(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[ks(c)[0]]),r=t.map(c=>ks(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return K(()=>{let c=new Iv(this.weightMap,l,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=ks(f),y=[];y[g]=e[f],p[m]=y});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=kv(m,p,c,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>Zn(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=WH(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];if(c===1){if(!this.keepTensorForDebug)u.dispose();else{let[p,d]=Wr(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=u:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=u)}delete o[u.id]}else c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Y().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let a=new Iv(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(u=>Zn(u,this.tensorsMap,a)),i=o.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[ks(x)[0]]),o=n.map(x=>ks(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:p}=Sv(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[A,b]=ks(x),w=[];w[b]=e[x],h[A]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(a,d,t,h,g,m,o,f,l);await Promise.all(x)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=i.filter(x=>!ck(x)&&!Zn(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw c!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. 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u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Wr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Zn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Zn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=ks(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n 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t=$n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push($n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=$n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new O3(xv.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=xv.Instance.transformGraph(e.modelInitializer);this.initializer=new O3(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=$n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof st)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Uj(e,t={}){if(e==null)throw 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i=e[o],l=Im(i,t,n,s);a[o]=l}return s.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function Zj(e,t=hk){return pk(e,t)}function pk(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(nc(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(u=>u[o]),l=pk(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function hk(e){return e===null?null:nc(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function fk(e,t){let n=new Map;Im(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let o=await a;n.set(r,o)}}return Im(e,t,n)}function nc(e){let t=!1;if(Y().get("IS_BROWSER"))t=e instanceof 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RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},G5=class extends mk{constructor(){super(G5.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};G5.INITIAL_CAPACITY=32;function gk(e){return new sq(e)}function H5(e){return new rq(e)}function tq(e,t){return new yk(e,t)}function nq(e,t=Va.FAIL){return new hq(e,t)}var kn=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new dq(this,e)}filter(e){return new uq(this,e)}map(e){return new cq(this,e)}mapAsync(e){return new Cv(this,e)}serialMapAsync(e){return new Cv(this,e).serial()}flatmap(e){return new pq(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new lq(this,e,t)}columnMajorBatch(e,t=!0,n=hk){return this.rowMajorBatch(e,t).map(r=>Zj(r,n))}concatenate(e,t){return new yk(gk([this,e]),t)}take(e){return e<0||e==null?this:new iq(this,e)}skip(e){return e<0||e==null?this:new oq(this,e)}prefetch(e){return new Ak(this,e)}shuffle(e,t){return new fq(this,e,t)}serial(){return new aq(this)}},sq=class extends kn{constructor(e){super(),this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:Qj(e),done:!1}}},rq=class extends kn{constructor(e){super(),this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},aq=class extends kn{constructor(e){super(),this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},oq=class extends kn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;ne(e.value)}return this.upstream.next()}},iq=class extends kn{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},lq=class extends kn{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},uq=class extends kn{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;ne(e.value)}}},cq=class extends kn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=kr.getTensorsInContainer(e.value),n=this.transform(e.value),s=kr.getTensorsInContainer(n);for(let r of t)kr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},dq=class extends kn{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Cv=class extends kn{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=kr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=kr.getTensorsInContainer(n);for(let r of t)kr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},j5=class extends kn{constructor(){super(),this.outputQueue=new G5,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},pq=class extends j5{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=kr.getTensorsInContainer(e.value),n=this.transform(e.value),s=kr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)kr.isTensorInList(r,s)||r.dispose();return!0}},yk=class extends kn{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Va;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Va||(Va={}));var hq=class extends kn{constructor(e,t=Va.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof kn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await fk(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case Va.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case Va.SHORTEST:return{value:null,done:!0};case Va.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Ak=class extends kn{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new mk(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},fq=class extends Ak{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Xj.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},Kc=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ws(async()=>(await n.iterator()).columnMajorBatch(e,t,yq),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ws(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ws(async()=>(await t.iterator()).filter(s=>K(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ws(async()=>(await t.iterator()).map(n=>K(()=>e(n))),this.size)}mapAsync(e){let t=this;return ws(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ws(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ws(async()=>{let s=H5(async()=>({value:await t.iterator(),done:!1}));return tq(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ws(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=qj.alea(t||v.now().toString());return ws(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,ws(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Kc.MAX_BUFFER_SIZE=1e4;function ws(e,t=null){return new class extends Kc{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function mq(e){return ws(async()=>gk(e),e.length)}function gq(e){if(!nc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return ws(async()=>{let n=await fk(e,s=>{if(s instanceof Kc)return{value:s.iterator(),recurse:!1};if(nc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return nq(n,Va.SHORTEST)},t)}function yq(e){if(e===null)return null;let t=e[0];return Yj(t)?{value:Aq(e),recurse:!1}:{value:null,recurse:!0}}function Aq(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof st?ln(e):yt(e)}var xk=class extends Kc{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Lf='"',Zd=Symbol("out"),Tv=Symbol("field"),Bf=Symbol("quote"),Zg=Symbol("quoteafterquote"),Nv=Symbol("quoteinquote"),bk=class extends Kc{constructor(e,t){super(),this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new xk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let u=Number(i);if(isNaN(u))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=u;else switch(o.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(i);break;default:l=u}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Zd;for(let o=0;o<r;o++)switch(a){case Zd:switch(e.charAt(o)){case Lf:s=o+1,a=Bf;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Zd;break;default:a=Tv,s=o;break}break;case Tv:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=Zd,s=o+1;break;default:}break;case Bf:switch(e.charAt(o)){case Lf:a=Zg;break;default:}break;case Zg:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=Zd,s=o+1;break;case Lf:a=Bf;break;default:a=Nv;break}break;case Nv:switch(e.charAt(o)){case Lf:a=Bf;break;default:}break;default:}if(a===Zg?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},vk=class extends kn{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!Y().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new vk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),yt(n,t)}},wk=class extends kn{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=$t([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=Ir([a,r,i,o],[1,4])}else this.cropBox=Ir([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!Y().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new wk(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Xs.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Zt(he(e,"float32"),0),n;n=Se.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return U(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},kk=class{},Ik=class extends kn{split(e){return new xq(this,e)}},xq=class extends Ik{constructor(e,t){super(),this.upstream=e,this.impl=new bq(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},bq=class extends j5{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},vq=class extends kn{decodeUTF8(){return new wq(this)}},wq=class extends Ik{constructor(e){super(),this.upstream=e,this.impl=new kq(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},kq=class extends j5{constructor(e){if(super(),this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=w7();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},Sk=class extends vq{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function Iq(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=Sq(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new Sk(o,t)}else throw new Error(a.statusText)}var Sq=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function Ck(e){return typeof e=="string"&&e.slice(0,7)==="file://"}var Tk=class extends kk{constructor(e,t={}){super(),this.input=e,this.options=t}async iterator(){if(Ck(this.input)&&Y().get("IS_NODE")){let e=ny();this.input=e.readFileSync(this.input.slice(7))}return new Sk(this.input,this.options)}},Nk=class extends kk{constructor(e,t={}){super(),this.url=e,this.fileOptions=t}async iterator(){return Ck(this.url)?new Tk(this.url,this.fileOptions).iterator():Iq(this.url,this.fileOptions)}};function Cq(e,t={}){return new bk(new Nk(e),t)}function Tq(e){let t=H5(e);return ws(async()=>t)}function Nq(e){return ws(async()=>{let t=await e();return H5(()=>t.next())})}async function Eq(e,t){return wk.create(e,t)}async function Rq(e){return vk.create(e)}var _q="3.18.0";function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var Dq=ir.whereImpl,q5=class extends ic{constructor(){super(),this.blockSize=48,this.firstUse=!0,this.data=new Ep(this,sn())}nextDataId(){return q5.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&T.warn(`
+============================
+Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. 
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n.makeTensorInfo(x.shape,x.dtype,x.values)}var zK={kernelName:Bm,backendName:"cpu",kernelFunc:MK};function LK(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s;Te([r,a],"conv2dBackpropInput");let p=v.computeStrides(a.shape),d=v.computeStrides(r.shape),h=T.convertConv2DDataFormat(u),f=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,h),m=new fn(f.inShape,"float32"),g=m.values,y=n.data.get(r.dataId).values,x=n.data.get(a.dataId).values,[A,b,w]=p,{batchSize:k,filterHeight:S,filterWidth:E,inChannels:R,inHeight:$,inWidth:_,outChannels:D,outHeight:C,outWidth:P,strideHeight:V,strideWidth:j}=f;h=f.dataFormat;let z=S-1-f.padInfo.top,Z=E-1-f.padInfo.left,W=h==="channelsLast",ee=m.strides[0],Q=W?m.strides[1]:m.strides[2],ie=W?m.strides[2]:1,J=W?1:m.strides[1],ae=d[0],le=W?d[1]:d[2],ye=W?d[2]:1,we=W?1:d[1];for(let Re=0;Re<k;++Re)for(let _e=0;_e<R;++_e)for(let Be=0;Be<$;++Be){let 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u=v.computeStrides(r.shape),c=v.computeStrides(a.shape),p=T.computeConv3DInfo(r.shape,l,o,1,i),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,y=p.filterWidth,x=new fn(p.filterShape,"float32"),A=x.values,[b,w,k,S]=x.strides,E=n.data.get(a.dataId).values,[R,$,_,D]=c,C=n.data.get(r.dataId).values,[P,V,j,z]=u,Z=p.padInfo.front,W=p.padInfo.left,ee=p.padInfo.top;for(let Q=0;Q<m;++Q){let ie=Math.max(0,Math.ceil((Z-Q)/d)),J=Math.min(p.outDepth,(p.inDepth+Z-Q)/d),ae=Q*b;for(let le=0;le<g;++le){let ye=Math.max(0,Math.ceil((ee-le)/h)),we=Math.min(p.outHeight,(p.inHeight+ee-le)/h),Re=le*w+ae;for(let _e=0;_e<y;++_e){let Be=Math.max(0,Math.ceil((W-_e)/f)),Ue=Math.min(p.outWidth,(p.inWidth+W-_e)/f),it=_e*k+Re;for(let dt=0;dt<p.inChannels;++dt){let pt=dt*S+it;for(let At=0;At<p.outChannels;++At){let $e=0;for(let Nt=0;Nt<p.batchSize;++Nt){let kt=Nt*P,Wn=Nt*R;for(let en=ie;en<J;++en){let pn=(Q+en*d-Z)*V+kt,Vn=en*$+Wn;for(let xs=ye;xs<we;++xs){let 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e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${t}`}}function wa(e,t,n){let s=Ie(e,()=>t());if(s==null)throw new Error(n);return s}function jI(e,t){let n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,s=t+e.TEXTURE0;if(s<e.TEXTURE0||s>n){let r=`[gl.TEXTURE0, gl.TEXTURE${n}]`;throw new Error(`textureUnit must be in ${r}.`)}}function tl(e,t=2){return v.sizeFromShape(e.slice(0,e.length-t))}function nl(e){if(e.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]}function Yf(e){let t=[1,1,1];return e.length===0||e.length===1&&e[0]===1||(t=[tl(e),...nl(e)]),t}function qI(e,t=!1){let n=Y().getNumber("WEBGL_MAX_TEXTURE_SIZE");t&&(n=n*2,e=e.map((r,a)=>a>=e.length-2?v.nearestLargerEven(e[a]):e[a]),e.length===1&&(e=[2,e[0]])),e.length!==2&&(e=v.squeezeShape(e).newShape);let s=v.sizeFromShape(e);if(e.length<=1&&s<=n)return[1,s];if(e.length===2&&e[0]<=n&&e[1]<=n)return e;if(e.length===3&&e[0]*e[1]<=n&&e[2]<=n)return[e[0]*e[1],e[2]];if(e.length===3&&e[0]<=n&&e[1]*e[2]<=n)return[e[0],e[1]*e[2]];if(e.length===4&&e[0]*e[1]*e[2]<=n&&e[3]<=n)return[e[0]*e[1]*e[2],e[3]];if(e.length===4&&e[0]<=n&&e[1]*e[2]*e[3]<=n)return[e[0],e[1]*e[2]*e[3]];if(t){let r=tl(e),a=2,o=2;return e.length&&([a,o]=nl(e)),s=r*(a/2)*(o/2),v.sizeToSquarishShape(s).map(i=>i*2)}return v.sizeToSquarishShape(s)}function Vf(e){return e%2===0}function Ip(e,t){if(e=e.slice(-2),t=t.slice(-2),v.arraysEqual(e,t)||!e.length||!t.length||e[0]===0||e[1]===0||t[0]===0||t[1]===0)return!0;if(e.length!==t.length){let n=e.slice(-1)[0],s=t.slice(-1)[0];if(n===s||Vf(n)&&Vf(s)&&(e[0]===1||t[0]===1))return!0}return e[1]===t[1]&&Vf(e[0])&&Vf(t[0])}var Jf,Qf;function XI(e){if(Jf==null){let t=Er(e);Jf=t.getParameter(t.MAX_TEXTURE_SIZE)}return Jf}function QQ(){Jf=null}function eee(){Qf=null}function KI(e){if(Qf==null){let t=Er(e);Qf=t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,Qf)}function ZI(e){if(e===0)return 0;let t,n=Er(e);return Hs(n,"EXT_disjoint_timer_query_webgl2")&&e===2?t=2:Hs(n,"EXT_disjoint_timer_query")?t=1:t=0,t}function Hs(e,t){return e.getExtension(t)!=null}function W3(e){try{if(Er(e)!=null)return!0}catch(t){return console.log("Error when getting WebGL context: ",t),!1}return!1}function YI(e){if(e===0)return!1;let t=Er(e);if(e===1){if(!Hs(t,"OES_texture_float"))return!1}else if(!Hs(t,"EXT_color_buffer_float"))return!1;return V3(t)}function JI(e){if(e===0)return!1;let t=Er(e);if(e===1){if(!Hs(t,"OES_texture_float")||!Hs(t,"WEBGL_color_buffer_float"))return!1}else{if(Hs(t,"EXT_color_buffer_float"))return V3(t);let s="EXT_color_buffer_half_float";if(Hs(t,s)){let r=t.getExtension(s);return tee(t,r)}return!1}return V3(t)}function V3(e){let t=ox(e),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function tee(e,t){let n=ox(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function QI(e){return e!==2?!1:Er(e).fenceSync!=null}function Jc(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Fe=Y();Fe.registerFlag("HAS_WEBGL",()=>Fe.getNumber("WEBGL_VERSION")>0);Fe.registerFlag("WEBGL_VERSION",()=>W3(2)?2:W3(1)?1:0);Fe.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Fe.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Fe.get("WEBGL_VERSION")===2);Fe.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Fe.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Fe.registerFlag("WEBGL_PACK",()=>Fe.getBool("HAS_WEBGL"));Fe.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_CLIP",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_PACK_REDUCE",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_LAZILY_UNPACK",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_CONV_IM2COL",()=>Fe.getBool("WEBGL_PACK"));Fe.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>XI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>KI(Fe.getNumber("WEBGL_VERSION")));Fe.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Fe.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);Fe.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);Fe.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);Fe.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function ss(){let e,t,n,s,r,a,o,i,l,u;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
+      bool isnan_custom(float val) {
+        uint floatToUint = floatBitsToUint(val);
+        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
+      }
+
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan_custom(val.x),
+          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
+      }
+
+      #define isnan(value) isnan_custom(value)
+    `,l="",u=`
+      #define round(value) newRound(value)
+      int newRound(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 newRound(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
+      #define isnan(value) isnan_custom(value)
+      bool isnan_custom(float val) {
+        return (val > 0. || val < 1. || val == 0.) ? false : true;
+      }
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
+      }
+    `,l=`
+      uniform float INFINITY;
+
+      bool isinf(float val) {
+        return abs(val) == INFINITY;
+      }
+      bvec4 isinf(vec4 val) {
+        return equal(abs(val), vec4(INFINITY));
+      }
+    `,u=`
+      int round(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 round(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function eu(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function u2(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function nee(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function see(e,t,n="index"){let s=e.map((a,o)=>o),r=nee(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function lx(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
+  }
+`}function ux(){return`
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
+  }
+`}var eS=`
+  const float FLOAT_MAX = 1.70141184e38;
+  const float FLOAT_MIN = 1.17549435e-38;
+
+  lowp vec4 encode_float(highp float v) {
+    if (isnan(v)) {
+      return vec4(255, 255, 255, 255);
+    }
+
+    highp float av = abs(v);
+
+    if(av < FLOAT_MIN) {
+      return vec4(0.0, 0.0, 0.0, 0.0);
+    } else if(v > FLOAT_MAX) {
+      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
+    } else if(v < -FLOAT_MAX) {
+      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
+    }
+
+    highp vec4 c = vec4(0,0,0,0);
+
+    highp float e = floor(log2(av));
+    highp float m = exp2(fract(log2(av))) - 1.0;
+
+    c[2] = floor(128.0 * m);
+    m -= c[2] / 128.0;
+    c[1] = floor(32768.0 * m);
+    m -= c[1] / 32768.0;
+    c[0] = floor(8388608.0 * m);
+
+    highp float ebias = e + 127.0;
+    c[3] = floor(ebias / 2.0);
+    ebias -= c[3] * 2.0;
+    c[2] += floor(ebias) * 128.0;
+
+    c[3] += 128.0 * step(0.0, -v);
+
+    return c / 255.0;
+  }
+`,{getBroadcastDims:tS}=T;function ree(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=cx(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
+`),a=e.map(h=>aee(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
+`),o=t.texShape,i=ss(),l=lee(i),u,c,p=dee(i);return t.isPacked?(u=oee(t.logicalShape,o,n.enableShapeUniforms),c=cee(i)):(u=iee(t.logicalShape,o,n.enableShapeUniforms),c=uee(i)),n.packedInputs&&(p+=mee),[p,l,c,r,u,a,n.userCode].join(`
+`)}function Qc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return Tee(e,t);case 1:return Eee(e,t);case 2:return _ee(e,t);case 3:return $ee(e,t);case 4:return Pee(e,t);case 5:return Oee(e);case 6:return Mee(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function nS(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Cee(e);case 1:return Nee(e,t);case 2:return Ree(e,t);case 3:return Dee(e,t);default:return Fee(e,t)}}function aee(e,t,n=!1,s){let r="";n?r+=nS(e,s):r+=Qc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=zee(e,t):r+=Lee(e,t)),r}function oee(e,t,n){switch(e.length){case 0:return sS();case 1:return gee(e,t,n);case 2:return Iee(e,t,n);case 3:return Aee(e,t,n);default:return bee(e,t,n)}}function iee(e,t,n){switch(e.length){case 0:return sS();case 1:return yee(e,t,n);case 2:return See(e,t,n);case 3:return xee(e,t,n);case 4:return vee(e,t,n);case 5:return wee(e,t);case 6:return kee(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function lee(e){return`
+    float sampleTexture(sampler2D textureSampler, vec2 uv) {
+      return ${e.texture2D}(textureSampler, uv).r;
+    }
+  `}function uee(e){return`
+    void setOutput(float val) {
+      ${e.output} = vec4(val, 0, 0, 0);
+    }
+  `}function cee(e){return`
+    void setOutput(vec4 val) {
+      ${e.output} = val;
+    }
+  `}function dee(e){return`${e.version}
+    precision highp float;
+    precision highp int;
+    precision highp sampler2D;
+    ${e.varyingFs} vec2 resultUV;
+    ${e.defineOutput}
+    const vec2 halfCR = vec2(0.5, 0.5);
+
+    struct ivec5
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+    };
+
+    struct ivec6
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+      int v;
+    };
+
+    uniform float NAN;
+    ${e.defineSpecialNaN}
+    ${e.defineSpecialInf}
+    ${e.defineRound}
+
+    int imod(int x, int y) {
+      return x - y * (x / y);
+    }
+
+    int idiv(int a, int b, float sign) {
+      int res = a / b;
+      int mod = imod(a, b);
+      if (sign < 0. && mod != 0) {
+        res -= 1;
+      }
+      return res;
+    }
+
+    //Based on the work of Dave Hoskins
+    //https://www.shadertoy.com/view/4djSRW
+    #define HASHSCALE1 443.8975
+    float random(float seed){
+      vec2 p = resultUV * seed;
+      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
+      p3 += dot(p3, p3.yzx + 19.19);
+      return fract((p3.x + p3.y) * p3.z);
+    }
+
+    ${pee}
+    ${hee}
+    ${fee}
+  `}var pee=`
+vec2 uvFromFlat(int texNumR, int texNumC, int index) {
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
+  int texelIndex = index / 2;
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`,hee=`
+vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
+  int texNumC, int row, int col) {
+  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`,fee=`
+vec2 packedUVfrom3D(int texNumR, int texNumC,
+    int texelsInBatch, int texelsInLogicalRow, int b,
+    int row, int col) {
+  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`,mee=`
+  float getChannel(vec4 frag, vec2 innerDims) {
+    vec2 modCoord = mod(innerDims, 2.);
+    return modCoord.x == 0. ?
+      (modCoord.y == 0. ? frag.r : frag.g) :
+      (modCoord.y == 0. ? frag.b : frag.a);
+  }
+  float getChannel(vec4 frag, int dim) {
+    float modCoord = mod(float(dim), 2.);
+    return modCoord == 0. ? frag.r : frag.g;
+  }
+`;function sS(){return`
+    int getOutputCoords() {
+      return 0;
+    }
+  `}function gee(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
+      }
+    `:`
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ${s[1]}.0);
+      }
+    `:s[1]===1?n?`
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
+      }
+    `:`
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ${s[0]}.0);
+      }
+    `:n?`
+    int getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
+    }
+  `:`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
+    }
+  `}function yee(e,t,n){return t[0]===1?n?`
+      int getOutputCoords() {
+        return int(resultUV.x * float(outTexShape[1]));
+      }
+    `:`
+      int getOutputCoords() {
+        return int(resultUV.x * ${t[1]}.0);
+      }
+    `:t[1]===1?n?`
+      int getOutputCoords() {
+        return int(resultUV.y * float(outTexShape[0]));
+      }
+    `:`
+      int getOutputCoords() {
+        return int(resultUV.y * ${t[0]}.0);
+      }
+    `:n?`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(outTexShape[0], outTexShape[1]));
+      return resTexRC.x * outTexShape[1] + resTexRC.y;
+    }
+  `:`
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${t[0]}, ${t[1]}));
+      return resTexRC.x * ${t[1]} + resTexRC.y;
+    }
+  `}function Aee(e,t,n){if(n)return`
+    ivec3 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
+      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+
+      int b = index / texelsInBatch;
+      index -= b * texelsInBatch;
+
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+      int index = resTexRC.x * ${s[1]} + resTexRC.y;
+
+      int b = index / ${a};
+      index -= b * ${a};
+
+      int r = 2 * (index / ${r});
+      int c = imod(index, ${r}) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `}function xee(e,t,n){if(n)return`
+  ivec3 getOutputCoords() {
+    ivec2 resTexRC = ivec2(resultUV.yx *
+                           vec2(outTexShape[0], outTexShape[1]));
+    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+    ${u2(["r","c","d"],e)}
+    return ivec3(r, c, d);
+  }
+`;let s=eu(["r","c","d"],e);return`
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      ${s}
+      return ivec3(r, c, d);
+    }
+  `}function bee(e,t,n){if(n)return`
+    ivec4 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+
+      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
+      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
+      int texelsInBatchN = texelsInBatch * outShape[1];
+
+      int b2 = index / texelsInBatchN;
+      index -= b2 * texelsInBatchN;
+
+      int b = index / texelsInBatch;
+      index -= b * texelsInBatch;
+
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec4(b2, b, r, c);
+    }
+  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let u=2;u<e.length-1;u++)o*=e[e.length-u-1],i=`
+      int b${u} = index / ${o};
+      index -= b${u} * ${o};
+    `+i,l=`b${u}, `+l;return`
+    ivec${e.length} getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+      int index = resTexRC.x * ${s[1]} + resTexRC.y;
+
+      ${i}
+
+      int b = index / ${a};
+      index -= b * ${a};
+
+      int r = 2 * (index / ${r});
+      int c = imod(index, ${r}) * 2;
+
+      return ivec${e.length}(${l});
+    }
+  `}function vee(e,t,n){if(n)return`
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(outTexShape[0], outTexShape[1]));
+      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+      ${u2(["r","c","d","d2"],e)}
+      return ivec4(r, c, d, d2);
+    }
+  `;let s=eu(["r","c","d","d2"],e);return`
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      ${s}
+      return ivec4(r, c, d, d2);
+    }
+  `}function wee(e,t){let n=eu(["r","c","d","d2","d3"],e);return`
+    ivec5 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
+                             ${t[1]}));
+
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+
+      ${n}
+
+      ivec5 outShape = ivec5(r, c, d, d2, d3);
+      return outShape;
+    }
+  `}function kee(e,t){let n=eu(["r","c","d","d2","d3","d4"],e);return`
+    ivec6 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+
+      ${n}
+
+      ivec6 result = ivec6(r, c, d, d2, d3, d4);
+      return result;
+    }
+  `}function Iee(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
+      ivec2 getOutputCoords() {
+        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
+      }
+    `:`
+      ivec2 getOutputCoords() {
+        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
+      }
+    `;let r=Math.ceil(e[1]/2);return n?`
+    ivec2 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec2(r, c);
+    }
+  `:`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${s[0]}, ${s[1]}));
+
+      int index = resTexRC.x * ${s[1]} + resTexRC.y;
+      int r = 2 * (index / ${r});
+      int c = imod(index, ${r}) * 2;
+
+      return ivec2(r, c);
+    }
+  `}function See(e,t,n){return v.arraysEqual(e,t)?n?`
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
+      }
+    `:`
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
+      }
+    `:e[1]===1?n?`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(outTexShape[0], outTexShape[1]));
+        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `:`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${t[0]}, ${t[1]}));
+        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `:e[0]===1?n?`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(outTexShape[0], outTexShape[1]));
+        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `:`
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${t[0]}, ${t[1]}));
+        int index = resTexRC.x * ${t[1]} + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `:n?`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(outTexShape[0], outTexShape[1]));
+      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+      int r = index / outShape[1];
+      int c = index - r * outShape[1];
+      return ivec2(r, c);
+    }
+  `:`
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${t[0]}, ${t[1]}));
+      int index = resTexRC.x * ${t[1]} + resTexRC.y;
+      int r = index / ${e[1]};
+      int c = index - r * ${e[1]};
+      return ivec2(r, c);
+    }
+  `}function tu(e){return`offset${e}`}function Cee(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=ss();return`
+    vec4 ${n}() {
+      return ${s.texture2D}(${t}, halfCR);
+    }
+  `}function Tee(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+      float ${s}() {
+        return sampleTexture(${n}, halfCR);
+      }
+    `;let o=tu(n);if(t)return`
+    float ${s}() {
+      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
+      return sampleTexture(${n}, uv);
+    }
+  `;let[i,l]=e.shapeInfo.texShape;return`
+    float ${s}() {
+      vec2 uv = uvFromFlat(${i}, ${l}, ${o});
+      return sampleTexture(${n}, uv);
+    }
+  `}function Nee(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=ss();if(t)return`
+    vec4 ${s}(int index) {
+      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
+      vec2 uv = packedUVfrom1D(
+        packedTexShape[0], packedTexShape[1], index);
+      return ${a.texture2D}(${n}, uv);
+    }
+  `;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
+    vec4 ${s}(int index) {
+      vec2 uv = packedUVfrom1D(
+        ${o[0]}, ${o[1]}, index);
+      return ${a.texture2D}(${n}, uv);
+    }
+  `}function Eee(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
+      float ${s}(int index) {
+        ${ed(e)}
+      }
+    `;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
+      float ${s}(int index) {
+        return sampleTexture(${n}, halfCR);
+      }
+    `;let i=tu(n);return o===1?t?`
+      float ${s}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
+        return sampleTexture(${n}, uv);
+      }
+    `:`
+      float ${s}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
+        return sampleTexture(${n}, uv);
+      }
+    `:a===1?t?`
+      float ${s}(int index) {
+        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
+        return sampleTexture(${n}, uv);
+      }
+    `:`
+      float ${s}(int index) {
+        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
+        return sampleTexture(${n}, uv);
+      }
+    `:t?`
+    float ${s}(int index) {
+      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
+      return sampleTexture(${n}, uv);
+    }
+  `:`
+    float ${s}(int index) {
+      vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
+      return sampleTexture(${n}, uv);
+    }
+  `}function Ree(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=ss();if(a!=null&&v.arraysEqual(n,a))return t?`
+      vec4 ${r}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
+
+        return ${l.texture2D}(${s}, uv);
+      }
+    `:`
+      vec4 ${r}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
+
+        return ${l.texture2D}(${s}, uv);
+      }
+    `;if(t)return`
+    vec4 ${r}(int row, int col) {
+      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
+      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
+      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
+      return ${l.texture2D}(${s}, uv);
+    }
+  `;let u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`
+    vec4 ${r}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
+      return ${l.texture2D}(${s}, uv);
+    }
+  `}function _ee(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
+      float ${r}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
+        return sampleTexture(${s}, uv);
+      }
+    `;let d=a[0],h=a[1];return`
+    float ${r}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
+      return sampleTexture(${s}, uv);
+    }
+  `}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let d=td(e,l),h=["row","col"];return`
+      ${Qc(d,t)}
+      float ${r}(int row, int col) {
+        return ${r}(${nd(h,i)});
+      }
+    `}if(e.shapeInfo.isUniform)return`
+      float ${r}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
+        ${ed(e)}
+      }
+    `;let u=a[0],c=a[1],p=tu(s);return c===1?t?`
+      float ${r}(int row, int col) {
+        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
+        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+    float ${r}(int row, int col) {
+      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
+      return sampleTexture(${s}, uv);
+    }
+  `:u===1?t?`
+      float ${r}(int row, int col) {
+        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
+        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+    float ${r}(int row, int col) {
+      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);
+      return sampleTexture(${s}, uv);
+    }
+  `:t?`
+      float ${r}(int row, int col) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${s}Shape[1] + col + ${p};
+        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+  float ${r}(int row, int col) {
+    // Explicitly use integer operations as dot() only works on floats.
+    int index = row * ${n[1]} + col + ${p};
+    vec2 uv = uvFromFlat(${u}, ${c}, index);
+    return sampleTexture(${s}, uv);
+  }
+`}function Dee(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=td(e,d),m=["b","row","col"];return`
+        ${nS(f,t)}
+        vec4 ${r}(int b, int row, int col) {
+          return ${r}(${nd(m,h)});
+        }
+      `}let i=ss();if(t)return`
+    vec4 ${r}(int b, int row, int col) {
+      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
+      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
+      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
+      vec2 uv = packedUVfrom3D(
+        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
+      return ${i.texture2D}(${s}, uv);
+    }
+  `;let l=o[0],u=o[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`
+    vec4 ${r}(int b, int row, int col) {
+      vec2 uv = packedUVfrom3D(
+        ${l}, ${u}, ${p}, ${c}, b, row, col);
+      return ${i.texture2D}(${s}, uv);
+    }
+  `}function $ee(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),u=i;if(u.length<n.length){let m=td(e,u),g=["row","col","depth"];return`
+        ${Qc(m,t)}
+        float ${r}(int row, int col, int depth) {
+          return ${r}(${nd(g,l)});
+        }
+      `}if(e.shapeInfo.isUniform)return`
+      float ${r}(int row, int col, int depth) {
+        int index = round(dot(vec3(row, col, depth),
+                          vec3(${a}, ${o}, 1)));
+        ${ed(e)}
+      }
+    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
+      float ${r}(int row, int col, int depth) {
+        int stride1 = ${s}Shape[2];
+        float texR = float(row);
+        float texC = dot(vec2(col, depth), vec2(stride1, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${s}TexShape[1], ${s}TexShape[0]);
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+        float ${r}(int row, int col, int depth) {
+          float texR = float(row);
+          float texC = dot(vec2(col, depth), vec2(${o}, 1));
+          vec2 uv = (vec2(texC, texR) + halfCR) /
+                     vec2(${d}.0, ${p}.0);
+          return sampleTexture(${s}, uv);
+        }
+      `;if(d===o&&h==null)return t?`
+      float ${r}(int row, int col, int depth) {
+        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
+        float texC = float(depth);
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+    float ${r}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
+      float texC = float(depth);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
+      return sampleTexture(${s}, uv);
+    }
+  `;let f=tu(s);return t?`
+    float ${r}(int row, int col, int depth) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int stride0 = ${s}Shape[1] * ${s}Shape[2];
+      int stride1 = ${s}Shape[2];
+      int index = row * ${a} + col * ${o} + depth + ${f};
+      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
+      return sampleTexture(${s}, uv);
+    }
+    `:`
+      float ${r}(int row, int col, int depth) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${a} + col * ${o} + depth + ${f};
+        vec2 uv = uvFromFlat(${p}, ${d}, index);
+        return sampleTexture(${s}, uv);
+      }
+  `}function Fee(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=ss();if(t)return`
+    vec4 ${s}(int b2, int b, int row, int col) {
+      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
+      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
+      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
+      texelsInBatch *= ${n}Shape[1];
+      index = b2 * texelsInBatch + index;
+      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
+      int texR = index / packedTexShape[1];
+      int texC = index - texR * packedTexShape[1];
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
+    }
+  `;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],u=l[0],c=l[1],p=Math.ceil(a[o-1]/2),d=p*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,d*=a[o-m-1],f=`b${m} * ${d} + `+f;return`
+    vec4 ${s}(${h}) {
+      int index = ${f};
+      int texR = index / ${c};
+      int texC = index - texR * ${c};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
+      return ${r.texture2D}(${n}, uv);
+    }
+  `}function Pee(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=v.squeezeShape(n);if(l.length<n.length){let x=td(e,l),A=["row","col","depth","depth2"];return`
+      ${Qc(x,t)}
+      float ${r}(int row, int col, int depth, int depth2) {
+        return ${r}(${nd(A,u)});
+      }
+    `}if(e.shapeInfo.isUniform)return`
+      float ${r}(int row, int col, int depth, int depth2) {
+        int index = round(dot(vec4(row, col, depth, depth2),
+                          vec4(${i}, ${o}, ${a}, 1)));
+        ${ed(e)}
+      }
+    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
+      float ${r}(int row, int col, int depth, int depth2) {
+        ${f}
+        ${m}
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(stride1, stride2, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${s}TexShape[1], ${s}TexShape[0]);
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+      float ${r}(int row, int col, int depth, int depth2) {
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(${o}, ${a}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${h}.0, ${d}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;if(h===a&&c==null)return t?`
+      float ${r}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${s}TexShape[1], ${s}TexShape[0]);
+        return sampleTexture(${s}, uv);
+      }
+    `:`
+      float ${r}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${n[1]*n[2]}, ${n[2]}, 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${h}.0, ${d}.0);
+        return sampleTexture(${s}, uv);
+      }
+    `;let y=tu(s);return t?`
+    float ${r}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      ${f}
+      ${m}
+      ${g}
+      int index = row * stride0 + col * stride1 +
+          depth * stride2 + depth2;
+      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${y});
+      return sampleTexture(${s}, uv);
+    }
+  `:`
+    float ${r}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${i} + col * ${o} +
+          depth * ${a} + depth2;
+      vec2 uv = uvFromFlat(${d}, ${h}, index + ${y});
+      return sampleTexture(${s}, uv);
+    }
+  `}function Oee(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=td(e,l),g=["row","col","depth","depth2","depth3"];return`
+      ${Qc(m)}
+      float ${s}(int row, int col, int depth, int depth2, int depth3) {
+        return ${s}(${nd(g,u)});
+      }
+    `}if(e.shapeInfo.isUniform)return`
+      float ${s}(int row, int col, int depth, int depth2, int depth3) {
+        float index = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${i}, ${o}, ${a}, ${r})) +
+          depth3;
+        ${ed(e)}
+      }
+    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&c==null)return`
+      float ${s}(int row, int col, int depth, int depth2, int depth3) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+                         vec4(${o}, ${a}, ${r}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${h}.0, ${d}.0);
+        return sampleTexture(${n}, uv);
+      }
+    `;if(h===r&&c==null)return`
+      float ${s}(int row, int col, int depth, int depth2, int depth3) {
+        float texR = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${t[1]*t[2]*t[3]},
+               ${t[2]*t[3]}, ${t[3]}, 1));
+        int texC = depth3;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${h}.0, ${d}.0);
+        return sampleTexture(${n}, uv);
+      }
+    `;let f=tu(n);return`
+    float ${s}(int row, int col, int depth, int depth2, int depth3) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${i} + col * ${o} + depth * ${a} +
+          depth2 * ${r} + depth3 + ${f};
+      vec2 uv = uvFromFlat(${d}, ${h}, index);
+      return sampleTexture(${n}, uv);
+    }
+  `}function Mee(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=td(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${Qc(g)}
+      float ${s}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        return ${s}(${nd(y,a)});
+      }
+    `}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
+      float ${s}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+        int index = round(dot(
+          vec4(row, col, depth, depth2),
+          vec4(${c}, ${u}, ${l}, ${i})) +
+          dot(
+            vec2(depth3, depth4),
+            vec2(${o}, 1)));
+        ${ed(e)}
+      }
+    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
+      float ${s}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+          vec4(${u}, ${l}, ${i}, ${o})) +
+               float(depth4);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${f}.0, ${h}.0);
+        return sampleTexture(${n}, uv);
+      }
+    `;if(f===o&&p==null)return`
+      float ${s}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        float texR = dot(vec4(row, col, depth, depth2),
+          vec4(${t[1]*t[2]*t[3]*t[4]},
+               ${t[2]*t[3]*t[4]},
+               ${t[3]*t[4]},
+               ${t[4]})) + float(depth3);
+        int texC = depth4;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${f}.0, ${h}.0);
+        return sampleTexture(${n}, uv);
+      }
+    `;let m=tu(n);return`
+    float ${s}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${c} + col * ${u} + depth * ${l} +
+          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
+      vec2 uv = uvFromFlat(${h}, ${f}, index);
+      return sampleTexture(${n}, uv);
+    }
+  `}function ed(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+    for (int i = 0; i < ${n}; i++) {
+      if (i == index) {
+        return ${t}[i];
+      }
+    }
+  `}function zee(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=tS(e.shapeInfo.logicalShape,t.logicalShape),l=Ct(o),u=o-a,c,p=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(x=>`coords.${p[x+u]} = 0;`).join(`
+`);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((x,A)=>`coords.${p[A+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,y=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)h=`
+      return vec4(outputValue.xy, outputValue.xy);
+    `;else if(m&&!y)o===1?h=`
+        return vec4(outputValue.x, outputValue.x, 0., 0.);
+      `:h=`
+        return vec4(outputValue.x);
+      `;else if(i.length){let x=a-2,A=a-1;i.indexOf(x)>-1&&i.indexOf(A)>-1?h="return vec4(outputValue.x);":i.indexOf(x)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(A)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
+    vec4 ${r}() {
+      ${l} coords = getOutputCoords();
+      ${c}
+      vec4 outputValue = get${s}(${d});
+      ${h}
+    }
+  `}function Lee(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
+      float ${r}() {
+        return sampleTexture(${n}, resultUV);
+      }
+    `;let u=Ct(l),c=tS(e.shapeInfo.logicalShape,t.logicalShape),p=l-i,d,h=["x","y","z","w","u","v"];i===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
+`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
+    float ${r}() {
+      ${u} coords = getOutputCoords();
+      ${d}
+      return get${s}(${f});
+    }
+  `}function Ct(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function cx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function td(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function nd(e,t){return t.map(n=>e[n]).join(", ")}function Bee(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=ree(r,o,t),l=$I(e.gl,i),u=e.createProgram(l);return Y().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o},rS(e,t,u))}function rS(e,t,n){let s={},r={},a={},o=[],i,l,u,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),Y().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),u=e.getUniformLocation(n,"outShapeStrides",d),l=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}}function _v(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function Wee(e,t,n,s,r){t.program.enableShapeUniforms||(_v(t.inShapeInfos,n),_v([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=cx(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(p,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,p,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],p=r[u];if(l.type==="float")e.gl.uniform1fv(c,p);else if(l.type==="vec2")e.gl.uniform2fv(c,p);else if(l.type==="vec3")e.gl.uniform3fv(c,p);else if(l.type==="vec4")e.gl.uniform4fv(c,p);else if(l.type==="int")e.gl.uniform1iv(c,p);else if(l.type==="ivec2")e.gl.uniform2iv(c,p);else if(l.type==="ivec3")e.gl.uniform3iv(c,p);else if(l.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Vee(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:p}=cx(e.packedInputs,o.shape,l),d="",h="",f="";if(c.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let w=v.computeStrides(c);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&v.arraysEqual(o.shape,l),y=v.sizeFromShape(o.shape)===1,x=T.getBroadcastDims(o.shape,n.shape),A=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${A}_${u?p:""}_${c.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Y().getNumber("WEBGL_VERSION")}`,a}function ys(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var Uee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=kp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=ss();this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length),this.userCode=`
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${this.enableShapeUniforms?u2(["r","c","d"],e):eu(["r","c","d"],e)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
+        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getA(rc.x, rc.y, rc.z);
+        }
+
+        ${t.output} = result;
+      }
+    `}},Gee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=kp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=ss();this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length),this.userCode=`
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${this.enableShapeUniforms?u2(["r","c","d"],e):eu(["r","c","d"],e)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
+        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
+        }
+
+        ${t.output} = result;
+      }
+    `}},Hee=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Gs.DOWNLOAD;let t=ss();this.outputShape=e,this.userCode=`
+      ${eS}
+
+      void main() {
+        float x = getAAtOutCoords();
+        ${t.output} = encode_float(x);
+      }
+    `}},jee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Gs.DOWNLOAD;let t=ss();this.outputShape=e,this.userCode=`
+      ${eS}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
+        ${t.output} = encode_float(x);
+      }
+    `}},qee=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=ss();this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
+      ${this.enableShapeUniforms?ux():lx(e)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        int flatIndex = getFlatIndex(coords);
+        int offset = imod(flatIndex, 4);
+
+        flatIndex = idiv(flatIndex, 4, 1.);
+
+        int r = flatIndex / texShape[1];
+        int c = imod(flatIndex, texShape[1]);
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
+        vec4 values = ${n.texture2D}(A, uv);
+
+        float result;
+
+        if(offset == 0) {
+          result = values[0];
+        } else if(offset == 1) {
+          result = values[1];
+        } else if(offset == 2) {
+          result = values[2];
+        } else {
+          result = values[3];
+        }
+
+        ${n.output} = vec4(${s}, 0., 0., 0.);
+      }
+    `}},Xee=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=ss();this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
+          localCoords = coords;
+          if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
+          localCoords[2] += ${o};
+          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
+            localCoords[1] += ${a};
+
+            flatIndex = getFlatIndex(localCoords);
+            offset = imod(flatIndex, 4);
+
+            flatIndex = idiv(flatIndex, 4, 1.);
+
+            int r = flatIndex / texShape[1];
+            int c = imod(flatIndex, texShape[1]);
+            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
+            values = ${n.texture2D}(A, uv);
+
+            if (offset == 0) {
+              result[${i}] = values[0];
+            } else if (offset == 1) {
+              result[${i}] = values[1];
+            } else if (offset == 2) {
+              result[${i}] = values[2];
+            } else {
+              result[${i}] = values[3];
+            }
+          }
+        }
+        `}this.userCode=`
+        ${this.enableShapeUniforms?ux():lx(e)}
+
+        void main() {
+          ivec3 coords = getOutputCoords();
+
+          vec4 result = vec4(0.);
+          int flatIndex, r, c, offset;
+          ivec3 localCoords;
+          vec2 uv;
+          vec4 values;
+
+          ${s}
+
+          ${n.output} = ${r};
+        }
+    `}},aS={};Ve(aS,{bindVertexProgramAttributeStreams:()=>fS,createBufferFromOutputTexture:()=>yS,createFloat16MatrixTexture:()=>cS,createFloat16PackedMatrixTexture:()=>hS,createFloat32MatrixTexture:()=>uS,createIndexBuffer:()=>lS,createPackedMatrixTexture:()=>pS,createUnsignedBytesMatrixTexture:()=>dS,createVertexBuffer:()=>iS,createVertexShader:()=>oS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>xS,downloadFloat32MatrixFromBuffer:()=>AS,downloadMatrixFromPackedOutputTexture:()=>vS,downloadPackedMatrixFromBuffer:()=>bS,getInternalFormatForFloat16MatrixTexture:()=>px,getInternalFormatForFloat16PackedMatrixTexture:()=>mx,getInternalFormatForFloat32MatrixTexture:()=>dx,getInternalFormatForPackedMatrixTexture:()=>fx,getInternalFormatForUnsignedBytesMatrixTexture:()=>hx,uploadDenseMatrixToTexture:()=>mS,uploadPixelDataToTexture:()=>gS});function oS(e){let t=ss(),n=`${t.version}
+    precision highp float;
+    ${t.attribute} vec3 clipSpacePos;
+    ${t.attribute} vec2 uv;
+    ${t.varyingVs} vec2 resultUV;
+
+    void main() {
+      gl_Position = vec4(clipSpacePos, 1);
+      resultUV = uv;
+    }`;return DI(e,n)}function iS(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return OI(e,t)}function lS(e){let t=new Uint16Array([0,1,2,2,1,3]);return MI(e,t)}function Th(e,t,n,s,r,a){LI(t,n);let o=zI(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Y().getNumber("WEBGL_VERSION")===1?Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)):Ie(e,()=>e.texStorage2D(i,1,s,t,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function dx(e){return e.internalFormatFloat}function uS(e,t,n,s){let[r,a]=Ch(t,n);return Th(e,r,a,dx(s),s.textureFormatFloat,e.FLOAT)}function px(e){return e.internalFormatHalfFloat}function cS(e,t,n,s){let[r,a]=Ch(t,n);return Th(e,r,a,px(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function hx(e){return e.downloadTextureFormat}function dS(e,t,n,s){let[r,a]=Ch(t,n);return Th(e,r,a,hx(s),e.RGBA,e.UNSIGNED_BYTE)}function fx(e){return e.internalFormatPackedFloat}function pS(e,t,n,s){let[r,a]=Yc(t,n);return Th(e,r,a,fx(s),e.RGBA,e.FLOAT)}function mx(e){return e.internalFormatPackedHalfFloat}function hS(e,t,n,s){let[r,a]=Yc(t,n);return Th(e,r,a,mx(s),e.RGBA,s.textureTypeHalfFloat)}function fS(e,t,n){return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),L3(e,t,"clipSpacePos",n,3,20,0)&&L3(e,t,"uv",n,2,20,12)}function mS(e,t,n,s,r,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Y().getNumber("WEBGL_VERSION")===2?Ie(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,i,o)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function gS(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Y().getNumber("WEBGL_VERSION")===2?Ie(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Y().getNumber("WEBGL_VERSION")===2?Ie(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function yS(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function AS(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function xS(e,t,n,s){let[r,a]=Ch(t,n),o=4,i=new Uint8Array(GQ(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function bS(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(HQ(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function vS(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Gu=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,l2(t,e)):this.gl=Er(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=rp(this.gl,r),Hs(this.gl,a))this.textureHalfFloatExtension=rp(this.gl,a);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Hs(this.gl,s))this.colorBufferHalfFloatExtension=rp(this.gl,s);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Hs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Hs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=iS(this.gl),this.indexBuffer=lS(this.gl),this.framebuffer=BI(this.gl),this.textureConfig=ox(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),uS(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),cS(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),dS(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),gS(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),mS(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),hS(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),pS(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(B3(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>xS(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return bS(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return AS(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=yS(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>vS(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=oS(t));let n=FI(t);return Ie(t,()=>t.attachShader(n,this.vertexShader)),Ie(t,()=>t.attachShader(n,e)),PI(t,n),this.debug&&Kf(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=fS(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Kf(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?VI(this.gl,e,t):UI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),GI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=Yc(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Kf(this.gl,this.program),ap(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=rp(this.gl,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Kee(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Zf(this.gl,e,this.framebuffer),this.debug&&ap(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Zf(this.gl,this.outputTexture,this.framebuffer),this.debug&&ap(this.gl)):B3(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;Zf(s,e,this.framebuffer),this.debug&&ap(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Kee(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:Zee,bincountImpl:wS,bincountReduceImpl:Yee,ceilImpl:Jee,concatImpl:Qee,equalImpl:ete,expImpl:tte,expm1Impl:nte,floorImpl:ste,gatherNdImpl:rte,gatherV2Impl:ate,greaterImpl:ote,greaterEqualImpl:ite,lessImpl:lte,lessEqualImpl:ute,linSpaceImpl:cte,logImpl:dte,maxImpl:pte,maximumImpl:hte,minimumImpl:fte,multiplyImpl:mte,negImpl:gte,notEqualImpl:yte,prodImpl:Ate,rangeImpl:xte,rsqrtImpl:bte,scatterImpl:vte,sigmoidImpl:wte,simpleAbsImpl:kS,sliceImpl:kte,sparseFillEmptyRowsImpl:Ite,sparseReshapeImpl:Ste,sparseSegmentReductionImpl:IS,sqrtImpl:Cte,stridedSliceImpl:Tte,stringNGramsImpl:Nte,stringSplitImpl:Ete,stringToHashBucketFastImpl:Rte,subImpl:_te,tileImpl:Dte,topKImpl:$te,transposeImpl:gx,uniqueImpl:Fte}=X5;function SS(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Yn(e,t){return t===1?[e]:SS(e,t)}function Pte(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var Ote=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=ys(this.outputShape.length),this.rank===0)this.userCode=`
+        void main() {
+          setOutput(vec4(getA(), 0., 0., 0.));
+        }
+      `;else{let t=Yn("rc",this.rank),n=Ct(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
+        void main() {
+          ${n} rc = getOutputCoords();
+
+          if(${s}) {
+            setOutput(vec4(0));
+          } else {
+            ${r}
+
+            setOutput(vec4(${a}));
+          }
+        }
+      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
+      int r = ${t[0]};
+      int c = ${t[1]};
+      int rp1 = r + 1;
+      int cp1 = c + 1;
+
+      bool cEdge = cp1 >= ${n};
+      bool rEdge = rp1 >= ${s};
+    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
+            cEdge ? 0. : getA(${t[1]}),
+            rEdge ? 0. : getA(${t[2]}),
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},CS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
+        ${r}
+        ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
+          int flatIndex = getFlatIndex(thisRC);
+
+          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
+          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
+
+          result[${s}] =
+            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
+        ${s>0?"}":""}
+      `}this.userCode=`
+      ${Mte(t,this.enableShapeUniforms)}
+      ${this.enableShapeUniforms?ux():lx(e)}
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+
+        vec4 result = vec4(0.);
+
+        ivec3 thisRC;
+        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
+        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
+
+        ${n}
+
+        setOutput(result);
+      }
+    `}};function Mte(e,t){return`
+    ivec3 inputCoordsFromReshapedOutCoords(int index) {
+      ${t?see(["r","c","d"],"inputShape"):eu(["r","c","d"],e)}
+      return ivec3(r, c, d);
+    }
+  `}var zte=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=$v(t,n),r=Fv(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=Dv(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Sn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Sn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Sn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Sn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=$v(n,s),a=Fv(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=Dv(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Lte(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function Dv(e,t,n,s,r){let a=Bte(t,s),o;if(r){let[l,u]=Yc(e[0],e[1]);o=l*u}else{let[l,u]=Ch(e[0],e[1]);o=l*u}let i=Lte(n,a);return o*i}function Bte(e,t){switch(e){case Sn.PACKED_2X2_FLOAT32:return fx(t);case Sn.PACKED_2X2_FLOAT16:return mx(t);case Sn.UNPACKED_FLOAT32:return dx(t);case Sn.UNPACKED_FLOAT16:return px(t);case Sn.PACKED_4X1_UNSIGNED_BYTE:return hx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Wte(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Sn.PACKED_2X2_FLOAT32:Sn.UNPACKED_FLOAT32:e?Sn.PACKED_2X2_FLOAT16:Sn.UNPACKED_FLOAT16}function $v(e,t){if(e===Gs.UPLOAD)return Sn.PACKED_2X2_FLOAT32;if(e===Gs.RENDER||e==null)return Wte(t);if(e===Gs.DOWNLOAD||e===Gs.PIXELS)return Sn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function Fv(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ua=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length),this.userCode=`
+      float unaryOperation(float x) {
+        ${t}
+      }
+
+      void main() {
+        float x = getAAtOutCoords();
+        float y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `}},ur="if (isnan(x)) return x;",Vte="return x;",Pv="return abs(x);",Ute="return (x >= 0.0) ? x : (exp(x) - 1.0);",Gte=ur+`
+  return (x < 0.0) ? 0.0 : x;
+`,Hte=ur+`
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`,_u="return x;",jte="return 1.0 / (1.0 + exp(-1.0 * x));",qte="return x;",Xte=`
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`,Kte=`
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,Zte=`
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,Yte="return 1.0 / (1.0 + exp(-1.0 * x));",zi=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length),this.userCode=`
+      vec4 unaryOperation(vec4 x) {
+        ${t}
+      }
+
+      void main() {
+        vec4 x = getAAtOutCoords();
+        vec4 y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `}},Jte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length);let t=e.length,n=Yn("rc",t),s=Ct(t),r=Pte(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
+      void main() {
+        ${s} rc = getOutputCoords();
+        vec4 packedInput = getA(${r});
+
+        setOutput(getChannel(packedInput, ${o}));
+      }
+    `}},Qte=ir.whereImpl,ene=1e-7,tne=1e-4,Uf={};function nne(e){return e in Uf||(Uf[e]={}),Uf[e]}var sne=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),rne=600;function ane(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*rne/1024/1024}var sd=class extends ic{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof Gu)t=e;else{let n=Er(Y().getNumber("WEBGL_VERSION"),e);t=new Gu(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Er(Y().getNumber("WEBGL_VERSION"));t=new Gu(n),this.binaryCache=nne(Y().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new zte(this.gpgpu),this.numMBBeforeWarning=ane(),this.texData=new Ep(this,sn())}nextDataId(){return sd.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Gs.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Gs.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new zi(o,_u):p=new ua(o,_u);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=T.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new zi(s,_u):h=new ua(s,_u);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...Wf(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=T.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&sn().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:o,isPacked:i,texture:l}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new zi(r,_u):d=new ua(r,_u);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),c=sn().makeTensorFromTensorInfo(u),p=this.texData.get(u.dataId);return Object.assign({tensorRef:c},p.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>v.decodeString(s));return We(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!RI(n))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Wf(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Yf(t):t,i=a?new jee(o):new Hee(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=sne){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Qte(e.shape,t)}packedUnaryOp(e,t,n){let s=new zi(e.shape,t),r=this.compileAndRun(s,[e],n);return sn().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=kS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,Pv,e.dtype);let t=new ua(e.shape,Pv),n=this.compileAndRun(t,[e]);return sn().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return sn().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new Jte(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Ote(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[tl(e.shape),...nl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[tl(t),...nl(t)],a=new CS(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=Yf(r),i;s?i=new Gee(o):i=new Uee(o);let l=!0,u=[t!=null?t:Wf(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,u,l,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===kp.DENSE){let g=a!=null?a:Wf(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(o.shape)===0)return i.values=v.getTypedArrayFromDType(o.dtype,0),o;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Ip(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=Vee(e,u,c),d=this.getAndSaveBinary(p,()=>Bee(this.gpgpu,e,u,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),Y().get("ENGINE_COMPILE_ONLY")||Wee(this.gpgpu,d,u,c,s),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=Y().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ene:tne}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=qI(n,i),t.texShape=c),r!=null){let p=Yf(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=Yc(c[0],c[1])),i?d=new Xee(p,m):d=new qee(p,m);let g=m?[f,h]:c,y=this.makeTensorInfo(g,s),x=this.texData.get(y.dataId);m?x.usage=Gs.PIXELS:x.usage=Gs.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,w=this.runWebGLProgram(d,[y],s,A,b),k=this.texData.get(w.dataId);t.texShape=k.texShape,t.isPacked=k.isPacked,t.usage=k.usage,Y().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=k.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let p=this.acquireTexture(c,o,s,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=one(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await mA(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ix(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}=rS(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}};sd.nextDataId=0;function one(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var ine="3.18.0";function TS(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Zp.isBrowser()&&Hl("webgl",()=>new sd,2);var lne={forceHalfFloat:TS},NS=`
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`,oc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=ys(this.outputShape.length),this.userCode=`
+      float binaryOperation(float a, float b) {
+        ${e}
+      }
+
+      void main() {
+        float a = getAAtOutCoords();
+        float b = getBAtOutCoords();
+        setOutput(binaryOperation(a, b));
+      }
+    `}},c2=`
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`,Nh=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=ys(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
+          result.y = 0.;
+          result.z = 0.;
+          result.w = 0.;
+        `;else if(a=`
+          ${Ct(r)} coords = getOutputCoords();
+        `,r===1)this.enableShapeUniforms?a+=`
+            result.y = (coords + 1) >= outShape ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `:a+=`
+            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;else{let i=Yn("coords",r);this.enableShapeUniforms?a+=`
+            bool nextRowOutOfBounds =
+              (${i[r-2]} + 1) >= outShape[${r} - 2];
+            bool nextColOutOfBounds =
+              (${i[r-1]} + 1) >= outShape[${r} - 1];
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `:a+=`
+            bool nextRowOutOfBounds =
+              (${i[r-2]} + 1) >= ${this.outputShape[r-2]};
+            bool nextColOutOfBounds =
+              (${i[r-1]} + 1) >= ${this.outputShape[r-1]};
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `}this.userCode=`
+      vec4 binaryOperation(vec4 a, vec4 b) {
+        ${e}
+      }
+
+      void main() {
+        vec4 a = getAAtOutCoords();
+        vec4 b = getBAtOutCoords();
+
+        vec4 result = binaryOperation(a, b);
+        ${a}
+
+        setOutput(result);
+      }
+    `}};function Ds(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var une={kernelName:Co,backendName:"webgl",kernelFunc:Ds};function ai(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Ds({inputs:{x:s},backend:n}),l=Ds({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var cne={kernelName:_p,backendName:"webgl",kernelFunc:ai},ES="return (a < 0.) ? b * a : a;",RS=`
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`;function dne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(RS,r.shape,o.shape):new oc(ES,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var pne={kernelName:To,backendName:"webgl",kernelFunc:dne},_S="return (a < 0.) ? b * a : a;",DS=`
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`;function hne(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(DS,s.shape,r.shape):new oc(_S,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var fne={kernelName:Lo,backendName:"webgl",kernelFunc:hne},rd="if (isnan(x)) return x;",mne=`
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`,gne=`
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`;function ct({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,l);return i.makeTensorInfo(o.shape,l,d)}let u=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new zi(o.shape,t):c=new ua(o.shape,e),i.runWebGLProgram(c,[o],l)}}function Nn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,w]=A,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new oc(e,l.shape,u.shape);return c.runWebGLProgram(E,[k,S],Fn(b.dtype,w.dtype))}),x=ai({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),x}let p=a||Fn(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,y=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,p),b=c.makeTensorInfo(A,p),w=c.texData.get(b.dataId);return w.values=x,b}let d=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Nh(t,l.shape,u.shape,n):h=new oc(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],p)}}function d2(e,t=!1){if(e==="linear")return t?qte:Vte;if(e==="relu")return t?Kte:Gte;if(e==="elu")return t?Xte:Ute;if(e==="relu6")return t?Zte:Hte;if(e==="prelu")return t?DS:_S;if(e==="leakyrelu")return t?RS:ES;if(e==="sigmoid")return t?Yte:jte;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var $S=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=ys(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${o}
+        }`:l?m=`vec4 activation(vec4 a) {
+          vec4 b = getLeakyreluAlphaAtOutCoords();
+          ${o}
+        }`:m=`vec4 activation(vec4 x) {
+          ${o}
+        }`,g="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",A="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(A=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
+      ${m}
+      // Don't use uniform for sharedDimensionPacked for performance.
+      const float sharedDimension = ${c}.0;
+
+      vec4 dot2x2ARowBCol(ivec3 rc) {
+        vec4 result = vec4(0);
+        for (int i = 0; i < ${c}; i++) {
+          int batchA = ${x};
+          int batchB = ${A};
+          vec4 a = getMatrixA(batchA, ${p});
+          vec4 b = getMatrixB(batchB, ${d});
+
+          // These swizzled products need to be separately added.
+          // See: https://github.com/tensorflow/tfjs/issues/1735
+          result += (${h[0]} * ${f[0]});
+          result += (${h[1]} * ${f[1]});
+        }
+        return result;
+      }
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+        vec4 result = dot2x2ARowBCol(rc);
+
+        ${y}
+
+        ${g}
+
+        setOutput(result);
+      }
+    `}},Ov={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Mv=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.userCode=`
+      float binaryOpComplex(
+          float areal, float aimag, float breal, float bimag) {
+        ${e}
+      }
+
+      void main() {
+        float areal = getARealAtOutCoords();
+        float aimag = getAImagAtOutCoords();
+        float breal = getBRealAtOutCoords();
+        float bimag = getBImagAtOutCoords();
+        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
+      }
+    `}},zv="return a * b;";function yx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=T.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new Mv(Ov.REAL,s.shape,r.shape),c=new Mv(Ov.IMAG,s.shape,r.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=ai({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=mte(s.shape,r.shape,i.values,l.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=u,p}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Nh(zv,s.shape,r.shape):o=new oc(zv,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var yne={kernelName:Oo,backendName:"webgl",kernelFunc:yx};function Ane(e,t,n){let s=[tl(e.shape),...nl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[tl(t),...nl(t)],o=new CS(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),u=v.sizeFromShape(l);v.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Ip(r.shape,l)&&!(c.texture!==null&&Ip(c.shape,l))?Ane(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var xne={kernelName:El,backendName:"webgl",kernelFunc:ve},Lv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
+        if (inIdx < 0 || inIdx >= ${r}) {
+          return 0.0;
+        }
+      `),this.userCode=`
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${u}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${n};
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${o}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${l}
+        }
+
+        int inIdx = inOffset + ${o};
+        if (${i===1}) {
+          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
+
+          ${l}
+        } else if (${i===2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1), 0.0, 0.0);
+
+          ${l}
+        } else if (${i===3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2), 0.0);
+
+          ${l}
+        }
+        setOutput(sumValue);
+      }
+    `}},bne=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,p=`
+      if (${t==="sum"}) {
+        sumValue += dot(values, ones);
+      } else if (${t==="prod"}) {
+        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
+        prodValue *= tmp[0] * tmp[1];
+      } else {
+        minMaxValue = ${i}(values, minMaxValue);
+        if (${t==="min"} || ${t==="max"}) {
+          minMaxValue = ${i}(values, minMaxValue);
+          bvec4 isNaN = isnan(values);
+          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
+            minMaxValue = vec4(NAN);
+          }
+        }
+      }
+    `,d="vec4";t==="all"?(o="1.0",p=`
+        bool reducedAllValue = all(values);
+        float floatedReducedAllValue = float(reducedAllValue);
+        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
+      `,d="bvec4"):t==="any"&&(o="0.0",p=`
+        bool reducedAnyValue = any(values);
+        float floatedReducedAnyValue = float(reducedAnyValue);
+        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
+      `,d="bvec4");let h="";r%n>0&&(h=`
+        if (inIdx < 0 || inIdx >= ${r}) {
+          return initializationValue;
+        }
+      `),this.userCode=`
+      const float initializationValue = ${o};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${h}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${n};
+
+        vec4 minMaxValue = vec4(${o});
+        float prodValue = 1.0;
+        float sumValue = 0.0;
+        float allValue = 1.0;
+        float anyValue = 0.0;
+
+        for (int i = 0; i < ${u}; i += 4) {
+          int inIdx = inOffset + i;
+          ${d} values = ${d}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${p}
+        }
+
+        int inIdx = inOffset + ${u};
+        if (${c===1}) {
+          ${d} values = ${d}(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          ${p}
+        } else if (${c===2}) {
+          ${d} values = ${d}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          ${p}
+        } else if (${c===3}) {
+          ${d} values = ${d}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          ${p}
+        }
+        setOutput(${l});
+      }
+    `}};function vne(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function nu(e,t,n,s){let r=vne(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,p;n==="mean"?c=o===0?new Lv({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new Lv({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new bne({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var wne=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=Ct(this.rank),r=kne(t);this.userCode=`
+    void main() {
+      ${s} resRC = getOutputCoords();
+      setOutput(getA(${r}));
+    }
+    `}};function kne(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var Ine=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=Ct(this.rank),r=SS("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
+    void main() {
+      ${s} rc = getOutputCoords();
+      vec4 result = vec4(0.);
+      result[0] = ${l};
+      if(${i}) {
+        result[1] = ${l};
+      }
+      --${r[this.rank-1]};
+      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
+        result[2] = ${l};
+        if(${i}) {
+          result[3] = ${l};
+        }
+      }
+      setOutput(result);
+    }
+    `}};function p2(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ine(e.shape,t):new wne(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function Sne(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=T.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=p2(e,l,s),i=T.getInnerMostAxes(i.length,a)),T.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=T.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=T.expandShapeToKeepDim(p,o));let f=v.sizeFromShape(d),g=v.sizeFromShape(e.shape)/f,y=ve({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),x=Kp(e.dtype),A=nu(y,x,"sum",s),b=ve({inputs:{x:A},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(A),u&&s.disposeIntermediateTensorInfo(c),b}function h2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Sne(r,a,o,n)}var Cne={kernelName:Xo,backendName:"webgl",kernelFunc:h2};function an(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let p=o.texData.get(r.dataId).values,d=gx(p,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(u.dataId);h.values=d}else u=p2(r,a,o);return u}var Tne={kernelName:Vr,backendName:"webgl",kernelFunc:an},FS=1e3;function Rm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=jl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,p,h]:[y,h,p],k=s?[x,f,d]:[x,d,f],S=ve({inputs:{x:e},backend:r,attrs:{shape:w}}),E=ve({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[S,E],$=Math.max(y,x),_=n?S.shape[1]:S.shape[2],D=a!=null,C=o!=null,P=l==="leakyrelu",V=l!=null?d2(l,!0):null,j=D||C||P||V!=null,z;if((h===1||f===1)&&_>FS&&j===!1){let W=S,ee=E;n&&(W=an({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),R.push(W)),s&&(ee=an({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),R.push(ee));let Q=f!==1,ie=f===1,J=W;Q&&(J=ve({inputs:{x:W},backend:r,attrs:{shape:[$,_,1]}}),R.push(J));let ae=f===1?2:1,le=ee;ie&&(le=ve({inputs:{x:ee},backend:r,attrs:{shape:[$,1,_]}}),R.push(le));let ye=yx({inputs:{a:J,b:le},backend:r});z=h2({inputs:{x:ye},backend:r,attrs:{axis:ae,keepDims:!0}}),R.push(ye)}else{let W=Fn(e.dtype,t.dtype),ee=new $S(w,k,[$,h,f],n,s,D,V,C,P),Q=[S,E];if(a!=null&&Q.push(a),C&&Q.push(o),P){let ie=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));Q.push(ie),R.push(ie)}z=r.runWebGLProgram(ee,Q,W)}let Z=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let W of R)r.disposeIntermediateTensorInfo(W);return Z}function Nne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s;return Rm({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var Ene={kernelName:Xa,backendName:"webgl",kernelFunc:Nne},Bv="return abs(x);";function Rne(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=kS(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new zi(s.shape,Bv):r=new ua(s.shape,Bv),n.runWebGLProgram(r,[s],s.dtype)}var _ne={kernelName:ol,backendName:"webgl",kernelFunc:Rne},Dne=ur+`
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return acos(x);
+`,$ne=ct({opSnippet:Dne}),Fne={kernelName:uc,backendName:"webgl",kernelFunc:$ne},Pne=ur+`
+  if (x < 1.0) return NAN;
+return log(x + sqrt(x * x - 1.0));`,One=ct({opSnippet:Pne}),Mne={kernelName:cc,backendName:"webgl",kernelFunc:One},Wv="return a + b;",zne=Nn({opSnippet:Wv,packedOpSnippet:Wv,supportsComplex:!0,cpuKernelImpl:Zee}),Lne={kernelName:ya,backendName:"webgl",kernelFunc:zne},Bne=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+      void main() {
+        ${n.join(`
+        `)}
+
+        float result = ${s};
+        setOutput(result);
+      }
+    `}},Wne=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+      void main() {
+        ${n.join(`
+        `)}
+
+        vec4 result = ${s};
+        setOutput(result);
+      }
+    `}};function em(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Ds({inputs:{x:s[0]},backend:n});if(s.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=em({inputs:s.slice(0,l),backend:n}),c=em({inputs:s.slice(l),backend:n});return em({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Fn(l,u)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new Wne(s[0].shape,a):new Bne(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Vne={kernelName:oo,backendName:"webgl",kernelFunc:em};function Une(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=an({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("all",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=nu(m,m.dtype,"all",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Gne={kernelName:dc,backendName:"webgl",kernelFunc:Une};function Hne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=an({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("any",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=nu(m,m.dtype,"any",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var jne={kernelName:pc,backendName:"webgl",kernelFunc:Hne},qne=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${s};
+
+        int bestIndex = inOffset;
+        float bestValue = getA(batch, bestIndex);
+
+        for (int i = 0; i < ${s}; i++) {
+          int inIdx = ${i};
+          float candidate = getA(batch, inIdx);
+          if (candidate ${o} bestValue) {
+            bestValue = candidate;
+            bestIndex = inIdx;
+          }
+        }
+        setOutput(float(bestIndex));
+      }
+    `}},Xne=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=Ct(i),u=Yn("coords",i),c,p;if(a===1){p=i+1;let S=Ct(p);c=`
+        ${S} sourceLocR = ${S}(${u.join()}, 0);
+        ++${u[i-1]};
+        ${S} sourceLocG = ${S}(${u.join()}, 0);
+        ++${u[i-2]};
+        ${S} sourceLocA = ${S}(${u.join()}, 0);
+        --${u[i-1]};
+        ${S} sourceLocB = ${S}(${u.join()}, 0);
+        --${u[i-2]};`}else p=i,c=`
+        ${l} sourceLocR = coords;
+        ++${u[i-1]};
+        ${l} sourceLocG = coords;
+        ++${u[i-2]};
+        ${l} sourceLocA = coords;
+        --${u[i-1]};
+        ${l} sourceLocB = coords;
+        --${u[i-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map(S=>"int "+S),m=Yn("sourceLocR",p-1).concat("inIdx.r"),g=Yn("sourceLocG",p-1).concat("inIdx.g"),y=Yn("sourceLocB",p-1).concat("inIdx.b"),x=Yn("sourceLocA",p-1).concat("inIdx.a"),A=n==="max"?"greaterThan":"lessThan",b=s?"":`
+          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
+                             getBestIndicesAChannel(${g.join()}),
+                             getBestIndicesAChannel(${y.join()}),
+                             getBestIndicesAChannel(${x.join()})));`,w=`vec4(
+            getAChannel(${m.join()}),
+            hasNextCol ? getAChannel(${g.join()}) : 0.,
+            hasNextRow ? getAChannel(${y.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,k=s?"":`
+      float getBestIndicesAChannel(${f.join()}) {
+        return getChannel(getBestIndicesA(${d.join()}),
+                                          vec2(${d.slice(-2).join()}));
+      }`;this.userCode=`
+      float getAChannel(${f.join()}) {
+        return getChannel(getA(${d.join()}),
+                               vec2(${d.slice(-2).join()}));
+      }
+      ${k}
+      void main() {
+        ${l} coords = getOutputCoords();
+        bool hasNextCol = ${u[i-1]} < ${o[i-1]-1};
+        bool hasNextRow = ${u[i-2]} < ${o[i-2]-1};
+        ${c}
+        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
+          sourceLocB${h}, sourceLocA${h}) * ${t};
+        ivec4 inIdx = srcIdx;
+        vec4 bestIndex = vec4(inIdx);
+        vec4 bestValue = ${w};
+
+        for (int i = 0; i < ${t}; i++) {
+          inIdx = srcIdx;
+          ${b}
+          vec4 candidate = ${w};
+          bvec4 nan = isnan(candidate);
+          bvec4 replace = bvec4(
+            vec4(${A}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+
+          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
+                           replace.y  ? candidate.y : bestValue.y,
+                           replace.z  ? candidate.z : bestValue.z,
+                           replace.w  ? candidate.w : bestValue.w);
+          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
+          srcIdx++;
+        }
+        setOutput(bestIndex);
+      }
+    `}};function PS(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=T.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new qne(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=PS(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function OS(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=T.computeOptimalWindowSize(a),i=new Xne(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=OS(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function MS(e,t,n,s){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=T.computeOutAndReduceShapes(l.shape,r),p=v.sizeFromShape(c),d=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=PS(e,d,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return OS(e,t,s)}function Kne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=an({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=MS(n,l,o[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var Zne={kernelName:io,backendName:"webgl",kernelFunc:Kne};function Yne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=an({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=MS(n,l,o[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var Jne={kernelName:hc,backendName:"webgl",kernelFunc:Yne},Qne=ur+`
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return asin(x);
+`,ese=ct({opSnippet:Qne}),tse={kernelName:fc,backendName:"webgl",kernelFunc:ese},nse=ur+"return log(x + sqrt(x * x + 1.0));",sse=ct({opSnippet:nse}),rse={kernelName:mc,backendName:"webgl",kernelFunc:sse},ase=ur+`
+  return atan(x);
+`,ose=ct({opSnippet:ase}),ise={kernelName:gc,backendName:"webgl",kernelFunc:ose},lse=mne+`
+  return atan(a, b);
+`,use=`
+  vec4 result = atan(a, b);
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+gne+`
+  return result;
+`,cse=Nn({opSnippet:lse,packedOpSnippet:use}),dse={kernelName:Ac,backendName:"webgl",kernelFunc:cse},pse=ur+`
+  if ((x < -1.0) || (x > 1.0)) return NAN;
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,hse=ct({opSnippet:pse}),fse={kernelName:yc,backendName:"webgl",kernelFunc:hse},Sp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
+        const ivec2 strides = ivec2(${o}, ${i});
+        const ivec2 pads = ivec2(${d}, ${h});
+
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int batch = coords[0];
+          int d = coords[3];
+
+          ivec2 xRCCorner = coords.yz * strides - pads;
+          int xRCorner = xRCCorner.x;
+          int xCCorner = xRCCorner.y;
+
+          // max/min x(?, ?, d) to get y(yR, yC, d).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+          float avgValue = 0.0;
+
+          for (int wR = 0; wR < ${c};
+              wR += ${l}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${p};
+                wC += ${u}) {
+              int xC = xCCorner + wC;
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              float value = getX(batch, xR, xC, d);
+
+              // If a min / max value has already been found, use it. If not,
+              // use the current value.
+              float currMinMaxValue = mix(
+                  value, minMaxValue, minMaxValueFound);
+              if (value ${S} currMinMaxValue) {
+                minMaxValue = value;
+                minMaxValueFound = 1.0;
+                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;return}let x="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,k=`
+      if (${f}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${x}(values, minMaxValue);
+      }
+    `;this.userCode=`
+      const ivec2 strides = ivec2(${o}, ${i});
+      const ivec2 pads = ivec2(${d}, ${h});
+      const float initializationValue = ${y};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xR, int xC, int d) {
+        if (xC < 0 || xC >= ${e.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xR, xC, d);
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d = coords[3];
+
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // max/min x(?, ?, d) to get y(yR, yC, d).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${y});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wR = 0; wR < ${c};
+            wR += ${l}) {
+          int xR = xRCorner + wR;
+
+          if (xR < 0 || xR >= ${e.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${b}; wC += 4) {
+            int xC = xCCorner + wC * ${u};
+
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + 2 * ${u}, d),
+              getValue(batch, xR, xC + 3 * ${u}, d)
+            );
+
+            ${k}
+          }
+
+          int xC = xCCorner + ${b};
+          if (${w===1}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              initializationValue,
+              initializationValue,
+              initializationValue
+            );
+
+            ${k}
+          } else if (${w===2}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${u}, d),
+              initializationValue,
+              initializationValue
+            );
+
+            ${k}
+          } else if (${w===3}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${u}, d),
+              getValue(batch, xR, xC + 2 * ${u}, d),
+              initializationValue
+            );
+
+            ${k}
+          }
+        }
+        setOutput(${A});
+      }
+    `}},Ax=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
+        const ivec3 strides =
+            ivec3(${o}, ${i}, ${l});
+        const ivec3 pads = ivec3(${m}, ${g}, ${y});
+
+        void main() {
+          ivec5 coords = getOutputCoords();
+          int batch = coords.x;
+          int ch = coords.u;
+
+          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+          int xDCorner = xCorner.x;
+          int xRCorner = xCorner.y;
+          int xCCorner = xCorner.z;
+
+          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+
+          for (int wD = 0; wD < ${d};
+              wD += ${u}) {
+            int xD = xDCorner + wD;
+
+            if (xD < 0 || xD >= ${e.inDepth}) {
+              continue;
+            }
+
+            for (int wR = 0; wR < ${h};
+                wR += ${c}) {
+              int xR = xRCorner + wR;
+
+              if (xR < 0 || xR >= ${e.inHeight}) {
+                continue;
+              }
+
+              for (int wC = 0; wC < ${f};
+                  wC += ${p}) {
+                int xC = xCCorner + wC;
+
+                if (xC < 0 || xC >= ${e.inWidth}) {
+                  continue;
+                }
+
+                float value = getX(batch, xD, xR, xC, ch);
+
+                // If a min / max value has already been found, use it. If not,
+                // use the current value.
+                float currMinMaxValue = mix(
+                    value, minMaxValue, minMaxValueFound);
+                if (value ${R} currMinMaxValue) {
+                  minMaxValue = value;
+                  minMaxValueFound = 1.0;
+                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
+                      wR * ${f} + wC`};
+                }
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let k=Math.floor(a/4)*4,S=a%4,E=`
+      if (${x}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${b}(values, minMaxValue);
+      }
+    `;this.userCode=`
+      const ivec3 strides =
+        ivec3(${o}, ${i}, ${l});
+      const ivec3 pads = ivec3(${m}, ${g}, ${y});
+      const float initializationValue = ${A};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xD, int xR, int xC, int ch) {
+        if (xC < 0 || xC >= ${e.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xD, xR, xC, ch);
+      }
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xDCorner = xCorner.x;
+        int xRCorner = xCorner.y;
+        int xCCorner = xCorner.z;
+
+        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${A});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wD = 0; wD < ${d};
+            wD += ${u}) {
+          int xD = xDCorner + wD;
+
+          if (xD < 0 || xD >= ${e.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${h};
+            wR += ${c}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${k}; wC += 4) {
+              int xC = xCCorner + wC * ${p};
+
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${p}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${p}, ch)
+              );
+
+              ${E}
+            }
+
+            int xC = xCCorner + ${k};
+            if (${S===1}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                initializationValue,
+                initializationValue,
+                initializationValue
+              );
+
+              ${E}
+            } else if (${S===2}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${p}, ch),
+                initializationValue,
+                initializationValue
+              );
+
+              ${E}
+            } else if (${S===3}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${p}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${p}, ch),
+                initializationValue
+              );
+
+              ${E}
+            }
+          }
+          setOutput(${w});
+        }
+      }
+    `}};function mse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Jc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;v.assert(T.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Ds({inputs:{x:r},backend:n});let p=new Sp(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var gse={kernelName:lo,backendName:"webgl",kernelFunc:mse};function yse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],p=T.computePool3DInfo(r.shape,a,o,c,i,l,u),d=new Ax(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var Ase={kernelName:Rp,backendName:"webgl",kernelFunc:yse},xse=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
+      const ivec2 pads = ivec2(${u}, ${c});
+      const float avgMultiplier = float(${p});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${i};
+            wR += ${a}) {
+          float dyR = float(dyRCorner + wR) / ${s}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${l};
+            wC+= ${o}) {
+            float dyC = float(dyCCorner + wC) / ${r}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+
+            dotProd += dyValue * avgMultiplier;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},bse=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
+      const ivec3 pads = ivec3(${h}, ${f}, ${m});
+      const float avgMultiplier = float(${g});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${c};
+            wD += ${i}) {
+          float dyD = float(dyDCorner + wD) / ${r}.0;
+
+          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${p};
+              wR += ${l}) {
+            float dyR = float(dyRCorner + wR) / ${a}.0;
+
+            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${d};
+                wC += ${u}) {
+              float dyC = float(dyCCorner + wC) / ${o}.0;
+
+              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+
+              dotProd += dyValue * avgMultiplier;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};function vse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,l,p,u,c),h=new bse(d);return n.runWebGLProgram(h,[r],o.dtype)}var wse={kernelName:Mm,backendName:"webgl",kernelFunc:vse};function kse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Jc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=T.computePool2DInfo(o.shape,i,l,1,u),p=new xse(c);return n.runWebGLProgram(p,[r],o.dtype)}var Ise={kernelName:Om,backendName:"webgl",kernelFunc:kse};function Sse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Rm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Cse={kernelName:uo,backendName:"webgl",kernelFunc:Sse},Tse=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+      void main() {
+        float x = getXAtOutCoords();
+        float mean = getMeanAtOutCoords();
+        float variance = getVarianceAtOutCoords();
+        float offset = ${o};
+        float scale = ${i};
+        float inv = scale * inversesqrt(variance + float(${a}));
+        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
+      }
+    `}},Nse=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+      void main() {
+        vec4 offset = ${o};
+        vec4 scale = ${i};
+
+        vec4 x = getXAtOutCoords();
+        vec4 mean = getMeanAtOutCoords();
+        vec4 variance = getVarianceAtOutCoords();
+
+        vec4 inv = scale * inversesqrt(variance + vec4(${a}));
+
+        setOutput((x - mean) * inv + offset);
+      }
+    `}},Ese=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let p=null;i!=null&&(p=i.shape,u.push(i));let d=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Nse(s.shape,r.shape,a.shape,c,p,l):new Tse(s.shape,r.shape,a.shape,c,p,l);return t.runWebGLProgram(d,u,u[0].dtype)},Rse={kernelName:Io,backendName:"webgl",kernelFunc:Ese},_se=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Ct(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Dse(this.rank),s,r=e.map((a,o)=>`sourceLoc.${U3[o]} = start[${o}] + coords.${U3[o]};`);s=`
+        ${t} sourceLoc;
+        ${t} coords = getOutputCoords();
+        ${r.join(`
+`)}
+      `,this.userCode=`
+      void main() {
+        ${s}
+        setOutput(getSource(${n}));
+      }
+    `}},U3=["x","y","z","w","u","v"];function Dse(e){if(e===1)return"sourceLoc";if(e<=6)return U3.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var $se=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=Ct(this.rank),n=Yn("coords",this.rank),s=Yn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
+      result.x = ${a};
+      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
+        ++${s[this.rank-1]};
+        result.y = ${a};
+        --${s[this.rank-1]};
+      }
+    `,i=this.rank===1?"":`
+      --${n[this.rank-1]};
+      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
+        ++${s[this.rank-2]};
+        result.z = ${a};
+        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
+          ++${s[this.rank-1]};
+          result.w = ${a};
+        }
+      }
+    `,l=this.rank<=4?`sourceLoc = coords +
+            ${t}(${e.map((u,c)=>`start[${c}]`).join()});`:e.map((u,c)=>`${s[c]} = ${n[c]} + start[${c}];`).join(`
+`);this.userCode=`
+      void main() {
+        ${t} coords = getOutputCoords();
+        ${t} sourceLoc;
+        ${l}
+        vec4 result = vec4(0.);
+        ${o}
+        ${i}
+        setOutput(result);
+      }
+    `}};function Fse(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Gt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function ad(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Gt.parseSliceParams(r,a,o);if(Gt.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=kte(p.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=n.texData.get(r.dataId),c=Gt.isSliceContinous(r.shape,i,l);if(u||!c){let p=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $se(l):new _se(l),d=[i];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),Fse(r,i,l,n)}var Pse={kernelName:Fl,backendName:"webgl",kernelFunc:ad},Ose=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=an({inputs:{x:f},backend:n,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:c}}),y=ad({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),y},Mse={kernelName:il,backendName:"webgl",kernelFunc:Ose};function zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),u=wS(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var Lse={kernelName:zm,backendName:"webgl",kernelFunc:zse};function Bse(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Wse={kernelName:Lm,backendName:"webgl",kernelFunc:Bse},Vse="return float(a != b);",zS=Nn({opSnippet:Vse,cpuKernelImpl:yte,dtype:"bool"}),Use={kernelName:kl,backendName:"webgl",kernelFunc:zS};function Eh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Ds({inputs:{x:r.complexTensorInfos.real},backend:n})}var Gse={kernelName:Bp,backendName:"webgl",kernelFunc:Eh},Hse="return float(int(x));";function jse(e,t){let n=new ua(e.shape,Hse),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function G3(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Ds({inputs:{x:r},backend:n});let o=Vt(r.shape),i=G3({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ai({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Eh({inputs:{input:r},backend:n}),i=G3({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Ds({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return jse(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=zS({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var qse={kernelName:co,backendName:"webgl",kernelFunc:G3},Vv="return ceil(x);",Xse=ct({opSnippet:Vv,packedOpSnippet:Vv,cpuKernelImpl:Jee}),Kse={kernelName:po,backendName:"webgl",kernelFunc:Xse},Zse=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+
+      void main() {
+        float value = getAAtOutCoords();
+        if (isnan(value)) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, minVal, maxVal));
+      }
+    `}},Yse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+      void main() {
+        vec4 value = getAAtOutCoords();
+
+        if (any(isnan(value))) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
+      }
+    `}};function Jse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Y().getBool("WEBGL_PACK_CLIP")?i=new Yse(r.shape):i=new Zse(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var Qse={kernelName:Aa,backendName:"webgl",kernelFunc:Jse},ere=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+      void main() {
+        float re = abs(getRealAtOutCoords());
+        float im = abs(getImagAtOutCoords());
+        float mx = max(re, im);
+
+        // sadly the length function in glsl is not underflow-safe
+        // (at least not on Intel GPUs). So the safe solution is
+        // to ensure underflow-safety in all cases.
+        setOutput(
+          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
+        );
+      }
+    `}};function Uv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function tre(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new ere(s.shape),o=[Uv(s,r.complexTensorInfos.real),Uv(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var nre={kernelName:Dp,backendName:"webgl",kernelFunc:tre},sre=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int yR = coords.x;
+        int yC = coords.y;
+
+        ${n.join(`
+        `)}
+      }
+    `}},rre=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=T.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=Ct(s),a=Yn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),p=`if (${l} < ${i[0]}) {
+        return getChannel(
+            getT0(${c}), vec2(${u.join()}));
+        }`;for(let f=1;f<i.length;f++){let m=i[f-1];p+=`
+        if (${l} < ${i[f]}  && ${l} >= ${i[f-1]}) {
+          return getChannel(
+            getT${f}(${Gf(o,l,m)}),
+            vec2(${Gf(u,l,m)}));
+        }`}let d=i.length,h=i[i.length-1];p+=`
+        return getChannel(
+          getT${d}(${Gf(o,l,h)}),
+          vec2(${Gf(u,l,h)}));`,this.userCode=`
+      float getValue(${o.map(f=>"int "+f)}) {
+        ${p}
+      }
+
+      void main() {
+        ${r} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${a}), 0., 0., 0.);
+
+        ${a[s-1]} = ${a[s-1]} + 1;
+        if (${a[s-1]} < ${n[s-1]}) {
+          result.g = getValue(${a});
+        }
+
+        ${a[s-2]} = ${a[s-2]} + 1;
+        if (${a[s-2]} < ${n[s-2]}) {
+          result.a = getValue(${a});
+        }
+
+        ${a[s-1]} = ${a[s-1]} - 1;
+        if (${a[s-2]} < ${n[s-2]} &&
+            ${a[s-1]} < ${n[s-1]}) {
+          result.b = getValue(${a});
+        }
+        setOutput(result);
+      }
+    `}};function Gf(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function f2(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Ds({inputs:{x:r.complexTensorInfos.imag},backend:n})}var are={kernelName:Op,backendName:"webgl",kernelFunc:f2};function Mu(e,t,n){let s=e[0].dtype;if(s==="complex64"){let c=e.map(m=>Eh({inputs:{input:m},backend:n})),p=e.map(m=>f2({inputs:{input:m},backend:n})),d=Mu(c,t,n),h=Mu(p,t,n),f=ai({inputs:{real:d,imag:h},backend:n});return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),p.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let c=e.map(y=>{let x=v.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:n,attrs:{shape:[-1,x]}})}),p=c.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),d=T.computeOutShape(c.map(y=>y.shape),1),h=c[0].shape[0]===1,f=Qee(p,d,s,h),m=T.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let c=Math.floor(e.length/2),p=Mu(e.slice(0,c),t,n),d=Mu(e.slice(c),t,n),h=Mu([p,d],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let c=new rre(e.map(p=>p.shape),t);return n.runWebGLProgram(c,e,s)}let{tensors2D:a,outShape:o}=ore(e,t,n),i=new sre(a.map(c=>c.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(c=>n.disposeIntermediateTensorInfo(c));let u=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),u}function ore(e,t,n){let s=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function LS(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=T.computeOutShape(t.map(u=>u.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>v.sizeFromShape(u.shape)>0);if(i.length===1)return Ds({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,a),Mu(i,a,n)}var ire={kernelName:ll,backendName:"webgl",kernelFunc:LS},BS=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";n&&(s?A=`float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${n}
+        }`:r?A=`float activation(float a) {
+          float b = getLeakyreluAlphaAtOutCoords();
+          ${n}
+        }`:A=`
+          float activation(float x) {
+            ${n}
+          }
+        `,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${A}
+
+      const ivec2 strides = ivec2(${i}, ${l});
+      const ivec2 pads = ivec2(${a}, ${o});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d2 = coords[${x}];
+
+        ivec2 xRCCorner =
+            ivec2(coords[${g}], coords[${y}]) * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${p}; wR++) {
+          int xR = xRCorner + wR * ${u};
+
+          if (xR < 0 || xR >= ${e.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${d}; wC++) {
+            int xC = xCCorner + wC * ${c};
+
+            if (xC < 0 || xC >= ${e.inWidth}) {
+              continue;
+            }
+
+            for (int d1 = 0; d1 < ${h}; d1 += 4) {
+              vec4 wValues = vec4(
+                getW(wR, wC, d1, d2),
+                getW(wR, wC, d1 + 1, d2),
+                getW(wR, wC, d1 + 2, d2),
+                getW(wR, wC, d1 + 3, d2)
+              );
+
+              if (${m}) {
+                vec4 xValues = vec4(
+                  getX(batch, xR, xC, d1),
+                  getX(batch, xR, xC, d1 + 1),
+                  getX(batch, xR, xC, d1 + 2),
+                  getX(batch, xR, xC, d1 + 3)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec4 xValues = vec4(
+                  getX(batch, d1, xR, xC),
+                  getX(batch, d1 + 1, xR, xC),
+                  getX(batch, d1 + 2, xR, xC),
+                  getX(batch, d1 + 3, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+
+            if (${f===1}) {
+
+              if (${m}) {
+                dotProd +=
+                    getX(batch, xR, xC, ${h}) *
+                    getW(wR, wC, ${h}, d2);
+              } else {
+                dotProd +=
+                    getX(batch, ${h}, xR, xC) *
+                    getW(wR, wC, ${h}, d2);
+              }
+
+            } else if (${f===2}) {
+              vec2 wValues = vec2(
+                getW(wR, wC, ${h}, d2),
+                getW(wR, wC, ${h} + 1, d2)
+              );
+
+              if (${m}) {
+                vec2 xValues = vec2(
+                  getX(batch, xR, xC, ${h}),
+                  getX(batch, xR, xC, ${h} + 1)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec2 xValues = vec2(
+                  getX(batch, ${h}, xR, xC),
+                  getX(batch, ${h} + 1, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            } else if (${f===3}) {
+              vec3 wValues = vec3(
+                getW(wR, wC, ${h}, d2),
+                getW(wR, wC, ${h} + 1, d2),
+                getW(wR, wC, ${h} + 2, d2)
+              );
+
+              if (${m}) {
+                vec3 xValues = vec3(
+                  getX(batch, xR, xC, ${h}),
+                  getX(batch, xR, xC, ${h} + 1),
+                  getX(batch, xR, xC, ${h} + 2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec3 xValues = vec3(
+                  getX(batch, ${h}, xR, xC),
+                  getX(batch, ${h} + 1, xR, xC),
+                  getX(batch, ${h} + 2, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            }
+          }
+        }
+
+        float result = dotProd;
+        ${w}
+        ${b}
+        setOutput(result);
+      }
+    `}},lre=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+      const ivec3 strides = ivec3(${r}, ${a}, ${o});
+      const ivec3 pads = ivec3(${t}, ${n}, ${s});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d2 = coords.u;
+
+        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xFCorner = xFRCCorner.x;
+        int xRCorner = xFRCCorner.y;
+        int xCCorner = xFRCCorner.z;
+
+        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
+        // y(yF, yR, yC, d2). ? = to be determined. : = across all
+        // values in that axis.
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${c}; wF++) {
+          int xF = xFCorner + wF * ${i};
+
+          if (xF < 0 || xF >= ${e.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${p}; wR++) {
+            int xR = xRCorner + wR * ${l};
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${d}; wC++) {
+              int xC = xCCorner + wC * ${u};
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              for (int d1 = 0; d1 < ${h}; d1 += 4) {
+                vec4 xValues = vec4(
+                  getX(batch, xF, xR, xC, d1),
+                  getX(batch, xF, xR, xC, d1 + 1),
+                  getX(batch, xF, xR, xC, d1 + 2),
+                  getX(batch, xF, xR, xC, d1 + 3)
+                );
+                vec4 wValues = vec4(
+                  getW(wF, wR, wC, d1, d2),
+                  getW(wF, wR, wC, d1 + 1, d2),
+                  getW(wF, wR, wC, d1 + 2, d2),
+                  getW(wF, wR, wC, d1 + 3, d2)
+                );
+
+                dotProd += dot(xValues, wValues);
+              }
+
+              if (${f===1}) {
+                dotProd +=
+                  getX(batch, xF, xR, xC, ${h}) *
+                  getW(wF, wR, wC, ${h}, d2);
+              } else if (${f===2}) {
+                vec2 xValues = vec2(
+                  getX(batch, xF, xR, xC, ${h}),
+                  getX(batch, xF, xR, xC, ${h} + 1)
+                );
+                vec2 wValues = vec2(
+                  getW(wF, wR, wC, ${h}, d2),
+                  getW(wF, wR, wC, ${h} + 1, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else if (${f===3}) {
+                vec3 xValues = vec3(
+                  getX(batch, xF, xR, xC, ${h}),
+                  getX(batch, xF, xR, xC, ${h} + 1),
+                  getX(batch, xF, xR, xC, ${h} + 2)
+                );
+                vec3 wValues = vec3(
+                  getW(wF, wR, wC, ${h}, d2),
+                  getW(wF, wR, wC, ${h} + 1, d2),
+                  getW(wF, wR, wC, ${h} + 2, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},ure=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=ys(this.outputShape.length);let{dataFormat:n}=t,s=ss(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
+          blockIndex = rc.y + ${c};
+          pos = rc.x + ${u};
+
+          ${i}
+            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
+            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
+
+            if(d0 < inputShape[${a}] && d0 >= 0) {
+              // Use custom imod instead mod. On Intel GPU, mod may generate
+              // unexpected value.
+              // https://github.com/tensorflow/tfjs/issues/5447
+              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
+              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
+                  inChannels);
+
+              if(d1 < inputShape[${o}] && d1 >= 0) {
+
+                ch = imod(pos, inChannels);
+
+                if (${r}) {
+                  innerDims = vec2(d1, ch);
+                  result[${u*2+c}] = getChannel(
+                    getA(d0, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                } else {
+                  innerDims = vec2(d0, d1);
+                  result[${u*2+c}] = getChannel(
+                    getA(ch, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                }
+              }
+            }
+          }
+        `;this.userCode=`
+      void main() {
+        ivec2 rc = getOutputCoords();
+
+        vec4 result = vec4(0);
+
+        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
+        vec2 innerDims;
+
+        ${l}
+
+        ${s.output} = result;
+      }
+    `}};function WS({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(a!=null&&!h&&a.shape.length===3){let b=an({inputs:{x:a},backend:s,attrs:{perm:[1,2,0]}});y.push(b),a=b}if(!((p===1||d===1)&&c>FS)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Ip(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let S=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let E=Rm({a:w,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(E.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,R.shape=n.outShape,g=Ds({inputs:{x:E},backend:s}),g.shape=n.outShape,y.push(E)}else{let b=h?e:an({inputs:{x:e},backend:s,attrs:{perm:[0,2,3,1]}}),w=b.shape,k=w[0]*w[1]*w[2],S=ve({inputs:{x:b},backend:s,attrs:{shape:[1,k,n.inChannels]}}),E=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),R=Rm({a:S,b:E,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),$=[n.batchSize,n.outHeight,n.outWidth,n.outChannels],_=ve({inputs:{x:R},backend:s,attrs:{shape:$}});g=h?_:an({inputs:{x:_},backend:s,attrs:{perm:[0,3,1,2]}}),h||(y.push(b),y.push(_)),y.push(S),y.push(E),y.push(R)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function VS({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=d*p,y=[m,g],x=!0,A=!1,b=[];if(a!=null&&!f&&a.shape.length===3){let Q=an({inputs:{x:a},backend:s,attrs:{perm:[1,2,0]}});b.push(Q),a=Q}let w=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let S=new ure(y,n),E=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(S,[w],"float32",E),$=ve({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push($);let _=r!=null,D=a!=null,C=i==="leakyrelu",P=i?d2(i,!0):null,V=new $S($.shape,k.shape,[1,g,n.outChannels],x,A,_,P,D,C),j=[$,k];if(r&&j.push(r),D&&j.push(a),C){let Q=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));j.push(Q),b.push(Q)}let z=s.runWebGLProgram(V,j,"float32"),Z=[1,d,p,n.outChannels],W=ve({inputs:{x:z},backend:s,attrs:{shape:Z}}),ee=f?W:an({inputs:{x:W},backend:s,attrs:{perm:[0,3,1,2]}});f||b.push(W),b.push(z);for(let Q of b)s.disposeIntermediateTensorInfo(Q);return ee}function cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=WS({x:r,filter:a,convInfo:d,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=VS({x:r,filter:a,convInfo:d,backend:n});else{let m=new BS(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var dre={kernelName:ho,backendName:"webgl",kernelFunc:cre},pre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int d2 = coords.w;
+
+        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${e.batchSize}; b++) {
+          for (int yR = 0; yR < ${e.outHeight}; yR++) {
+            int xR = wR + yR * ${t} - ${s};
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${e.outWidth}; yC++) {
+              int xC = wC + yC * ${n} - ${r};
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              if (${a}) {
+                float dyValue = getDy(b, yR, yC, d2);
+                float xValue = getX(b, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              } else {
+                float dyValue = getDy(b, d2, yR, yC);
+                float xValue = getX(b, d1, xR, xC);
+                dotProd += (xValue * dyValue);
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},hre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
+      const ivec2 pads = ivec2(${o}, ${i});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[${c}];
+
+        ivec2 dyCorner = ivec2(coords[${l}], coords[${u}]) - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${t}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${s}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${t} - 1 - wR;
+
+          for (int wC = 0; wC < ${n}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${r}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${n} - 1 - wC;
+
+            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
+
+              if (${a}) {
+                float xValue = getDy(batch, idyR, idyC, d2);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              } else {
+                float xValue = getDy(batch, d2, idyR, idyC);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},fre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int wF = coords.x;
+        int wR = coords.y;
+        int wC = coords.z;
+        int d1 = coords.w;
+        int d2 = coords.u;
+
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${e.batchSize}; b++) {
+          for (int yF = 0; yF < ${e.outDepth}; yF++) {
+            int xF = wF + yF * ${t} - ${r};
+
+            if (xF < 0 || xF >= ${e.inDepth}) {
+              continue;
+            }
+
+            for (int yR = 0; yR < ${e.outHeight}; yR++) {
+              int xR = wR + yR * ${n} - ${a};
+
+              if (xR < 0 || xR >= ${e.inHeight}) {
+                continue;
+              }
+
+              for (int yC = 0; yC < ${e.outWidth}; yC++) {
+                int xC = wC + yC * ${s} - ${o};
+
+                if (xC < 0 || xC >= ${e.inWidth}) {
+                  continue;
+                }
+
+                float dyValue = getDy(b, yF, yR, yC, d2);
+                float xValue = getX(b, xF, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},mre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
+      const ivec3 pads = ivec3(${i}, ${l}, ${u});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.u;
+
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyFCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${t}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${r}.0;
+
+          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
+            continue;
+          }
+          int idyF = int(dyF);
+
+          int wFPerm = ${t} - 1 - wF;
+
+          for (int wR = 0; wR < ${n}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${a}.0;
+
+            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
+              fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            int wRPerm = ${n} - 1 - wR;
+
+            for (int wC = 0; wC < ${s}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${o}.0;
+
+              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              int wCPerm = ${s} - 1 - wC;
+
+              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
+                float xValue = getDy(batch, idyF, idyR, idyC, d2);
+                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};function gre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,c,o,1,i,u,!1,p),h=new pre(d);return n.runWebGLProgram(h,[r,a],"float32")}var yre={kernelName:Bm,backendName:"webgl",kernelFunc:gre};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=new hre(d);return n.runWebGLProgram(h,[r,a],"float32")}var xre={kernelName:fo,backendName:"webgl",kernelFunc:Are};function bre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=T.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new lre(u);return n.runWebGLProgram(c,[r,a],"float32")}var vre={kernelName:$p,backendName:"webgl",kernelFunc:bre};function wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=T.computeConv3DInfo(r.shape,l,o,1,i),c=new fre(u);return n.runWebGLProgram(c,[r,a],"float32")}var kre={kernelName:Wm,backendName:"webgl",kernelFunc:wre};function Ire(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=T.computeConv3DInfo(l,a.shape,i,1,o),c=new mre(u);return n.runWebGLProgram(c,[r,a],"float32")}var Sre={kernelName:Vm,backendName:"webgl",kernelFunc:Ire},Cre=rd+`
+  return cos(x);
+`,Tre=ct({opSnippet:Cre}),Nre={kernelName:mo,backendName:"webgl",kernelFunc:Tre},Ere=`
+  float e2x = exp(-x);
+  return (e2x + 1.0 / e2x) / 2.0;
+`,Rre=ct({opSnippet:Ere}),_re={kernelName:go,backendName:"webgl",kernelFunc:Rre},Dre=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,p]=n;this.outputShape=[u,c,p,l];let d=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+      const float height_ratio = float(${m});
+      const float width_ratio = float(${x});
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int y = coords[1];
+        int x = coords[2];
+        int d = coords[3];
+
+        // get box vals
+        float y1 = getBoxes(b,0);
+        float x1 = getBoxes(b,1);
+        float y2 = getBoxes(b,2);
+        float x2 = getBoxes(b,3);
+
+        // get image in batch index
+        int bInd = round(getBoxInd(b));
+        if(bInd < 0 || bInd >= ${a}) {
+          return;
+        }
+
+        float height_scale = ${g};
+        float width_scale = ${A};
+
+        float in_y = ${y};
+        if( in_y < 0.0 || in_y > ${h} ) {
+          setOutput(float(${r}));
+          return;
+        }
+        float in_x = ${b};
+        if( in_x < 0.0 || in_x > ${f} ) {
+          setOutput(float(${r}));
+          return;
+        }
+
+        vec2 sourceFracIndexCR = vec2(in_x,in_y);
+        if(${d} == 1) {
+          // Compute the four integer indices.
+          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
+          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
+
+          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
+          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
+          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
+          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
+
+          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
+
+          float top = topLeft + (topRight - topLeft) * fracCR.x;
+          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          float newValue = top + (bottom - top) * fracCR.y;
+          setOutput(newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          ivec2 sourceNearestCR = ivec2(floor(
+            sourceFracIndexCR + vec2(0.5,0.5)));
+          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutput(newValue);
+        }
+      }
+    `}},$re=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new Dre(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},Fre={kernelName:cl,backendName:"webgl",kernelFunc:$re},Cp;(function(e){e.Prod="*",e.Sum="+"})(Cp||(Cp={}));var Gv=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,a=this.op===Cp.Prod?"1.0":"0.0",o=n?a:`getX(${Hv(r,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=s?`end != ${i-1}`:"end != 0",u=s?"end + 1":"end - 1"):(l=s?`end + pow2 < ${i}`:"end >= pow2",u=s?"end + pow2":"end - pow2"),this.userCode=`
+      void main() {
+        ${Ct(r)} coords = getOutputCoords();
+        int end = ${jv(r,"coords",this.op)};
+        float val = ${o};
+        int pow2 = int(pow(2.0, index));
+        if (${l}) {
+          int idx = ${u};
+          ${jv(r,"coords",this.op)} = idx;
+          val ${this.op}= getX(${Hv(r,"coords",this.op)});
+        }
+        setOutput(val);
+      }
+    `}};function Hv(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function jv(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function US(e,t,n,s,r,a){let o=t.shape.length,i=T.getAxesPermutation([s],o),l=t;i!=null&&(l=an({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=T.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=l.shape[u],p=Ds({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new Gv(e,l.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new Gv(e,l.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=an({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(l),h}return p}function Pre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return US(Cp.Prod,r,n,a,o,i)}var Ore={kernelName:ul,backendName:"webgl",kernelFunc:Pre};function Mre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return US(Cp.Sum,r,n,a,o,i)}var zre={kernelName:yo,backendName:"webgl",kernelFunc:Mre};function Lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=wS(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=Yee(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Bre={kernelName:Um,backendName:"webgl",kernelFunc:Lre},Wre=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    void main() {
+      ivec4 coords = getOutputCoords();
+      int b = coords[0];
+      int h = ${this.getHeightCoordString()};
+      int w = ${this.getWidthCoordString()};
+      int d = ${this.getDepthCoordString()};
+
+      int in_h = h / ${t};
+      int offset_h = imod(h, ${t});
+      int in_w = w / ${t};
+      int offset_w = imod(w, ${t});
+      int offset_d = (offset_h * ${t} + offset_w) *
+        ${this.getOutputDepthSize()};
+      int in_d = d + offset_d;
+
+      float result = ${this.getInputSamplingString()};
+      setOutput(result);
+    }
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Vre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new Wre(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Ure={kernelName:dl,backendName:"webgl",kernelFunc:Vre},GS=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ys(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${n}
+        }`:r?l=`float activation(float a) {
+          float b = getLeakyreluAlphaAtOutCoords();
+          ${n}
+        }`:l=`
+          float activation(float x) {
+            ${n}
+          }
+        `,u="result = activation(result);");let c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${l}
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${i};
+        int q = d2 - d1 * ${i};
+
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
+        for (int wR = 0; wR < ${a}; wR++) {
+          int xR = xRCorner + wR * dilations[0];
+
+          if (xR < 0 || xR >= inDims[0]) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${o}; wC++) {
+            int xC = xCCorner + wC * dilations[1];
+
+            if (xC < 0 || xC >= inDims[1]) {
+              continue;
+            }
+
+            float xVal = getX(batch, xR, xC, d1);
+            float wVal = getW(wR, wC, d1, q);
+            dotProd += xVal * wVal;
+          }
+        }
+
+        float result = dotProd;
+        ${c}
+        ${u}
+        setOutput(result);
+      }
+    `}},HS=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ys(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,p=c,d=`
+      int xR; int xC; int xCOffset;
+      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
+          vec4 xTexelC${g*2};
+          int xTexelC${g*2}Ready;
+          vec4 xTexelC${g*2+1};
+          int xTexelC${g*2+1}Ready;
+          vec4 xC${g};`;d+=`
+    for (int r = 0; r < ${u}; r++) {
+      `;for(let g=0;g<c;g++)d+=`
+          xTexelC${g*2} = vec4(0.0);
+          xTexelC${g*2}Ready = 0;
+          xTexelC${g*2+1} = vec4(0.0);
+          xTexelC${g*2+1}Ready = 0;
+          xC${g} = vec4(0.0);`;d+=`
+        xR = xRCorner + r * dilations[0];
+        if (xR >=0 && xR < inDims[0]) {
+      `;for(let g=0;g<(p+1)/2;g++){let y=g*2;if(d+=`
+          xC = xCCorner + ${y*l};
+          `,i===1){if(y<c&&(o%2===1?(d+=`
+                xCOffset = xC + 1;
+                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
+                  xTexelC${y} = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${y}.zw = vec2(0.0);
+                  }
+                  xTexelC${y}Ready = 1;
+                }
+              `,l===1&&y>0?d+=`
+                xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
+                `:d+=`
+                  xCOffset = xC + 1 - 2;
+
+                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
+                    previous = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= inDims[1]) {
+                      previous.zw = vec2(0.0);
+                    }
+
+                    xC${y} = vec4(previous.zw, xTexelC${y}.xy);
+                  } else {
+                    xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
+                  }
+                  `):d+=`
+                if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
+                  xTexelC${y} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= inDims[1]) {
+                    xTexelC${y}.zw = vec2(0.0);
+                  }
+                  xTexelC${y}Ready = 1;
+                }
+
+                xC${y} = xTexelC${y};
+                `,y+1<c)){let x=o%2===0?v.nearestLargerEven(l):l;l%2===0&&o%2===1||l%2!==0&&o%2!==1?(d+=`
+                  xCOffset = xC + imod(pads[1], 2) + ${x};
+
+                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
+                    xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= inDims[1]) {
+                      xTexelC${y+1}.zw = vec2(0.0);
+                    }
+                    xTexelC${y+1}Ready = 1;
+                  }
+                  `,l>1&&(d+=`
+                    xCOffset -= 2;
+                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
+                      xTexelC${y} = getX(batch, xR, xCOffset, d1);
+                      xTexelC${y}Ready = 1;
+                    }
+                    `),d+=`
+                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
+                  `):x===1?d+=`
+                    xC${y+1} = xTexelC${y};
+                    `:d+=`
+                    xCOffset = xC + ${x};
+
+                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
+                      xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
+                      if (xCOffset + 1 >= inDims[1]) {
+                        xTexelC${y+1}.zw = vec2(0.0);
+                      }
+                      xTexelC${y+1}Ready = 1;
+                    }
+
+                    xC${y+1} = xTexelC${y+1};
+                    `}}else y<c&&(o%2===1?(d+=`
+                xCOffset = xC + 1 - strides[1];
+                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
+                  xTexelC${y} = getX(batch, xR, xCOffset, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${y}.zw = vec2(0.0);
+                  }
+                  xTexelC${y}Ready = 1;
+                }
+
+                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
+                  xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xC + 2 >= inDims[1]) {
+                    xTexelC${y+1}.zw = vec2(0.0);
+                  }
+                  xTexelC${y+1}Ready = 1;
+                }
+
+                xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
+              `,y+1<c&&(d+=`
+                  final = vec4(0.0);
+                  xCOffset = xC + 1 + strides[1];
+                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
+                    final = getX(batch, xR, xCOffset, d1);
+                  }
+                  xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
+                `)):(d+=`
+                if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
+                  xTexelC${y} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= inDims[1]) {
+                    xTexelC${y}.zw = vec2(0.0);
+                  }
+                  xTexelC${y}Ready = 1;
+                }
+
+                xCOffset = xC + strides[1];
+                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
+                  xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${y+1}.zw = vec2(0.);
+                  }
+                  xTexelC${y+1}Ready = 1;
+                }
+
+                xC${y} = vec4(
+                  xTexelC${y}.xy, xTexelC${y+1}.xy);
+              `,y+1<c&&(d+=`
+                  xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
+                `)));y<c&&(d+=`
+            wTexel = getW(r, ${y}, d1, q);
+            dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
+          `,y+1<c&&(d+=`
+              wTexel = getW(r, ${y+1}, d1, q);
+              dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
+            `))}d+=`
+    }
+  `,d+=`
+      }
+    `;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${n}
+        }`:r?h=`vec4 activation(vec4 a) {
+          vec4 b = getLeakyreluAlphaAtOutCoords();
+          ${n}
+        }`:h=`vec4 activation(vec4 x) {
+          ${n}
+        }`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
+      ${h}
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${a};
+        int q = d2 - d1 * ${a};
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
+        vec4 dotProd = vec4(0.000000000000001);
+
+        ${d}
+
+        vec4 result = dotProd - vec4(0.000000000000001);
+        ${m}
+        ${f}
+        setOutput(result);
+      }
+    `}};function Gre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=T.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),d;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new HS(p):d=new GS(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var Hre={kernelName:Ao,backendName:"webgl",kernelFunc:Gre},jre=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int dm = coords.w;
+        int d2 = d1 * ${a} + dm;
+
+        float dotProd = 0.0;
+
+        // TO DO: Vec4 over the batch size
+        for (int b = 0; b < ${e.batchSize}; b++) {
+          for (int yR = 0; yR < ${e.outHeight}; yR++) {
+            int xR = wR + yR * ${t} - ${s};
+
+            if (xR < 0 || xR >= ${e.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${e.outWidth}; yC++) {
+              int xC = wC + yC * ${n} - ${r};
+
+              if (xC < 0 || xC >= ${e.inWidth}) {
+                continue;
+              }
+
+              float dyValue = getDy(b, yR, yC, d2);
+              float xValue = getX(b, xR, xC, d1);
+              dotProd += (xValue * dyValue);
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},qre=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
+      const ivec2 pads = ivec2(${a}, ${o});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[3];
+        ivec2 dyCorner = coords.yz - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        float dotProd = 0.0;
+
+        for (int wR = 0; wR < ${t}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${s}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${t} - 1 - wR;
+
+          for (int wC = 0; wC < ${n}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${r}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${n} - 1 - wC;
+
+            // TO DO: Vec4 over the channelMul
+            for (int dm = 0; dm < ${i}; dm++) {
+              int d2 = d1 * ${i} + dm;
+              float xValue = getDy(batch, idyR, idyC, d2);
+              float wValue = getW(wRPerm, wCPerm, d1, dm);
+              dotProd += xValue * wValue;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};function Xre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,p=T.computeConv2DInfo(r.shape,c,o,i,l,u,!0),d=new jre(p);return n.runWebGLProgram(d,[r,a],"float32")}var Kre={kernelName:Gm,backendName:"webgl",kernelFunc:Xre};function Zre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,p=T.computeConv2DInfo(c,a.shape,o,i,l,u,!0),d=new qre(p);return n.runWebGLProgram(d,[r,a],"float32")}var Yre={kernelName:Hm,backendName:"webgl",kernelFunc:Zre},Jre=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+      void main() {
+          ivec2 coords = getOutputCoords();
+          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
+          setOutput(val);
+      }
+    `}};function Qre(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Jre(a),l=n.runWebGLProgram(i,[o],o.dtype),u=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var eae={kernelName:jm,backendName:"webgl",kernelFunc:Qre},tae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:p}=s;this.userCode=`
+      const ivec2 strides = ivec2(${r}, ${a});
+      const ivec2 pads = ivec2(${c}, ${p});
+      const float neg_infinity = -3.4e38;
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.w;
+        ivec2 outTopLeftCorner =
+            coords.yz * strides - pads;
+        int hBeg = outTopLeftCorner.x;
+        int wBeg = outTopLeftCorner.y;
+
+        float curVal = neg_infinity;
+        for (int h = 0; h < ${o}; h++) {
+          int hIn = hBeg + h * ${l};
+
+          if (hIn >= 0 && hIn < ${t}) {
+            for (int w = 0; w < ${i}; w++) {
+              int wIn = wBeg + w * ${u};
+
+              if (wIn >= 0 && wIn < ${n}) {
+                float xVal = getX(batch, hIn, wIn, d1);
+                float wVal = getW(h, w, d1);
+
+                float val = xVal + wVal;
+                if (val > curVal) {
+                  curVal = val;
+                }
+              }
+            }
+          }
+        }
+
+        float result = curVal;
+        setOutput(result);
+      }
+    `}};function nae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=T.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,p=new tae(u);c=n.runWebGLProgram(p,[r,a],"float32");let d=ve({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var sae={kernelName:Fp,backendName:"webgl",kernelFunc:nae};function rae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=T.decodeEinsumEquation(r,a.length);T.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=T.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=a[g]:(A=an({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=ve({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=yx({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=h2({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var aae={kernelName:Pp,backendName:"webgl",kernelFunc:rae},oae="return (x >= 0.0) ? x : (exp(x) - 1.0);",iae=`
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`,lae=ct({opSnippet:oae,packedOpSnippet:iae}),uae={kernelName:bo,backendName:"webgl",kernelFunc:lae},cae="return (b >= 1.0) ? a : a * (b + 1.0);",dae=`
+  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
+  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
+`,pae=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(dae,s.shape,r.shape):new oc(cae,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},hae={kernelName:qm,backendName:"webgl",kernelFunc:pae},fae=`
+  return vec4(equal(a, b));
+`,mae="return float(a == b);",gae=Nn({opSnippet:mae,packedOpSnippet:fae,dtype:"bool",cpuKernelImpl:ete}),yae={kernelName:pl,backendName:"webgl",kernelFunc:gae},Aae=`
+  // Error function is calculated approximately with elementary function.
+  // See "Handbook of Mathematical Functions with Formulas,
+  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
+  float p = ${T.ERF_P};
+  float a1 = ${T.ERF_A1};
+  float a2 = ${T.ERF_A2};
+  float a3 = ${T.ERF_A3};
+  float a4 = ${T.ERF_A4};
+  float a5 = ${T.ERF_A5};
+
+  float sign = sign(x);
+  x = abs(x);
+  float t = 1.0 / (1.0 + p * x);
+  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
+`,xae=ct({opSnippet:Aae}),bae={kernelName:xc,backendName:"webgl",kernelFunc:xae},vae=rd+`
+  return exp(x);
+`,wae=`
+  vec4 result = exp(x);
+  bvec4 isNaN = isnan(x);
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,jS=ct({opSnippet:vae,packedOpSnippet:wae,cpuKernelImpl:tte,dtype:"float32"}),kae={kernelName:vo,backendName:"webgl",kernelFunc:jS};function H3(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var Iae={kernelName:hl,backendName:"webgl",kernelFunc:H3},qv="return exp(x) - 1.0;",Sae=ct({opSnippet:qv,packedOpSnippet:qv,cpuKernelImpl:nte}),Cae={kernelName:fl,backendName:"webgl",kernelFunc:Sae},Xv=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+      const float exponentMultiplier = ${r};
+
+      float unaryOpComplex(float real, float expR, float imag, float expI) {
+        ${o}
+      }
+
+      float mulMatDFT(int batch, int index) {
+        float indexRatio = float(index) / float(${s});
+        float exponentMultiplierTimesIndexRatio =
+            exponentMultiplier * indexRatio;
+
+        float result = 0.0;
+
+        for (int i = 0; i < ${s}; i++) {
+          // x = (-2|2 * PI / N) * index * i;
+          float x = exponentMultiplierTimesIndexRatio * float(i);
+          float expR = cos(x);
+          float expI = sin(x);
+          float real = getReal(batch, i);
+          float imag = getImag(batch, i);
+
+          result +=
+              unaryOpComplex(real, expR, imag, expI) / ${a};
+        }
+
+        return result;
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        setOutput(mulMatDFT(coords[0], coords[1]));
+      }
+    `}};function qS(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new Xv("real",l,t),c=new Xv("imag",l,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=ai({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function Tae(e){let{inputs:t,backend:n}=e,{input:s}=t;return qS(s,!1,n)}var Nae={kernelName:Xm,backendName:"webgl",kernelFunc:Tae},Eae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+      void main() {
+        // Input can be obtained from uniform value.
+        setOutput(value);
+      }
+    `}};function Rh(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new Eae(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Rae={kernelName:bc,backendName:"webgl",kernelFunc:Rh},_ae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+
+          int coordX = ${t} - x - 1;
+          float outputValue;
+          if(coordX >= 0 && coordX < ${t}) {
+            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
+          } else {
+            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `}},Dae={kernelName:ml,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new _ae(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Kv="return floor(x);",$ae=ct({opSnippet:Kv,packedOpSnippet:Kv,cpuKernelImpl:ste}),Fae={kernelName:wo,backendName:"webgl",kernelFunc:$ae},Pae=`
+  float s = sign(a) * sign(b);
+  int ia = round(a);
+  int ib = round(b);
+  if (ib != 0) {
+    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+    return float(idiv(ia, ib, s));
+  } else {
+    return NAN;
+  }
+`,Oae=`
+  ivec4 ia = round(a);
+  ivec4 ib = round(b);
+  bvec4 cond = notEqual(ib, ivec4(0));
+  ivec4 result = ivec4(0);
+  vec4 s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    result[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    result[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    result[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    result[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4(result);
+`,Mae=Nn({opSnippet:Pae,packedOpSnippet:Oae,dtype:"int32"}),zae={kernelName:ko,backendName:"webgl",kernelFunc:Mae},Lae=class{constructor(e){this.variableNames=["A"];let t=ss(),[n,s]=e;this.outputShape=e,this.userCode=`
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
+
+        vec4 values = ${t.texture2D}(A, uv);
+        float value;
+        if (depth == 0) {
+          value = values.r;
+        } else if (depth == 1) {
+          value = values.g;
+        } else if (depth == 2) {
+          value = values.b;
+        } else if (depth == 3) {
+          value = values.a;
+        }
+
+        setOutput(floor(value * 255.0 + 0.5));
+      }
+    `}},Bae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=ss(),[n,s]=e;this.outputShape=e,this.userCode=`
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+
+        vec4 result = vec4(0.);
+
+        for(int row=0; row<=1; row++) {
+          for(int col=0; col<=1; col++) {
+            texC = coords[1] + row;
+            depth = coords[2] + col;
+
+            vec2 uv = (vec2(texC, texR) + halfCR) /
+                       vec2(${s}.0, ${n}.0);
+            vec4 values = ${t.texture2D}(A, uv);
+            float value;
+            if (depth == 0) {
+              value = values.r;
+            } else if (depth == 1) {
+              value = values.g;
+            } else if (depth == 2) {
+              value = values.b;
+            } else if (depth == 3) {
+              value = values.a;
+            }
+
+            result[row * 2 + col] = floor(value * 255.0 + 0.5);
+          }
+        }
+
+        ${t.output} = result;
+      }
+    `}},Wae={kernelName:pp,backendName:"webgl",kernelFunc:Vae},Du;function Vae(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],p=[u,l,a];(i||o)&&(Du==null&&(Du=document.createElement("canvas").getContext("2d")),Du.canvas.width=l,Du.canvas.height=u,Du.drawImage(r,0,0,l,u),r=Du.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Gs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=Y().getBool("WEBGL_PACK")?new Bae(p):new Lae(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function Uae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!1,m),y,x=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=WS({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=VS({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",S=h?d2(h,!1):null,E=new BS(g,b,S,w,k),R=[r,a],$=(_,D)=>{if(D==="NCHW"&&_.shape.length===1&&_.shape[0]!==1){let C=ve({inputs:{x:_},backend:n,attrs:{shape:[_.shape[0],1,1]}});return x.push(C),C}return _};if(b&&R.push($(o,c)),w&&R.push($(i,c)),k){let _=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(_),x.push(_)}y=n.runWebGLProgram(E,R,"float32")}let A=ve({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var Gae={kernelName:Ka,backendName:"webgl",kernelFunc:Uae};function Hae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,a.shape,l,m,u,p,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?d2(d,y):null,A=[r,a],b=o!=null,w=i!=null,k=d==="leakyrelu";if(b&&A.push(o),w&&A.push(i),k){let $=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push($),f.push($)}let S;y?S=new HS(g,b,x,w,k):S=new GS(g,b,x,w,k);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(S,A,"float32",E);return f.forEach($=>n.disposeIntermediateTensorInfo($)),R}var jae={kernelName:Za,backendName:"webgl",kernelFunc:Hae},qae=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=Ct(t.length),r=Ct(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+        ${s} strides = ${s}(${this.strides});
+         void main() {
+          ${r} coords = getOutputCoords();
+          int flattenIndex = 0;
+          for (int j = 0; j < ${this.sliceDim}; j++) {
+            int index = round(getIndices(coords[0], j));
+            flattenIndex += index * ${a};
+          }
+          setOutput(getX(flattenIndex, coords[1]));
+        }
+      `}};function Xae(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,u,c,p]=T.prepareAndValidate(s,r),d=ve({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),x=n.bufferSync(s),A=rte(y,x,s.dtype,u,o,c,p,s.shape,i);return n.makeTensorInfo(l,s.dtype,A.values)}let f=new qae(o,p,[u,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Kae={kernelName:yl,backendName:"webgl",kernelFunc:Xae},Zae=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=Ct(this.rank),s=Yae(e,2);this.userCode=`
+      void main() {
+        ${n} resRC = getOutputCoords();
+        int index = int(getIndices(resRC.x, resRC.z));
+        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
+        setOutput(inBounds * getA(${s}));
+      }
+    `}};function Yae(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function XS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0];if(Y().get("DEBUG")){let x=n.readSync(a.dataId),A=r.shape[l];for(let b=0;b<x.length;++b){let w=x[b];v.assert(w<=A-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${A-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=v.sizeFromShape(a.shape),p=[],d=ve({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=ve({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let x=n.bufferSync(h),A=n.bufferSync(d),b=ate(A,x,f);return p.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Zae(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let y=ve({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeIntermediateTensorInfo(x)),y}var Jae={kernelName:gl,backendName:"webgl",kernelFunc:XS},Qae="return float(a > b);",eoe=`
+  return vec4(greaterThan(a, b));
+`,toe=Nn({opSnippet:Qae,packedOpSnippet:eoe,cpuKernelImpl:ote,dtype:"bool"}),noe={kernelName:Al,backendName:"webgl",kernelFunc:toe},soe="return float(a >= b);",roe=`
+  return vec4(greaterThanEqual(a, b));
+`,aoe=Nn({opSnippet:soe,packedOpSnippet:roe,dtype:"bool",cpuKernelImpl:ite}),ooe={kernelName:So,backendName:"webgl",kernelFunc:aoe};function ioe(e){let{inputs:t,backend:n}=e,{input:s}=t;return qS(s,!0,n)}var loe={kernelName:Km,backendName:"webgl",kernelFunc:ioe},uoe="return float(!isnan(x) && !isinf(x));",coe=ct({opSnippet:uoe,dtype:"bool"}),doe={kernelName:vc,backendName:"webgl",kernelFunc:coe},poe="return float(isinf(x));",hoe=ct({opSnippet:poe,dtype:"bool"}),foe={kernelName:wc,backendName:"webgl",kernelFunc:hoe},moe="return float(isnan(x));",goe=ct({opSnippet:moe,dtype:"bool"}),yoe={kernelName:kc,backendName:"webgl",kernelFunc:goe},Aoe="return float(a < b);",xoe=`
+  return vec4(lessThan(a, b));
+`,boe=Nn({opSnippet:Aoe,packedOpSnippet:xoe,cpuKernelImpl:lte,dtype:"bool"}),voe={kernelName:xl,backendName:"webgl",kernelFunc:boe},woe="return float(a <= b);",koe=`
+  return vec4(lessThanEqual(a, b));
+`,Ioe=Nn({opSnippet:woe,packedOpSnippet:koe,cpuKernelImpl:ute,dtype:"bool"}),Soe={kernelName:bl,backendName:"webgl",kernelFunc:Ioe};function Coe(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=cte(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var Toe={kernelName:Zm,backendName:"webgl",kernelFunc:Coe},Noe=rd+`
+  return x < 0.0 ? 0./0. : log(x);
+`,Eoe=`
+  vec4 result = log(x);
+  bvec4 isNaN = isnan(x);
+  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
+  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
+  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
+  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
+  return result;
+`,Roe=ct({opSnippet:Noe,packedOpSnippet:Eoe,cpuKernelImpl:dte}),_oe={kernelName:No,backendName:"webgl",kernelFunc:Roe},Doe=rd+`
+  return log(1.0 + x);
+`,$oe=ct({opSnippet:Doe}),Foe={kernelName:Ic,backendName:"webgl",kernelFunc:$oe},Poe="return float(a >= 1.0 && b >= 1.0);",Ooe=`
+  return vec4(
+    vec4(greaterThanEqual(a, vec4(1.0))) *
+    vec4(greaterThanEqual(b, vec4(1.0))));
+`,Moe=Nn({opSnippet:Poe,packedOpSnippet:Ooe,dtype:"bool"}),zoe={kernelName:vl,backendName:"webgl",kernelFunc:Moe},Loe="return float(!(x >= 1.0));",Boe=ct({opSnippet:Loe}),Woe={kernelName:Sc,backendName:"webgl",kernelFunc:Boe},Voe="return float(a >= 1.0 || b >= 1.0);",Uoe=`
+  return min(
+    vec4(greaterThanEqual(a, vec4(1.0))) +
+    vec4(greaterThanEqual(b, vec4(1.0))),
+    vec4(1.0));
+`,Goe=Nn({opSnippet:Voe,packedOpSnippet:Uoe,dtype:"bool"}),Hoe={kernelName:Mp,backendName:"webgl",kernelFunc:Goe},joe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+        int d = coords[3];
+        float x = getX(b, r, c, d);
+        float sum = 0.0;
+        for (int j = -${a}; j <= ${a}; j++) {
+          int idx = d + j;
+          if (idx >= 0 && idx <=  ${o}) {
+            float z = getX(b, r, c, idx);
+            sum += z * z;
+          }
+        }
+        float val = x * ${i};
+        setOutput(val);
+      }
+    `}},qoe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords.x;
+        int r = coords.y;
+        int c = coords.z;
+        int d = coords.w;
+
+        bool hasNextCol = d < ${this.outputShape[3]};
+        bool hasNextRow = c < ${this.outputShape[2]};
+
+        vec4 sum = vec4(0.);
+        vec4 xFragAtOutputCoords = getX(b, r, c, d);
+
+        vec4 xAtOutputCoords = vec4(
+          getChannel(xFragAtOutputCoords, vec2(c, d)),
+          hasNextCol ?
+            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
+          hasNextRow ?
+            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
+        );
+
+        int firstChannel = d - ${a};
+        vec2 cache = vec2(0.);
+        if(firstChannel >= 0){
+          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
+          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
+            if(hasNextRow){
+              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
+            }
+        }
+
+        ivec2 depth = ivec2(d, d + 1);
+        for (int j = - ${a}; j <= ${a}; j++) {
+          ivec2 idx = depth + j;
+          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
+
+          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
+          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
+
+          if(depthInRange || depthPlusOneInRange){
+            vec4 z = vec4(0.);
+            vec4 xFragAtCurrentDepth;
+            z.xz = cache.xy;
+            if(depthPlusOneInRange && hasNextCol){
+              xFragAtCurrentDepth = idx.y != d ?
+                getX(b, r, c, idx.y) : xFragAtOutputCoords;
+              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
+              if(hasNextRow){
+                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
+              }
+            }
+            cache.xy = z.yw;
+            sum += z * z;
+          }
+        }
+        vec4 result = xAtOutputCoords * ${i};
+        setOutput(result);
+      }
+    `}},Xoe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=Y().getBool("WEBGL_PACK_NORMALIZATION")?new qoe(r.shape,a,o,i,l):new joe(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},Koe={kernelName:zp,backendName:"webgl",kernelFunc:Xoe},Zoe=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+
+        float result = 0.0;
+        for (int d = 0; d < ${this.depth}; ++d) {
+          int depthBegin = int(max(0.0, float(d - ${t})));
+          int depthEnd = int(min(float(${this.depth}),
+              float(d + ${t} + 1)));
+
+          const int MIN_DEPTH_BEGIN = 0;
+          const int MAX_DEPTH_END = ${this.depth};
+
+          float norm = 0.0;
+          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd) {
+              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
+            }
+            else {
+              break;
+            }
+          }
+
+          norm = float(${s}) * norm + float(${n});
+
+          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd){
+              float dyi = -2.0 * float(${s})
+                * float(${r})
+                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
+                / norm;
+              if (k == d) {
+                dyi += pow(norm, -1.0 * ${r});
+              }
+              if (k == coords[3]) {
+                dyi *= getDy(b, r, c, d);
+                result += dyi;
+              }
+            }
+            else {
+              break;
+            }
+          }
+      }
+      setOutput(result);
+      }
+    `}},Yoe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,p=new Zoe(r.shape,i,l,u,c);return n.runWebGLProgram(p,[r,a,o],r.dtype)},Joe={kernelName:Ym,backendName:"webgl",kernelFunc:Yoe};function Qoe(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=nu(i,e.dtype,"max",s),u=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function KS(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let A=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[c[S]];let w=gx(A,r.shape,r.dtype,c,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=p2(r,c,n);u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("max",u,i);let[f,m]=T.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=T.expandShapeToKeepDim(f,l));let y;if(d){let A=n.texData.get(h.dataId).values,b=pte(A,v.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=b}else y=Qoe(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var eie={kernelName:Eo,backendName:"webgl",kernelFunc:KS},tie=NS+`
+  return max(a, b);
+`,nie=`
+  vec4 result = vec4(max(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+c2+`
+  return result;
+`,sie=Nn({opSnippet:tie,packedOpSnippet:nie,cpuKernelImpl:hte}),rie={kernelName:Ro,backendName:"webgl",kernelFunc:sie};function aie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;Jc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;v.assert(T.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Ds({inputs:{x:r},backend:n});let p=new Sp(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var oie={kernelName:_o,backendName:"webgl",kernelFunc:aie};function iie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],p=T.computePool3DInfo(r.shape,a,o,c,i,u,l),d=new Ax(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var lie={kernelName:Lp,backendName:"webgl",kernelFunc:iie},uie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
+      const ivec2 pads = ivec2(${o}, ${i});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${r};
+          wR += ${s}) {
+          float dyR = float(dyRCorner + wR) / ${t}.0;
+
+          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${a}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${n}.0;
+
+            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+            int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
+
+            // Get the current value, check it against the value from the
+            // position matrix.
+            int curPosValue = wR * ${a} + wC;
+            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+            dotProd += dyValue * mask;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}},cie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
+      const ivec3 pads = ivec3(${c}, ${p}, ${d});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${i};
+           wD += ${r}) {
+          float dyD = float(dyDCorner + wD) / ${t}.0;
+
+          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${l};
+              wR += ${a}) {
+            float dyR = float(dyRCorner + wR) / ${n}.0;
+
+            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${u};
+                wC += ${o}) {
+              float dyC = float(dyCCorner + wC) / ${s}.0;
+
+              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+              int maxPosValue = ${h} -
+                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
+
+              // Get the current value, check it against the value from the
+              // position matrix.
+              int curPosValue =
+                  wD * ${l} * ${u} +
+                  wR * ${u} + wC;
+              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+              dotProd += dyValue * mask;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `}};function die(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,l,p,u,c),h=new Ax(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new cie(d),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var pie={kernelName:Qm,backendName:"webgl",kernelFunc:die};function hie(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;Jc([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=s,d=T.computePool2DInfo(i.shape,l,u,1,c,p),h=!0,f=new Sp(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new uie(d),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var fie={kernelName:Jm,backendName:"webgl",kernelFunc:hie};function mie(e,t,n,s){let r=new Sp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Sp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var gie={kernelName:e0,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=T.computePool2DInfo(s.shape,r,a,u,o),[p,d]=mie(s,i,c,l);return[p,d]}};function yie(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=nu(i,"float32","mean",s),u=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var Aie={kernelName:Do,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,d=o.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=s.shape[c[E]];let k=gx(b,s.shape,s.dtype,c,w);f=o.makeTensorInfo(w,s.dtype);let S=o.texData.get(f.dataId);S.values=k}else f=p2(s,c,o);h.push(f),u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=T.computeOutAndReduceShapes(f.shape,u),y=m;r&&(y=T.expandShapeToKeepDim(m,l));let x=yie(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function xie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=an({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=nu(m,m.dtype,"min",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var bie={kernelName:$o,backendName:"webgl",kernelFunc:xie},vie=NS+`
+  return min(a, b);
+`,wie=`
+  vec4 result = vec4(min(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  `+c2+`
+  return result;
+`,kie=Nn({opSnippet:vie,packedOpSnippet:wie,cpuKernelImpl:fte}),Iie={kernelName:Fo,backendName:"webgl",kernelFunc:kie},Sie=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=Ct(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
+        int start = ${a};
+        int end = ${o};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start) {
+            outC = start * 2 - outC - ${l};
+          } else if(outC >= end) {
+            outC = (end - 1) * 2 - outC + ${l};
+          }
+          setOutput(getX(outC - start));
+        }
+      `;return}this.userCode=`
+      ${r} start = ${r}(${a});
+      ${r} end = ${r}(${o});
+
+      void main() {
+        ${r} outC = getOutputCoords();
+        for (int i = 0; i < ${s}; i++) {
+          if (outC[i] < start[i]) {
+            outC[i] = start[i] * 2 - outC[i] - ${l};
+          } else if(outC[i] >= end[i]) {
+            outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
+          }
+        }
+        ${r} coords = outC - start;
+        setOutput(getX(${i}));
+      }
+    `}},Cie=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=Ct(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Yn("rc",s),l=Yn("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
+        ${r} source = rc;
+        if (source < start) {
+          source = start * 2 - source - ${p};
+        } else if (source >= end) {
+          source = (end - 1) * 2 - source + ${p};
+        }
+        source -= start;
+      `;d=`
+        ${r} rc = outputLoc;
+        ${h}
+        result[0] = getChannel(getX(${l.join()}), ${c});
+        ${i[s-1]} += 1;
+        if(${u}) {
+          ${h}
+          result[1] = getChannel(getX(${l.join()}), ${c});
+        }
+      `}else{let h=`
+        ${r} source = rc;
+        ${r} lt = ${r}(lessThan(source, start));
+        ${r} gte = ${r}(greaterThanEqual(source, end));
+        ${r} orig = 1 - (lt + gte);
+        source = orig * source +
+                lt * (start * 2 - source - ${p}) +
+                gte * ((end - 1) * 2 - source + ${p});
+        source -= start;
+      `;d=`
+        ${r} rc = outputLoc;
+        ${h}
+        result[0] = getChannel(getX(${l.join()}), ${c});
+        ${i[s-1]} += 1;
+        if(${u}) {
+          ${h}
+          result[1] = getChannel(getX(${l.join()}), ${c});
+        }
+        rc = outputLoc;
+        ${i[s-2]} += 1;
+        if(${i[s-2]} < ${this.outputShape[s-2]}) {
+          ${h}
+          result[2] = getChannel(getX(${l.join()}), ${c});
+          ${i[s-1]} += 1;
+          if(${u}) {
+            ${h}
+            result[3] = getChannel(getX(${l.join()}), ${c});
+          }
+        }
+      `}this.userCode=`
+      const ${r} start = ${r}(${a});
+      const ${r} end = ${r}(${o});
+
+      void main() {
+        ${r} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${d}
+        setOutput(result);
+      }
+    `}},Tie=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Cie(s.shape,r,a):new Sie(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Nie={kernelName:Po,backendName:"webgl",kernelFunc:Tie},Eie=`if (b == 0.0) return NAN;
+  return mod(a, b);`,Rie=`
+  vec4 result = mod(a, b);
+  vec4 isNaN = vec4(equal(b, vec4(0.0)));
+  `+c2+`
+  return result;
+`,_ie=Nn({opSnippet:Eie,packedOpSnippet:Rie}),Die={kernelName:Cc,backendName:"webgl",kernelFunc:_ie},$ie=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+
+        float r = random(seed);
+        float cdf = 0.0;
+
+        for (int i = 0; i < ${t-1}; i++) {
+          cdf += getProbs(batch, i);
+
+          if (r < cdf) {
+            setOutput(float(i));
+            return;
+          }
+        }
+
+        // If no other event happened, last event happened.
+        setOutput(float(${t-1}));
+      }
+    `}},Fie=`
+if (a == b) {
+  return 1.0;
+};
+return a / b;`,Pie=`
+  // vec4 one = vec4(equal(a, b));
+  // return one + (vec4(1.0) - one) * a / b;
+  vec4 result = a / b;
+  if(a.x == b.x) {
+    result.x = 1.;
+  }
+  if(a.y == b.y) {
+    result.y = 1.;
+  }
+  if(a.z == b.z) {
+    result.z = 1.;
+  }
+  if(a.w == b.w) {
+    result.w = 1.;
+  }
+
+  return result;
+`,ZS=Nn({opSnippet:Fie,packedOpSnippet:Pie,checkOutOfBounds:!0}),Oie={kernelName:xo,backendName:"webgl",kernelFunc:ZS},Zv="return a - b;",YS=Nn({opSnippet:Zv,packedOpSnippet:Zv,supportsComplex:!0,cpuKernelImpl:_te}),Mie={kernelName:Yo,backendName:"webgl",kernelFunc:YS};function JS(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=KS({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=T.expandShapeToKeepDim(i.shape,o),u=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),c=YS({inputs:{a:r,b:u},backend:n}),p=jS({inputs:{x:c},backend:n}),d=h2({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:d},backend:n,attrs:{shape:l}}),f=ZS({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var zie={kernelName:Ko,backendName:"webgl",kernelFunc:JS};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:JS({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new $ie(u,c,a),d=[[o]],h=n.runWebGLProgram(p,[l],"int32",d);return i||n.disposeIntermediateTensorInfo(l),h}var Bie={kernelName:t0,backendName:"webgl",kernelFunc:Lie},Wie=ur+`
+  return -x;
+`,Vie=`
+  vec4 result = -x;
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;function Uie(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=gte(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new zi(s.shape,Vie):r=new ua(s.shape,Wie),n.runWebGLProgram(r,[s],s.dtype)}var Gie={kernelName:wl,backendName:"webgl",kernelFunc:Uie},Hie=ir.nonMaxSuppressionV3Impl;function jie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=Hie(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var qie={kernelName:Il,backendName:"webgl",kernelFunc:jie},Xie=ir.nonMaxSuppressionV4Impl;function Kie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=Xie(c,p,o,i,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Zie={kernelName:Tc,backendName:"webgl",kernelFunc:Kie},Yie=ir.nonMaxSuppressionV5Impl;function Jie(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=Yie(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Qie={kernelName:Sl,backendName:"webgl",kernelFunc:Jie},ele=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int index = round(getIndices(coords.x));
+        setOutput(mix(float(${s}), float(${n}),
+                      float(index == coords.y)));
+      }
+    `}},tle=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),u=new ele(l,a,o,i),c=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),p=n.runWebGLProgram(u,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=ve({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},nle={kernelName:Tl,backendName:"webgl",kernelFunc:tle};function _m(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Eh({inputs:{input:s},backend:n}),a=_m({inputs:{x:r},backend:n}),o=f2({inputs:{input:s},backend:n}),i=_m({inputs:{x:o},backend:n}),l=ai({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Rh({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var sle={kernelName:Ul,backendName:"webgl",kernelFunc:_m};function QS(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Eh({inputs:{input:s},backend:n}),a=QS({inputs:{x:r},backend:n}),o=f2({inputs:{input:s},backend:n}),i=_m({inputs:{x:o},backend:n}),l=ai({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Rh({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var rle={kernelName:Cl,backendName:"webgl",kernelFunc:QS};function ale(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return H3({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=H3({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),u=LS({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var ole={kernelName:Nl,backendName:"webgl",kernelFunc:ale},ile=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=Ct(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
+        int start = ${a};
+        int end = ${o};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start || outC >= end) {
+            setOutput(value);
+          } else {
+            setOutput(getX(outC - start));
+          }
+        }
+      `;return}this.userCode=`
+      ${r} start = ${r}(${a});
+      ${r} end = ${r}(${o});
+
+      void main() {
+        ${r} outC = getOutputCoords();
+        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
+          setOutput(value);
+        } else {
+          ${r} coords = outC - start;
+          setOutput(getX(${i}));
+        }
+      }
+    `}},lle=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=Ct(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Yn("rc",s),l=Yn("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
+       if(${u}) {
+      `,s===1?"":`}
+       rc = outputLoc;
+       ${i[s-2]} += 1;
+       if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":`  ${i[s-1]} += 1;
+         if(${u}) {`],d=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
+        ${p[f]}
+        if (${d}) {
+          result[${f}] = float(value);
+        } else {
+          ${r} source = rc - start;
+          result[${f}] = getChannel(getX(${l.join()}), ${c});
+        }
+      `;h+=s===1?"} ":"}}",this.userCode=`
+      const ${r} start = ${r}(${a});
+      const ${r} end = ${r}(${o});
+
+      void main() {
+        ${r} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${h}
+        setOutput(result);
+      }
+    `}},e9=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let u=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return Rh({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new lle(r.shape,a,o):new ile(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},ule={kernelName:Mo,backendName:"webgl",kernelFunc:e9},cle=`
+  if(a < 0.0 && floor(b) < b){
+    return NAN;
+  }
+  if (b == 0.0) {
+    return 1.0;
+  }
+  return (round(mod(b, 2.0)) != 1) ?
+      pow(abs(a), b) : sign(a) * pow(abs(a), b);
+`,dle=`
+  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
+  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
+  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
+  vec4 result = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  bvec4 isExpZero = equal(b, vec4(0.0));
+  result.r = isExpZero.r ? 1.0 : result.r;
+  result.g = isExpZero.g ? 1.0 : result.g;
+  result.b = isExpZero.b ? 1.0 : result.b;
+  result.a = isExpZero.a ? 1.0 : result.a;
+
+  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
+  `+c2+`
+  return result;
+`,ple=Nn({opSnippet:cle,packedOpSnippet:dle}),hle={kernelName:zo,backendName:"webgl",kernelFunc:ple};function fle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=v.parseAxisParam(a,r.shape),c=u,p=T.getAxesPermutation(c,i),d=r;p!=null&&(d=an({inputs:{x:r},backend:n,attrs:{perm:p}}),c=T.getInnerMostAxes(c.length,i),l.push(d)),T.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=Ate(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=T.computeOutAndReduceShapes(d.shape,c),g=v.sizeFromShape(m),y=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),x=Kp(r.dtype),A=nu(y,x,"prod",n);h=ve({inputs:{x:A},backend:n,attrs:{shape:f}}),l.push(y),l.push(A)}if(o){l.push(h);let f=T.expandShapeToKeepDim(h.shape,u);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var mle={kernelName:Bo,backendName:"webgl",kernelFunc:fle},t9=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=xte(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},gle={kernelName:Nc,backendName:"webgl",kernelFunc:t9},yle="return 1.0 / x;",Ale=ct({opSnippet:yle}),xle={kernelName:Ec,backendName:"webgl",kernelFunc:Ale},ble=ur+`
+  return (x < 0.0) ? 0.0 : x;
+`,vle=`
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,wle=ct({opSnippet:ble,packedOpSnippet:vle}),kle={kernelName:Wo,backendName:"webgl",kernelFunc:wle},Ile=ur+`
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`,Sle=`
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,Cle=ct({opSnippet:Ile,packedOpSnippet:Sle}),Tle={kernelName:Uo,backendName:"webgl",kernelFunc:Cle},Nle=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${u[0]/c[0]},
+          ${u[1]/c[1]});
+      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = ${p};
+
+        // Compute the four integer indices.
+        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
+        ivec2 sourceCeilRC = ivec2(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
+        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
+        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
+        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
+
+        float top = topLeft + (topRight - topLeft) * fracRC.y;
+        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+        float newValue = top + (bottom - top) * fracRC.x;
+
+        setOutput(newValue);
+      }
+    `}},Ele=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${u[0]/c[0]},
+          ${u[1]/c[1]},
+          ${u[1]/c[1]});
+      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
+                                     ${i}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = ${p};
+
+        // Compute the four integer indices.
+        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
+        ivec3 sourceCeilRC = ivec3(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${l-1};
+        bool hasNextRow = coords.z < ${n-1};
+
+        // In parallel, construct four corners for all four components in
+        // packed 2x2 cell.
+        vec4 topLeft = vec4(
+          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 bottomLeft = vec4(
+          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 topRight = vec4(
+          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec4 bottomRight = vec4(
+          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
+
+        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
+        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
+        vec4 newValue = mix(top, bottom, fracRC.x);
+
+        setOutput(newValue);
+      }
+    `}};function Rle(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ele(r.shape,l,u,a,o):new Nle(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var _le={kernelName:Vo,backendName:"webgl",kernelFunc:Rle},Dle=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${u});
+        const float widthScale = float(${c});
+
+        const float invHeightScale = float(${p});
+        const float invWidthScale = float(${d});
+
+        const int winHeight = int(${h});
+        const int winWidth = int(${f});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(startRLerp - float(winHeight / 2));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(startCLerp - float(winWidth / 2));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${a}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${o}) {
+              continue;
+            }
+
+            float dxR = float(dyR) * heightScale;
+            int topDxRIndex = int(floor(dxR));
+            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
+            float dxRLerp = dxR - float(topDxRIndex);
+            float inverseDxRLerp = 1.0 - dxRLerp;
+
+            float dxC = float(dyC) * widthScale;
+            int leftDxCIndex = int(floor(dxC));
+            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
+            float dxCLerp = dxC - float(leftDxCIndex);
+            float inverseDxCLerp = 1.0 - dxCLerp;
+
+            if (r == topDxRIndex && c == leftDxCIndex) {
+              // topLeft
+              accumulator +=
+                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
+            }
+
+            if (r == topDxRIndex && c == rightDxCIndex) {
+              // topRight
+              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == leftDxCIndex) {
+              // bottomLeft
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == rightDxCIndex) {
+              // bottomRight
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `}};function $le(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Dle(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Fle={kernelName:s0,backendName:"webgl",kernelFunc:$le},Ple=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${u[0]/c[0]},
+          ${u[1]/c[1]});
+      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = ${d};
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec2 sourceNearestRC = ivec2(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
+        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+        setOutput(newValue);
+      }
+    `}},Ole=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${u[0]/c[0]},
+          ${u[1]/c[1]},
+          ${u[1]/c[1]});
+      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
+                                     ${i}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = ${d};
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec3 sourceNearestRC = ivec3(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${l-1};
+        bool hasNextRow = coords.z < ${n-1};
+
+        vec4 newValue = vec4(
+          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
+          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
+
+        setOutput(newValue);
+      }
+    `}};function Mle(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ole(r.shape,l,u,a,o):new Ple(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var zle={kernelName:Rc,backendName:"webgl",kernelFunc:Mle},Lle=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${u});
+        const float widthScale = float(${c});
+
+        const float invHeightScale = float(${p});
+        const float invWidthScale = float(${d});
+
+        const int winHeight = int(${h});
+        const int winWidth = int(${f});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(floor(startRLerp - float(winHeight / 2)));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(floor(startCLerp - float(winWidth / 2)));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${a}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${o}) {
+              continue;
+            }
+
+            float sourceFracRow =
+              float(${i[0]}) *
+                (float(dyR) / float(${l[0]}));
+
+            float sourceFracCol =
+                float(${i[1]}) *
+                  (float(dyC) / float(${l[1]}));
+
+            int sourceNearestRow = int(min(
+                float(int(${s}) - 1),
+                ${n} ? float(round(sourceFracRow)) :
+                                  float(floor(sourceFracRow))));
+
+            int sourceNearestCol = int(min(
+                float(int(${r}) - 1),
+                ${n} ? float(round(sourceFracCol)) :
+                                  float(floor(sourceFracCol))));
+
+            if (r == sourceNearestRow && c == sourceNearestCol) {
+              accumulator += getDy(b, dyR, dyC, d);
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `}};function Ble(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Lle(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Wle={kernelName:n0,backendName:"webgl",kernelFunc:Ble},Vle=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+        void main() {
+          int coord = getOutputCoords();
+          setOutput(getX(${e[0]} - coord - 1));
+        }
+      `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=Ct(n);this.userCode=`
+      void main() {
+        ${a} coords = getOutputCoords();
+        setOutput(getX(${r}));
+      }
+    `}},Ule=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=Yn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=Ct(n);n===1?this.userCode=`
+        void main(){
+          int rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = getChannel(getX(${e[0]} - rc - 1),
+            ${e[0]} - rc - 1);
+          if(${r}){
+              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),
+                ${e[0]} - (rc  + 1) - 1);
+          }
+          setOutput(result);
+        }
+      `:this.userCode=`
+        void main() {
+          ${o} rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = ${i(s.slice())};
+          if(${r}){
+            result.g = ${l(s.slice())};
+          }
+          if(${a}) {
+            result.b = ${u(s.slice())};
+            if(${r}) {
+              result.a = ${c(s.slice())};
+            }
+          }
+          setOutput(result);
+        }
+    `;function i(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((y,x)=>d(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Gle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Ds({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ule(r.shape,i):new Vle(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Hle={kernelName:Rl,backendName:"webgl",kernelFunc:Gle},jle=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
+        vec3 fill = vec3(${t.join(",")});
+        float outputValue = fill[coords[3]];`,this.userCode=`
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+          int y = coords[1];
+          float coordXFloat = (float(x) - params[0]) * params[3] -
+            (float(y) - params[1]) * params[2];
+          float coordYFloat = (float(x) - params[0]) * params[2] +
+            (float(y) - params[1]) * params[3];
+          int coordX = int(round(coordXFloat + params[0]));
+          int coordY = int(round(coordYFloat + params[1]));
+          ${r}
+          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
+            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `}},qle={kernelName:Gl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new jle(s.shape,a),[u,c]=T.getImageCenter(o,s.shape[1],s.shape[2]),p=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,p)}},Xle=`
+  // OpenGL ES does not support round function.
+  // The algorithm is based on banker's rounding.
+  float base = floor(x);
+  if ((x - base) < 0.5) {
+    return floor(x);
+  } else if ((x - base) > 0.5) {
+    return ceil(x);
+  } else {
+    if (mod(base, 2.0) == 0.0) {
+      return base;
+    } else {
+      return base + 1.0;
+    }
+  }
+`,Kle=ct({opSnippet:Xle}),Zle={kernelName:_l,backendName:"webgl",kernelFunc:Kle},Yle="return inversesqrt(x);",Jle=ct({opSnippet:Yle,cpuKernelImpl:bte}),Qle={kernelName:Go,backendName:"webgl",kernelFunc:Jle},n9=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=Ct(r.length),l=Ct(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+        ${i} strides = ${i}(${r});
+
+        void main() {
+          ${l} coords = getOutputCoords();
+          float sum = 0.0;
+          bool found = false;
+          for (int i = 0; i < ${e}; i++) {
+            int flattenedIndex = 0;
+            for (int j = 0; j < ${t}; j++) {
+              int index = round(${c});
+              flattenedIndex += index * ${h};
+            }
+            if (flattenedIndex == coords[0]) {
+              sum += ${d};
+              found = true;
+            }
+          }
+          setOutput(mix(getDefaultValue(), sum, float(found)));
+        }
+      `}};function eue(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(a,r,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new n9(l,i,h.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var tue={kernelName:Dl,backendName:"webgl",kernelFunc:eue},nue=class{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=Y().getNumber("WEBGL_VERSION")===2?r:a,i=s==="left"?"<":"<=";this.userCode=`
+       int findBound(int batch, float value) {
+         int left = 0;
+         int right = numInputs;
+         int mid;
+         ${o}
+           mid = (left + right) / 2;
+           if (getSortedSequence(batch, mid) ${i} value) {
+             left = mid + 1;
+           } else {
+             right = mid;
+           }
+         }
+         return right;
+       }
+
+       void main() {
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int valueIndex = coords[1];
+
+         float value = getValues(batch, valueIndex);
+
+         setOutput(float(findBound(batch, value)));
+       }
+     `}};function sue(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:o}=s,i=new nue(r.shape[0],r.shape[1],a.shape[1],o),l=[[r.shape[1]]];return n.runWebGLProgram(i,[r,a],"int32",l)}var rue={kernelName:r0,backendName:"webgl",kernelFunc:sue},aue=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=Ct(n);this.userCode=`
+      void main() {
+        ${a} resRC = getOutputCoords();
+        float cVal = getC(${s});
+        if (cVal >= 1.0) {
+          setOutput(getA(${r}));
+        } else {
+          setOutput(getB(${r}));
+        }
+      }
+    `}};function oue(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new aue(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Fn(r.dtype,a.dtype))}var iue={kernelName:$l,backendName:"webgl",kernelFunc:oue},lue=`
+  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
+  // see: https://arxiv.org/abs/1706.02515
+  float scaleAlpha = ${T.SELU_SCALEALPHA};
+  float scale = ${T.SELU_SCALE};
+  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
+`,uue=ct({opSnippet:lue}),cue={kernelName:_c,backendName:"webgl",kernelFunc:uue},due=rd+`
+  return 1.0 / (1.0 + exp(-1.0 * x));
+`,pue=`
+  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`,hue=ct({opSnippet:due,packedOpSnippet:pue,cpuKernelImpl:wte}),fue={kernelName:jo,backendName:"webgl",kernelFunc:hue},mue=`
+  if (isnan(x)) { return 0.0; }
+  return sign(x);
+`,gue=ct({opSnippet:mue}),yue={kernelName:Dc,backendName:"webgl",kernelFunc:gue},Aue=rd+`
+  return sin(x);
+`,xue=ct({opSnippet:Aue}),bue={kernelName:Ho,backendName:"webgl",kernelFunc:xue},vue=`
+  float e2x = exp(x);
+  return (e2x - 1.0 / e2x) / 2.0;
+`,wue=ct({opSnippet:vue}),kue={kernelName:Pl,backendName:"webgl",kernelFunc:wue},Iue=`
+  float epsilon = 1.1920928955078125e-7;
+  float threshold = log(epsilon) + 2.0;
+
+  bool too_large = x > -threshold;
+  bool too_small = x < threshold;
+
+  float result;
+  float exp_x = exp(x);
+
+  if (too_large){
+    result = x;
+  }
+  else if (too_small){
+    result = exp_x;
+  }
+  else{
+    result = log(exp_x + 1.0);
+  }
+  return result;
+`,Sue=ct({opSnippet:Iue}),Cue={kernelName:$c,backendName:"webgl",kernelFunc:Sue},Tue=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let u=[],c=e9({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(c.shape,a,i,!1),d=T.getPermuted(p.length,a.length,!1),h=T.getReshapedPermuted(c.shape,a,i,!1),f=ve({inputs:{x:c},backend:n,attrs:{shape:p}}),m=an({inputs:{x:f},backend:n,attrs:{perm:d}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Nue={kernelName:Ol,backendName:"webgl",kernelFunc:Tue};function Eue(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+         ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
+         ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
+         ${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
+        ${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[p,d,h,f,m]=Ite(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Rue={kernelName:Wp,backendName:"webgl",kernelFunc:Eue};function _ue(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,p]=Ste(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Due={kernelName:Fc,backendName:"webgl",kernelFunc:_ue};function $ue(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
+              ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=IS(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Fue={kernelName:Vp,backendName:"webgl",kernelFunc:$ue};function Pue(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+             ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
+             ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=IS(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var Oue={kernelName:Up,backendName:"webgl",kernelFunc:Pue};function Mue(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=T.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let y=n.bufferSync(r),x=n.bufferSync(a),A=v.decodeString(n.readSync(o.dataId)[0]),b=vte(y,x,i,d,c,u,l,p,A,h);return n.makeTensorInfo(i,b.dtype,b.values)}let f=new n9(u,l,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,o],a.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var zue={kernelName:Gp,backendName:"webgl",kernelFunc:Mue};function Lue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=T.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=ad({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var Bue={kernelName:Ml,backendName:"webgl",kernelFunc:Lue},Yv="return sqrt(x);",Wue=ct({opSnippet:Yv,packedOpSnippet:Yv,cpuKernelImpl:Cte}),Vue={kernelName:qo,backendName:"webgl",kernelFunc:Wue},Uue="return x * x;",Gue=ct({opSnippet:Uue}),Hue={kernelName:Pc,backendName:"webgl",kernelFunc:Gue},Jv="return (a - b) * (a - b);",jue=Nn({opSnippet:Jv,packedOpSnippet:Jv}),que={kernelName:Zo,backendName:"webgl",kernelFunc:jue};function Xue({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ur+`
+    return x > 0.0 ? 1.0 : float(${t.alpha});
+  `,a=new ua(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Kue={kernelName:Qo,backendName:"webgl",kernelFunc:Xue},Zue=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=Ct(n.length),a=Ct(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
+      ${r} begin = ${r}(${e});
+      ${r} strides = ${r}(${t});
+
+      void main() {
+        ${a} coords = getOutputCoords();
+        setOutput(getX(${o}));
+      }
+    `}};function Yue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Gt.sliceInfo(r.shape,a,o,i,l,u,c,p,d),w;if(m)w=ve({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=Gt.computeOutShape(x,A,b),E=ad({inputs:{x:r},backend:n,attrs:{begin:x,size:S}});w=ve({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),R=We(r.shape,r.dtype,E),$=Tte(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,$.values)}else{let E=new Zue(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let k=ve({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var Jue={kernelName:zl,backendName:"webgl",kernelFunc:Yue};function Que(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Nte(d,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var ece={kernelName:Hp,backendName:"webgl",kernelFunc:Que};function tce(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,p]=Ete(i,l,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",u),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var nce={kernelName:a0,backendName:"webgl",kernelFunc:tce};function sce(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=Rte(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var rce={kernelName:o0,backendName:"webgl",kernelFunc:sce},ace="return tan(x);",oce=ct({opSnippet:ace}),ice={kernelName:Ll,backendName:"webgl",kernelFunc:oce},lce=`
+  float e2x = exp(-2.0 * abs(x));
+  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
+`,uce=ct({opSnippet:lce}),cce={kernelName:Jo,backendName:"webgl",kernelFunc:uce},dce=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=Ct(this.rank),r=pce(e);this.userCode=`
+      void main() {
+        ${s} resRC = getOutputCoords();
+        setOutput(getA(${r}));
+      }
+    `}};function pce(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function s9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(d=>v.decodeString(d)):l,c=We(r.shape,r.dtype,u),p=Dte(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new dce(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var hce={kernelName:xa,backendName:"webgl",kernelFunc:s9},fce=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
+       void main() {
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int elemIdx = coords[1];
+
+         // We compare elements pair-wise within a group of size 2 * inc.
+         // The comparing rule for each group alternates between ascending
+         // and descending. Within each group, we compare each pair at
+         // positions i and i+inc. To decide whether an element at position i
+         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
+         // inc, it is in the first half of the group, we denote it as x0,
+         // otherwise we denote it as x1.
+         // For example, as shown in the Bitonic top K paper referenced above,
+         // Figure5(a) shows that element[1] is in the
+         // second half of the group when group size is 2, but it is in the
+         // first half of the group when group size is 4.
+
+         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
+         int i = isFirstInPair ? elemIdx : elemIdx - inc;
+
+         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
+         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
+         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
+         float x1 = i1 < n ? getX(batch, i1) : negativeInf;
+
+         // Denotes which direction indices are in (ascending or descending).
+         bool reverse = imod(elemIdx, 2 * dir) >= dir;
+         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
+         if (reverse == isGreater) { // Elements in opposite order of direction
+           int iTemp = i0;
+           i0 = i1;
+           i1 = iTemp;
+         }
+         if (isFirstInPair) {
+            setOutput(float(i0));
+         } else {
+            setOutput(float(i1));
+         }
+       }
+     `}},mce=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
+    void main() {
+         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int elemIdx = coords[1];
+
+         // The output size is half of the previous size.
+         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
+         // we only need to output the indices at positions |, the indices at
+         // positions _ can be thrown away, see Figure5(b) After Phase 2
+         // (Merge phase) in the Bitonic Top K paper referenced above.
+         // For example, the paper shows we only need to output the orange bars.
+         // The output sequence should look like this | | | | | | | |.
+         // Because the sequence is halved, to map the output index back
+         // to the previous sequence to find the corresponding value,
+         // we need to double the index. When we double the index,
+         // we basically interpolate a position, so 2i looks like
+         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
+         // of each 2k positions by - elemIdx % k. E.g. for output at
+         // index 4,5,6,7, we want to get the corresponding element at
+         // original index 8,9,10,11, for output at index 8,9,10,11,
+         // we want to get the corresponding element at original index
+         // 16,17,18,19, so on and so forth.
+
+         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
+         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
+         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
+
+         float x0 = getX(batch, i0);
+         float x1 = i1 < n ? getX(batch, i1) : x0;
+
+         setOutput(x0 >= x1 ? float(i0) : float(i1));
+       }
+     `}};function Ni(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Qv(e){let t=1;for(;t<e;)t*=2;return t}function gce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let $=n.readSync(r.dataId),[_,D]=$te($,u,r.dtype,a,o);return[n.makeTensorInfo(_.shape,_.dtype,_.values),n.makeTensorInfo(D.shape,D.dtype,D.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,Rh({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=v.sizeFromShape(u)/c,g=ve({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Ni(n,h);let y=Qv(a),x=Qv(c),A=null,b=()=>A===null?[g,g]:[g,A],w=($,_,D)=>{let C=b(),P=new fce(D),j=[[c],[A===null?1:0],[Number.NEGATIVE_INFINITY],[$],[_]],z=A;A=n.runWebGLProgram(P,C,"int32",j),Ni(n,z)};for(let $=1;$<y;$*=2){let _=$*2;for(let D=$;D>=1;D/=2)w(_,D,[m,x])}for(let $=x;$>y;$/=2){let _=b(),D=new mce([m,$/2]),P=[[c],[A===null?1:0],[y]],V=A;A=n.runWebGLProgram(D,_,"int32",P),Ni(n,V);let j=y/2,z=j*2;for(let Z=j;Z>=1;Z/=2)w(z,Z,A.shape)}let k=A;A=ad({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),Ni(n,k);let S=XS({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});Ni(n,g);let E=u.slice(0,-1);E.push(a),k=A,A=ve({inputs:{x:A},attrs:{shape:E},backend:n}),Ni(n,k);let R=S;return S=ve({inputs:{x:S},attrs:{shape:E},backend:n}),Ni(n,R),[S,A]}var yce={kernelName:Bl,backendName:"webgl",kernelFunc:gce},Ace=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
+            float mapCoord(float outCoord, float len) {
+              float inCoord = outCoord;
+              if(${i} == 2) {
+                if (inCoord < 0.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz2 = 2.0 * len;
+                    if (inCoord < sz2) {
+                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
+                      inCoord;
+                    }
+                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
+                  }
+                } else if (inCoord > len - 1.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz2 = 2.0 * len;
+                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
+                    if (inCoord >= len) {
+                      inCoord = sz2 - inCoord - 1.0;
+                    }
+                  }
+                }
+                return clamp(inCoord, 0.0, len - 1.0);
+              } else if (${i} == 3) {
+                if (inCoord < 0.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz = len - 1.0;
+                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
+                  }
+                } else if (inCoord > len - 1.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz = len - 1.0;
+                    inCoord -= len * float(int(float(inCoord / sz)));
+                  }
+                }
+                return clamp(inCoord, 0.0, len - 1.0);
+              } else if (${i} == 4) {
+                return clamp(outCoord, 0.0, len - 1.0);
+              } else {
+                return outCoord;
+              }
+            }
+
+            float readWithFillValue(int batch, int coordY, int coordX,
+              int channel) {
+              float outputValue;
+              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
+                  outputValue = getImage(batch, coordY, coordX, channel);
+              } else {
+                outputValue = float(${r});
+              }
+              return outputValue;
+            }
+
+            void main() {
+              ivec4 coords = getOutputCoords();
+              float outputValue;
+              int batch = coords[0];
+              int x = coords[2];
+              int y = coords[1];
+              int channel = coords[3];
+              float xf = float(x);
+              float yf = float(y);
+              float a1 = getTransforms(batch, 0);
+              float a2 = getTransforms(batch, 1);
+              float a3 = getTransforms(batch, 2);
+              float b1 = getTransforms(batch, 3);
+              float b2 = getTransforms(batch, 4);
+              float b3 = getTransforms(batch, 5);
+              float c1 = getTransforms(batch, 6);
+              float c2 = getTransforms(batch, 7);
+              float projection = c1 * xf + c2 * yf + 1.0;
+              if (projection == 0.0) {
+                outputValue = float(${r});
+              } else {
+                float inX = (a1 * xf + a2 * yf + a3) / projection;
+                float inY = (b1 * xf + b2 * yf + b3) / projection;
+                float mapX = mapCoord(inX, float(${t}));
+                float mapY = mapCoord(inY, float(${e}));
+
+                if (${o} == 1) {
+                  int coordY = int(round(mapY));
+                  int coordX = int(round(mapX));
+                  outputValue = readWithFillValue(batch, coordY, coordX,
+                    channel);
+                } else {
+                  float yFloor = floor(mapY);
+                  float xFloor = floor(mapX);
+                  float yCeil = yFloor + 1.0;
+                  float xCeil = xFloor + 1.0;
+                  float valueYFloor = (xCeil - mapX) *
+                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
+                  float valueYCeil = (xCeil - mapX) *
+                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
+                  outputValue = (yCeil - mapY) * valueYFloor +
+                  (mapY - yFloor) * valueYCeil;
+                }
+              }
+              setOutput(outputValue);
+            }
+        `}};function xce(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new Ace(p,d,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var bce={kernelName:Wl,backendName:"webgl",kernelFunc:xce};function vce(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;Jc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=Fte(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var wce={kernelName:i0,backendName:"webgl",kernelFunc:vce};function kce(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=ad({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=ve({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Ice={kernelName:Vl,backendName:"webgl",kernelFunc:kce},Sce=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,p=`
+        sumValue += dot(values, segFilter);
+    `,d="";r%n>0&&(d=`
+        if (inIdx < 0 || inIdx >= ${r}) {
+          return initializationValue;
+        }
+      `);let h="";r%n>0&&(h=`
+        if (inIdx < 0 || inIdx >= ${r}) {
+          return -1.0;
+        }
+      `),this.userCode=`
+      const float initializationValue = ${i};
+
+      float getValue(int batch, int inIdx) {
+        ${d}
+        return getX(batch, inIdx);
+      }
+
+      float getSegmentIdAtIndex(int inIdx) {
+        ${h}
+        return getSegmentIds(inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = int(floor(float(outIdx) / float(
+          ${a})) * float(${n}));
+        int currentSeg = int(mod(float(outIdx), float(${a})));
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${u}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
+          );
+
+          ${p}
+        }
+
+        int inIdx = inOffset + ${u};
+        if (${c===1}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            0,
+            0,
+            0
+          );
+
+          ${p}
+        } else if (${c===2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+              0,
+              0
+          );
+
+          ${p}
+        } else if (${c===3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            0
+          );
+
+          ${p}
+        }
+        setOutput(${l});
+      }
+    `}};function Cce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=T.getAxesPermutation([u],i),p=r;c!=null&&(p=an({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=T.getInnerMostAxes(1,i)[0]);let d=T.segment_util.computeOutShape(p.shape,u,o),h=v.sizeFromShape([p.shape[u]]),f=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Kp(r.dtype),g=(b,w,k,S,E)=>{let R=b.shape[0],$=b.shape[1],_=T.segment_util.segOpComputeOptimalWindowSize($,E),D={windowSize:_,inSize:$,batchSize:R,numSegments:E},C=new Sce(D,w),P=n.compileAndRun(C,[b,k],S);if(l.push(P),P.shape[1]===E)return P;let V=t9({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),j=s9({inputs:{x:V},backend:n,attrs:{reps:[$/_]}});return l.push(V),l.push(j),g(P,w,j,S,E)},y=g(f,"unsortedSegmentSum",a,m,o),x=ve({inputs:{x:y},backend:n,attrs:{shape:d}}),A=x;if(c!=null){l.push(x);let b=T.getUndoAxesPermutation(c);A=an({inputs:{x:A},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var Tce={kernelName:jp,backendName:"webgl",kernelFunc:Cce},Nce=[Ene,_ne,Fne,Mne,Lne,Vne,Gne,jne,Zne,Jne,tse,rse,ise,dse,fse,gse,Ase,wse,Ise,Cse,Rse,Mse,Lse,Wse,qse,Kse,Qse,cne,nre,ire,dre,yre,xre,vre,kre,Sre,Nre,_re,Fre,Ore,zre,Bre,Ure,Hre,Kre,Yre,eae,sae,aae,uae,hae,yae,bae,kae,Iae,Cae,Nae,Rae,Dae,Fae,zae,Wae,Gae,jae,Kae,Jae,noe,ooe,une,loe,are,doe,foe,yoe,pne,voe,Soe,Toe,_oe,Foe,zoe,Woe,Hoe,Koe,Joe,eie,rie,oie,lie,pie,fie,gie,Aie,bie,Iie,Nie,Die,Bie,yne,Gie,qie,Zie,Qie,Use,nle,rle,ole,ule,hle,fne,mle,gle,Gse,Oie,xle,kle,Tle,xne,_le,Fle,zle,Wle,Hle,qle,Zle,Qle,tue,rue,iue,cue,fue,yue,bue,kue,Pse,zie,Cue,Nue,Rue,Due,Fue,Oue,zue,Bue,Vue,Hue,que,Kue,Jue,ece,nce,rce,Mie,Cne,ice,cce,hce,yce,bce,Tne,wce,Ice,Tce,sle];for(let e of Nce)or(e);var jt;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(jt||(jt={}));var Tp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Tp||(Tp={}));var r9;function Ece(e){r9=e.wasm.cwrap(Xa,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Rce(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=Tp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],x=u?a.shape[1]:a.shape[2],A=jl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),b=n.makeOutput([...A,y,x],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return r9(d,k,r.shape.length,h,S,a.shape.length,l,u,g,f,m,p||0,w),b}var _ce={kernelName:Xa,backendName:"wasm",setupFunc:Ece,kernelFunc:Rce};function En(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,u=o.makeOutput(i.shape,t||i.dtype),c=o.dataIdMap.get(u.dataId).id;return v.sizeFromShape(u.shape)===0||n(l,jt[i.dtype],c),u}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Dce=En(ol);function rs(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,d=i.dataIdMap.get(c.dataId).id,h=n!=null?n:u.dtype,f=T.assertAndGetBroadcastShape(u.shape,c.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),x=i.dataIdMap.get(m.dataId).id;return(()=>s(p,g,u.shape.length,d,y,c.shape.length,jt[u.dtype],x))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var $ce=!0,Fce=rs(ya,$ce),a9;function Pce(e){a9=e.wasm.cwrap(oo,null,["array","number","number","number"])}function Oce(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return a9(a,r.length,jt[s.dtype],o),s}var Mce={kernelName:oo,backendName:"wasm",setupFunc:Pce,kernelFunc:Oce};function m2(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var zce={kernelName:Co,backendName:"wasm",kernelFunc:m2},o9;function Lce(e){o9=e.wasm.cwrap(Vr,null,["number","array","number","number","number","array","number"])}function ro(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Wce(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Bce(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=m2({inputs:t,backend:n});return f.shape=i,f}let u=n.makeOutput(i,l.dtype),c=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(u.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return o9(c,h,l.shape.length,jt[l.dtype],p,d,a.length),u}function Bce(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Wce(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var Vce={kernelName:Vr,backendName:"wasm",kernelFunc:ro,setupFunc:Lce};function oi(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=T.getAxesPermutation(o,r),l=null,u=!1;if(i!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[i[h]];o=T.getInnerMostAxes(o.length,r),l=ro({inputs:{x:e},attrs:{perm:i},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:u}}var i9;function Uce(e){i9=e.wasm.cwrap(dc,null,["number, number, number"])}function Gce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=oi(o,r,t);if(h){let A=t.dataIdMap.get(c.dataId).id;u=c,l=A}let f=u.shape.length;T.assertAxesAreInnerMostDims("all",p,f);let[m,g]=T.computeOutAndReduceShapes(u.shape,p),y=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;i9(l,y,A)}if(h&&t.disposeData(c.dataId),a){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Hce={kernelName:dc,backendName:"wasm",setupFunc:Uce,kernelFunc:Gce},l9;function jce(e){l9=e.wasm.cwrap(pc,null,["number, number, number"])}function qce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=oi(o,r,t);if(h){let A=t.dataIdMap.get(c.dataId).id;u=c,l=A}let f=u.shape.length;T.assertAxesAreInnerMostDims("any",p,f);let[m,g]=T.computeOutAndReduceShapes(u.shape,p),y=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;l9(l,y,A)}if(h&&t.disposeData(c.dataId),a){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Xce={kernelName:pc,backendName:"wasm",setupFunc:jce,kernelFunc:qce},u9;function Kce(e){u9=e.wasm.cwrap(io,null,["number","number","number","number","number"])}function Zce(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:u,axes:c,inputWasTransposed:p}=oi(a,r,t);if(p){let y=t.dataIdMap.get(u.dataId).id;y!==o&&(l=u,i=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[c[0]];return u9(i,jt[l.dtype],m,g,f),p&&t.disposeData(u.dataId),h}var Yce={kernelName:io,backendName:"wasm",kernelFunc:Zce,setupFunc:Kce},c9;function Jce(e){c9=e.wasm.cwrap(lo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Qce(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=T.computePool2DInfo(r.shape,o,i,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,y=c.strideHeight,x=c.strideWidth,A=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=s.makeOutput(c.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return c9(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,y,x,A,w),b}var ede={kernelName:lo,backendName:"wasm",setupFunc:Jce,kernelFunc:Qce};function ds(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var tde={kernelName:El,backendName:"wasm",kernelFunc:ds},d9;function nde(e){d9=e.wasm.cwrap(uo,null,["number","array","number","number","array","number","number","number","number"])}function sde(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],p=i?a.shape[u-1]:a.shape[u-2],d=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=jl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);v.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,c,d]:[g,d,c],w=i?[y,h,p]:[y,p,h],k=ds({inputs:{x:r},backend:n,attrs:{shape:b}}),S=ds({inputs:{x:a},backend:n,attrs:{shape:w}}),E=n.dataIdMap.get(k.dataId).id,R=n.dataIdMap.get(S.dataId).id,$=o?k.shape[2]:k.shape[1],_=i?S.shape[1]:S.shape[2],D=Math.max(g,y),C=n.makeOutput([D,$,_],k.dtype),P=n.dataIdMap.get(C.dataId).id,V=new Uint8Array(new Int32Array(k.shape).buffer),j=new Uint8Array(new Int32Array(S.shape).buffer);return d9(E,V,k.shape.length,R,j,S.shape.length,o,i,P),n.disposeData(k.dataId),n.disposeData(S.dataId),C.shape=A,C}var rde={kernelName:uo,backendName:"wasm",setupFunc:nde,kernelFunc:sde};function sl(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Gt.parseSliceParams(t,n,s),i=Gt.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=v.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(i){let f=Gt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+v.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Cm(l,a,o,t.shape,t.dtype);return p.stringBytes=f,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)ade(l,c[0],d,a,o);else if(h===3)ode(l,c[0],c[1],d,a,o);else if(h===4)ide(l,c[0],c[1],c[2],d,a,o);else{let f=Cm(l,a,o,t.shape,t.dtype);d.set(f)}return u}function ade(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function ode(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],u=r[2],c=i+a[0],p=l+a[1];for(let d=i;d<c;d++)for(let h=l;h<p;h++){let f=d*t+h*n+u;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function ide(e,t,n,s,r,a,o){let i=0,l=a[0],u=a[1],c=a[2],p=l+o[0],d=u+o[1],h=c+o[2],f=a[3];for(let m=l;m<p;m++)for(let g=u;g<d;g++)for(let y=c;y<h;y++){let x=m*t+g*n+y*s+f;r.set(e.subarray(x,x+o[3]),i),i+=o[3]}}var lde={kernelName:Fl,backendName:"wasm",kernelFunc:sl};function ude(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=ds({inputs:{x:r},backend:n,attrs:{shape:l}}),f=ro({inputs:{x:h},backend:n,attrs:{perm:u}}),m=ds({inputs:{x:f},backend:n,attrs:{shape:c}}),g=sl({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var cde={kernelName:il,backendName:"wasm",kernelFunc:ude};function _h(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var dde={kernelName:co,backendName:"wasm",kernelFunc:_h},pde=En(po),p9;function hde(e){p9=e.wasm.cwrap(Aa,null,["number","number","number","number"])}function fde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return p9(i,a,o,u),l}var mde={kernelName:Aa,backendName:"wasm",setupFunc:hde,kernelFunc:fde};function h9(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=T.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return m2({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(T.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(A=>{let b=v.sizeFromShape(A.shape.slice(s));return ds({inputs:{x:A},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(A=>({vals:n.readSync(A.dataId),shape:A.shape}));r=T.computeOutShape(h.map(A=>A.shape),1);let m=h[0].shape[0]===1,g=Y5(f,r,t[0].dtype,m),y=T.computeOutShape(a.map(A=>A.shape),s);o.shape=y;let x=n.dataIdMap.get(o.dataId);return x.stringBytes=T.fromStringArrayToUint8(g),h.forEach(A=>n.disposeData(A.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return u+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*u;for(let m=0;m<p.length;m++){let g=c[m],y=h*g,x=p[m].subarray(y,y+g);d.set(x,f),f+=g}}return o}var gde={kernelName:ll,backendName:"wasm",kernelFunc:h9},f9;function yde(e){f9=e.wasm.cwrap(ho,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ade(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=T.convertConv2DDataFormat(d),f=T.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,x=f.padInfo.right,A=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,S=f.strideHeight,E=f.strideWidth,R=f.inChannels,$=f.outChannels,_=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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vde(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=s,p=1,d=T.convertConv2DDataFormat(l),h=T.computeConv2DInfo(c,a.shape,o,p,i,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:w,outWidth:k,strideHeight:S,strideWidth:E}=h,R=m-1-h.padInfo.top,$=g-1-h.padInfo.left,_=h.dataFormat==="channelsLast",D=v.computeStrides(h.inShape),C=v.computeStrides(r.shape),[P,V,j]=v.computeStrides(a.shape),z=D[0],Z=_?D[1]:D[2],W=_?D[2]:1,ee=_?1:D[1],Q=C[0],ie=_?C[1]:C[2],J=_?C[2]:1,ae=_?1:C[1],le=t.makeOutput(h.inShape,"float32"),ye=t.dataIdMap.get(le.dataId).id,we=t.dataIdMap.get(r.dataId).id,Re=t.dataIdMap.get(a.dataId).id;return m9(we,Re,f,m,g,x,A,y,w,k,b,S,E,R,$,P,V,j,z,Z,W,ee,Q,ie,J,ae,ye),le}var wde={kernelName:fo,backendName:"wasm",setupFunc:bde,kernelFunc:vde},kde=En(mo),Ide=En(go),j3;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(j3||(j3={}));var g9;function Sde(e){g9=e.wasm.cwrap(cl,null,["number","number","number","number","array","number","number","number","number","number"])}function Cde(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=_h({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(u.dataId).id,A=t.makeOutput(h,"float32"),b=t.dataIdMap.get(A.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return g9(g,y,x,c,w,p,d,j3[r],a,b),m!=null&&t.disposeData(m.dataId),A}var Tde={kernelName:cl,backendName:"wasm",setupFunc:Sde,kernelFunc:Cde},y9;function Nde(e){y9=e.wasm.cwrap(ul,null,["number","number","number","number","number","number"])}function Ede(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([a],l),c=r;u!==null&&(c=ro({inputs:{x:r},attrs:{perm:u},backend:n}));let p=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumprod",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;y9(f,o?1:0,i?1:0,h,m,jt[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=ro({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Rde={kernelName:ul,backendName:"wasm",setupFunc:Nde,kernelFunc:Ede},A9;function _de(e){A9=e.wasm.cwrap(yo,null,["number","number","number","number","number","number"])}function Dde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([a],l),c=r;u!==null&&(c=ro({inputs:{x:r},attrs:{perm:u},backend:n}));let p=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;A9(f,o?1:0,i?1:0,h,m,jt[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=ro({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var $de={kernelName:yo,backendName:"wasm",setupFunc:_de,kernelFunc:Dde},x9;function Fde(e){x9=e.wasm.cwrap(dl,null,["number","number","number","array","number","array","array","number","number"])}function Pde(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return x9(y,a,o==="NHWC"?1:0,x,r.shape.length-1,A,b,f.length,w),m}var Ode={kernelName:dl,backendName:"wasm",setupFunc:Fde,kernelFunc:Pde},b9;function Mde(e){b9=e.wasm.cwrap(Ao,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function zde(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,S=h.strideWidth,E=h.inChannels,R=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let _=s.makeOutput(h.outShape,"float32"),D=s.dataIdMap.get(_.dataId).id;return b9(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,y,x,A,$,b,w,k,S,E,R,D),_}var Lde={kernelName:Ao,backendName:"wasm",setupFunc:Mde,kernelFunc:zde},Bde=En(bo),Wde=!1,Vde=rs(pl,Wde,"bool"),Ude=En(vo,"float32");function q3(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),ds({inputs:{x:r},backend:s,attrs:{shape:i}})}var Gde={kernelName:hl,backendName:"wasm",kernelFunc:q3};function v9(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var Hde={kernelName:bc,backendName:"wasm",kernelFunc:v9},w9;function jde(e){w9=e.wasm.cwrap(ml,null,["number","number","number","number","number","number"])}function qde(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,u,c]=s.shape;return w9(a,i,l,u,c,o),r}var Xde={kernelName:ml,backendName:"wasm",kernelFunc:qde,setupFunc:jde},Kde=En(wo),Zde=!1,Yde=rs(ko,Zde),k9;function Jde(e){k9=e.wasm.cwrap(Io,null,["number","number","number","number","number","number","number"])}function Qde(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:u}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=u!=null?t.dataIdMap.get(u.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return k9(c,p,d,h,f,r,g),m}var epe={kernelName:Io,backendName:"wasm",setupFunc:Jde,kernelFunc:Qde},I9;function tpe(e){I9=e.wasm.cwrap(Ka,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function npe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.computeConv2DInfo(r.shape,a.shape,l,c,u,d),g=Tp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let J=s.dataIdMap.get(o.dataId);if(J.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${J.shape.length}.`);if(J.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${J.shape}) does not match the number of output channels (${A})`);b=J.id}let w=m.filterHeight,k=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,$=m.padInfo.left,_=m.dilationHeight,D=m.dilationWidth,C=m.strideHeight,P=m.strideWidth,V=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,Z=m.inHeight,W=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. 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Bpe={kernelName:$o,backendName:"wasm",setupFunc:zpe,kernelFunc:Lpe},Wpe=!1,Vpe=rs(Fo,Wpe),X3;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(X3||(X3={}));var $9;function Upe(e){$9=e.wasm.cwrap(Po,null,["number","array","number","number","array","array","number","number"])}function Gpe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return $9(o,u,t.shape.length,jt[t.dtype],d,h,X3[r],l),i}var Hpe={kernelName:Po,backendName:"wasm",kernelFunc:Gpe,setupFunc:Upe},jpe=!0,qpe=rs(Oo,jpe),Xpe=En(wl);function xx(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var F9;function Kpe(e){F9=e.wasm.cwrap(Il,"number",["number","number","number","number","number"])}function Zpe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=F9(u,c,a,r,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=xx(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Ype={kernelName:Il,backendName:"wasm",setupFunc:Kpe,kernelFunc:Zpe},P9;function Jpe(e){P9=e.wasm.cwrap(Tc,"number",["number","number","number","number","number","bool"])}function Qpe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=P9(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=xx(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var ehe={kernelName:Tc,backendName:"wasm",setupFunc:Jpe,kernelFunc:Qpe},O9;function the(e){O9=e.wasm.cwrap(Sl,"number",["number","number","number","number","number","number"])}function nhe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=O9(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=xx(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var she={kernelName:Sl,backendName:"wasm",setupFunc:the,kernelFunc:nhe},rhe=!1,ahe=rs(kl,rhe,"bool"),M9;function ohe(e){M9=e.wasm.cwrap(Tl,null,["number","number","number","number","number"])}function ihe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),u=n.dataIdMap.get(l.dataId).id,p=n.dataIdMap.get(r.dataId).id;return M9(p,a,o,i,u),l}var lhe={kernelName:Tl,backendName:"wasm",setupFunc:ohe,kernelFunc:ihe};function uhe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var che={kernelName:Cl,backendName:"wasm",kernelFunc:uhe};function dhe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return q3({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching 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+");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else 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o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e,t,n){let{memoryOffset:s,dtype:r,shape:a,stringBytes:o}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(n==null||n>=o.length)?o:o.slice(t,n);t=t||0,n=n||v.sizeFromShape(a);let i=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(s+t*i,s+n*i);return Xfe(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function jfe(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at 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+  vec4<f32>(b >= vec4<f32>(1.0)));`,bme=`
+  if (isnan(a)) { return a; }
+  if (isnan(b)) { return b; }
+  `,lC=`
+  if (isNaN.r) {
+    resultTemp.r = uniforms.NAN;
+  }
+  if (isNaN.g) {
+    resultTemp.g = uniforms.NAN;
+  }
+  if (isNaN.b) {
+    resultTemp.b = uniforms.NAN;
+  }
+  if (isNaN.a) {
+    resultTemp.a = uniforms.NAN;
+  }
+  `,vme=`
+  let s = sign(a) * sign(b);
+  let ia = i32(round(a));
+  let ib = i32(round(b));
+  return f32(idiv(ia, ib, s));
+  `,wme=`
+  let ia = vec4<i32>(round(a));
+  let ib = vec4<i32>(round(b));
+  let cond = ib != vec4<i32>(0);
+  var resultTemp = vec4<i32>(0);
+  let s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    resultTemp[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    resultTemp[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    resultTemp[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    resultTemp[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4<f32>(resultTemp);
+  `,kme="return f32(a != b);",Ime="return vec4<f32>(a != b);",Sme=`
+  if(a < 0.0 && floor(b) < b) {
+    return uniforms.NAN;
+  }
+  if (b == 0.0) {
+    return 1.0;
+  }
+  if (round(abs(b) % 2.0) != 1.0) {
+    return pow(abs(a), b);
+  }
+  return sign(a) * pow(abs(a), b);
+  `,Cme=`
+  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
+  let isModRound1 = vec4<f32>(isModRound1Bool);
+  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
+  var resultTemp = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  let isExpZero = b == vec4<f32>(0.0);
+  if (isExpZero.r) {
+    resultTemp.r = 1.0;
+  }
+  if (isExpZero.g) {
+    resultTemp.g = 1.0;
+  }
+  if (isExpZero.b) {
+    resultTemp.b = 1.0;
+  }
+  if (isExpZero.a) {
+    resultTemp.a = 1.0;
+  }
+  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
+  ${lC}
+  return resultTemp;
+  `,Tme="if (a < 0.0) { return b * a; }  return a;",Nme=`
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+  `;function n7(e,t){let n=t?lC:bme;return t?`
+    var resultTemp = vec4<f32>(${e}(a, b));
+    let isNaN = isnanVec4(a) | isnanVec4(b);
+    `+n+`
+    return resultTemp;
+  `:n+`
+    return ${e}(a, b);
+  `}function Dh(e,t){switch(e){case Ze.MUL:return ame;case Ze.ADD:return tme;case Ze.SUB:return ime;case Ze.DIV:return rme;case Ze.EQUAL:return t?ume:lme;case Ze.GREATER:return t?dme:cme;case Ze.GREATER_EQUAL:return t?hme:pme;case Ze.LESS:return t?mme:fme;case Ze.LESS_EQUAL:return t?yme:gme;case Ze.LOGICAL_AND:return t?xme:Ame;case Ze.NOT_EQUAL:return t?Ime:kme;case Ze.SQUARED_DIFFERENCE:return ome;case Ze.INT_DIV:return t?wme:vme;case Ze.PRELU:return t?Nme:Tme;case Ze.MAX:return n7("max",t);case Ze.MIN:return n7("min",t);case Ze.POW:return t?Cme:Sme;case Ze.COMPLEX_MULTIPLY_REAL:return nme;case Ze.COMPLEX_MULTIPLY_IMAG:return sme;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Pe;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.RELU=12]="RELU",e[e.RELU6=13]="RELU6",e[e.LEAKYRELU=14]="LEAKYRELU",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(Pe||(Pe={}));var Eme="return abs(a);",Rme="return ceil(a);",_me="return cos(a);",Dme=`
+  let e2x = exp(-a);
+  return (e2x + 1.0 / e2x) / 2.0;
+`,$me="return exp(a) - 1.0;",Fme="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",Pme=`
+  var resFloat = exp(a) - vec4<f32>(1.0);
+  if (a.r >= 0.0) {
+    resFloat.r = a.r;
+  }
+  if (a.g >= 0.0) {
+    resFloat.g = a.g;
+  }
+  if (a.b >= 0.0) {
+    resFloat.b = a.b;
+  }
+  if (a.a >= 0.0) {
+    resFloat.a = a.a;
+  }
+  return resFloat;
+`,Ome="return exp(a);",Mme="return floor(a);",zme="return a;",Lme=`if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`,Bme="return f32(!(a >= 1.0));",Wme="return -a;",Vme="if (a < 0.0) { return uniforms.alpha * a; } return a;",Ume=`
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+`,Gme="return select(a, 0.0, a < 0.0);",Hme="return clamp(a, 0.0, 6.0);",jme="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",qme=`
+  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
+`,Xme="return 1.0/sqrt(a);",Kme="return 1.0 / (1.0 + exp(-1.0 * a));",Zme="return sin(a);",Yme=`
+  let e2x = exp(a);
+  return (e2x - 1.0 / e2x) / 2.0;
+`,Jme="return sqrt(a);",Qme="return a * a;",e0e=`
+  let e2x = exp(-2.0 * abs(a));
+  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
+`,t0e="return f32(i32((a)));";function _i(e,t){switch(e){case Pe.ABS:return Eme;case Pe.COS:return _me;case Pe.COSH:return Dme;case Pe.CEIL:return Rme;case Pe.ELU:return t?Pme:Fme;case Pe.EXP:return Ome;case Pe.EXPM1:return $me;case Pe.FLOOR:return Mme;case Pe.LINEAR:return zme;case Pe.LOG:return Lme;case Pe.LOGICAL_NOT:return Bme;case Pe.NEG:return Wme;case Pe.LEAKYRELU:return t?Ume:Vme;case Pe.RELU:return t?qme:Gme;case Pe.RELU6:return t?jme:Hme;case Pe.RSQRT:return Xme;case Pe.SIGMOID:return Kme;case Pe.SIN:return Zme;case Pe.SINH:return Yme;case Pe.SQRT:return Jme;case Pe.SQUARE:return Qme;case Pe.TANH:return e0e;case Pe.TO_INT:return t0e;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function li(e,t=!1){if(e===null)return null;if(e==="linear")return _i(Pe.LINEAR);if(e==="relu")return _i(Pe.RELU,t);if(e==="elu")return _i(Pe.ELU,t);if(e==="relu6")return _i(Pe.RELU6,t);if(e==="prelu")return Dh(Ze.PRELU,t);if(e==="sigmoid")return _i(Pe.SIGMOID,t);if(e==="leakyrelu")return _i(Pe.LEAKYRELU,t);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function n0e(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function wn(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ja(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function tm(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function wx(){return`
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+`}function su(){return`
+  ${wx()}
+  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
+          @builtin(global_invocation_id) GlobalId : vec3<u32>,
+          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
+    localId = LocalId;
+    globalId = GlobalId;
+    numWorkgroups = NumWorkgroups;
+`}function ot(){return`
+    ${su()}
+      let index = getGlobalIndex();
+`}function s0e(e,t,n,s=!1){let r=[];if(r.push(`
+    let workGroupSizeX = ${n.workGroupSize[0]}u;
+    let workGroupSizeY = ${n.workGroupSize[1]}u;
+    let workGroupSizeZ = ${n.workGroupSize[2]}u;
+
+    var<private> localId: vec3<u32>;
+    var<private> globalId: vec3<u32>;
+    var<private> numWorkgroups: vec3<u32>;
+
+    // Only used when the y/z dimension of workgroup size is 1.
+    fn getGlobalIndex() -> i32 {
+      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
+        return i32(globalId.x);
+      }
+
+      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+          localId.y * workGroupSizeX + localId.x;
+      let workGroupID = (globalId - localId)/vec3<u32>(
+          workGroupSizeX, workGroupSizeY, workGroupSizeZ);
+
+      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
+        workGroupID.y * numWorkgroups.x + workGroupID.x) *
+        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
+        localInvocationIndex);
+    }
+  `),s===!0)return r.push(`
+      struct Uniform {
+        size            : i32,
+        numChannels     : i32,
+        outShapeStrides : vec2<i32>,
+        dispatchSize    : vec3<u32>,
+      };
+
+      @group(0) @binding(0) var<storage, write> result: array<${tm(t.dtype,n.isVec4)}>;
+      @group(0) @binding(2) var<uniform> uniforms: Uniform;
+    `),[s7,r.join(`
+`),r7(t.shape),n.getUserCode()].join(`
+`);let a=!1,o=!1,i="struct Uniforms { NAN : f32, ";n.variableNames.forEach((m,g)=>{let y=wn(e[g].shape.length);(y==="vec5"||y==="vec6")&&(o=!0),(a||o)&&(i+="@align(16) "),a=o,i+=`${m.charAt(0).toLowerCase()+m.slice(1)}Shape : ${y}, `});let l=wn(t.shape.length);o=l==="vec5"||l==="vec6",(a||o)&&(i+="@align(16) "),a=o,i+=`outShape : ${l}, `;let u=t.shape.length-1,c=wn(u);o=c==="vec5"||c==="vec6",(a||o)&&(i+="@align(16) "),a=o,i+=`
+       outShapeStrides: ${c}, `,n.size&&(a&&(i+="@align(16) "),a=!1,i+="size : i32, "),n.uniforms&&(a&&(i+="@align(16) "),i+=n.uniforms),i+="};",r.push(i),n.atomic?r.push(`
+    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
+  `):r.push(`
+    @group(0) @binding(0) var<storage, write> result: array<${tm(t.dtype,n.isVec4)}>;
+  `),n.variableNames.forEach((m,g)=>{r.push(`
+    @group(0) @binding(${1+g}) var<storage, read> ${m}: array<${n.variableTypes?n.variableTypes[g]:tm(e[g].dtype,n.isVec4)}>;
+      `)}),i!==""&&r.push(`
+    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
+    `);let[p,d]=u0e(t.shape,n.dispatchLayout),h=[s7,r.join(`
+`),r7(t.shape),p,r0e(t.shape.length)];if(n.atomic||h.push(a0e(t.shape,t.dtype,n.isVec4)),d===t.shape.length){let m=e.map((g,y)=>o0e(g,t.shape,n.variableTypes?n.variableTypes[y]==="vec4<f32>":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
+`);h.push(m)}return h.push(n.getUserCode()),h.join(`
+`)}var s7=`
+  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
+  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
+
+  // Checks whether coordinates lie within the bounds of the shape.
+  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
+    return all(coord >= vec2<i32>(0)) && all(coord < shape);
+  }
+  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
+    return all(coord >= vec3<i32>(0)) && all(coord < shape);
+  }
+  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
+    return all(coord >= vec4<i32>(0)) && all(coord < shape);
+  }
+
+  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
+    return coord;
+  }
+  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
+    return dot(coords, vec2<i32>(shape.y, 1));
+  }
+  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
+    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
+  }
+  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
+    return dot(coords, vec4<i32>(
+        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
+  }
+  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
+    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
+    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
+  }
+  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
+    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
+    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
+  }
+
+  fn idiv(a: i32, b: i32, sign: f32) -> i32 {
+    var res: i32 = a / b;
+    let mod: i32 = a % b;
+    if (sign < 0. && mod != 0) {
+      res = res - 1;
+    }
+    return res;
+  }
+
+  // NaN defination in IEEE 754-1985 is :
+  //   - sign = either 0 or 1.
+  //   - biased exponent = all 1 bits.
+  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
+  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
+  fn isnan(val: f32) -> bool {
+    let floatToUint: u32 = bitcast<u32>(val);
+    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
+  }
+  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
+    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
+  }
+`;function r0e(e){let t="";switch(e){case 0:case 1:t+=`
+        fn getOutputIndexFromCoords(coords : i32) -> i32 {
+          return coords;
+        }
+        `;break;case 2:t+=`
+        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
+          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
+        }
+        `;break;case 3:t+=`
+        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
+          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
+        }
+        `;break;case 4:t+=`
+        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
+          return dot(coords, vec4<i32>(
+            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
+        }
+        `;break;case 5:t+=`
+        fn getOutputIndexFromCoords(coords : vec5) -> i32 {
+          return coords.x * uniforms.outShapeStrides.x +
+              coords.y * uniforms.outShapeStrides.y +
+              coords.z * uniforms.outShapeStrides.z +
+              coords.w * uniforms.outShapeStrides.w +
+              coords.u;
+        }
+        `;break;case 6:t+=`
+        fn getOutputIndexFromCoords(coords : vec6) -> i32 {
+          return coords.x * uniforms.outShapeStrides.x +
+              coords.y * uniforms.outShapeStrides.y +
+              coords.z * uniforms.outShapeStrides.z +
+              coords.w * uniforms.outShapeStrides.w +
+              coords.u * uniforms.outShapeStrides.u +
+              coords.v;
+        }
+        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function a0e(e,t,n){let s=e.length,r=tm(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+      result[flatIndex] = ${r}(value);
+    }
+    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
+      result[flatIndex] = ${r}(value);
+    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {
+      result[flatIndex] = ${r}(value);
+    }
+    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
+      result[flatIndex] = ${r}(value);
+    }`,s>=2){let o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=wn(s);n?a+=`
+      fn setOutputAtCoords(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
+        setOutputAtIndex(flatIndex / 4, value);
+      }
+      fn setOutputAtCoordsI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
+        setOutputAtIndexI32(flatIndex / 4, value);
+      }
+    `:a+=`
+      fn setOutputAtCoords(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
+        setOutputAtIndex(flatIndex, value);
+      }
+      fn setOutputAtCoordsI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
+        let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
+        setOutputAtIndexI32(flatIndex, value);
+      }
+    `}return a}function o0e(e,t,n,s){let r=i0e(e,n);return e.shape.length<=t.length&&(r+=l0e(e,t,n,s)),r}function i0e(e,t){let n=e.name,s=e.shape.length,r=wn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=o.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
+        fn ${a}() -> vec4<f32> {
+          return vec4<f32>(${n}[0]);
+        }
+      `:`
+      fn ${a}() ->f32 {
+        return f32(${n}[0]);
+      }
+    `;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,u=`${s}D`;return s===0&&(u="1D"),t?`
+      fn ${a}(${i}) -> vec4<f32> {
+        return vec4<f32>(${n}[getIndexFromCoords${u}(${r}(${o.join(",")}),
+          ${l}) / 4]);
+      }
+      `:`
+    fn ${a}(${i}) -> f32 {
+      return f32(${n}[getIndexFromCoords${u}(${r}(${o.join(",")}),
+        ${l})]);
+    }
+   `}function l0e(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"ByOutput",i=e.shape.length,l=t.length,u=wn(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
+        fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+          return vec4<f32>(${r}[globalIndex]);
+        }
+
+        fn ${o}Coords(coords : ${u}) -> vec4<f32> {
+          return vec4<f32>(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"} / 4]);
+        }
+        `:`
+      fn ${o}Index(globalIndex : i32) -> f32 {
+        return f32(${r}[globalIndex]);
+      }
+
+      fn ${o}Coords(coords : ${u}) -> f32 {
+        return f32(${r}[${l>1?"getOutputIndexFromCoords(coords)":"coords"}]);
+      }
+      `;let c=T.getBroadcastDims(e.shape,t),p=l-i,d="";if(i===0)return n?`
+      fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+        return get${a}();
+      }
+
+      fn ${o}Coords(coords : ${u}) -> vec4<f32> {
+        return get${a}();
+      }
+    `:`
+      fn ${o}Index(globalIndex : i32) -> f32{
+        return get${a}();
+      }
+
+      fn ${o}Coords(coords : ${u}) -> f32{
+        return get${a}();
+      }
+    `;l<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords.${ja(g+p)} = 0;`).join(`
+`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=wn(i),y=e.shape.map((x,A)=>`coords.${ja(A+p)}`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
+      fn ${o}Index(globalIndex : i32) -> vec4<f32> {
+        var coords = getCoordsFromIndex(globalIndex);
+        ${d}
+        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
+      }
+
+      fn ${o}Coords(coordsIn : ${u}) -> vec4<f32> {
+        var coords = coordsIn;
+        ${d}
+        return ${r}[getIndexFromCoords${m}(${h}, ${f}) / 4];
+      }
+    `:`
+    fn ${o}Index(globalIndex : i32) -> f32 {
+      var coords = getCoordsFromIndex(globalIndex);
+      ${d}
+      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
+    }
+
+    fn ${o}Coords(coordsIn : ${u}) -> f32 {
+      var coords = coordsIn;
+      ${d}
+      return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
+    }
+  `}function u0e(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${wn(a)}{
+      let globalIndex = getGlobalIndex();
+      return getCoordsFromIndex(globalIndex);
+    }
+    `,a];let o="",i=[n,s,r],l=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=n0e(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let u=[];for(let d=0;d<l;d++)u.push(`d${d}`);let c=wn(l),p=`fn getOutputCoords() -> ${c} {
+    ${o}
+  `;return u.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${u.join(",")}); }`,[p,l]}function r7(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=wn(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
+      return vec2<i32>(d0, d1);
+    }`;let a;return a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides.${ja(i)}`,u=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${ja(i)}`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${ja(i)}`;return`${l}; ${u};`}).join(""),`
+    fn getCoordsFromIndex(index : i32) -> ${s} {
+      ${a}
+      return ${s}(${r.join(",")});
+    }
+  `}var uC={};Ve(uC,{ArrayBufferToTypedArray:()=>dC,GPUBytesPerElement:()=>nm,computeDispatch:()=>Le,computeWorkGroupSizeForConv2d:()=>kx,computeWorkGroupSizeForMatMul:()=>cC,computeWorkPerThreadForConv2d:()=>Ix,flatDispatchLayout:()=>tt,isWebGPUSupported:()=>Sx,tilesFitEvenlyIntoShape:()=>ma});var Vi=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ma(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function Le(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(Vi(e.x.map(i=>t[i]))/(n[0]*s[0])),e.y?Math.ceil(Vi(e.y.map(i=>t[i]))/(n[1]*s[1])):1,e.z?Math.ceil(Vi(e.z.map(i=>t[i]))/(n[2]*s[2])):1];return[r,a,o]}function kx(e,t){let n=Vi(e.x.map(r=>t[r])),s=Vi(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function cC(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Ix(e,t){let n=Vi(e.x.map(r=>t[r])),s=Vi(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function tt(e){return{x:e.map((t,n)=>n)}}function nm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function dC(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Sx(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}function pC(e,t,n,s,r=4){return v.assert((s%4===0||s%3===0)&&e[0]===4&&(r===3||r===4),()=>`tileInner must be divisible by 4|3. ColPerThread must be 4.
+           innerElementSize must be 3|4.`),`
+  var<workgroup> mm_Asub : array<array<vec${r}<f32>, ${s/r}>, ${t}>;
+  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
+
+  let RowPerThread = ${e[1]};
+  let ColPerThread = ${e[0]};
+  let InnerElementSize = ${r};
+  let TileInner = ${s};
+
+  ${su()}
+
+    let tileRow = ${t===1?"0":"i32(localId.y) * RowPerThread"};
+    let tileCol = i32(localId.x);
+
+    let globalRow = ${t===1?"0":"i32(globalId.y) * RowPerThread"};
+    let globalCol = i32(globalId.x);
+    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
+
+    var acc: array<vec4<f32>, RowPerThread>;
+    var BCached : array<vec4<f32>, 4>;
+
+    // Loop over shared dimension.
+    var globalColA = tileCol;
+    let RowPerThreadB = TileInner / i32(workGroupSizeY);
+    let tileRowB = i32(localId.y) * RowPerThreadB;
+    for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileCol;
+            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
+        }
+        globalColA = globalColA + TileInner / InnerElementSize;
+
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol;
+            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
+            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
+            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
+            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
+            ${r===3?"":"BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
+
+            for (var i = 0; i < RowPerThread; i = i + 1) {
+                let ACached = mm_Asub[tileRow + i][k];
+                acc[i] = BCached[0] * ACached.x + acc[i];
+                acc[i] = BCached[1] * ACached.y + acc[i];
+                acc[i] = BCached[2] * ACached.z + acc[i];
+                ${r===3?"":"acc[i] = BCached[3] * ACached.w + acc[i];"}
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+        mm_write(globalRow + innerRow,
+                 globalCol,
+                 acc[innerRow], globalId);
+    }
+  }`}var c0e=class{constructor(e,t,n,s,r,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let l=a!=null,u=i!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=u,this.batchAEqualOne=s,this.batchBEqualOne=r,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[ma(s,this.aShape.slice(1)),ma(r,n.slice(1))]}getUserCode(){let e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
+        }
+        return vec4<f32>(0.0)`,t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
+        }
+        return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=li(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${o}
+                }`:n=`
+            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+              ${o}
+            }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+      ${n}
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchAEqualOne?`
+          let batchASize = 0;
+          let batch = 0;
+        `:`
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
+          let batch = i32(globalId.z);
+        `}
+
+        ${e};
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchBEqualOne?`
+          let batchBSize = 0;
+          let batch = 0;
+          `:`
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
+          let batch = i32(globalId.z);
+       `}
+        ${t};
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
+        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
+        {
+          var value = valueIn;
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col * 4);
+          ${r}
+          ${s}
+          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
+        }
+      }
+      ${pC(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
+    `}};function Cx(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
+    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
+    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
+    ${su()}
+      let tileRow = i32(localId.y) * ${e[1]};
+      let tileCol = i32(localId.x) * ${e[0]};
+
+      let globalRow = i32(globalId.y) * ${e[1]};
+      let globalCol = i32(globalId.x) * ${e[0]};
+
+      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
+
+      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
+      var ACached : f32;
+      var BCached : array<f32, ${e[0]}>;
+
+      // Without this initialization strange values show up in acc.
+      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
+        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
+          acc[innerRow][innerCol] = 0.0;
+        }
+      }
+
+      let ColPerThreadA = ${r} / ${t[0]};
+      let tileColA = i32(localId.x) * ColPerThreadA;
+      let RowPerThreadB = ${r} / ${t[1]};
+      let tileRowB = i32(localId.y) * RowPerThreadB;
+
+      // Loop over shared dimension.
+      for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
+          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileColA + innerCol;
+
+            mm_Asub[inputRow][inputCol] = mm_readA(
+                globalRow + innerRow,
+                t * ${r} + inputCol, globalId);
+          }
+        }
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol + innerCol;
+
+            mm_Bsub[inputRow][inputCol] = mm_readB(
+              t * ${r} + inputRow,
+              globalCol + innerCol, globalId);
+          }
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < ${r}; k = k + 1) {
+          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
+            BCached[inner] = mm_Bsub[k][tileCol + inner];
+          }
+
+          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
+            ACached = mm_Asub[tileRow + innerRow][k];
+            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
+              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
+            }
+          }
+        }
+
+        workgroupBarrier();
+      }
+
+      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
+        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
+
+          if ((globalCol + innerCol) < uniforms.dimBOuter &&
+              (globalRow + innerRow) < uniforms.dimAOuter) {
+            mm_write(globalRow + innerRow,
+                     globalCol + innerCol,
+                     acc[innerRow][innerCol], globalId);
+          }
+        }
+      }
+    }
+  `}function d0e(e){return`
+    let TileSize = ${e[0]*4};
+    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
+
+    ${su()}
+      let tileCol = i32(localId.x);
+      let globalCol = i32(globalId.x);
+      let globalRow = i32(globalId.y);
+
+      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
+
+      // Without this initialization strange values show up in acc.
+      var acc = 0.0;
+
+      // Loop over shared dimension.
+      for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        let colA = t * TileSize + tileCol * 4;
+        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
+                                mm_readA(globalRow, colA + 1, globalId),
+                                mm_readA(globalRow, colA + 2, globalId),
+                                mm_readA(globalRow, colA + 3, globalId));
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileSize / 4; k = k + 1) {
+          let rowB = t * TileSize + k * 4;
+          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
+                              mm_readB(rowB + 1, globalCol, globalId),
+                              mm_readB(rowB + 2, globalCol, globalId),
+                              mm_readB(rowB + 3, globalCol, globalId));
+
+          let ACached = mm_Asub[k];
+          acc = acc + dot(ACached, BCached);
+        }
+
+        workgroupBarrier();
+      }
+
+      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
+        mm_write(globalRow, globalCol, acc, globalId);
+      }
+    }
+  `}var p0e=class{constructor(e,t,n,s,r,a=!1,o=!1,i=null,l=null,u=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let c=a?e[1]:e[2];this.workGroupSize=cC(t[1],c,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let p=i!=null,d=u!=null;p&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=a,this.transposeB=o,this.addBias=p,this.activation=l,this.hasPreluActivationWeights=d,this.batchAEqualOne=s,this.batchBEqualOne=r;let h=this.outputShape[2],f=this.transposeB?[this.outputShape[0],h,c]:[this.outputShape[0],c,h];[this.fitA,this.fitB]=this.getShapeFit(f),this.shaderKey=`matMulPacked_${this.workPerThread}_${a}_${o}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ma(r,this.aShape.slice(1)),ma(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+             return A[batch * batchASize + row * uniforms.dimInner + col];
+           }
+           return 0.0;`:e=this.fitA?"return A[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+             return A[batch* batchASize + col * uniforms.dimAOuter + row];
+           }
+           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter + col];
+           }
+           return 0.0;`:t=this.fitB?"return B[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + col * uniforms.dimInner + row];
+           }
+           return 0.0;`;let n="",s="";if(this.activation){let o=li(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+               let b = getPreluActivationWeightsByOutputCoords(outCoord);
+               ${o}
+            }`:n=`
+              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+                ${o}
+              }
+            `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+      ${n}
+
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchAEqualOne?`
+        let batch = 0;
+        let batchASize = 0;
+        `:`
+        let batch = i32(globalId.z);
+        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+        `}
+        ${e}
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchBEqualOne?`
+        let batch = 0;
+        let batchBSize = 0;
+        `:`
+        let batch = i32(globalId.z);
+        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+        `}
+        ${t}
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        var value = valueIn;
+        let batch = i32(globalId.z);
+        let outCoord = vec3<i32>(batch, row, col);
+        ${r}
+        ${s}
+        setOutputAtCoords(batch, row, col, value);
+      }
+      ${this.outputShape[1]>1?Cx([this.workPerThread,this.workPerThread,1],this.workGroupSize):d0e(this.workGroupSize)}
+    `}};function h0e(){return`
+    var<workgroup> sumValues : array<f32, workGroupSizeX>;
+    ${su()}
+      let coords = getOutputCoords();
+      let batch = coords[0];
+      let row = coords[1];
+      let col = coords[2];
+      var sum = 0.0;
+      let Length = uniforms.dimInner;
+      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
+        let dataA = mm_readA(batch, row, k);
+        let dataB = mm_readB(batch, k, col);
+        sum = sum + dataA * dataB;
+      }
+      sumValues[localId.x] = sum;
+      workgroupBarrier();
+
+      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
+          currentSize = currentSize / 2u) {
+        if (localId.x < currentSize)
+        {
+          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
+        }
+        workgroupBarrier();
+      }
+
+      if (localId.x == 0u) {
+        sum = sumValues[0] + sumValues[1];
+        mm_write(batch, row, col, sum);
+      }
+    }
+  `}var f0e=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=a!=null,u=i!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e;this.transposeA===!1?e="return f32(A[batch * batchASize + row * uniforms.dimInner + col]);":e="return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);";let t;this.transposeB===!1?t="return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);":t="return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);";let n="",s="";if(this.activation){let o=li(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+               let b = getPreluActivationWeightsByOutputCoords(outCoord);
+               ${o}
+            }`:n=`
+              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+                ${o}
+              }
+            `,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+      ${n}
+
+      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {
+        ${this.batchAEqualOne?`
+          let batchASize = 0;
+          let batch = 0;
+          `:`
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+          let batch = batchIn;
+          `}
+        ${e}
+      }
+
+      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
+        ${this.batchBEqualOne?`
+          let batch = 0;
+          let batchBSize = 0;
+          `:`
+          let batch = batchIn;
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+          `}
+        ${t}
+      }
+
+      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
+        var value = valueIn;
+        let outCoord = vec3<i32>(batch, row, col);
+        ${r}
+        ${s}
+        setOutputAtCoords(batch, row, col, value);
+      }
+      ${h0e()}
+    `}};function m0e(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
+  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
+  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
+  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
+  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
+
+  // If the output size is small for matrix multiplication, avoid to use vec4
+  // and handle some elements per thread to optimally utilize the ALU.
+  // Introduces two shared memory buffers, some logical threads could handle
+  // arithmetic operations and others handle IO operations between barrier api,
+  // makes ALUs and load/store units work simultaneously, could improves
+  // the performance.
+  ${su()}
+    let tileRow = i32(localId.y);
+    let tileCol = i32(localId.x);
+    let globalRow = i32(globalId.y);
+    let globalCol = i32(globalId.x);
+
+    // uniforms.dimInner should be greater than 0.
+    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
+    var acc = 0.0;
+
+    var globalColA = tileCol;
+    var globalRowB = tileRow;
+    for (var t = 0; t < numTiles; t = t + 1) {
+      if (t == 0) {
+        if (tileRow < ${t}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub1[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${s};
+          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${s};
+        }
+      } else {
+        if (tileRow < ${t}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub1[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${s};
+          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${s};
+        } else {
+          // Compute acc values for a single thread.
+          for (var k = 0; k < ${s}; k = k + 1) {
+            let subRow = tileRow - ${t};
+            if (subRow < 0) {
+              continue;
+            }
+            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
+          }
+        }
+      }
+      workgroupBarrier();
+      if (t != 0) {
+        t = t + 1;
+      }
+
+      if (t < numTiles) {
+        if (tileRow < ${t}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub2[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${s};
+          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${s};
+        } else {
+          // Compute acc values for a single thread.
+          for (var k = 0; k < ${s}; k = k + 1) {
+            let subRow = tileRow - ${t};
+            if (subRow < 0) {
+              continue;
+            }
+            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
+          }
+        }
+      }
+      workgroupBarrier();
+    }
+    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
+    if (tileRow >= ${t} && writeCol >= 0) {
+      mm_write(writeCol, globalCol, acc, globalId);
+    }
+  }
+  `}var g0e=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+          return A[batch * batchASize + row * uniforms.dimInner + col];
+        }
+        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return B[batch * batchBSize + row * uniforms.dimBOuter + col];
+         }
+         return 0.0;`,n="",s="";if(this.activation){let o=li(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+            let b = getPreluActivationWeightsByOutputCoords(outCoord);
+            ${o}
+            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+            ${o}
+        }`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+      ${n}
+
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchAEqualOne?`
+          let batch = 0;
+          let batchASize = 0;
+          `:`
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+          let batch = i32(globalId.z);
+          `}
+        ${e}
+      }
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchBEqualOne?`
+          let batch = 0;
+          let batchBSize = 0;
+          `:`
+          let batch = i32(globalId.z);
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+          `}
+        ${t}
+      }
+      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col);
+          var value = valueIn;
+          ${r}
+          ${s}
+          setOutputAtCoords(batch, row, col, value);
+        }
+      }
+      ${m0e(this.workGroupSize)}
+    `}};function at(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var y0e={kernelName:El,backendName:"webgpu",kernelFunc:at};function Tx({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=jl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,p,h]:[y,h,p],k=s?[x,f,d]:[x,d,f],S=at({inputs:{x:e},backend:r,attrs:{shape:w}}),E=at({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[S,E],$=Math.max(y,x),_=y===1,D=x===1,C=p%4===0&&f%4===0&&!n&&!s,P;h*f<=32?P=new f0e([$,h,f],_,D,n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?P=new g0e(w,k,[$,h,f],a,l,o):C?P=new c0e(w,[$,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),_,D,a,l,o):P=new p0e(w,[$,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),_,D,n,s,a,l,o);let V=[S,E];a&&V.push(a),o&&V.push(o);let j=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}];l==="leakyrelu"&&(j.push({type:"float32",data:[i]}),P.uniforms+=" alpha : f32,");let z=r.runWebGPUProgram(P,V,e.dtype,j),Z=at({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let W of R)r.disposeData(W.dataId);return Z}function A0e(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s;return Tx({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var x0e={kernelName:Xa,backendName:"webgpu",kernelFunc:A0e},a7=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+      fn binaryOpComplex(
+          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
+        ${Dh(this.op,!1)}
+      }
+
+      ${ot()}
+        if(index < uniforms.size) {
+          let areal = getARealByOutputIndex(index);
+          let aimag = getAImagByOutputIndex(index);
+          let breal = getBRealByOutputIndex(index);
+          let bimag = getBImagByOutputIndex(index);
+          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
+        }
+      }
+    `}},b0e=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=tt(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
+         let b = getBByOutputCoords(coords);`;return`
+        fn binaryOperation(a : f32, b : f32) -> f32 {
+          ${Dh(this.op,!1)}
+        }
+        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
+        ${ot()}
+
+          // Fill in the shared memory buffer. Here we need a loop to make sure
+          // that all data in A|B are uploaded when |sharedMemorySize| is larger
+          // than work group size.
+          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
+            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}[localIndex]);
+          }
+          workgroupBarrier();
+
+          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+            let flatIndex = index * ${this.workPerThread} + i;
+            if(flatIndex < uniforms.size) {
+              let coords = getCoordsFromIndex(flatIndex);
+
+              ${t}
+              setOutputAtIndex(flatIndex, binaryOperation(a, b));
+            }
+          }
+        }
+        `}},v0e=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
+      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
+        ${Dh(this.op,this.isVec4)}
+      }
+      ${ot()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          let b = getBByOutputIndex(index);
+          setOutputAtIndex(index, binaryOperation(a, b));
+        }
+      }
+    `}},hC=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+      fn binaryOperation(a : f32, b : f32) -> f32 {
+        ${Dh(this.op,!1)}
+      }
+      ${ot()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          let b = getBByOutputIndex(index);
+          setOutputAtIndex(index, binaryOperation(a, b));
+        }
+      }
+      `}};function o7(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4===0)return new v0e(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new b0e(e,t,n,a):new hC(e,t,n)}function js(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var w0e={kernelName:Co,backendName:"webgpu",kernelFunc:js};function od(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=js({inputs:{x:s},backend:n}),l=js({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var k0e={kernelName:_p,backendName:"webgpu",kernelFunc:od},$h=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      fn unaryOperation(a : f32) -> f32 {
+        ${_i(this.op,!1)}
+      }
+      ${ot()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          setOutputAtIndex(index, unaryOperation(a));
+        }
+      }
+      `}};function Rn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let u=o.tensorMap.get(a.dataId),c=t(u.values,i);return o.makeTensorInfo(a.shape,i,c)}let l=new $h(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function as({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let p=l.tensorMap.get(o.dataId),d=l.tensorMap.get(i.dataId),h,f;if(e!==Ze.MUL)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=o7(e,o.shape,i.shape);return l.runWebGPUProgram(w,[A,b],Fn(y.dtype,x.dtype))});else{let g=new a7(Ze.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new a7(Ze.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=od({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=s||Fn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let p=l.tensorMap.get(o.dataId).values,d=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?T.fromUint8ToStringArray(p):p,f=o.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let c=o7(e,o.shape,i.shape);return l.runWebGPUProgram(c,[o,i],u)}}var{addImpl:I0e,ceilImpl:S0e,concatImpl:C0e,equalImpl:T0e,expImpl:N0e,expm1Impl:E0e,floorImpl:R0e,gatherNdImpl:_0e,gatherV2Impl:D0e,greaterEqualImpl:$0e,greaterImpl:F0e,lessEqualImpl:P0e,lessImpl:O0e,logImpl:M0e,maxImpl:z0e,maximumImpl:L0e,minimumImpl:B0e,multiplyImpl:W0e,negImpl:V0e,notEqualImpl:U0e,prodImpl:G0e,rangeImpl:H0e,rsqrtImpl:j0e,scatterImpl:q0e,simpleAbsImpl:X0e,sliceImpl:K0e,stridedSliceImpl:Z0e,stringNGramsImpl:Y0e,subImpl:J0e,tileImpl:Q0e,topKImpl:e2e,transposeImpl:t2e,uniqueImpl:axe}=X5,n2e=Rn({opType:Pe.ABS,cpuKernelImpl:X0e}),s2e={kernelName:ol,backendName:"webgpu",kernelFunc:n2e},r2e=as({opSnippet:Ze.ADD,cpuKernelImpl:I0e,supportsComplex:!0}),a2e={kernelName:ya,backendName:"webgpu",kernelFunc:r2e},o2e=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
+      ${ot()}
+        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if (flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndex);
+            ${e.join(`
+        `)}
+            setOutputAtIndex(flatIndex, ${t});
+          }
+        }
+      }
+    `}};function i2e(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return js({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Fn(i,l)),a=s.map(i=>i.shape),o=new o2e(a);return n.runWebGPUProgram(o,s,r)}var l2e={kernelName:oo,backendName:"webgpu",kernelFunc:i2e},fC=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let s=[t];T.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=T.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
+      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
+    `,t=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${ja(this.inputShape.length-1)}`,n=()=>{let r="";if(this.outputShape.length===1)this.inputShape.length!==1&&(r+="outputCoords,");else for(let a=0;a<this.outputShape.length;a++)r+=`outputCoords.${ja(a)},`;return r};return`
+      fn DIV_CEIL(a : u32, b : u32) -> u32 {
+        return ((a - 1u) / b + 1u);
+      }
+
+      ${e}
+
+      ${ot()}
+        let outputIndex = index / i32(workGroupSizeX);
+        let reduceLength = ${t()};
+
+        var bestIndex = i32(localId.x);
+        var bestValue = uniforms.infinityValue;
+        let outputCoords = getCoordsFromIndex(outputIndex);
+        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
+            k = k + i32(workGroupSizeX)) {
+          let candidate = getX(${n()} k);
+          if (!isnan(candidate) && candidate ${this.op} bestValue) {
+            bestValue = candidate;
+            bestIndex = k;
+          }
+        }
+        xBestValues[localId.x] = bestValue;
+        xBestIndices[localId.x] = bestIndex;
+        workgroupBarrier();
+
+        var reduceSize = min(u32(reduceLength), workGroupSizeX);
+        for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+            currentSize = reduceSize / 2u) {
+          let interval = DIV_CEIL(reduceSize, 2u);
+          if (localId.x < currentSize) {
+            let candidate = xBestValues[localId.x + interval];
+            if (candidate ${this.op} bestValue) {
+              bestValue = candidate;
+              xBestValues[localId.x] = bestValue;
+              xBestIndices[localId.x] = xBestIndices[localId.x + interval];
+            }
+          }
+          reduceSize = interval;
+          workgroupBarrier();
+        }
+
+        if (localId.x == 0u && outputIndex < uniforms.size) {
+          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
+        }
+      }
+    `}},u2e=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+      let TILE_DIM = ${this.workGroupSize[0]};
+      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
+      ${wx()}
+      fn main(@builtin(local_invocation_id) localId : vec3<u32>,
+              @builtin(workgroup_id) workgroupId : vec3<u32>) {
+        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
+        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
+        let width = uniforms.outShape[0];
+        let height = uniforms.outShape[1];
+        if (x < width && y < height) {
+          tile[localId.y][localId.x] = A[y * width + x];
+        }
+        workgroupBarrier();
+
+        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
+        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
+        if (x < height && y < width) {
+          setOutputAtIndex((y * height + x), tile[localId.x]
+            [localId.y]);
+        }
+      }
+    `}},c2e=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=wn(this.outputShape.length),t=d2e(this.newDim);return`
+      ${ot()}
+
+        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if(flatIndex < uniforms.size) {
+            let resRC = getCoordsFromIndex(flatIndex);
+            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
+              ${e}(${t}), uniforms.aShape)]);
+          }
+        }
+      }
+    `}};function d2e(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${ja(s)}`;return n.join()}function ga(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=o.tensorMap.get(r.dataId).values,d=t2e(p,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,d)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let c=new u2e(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}let u=new c2e(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}var p2e={kernelName:Vr,backendName:"webgpu",kernelFunc:ga};function h2e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ga({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=new fC(l.shape,o[0],"max"),p=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var f2e={kernelName:io,backendName:"webgpu",kernelFunc:h2e};function m2e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=ga({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=new fC(l.shape,o[0],"min"),p=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var g2e={kernelName:hc,backendName:"webgpu",kernelFunc:m2e},mC=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+      ${ot()}
+      if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+          let batch = coords[0];
+          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
+          let xRCorner = xRCCorner.x;
+          let xCCorner = xRCCorner.y;
+
+          var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
+          var count = 0.0;
+
+          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
+            let xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= uniforms.convDims.x) {
+              continue;
+            }
+
+            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
+              let xC = xCCorner + wC;
+              if (xC < 0 || xC >= uniforms.convDims.y) {
+                continue;
+              }
+
+              let value = getX(batch, xR, xC, coords[3]);
+              ${e}
+            }
+          }
+
+          setOutputAtIndex(index, ${t});
+        }
+      }
+    `}},gC=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let batch = coords[0];
+          let d = coords[3];
+
+          let xRCCorner = coords.yz * uniforms.stride;
+          let xRCorner = xRCCorner.x;
+          let xCCorner = xRCCorner.y;
+
+          let value = getX(batch, xRCorner, xCCorner, d);
+          setOutputAtIndex(index, value);
+        }
+      }
+    `}};function y2e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1,c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return js({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new gC(c):(p=new mC(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var A2e={kernelName:lo,backendName:"webgpu",kernelFunc:y2e};function x2e(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Tx({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var b2e={kernelName:uo,backendName:"webgpu",kernelFunc:x2e},v2e=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${wn(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=wn(this.rank),t=w2e(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Y3[a]} = uniforms.start[${a}] + coords.${Y3[a]};`),`
+      ${ot()}
+        if (index < uniforms.size) {
+          var sourceLoc : ${e};
+          let coords = getCoordsFromIndex(index);
+          ${n.join(`
+`)}
+          setOutputAtIndex(index, getSource(${t}));
+        }
+      }
+    `}},Y3=["x","y","z","w","u","v"];function w2e(e){if(e===1)return"sourceLoc";if(e<=6)return Y3.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function id(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Gt.parseSliceParams(r,a,o);if(Gt.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=K0e(p.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let u=new v2e(i,l),c=[{type:"int32",data:i}];return n.runWebGPUProgram(u,[r],r.dtype,c)}var k2e={kernelName:Fl,backendName:"webgpu",kernelFunc:id},I2e=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=[],f=at({inputs:{x:r},backend:n,attrs:{shape:l}}),m=ga({inputs:{x:f},backend:n,attrs:{perm:u}}),g=at({inputs:{x:m},backend:n,attrs:{shape:c}}),y=id({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),y},S2e={kernelName:il,backendName:"webgpu",kernelFunc:I2e},yC=as({opSnippet:Ze.NOT_EQUAL,dtype:"bool",cpuKernelImpl:U0e}),C2e={kernelName:kl,backendName:"webgpu",kernelFunc:yC};function Fh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return js({inputs:{x:r.complexTensorInfos.real},backend:n})}var T2e={kernelName:Bp,backendName:"webgpu",kernelFunc:Fh};function N2e(e,t){let n=new $h(e.shape,Pe.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function J3(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return js({inputs:{x:r},backend:n});let o=Vt(r.shape),i=J3({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=od({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Fh({inputs:{input:r},backend:n}),i=J3({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=js({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return N2e(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=yC({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var E2e={kernelName:co,backendName:"webgpu",kernelFunc:J3},R2e=Rn({opType:Pe.CEIL,cpuKernelImpl:S0e}),_2e={kernelName:po,backendName:"webgpu",kernelFunc:R2e},D2e=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+      ${ot()}
+        if(index < uniforms.size) {
+          let value = getAByOutputIndex(index);
+          var clampedValue : vec4<f32>;
+          for (var i = 0; i < 4; i = i + 1) {
+            if (isnan(value[i])) {
+              clampedValue[i] = value[i];
+            } else {
+              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
+            }
+          }
+
+          setOutputAtIndex(index, clampedValue);
+        }
+      }
+    `}},$2e=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+      ${ot()}
+        if(index < uniforms.size) {
+          let value = getAByOutputIndex(index);
+          if (isnan(value)) {
+            setOutputAtIndex(index, value);
+            return;
+          }
+          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
+        }
+      }
+    `}};function F2e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4===0?i=new D2e(r.shape):i=new $2e(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var P2e={kernelName:Aa,backendName:"webgpu",kernelFunc:F2e},O2e=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+      ${ot()}
+        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if(flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndex);
+            let yR = coords.x;
+            let yC = coords.y;
+
+            ${e.join(`
+        `)}
+          }
+        }
+      }
+    `}};function g2(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return js({inputs:{x:r.complexTensorInfos.imag},backend:n})}var M2e={kernelName:Op,backendName:"webgpu",kernelFunc:g2};function Q3(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(x=>Fh({inputs:{input:x},backend:n})),f=e.map(x=>g2({inputs:{input:x},backend:n})),m=Q3(h,t,n),g=Q3(f,t,n),y=od({inputs:{real:m,imag:g},backend:n});return h.forEach(x=>n.disposeData(x.dataId)),f.forEach(x=>n.disposeData(x.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),y}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return at({inputs:{x:b},backend:n,attrs:{shape:[-1,w]}})}),f=h.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),m=T.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=C0e(f,m,s,g),x=T.computeOutShape(e.map(b=>b.shape),t),A=n.makeTensorInfo(x,s,y);return h.forEach(b=>n.disposeData(b.dataId)),A}let{tensors2D:a,outShape:o}=z2e(e,t,n),i=a.map(h=>h.shape),l=new O2e(i),u=[],c=new Array(i.length-1);if(c.length>0){c[0]=i[0][1],u.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+i[h][1],u.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(l,a,a[0].dtype,u);a.forEach(h=>n.disposeData(h.dataId));let d=at({inputs:{x:p},backend:n,attrs:{shape:o}});return n.disposeData(p.dataId),d}function z2e(e,t,n){let s=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>at({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function AC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=T.computeOutShape(t.map(u=>u.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>v.sizeFromShape(u.shape)>0);if(i.length===1)return js({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,a),Q3(i,a,n)}var L2e={kernelName:ll,backendName:"webgpu",kernelFunc:AC},B2e=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.innerElementSize=this.convInfo.inChannels%4===0?4:3,this.innerElementSize===3?this.variableTypes=["f32","vec4<f32>"]:this.variableTypes=["vec4<f32>","vec4<f32>"],this.addBias&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),this.hasPreluActivationWeights&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>")),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.workGroupSize[0]*this.innerElementSize,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ma(e,[n,r]),ma(t,[r,s])]}getUserCode(){let e=pC(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner,this.innerElementSize),t=`let outRow = r / uniforms.outShape[2];
+        let outCol = r % uniforms.outShape[2];
+        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
+        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
+        let inChCoord = c % uniforms.xShape[3];
+        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+
+        var resData = vec${this.innerElementSize}<f32>(0.0);
+        // The bounds checking is always needed since we use it to pad zero for
+        // the 'same' padding type.
+        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
+          var coord = vec4<i32>(
+            batch,
+            xRow,
+            xCol,
+            inChCoord);
+          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
+          ${this.innerElementSize===3?"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);":"resData = x[xIndex / 4];"}
+        }
+        return resData;`,n=this.fitA?`${t}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
+          ${t}
+         }
+         return vec${this.innerElementSize}<f32>(0.0);
+        `,s=this.fitB?"return W[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return W[row * uniforms.dimBOuter / 4 + col];
+         }
+         return vec4<f32>(0.0);
+        `,r="",a="";if(this.activation){let l=li(this.activation,this.isVec4);this.hasPreluActivationWeights?r=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${l}
+        }`:r=`
+        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          ${l}
+        }`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+        ${r}
+        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
+          let r = row;
+          let c = col * ${this.innerElementSize};
+          var batch = i32(globalId.z);
+          ${n}
+        }
+
+        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
+          ${s}
+        }
+
+        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
+          var batch = i32(globalId.z);
+          var value = valueInput;
+          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
+          {
+            let outCoord = vec4<i32>(
+              batch,
+              row / uniforms.outShape[2],
+              row % uniforms.outShape[2],
+              col * 4);
+            ${o}
+            ${a}
+            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
+              value);
+          }
+        }
+        ${e}
+      `}},W2e=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[1],y:[2,3],z:[0]},this.workGroupSize=kx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ix(this.dispatchLayout,this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.convInfo.outHeight*this.convInfo.outWidth,o=this.convInfo.outChannels,i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ma(s,[a,i]),ma(r,[i,o])]}getUserCode(){let e=this.isChannelsLast?`
+    let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);
+    `:`
+    let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);
+    `,t=this.isChannelsLast?`
+    let outCoord = vec4<i32>(
+      batch,
+      row / outWidth,
+      row % outWidth,
+      col);
+    `:`
+    let outCoord = vec4<i32>(
+      batch,
+      col,
+      row / outWidth,
+      row % outWidth);
+    `,n=Cx(this.elementsPerThread,this.workGroupSize),s=`
+    let inChannels = uniforms.wShape[2];
+    let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
+    let outRow = row / outWidth;
+    let outCol = row % outWidth;
+
+    let WRow = col / (uniforms.filterDims[1] * inChannels);
+    let WCol = col / inChannels % uniforms.filterDims[1];
+    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+    ${e}
+    // The bounds checking is always needed since we use it to pad zero for the
+    // 'same' padding type.
+    if(coordsInBounds4D(coord, uniforms.xShape)) {
+      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
+    }
+    return 0.0;`,r=this.fitA?`${s}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+      ${s}
+    }
+    return 0.0;
+    `,a=this.fitB?"return W[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return W[row * uniforms.dimBOuter + col];
+	 }
+	 return 0.0;
+	 `,o="",i="";if(this.activation){let c=li(this.activation,!1);this.hasPreluActivationWeights?o=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${c}
+                }`:o=`
+                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+                    ${c}
+                  }
+                `,i="value = activation(value, outCoord);"}let l=this.addBias?"value = value + getBiasByOutputCoords(outCoord);":"";return`
+    ${o}
+    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      var batch = i32(globalId.z);
+      ${r}
+    }
+
+    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      ${a}
+    }
+
+    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      var batch = i32(globalId.z);
+      var value = valueInput;
+      let outWidth = ${this.isChannelsLast?"uniforms.outShape[2]":"uniforms.outShape[3]"};
+      ${t}
+      ${l}
+      ${i}
+      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+    }
+    ${n}
+  `}};function V2e({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=n.dataFormat==="channelsLast",u=!l,c=!1,p=l&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",d,h;if(p){let g=n.inHeight*n.inWidth*n.inChannels;d=at({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,g]}}),h=at({inputs:{x:t},backend:s,attrs:{shape:[1,g,n.outChannels]}})}else d=at({inputs:{x:e},backend:s,attrs:{shape:l?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),h=at({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});let f=Tx({a:l?d:h,b:l?h:d,transposeA:u,transposeB:c,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),m=at({inputs:{x:f},backend:s,attrs:{shape:n.outShape}});return s.disposeData(d.dataId),s.disposeData(h.dataId),s.disposeData(f.dataId),m}function xC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=r!=null,u=a!=null,c=n.dataFormat==="channelsLast",p;if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return V2e({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});let h=(n.inChannels%4===0||n.inChannels%3===0)&&n.outChannels%4===0&&c,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];h?p=new B2e(n,l,i,u):p=new W2e(n,l,i,u);let g=n.outHeight*n.outWidth,y=n.outChannels,x=n.filterHeight*n.filterWidth*n.inChannels;m.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[x]});let A=[e,t];return l&&A.push(r),u&&A.push(a),i==="leakyrelu"&&(m.push({type:"float32",data:[o]}),p.uniforms+=" alpha : f32,"),s.runWebGPUProgram(p,A,e.dtype,m)}function U2e(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,p);return xC({x:r,filter:a,convInfo:d,backend:s})}var G2e={kernelName:ho,backendName:"webgpu",kernelFunc:U2e},H2e=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Ix(this.dispatchLayout,this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      var batch = i32(globalId.z);
+      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+      
+    let outRow = row / uniforms.outShape[2];
+    let outCol = row % uniforms.outShape[2];
+
+    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
+    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
+    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
+    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
+    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
+      return 0.0;
+    }
+    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
+      return 0.0;
+    }
+    let coord = vec4<i32>(
+        batch,
+        i32(xR),
+        i32(xC),
+        col % uniforms.outBackprop[3]);
+    return x[getIndexFromCoords4D(coord, uniforms.xShape)];
+    }
+    return 0.0;
+    }
+
+    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      let coordX = uniforms.filterDims.x - 1 -
+          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
+      let coordY = uniforms.filterDims.y - 1 -
+          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
+      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
+          coordX >= 0 && coordY >= 0) {
+        let coord = vec4<i32>(coordX, coordY, col,
+            row % uniforms.outBackprop[3]);
+        return W[getIndexFromCoords4D(coord, uniforms.wShape)];
+      }
+      return 0.0;
+    }
+
+    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      var batch = i32(globalId.z);
+      var value = valueInput;
+      let outCoord = vec4<i32>(
+          batch,
+          row / uniforms.outShape[2],
+          row % uniforms.outShape[2],
+          col);
+      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+    }
+
+    ${Cx(this.elementsPerThread,this.workGroupSize)}
+  `}},j2e=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
+    ${ot()} {
+      if(index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+        let batch = coords[0];
+        let d1 = coords[${n}];
+
+        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
+        let dyRCorner = dyCorner.x;
+        let dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        var dotProd = 0.0;
+        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
+          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
+          let wRPerm = uniforms.filterDims.x - 1 - wR;
+          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
+              wRPerm < 0) {
+            continue;
+          }
+          let idyR = dyR;
+
+          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
+            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
+            let wCPerm = uniforms.filterDims.y - 1 - wC;
+            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
+                fract(dyC) > 0.0 || wCPerm < 0) {
+              continue;
+            }
+            let idyC = dyC;
+
+            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
+              if (${this.isChannelsLast}) {
+                let xValue = getDy(batch, idyR, idyC, d2);
+                let wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd = dotProd + xValue * wValue;
+              } else {
+                let xValue = getDy(batch, d2, idyR, idyC);
+                let wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd = dotProd + xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutputAtIndex(index, dotProd);
+      }
+    }
+  `}};function q2e(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new j2e(d);else{f=new H2e(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var X2e={kernelName:fo,backendName:"webgpu",kernelFunc:q2e},K2e=Rn({opType:Pe.COS}),Z2e={kernelName:mo,backendName:"webgpu",kernelFunc:K2e},Y2e=Rn({opType:Pe.COSH}),J2e={kernelName:go,backendName:"webgpu",kernelFunc:Y2e},Q2e=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+      ${ot()}
+      if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+        let height_ratio = f32(${n});
+        let width_ratio = f32(${a});
+        let b = coords[0];
+        let y = coords[1];
+        let x = coords[2];
+        let d = coords[3];
+        // get box vals
+        let y1 = getBoxes(b, 0);
+        let x1 = getBoxes(b, 1);
+        let y2 = getBoxes(b, 2);
+        let x2 = getBoxes(b, 3);
+        // get image in batch index
+        let bInd = i32(round(getBoxInd(b)));
+        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
+          return;
+        }
+        let height_scale = ${s};
+        let width_scale = ${o};
+        let in_y = ${r};
+        if( in_y < 0.0 || in_y > ${e} ) {
+          setOutputAtIndex(index, uniforms.extrapolationValue);
+          return;
+        }
+        let in_x = ${i};
+        if( in_x < 0.0 || in_x > ${t} ) {
+          setOutputAtIndex(index, uniforms.extrapolationValue);
+          return;
+        }
+        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
+        if(${this.methodId} == 1) {
+          // Compute the four integer indices.
+          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
+          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
+          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
+          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
+          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
+          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
+          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
+          let top = topLeft + (topRight - topLeft) * fracCR.x;
+          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          let newValue = top + (bottom - top) * fracCR.y;
+          setOutputAtIndex(index, newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          let sourceNearestCR = vec2<i32>(floor(
+            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
+          let newValue = getImage(
+            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    }
+    `}},e1e=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new Q2e(r.shape[3],a.shape,i,l),p=[{type:"float32",data:[u]}];return n.runWebGPUProgram(c,[r,a,o],"float32",p)},t1e={kernelName:cl,backendName:"webgpu",kernelFunc:e1e},Np;(function(e){e.Prod="*",e.Sum="+"})(Np||(Np={}));var i7=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Np.Prod?"1.0":"0.0",n=this.exclusive?t:`getX(${l7(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
+      ${ot()}
+       if (index < uniforms.size) {
+         var coords = getCoordsFromIndex(index);
+
+         let end = ${u7(e,"coords",this.op)};
+         var val = ${n};
+         let pow2 = i32(pow(2.0, uniforms.index));
+         if (${r}) {
+           let idx = ${a};
+           ${u7(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${l7(e,"coords",this.op)});
+         }
+         setOutputAtIndex(index, val);
+       }
+      }
+    `}};function l7(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function u7(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function bC(e,t,n,s,r,a){let o=t.shape.length,i=T.getAxesPermutation([s],o),l=t;i!=null&&(l=ga({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=T.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=l.shape[u],p=js({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new i7(e,l.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new i7(e,l.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=ga({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(l.dataId),h}return p}function n1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return bC(Np.Prod,r,n,a,o,i)}var s1e={kernelName:ul,backendName:"webgpu",kernelFunc:n1e};function r1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return bC(Np.Sum,r,n,a,o,i)}var a1e={kernelName:yo,backendName:"webgpu",kernelFunc:r1e},o1e=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let h = ${this.getHeightCoordString()};
+          let w = ${this.getWidthCoordString()};
+          let d = ${this.getDepthCoordString()};
+
+          let in_h = h / uniforms.blockSize;
+          let offset_h = h % uniforms.blockSize;
+          let in_w = w / uniforms.blockSize;
+          let offset_w = w % uniforms.blockSize;
+          let offset_d = (offset_h * uniforms.blockSize + offset_w) *
+            ${this.getOutputDepthSize()};
+          let in_d = d + offset_d;
+
+          let rlt = ${this.getInputSamplingString()};
+          setOutputAtIndex(index, rlt);
+        }
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function i1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=[{type:"int32",data:[a]}],g=new o1e(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var l1e={kernelName:dl,backendName:"webgpu",kernelFunc:i1e},vC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=li(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${r}
+        }`:e=`
+        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+            ${r}
+          }
+        `,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
+      ${e}
+
+      ${wx()}
+      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
+        let batch = 0;
+        let r = i32(globalId.x);
+        let c = i32(globalId.y) * 4;
+        let d2 = i32(globalId.z) * 4;
+        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
+        let d1 = d2;
+        let q = 0;
+
+        let xRCorner = xRCCorner.x;
+        let xCCorner = xRCCorner.y;
+
+        var wVals : array<vec4<f32>, 9>;
+        wVals[0] = getW(0, 0, d1, q);
+        wVals[1] = getW(0, 1, d1, q);
+        wVals[2] = getW(0, 2, d1, q);
+        wVals[3] = getW(1, 0, d1, q);
+        wVals[4] = getW(1, 1, d1, q);
+        wVals[5] = getW(1, 2, d1, q);
+        wVals[6] = getW(2, 0, d1, q);
+        wVals[7] = getW(2, 1, d1, q);
+        wVals[8] = getW(2, 2, d1, q);
+
+        var xVals : array<array<vec4<f32>, 6>, 3>;
+        for (var wR = 0; wR < 3; wR = wR + 1) {
+          let xR = xRCorner + wR * uniforms.dilation[0];
+          for (var wC = 0; wC < 6; wC = wC + 1) {
+            let xC = xCCorner + wC * uniforms.dilation[1];
+            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
+              xVals[wR][wC] = vec4<f32>(0.0);
+            } else {
+              xVals[wR][wC] = getX(batch, xR, xC, d1);
+            }
+          }
+        }
+
+        var dotProd : array<vec4<f32>, 4>;
+        dotProd[0] = vec4<f32>(0.0);
+        dotProd[1] = vec4<f32>(0.0);
+        dotProd[2] = vec4<f32>(0.0);
+        dotProd[3] = vec4<f32>(0.0);
+
+        for (var wR = 0; wR < 3; wR = wR + 1) {
+          for (var wC = 0; wC < 3; wC = wC + 1) {
+            let indexW = wR * 3 + wC;
+            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
+            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
+            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
+            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
+          }
+        }
+
+        for (var i = 0; i < 4; i = i + 1) {
+          let coords = vec4<i32>(batch, r, c + i, d2);
+          if (coordsInBounds4D(coords, uniforms.outShape)) {
+            ${n}
+            ${t}
+            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
+          }
+        }
+      }
+    `}},wC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
+      channelMul : i32,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=li(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${r}
+        }`:e=`
+          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+            ${r}
+          }
+        `,t="dotProd = activation(dotProd, coords);"}let n=this.addBias?"dotProd = dotProd + getBiasByOutputCoords(coords);":"";return`
+      ${e}
+
+      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
+          value : f32) {
+        let coord = vec4<i32>(batch, row, col, chan);
+        if (coordsInBounds4D(coord, uniforms.outShape)) {
+          setOutputAtCoords(batch, row, col, chan, value);
+        }
+      }
+
+      ${su()}
+        let coords = getOutputCoords();
+        let batch = coords[0];
+        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
+        let d2 = coords[3];
+        let d1 = d2 / uniforms.channelMul;
+        let q = d2 - d1 * uniforms.channelMul;
+
+        let inputRowStart = xRCCorner.x;
+        let inputColStart = xRCCorner.y;
+        let inputRowEnd = inputRowStart + uniforms.filterHeight *
+            uniforms.dilation[0];
+        let inputColEnd = inputColStart + uniforms.filterWidth *
+            uniforms.dilation[1];
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        var dotProd = 0.0;
+
+        // Extract if checking out of for loop for performance.
+        if (inputRowStart >= 0 && inputColStart >= 0 &&
+          inputRowEnd < uniforms.inDims[0] &&
+              inputColEnd < uniforms.inDims[1]) {
+            // Here using a constant value |this.convInfo.filterHeight| instead
+            // of uniform value is in order to loop unrolling.
+            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
+              let xR = inputRowStart + wR * uniforms.dilation[0];
+
+              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
+                let xC = inputColStart + wC * uniforms.dilation[1];
+
+                let xVal = getX(batch, xR, xC, d1);
+                let wVal = getW(wR, wC, d1, q);
+                dotProd = dotProd + xVal * wVal;
+              }
+            }
+          } else {
+            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
+              let xR = inputRowStart + wR * uniforms.dilation[0];
+
+              if (xR < 0 || xR >= uniforms.inDims[0]) {
+                continue;
+              }
+
+              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
+                let xC = inputColStart + wC * uniforms.dilation[1];
+
+                if (xC < 0 || xC >= uniforms.inDims[1]) {
+                  continue;
+                }
+
+                let xVal = getX(batch, xR, xC, d1);
+                let wVal = getW(wR, wC, d1, q);
+                dotProd = dotProd + xVal * wVal;
+              }
+            }
+          }
+
+        ${n}
+        ${t}
+        writeResult(batch, coords[1], coords[2], d2, dotProd);
+      }
+    `}};function u1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]);let p=T.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.dilationHeight===1&&p.dilationWidth===1&&p.filterHeight===3&&p.inChannels%4===0?h=new vC(p):(h=new wC(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var c1e={kernelName:Ao,backendName:"webgpu",kernelFunc:u1e},kC=as({opSnippet:Ze.MUL,cpuKernelImpl:W0e,supportsComplex:!0}),d1e={kernelName:Oo,backendName:"webgpu",kernelFunc:kC},p1e=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=T.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+         if (isnan(candidate)) {
+          bestValue = uniforms.NAN;
+         } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
+           {  bestValue = candidate; }`,t="f32(x[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
+       fn DIV_CEIL(a : u32, b : u32) -> u32 {
+        return ((a - 1u) / b + 1u);
+       }
+
+       ${`
+         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
+       `}
+       fn getOffset(outputIndex : i32) -> i32 {
+         let outputCoords = getCoordsFromIndex(outputIndex);
+         let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
+          return offset;
+       }
+       ${ot()}
+         let outputIndex = index / i32(workGroupSizeX);
+         let offset = getOffset(outputIndex);
+         var bestValue = ${t};
+         let Length = uniforms.reduceSize;
+         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
+         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
+             k = k + i32(workGroupSizeX)) {
+           let candidate = f32(x[offset + k]);
+           ${e}
+         }
+         xBestValues[localId.x] = bestValue;
+         workgroupBarrier();
+
+         var reduceSize = min(u32(Length), workGroupSizeX);
+         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+             currentSize = reduceSize / 2u) {
+           let interval = DIV_CEIL(reduceSize, 2u);
+           if (localId.x < currentSize) {
+            let candidate = xBestValues[localId.x + interval];
+            ${e}
+            xBestValues[localId.x] = bestValue;
+           }
+           reduceSize = interval;
+           workgroupBarrier();
+         }
+
+         if (localId.x == 0u && outputIndex < uniforms.size) {
+          ${n}
+        }
+       }
+     `}};function Ph(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,u=T.getAxesPermutation(l,a),c=e;u!=null&&(c=ga({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,a),o.push(c)),T.assertAxesAreInnerMostDims(s,l,a);let[p,d]=T.computeOutAndReduceShapes(c.shape,l),h=p;n&&(h=T.expandShapeToKeepDim(p,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=z0e(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=G0e(c.shape,c.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),y=v.sizeFromShape(c.shape)/m,x={windowSize:m,inSize:m,batchSize:y,outSize:1},A=s==="mean"?"float32":Kp(e.dtype),b=[{type:"int32",data:[m]}],w=new p1e(x,s),k=r.runWebGPUProgram(w,[c],A,b);o.push(k),f=at({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Nx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ph(r,a,o,"sum",n)}var h1e={kernelName:Xo,backendName:"webgpu",kernelFunc:Nx};function f1e(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=T.decodeEinsumEquation(r,a.length);T.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=T.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=a[g]:(A=ga({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=at({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=kC({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=Nx({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var m1e={kernelName:Pp,backendName:"webgpu",kernelFunc:f1e},g1e=Rn({opType:Pe.ELU}),y1e={kernelName:bo,backendName:"webgpu",kernelFunc:g1e},A1e=as({opSnippet:Ze.EQUAL,dtype:"bool",cpuKernelImpl:T0e}),x1e={kernelName:pl,backendName:"webgpu",kernelFunc:A1e},IC=Rn({opType:Pe.EXP,cpuKernelImpl:N0e,dtype:"float32"}),b1e={kernelName:vo,backendName:"webgpu",kernelFunc:IC};function ey(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),at({inputs:{x:a},backend:s,attrs:{shape:i}})}var v1e={kernelName:hl,backendName:"webgpu",kernelFunc:ey},w1e=Rn({opType:Pe.EXPM1,cpuKernelImpl:E0e}),k1e={kernelName:fl,backendName:"webgpu",kernelFunc:w1e},I1e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+    ${ot()}
+      if (index < uniforms.size) {
+        setOutputAtIndex(index, uniforms.value);
+      }
+    }
+  `}};function ld(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new I1e(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var S1e={kernelName:bc,backendName:"webgpu",kernelFunc:ld},C1e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let coordX = uniforms.xShape[2] - coords[2] - 1;
+          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
+          setOutputAtIndex(index, outputValue);
+        }
+      }
+    `}},T1e={kernelName:ml,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new C1e(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},N1e=Rn({opType:Pe.FLOOR,cpuKernelImpl:R0e}),E1e={kernelName:wo,backendName:"webgpu",kernelFunc:N1e},R1e=as({opSnippet:Ze.INT_DIV,dtype:"int32"}),_1e={kernelName:ko,backendName:"webgpu",kernelFunc:R1e},D1e=class{constructor(e,t=!1){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.useImport=t,this.shaderKey=`fromPixels_${this.useImport}`}getUserCode(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+      @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
+
+      ${ot()}
+        let flatIndexBase = index * uniforms.numChannels;
+        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
+          let flatIndex = flatIndexBase + i;
+          if (flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndexBase);
+            let values = ${e};
+            result[flatIndex] = i32(floor(255.0 * values[i]));
+          }
+        }
+      }
+  `}},$1e={kernelName:pp,backendName:"webgpu",kernelFunc:F1e},$u;function F1e(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(Y().getBool("WEBGPU_USE_IMPORT")&&o)return c7({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((o||i)&&($u==null&&($u=document.createElement("canvas").getContext("2d")),$u.canvas.width=c,$u.canvas.height=p,$u.drawImage(r,0,0,c,p),r=$u.canvas),u||l||o||i)return c7({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let y=h.length,x=0;for(let A=0;A<y;A++)A%4<a&&(f[x++]=h[A])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}function c7(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),u=new D1e(r,a),c=[{type:"uint32",data:[i]},{type:"uint32",data:[o]},{type:"uint32",data:[...l]},{type:"uint32",data:[...u.dispatch]}];return n.runFromPixelsProgram(u,r,c,a,t)}var P1e=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+      ${ot()}
+        if (index < uniforms.size)
+        {
+          let xValue = getXByOutputIndex(index);
+          let meanValue = getMeanByOutputIndex(index);
+          let varianValue = getVarianceByOutputIndex(index);
+          let offsetValue = ${e};
+          let scaleValue = ${t};
+          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
+          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
+        }
+      }
+  `}},O1e={kernelName:Io,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,u=n,c=[s,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new P1e(s.shape,o.shape,i.shape,p,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,c,s.dtype,f)}};function M1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s;if(c!=="NHWC")throw new Error(`WebGPU backend FusedConv2D does not support dataFormat:'${c}'. Please use 'NHWC'.`);let m=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!1,m);return xC({x:r,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var z1e={kernelName:Ka,backendName:"webgpu",kernelFunc:M1e};function L1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,a.shape,l,f,u,p,!0),g=[r,a],y=o!=null,x=i!=null;y&&g.push(o),x&&g.push(i);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.batchSize===1&&m.inHeight===m.outHeight&&m.inWidth===m.outWidth&&m.strideHeight===1&&m.strideWidth===1&&m.filterHeight===m.filterWidth&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.filterHeight===3&&m.inChannels%4===0?b=new vC(m,y,d,x):(b=new wC(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.outChannels/m.inChannels]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),n.runWebGPUProgram(b,g,"float32",A)}var B1e={kernelName:Za,backendName:"webgpu",kernelFunc:L1e},W1e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${wn(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+        ${ot()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          var flattenIndex = 0;
+          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+            let indexTemp = i32(round(getIndices(coords[0], j)));
+            let strideNum = ${e};
+            flattenIndex = flattenIndex + indexTemp * strideNum;
+          }
+
+          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
+        }
+      }
+      `}};function V1e(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,u,c,p]=T.prepareAndValidate(s,r),d=at({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=at({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),A=n.bufferSync(s),b=_0e(x,A,s.dtype,u,o,c,p,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new W1e(o,[u,c]),m=[{type:"int32",data:[o]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),y=at({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var U1e={kernelName:yl,backendName:"webgpu",kernelFunc:V1e},G1e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=H1e(this.aShape);return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          let indexZ = i32(getIndices(resRC.x, resRC.z));
+          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
+          setOutputAtIndex(index, inBounds * getA(${e}));
+        }
+      }
+    `}};function H1e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function SC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=v.sizeFromShape(a.shape),p=[],d=at({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=at({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let A=n.tensorMap.get(h.dataId).values,b=We(h.shape,h.dtype,A),k=n.tensorMap.get(d.dataId).values,S=We(d.shape,d.dtype,k),E=D0e(S,b,f);return p.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(u.outputShape,E.dtype,E.values)}let m=new G1e(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let y=at({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeData(x.dataId)),y}var j1e={kernelName:gl,backendName:"webgpu",kernelFunc:SC},q1e=as({opSnippet:Ze.GREATER,cpuKernelImpl:F0e,dtype:"bool"}),X1e={kernelName:Al,backendName:"webgpu",kernelFunc:q1e},K1e=as({opSnippet:Ze.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:$0e}),Z1e={kernelName:So,backendName:"webgpu",kernelFunc:K1e};function Y1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new $h(r.shape,Pe.LEAKYRELU);return i.uniforms="alpha : f32,",n.runWebGPUProgram(i,[r],"float32",o)}var J1e={kernelName:To,backendName:"webgpu",kernelFunc:Y1e},Q1e=as({opSnippet:Ze.LESS,dtype:"bool",cpuKernelImpl:O0e}),ege={kernelName:xl,backendName:"webgpu",kernelFunc:Q1e},tge=as({opSnippet:Ze.LESS_EQUAL,dtype:"bool",cpuKernelImpl:P0e}),nge={kernelName:bl,backendName:"webgpu",kernelFunc:tge},sge=Rn({opType:Pe.LOG,cpuKernelImpl:M0e}),rge={kernelName:No,backendName:"webgpu",kernelFunc:sge},age=as({opSnippet:Ze.LOGICAL_AND,dtype:"bool"}),oge={kernelName:vl,backendName:"webgpu",kernelFunc:age},ige=Rn({opType:Pe.LOGICAL_NOT}),lge={kernelName:Sc,backendName:"webgpu",kernelFunc:ige};function CC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Ph(r,a,o,"max",n)}var uge={kernelName:Eo,backendName:"webgpu",kernelFunc:CC},cge=as({opSnippet:Ze.MAX,cpuKernelImpl:L0e}),dge={kernelName:Ro,backendName:"webgpu",kernelFunc:cge};function pge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1,c=T.computePool2DInfo(r.shape,a,o,u,i,l),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(v.arraysEqual(c.inShape,c.outShape))return js({inputs:{x:r},backend:n});p=new gC(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new mC(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var hge={kernelName:_o,backendName:"webgpu",kernelFunc:pge};function fge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Ph(r,o,a,"mean",n)}var mge={kernelName:Do,backendName:"webgpu",kernelFunc:fge};function gge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ph(r,a,o,"min",n)}var yge={kernelName:$o,backendName:"webgpu",kernelFunc:gge},Age=as({opSnippet:Ze.MIN,cpuKernelImpl:B0e}),xge={kernelName:Fo,backendName:"webgpu",kernelFunc:Age},bge=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),n=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=wn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let start = ${o}(${t});
+          let end = ${o}(${n});
+          var outC = getCoordsFromIndex(index);
+          for (var i = 0; i < ${e}; i = i + 1) {
+            if (${a} < ${s}) {
+              ${a} = ${s} * 2 - ${a} - ${this.offset};
+            } else if(${a} >= ${r}) {
+              ${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
+            }
+          }
+          let coords = outC - start;
+          setOutputAtIndex(index, getX(${i}));
+        }
+      }
+    `}},vge={kernelName:Po,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(c=>({type:"int32",data:[c[0],c[1]]})),l=new bge(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function wge(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=V0e(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new $h(s.shape,Pe.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var kge={kernelName:wl,backendName:"webgpu",kernelFunc:wge};function Ige(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=ir.nonMaxSuppressionV3Impl(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Sge={kernelName:Il,backendName:"webgpu",kernelFunc:Ige};function Cge(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=ir.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Tge={kernelName:Sl,backendName:"webgpu",kernelFunc:Cge};function $m(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Fh({inputs:{input:s},backend:n}),a=$m({inputs:{x:r},backend:n}),o=g2({inputs:{input:s},backend:n}),i=$m({inputs:{x:o},backend:n}),l=od({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return ld({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Nge={kernelName:Ul,backendName:"webgpu",kernelFunc:$m};function TC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Fh({inputs:{input:s},backend:n}),a=TC({inputs:{x:r},backend:n}),o=g2({inputs:{input:s},backend:n}),i=$m({inputs:{x:o},backend:n}),l=od({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return ld({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Ege={kernelName:Cl,backendName:"webgpu",kernelFunc:TC};function Rge(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ey({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=ey({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),u=AC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),u}var _ge={kernelName:Nl,backendName:"webgpu",kernelFunc:Rge},Dge=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=wn(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let start = ${r};
+          let end = ${a};
+          let outC = getCoordsFromIndex(index);
+
+          if (${o} || ${i}) {
+            setOutputAtIndex(index, uniforms.constantValue);
+          } else {
+            let coords = outC - start;
+            setOutputAtIndex(index, getX(${l}));
+          }
+        }
+      }
+    `}},NC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(u=>v.arraysEqual(u,[0,0])))return js({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let u=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return ld({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let l=new Dge(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},$ge={kernelName:Mo,backendName:"webgpu",kernelFunc:NC},Fge=as({opSnippet:Ze.POW}),Pge={kernelName:zo,backendName:"webgpu",kernelFunc:Fge};function Oge(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new hC(Ze.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Mge={kernelName:Lo,backendName:"webgpu",kernelFunc:Oge};function zge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ph(r,a,o,"prod",n)}var Lge={kernelName:Bo,backendName:"webgpu",kernelFunc:zge},Bge=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=H0e(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Wge={kernelName:Nc,backendName:"webgpu",kernelFunc:Bge},EC=as({opSnippet:Ze.DIV}),Vge={kernelName:xo,backendName:"webgpu",kernelFunc:EC},Uge=Rn({opType:Pe.RELU}),Gge={kernelName:Wo,backendName:"webgpu",kernelFunc:Uge},Hge=Rn({opType:Pe.RELU6}),jge={kernelName:Uo,backendName:"webgpu",kernelFunc:Hge},qge=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+      ${ot()}
+        if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let d = coords[3];
+          let rc = coords.yz;
+
+          let effectiveInSize = vec2<f32>(
+            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveOutSize = vec2<f32>(
+            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveInputOverOutputRatioRC =
+              effectiveInSize / effectiveOutSize;
+
+          // Fractional source index
+          let sourceFracIndexRC =
+            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
+            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
+
+          // Compute the four integer indices.
+          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
+          let sourceCeilRC = vec2<i32>(
+            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
+
+          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
+          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
+          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
+          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
+
+          let top = topLeft + (topRight - topLeft) * fracRC.y;
+          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+          let newValue = top + (bottom - top) * fracRC.x;
+
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    `}};function Xge(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,u]=o,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[i?.5:0]}],f=new qge(r.shape,l,u);return n.runWebGPUProgram(f,[r],"float32",h)}var Kge={kernelName:Vo,backendName:"webgpu",kernelFunc:Xge},Zge=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+      ${ot()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let d = coords[3];
+          let rc = coords.yz;
+
+          let effectiveInSize = vec2<f32>(
+            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveOutSize = vec2<f32>(
+            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveInputOverOutputRatioRC =
+              effectiveInSize / effectiveOutSize;
+
+          // Fractional source index
+          let sourceFracIndexRC = ${e};
+
+          // Compute the coordinators of nearest neighbor point.
+          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
+          let sourceNearestRC = vec2<i32>(
+            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
+          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    `}};function Yge(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Zge(r.shape,l,u,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Jge={kernelName:Rc,backendName:"webgpu",kernelFunc:Yge},Qge=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+          cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
+        ${ot()}
+
+          if (index < uniforms.size) {
+            let coords = getCoordsFromIndex(index);
+            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
+                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
+                uniforms.sinRadians;
+            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
+                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
+                uniforms.cosRadians;
+            let coordX = i32(round(coordXFloat + uniforms.centerX));
+            let coordY = i32(round(coordYFloat + uniforms.centerY));
+            ${this.fillSnippet}
+            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
+                coordY < uniforms.xShape[1]) {
+              outputValue = getX(coords[0], coordY, coordX, coords[3]);
+            }
+            setOutputAtIndex(index, outputValue);
+          }
+        }
+      `}},e3e={kernelName:Gl,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Qge(s.shape,a),[u,c]=T.getImageCenter(o,s.shape[1],s.shape[2]),p=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,p)}},t3e=Rn({opType:Pe.RSQRT,cpuKernelImpl:j0e}),n3e={kernelName:Go,backendName:"webgpu",kernelFunc:t3e},s3e=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=tt(e),this.dispatch=Le(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=wn(r.length);this.uniforms=`sliceDim : i32, strides: ${i}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
+      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
+        return index;
+      }
+      `):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
+      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
+        let d0 = index / uniforms.updatesShape[1];
+        let d1 = index - d0 * uniforms.updatesShape[1];
+        return vec2<i32>(d0, d1);
+      }
+      `);let o=`getUpdates(${s})`,i=this.type==="int32"?"atomicAdd(&(result[flatIndex]), i32(updateValue));":`
+     var assumed = atomicLoad(&(result[flatIndex]));
+     var success = 0;
+     for (; success == 0;) {
+       let new = bitcast<f32>(assumed) + updateValue;
+       let newI32 = bitcast<i32>(new);
+       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
+       assumed = resValue[0];
+       success = resValue[1];
+     }
+     `;return`
+    ${a}
+
+      ${ot()}
+
+        if (index < uniforms.size) {
+          let coords = getUpdatesCoordsFromFlatIndex(index);
+          var flattenedIndex = 0;
+          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+            let indexInside = i32(round(${t}));
+            flattenedIndex = flattenedIndex + indexInside * ${n};
+          }
+          let updateValue = ${o};
+          let flatIndex = getOutputIndexFromCoords(${r});
+
+         ${i}
+        }
+      }`}};function r3e(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(a,r,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=at({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=at({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=f.dtype,g=ld({backend:n,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[y]}],A=new s3e(f.shape,i,h.shape.length,f.shape.length,c,d,m),b=n.runWebGPUProgram(A,[f,h],m,x,g),w=at({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var a3e={kernelName:Dl,backendName:"webgpu",kernelFunc:r3e},o3e=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          let cVal = getC(${e});
+          if (cVal >= 1.0) {
+            setOutputAtIndex(index, getA(${t}));
+          } else {
+            setOutputAtIndex(index, getB(${t}));
+          }
+        }
+      }
+    `}};function i3e(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new o3e(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Fn(r.dtype,a.dtype))}var l3e={kernelName:$l,backendName:"webgpu",kernelFunc:i3e},u3e=Rn({opType:Pe.SIGMOID}),c3e={kernelName:jo,backendName:"webgpu",kernelFunc:u3e},d3e=Rn({opType:Pe.SIN}),p3e={kernelName:Ho,backendName:"webgpu",kernelFunc:d3e},h3e=Rn({opType:Pe.SINH}),f3e={kernelName:Pl,backendName:"webgpu",kernelFunc:h3e},RC=as({opSnippet:Ze.SUB,cpuKernelImpl:J0e,supportsComplex:!0}),m3e={kernelName:Yo,backendName:"webgpu",kernelFunc:RC};function g3e(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=CC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=T.expandShapeToKeepDim(i.shape,o),u=at({inputs:{x:i},backend:n,attrs:{shape:l}}),c=RC({inputs:{a:r,b:u},backend:n}),p=IC({inputs:{x:c},backend:n}),d=Nx({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=at({inputs:{x:d},backend:n,attrs:{shape:l}}),f=EC({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(u.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var y3e={kernelName:Ko,backendName:"webgpu",kernelFunc:g3e},A3e=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let u=[],c=NC({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(c.shape,a,i,!1),d=T.getPermuted(p.length,a.length,!1),h=T.getReshapedPermuted(c.shape,a,i,!1),f=at({inputs:{x:c},backend:n,attrs:{shape:p}}),m=ga({inputs:{x:f},backend:n,attrs:{perm:d}}),g=at({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeData(y.dataId)),g},x3e={kernelName:Ol,backendName:"webgpu",kernelFunc:A3e},b3e=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let l=wn(r.length);this.uniforms=`updateSize : i32, sliceDim : i32, strides: ${l},`;let u="";n===1?u="i":n===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+      ${ot()}
+
+        let globalIndex = index * ${this.workPerThread};
+        if (globalIndex < uniforms.size) {
+          var sum = vec4<f32>(0.0);
+          var found = vec4<bool>(false);
+          for (var i = 0; i < uniforms.updateSize; i = i + 1) {
+            var flattenedIndex = 0;
+            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+              let indexInside = i32(round(${this.indicesSnippet}));
+              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
+            }
+            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
+              let curIndex = globalIndex + innerIndex;
+              let coords = getCoordsFromIndex(curIndex);
+              if (flattenedIndex == coords[0]) {
+                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
+                found[innerIndex] = true;
+              }
+            }
+          }
+          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
+            let curIndex = globalIndex + innerIndex;
+            if (curIndex < uniforms.size)
+            {
+              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
+            }
+          }
+        }
+      }`}};function v3e(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=T.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let x=n.bufferSync(r),A=n.bufferSync(a),b=v.decodeString(n.readSync(o.dataId)[0]),w=q0e(x,A,i,d,c,u,l,p,b,h);return n.makeTensorInfo(i,w.dtype,w.values)}let f=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:p}],m=new b3e(u,l,r.shape.length,a.shape.length,p,[d,1],h),g=n.runWebGPUProgram(m,[a,r,o],a.dtype,f),y=at({inputs:{x:g},backend:n,attrs:{shape:i}});return n.disposeData(g.dataId),y}var w3e={kernelName:Gp,backendName:"webgpu",kernelFunc:v3e};function k3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=T.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=id({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var I3e={kernelName:Ml,backendName:"webgpu",kernelFunc:k3e},S3e=Rn({opType:Pe.SQRT}),C3e={kernelName:qo,backendName:"webgpu",kernelFunc:S3e},T3e={kernelName:Pc,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new $h(n.shape,Pe.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},N3e=as({opSnippet:Ze.SQUARED_DIFFERENCE}),E3e={kernelName:Zo,backendName:"webgpu",kernelFunc:N3e},R3e=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=wn(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+       ${ot()}
+         if (index < uniforms.size) {
+           let coords = getCoordsFromIndex(index);
+           setOutputAtIndex(index, getX(${t}));
+         }
+       }
+     `}};function _3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Gt.sliceInfo(r.shape,a,o,i,l,u,c,p,d),w;if(m)w=at({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Gt.computeOutShape(x,A,b),S=id({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=at({inputs:{x:S},backend:n,attrs:{shape:f}}),n.disposeData(S.dataId)}else if(n.shouldExecuteOnCPU([r])){let S=n.readSync(r.dataId),E=We(r.shape,r.dtype,S),R=Z0e(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let S=new R3e(h),E=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(S,[r],r.dtype,E);w=at({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var D3e={kernelName:zl,backendName:"webgpu",kernelFunc:_3e};function $3e(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Y0e(d,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var F3e={kernelName:Hp,backendName:"webgpu",kernelFunc:$3e},P3e=Rn({opType:Pe.TANH}),O3e={kernelName:Jo,backendName:"webgpu",kernelFunc:P3e},M3e=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=z3e(this.rank,"uniforms.");return`
+      ${ot()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          setOutputAtIndex(index, getA(${e}));
+        }
+      }
+    `}};function z3e(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function L3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(d=>v.decodeString(d)):l,c=We(r.shape,r.dtype,u),p=Q0e(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new M3e(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var B3e={kernelName:xa,backendName:"webgpu",kernelFunc:L3e},W3e=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+        dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
+        ${ot()}
+          if (index < uniforms.size) {
+            let outC = getCoordsFromIndex(index);
+            let batch = outC[0];
+            let elemIdx = outC[1];
+            // We compare elements pair-wise within a group of size 2 * inc.
+            // The comparing rule for each group alternates between ascending
+            // and descending. Within each group, we compare each pair at
+            // positions i and i+inc. To decide whether an element at position i
+            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
+            // inc, it is in the first half of the group, we denote it as x0,
+            // otherwise we denote it as x1.
+            // For example, as shown in the Bitonic top K paper referenced
+            // above, Figure5(a) shows that element[1] is in the second half of
+            // the group when group size is 2, but it is in the first half of
+            // the group when group size is 4.
+            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
+            var i = 0;
+            if (isFirstInPair) {
+              i = elemIdx;
+            } else {
+              i = elemIdx - uniforms.inc;
+            }
+
+            var i0 = 0;
+            if (uniforms.firstPass == 1) {
+              i0 = i;
+            } else {
+              i0 = i32(getIndices(batch, i));
+            }
+
+            var i1 = 0;
+            if (uniforms.firstPass == 1) {
+              i1 = i + uniforms.inc;
+            } else {
+              i1 = i32(getIndices(batch, i + uniforms.inc));
+            }
+
+            var x0 = f32(0.0);
+            var x1 = f32(0.0);
+            if (i0 < uniforms.inputSize) {
+              x0 = getX(batch, i0);
+            } else {
+              x0 = uniforms.negativeInf;
+            }
+            if (i1 < uniforms.inputSize) {
+              x1 = getX(batch, i1);
+            } else {
+              x1 = uniforms.negativeInf;
+            }
+
+            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
+            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
+            if (reverse == isGreater) {
+              // Elements in opposite order of direction
+              let iTemp = i0;
+              i0 = i1;
+              i1 = iTemp;
+            }
+            if (isFirstInPair) {
+              setOutputAtIndex(index, f32(i0));
+            } else {
+              setOutputAtIndex(index, f32(i1));
+            }
+          }
+        }
+      `}},V3e=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+        ${ot()}
+          if (index < uniforms.size) {
+            let outC = getCoordsFromIndex(index);
+            let batch = outC[0];
+            let elemIdx = outC[1];
+            // The output size is half of the previous size.
+            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _
+            // (k=4), we only need to output the indices at positions |, the
+            // indices at positions _ can be thrown away, see Figure5(b) After
+            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced
+            // above.
+            // For example, the paper shows we only need to output the orange
+            // bars. The output sequence should look like this | | | | | | | |.
+            // Because the sequence is halved, to map the output index back to
+            // the previous sequence to find the corresponding value, we need
+            // to double the index. When we double the index, we basically
+            // interpolate a position, so 2i looks like
+            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
+            // position of each 2k positions by - elemIdx % k. E.g. for output
+            // at index 4,5,6,7, we want to get the corresponding element at
+            // original index 8,9,10,11, for output at index 8,9,10,11,
+            // we want to get the corresponding element at original index
+            // 16,17,18,19, so on and so forth.
+
+            var i = 0;
+            if (elemIdx < uniforms.k) {
+              i = elemIdx;
+            } else {
+              i = elemIdx * 2 - elemIdx % uniforms.k;
+            }
+            var i0 = 0;
+            if (uniforms.firstPass == 1) {
+              i0 = i;
+            } else {
+              i0 = i32(getIndices(batch, i));
+            }
+            var i1 = 0;
+            if (uniforms.firstPass == 1) {
+              i1 = i + uniforms.k;
+            } else {
+              i1 = i32(getIndices(batch, i + uniforms.k));
+            }
+
+            let x0 = getX(batch, i0);
+            var x1 = f32(0.0);
+            if (i1 < uniforms.inputSize) {
+              x1 = getX(batch, i1);
+            } else {
+              x1 = x0;
+            }
+
+            if (x0 >= x1) {
+              setOutputAtIndex(index, f32(i0));
+            } else {
+              setOutputAtIndex(index, f32(i1));
+            }
+          }
+        }
+      `}};function Fu(e,t){t!==null&&e.disposeData(t.dataId)}function d7(e){let t=1;for(;t<e;)t*=2;return t}function U3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[k,S]=e2e(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(S.shape,S.dtype,S.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,ld({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=v.sizeFromShape(i)/l,p=at({inputs:{x:r},attrs:{shape:[c,l]},backend:n}),d=d7(a),h=d7(l),f=null,m=()=>f===null?[p,p]:[p,f],g=(w,k,S)=>{let E=m(),R=new W3e(S),_=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],D=f;f=n.runWebGPUProgram(R,E,"int32",_),Fu(n,D)};for(let w=1;w<d;w*=2){let k=w*2;for(let S=w;S>=1;S/=2)g(k,S,[c,h])}for(let w=h;w>d;w/=2){let k=m(),S=new V3e([c,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],$=f;f=n.runWebGPUProgram(S,k,"int32",R),Fu(n,$);let _=d/2,D=_*2;for(let C=_;C>=1;C/=2)g(D,C,f.shape)}let y=f;f=id({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Fu(n,y);let x=SC({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Fu(n,p);let A=i.slice(0,-1);A.push(a),y=f,f=at({inputs:{x:f},attrs:{shape:A},backend:n}),Fu(n,y);let b=x;return x=at({inputs:{x},attrs:{shape:A},backend:n}),Fu(n,b),[x,f]}var G3e={kernelName:Bl,backendName:"webgpu",kernelFunc:U3e},H3e=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=tt(this.outputShape),this.dispatch=Le(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+          fn mapCoord(outCoord : f32, len : f32) -> f32{
+            var inCoord = outCoord;
+            if(uniforms.fillModeId == 2) {
+              if (inCoord < 0.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz2 = 2.0 * len;
+                  if (inCoord < sz2) {
+                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
+                    inCoord;
+                  }
+                  if (inCoord < -len) {
+                    inCoord = inCoord + sz2;
+                  } else {
+                    inCoord = -inCoord - 1.0;
+                  }
+                }
+              } else if (inCoord > len - 1.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz2 = 2.0 * len;
+                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
+                  if (inCoord >= len) {
+                    inCoord = sz2 - inCoord - 1.0;
+                  }
+                }
+              }
+              return clamp(inCoord, 0.0, len - 1.0);
+            } else if (uniforms.fillModeId == 3) {
+              if (inCoord < 0.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz = len - 1.0;
+                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
+                }
+              } else if (inCoord > len - 1.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz = len - 1.0;
+                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
+                }
+              }
+              return clamp(inCoord, 0.0, len - 1.0);
+            } else if (uniforms.fillModeId == 4) {
+              return clamp(outCoord, 0.0, len - 1.0);
+            }
+            return outCoord;
+          }
+          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
+            channel : i32) -> f32 {
+            var outputValue : f32;
+            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
+                outputValue = getImage(batch, coordY, coordX, channel);
+            } else {
+              outputValue = uniforms.fillValue;
+            }
+            return outputValue;
+          }
+
+          ${ot()}
+            if (index < uniforms.size) {
+              let coords = getCoordsFromIndex(index);
+              var outputValue : f32;
+              let batch = coords[0];
+              let x = coords[2];
+              let y = coords[1];
+              let channel = coords[3];
+              let xf = f32(x);
+              let yf = f32(y);
+              let a1 = getTransforms(batch, 0);
+              let a2 = getTransforms(batch, 1);
+              let a3 = getTransforms(batch, 2);
+              let b1 = getTransforms(batch, 3);
+              let b2 = getTransforms(batch, 4);
+              let b3 = getTransforms(batch, 5);
+              let c1 = getTransforms(batch, 6);
+              let c2 = getTransforms(batch, 7);
+              let projection = c1 * xf + c2 * yf + 1.0;
+              if (projection == 0.0) {
+                outputValue = uniforms.fillValue;
+              } else {
+                let inX = (a1 * xf + a2 * yf + a3) / projection;
+                let inY = (b1 * xf + b2 * yf + b3) / projection;
+                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
+                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
+
+                if (uniforms.interpolationModeId == 1) {
+                  let coordY = i32(round(mapY));
+                  let coordX = i32(round(mapX));
+                  outputValue = readWithFillValue(batch, coordY, coordX,
+                    channel);
+                } else {
+                  let yFloor = floor(mapY);
+                  let xFloor = floor(mapX);
+                  let yCeil = yFloor + 1.0;
+                  let xCeil = xFloor + 1.0;
+                  let valueYFloor = (xCeil - mapX) *
+                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
+                  let valueYCeil = (xCeil - mapX) *
+                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
+                  outputValue = (yCeil - mapY) * valueYFloor +
+                  (mapY - yFloor) * valueYCeil;
+                }
+              }
+              setOutputAtIndex(index, outputValue);
+            }
+          }
+        `}};function j3e(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new H3e(g),x=o==="nearest"?1:2,A;switch(i){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var q3e={kernelName:Wl,backendName:"webgpu",kernelFunc:j3e};function X3e(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=id({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=at({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var K3e={kernelName:Vl,backendName:"webgpu",kernelFunc:X3e},Z3e=[x0e,s2e,a2e,l2e,f2e,g2e,A2e,b2e,S2e,E2e,_2e,P2e,k0e,L2e,G2e,X2e,Z2e,J2e,t1e,s1e,a1e,l1e,c1e,m1e,y1e,x1e,b1e,v1e,k1e,S1e,T1e,$1e,E1e,_1e,O1e,z1e,B1e,U1e,j1e,X1e,Z1e,w0e,M2e,J1e,ege,nge,rge,oge,lge,uge,dge,hge,mge,yge,xge,vge,d1e,kge,Sge,Tge,C2e,Ege,_ge,$ge,Pge,Mge,Lge,Wge,T2e,Vge,Gge,jge,y0e,Kge,Jge,e3e,n3e,a3e,l3e,c3e,p3e,f3e,k2e,D3e,F3e,y3e,x3e,w3e,I3e,C3e,T3e,E3e,m3e,h1e,O3e,B3e,G3e,q3e,p2e,K3e,Nge];for(let e of Z3e)or(e);var Y3e=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=p7(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=p7(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function p7(e,t){return`${e}_${t}`}var J3e=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,n,s){let r=f7(n),a=e*t*r,o=h7(e,t,n,s);if(this.freeTextures.has(o)||this.freeTextures.set(o,[]),this.usedTextures.has(o)||this.usedTextures.set(o,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(o).length>0){this.numFreeTextures--;let l=this.freeTextures.get(o).shift();return this.usedTextures.get(o).push(l),l}this.numBytesAllocated+=a;let i=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(o).push(i),i}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=h7(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let o=this.usedTextures.get(a),i=o.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");o.splice(i,1);let l=f7(s),u=t*n*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function h7(e,t,n,s){return`${e}_${t}_${n}_${s}`}function f7(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}var Q3e=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},m7=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=s0e(s,o,t,a),l=e.createShaderModule({code:i,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function g7(e,t,n=[],s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}var eye=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),y7=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(o=>o<=n))return r;v.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),v.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},y2=class extends ic{constructor(e,t=!1){if(super(),this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,this.fromPixelTextureLayout=null,this.fromPixelImportTextureLayout=null,!Sx())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Y3e(this.device),this.textureManager=new J3e(this.device),this.tensorMap=new Ep(this,sn()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return y2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.textureDisposalQueue.forEach(e=>this.textureManager.releaseTexture(e.texture,e.width,e.height,e.format,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.textureDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}getTextureManager(){return this.textureManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*nm(n);return this.tensorMap.set(s,{dtype:n,shape:t,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*nm(s);this.tensorMap.set(e,{dtype:s,shape:n,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let n=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=T.mergeRealAndImagArrays(a,o)}else{let r=t.values!=null?t.values:await this.getBufferData(t.bufferInfo.buffer,t.bufferInfo.byteSize);s=dC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,bufferInfo:a}=t;if(s==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a.buffer==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let o=v.sizeFromShape(r)*nm(s),i=this.acquireBuffer(o);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,i,0,o),this.submitQueue();let l=this.makeTensorInfo(r,s),u=sn().makeTensorFromTensorInfo(l),c=this.tensorMap.get(l.dataId);return c.bufferInfo.buffer=i,{tensorRef:u,buffer:i,bufSize:o}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>v.decodeString(s));return We(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,t)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let l;switch(i.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}(n===5||n===6)&&(l=16),t=Math.ceil(t/l)*l,n=i.data.length,s.push(t),t+=i.data.length*4});let r=new ArrayBuffer(t);e.forEach((i,l)=>{let u=s[l];i.type==="int32"?new Int32Array(r,u,i.data.length).set(i.data):i.type==="uint32"?new Uint32Array(r,u,i.data.length).set(i.data):new Float32Array(r,u,i.data.length).set(i.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let o={byteSize:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformDisposalQueue.push(o),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),v.sizeFromShape(r.shape)===0){let k=this.tensorMap.get(r.dataId);return k.values=v.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=y7(this.device,e);let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(k=>k.shape),i="int32";o.map(k=>{a.push({type:i,data:k})});let l=v.computeStrides(r.shape);if(a.push({type:i,data:l}),e.size){let k=v.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?k/4:k]})}s&&(a=[...a,...s]);let u=this.makeUniforms(a),c=t.map((k,S)=>{if(k.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(k.dataId),{dtype:this.tensorMap.get(k.dataId).dtype,shape:k.shape,name:e.variableNames[S]}}),p=c.map(k=>k.dtype).concat(r.dtype),d=c.map(k=>T.getBroadcastDims(k.shape,r.shape)),h=c.map(k=>v.arraysEqual(k.shape,r.shape)).join("_"),f=d.map(k=>k.join("_")).join(";"),m=g7(e,o,p,f,h),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),x=this.getAndSavePipeline(m,()=>m7(this.device,e,y,c,r)),A=this.activeTimers!=null,b=Q3e(this.device,g,t.map(k=>this.tensorToBinding(k)),this.tensorToBinding(r),u);this.ensureCommandEncoderReady();let w=this.getComputePass();return A&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,0),w.setPipeline(x),w.setBindGroup(0,b),w.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),A&&this.supportTimeQuery&&w.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(k=>{this.commandQueueOwnedIds.add(k.dataId)}),this.commandQueueOwnedIds.add(r.dataId),Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),A&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}getFromPixelTextureLayout(e){return e?(this.fromPixelImportTextureLayout===null&&(this.fromPixelImportTextureLayout=this.createFromPixelTextureLayout(!0)),this.fromPixelImportTextureLayout):(this.fromPixelTextureLayout===null&&(this.fromPixelTextureLayout=this.createFromPixelTextureLayout(!1)),this.fromPixelTextureLayout)}createFromPixelTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),e?t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}):t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}copyExternalImageToTexture(e,t){let n=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,s="rgba8unorm",r=this.textureManager.acquireTexture(t[1],t[0],s,n),a=r.createView();this.queue.copyExternalImageToTexture({source:e},{texture:r},[t[1],t[0]]);let o={width:t[1],height:t[0],format:s,usage:n,texture:r};return this.textureDisposalQueue.push(o),a}runFromPixelsProgram(e,t,n,s,r){e.dispatch=y7(this.device,e);let a=this.makeTensorInfo(t,"int32");if(v.sizeFromShape(a.shape)===0){let m=this.tensorMap.get(a.dataId);return m.values=v.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId);let o=g7(e,[a.shape]),i=this.getFromPixelTextureLayout(s),l=this.getAndSavePipeline(o,()=>m7(this.device,e,i.pipelineLayout,[],a,!0)),u;if(s){let m={source:r};u=this.device.importExternalTexture(m)}else u=this.copyExternalImageToTexture(r,a.shape);let c=this.tensorToBinding(a),p=this.makeUniforms(n),d=this.device.createBindGroup({layout:i.bindGroupLayout,entries:[{binding:0,resource:{buffer:c.buffer}},{binding:1,resource:u},{binding:2,resource:{buffer:p.buffer}}]});this.ensureCommandEncoderReady();let h=this.getComputePass(),f=this.activeTimers!=null;return f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,0),h.setPipeline(l),h.setBindGroup(0,d),h.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),f&&this.supportTimeQuery&&h.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a.dataId),this.dispatchNumberInEncoder++,Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),f&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=eye){return Y().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};y2.nextDataId=0;var _C={};Ve(_C,{WebGPUBackend:()=>y2,webgpu_util:()=>uC});Sx()&&Hl("webgpu",async()=>{Y().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Y().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new y2(a,r)},3);var tye="3.18.0",nye="3.18.0",sye="3.18.0",rye="3.18.0",aye="3.18.0",oye="3.18.0",iye="3.18.0",Oh={tfjs:tye,"tfjs-core":nye,"tfjs-data":sye,"tfjs-layers":rye,"tfjs-converter":aye,"tfjs-backend-webgl":oye,"tfjs-backend-wasm":iye};var DC=`
+  precision highp float;
+  attribute vec2 pos;
+  attribute vec2 uv;
+  varying vec2 vUv;
+  uniform float flipY;
+  void main(void) {
+    vUv = uv;
+    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
+  }
+`;var $C=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
+    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
+  }
+`,FC=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
+    gl_FragColor.a = c.a;
+  }
+`,PC=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform vec2 size;
+  uniform sampler2D texture;
+  vec2 pixelate(vec2 coord, vec2 size) {
+    return floor( coord / size ) * size;
+  }
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    vec2 coord = pixelate(vUv, size);
+    gl_FragColor += texture2D(texture, coord);
+  }
+`,OC=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
+    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;
+    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
+  }
+`,MC=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  uniform float m[9];
+  void main(void) {
+    vec4 c11 = texture2D(texture, vUv - px); // top left
+    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
+    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
+    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
+    vec4 c22 = texture2D(texture, vUv); // mid center
+    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
+    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
+    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
+    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
+    gl_FragColor = 
+    c11 * m[0] + c12 * m[1] + c22 * m[2] +
+    c21 * m[3] + c22 * m[4] + c23 * m[5] +
+    c31 * m[6] + c32 * m[7] + c33 * m[8];
+    gl_FragColor.a = c22.a;
+  }
+`;var Ex=(e,t,n)=>{let s=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(s,(r,a)=>(n[a]=0,r))},Rx=class{constructor(t,n,s){ge(this,"uniform",{});ge(this,"attribute",{});ge(this,"gl");ge(this,"id");ge(this,"compile",(t,n)=>{let s=this.gl.createShader(n);return s?(this.gl.shaderSource(s,t),this.gl.compileShader(s),this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS)?s:(oe(`filter: gl compile failed: ${this.gl.getShaderInfoLog(s)}`),null)):(oe("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),a=this.compile(s,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!a)){if(!this.id){oe("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,a),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){oe(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),Ex(n,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=this.gl.getAttribLocation(this.id,o);Ex(n,"uniform",this.uniform),Ex(s,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=this.gl.getUniformLocation(this.id,o)}}};function zC(){let e=0,t=null,n=!1,s=-1,r=[null,null],a=[],o=null,i=null,l=os(100,100),u={},c={INTERMEDIATE:1},p=l.getContext("webgl");if(!p){oe("filter: cannot get webgl context");return}this.gl=p;function d(x,A){if(!(x===l.width&&A===l.height)){if(l.width=x,l.height=A,!o){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);o=p.createBuffer(),p.bindBuffer(p.ARRAY_BUFFER,o),p.bufferData(p.ARRAY_BUFFER,b,p.STATIC_DRAW),p.pixelStorei(p.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}p.viewport(0,0,l.width,l.height),r=[null,null]}}function h(x,A){let b=p.createFramebuffer();p.bindFramebuffer(p.FRAMEBUFFER,b);let w=p.createRenderbuffer();p.bindRenderbuffer(p.RENDERBUFFER,w);let k=p.createTexture();return 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t={};t.boxStarts=Oe(e,[0,1],[-1,2]),t.centers=ce(t.boxStarts,mT),t.boxSizes=Oe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Lh),t.centersNormalized=pe(t.centers,Lh),t.halfBoxSize=pe(t.boxSizesNormalized,nt.tf2),t.starts=fe(t.centersNormalized,t.halfBoxSize),t.ends=ce(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Lh),t.endNormalized=L(t.ends,Lh);let n=zc([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>ne(t[s])),n}async function yT(e,t){var i,l,u,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=Se.resizeBilinear(e,[ci,ci]),n.div=pe(n.resized,nt.tf127),n.normalized=fe(n.div,nt.tf05);let s=Yr==null?void 0:Yr.execute(n.normalized);if(Array.isArray(s)&&s.length>2){let p=s.sort((d,h)=>d.size-h.size);n.concat384=St([p[0],p[2]],2),n.concat512=St([p[1],p[3]],2),n.concat=St([n.concat512,n.concat384],1),n.batch=et(n.concat,0)}else 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E2={};ra(E2,{connected:()=>Yx,kpt:()=>Zx});var Zx=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Yx={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var xT=224,SAe,CAe=5,R2=[8,16,32,32,32];async function bT(){let e=[],t=0;for(;t<CAe;){let n=0,s=t;for(;s<R2.length&&R2[s]===R2[t];)n+=2,s++;let r=R2[t],a=Math.ceil(xT/r),o=Math.ceil(xT/r);for(let i=0;i<a;++i)for(let l=0;l<o;++l)for(let u=0;u<n;++u)e.push({x:(l+.5)/o,y:(i+.5)/a});t=s}SAe={x:$t(e.map(n=>n.x)),y:$t(e.map(n=>n.y))}}function Ia(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function vT(e,t=[1,1]){let n=[e.map(u=>u[0]),e.map(u=>u[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function _2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var IT={initial:!0},Fs={detector:null,landmarks:null},hd={detector:[224,224],landmarks:[256,256]},Jx=Number.MAX_SAFE_INTEGER,NAe={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},$2=null,Bh,di=[[0,0],[0,0],[0,0],[0,0]],wT=0,kT=e=>1-1/(1+Math.exp(e));async function ST(e){if(IT.initial&&(Fs.detector=null),!Fs.detector&&e.body.detector&&e.body.detector.modelPath){Fs.detector=await je(e.body.detector.modelPath);let t=Object.values(Fs.detector.modelSignature.inputs);hd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,hd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&Fs.detector&&oe("cached model:",Fs.detector.modelUrl);return await bT(),Fs.detector}async function CT(e){if(IT.initial&&(Fs.landmarks=null),Fs.landmarks)e.debug&&oe("cached model:",Fs.landmarks.modelUrl);else{Fs.landmarks=await je(e.body.modelPath);let t=Object.values(Fs.landmarks.modelSignature.inputs);hd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,hd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return Fs.landmarks}async function EAe(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let s;if(Bh&&(n.cropped=Se.cropAndResize(e,[Bh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],a=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];di=[[0,0],r,a,[0,0]],n.pad=Ks(n.cropped||e,di),n.resize=Se.resizeBilinear(n.pad,[t,t]),s=pe(n.resize,nt.tf255)}else e.shape[1]!==t?(n.resize=Se.resizeBilinear(n.cropped||e,[t,t]),s=pe(n.resize,nt.tf255)):s=pe(n.cropped||e,nt.tf255);return Object.keys(n).forEach(r=>ne(n[r])),s}function RAe(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+di[2][0]+di[2][1])/t[0]-di[2][0]),Math.trunc(n.position[1]*(t[1]+di[1][0]+di[1][1])/t[1]-di[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(Bh)for(let n of e)n.positionRaw=[n.positionRaw[0]+Bh[1],n.positionRaw[1]+Bh[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}async function _Ae(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),s=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(s.position[2]||0))/2;let r=e.find(i=>i.part==="rightPalm"),a=e.find(i=>i.part==="rightWrist"),o=e.find(i=>i.part==="rightIndex");r.position[2]=((a.position[2]||0)+(o.position[2]||0))/2}async function DAe(e,t,n){var f;let s={};[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(f=Fs.landmarks)==null?void 0:f.execute(e,NAe.landmarks);let r=(await s.poseflag.data())[0],a=await s.ld.data(),o=await s.world.data();Object.keys(s).forEach(m=>ne(s[m]));let i=[],l=5;for(let m=0;m<a.length/l;m++){let g=kT(a[l*m+3]),y=kT(a[l*m+4]),x=Math.trunc(100*g*y*r)/100,A=[a[l*m+0]/hd.landmarks[0],a[l*m+1]/hd.landmarks[1],a[l*m+2]+0],b=[Math.trunc(n[0]*A[0]),Math.trunc(n[1]*A[1]),A[2]],w=[o[l*m+0],o[l*m+1],o[l*m+2]+0];i.push({part:Zx[m],positionRaw:A,position:b,distance:w,score:x})}if(r<(t.body.minConfidence||0))return null;_Ae(i);let u=RAe(i,n),c=u.map(m=>m.position),p=Ia(c,[n[0],n[1]]),d={};for(let[m,g]of Object.entries(Yx)){let y=[];for(let x=0;x<g.length-1;x++){let A=u.find(w=>w.part===g[x]),b=u.find(w=>w.part===g[x+1]);A&&b&&y.push([A.position,b.position])}d[m]=y}return{id:0,score:Math.trunc(100*r)/100,box:p.box,boxRaw:p.boxRaw,keypoints:u,annotations:d}}async function Qx(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ue()-wT,r=Jx<(t.body.skipFrames||0);if(t.skipAllowed&&s&&r&&$2!==null)Jx++;else{let a={};a.landmarks=await EAe(e,256),$2=await DAe(a.landmarks,t,n),Object.keys(a).forEach(o=>ne(a[o])),wT=ue(),Jx=0}return $2?[$2]:[]}var fd=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Sa,iu=0,eb=[],NT=0,tb=Number.MAX_SAFE_INTEGER;async function ET(e){if(me.initial&&(Sa=null),Sa)e.debug&&oe("cached model:",Sa.modelUrl);else{Sa=await je(e.object.modelPath);let t=Object.values(Sa.modelSignature.inputs);iu=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return Sa}async function $Ae(e,t,n){if(!e)return[];let s={},r=[],a=await e.array();s.squeeze=et(e);let o=Yt(s.squeeze,6,1);s.stack=ln([o[1],o[0],o[3],o[2]],1),s.boxes=et(s.stack),s.scores=et(o[4]),s.classes=et(o[5]),ne([e,...o]),s.nms=await Se.nonMaxSuppressionAsync(s.boxes,s.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence||0);let i=await s.nms.data(),l=0;for(let u of Array.from(i)){let c=Math.trunc(100*a[0][u][4])/100,p=a[0][u][5],d=fd[p].label,[h,f]=[a[0][u][0]/iu,a[0][u][1]/iu],m=[h,f,a[0][u][2]/iu-h,a[0][u][3]/iu-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];r.push({id:l++,score:c,class:p,label:d,box:g,boxRaw:m})}return Object.keys(s).forEach(u=>ne(s[u])),r}async function nb(e,t){let n=(t.object.skipTime||0)>ue()-NT,s=tb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&eb.length>0?(tb++,eb):(tb=0,new Promise(async r=>{let a=[e.shape[2]||0,e.shape[1]||0],o=Se.resizeBilinear(e,[iu,iu]),i=t.object.enabled?Sa==null?void 0:Sa.execute(o,["tower_0/detections"]):null;NT=ue(),ne(o);let l=await $Ae(i,a,t);eb=l,r(l)}))}var F2={};ra(F2,{connected:()=>rb,kpt:()=>sb});var sb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],rb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Mn,_T=0,ls={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},ab=Number.MAX_SAFE_INTEGER;async function DT(e){return me.initial&&(Mn=null),Mn?e.debug&&oe("cached model:",Mn.modelUrl):Mn=await je(e.body.modelPath),Mn}async function FAe(e,t){let[n,s]=e.shape,r=U(e,[s*n]),a=mn(r,0),o=(await a.data())[0];if(ne([r,a]),o>t){let i=Cs(r,0),l=Xl(i,n),u=(await l.data())[0],c=pe(i,Ce(n,"int32")),p=(await c.data())[0];return ne([l,c]),[u,p,o]}return[0,0,o]}async function ob(e,t){let n=(t.body.skipTime||0)>ue()-_T,s=ab<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(ls.keypoints).length>0?(ab++,[ls]):(ab=0,new Promise(async r=>{var p;let a=K(()=>{if(!(Mn!=null&&Mn.inputs[0].shape))return null;let d=Se.resizeBilinear(e,[Mn.inputs[0].shape[2],Mn.inputs[0].shape[1]],!1),h=L(d,nt.tf2);return fe(h,nt.tf1)}),o;if(t.body.enabled&&(o=Mn==null?void 0:Mn.execute(a)),_T=ue(),ne(a),o){ls.keypoints.length=0;let d=o.squeeze();ne(o);let h=d.unstack(2);ne(d);for(let f=0;f<h.length;f++){let[m,g,y]=await FAe(h[f],t.body.minConfidence);y>(((p=t.body)==null?void 0:p.minConfidence)||0)&&ls.keypoints.push({score:Math.round(100*y)/100,part:sb[f],positionRaw:[m/Mn.inputs[0].shape[2],g/Mn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Mn.inputs[0].shape[2]),Math.round(e.shape[1]*g/Mn.inputs[0].shape[1])]})}h.forEach(f=>ne(f))}ls.score=ls.keypoints.reduce((d,h)=>h.score>d?h.score:d,0);let i=ls.keypoints.map(d=>d.position[0]),l=ls.keypoints.map(d=>d.position[1]);ls.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let u=ls.keypoints.map(d=>d.positionRaw[0]),c=ls.keypoints.map(d=>d.positionRaw[1]);ls.boxRaw=[Math.min(...u),Math.min(...c),Math.max(...u)-Math.min(...u),Math.max(...c)-Math.min(...c)];for(let[d,h]of Object.entries(rb)){let f=[];for(let m=0;m<h.length-1;m++){let g=ls.keypoints.find(x=>x.part===h[m]),y=ls.keypoints.find(x=>x.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}ls.annotations[d]=f}r([ls])}))}var PAe=["angry","disgust","fear","happy","sad","surprise","neutral"],Ys,P2=[],FT=0,PT=0,ib=Number.MAX_SAFE_INTEGER;async function OT(e){var t;return me.initial&&(Ys=null),Ys?e.debug&&oe("cached model:",Ys.modelUrl):Ys=await je((t=e.face.emotion)==null?void 0:t.modelPath),Ys}async function lb(e,t,n,s){var o,i;if(!Ys)return[];let r=ib<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ue()-PT;return t.skipAllowed&&a&&r&&FT===s&&P2[n]&&P2[n].length>0?(ib++,P2[n]):(ib=0,new Promise(async l=>{var c,p;let u=[];if((c=t.face.emotion)!=null&&c.enabled){let d={},h=Ys!=null&&Ys.inputs[0].shape?Ys.inputs[0].shape[2]:0;d.resize=Se.resizeBilinear(e,[h,h],!1),d.channels=L(d.resize,nt.rgb),d.grayscale=ke(d.channels,3,!0),d.grayscaleSub=fe(d.grayscale,nt.tf05),d.grayscaleMul=L(d.grayscaleSub,nt.tf2),d.emotion=Ys==null?void 0:Ys.execute(d.grayscaleMul),PT=ue();let f=await d.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((p=t.face.emotion)==null?void 0:p.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:PAe[m]});u.sort((m,g)=>g.score-m.score),Object.keys(d).forEach(m=>ne(d[m]))}P2[n]=u,FT=s,l(u)}))}var Ps,ub=[],zT=0,LT=0,BT=Number.MAX_SAFE_INTEGER;async function WT(e){return me.initial&&(Ps=null),Ps?e.debug&&oe("cached model:",Ps.modelUrl):Ps=await je(e.face.mobilefacenet.modelPath),Ps}async function cb(e,t,n,s){var o,i;if(!Ps)return[];let r=BT<(((o=t.face.embedding)==null?void 0:o.skipFrames)||0),a=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>ue()-LT;return t.skipAllowed&&a&&r&&zT===s&&ub[n]?(BT++,ub[n]):new Promise(async l=>{var c;let u=[];if(((c=t.face.embedding)==null?void 0:c.enabled)&&(Ps==null?void 0:Ps.inputs[0].shape)){let p={};p.crop=Se.resizeBilinear(e,[Ps.inputs[0].shape[2],Ps.inputs[0].shape[1]],!1),p.data=Ps==null?void 0:Ps.execute(p.crop);let d=await p.data.data();u=Array.from(d)}ub[n]=u,zT=s,LT=ue(),l(u)})}var Ca,pi=0,OAe=2.3,db=pr.leftEyeLower0,pb=pr.rightEyeLower0,md={leftBounds:[db[0],db[db.length-1]],rightBounds:[pb[0],pb[pb.length-1]]},gd={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function jT(e){var t;return me.initial&&(Ca=null),Ca?e.debug&&oe("cached model:",Ca.modelUrl):Ca=await je((t=e.face.iris)==null?void 0:t.modelPath),pi=Ca.inputs[0].shape?Ca.inputs[0].shape[2]:0,pi===-1&&(pi=64),Ca}function O2(e,t,n,s){for(let r=0;r<Hx.length;r++){let{key:a,indices:o}=Hx[r],i=pr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var MAe=e=>{let t=e[md.leftBounds[0]][2],n=e[md.rightBounds[0]][2];return t-n},UT=(e,t,n,s,r,a=!1)=>{let o=N2(T2(lT([e[n],e[s]]),OAe)),i=dd(o),l=Se.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[pi,pi]);if(a&&me.kernels.includes("flipleftright")){let u=Se.flipLeftRight(l);ne(l),l=u}return{box:o,boxSize:i,crop:l}},GT=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<gd.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/pi:o/pi)*n[0]+t.startPoint[0],i/pi*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(gd.index)}},HT=(e,t,n)=>{let s=e[pr[`${n}EyeUpper0`][gd.upperCenter]][2],r=e[pr[`${n}EyeLower0`][gd.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function qT(e,t,n,s){if(!Ca)return n.debug&&oe("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=UT(e,t,md.leftBounds[0],md.leftBounds[1],s,!0),{box:i,boxSize:l,crop:u}=UT(e,t,md.rightBounds[0],md.rightBounds[1],s,!0),c=St([o,u]);ne(o),ne(u);let p=Ca.execute(c);ne(c);let d=await p.data();ne(p);let h=d.slice(0,gd.numCoordinates*3),{rawCoords:f,iris:m}=GT(h,r,a,!0),g=d.slice(gd.numCoordinates*3),{rawCoords:y,iris:x}=GT(g,i,l,!1),A=MAe(e);Math.abs(A)<30?(O2(e,f,"left",null),O2(e,y,"right",null)):A<1?O2(e,f,"left",["EyeUpper0","EyeLower0"]):O2(e,y,"right",["EyeUpper0","EyeLower0"]);let b=HT(e,m,"left"),w=HT(e,x,"right");return e.concat(b).concat(w)}var zAe=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],LAe=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],BAe=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],WAe=[[474,475],[475,476],[476,477],[477,474]],VAe=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],UAe=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],GAe=[[469,470],[470,471],[471,472],[472,469]],HAe=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function hi(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var jAe={lips:hi(zAe),leftEye:hi(LAe),leftEyebrow:hi(BAe),leftIris:hi(WAe),rightEye:hi(VAe),rightEyebrow:hi(UAe),rightIris:hi(GAe),faceOval:hi(HAe)},qAe=Object.entries(jAe).map(([e,t])=>t.map(n=>[n,e])).flat(),U8e=new Map(qAe),Wh=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],lu=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],uu=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function ZT(e,t){let n={lips:await t.filter(a=>a.size===160)[0].data(),irisL:await t.filter(a=>a.size===10)[0].data(),eyeL:await t.filter(a=>a.size===142)[0].data(),irisR:await t.filter(a=>a.size===10)[1].data(),eyeR:await t.filter(a=>a.size===142)[1].data()},s=lu.reduce((a,o)=>a+=e[o][2],0)/lu.length;for(let a=0;a<n.irisL.length/2;a++)e.push([n.irisL[2*a+0],n.irisL[2*a+1],s]);let r=uu.reduce((a,o)=>a+=e[o][2],0)/uu.length;for(let a=0;a<n.irisR.length/2;a++)e.push([n.irisR[2*a+0],n.irisR[2*a+1],r]);for(let a=0;a<n.eyeL.length/2;a++)e[lu[a]]=[n.eyeL[2*a+0],n.eyeL[2*a+1],e[lu[a]][2]];for(let a=0;a<n.eyeR.length/2;a++)e[uu[a]]=[n.eyeR[2*a+0],n.eyeR[2*a+1],e[uu[a]][2]];for(let a=0;a<n.lips.length/2;a++)e[Wh[a]]=[n.lips[2*a+0],n.lips[2*a+1],e[Wh[a]][2]];return e}var Jr={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},zn=null,cu=0;async function YT(e,t){var i,l,u,c,p,d,h,f,m,g,y;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ue()-Jr.timestamp,s=Jr.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!n||!s||Jr.boxes.length===0?(Jr.boxes=await yT(e,t),Jr.timestamp=ue(),Jr.skipped=0):Jr.skipped++;let r=[],a=[],o=0;for(let x=0;x<Jr.boxes.length;x++){let A=Jr.boxes[x],b=0,w,k={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,k.tensor]=pT((u=t.face.detector)==null?void 0:u.rotation,A,e,(c=t.face.mesh)!=null&&c.enabled?cu:pd()),(p=t==null?void 0:t.filter)!=null&&p.equalization){let S=await A2(k.tensor);ne(k.tensor),k.tensor=S}if(k.boxScore=Math.round(100*A.confidence)/100,(d=t.face.mesh)!=null&&d.enabled)if(!zn)t.debug&&oe("face mesh detection requested, but model is not loaded");else{let S=zn.execute(k.tensor),R=await S.find($=>$.shape[$.shape.length-1]===1).data();if(k.faceScore=Math.round(100*R[0])/100,k.faceScore<(((h=t.face.detector)==null?void 0:h.minConfidence)||1)){if(A.confidence=k.faceScore,(f=t.face.mesh)!=null&&f.keepInvalid){k.box=S2(A,e),k.boxRaw=C2(A,e),k.score=k.boxScore,k.mesh=A.landmarks.map($=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*$[0]/pd(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*$[1]/pd()]),k.meshRaw=k.mesh.map($=>[$[0]/(e.shape[2]||1),$[1]/(e.shape[1]||1),($[2]||0)/cu]);for(let $ of Object.keys(ru))k.annotations[$]=[k.mesh[ru[$]]]}}else{let $=S.find(P=>P.shape[P.shape.length-1]===1404),_=U($,[-1,3]),D=await _.array();ne(_),(m=t.face.attention)!=null&&m.enabled?D=await ZT(D,S):(g=t.face.iris)!=null&&g.enabled&&(D=await qT(D,k.tensor,t,cu)),k.mesh=dT(D,A,b,w,cu),k.meshRaw=k.mesh.map(P=>[P[0]/(e.shape[2]||0),P[1]/(e.shape[1]||0),(P[2]||0)/cu]);for(let P of Object.keys(pr))k.annotations[P]=pr[P].map(V=>k.mesh[V]);k.score=k.faceScore;let C={...hT(k.mesh,A),confidence:A.confidence,landmarks:A.landmarks};k.box=S2(C,e),k.boxRaw=C2(C,e),a.push(C)}ne(S)}else{k.box=S2(A,e),k.boxRaw=C2(A,e),k.score=k.boxScore,k.mesh=A.landmarks.map(S=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*S[0]/pd(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*S[1]/pd()]),k.meshRaw=k.mesh.map(S=>[S[0]/(e.shape[2]||0),S[1]/(e.shape[1]||0),(S[2]||0)/cu]);for(let S of Object.keys(ru))k.annotations[S]=[k.mesh[ru[S]]]}k.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(k):ne(k.tensor)}return Jr.boxes=a,r}async function JT(e){var t,n,s,r,a,o;return me.initial&&(zn=null),((n=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:n.enabled)&&(zn==null?void 0:zn.signature)&&Object.keys(((s=zn==null?void 0:zn.signature)==null?void 0:s.outputs)||{}).length<6&&(zn=null),zn?e.debug&&oe("cached model:",zn.modelUrl):(r=e.face.attention)!=null&&r.enabled?zn=await je((a=e.face.attention)==null?void 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s={};s.reshape=U(t,[-1,7,2]),s.div=pe(s.reshape,this.inputSizeTensor),s.landmarks=ce(s.div,this.anchors[n]);let r=L(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>ne(s[a])),r}async predict(t,n){let s={};s.resize=Se.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=pe(s.resize,nt.tf127),s.image=fe(s.div,nt.tf1),s.batched=this.model.execute(s.image),s.predictions=et(s.batched),s.slice=Oe(s.predictions,[0,0],[-1,1]),s.sigmoid=Cn(s.slice),s.scores=et(s.sigmoid);let r=await s.scores.data();s.boxes=Oe(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await Se.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l={};l.box=Oe(s.norm,[i,0],[1,-1]),l.slice=Oe(s.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=U(l.norm,[-1,2]);let u=await l.box.data(),c=u.slice(0,2),p=u.slice(2,4),d=await l.palmLandmarks.array(),h={startPoint:c,endPoint:p,palmLandmarks:d,confidence:r[i]},f=iN(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>ne(l[m]))}return Object.keys(s).forEach(i=>ne(s[i])),o}};var QAe=5,pN=1.65,hN=[0,5,9,13,17,1,2],e5e=0,t5e=2,fN=0,V2=class{constructor(t,n){ge(this,"handDetector");ge(this,"handPoseModel");ge(this,"inputSize");ge(this,"storedBoxes");ge(this,"skipped");ge(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>xb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return L2(B2(r),QAe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=L2(B2(n),pN);s.palmLandmarks=[];for(let r=0;r<hN.length;r++)s.palmLandmarks.push(t[hN[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=z2(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=Ab(s,[0,0]),u=i.map(h=>[...xb(h,l),h[2]]),c=uN(r),p=[...Vh(n),1],d=[fi(p,c[0]),fi(p,c[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ue()-fN,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(n.hand.landmarks){let c=n.hand.rotation?lN(u.palmLandmarks[e5e],u.palmLandmarks[t5e]):0,p=Vh(u),d=[p[0]/t.shape[2],p[1]/t.shape[1]],h=n.hand.rotation&&me.kernels.includes("rotatewithoffset")?Se.rotateWithOffset(t,c,0,d):t.clone(),f=Ab(-c,p),m=s?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=oN(m,h,[this.inputSize,this.inputSize]),y=pe(g,nt.tf255);ne(g),ne(h);let[x,A]=this.handPoseModel.execute(y);fN=ue(),ne(y);let b=(await x.data())[0];if(ne(x),b>=n.hand.minConfidence/4){let w=U(A,[-1,3]),k=await w.array();ne(A),ne(w);let S=this.transformRawCoords(k,m,c,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(R)}else this.storedBoxes[l]=null;ne(A)}else{let 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us={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>us.nameMapping[e],getPoints:e=>us.pointsMapping[e]},gi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>gi.nameMapping[e]},qt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>qt.nameMapping[e]},mi=class{constructor(t){ge(this,"name");ge(this,"curls");ge(this,"directions");ge(this,"weights");ge(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof 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g=Math.sqrt(r*r+i*i),y=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+u*u),A=Math.max(g,y,x),b=e[0],w=e[1],k=n[0],S=n[1];A===g?(k=n[0],S=n[1]):A===x&&(b=t[0],w=t[1]);let $=AN([b,w],[k,S]),_=yN($,hu.TOTAL_ANGLE_VOTE_POWER);d+=_[0],h+=_[1],f+=_[2];for(let C of s){let P=yN(C,hu.SINGLE_ANGLE_VOTE_POWER);d+=P[0],h+=P[1],f+=P[2]}let D;return d===Math.max(d,h,f)?D=bN(l,i,u,p):f===Math.max(h,f)?D=xN(a,r,o,c):D=l5e(l,i,u,p,a,r,o,c),D}function vN(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of us.all){let o=us.getPoints(a),i=[],l=[];for(let u of o){let c=e[u[0]],p=e[u[1]],d=AN(c,p),h=d[0],f=d[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of us.all){let o=a===us.thumb?1:0,i=us.getPoints(a),l=e[i[o][0]],u=e[i[o+1][1]],c=e[i[3][1]],p=i5e(l,u,c),d=u5e(l,u,c,t[a].slice(o));s[a]=p,r[a]=d}return{curls:s,directions:r}}function U2(e){if(!e||e.length===0)return null;let t=vN(e),n={};for(let s of us.all)n[us.getName(s)]={curl:gi.getName(t.curls[s]),direction:qt.getName(t.directions[s])};return n}function wN(e){let t=[];if(!e||e.length===0)return t;let n=vN(e);for(let s of mN){let r=s.matchAgainst(n.curls,n.directions);r>=o5e&&t.push({name:s.name,confidence:r})}return t}var kN={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},bd,vd,IN;async function wb(e,t){let n=await IN.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let c of Object.keys(kN))a[c]=kN[c].map(p=>n[r].landmarks[p]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let c of o)c[0]<i[0]&&(i[0]=c[0]),c[1]<i[1]&&(i[1]=c[1]),c[0]>i[2]&&(i[2]=c[0]),c[1]>i[3]&&(i[3]=c[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let u=U2(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:u})}return s}async function kb(e){var n,s;me.initial&&(bd=null,vd=null),!bd||!vd?[bd,vd]=await Promise.all([e.hand.enabled?je((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?je((s=e.hand.skeleton)==null?void 0:s.modelPath):null]):(e.debug&&oe("cached 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n=Object.values(An[0].modelSignature.inputs);bi[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,bi[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return An[0]}async function RN(e){var t;if(me.initial&&(An[1]=null),An[1])e.debug&&oe("cached model:",An[1].modelUrl);else{An[1]=await je((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=Object.values(An[1].modelSignature.inputs);bi[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,bi[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return An[1]}async function f5e(e,t){let n=[];if(!e||!An[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,p5e),o=Math.round(a*r/8)*8;s.resize=Se.resizeBilinear(e,[a,o]),s.cast=he(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await An[0].executeAsync(s.cast,c5e),s.boxes=et(s.rawBoxes,[0,2]),s.scores=et(s.rawScores,[0]);let i=Qn(s.scores,1);ne(i[CN]),i.splice(CN,1),s.filtered=ln(i,1),ne(i),s.max=mn(s.filtered,1),s.argmax=Cs(s.filtered,1);let l=0;s.nms=await Se.nonMaxSuppressionAsync(s.boxes,s.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await s.nms.data(),c=await s.max.data(),p=await s.argmax.data();for(let d of Array.from(u)){let h=Oe(s.boxes,d,1),f=await h.data();ne(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=_2(m,h5e),y=[Math.trunc(m[0]*Ea[0]),Math.trunc(m[1]*Ea[1]),Math.trunc(m[2]*Ea[0]),Math.trunc(m[3]*Ea[1])],x=c[d],A=d5e[p[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};n.push(b)}return Object.keys(s).forEach(d=>ne(s[d])),n.sort((d,h)=>h.score-d.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Sb(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&An[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},a=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Se.cropAndResize(e,[a],[0],[bi[1][0],bi[1][1]],"bilinear"),r.div=pe(r.crop,nt.tf255),[r.score,r.keypoints]=An[1].execute(r.div,["Identity_1","Identity"]);let o=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(o))))/100;if(i>=(n.hand.minConfidence||0)){s.fingerScore=i,r.reshaped=U(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(p=>[p[0]/bi[1][1],p[1]/bi[1][0],p[2]||0]).map(p=>[p[0]*t.boxRaw[2],p[1]*t.boxRaw[3],p[2]||0]);s.keypoints=c.map(p=>[Ea[0]*(p[0]+t.boxRaw[0]),Ea[1]*(p[1]+t.boxRaw[1]),p[2]||0]),s.landmarks=U2(s.keypoints);for(let p of Object.keys(NN))s.annotations[p]=NN[p].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(l=>ne(r[l]))}return s}async function Cb(e,t){var r,a;if(!An[0]||!An[1]||!((r=An[0])!=null&&r.inputs[0].shape)||!((a=An[1])!=null&&a.inputs[0].shape))return[];Ea=[e.shape[2]||0,e.shape[1]||0],G2++;let n=(t.hand.skipTime||0)>ue()-Ib,s=G2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?Qt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ue()-Ib,l=G2<3*(t.hand.skipFrames||0);t.skipAllowed&&Qt.hands.length===t.hand.maxDetected?Qt.hands=await Promise.all(Qt.boxes.map(c=>Sb(e,c,t))):t.skipAllowed&&i&&l&&Qt.hands.length>0?Qt.hands=await Promise.all(Qt.boxes.map(c=>Sb(e,c,t))):(Qt.boxes=await f5e(e,t),Ib=ue(),Qt.hands=await Promise.all(Qt.boxes.map(c=>Sb(e,c,t))),G2=0);let u=[...Qt.boxes];if(Qt.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<Qt.hands.length;c++){let p=vT(Qt.hands[c].keypoints,Ea);if(p.box[2]/(e.shape[2]||1)>.05&&p.box[3]/(e.shape[1]||1)>.05&&Qt.hands[c].fingerScore&&Qt.hands[c].fingerScore>(t.hand.minConfidence||0)){let d=_2(p.box,TN),h=_2(p.boxRaw,TN);Qt.boxes.push({...u[c],box:d,boxRaw:h})}}for(let c=0;c<Qt.hands.length;c++){let p=Ia(Qt.hands[c].keypoints,Ea);Qt.hands[c].box=p.box,Qt.hands[c].boxRaw=p.boxRaw}o(Qt.hands)})}var Ln,j2=[],Tb=Number.MAX_SAFE_INTEGER,DN=0,$N=0;async function FN(e){var t;return me.initial&&(Ln=null),Ln?e.debug&&oe("cached model:",Ln.modelUrl):Ln=await je((t=e.face.liveness)==null?void 0:t.modelPath),Ln}async function Nb(e,t,n,s){var o,i;if(!Ln)return 0;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ue()-$N,a=Tb<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&DN===s&&j2[n]?(Tb++,j2[n]):(Tb=0,new Promise(async l=>{let u=Se.resizeBilinear(e,[Ln!=null&&Ln.inputs[0].shape?Ln.inputs[0].shape[2]:0,Ln!=null&&Ln.inputs[0].shape?Ln.inputs[0].shape[1]:0],!1),c=Ln==null?void 0:Ln.execute(u),p=(await c.data())[0];j2[n]=Math.round(100*p)/100,DN=s,$N=ue(),ne([u,c]),l(j2[n])}))}var Uh={};ra(Uh,{connected:()=>X2,horizontal:()=>Eb,kpt:()=>q2,relative:()=>_b,vertical:()=>Rb});var q2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Eb=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Rb=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],_b=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],X2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var ON=.005,Ms={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Db(e){for(let t of Eb){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of Rb){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of _b){let s=e.keypoints.findIndex(u=>u&&u.part===t[0]),r=e.keypoints.findIndex(u=>u&&u.part===t[1]),a=e.keypoints.findIndex(u=>u&&u.part===n[0]),o=e.keypoints.findIndex(u=>u&&u.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let u=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=u}}}function MN(e){for(let t=0;t<e.length;t++)if(e[t]&&Ms.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-Ms.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-Ms.keypoints[t].positionRaw[1])];n[0]<ON&&n[1]<ON?e[t]=Ms.keypoints[t]:Ms.keypoints[t]=e[t]}else Ms.keypoints[t]=e[t];return e}function zN(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;Ms.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=Ks(e,Ms.padding),n.resize=Se.resizeBilinear(n.pad,[t,t]);let s=he(n.resize,"int32");return Object.keys(n).forEach(r=>ne(n[r])),s}function LN(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+Ms.padding[2][0]+Ms.padding[2][1])/t[0]-Ms.padding[2][0],s.position[1]*(t[1]+Ms.padding[1][0]+Ms.padding[1][1])/t[1]-Ms.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Ia(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var zs,K2=0,$b=Number.MAX_SAFE_INTEGER,fu={boxes:[],bodies:[],last:0};async function BN(e){return me.initial&&(zs=null),zs?e.debug&&oe("cached model:",zs.modelUrl):(H2(["size"],e),zs=await je(e.body.modelPath)),K2=zs.inputs[0].shape?zs.inputs[0].shape[2]:0,K2<64&&(K2=256),zs}async function g5e(e,t,n){let s=e[0][0],r=[],a=0;for(let c=0;c<s.length;c++)if(a=s[c][2],a>t.body.minConfidence){let p=[s[c][1],s[c][0]];r.push({score:Math.round(100*a)/100,part:q2[c],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}a=r.reduce((c,p)=>p.score>c?p.score:c,0);let o=[],i=Ia(r.map(c=>c.position),[n.shape[2],n.shape[1]]),l={};for(let[c,p]of Object.entries(X2)){let d=[];for(let h=0;h<p.length-1;h++){let f=r.find(g=>g.part===p[h]),m=r.find(g=>g.part===p[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&d.push([f.position,m.position])}l[c]=d}let u={id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:r,annotations:l};return Db(u),o.push(u),o}async function y5e(e,t,n){let s=[];for(let r=0;r<e[0].length;r++){let a=e[0][r],o=Math.round(100*a[51+4])/100;if(o>t.body.minConfidence){let i=[];for(let p=0;p<17;p++){let d=a[3*p+2];if(d>t.body.minConfidence){let h=[a[3*p+1],a[3*p+0]];i.push({part:q2[p],score:Math.round(100*d)/100,positionRaw:h,position:[Math.round((n.shape[2]||0)*h[0]),Math.round((n.shape[1]||0)*h[1])]})}}let l=Ia(i.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,d]of Object.entries(X2)){let h=[];for(let f=0;f<d.length-1;f++){let m=i.find(y=>y.part===d[f]),g=i.find(y=>y.part===d[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}u[p]=h}let c={id:r,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:u};Db(c),s.push(c)}}return s.sort((r,a)=>a.score-r.score),s.length>t.body.maxDetected&&(s.length=t.body.maxDetected),s}async function Fb(e,t){if(!zs||!(zs!=null&&zs.inputs[0].shape))return[];t.skipAllowed||(fu.boxes.length=0),$b++;let n=(t.body.skipTime||0)>ue()-fu.last,s=$b<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?fu.bodies:new Promise(async r=>{let a={};$b=0,a.input=zN(e,K2),a.res=zs==null?void 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x=(.5+Math.trunc(m%u))/u,A=(.5+Math.trunc(m/u))/u,b=h[m].map(D=>D*(u/l/J2)),[w,k]=[x-Y2/l*b[0],A-Y2/l*b[1]],[S,E]=[x+Y2/l*b[2]-w,A+Y2/l*b[3]-k],R=[w,k,S,E];R=R.map(D=>Math.max(0,Math.min(D,1)));let $=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],_={id:s++,score:Math.round(100*y)/100,class:g+1,label:fd[g].label,box:$.map(D=>Math.trunc(D)),boxRaw:R};r.push(_)}}});e.forEach(l=>ne(l));let a=r.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),o=r.map(l=>l.score),i=[];if(a&&a.length>0){let l=await Se.nonMaxSuppressionAsync(a,o,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);i=await l.data(),ne(l)}return r=r.filter((l,u)=>i.includes(u)).sort((l,u)=>u.score-l.score),r}async function Ob(e,t){let n=(t.object.skipTime||0)>ue()-VN,s=Pb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&Z2.length>0?(Pb++,Z2):(Pb=0,!me.kernels.includes("mod")||!me.kernels.includes("sparsetodense")?Z2:new Promise(async r=>{let a=[e.shape[2]||0,e.shape[1]||0],o=Se.resizeBilinear(e,[J2,J2],!1),i=pe(o,nt.tf255),l=i.transpose([0,3,1,2]);ne(i),ne(o);let u;t.object.enabled&&(u=wd.execute(l)),VN=ue(),ne(l);let c=await A5e(u,a,t);Z2=c,r(c)}))}var Hh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],x5e=Hh.length,Gh=Hh.reduce((e,t,n)=>(e[t]=n,e),{}),b5e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Rke=b5e.map(([e,t])=>[Gh[e],Gh[t]]),HN=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function jN(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function qN(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(u,c)=>({id:c,score:u.score,boxRaw:[u.box[0]/r,u.box[1]/s,u.box[2]/r,u.box[3]/s],box:[Math.trunc(u.box[0]*o),Math.trunc(u.box[1]*a),Math.trunc(u.box[2]*o),Math.trunc(u.box[3]*a)],keypoints:u.keypoints.map(({score:p,part:d,position:h})=>({score:p,part:d,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]})),annotations:{}});return e.map((u,c)=>i(u,c))}var Q2=class{constructor(t,n){ge(this,"priorityQueue");ge(this,"numberOfElements");ge(this,"getElementValue");this.priorityQueue=new 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Mb(e,t,n,s){return{y:s.get(e,t,n),x:s.get(e,t,n+x5e)}}function zb(e,t,n){let{heatmapY:s,heatmapX:r,id:a}=e,{y:o,x:i}=Mb(s,r,a,n);return{x:e.heatmapX*t+i,y:e.heatmapY*t+o}}function Lb(e,t,n){return e<t?t:e>n?n:e}function XN(e,t,n,s){let r=n-e,a=s-t;return r*r+a*a}function Bb(e,t){return{x:e.x+t.x,y:e.y+t.y}}var Mr,w5e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],e1=1,kd=16,k5e=50**2;function KN(e,t,n,s,r,a,o=2){let i=y=>({y:a.get(y.y,y.x,e),x:a.get(y.y,y.x,a.shape[2]/2+e)}),l=(y,x,A)=>({y:Lb(Math.round(y.y/kd),0,x-1),x:Lb(Math.round(y.x/kd),0,A-1)}),[u,c]=s.shape,p=l(t.position,u,c),d=i(p),f=Bb(t.position,d);for(let y=0;y<o;y++){let x=l(f,u,c),A=Mb(x.y,x.x,n,r);f=Bb({x:x.x*kd,y:x.y*kd},{x:A.x,y:A.y})}let m=l(f,u,c),g=s.get(m.y,m.x,n);return{position:f,part:Hh[n],score:g}}function I5e(e,t,n,s,r){let 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n=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,s=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],n,s,0,0,2*Math.PI),t.stroke(),mt.fillPolygons&&(t.fillStyle=mt.useDepth?"rgba(255, 255, 200, 0.3)":mt.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=mt.useDepth?"rgba(255, 200, 255, 0.3)":mt.color,t.beginPath();let n=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,s=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],n,s,0,0,2*Math.PI),t.stroke(),mt.fillPolygons&&(t.fillStyle=mt.useDepth?"rgba(255, 255, 200, 0.3)":mt.color,t.fill())}}function F5e(e,t){var n;if(mt.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let s=e.box[0]+e.box[2]/2-e.box[3]*mu(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*mu(e.rotation.angle.pitch)/90,a=new Path2D(`
+      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+      C
+        ${s} ${e.box[1]},
+        ${s} ${e.box[1]+e.box[3]},
+        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
+    `),o=new Path2D(`
+      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+      C 
+        ${e.box[0]} ${r},
+        ${e.box[0]+e.box[2]} ${r},
+        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
+    `);t.stroke(o),t.stroke(a)}}function P5e(e,t){var n,s,r,a;if(mt.drawGaze&&((s=(n=e.rotation)==null?void 0:n.gaze)==null?void 0:s.strength)&&((a=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:a.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Xb(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let i=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Xb(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[i[0],i[1]],4)}}function O5e(e,t){if(mt.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<au.length/3;n++){let s=[au[n*3+0],au[n*3+1],au[n*3+2]].map(r=>e.mesh[r]);qb(t,s,mt)}$5e(e,t)}}function M5e(e,t){if(mt.drawPoints&&e.mesh.length>=468)for(let n=0;n<e.mesh.length;n++)_a(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2],mt),mt.drawAttention&&(Wh.includes(n)&&_a(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]+127,mt),lu.includes(n)&&_a(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,mt),uu.includes(n)&&_a(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,mt))}function z5e(e,t){mt.drawBoxes&&ea(t,e.box[0],e.box[1],e.box[2],e.box[3],mt)}async function Id(e,t,n){if(mt=Kt(Bn,n),!t||!e)return;let s=Js(e);if(!!s){s.font=mt.font,s.strokeStyle=mt.color,s.fillStyle=mt.color;for(let r of t)z5e(r,s),D5e(r,s),r.mesh&&r.mesh.length>0&&(M5e(r,s),O5e(r,s),F5e(r,s),P5e(r,s))}}async function Sd(e,t,n){var a;let s=Kt(Bn,n);if(!t||!e)return;let r=Js(e);if(!!r){r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(ea(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)!t[o].keypoints[i].score||t[o].keypoints[i].score===0||(r.fillStyle=Ra(t[o].keypoints[i].position[2],s),_a(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s));if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)!i.score||i.score===0||(r.fillStyle=Ra(i.position[2],s),r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4))}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)sE(r,l,s)}}}async function Cd(e,t,n){let s=Kt(Bn,n);if(!t||!e)return;let r=Js(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,ea(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=Ra(o[2],s),_a(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{if(!i||i.length===0||!i[0])return;let u=i[i.length-1][2]||-256;r.fillStyle=Ra(u,s),r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4)};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++){r.beginPath();let u=i[l][2]||0;r.strokeStyle=Ra(l*u,s),r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()}};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function Td(e,t,n){let s=Kt(Bn,n);if(!t||!e)return;let r=Js(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,ea(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}}async function Nd(e,t,n){let s=Kt(Bn,n);if(!(!t||!e)&&s.drawGestures){let r=Js(e);if(!r)return;r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let u=i[1]>0?`#${i[1]}`:"",c=`${i[0]} ${u}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(c,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(c,6,0+a*s.lineHeight),a+=1}}}}var Kb=0;async function Zb(e,t,n){let s=Kt(Bn,n);if(!t||!e)return;let r=Js(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,ea(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person 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pr.silhouette)r.push({x:(e.mesh[o][0]-e.box[0])/e.box[2],y:(e.mesh[o][1]-e.box[1])/e.box[3]});Ed&&Ed>0&&(r=r.map(o=>({x:o.x>.5?o.x+Ed:o.x-Ed,y:o.y>.5?o.y+Ed:o.y-Ed})));for(let o=0;o<t;o++)for(let i=0;i<n;i++)L5e(o/t,i/t,r)||(s.set(e4*s.get(0,i,o,0),0,i,o,0),s.set(e4*s.get(0,i,o,1),0,i,o,1),s.set(e4*s.get(0,i,o,2),0,i,o,2));let a=s.toTensor();return ne(s),a}var W5e=e=>{let t=(p,d)=>Math.atan2(p[1]-d[1],p[0]-d[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},aE=(e,t)=>{let n=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},s=(m,g)=>{let y=m[0]-g[0],x=m[1]-g[1],A=m[2]-g[2];return[y,x,A]},r=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],x=m[2]*g[0]-m[0]*g[2],A=m[0]*g[1]-m[1]*g[0];return[y,x,A]},a=m=>{let[g,y,x,A,b,w,k,S,E]=m,R,$,_;return A<1?A>-1?(_=Math.asin(A),$=Math.atan2(-k,g),R=Math.atan2(-w,b)):(_=-Math.PI/2,$=-Math.atan2(S,E),R=0):(_=Math.PI/2,$=Math.atan2(S,E),R=0),isNaN(R)&&(R=0),isNaN($)&&($=0),isNaN(_)&&(_=0),{pitch:2*-R,yaw:2*-$,roll:2*-_}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),u=n(s(l[1],l[0])),c=n(s(l[3],l[2])),p=n(r(c,u));c=r(u,p);let d=[c[0],c[1],c[2],u[0],u[1],u[2],p[0],p[1],p[2]],h=a(d),f=o.length===478?W5e(e):{bearing:0,strength:0};return{angle:h,matrix:d,gaze:f}};var t4=async(e,t)=>{var h,f,m,g,y,x,A,b,w,k,S,E,R,$,_,D,C,P,V,j,z,Z;let n=ue(),s,r,a,o,i,l,u,c,p=[];e.state="run:face";let d=await YT(t,e.config);if(e.performance.face=me.perfadd?(e.performance.face||0)+Math.trunc(ue()-n):Math.trunc(ue()-n),!t.shape||t.shape.length!==4)return[];if(!d)return[];for(let W=0;W<d.length;W++){if(e.analyze("Get Face"),!d[W].tensor||d[W].tensor.isDisposedInternal){oe("Face object is disposed:",d[W].tensor);continue}if((h=e.config.face.detector)!=null&&h.mask){let ae=await rE(d[W]);ne(d[W].tensor),d[W].tensor=ae}let ee=d[W].mesh&&d[W].mesh.length>200?aE(d[W],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=(f=e.config.face.emotion)!=null&&f.enabled?lb(d[W].tensor||yt([]),e.config,W,d.length):[]:(e.state="run:emotion",n=ue(),o=(m=e.config.face.emotion)!=null&&m.enabled?await lb(d[W].tensor||yt([]),e.config,W,d.length):[],e.performance.emotion=me.perfadd?(e.performance.emotion||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?Ux(d[W].tensor||yt([]),e.config,W,d.length):0:(e.state="run:antispoof",n=ue(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await Ux(d[W].tensor||yt([]),e.config,W,d.length):0,e.performance.antispoof=me.perfadd?(e.performance.antispoof||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(x=e.config.face.liveness)!=null&&x.enabled?Nb(d[W].tensor||yt([]),e.config,W,d.length):0:(e.state="run:liveness",n=ue(),u=(A=e.config.face.liveness)!=null&&A.enabled?await Nb(d[W].tensor||yt([]),e.config,W,d.length):0,e.performance.liveness=me.perfadd?(e.performance.antispoof||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(b=e.config.face.gear)!=null&&b.enabled?Ox(d[W].tensor||yt([]),e.config,W,d.length):null:(e.state="run:gear",n=ue(),r=(w=e.config.face.gear)!=null&&w.enabled?await Ox(d[W].tensor||yt([]),e.config,W,d.length):null,e.performance.gear=Math.trunc(ue()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=(k=e.config.face.ssrnet)!=null&&k.enabled?zx(d[W].tensor||yt([]),e.config,W,d.length):null,a=(S=e.config.face.ssrnet)!=null&&S.enabled?Wx(d[W].tensor||yt([]),e.config,W,d.length):null):(e.state="run:ssrnet",n=ue(),s=(E=e.config.face.ssrnet)!=null&&E.enabled?await zx(d[W].tensor||yt([]),e.config,W,d.length):null,a=(R=e.config.face.ssrnet)!=null&&R.enabled?await Wx(d[W].tensor||yt([]),e.config,W,d.length):null,e.performance.ssrnet=Math.trunc(ue()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=($=e.config.face.mobilefacenet)!=null&&$.enabled?cb(d[W].tensor||yt([]),e.config,W,d.length):null:(e.state="run:mobilefacenet",n=ue(),i=(_=e.config.face.mobilefacenet)!=null&&_.enabled?await cb(d[W].tensor||yt([]),e.config,W,d.length):null,e.performance.mobilefacenet=Math.trunc(ue()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(D=e.config.face.description)!=null&&D.enabled?gb(d[W].tensor||yt([]),e.config,W,d.length):null:(e.state="run:description",n=ue(),c=(C=e.config.face.description)!=null&&C.enabled?await gb(d[W].tensor||yt([]),e.config,W,d.length):null,e.performance.description=me.perfadd?(e.performance.description||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,c,r,l,u]=await Promise.all([s,a,o,i,c,r,l,u])),e.analyze("Finish Face:"),((P=e.config.face.ssrnet)==null?void 0:P.enabled)&&s&&a&&(c={...c,age:s.age,gender:a.gender,genderScore:a.genderScore}),((V=e.config.face.gear)==null?void 0:V.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((j=e.config.face.mobilefacenet)==null?void 0:j.enabled)&&i&&(c.descriptor=i),(z=e.config.face.iris)!=null&&z.enabled;let Q=d[W].annotations&&d[W].annotations.leftEyeIris&&d[W].annotations.leftEyeIris[0]&&d[W].annotations.rightEyeIris&&d[W].annotations.rightEyeIris[0]&&d[W].annotations.leftEyeIris.length>0&&d[W].annotations.rightEyeIris.length>0&&d[W].annotations.leftEyeIris[0]!==null&&d[W].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[W].annotations.leftEyeIris[3][0]-d[W].annotations.leftEyeIris[1][0]),Math.abs(d[W].annotations.rightEyeIris[4][1]-d[W].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ie=(Z=e.config.face.detector)!=null&&Z.return?et(d[W].tensor):null;ne(d[W].tensor),d[W].tensor&&delete d[W].tensor;let J={...d[W],id:W};c!=null&&c.age&&(J.age=c.age),c!=null&&c.gender&&(J.gender=c.gender),c!=null&&c.genderScore&&(J.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(J.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(J.race=c==null?void 0:c.race),o&&(J.emotion=o),l&&(J.real=l),u&&(J.live=u),Q&&Q!==0&&(J.iris=Math.trunc(500/Q/11.7)/100),ee&&(J.rotation=ee),ie&&(J.tensor=ie),p.push(J),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),p};var oE=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},iE=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(s/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},lE=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),u=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(u=!0,t.push({iris:n,gesture:"facing center"}));let p=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],d=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(p>.06||d>.06)&&(u=!1),p>d?p>.05&&t.push({iris:n,gesture:"looking right"}):d>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(u=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),u&&t.push({iris:n,gesture:"looking center"})}return t},uE=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>(o.position[2]||0)<(i.position[2]||0)?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=wN(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Ee={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},n4=0;function cE(e,t){var o,i,l,u,c,p,d,h,f,m,g,y,x,A,b,w,k,S,E,R,$,_,D,C,P,V,j;let n=ue();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(e.canvas&&(Ee.canvas=e.canvas),e.error&&(Ee.error=e.error),!Ee.body||e.body.length!==Ee.body.length)Ee.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let Z=e.body[z].box.map((J,ae)=>((r-1)*Ee.body[z].box[ae]+J)/r),W=e.body[z].boxRaw.map((J,ae)=>((r-1)*Ee.body[z].boxRaw[ae]+J)/r),ee=e.body[z].keypoints.map((J,ae)=>{var le,ye,we,Re,_e,Be,Ue,it,dt;return{score:J.score,part:J.part,position:[Ee.body[z].keypoints[ae]?((r-1)*(Ee.body[z].keypoints[ae].position[0]||0)+(J.position[0]||0))/r:J.position[0],Ee.body[z].keypoints[ae]?((r-1)*(Ee.body[z].keypoints[ae].position[1]||0)+(J.position[1]||0))/r:J.position[1],Ee.body[z].keypoints[ae]?((r-1)*(Ee.body[z].keypoints[ae].position[2]||0)+(J.position[2]||0))/r:J.position[2]],positionRaw:[Ee.body[z].keypoints[ae]?((r-1)*(Ee.body[z].keypoints[ae].positionRaw[0]||0)+(J.positionRaw[0]||0))/r:J.positionRaw[0],Ee.body[z].keypoints[ae]?((r-1)*(Ee.body[z].keypoints[ae].positionRaw[1]||0)+(J.positionRaw[1]||0))/r:J.positionRaw[1],Ee.body[z].keypoints[ae]?((r-1)*(Ee.body[z].keypoints[ae].positionRaw[2]||0)+(J.positionRaw[2]||0))/r:J.positionRaw[2]],distance:[Ee.body[z].keypoints[ae]?((r-1)*(((le=Ee.body[z].keypoints[ae].distance)==null?void 0:le[0])||0)+(((ye=J.distance)==null?void 0:ye[0])||0))/r:(we=J.distance)==null?void 0:we[0],Ee.body[z].keypoints[ae]?((r-1)*(((Re=Ee.body[z].keypoints[ae].distance)==null?void 0:Re[1])||0)+(((_e=J.distance)==null?void 0:_e[1])||0))/r:(Be=J.distance)==null?void 0:Be[1],Ee.body[z].keypoints[ae]?((r-1)*(((Ue=Ee.body[z].keypoints[ae].distance)==null?void 0:Ue[2])||0)+(((it=J.distance)==null?void 0:it[2])||0))/r:(dt=J.distance)==null?void 0:dt[2]]}}),Q={},ie={connected:{}};(i=(o=t.body)==null?void 0:o.modelPath)!=null&&i.includes("efficientpose")?ie=F2:(u=(l=t.body)==null?void 0:l.modelPath)!=null&&u.includes("blazepose")?ie=E2:(p=(c=t.body)==null?void 0:c.modelPath)!=null&&p.includes("movenet")&&(ie=Uh);for(let[J,ae]of Object.entries(ie.connected)){let le=[];for(let ye=0;ye<ae.length-1;ye++){let we=ee.find(_e=>_e.part===ae[ye]),Re=ee.find(_e=>_e.part===ae[ye+1]);we&&Re&&le.push([we.position,Re.position])}Q[J]=le}Ee.body[z]={...e.body[z],box:Z,boxRaw:W,keypoints:ee,annotations:Q}}if(!Ee.hand||e.hand.length!==Ee.hand.length)Ee.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let Z=e.hand[z].box.map((ie,J)=>((r-1)*Ee.hand[z].box[J]+ie)/r),W=e.hand[z].boxRaw.map((ie,J)=>((r-1)*Ee.hand[z].boxRaw[J]+ie)/r);Ee.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Ee.hand[z].keypoints=e.hand[z].keypoints);let ee=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((ie,J)=>ie.map((ae,le)=>((r-1)*(Ee.hand[z].keypoints[J][le]||1)+(ae||0))/r)):[],Q={};if(Object.keys(Ee.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Ee.hand[z].annotations=e.hand[z].annotations,Q=Ee.hand[z].annotations;else if(e.hand[z].annotations)for(let ie of Object.keys(e.hand[z].annotations))Q[ie]=e.hand[z].annotations[ie]&&e.hand[z].annotations[ie][0]?e.hand[z].annotations[ie].map((J,ae)=>J.map((le,ye)=>((r-1)*Ee.hand[z].annotations[ie][ae][ye]+le)/r)):null;Ee.hand[z]={...e.hand[z],box:Z,boxRaw:W,keypoints:ee,annotations:Q}}if(!Ee.face||e.face.length!==Ee.face.length)Ee.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let Z=e.face[z].box.map((ee,Q)=>((r-1)*Ee.face[z].box[Q]+ee)/r),W=e.face[z].boxRaw.map((ee,Q)=>((r-1)*Ee.face[z].boxRaw[Q]+ee)/r);if(e.face[z].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(d=e.face[z].rotation)==null?void 0:d.matrix,ee.angle={roll:((r-1)*(((f=(h=Ee.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((x=(y=Ee.face[z].rotation)==null?void 0:y.angle)==null?void 0:x.yaw)||0)+(((b=(A=e.face[z].rotation)==null?void 0:A.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(w=Ee.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((E=(S=e.face[z].rotation)==null?void 0:S.angle)==null?void 0:E.pitch)||0))/r},ee.gaze={bearing:((r-1)*((($=(R=Ee.face[z].rotation)==null?void 0:R.gaze)==null?void 0:$.bearing)||0)+(((D=(_=e.face[z].rotation)==null?void 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n4=me.perfadd?n4+Math.round(a-n):Math.round(a-n),e.performance&&(Ee.performance={...e.performance,interpolate:n4}),Ee}var a4={};ra(a4,{distance:()=>qh,match:()=>r4,similarity:()=>s4});function qh(e,t,n={order:2,multiplier:25}){let s=0;for(let r=0;r<e.length;r++){let a=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);s+=!n.order||n.order===2?a*a:a**n.order}return(n.multiplier||20)*s}var dE=(e,t,n,s)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),a=(1-r/100-n)/(s-n);return Math.max(Math.min(a,1),0)};function s4(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=qh(e,t,n);return dE(s,n.order||2,n.min||0,n.max||1)}function r4(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let s=Number.MAX_SAFE_INTEGER,r=-1;for(let o=0;o<t.length;o++){let i=qh(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let 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l=[],u=[],c=[],p=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?t4(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ue(),l=this.config.face.enabled?await t4(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Kt(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?Wb(i.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?Qx(i.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?ob(i.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?Fb(i.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ue(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await Wb(i.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await Qx(i.tensor,d):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?u=this.config.body.enabled?await ob(i.tensor,d):[]:(S=this.config.body.modelPath)!=null&&S.includes("movenet")&&(u=this.config.body.enabled?await Fb(i.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Kt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?c=this.config.hand.enabled?wb(i.tensor,h):[]:(_=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&_.includes("handtrack")&&(c=this.config.hand.enabled?Cb(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ue(),(C=(D=this.config.hand.detector)==null?void 0:D.modelPath)!=null&&C.includes("handdetect")?c=this.config.hand.enabled?await wb(i.tensor,h):[]:(V=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&V.includes("handtrack")&&(c=this.config.hand.enabled?await Cb(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((j=this.config.object.modelPath)!=null&&j.includes("nanodet")?p=this.config.object.enabled?Ob(i.tensor,this.config):[]:(z=this.config.object.modelPath)!=null&&z.includes("centernet")&&(p=this.config.object.enabled?nb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ue(),(Z=this.config.object.modelPath)!=null&&Z.includes("nanodet")?p=this.config.object.enabled?await Ob(i.tensor,this.config):[]:(W=this.config.object.modelPath)!=null&&W.includes("centernet")&&(p=this.config.object.enabled?await nb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,c,p]=await Promise.all([l,u,c,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ue(),f=[...iE(l),...oE(u),...uE(c),...lE(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ue()-o):Math.trunc(ue()-o);let m=((Q=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Q.shape)||[];this.result={face:l,body:u,hand:c,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return pE(l,u,c,f,m)}},ne(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Rd=new WeakMap,Xh=new WeakMap,Kh=new WeakMap,o1=new WeakMap;return pR(Q5e);})();
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use backend file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the License);
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the License);
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the 'License');
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an 'AS IS' BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * Human main module
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
+/**
+* @license
+* Copyright 2018 Google LLC. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+* =============================================================================
+*/
+/** @license See the LICENSE file. */
diff --git a/dist/human.node-gpu.d.ts b/dist/human.node-gpu.d.ts
new file mode 100644
index 00000000..fed34cea
--- /dev/null
+++ b/dist/human.node-gpu.d.ts
@@ -0,0 +1,2643 @@
+/// <reference types="@webgpu/types/dist" />
+/// <reference types="offscreencanvas" />
+
+/** meta-function that performs draw for: canvas, face, body, hand */
+declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
+
+/** Defines all possible canvas types */
+export declare type AnyCanvas = HTMLCanvasElement | OffscreenCanvas;
+
+/** Defines all possible image types */
+export declare type AnyImage = HTMLImageElement | typeof Image;
+
+/** Defines all possible video types */
+export declare type AnyVideo = HTMLMediaElement | HTMLVideoElement;
+
+/** @docalias number[] */
+declare interface ArrayMap {
+    R0: number;
+    R1: number[];
+    R2: number[][];
+    R3: number[][][];
+    R4: number[][][][];
+    R5: number[][][][][];
+    R6: number[][][][][][];
+}
+
+/** Possible TensorFlow backends */
+export declare type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];
+
+/** draw detected bodies */
+declare function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+export declare type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;
+
+export declare type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';
+
+export declare type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';
+
+/** Configures all body detection specific options */
+export declare interface BodyConfig extends GenericConfig {
+    /** maximum number of detected bodies */
+    maxDetected: number;
+    /** minimum confidence for a detected body before results are discarded */
+    minConfidence: number;
+}
+
+/** body gesture type */
+export declare type BodyGesture = `leaning ${'left' | 'right'}` | `raise ${'left' | 'right'} hand` | 'i give up';
+
+/** Body Result keypoints */
+export declare interface BodyKeypoint {
+    /** body part name */
+    part: BodyLandmark;
+    /** body part position */
+    position: Point;
+    /** body part position normalized to 0..1 */
+    positionRaw: Point;
+    /** body part position relative to body center in meters */
+    distance?: Point;
+    /** body part detection score */
+    score: number;
+}
+
+export declare type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;
+
+export declare type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle' | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';
+
+export declare type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';
+
+export declare type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+export declare type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+/** Body results */
+export declare interface BodyResult {
+    /** body id */
+    id: number;
+    /** body detection score */
+    score: number;
+    /** detected body box */
+    box: Box;
+    /** detected body box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected body keypoints */
+    keypoints: Array<BodyKeypoint>;
+    /** detected body keypoints combined into annotated parts */
+    annotations: Record<BodyAnnotation, Point[][]>;
+}
+
+/** generic box as [x, y, width, height] */
+export declare type Box = [number, number, number, number];
+
+/**
+ * Creates an IOHandler that loads model artifacts from user-selected files.
+ *
+ * This method can be used for loading from files such as user-selected files
+ * in the browser.
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * // Note: This code snippet won't run properly without the actual file input
+ * //   elements in the HTML DOM.
+ *
+ * // Suppose there are two HTML file input (`<input type="file" ...>`)
+ * // elements.
+ * const uploadJSONInput = document.getElementById('upload-json');
+ * const uploadWeightsInput = document.getElementById('upload-weights');
+ * const model = await tf.loadLayersModel(tf.io.browserFiles(
+ *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+ * ```
+ *
+ * @param files `File`s to load from. Currently, this function supports only
+ *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+ *   which an `Array` of `File`s is expected (in that order):
+ *   - A JSON file containing the model topology and weight manifest.
+ *   - Optionally, One or more binary files containing the binary weights.
+ *     These files must have names that match the paths in the `weightsManifest`
+ *     contained by the aforementioned JSON file, or errors will be thrown
+ *     during loading. These weights files have the same format as the ones
+ *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+ *     Python PIP package. If no weights files are provided, only the model
+ *     topology will be loaded from the JSON file above.
+ * @returns An instance of `Files` `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function browserFiles(files: File[]): IOHandler;
+
+/**
+ * Deprecated. Use `tf.io.http`.
+ * @param path
+ * @param loadOptions
+ */
+declare function browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** draw processed canvas */
+declare function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas): Promise<void>;
+
+/**
+ * Concatenate a number of ArrayBuffers into one.
+ *
+ * @param buffers A number of array buffers to concatenate.
+ * @returns Result of concatenating `buffers` in order.
+ */
+declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+
+/**
+ * Configuration interface definition for **Human** library
+ * Contains all configurable parameters
+ * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+ */
+export declare interface Config {
+    /** Backend used for TFJS operations
+     * valid build-in backends are:
+     * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`
+     * - NodeJS: `cpu`, `wasm`, `tensorflow`
+     * default: `humangl` for browser and `tensorflow` for nodejs
+     */
+    backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+    /** Path to *.wasm files if backend is set to `wasm`
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPath: string;
+    /** Force WASM loader to use platform fetch
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPlatformFetch: boolean;
+    /** Print debug statements to console
+     *
+     * default: `true`
+     */
+    debug: boolean;
+    /** Perform model loading and inference concurrently or sequentially
+     *
+     * default: `true`
+     */
+    async: boolean;
+    /** What to use for `human.warmup()`
+     * - warmup pre-initializes all models for faster inference but can take significant time on startup
+     * - used by `webgl`, `humangl` and `webgpu` backends
+     *
+     * default: `full`
+     */
+    warmup: '' | 'none' | 'face' | 'full' | 'body';
+    /** Base model path (typically starting with file://, http:// or https://) for all models
+     * - individual modelPath values are relative to this path
+     *
+     * default: `../models/` for browsers and `file://models/` for nodejs
+     */
+    modelBasePath: string;
+    /** Cache models in IndexDB on first sucessfull load
+     * default: true if indexdb is available (browsers), false if its not (nodejs)
+     */
+    cacheModels: boolean;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+     *
+     * default: 0.7
+     */
+    cacheSensitivity: number;
+    /** Perform immediate garbage collection on deallocated tensors instead of caching them */
+    deallocate: boolean;
+    /** Internal Variable */
+    skipAllowed: boolean;
+    /** Filter config {@link FilterConfig} */
+    filter: Partial<FilterConfig>;
+    /** Gesture config {@link GestureConfig} */
+    gesture: Partial<GestureConfig>;
+    /** Face config {@link FaceConfig} */
+    face: Partial<FaceConfig>;
+    /** Body config {@link BodyConfig} */
+    body: Partial<BodyConfig>;
+    /** Hand config {@link HandConfig} */
+    hand: Partial<HandConfig>;
+    /** Object config {@link ObjectConfig} */
+    object: Partial<ObjectConfig>;
+    /** Segmentation config {@link SegmentationConfig} */
+    segmentation: Partial<SegmentationConfig>;
+}
+
+/**
+ * Copy a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Copying within a storage medium, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Copying between two storage mediums, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Copy the model, from Local Storage to IndexedDB.
+ * await tf.io.copyModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove both models.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of copying.
+ * @param destURL Destination URL of copying.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * We wrap data id since we use weak map to avoid memory leaks.
+ * Since we have our own memory management, we have a reference counter
+ * mapping a tensor to its data, so there is always a pointer (even if that
+ * data is otherwise garbage collectable).
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
+ * Global_Objects/WeakMap
+ */
+declare type DataId = object;
+
+declare type DataToGPUOptions = DataToGPUWebGLOption;
+
+declare interface DataToGPUWebGLOption {
+    customTexShape?: [number, number];
+}
+
+/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
+declare type DataType = keyof DataTypeMap;
+
+declare interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+
+/**
+ * Decode flat ArrayBuffer as weights.
+ *
+ * This function does not handle sharding.
+ *
+ * This function is the reverse of `encodeWeights`.
+ *
+ * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+ *   concatenated in the order specified in `specs`.
+ * @param specs Specifications of the names, dtypes and shapes of the tensors
+ *   whose value are encoded by `buffer`.
+ * @return A map from tensor name to tensor value, with the names corresponding
+ *   to names in `specs`.
+ * @throws Error, if any of the tensors has unsupported dtype.
+ */
+declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+
+/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
+export declare const defaults: Config;
+
+/** Face descriptor type as number array */
+export declare type Descriptor = Array<number>;
+
+/** Calculates distance between two descriptors
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ */
+declare function distance(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare namespace draw {
+    export {
+        person,
+        canvas,
+        all,
+        options,
+        face,
+        body,
+        hand,
+        object,
+        gesture
+    }
+}
+export { draw }
+
+/** Draw Options
+ * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter
+ */
+export declare type DrawOptions = {
+    /** draw line color */
+    color: string;
+    /** alpha value used for lines */
+    alpha: number;
+    /** label color */
+    labelColor: string;
+    /** label shadow color */
+    shadowColor: string;
+    /** label font */
+    font: string;
+    /** line spacing between labels */
+    lineHeight: number;
+    /** line width for drawn lines */
+    lineWidth: number;
+    /** size of drawn points */
+    pointSize: number;
+    /** draw rounded boxes by n pixels */
+    roundRect: number;
+    /** should points be drawn? */
+    drawPoints: boolean;
+    /** should labels be drawn? */
+    drawLabels: boolean;
+    /** should face attention keypoints be highlighted */
+    drawAttention: boolean;
+    /** should detected gestures be drawn? */
+    drawGestures: boolean;
+    /** should draw boxes around detection results? */
+    drawBoxes: boolean;
+    /** should draw polygons from detection points? */
+    drawPolygons: boolean;
+    /** should draw gaze arrows? */
+    drawGaze: boolean;
+    /** should fill polygons? */
+    fillPolygons: boolean;
+    /** use z-coordinate when available */
+    useDepth: boolean;
+    /** should lines be curved? */
+    useCurves: boolean;
+};
+
+export declare type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';
+
+/**
+ * Encode a map from names to weight values as an ArrayBuffer, along with an
+ * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+ *
+ * This function does not perform sharding.
+ *
+ * This function is the reverse of `decodeWeights`.
+ *
+ * @param tensors A map ("dict") from names to tensors.
+ * @param group Group to which the weights belong (optional).
+ * @returns A `Promise` of
+ *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+ *     concatenated.
+ *   - An `Array` of `WeightManifestEntry`s, carrying information including
+ *     tensor names, `dtype`s and shapes.
+ * @throws Error: on unsupported tensor `dtype`.
+ */
+declare function encodeWeights(tensors: NamedTensorMap | NamedTensor[], group?: WeightGroup): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+
+/** Env class that holds detected capabilities */
+export declare class Env {
+    /** Running in Browser */
+    browser: boolean;
+    /** Running in NodeJS */
+    node: boolean;
+    /** Running in WebWorker thread */
+    worker: boolean;
+    /** Detected platform */
+    platform: string;
+    /** Detected agent */
+    agent: string;
+    /** List of supported backends */
+    backends: string[];
+    /** Has any work been performed so far */
+    initial: boolean;
+    /** Are image filters supported? */
+    filter: boolean | undefined;
+    /** TFJS instance details */
+    tfjs: {
+        version: undefined | string;
+    };
+    /** Is offscreenCanvas supported? */
+    offscreen: undefined | boolean;
+    /** Are performance counter instant values or additive */
+    perfadd: boolean;
+    /** WASM detected capabilities */
+    wasm: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        simd: undefined | boolean;
+        multithread: undefined | boolean;
+    };
+    /** WebGL detected capabilities */
+    webgl: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        version: undefined | string;
+        renderer: undefined | string;
+    };
+    /** WebGPU detected capabilities */
+    webgpu: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        adapter: undefined | string;
+    };
+    /** CPU info */
+    cpu: {
+        model: undefined | string;
+        flags: string[];
+    };
+    /** List of supported kernels for current backend */
+    kernels: string[];
+    /** MonkeyPatch for Canvas */
+    Canvas: undefined;
+    /** MonkeyPatch for Image */
+    Image: undefined;
+    /** MonkeyPatch for ImageData */
+    ImageData: undefined;
+    constructor();
+    /** update backend information */
+    updateBackend(): Promise<void>;
+    /** update cpu information */
+    updateCPU(): Promise<void>;
+}
+
+export declare const env: Env;
+
+/** Events dispatched by `human.events`
+ * - `create`: triggered when Human object is instantiated
+ * - `load`: triggered when models are loaded (explicitly or on-demand)
+ * - `image`: triggered when input image is processed
+ * - `result`: triggered when detection is complete
+ * - `warmup`: triggered when warmup is complete
+ */
+export declare type Events = 'create' | 'load' | 'image' | 'result' | 'warmup' | 'error';
+
+/** Defines possible externally defined canvas */
+export declare type ExternalCanvas = typeof env.Canvas;
+
+/** draw detected faces */
+declare function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Anti-spoofing part of face configuration */
+export declare interface FaceAntiSpoofConfig extends GenericConfig {
+}
+
+/** Attention part of face configuration */
+export declare interface FaceAttentionConfig extends GenericConfig {
+}
+
+/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */
+export declare interface FaceConfig extends GenericConfig {
+    detector: Partial<FaceDetectorConfig>;
+    mesh: Partial<FaceMeshConfig>;
+    attention: Partial<FaceAttentionConfig>;
+    iris: Partial<FaceIrisConfig>;
+    description: Partial<FaceDescriptionConfig>;
+    emotion: Partial<FaceEmotionConfig>;
+    antispoof: Partial<FaceAntiSpoofConfig>;
+    liveness: Partial<FaceLivenessConfig>;
+    gear: Partial<FaceGearConfig>;
+}
+
+/** Description or face embedding part of face configuration
+ * - also used by age and gender detection
+ */
+export declare interface FaceDescriptionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Detector part of face configuration */
+export declare interface FaceDetectorConfig extends GenericConfig {
+    /** is face rotation correction performed after detecting face?
+     * used to correctly analyze faces under high angles
+     */
+    rotation: boolean;
+    /** maximum number of detected faces */
+    maxDetected: number;
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected faces before one is discarded */
+    iouThreshold: number;
+    /** should child models perform on masked image of a face */
+    mask: boolean;
+    /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?
+     * if enabled it must be manually deallocated to avoid memory leak */
+    return: boolean;
+}
+
+/** Emotion part of face configuration */
+export declare interface FaceEmotionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Gear part of face configuration */
+export declare interface FaceGearConfig extends GenericConfig {
+    /** minimum confidence for a detected race before results are discarded */
+    minConfidence: number;
+}
+
+/** face gesture type */
+export declare type FaceGesture = `facing ${'left' | 'center' | 'right'}` | `blink ${'left' | 'right'} eye` | `mouth ${number}% open` | `head ${'up' | 'down'}`;
+
+/** Iris part of face configuration */
+export declare interface FaceIrisConfig extends GenericConfig {
+}
+
+export declare type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette' | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner' | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris' | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris' | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';
+
+/** Liveness part of face configuration */
+export declare interface FaceLivenessConfig extends GenericConfig {
+}
+
+/** Mesh part of face configuration */
+export declare interface FaceMeshConfig extends GenericConfig {
+    /** Keep detected faces that cannot be verified using facemesh */
+    keepInvalid: boolean;
+}
+
+/** Face results
+ * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
+ * - Some values may be null if specific model is not enabled
+ */
+export declare interface FaceResult {
+    /** face id */
+    id: number;
+    /** overall face score */
+    score: number;
+    /** detection score */
+    boxScore: number;
+    /** mesh score */
+    faceScore: number;
+    /** detected face box */
+    box: Box;
+    /** detected face box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected face mesh */
+    mesh: Array<Point>;
+    /** detected face mesh normalized to 0..1 */
+    meshRaw: Array<Point>;
+    /** face contours as array of 2d points normalized to 0..1 */
+    /** face contours as array of 2d points */
+    /** mesh keypoints combined into annotated results */
+    annotations: Record<FaceLandmark, Point[]>;
+    /** detected age */
+    age?: number;
+    /** detected gender */
+    gender?: Gender;
+    /** gender detection score */
+    genderScore?: number;
+    /** detected emotions */
+    emotion?: Array<{
+        score: number;
+        emotion: Emotion;
+    }>;
+    /** detected race */
+    race?: Array<{
+        score: number;
+        race: Race;
+    }>;
+    /** face descriptor */
+    embedding?: Array<number>;
+    /** face iris distance from camera */
+    iris?: number;
+    /** face anti-spoofing result confidence */
+    real?: number;
+    /** face liveness result confidence */
+    live?: number;
+    /** face rotation details */
+    rotation?: {
+        angle: {
+            roll: number;
+            yaw: number;
+            pitch: number;
+        };
+        matrix: [number, number, number, number, number, number, number, number, number];
+        gaze: {
+            bearing: number;
+            strength: number;
+        };
+    } | null;
+    /** detected face as tensor that can be used in further pipelines */
+    tensor?: Tensor;
+}
+
+/** Run input through image filters before inference
+ * - available only in Browser environments
+ * - image filters run with near-zero latency as they are executed on the GPU using WebGL
+ */
+export declare interface FilterConfig {
+    /** are image filters enabled? */
+    enabled: boolean;
+    /** perform image histogram equalization
+     * - equalization is performed on input as a whole and detected face before its passed for further analysis
+     */
+    equalization: boolean;
+    /** resize input width
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    width: number;
+    /** resize input height
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    height: number;
+    /** return processed canvas imagedata in result */
+    return: boolean;
+    /** flip input as mirror image */
+    flip: boolean;
+    /** range: -1 (darken) to 1 (lighten) */
+    brightness: number;
+    /** range: -1 (reduce contrast) to 1 (increase contrast) */
+    contrast: number;
+    /** range: 0 (no sharpening) to 1 (maximum sharpening) */
+    sharpness: number;
+    /** range: 0 (no blur) to N (blur radius in pixels) */
+    blur: number;
+    /** range: -1 (reduce saturation) to 1 (increase saturation) */
+    saturation: number;
+    /** range: 0 (no change) to 360 (hue rotation in degrees) */
+    hue: number;
+    /** image negative */
+    negative: boolean;
+    /** image sepia colors */
+    sepia: boolean;
+    /** image vintage colors */
+    vintage: boolean;
+    /** image kodachrome colors */
+    kodachrome: boolean;
+    /** image technicolor colors */
+    technicolor: boolean;
+    /** image polaroid camera effect */
+    polaroid: boolean;
+    /** range: 0 (no pixelate) to N (number of pixels to pixelate) */
+    pixelate: number;
+}
+
+export declare type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';
+
+export declare type FingerCurl = 'none' | 'half' | 'full';
+
+export declare type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandler` that simply loads the provided data.
+ */
+declare function fromMemory(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler;
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
+ */
+declare function fromMemorySync(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandlerSync;
+
+export declare type Gender = 'male' | 'female' | 'unknown';
+
+/** Generic config type inherited by all module types */
+export declare interface GenericConfig {
+    /** is module enabled? */
+    enabled: boolean;
+    /** path to model json file (relative to `modelBasePath` */
+    modelPath: string;
+    /** how many max frames to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipFrames: number;
+    /** how many max milliseconds to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipTime: number;
+}
+
+/** draw detected gestures */
+declare function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Controlls gesture detection */
+export declare interface GestureConfig {
+    /** is gesture detection enabled? */
+    enabled: boolean;
+}
+
+/** Gesture combined results
+ * Each result has:
+ * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`
+ * - gesture: gesture detected
+ */
+export declare type GestureResult = {
+    'face': number;
+    gesture: FaceGesture;
+} | {
+    'iris': number;
+    gesture: IrisGesture;
+} | {
+    'body': number;
+    gesture: BodyGesture;
+} | {
+    'hand': number;
+    gesture: HandGesture;
+};
+
+declare const getLoadHandlers: (url: string | string[], loadOptions?: LoadOptions) => IOHandler[];
+
+/**
+ * Create `ModelArtifacts` from a JSON file.
+ *
+ * @param modelJSON Object containing the parsed JSON of `model.json`
+ * @param loadWeights Function that takes the JSON file's weights manifest,
+ *     reads weights from the listed path(s), and returns a Promise of the
+ *     weight manifest entries along with the weights data.
+ * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+ */
+declare function getModelArtifactsForJSON(modelJSON: ModelJSON, loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[WeightsManifestEntry[], /* weightData */ ArrayBuffer]>): Promise<ModelArtifacts>;
+
+/**
+ * Populate ModelArtifactsInfo fields for a model with JSON topology.
+ * @param modelArtifacts
+ * @returns A ModelArtifactsInfo object.
+ */
+declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
+
+declare const getModelStats: (instance: Human) => ModelStats;
+
+declare const getSaveHandlers: (url: string | string[]) => IOHandler[];
+
+declare interface GPUData {
+    tensorRef: Tensor;
+    texture?: WebGLTexture;
+    buffer?: GPUBuffer;
+    texShape?: [number, number];
+    bufSize?: number;
+}
+
+/**
+ * A `tf.GraphModel` is a directed, acyclic graph built from a
+ * SavedModel GraphDef and allows inference execution.
+ *
+ * A `tf.GraphModel` can only be created by loading from a model converted from
+ * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+ * the command line converter tool and loaded via `tf.loadGraphModel`.
+ *
+ * @doc {heading: 'Models', subheading: 'Classes'}
+ */
+export declare class GraphModel<ModelURL extends Url = string | io.IOHandler> implements InferenceModel {
+    private modelUrl;
+    private loadOptions;
+    private executor;
+    private version;
+    private handler;
+    private artifacts;
+    private initializer;
+    private resourceManager;
+    private signature;
+    readonly modelVersion: string;
+    readonly inputNodes: string[];
+    readonly outputNodes: string[];
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
+    readonly metadata: {};
+    readonly modelSignature: {};
+    /**
+     * @param modelUrl url for the model, or an `io.IOHandler`.
+     * @param weightManifestUrl url for the weight file generated by
+     * scripts/convert.py script.
+     * @param requestOption options for Request, which allows to send credentials
+     * and custom headers.
+     * @param onProgress Optional, progress callback function, fired periodically
+     * before the load is completed.
+     */
+    constructor(modelUrl: ModelURL, loadOptions?: io.LoadOptions);
+    private findIOHandler;
+    /**
+     * Loads the model and weight files, construct the in memory weight map and
+     * compile the inference graph.
+     */
+    load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync ? boolean : Promise<boolean>;
+    /**
+     * Synchronously construct the in memory weight map and
+     * compile the inference graph. Also initialize hashtable if any.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    loadSync(artifacts: io.ModelArtifacts): boolean;
+    /**
+     * Save the configuration and/or weights of the GraphModel.
+     *
+     * An `IOHandler` is an object that has a `save` method of the proper
+     * signature defined. The `save` method manages the storing or
+     * transmission of serialized data ("artifacts") that represent the
+     * model's topology and weights onto or via a specific medium, such as
+     * file downloads, local storage, IndexedDB in the web browser and HTTP
+     * requests to a server. TensorFlow.js provides `IOHandler`
+     * implementations for a number of frequently used saving mediums, such as
+     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+     * for more details.
+     *
+     * This method also allows you to refer to certain types of `IOHandler`s
+     * as URL-like string shortcuts, such as 'localstorage://' and
+     * 'indexeddb://'.
+     *
+     * Example 1: Save `model`'s topology and weights to browser [local
+     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+     * then load it back.
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     *
+     * const saveResults = await model.save('localstorage://my-model-1');
+     *
+     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+     * console.log('Prediction from loaded model:');
+     * model.predict(zeros).print();
+     * ```
+     *
+     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+     * scheme-based string shortcut for `IOHandler`.
+     * @param config Options for saving the model.
+     * @returns A `Promise` of `SaveResult`, which summarizes the result of
+     * the saving, such as byte sizes of the saved artifacts for the model's
+     *   topology and weight values.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+     * inputs params should be in either `tf.Tensor`[] if the input order is
+     * fixed, or otherwise NamedTensorMap format.
+     *
+     * For model with multiple inputs, we recommend you use NamedTensorMap as the
+     * input type, if you use `tf.Tensor`[], the order of the array needs to
+     * follow the
+     * order of inputNodes array. @see {@link GraphModel.inputNodes}
+     *
+     * You can also feed any intermediate nodes using the NamedTensorMap as the
+     * input type. For example, given the graph
+     *    InputNode => Intermediate => OutputNode,
+     * you can execute the subgraph Intermediate => OutputNode by calling
+     *    model.execute('IntermediateNode' : tf.tensor(...));
+     *
+     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+     * state needs to be fed manually.
+     *
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size and
+     * output node names. Currently the batch size option is ignored for graph
+     * model.
+     *
+     * @returns Inference result tensors. The output would be single `tf.Tensor`
+     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+     * will be returned for model with multiple outputs.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    private normalizeInputs;
+    private normalizeOutputs;
+    /**
+     * Executes inference for the model for given input tensors.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no
+     * outputs are specified, the default outputs of the model would be used.
+     * You can inspect intermediate nodes of the model by adding them to the
+     * outputs array.
+     *
+     * @returns A single tensor if provided with a single output or no outputs
+     * are provided and there is only one default output, otherwise return a
+     * tensor array. The order of the tensor array is the same as the outputs
+     * if provided, otherwise the order of outputNodes attribute of the model.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
+    /**
+     * Executes inference for the model for given input tensors in async
+     * fashion, use this method when your model contains control flow ops.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no outputs
+     * are specified, the default outputs of the model would be used. You can
+     * inspect intermediate nodes of the model by adding them to the outputs
+     * array.
+     *
+     * @returns A Promise of single tensor if provided with a single output or
+     * no outputs are provided and there is only one default output, otherwise
+     * return a tensor map.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
+    /**
+     * Get intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    getIntermediateTensors(): NamedTensorsMap;
+    /**
+     * Dispose intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    disposeIntermediateTensors(): void;
+    private convertTensorMapToTensorsMap;
+    /**
+     * Releases the memory used by the weight tensors and resourceManager.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    dispose(): void;
+}
+
+/** draw detected hands */
+declare function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all hand detection specific options */
+export declare interface HandConfig extends GenericConfig {
+    /** should hand rotation correction be performed after hand detection? */
+    rotation: boolean;
+    /** minimum confidence for a detected hand before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected hands before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected hands */
+    maxDetected: number;
+    /** should hand landmarks be detected or just return detected hand box */
+    landmarks: boolean;
+    detector: {
+        /** path to hand detector model json */
+        modelPath?: string;
+    };
+    skeleton: {
+        /** path to hand skeleton model json */
+        modelPath?: string;
+    };
+}
+
+/** hand gesture type */
+export declare type HandGesture = `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward` | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up` | 'victory' | 'thumbs up';
+
+/** Hand results */
+export declare interface HandResult {
+    /** hand id */
+    id: number;
+    /** hand overal score */
+    score: number;
+    /** hand detection score */
+    boxScore: number;
+    /** hand skelton score */
+    fingerScore: number;
+    /** detected hand box */
+    box: Box;
+    /** detected hand box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected hand keypoints */
+    keypoints: Array<Point>;
+    /** detected hand class */
+    label: HandType;
+    /** detected hand keypoints combined into annotated parts */
+    annotations: Record<Finger, Array<Point>>;
+    /** detected hand parts annotated with part gestures */
+    landmarks: Record<Finger, {
+        curl: FingerCurl;
+        direction: FingerDirection;
+    }>;
+}
+
+export declare type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';
+
+/**
+ * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+ *
+ * An HTTP request of the `multipart/form-data` mime type will be sent to the
+ * `path` URL. The form data includes artifacts that represent the topology
+ * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+ * blobs (files) exist in form-data:
+ *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+ *   - A binary weights file consisting of the concatenated weight values.
+ * These files are in the same format as the one generated by
+ * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+ *
+ * The following code snippet exemplifies the client-side code that uses this
+ * function:
+ *
+ * ```js
+ * const model = tf.sequential();
+ * model.add(
+ *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+ *
+ * const saveResult = await model.save(tf.io.http(
+ *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+ * console.log(saveResult);
+ * ```
+ *
+ * If the default `POST` method is to be used, without any custom parameters
+ * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+ *
+ * ```js
+ * const saveResult = await model.save('http://model-server:5000/upload');
+ * ```
+ *
+ * The following GitHub Gist
+ * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+ * implements a server based on [flask](https://github.com/pallets/flask) that
+ * can receive the request. Upon receiving the model artifacts via the requst,
+ * this particular server reconsistutes instances of [Keras
+ * Models](https://keras.io/models/model/) in memory.
+ *
+ *
+ * @param path A URL path to the model.
+ *   Can be an absolute HTTP path (e.g.,
+ *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+ *   './model-upload').
+ * @param requestInit Request configurations to be used when sending
+ *    HTTP request to server using `fetch`. It can contain fields such as
+ *    `method`, `credentials`, `headers`, `mode`, etc. See
+ *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+ *    for more information. `requestInit` must not have a body, because the
+ * body will be set by TensorFlow.js. File blobs representing the model
+ * topology (filename: 'model.json') and the weights of the model (filename:
+ * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+ * `body`, an Error will be thrown.
+ * @param loadOptions Optional configuration for the loading. It includes the
+ *   following fields:
+ *   - weightPathPrefix Optional, this specifies the path prefix for weight
+ *     files, by default this is calculated from the path param.
+ *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+ *     the `fetch` from node-fetch can be used here.
+ *   - onProgress Optional, progress callback function, fired periodically
+ *     before the load is completed.
+ * @returns An instance of `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function http(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** **Human** library main class
+ *
+ * All methods and properties are available only as members of Human class
+ *
+ * - Configuration object definition: {@link Config}
+ * - Results object definition: {@link Result}
+ * - Possible inputs: {@link Input}
+ *
+ * @param userConfig - {@link Config}
+ * @returns instance of {@link Human}
+ */
+declare class Human {
+    #private;
+    /** Current version of Human library in *semver* format */
+    version: string;
+    /** Current configuration
+     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+     */
+    config: Config;
+    /** Last known result of detect run
+     * - Can be accessed anytime after initial detection
+     */
+    result: Result;
+    /** Current state of Human library
+     * - Can be polled to determine operations that are currently executed
+     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
+     */
+    state: string;
+    /** currenty processed image tensor and canvas */
+    process: {
+        tensor: Tensor | null;
+        canvas: AnyCanvas | null;
+    };
+    /** Instance of TensorFlow/JS used by Human
+     *  - Can be embedded or externally provided
+     * [TFJS API](https://js.tensorflow.org/api/latest/)
+     */
+    tf: any;
+    /** Object containing environment information used for diagnostics */
+    env: Env;
+    /** Draw helper classes that can draw detected objects on canvas using specified draw
+     * - canvas: draws input to canvas
+     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
+     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
+     */
+    draw: {
+        canvas: typeof draw.canvas;
+        face: typeof draw.face;
+        body: typeof draw.body;
+        hand: typeof draw.hand;
+        gesture: typeof draw.gesture;
+        object: typeof draw.object;
+        person: typeof draw.person;
+        all: typeof draw.all;
+        options: DrawOptions;
+    };
+    /** Currently loaded models
+     * @internal
+     * {@link Models}
+     */
+    models: models.Models;
+    /** Container for events dispatched by Human
+     * Possible events:
+     * - `create`: triggered when Human object is instantiated
+     * - `load`: triggered when models are loaded (explicitly or on-demand)
+     * - `image`: triggered when input image is processed
+     * - `result`: triggered when detection is complete
+     * - `warmup`: triggered when warmup is complete
+     * - `error`: triggered on some errors
+     */
+    events: EventTarget | undefined;
+    /** Reference face triangualtion array of 468 points, used for triangle references between points */
+    faceTriangulation: number[];
+    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
+    faceUVMap: [number, number][];
+    /** Performance object that contains values for all recently performed operations */
+    performance: Record<string, number>;
+    /** WebGL debug info */
+    gl: Record<string, unknown>;
+    /** Constructor for **Human** library that is futher used for all operations
+     * @param userConfig - user configuration object {@link Config}
+     */
+    constructor(userConfig?: Partial<Config>);
+    /** internal function to measure tensor leaks */
+    analyze: (...msg: string[]) => void;
+    /** Reset configuration to default values */
+    reset(): void;
+    /** Validate current configuration schema */
+    validate(userConfig?: Partial<Config>): {
+        reason: string;
+        where: string;
+        expected?: string;
+    }[];
+    /** Exports face matching methods {@link match#similarity} */
+    similarity: typeof match.similarity;
+    /** Exports face matching methods {@link match#distance} */
+    distance: typeof match.distance;
+    /** Exports face matching methods {@link match#match} */
+    match: typeof match.match;
+    /** Utility wrapper for performance.now() */
+    now(): number;
+    /** Process input as return canvas and tensor
+     *
+     * @param input - any input {@link Input}
+     * @param getTensor - should image processing also return tensor or just canvas
+     * Returns object with `tensor` and `canvas`
+     */
+    image(input: Input, getTensor?: boolean): Promise<{
+        tensor: Tensor<Rank> | null;
+        canvas: AnyCanvas | null;
+    }>;
+    /** Segmentation method takes any input and returns processed canvas with body segmentation
+     *  - Segmentation is not triggered as part of detect process
+     * @param input - {@link Input}
+     * @param background - {@link Input}
+     *  - Optional parameter background is used to fill the background with specific input
+     *  Returns:
+     *  - `data` as raw data array with per-pixel segmentation values
+     *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
+     *  - `alpha` as grayscale canvas that represents segmentation alpha values
+     */
+    segmentation(input: Input, background?: Input): Promise<{
+        data: number[] | Tensor;
+        canvas: AnyCanvas | null;
+        alpha: AnyCanvas | null;
+    }>;
+    /** Enhance method performs additional enhacements to face image previously detected for futher processing
+     *
+     * @param input - Tensor as provided in human.result.face[n].tensor
+     * @returns Tensor
+     */
+    enhance(input: Tensor): Tensor | null;
+    /** Compare two input tensors for pixel simmilarity
+     * - use `human.image` to process any valid input and get a tensor that can be used for compare
+     * - when passing manually generated tensors:
+     *  - both input tensors must be in format [1, height, width, 3]
+     *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
+     * - return value is pixel similarity score normalized by input resolution and rgb channels
+     */
+    compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
+    /** Explicit backend initialization
+     *  - Normally done implicitly during initial load phase
+     *  - Call to explictly register and initialize TFJS backend without any other operations
+     *  - Use when changing backend during runtime
+     */
+    init(): Promise<void>;
+    /** Load method preloads all configured models on-demand
+     * - Not explicitly required as any required model is load implicitly on it's first run
+     *
+     * @param userConfig - {@link Config}
+     */
+    load(userConfig?: Partial<Config>): Promise<void>;
+    /** emit event */
+    emit: (event: string) => void;
+    /** Runs interpolation using last known result and returns smoothened result
+     * Interpolation is based on time since last known result so can be called independently
+     *
+     * @param result - {@link Result} optional use specific result set to run interpolation on
+     * @returns result - {@link Result}
+     */
+    next(result?: Result): Result;
+    /** get model loading/loaded stats */
+    getModelStats(): ModelStats;
+    /** Warmup method pre-initializes all configured models for faster inference
+     * - can take significant time on startup
+     * - only used for `webgl` and `humangl` backends
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    warmup(userConfig?: Partial<Config>): Promise<Result | undefined>;
+    /** Run detect with tensorflow profiling
+     * - result object will contain total exeuction time information for top-20 kernels
+     * - actual detection object can be accessed via `human.result`
+     */
+    profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>>;
+    /** Main detection method
+     * - Analyze configuration: {@link Config}
+     * - Pre-process input: {@link Input}
+     * - Run inference for all configured models
+     * - Process and return result: {@link Result}
+     *
+     * @param input - {@link Input}
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    detect(input: Input, userConfig?: Partial<Config>): Promise<Result>;
+}
+export { Human }
+export default Human;
+
+/** Defines all possible image objects */
+export declare type ImageObjects = ImageData | ImageBitmap;
+
+/**
+ * Common interface for a machine learning model that can do inference.
+ */
+declare interface InferenceModel {
+    /**
+     * Return the array of input tensor info.
+     */
+    readonly inputs: ModelTensorInfo[];
+    /**
+     * Return the array of output tensor info.
+     */
+    readonly outputs: ModelTensorInfo[];
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size.
+     *
+     * @returns Inference result tensors. The output would be single Tensor if
+     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
+     * be returned for model with multiple outputs.
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    /**
+     * Single Execute the inference for the input tensors and return activation
+     * values for specified output node names without batching.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     *
+     * @param outputs string|string[]. List of output node names to retrieve
+     * activation from.
+     *
+     * @returns Activation values for the output nodes result tensors. The return
+     * type matches specified parameter outputs type. The output would be single
+     * Tensor if single output is specified, otherwise Tensor[] for multiple
+     * outputs.
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
+
+/** Defines all possible input types for **Human** detection */
+export declare type Input = Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas;
+
+declare namespace io {
+    export {
+        copyModel,
+        listModels,
+        moveModel,
+        removeModel,
+        browserFiles,
+        browserHTTPRequest,
+        concatenateArrayBuffers,
+        decodeWeights,
+        encodeWeights,
+        fromMemory,
+        fromMemorySync,
+        getLoadHandlers,
+        getModelArtifactsForJSON,
+        getModelArtifactsInfoForJSON,
+        getSaveHandlers,
+        http,
+        IOHandler,
+        IOHandlerSync,
+        isHTTPScheme,
+        LoadHandler,
+        LoadOptions,
+        loadWeights,
+        ModelArtifacts,
+        ModelArtifactsInfo,
+        ModelJSON,
+        ModelStoreManager,
+        OnProgressCallback,
+        registerLoadRouter,
+        registerSaveRouter,
+        RequestDetails,
+        SaveConfig,
+        SaveHandler,
+        SaveResult,
+        TrainingConfig,
+        WeightGroup,
+        weightsLoaderFactory,
+        WeightsManifestConfig,
+        WeightsManifestEntry,
+        withSaveHandler,
+        withSaveHandlerSync
+    }
+}
+
+/**
+ * Interface for a model import/export handler.
+ *
+ * The `save` and `load` handlers are both optional, in order to allow handlers
+ * that support only saving or loading.
+ */
+declare interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+
+declare type IOHandlerSync = {
+    [K in keyof IOHandler]: Syncify<IOHandler[K]>;
+};
+
+declare type IORouter = (url: string | string[], loadOptions?: LoadOptions) => IOHandler;
+
+/** iris gesture type */
+export declare type IrisGesture = 'facing center' | `looking ${'left' | 'right' | 'up' | 'down'}` | 'looking center';
+
+declare function isHTTPScheme(url: string): boolean;
+
+/**
+ * List all models stored in registered storage mediums.
+ *
+ * For a web browser environment, the registered mediums are Local Storage and
+ * IndexedDB.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @returns A `Promise` of a dictionary mapping URLs of existing models to
+ * their model artifacts info. URLs include medium-specific schemes, e.g.,
+ *   'indexeddb://my/model/1'. Model artifacts info include type of the
+ * model's topology, byte sizes of the topology, weights, etc.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+
+/** Load method preloads all instance.configured models on-demand */
+declare function load(instance: Human): Promise<void>;
+
+/**
+ * Type definition for handlers of loading operations.
+ */
+declare type LoadHandler = () => Promise<ModelArtifacts>;
+
+/** @innamespace io */
+declare interface LoadOptions {
+    /**
+     * RequestInit (options) for HTTP requests.
+     *
+     * For detailed information on the supported fields, see
+     * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
+     *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
+     */
+    requestInit?: RequestInit;
+    /**
+     * Progress callback.
+     */
+    onProgress?: OnProgressCallback;
+    /**
+     * A function used to override the `window.fetch` function.
+     */
+    fetchFunc?: Function;
+    /**
+     * Strict loading model: whether extraneous weights or missing
+     * weights should trigger an `Error`.
+     *
+     * If `true`, require that the provided weights exactly match those
+     * required by the layers. `false` means that both extra weights
+     * and missing weights will be silently ignored.
+     *
+     * Default: `true`.
+     */
+    strict?: boolean;
+    /**
+     * Path prefix for weight files, by default this is calculated from the
+     * path of the model JSON file.
+     *
+     * For instance, if the path to the model JSON file is
+     * `http://localhost/foo/model.json`, then the default path prefix will be
+     * `http://localhost/foo/`. If a weight file has the path value
+     * `group1-shard1of2` in the weight manifest, then the weight file will be
+     * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
+     * if you provide a `weightPathPrefix` value of
+     * `http://localhost/foo/alt-weights`, then the weight file will be loaded
+     * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
+     */
+    weightPathPrefix?: string;
+    /**
+     * Whether the module or model is to be loaded from TF Hub.
+     *
+     * Setting this to `true` allows passing a TF-Hub module URL, omitting the
+     * standard model file name and the query parameters.
+     *
+     * Default: `false`.
+     */
+    fromTFHub?: boolean;
+    /**
+     * An async function to convert weight file name to URL. The weight file
+     * names are stored in model.json's weightsManifest.paths field. By default we
+     * consider weight files are colocated with the model.json file. For example:
+     *     model.json URL: https://www.google.com/models/1/model.json
+     *     group1-shard1of1.bin url:
+     *        https://www.google.com/models/1/group1-shard1of1.bin
+     *
+     * With this func you can convert the weight file name to any URL.
+     */
+    weightUrlConverter?: (weightFileName: string) => Promise<string>;
+}
+
+/**
+ * Reads a weights manifest JSON configuration, fetches the weights and
+ * returns them as `Tensor`s.
+ *
+ * @param manifest The weights manifest JSON.
+ * @param filePathPrefix The path prefix for filenames given in the manifest.
+ *     Defaults to the empty string.
+ * @param weightNames The names of the weights to be fetched.
+ */
+declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestInit?: RequestInit): Promise<NamedTensorMap>;
+
+declare namespace match {
+    export {
+        distance,
+        similarity,
+        match_2 as match,
+        Descriptor,
+        MatchOptions
+    }
+}
+export { match }
+
+/** Matches given descriptor to a closest entry in array of descriptors
+ * @param descriptor - face descriptor
+ * @param descriptors - array of face descriptors to commpare given descriptor to
+ * @param options - see `similarity` method for options description
+ * Returns
+ * - `index` index array index where best match was found or -1 if no matches
+ * - `distance` calculated `distance` of given descriptor to the best match
+ * - `similarity` calculated normalized `similarity` of given descriptor to the best match
+ */
+declare function match_2(descriptor: Descriptor, descriptors: Array<Descriptor>, options?: MatchOptions): {
+    index: number;
+    distance: number;
+    similarity: number;
+};
+
+declare type MatchOptions = {
+    order?: number;
+    threshold?: number;
+    multiplier?: number;
+    min?: number;
+    max?: number;
+} | undefined;
+
+/**
+ * The serialized artifacts of a model, including topology and weights.
+ *
+ * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
+ * of this interface are optional, in order to support topology- or weights-only
+ * saving and loading.
+ *
+ * Note this interface is used internally in IOHandlers.  For the file format
+ * written to disk as `model.json`, see `ModelJSON`.
+ */
+declare interface ModelArtifacts {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology?: {} | ArrayBuffer;
+    /**
+     * Serialized configuration for the model's training.
+     */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weight specifications.
+     *
+     * This corresponds to the weightsData below.
+     */
+    weightSpecs?: WeightsManifestEntry[];
+    /**
+     * Binary buffer for all weight values concatenated in the order specified
+     * by `weightSpecs`.
+     */
+    weightData?: ArrayBuffer;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelArtifactsInfo {
+    /**
+     * Timestamp for when the model is saved.
+     */
+    dateSaved: Date;
+    /**
+     * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
+     * come in a JSON format and none of our IOHandlers support a non json
+     * format. We could conder replacing this with 'Binary' if we want to
+     * allow future handlers to save to non json formats (though they will
+     * probably want more information than 'Binary').
+     * Type of the model topology
+     *
+     * Type of the model topology
+     *
+     * Possible values:
+     *   - JSON: JSON config (human-readable, e.g., Keras JSON).
+     *   - GraphDef: TensorFlow
+     *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
+     *     protocol buffer (binary).
+     */
+    modelTopologyType: 'JSON' | 'GraphDef';
+    /**
+     * Size of model topology (Keras JSON or GraphDef), in bytes.
+     */
+    modelTopologyBytes?: number;
+    /**
+     * Size of weight specification or manifest, in bytes.
+     */
+    weightSpecsBytes?: number;
+    /**
+     * Size of weight value data, in bytes.
+     */
+    weightDataBytes?: number;
+}
+
+export declare type ModelInfo = {
+    name: string;
+    inCache: boolean;
+    sizeDesired: number;
+    sizeFromManifest: number;
+    sizeLoadedWeights: number;
+};
+
+/**
+ * The on-disk format of the `model.json` file.
+ *
+ * TF.js 1.0 always populates the optional fields when writing model.json.
+ * Prior versions did not provide those fields.
+ */
+declare interface ModelJSON {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology: {};
+    /** Model training configuration. */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weights manifest.
+     *
+     * The weights manifest consists of an ordered list of weight-manifest
+     * groups. Each weight-manifest group consists of a number of weight values
+     * stored in a number of paths. See the documentation of
+     * `WeightsManifestConfig` for more details.
+     */
+    weightsManifest: WeightsManifestConfig;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelPredictConfig {
+    /**
+     * Optional. Batch size (Integer). If unspecified, it will default to 32.
+     */
+    batchSize?: number;
+    /**
+     * Optional. Verbosity mode. Defaults to false.
+     */
+    verbose?: boolean;
+}
+
+/** Instances of all possible TFJS Graph Models used by Human
+ * - loaded as needed based on configuration
+ * - initialized explictly with `human.load()` method
+ * - initialized implicity on first call to `human.detect()`
+ * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
+ */
+export declare class Models {
+    ssrnetage: null | GraphModel | Promise<GraphModel>;
+    gear: null | GraphModel | Promise<GraphModel>;
+    blazeposedetect: null | GraphModel | Promise<GraphModel>;
+    blazepose: null | GraphModel | Promise<GraphModel>;
+    centernet: null | GraphModel | Promise<GraphModel>;
+    efficientpose: null | GraphModel | Promise<GraphModel>;
+    mobilefacenet: null | GraphModel | Promise<GraphModel>;
+    emotion: null | GraphModel | Promise<GraphModel>;
+    facedetect: null | GraphModel | Promise<GraphModel>;
+    faceiris: null | GraphModel | Promise<GraphModel>;
+    facemesh: null | GraphModel | Promise<GraphModel>;
+    faceres: null | GraphModel | Promise<GraphModel>;
+    ssrnetgender: null | GraphModel | Promise<GraphModel>;
+    handpose: null | GraphModel | Promise<GraphModel>;
+    handskeleton: null | GraphModel | Promise<GraphModel>;
+    handtrack: null | GraphModel | Promise<GraphModel>;
+    liveness: null | GraphModel | Promise<GraphModel>;
+    movenet: null | GraphModel | Promise<GraphModel>;
+    nanodet: null | GraphModel | Promise<GraphModel>;
+    posenet: null | GraphModel | Promise<GraphModel>;
+    segmentation: null | GraphModel | Promise<GraphModel>;
+    antispoof: null | GraphModel | Promise<GraphModel>;
+}
+
+declare namespace models {
+    export {
+        reset,
+        load,
+        validate,
+        Models,
+        ModelStats,
+        getModelStats
+    }
+}
+export { models }
+
+export declare type ModelStats = {
+    numLoadedModels: number;
+    numEnabledModels: undefined;
+    numDefinedModels: number;
+    percentageLoaded: number;
+    totalSizeFromManifest: number;
+    totalSizeWeights: number;
+    totalSizeLoading: number;
+    totalSizeEnabled: undefined;
+    modelStats: ModelInfo[];
+};
+
+/**
+ * An interface for the manager of a model store.
+ *
+ * A model store is defined as a storage medium on which multiple models can
+ * be stored. Each stored model has a unique `path` as its identifier.
+ * A `ModelStoreManager` for the store allows actions including
+ *
+ * - Listing the models stored in the store.
+ * - Deleting a model from the store.
+ */
+declare interface ModelStoreManager {
+    /**
+     * List all models in the model store.
+     *
+     * @returns A dictionary mapping paths of existing models to their
+     *   model artifacts info. Model artifacts info include type of the model's
+     *   topology, byte sizes of the topology, weights, etc.
+     */
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    /**
+     * Remove a model specified by `path`.
+     *
+     * @param path
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     */
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
+
+/**
+ * Interface for model input/output tensor info.
+ */
+declare interface ModelTensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+    tfDtype?: string;
+}
+
+/**
+ * Move a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Moving within a storage medium, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Moving between two storage mediums, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Move the model, from Local Storage to IndexedDB.
+ * await tf.io.moveModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove the moved model.
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of moving.
+ * @param destURL Destination URL of moving.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+declare interface NamedTensor {
+    name: string;
+    tensor: Tensor;
+}
+
+/** @docalias {[name: string]: Tensor} */
+declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+
+declare type NamedTensorsMap = {
+    [key: string]: Tensor[];
+};
+
+declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+
+/** draw detected objects */
+declare function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all object detection specific options */
+export declare interface ObjectConfig extends GenericConfig {
+    /** minimum confidence for a detected objects before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected objects before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected objects */
+    maxDetected: number;
+}
+
+/** Object results */
+export declare interface ObjectResult {
+    /** object id */
+    id: number;
+    /** object detection score */
+    score: number;
+    /** detected object class id */
+    class: number;
+    /** detected object class name */
+    label: ObjectType;
+    /** detected object box */
+    box: Box;
+    /** detected object box normalized to 0..1 */
+    boxRaw: Box;
+}
+
+export declare type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter' | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee' | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork' | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant' | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book' | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';
+
+/**
+ * Callback for the progress of a long-running action such as an HTTP
+ * request for a large binary object.
+ *
+ * `fraction` should be a number in the [0, 1] interval, indicating how
+ * much of the action has completed.
+ */
+declare type OnProgressCallback = (fraction: number) => void;
+
+/** currently set draw options {@link DrawOptions} */
+declare const options: DrawOptions;
+
+/** draw combined person results instead of individual detection result objects */
+declare function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Person getter
+ * - Triggers combining all individual results into a virtual person object
+ */
+export declare interface PersonResult {
+    /** person id */
+    id: number;
+    /** face result that belongs to this person */
+    face: FaceResult;
+    /** body result that belongs to this person */
+    body: BodyResult | null;
+    /** left and right hand results that belong to this person */
+    hands: {
+        left: HandResult | null;
+        right: HandResult | null;
+    };
+    /** detected gestures specific to this person */
+    gestures: Array<GestureResult>;
+    /** box that defines the person */
+    box: Box;
+    /** box that defines the person normalized to 0..1 */
+    boxRaw?: Box;
+}
+
+/** generic point as [x, y, z?] */
+export declare type Point = [number, number, number?];
+
+declare type PromiseFunction = (...args: unknown[]) => Promise<unknown>;
+
+export declare type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';
+
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
+declare const registerLoadRouter: (loudRouter: IORouter) => void;
+
+declare const registerSaveRouter: (loudRouter: IORouter) => void;
+
+/**
+ * Remove a model specified by URL from a reigstered storage medium.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @param url A URL to a stored model, with a scheme prefix, e.g.,
+ *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+ * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+ *   is successful).
+ * @throws Error if deletion fails, e.g., if no model exists at `path`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * Additional options for Platform.fetch
+ */
+declare interface RequestDetails {
+    /**
+     * Is this request for a binary file (as opposed to a json file)
+     */
+    isBinary?: boolean;
+}
+
+declare function reset(instance: Human): void;
+
+/**
+ * Result interface definition for **Human** library
+ *
+ * Contains all possible detection results
+ */
+export declare interface Result {
+    /** {@link FaceResult}: detection & analysis results */
+    face: Array<FaceResult>;
+    /** {@link BodyResult}: detection & analysis results */
+    body: Array<BodyResult>;
+    /** {@link HandResult}: detection & analysis results */
+    hand: Array<HandResult>;
+    /** {@link GestureResult}: detection & analysis results */
+    gesture: Array<GestureResult>;
+    /** {@link ObjectResult}: detection & analysis results */
+    object: Array<ObjectResult>;
+    /** global performance object with timing values for each operation */
+    performance: Record<string, number>;
+    /** optional processed canvas that can be used to draw input on screen */
+    canvas?: AnyCanvas | null;
+    /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
+    readonly timestamp: number;
+    /** getter property that returns unified persons object  */
+    persons: Array<PersonResult>;
+    /** Last known error message */
+    error: string | null;
+}
+
+/**
+ * Options for saving a model.
+ * @innamespace io
+ */
+declare interface SaveConfig {
+    /**
+     * Whether to save only the trainable weights of the model, ignoring the
+     * non-trainable ones.
+     */
+    trainableOnly?: boolean;
+    /**
+     * Whether the optimizer will be saved (if exists).
+     *
+     * Default: `false`.
+     */
+    includeOptimizer?: boolean;
+}
+
+/**
+ * Type definition for handlers of saving operations.
+ */
+declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+
+/**
+ * Result of a saving operation.
+ */
+declare interface SaveResult {
+    /**
+     * Information about the model artifacts saved.
+     */
+    modelArtifactsInfo: ModelArtifactsInfo;
+    /**
+     * HTTP responses from the server that handled the model-saving request (if
+     * any). This is applicable only to server-based saving routes.
+     */
+    responses?: Response[];
+    /**
+     * Error messages and related data (if any).
+     */
+    errors?: Array<{} | string>;
+}
+
+/** Configures all body segmentation module
+ * removes background from input containing person
+ * if segmentation is enabled it will run as preprocessing task before any other model
+ * alternatively leave it disabled and use it on-demand using human.segmentation method which can
+ * remove background or replace it with user-provided background
+ */
+export declare interface SegmentationConfig extends GenericConfig {
+    /** blur segmentation output by <number> pixels for more realistic image */
+    blur: number;
+}
+
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/// <amd-module name="@tensorflow/tfjs-core/dist/types" />
+/** @docalias number[] */
+declare interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+
+/** Calculates normalized similarity between two face descriptors based on their `distance`
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ * - min - normalize similarity result to a given range
+ * - max - normalzie similarity resutl to a given range
+ *   default is 0.2...0.8
+ * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
+ */
+declare function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+
+declare type Syncify<T extends PromiseFunction> = T extends (...args: infer Args) => Promise<infer R> ? (...args: Args) => R : never;
+
+export declare namespace Tensor { }
+
+/**
+ * A `tf.Tensor` object represents an immutable, multidimensional array of
+ * numbers that has a shape and a data type.
+ *
+ * For performance reasons, functions that create tensors do not necessarily
+ * perform a copy of the data passed to them (e.g. if the data is passed as a
+ * `Float32Array`), and changes to the data will change the tensor. This is not
+ * a feature and is not supported. To avoid this behavior, use the tensor before
+ * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+ *
+ * See `tf.tensor` for details on how to create a `tf.Tensor`.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+export declare class Tensor<R extends Rank = Rank> {
+    /** Unique id of this tensor. */
+    readonly id: number;
+    /**
+     * Id of the bucket holding the data for this tensor. Multiple arrays can
+     * point to the same bucket (e.g. when calling array.reshape()).
+     */
+    dataId: DataId;
+    /** The shape of the tensor. */
+    readonly shape: ShapeMap[R];
+    /** Number of elements in the tensor. */
+    readonly size: number;
+    /** The data type for the array. */
+    readonly dtype: DataType;
+    /** The rank type for the array (see `Rank` enum). */
+    readonly rankType: R;
+    /** Whether this tensor has been globally kept. */
+    kept: boolean;
+    /** The id of the scope this tensor is being tracked in. */
+    scopeId: number;
+    /**
+     * Number of elements to skip in each dimension when indexing. See
+     * https://docs.scipy.org/doc/numpy/reference/generated/\
+     * numpy.ndarray.strides.html
+     */
+    readonly strides: number[];
+    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
+    readonly rank: number;
+    /**
+     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
+    /**
+     * Returns a `tf.TensorBuffer` that holds the underlying data.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * asynchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    array(): Promise<ArrayMap[R]>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * synchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    arraySync(): ArrayMap[R];
+    /**
+     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+     * promise of `TypedArray` that resolves when the computation has finished.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    /**
+     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
+     * and `data()`, this method prevents data from being downloaded to CPU.
+     *
+     * For WebGL backend, the data will be stored on a densely packed texture.
+     * This means that the texture will use the RGBA channels to store value.
+     *
+     * For WebGPU backend, the data will be stored on a buffer. There is no
+     * parameter, so can not use an user defined size to create the buffer.
+     *
+     * @param options:
+     *     For WebGL,
+     *         - customTexShape: Optional. If set, will use the user defined
+     *     texture shape to create the texture.
+     *
+     * @returns For WebGL backend, a GPUData contains the new texture and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this texture,
+     *        texture: WebGLTexture,
+     *        texShape: [number, number] // [height, width]
+     *     }
+     *
+     *     For WebGPU backend, a GPUData contains the new buffer and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this buffer,
+     *        buffer: GPUBuffer,
+     *        bufSize: number
+     *     }
+     *
+     *     Remember to dispose the GPUData after it is used by
+     *     `res.tensorRef.dispose()`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataToGPU(options?: DataToGPUOptions): GPUData;
+    /**
+     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+     * UI thread until the values are ready, which can cause performance issues.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    /** Returns the underlying bytes of the tensor's data. */
+    bytes(): Promise<Uint8Array[] | Uint8Array>;
+    /**
+     * Disposes `tf.Tensor` from memory.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dispose(): void;
+    protected isDisposedInternal: boolean;
+    readonly isDisposed: boolean;
+    throwIfDisposed(): void;
+    /**
+     * Prints the `tf.Tensor`. See `tf.print` for details.
+     *
+     * @param verbose Whether to print verbose information about the tensor,
+     *    including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    print(verbose?: boolean): void;
+    /**
+     * Returns a copy of the tensor. See `tf.clone` for details.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    clone<T extends Tensor>(this: T): T;
+    /**
+     * Returns a human-readable description of the tensor. Useful for logging.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    toString(verbose?: boolean): string;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+}
+
+/**
+ * A mutable object, similar to `tf.Tensor`, that allows users to set values
+ * at locations before converting to an immutable `tf.Tensor`.
+ *
+ * See `tf.buffer` for creating a tensor buffer.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    /**
+     * Sets a value in the buffer at a given location.
+     *
+     * @param value The value to set.
+     * @param locs  The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    /**
+     * Returns the value in the buffer at the provided location.
+     *
+     * @param locs The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    /**
+     * Creates an immutable `tf.Tensor` object from the buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    toTensor(): Tensor<R>;
+}
+
+declare interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
+
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
+/** Model training configuration. */
+declare interface TrainingConfig {
+    /** Optimizer used for the model training. */
+    optimizer_config: {};
+    /** Loss function(s) for the model's output(s). */
+    loss: string | string[] | {
+        [key: string]: string;
+    };
+    /** Metric function(s) for the model's output(s). */
+    metrics?: string[] | {
+        [key: string]: string;
+    };
+    weighted_metrics?: string[];
+    sample_weight_mode?: string;
+    loss_weights?: number[] | {
+        [key: string]: number;
+    };
+}
+
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
+declare type Url = string | io.IOHandler | io.IOHandlerSync;
+
+declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
+
+declare function validate(instance: Human): Promise<void>;
+
+/**
+ * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
+    /**
+     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+     * the same shape and dtype as the old `tf.Tensor`.
+     *
+     * @param newValue New tensor to be assigned to this variable.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    assign(newValue: Tensor<R>): void;
+    dispose(): void;
+}
+
+/** Possible values for `human.warmup` */
+export declare type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];
+
+/**
+ * Group to which the weight belongs.
+ *
+ * - 'optimizer': Weight from a stateful optimizer.
+ */
+declare type WeightGroup = 'model' | 'optimizer';
+
+/**
+ * Creates a function, which reads a weights manifest JSON configuration,
+ * fetches the weight files using the specified function and returns them as
+ * `Tensor`s.
+ *
+ * ```js
+ * // example for creating a nodejs weight loader, which reads the weight files
+ * // from disk using fs.readFileSync
+ *
+ * import * as fs from 'fs'
+ *
+ * const fetchWeightsFromDisk = (filePaths: string[]) =>
+ *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+ *
+ * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+ *
+ * const manifest = JSON.parse(
+ *   fs.readFileSync('./my_model-weights_manifest').toString()
+ * )
+ * const weightMap = await loadWeights(manifest, './')
+ * ```
+ * @param fetchWeightsFunction The function used for fetching the weight files.
+ * @returns Weight loading function.
+ */
+declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
+
+/**
+ * A weight manifest.
+ *
+ * The weight manifest consists of an ordered list of weight-manifest groups.
+ * Each weight-manifest group ("group" for short hereafter) consists of a
+ * number of weight values stored in a number of paths.
+ * See the documentation of `WeightManifestGroupConfig` below for more details.
+ */
+declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+
+/**
+ * An entry in the weight manifest.
+ *
+ * The entry contains specification of a weight.
+ */
+declare interface WeightsManifestEntry {
+    /**
+     * Name of the weight, e.g., 'Dense_1/bias'
+     */
+    name: string;
+    /**
+     * Shape of the weight.
+     */
+    shape: number[];
+    /**
+     * Data type of the weight.
+     */
+    dtype: 'float32' | 'int32' | 'bool' | 'string' | 'complex64';
+    /**
+     * Type of the weight.
+     *
+     * Optional.
+     *
+     * The value 'optimizer' indicates the weight belongs to an optimizer
+     * (i.e., used only during model training and not during inference).
+     */
+    group?: WeightGroup;
+    /**
+     * Information for dequantization of the weight.
+     */
+    quantization?: {
+        scale?: number;
+        min?: number;
+        dtype: 'uint16' | 'uint8' | 'float16';
+    };
+}
+
+/**
+ * A weight-manifest group.
+ *
+ * Consists of an ordered list of weight values encoded in binary format,
+ * stored in an ordered list of paths.
+ */
+declare interface WeightsManifestGroupConfig {
+    /**
+     * An ordered list of paths.
+     *
+     * Paths are intentionally abstract in order to be general. For example, they
+     * can be relative URL paths or relative paths on the file system.
+     */
+    paths: string[];
+    /**
+     * Specifications of the weights stored in the paths.
+     */
+    weights: WeightsManifestEntry[];
+}
+
+/**
+ * Creates an IOHandler that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     promise that resolves to a `SaveResult`.
+ */
+declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
+
+/**
+ * Creates an IOHandlerSync that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     `SaveResult`.
+ */
+declare function withSaveHandlerSync(saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync;
+
+export { }
diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
new file mode 100644
index 00000000..83899c59
--- /dev/null
+++ b/dist/human.node-gpu.js
@@ -0,0 +1,838 @@
+"use strict";/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var An=Object.create;var h2=Object.defineProperty;var sn=Object.getOwnPropertyDescriptor;var an=Object.getOwnPropertyNames;var ln=Object.getPrototypeOf,yn=Object.prototype.hasOwnProperty;var xn=(e,t,o)=>t in e?h2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var cn=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ue=(e,t)=>{for(var o in t)h2(e,o,{get:t[o],enumerable:!0})},dt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of an(t))!yn.call(e,r)&&r!==o&&h2(e,r,{get:()=>t[r],enumerable:!(n=sn(t,r))||n.enumerable});return e};var Z=(e,t,o)=>(o=e!=null?An(ln(e)):{},dt(t||!e||!e.__esModule?h2(o,"default",{value:e,enumerable:!0}):o,e)),dn=e=>dt(h2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(xn(e,typeof t!="symbol"?t+"":t,o),o),ft=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var b2=(e,t,o)=>(ft(e,t,"read from private field"),o?o.call(e):t.get(e)),g2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},M2=(e,t,o,n)=>(ft(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var D=cn((f7,S5)=>{"use strict";var pt=Object.defineProperty,fn=Object.getOwnPropertyDescriptor,mn=Object.getOwnPropertyNames,pn=Object.prototype.hasOwnProperty,z5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of mn(t))!pn.call(e,r)&&r!==o&&pt(e,r,{get:()=>t[r],enumerable:!(n=fn(t,r))||n.enumerable});return e},un=(e,t,o)=>(z5(e,t,"default"),o&&z5(o,t,"default")),hn=e=>z5(pt({},"__esModule",{value:!0}),e),ut={};S5.exports=hn(ut);un(ut,require("@tensorflow/tfjs-node-gpu"),S5.exports)});var y7={};ue(y7,{Human:()=>it,default:()=>it,defaults:()=>he,draw:()=>tt,env:()=>v,match:()=>at,models:()=>R5});module.exports=dn(y7);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function mt(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function E5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")E5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var he={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var p0=Z(D());var N=Z(D());var ht=`
+  precision highp float;
+  attribute vec2 pos;
+  attribute vec2 uv;
+  varying vec2 vUv;
+  uniform float flipY;
+  void main(void) {
+    vUv = uv;
+    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
+  }
+`;var bt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
+    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
+  }
+`,gt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
+    gl_FragColor.a = c.a;
+  }
+`,Mt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform vec2 size;
+  uniform sampler2D texture;
+  vec2 pixelate(vec2 coord, vec2 size) {
+    return floor( coord / size ) * size;
+  }
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    vec2 coord = pixelate(vUv, size);
+    gl_FragColor += texture2D(texture, coord);
+  }
+`,Pt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
+    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;
+    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
+  }
+`,Rt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  uniform float m[9];
+  void main(void) {
+    vec4 c11 = texture2D(texture, vUv - px); // top left
+    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
+    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
+    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
+    vec4 c22 = texture2D(texture, vUv); // mid center
+    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
+    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
+    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
+    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
+    gl_FragColor = 
+    c11 * m[0] + c12 * m[1] + c22 * m[2] +
+    c21 * m[3] + c22 * m[4] + c23 * m[5] +
+    c31 * m[6] + c32 * m[7] + c33 * m[8];
+    gl_FragColor.a = c22.a;
+  }
+`;var C5=(e,t,o)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(r,A)=>(o[A]=0,r))},j5=class{constructor(t,o,n){T(this,"uniform",{});T(this,"attribute",{});T(this,"gl");T(this,"id");T(this,"compile",(t,o)=>{let n=this.gl.createShader(o);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(p(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)}`),null)):(p("filter: could not create shader"),null)});this.gl=t;let r=this.compile(o,this.gl.VERTEX_SHADER),A=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!A)){if(!this.id){p("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,A),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){p(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),C5(o,"attribute",this.attribute);for(let s in this.attribute)this.attribute[s]=this.gl.getAttribLocation(this.id,s);C5(o,"uniform",this.uniform),C5(n,"uniform",this.uniform);for(let s in this.uniform)this.uniform[s]=this.gl.getUniformLocation(this.id,s)}}};function vt(){let e=0,t=null,o=!1,n=-1,r=[null,null],A=[],s=null,a=null,i=R0(100,100),x={},c={INTERMEDIATE:1},l=i.getContext("webgl");if(!l){p("filter: cannot get webgl context");return}this.gl=l;function y(b,d){if(!(b===i.width&&d===i.height)){if(i.width=b,i.height=d,!s){let M=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);s=l.createBuffer(),l.bindBuffer(l.ARRAY_BUFFER,s),l.bufferData(l.ARRAY_BUFFER,M,l.STATIC_DRAW),l.pixelStorei(l.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}l.viewport(0,0,i.width,i.height),r=[null,null]}}function f(b,d){let M=l.createFramebuffer();l.bindFramebuffer(l.FRAMEBUFFER,M);let S=l.createRenderbuffer();l.bindRenderbuffer(l.RENDERBUFFER,S);let w=l.createTexture();return l.bindTexture(l.TEXTURE_2D,w),l.texImage2D(l.TEXTURE_2D,0,l.RGBA,b,d,0,l.RGBA,l.UNSIGNED_BYTE,null),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_MAG_FILTER,l.LINEAR),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_MIN_FILTER,l.LINEAR),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_WRAP_S,l.CLAMP_TO_EDGE),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_WRAP_T,l.CLAMP_TO_EDGE),l.framebufferTexture2D(l.FRAMEBUFFER,l.COLOR_ATTACHMENT0,l.TEXTURE_2D,w,0),l.bindTexture(l.TEXTURE_2D,null),l.bindFramebuffer(l.FRAMEBUFFER,null),{fbo:M,texture:w}}function u(b){return r[b]=r[b]||f(i.width,i.height),r[b]}function m(b=0){if(!a)return;let d=null,M=null,S=!1;e===0?d=t:d=u(n).texture||null,e++,o&&!(b&c.INTERMEDIATE)?(M=null,S=e%2===0):(n=(n+1)%2,M=u(n).fbo||null),l.bindTexture(l.TEXTURE_2D,d),l.bindFramebuffer(l.FRAMEBUFFER,M),l.uniform1f(a.uniform.flipY,S?-1:1),l.drawArrays(l.TRIANGLES,0,6)}function P(b){if(x[b])return a=x[b],l.useProgram((a?a.id:null)||null),a;if(a=new j5(l,ht,b),!a)return p("filter: could not get webgl 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d=Math.cos(b),M=Math.sin(b),S=.213,w=.715,j=.072;g.colorMatrix([S+d*(1-S)+M*-S,w+d*-w+M*-w,j+d*-j+M*(1-j),0,0,S+d*-S+M*.143,w+d*(1-w)+M*.14,j+d*-j+M*-.283,0,0,S+d*-S+M*-(1-S),w+d*-w+M*w,j+d*(1-j)+M*j,0,0,0,0,0,1,0])},desaturateLuminance:()=>{g.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{g.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{g.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{g.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{g.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{g.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{g.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{g.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:b=>{let d=new Float32Array(b),M=1/i.width,S=1/i.height,w=P(Rt);!w||(l.uniform1fv(w.uniform.m,d),l.uniform2f(w.uniform.px,M,S),m())},detectEdges:()=>{g.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{g.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{g.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:b=>{let d=b||1;g.convolution.call(this,[0,-1*d,0,-1*d,1+4*d,-1*d,0,-1*d,0])},emboss:b=>{let d=b||1;g.convolution.call(this,[-2*d,-1*d,0,-1*d,1,1*d,0,1*d,2*d])},blur:b=>{let d=b/7/i.width,M=b/7/i.height,S=P(Pt);!S||(l.uniform2f(S.uniform.px,0,M),m(c.INTERMEDIATE),l.uniform2f(S.uniform.px,d,0),m())},pixelate:b=>{let d=b/i.width,M=b/i.height,S=P(Mt);!S||(l.uniform2f(S.uniform.size,d,M),m())}};this.add=function(b){let d=Array.prototype.slice.call(arguments,1),M=g[b];A.push({func:M,args:d})},this.reset=function(){A=[]},this.get=function(){return 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o;if(!ae.inputTensor)ae.inputTensor=N.clone(t);else if(ae.inputTensor.shape[1]!==t.shape[1]||ae.inputTensor.shape[2]!==t.shape[2])N.dispose(ae.inputTensor),ae.inputTensor=N.clone(t);else{let n={};n.diff=N.sub(t,ae.inputTensor),n.squared=N.mul(n.diff,n.diff),n.sum=N.sum(n.squared);let A=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;N.dispose([ae.inputTensor,n.diff,n.squared,n.sum]),ae.inputTensor=N.clone(t),o=A<=(e.cacheSensitivity||0)}return o}async function wt(e,t,o){let n={};if(!t||!o||t.shape.length!==4||t.shape.length!==o.shape.length)return e.debug||p("invalid input tensor or tensor shapes do not match:",t.shape,o.shape),0;if(t.shape[0]!==1||o.shape[0]!==1||t.shape[3]!==3||o.shape[3]!==3)return e.debug||p("input tensors must be of shape [1, height, width, 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Kr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Jr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Qr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],_r=[[474,475],[475,476],[476,477],[477,474]],$r=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],eA=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],tA=[[469,470],[470,471],[471,472],[472,469]],oA=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ve(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var nA={lips:ve(Kr),leftEye:ve(Jr),leftEyebrow:ve(Qr),leftIris:ve(_r),rightEye:ve($r),rightEyebrow:ve(eA),rightIris:ve(tA),faceOval:ve(oA)},rA=Object.entries(nA).map(([e,t])=>t.map(o=>[o,e])).flat(),f4=new Map(rA),w2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],We=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Fe=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function W3(e,t){let o={lips:await t.filter(A=>A.size===160)[0].data(),irisL:await t.filter(A=>A.size===10)[0].data(),eyeL:await t.filter(A=>A.size===142)[0].data(),irisR:await t.filter(A=>A.size===10)[1].data(),eyeR:await t.filter(A=>A.size===142)[1].data()},n=We.reduce((A,s)=>A+=e[s][2],0)/We.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=Fe.reduce((A,s)=>A+=e[s][2],0)/Fe.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[We[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[We[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[Fe[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[Fe[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[w2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[w2[A]][2]];return e}var oe={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},g0=null,Ge=0;async function F3(e,t){var a,i,x,c,l,y,f,u,m,P,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-oe.timestamp,n=oe.skipped<(((i=t.face.detector)==null?void 0:i.skipFrames)||0);!t.skipAllowed||!o||!n||oe.boxes.length===0?(oe.boxes=await n3(e,t),oe.timestamp=h(),oe.skipped=0):oe.skipped++;let r=[],A=[],s=0;for(let b=0;b<oe.boxes.length;b++){let d=oe.boxes[b],M=0,S,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([M,S,w.tensor]=_t((x=t.face.detector)==null?void 0:x.rotation,d,e,(c=t.face.mesh)!=null&&c.enabled?Ge:Qe()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let j=await O2(w.tensor);Be.dispose(w.tensor),w.tensor=j}if(w.boxScore=Math.round(100*d.confidence)/100,(y=t.face.mesh)!=null&&y.enabled)if(!g0)t.debug&&p("face mesh detection requested, but model is not loaded");else{let j=g0.execute(w.tensor),F=await j.find(H=>H.shape[H.shape.length-1]===1).data();if(w.faceScore=Math.round(100*F[0])/100,w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=X2(d,e),w.boxRaw=q2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(H=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*H[0]/Qe(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*H[1]/Qe()]),w.meshRaw=w.mesh.map(H=>[H[0]/(e.shape[2]||1),H[1]/(e.shape[1]||1),(H[2]||0)/Ge]);for(let H of Object.keys(Ie))w.annotations[H]=[w.mesh[Ie[H]]]}}else{let H=j.find(E=>E.shape[E.shape.length-1]===1404),Y=Be.reshape(H,[-1,3]),J=await Y.array();Be.dispose(Y),(m=t.face.attention)!=null&&m.enabled?J=await W3(J,j):(P=t.face.iris)!=null&&P.enabled&&(J=await N3(J,w.tensor,t,Ge)),w.mesh=Qt(J,d,M,S,Ge),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/Ge]);for(let E of Object.keys(X0))w.annotations[E]=X0[E].map(G0=>w.mesh[G0]);w.score=w.faceScore;let x0={...$t(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=X2(x0,e),w.boxRaw=q2(x0,e),A.push(x0)}Be.dispose(j)}else{w.box=X2(d,e),w.boxRaw=q2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Qe(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Qe()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/Ge]);for(let j of Object.keys(Ie))w.annotations[j]=[w.mesh[Ie[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):Be.dispose(w.tensor)}return oe.boxes=A,r}async function G3(e){var t,o,n,r,A,s;return v.initial&&(g0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(g0==null?void 0:g0.signature)&&Object.keys(((n=g0==null?void 0:g0.signature)==null?void 0:n.outputs)||{}).length<6&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):(r=e.face.attention)!=null&&r.enabled?g0=await 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Object.keys(o).forEach(r=>W.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=W.reshape(t,[-1,7,2]),n.div=W.div(n.reshape,this.inputSizeTensor),n.landmarks=W.add(n.div,this.anchors[o]);let r=W.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>W.dispose(n[A])),r}async predict(t,o){let n={};n.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=W.div(n.resize,V.tf127),n.image=W.sub(n.div,V.tf1),n.batched=this.model.execute(n.image),n.predictions=W.squeeze(n.batched),n.slice=W.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=W.sigmoid(n.slice),n.scores=W.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=W.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await W.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=W.slice(n.norm,[a,0],[1,-1]),i.slice=W.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=W.reshape(i.norm,[-1,2]);let x=await i.box.data(),c=x.slice(0,2),l=x.slice(2,4),y=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:y,confidence:r[a]},u=K3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>W.dispose(i[m]))}return Object.keys(n).forEach(a=>W.dispose(n[a])),s}};var L0=Z(D());var yA=5,eo=1.65,to=[0,5,9,13,17,1,2],xA=0,cA=2,oo=0,i5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>M1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return A5(s5(r),yA)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=A5(s5(o),eo);n.palmLandmarks=[];for(let r=0;r<to.length;r++)n.palmLandmarks.push(t[to[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=r5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=g1(n,[0,0]),x=a.map(f=>[...M1(f,i),f[2]]),c=Q3(r),l=[...k2(o),1],y=[Te(l,c[0]),Te(l,c[1])];return x.map(f=>[Math.trunc(f[0]+y[0]),Math.trunc(f[1]+y[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-oo,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await 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a=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let x=l5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:x})}return n}async function T1(e){var o,n;v.initial&&(A2=null,s2=null),!A2||!s2?[A2,s2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached 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a=Q.unstack(n.scores,1);Q.dispose(a[mo]),a.splice(mo,1),n.filtered=Q.stack(a,1),Q.dispose(a),n.max=Q.max(n.filtered,1),n.argmax=Q.argMax(n.filtered,1);let i=0;n.nms=await Q.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let y of Array.from(x)){let f=Q.slice(n.boxes,y,1),u=await f.data();Q.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],P=Q2(m,vA),g=[Math.trunc(m[0]*fe[0]),Math.trunc(m[1]*fe[1]),Math.trunc(m[2]*fe[0]),Math.trunc(m[3]*fe[1])],b=c[y],d=PA[l[y]],M={id:i++,score:b,box:g,boxRaw:P,label:d};o.push(M)}return Object.keys(n).forEach(y=>Q.dispose(n[y])),o.sort((y,f)=>f.score-y.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function k1(e,t,o){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&d0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[A],[0],[Ce[1][0],Ce[1][1]],"bilinear"),r.div=Q.div(r.crop,V.tf255),[r.score,r.keypoints]=d0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ce[1][1],l[1]/Ce[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[fe[0]*(l[0]+t.boxRaw[0]),fe[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=l5(n.keypoints);for(let l of 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S2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],SA=S2.length,z2=S2.reduce((e,t,o)=>(e[t]=o,e),{}),CA=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],ts=CA.map(([e,t])=>[z2[e],z2[t]]),Oo=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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r,A,s;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(o[0],o[1]),r=Math.atan2(o[1]-t[1],o[0]-t[0]),A=n*Math.cos(r)+o[0],s=n*Math.sin(r)+o[1],e.moveTo(A,s),r+=1/3*(2*Math.PI),A=n*Math.cos(r)+o[0],s=n*Math.sin(r)+o[1],e.lineTo(A,s),r+=1/3*(2*Math.PI),A=n*Math.cos(r)+o[0],s=n*Math.sin(r)+o[1],e.lineTo(A,s),e.closePath(),e.stroke(),e.fill()}var P0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawAttention:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1};var U;function DA(e,t){if(U.drawLabels){let o=[];if(o.push(`face: ${Math.trunc(100*e.score)}%`),e.genderScore&&o.push(`${e.gender||""} ${Math.trunc(100*e.genderScore)}%`),e.age&&o.push(`age: ${e.age||""}`),e.iris&&o.push(`distance: ${e.iris}`),e.real&&o.push(`real: ${Math.trunc(100*e.real)}%`),e.live&&o.push(`live: ${Math.trunc(100*e.live)}%`),e.emotion&&e.emotion.length>0){let n=e.emotion.map(r=>`${Math.trunc(100*r.score)}% ${r.emotion}`);n.length>3&&(n.length=3),o.push(n.join(" "))}e.rotation&&e.rotation.angle&&e.rotation.gaze&&(e.rotation.angle.roll&&o.push(`roll: ${Xe(e.rotation.angle.roll)}\xB0 yaw:${Xe(e.rotation.angle.yaw)}\xB0 pitch:${Xe(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&o.push(`gaze: ${Xe(e.rotation.gaze.bearing)}\xB0`)),o.length===0&&o.push("face"),t.fillStyle=U.color;for(let n=o.length-1;n>=0;n--){let r=Math.max(e.box[0],0),A=n*U.lineHeight+e.box[1];U.shadowColor&&U.shadowColor!==""&&(t.fillStyle=U.shadowColor,t.fillText(o[n],r+5,A+16)),t.fillStyle=U.labelColor,t.fillText(o[n],r+4,A+15)}}}function ZA(e,t){if(e.annotations&&e.annotations.leftEyeIris&&e.annotations.leftEyeIris[0]){t.strokeStyle=U.useDepth?"rgba(255, 200, 255, 0.3)":U.color,t.beginPath();let o=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),U.fillPolygons&&(t.fillStyle=U.useDepth?"rgba(255, 255, 200, 0.3)":U.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=U.useDepth?"rgba(255, 200, 255, 0.3)":U.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),U.fillPolygons&&(t.fillStyle=U.useDepth?"rgba(255, 255, 200, 0.3)":U.color,t.fill())}}function XA(e,t){var o;if(U.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Xe(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Xe(e.rotation.angle.pitch)/90,A=new Path2D(`
+      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+      C
+        ${n} ${e.box[1]},
+        ${n} ${e.box[1]+e.box[3]},
+        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
+    `),s=new Path2D(`
+      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+      C 
+        ${e.box[0]} ${r},
+        ${e.box[0]+e.box[2]} ${r},
+        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
+    `);t.stroke(s),t.stroke(A)}}function qA(e,t){var o,n,r,A;if(U.drawGaze&&((n=(o=e.rotation)==null?void 0:o.gaze)==null?void 0:n.strength)&&((A=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:A.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let s=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];J1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[s[0],s[1]],4);let a=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];J1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[a[0],a[1]],4)}}function UA(e,t){if(U.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let o=0;o<Ne.length/3;o++){let n=[Ne[o*3+0],Ne[o*3+1],Ne[o*3+2]].map(r=>e.mesh[r]);K1(t,n,U)}ZA(e,t)}}function YA(e,t){if(U.drawPoints&&e.mesh.length>=468)for(let o=0;o<e.mesh.length;o++)pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2],U),U.drawAttention&&(w2.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]+127,U),We.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,U),Fe.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,U))}function KA(e,t){U.drawBoxes&&re(t,e.box[0],e.box[1],e.box[2],e.box[3],U)}async function l2(e,t,o){if(U=r0(P0,o),!t||!e)return;let n=V0(e);if(!!n){n.font=U.font,n.strokeStyle=U.color,n.fillStyle=U.color;for(let r of t)KA(r,n),DA(r,n),r.mesh&&r.mesh.length>0&&(YA(r,n),UA(r,n),XA(r,n),qA(r,n))}}async function y2(e,t,o){var A;let n=r0(P0,o);if(!t||!e)return;let r=V0(e);if(!!r){r.lineJoin="round";for(let s=0;s<t.length;s++){if(r.strokeStyle=n.color,r.fillStyle=n.color,r.lineWidth=n.lineWidth,r.font=n.font,n.drawBoxes&&t[s].box&&((A=t[s].box)==null?void 0:A.length)===4&&(re(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(r.fillStyle=n.shadowColor,r.fillText(`body ${100*t[s].score}%`,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),r.fillStyle=n.labelColor,r.fillText(`body ${100*t[s].score}%`,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2]))),n.drawPoints&&t[s].keypoints)for(let a=0;a<t[s].keypoints.length;a++)!t[s].keypoints[a].score||t[s].keypoints[a].score===0||(r.fillStyle=me(t[s].keypoints[a].position[2],n),pe(r,t[s].keypoints[a].position[0],t[s].keypoints[a].position[1],0,n));if(n.drawLabels&&t[s].keypoints){r.font=n.font;for(let a of t[s].keypoints)!a.score||a.score===0||(r.fillStyle=me(a.position[2],n),r.fillText(`${a.part} 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x=a[a.length-1][2]||-256;r.fillStyle=me(x,n),r.fillText(i,a[a.length-1][0]+4,a[a.length-1][1]+4)};r.font=n.font,s(A.annotations.index,"index"),s(A.annotations.middle,"middle"),s(A.annotations.ring,"ring"),s(A.annotations.pinky,"pinky"),s(A.annotations.thumb,"thumb"),s(A.annotations.palm,"palm")}if(n.drawPolygons&&A.annotations){let s=a=>{if(!(!a||a.length===0||!a[0]))for(let i=0;i<a.length;i++){r.beginPath();let x=a[i][2]||0;r.strokeStyle=me(i*x,n),r.moveTo(a[i>0?i-1:0][0],a[i>0?i-1:0][1]),r.lineTo(a[i][0],a[i][1]),r.stroke()}};r.lineWidth=n.lineWidth,s(A.annotations.index),s(A.annotations.middle),s(A.annotations.ring),s(A.annotations.pinky),s(A.annotations.thumb)}}}}async function c2(e,t,o){let n=r0(P0,o);if(!t||!e)return;let r=V0(e);if(!!r){r.lineJoin="round",r.font=n.font;for(let A of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,re(r,A.box[0],A.box[1],A.box[2],A.box[3],n),n.drawLabels){let s=`${A.label} 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X0.silhouette)r.push({x:(e.mesh[s][0]-e.box[0])/e.box[2],y:(e.mesh[s][1]-e.box[1])/e.box[3]});f2&&f2>0&&(r=r.map(s=>({x:s.x>.5?s.x+f2:s.x-f2,y:s.y>.5?s.y+f2:s.y-f2})));for(let s=0;s<t;s++)for(let a=0;a<o;a++)JA(s/t,a/t,r)||(n.set(ot*n.get(0,a,s,0),0,a,s,0),n.set(ot*n.get(0,a,s,1),0,a,s,1),n.set(ot*n.get(0,a,s,2),0,a,s,2));let A=n.toTensor();return Yo.dispose(n),A}var _A=e=>{let t=(l,y)=>Math.atan2(l[1]-y[1],l[0]-y[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let o=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=r?e.mesh[473]:e.mesh[468],s=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],i=[(s[0]-A[0])/a[0]-o[0],n*(A[1]-s[1])/a[1]-o[1]],x=Math.sqrt(i[0]*i[0]+i[1]*i[1]);return x=Math.min(x,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],i)+Math.PI/2)%Math.PI,strength:x}},Jo=(e,t)=>{let o=m=>{let P=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=P,m[1]/=P,m[2]/=P,m},n=(m,P)=>{let g=m[0]-P[0],b=m[1]-P[1],d=m[2]-P[2];return[g,b,d]},r=(m,P)=>{let g=m[1]*P[2]-m[2]*P[1],b=m[2]*P[0]-m[0]*P[2],d=m[0]*P[1]-m[1]*P[0];return[g,b,d]},A=m=>{let[P,g,b,d,M,S,w,j,C]=m,F,H,Y;return d<1?d>-1?(Y=Math.asin(d),H=Math.atan2(-w,P),F=Math.atan2(-S,M)):(Y=-Math.PI/2,H=-Math.atan2(j,C),F=0):(Y=Math.PI/2,H=Math.atan2(j,C),F=0),isNaN(F)&&(F=0),isNaN(H)&&(H=0),isNaN(Y)&&(Y=0),{pitch:2*-F,yaw:2*-H,roll:2*-Y}},s=e.meshRaw;if(!s||s.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let a=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,i=[s[10],s[152],s[234],s[454]].map(m=>[m[0]*t[0]/a,m[1]*t[1]/a,m[2]]),x=o(n(i[1],i[0])),c=o(n(i[3],i[2])),l=o(r(c,x));c=r(x,l);let y=[c[0],c[1],c[2],x[0],x[1],x[2],l[0],l[1],l[2]],f=A(y),u=s.length===478?_A(e):{bearing:0,strength:0};return{angle:f,matrix:y,gaze:u}};var nt=async(e,t)=>{var f,u,m,P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0,R,c0;let o=h(),n,r,A,s,a,i,x,c,l=[];e.state="run:face";let y=await F3(t,e.config);if(e.performance.face=v.perfadd?(e.performance.face||0)+Math.trunc(h()-o):Math.trunc(h()-o),!t.shape||t.shape.length!==4)return[];if(!y)return[];for(let k=0;k<y.length;k++){if(e.analyze("Get Face"),!y[k].tensor||y[k].tensor.isDisposedInternal){p("Face object is disposed:",y[k].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let K=await Ko(y[k]);s0.dispose(y[k].tensor),y[k].tensor=K}let $=y[k].mesh&&y[k].mesh.length>200?Jo(y[k],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?s=(u=e.config.face.emotion)!=null&&u.enabled?y1(y[k].tensor||s0.tensor([]),e.config,k,y.length):[]:(e.state="run:emotion",o=h(),s=(m=e.config.face.emotion)!=null&&m.enabled?await y1(y[k].tensor||s0.tensor([]),e.config,k,y.length):[],e.performance.emotion=v.perfadd?(e.performance.emotion||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?i=(P=e.config.face.antispoof)!=null&&P.enabled?X5(y[k].tensor||s0.tensor([]),e.config,k,y.length):0:(e.state="run:antispoof",o=h(),i=(g=e.config.face.antispoof)!=null&&g.enabled?await X5(y[k].tensor||s0.tensor([]),e.config,k,y.length):0,e.performance.antispoof=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?x=(b=e.config.face.liveness)!=null&&b.enabled?S1(y[k].tensor||s0.tensor([]),e.config,k,y.length):0:(e.state="run:liveness",o=h(),x=(d=e.config.face.liveness)!=null&&d.enabled?await S1(y[k].tensor||s0.tensor([]),e.config,k,y.length):0,e.performance.liveness=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(M=e.config.face.gear)!=null&&M.enabled?F5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:gear",o=h(),r=(S=e.config.face.gear)!=null&&S.enabled?await F5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.gear=Math.trunc(h()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(w=e.config.face.ssrnet)!=null&&w.enabled?B5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,A=(j=e.config.face.ssrnet)!=null&&j.enabled?D5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null):(e.state="run:ssrnet",o=h(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await B5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,A=(F=e.config.face.ssrnet)!=null&&F.enabled?await D5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.ssrnet=Math.trunc(h()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?a=(H=e.config.face.mobilefacenet)!=null&&H.enabled?c1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:mobilefacenet",o=h(),a=(Y=e.config.face.mobilefacenet)!=null&&Y.enabled?await c1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.mobilefacenet=Math.trunc(h()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(J=e.config.face.description)!=null&&J.enabled?h1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:description",o=h(),c=(x0=e.config.face.description)!=null&&x0.enabled?await h1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Description:"),e.config.async&&([n,A,s,a,c,r,i,x]=await Promise.all([n,A,s,a,c,r,i,x])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&n&&A&&(c={...c,age:n.age,gender:A.gender,genderScore:A.genderScore}),((G0=e.config.face.gear)==null?void 0:G0.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((B0=e.config.face.mobilefacenet)==null?void 0:B0.enabled)&&a&&(c.descriptor=a),(R=e.config.face.iris)!=null&&R.enabled;let a0=y[k].annotations&&y[k].annotations.leftEyeIris&&y[k].annotations.leftEyeIris[0]&&y[k].annotations.rightEyeIris&&y[k].annotations.rightEyeIris[0]&&y[k].annotations.leftEyeIris.length>0&&y[k].annotations.rightEyeIris.length>0&&y[k].annotations.leftEyeIris[0]!==null&&y[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(y[k].annotations.leftEyeIris[3][0]-y[k].annotations.leftEyeIris[1][0]),Math.abs(y[k].annotations.rightEyeIris[4][1]-y[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,f0=(c0=e.config.face.detector)!=null&&c0.return?s0.squeeze(y[k].tensor):null;s0.dispose(y[k].tensor),y[k].tensor&&delete y[k].tensor;let L={...y[k],id:k};c!=null&&c.age&&(L.age=c.age),c!=null&&c.gender&&(L.gender=c.gender),c!=null&&c.genderScore&&(L.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(L.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(L.race=c==null?void 0:c.race),s&&(L.emotion=s),i&&(L.real=i),x&&(L.live=x),a0&&a0!==0&&(L.iris=Math.trunc(500/a0/11.7)/100),$&&(L.rotation=$),f0&&(L.tensor=f0),l.push(L),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Qo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give up"}):A&&n&&n.position[1]<A.position[1]?t.push({body:o,gesture:"raise left hand"}):A&&r&&r.position[1]<A.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(i=>i.part==="leftShoulder"),a=e[o].keypoints.find(i=>i.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},_o=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},$o=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),x=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(x=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],y=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||y>.06)&&(x=!1),l>y?l>.05&&t.push({iris:o,gesture:"looking right"}):y>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(x=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),x&&t.push({iris:o,gesture:"looking center"})}return t},en=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=yo(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},rt=0;function tn(e,t){var s,a,i,x,c,l,y,f,u,m,P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let c0=e.body[R].box.map((L,K)=>((r-1)*z.body[R].box[K]+L)/r),k=e.body[R].boxRaw.map((L,K)=>((r-1)*z.body[R].boxRaw[K]+L)/r),$=e.body[R].keypoints.map((L,K)=>{var Ae,se,p2,u2,qe,lt,yt,xt,ct;return{score:L.score,part:L.part,position:[z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].position[0]||0)+(L.position[0]||0))/r:L.position[0],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].position[1]||0)+(L.position[1]||0))/r:L.position[1],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].position[2]||0)+(L.position[2]||0))/r:L.position[2]],positionRaw:[z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].positionRaw[0]||0)+(L.positionRaw[0]||0))/r:L.positionRaw[0],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].positionRaw[1]||0)+(L.positionRaw[1]||0))/r:L.positionRaw[1],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].positionRaw[2]||0)+(L.positionRaw[2]||0))/r:L.positionRaw[2]],distance:[z.body[R].keypoints[K]?((r-1)*(((Ae=z.body[R].keypoints[K].distance)==null?void 0:Ae[0])||0)+(((se=L.distance)==null?void 0:se[0])||0))/r:(p2=L.distance)==null?void 0:p2[0],z.body[R].keypoints[K]?((r-1)*(((u2=z.body[R].keypoints[K].distance)==null?void 0:u2[1])||0)+(((qe=L.distance)==null?void 0:qe[1])||0))/r:(lt=L.distance)==null?void 0:lt[1],z.body[R].keypoints[K]?((r-1)*(((yt=z.body[R].keypoints[K].distance)==null?void 0:yt[2])||0)+(((xt=L.distance)==null?void 0:xt[2])||0))/r:(ct=L.distance)==null?void 0:ct[2]]}}),a0={},f0={connected:{}};(a=(s=t.body)==null?void 0:s.modelPath)!=null&&a.includes("efficientpose")?f0=e5:(x=(i=t.body)==null?void 0:i.modelPath)!=null&&x.includes("blazepose")?f0=K2:(l=(c=t.body)==null?void 0:c.modelPath)!=null&&l.includes("movenet")&&(f0=E2);for(let[L,K]of Object.entries(f0.connected)){let Ae=[];for(let se=0;se<K.length-1;se++){let p2=$.find(qe=>qe.part===K[se]),u2=$.find(qe=>qe.part===K[se+1]);p2&&u2&&Ae.push([p2.position,u2.position])}a0[L]=Ae}z.body[R]={...e.body[R],box:c0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R<e.hand.length;R++){let c0=e.hand[R].box.map((f0,L)=>((r-1)*z.hand[R].box[L]+f0)/r),k=e.hand[R].boxRaw.map((f0,L)=>((r-1)*z.hand[R].boxRaw[L]+f0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((f0,L)=>f0.map((K,Ae)=>((r-1)*(z.hand[R].keypoints[L][Ae]||1)+(K||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let f0 of Object.keys(e.hand[R].annotations))a0[f0]=e.hand[R].annotations[f0]&&e.hand[R].annotations[f0][0]?e.hand[R].annotations[f0].map((L,K)=>L.map((Ae,se)=>((r-1)*z.hand[R].annotations[f0][K][se]+Ae)/r)):null;z.hand[R]={...e.hand[R],box:c0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let c0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(y=e.face[R].rotation)==null?void 0:y.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((P=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:P.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((M=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:M.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((H=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:H.bearing)||0)+(((J=(Y=e.face[R].rotation)==null?void 0:Y.gaze)==null?void 0:J.bearing)||0))/r,strength:((r-1)*(((E=(x0=z.face[R].rotation)==null?void 0:x0.gaze)==null?void 0:E.strength)||0)+(((B0=(G0=e.face[R].rotation)==null?void 0:G0.gaze)==null?void 0:B0.strength)||0))/r},z.face[R]={...e.face[R],rotation:$,box:c0,boxRaw:k}}z.face[R]={...e.face[R],box:c0,boxRaw:k}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let c0=e.object[R].box.map(($,a0)=>((r-1)*z.object[R].box[a0]+$)/r),k=e.object[R].boxRaw.map(($,a0)=>((r-1)*z.object[R].boxRaw[a0]+$)/r);z.object[R]={...e.object[R],box:c0,boxRaw:k}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let c0=0;c0<R.length;c0++)z.persons[c0].box=R[c0].box.map((k,$)=>((r-1)*z.persons[c0].box[$]+k)/r)}e.gesture&&(z.gesture=e.gesture);let A=h();return rt=v.perfadd?rt+Math.round(A-o):Math.round(A-o),e.performance&&(z.performance={...e.performance,interpolate:rt}),z}var at={};ue(at,{distance:()=>j2,match:()=>st,similarity:()=>At});function j2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var on=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function At(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=j2(e,t,o);return on(n,o.order||2,o.min||0,o.max||1)}function st(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=j2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=on(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function nn(e,t,o,n,r){var a,i,x,c,l,y,f,u,m,P,g,b,d,M,S,w;let A=0,s=[];for(let j of e){let C={id:A++,face:j,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let E of t)j.box[0]>E.box[0]&&j.box[0]<E.box[0]+E.box[2]&&j.box[1]+j.box[3]>E.box[1]&&j.box[1]+j.box[3]<E.box[1]+E.box[3]&&(C.body=E);if(C.body)for(let E of o)E.box[0]+E.box[2]>C.body.box[0]&&E.box[0]+E.box[2]<C.body.box[0]+C.body.box[2]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=E),E.box[0]<C.body.box[0]+C.body.box[2]&&E.box[0]>C.body.box[0]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=E);for(let E of n)E.face!==void 0&&E.face===j.id?(a=C.gestures)==null||a.push(E):E.iris!==void 0&&E.iris===j.id?(i=C.gestures)==null||i.push(E):E.body!==void 0&&E.body===((x=C.body)==null?void 0:x.id)?(c=C.gestures)==null||c.push(E):E.hand!==void 0&&E.hand===((y=(l=C.hands)==null?void 0:l.left)==null?void 0:y.id)?(f=C.gestures)==null||f.push(E):E.hand!==void 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0:n.tfjs)||ee.version_core).replace(/-(.*)/,"");he.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${o}/dist/`,he.modelBasePath=v.browser?"../models/":"file://models/",he.backend=v.browser?"humangl":"tensorflow",this.version=O5,Object.defineProperty(this,"version",{value:O5}),this.config=JSON.parse(JSON.stringify(he)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=r0(this.config,t)),kt(this.config),this.tf=ee,this.state="idle",M2(this,m2,0),M2(this,I2,!1),M2(this,N2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new C2,this.draw={options:P0,canvas:(r,A)=>$1(r,A),face:(r,A,s)=>l2(r,A,s),body:(r,A,s)=>y2(r,A,s),hand:(r,A,s)=>x2(r,A,s),gesture:(r,A,s)=>d2(r,A,s),object:(r,A,s)=>c2(r,A,s),person:(r,A,s)=>_1(r,A,s),all:(r,A,s)=>et(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=B3,this.faceUVMap=H3,this.gl=t0,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(he)),this.config.backend=t}validate(t){return E5(he,t||this.config)}now(){return h()}image(t,o=!0){return Ye(t,this.config,o)}async segmentation(t,o){return Do(t,o,this.config)}enhance(t){return u1(t)}compare(t,o){return wt(this.config,t,o)}async init(){await v5(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=h(),n=Object.values(this.models).filter(s=>s).length;t&&(this.config=r0(this.config,t)),this.env.initial&&(this.config.debug&&p(`version: ${this.version}`),this.config.debug&&p(`tfjs version: ${this.tf.version["tfjs-core"]}`),await v5(this)||p("error: backend check failed"),await ee.ready(),this.env.browser&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("environment:",this.env),this.config.debug&&p("tf flags:",this.tf.ENV.flags))),await U1(this),this.env.initial&&this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==n&&(await Y1(this),this.emit("load"));let A=Math.trunc(h()-o);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return tn(t,this.config)}getModelStats(){return q1(this)}async warmup(t){let o=h(),n=await rn(this,t),r=h();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let a of n.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs;let A=[];Object.entries(r).forEach(a=>A.push({name:a[0],ms:a[1]})),A.sort((a,i)=>i.ms-a.ms),A.length=20;let s={};for(let a of A)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async n=>{var P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0,R,c0,k,$,a0;this.state="config";let r;this.config=r0(this.config,o),this.state="check";let A=b2(this,k5).call(this,t);A&&(p(A,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:A}));let s=h();await v5(this),await this.load(),r=h(),this.state="image";let a=await Ye(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&p("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=h(),this.config.skipAllowed=await Tt(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Check Changed:");let i=[],x=[],c=[],l=[];this.state="detect:face",this.config.async?(i=this.config.face.enabled?nt(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=h(),i=this.config.face.enabled?await nt(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(i=await i),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?r0(this.config,{body:{maxDetected:this.config.face.enabled?1*i.length:1}}):this.config;this.config.async?((P=this.config.body.modelPath)!=null&&P.includes("posenet")?x=this.config.body.enabled?D1(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("blazepose")?x=this.config.body.enabled?t1(a.tensor,y):[]:(b=this.config.body.modelPath)!=null&&b.includes("efficientpose")?x=this.config.body.enabled?i1(a.tensor,y):[]:(d=this.config.body.modelPath)!=null&&d.includes("movenet")&&(x=this.config.body.enabled?L1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=h(),(M=this.config.body.modelPath)!=null&&M.includes("posenet")?x=this.config.body.enabled?await D1(a.tensor,y):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?x=this.config.body.enabled?await t1(a.tensor,y):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?x=this.config.body.enabled?await i1(a.tensor,y):[]:(j=this.config.body.modelPath)!=null&&j.includes("movenet")&&(x=this.config.body.enabled?await L1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?r0(this.config,{hand:{maxDetected:this.config.face.enabled?2*i.length:1}}):this.config;this.config.async?((F=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&F.includes("handdetect")?c=this.config.hand.enabled?v1(a.tensor,f):[]:(Y=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&Y.includes("handtrack")&&(c=this.config.hand.enabled?E1(a.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=h(),(x0=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&x0.includes("handdetect")?c=this.config.hand.enabled?await v1(a.tensor,f):[]:(G0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&G0.includes("handtrack")&&(c=this.config.hand.enabled?await E1(a.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((B0=this.config.object.modelPath)!=null&&B0.includes("nanodet")?l=this.config.object.enabled?F1(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?r1(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=h(),(c0=this.config.object.modelPath)!=null&&c0.includes("nanodet")?l=this.config.object.enabled?await F1(a.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(l=this.config.object.enabled?await r1(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([i,x,c,l]=await Promise.all([i,x,c,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(r=h(),u=[..._o(i),...Qo(x),...en(c),...$o(i)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(h()-s):Math.trunc(h()-s);let m=((a0=($=this.process)==null?void 0:$.tensor)==null?void 0:a0.shape)||[];this.result={face:i,body:x,hand:c,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return nn(i,x,c,u,m)}},ee.dispose(a.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};m2=new WeakMap,I2=new WeakMap,N2=new WeakMap,k5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
+/**
+ * Human main module
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
diff --git a/dist/human.node-wasm.d.ts b/dist/human.node-wasm.d.ts
new file mode 100644
index 00000000..fed34cea
--- /dev/null
+++ b/dist/human.node-wasm.d.ts
@@ -0,0 +1,2643 @@
+/// <reference types="@webgpu/types/dist" />
+/// <reference types="offscreencanvas" />
+
+/** meta-function that performs draw for: canvas, face, body, hand */
+declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
+
+/** Defines all possible canvas types */
+export declare type AnyCanvas = HTMLCanvasElement | OffscreenCanvas;
+
+/** Defines all possible image types */
+export declare type AnyImage = HTMLImageElement | typeof Image;
+
+/** Defines all possible video types */
+export declare type AnyVideo = HTMLMediaElement | HTMLVideoElement;
+
+/** @docalias number[] */
+declare interface ArrayMap {
+    R0: number;
+    R1: number[];
+    R2: number[][];
+    R3: number[][][];
+    R4: number[][][][];
+    R5: number[][][][][];
+    R6: number[][][][][][];
+}
+
+/** Possible TensorFlow backends */
+export declare type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];
+
+/** draw detected bodies */
+declare function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+export declare type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;
+
+export declare type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';
+
+export declare type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';
+
+/** Configures all body detection specific options */
+export declare interface BodyConfig extends GenericConfig {
+    /** maximum number of detected bodies */
+    maxDetected: number;
+    /** minimum confidence for a detected body before results are discarded */
+    minConfidence: number;
+}
+
+/** body gesture type */
+export declare type BodyGesture = `leaning ${'left' | 'right'}` | `raise ${'left' | 'right'} hand` | 'i give up';
+
+/** Body Result keypoints */
+export declare interface BodyKeypoint {
+    /** body part name */
+    part: BodyLandmark;
+    /** body part position */
+    position: Point;
+    /** body part position normalized to 0..1 */
+    positionRaw: Point;
+    /** body part position relative to body center in meters */
+    distance?: Point;
+    /** body part detection score */
+    score: number;
+}
+
+export declare type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;
+
+export declare type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle' | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';
+
+export declare type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';
+
+export declare type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+export declare type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+/** Body results */
+export declare interface BodyResult {
+    /** body id */
+    id: number;
+    /** body detection score */
+    score: number;
+    /** detected body box */
+    box: Box;
+    /** detected body box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected body keypoints */
+    keypoints: Array<BodyKeypoint>;
+    /** detected body keypoints combined into annotated parts */
+    annotations: Record<BodyAnnotation, Point[][]>;
+}
+
+/** generic box as [x, y, width, height] */
+export declare type Box = [number, number, number, number];
+
+/**
+ * Creates an IOHandler that loads model artifacts from user-selected files.
+ *
+ * This method can be used for loading from files such as user-selected files
+ * in the browser.
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * // Note: This code snippet won't run properly without the actual file input
+ * //   elements in the HTML DOM.
+ *
+ * // Suppose there are two HTML file input (`<input type="file" ...>`)
+ * // elements.
+ * const uploadJSONInput = document.getElementById('upload-json');
+ * const uploadWeightsInput = document.getElementById('upload-weights');
+ * const model = await tf.loadLayersModel(tf.io.browserFiles(
+ *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+ * ```
+ *
+ * @param files `File`s to load from. Currently, this function supports only
+ *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+ *   which an `Array` of `File`s is expected (in that order):
+ *   - A JSON file containing the model topology and weight manifest.
+ *   - Optionally, One or more binary files containing the binary weights.
+ *     These files must have names that match the paths in the `weightsManifest`
+ *     contained by the aforementioned JSON file, or errors will be thrown
+ *     during loading. These weights files have the same format as the ones
+ *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+ *     Python PIP package. If no weights files are provided, only the model
+ *     topology will be loaded from the JSON file above.
+ * @returns An instance of `Files` `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function browserFiles(files: File[]): IOHandler;
+
+/**
+ * Deprecated. Use `tf.io.http`.
+ * @param path
+ * @param loadOptions
+ */
+declare function browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** draw processed canvas */
+declare function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas): Promise<void>;
+
+/**
+ * Concatenate a number of ArrayBuffers into one.
+ *
+ * @param buffers A number of array buffers to concatenate.
+ * @returns Result of concatenating `buffers` in order.
+ */
+declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+
+/**
+ * Configuration interface definition for **Human** library
+ * Contains all configurable parameters
+ * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+ */
+export declare interface Config {
+    /** Backend used for TFJS operations
+     * valid build-in backends are:
+     * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`
+     * - NodeJS: `cpu`, `wasm`, `tensorflow`
+     * default: `humangl` for browser and `tensorflow` for nodejs
+     */
+    backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+    /** Path to *.wasm files if backend is set to `wasm`
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPath: string;
+    /** Force WASM loader to use platform fetch
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPlatformFetch: boolean;
+    /** Print debug statements to console
+     *
+     * default: `true`
+     */
+    debug: boolean;
+    /** Perform model loading and inference concurrently or sequentially
+     *
+     * default: `true`
+     */
+    async: boolean;
+    /** What to use for `human.warmup()`
+     * - warmup pre-initializes all models for faster inference but can take significant time on startup
+     * - used by `webgl`, `humangl` and `webgpu` backends
+     *
+     * default: `full`
+     */
+    warmup: '' | 'none' | 'face' | 'full' | 'body';
+    /** Base model path (typically starting with file://, http:// or https://) for all models
+     * - individual modelPath values are relative to this path
+     *
+     * default: `../models/` for browsers and `file://models/` for nodejs
+     */
+    modelBasePath: string;
+    /** Cache models in IndexDB on first sucessfull load
+     * default: true if indexdb is available (browsers), false if its not (nodejs)
+     */
+    cacheModels: boolean;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+     *
+     * default: 0.7
+     */
+    cacheSensitivity: number;
+    /** Perform immediate garbage collection on deallocated tensors instead of caching them */
+    deallocate: boolean;
+    /** Internal Variable */
+    skipAllowed: boolean;
+    /** Filter config {@link FilterConfig} */
+    filter: Partial<FilterConfig>;
+    /** Gesture config {@link GestureConfig} */
+    gesture: Partial<GestureConfig>;
+    /** Face config {@link FaceConfig} */
+    face: Partial<FaceConfig>;
+    /** Body config {@link BodyConfig} */
+    body: Partial<BodyConfig>;
+    /** Hand config {@link HandConfig} */
+    hand: Partial<HandConfig>;
+    /** Object config {@link ObjectConfig} */
+    object: Partial<ObjectConfig>;
+    /** Segmentation config {@link SegmentationConfig} */
+    segmentation: Partial<SegmentationConfig>;
+}
+
+/**
+ * Copy a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Copying within a storage medium, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Copying between two storage mediums, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Copy the model, from Local Storage to IndexedDB.
+ * await tf.io.copyModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove both models.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of copying.
+ * @param destURL Destination URL of copying.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * We wrap data id since we use weak map to avoid memory leaks.
+ * Since we have our own memory management, we have a reference counter
+ * mapping a tensor to its data, so there is always a pointer (even if that
+ * data is otherwise garbage collectable).
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
+ * Global_Objects/WeakMap
+ */
+declare type DataId = object;
+
+declare type DataToGPUOptions = DataToGPUWebGLOption;
+
+declare interface DataToGPUWebGLOption {
+    customTexShape?: [number, number];
+}
+
+/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
+declare type DataType = keyof DataTypeMap;
+
+declare interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+
+/**
+ * Decode flat ArrayBuffer as weights.
+ *
+ * This function does not handle sharding.
+ *
+ * This function is the reverse of `encodeWeights`.
+ *
+ * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+ *   concatenated in the order specified in `specs`.
+ * @param specs Specifications of the names, dtypes and shapes of the tensors
+ *   whose value are encoded by `buffer`.
+ * @return A map from tensor name to tensor value, with the names corresponding
+ *   to names in `specs`.
+ * @throws Error, if any of the tensors has unsupported dtype.
+ */
+declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+
+/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
+export declare const defaults: Config;
+
+/** Face descriptor type as number array */
+export declare type Descriptor = Array<number>;
+
+/** Calculates distance between two descriptors
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ */
+declare function distance(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare namespace draw {
+    export {
+        person,
+        canvas,
+        all,
+        options,
+        face,
+        body,
+        hand,
+        object,
+        gesture
+    }
+}
+export { draw }
+
+/** Draw Options
+ * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter
+ */
+export declare type DrawOptions = {
+    /** draw line color */
+    color: string;
+    /** alpha value used for lines */
+    alpha: number;
+    /** label color */
+    labelColor: string;
+    /** label shadow color */
+    shadowColor: string;
+    /** label font */
+    font: string;
+    /** line spacing between labels */
+    lineHeight: number;
+    /** line width for drawn lines */
+    lineWidth: number;
+    /** size of drawn points */
+    pointSize: number;
+    /** draw rounded boxes by n pixels */
+    roundRect: number;
+    /** should points be drawn? */
+    drawPoints: boolean;
+    /** should labels be drawn? */
+    drawLabels: boolean;
+    /** should face attention keypoints be highlighted */
+    drawAttention: boolean;
+    /** should detected gestures be drawn? */
+    drawGestures: boolean;
+    /** should draw boxes around detection results? */
+    drawBoxes: boolean;
+    /** should draw polygons from detection points? */
+    drawPolygons: boolean;
+    /** should draw gaze arrows? */
+    drawGaze: boolean;
+    /** should fill polygons? */
+    fillPolygons: boolean;
+    /** use z-coordinate when available */
+    useDepth: boolean;
+    /** should lines be curved? */
+    useCurves: boolean;
+};
+
+export declare type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';
+
+/**
+ * Encode a map from names to weight values as an ArrayBuffer, along with an
+ * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+ *
+ * This function does not perform sharding.
+ *
+ * This function is the reverse of `decodeWeights`.
+ *
+ * @param tensors A map ("dict") from names to tensors.
+ * @param group Group to which the weights belong (optional).
+ * @returns A `Promise` of
+ *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+ *     concatenated.
+ *   - An `Array` of `WeightManifestEntry`s, carrying information including
+ *     tensor names, `dtype`s and shapes.
+ * @throws Error: on unsupported tensor `dtype`.
+ */
+declare function encodeWeights(tensors: NamedTensorMap | NamedTensor[], group?: WeightGroup): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+
+/** Env class that holds detected capabilities */
+export declare class Env {
+    /** Running in Browser */
+    browser: boolean;
+    /** Running in NodeJS */
+    node: boolean;
+    /** Running in WebWorker thread */
+    worker: boolean;
+    /** Detected platform */
+    platform: string;
+    /** Detected agent */
+    agent: string;
+    /** List of supported backends */
+    backends: string[];
+    /** Has any work been performed so far */
+    initial: boolean;
+    /** Are image filters supported? */
+    filter: boolean | undefined;
+    /** TFJS instance details */
+    tfjs: {
+        version: undefined | string;
+    };
+    /** Is offscreenCanvas supported? */
+    offscreen: undefined | boolean;
+    /** Are performance counter instant values or additive */
+    perfadd: boolean;
+    /** WASM detected capabilities */
+    wasm: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        simd: undefined | boolean;
+        multithread: undefined | boolean;
+    };
+    /** WebGL detected capabilities */
+    webgl: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        version: undefined | string;
+        renderer: undefined | string;
+    };
+    /** WebGPU detected capabilities */
+    webgpu: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        adapter: undefined | string;
+    };
+    /** CPU info */
+    cpu: {
+        model: undefined | string;
+        flags: string[];
+    };
+    /** List of supported kernels for current backend */
+    kernels: string[];
+    /** MonkeyPatch for Canvas */
+    Canvas: undefined;
+    /** MonkeyPatch for Image */
+    Image: undefined;
+    /** MonkeyPatch for ImageData */
+    ImageData: undefined;
+    constructor();
+    /** update backend information */
+    updateBackend(): Promise<void>;
+    /** update cpu information */
+    updateCPU(): Promise<void>;
+}
+
+export declare const env: Env;
+
+/** Events dispatched by `human.events`
+ * - `create`: triggered when Human object is instantiated
+ * - `load`: triggered when models are loaded (explicitly or on-demand)
+ * - `image`: triggered when input image is processed
+ * - `result`: triggered when detection is complete
+ * - `warmup`: triggered when warmup is complete
+ */
+export declare type Events = 'create' | 'load' | 'image' | 'result' | 'warmup' | 'error';
+
+/** Defines possible externally defined canvas */
+export declare type ExternalCanvas = typeof env.Canvas;
+
+/** draw detected faces */
+declare function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Anti-spoofing part of face configuration */
+export declare interface FaceAntiSpoofConfig extends GenericConfig {
+}
+
+/** Attention part of face configuration */
+export declare interface FaceAttentionConfig extends GenericConfig {
+}
+
+/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */
+export declare interface FaceConfig extends GenericConfig {
+    detector: Partial<FaceDetectorConfig>;
+    mesh: Partial<FaceMeshConfig>;
+    attention: Partial<FaceAttentionConfig>;
+    iris: Partial<FaceIrisConfig>;
+    description: Partial<FaceDescriptionConfig>;
+    emotion: Partial<FaceEmotionConfig>;
+    antispoof: Partial<FaceAntiSpoofConfig>;
+    liveness: Partial<FaceLivenessConfig>;
+    gear: Partial<FaceGearConfig>;
+}
+
+/** Description or face embedding part of face configuration
+ * - also used by age and gender detection
+ */
+export declare interface FaceDescriptionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Detector part of face configuration */
+export declare interface FaceDetectorConfig extends GenericConfig {
+    /** is face rotation correction performed after detecting face?
+     * used to correctly analyze faces under high angles
+     */
+    rotation: boolean;
+    /** maximum number of detected faces */
+    maxDetected: number;
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected faces before one is discarded */
+    iouThreshold: number;
+    /** should child models perform on masked image of a face */
+    mask: boolean;
+    /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?
+     * if enabled it must be manually deallocated to avoid memory leak */
+    return: boolean;
+}
+
+/** Emotion part of face configuration */
+export declare interface FaceEmotionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Gear part of face configuration */
+export declare interface FaceGearConfig extends GenericConfig {
+    /** minimum confidence for a detected race before results are discarded */
+    minConfidence: number;
+}
+
+/** face gesture type */
+export declare type FaceGesture = `facing ${'left' | 'center' | 'right'}` | `blink ${'left' | 'right'} eye` | `mouth ${number}% open` | `head ${'up' | 'down'}`;
+
+/** Iris part of face configuration */
+export declare interface FaceIrisConfig extends GenericConfig {
+}
+
+export declare type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette' | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner' | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris' | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris' | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';
+
+/** Liveness part of face configuration */
+export declare interface FaceLivenessConfig extends GenericConfig {
+}
+
+/** Mesh part of face configuration */
+export declare interface FaceMeshConfig extends GenericConfig {
+    /** Keep detected faces that cannot be verified using facemesh */
+    keepInvalid: boolean;
+}
+
+/** Face results
+ * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
+ * - Some values may be null if specific model is not enabled
+ */
+export declare interface FaceResult {
+    /** face id */
+    id: number;
+    /** overall face score */
+    score: number;
+    /** detection score */
+    boxScore: number;
+    /** mesh score */
+    faceScore: number;
+    /** detected face box */
+    box: Box;
+    /** detected face box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected face mesh */
+    mesh: Array<Point>;
+    /** detected face mesh normalized to 0..1 */
+    meshRaw: Array<Point>;
+    /** face contours as array of 2d points normalized to 0..1 */
+    /** face contours as array of 2d points */
+    /** mesh keypoints combined into annotated results */
+    annotations: Record<FaceLandmark, Point[]>;
+    /** detected age */
+    age?: number;
+    /** detected gender */
+    gender?: Gender;
+    /** gender detection score */
+    genderScore?: number;
+    /** detected emotions */
+    emotion?: Array<{
+        score: number;
+        emotion: Emotion;
+    }>;
+    /** detected race */
+    race?: Array<{
+        score: number;
+        race: Race;
+    }>;
+    /** face descriptor */
+    embedding?: Array<number>;
+    /** face iris distance from camera */
+    iris?: number;
+    /** face anti-spoofing result confidence */
+    real?: number;
+    /** face liveness result confidence */
+    live?: number;
+    /** face rotation details */
+    rotation?: {
+        angle: {
+            roll: number;
+            yaw: number;
+            pitch: number;
+        };
+        matrix: [number, number, number, number, number, number, number, number, number];
+        gaze: {
+            bearing: number;
+            strength: number;
+        };
+    } | null;
+    /** detected face as tensor that can be used in further pipelines */
+    tensor?: Tensor;
+}
+
+/** Run input through image filters before inference
+ * - available only in Browser environments
+ * - image filters run with near-zero latency as they are executed on the GPU using WebGL
+ */
+export declare interface FilterConfig {
+    /** are image filters enabled? */
+    enabled: boolean;
+    /** perform image histogram equalization
+     * - equalization is performed on input as a whole and detected face before its passed for further analysis
+     */
+    equalization: boolean;
+    /** resize input width
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    width: number;
+    /** resize input height
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    height: number;
+    /** return processed canvas imagedata in result */
+    return: boolean;
+    /** flip input as mirror image */
+    flip: boolean;
+    /** range: -1 (darken) to 1 (lighten) */
+    brightness: number;
+    /** range: -1 (reduce contrast) to 1 (increase contrast) */
+    contrast: number;
+    /** range: 0 (no sharpening) to 1 (maximum sharpening) */
+    sharpness: number;
+    /** range: 0 (no blur) to N (blur radius in pixels) */
+    blur: number;
+    /** range: -1 (reduce saturation) to 1 (increase saturation) */
+    saturation: number;
+    /** range: 0 (no change) to 360 (hue rotation in degrees) */
+    hue: number;
+    /** image negative */
+    negative: boolean;
+    /** image sepia colors */
+    sepia: boolean;
+    /** image vintage colors */
+    vintage: boolean;
+    /** image kodachrome colors */
+    kodachrome: boolean;
+    /** image technicolor colors */
+    technicolor: boolean;
+    /** image polaroid camera effect */
+    polaroid: boolean;
+    /** range: 0 (no pixelate) to N (number of pixels to pixelate) */
+    pixelate: number;
+}
+
+export declare type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';
+
+export declare type FingerCurl = 'none' | 'half' | 'full';
+
+export declare type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandler` that simply loads the provided data.
+ */
+declare function fromMemory(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler;
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
+ */
+declare function fromMemorySync(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandlerSync;
+
+export declare type Gender = 'male' | 'female' | 'unknown';
+
+/** Generic config type inherited by all module types */
+export declare interface GenericConfig {
+    /** is module enabled? */
+    enabled: boolean;
+    /** path to model json file (relative to `modelBasePath` */
+    modelPath: string;
+    /** how many max frames to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipFrames: number;
+    /** how many max milliseconds to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipTime: number;
+}
+
+/** draw detected gestures */
+declare function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Controlls gesture detection */
+export declare interface GestureConfig {
+    /** is gesture detection enabled? */
+    enabled: boolean;
+}
+
+/** Gesture combined results
+ * Each result has:
+ * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`
+ * - gesture: gesture detected
+ */
+export declare type GestureResult = {
+    'face': number;
+    gesture: FaceGesture;
+} | {
+    'iris': number;
+    gesture: IrisGesture;
+} | {
+    'body': number;
+    gesture: BodyGesture;
+} | {
+    'hand': number;
+    gesture: HandGesture;
+};
+
+declare const getLoadHandlers: (url: string | string[], loadOptions?: LoadOptions) => IOHandler[];
+
+/**
+ * Create `ModelArtifacts` from a JSON file.
+ *
+ * @param modelJSON Object containing the parsed JSON of `model.json`
+ * @param loadWeights Function that takes the JSON file's weights manifest,
+ *     reads weights from the listed path(s), and returns a Promise of the
+ *     weight manifest entries along with the weights data.
+ * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+ */
+declare function getModelArtifactsForJSON(modelJSON: ModelJSON, loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[WeightsManifestEntry[], /* weightData */ ArrayBuffer]>): Promise<ModelArtifacts>;
+
+/**
+ * Populate ModelArtifactsInfo fields for a model with JSON topology.
+ * @param modelArtifacts
+ * @returns A ModelArtifactsInfo object.
+ */
+declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
+
+declare const getModelStats: (instance: Human) => ModelStats;
+
+declare const getSaveHandlers: (url: string | string[]) => IOHandler[];
+
+declare interface GPUData {
+    tensorRef: Tensor;
+    texture?: WebGLTexture;
+    buffer?: GPUBuffer;
+    texShape?: [number, number];
+    bufSize?: number;
+}
+
+/**
+ * A `tf.GraphModel` is a directed, acyclic graph built from a
+ * SavedModel GraphDef and allows inference execution.
+ *
+ * A `tf.GraphModel` can only be created by loading from a model converted from
+ * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+ * the command line converter tool and loaded via `tf.loadGraphModel`.
+ *
+ * @doc {heading: 'Models', subheading: 'Classes'}
+ */
+export declare class GraphModel<ModelURL extends Url = string | io.IOHandler> implements InferenceModel {
+    private modelUrl;
+    private loadOptions;
+    private executor;
+    private version;
+    private handler;
+    private artifacts;
+    private initializer;
+    private resourceManager;
+    private signature;
+    readonly modelVersion: string;
+    readonly inputNodes: string[];
+    readonly outputNodes: string[];
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
+    readonly metadata: {};
+    readonly modelSignature: {};
+    /**
+     * @param modelUrl url for the model, or an `io.IOHandler`.
+     * @param weightManifestUrl url for the weight file generated by
+     * scripts/convert.py script.
+     * @param requestOption options for Request, which allows to send credentials
+     * and custom headers.
+     * @param onProgress Optional, progress callback function, fired periodically
+     * before the load is completed.
+     */
+    constructor(modelUrl: ModelURL, loadOptions?: io.LoadOptions);
+    private findIOHandler;
+    /**
+     * Loads the model and weight files, construct the in memory weight map and
+     * compile the inference graph.
+     */
+    load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync ? boolean : Promise<boolean>;
+    /**
+     * Synchronously construct the in memory weight map and
+     * compile the inference graph. Also initialize hashtable if any.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    loadSync(artifacts: io.ModelArtifacts): boolean;
+    /**
+     * Save the configuration and/or weights of the GraphModel.
+     *
+     * An `IOHandler` is an object that has a `save` method of the proper
+     * signature defined. The `save` method manages the storing or
+     * transmission of serialized data ("artifacts") that represent the
+     * model's topology and weights onto or via a specific medium, such as
+     * file downloads, local storage, IndexedDB in the web browser and HTTP
+     * requests to a server. TensorFlow.js provides `IOHandler`
+     * implementations for a number of frequently used saving mediums, such as
+     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+     * for more details.
+     *
+     * This method also allows you to refer to certain types of `IOHandler`s
+     * as URL-like string shortcuts, such as 'localstorage://' and
+     * 'indexeddb://'.
+     *
+     * Example 1: Save `model`'s topology and weights to browser [local
+     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+     * then load it back.
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     *
+     * const saveResults = await model.save('localstorage://my-model-1');
+     *
+     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+     * console.log('Prediction from loaded model:');
+     * model.predict(zeros).print();
+     * ```
+     *
+     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+     * scheme-based string shortcut for `IOHandler`.
+     * @param config Options for saving the model.
+     * @returns A `Promise` of `SaveResult`, which summarizes the result of
+     * the saving, such as byte sizes of the saved artifacts for the model's
+     *   topology and weight values.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+     * inputs params should be in either `tf.Tensor`[] if the input order is
+     * fixed, or otherwise NamedTensorMap format.
+     *
+     * For model with multiple inputs, we recommend you use NamedTensorMap as the
+     * input type, if you use `tf.Tensor`[], the order of the array needs to
+     * follow the
+     * order of inputNodes array. @see {@link GraphModel.inputNodes}
+     *
+     * You can also feed any intermediate nodes using the NamedTensorMap as the
+     * input type. For example, given the graph
+     *    InputNode => Intermediate => OutputNode,
+     * you can execute the subgraph Intermediate => OutputNode by calling
+     *    model.execute('IntermediateNode' : tf.tensor(...));
+     *
+     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+     * state needs to be fed manually.
+     *
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size and
+     * output node names. Currently the batch size option is ignored for graph
+     * model.
+     *
+     * @returns Inference result tensors. The output would be single `tf.Tensor`
+     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+     * will be returned for model with multiple outputs.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    private normalizeInputs;
+    private normalizeOutputs;
+    /**
+     * Executes inference for the model for given input tensors.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no
+     * outputs are specified, the default outputs of the model would be used.
+     * You can inspect intermediate nodes of the model by adding them to the
+     * outputs array.
+     *
+     * @returns A single tensor if provided with a single output or no outputs
+     * are provided and there is only one default output, otherwise return a
+     * tensor array. The order of the tensor array is the same as the outputs
+     * if provided, otherwise the order of outputNodes attribute of the model.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
+    /**
+     * Executes inference for the model for given input tensors in async
+     * fashion, use this method when your model contains control flow ops.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no outputs
+     * are specified, the default outputs of the model would be used. You can
+     * inspect intermediate nodes of the model by adding them to the outputs
+     * array.
+     *
+     * @returns A Promise of single tensor if provided with a single output or
+     * no outputs are provided and there is only one default output, otherwise
+     * return a tensor map.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
+    /**
+     * Get intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    getIntermediateTensors(): NamedTensorsMap;
+    /**
+     * Dispose intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    disposeIntermediateTensors(): void;
+    private convertTensorMapToTensorsMap;
+    /**
+     * Releases the memory used by the weight tensors and resourceManager.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    dispose(): void;
+}
+
+/** draw detected hands */
+declare function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all hand detection specific options */
+export declare interface HandConfig extends GenericConfig {
+    /** should hand rotation correction be performed after hand detection? */
+    rotation: boolean;
+    /** minimum confidence for a detected hand before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected hands before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected hands */
+    maxDetected: number;
+    /** should hand landmarks be detected or just return detected hand box */
+    landmarks: boolean;
+    detector: {
+        /** path to hand detector model json */
+        modelPath?: string;
+    };
+    skeleton: {
+        /** path to hand skeleton model json */
+        modelPath?: string;
+    };
+}
+
+/** hand gesture type */
+export declare type HandGesture = `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward` | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up` | 'victory' | 'thumbs up';
+
+/** Hand results */
+export declare interface HandResult {
+    /** hand id */
+    id: number;
+    /** hand overal score */
+    score: number;
+    /** hand detection score */
+    boxScore: number;
+    /** hand skelton score */
+    fingerScore: number;
+    /** detected hand box */
+    box: Box;
+    /** detected hand box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected hand keypoints */
+    keypoints: Array<Point>;
+    /** detected hand class */
+    label: HandType;
+    /** detected hand keypoints combined into annotated parts */
+    annotations: Record<Finger, Array<Point>>;
+    /** detected hand parts annotated with part gestures */
+    landmarks: Record<Finger, {
+        curl: FingerCurl;
+        direction: FingerDirection;
+    }>;
+}
+
+export declare type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';
+
+/**
+ * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+ *
+ * An HTTP request of the `multipart/form-data` mime type will be sent to the
+ * `path` URL. The form data includes artifacts that represent the topology
+ * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+ * blobs (files) exist in form-data:
+ *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+ *   - A binary weights file consisting of the concatenated weight values.
+ * These files are in the same format as the one generated by
+ * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+ *
+ * The following code snippet exemplifies the client-side code that uses this
+ * function:
+ *
+ * ```js
+ * const model = tf.sequential();
+ * model.add(
+ *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+ *
+ * const saveResult = await model.save(tf.io.http(
+ *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+ * console.log(saveResult);
+ * ```
+ *
+ * If the default `POST` method is to be used, without any custom parameters
+ * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+ *
+ * ```js
+ * const saveResult = await model.save('http://model-server:5000/upload');
+ * ```
+ *
+ * The following GitHub Gist
+ * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+ * implements a server based on [flask](https://github.com/pallets/flask) that
+ * can receive the request. Upon receiving the model artifacts via the requst,
+ * this particular server reconsistutes instances of [Keras
+ * Models](https://keras.io/models/model/) in memory.
+ *
+ *
+ * @param path A URL path to the model.
+ *   Can be an absolute HTTP path (e.g.,
+ *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+ *   './model-upload').
+ * @param requestInit Request configurations to be used when sending
+ *    HTTP request to server using `fetch`. It can contain fields such as
+ *    `method`, `credentials`, `headers`, `mode`, etc. See
+ *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+ *    for more information. `requestInit` must not have a body, because the
+ * body will be set by TensorFlow.js. File blobs representing the model
+ * topology (filename: 'model.json') and the weights of the model (filename:
+ * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+ * `body`, an Error will be thrown.
+ * @param loadOptions Optional configuration for the loading. It includes the
+ *   following fields:
+ *   - weightPathPrefix Optional, this specifies the path prefix for weight
+ *     files, by default this is calculated from the path param.
+ *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+ *     the `fetch` from node-fetch can be used here.
+ *   - onProgress Optional, progress callback function, fired periodically
+ *     before the load is completed.
+ * @returns An instance of `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function http(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** **Human** library main class
+ *
+ * All methods and properties are available only as members of Human class
+ *
+ * - Configuration object definition: {@link Config}
+ * - Results object definition: {@link Result}
+ * - Possible inputs: {@link Input}
+ *
+ * @param userConfig - {@link Config}
+ * @returns instance of {@link Human}
+ */
+declare class Human {
+    #private;
+    /** Current version of Human library in *semver* format */
+    version: string;
+    /** Current configuration
+     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+     */
+    config: Config;
+    /** Last known result of detect run
+     * - Can be accessed anytime after initial detection
+     */
+    result: Result;
+    /** Current state of Human library
+     * - Can be polled to determine operations that are currently executed
+     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
+     */
+    state: string;
+    /** currenty processed image tensor and canvas */
+    process: {
+        tensor: Tensor | null;
+        canvas: AnyCanvas | null;
+    };
+    /** Instance of TensorFlow/JS used by Human
+     *  - Can be embedded or externally provided
+     * [TFJS API](https://js.tensorflow.org/api/latest/)
+     */
+    tf: any;
+    /** Object containing environment information used for diagnostics */
+    env: Env;
+    /** Draw helper classes that can draw detected objects on canvas using specified draw
+     * - canvas: draws input to canvas
+     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
+     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
+     */
+    draw: {
+        canvas: typeof draw.canvas;
+        face: typeof draw.face;
+        body: typeof draw.body;
+        hand: typeof draw.hand;
+        gesture: typeof draw.gesture;
+        object: typeof draw.object;
+        person: typeof draw.person;
+        all: typeof draw.all;
+        options: DrawOptions;
+    };
+    /** Currently loaded models
+     * @internal
+     * {@link Models}
+     */
+    models: models.Models;
+    /** Container for events dispatched by Human
+     * Possible events:
+     * - `create`: triggered when Human object is instantiated
+     * - `load`: triggered when models are loaded (explicitly or on-demand)
+     * - `image`: triggered when input image is processed
+     * - `result`: triggered when detection is complete
+     * - `warmup`: triggered when warmup is complete
+     * - `error`: triggered on some errors
+     */
+    events: EventTarget | undefined;
+    /** Reference face triangualtion array of 468 points, used for triangle references between points */
+    faceTriangulation: number[];
+    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
+    faceUVMap: [number, number][];
+    /** Performance object that contains values for all recently performed operations */
+    performance: Record<string, number>;
+    /** WebGL debug info */
+    gl: Record<string, unknown>;
+    /** Constructor for **Human** library that is futher used for all operations
+     * @param userConfig - user configuration object {@link Config}
+     */
+    constructor(userConfig?: Partial<Config>);
+    /** internal function to measure tensor leaks */
+    analyze: (...msg: string[]) => void;
+    /** Reset configuration to default values */
+    reset(): void;
+    /** Validate current configuration schema */
+    validate(userConfig?: Partial<Config>): {
+        reason: string;
+        where: string;
+        expected?: string;
+    }[];
+    /** Exports face matching methods {@link match#similarity} */
+    similarity: typeof match.similarity;
+    /** Exports face matching methods {@link match#distance} */
+    distance: typeof match.distance;
+    /** Exports face matching methods {@link match#match} */
+    match: typeof match.match;
+    /** Utility wrapper for performance.now() */
+    now(): number;
+    /** Process input as return canvas and tensor
+     *
+     * @param input - any input {@link Input}
+     * @param getTensor - should image processing also return tensor or just canvas
+     * Returns object with `tensor` and `canvas`
+     */
+    image(input: Input, getTensor?: boolean): Promise<{
+        tensor: Tensor<Rank> | null;
+        canvas: AnyCanvas | null;
+    }>;
+    /** Segmentation method takes any input and returns processed canvas with body segmentation
+     *  - Segmentation is not triggered as part of detect process
+     * @param input - {@link Input}
+     * @param background - {@link Input}
+     *  - Optional parameter background is used to fill the background with specific input
+     *  Returns:
+     *  - `data` as raw data array with per-pixel segmentation values
+     *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
+     *  - `alpha` as grayscale canvas that represents segmentation alpha values
+     */
+    segmentation(input: Input, background?: Input): Promise<{
+        data: number[] | Tensor;
+        canvas: AnyCanvas | null;
+        alpha: AnyCanvas | null;
+    }>;
+    /** Enhance method performs additional enhacements to face image previously detected for futher processing
+     *
+     * @param input - Tensor as provided in human.result.face[n].tensor
+     * @returns Tensor
+     */
+    enhance(input: Tensor): Tensor | null;
+    /** Compare two input tensors for pixel simmilarity
+     * - use `human.image` to process any valid input and get a tensor that can be used for compare
+     * - when passing manually generated tensors:
+     *  - both input tensors must be in format [1, height, width, 3]
+     *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
+     * - return value is pixel similarity score normalized by input resolution and rgb channels
+     */
+    compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
+    /** Explicit backend initialization
+     *  - Normally done implicitly during initial load phase
+     *  - Call to explictly register and initialize TFJS backend without any other operations
+     *  - Use when changing backend during runtime
+     */
+    init(): Promise<void>;
+    /** Load method preloads all configured models on-demand
+     * - Not explicitly required as any required model is load implicitly on it's first run
+     *
+     * @param userConfig - {@link Config}
+     */
+    load(userConfig?: Partial<Config>): Promise<void>;
+    /** emit event */
+    emit: (event: string) => void;
+    /** Runs interpolation using last known result and returns smoothened result
+     * Interpolation is based on time since last known result so can be called independently
+     *
+     * @param result - {@link Result} optional use specific result set to run interpolation on
+     * @returns result - {@link Result}
+     */
+    next(result?: Result): Result;
+    /** get model loading/loaded stats */
+    getModelStats(): ModelStats;
+    /** Warmup method pre-initializes all configured models for faster inference
+     * - can take significant time on startup
+     * - only used for `webgl` and `humangl` backends
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    warmup(userConfig?: Partial<Config>): Promise<Result | undefined>;
+    /** Run detect with tensorflow profiling
+     * - result object will contain total exeuction time information for top-20 kernels
+     * - actual detection object can be accessed via `human.result`
+     */
+    profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>>;
+    /** Main detection method
+     * - Analyze configuration: {@link Config}
+     * - Pre-process input: {@link Input}
+     * - Run inference for all configured models
+     * - Process and return result: {@link Result}
+     *
+     * @param input - {@link Input}
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    detect(input: Input, userConfig?: Partial<Config>): Promise<Result>;
+}
+export { Human }
+export default Human;
+
+/** Defines all possible image objects */
+export declare type ImageObjects = ImageData | ImageBitmap;
+
+/**
+ * Common interface for a machine learning model that can do inference.
+ */
+declare interface InferenceModel {
+    /**
+     * Return the array of input tensor info.
+     */
+    readonly inputs: ModelTensorInfo[];
+    /**
+     * Return the array of output tensor info.
+     */
+    readonly outputs: ModelTensorInfo[];
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size.
+     *
+     * @returns Inference result tensors. The output would be single Tensor if
+     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
+     * be returned for model with multiple outputs.
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    /**
+     * Single Execute the inference for the input tensors and return activation
+     * values for specified output node names without batching.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     *
+     * @param outputs string|string[]. List of output node names to retrieve
+     * activation from.
+     *
+     * @returns Activation values for the output nodes result tensors. The return
+     * type matches specified parameter outputs type. The output would be single
+     * Tensor if single output is specified, otherwise Tensor[] for multiple
+     * outputs.
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
+
+/** Defines all possible input types for **Human** detection */
+export declare type Input = Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas;
+
+declare namespace io {
+    export {
+        copyModel,
+        listModels,
+        moveModel,
+        removeModel,
+        browserFiles,
+        browserHTTPRequest,
+        concatenateArrayBuffers,
+        decodeWeights,
+        encodeWeights,
+        fromMemory,
+        fromMemorySync,
+        getLoadHandlers,
+        getModelArtifactsForJSON,
+        getModelArtifactsInfoForJSON,
+        getSaveHandlers,
+        http,
+        IOHandler,
+        IOHandlerSync,
+        isHTTPScheme,
+        LoadHandler,
+        LoadOptions,
+        loadWeights,
+        ModelArtifacts,
+        ModelArtifactsInfo,
+        ModelJSON,
+        ModelStoreManager,
+        OnProgressCallback,
+        registerLoadRouter,
+        registerSaveRouter,
+        RequestDetails,
+        SaveConfig,
+        SaveHandler,
+        SaveResult,
+        TrainingConfig,
+        WeightGroup,
+        weightsLoaderFactory,
+        WeightsManifestConfig,
+        WeightsManifestEntry,
+        withSaveHandler,
+        withSaveHandlerSync
+    }
+}
+
+/**
+ * Interface for a model import/export handler.
+ *
+ * The `save` and `load` handlers are both optional, in order to allow handlers
+ * that support only saving or loading.
+ */
+declare interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+
+declare type IOHandlerSync = {
+    [K in keyof IOHandler]: Syncify<IOHandler[K]>;
+};
+
+declare type IORouter = (url: string | string[], loadOptions?: LoadOptions) => IOHandler;
+
+/** iris gesture type */
+export declare type IrisGesture = 'facing center' | `looking ${'left' | 'right' | 'up' | 'down'}` | 'looking center';
+
+declare function isHTTPScheme(url: string): boolean;
+
+/**
+ * List all models stored in registered storage mediums.
+ *
+ * For a web browser environment, the registered mediums are Local Storage and
+ * IndexedDB.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @returns A `Promise` of a dictionary mapping URLs of existing models to
+ * their model artifacts info. URLs include medium-specific schemes, e.g.,
+ *   'indexeddb://my/model/1'. Model artifacts info include type of the
+ * model's topology, byte sizes of the topology, weights, etc.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+
+/** Load method preloads all instance.configured models on-demand */
+declare function load(instance: Human): Promise<void>;
+
+/**
+ * Type definition for handlers of loading operations.
+ */
+declare type LoadHandler = () => Promise<ModelArtifacts>;
+
+/** @innamespace io */
+declare interface LoadOptions {
+    /**
+     * RequestInit (options) for HTTP requests.
+     *
+     * For detailed information on the supported fields, see
+     * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
+     *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
+     */
+    requestInit?: RequestInit;
+    /**
+     * Progress callback.
+     */
+    onProgress?: OnProgressCallback;
+    /**
+     * A function used to override the `window.fetch` function.
+     */
+    fetchFunc?: Function;
+    /**
+     * Strict loading model: whether extraneous weights or missing
+     * weights should trigger an `Error`.
+     *
+     * If `true`, require that the provided weights exactly match those
+     * required by the layers. `false` means that both extra weights
+     * and missing weights will be silently ignored.
+     *
+     * Default: `true`.
+     */
+    strict?: boolean;
+    /**
+     * Path prefix for weight files, by default this is calculated from the
+     * path of the model JSON file.
+     *
+     * For instance, if the path to the model JSON file is
+     * `http://localhost/foo/model.json`, then the default path prefix will be
+     * `http://localhost/foo/`. If a weight file has the path value
+     * `group1-shard1of2` in the weight manifest, then the weight file will be
+     * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
+     * if you provide a `weightPathPrefix` value of
+     * `http://localhost/foo/alt-weights`, then the weight file will be loaded
+     * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
+     */
+    weightPathPrefix?: string;
+    /**
+     * Whether the module or model is to be loaded from TF Hub.
+     *
+     * Setting this to `true` allows passing a TF-Hub module URL, omitting the
+     * standard model file name and the query parameters.
+     *
+     * Default: `false`.
+     */
+    fromTFHub?: boolean;
+    /**
+     * An async function to convert weight file name to URL. The weight file
+     * names are stored in model.json's weightsManifest.paths field. By default we
+     * consider weight files are colocated with the model.json file. For example:
+     *     model.json URL: https://www.google.com/models/1/model.json
+     *     group1-shard1of1.bin url:
+     *        https://www.google.com/models/1/group1-shard1of1.bin
+     *
+     * With this func you can convert the weight file name to any URL.
+     */
+    weightUrlConverter?: (weightFileName: string) => Promise<string>;
+}
+
+/**
+ * Reads a weights manifest JSON configuration, fetches the weights and
+ * returns them as `Tensor`s.
+ *
+ * @param manifest The weights manifest JSON.
+ * @param filePathPrefix The path prefix for filenames given in the manifest.
+ *     Defaults to the empty string.
+ * @param weightNames The names of the weights to be fetched.
+ */
+declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestInit?: RequestInit): Promise<NamedTensorMap>;
+
+declare namespace match {
+    export {
+        distance,
+        similarity,
+        match_2 as match,
+        Descriptor,
+        MatchOptions
+    }
+}
+export { match }
+
+/** Matches given descriptor to a closest entry in array of descriptors
+ * @param descriptor - face descriptor
+ * @param descriptors - array of face descriptors to commpare given descriptor to
+ * @param options - see `similarity` method for options description
+ * Returns
+ * - `index` index array index where best match was found or -1 if no matches
+ * - `distance` calculated `distance` of given descriptor to the best match
+ * - `similarity` calculated normalized `similarity` of given descriptor to the best match
+ */
+declare function match_2(descriptor: Descriptor, descriptors: Array<Descriptor>, options?: MatchOptions): {
+    index: number;
+    distance: number;
+    similarity: number;
+};
+
+declare type MatchOptions = {
+    order?: number;
+    threshold?: number;
+    multiplier?: number;
+    min?: number;
+    max?: number;
+} | undefined;
+
+/**
+ * The serialized artifacts of a model, including topology and weights.
+ *
+ * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
+ * of this interface are optional, in order to support topology- or weights-only
+ * saving and loading.
+ *
+ * Note this interface is used internally in IOHandlers.  For the file format
+ * written to disk as `model.json`, see `ModelJSON`.
+ */
+declare interface ModelArtifacts {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology?: {} | ArrayBuffer;
+    /**
+     * Serialized configuration for the model's training.
+     */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weight specifications.
+     *
+     * This corresponds to the weightsData below.
+     */
+    weightSpecs?: WeightsManifestEntry[];
+    /**
+     * Binary buffer for all weight values concatenated in the order specified
+     * by `weightSpecs`.
+     */
+    weightData?: ArrayBuffer;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelArtifactsInfo {
+    /**
+     * Timestamp for when the model is saved.
+     */
+    dateSaved: Date;
+    /**
+     * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
+     * come in a JSON format and none of our IOHandlers support a non json
+     * format. We could conder replacing this with 'Binary' if we want to
+     * allow future handlers to save to non json formats (though they will
+     * probably want more information than 'Binary').
+     * Type of the model topology
+     *
+     * Type of the model topology
+     *
+     * Possible values:
+     *   - JSON: JSON config (human-readable, e.g., Keras JSON).
+     *   - GraphDef: TensorFlow
+     *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
+     *     protocol buffer (binary).
+     */
+    modelTopologyType: 'JSON' | 'GraphDef';
+    /**
+     * Size of model topology (Keras JSON or GraphDef), in bytes.
+     */
+    modelTopologyBytes?: number;
+    /**
+     * Size of weight specification or manifest, in bytes.
+     */
+    weightSpecsBytes?: number;
+    /**
+     * Size of weight value data, in bytes.
+     */
+    weightDataBytes?: number;
+}
+
+export declare type ModelInfo = {
+    name: string;
+    inCache: boolean;
+    sizeDesired: number;
+    sizeFromManifest: number;
+    sizeLoadedWeights: number;
+};
+
+/**
+ * The on-disk format of the `model.json` file.
+ *
+ * TF.js 1.0 always populates the optional fields when writing model.json.
+ * Prior versions did not provide those fields.
+ */
+declare interface ModelJSON {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology: {};
+    /** Model training configuration. */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weights manifest.
+     *
+     * The weights manifest consists of an ordered list of weight-manifest
+     * groups. Each weight-manifest group consists of a number of weight values
+     * stored in a number of paths. See the documentation of
+     * `WeightsManifestConfig` for more details.
+     */
+    weightsManifest: WeightsManifestConfig;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelPredictConfig {
+    /**
+     * Optional. Batch size (Integer). If unspecified, it will default to 32.
+     */
+    batchSize?: number;
+    /**
+     * Optional. Verbosity mode. Defaults to false.
+     */
+    verbose?: boolean;
+}
+
+/** Instances of all possible TFJS Graph Models used by Human
+ * - loaded as needed based on configuration
+ * - initialized explictly with `human.load()` method
+ * - initialized implicity on first call to `human.detect()`
+ * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
+ */
+export declare class Models {
+    ssrnetage: null | GraphModel | Promise<GraphModel>;
+    gear: null | GraphModel | Promise<GraphModel>;
+    blazeposedetect: null | GraphModel | Promise<GraphModel>;
+    blazepose: null | GraphModel | Promise<GraphModel>;
+    centernet: null | GraphModel | Promise<GraphModel>;
+    efficientpose: null | GraphModel | Promise<GraphModel>;
+    mobilefacenet: null | GraphModel | Promise<GraphModel>;
+    emotion: null | GraphModel | Promise<GraphModel>;
+    facedetect: null | GraphModel | Promise<GraphModel>;
+    faceiris: null | GraphModel | Promise<GraphModel>;
+    facemesh: null | GraphModel | Promise<GraphModel>;
+    faceres: null | GraphModel | Promise<GraphModel>;
+    ssrnetgender: null | GraphModel | Promise<GraphModel>;
+    handpose: null | GraphModel | Promise<GraphModel>;
+    handskeleton: null | GraphModel | Promise<GraphModel>;
+    handtrack: null | GraphModel | Promise<GraphModel>;
+    liveness: null | GraphModel | Promise<GraphModel>;
+    movenet: null | GraphModel | Promise<GraphModel>;
+    nanodet: null | GraphModel | Promise<GraphModel>;
+    posenet: null | GraphModel | Promise<GraphModel>;
+    segmentation: null | GraphModel | Promise<GraphModel>;
+    antispoof: null | GraphModel | Promise<GraphModel>;
+}
+
+declare namespace models {
+    export {
+        reset,
+        load,
+        validate,
+        Models,
+        ModelStats,
+        getModelStats
+    }
+}
+export { models }
+
+export declare type ModelStats = {
+    numLoadedModels: number;
+    numEnabledModels: undefined;
+    numDefinedModels: number;
+    percentageLoaded: number;
+    totalSizeFromManifest: number;
+    totalSizeWeights: number;
+    totalSizeLoading: number;
+    totalSizeEnabled: undefined;
+    modelStats: ModelInfo[];
+};
+
+/**
+ * An interface for the manager of a model store.
+ *
+ * A model store is defined as a storage medium on which multiple models can
+ * be stored. Each stored model has a unique `path` as its identifier.
+ * A `ModelStoreManager` for the store allows actions including
+ *
+ * - Listing the models stored in the store.
+ * - Deleting a model from the store.
+ */
+declare interface ModelStoreManager {
+    /**
+     * List all models in the model store.
+     *
+     * @returns A dictionary mapping paths of existing models to their
+     *   model artifacts info. Model artifacts info include type of the model's
+     *   topology, byte sizes of the topology, weights, etc.
+     */
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    /**
+     * Remove a model specified by `path`.
+     *
+     * @param path
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     */
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
+
+/**
+ * Interface for model input/output tensor info.
+ */
+declare interface ModelTensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+    tfDtype?: string;
+}
+
+/**
+ * Move a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Moving within a storage medium, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Moving between two storage mediums, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Move the model, from Local Storage to IndexedDB.
+ * await tf.io.moveModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove the moved model.
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of moving.
+ * @param destURL Destination URL of moving.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+declare interface NamedTensor {
+    name: string;
+    tensor: Tensor;
+}
+
+/** @docalias {[name: string]: Tensor} */
+declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+
+declare type NamedTensorsMap = {
+    [key: string]: Tensor[];
+};
+
+declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+
+/** draw detected objects */
+declare function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all object detection specific options */
+export declare interface ObjectConfig extends GenericConfig {
+    /** minimum confidence for a detected objects before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected objects before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected objects */
+    maxDetected: number;
+}
+
+/** Object results */
+export declare interface ObjectResult {
+    /** object id */
+    id: number;
+    /** object detection score */
+    score: number;
+    /** detected object class id */
+    class: number;
+    /** detected object class name */
+    label: ObjectType;
+    /** detected object box */
+    box: Box;
+    /** detected object box normalized to 0..1 */
+    boxRaw: Box;
+}
+
+export declare type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter' | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee' | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork' | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant' | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book' | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';
+
+/**
+ * Callback for the progress of a long-running action such as an HTTP
+ * request for a large binary object.
+ *
+ * `fraction` should be a number in the [0, 1] interval, indicating how
+ * much of the action has completed.
+ */
+declare type OnProgressCallback = (fraction: number) => void;
+
+/** currently set draw options {@link DrawOptions} */
+declare const options: DrawOptions;
+
+/** draw combined person results instead of individual detection result objects */
+declare function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Person getter
+ * - Triggers combining all individual results into a virtual person object
+ */
+export declare interface PersonResult {
+    /** person id */
+    id: number;
+    /** face result that belongs to this person */
+    face: FaceResult;
+    /** body result that belongs to this person */
+    body: BodyResult | null;
+    /** left and right hand results that belong to this person */
+    hands: {
+        left: HandResult | null;
+        right: HandResult | null;
+    };
+    /** detected gestures specific to this person */
+    gestures: Array<GestureResult>;
+    /** box that defines the person */
+    box: Box;
+    /** box that defines the person normalized to 0..1 */
+    boxRaw?: Box;
+}
+
+/** generic point as [x, y, z?] */
+export declare type Point = [number, number, number?];
+
+declare type PromiseFunction = (...args: unknown[]) => Promise<unknown>;
+
+export declare type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';
+
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
+declare const registerLoadRouter: (loudRouter: IORouter) => void;
+
+declare const registerSaveRouter: (loudRouter: IORouter) => void;
+
+/**
+ * Remove a model specified by URL from a reigstered storage medium.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @param url A URL to a stored model, with a scheme prefix, e.g.,
+ *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+ * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+ *   is successful).
+ * @throws Error if deletion fails, e.g., if no model exists at `path`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * Additional options for Platform.fetch
+ */
+declare interface RequestDetails {
+    /**
+     * Is this request for a binary file (as opposed to a json file)
+     */
+    isBinary?: boolean;
+}
+
+declare function reset(instance: Human): void;
+
+/**
+ * Result interface definition for **Human** library
+ *
+ * Contains all possible detection results
+ */
+export declare interface Result {
+    /** {@link FaceResult}: detection & analysis results */
+    face: Array<FaceResult>;
+    /** {@link BodyResult}: detection & analysis results */
+    body: Array<BodyResult>;
+    /** {@link HandResult}: detection & analysis results */
+    hand: Array<HandResult>;
+    /** {@link GestureResult}: detection & analysis results */
+    gesture: Array<GestureResult>;
+    /** {@link ObjectResult}: detection & analysis results */
+    object: Array<ObjectResult>;
+    /** global performance object with timing values for each operation */
+    performance: Record<string, number>;
+    /** optional processed canvas that can be used to draw input on screen */
+    canvas?: AnyCanvas | null;
+    /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
+    readonly timestamp: number;
+    /** getter property that returns unified persons object  */
+    persons: Array<PersonResult>;
+    /** Last known error message */
+    error: string | null;
+}
+
+/**
+ * Options for saving a model.
+ * @innamespace io
+ */
+declare interface SaveConfig {
+    /**
+     * Whether to save only the trainable weights of the model, ignoring the
+     * non-trainable ones.
+     */
+    trainableOnly?: boolean;
+    /**
+     * Whether the optimizer will be saved (if exists).
+     *
+     * Default: `false`.
+     */
+    includeOptimizer?: boolean;
+}
+
+/**
+ * Type definition for handlers of saving operations.
+ */
+declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+
+/**
+ * Result of a saving operation.
+ */
+declare interface SaveResult {
+    /**
+     * Information about the model artifacts saved.
+     */
+    modelArtifactsInfo: ModelArtifactsInfo;
+    /**
+     * HTTP responses from the server that handled the model-saving request (if
+     * any). This is applicable only to server-based saving routes.
+     */
+    responses?: Response[];
+    /**
+     * Error messages and related data (if any).
+     */
+    errors?: Array<{} | string>;
+}
+
+/** Configures all body segmentation module
+ * removes background from input containing person
+ * if segmentation is enabled it will run as preprocessing task before any other model
+ * alternatively leave it disabled and use it on-demand using human.segmentation method which can
+ * remove background or replace it with user-provided background
+ */
+export declare interface SegmentationConfig extends GenericConfig {
+    /** blur segmentation output by <number> pixels for more realistic image */
+    blur: number;
+}
+
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/// <amd-module name="@tensorflow/tfjs-core/dist/types" />
+/** @docalias number[] */
+declare interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+
+/** Calculates normalized similarity between two face descriptors based on their `distance`
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ * - min - normalize similarity result to a given range
+ * - max - normalzie similarity resutl to a given range
+ *   default is 0.2...0.8
+ * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
+ */
+declare function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+
+declare type Syncify<T extends PromiseFunction> = T extends (...args: infer Args) => Promise<infer R> ? (...args: Args) => R : never;
+
+export declare namespace Tensor { }
+
+/**
+ * A `tf.Tensor` object represents an immutable, multidimensional array of
+ * numbers that has a shape and a data type.
+ *
+ * For performance reasons, functions that create tensors do not necessarily
+ * perform a copy of the data passed to them (e.g. if the data is passed as a
+ * `Float32Array`), and changes to the data will change the tensor. This is not
+ * a feature and is not supported. To avoid this behavior, use the tensor before
+ * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+ *
+ * See `tf.tensor` for details on how to create a `tf.Tensor`.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+export declare class Tensor<R extends Rank = Rank> {
+    /** Unique id of this tensor. */
+    readonly id: number;
+    /**
+     * Id of the bucket holding the data for this tensor. Multiple arrays can
+     * point to the same bucket (e.g. when calling array.reshape()).
+     */
+    dataId: DataId;
+    /** The shape of the tensor. */
+    readonly shape: ShapeMap[R];
+    /** Number of elements in the tensor. */
+    readonly size: number;
+    /** The data type for the array. */
+    readonly dtype: DataType;
+    /** The rank type for the array (see `Rank` enum). */
+    readonly rankType: R;
+    /** Whether this tensor has been globally kept. */
+    kept: boolean;
+    /** The id of the scope this tensor is being tracked in. */
+    scopeId: number;
+    /**
+     * Number of elements to skip in each dimension when indexing. See
+     * https://docs.scipy.org/doc/numpy/reference/generated/\
+     * numpy.ndarray.strides.html
+     */
+    readonly strides: number[];
+    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
+    readonly rank: number;
+    /**
+     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
+    /**
+     * Returns a `tf.TensorBuffer` that holds the underlying data.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * asynchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    array(): Promise<ArrayMap[R]>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * synchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    arraySync(): ArrayMap[R];
+    /**
+     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+     * promise of `TypedArray` that resolves when the computation has finished.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    /**
+     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
+     * and `data()`, this method prevents data from being downloaded to CPU.
+     *
+     * For WebGL backend, the data will be stored on a densely packed texture.
+     * This means that the texture will use the RGBA channels to store value.
+     *
+     * For WebGPU backend, the data will be stored on a buffer. There is no
+     * parameter, so can not use an user defined size to create the buffer.
+     *
+     * @param options:
+     *     For WebGL,
+     *         - customTexShape: Optional. If set, will use the user defined
+     *     texture shape to create the texture.
+     *
+     * @returns For WebGL backend, a GPUData contains the new texture and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this texture,
+     *        texture: WebGLTexture,
+     *        texShape: [number, number] // [height, width]
+     *     }
+     *
+     *     For WebGPU backend, a GPUData contains the new buffer and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this buffer,
+     *        buffer: GPUBuffer,
+     *        bufSize: number
+     *     }
+     *
+     *     Remember to dispose the GPUData after it is used by
+     *     `res.tensorRef.dispose()`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataToGPU(options?: DataToGPUOptions): GPUData;
+    /**
+     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+     * UI thread until the values are ready, which can cause performance issues.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    /** Returns the underlying bytes of the tensor's data. */
+    bytes(): Promise<Uint8Array[] | Uint8Array>;
+    /**
+     * Disposes `tf.Tensor` from memory.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dispose(): void;
+    protected isDisposedInternal: boolean;
+    readonly isDisposed: boolean;
+    throwIfDisposed(): void;
+    /**
+     * Prints the `tf.Tensor`. See `tf.print` for details.
+     *
+     * @param verbose Whether to print verbose information about the tensor,
+     *    including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    print(verbose?: boolean): void;
+    /**
+     * Returns a copy of the tensor. See `tf.clone` for details.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    clone<T extends Tensor>(this: T): T;
+    /**
+     * Returns a human-readable description of the tensor. Useful for logging.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    toString(verbose?: boolean): string;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+}
+
+/**
+ * A mutable object, similar to `tf.Tensor`, that allows users to set values
+ * at locations before converting to an immutable `tf.Tensor`.
+ *
+ * See `tf.buffer` for creating a tensor buffer.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    /**
+     * Sets a value in the buffer at a given location.
+     *
+     * @param value The value to set.
+     * @param locs  The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    /**
+     * Returns the value in the buffer at the provided location.
+     *
+     * @param locs The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    /**
+     * Creates an immutable `tf.Tensor` object from the buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    toTensor(): Tensor<R>;
+}
+
+declare interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
+
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
+/** Model training configuration. */
+declare interface TrainingConfig {
+    /** Optimizer used for the model training. */
+    optimizer_config: {};
+    /** Loss function(s) for the model's output(s). */
+    loss: string | string[] | {
+        [key: string]: string;
+    };
+    /** Metric function(s) for the model's output(s). */
+    metrics?: string[] | {
+        [key: string]: string;
+    };
+    weighted_metrics?: string[];
+    sample_weight_mode?: string;
+    loss_weights?: number[] | {
+        [key: string]: number;
+    };
+}
+
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
+declare type Url = string | io.IOHandler | io.IOHandlerSync;
+
+declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
+
+declare function validate(instance: Human): Promise<void>;
+
+/**
+ * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
+    /**
+     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+     * the same shape and dtype as the old `tf.Tensor`.
+     *
+     * @param newValue New tensor to be assigned to this variable.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    assign(newValue: Tensor<R>): void;
+    dispose(): void;
+}
+
+/** Possible values for `human.warmup` */
+export declare type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];
+
+/**
+ * Group to which the weight belongs.
+ *
+ * - 'optimizer': Weight from a stateful optimizer.
+ */
+declare type WeightGroup = 'model' | 'optimizer';
+
+/**
+ * Creates a function, which reads a weights manifest JSON configuration,
+ * fetches the weight files using the specified function and returns them as
+ * `Tensor`s.
+ *
+ * ```js
+ * // example for creating a nodejs weight loader, which reads the weight files
+ * // from disk using fs.readFileSync
+ *
+ * import * as fs from 'fs'
+ *
+ * const fetchWeightsFromDisk = (filePaths: string[]) =>
+ *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+ *
+ * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+ *
+ * const manifest = JSON.parse(
+ *   fs.readFileSync('./my_model-weights_manifest').toString()
+ * )
+ * const weightMap = await loadWeights(manifest, './')
+ * ```
+ * @param fetchWeightsFunction The function used for fetching the weight files.
+ * @returns Weight loading function.
+ */
+declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
+
+/**
+ * A weight manifest.
+ *
+ * The weight manifest consists of an ordered list of weight-manifest groups.
+ * Each weight-manifest group ("group" for short hereafter) consists of a
+ * number of weight values stored in a number of paths.
+ * See the documentation of `WeightManifestGroupConfig` below for more details.
+ */
+declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+
+/**
+ * An entry in the weight manifest.
+ *
+ * The entry contains specification of a weight.
+ */
+declare interface WeightsManifestEntry {
+    /**
+     * Name of the weight, e.g., 'Dense_1/bias'
+     */
+    name: string;
+    /**
+     * Shape of the weight.
+     */
+    shape: number[];
+    /**
+     * Data type of the weight.
+     */
+    dtype: 'float32' | 'int32' | 'bool' | 'string' | 'complex64';
+    /**
+     * Type of the weight.
+     *
+     * Optional.
+     *
+     * The value 'optimizer' indicates the weight belongs to an optimizer
+     * (i.e., used only during model training and not during inference).
+     */
+    group?: WeightGroup;
+    /**
+     * Information for dequantization of the weight.
+     */
+    quantization?: {
+        scale?: number;
+        min?: number;
+        dtype: 'uint16' | 'uint8' | 'float16';
+    };
+}
+
+/**
+ * A weight-manifest group.
+ *
+ * Consists of an ordered list of weight values encoded in binary format,
+ * stored in an ordered list of paths.
+ */
+declare interface WeightsManifestGroupConfig {
+    /**
+     * An ordered list of paths.
+     *
+     * Paths are intentionally abstract in order to be general. For example, they
+     * can be relative URL paths or relative paths on the file system.
+     */
+    paths: string[];
+    /**
+     * Specifications of the weights stored in the paths.
+     */
+    weights: WeightsManifestEntry[];
+}
+
+/**
+ * Creates an IOHandler that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     promise that resolves to a `SaveResult`.
+ */
+declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
+
+/**
+ * Creates an IOHandlerSync that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     `SaveResult`.
+ */
+declare function withSaveHandlerSync(saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync;
+
+export { }
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
new file mode 100644
index 00000000..5337c474
--- /dev/null
+++ b/dist/human.node-wasm.js
@@ -0,0 +1,838 @@
+"use strict";/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var sn=Object.create;var h2=Object.defineProperty;var an=Object.getOwnPropertyDescriptor;var ln=Object.getOwnPropertyNames;var yn=Object.getPrototypeOf,xn=Object.prototype.hasOwnProperty;var cn=(e,t,o)=>t in e?h2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var dn=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ue=(e,t)=>{for(var o in t)h2(e,o,{get:t[o],enumerable:!0})},ft=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of ln(t))!xn.call(e,r)&&r!==o&&h2(e,r,{get:()=>t[r],enumerable:!(n=an(t,r))||n.enumerable});return e};var Z=(e,t,o)=>(o=e!=null?sn(yn(e)):{},ft(t||!e||!e.__esModule?h2(o,"default",{value:e,enumerable:!0}):o,e)),fn=e=>ft(h2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(cn(e,typeof t!="symbol"?t+"":t,o),o),mt=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var b2=(e,t,o)=>(mt(e,t,"read from private field"),o?o.call(e):t.get(e)),g2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},M2=(e,t,o,n)=>(mt(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var D=dn((f7,O2)=>{"use strict";var ut=Object.defineProperty,mn=Object.getOwnPropertyDescriptor,pn=Object.getOwnPropertyNames,un=Object.prototype.hasOwnProperty,S5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of pn(t))!un.call(e,r)&&r!==o&&ut(e,r,{get:()=>t[r],enumerable:!(n=mn(t,r))||n.enumerable});return e},ht=(e,t,o)=>(S5(e,t,"default"),o&&S5(o,t,"default")),hn=e=>S5(ut({},"__esModule",{value:!0}),e),C5={};O2.exports=hn(C5);ht(C5,require("@tensorflow/tfjs"),O2.exports);ht(C5,require("@tensorflow/tfjs-backend-wasm"),O2.exports)});var y7={};ue(y7,{Human:()=>lt,default:()=>lt,defaults:()=>he,draw:()=>ot,env:()=>v,match:()=>it,models:()=>v5});module.exports=fn(y7);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function pt(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function z5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")z5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var he={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var p0=Z(D());var N=Z(D());var bt=`
+  precision highp float;
+  attribute vec2 pos;
+  attribute vec2 uv;
+  varying vec2 vUv;
+  uniform float flipY;
+  void main(void) {
+    vUv = uv;
+    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
+  }
+`;var gt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
+    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
+  }
+`,Mt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
+    gl_FragColor.a = c.a;
+  }
+`,Pt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform vec2 size;
+  uniform sampler2D texture;
+  vec2 pixelate(vec2 coord, vec2 size) {
+    return floor( coord / size ) * size;
+  }
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    vec2 coord = pixelate(vUv, size);
+    gl_FragColor += texture2D(texture, coord);
+  }
+`,Rt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
+    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;
+    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
+  }
+`,vt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  uniform float m[9];
+  void main(void) {
+    vec4 c11 = texture2D(texture, vUv - px); // top left
+    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
+    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
+    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
+    vec4 c22 = texture2D(texture, vUv); // mid center
+    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
+    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
+    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
+    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
+    gl_FragColor = 
+    c11 * m[0] + c12 * m[1] + c22 * m[2] +
+    c21 * m[3] + c22 * m[4] + c23 * m[5] +
+    c31 * m[6] + c32 * m[7] + c33 * m[8];
+    gl_FragColor.a = c22.a;
+  }
+`;var j5=(e,t,o)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(r,A)=>(o[A]=0,r))},I5=class{constructor(t,o,n){T(this,"uniform",{});T(this,"attribute",{});T(this,"gl");T(this,"id");T(this,"compile",(t,o)=>{let n=this.gl.createShader(o);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(p(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)}`),null)):(p("filter: could not create shader"),null)});this.gl=t;let r=this.compile(o,this.gl.VERTEX_SHADER),A=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!A)){if(!this.id){p("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,A),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){p(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),j5(o,"attribute",this.attribute);for(let s in 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Kr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Jr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Qr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],_r=[[474,475],[475,476],[476,477],[477,474]],$r=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],eA=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],tA=[[469,470],[470,471],[471,472],[472,469]],oA=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ve(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var nA={lips:ve(Kr),leftEye:ve(Jr),leftEyebrow:ve(Qr),leftIris:ve(_r),rightEye:ve($r),rightEyebrow:ve(eA),rightIris:ve(tA),faceOval:ve(oA)},rA=Object.entries(nA).map(([e,t])=>t.map(o=>[o,e])).flat(),f4=new Map(rA),w2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],We=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Fe=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function F3(e,t){let o={lips:await t.filter(A=>A.size===160)[0].data(),irisL:await t.filter(A=>A.size===10)[0].data(),eyeL:await t.filter(A=>A.size===142)[0].data(),irisR:await t.filter(A=>A.size===10)[1].data(),eyeR:await t.filter(A=>A.size===142)[1].data()},n=We.reduce((A,s)=>A+=e[s][2],0)/We.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=Fe.reduce((A,s)=>A+=e[s][2],0)/Fe.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[We[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[We[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[Fe[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[Fe[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[w2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[w2[A]][2]];return e}var oe={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},g0=null,Ge=0;async function G3(e,t){var a,i,x,c,l,y,f,u,m,P,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-oe.timestamp,n=oe.skipped<(((i=t.face.detector)==null?void 0:i.skipFrames)||0);!t.skipAllowed||!o||!n||oe.boxes.length===0?(oe.boxes=await r3(e,t),oe.timestamp=h(),oe.skipped=0):oe.skipped++;let r=[],A=[],s=0;for(let b=0;b<oe.boxes.length;b++){let d=oe.boxes[b],M=0,S,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([M,S,w.tensor]=$t((x=t.face.detector)==null?void 0:x.rotation,d,e,(c=t.face.mesh)!=null&&c.enabled?Ge:Qe()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let j=await L2(w.tensor);Be.dispose(w.tensor),w.tensor=j}if(w.boxScore=Math.round(100*d.confidence)/100,(y=t.face.mesh)!=null&&y.enabled)if(!g0)t.debug&&p("face mesh detection requested, but model is not loaded");else{let j=g0.execute(w.tensor),F=await j.find(H=>H.shape[H.shape.length-1]===1).data();if(w.faceScore=Math.round(100*F[0])/100,w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=q2(d,e),w.boxRaw=U2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(H=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*H[0]/Qe(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*H[1]/Qe()]),w.meshRaw=w.mesh.map(H=>[H[0]/(e.shape[2]||1),H[1]/(e.shape[1]||1),(H[2]||0)/Ge]);for(let H of Object.keys(Ie))w.annotations[H]=[w.mesh[Ie[H]]]}}else{let H=j.find(E=>E.shape[E.shape.length-1]===1404),Y=Be.reshape(H,[-1,3]),J=await Y.array();Be.dispose(Y),(m=t.face.attention)!=null&&m.enabled?J=await F3(J,j):(P=t.face.iris)!=null&&P.enabled&&(J=await O3(J,w.tensor,t,Ge)),w.mesh=_t(J,d,M,S,Ge),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/Ge]);for(let E of Object.keys(X0))w.annotations[E]=X0[E].map(G0=>w.mesh[G0]);w.score=w.faceScore;let x0={...e3(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=q2(x0,e),w.boxRaw=U2(x0,e),A.push(x0)}Be.dispose(j)}else{w.box=q2(d,e),w.boxRaw=U2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Qe(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Qe()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/Ge]);for(let j of Object.keys(Ie))w.annotations[j]=[w.mesh[Ie[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):Be.dispose(w.tensor)}return oe.boxes=A,r}async function B3(e){var t,o,n,r,A,s;return v.initial&&(g0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(g0==null?void 0:g0.signature)&&Object.keys(((n=g0==null?void 0:g0.signature)==null?void 0:n.outputs)||{}).length<6&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):(r=e.face.attention)!=null&&r.enabled?g0=await 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Object.keys(o).forEach(r=>W.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=W.reshape(t,[-1,7,2]),n.div=W.div(n.reshape,this.inputSizeTensor),n.landmarks=W.add(n.div,this.anchors[o]);let r=W.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>W.dispose(n[A])),r}async predict(t,o){let n={};n.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=W.div(n.resize,V.tf127),n.image=W.sub(n.div,V.tf1),n.batched=this.model.execute(n.image),n.predictions=W.squeeze(n.batched),n.slice=W.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=W.sigmoid(n.slice),n.scores=W.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=W.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await W.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=W.slice(n.norm,[a,0],[1,-1]),i.slice=W.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=W.reshape(i.norm,[-1,2]);let x=await i.box.data(),c=x.slice(0,2),l=x.slice(2,4),y=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:y,confidence:r[a]},u=J3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>W.dispose(i[m]))}return Object.keys(n).forEach(a=>W.dispose(n[a])),s}};var L0=Z(D());var yA=5,to=1.65,oo=[0,5,9,13,17,1,2],xA=0,cA=2,no=0,l5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>P1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return s5(a5(r),yA)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=s5(a5(o),to);n.palmLandmarks=[];for(let r=0;r<oo.length;r++)n.palmLandmarks.push(t[oo[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=A5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=M1(n,[0,0]),x=a.map(f=>[...P1(f,i),f[2]]),c=_3(r),l=[...k2(o),1],y=[Te(l,c[0]),Te(l,c[1])];return x.map(f=>[Math.trunc(f[0]+y[0]),Math.trunc(f[1]+y[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-no,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await 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j=this.transformRawCoords(w,m,c,u),C=this.getBoxForHandLandmarks(j);this.storedBoxes[i]={...C,confidence:M};let F={landmarks:j,confidence:M,boxConfidence:x.confidence,fingerConfidence:M,box:{topLeft:C.startPoint,bottomRight:C.endPoint}};a.push(F)}else this.storedBoxes[i]=null;L0.dispose(d)}else{let c=s5(a5(x),to),l={confidence:x.confidence,boxConfidence:x.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};a.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=a.length,a.length>o.hand.maxDetected&&(a.length=o.hand.maxDetected),a}};var 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a=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let x=y5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:x})}return n}async function w1(e){var o,n;v.initial&&(A2=null,s2=null),!A2||!s2?[A2,s2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached model:",A2.modelUrl),e.debug&&p("cached model:",s2.modelUrl));let t=new i5(A2);return fo=new l5(t,s2),[A2,s2]}var Q=Z(D());var d0=[null,null],MA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ce=[[0,0],[0,0]],PA=["hand","fist","pinch","point","face","tip","pinchtip"],po=4,uo=1.6,RA=512,vA=1.4,x5=Number.MAX_SAFE_INTEGER,k1=0,fe=[0,0],A0={boxes:[],hands:[]},ho={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function bo(e){var t;if(v.initial&&(d0[0]=null),d0[0])e.debug&&p("cached model:",d0[0].modelUrl);else{c5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),d0[0]=await G((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values(d0[0].modelSignature.inputs);Ce[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ce[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return d0[0]}async function go(e){var t;if(v.initial&&(d0[1]=null),d0[1])e.debug&&p("cached model:",d0[1].modelUrl);else{d0[1]=await G((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values(d0[1].modelSignature.inputs);Ce[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,Ce[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return d0[1]}async function TA(e,t){let o=[];if(!e||!d0[0])return o;let n={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,RA),s=Math.round(A*r/8)*8;n.resize=Q.image.resizeBilinear(e,[A,s]),n.cast=Q.cast(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await d0[0].executeAsync(n.cast,MA),n.boxes=Q.squeeze(n.rawBoxes,[0,2]),n.scores=Q.squeeze(n.rawScores,[0]);let a=Q.unstack(n.scores,1);Q.dispose(a[po]),a.splice(po,1),n.filtered=Q.stack(a,1),Q.dispose(a),n.max=Q.max(n.filtered,1),n.argmax=Q.argMax(n.filtered,1);let i=0;n.nms=await Q.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let y of Array.from(x)){let f=Q.slice(n.boxes,y,1),u=await f.data();Q.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],P=_2(m,vA),g=[Math.trunc(m[0]*fe[0]),Math.trunc(m[1]*fe[1]),Math.trunc(m[2]*fe[0]),Math.trunc(m[3]*fe[1])],b=c[y],d=PA[l[y]],M={id:i++,score:b,box:g,boxRaw:P,label:d};o.push(M)}return Object.keys(n).forEach(y=>Q.dispose(n[y])),o.sort((y,f)=>f.score-y.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function E1(e,t,o){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&d0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[A],[0],[Ce[1][0],Ce[1][1]],"bilinear"),r.div=Q.div(r.crop,V.tf255),[r.score,r.keypoints]=d0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ce[1][1],l[1]/Ce[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[fe[0]*(l[0]+t.boxRaw[0]),fe[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=y5(n.keypoints);for(let l of 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o=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),U.fillPolygons&&(t.fillStyle=U.useDepth?"rgba(255, 255, 200, 0.3)":U.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=U.useDepth?"rgba(255, 200, 255, 0.3)":U.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),U.fillPolygons&&(t.fillStyle=U.useDepth?"rgba(255, 255, 200, 0.3)":U.color,t.fill())}}function XA(e,t){var o;if(U.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Xe(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Xe(e.rotation.angle.pitch)/90,A=new Path2D(`
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+      C
+        ${n} ${e.box[1]},
+        ${n} ${e.box[1]+e.box[3]},
+        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
+    `),s=new Path2D(`
+      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+      C 
+        ${e.box[0]} ${r},
+        ${e.box[0]+e.box[2]} ${r},
+        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
+    `);t.stroke(s),t.stroke(A)}}function qA(e,t){var o,n,r,A;if(U.drawGaze&&((n=(o=e.rotation)==null?void 0:o.gaze)==null?void 0:n.strength)&&((A=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:A.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let s=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Q1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[s[0],s[1]],4);let a=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Q1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[a[0],a[1]],4)}}function UA(e,t){if(U.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let o=0;o<Ne.length/3;o++){let n=[Ne[o*3+0],Ne[o*3+1],Ne[o*3+2]].map(r=>e.mesh[r]);J1(t,n,U)}ZA(e,t)}}function YA(e,t){if(U.drawPoints&&e.mesh.length>=468)for(let o=0;o<e.mesh.length;o++)pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2],U),U.drawAttention&&(w2.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]+127,U),We.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,U),Fe.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,U))}function KA(e,t){U.drawBoxes&&re(t,e.box[0],e.box[1],e.box[2],e.box[3],U)}async function l2(e,t,o){if(U=r0(P0,o),!t||!e)return;let n=V0(e);if(!!n){n.font=U.font,n.strokeStyle=U.color,n.fillStyle=U.color;for(let r of t)KA(r,n),DA(r,n),r.mesh&&r.mesh.length>0&&(YA(r,n),UA(r,n),XA(r,n),qA(r,n))}}async function y2(e,t,o){var A;let n=r0(P0,o);if(!t||!e)return;let r=V0(e);if(!!r){r.lineJoin="round";for(let 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X0.silhouette)r.push({x:(e.mesh[s][0]-e.box[0])/e.box[2],y:(e.mesh[s][1]-e.box[1])/e.box[3]});f2&&f2>0&&(r=r.map(s=>({x:s.x>.5?s.x+f2:s.x-f2,y:s.y>.5?s.y+f2:s.y-f2})));for(let s=0;s<t;s++)for(let a=0;a<o;a++)JA(s/t,a/t,r)||(n.set(nt*n.get(0,a,s,0),0,a,s,0),n.set(nt*n.get(0,a,s,1),0,a,s,1),n.set(nt*n.get(0,a,s,2),0,a,s,2));let A=n.toTensor();return Ko.dispose(n),A}var _A=e=>{let t=(l,y)=>Math.atan2(l[1]-y[1],l[0]-y[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let o=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=r?e.mesh[473]:e.mesh[468],s=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],i=[(s[0]-A[0])/a[0]-o[0],n*(A[1]-s[1])/a[1]-o[1]],x=Math.sqrt(i[0]*i[0]+i[1]*i[1]);return 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y=[c[0],c[1],c[2],x[0],x[1],x[2],l[0],l[1],l[2]],f=A(y),u=s.length===478?_A(e):{bearing:0,strength:0};return{angle:f,matrix:y,gaze:u}};var rt=async(e,t)=>{var f,u,m,P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0,R,c0;let o=h(),n,r,A,s,a,i,x,c,l=[];e.state="run:face";let y=await G3(t,e.config);if(e.performance.face=v.perfadd?(e.performance.face||0)+Math.trunc(h()-o):Math.trunc(h()-o),!t.shape||t.shape.length!==4)return[];if(!y)return[];for(let k=0;k<y.length;k++){if(e.analyze("Get Face"),!y[k].tensor||y[k].tensor.isDisposedInternal){p("Face object is disposed:",y[k].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let K=await Jo(y[k]);s0.dispose(y[k].tensor),y[k].tensor=K}let $=y[k].mesh&&y[k].mesh.length>200?Qo(y[k],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?s=(u=e.config.face.emotion)!=null&&u.enabled?x1(y[k].tensor||s0.tensor([]),e.config,k,y.length):[]:(e.state="run:emotion",o=h(),s=(m=e.config.face.emotion)!=null&&m.enabled?await x1(y[k].tensor||s0.tensor([]),e.config,k,y.length):[],e.performance.emotion=v.perfadd?(e.performance.emotion||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?i=(P=e.config.face.antispoof)!=null&&P.enabled?q5(y[k].tensor||s0.tensor([]),e.config,k,y.length):0:(e.state="run:antispoof",o=h(),i=(g=e.config.face.antispoof)!=null&&g.enabled?await q5(y[k].tensor||s0.tensor([]),e.config,k,y.length):0,e.performance.antispoof=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?x=(b=e.config.face.liveness)!=null&&b.enabled?C1(y[k].tensor||s0.tensor([]),e.config,k,y.length):0:(e.state="run:liveness",o=h(),x=(d=e.config.face.liveness)!=null&&d.enabled?await C1(y[k].tensor||s0.tensor([]),e.config,k,y.length):0,e.performance.liveness=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(M=e.config.face.gear)!=null&&M.enabled?G5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:gear",o=h(),r=(S=e.config.face.gear)!=null&&S.enabled?await G5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.gear=Math.trunc(h()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(w=e.config.face.ssrnet)!=null&&w.enabled?H5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,A=(j=e.config.face.ssrnet)!=null&&j.enabled?Z5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null):(e.state="run:ssrnet",o=h(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await H5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,A=(F=e.config.face.ssrnet)!=null&&F.enabled?await Z5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.ssrnet=Math.trunc(h()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?a=(H=e.config.face.mobilefacenet)!=null&&H.enabled?d1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:mobilefacenet",o=h(),a=(Y=e.config.face.mobilefacenet)!=null&&Y.enabled?await d1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.mobilefacenet=Math.trunc(h()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(J=e.config.face.description)!=null&&J.enabled?b1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:description",o=h(),c=(x0=e.config.face.description)!=null&&x0.enabled?await b1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Description:"),e.config.async&&([n,A,s,a,c,r,i,x]=await Promise.all([n,A,s,a,c,r,i,x])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&n&&A&&(c={...c,age:n.age,gender:A.gender,genderScore:A.genderScore}),((G0=e.config.face.gear)==null?void 0:G0.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((B0=e.config.face.mobilefacenet)==null?void 0:B0.enabled)&&a&&(c.descriptor=a),(R=e.config.face.iris)!=null&&R.enabled;let a0=y[k].annotations&&y[k].annotations.leftEyeIris&&y[k].annotations.leftEyeIris[0]&&y[k].annotations.rightEyeIris&&y[k].annotations.rightEyeIris[0]&&y[k].annotations.leftEyeIris.length>0&&y[k].annotations.rightEyeIris.length>0&&y[k].annotations.leftEyeIris[0]!==null&&y[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(y[k].annotations.leftEyeIris[3][0]-y[k].annotations.leftEyeIris[1][0]),Math.abs(y[k].annotations.rightEyeIris[4][1]-y[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,f0=(c0=e.config.face.detector)!=null&&c0.return?s0.squeeze(y[k].tensor):null;s0.dispose(y[k].tensor),y[k].tensor&&delete y[k].tensor;let L={...y[k],id:k};c!=null&&c.age&&(L.age=c.age),c!=null&&c.gender&&(L.gender=c.gender),c!=null&&c.genderScore&&(L.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(L.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(L.race=c==null?void 0:c.race),s&&(L.emotion=s),i&&(L.real=i),x&&(L.live=x),a0&&a0!==0&&(L.iris=Math.trunc(500/a0/11.7)/100),$&&(L.rotation=$),f0&&(L.tensor=f0),l.push(L),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var _o=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give up"}):A&&n&&n.position[1]<A.position[1]?t.push({body:o,gesture:"raise left hand"}):A&&r&&r.position[1]<A.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(i=>i.part==="leftShoulder"),a=e[o].keypoints.find(i=>i.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},$o=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},en=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),x=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(x=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],y=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||y>.06)&&(x=!1),l>y?l>.05&&t.push({iris:o,gesture:"looking right"}):y>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(x=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),x&&t.push({iris:o,gesture:"looking center"})}return t},tn=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=xo(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},At=0;function on(e,t){var s,a,i,x,c,l,y,f,u,m,P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let c0=e.body[R].box.map((L,K)=>((r-1)*z.body[R].box[K]+L)/r),k=e.body[R].boxRaw.map((L,K)=>((r-1)*z.body[R].boxRaw[K]+L)/r),$=e.body[R].keypoints.map((L,K)=>{var 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0:p2[0],z.body[R].keypoints[K]?((r-1)*(((u2=z.body[R].keypoints[K].distance)==null?void 0:u2[1])||0)+(((qe=L.distance)==null?void 0:qe[1])||0))/r:(yt=L.distance)==null?void 0:yt[1],z.body[R].keypoints[K]?((r-1)*(((xt=z.body[R].keypoints[K].distance)==null?void 0:xt[2])||0)+(((ct=L.distance)==null?void 0:ct[2])||0))/r:(dt=L.distance)==null?void 0:dt[2]]}}),a0={},f0={connected:{}};(a=(s=t.body)==null?void 0:s.modelPath)!=null&&a.includes("efficientpose")?f0=t5:(x=(i=t.body)==null?void 0:i.modelPath)!=null&&x.includes("blazepose")?f0=J2:(l=(c=t.body)==null?void 0:c.modelPath)!=null&&l.includes("movenet")&&(f0=E2);for(let[L,K]of Object.entries(f0.connected)){let Ae=[];for(let se=0;se<K.length-1;se++){let p2=$.find(qe=>qe.part===K[se]),u2=$.find(qe=>qe.part===K[se+1]);p2&&u2&&Ae.push([p2.position,u2.position])}a0[L]=Ae}z.body[R]={...e.body[R],box:c0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R<e.hand.length;R++){let c0=e.hand[R].box.map((f0,L)=>((r-1)*z.hand[R].box[L]+f0)/r),k=e.hand[R].boxRaw.map((f0,L)=>((r-1)*z.hand[R].boxRaw[L]+f0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((f0,L)=>f0.map((K,Ae)=>((r-1)*(z.hand[R].keypoints[L][Ae]||1)+(K||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let f0 of Object.keys(e.hand[R].annotations))a0[f0]=e.hand[R].annotations[f0]&&e.hand[R].annotations[f0][0]?e.hand[R].annotations[f0].map((L,K)=>L.map((Ae,se)=>((r-1)*z.hand[R].annotations[f0][K][se]+Ae)/r)):null;z.hand[R]={...e.hand[R],box:c0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let c0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(y=e.face[R].rotation)==null?void 0:y.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((P=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:P.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((M=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:M.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((H=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:H.bearing)||0)+(((J=(Y=e.face[R].rotation)==null?void 0:Y.gaze)==null?void 0:J.bearing)||0))/r,strength:((r-1)*(((E=(x0=z.face[R].rotation)==null?void 0:x0.gaze)==null?void 0:E.strength)||0)+(((B0=(G0=e.face[R].rotation)==null?void 0:G0.gaze)==null?void 0:B0.strength)||0))/r},z.face[R]={...e.face[R],rotation:$,box:c0,boxRaw:k}}z.face[R]={...e.face[R],box:c0,boxRaw:k}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let c0=e.object[R].box.map(($,a0)=>((r-1)*z.object[R].box[a0]+$)/r),k=e.object[R].boxRaw.map(($,a0)=>((r-1)*z.object[R].boxRaw[a0]+$)/r);z.object[R]={...e.object[R],box:c0,boxRaw:k}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let c0=0;c0<R.length;c0++)z.persons[c0].box=R[c0].box.map((k,$)=>((r-1)*z.persons[c0].box[$]+k)/r)}e.gesture&&(z.gesture=e.gesture);let A=h();return At=v.perfadd?At+Math.round(A-o):Math.round(A-o),e.performance&&(z.performance={...e.performance,interpolate:At}),z}var it={};ue(it,{distance:()=>j2,match:()=>at,similarity:()=>st});function j2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var nn=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function st(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=j2(e,t,o);return nn(n,o.order||2,o.min||0,o.max||1)}function at(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=j2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=nn(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function rn(e,t,o,n,r){var a,i,x,c,l,y,f,u,m,P,g,b,d,M,S,w;let A=0,s=[];for(let j of e){let C={id:A++,face:j,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let E of t)j.box[0]>E.box[0]&&j.box[0]<E.box[0]+E.box[2]&&j.box[1]+j.box[3]>E.box[1]&&j.box[1]+j.box[3]<E.box[1]+E.box[3]&&(C.body=E);if(C.body)for(let E of o)E.box[0]+E.box[2]>C.body.box[0]&&E.box[0]+E.box[2]<C.body.box[0]+C.body.box[2]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=E),E.box[0]<C.body.box[0]+C.body.box[2]&&E.box[0]>C.body.box[0]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=E);for(let E of n)E.face!==void 0&&E.face===j.id?(a=C.gestures)==null||a.push(E):E.iris!==void 0&&E.iris===j.id?(i=C.gestures)==null||i.push(E):E.body!==void 0&&E.body===((x=C.body)==null?void 0:x.id)?(c=C.gestures)==null||c.push(E):E.hand!==void 0&&E.hand===((y=(l=C.hands)==null?void 0:l.left)==null?void 0:y.id)?(f=C.gestures)==null||f.push(E):E.hand!==void 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0:n.tfjs)||ee.version_core).replace(/-(.*)/,"");he.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${o}/dist/`,he.modelBasePath=v.browser?"../models/":"file://models/",he.backend=v.browser?"humangl":"tensorflow",this.version=L5,Object.defineProperty(this,"version",{value:L5}),this.config=JSON.parse(JSON.stringify(he)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=r0(this.config,t)),Et(this.config),this.tf=ee,this.state="idle",M2(this,m2,0),M2(this,I2,!1),M2(this,N2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new 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n.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs;let A=[];Object.entries(r).forEach(a=>A.push({name:a[0],ms:a[1]})),A.sort((a,i)=>i.ms-a.ms),A.length=20;let s={};for(let a of A)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async n=>{var P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0,R,c0,k,$,a0;this.state="config";let r;this.config=r0(this.config,o),this.state="check";let A=b2(this,E5).call(this,t);A&&(p(A,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:A}));let s=h();await T5(this),await this.load(),r=h(),this.state="image";let a=await Ye(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&p("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=h(),this.config.skipAllowed=await wt(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Check Changed:");let i=[],x=[],c=[],l=[];this.state="detect:face",this.config.async?(i=this.config.face.enabled?rt(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=h(),i=this.config.face.enabled?await rt(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(i=await i),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?r0(this.config,{body:{maxDetected:this.config.face.enabled?1*i.length:1}}):this.config;this.config.async?((P=this.config.body.modelPath)!=null&&P.includes("posenet")?x=this.config.body.enabled?Z1(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("blazepose")?x=this.config.body.enabled?o1(a.tensor,y):[]:(b=this.config.body.modelPath)!=null&&b.includes("efficientpose")?x=this.config.body.enabled?l1(a.tensor,y):[]:(d=this.config.body.modelPath)!=null&&d.includes("movenet")&&(x=this.config.body.enabled?W1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=h(),(M=this.config.body.modelPath)!=null&&M.includes("posenet")?x=this.config.body.enabled?await Z1(a.tensor,y):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?x=this.config.body.enabled?await o1(a.tensor,y):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?x=this.config.body.enabled?await l1(a.tensor,y):[]:(j=this.config.body.modelPath)!=null&&j.includes("movenet")&&(x=this.config.body.enabled?await W1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?r0(this.config,{hand:{maxDetected:this.config.face.enabled?2*i.length:1}}):this.config;this.config.async?((F=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&F.includes("handdetect")?c=this.config.hand.enabled?T1(a.tensor,f):[]:(Y=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&Y.includes("handtrack")&&(c=this.config.hand.enabled?z1(a.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=h(),(x0=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&x0.includes("handdetect")?c=this.config.hand.enabled?await T1(a.tensor,f):[]:(G0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&G0.includes("handtrack")&&(c=this.config.hand.enabled?await z1(a.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((B0=this.config.object.modelPath)!=null&&B0.includes("nanodet")?l=this.config.object.enabled?G1(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?A1(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=h(),(c0=this.config.object.modelPath)!=null&&c0.includes("nanodet")?l=this.config.object.enabled?await G1(a.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(l=this.config.object.enabled?await A1(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([i,x,c,l]=await Promise.all([i,x,c,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(r=h(),u=[...$o(i),..._o(x),...tn(c),...en(i)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(h()-s):Math.trunc(h()-s);let m=((a0=($=this.process)==null?void 0:$.tensor)==null?void 0:a0.shape)||[];this.result={face:i,body:x,hand:c,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return rn(i,x,c,u,m)}},ee.dispose(a.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};m2=new WeakMap,I2=new WeakMap,N2=new WeakMap,E5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
+/**
+ * Human main module
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
diff --git a/dist/human.node.d.ts b/dist/human.node.d.ts
new file mode 100644
index 00000000..fed34cea
--- /dev/null
+++ b/dist/human.node.d.ts
@@ -0,0 +1,2643 @@
+/// <reference types="@webgpu/types/dist" />
+/// <reference types="offscreencanvas" />
+
+/** meta-function that performs draw for: canvas, face, body, hand */
+declare function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>): Promise<[void, void, void, void, void] | null>;
+
+/** Defines all possible canvas types */
+export declare type AnyCanvas = HTMLCanvasElement | OffscreenCanvas;
+
+/** Defines all possible image types */
+export declare type AnyImage = HTMLImageElement | typeof Image;
+
+/** Defines all possible video types */
+export declare type AnyVideo = HTMLMediaElement | HTMLVideoElement;
+
+/** @docalias number[] */
+declare interface ArrayMap {
+    R0: number;
+    R1: number[];
+    R2: number[][];
+    R3: number[][][];
+    R4: number[][][][];
+    R5: number[][][][][];
+    R6: number[][][][][][];
+}
+
+/** Possible TensorFlow backends */
+export declare type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];
+
+/** draw detected bodies */
+declare function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+export declare type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;
+
+export declare type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';
+
+export declare type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';
+
+/** Configures all body detection specific options */
+export declare interface BodyConfig extends GenericConfig {
+    /** maximum number of detected bodies */
+    maxDetected: number;
+    /** minimum confidence for a detected body before results are discarded */
+    minConfidence: number;
+}
+
+/** body gesture type */
+export declare type BodyGesture = `leaning ${'left' | 'right'}` | `raise ${'left' | 'right'} hand` | 'i give up';
+
+/** Body Result keypoints */
+export declare interface BodyKeypoint {
+    /** body part name */
+    part: BodyLandmark;
+    /** body part position */
+    position: Point;
+    /** body part position normalized to 0..1 */
+    positionRaw: Point;
+    /** body part position relative to body center in meters */
+    distance?: Point;
+    /** body part detection score */
+    score: number;
+}
+
+export declare type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;
+
+export declare type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle' | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';
+
+export declare type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';
+
+export declare type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+export declare type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';
+
+/** Body results */
+export declare interface BodyResult {
+    /** body id */
+    id: number;
+    /** body detection score */
+    score: number;
+    /** detected body box */
+    box: Box;
+    /** detected body box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected body keypoints */
+    keypoints: Array<BodyKeypoint>;
+    /** detected body keypoints combined into annotated parts */
+    annotations: Record<BodyAnnotation, Point[][]>;
+}
+
+/** generic box as [x, y, width, height] */
+export declare type Box = [number, number, number, number];
+
+/**
+ * Creates an IOHandler that loads model artifacts from user-selected files.
+ *
+ * This method can be used for loading from files such as user-selected files
+ * in the browser.
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * // Note: This code snippet won't run properly without the actual file input
+ * //   elements in the HTML DOM.
+ *
+ * // Suppose there are two HTML file input (`<input type="file" ...>`)
+ * // elements.
+ * const uploadJSONInput = document.getElementById('upload-json');
+ * const uploadWeightsInput = document.getElementById('upload-weights');
+ * const model = await tf.loadLayersModel(tf.io.browserFiles(
+ *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
+ * ```
+ *
+ * @param files `File`s to load from. Currently, this function supports only
+ *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
+ *   which an `Array` of `File`s is expected (in that order):
+ *   - A JSON file containing the model topology and weight manifest.
+ *   - Optionally, One or more binary files containing the binary weights.
+ *     These files must have names that match the paths in the `weightsManifest`
+ *     contained by the aforementioned JSON file, or errors will be thrown
+ *     during loading. These weights files have the same format as the ones
+ *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
+ *     Python PIP package. If no weights files are provided, only the model
+ *     topology will be loaded from the JSON file above.
+ * @returns An instance of `Files` `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function browserFiles(files: File[]): IOHandler;
+
+/**
+ * Deprecated. Use `tf.io.http`.
+ * @param path
+ * @param loadOptions
+ */
+declare function browserHTTPRequest(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** draw processed canvas */
+declare function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas): Promise<void>;
+
+/**
+ * Concatenate a number of ArrayBuffers into one.
+ *
+ * @param buffers A number of array buffers to concatenate.
+ * @returns Result of concatenating `buffers` in order.
+ */
+declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+
+/**
+ * Configuration interface definition for **Human** library
+ * Contains all configurable parameters
+ * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+ */
+export declare interface Config {
+    /** Backend used for TFJS operations
+     * valid build-in backends are:
+     * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`
+     * - NodeJS: `cpu`, `wasm`, `tensorflow`
+     * default: `humangl` for browser and `tensorflow` for nodejs
+     */
+    backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+    /** Path to *.wasm files if backend is set to `wasm`
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPath: string;
+    /** Force WASM loader to use platform fetch
+     *
+     * default: auto-detects to link to CDN `jsdelivr` when running in browser
+     */
+    wasmPlatformFetch: boolean;
+    /** Print debug statements to console
+     *
+     * default: `true`
+     */
+    debug: boolean;
+    /** Perform model loading and inference concurrently or sequentially
+     *
+     * default: `true`
+     */
+    async: boolean;
+    /** What to use for `human.warmup()`
+     * - warmup pre-initializes all models for faster inference but can take significant time on startup
+     * - used by `webgl`, `humangl` and `webgpu` backends
+     *
+     * default: `full`
+     */
+    warmup: '' | 'none' | 'face' | 'full' | 'body';
+    /** Base model path (typically starting with file://, http:// or https://) for all models
+     * - individual modelPath values are relative to this path
+     *
+     * default: `../models/` for browsers and `file://models/` for nodejs
+     */
+    modelBasePath: string;
+    /** Cache models in IndexDB on first sucessfull load
+     * default: true if indexdb is available (browsers), false if its not (nodejs)
+     */
+    cacheModels: boolean;
+    /** Cache sensitivity
+     * - values 0..1 where 0.01 means reset cache if input changed more than 1%
+     * - set to 0 to disable caching
+     *
+     * default: 0.7
+     */
+    cacheSensitivity: number;
+    /** Perform immediate garbage collection on deallocated tensors instead of caching them */
+    deallocate: boolean;
+    /** Internal Variable */
+    skipAllowed: boolean;
+    /** Filter config {@link FilterConfig} */
+    filter: Partial<FilterConfig>;
+    /** Gesture config {@link GestureConfig} */
+    gesture: Partial<GestureConfig>;
+    /** Face config {@link FaceConfig} */
+    face: Partial<FaceConfig>;
+    /** Body config {@link BodyConfig} */
+    body: Partial<BodyConfig>;
+    /** Hand config {@link HandConfig} */
+    hand: Partial<HandConfig>;
+    /** Object config {@link ObjectConfig} */
+    object: Partial<ObjectConfig>;
+    /** Segmentation config {@link SegmentationConfig} */
+    segmentation: Partial<SegmentationConfig>;
+}
+
+/**
+ * Copy a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Copying within a storage medium, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Copying between two storage mediums, e.g.,
+ *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Copy the model, from Local Storage to IndexedDB.
+ * await tf.io.copyModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove both models.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of copying.
+ * @param destURL Destination URL of copying.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * We wrap data id since we use weak map to avoid memory leaks.
+ * Since we have our own memory management, we have a reference counter
+ * mapping a tensor to its data, so there is always a pointer (even if that
+ * data is otherwise garbage collectable).
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
+ * Global_Objects/WeakMap
+ */
+declare type DataId = object;
+
+declare type DataToGPUOptions = DataToGPUWebGLOption;
+
+declare interface DataToGPUWebGLOption {
+    customTexShape?: [number, number];
+}
+
+/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */
+declare type DataType = keyof DataTypeMap;
+
+declare interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+
+/**
+ * Decode flat ArrayBuffer as weights.
+ *
+ * This function does not handle sharding.
+ *
+ * This function is the reverse of `encodeWeights`.
+ *
+ * @param buffer A flat ArrayBuffer carrying the binary values of the tensors
+ *   concatenated in the order specified in `specs`.
+ * @param specs Specifications of the names, dtypes and shapes of the tensors
+ *   whose value are encoded by `buffer`.
+ * @return A map from tensor name to tensor value, with the names corresponding
+ *   to names in `specs`.
+ * @throws Error, if any of the tensors has unsupported dtype.
+ */
+declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+
+/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */
+export declare const defaults: Config;
+
+/** Face descriptor type as number array */
+export declare type Descriptor = Array<number>;
+
+/** Calculates distance between two descriptors
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ */
+declare function distance(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare namespace draw {
+    export {
+        person,
+        canvas,
+        all,
+        options,
+        face,
+        body,
+        hand,
+        object,
+        gesture
+    }
+}
+export { draw }
+
+/** Draw Options
+ * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter
+ */
+export declare type DrawOptions = {
+    /** draw line color */
+    color: string;
+    /** alpha value used for lines */
+    alpha: number;
+    /** label color */
+    labelColor: string;
+    /** label shadow color */
+    shadowColor: string;
+    /** label font */
+    font: string;
+    /** line spacing between labels */
+    lineHeight: number;
+    /** line width for drawn lines */
+    lineWidth: number;
+    /** size of drawn points */
+    pointSize: number;
+    /** draw rounded boxes by n pixels */
+    roundRect: number;
+    /** should points be drawn? */
+    drawPoints: boolean;
+    /** should labels be drawn? */
+    drawLabels: boolean;
+    /** should face attention keypoints be highlighted */
+    drawAttention: boolean;
+    /** should detected gestures be drawn? */
+    drawGestures: boolean;
+    /** should draw boxes around detection results? */
+    drawBoxes: boolean;
+    /** should draw polygons from detection points? */
+    drawPolygons: boolean;
+    /** should draw gaze arrows? */
+    drawGaze: boolean;
+    /** should fill polygons? */
+    fillPolygons: boolean;
+    /** use z-coordinate when available */
+    useDepth: boolean;
+    /** should lines be curved? */
+    useCurves: boolean;
+};
+
+export declare type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';
+
+/**
+ * Encode a map from names to weight values as an ArrayBuffer, along with an
+ * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
+ *
+ * This function does not perform sharding.
+ *
+ * This function is the reverse of `decodeWeights`.
+ *
+ * @param tensors A map ("dict") from names to tensors.
+ * @param group Group to which the weights belong (optional).
+ * @returns A `Promise` of
+ *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
+ *     concatenated.
+ *   - An `Array` of `WeightManifestEntry`s, carrying information including
+ *     tensor names, `dtype`s and shapes.
+ * @throws Error: on unsupported tensor `dtype`.
+ */
+declare function encodeWeights(tensors: NamedTensorMap | NamedTensor[], group?: WeightGroup): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+
+/** Env class that holds detected capabilities */
+export declare class Env {
+    /** Running in Browser */
+    browser: boolean;
+    /** Running in NodeJS */
+    node: boolean;
+    /** Running in WebWorker thread */
+    worker: boolean;
+    /** Detected platform */
+    platform: string;
+    /** Detected agent */
+    agent: string;
+    /** List of supported backends */
+    backends: string[];
+    /** Has any work been performed so far */
+    initial: boolean;
+    /** Are image filters supported? */
+    filter: boolean | undefined;
+    /** TFJS instance details */
+    tfjs: {
+        version: undefined | string;
+    };
+    /** Is offscreenCanvas supported? */
+    offscreen: undefined | boolean;
+    /** Are performance counter instant values or additive */
+    perfadd: boolean;
+    /** WASM detected capabilities */
+    wasm: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        simd: undefined | boolean;
+        multithread: undefined | boolean;
+    };
+    /** WebGL detected capabilities */
+    webgl: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        version: undefined | string;
+        renderer: undefined | string;
+    };
+    /** WebGPU detected capabilities */
+    webgpu: {
+        supported: undefined | boolean;
+        backend: undefined | boolean;
+        adapter: undefined | string;
+    };
+    /** CPU info */
+    cpu: {
+        model: undefined | string;
+        flags: string[];
+    };
+    /** List of supported kernels for current backend */
+    kernels: string[];
+    /** MonkeyPatch for Canvas */
+    Canvas: undefined;
+    /** MonkeyPatch for Image */
+    Image: undefined;
+    /** MonkeyPatch for ImageData */
+    ImageData: undefined;
+    constructor();
+    /** update backend information */
+    updateBackend(): Promise<void>;
+    /** update cpu information */
+    updateCPU(): Promise<void>;
+}
+
+export declare const env: Env;
+
+/** Events dispatched by `human.events`
+ * - `create`: triggered when Human object is instantiated
+ * - `load`: triggered when models are loaded (explicitly or on-demand)
+ * - `image`: triggered when input image is processed
+ * - `result`: triggered when detection is complete
+ * - `warmup`: triggered when warmup is complete
+ */
+export declare type Events = 'create' | 'load' | 'image' | 'result' | 'warmup' | 'error';
+
+/** Defines possible externally defined canvas */
+export declare type ExternalCanvas = typeof env.Canvas;
+
+/** draw detected faces */
+declare function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Anti-spoofing part of face configuration */
+export declare interface FaceAntiSpoofConfig extends GenericConfig {
+}
+
+/** Attention part of face configuration */
+export declare interface FaceAttentionConfig extends GenericConfig {
+}
+
+/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */
+export declare interface FaceConfig extends GenericConfig {
+    detector: Partial<FaceDetectorConfig>;
+    mesh: Partial<FaceMeshConfig>;
+    attention: Partial<FaceAttentionConfig>;
+    iris: Partial<FaceIrisConfig>;
+    description: Partial<FaceDescriptionConfig>;
+    emotion: Partial<FaceEmotionConfig>;
+    antispoof: Partial<FaceAntiSpoofConfig>;
+    liveness: Partial<FaceLivenessConfig>;
+    gear: Partial<FaceGearConfig>;
+}
+
+/** Description or face embedding part of face configuration
+ * - also used by age and gender detection
+ */
+export declare interface FaceDescriptionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Detector part of face configuration */
+export declare interface FaceDetectorConfig extends GenericConfig {
+    /** is face rotation correction performed after detecting face?
+     * used to correctly analyze faces under high angles
+     */
+    rotation: boolean;
+    /** maximum number of detected faces */
+    maxDetected: number;
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected faces before one is discarded */
+    iouThreshold: number;
+    /** should child models perform on masked image of a face */
+    mask: boolean;
+    /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?
+     * if enabled it must be manually deallocated to avoid memory leak */
+    return: boolean;
+}
+
+/** Emotion part of face configuration */
+export declare interface FaceEmotionConfig extends GenericConfig {
+    /** minimum confidence for a detected face before results are discarded */
+    minConfidence: number;
+}
+
+/** Gear part of face configuration */
+export declare interface FaceGearConfig extends GenericConfig {
+    /** minimum confidence for a detected race before results are discarded */
+    minConfidence: number;
+}
+
+/** face gesture type */
+export declare type FaceGesture = `facing ${'left' | 'center' | 'right'}` | `blink ${'left' | 'right'} eye` | `mouth ${number}% open` | `head ${'up' | 'down'}`;
+
+/** Iris part of face configuration */
+export declare interface FaceIrisConfig extends GenericConfig {
+}
+
+export declare type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette' | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner' | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris' | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris' | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';
+
+/** Liveness part of face configuration */
+export declare interface FaceLivenessConfig extends GenericConfig {
+}
+
+/** Mesh part of face configuration */
+export declare interface FaceMeshConfig extends GenericConfig {
+    /** Keep detected faces that cannot be verified using facemesh */
+    keepInvalid: boolean;
+}
+
+/** Face results
+ * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
+ * - Some values may be null if specific model is not enabled
+ */
+export declare interface FaceResult {
+    /** face id */
+    id: number;
+    /** overall face score */
+    score: number;
+    /** detection score */
+    boxScore: number;
+    /** mesh score */
+    faceScore: number;
+    /** detected face box */
+    box: Box;
+    /** detected face box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected face mesh */
+    mesh: Array<Point>;
+    /** detected face mesh normalized to 0..1 */
+    meshRaw: Array<Point>;
+    /** face contours as array of 2d points normalized to 0..1 */
+    /** face contours as array of 2d points */
+    /** mesh keypoints combined into annotated results */
+    annotations: Record<FaceLandmark, Point[]>;
+    /** detected age */
+    age?: number;
+    /** detected gender */
+    gender?: Gender;
+    /** gender detection score */
+    genderScore?: number;
+    /** detected emotions */
+    emotion?: Array<{
+        score: number;
+        emotion: Emotion;
+    }>;
+    /** detected race */
+    race?: Array<{
+        score: number;
+        race: Race;
+    }>;
+    /** face descriptor */
+    embedding?: Array<number>;
+    /** face iris distance from camera */
+    iris?: number;
+    /** face anti-spoofing result confidence */
+    real?: number;
+    /** face liveness result confidence */
+    live?: number;
+    /** face rotation details */
+    rotation?: {
+        angle: {
+            roll: number;
+            yaw: number;
+            pitch: number;
+        };
+        matrix: [number, number, number, number, number, number, number, number, number];
+        gaze: {
+            bearing: number;
+            strength: number;
+        };
+    } | null;
+    /** detected face as tensor that can be used in further pipelines */
+    tensor?: Tensor;
+}
+
+/** Run input through image filters before inference
+ * - available only in Browser environments
+ * - image filters run with near-zero latency as they are executed on the GPU using WebGL
+ */
+export declare interface FilterConfig {
+    /** are image filters enabled? */
+    enabled: boolean;
+    /** perform image histogram equalization
+     * - equalization is performed on input as a whole and detected face before its passed for further analysis
+     */
+    equalization: boolean;
+    /** resize input width
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    width: number;
+    /** resize input height
+     * - if both width and height are set to 0, there is no resizing
+     * - if just one is set, second one is scaled automatically
+     * - if both are set, values are used as-is
+     */
+    height: number;
+    /** return processed canvas imagedata in result */
+    return: boolean;
+    /** flip input as mirror image */
+    flip: boolean;
+    /** range: -1 (darken) to 1 (lighten) */
+    brightness: number;
+    /** range: -1 (reduce contrast) to 1 (increase contrast) */
+    contrast: number;
+    /** range: 0 (no sharpening) to 1 (maximum sharpening) */
+    sharpness: number;
+    /** range: 0 (no blur) to N (blur radius in pixels) */
+    blur: number;
+    /** range: -1 (reduce saturation) to 1 (increase saturation) */
+    saturation: number;
+    /** range: 0 (no change) to 360 (hue rotation in degrees) */
+    hue: number;
+    /** image negative */
+    negative: boolean;
+    /** image sepia colors */
+    sepia: boolean;
+    /** image vintage colors */
+    vintage: boolean;
+    /** image kodachrome colors */
+    kodachrome: boolean;
+    /** image technicolor colors */
+    technicolor: boolean;
+    /** image polaroid camera effect */
+    polaroid: boolean;
+    /** range: 0 (no pixelate) to N (number of pixels to pixelate) */
+    pixelate: number;
+}
+
+export declare type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';
+
+export declare type FingerCurl = 'none' | 'half' | 'full';
+
+export declare type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandler` that simply loads the provided data.
+ */
+declare function fromMemory(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandler;
+
+/**
+ * Creates an IOHandler that loads model artifacts from memory.
+ *
+ * When used in conjunction with `tf.loadLayersModel`, an instance of
+ * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
+ *
+ * ```js
+ * const model = await tf.loadLayersModel(tf.io.fromMemory(
+ *     modelTopology, weightSpecs, weightData));
+ * ```
+ *
+ * @param modelArtifacts a object containing model topology (i.e., parsed from
+ *   the JSON format).
+ * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
+ *   names, shapes, types, and quantization of the weight data. Optional.
+ * @param weightData A single `ArrayBuffer` containing the weight data,
+ *   concatenated in the order described by the weightSpecs. Optional.
+ * @param trainingConfig Model training configuration. Optional.
+ *
+ * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
+ */
+declare function fromMemorySync(modelArtifacts: {} | ModelArtifacts, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer, trainingConfig?: TrainingConfig): IOHandlerSync;
+
+export declare type Gender = 'male' | 'female' | 'unknown';
+
+/** Generic config type inherited by all module types */
+export declare interface GenericConfig {
+    /** is module enabled? */
+    enabled: boolean;
+    /** path to model json file (relative to `modelBasePath` */
+    modelPath: string;
+    /** how many max frames to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipFrames: number;
+    /** how many max milliseconds to go without re-running model if cached results are acceptable
+     * for two-phase models such as face and hand caching applies to bounding boxes detection only */
+    skipTime: number;
+}
+
+/** draw detected gestures */
+declare function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Controlls gesture detection */
+export declare interface GestureConfig {
+    /** is gesture detection enabled? */
+    enabled: boolean;
+}
+
+/** Gesture combined results
+ * Each result has:
+ * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`
+ * - gesture: gesture detected
+ */
+export declare type GestureResult = {
+    'face': number;
+    gesture: FaceGesture;
+} | {
+    'iris': number;
+    gesture: IrisGesture;
+} | {
+    'body': number;
+    gesture: BodyGesture;
+} | {
+    'hand': number;
+    gesture: HandGesture;
+};
+
+declare const getLoadHandlers: (url: string | string[], loadOptions?: LoadOptions) => IOHandler[];
+
+/**
+ * Create `ModelArtifacts` from a JSON file.
+ *
+ * @param modelJSON Object containing the parsed JSON of `model.json`
+ * @param loadWeights Function that takes the JSON file's weights manifest,
+ *     reads weights from the listed path(s), and returns a Promise of the
+ *     weight manifest entries along with the weights data.
+ * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
+ */
+declare function getModelArtifactsForJSON(modelJSON: ModelJSON, loadWeights: (weightsManifest: WeightsManifestConfig) => Promise<[WeightsManifestEntry[], /* weightData */ ArrayBuffer]>): Promise<ModelArtifacts>;
+
+/**
+ * Populate ModelArtifactsInfo fields for a model with JSON topology.
+ * @param modelArtifacts
+ * @returns A ModelArtifactsInfo object.
+ */
+declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
+
+declare const getModelStats: (instance: Human) => ModelStats;
+
+declare const getSaveHandlers: (url: string | string[]) => IOHandler[];
+
+declare interface GPUData {
+    tensorRef: Tensor;
+    texture?: WebGLTexture;
+    buffer?: GPUBuffer;
+    texShape?: [number, number];
+    bufSize?: number;
+}
+
+/**
+ * A `tf.GraphModel` is a directed, acyclic graph built from a
+ * SavedModel GraphDef and allows inference execution.
+ *
+ * A `tf.GraphModel` can only be created by loading from a model converted from
+ * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
+ * the command line converter tool and loaded via `tf.loadGraphModel`.
+ *
+ * @doc {heading: 'Models', subheading: 'Classes'}
+ */
+export declare class GraphModel<ModelURL extends Url = string | io.IOHandler> implements InferenceModel {
+    private modelUrl;
+    private loadOptions;
+    private executor;
+    private version;
+    private handler;
+    private artifacts;
+    private initializer;
+    private resourceManager;
+    private signature;
+    readonly modelVersion: string;
+    readonly inputNodes: string[];
+    readonly outputNodes: string[];
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    readonly weights: NamedTensorsMap;
+    readonly metadata: {};
+    readonly modelSignature: {};
+    /**
+     * @param modelUrl url for the model, or an `io.IOHandler`.
+     * @param weightManifestUrl url for the weight file generated by
+     * scripts/convert.py script.
+     * @param requestOption options for Request, which allows to send credentials
+     * and custom headers.
+     * @param onProgress Optional, progress callback function, fired periodically
+     * before the load is completed.
+     */
+    constructor(modelUrl: ModelURL, loadOptions?: io.LoadOptions);
+    private findIOHandler;
+    /**
+     * Loads the model and weight files, construct the in memory weight map and
+     * compile the inference graph.
+     */
+    load(): UrlIOHandler<ModelURL> extends io.IOHandlerSync ? boolean : Promise<boolean>;
+    /**
+     * Synchronously construct the in memory weight map and
+     * compile the inference graph. Also initialize hashtable if any.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    loadSync(artifacts: io.ModelArtifacts): boolean;
+    /**
+     * Save the configuration and/or weights of the GraphModel.
+     *
+     * An `IOHandler` is an object that has a `save` method of the proper
+     * signature defined. The `save` method manages the storing or
+     * transmission of serialized data ("artifacts") that represent the
+     * model's topology and weights onto or via a specific medium, such as
+     * file downloads, local storage, IndexedDB in the web browser and HTTP
+     * requests to a server. TensorFlow.js provides `IOHandler`
+     * implementations for a number of frequently used saving mediums, such as
+     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
+     * for more details.
+     *
+     * This method also allows you to refer to certain types of `IOHandler`s
+     * as URL-like string shortcuts, such as 'localstorage://' and
+     * 'indexeddb://'.
+     *
+     * Example 1: Save `model`'s topology and weights to browser [local
+     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
+     * then load it back.
+     *
+     * ```js
+     * const modelUrl =
+     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
+     * const model = await tf.loadGraphModel(modelUrl);
+     * const zeros = tf.zeros([1, 224, 224, 3]);
+     * model.predict(zeros).print();
+     *
+     * const saveResults = await model.save('localstorage://my-model-1');
+     *
+     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
+     * console.log('Prediction from loaded model:');
+     * model.predict(zeros).print();
+     * ```
+     *
+     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
+     * scheme-based string shortcut for `IOHandler`.
+     * @param config Options for saving the model.
+     * @returns A `Promise` of `SaveResult`, which summarizes the result of
+     * the saving, such as byte sizes of the saved artifacts for the model's
+     *   topology and weight values.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
+     */
+    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
+     * inputs params should be in either `tf.Tensor`[] if the input order is
+     * fixed, or otherwise NamedTensorMap format.
+     *
+     * For model with multiple inputs, we recommend you use NamedTensorMap as the
+     * input type, if you use `tf.Tensor`[], the order of the array needs to
+     * follow the
+     * order of inputNodes array. @see {@link GraphModel.inputNodes}
+     *
+     * You can also feed any intermediate nodes using the NamedTensorMap as the
+     * input type. For example, given the graph
+     *    InputNode => Intermediate => OutputNode,
+     * you can execute the subgraph Intermediate => OutputNode by calling
+     *    model.execute('IntermediateNode' : tf.tensor(...));
+     *
+     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
+     * state needs to be fed manually.
+     *
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size and
+     * output node names. Currently the batch size option is ignored for graph
+     * model.
+     *
+     * @returns Inference result tensors. The output would be single `tf.Tensor`
+     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+     * will be returned for model with multiple outputs.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    private normalizeInputs;
+    private normalizeOutputs;
+    /**
+     * Executes inference for the model for given input tensors.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no
+     * outputs are specified, the default outputs of the model would be used.
+     * You can inspect intermediate nodes of the model by adding them to the
+     * outputs array.
+     *
+     * @returns A single tensor if provided with a single output or no outputs
+     * are provided and there is only one default output, otherwise return a
+     * tensor array. The order of the tensor array is the same as the outputs
+     * if provided, otherwise the order of outputNodes attribute of the model.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
+    /**
+     * Executes inference for the model for given input tensors in async
+     * fashion, use this method when your model contains control flow ops.
+     * @param inputs tensor, tensor array or tensor map of the inputs for the
+     * model, keyed by the input node names.
+     * @param outputs output node name from the Tensorflow model, if no outputs
+     * are specified, the default outputs of the model would be used. You can
+     * inspect intermediate nodes of the model by adding them to the outputs
+     * array.
+     *
+     * @returns A Promise of single tensor if provided with a single output or
+     * no outputs are provided and there is only one default output, otherwise
+     * return a tensor map.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
+    /**
+     * Get intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    getIntermediateTensors(): NamedTensorsMap;
+    /**
+     * Dispose intermediate tensors for model debugging mode (flag
+     * KEEP_INTERMEDIATE_TENSORS is true).
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    disposeIntermediateTensors(): void;
+    private convertTensorMapToTensorsMap;
+    /**
+     * Releases the memory used by the weight tensors and resourceManager.
+     *
+     * @doc {heading: 'Models', subheading: 'Classes'}
+     */
+    dispose(): void;
+}
+
+/** draw detected hands */
+declare function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all hand detection specific options */
+export declare interface HandConfig extends GenericConfig {
+    /** should hand rotation correction be performed after hand detection? */
+    rotation: boolean;
+    /** minimum confidence for a detected hand before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected hands before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected hands */
+    maxDetected: number;
+    /** should hand landmarks be detected or just return detected hand box */
+    landmarks: boolean;
+    detector: {
+        /** path to hand detector model json */
+        modelPath?: string;
+    };
+    skeleton: {
+        /** path to hand skeleton model json */
+        modelPath?: string;
+    };
+}
+
+/** hand gesture type */
+export declare type HandGesture = `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward` | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up` | 'victory' | 'thumbs up';
+
+/** Hand results */
+export declare interface HandResult {
+    /** hand id */
+    id: number;
+    /** hand overal score */
+    score: number;
+    /** hand detection score */
+    boxScore: number;
+    /** hand skelton score */
+    fingerScore: number;
+    /** detected hand box */
+    box: Box;
+    /** detected hand box normalized to 0..1 */
+    boxRaw: Box;
+    /** detected hand keypoints */
+    keypoints: Array<Point>;
+    /** detected hand class */
+    label: HandType;
+    /** detected hand keypoints combined into annotated parts */
+    annotations: Record<Finger, Array<Point>>;
+    /** detected hand parts annotated with part gestures */
+    landmarks: Record<Finger, {
+        curl: FingerCurl;
+        direction: FingerDirection;
+    }>;
+}
+
+export declare type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';
+
+/**
+ * Creates an IOHandler subtype that sends model artifacts to HTTP server.
+ *
+ * An HTTP request of the `multipart/form-data` mime type will be sent to the
+ * `path` URL. The form data includes artifacts that represent the topology
+ * and/or weights of the model. In the case of Keras-style `tf.Model`, two
+ * blobs (files) exist in form-data:
+ *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
+ *   - A binary weights file consisting of the concatenated weight values.
+ * These files are in the same format as the one generated by
+ * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
+ *
+ * The following code snippet exemplifies the client-side code that uses this
+ * function:
+ *
+ * ```js
+ * const model = tf.sequential();
+ * model.add(
+ *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
+ *
+ * const saveResult = await model.save(tf.io.http(
+ *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
+ * console.log(saveResult);
+ * ```
+ *
+ * If the default `POST` method is to be used, without any custom parameters
+ * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
+ *
+ * ```js
+ * const saveResult = await model.save('http://model-server:5000/upload');
+ * ```
+ *
+ * The following GitHub Gist
+ * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
+ * implements a server based on [flask](https://github.com/pallets/flask) that
+ * can receive the request. Upon receiving the model artifacts via the requst,
+ * this particular server reconsistutes instances of [Keras
+ * Models](https://keras.io/models/model/) in memory.
+ *
+ *
+ * @param path A URL path to the model.
+ *   Can be an absolute HTTP path (e.g.,
+ *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
+ *   './model-upload').
+ * @param requestInit Request configurations to be used when sending
+ *    HTTP request to server using `fetch`. It can contain fields such as
+ *    `method`, `credentials`, `headers`, `mode`, etc. See
+ *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
+ *    for more information. `requestInit` must not have a body, because the
+ * body will be set by TensorFlow.js. File blobs representing the model
+ * topology (filename: 'model.json') and the weights of the model (filename:
+ * 'model.weights.bin') will be appended to the body. If `requestInit` has a
+ * `body`, an Error will be thrown.
+ * @param loadOptions Optional configuration for the loading. It includes the
+ *   following fields:
+ *   - weightPathPrefix Optional, this specifies the path prefix for weight
+ *     files, by default this is calculated from the path param.
+ *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
+ *     the `fetch` from node-fetch can be used here.
+ *   - onProgress Optional, progress callback function, fired periodically
+ *     before the load is completed.
+ * @returns An instance of `IOHandler`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Loading',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function http(path: string, loadOptions?: LoadOptions): IOHandler;
+
+/** **Human** library main class
+ *
+ * All methods and properties are available only as members of Human class
+ *
+ * - Configuration object definition: {@link Config}
+ * - Results object definition: {@link Result}
+ * - Possible inputs: {@link Input}
+ *
+ * @param userConfig - {@link Config}
+ * @returns instance of {@link Human}
+ */
+declare class Human {
+    #private;
+    /** Current version of Human library in *semver* format */
+    version: string;
+    /** Current configuration
+     * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)
+     */
+    config: Config;
+    /** Last known result of detect run
+     * - Can be accessed anytime after initial detection
+     */
+    result: Result;
+    /** Current state of Human library
+     * - Can be polled to determine operations that are currently executed
+     * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'
+     */
+    state: string;
+    /** currenty processed image tensor and canvas */
+    process: {
+        tensor: Tensor | null;
+        canvas: AnyCanvas | null;
+    };
+    /** Instance of TensorFlow/JS used by Human
+     *  - Can be embedded or externally provided
+     * [TFJS API](https://js.tensorflow.org/api/latest/)
+     */
+    tf: any;
+    /** Object containing environment information used for diagnostics */
+    env: Env;
+    /** Draw helper classes that can draw detected objects on canvas using specified draw
+     * - canvas: draws input to canvas
+     * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}
+     * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas
+     */
+    draw: {
+        canvas: typeof draw.canvas;
+        face: typeof draw.face;
+        body: typeof draw.body;
+        hand: typeof draw.hand;
+        gesture: typeof draw.gesture;
+        object: typeof draw.object;
+        person: typeof draw.person;
+        all: typeof draw.all;
+        options: DrawOptions;
+    };
+    /** Currently loaded models
+     * @internal
+     * {@link Models}
+     */
+    models: models.Models;
+    /** Container for events dispatched by Human
+     * Possible events:
+     * - `create`: triggered when Human object is instantiated
+     * - `load`: triggered when models are loaded (explicitly or on-demand)
+     * - `image`: triggered when input image is processed
+     * - `result`: triggered when detection is complete
+     * - `warmup`: triggered when warmup is complete
+     * - `error`: triggered on some errors
+     */
+    events: EventTarget | undefined;
+    /** Reference face triangualtion array of 468 points, used for triangle references between points */
+    faceTriangulation: number[];
+    /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */
+    faceUVMap: [number, number][];
+    /** Performance object that contains values for all recently performed operations */
+    performance: Record<string, number>;
+    /** WebGL debug info */
+    gl: Record<string, unknown>;
+    /** Constructor for **Human** library that is futher used for all operations
+     * @param userConfig - user configuration object {@link Config}
+     */
+    constructor(userConfig?: Partial<Config>);
+    /** internal function to measure tensor leaks */
+    analyze: (...msg: string[]) => void;
+    /** Reset configuration to default values */
+    reset(): void;
+    /** Validate current configuration schema */
+    validate(userConfig?: Partial<Config>): {
+        reason: string;
+        where: string;
+        expected?: string;
+    }[];
+    /** Exports face matching methods {@link match#similarity} */
+    similarity: typeof match.similarity;
+    /** Exports face matching methods {@link match#distance} */
+    distance: typeof match.distance;
+    /** Exports face matching methods {@link match#match} */
+    match: typeof match.match;
+    /** Utility wrapper for performance.now() */
+    now(): number;
+    /** Process input as return canvas and tensor
+     *
+     * @param input - any input {@link Input}
+     * @param getTensor - should image processing also return tensor or just canvas
+     * Returns object with `tensor` and `canvas`
+     */
+    image(input: Input, getTensor?: boolean): Promise<{
+        tensor: Tensor<Rank> | null;
+        canvas: AnyCanvas | null;
+    }>;
+    /** Segmentation method takes any input and returns processed canvas with body segmentation
+     *  - Segmentation is not triggered as part of detect process
+     * @param input - {@link Input}
+     * @param background - {@link Input}
+     *  - Optional parameter background is used to fill the background with specific input
+     *  Returns:
+     *  - `data` as raw data array with per-pixel segmentation values
+     *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
+     *  - `alpha` as grayscale canvas that represents segmentation alpha values
+     */
+    segmentation(input: Input, background?: Input): Promise<{
+        data: number[] | Tensor;
+        canvas: AnyCanvas | null;
+        alpha: AnyCanvas | null;
+    }>;
+    /** Enhance method performs additional enhacements to face image previously detected for futher processing
+     *
+     * @param input - Tensor as provided in human.result.face[n].tensor
+     * @returns Tensor
+     */
+    enhance(input: Tensor): Tensor | null;
+    /** Compare two input tensors for pixel simmilarity
+     * - use `human.image` to process any valid input and get a tensor that can be used for compare
+     * - when passing manually generated tensors:
+     *  - both input tensors must be in format [1, height, width, 3]
+     *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor
+     * - return value is pixel similarity score normalized by input resolution and rgb channels
+     */
+    compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number>;
+    /** Explicit backend initialization
+     *  - Normally done implicitly during initial load phase
+     *  - Call to explictly register and initialize TFJS backend without any other operations
+     *  - Use when changing backend during runtime
+     */
+    init(): Promise<void>;
+    /** Load method preloads all configured models on-demand
+     * - Not explicitly required as any required model is load implicitly on it's first run
+     *
+     * @param userConfig - {@link Config}
+     */
+    load(userConfig?: Partial<Config>): Promise<void>;
+    /** emit event */
+    emit: (event: string) => void;
+    /** Runs interpolation using last known result and returns smoothened result
+     * Interpolation is based on time since last known result so can be called independently
+     *
+     * @param result - {@link Result} optional use specific result set to run interpolation on
+     * @returns result - {@link Result}
+     */
+    next(result?: Result): Result;
+    /** get model loading/loaded stats */
+    getModelStats(): ModelStats;
+    /** Warmup method pre-initializes all configured models for faster inference
+     * - can take significant time on startup
+     * - only used for `webgl` and `humangl` backends
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    warmup(userConfig?: Partial<Config>): Promise<Result | undefined>;
+    /** Run detect with tensorflow profiling
+     * - result object will contain total exeuction time information for top-20 kernels
+     * - actual detection object can be accessed via `human.result`
+     */
+    profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>>;
+    /** Main detection method
+     * - Analyze configuration: {@link Config}
+     * - Pre-process input: {@link Input}
+     * - Run inference for all configured models
+     * - Process and return result: {@link Result}
+     *
+     * @param input - {@link Input}
+     * @param userConfig - {@link Config}
+     * @returns result - {@link Result}
+     */
+    detect(input: Input, userConfig?: Partial<Config>): Promise<Result>;
+}
+export { Human }
+export default Human;
+
+/** Defines all possible image objects */
+export declare type ImageObjects = ImageData | ImageBitmap;
+
+/**
+ * Common interface for a machine learning model that can do inference.
+ */
+declare interface InferenceModel {
+    /**
+     * Return the array of input tensor info.
+     */
+    readonly inputs: ModelTensorInfo[];
+    /**
+     * Return the array of output tensor info.
+     */
+    readonly outputs: ModelTensorInfo[];
+    /**
+     * Execute the inference for the input tensors.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     * For batch inference execution, the tensors for each input need to be
+     * concatenated together. For example with mobilenet, the required input shape
+     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+     * If we are provide a batched data of 100 images, the input tensor should be
+     * in the shape of [100, 244, 244, 3].
+     *
+     * @param config Prediction configuration for specifying the batch size.
+     *
+     * @returns Inference result tensors. The output would be single Tensor if
+     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
+     * be returned for model with multiple outputs.
+     */
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    /**
+     * Single Execute the inference for the input tensors and return activation
+     * values for specified output node names without batching.
+     *
+     * @param input The input tensors, when there is single input for the model,
+     * inputs param should be a Tensor. For models with multiple inputs, inputs
+     * params should be in either Tensor[] if the input order is fixed, or
+     * otherwise NamedTensorMap format.
+     *
+     * @param outputs string|string[]. List of output node names to retrieve
+     * activation from.
+     *
+     * @returns Activation values for the output nodes result tensors. The return
+     * type matches specified parameter outputs type. The output would be single
+     * Tensor if single output is specified, otherwise Tensor[] for multiple
+     * outputs.
+     */
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
+
+/** Defines all possible input types for **Human** detection */
+export declare type Input = Tensor | AnyCanvas | AnyImage | AnyVideo | ImageObjects | ExternalCanvas;
+
+declare namespace io {
+    export {
+        copyModel,
+        listModels,
+        moveModel,
+        removeModel,
+        browserFiles,
+        browserHTTPRequest,
+        concatenateArrayBuffers,
+        decodeWeights,
+        encodeWeights,
+        fromMemory,
+        fromMemorySync,
+        getLoadHandlers,
+        getModelArtifactsForJSON,
+        getModelArtifactsInfoForJSON,
+        getSaveHandlers,
+        http,
+        IOHandler,
+        IOHandlerSync,
+        isHTTPScheme,
+        LoadHandler,
+        LoadOptions,
+        loadWeights,
+        ModelArtifacts,
+        ModelArtifactsInfo,
+        ModelJSON,
+        ModelStoreManager,
+        OnProgressCallback,
+        registerLoadRouter,
+        registerSaveRouter,
+        RequestDetails,
+        SaveConfig,
+        SaveHandler,
+        SaveResult,
+        TrainingConfig,
+        WeightGroup,
+        weightsLoaderFactory,
+        WeightsManifestConfig,
+        WeightsManifestEntry,
+        withSaveHandler,
+        withSaveHandlerSync
+    }
+}
+
+/**
+ * Interface for a model import/export handler.
+ *
+ * The `save` and `load` handlers are both optional, in order to allow handlers
+ * that support only saving or loading.
+ */
+declare interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+
+declare type IOHandlerSync = {
+    [K in keyof IOHandler]: Syncify<IOHandler[K]>;
+};
+
+declare type IORouter = (url: string | string[], loadOptions?: LoadOptions) => IOHandler;
+
+/** iris gesture type */
+export declare type IrisGesture = 'facing center' | `looking ${'left' | 'right' | 'up' | 'down'}` | 'looking center';
+
+declare function isHTTPScheme(url: string): boolean;
+
+/**
+ * List all models stored in registered storage mediums.
+ *
+ * For a web browser environment, the registered mediums are Local Storage and
+ * IndexedDB.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @returns A `Promise` of a dictionary mapping URLs of existing models to
+ * their model artifacts info. URLs include medium-specific schemes, e.g.,
+ *   'indexeddb://my/model/1'. Model artifacts info include type of the
+ * model's topology, byte sizes of the topology, weights, etc.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+
+/** Load method preloads all instance.configured models on-demand */
+declare function load(instance: Human): Promise<void>;
+
+/**
+ * Type definition for handlers of loading operations.
+ */
+declare type LoadHandler = () => Promise<ModelArtifacts>;
+
+/** @innamespace io */
+declare interface LoadOptions {
+    /**
+     * RequestInit (options) for HTTP requests.
+     *
+     * For detailed information on the supported fields, see
+     * [https://developer.mozilla.org/en-US/docs/Web/API/Request/Request](
+     *     https://developer.mozilla.org/en-US/docs/Web/API/Request/Request)
+     */
+    requestInit?: RequestInit;
+    /**
+     * Progress callback.
+     */
+    onProgress?: OnProgressCallback;
+    /**
+     * A function used to override the `window.fetch` function.
+     */
+    fetchFunc?: Function;
+    /**
+     * Strict loading model: whether extraneous weights or missing
+     * weights should trigger an `Error`.
+     *
+     * If `true`, require that the provided weights exactly match those
+     * required by the layers. `false` means that both extra weights
+     * and missing weights will be silently ignored.
+     *
+     * Default: `true`.
+     */
+    strict?: boolean;
+    /**
+     * Path prefix for weight files, by default this is calculated from the
+     * path of the model JSON file.
+     *
+     * For instance, if the path to the model JSON file is
+     * `http://localhost/foo/model.json`, then the default path prefix will be
+     * `http://localhost/foo/`. If a weight file has the path value
+     * `group1-shard1of2` in the weight manifest, then the weight file will be
+     * loaded from `http://localhost/foo/group1-shard1of2` by default. However,
+     * if you provide a `weightPathPrefix` value of
+     * `http://localhost/foo/alt-weights`, then the weight file will be loaded
+     * from the path `http://localhost/foo/alt-weights/group1-shard1of2` instead.
+     */
+    weightPathPrefix?: string;
+    /**
+     * Whether the module or model is to be loaded from TF Hub.
+     *
+     * Setting this to `true` allows passing a TF-Hub module URL, omitting the
+     * standard model file name and the query parameters.
+     *
+     * Default: `false`.
+     */
+    fromTFHub?: boolean;
+    /**
+     * An async function to convert weight file name to URL. The weight file
+     * names are stored in model.json's weightsManifest.paths field. By default we
+     * consider weight files are colocated with the model.json file. For example:
+     *     model.json URL: https://www.google.com/models/1/model.json
+     *     group1-shard1of1.bin url:
+     *        https://www.google.com/models/1/group1-shard1of1.bin
+     *
+     * With this func you can convert the weight file name to any URL.
+     */
+    weightUrlConverter?: (weightFileName: string) => Promise<string>;
+}
+
+/**
+ * Reads a weights manifest JSON configuration, fetches the weights and
+ * returns them as `Tensor`s.
+ *
+ * @param manifest The weights manifest JSON.
+ * @param filePathPrefix The path prefix for filenames given in the manifest.
+ *     Defaults to the empty string.
+ * @param weightNames The names of the weights to be fetched.
+ */
+declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestInit?: RequestInit): Promise<NamedTensorMap>;
+
+declare namespace match {
+    export {
+        distance,
+        similarity,
+        match_2 as match,
+        Descriptor,
+        MatchOptions
+    }
+}
+export { match }
+
+/** Matches given descriptor to a closest entry in array of descriptors
+ * @param descriptor - face descriptor
+ * @param descriptors - array of face descriptors to commpare given descriptor to
+ * @param options - see `similarity` method for options description
+ * Returns
+ * - `index` index array index where best match was found or -1 if no matches
+ * - `distance` calculated `distance` of given descriptor to the best match
+ * - `similarity` calculated normalized `similarity` of given descriptor to the best match
+ */
+declare function match_2(descriptor: Descriptor, descriptors: Array<Descriptor>, options?: MatchOptions): {
+    index: number;
+    distance: number;
+    similarity: number;
+};
+
+declare type MatchOptions = {
+    order?: number;
+    threshold?: number;
+    multiplier?: number;
+    min?: number;
+    max?: number;
+} | undefined;
+
+/**
+ * The serialized artifacts of a model, including topology and weights.
+ *
+ * The `modelTopology`, `trainingConfig`, `weightSpecs` and `weightData` fields
+ * of this interface are optional, in order to support topology- or weights-only
+ * saving and loading.
+ *
+ * Note this interface is used internally in IOHandlers.  For the file format
+ * written to disk as `model.json`, see `ModelJSON`.
+ */
+declare interface ModelArtifacts {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology?: {} | ArrayBuffer;
+    /**
+     * Serialized configuration for the model's training.
+     */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weight specifications.
+     *
+     * This corresponds to the weightsData below.
+     */
+    weightSpecs?: WeightsManifestEntry[];
+    /**
+     * Binary buffer for all weight values concatenated in the order specified
+     * by `weightSpecs`.
+     */
+    weightData?: ArrayBuffer;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelArtifactsInfo {
+    /**
+     * Timestamp for when the model is saved.
+     */
+    dateSaved: Date;
+    /**
+     * TODO (cais,yassogba) consider removing GraphDef as GraphDefs now
+     * come in a JSON format and none of our IOHandlers support a non json
+     * format. We could conder replacing this with 'Binary' if we want to
+     * allow future handlers to save to non json formats (though they will
+     * probably want more information than 'Binary').
+     * Type of the model topology
+     *
+     * Type of the model topology
+     *
+     * Possible values:
+     *   - JSON: JSON config (human-readable, e.g., Keras JSON).
+     *   - GraphDef: TensorFlow
+     *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)
+     *     protocol buffer (binary).
+     */
+    modelTopologyType: 'JSON' | 'GraphDef';
+    /**
+     * Size of model topology (Keras JSON or GraphDef), in bytes.
+     */
+    modelTopologyBytes?: number;
+    /**
+     * Size of weight specification or manifest, in bytes.
+     */
+    weightSpecsBytes?: number;
+    /**
+     * Size of weight value data, in bytes.
+     */
+    weightDataBytes?: number;
+}
+
+export declare type ModelInfo = {
+    name: string;
+    inCache: boolean;
+    sizeDesired: number;
+    sizeFromManifest: number;
+    sizeLoadedWeights: number;
+};
+
+/**
+ * The on-disk format of the `model.json` file.
+ *
+ * TF.js 1.0 always populates the optional fields when writing model.json.
+ * Prior versions did not provide those fields.
+ */
+declare interface ModelJSON {
+    /**
+     * Model topology.
+     *
+     * For Keras-style `tf.Model`s, this is a JSON object.
+     * For TensorFlow-style models (e.g., `SavedModel`), this is the JSON
+     * encoding of the `GraphDef` protocol buffer.
+     */
+    modelTopology: {};
+    /** Model training configuration. */
+    trainingConfig?: TrainingConfig;
+    /**
+     * Weights manifest.
+     *
+     * The weights manifest consists of an ordered list of weight-manifest
+     * groups. Each weight-manifest group consists of a number of weight values
+     * stored in a number of paths. See the documentation of
+     * `WeightsManifestConfig` for more details.
+     */
+    weightsManifest: WeightsManifestConfig;
+    /**
+     * Hard-coded format name for models saved from TensorFlow.js or converted
+     * by TensorFlow.js Converter.
+     */
+    format?: string;
+    /**
+     * What library is responsible for originally generating this artifact.
+     *
+     * Used for debugging purposes. E.g., 'TensorFlow.js v1.0.0'.
+     */
+    generatedBy?: string;
+    /**
+     * What library or tool is responsible for converting the original model
+     * to this format, applicable only if the model is output by a converter.
+     *
+     * Used for debugging purposes.  E.g., 'TensorFlow.js Converter v1.0.0'.
+     *
+     * A value of `null` means the model artifacts are generated without any
+     * conversion process (e.g., saved directly from a TensorFlow.js
+     * `tf.LayersModel` instance.)
+     */
+    convertedBy?: string | null;
+    /**
+     * Inputs and outputs signature for saved model.
+     */
+    signature?: {};
+    /**
+     * User-defined metadata about the model.
+     */
+    userDefinedMetadata?: {
+        [key: string]: {};
+    };
+    /**
+     * Initializer for the model.
+     */
+    modelInitializer?: {};
+}
+
+declare interface ModelPredictConfig {
+    /**
+     * Optional. Batch size (Integer). If unspecified, it will default to 32.
+     */
+    batchSize?: number;
+    /**
+     * Optional. Verbosity mode. Defaults to false.
+     */
+    verbose?: boolean;
+}
+
+/** Instances of all possible TFJS Graph Models used by Human
+ * - loaded as needed based on configuration
+ * - initialized explictly with `human.load()` method
+ * - initialized implicity on first call to `human.detect()`
+ * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading
+ */
+export declare class Models {
+    ssrnetage: null | GraphModel | Promise<GraphModel>;
+    gear: null | GraphModel | Promise<GraphModel>;
+    blazeposedetect: null | GraphModel | Promise<GraphModel>;
+    blazepose: null | GraphModel | Promise<GraphModel>;
+    centernet: null | GraphModel | Promise<GraphModel>;
+    efficientpose: null | GraphModel | Promise<GraphModel>;
+    mobilefacenet: null | GraphModel | Promise<GraphModel>;
+    emotion: null | GraphModel | Promise<GraphModel>;
+    facedetect: null | GraphModel | Promise<GraphModel>;
+    faceiris: null | GraphModel | Promise<GraphModel>;
+    facemesh: null | GraphModel | Promise<GraphModel>;
+    faceres: null | GraphModel | Promise<GraphModel>;
+    ssrnetgender: null | GraphModel | Promise<GraphModel>;
+    handpose: null | GraphModel | Promise<GraphModel>;
+    handskeleton: null | GraphModel | Promise<GraphModel>;
+    handtrack: null | GraphModel | Promise<GraphModel>;
+    liveness: null | GraphModel | Promise<GraphModel>;
+    movenet: null | GraphModel | Promise<GraphModel>;
+    nanodet: null | GraphModel | Promise<GraphModel>;
+    posenet: null | GraphModel | Promise<GraphModel>;
+    segmentation: null | GraphModel | Promise<GraphModel>;
+    antispoof: null | GraphModel | Promise<GraphModel>;
+}
+
+declare namespace models {
+    export {
+        reset,
+        load,
+        validate,
+        Models,
+        ModelStats,
+        getModelStats
+    }
+}
+export { models }
+
+export declare type ModelStats = {
+    numLoadedModels: number;
+    numEnabledModels: undefined;
+    numDefinedModels: number;
+    percentageLoaded: number;
+    totalSizeFromManifest: number;
+    totalSizeWeights: number;
+    totalSizeLoading: number;
+    totalSizeEnabled: undefined;
+    modelStats: ModelInfo[];
+};
+
+/**
+ * An interface for the manager of a model store.
+ *
+ * A model store is defined as a storage medium on which multiple models can
+ * be stored. Each stored model has a unique `path` as its identifier.
+ * A `ModelStoreManager` for the store allows actions including
+ *
+ * - Listing the models stored in the store.
+ * - Deleting a model from the store.
+ */
+declare interface ModelStoreManager {
+    /**
+     * List all models in the model store.
+     *
+     * @returns A dictionary mapping paths of existing models to their
+     *   model artifacts info. Model artifacts info include type of the model's
+     *   topology, byte sizes of the topology, weights, etc.
+     */
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    /**
+     * Remove a model specified by `path`.
+     *
+     * @param path
+     * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+     *   is successful).
+     * @throws Error if deletion fails, e.g., if no model exists at `path`.
+     */
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
+
+/**
+ * Interface for model input/output tensor info.
+ */
+declare interface ModelTensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+    tfDtype?: string;
+}
+
+/**
+ * Move a model from one URL to another.
+ *
+ * This function supports:
+ *
+ * 1. Moving within a storage medium, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
+ * 2. Moving between two storage mediums, e.g.,
+ *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Move the model, from Local Storage to IndexedDB.
+ * await tf.io.moveModel(
+ *     'localstorage://demo/management/model1',
+ *     'indexeddb://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Remove the moved model.
+ * await tf.io.removeModel('indexeddb://demo/management/model1');
+ * ```
+ *
+ * @param sourceURL Source URL of moving.
+ * @param destURL Destination URL of moving.
+ * @returns ModelArtifactsInfo of the copied model (if and only if copying
+ *   is successful).
+ * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
+ *   if `oldPath` and `newPath` are identical.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+
+declare interface NamedTensor {
+    name: string;
+    tensor: Tensor;
+}
+
+/** @docalias {[name: string]: Tensor} */
+declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+
+declare type NamedTensorsMap = {
+    [key: string]: Tensor[];
+};
+
+declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+
+/** draw detected objects */
+declare function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Configures all object detection specific options */
+export declare interface ObjectConfig extends GenericConfig {
+    /** minimum confidence for a detected objects before results are discarded */
+    minConfidence: number;
+    /** minimum overlap between two detected objects before one is discarded */
+    iouThreshold: number;
+    /** maximum number of detected objects */
+    maxDetected: number;
+}
+
+/** Object results */
+export declare interface ObjectResult {
+    /** object id */
+    id: number;
+    /** object detection score */
+    score: number;
+    /** detected object class id */
+    class: number;
+    /** detected object class name */
+    label: ObjectType;
+    /** detected object box */
+    box: Box;
+    /** detected object box normalized to 0..1 */
+    boxRaw: Box;
+}
+
+export declare type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter' | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee' | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork' | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant' | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book' | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';
+
+/**
+ * Callback for the progress of a long-running action such as an HTTP
+ * request for a large binary object.
+ *
+ * `fraction` should be a number in the [0, 1] interval, indicating how
+ * much of the action has completed.
+ */
+declare type OnProgressCallback = (fraction: number) => void;
+
+/** currently set draw options {@link DrawOptions} */
+declare const options: DrawOptions;
+
+/** draw combined person results instead of individual detection result objects */
+declare function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>): Promise<void>;
+
+/** Person getter
+ * - Triggers combining all individual results into a virtual person object
+ */
+export declare interface PersonResult {
+    /** person id */
+    id: number;
+    /** face result that belongs to this person */
+    face: FaceResult;
+    /** body result that belongs to this person */
+    body: BodyResult | null;
+    /** left and right hand results that belong to this person */
+    hands: {
+        left: HandResult | null;
+        right: HandResult | null;
+    };
+    /** detected gestures specific to this person */
+    gestures: Array<GestureResult>;
+    /** box that defines the person */
+    box: Box;
+    /** box that defines the person normalized to 0..1 */
+    boxRaw?: Box;
+}
+
+/** generic point as [x, y, z?] */
+export declare type Point = [number, number, number?];
+
+declare type PromiseFunction = (...args: unknown[]) => Promise<unknown>;
+
+export declare type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';
+
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+
+declare interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+
+declare const registerLoadRouter: (loudRouter: IORouter) => void;
+
+declare const registerSaveRouter: (loudRouter: IORouter) => void;
+
+/**
+ * Remove a model specified by URL from a reigstered storage medium.
+ *
+ * ```js
+ * // First create and save a model.
+ * const model = tf.sequential();
+ * model.add(tf.layers.dense(
+ *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
+ * await model.save('localstorage://demo/management/model1');
+ *
+ * // Then list existing models.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ *
+ * // Delete the model.
+ * await tf.io.removeModel('localstorage://demo/management/model1');
+ *
+ * // List models again.
+ * console.log(JSON.stringify(await tf.io.listModels()));
+ * ```
+ *
+ * @param url A URL to a stored model, with a scheme prefix, e.g.,
+ *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
+ * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
+ *   is successful).
+ * @throws Error if deletion fails, e.g., if no model exists at `path`.
+ *
+ * @doc {
+ *   heading: 'Models',
+ *   subheading: 'Management',
+ *   namespace: 'io',
+ *   ignoreCI: true
+ * }
+ */
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+
+/**
+ * Additional options for Platform.fetch
+ */
+declare interface RequestDetails {
+    /**
+     * Is this request for a binary file (as opposed to a json file)
+     */
+    isBinary?: boolean;
+}
+
+declare function reset(instance: Human): void;
+
+/**
+ * Result interface definition for **Human** library
+ *
+ * Contains all possible detection results
+ */
+export declare interface Result {
+    /** {@link FaceResult}: detection & analysis results */
+    face: Array<FaceResult>;
+    /** {@link BodyResult}: detection & analysis results */
+    body: Array<BodyResult>;
+    /** {@link HandResult}: detection & analysis results */
+    hand: Array<HandResult>;
+    /** {@link GestureResult}: detection & analysis results */
+    gesture: Array<GestureResult>;
+    /** {@link ObjectResult}: detection & analysis results */
+    object: Array<ObjectResult>;
+    /** global performance object with timing values for each operation */
+    performance: Record<string, number>;
+    /** optional processed canvas that can be used to draw input on screen */
+    canvas?: AnyCanvas | null;
+    /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */
+    readonly timestamp: number;
+    /** getter property that returns unified persons object  */
+    persons: Array<PersonResult>;
+    /** Last known error message */
+    error: string | null;
+}
+
+/**
+ * Options for saving a model.
+ * @innamespace io
+ */
+declare interface SaveConfig {
+    /**
+     * Whether to save only the trainable weights of the model, ignoring the
+     * non-trainable ones.
+     */
+    trainableOnly?: boolean;
+    /**
+     * Whether the optimizer will be saved (if exists).
+     *
+     * Default: `false`.
+     */
+    includeOptimizer?: boolean;
+}
+
+/**
+ * Type definition for handlers of saving operations.
+ */
+declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+
+/**
+ * Result of a saving operation.
+ */
+declare interface SaveResult {
+    /**
+     * Information about the model artifacts saved.
+     */
+    modelArtifactsInfo: ModelArtifactsInfo;
+    /**
+     * HTTP responses from the server that handled the model-saving request (if
+     * any). This is applicable only to server-based saving routes.
+     */
+    responses?: Response[];
+    /**
+     * Error messages and related data (if any).
+     */
+    errors?: Array<{} | string>;
+}
+
+/** Configures all body segmentation module
+ * removes background from input containing person
+ * if segmentation is enabled it will run as preprocessing task before any other model
+ * alternatively leave it disabled and use it on-demand using human.segmentation method which can
+ * remove background or replace it with user-provided background
+ */
+export declare interface SegmentationConfig extends GenericConfig {
+    /** blur segmentation output by <number> pixels for more realistic image */
+    blur: number;
+}
+
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/// <amd-module name="@tensorflow/tfjs-core/dist/types" />
+/** @docalias number[] */
+declare interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+
+/** Calculates normalized similarity between two face descriptors based on their `distance`
+ * @param options - calculation options
+ * - order - algorithm to use
+ *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2
+ * - multiplier - by how much to enhance difference analysis in range of 1..100
+ *   default is 20 which normalizes results to similarity above 0.5 can be considered a match
+ * - min - normalize similarity result to a given range
+ * - max - normalzie similarity resutl to a given range
+ *   default is 0.2...0.8
+ * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
+ */
+declare function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options?: MatchOptions): number;
+
+declare interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+
+declare type Syncify<T extends PromiseFunction> = T extends (...args: infer Args) => Promise<infer R> ? (...args: Args) => R : never;
+
+export declare namespace Tensor { }
+
+/**
+ * A `tf.Tensor` object represents an immutable, multidimensional array of
+ * numbers that has a shape and a data type.
+ *
+ * For performance reasons, functions that create tensors do not necessarily
+ * perform a copy of the data passed to them (e.g. if the data is passed as a
+ * `Float32Array`), and changes to the data will change the tensor. This is not
+ * a feature and is not supported. To avoid this behavior, use the tensor before
+ * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
+ *
+ * See `tf.tensor` for details on how to create a `tf.Tensor`.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+export declare class Tensor<R extends Rank = Rank> {
+    /** Unique id of this tensor. */
+    readonly id: number;
+    /**
+     * Id of the bucket holding the data for this tensor. Multiple arrays can
+     * point to the same bucket (e.g. when calling array.reshape()).
+     */
+    dataId: DataId;
+    /** The shape of the tensor. */
+    readonly shape: ShapeMap[R];
+    /** Number of elements in the tensor. */
+    readonly size: number;
+    /** The data type for the array. */
+    readonly dtype: DataType;
+    /** The rank type for the array (see `Rank` enum). */
+    readonly rankType: R;
+    /** Whether this tensor has been globally kept. */
+    kept: boolean;
+    /** The id of the scope this tensor is being tracked in. */
+    scopeId: number;
+    /**
+     * Number of elements to skip in each dimension when indexing. See
+     * https://docs.scipy.org/doc/numpy/reference/generated/\
+     * numpy.ndarray.strides.html
+     */
+    readonly strides: number[];
+    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
+    readonly rank: number;
+    /**
+     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
+    /**
+     * Returns a `tf.TensorBuffer` that holds the underlying data.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * asynchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    array(): Promise<ArrayMap[R]>;
+    /**
+     * Returns the tensor data as a nested array. The transfer of data is done
+     * synchronously.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    arraySync(): ArrayMap[R];
+    /**
+     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
+     * promise of `TypedArray` that resolves when the computation has finished.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    /**
+     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
+     * and `data()`, this method prevents data from being downloaded to CPU.
+     *
+     * For WebGL backend, the data will be stored on a densely packed texture.
+     * This means that the texture will use the RGBA channels to store value.
+     *
+     * For WebGPU backend, the data will be stored on a buffer. There is no
+     * parameter, so can not use an user defined size to create the buffer.
+     *
+     * @param options:
+     *     For WebGL,
+     *         - customTexShape: Optional. If set, will use the user defined
+     *     texture shape to create the texture.
+     *
+     * @returns For WebGL backend, a GPUData contains the new texture and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this texture,
+     *        texture: WebGLTexture,
+     *        texShape: [number, number] // [height, width]
+     *     }
+     *
+     *     For WebGPU backend, a GPUData contains the new buffer and
+     *     its information.
+     *     {
+     *        tensorRef: The tensor that is associated with this buffer,
+     *        buffer: GPUBuffer,
+     *        bufSize: number
+     *     }
+     *
+     *     Remember to dispose the GPUData after it is used by
+     *     `res.tensorRef.dispose()`.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataToGPU(options?: DataToGPUOptions): GPUData;
+    /**
+     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
+     * UI thread until the values are ready, which can cause performance issues.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    /** Returns the underlying bytes of the tensor's data. */
+    bytes(): Promise<Uint8Array[] | Uint8Array>;
+    /**
+     * Disposes `tf.Tensor` from memory.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    dispose(): void;
+    protected isDisposedInternal: boolean;
+    readonly isDisposed: boolean;
+    throwIfDisposed(): void;
+    /**
+     * Prints the `tf.Tensor`. See `tf.print` for details.
+     *
+     * @param verbose Whether to print verbose information about the tensor,
+     *    including dtype and size.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    print(verbose?: boolean): void;
+    /**
+     * Returns a copy of the tensor. See `tf.clone` for details.
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    clone<T extends Tensor>(this: T): T;
+    /**
+     * Returns a human-readable description of the tensor. Useful for logging.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    toString(verbose?: boolean): string;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+}
+
+/**
+ * A mutable object, similar to `tf.Tensor`, that allows users to set values
+ * at locations before converting to an immutable `tf.Tensor`.
+ *
+ * See `tf.buffer` for creating a tensor buffer.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    /**
+     * Sets a value in the buffer at a given location.
+     *
+     * @param value The value to set.
+     * @param locs  The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    /**
+     * Returns the value in the buffer at the provided location.
+     *
+     * @param locs The location indices.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    /**
+     * Creates an immutable `tf.Tensor` object from the buffer.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Creation'}
+     */
+    toTensor(): Tensor<R>;
+}
+
+declare interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
+
+/** @docalias TypedArray|Array */
+export declare type TensorLike = TypedArray | number | boolean | string | RecursiveArray<number | number[] | TypedArray> | RecursiveArray<boolean> | RecursiveArray<string> | Uint8Array[];
+
+/** Model training configuration. */
+declare interface TrainingConfig {
+    /** Optimizer used for the model training. */
+    optimizer_config: {};
+    /** Loss function(s) for the model's output(s). */
+    loss: string | string[] | {
+        [key: string]: string;
+    };
+    /** Metric function(s) for the model's output(s). */
+    metrics?: string[] | {
+        [key: string]: string;
+    };
+    weighted_metrics?: string[];
+    sample_weight_mode?: string;
+    loss_weights?: number[] | {
+        [key: string]: number;
+    };
+}
+
+declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+
+declare type Url = string | io.IOHandler | io.IOHandlerSync;
+
+declare type UrlIOHandler<T extends Url> = T extends string ? io.IOHandler : T;
+
+declare function validate(instance: Human): Promise<void>;
+
+/**
+ * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
+ *
+ * @doc {heading: 'Tensors', subheading: 'Classes'}
+ */
+declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
+    /**
+     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
+     * the same shape and dtype as the old `tf.Tensor`.
+     *
+     * @param newValue New tensor to be assigned to this variable.
+     *
+     * @doc {heading: 'Tensors', subheading: 'Classes'}
+     */
+    assign(newValue: Tensor<R>): void;
+    dispose(): void;
+}
+
+/** Possible values for `human.warmup` */
+export declare type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];
+
+/**
+ * Group to which the weight belongs.
+ *
+ * - 'optimizer': Weight from a stateful optimizer.
+ */
+declare type WeightGroup = 'model' | 'optimizer';
+
+/**
+ * Creates a function, which reads a weights manifest JSON configuration,
+ * fetches the weight files using the specified function and returns them as
+ * `Tensor`s.
+ *
+ * ```js
+ * // example for creating a nodejs weight loader, which reads the weight files
+ * // from disk using fs.readFileSync
+ *
+ * import * as fs from 'fs'
+ *
+ * const fetchWeightsFromDisk = (filePaths: string[]) =>
+ *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
+ *
+ * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+ *
+ * const manifest = JSON.parse(
+ *   fs.readFileSync('./my_model-weights_manifest').toString()
+ * )
+ * const weightMap = await loadWeights(manifest, './')
+ * ```
+ * @param fetchWeightsFunction The function used for fetching the weight files.
+ * @returns Weight loading function.
+ */
+declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
+
+/**
+ * A weight manifest.
+ *
+ * The weight manifest consists of an ordered list of weight-manifest groups.
+ * Each weight-manifest group ("group" for short hereafter) consists of a
+ * number of weight values stored in a number of paths.
+ * See the documentation of `WeightManifestGroupConfig` below for more details.
+ */
+declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+
+/**
+ * An entry in the weight manifest.
+ *
+ * The entry contains specification of a weight.
+ */
+declare interface WeightsManifestEntry {
+    /**
+     * Name of the weight, e.g., 'Dense_1/bias'
+     */
+    name: string;
+    /**
+     * Shape of the weight.
+     */
+    shape: number[];
+    /**
+     * Data type of the weight.
+     */
+    dtype: 'float32' | 'int32' | 'bool' | 'string' | 'complex64';
+    /**
+     * Type of the weight.
+     *
+     * Optional.
+     *
+     * The value 'optimizer' indicates the weight belongs to an optimizer
+     * (i.e., used only during model training and not during inference).
+     */
+    group?: WeightGroup;
+    /**
+     * Information for dequantization of the weight.
+     */
+    quantization?: {
+        scale?: number;
+        min?: number;
+        dtype: 'uint16' | 'uint8' | 'float16';
+    };
+}
+
+/**
+ * A weight-manifest group.
+ *
+ * Consists of an ordered list of weight values encoded in binary format,
+ * stored in an ordered list of paths.
+ */
+declare interface WeightsManifestGroupConfig {
+    /**
+     * An ordered list of paths.
+     *
+     * Paths are intentionally abstract in order to be general. For example, they
+     * can be relative URL paths or relative paths on the file system.
+     */
+    paths: string[];
+    /**
+     * Specifications of the weights stored in the paths.
+     */
+    weights: WeightsManifestEntry[];
+}
+
+/**
+ * Creates an IOHandler that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     promise that resolves to a `SaveResult`.
+ */
+declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
+
+/**
+ * Creates an IOHandlerSync that passes saved model artifacts to a callback.
+ *
+ * ```js
+ * function handleSave(artifacts) {
+ *   // ... do something with the artifacts ...
+ *   return {modelArtifactsInfo: {...}, ...};
+ * }
+ *
+ * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
+ * ```
+ *
+ * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
+ *     `SaveResult`.
+ */
+declare function withSaveHandlerSync(saveHandler: (artifacts: ModelArtifacts) => SaveResult): IOHandlerSync;
+
+export { }
diff --git a/dist/human.node.js b/dist/human.node.js
new file mode 100644
index 00000000..78992028
--- /dev/null
+++ b/dist/human.node.js
@@ -0,0 +1,838 @@
+"use strict";/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var An=Object.create;var h2=Object.defineProperty;var sn=Object.getOwnPropertyDescriptor;var an=Object.getOwnPropertyNames;var ln=Object.getPrototypeOf,yn=Object.prototype.hasOwnProperty;var xn=(e,t,o)=>t in e?h2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:o}):e[t]=o;var cn=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ue=(e,t)=>{for(var o in t)h2(e,o,{get:t[o],enumerable:!0})},dt=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of an(t))!yn.call(e,r)&&r!==o&&h2(e,r,{get:()=>t[r],enumerable:!(n=sn(t,r))||n.enumerable});return e};var Z=(e,t,o)=>(o=e!=null?An(ln(e)):{},dt(t||!e||!e.__esModule?h2(o,"default",{value:e,enumerable:!0}):o,e)),dn=e=>dt(h2({},"__esModule",{value:!0}),e);var T=(e,t,o)=>(xn(e,typeof t!="symbol"?t+"":t,o),o),ft=(e,t,o)=>{if(!t.has(e))throw TypeError("Cannot "+o)};var b2=(e,t,o)=>(ft(e,t,"read from private field"),o?o.call(e):t.get(e)),g2=(e,t,o)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,o)},M2=(e,t,o,n)=>(ft(e,t,"write to private field"),n?n.call(e,o):t.set(e,o),o);var D=cn((f7,S5)=>{"use strict";var pt=Object.defineProperty,fn=Object.getOwnPropertyDescriptor,mn=Object.getOwnPropertyNames,pn=Object.prototype.hasOwnProperty,z5=(e,t,o,n)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of mn(t))!pn.call(e,r)&&r!==o&&pt(e,r,{get:()=>t[r],enumerable:!(n=fn(t,r))||n.enumerable});return e},un=(e,t,o)=>(z5(e,t,"default"),o&&z5(o,t,"default")),hn=e=>z5(pt({},"__esModule",{value:!0}),e),ut={};S5.exports=hn(ut);un(ut,require("@tensorflow/tfjs-node"),S5.exports)});var y7={};ue(y7,{Human:()=>it,default:()=>it,defaults:()=>he,draw:()=>tt,env:()=>v,match:()=>at,models:()=>R5});module.exports=dn(y7);function p(...e){let t=new Date,o=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(o,"Human:",...e)}function mt(e,t){let o=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${o}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var h=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function E5(e,t,o="config",n=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")E5(e[r],t[r],r,n);else{let A=e&&typeof e[r]!="undefined";A||n.push({reason:"unknown property",where:`${o}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&n.push({reason:"property type mismatch",where:`${o}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&o==="config"&&n.length>0&&p("invalid configuration",n),n}function r0(...e){let t=o=>o&&typeof o=="object";return e.reduce((o,n)=>(Object.keys(n||{}).forEach(r=>{let A=o[r],s=n[r];Array.isArray(A)&&Array.isArray(s)?o[r]=A.concat(...s):t(A)&&t(s)?o[r]=r0(A,s):o[r]=s}),o),{})}var he={backend:"",modelBasePath:"",cacheModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var p0=Z(D());var N=Z(D());var ht=`
+  precision highp float;
+  attribute vec2 pos;
+  attribute vec2 uv;
+  varying vec2 vUv;
+  uniform float flipY;
+  void main(void) {
+    vUv = uv;
+    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
+  }
+`;var bt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
+    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
+  }
+`,gt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform float m[20];
+  void main(void) {
+    vec4 c = texture2D(texture, vUv);
+    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
+    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
+    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
+    gl_FragColor.a = c.a;
+  }
+`,Mt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform vec2 size;
+  uniform sampler2D texture;
+  vec2 pixelate(vec2 coord, vec2 size) {
+    return floor( coord / size ) * size;
+  }
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    vec2 coord = pixelate(vUv, size);
+    gl_FragColor += texture2D(texture, coord);
+  }
+`,Pt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  void main(void) {
+    gl_FragColor = vec4(0.0);
+    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
+    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;
+    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;
+    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;
+    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;
+    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;
+    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;
+    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
+    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
+  }
+`,Rt=`
+  precision highp float;
+  varying vec2 vUv;
+  uniform sampler2D texture;
+  uniform vec2 px;
+  uniform float m[9];
+  void main(void) {
+    vec4 c11 = texture2D(texture, vUv - px); // top left
+    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
+    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
+    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
+    vec4 c22 = texture2D(texture, vUv); // mid center
+    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
+    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
+    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
+    vec4 c33 = texture2D(texture, vUv + px ); // bottom right
+    gl_FragColor = 
+    c11 * m[0] + c12 * m[1] + c22 * m[2] +
+    c21 * m[3] + c22 * m[4] + c23 * m[5] +
+    c31 * m[6] + c32 * m[7] + c33 * m[8];
+    gl_FragColor.a = c22.a;
+  }
+`;var C5=(e,t,o)=>{let n=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(n,(r,A)=>(o[A]=0,r))},j5=class{constructor(t,o,n){T(this,"uniform",{});T(this,"attribute",{});T(this,"gl");T(this,"id");T(this,"compile",(t,o)=>{let n=this.gl.createShader(o);return n?(this.gl.shaderSource(n,t),this.gl.compileShader(n),this.gl.getShaderParameter(n,this.gl.COMPILE_STATUS)?n:(p(`filter: gl compile failed: ${this.gl.getShaderInfoLog(n)}`),null)):(p("filter: could not create shader"),null)});this.gl=t;let r=this.compile(o,this.gl.VERTEX_SHADER),A=this.compile(n,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!A)){if(!this.id){p("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,A),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){p(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),C5(o,"attribute",this.attribute);for(let s in 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Kr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Jr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Qr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],_r=[[474,475],[475,476],[476,477],[477,474]],$r=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],eA=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],tA=[[469,470],[470,471],[471,472],[472,469]],oA=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ve(e){let t=e.map(o=>o[0]);return t.push(e[e.length-1][1]),t}var nA={lips:ve(Kr),leftEye:ve(Jr),leftEyebrow:ve(Qr),leftIris:ve(_r),rightEye:ve($r),rightEyebrow:ve(eA),rightIris:ve(tA),faceOval:ve(oA)},rA=Object.entries(nA).map(([e,t])=>t.map(o=>[o,e])).flat(),f4=new Map(rA),w2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],We=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],Fe=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function W3(e,t){let o={lips:await t.filter(A=>A.size===160)[0].data(),irisL:await t.filter(A=>A.size===10)[0].data(),eyeL:await t.filter(A=>A.size===142)[0].data(),irisR:await t.filter(A=>A.size===10)[1].data(),eyeR:await t.filter(A=>A.size===142)[1].data()},n=We.reduce((A,s)=>A+=e[s][2],0)/We.length;for(let A=0;A<o.irisL.length/2;A++)e.push([o.irisL[2*A+0],o.irisL[2*A+1],n]);let r=Fe.reduce((A,s)=>A+=e[s][2],0)/Fe.length;for(let A=0;A<o.irisR.length/2;A++)e.push([o.irisR[2*A+0],o.irisR[2*A+1],r]);for(let A=0;A<o.eyeL.length/2;A++)e[We[A]]=[o.eyeL[2*A+0],o.eyeL[2*A+1],e[We[A]][2]];for(let A=0;A<o.eyeR.length/2;A++)e[Fe[A]]=[o.eyeR[2*A+0],o.eyeR[2*A+1],e[Fe[A]][2]];for(let A=0;A<o.lips.length/2;A++)e[w2[A]]=[o.lips[2*A+0],o.lips[2*A+1],e[w2[A]][2]];return e}var oe={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},g0=null,Ge=0;async function F3(e,t){var a,i,x,c,l,y,f,u,m,P,g;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>h()-oe.timestamp,n=oe.skipped<(((i=t.face.detector)==null?void 0:i.skipFrames)||0);!t.skipAllowed||!o||!n||oe.boxes.length===0?(oe.boxes=await n3(e,t),oe.timestamp=h(),oe.skipped=0):oe.skipped++;let r=[],A=[],s=0;for(let b=0;b<oe.boxes.length;b++){let d=oe.boxes[b],M=0,S,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([M,S,w.tensor]=_t((x=t.face.detector)==null?void 0:x.rotation,d,e,(c=t.face.mesh)!=null&&c.enabled?Ge:Qe()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let j=await O2(w.tensor);Be.dispose(w.tensor),w.tensor=j}if(w.boxScore=Math.round(100*d.confidence)/100,(y=t.face.mesh)!=null&&y.enabled)if(!g0)t.debug&&p("face mesh detection requested, but model is not loaded");else{let j=g0.execute(w.tensor),F=await j.find(H=>H.shape[H.shape.length-1]===1).data();if(w.faceScore=Math.round(100*F[0])/100,w.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(d.confidence=w.faceScore,(u=t.face.mesh)!=null&&u.keepInvalid){w.box=X2(d,e),w.boxRaw=q2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(H=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*H[0]/Qe(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*H[1]/Qe()]),w.meshRaw=w.mesh.map(H=>[H[0]/(e.shape[2]||1),H[1]/(e.shape[1]||1),(H[2]||0)/Ge]);for(let H of Object.keys(Ie))w.annotations[H]=[w.mesh[Ie[H]]]}}else{let H=j.find(E=>E.shape[E.shape.length-1]===1404),Y=Be.reshape(H,[-1,3]),J=await Y.array();Be.dispose(Y),(m=t.face.attention)!=null&&m.enabled?J=await W3(J,j):(P=t.face.iris)!=null&&P.enabled&&(J=await N3(J,w.tensor,t,Ge)),w.mesh=Qt(J,d,M,S,Ge),w.meshRaw=w.mesh.map(E=>[E[0]/(e.shape[2]||0),E[1]/(e.shape[1]||0),(E[2]||0)/Ge]);for(let E of Object.keys(X0))w.annotations[E]=X0[E].map(G0=>w.mesh[G0]);w.score=w.faceScore;let x0={...$t(w.mesh,d),confidence:d.confidence,landmarks:d.landmarks};w.box=X2(x0,e),w.boxRaw=q2(x0,e),A.push(x0)}Be.dispose(j)}else{w.box=X2(d,e),w.boxRaw=q2(d,e),w.score=w.boxScore,w.mesh=d.landmarks.map(j=>[(d.startPoint[0]+d.endPoint[0])/2+(d.endPoint[0]+d.startPoint[0])*j[0]/Qe(),(d.startPoint[1]+d.endPoint[1])/2+(d.endPoint[1]+d.startPoint[1])*j[1]/Qe()]),w.meshRaw=w.mesh.map(j=>[j[0]/(e.shape[2]||0),j[1]/(e.shape[1]||0),(j[2]||0)/Ge]);for(let j of Object.keys(Ie))w.annotations[j]=[w.mesh[Ie[j]]]}w.score>(((g=t.face.detector)==null?void 0:g.minConfidence)||1)?r.push(w):Be.dispose(w.tensor)}return oe.boxes=A,r}async function G3(e){var t,o,n,r,A,s;return v.initial&&(g0=null),((o=(t=e==null?void 0:e.face)==null?void 0:t.attention)==null?void 0:o.enabled)&&(g0==null?void 0:g0.signature)&&Object.keys(((n=g0==null?void 0:g0.signature)==null?void 0:n.outputs)||{}).length<6&&(g0=null),g0?e.debug&&p("cached model:",g0.modelUrl):(r=e.face.attention)!=null&&r.enabled?g0=await 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Object.keys(o).forEach(r=>W.dispose(o[r])),n}normalizeLandmarks(t,o){let n={};n.reshape=W.reshape(t,[-1,7,2]),n.div=W.div(n.reshape,this.inputSizeTensor),n.landmarks=W.add(n.div,this.anchors[o]);let r=W.mul(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(A=>W.dispose(n[A])),r}async predict(t,o){let n={};n.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=W.div(n.resize,V.tf127),n.image=W.sub(n.div,V.tf1),n.batched=this.model.execute(n.image),n.predictions=W.squeeze(n.batched),n.slice=W.slice(n.predictions,[0,0],[-1,1]),n.sigmoid=W.sigmoid(n.slice),n.scores=W.squeeze(n.sigmoid);let r=await n.scores.data();n.boxes=W.slice(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await W.image.nonMaxSuppressionAsync(n.norm,n.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let A=await n.nms.array(),s=[];for(let a of A){let i={};i.box=W.slice(n.norm,[a,0],[1,-1]),i.slice=W.slice(n.predictions,[a,5],[1,14]),i.norm=this.normalizeLandmarks(i.slice,a),i.palmLandmarks=W.reshape(i.norm,[-1,2]);let x=await i.box.data(),c=x.slice(0,2),l=x.slice(2,4),y=await i.palmLandmarks.array(),f={startPoint:c,endPoint:l,palmLandmarks:y,confidence:r[a]},u=K3(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(i).forEach(m=>W.dispose(i[m]))}return Object.keys(n).forEach(a=>W.dispose(n[a])),s}};var L0=Z(D());var yA=5,eo=1.65,to=[0,5,9,13,17,1,2],xA=0,cA=2,oo=0,i5=class{constructor(t,o){T(this,"handDetector");T(this,"handPoseModel");T(this,"inputSize");T(this,"storedBoxes");T(this,"skipped");T(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(s=>s[0]),n=t.map(s=>s[1]),r=[Math.min(...o),Math.min(...n)],A=[Math.max(...o),Math.max(...n)];return{startPoint:r,endPoint:A}}getBoxForPalmLandmarks(t,o){let n=t.map(A=>M1([...A,1],o)),r=this.calculateLandmarksBoundingBox(n);return A5(s5(r),yA)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),n=A5(s5(o),eo);n.palmLandmarks=[];for(let r=0;r<to.length;r++)n.palmLandmarks.push(t[to[r]].slice(0,2));return n}transformRawCoords(t,o,n,r){let A=r5(o),s=[A[0]/this.inputSize,A[1]/this.inputSize,(A[0]+A[1])/this.inputSize/2],a=t.map(f=>[s[0]*(f[0]-this.inputSize/2),s[1]*(f[1]-this.inputSize/2),s[2]*f[2]]),i=g1(n,[0,0]),x=a.map(f=>[...M1(f,i),f[2]]),c=Q3(r),l=[...k2(o),1],y=[Te(l,c[0]),Te(l,c[1])];return x.map(f=>[Math.trunc(f[0]+y[0]),Math.trunc(f[1]+y[1]),Math.trunc(f[2])])}async estimateHands(t,o){let n=!1,r,A=(o.hand.skipTime||0)>h()-oo,s=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&A&&s&&(r=await 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a=o[r].box?[Math.trunc(Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.max(0,o[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[r].box.bottomRight[0])-Math.max(0,o[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[r].box.bottomRight[1])-Math.max(0,o[r].box.topLeft[1]))]:[0,0,0,0],i=[o[r].box.topLeft[0]/(e.shape[2]||0),o[r].box.topLeft[1]/(e.shape[1]||0),(o[r].box.bottomRight[0]-o[r].box.topLeft[0])/(e.shape[2]||0),(o[r].box.bottomRight[1]-o[r].box.topLeft[1])/(e.shape[1]||0)];let x=l5(s);n.push({id:r,score:Math.round(100*o[r].confidence)/100,boxScore:Math.round(100*o[r].boxConfidence)/100,fingerScore:Math.round(100*o[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:i,keypoints:s,annotations:A,landmarks:x})}return n}async function T1(e){var o,n;v.initial&&(A2=null,s2=null),!A2||!s2?[A2,s2]=await Promise.all([e.hand.enabled?G((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?G((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&p("cached 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a=Q.unstack(n.scores,1);Q.dispose(a[mo]),a.splice(mo,1),n.filtered=Q.stack(a,1),Q.dispose(a),n.max=Q.max(n.filtered,1),n.argmax=Q.argMax(n.filtered,1);let i=0;n.nms=await Q.image.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let x=await n.nms.data(),c=await n.max.data(),l=await n.argmax.data();for(let y of Array.from(x)){let f=Q.slice(n.boxes,y,1),u=await f.data();Q.dispose(f);let m=[u[1],u[0],u[3]-u[1],u[2]-u[0]],P=Q2(m,vA),g=[Math.trunc(m[0]*fe[0]),Math.trunc(m[1]*fe[1]),Math.trunc(m[2]*fe[0]),Math.trunc(m[3]*fe[1])],b=c[y],d=PA[l[y]],M={id:i++,score:b,box:g,boxRaw:P,label:d};o.push(M)}return Object.keys(n).forEach(y=>Q.dispose(n[y])),o.sort((y,f)=>f.score-y.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function k1(e,t,o){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&d0[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Q.image.cropAndResize(e,[A],[0],[Ce[1][0],Ce[1][1]],"bilinear"),r.div=Q.div(r.crop,V.tf255),[r.score,r.keypoints]=d0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(o.hand.minConfidence||0)){n.fingerScore=a,r.reshaped=Q.reshape(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(l=>[l[0]/Ce[1][1],l[1]/Ce[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);n.keypoints=c.map(l=>[fe[0]*(l[0]+t.boxRaw[0]),fe[1]*(l[1]+t.boxRaw[1]),l[2]||0]),n.landmarks=l5(n.keypoints);for(let l of 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S2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],SA=S2.length,z2=S2.reduce((e,t,o)=>(e[t]=o,e),{}),CA=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],ts=CA.map(([e,t])=>[z2[e],z2[t]]),Oo=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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r,A,s;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(o[0],o[1]),r=Math.atan2(o[1]-t[1],o[0]-t[0]),A=n*Math.cos(r)+o[0],s=n*Math.sin(r)+o[1],e.moveTo(A,s),r+=1/3*(2*Math.PI),A=n*Math.cos(r)+o[0],s=n*Math.sin(r)+o[1],e.lineTo(A,s),r+=1/3*(2*Math.PI),A=n*Math.cos(r)+o[0],s=n*Math.sin(r)+o[1],e.lineTo(A,s),e.closePath(),e.stroke(),e.fill()}var P0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawAttention:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1};var U;function DA(e,t){if(U.drawLabels){let o=[];if(o.push(`face: ${Math.trunc(100*e.score)}%`),e.genderScore&&o.push(`${e.gender||""} ${Math.trunc(100*e.genderScore)}%`),e.age&&o.push(`age: ${e.age||""}`),e.iris&&o.push(`distance: ${e.iris}`),e.real&&o.push(`real: ${Math.trunc(100*e.real)}%`),e.live&&o.push(`live: ${Math.trunc(100*e.live)}%`),e.emotion&&e.emotion.length>0){let n=e.emotion.map(r=>`${Math.trunc(100*r.score)}% ${r.emotion}`);n.length>3&&(n.length=3),o.push(n.join(" "))}e.rotation&&e.rotation.angle&&e.rotation.gaze&&(e.rotation.angle.roll&&o.push(`roll: ${Xe(e.rotation.angle.roll)}\xB0 yaw:${Xe(e.rotation.angle.yaw)}\xB0 pitch:${Xe(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&o.push(`gaze: ${Xe(e.rotation.gaze.bearing)}\xB0`)),o.length===0&&o.push("face"),t.fillStyle=U.color;for(let n=o.length-1;n>=0;n--){let r=Math.max(e.box[0],0),A=n*U.lineHeight+e.box[1];U.shadowColor&&U.shadowColor!==""&&(t.fillStyle=U.shadowColor,t.fillText(o[n],r+5,A+16)),t.fillStyle=U.labelColor,t.fillText(o[n],r+4,A+15)}}}function ZA(e,t){if(e.annotations&&e.annotations.leftEyeIris&&e.annotations.leftEyeIris[0]){t.strokeStyle=U.useDepth?"rgba(255, 200, 255, 0.3)":U.color,t.beginPath();let o=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,n=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),U.fillPolygons&&(t.fillStyle=U.useDepth?"rgba(255, 255, 200, 0.3)":U.color,t.fill())}if(e.annotations&&e.annotations.rightEyeIris&&e.annotations.rightEyeIris[0]){t.strokeStyle=U.useDepth?"rgba(255, 200, 255, 0.3)":U.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,n=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,n,0,0,2*Math.PI),t.stroke(),U.fillPolygons&&(t.fillStyle=U.useDepth?"rgba(255, 255, 200, 0.3)":U.color,t.fill())}}function XA(e,t){var o;if(U.drawGaze&&((o=e.rotation)==null?void 0:o.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let n=e.box[0]+e.box[2]/2-e.box[3]*Xe(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Xe(e.rotation.angle.pitch)/90,A=new Path2D(`
+      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+      C
+        ${n} ${e.box[1]},
+        ${n} ${e.box[1]+e.box[3]},
+        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
+    `),s=new Path2D(`
+      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+      C 
+        ${e.box[0]} ${r},
+        ${e.box[0]+e.box[2]} ${r},
+        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
+    `);t.stroke(s),t.stroke(A)}}function qA(e,t){var o,n,r,A;if(U.drawGaze&&((n=(o=e.rotation)==null?void 0:o.gaze)==null?void 0:n.strength)&&((A=(r=e.rotation)==null?void 0:r.gaze)==null?void 0:A.bearing)&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let s=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];J1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[s[0],s[1]],4);let a=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];J1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[a[0],a[1]],4)}}function UA(e,t){if(U.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let o=0;o<Ne.length/3;o++){let n=[Ne[o*3+0],Ne[o*3+1],Ne[o*3+2]].map(r=>e.mesh[r]);K1(t,n,U)}ZA(e,t)}}function YA(e,t){if(U.drawPoints&&e.mesh.length>=468)for(let o=0;o<e.mesh.length;o++)pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2],U),U.drawAttention&&(w2.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]+127,U),We.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,U),Fe.includes(o)&&pe(t,e.mesh[o][0],e.mesh[o][1],e.mesh[o][2]-127,U))}function KA(e,t){U.drawBoxes&&re(t,e.box[0],e.box[1],e.box[2],e.box[3],U)}async function l2(e,t,o){if(U=r0(P0,o),!t||!e)return;let n=V0(e);if(!!n){n.font=U.font,n.strokeStyle=U.color,n.fillStyle=U.color;for(let r of t)KA(r,n),DA(r,n),r.mesh&&r.mesh.length>0&&(YA(r,n),UA(r,n),XA(r,n),qA(r,n))}}async function y2(e,t,o){var A;let n=r0(P0,o);if(!t||!e)return;let r=V0(e);if(!!r){r.lineJoin="round";for(let s=0;s<t.length;s++){if(r.strokeStyle=n.color,r.fillStyle=n.color,r.lineWidth=n.lineWidth,r.font=n.font,n.drawBoxes&&t[s].box&&((A=t[s].box)==null?void 0:A.length)===4&&(re(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(r.fillStyle=n.shadowColor,r.fillText(`body ${100*t[s].score}%`,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),r.fillStyle=n.labelColor,r.fillText(`body ${100*t[s].score}%`,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2]))),n.drawPoints&&t[s].keypoints)for(let a=0;a<t[s].keypoints.length;a++)!t[s].keypoints[a].score||t[s].keypoints[a].score===0||(r.fillStyle=me(t[s].keypoints[a].position[2],n),pe(r,t[s].keypoints[a].position[0],t[s].keypoints[a].position[1],0,n));if(n.drawLabels&&t[s].keypoints){r.font=n.font;for(let a of t[s].keypoints)!a.score||a.score===0||(r.fillStyle=me(a.position[2],n),r.fillText(`${a.part} 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x=a[a.length-1][2]||-256;r.fillStyle=me(x,n),r.fillText(i,a[a.length-1][0]+4,a[a.length-1][1]+4)};r.font=n.font,s(A.annotations.index,"index"),s(A.annotations.middle,"middle"),s(A.annotations.ring,"ring"),s(A.annotations.pinky,"pinky"),s(A.annotations.thumb,"thumb"),s(A.annotations.palm,"palm")}if(n.drawPolygons&&A.annotations){let s=a=>{if(!(!a||a.length===0||!a[0]))for(let i=0;i<a.length;i++){r.beginPath();let x=a[i][2]||0;r.strokeStyle=me(i*x,n),r.moveTo(a[i>0?i-1:0][0],a[i>0?i-1:0][1]),r.lineTo(a[i][0],a[i][1]),r.stroke()}};r.lineWidth=n.lineWidth,s(A.annotations.index),s(A.annotations.middle),s(A.annotations.ring),s(A.annotations.pinky),s(A.annotations.thumb)}}}}async function c2(e,t,o){let n=r0(P0,o);if(!t||!e)return;let r=V0(e);if(!!r){r.lineJoin="round",r.font=n.font;for(let A of t)if(n.drawBoxes){if(r.strokeStyle=n.color,r.fillStyle=n.color,re(r,A.box[0],A.box[1],A.box[2],A.box[3],n),n.drawLabels){let s=`${A.label} 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X0.silhouette)r.push({x:(e.mesh[s][0]-e.box[0])/e.box[2],y:(e.mesh[s][1]-e.box[1])/e.box[3]});f2&&f2>0&&(r=r.map(s=>({x:s.x>.5?s.x+f2:s.x-f2,y:s.y>.5?s.y+f2:s.y-f2})));for(let s=0;s<t;s++)for(let a=0;a<o;a++)JA(s/t,a/t,r)||(n.set(ot*n.get(0,a,s,0),0,a,s,0),n.set(ot*n.get(0,a,s,1),0,a,s,1),n.set(ot*n.get(0,a,s,2),0,a,s,2));let A=n.toTensor();return Yo.dispose(n),A}var _A=e=>{let t=(l,y)=>Math.atan2(l[1]-y[1],l[0]-y[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let o=[0,-.1],n=1,r=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=r?e.mesh[473]:e.mesh[468],s=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],a=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],i=[(s[0]-A[0])/a[0]-o[0],n*(A[1]-s[1])/a[1]-o[1]],x=Math.sqrt(i[0]*i[0]+i[1]*i[1]);return x=Math.min(x,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],i)+Math.PI/2)%Math.PI,strength:x}},Jo=(e,t)=>{let o=m=>{let P=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=P,m[1]/=P,m[2]/=P,m},n=(m,P)=>{let g=m[0]-P[0],b=m[1]-P[1],d=m[2]-P[2];return[g,b,d]},r=(m,P)=>{let g=m[1]*P[2]-m[2]*P[1],b=m[2]*P[0]-m[0]*P[2],d=m[0]*P[1]-m[1]*P[0];return[g,b,d]},A=m=>{let[P,g,b,d,M,S,w,j,C]=m,F,H,Y;return d<1?d>-1?(Y=Math.asin(d),H=Math.atan2(-w,P),F=Math.atan2(-S,M)):(Y=-Math.PI/2,H=-Math.atan2(j,C),F=0):(Y=Math.PI/2,H=Math.atan2(j,C),F=0),isNaN(F)&&(F=0),isNaN(H)&&(H=0),isNaN(Y)&&(Y=0),{pitch:2*-F,yaw:2*-H,roll:2*-Y}},s=e.meshRaw;if(!s||s.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let a=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,i=[s[10],s[152],s[234],s[454]].map(m=>[m[0]*t[0]/a,m[1]*t[1]/a,m[2]]),x=o(n(i[1],i[0])),c=o(n(i[3],i[2])),l=o(r(c,x));c=r(x,l);let y=[c[0],c[1],c[2],x[0],x[1],x[2],l[0],l[1],l[2]],f=A(y),u=s.length===478?_A(e):{bearing:0,strength:0};return{angle:f,matrix:y,gaze:u}};var nt=async(e,t)=>{var f,u,m,P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0,R,c0;let o=h(),n,r,A,s,a,i,x,c,l=[];e.state="run:face";let y=await F3(t,e.config);if(e.performance.face=v.perfadd?(e.performance.face||0)+Math.trunc(h()-o):Math.trunc(h()-o),!t.shape||t.shape.length!==4)return[];if(!y)return[];for(let k=0;k<y.length;k++){if(e.analyze("Get Face"),!y[k].tensor||y[k].tensor.isDisposedInternal){p("Face object is disposed:",y[k].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let K=await Ko(y[k]);s0.dispose(y[k].tensor),y[k].tensor=K}let $=y[k].mesh&&y[k].mesh.length>200?Jo(y[k],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?s=(u=e.config.face.emotion)!=null&&u.enabled?y1(y[k].tensor||s0.tensor([]),e.config,k,y.length):[]:(e.state="run:emotion",o=h(),s=(m=e.config.face.emotion)!=null&&m.enabled?await y1(y[k].tensor||s0.tensor([]),e.config,k,y.length):[],e.performance.emotion=v.perfadd?(e.performance.emotion||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?i=(P=e.config.face.antispoof)!=null&&P.enabled?X5(y[k].tensor||s0.tensor([]),e.config,k,y.length):0:(e.state="run:antispoof",o=h(),i=(g=e.config.face.antispoof)!=null&&g.enabled?await X5(y[k].tensor||s0.tensor([]),e.config,k,y.length):0,e.performance.antispoof=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?x=(b=e.config.face.liveness)!=null&&b.enabled?S1(y[k].tensor||s0.tensor([]),e.config,k,y.length):0:(e.state="run:liveness",o=h(),x=(d=e.config.face.liveness)!=null&&d.enabled?await S1(y[k].tensor||s0.tensor([]),e.config,k,y.length):0,e.performance.liveness=v.perfadd?(e.performance.antispoof||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(M=e.config.face.gear)!=null&&M.enabled?F5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:gear",o=h(),r=(S=e.config.face.gear)!=null&&S.enabled?await F5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.gear=Math.trunc(h()-o)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=(w=e.config.face.ssrnet)!=null&&w.enabled?B5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,A=(j=e.config.face.ssrnet)!=null&&j.enabled?D5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null):(e.state="run:ssrnet",o=h(),n=(C=e.config.face.ssrnet)!=null&&C.enabled?await B5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,A=(F=e.config.face.ssrnet)!=null&&F.enabled?await D5(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.ssrnet=Math.trunc(h()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?a=(H=e.config.face.mobilefacenet)!=null&&H.enabled?c1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:mobilefacenet",o=h(),a=(Y=e.config.face.mobilefacenet)!=null&&Y.enabled?await c1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.mobilefacenet=Math.trunc(h()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?c=(J=e.config.face.description)!=null&&J.enabled?h1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null:(e.state="run:description",o=h(),c=(x0=e.config.face.description)!=null&&x0.enabled?await h1(y[k].tensor||s0.tensor([]),e.config,k,y.length):null,e.performance.description=v.perfadd?(e.performance.description||0)+Math.trunc(h()-o):Math.trunc(h()-o)),e.analyze("End Description:"),e.config.async&&([n,A,s,a,c,r,i,x]=await Promise.all([n,A,s,a,c,r,i,x])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&n&&A&&(c={...c,age:n.age,gender:A.gender,genderScore:A.genderScore}),((G0=e.config.face.gear)==null?void 0:G0.enabled)&&r&&(c={...c,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((B0=e.config.face.mobilefacenet)==null?void 0:B0.enabled)&&a&&(c.descriptor=a),(R=e.config.face.iris)!=null&&R.enabled;let a0=y[k].annotations&&y[k].annotations.leftEyeIris&&y[k].annotations.leftEyeIris[0]&&y[k].annotations.rightEyeIris&&y[k].annotations.rightEyeIris[0]&&y[k].annotations.leftEyeIris.length>0&&y[k].annotations.rightEyeIris.length>0&&y[k].annotations.leftEyeIris[0]!==null&&y[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(y[k].annotations.leftEyeIris[3][0]-y[k].annotations.leftEyeIris[1][0]),Math.abs(y[k].annotations.rightEyeIris[4][1]-y[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,f0=(c0=e.config.face.detector)!=null&&c0.return?s0.squeeze(y[k].tensor):null;s0.dispose(y[k].tensor),y[k].tensor&&delete y[k].tensor;let L={...y[k],id:k};c!=null&&c.age&&(L.age=c.age),c!=null&&c.gender&&(L.gender=c.gender),c!=null&&c.genderScore&&(L.genderScore=c==null?void 0:c.genderScore),c!=null&&c.descriptor&&(L.embedding=c==null?void 0:c.descriptor),c!=null&&c.race&&(L.race=c==null?void 0:c.race),s&&(L.emotion=s),i&&(L.real=i),x&&(L.live=x),a0&&a0!==0&&(L.iris=Math.trunc(500/a0/11.7)/100),$&&(L.rotation=$),f0&&(L.tensor=f0),l.push(L),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Qo=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=e[o].keypoints.find(i=>i.part==="leftWrist"),r=e[o].keypoints.find(i=>i.part==="rightWrist"),A=e[o].keypoints.find(i=>i.part==="nose");A&&n&&r&&n.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:o,gesture:"i give up"}):A&&n&&n.position[1]<A.position[1]?t.push({body:o,gesture:"raise left hand"}):A&&r&&r.position[1]<A.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(i=>i.part==="leftShoulder"),a=e[o].keypoints.find(i=>i.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},_o=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let n=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),r=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(n/r)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let i=e[o].mesh[152][2]||0;Math.abs(i)>10&&t.push({face:o,gesture:`head ${i<0?"up":"down"}`})}return t},$o=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let n=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],r=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],A=Math.abs(n*r),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],i=Math.abs(s*a),x=!1;Math.abs(A-i)/Math.max(A,i)<.25&&(x=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],y=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||y>.06)&&(x=!1),l>y?l>.05&&t.push({iris:o,gesture:"looking right"}):y>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||f<.01||u>.022||f>.022)&&(x=!1),(u<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),x&&t.push({iris:o,gesture:"looking center"})}return t},en=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let n=[];if(e[o].annotations)for(let[r,A]of Object.entries(e[o].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&n.push({name:r.toLowerCase(),position:A[0]});if(n&&n.length>0){let r=n.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${r.name} forward`});let A=n.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${A.name} up`})}if(e[o].keypoints){let r=yo(e[o].keypoints);for(let A of r)t.push({hand:o,gesture:A.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},rt=0;function tn(e,t){var s,a,i,x,c,l,y,f,u,m,P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0;let o=h();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,r=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let c0=e.body[R].box.map((L,K)=>((r-1)*z.body[R].box[K]+L)/r),k=e.body[R].boxRaw.map((L,K)=>((r-1)*z.body[R].boxRaw[K]+L)/r),$=e.body[R].keypoints.map((L,K)=>{var Ae,se,p2,u2,qe,lt,yt,xt,ct;return{score:L.score,part:L.part,position:[z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].position[0]||0)+(L.position[0]||0))/r:L.position[0],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].position[1]||0)+(L.position[1]||0))/r:L.position[1],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].position[2]||0)+(L.position[2]||0))/r:L.position[2]],positionRaw:[z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].positionRaw[0]||0)+(L.positionRaw[0]||0))/r:L.positionRaw[0],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].positionRaw[1]||0)+(L.positionRaw[1]||0))/r:L.positionRaw[1],z.body[R].keypoints[K]?((r-1)*(z.body[R].keypoints[K].positionRaw[2]||0)+(L.positionRaw[2]||0))/r:L.positionRaw[2]],distance:[z.body[R].keypoints[K]?((r-1)*(((Ae=z.body[R].keypoints[K].distance)==null?void 0:Ae[0])||0)+(((se=L.distance)==null?void 0:se[0])||0))/r:(p2=L.distance)==null?void 0:p2[0],z.body[R].keypoints[K]?((r-1)*(((u2=z.body[R].keypoints[K].distance)==null?void 0:u2[1])||0)+(((qe=L.distance)==null?void 0:qe[1])||0))/r:(lt=L.distance)==null?void 0:lt[1],z.body[R].keypoints[K]?((r-1)*(((yt=z.body[R].keypoints[K].distance)==null?void 0:yt[2])||0)+(((xt=L.distance)==null?void 0:xt[2])||0))/r:(ct=L.distance)==null?void 0:ct[2]]}}),a0={},f0={connected:{}};(a=(s=t.body)==null?void 0:s.modelPath)!=null&&a.includes("efficientpose")?f0=e5:(x=(i=t.body)==null?void 0:i.modelPath)!=null&&x.includes("blazepose")?f0=K2:(l=(c=t.body)==null?void 0:c.modelPath)!=null&&l.includes("movenet")&&(f0=E2);for(let[L,K]of Object.entries(f0.connected)){let Ae=[];for(let se=0;se<K.length-1;se++){let p2=$.find(qe=>qe.part===K[se]),u2=$.find(qe=>qe.part===K[se+1]);p2&&u2&&Ae.push([p2.position,u2.position])}a0[L]=Ae}z.body[R]={...e.body[R],box:c0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R<e.hand.length;R++){let c0=e.hand[R].box.map((f0,L)=>((r-1)*z.hand[R].box[L]+f0)/r),k=e.hand[R].boxRaw.map((f0,L)=>((r-1)*z.hand[R].boxRaw[L]+f0)/r);z.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(z.hand[R].keypoints=e.hand[R].keypoints);let $=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((f0,L)=>f0.map((K,Ae)=>((r-1)*(z.hand[R].keypoints[L][Ae]||1)+(K||0))/r)):[],a0={};if(Object.keys(z.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)z.hand[R].annotations=e.hand[R].annotations,a0=z.hand[R].annotations;else if(e.hand[R].annotations)for(let f0 of Object.keys(e.hand[R].annotations))a0[f0]=e.hand[R].annotations[f0]&&e.hand[R].annotations[f0][0]?e.hand[R].annotations[f0].map((L,K)=>L.map((Ae,se)=>((r-1)*z.hand[R].annotations[f0][K][se]+Ae)/r)):null;z.hand[R]={...e.hand[R],box:c0,boxRaw:k,keypoints:$,annotations:a0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let c0=e.face[R].box.map(($,a0)=>((r-1)*z.face[R].box[a0]+$)/r),k=e.face[R].boxRaw.map(($,a0)=>((r-1)*z.face[R].boxRaw[a0]+$)/r);if(e.face[R].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(y=e.face[R].rotation)==null?void 0:y.matrix,$.angle={roll:((r-1)*(((u=(f=z.face[R].rotation)==null?void 0:f.angle)==null?void 0:u.roll)||0)+(((P=(m=e.face[R].rotation)==null?void 0:m.angle)==null?void 0:P.roll)||0))/r,yaw:((r-1)*(((b=(g=z.face[R].rotation)==null?void 0:g.angle)==null?void 0:b.yaw)||0)+(((M=(d=e.face[R].rotation)==null?void 0:d.angle)==null?void 0:M.yaw)||0))/r,pitch:((r-1)*(((w=(S=z.face[R].rotation)==null?void 0:S.angle)==null?void 0:w.pitch)||0)+(((C=(j=e.face[R].rotation)==null?void 0:j.angle)==null?void 0:C.pitch)||0))/r},$.gaze={bearing:((r-1)*(((H=(F=z.face[R].rotation)==null?void 0:F.gaze)==null?void 0:H.bearing)||0)+(((J=(Y=e.face[R].rotation)==null?void 0:Y.gaze)==null?void 0:J.bearing)||0))/r,strength:((r-1)*(((E=(x0=z.face[R].rotation)==null?void 0:x0.gaze)==null?void 0:E.strength)||0)+(((B0=(G0=e.face[R].rotation)==null?void 0:G0.gaze)==null?void 0:B0.strength)||0))/r},z.face[R]={...e.face[R],rotation:$,box:c0,boxRaw:k}}z.face[R]={...e.face[R],box:c0,boxRaw:k}}if(!z.object||e.object.length!==z.object.length)z.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let c0=e.object[R].box.map(($,a0)=>((r-1)*z.object[R].box[a0]+$)/r),k=e.object[R].boxRaw.map(($,a0)=>((r-1)*z.object[R].boxRaw[a0]+$)/r);z.object[R]={...e.object[R],box:c0,boxRaw:k}}if(e.persons){let R=e.persons;if(!z.persons||R.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(R));else for(let c0=0;c0<R.length;c0++)z.persons[c0].box=R[c0].box.map((k,$)=>((r-1)*z.persons[c0].box[$]+k)/r)}e.gesture&&(z.gesture=e.gesture);let A=h();return rt=v.perfadd?rt+Math.round(A-o):Math.round(A-o),e.performance&&(z.performance={...e.performance,interpolate:rt}),z}var at={};ue(at,{distance:()=>j2,match:()=>st,similarity:()=>At});function j2(e,t,o={order:2,multiplier:25}){let n=0;for(let r=0;r<e.length;r++){let A=!o.order||o.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);n+=!o.order||o.order===2?A*A:A**o.order}return(o.multiplier||20)*n}var on=(e,t,o,n)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-o)/(n-o);return Math.max(Math.min(A,1),0)};function At(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let n=j2(e,t,o);return on(n,o.order||2,o.min||0,o.max||1)}function st(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=j2(e,t[s],o);if(a<n&&(n=a,r=s),n<(o.threshold||0))break}let A=on(n,o.order||2,o.min||0,o.max||1);return{index:r,distance:n,similarity:A}}function nn(e,t,o,n,r){var a,i,x,c,l,y,f,u,m,P,g,b,d,M,S,w;let A=0,s=[];for(let j of e){let C={id:A++,face:j,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let E of t)j.box[0]>E.box[0]&&j.box[0]<E.box[0]+E.box[2]&&j.box[1]+j.box[3]>E.box[1]&&j.box[1]+j.box[3]<E.box[1]+E.box[3]&&(C.body=E);if(C.body)for(let E of o)E.box[0]+E.box[2]>C.body.box[0]&&E.box[0]+E.box[2]<C.body.box[0]+C.body.box[2]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=E),E.box[0]<C.body.box[0]+C.body.box[2]&&E.box[0]>C.body.box[0]&&E.box[1]+E.box[3]>C.body.box[1]&&E.box[1]+E.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=E);for(let E of n)E.face!==void 0&&E.face===j.id?(a=C.gestures)==null||a.push(E):E.iris!==void 0&&E.iris===j.id?(i=C.gestures)==null||i.push(E):E.body!==void 0&&E.body===((x=C.body)==null?void 0:x.id)?(c=C.gestures)==null||c.push(E):E.hand!==void 0&&E.hand===((y=(l=C.hands)==null?void 0:l.left)==null?void 0:y.id)?(f=C.gestures)==null||f.push(E):E.hand!==void 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o.checkCompileCompletionAsync();o.getUniformLocations(),p("compile pass models:",r),p("compile pass kernels:",A.length),m0.env().set("ENGINE_COMPILE_ONLY",!1);let s=m0.engine().state.numTensors;s-n>0&&p("tensor leak:",s-n)}async function rn(e,t){let o=h();return e.state="warmup",t&&(e.config=r0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:h(),persons:[],error:null}:new Promise(async n=>{await i7(e.models);let r=await a7(e),A=h();e.config.debug&&p("warmup",e.config.warmup,Math.round(A-o),"ms"),e.emit("warmup"),n(r)})}var m2,I2,N2,k5,it=class{constructor(t){T(this,"version");T(this,"config");T(this,"result");T(this,"state");T(this,"process");T(this,"tf");T(this,"env");T(this,"draw");T(this,"models");T(this,"events");T(this,"faceTriangulation");T(this,"faceUVMap");T(this,"performance");g2(this,m2,void 0);g2(this,I2,void 0);g2(this,N2,void 0);T(this,"gl");T(this,"analyze",(...t)=>{if(!b2(this,I2))return;let o=this.tf.engine().state.numTensors,n=b2(this,m2);M2(this,m2,o);let r=o-n;r!==0&&p(...t,r)});g2(this,k5,t=>{if(!b2(this,N2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ee.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});T(this,"similarity",At);T(this,"distance",j2);T(this,"match",st);T(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});var n;this.env=v;let o=(((n=ee.version)==null?void 0:n.tfjs)||ee.version_core).replace(/-(.*)/,"");he.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${o}/dist/`,he.modelBasePath=v.browser?"../models/":"file://models/",he.backend=v.browser?"humangl":"tensorflow",this.version=O5,Object.defineProperty(this,"version",{value:O5}),this.config=JSON.parse(JSON.stringify(he)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=r0(this.config,t)),kt(this.config),this.tf=ee,this.state="idle",M2(this,m2,0),M2(this,I2,!1),M2(this,N2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new C2,this.draw={options:P0,canvas:(r,A)=>$1(r,A),face:(r,A,s)=>l2(r,A,s),body:(r,A,s)=>y2(r,A,s),hand:(r,A,s)=>x2(r,A,s),gesture:(r,A,s)=>d2(r,A,s),object:(r,A,s)=>c2(r,A,s),person:(r,A,s)=>_1(r,A,s),all:(r,A,s)=>et(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=B3,this.faceUVMap=H3,this.gl=t0,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(he)),this.config.backend=t}validate(t){return E5(he,t||this.config)}now(){return h()}image(t,o=!0){return Ye(t,this.config,o)}async segmentation(t,o){return Do(t,o,this.config)}enhance(t){return u1(t)}compare(t,o){return wt(this.config,t,o)}async init(){await v5(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=h(),n=Object.values(this.models).filter(s=>s).length;t&&(this.config=r0(this.config,t)),this.env.initial&&(this.config.debug&&p(`version: ${this.version}`),this.config.debug&&p(`tfjs version: ${this.tf.version["tfjs-core"]}`),await v5(this)||p("error: backend check failed"),await ee.ready(),this.env.browser&&(this.config.debug&&p("configuration:",this.config),this.config.debug&&p("environment:",this.env),this.config.debug&&p("tf flags:",this.tf.ENV.flags))),await U1(this),this.env.initial&&this.config.debug&&p("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==n&&(await Y1(this),this.emit("load"));let A=Math.trunc(h()-o);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return tn(t,this.config)}getModelStats(){return q1(this)}async warmup(t){let o=h(),n=await rn(this,t),r=h();return this.performance.warmup=Math.trunc(r-o),n}async profile(t,o){let n=await this.tf.profile(()=>this.detect(t,o)),r={};for(let a of n.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs;let A=[];Object.entries(r).forEach(a=>A.push({name:a[0],ms:a[1]})),A.sort((a,i)=>i.ms-a.ms),A.length=20;let s={};for(let a of A)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async n=>{var P,g,b,d,M,S,w,j,C,F,H,Y,J,x0,E,G0,B0,R,c0,k,$,a0;this.state="config";let r;this.config=r0(this.config,o),this.state="check";let A=b2(this,k5).call(this,t);A&&(p(A,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:A}));let s=h();await v5(this),await this.load(),r=h(),this.state="image";let a=await Ye(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&p("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:h(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=h(),this.config.skipAllowed=await Tt(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(h()-r):Math.trunc(h()-r),this.analyze("Check Changed:");let i=[],x=[],c=[],l=[];this.state="detect:face",this.config.async?(i=this.config.face.enabled?nt(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=h(),i=this.config.face.enabled?await nt(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(i=await i),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?r0(this.config,{body:{maxDetected:this.config.face.enabled?1*i.length:1}}):this.config;this.config.async?((P=this.config.body.modelPath)!=null&&P.includes("posenet")?x=this.config.body.enabled?D1(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("blazepose")?x=this.config.body.enabled?t1(a.tensor,y):[]:(b=this.config.body.modelPath)!=null&&b.includes("efficientpose")?x=this.config.body.enabled?i1(a.tensor,y):[]:(d=this.config.body.modelPath)!=null&&d.includes("movenet")&&(x=this.config.body.enabled?L1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=h(),(M=this.config.body.modelPath)!=null&&M.includes("posenet")?x=this.config.body.enabled?await D1(a.tensor,y):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?x=this.config.body.enabled?await t1(a.tensor,y):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?x=this.config.body.enabled?await i1(a.tensor,y):[]:(j=this.config.body.modelPath)!=null&&j.includes("movenet")&&(x=this.config.body.enabled?await L1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?r0(this.config,{hand:{maxDetected:this.config.face.enabled?2*i.length:1}}):this.config;this.config.async?((F=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&F.includes("handdetect")?c=this.config.hand.enabled?v1(a.tensor,f):[]:(Y=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&Y.includes("handtrack")&&(c=this.config.hand.enabled?E1(a.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(r=h(),(x0=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&x0.includes("handdetect")?c=this.config.hand.enabled?await v1(a.tensor,f):[]:(G0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&G0.includes("handtrack")&&(c=this.config.hand.enabled?await E1(a.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((B0=this.config.object.modelPath)!=null&&B0.includes("nanodet")?l=this.config.object.enabled?F1(a.tensor,this.config):[]:(R=this.config.object.modelPath)!=null&&R.includes("centernet")&&(l=this.config.object.enabled?r1(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=h(),(c0=this.config.object.modelPath)!=null&&c0.includes("nanodet")?l=this.config.object.enabled?await F1(a.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(l=this.config.object.enabled?await r1(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([i,x,c,l]=await Promise.all([i,x,c,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(r=h(),u=[..._o(i),...Qo(x),...en(c),...$o(i)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(h()-r):Math.trunc(h()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(h()-s):Math.trunc(h()-s);let m=((a0=($=this.process)==null?void 0:$.tensor)==null?void 0:a0.shape)||[];this.result={face:i,body:x,hand:c,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return nn(i,x,c,u,m)}},ee.dispose(a.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};m2=new WeakMap,I2=new WeakMap,N2=new WeakMap,k5=new WeakMap;0&&(module.exports={Human,defaults,draw,env,match,models});
+/**
+ * Human main module
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
new file mode 100644
index 00000000..0c09c636
--- /dev/null
+++ b/dist/tfjs.esm.js
@@ -0,0 +1,75645 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var __create = Object.create;
+var __defProp = Object.defineProperty;
+var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
+var __getOwnPropNames = Object.getOwnPropertyNames;
+var __getProtoOf = Object.getPrototypeOf;
+var __hasOwnProp = Object.prototype.hasOwnProperty;
+var __commonJS = (cb, mod4) => function __require() {
+  return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports;
+};
+var __export = (target, all5) => {
+  for (var name in all5)
+    __defProp(target, name, { get: all5[name], enumerable: true });
+};
+var __copyProps = (to, from, except, desc) => {
+  if (from && typeof from === "object" || typeof from === "function") {
+    for (let key of __getOwnPropNames(from))
+      if (!__hasOwnProp.call(to, key) && key !== except)
+        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
+  }
+  return to;
+};
+var __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps(isNodeMode || !mod4 || !mod4.__esModule ? __defProp(target, "default", { value: mod4, enumerable: true }) : target, mod4));
+
+// node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js
+var require_long = __commonJS({
+  "node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js"(exports, module) {
+    module.exports = Long2;
+    var wasm = null;
+    try {
+      wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([
+        0,
+        97,
+        115,
+        109,
+        1,
+        0,
+        0,
+        0,
+        1,
+        13,
+        2,
+        96,
+        0,
+        1,
+        127,
+        96,
+        4,
+        127,
+        127,
+        127,
+        127,
+        1,
+        127,
+        3,
+        7,
+        6,
+        0,
+        1,
+        1,
+        1,
+        1,
+        1,
+        6,
+        6,
+        1,
+        127,
+        1,
+        65,
+        0,
+        11,
+        7,
+        50,
+        6,
+        3,
+        109,
+        117,
+        108,
+        0,
+        1,
+        5,
+        100,
+        105,
+        118,
+        95,
+        115,
+        0,
+        2,
+        5,
+        100,
+        105,
+        118,
+        95,
+        117,
+        0,
+        3,
+        5,
+        114,
+        101,
+        109,
+        95,
+        115,
+        0,
+        4,
+        5,
+        114,
+        101,
+        109,
+        95,
+        117,
+        0,
+        5,
+        8,
+        103,
+        101,
+        116,
+        95,
+        104,
+        105,
+        103,
+        104,
+        0,
+        0,
+        10,
+        191,
+        1,
+        6,
+        4,
+        0,
+        35,
+        0,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        126,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        127,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        128,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        129,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11,
+        36,
+        1,
+        1,
+        126,
+        32,
+        0,
+        173,
+        32,
+        1,
+        173,
+        66,
+        32,
+        134,
+        132,
+        32,
+        2,
+        173,
+        32,
+        3,
+        173,
+        66,
+        32,
+        134,
+        132,
+        130,
+        34,
+        4,
+        66,
+        32,
+        135,
+        167,
+        36,
+        0,
+        32,
+        4,
+        167,
+        11
+      ])), {}).exports;
+    } catch (e2) {
+    }
+    function Long2(low, high, unsigned) {
+      this.low = low | 0;
+      this.high = high | 0;
+      this.unsigned = !!unsigned;
+    }
+    Long2.prototype.__isLong__;
+    Object.defineProperty(Long2.prototype, "__isLong__", { value: true });
+    function isLong(obj) {
+      return (obj && obj["__isLong__"]) === true;
+    }
+    Long2.isLong = isLong;
+    var INT_CACHE = {};
+    var UINT_CACHE = {};
+    function fromInt(value, unsigned) {
+      var obj, cachedObj, cache;
+      if (unsigned) {
+        value >>>= 0;
+        if (cache = 0 <= value && value < 256) {
+          cachedObj = UINT_CACHE[value];
+          if (cachedObj)
+            return cachedObj;
+        }
+        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);
+        if (cache)
+          UINT_CACHE[value] = obj;
+        return obj;
+      } else {
+        value |= 0;
+        if (cache = -128 <= value && value < 128) {
+          cachedObj = INT_CACHE[value];
+          if (cachedObj)
+            return cachedObj;
+        }
+        obj = fromBits(value, value < 0 ? -1 : 0, false);
+        if (cache)
+          INT_CACHE[value] = obj;
+        return obj;
+      }
+    }
+    Long2.fromInt = fromInt;
+    function fromNumber(value, unsigned) {
+      if (isNaN(value))
+        return unsigned ? UZERO : ZERO;
+      if (unsigned) {
+        if (value < 0)
+          return UZERO;
+        if (value >= TWO_PWR_64_DBL)
+          return MAX_UNSIGNED_VALUE;
+      } else {
+        if (value <= -TWO_PWR_63_DBL)
+          return MIN_VALUE;
+        if (value + 1 >= TWO_PWR_63_DBL)
+          return MAX_VALUE;
+      }
+      if (value < 0)
+        return fromNumber(-value, unsigned).neg();
+      return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);
+    }
+    Long2.fromNumber = fromNumber;
+    function fromBits(lowBits, highBits, unsigned) {
+      return new Long2(lowBits, highBits, unsigned);
+    }
+    Long2.fromBits = fromBits;
+    var pow_dbl = Math.pow;
+    function fromString(str, unsigned, radix) {
+      if (str.length === 0)
+        throw Error("empty string");
+      if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity")
+        return ZERO;
+      if (typeof unsigned === "number") {
+        radix = unsigned, unsigned = false;
+      } else {
+        unsigned = !!unsigned;
+      }
+      radix = radix || 10;
+      if (radix < 2 || 36 < radix)
+        throw RangeError("radix");
+      var p2;
+      if ((p2 = str.indexOf("-")) > 0)
+        throw Error("interior hyphen");
+      else if (p2 === 0) {
+        return fromString(str.substring(1), unsigned, radix).neg();
+      }
+      var radixToPower = fromNumber(pow_dbl(radix, 8));
+      var result = ZERO;
+      for (var i2 = 0; i2 < str.length; i2 += 8) {
+        var size = Math.min(8, str.length - i2), value = parseInt(str.substring(i2, i2 + size), radix);
+        if (size < 8) {
+          var power = fromNumber(pow_dbl(radix, size));
+          result = result.mul(power).add(fromNumber(value));
+        } else {
+          result = result.mul(radixToPower);
+          result = result.add(fromNumber(value));
+        }
+      }
+      result.unsigned = unsigned;
+      return result;
+    }
+    Long2.fromString = fromString;
+    function fromValue(val, unsigned) {
+      if (typeof val === "number")
+        return fromNumber(val, unsigned);
+      if (typeof val === "string")
+        return fromString(val, unsigned);
+      return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned);
+    }
+    Long2.fromValue = fromValue;
+    var TWO_PWR_16_DBL = 1 << 16;
+    var TWO_PWR_24_DBL = 1 << 24;
+    var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;
+    var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;
+    var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;
+    var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);
+    var ZERO = fromInt(0);
+    Long2.ZERO = ZERO;
+    var UZERO = fromInt(0, true);
+    Long2.UZERO = UZERO;
+    var ONE = fromInt(1);
+    Long2.ONE = ONE;
+    var UONE = fromInt(1, true);
+    Long2.UONE = UONE;
+    var NEG_ONE = fromInt(-1);
+    Long2.NEG_ONE = NEG_ONE;
+    var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);
+    Long2.MAX_VALUE = MAX_VALUE;
+    var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);
+    Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
+    var MIN_VALUE = fromBits(0, 2147483648 | 0, false);
+    Long2.MIN_VALUE = MIN_VALUE;
+    var LongPrototype = Long2.prototype;
+    LongPrototype.toInt = function toInt() {
+      return this.unsigned ? this.low >>> 0 : this.low;
+    };
+    LongPrototype.toNumber = function toNumber() {
+      if (this.unsigned)
+        return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);
+      return this.high * TWO_PWR_32_DBL + (this.low >>> 0);
+    };
+    LongPrototype.toString = function toString(radix) {
+      radix = radix || 10;
+      if (radix < 2 || 36 < radix)
+        throw RangeError("radix");
+      if (this.isZero())
+        return "0";
+      if (this.isNegative()) {
+        if (this.eq(MIN_VALUE)) {
+          var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);
+          return div3.toString(radix) + rem1.toInt().toString(radix);
+        } else
+          return "-" + this.neg().toString(radix);
+      }
+      var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;
+      var result = "";
+      while (true) {
+        var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);
+        rem = remDiv;
+        if (rem.isZero())
+          return digits + result;
+        else {
+          while (digits.length < 6)
+            digits = "0" + digits;
+          result = "" + digits + result;
+        }
+      }
+    };
+    LongPrototype.getHighBits = function getHighBits() {
+      return this.high;
+    };
+    LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {
+      return this.high >>> 0;
+    };
+    LongPrototype.getLowBits = function getLowBits() {
+      return this.low;
+    };
+    LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {
+      return this.low >>> 0;
+    };
+    LongPrototype.getNumBitsAbs = function getNumBitsAbs() {
+      if (this.isNegative())
+        return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();
+      var val = this.high != 0 ? this.high : this.low;
+      for (var bit = 31; bit > 0; bit--)
+        if ((val & 1 << bit) != 0)
+          break;
+      return this.high != 0 ? bit + 33 : bit + 1;
+    };
+    LongPrototype.isZero = function isZero() {
+      return this.high === 0 && this.low === 0;
+    };
+    LongPrototype.eqz = LongPrototype.isZero;
+    LongPrototype.isNegative = function isNegative() {
+      return !this.unsigned && this.high < 0;
+    };
+    LongPrototype.isPositive = function isPositive() {
+      return this.unsigned || this.high >= 0;
+    };
+    LongPrototype.isOdd = function isOdd() {
+      return (this.low & 1) === 1;
+    };
+    LongPrototype.isEven = function isEven2() {
+      return (this.low & 1) === 0;
+    };
+    LongPrototype.equals = function equals(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)
+        return false;
+      return this.high === other.high && this.low === other.low;
+    };
+    LongPrototype.eq = LongPrototype.equals;
+    LongPrototype.notEquals = function notEquals(other) {
+      return !this.eq(other);
+    };
+    LongPrototype.neq = LongPrototype.notEquals;
+    LongPrototype.ne = LongPrototype.notEquals;
+    LongPrototype.lessThan = function lessThan(other) {
+      return this.comp(other) < 0;
+    };
+    LongPrototype.lt = LongPrototype.lessThan;
+    LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {
+      return this.comp(other) <= 0;
+    };
+    LongPrototype.lte = LongPrototype.lessThanOrEqual;
+    LongPrototype.le = LongPrototype.lessThanOrEqual;
+    LongPrototype.greaterThan = function greaterThan(other) {
+      return this.comp(other) > 0;
+    };
+    LongPrototype.gt = LongPrototype.greaterThan;
+    LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {
+      return this.comp(other) >= 0;
+    };
+    LongPrototype.gte = LongPrototype.greaterThanOrEqual;
+    LongPrototype.ge = LongPrototype.greaterThanOrEqual;
+    LongPrototype.compare = function compare(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      if (this.eq(other))
+        return 0;
+      var thisNeg = this.isNegative(), otherNeg = other.isNegative();
+      if (thisNeg && !otherNeg)
+        return -1;
+      if (!thisNeg && otherNeg)
+        return 1;
+      if (!this.unsigned)
+        return this.sub(other).isNegative() ? -1 : 1;
+      return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;
+    };
+    LongPrototype.comp = LongPrototype.compare;
+    LongPrototype.negate = function negate() {
+      if (!this.unsigned && this.eq(MIN_VALUE))
+        return MIN_VALUE;
+      return this.not().add(ONE);
+    };
+    LongPrototype.neg = LongPrototype.negate;
+    LongPrototype.add = function add5(addend) {
+      if (!isLong(addend))
+        addend = fromValue(addend);
+      var a48 = this.high >>> 16;
+      var a32 = this.high & 65535;
+      var a16 = this.low >>> 16;
+      var a00 = this.low & 65535;
+      var b48 = addend.high >>> 16;
+      var b32 = addend.high & 65535;
+      var b16 = addend.low >>> 16;
+      var b00 = addend.low & 65535;
+      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
+      c00 += a00 + b00;
+      c16 += c00 >>> 16;
+      c00 &= 65535;
+      c16 += a16 + b16;
+      c32 += c16 >>> 16;
+      c16 &= 65535;
+      c32 += a32 + b32;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c48 += a48 + b48;
+      c48 &= 65535;
+      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
+    };
+    LongPrototype.subtract = function subtract(subtrahend) {
+      if (!isLong(subtrahend))
+        subtrahend = fromValue(subtrahend);
+      return this.add(subtrahend.neg());
+    };
+    LongPrototype.sub = LongPrototype.subtract;
+    LongPrototype.multiply = function multiply4(multiplier) {
+      if (this.isZero())
+        return ZERO;
+      if (!isLong(multiplier))
+        multiplier = fromValue(multiplier);
+      if (wasm) {
+        var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high);
+        return fromBits(low, wasm.get_high(), this.unsigned);
+      }
+      if (multiplier.isZero())
+        return ZERO;
+      if (this.eq(MIN_VALUE))
+        return multiplier.isOdd() ? MIN_VALUE : ZERO;
+      if (multiplier.eq(MIN_VALUE))
+        return this.isOdd() ? MIN_VALUE : ZERO;
+      if (this.isNegative()) {
+        if (multiplier.isNegative())
+          return this.neg().mul(multiplier.neg());
+        else
+          return this.neg().mul(multiplier).neg();
+      } else if (multiplier.isNegative())
+        return this.mul(multiplier.neg()).neg();
+      if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))
+        return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);
+      var a48 = this.high >>> 16;
+      var a32 = this.high & 65535;
+      var a16 = this.low >>> 16;
+      var a00 = this.low & 65535;
+      var b48 = multiplier.high >>> 16;
+      var b32 = multiplier.high & 65535;
+      var b16 = multiplier.low >>> 16;
+      var b00 = multiplier.low & 65535;
+      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
+      c00 += a00 * b00;
+      c16 += c00 >>> 16;
+      c00 &= 65535;
+      c16 += a16 * b00;
+      c32 += c16 >>> 16;
+      c16 &= 65535;
+      c16 += a00 * b16;
+      c32 += c16 >>> 16;
+      c16 &= 65535;
+      c32 += a32 * b00;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c32 += a16 * b16;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c32 += a00 * b32;
+      c48 += c32 >>> 16;
+      c32 &= 65535;
+      c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;
+      c48 &= 65535;
+      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
+    };
+    LongPrototype.mul = LongPrototype.multiply;
+    LongPrototype.divide = function divide(divisor) {
+      if (!isLong(divisor))
+        divisor = fromValue(divisor);
+      if (divisor.isZero())
+        throw Error("division by zero");
+      if (wasm) {
+        if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {
+          return this;
+        }
+        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(this.low, this.high, divisor.low, divisor.high);
+        return fromBits(low, wasm.get_high(), this.unsigned);
+      }
+      if (this.isZero())
+        return this.unsigned ? UZERO : ZERO;
+      var approx, rem, res;
+      if (!this.unsigned) {
+        if (this.eq(MIN_VALUE)) {
+          if (divisor.eq(ONE) || divisor.eq(NEG_ONE))
+            return MIN_VALUE;
+          else if (divisor.eq(MIN_VALUE))
+            return ONE;
+          else {
+            var halfThis = this.shr(1);
+            approx = halfThis.div(divisor).shl(1);
+            if (approx.eq(ZERO)) {
+              return divisor.isNegative() ? ONE : NEG_ONE;
+            } else {
+              rem = this.sub(divisor.mul(approx));
+              res = approx.add(rem.div(divisor));
+              return res;
+            }
+          }
+        } else if (divisor.eq(MIN_VALUE))
+          return this.unsigned ? UZERO : ZERO;
+        if (this.isNegative()) {
+          if (divisor.isNegative())
+            return this.neg().div(divisor.neg());
+          return this.neg().div(divisor).neg();
+        } else if (divisor.isNegative())
+          return this.div(divisor.neg()).neg();
+        res = ZERO;
+      } else {
+        if (!divisor.unsigned)
+          divisor = divisor.toUnsigned();
+        if (divisor.gt(this))
+          return UZERO;
+        if (divisor.gt(this.shru(1)))
+          return UONE;
+        res = UZERO;
+      }
+      rem = this;
+      while (rem.gte(divisor)) {
+        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));
+        var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);
+        while (approxRem.isNegative() || approxRem.gt(rem)) {
+          approx -= delta;
+          approxRes = fromNumber(approx, this.unsigned);
+          approxRem = approxRes.mul(divisor);
+        }
+        if (approxRes.isZero())
+          approxRes = ONE;
+        res = res.add(approxRes);
+        rem = rem.sub(approxRem);
+      }
+      return res;
+    };
+    LongPrototype.div = LongPrototype.divide;
+    LongPrototype.modulo = function modulo(divisor) {
+      if (!isLong(divisor))
+        divisor = fromValue(divisor);
+      if (wasm) {
+        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(this.low, this.high, divisor.low, divisor.high);
+        return fromBits(low, wasm.get_high(), this.unsigned);
+      }
+      return this.sub(this.div(divisor).mul(divisor));
+    };
+    LongPrototype.mod = LongPrototype.modulo;
+    LongPrototype.rem = LongPrototype.modulo;
+    LongPrototype.not = function not() {
+      return fromBits(~this.low, ~this.high, this.unsigned);
+    };
+    LongPrototype.and = function and(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      return fromBits(this.low & other.low, this.high & other.high, this.unsigned);
+    };
+    LongPrototype.or = function or(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      return fromBits(this.low | other.low, this.high | other.high, this.unsigned);
+    };
+    LongPrototype.xor = function xor(other) {
+      if (!isLong(other))
+        other = fromValue(other);
+      return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);
+    };
+    LongPrototype.shiftLeft = function shiftLeft(numBits) {
+      if (isLong(numBits))
+        numBits = numBits.toInt();
+      if ((numBits &= 63) === 0)
+        return this;
+      else if (numBits < 32)
+        return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);
+      else
+        return fromBits(0, this.low << numBits - 32, this.unsigned);
+    };
+    LongPrototype.shl = LongPrototype.shiftLeft;
+    LongPrototype.shiftRight = function shiftRight(numBits) {
+      if (isLong(numBits))
+        numBits = numBits.toInt();
+      if ((numBits &= 63) === 0)
+        return this;
+      else if (numBits < 32)
+        return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);
+      else
+        return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);
+    };
+    LongPrototype.shr = LongPrototype.shiftRight;
+    LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {
+      if (isLong(numBits))
+        numBits = numBits.toInt();
+      numBits &= 63;
+      if (numBits === 0)
+        return this;
+      else {
+        var high = this.high;
+        if (numBits < 32) {
+          var low = this.low;
+          return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);
+        } else if (numBits === 32)
+          return fromBits(high, 0, this.unsigned);
+        else
+          return fromBits(high >>> numBits - 32, 0, this.unsigned);
+      }
+    };
+    LongPrototype.shru = LongPrototype.shiftRightUnsigned;
+    LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;
+    LongPrototype.toSigned = function toSigned() {
+      if (!this.unsigned)
+        return this;
+      return fromBits(this.low, this.high, false);
+    };
+    LongPrototype.toUnsigned = function toUnsigned() {
+      if (this.unsigned)
+        return this;
+      return fromBits(this.low, this.high, true);
+    };
+    LongPrototype.toBytes = function toBytes(le) {
+      return le ? this.toBytesLE() : this.toBytesBE();
+    };
+    LongPrototype.toBytesLE = function toBytesLE() {
+      var hi = this.high, lo = this.low;
+      return [
+        lo & 255,
+        lo >>> 8 & 255,
+        lo >>> 16 & 255,
+        lo >>> 24,
+        hi & 255,
+        hi >>> 8 & 255,
+        hi >>> 16 & 255,
+        hi >>> 24
+      ];
+    };
+    LongPrototype.toBytesBE = function toBytesBE() {
+      var hi = this.high, lo = this.low;
+      return [
+        hi >>> 24,
+        hi >>> 16 & 255,
+        hi >>> 8 & 255,
+        hi & 255,
+        lo >>> 24,
+        lo >>> 16 & 255,
+        lo >>> 8 & 255,
+        lo & 255
+      ];
+    };
+    Long2.fromBytes = function fromBytes(bytes, unsigned, le) {
+      return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);
+    };
+    Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {
+      return new Long2(bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned);
+    };
+    Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {
+      return new Long2(bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned);
+    };
+  }
+});
+
+// (disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js
+var require_browser = __commonJS({
+  "(disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js"() {
+  }
+});
+
+// (disabled):util
+var require_util = __commonJS({
+  "(disabled):util"() {
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js
+var require_alea = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/alea.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function Alea(seed) {
+        var me = this, mash = Mash();
+        me.next = function() {
+          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;
+          me.s0 = me.s1;
+          me.s1 = me.s2;
+          return me.s2 = t2 - (me.c = t2 | 0);
+        };
+        me.c = 1;
+        me.s0 = mash(" ");
+        me.s1 = mash(" ");
+        me.s2 = mash(" ");
+        me.s0 -= mash(seed);
+        if (me.s0 < 0) {
+          me.s0 += 1;
+        }
+        me.s1 -= mash(seed);
+        if (me.s1 < 0) {
+          me.s1 += 1;
+        }
+        me.s2 -= mash(seed);
+        if (me.s2 < 0) {
+          me.s2 += 1;
+        }
+        mash = null;
+      }
+      function copy(f, t2) {
+        t2.c = f.c;
+        t2.s0 = f.s0;
+        t2.s1 = f.s1;
+        t2.s2 = f.s2;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
+        prng.int32 = function() {
+          return xg.next() * 4294967296 | 0;
+        };
+        prng.double = function() {
+          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
+        };
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      function Mash() {
+        var n = 4022871197;
+        var mash = function(data) {
+          data = data.toString();
+          for (var i2 = 0; i2 < data.length; i2++) {
+            n += data.charCodeAt(i2);
+            var h = 0.02519603282416938 * n;
+            n = h >>> 0;
+            h -= n;
+            h *= n;
+            n = h >>> 0;
+            h -= n;
+            n += h * 4294967296;
+          }
+          return (n >>> 0) * 23283064365386963e-26;
+        };
+        return mash;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.alea = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js
+var require_xor128 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor128.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.next = function() {
+          var t2 = me.x ^ me.x << 11;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;
+        };
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor128 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js
+var require_xorwow = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorwow.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var t2 = me.x ^ me.x >>> 2;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          me.w = me.v;
+          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;
+        };
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.v = 0;
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          if (k == strseed.length) {
+            me.d = me.x << 10 ^ me.x >>> 4;
+          }
+          me.next();
+        }
+      }
+      function copy(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        t2.v = f.v;
+        t2.d = f.d;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorwow = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js
+var require_xorshift7 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xorshift7.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var X = me.x, i2 = me.i, t2, v, w;
+          t2 = X[i2];
+          t2 ^= t2 >>> 7;
+          v = t2 ^ t2 << 24;
+          t2 = X[i2 + 1 & 7];
+          v ^= t2 ^ t2 >>> 10;
+          t2 = X[i2 + 3 & 7];
+          v ^= t2 ^ t2 >>> 3;
+          t2 = X[i2 + 4 & 7];
+          v ^= t2 ^ t2 << 7;
+          t2 = X[i2 + 7 & 7];
+          t2 = t2 ^ t2 << 13;
+          v ^= t2 ^ t2 << 9;
+          X[i2] = v;
+          me.i = i2 + 1 & 7;
+          return v;
+        };
+        function init2(me2, seed2) {
+          var j, w, X = [];
+          if (seed2 === (seed2 | 0)) {
+            w = X[0] = seed2;
+          } else {
+            seed2 = "" + seed2;
+            for (j = 0; j < seed2.length; ++j) {
+              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
+            }
+          }
+          while (X.length < 8)
+            X.push(0);
+          for (j = 0; j < 8 && X[j] === 0; ++j)
+            ;
+          if (j == 8)
+            w = X[7] = -1;
+          else
+            w = X[j];
+          me2.x = X;
+          me2.i = 0;
+          for (j = 256; j > 0; --j) {
+            me2.next();
+          }
+        }
+        init2(me, seed);
+      }
+      function copy(f, t2) {
+        t2.x = f.x.slice();
+        t2.i = f.i;
+        return t2;
+      }
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.x)
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorshift7 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js
+var require_xor4096 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/xor4096.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var w = me.w, X = me.X, i2 = me.i, t2, v;
+          me.w = w = w + 1640531527 | 0;
+          v = X[i2 + 34 & 127];
+          t2 = X[i2 = i2 + 1 & 127];
+          v ^= v << 13;
+          t2 ^= t2 << 17;
+          v ^= v >>> 15;
+          t2 ^= t2 >>> 12;
+          v = X[i2] = v ^ t2;
+          me.i = i2;
+          return v + (w ^ w >>> 16) | 0;
+        };
+        function init2(me2, seed2) {
+          var t2, v, i2, j, w, X = [], limit = 128;
+          if (seed2 === (seed2 | 0)) {
+            v = seed2;
+            seed2 = null;
+          } else {
+            seed2 = seed2 + "\0";
+            v = 0;
+            limit = Math.max(limit, seed2.length);
+          }
+          for (i2 = 0, j = -32; j < limit; ++j) {
+            if (seed2)
+              v ^= seed2.charCodeAt((j + 32) % seed2.length);
+            if (j === 0)
+              w = v;
+            v ^= v << 10;
+            v ^= v >>> 15;
+            v ^= v << 4;
+            v ^= v >>> 13;
+            if (j >= 0) {
+              w = w + 1640531527 | 0;
+              t2 = X[j & 127] ^= v + w;
+              i2 = 0 == t2 ? i2 + 1 : 0;
+            }
+          }
+          if (i2 >= 128) {
+            X[(seed2 && seed2.length || 0) & 127] = -1;
+          }
+          i2 = 127;
+          for (j = 4 * 128; j > 0; --j) {
+            v = X[i2 + 34 & 127];
+            t2 = X[i2 = i2 + 1 & 127];
+            v ^= v << 13;
+            t2 ^= t2 << 17;
+            v ^= v >>> 15;
+            t2 ^= t2 >>> 12;
+            X[i2] = v ^ t2;
+          }
+          me2.w = w;
+          me2.X = X;
+          me2.i = i2;
+        }
+        init2(me, seed);
+      }
+      function copy(f, t2) {
+        t2.i = f.i;
+        t2.w = f.w;
+        t2.X = f.X.slice();
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.X)
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor4096 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js
+var require_tychei = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/lib/tychei.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var b = me.b, c = me.c, d = me.d, a6 = me.a;
+          b = b << 25 ^ b >>> 7 ^ c;
+          c = c - d | 0;
+          d = d << 24 ^ d >>> 8 ^ a6;
+          a6 = a6 - b | 0;
+          me.b = b = b << 20 ^ b >>> 12 ^ c;
+          me.c = c = c - d | 0;
+          me.d = d << 16 ^ c >>> 16 ^ a6;
+          return me.a = a6 - b | 0;
+        };
+        me.a = 0;
+        me.b = 0;
+        me.c = 2654435769 | 0;
+        me.d = 1367130551;
+        if (seed === Math.floor(seed)) {
+          me.a = seed / 4294967296 | 0;
+          me.b = seed | 0;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 20; k++) {
+          me.b ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy(f, t2) {
+        t2.a = f.a;
+        t2.b = f.b;
+        t2.c = f.c;
+        t2.d = f.d;
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.tychei = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// (disabled):crypto
+var require_crypto = __commonJS({
+  "(disabled):crypto"() {
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js
+var require_seedrandom = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/seedrandom.js"(exports, module) {
+    (function(pool3, math) {
+      var global2 = this, width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
+      function seedrandom5(seed, options, callback) {
+        var key = [];
+        options = options == true ? { entropy: true } : options || {};
+        var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
+        var arc4 = new ARC4(key);
+        var prng = function() {
+          var n = arc4.g(chunks), d = startdenom, x = 0;
+          while (n < significance) {
+            n = (n + x) * width;
+            d *= width;
+            x = arc4.g(1);
+          }
+          while (n >= overflow) {
+            n /= 2;
+            d /= 2;
+            x >>>= 1;
+          }
+          return (n + x) / d;
+        };
+        prng.int32 = function() {
+          return arc4.g(4) | 0;
+        };
+        prng.quick = function() {
+          return arc4.g(4) / 4294967296;
+        };
+        prng.double = prng;
+        mixkey(tostring(arc4.S), pool3);
+        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
+          if (state) {
+            if (state.S) {
+              copy(state, arc4);
+            }
+            prng2.state = function() {
+              return copy(arc4, {});
+            };
+          }
+          if (is_math_call) {
+            math[rngname] = prng2;
+            return seed2;
+          } else
+            return prng2;
+        })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
+      }
+      math["seed" + rngname] = seedrandom5;
+      function ARC4(key) {
+        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];
+        if (!keylen) {
+          key = [keylen++];
+        }
+        while (i2 < width) {
+          s2[i2] = i2++;
+        }
+        for (i2 = 0; i2 < width; i2++) {
+          s2[i2] = s2[j = mask & j + key[i2 % keylen] + (t2 = s2[i2])];
+          s2[j] = t2;
+        }
+        (me.g = function(count2) {
+          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;
+          while (count2--) {
+            t3 = s3[i3 = mask & i3 + 1];
+            r2 = r2 * width + s3[mask & (s3[i3] = s3[j2 = mask & j2 + t3]) + (s3[j2] = t3)];
+          }
+          me.i = i3;
+          me.j = j2;
+          return r2;
+        })(width);
+      }
+      function copy(f, t2) {
+        t2.i = f.i;
+        t2.j = f.j;
+        t2.S = f.S.slice();
+        return t2;
+      }
+      ;
+      function flatten4(obj, depth) {
+        var result = [], typ = typeof obj, prop;
+        if (depth && typ == "object") {
+          for (prop in obj) {
+            try {
+              result.push(flatten4(obj[prop], depth - 1));
+            } catch (e2) {
+            }
+          }
+        }
+        return result.length ? result : typ == "string" ? obj : obj + "\0";
+      }
+      function mixkey(seed, key) {
+        var stringseed = seed + "", smear, j = 0;
+        while (j < stringseed.length) {
+          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
+        }
+        return tostring(key);
+      }
+      function autoseed() {
+        try {
+          var out;
+          if (nodecrypto && (out = nodecrypto.randomBytes)) {
+            out = out(width);
+          } else {
+            out = new Uint8Array(width);
+            (global2.crypto || global2.msCrypto).getRandomValues(out);
+          }
+          return tostring(out);
+        } catch (e2) {
+          var browser = global2.navigator, plugins = browser && browser.plugins;
+          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
+        }
+      }
+      function tostring(a6) {
+        return String.fromCharCode.apply(0, a6);
+      }
+      mixkey(math.random(), pool3);
+      if (typeof module == "object" && module.exports) {
+        module.exports = seedrandom5;
+        try {
+          nodecrypto = require_crypto();
+        } catch (ex) {
+        }
+      } else if (typeof define == "function" && define.amd) {
+        define(function() {
+          return seedrandom5;
+        });
+      }
+    })([], Math);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js
+var require_seedrandom2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@2.4.3/node_modules/seedrandom/index.js"(exports, module) {
+    var alea5 = require_alea();
+    var xor128 = require_xor128();
+    var xorwow = require_xorwow();
+    var xorshift7 = require_xorshift7();
+    var xor4096 = require_xor4096();
+    var tychei = require_tychei();
+    var sr = require_seedrandom();
+    sr.alea = alea5;
+    sr.xor128 = xor128;
+    sr.xorwow = xorwow;
+    sr.xorshift7 = xorshift7;
+    sr.xor4096 = xor4096;
+    sr.tychei = tychei;
+    module.exports = sr;
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js
+var require_alea2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function Alea(seed) {
+        var me = this, mash = Mash();
+        me.next = function() {
+          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;
+          me.s0 = me.s1;
+          me.s1 = me.s2;
+          return me.s2 = t2 - (me.c = t2 | 0);
+        };
+        me.c = 1;
+        me.s0 = mash(" ");
+        me.s1 = mash(" ");
+        me.s2 = mash(" ");
+        me.s0 -= mash(seed);
+        if (me.s0 < 0) {
+          me.s0 += 1;
+        }
+        me.s1 -= mash(seed);
+        if (me.s1 < 0) {
+          me.s1 += 1;
+        }
+        me.s2 -= mash(seed);
+        if (me.s2 < 0) {
+          me.s2 += 1;
+        }
+        mash = null;
+      }
+      function copy(f, t2) {
+        t2.c = f.c;
+        t2.s0 = f.s0;
+        t2.s1 = f.s1;
+        t2.s2 = f.s2;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
+        prng.int32 = function() {
+          return xg.next() * 4294967296 | 0;
+        };
+        prng.double = function() {
+          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;
+        };
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      function Mash() {
+        var n = 4022871197;
+        var mash = function(data) {
+          data = String(data);
+          for (var i2 = 0; i2 < data.length; i2++) {
+            n += data.charCodeAt(i2);
+            var h = 0.02519603282416938 * n;
+            n = h >>> 0;
+            h -= n;
+            h *= n;
+            n = h >>> 0;
+            h -= n;
+            n += h * 4294967296;
+          }
+          return (n >>> 0) * 23283064365386963e-26;
+        };
+        return mash;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.alea = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js
+var require_xor1282 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.next = function() {
+          var t2 = me.x ^ me.x << 11;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;
+        };
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor128 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js
+var require_xorwow2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var t2 = me.x ^ me.x >>> 2;
+          me.x = me.y;
+          me.y = me.z;
+          me.z = me.w;
+          me.w = me.v;
+          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;
+        };
+        me.x = 0;
+        me.y = 0;
+        me.z = 0;
+        me.w = 0;
+        me.v = 0;
+        if (seed === (seed | 0)) {
+          me.x = seed;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 64; k++) {
+          me.x ^= strseed.charCodeAt(k) | 0;
+          if (k == strseed.length) {
+            me.d = me.x << 10 ^ me.x >>> 4;
+          }
+          me.next();
+        }
+      }
+      function copy(f, t2) {
+        t2.x = f.x;
+        t2.y = f.y;
+        t2.z = f.z;
+        t2.w = f.w;
+        t2.v = f.v;
+        t2.d = f.d;
+        return t2;
+      }
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorwow = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js
+var require_xorshift72 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var X = me.x, i2 = me.i, t2, v, w;
+          t2 = X[i2];
+          t2 ^= t2 >>> 7;
+          v = t2 ^ t2 << 24;
+          t2 = X[i2 + 1 & 7];
+          v ^= t2 ^ t2 >>> 10;
+          t2 = X[i2 + 3 & 7];
+          v ^= t2 ^ t2 >>> 3;
+          t2 = X[i2 + 4 & 7];
+          v ^= t2 ^ t2 << 7;
+          t2 = X[i2 + 7 & 7];
+          t2 = t2 ^ t2 << 13;
+          v ^= t2 ^ t2 << 9;
+          X[i2] = v;
+          me.i = i2 + 1 & 7;
+          return v;
+        };
+        function init2(me2, seed2) {
+          var j, w, X = [];
+          if (seed2 === (seed2 | 0)) {
+            w = X[0] = seed2;
+          } else {
+            seed2 = "" + seed2;
+            for (j = 0; j < seed2.length; ++j) {
+              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;
+            }
+          }
+          while (X.length < 8)
+            X.push(0);
+          for (j = 0; j < 8 && X[j] === 0; ++j)
+            ;
+          if (j == 8)
+            w = X[7] = -1;
+          else
+            w = X[j];
+          me2.x = X;
+          me2.i = 0;
+          for (j = 256; j > 0; --j) {
+            me2.next();
+          }
+        }
+        init2(me, seed);
+      }
+      function copy(f, t2) {
+        t2.x = f.x.slice();
+        t2.i = f.i;
+        return t2;
+      }
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.x)
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xorshift7 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js
+var require_xor40962 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this;
+        me.next = function() {
+          var w = me.w, X = me.X, i2 = me.i, t2, v;
+          me.w = w = w + 1640531527 | 0;
+          v = X[i2 + 34 & 127];
+          t2 = X[i2 = i2 + 1 & 127];
+          v ^= v << 13;
+          t2 ^= t2 << 17;
+          v ^= v >>> 15;
+          t2 ^= t2 >>> 12;
+          v = X[i2] = v ^ t2;
+          me.i = i2;
+          return v + (w ^ w >>> 16) | 0;
+        };
+        function init2(me2, seed2) {
+          var t2, v, i2, j, w, X = [], limit = 128;
+          if (seed2 === (seed2 | 0)) {
+            v = seed2;
+            seed2 = null;
+          } else {
+            seed2 = seed2 + "\0";
+            v = 0;
+            limit = Math.max(limit, seed2.length);
+          }
+          for (i2 = 0, j = -32; j < limit; ++j) {
+            if (seed2)
+              v ^= seed2.charCodeAt((j + 32) % seed2.length);
+            if (j === 0)
+              w = v;
+            v ^= v << 10;
+            v ^= v >>> 15;
+            v ^= v << 4;
+            v ^= v >>> 13;
+            if (j >= 0) {
+              w = w + 1640531527 | 0;
+              t2 = X[j & 127] ^= v + w;
+              i2 = 0 == t2 ? i2 + 1 : 0;
+            }
+          }
+          if (i2 >= 128) {
+            X[(seed2 && seed2.length || 0) & 127] = -1;
+          }
+          i2 = 127;
+          for (j = 4 * 128; j > 0; --j) {
+            v = X[i2 + 34 & 127];
+            t2 = X[i2 = i2 + 1 & 127];
+            v ^= v << 13;
+            t2 ^= t2 << 17;
+            v ^= v >>> 15;
+            t2 ^= t2 >>> 12;
+            X[i2] = v ^ t2;
+          }
+          me2.w = w;
+          me2.X = X;
+          me2.i = i2;
+        }
+        init2(me, seed);
+      }
+      function copy(f, t2) {
+        t2.i = f.i;
+        t2.w = f.w;
+        t2.X = f.X.slice();
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        if (seed == null)
+          seed = +new Date();
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (state.X)
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.xor4096 = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js
+var require_tychei2 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js"(exports, module) {
+    (function(global2, module2, define2) {
+      function XorGen(seed) {
+        var me = this, strseed = "";
+        me.next = function() {
+          var b = me.b, c = me.c, d = me.d, a6 = me.a;
+          b = b << 25 ^ b >>> 7 ^ c;
+          c = c - d | 0;
+          d = d << 24 ^ d >>> 8 ^ a6;
+          a6 = a6 - b | 0;
+          me.b = b = b << 20 ^ b >>> 12 ^ c;
+          me.c = c = c - d | 0;
+          me.d = d << 16 ^ c >>> 16 ^ a6;
+          return me.a = a6 - b | 0;
+        };
+        me.a = 0;
+        me.b = 0;
+        me.c = 2654435769 | 0;
+        me.d = 1367130551;
+        if (seed === Math.floor(seed)) {
+          me.a = seed / 4294967296 | 0;
+          me.b = seed | 0;
+        } else {
+          strseed += seed;
+        }
+        for (var k = 0; k < strseed.length + 20; k++) {
+          me.b ^= strseed.charCodeAt(k) | 0;
+          me.next();
+        }
+      }
+      function copy(f, t2) {
+        t2.a = f.a;
+        t2.b = f.b;
+        t2.c = f.c;
+        t2.d = f.d;
+        return t2;
+      }
+      ;
+      function impl(seed, opts) {
+        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
+          return (xg.next() >>> 0) / 4294967296;
+        };
+        prng.double = function() {
+          do {
+            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);
+          } while (result === 0);
+          return result;
+        };
+        prng.int32 = xg.next;
+        prng.quick = prng;
+        if (state) {
+          if (typeof state == "object")
+            copy(state, xg);
+          prng.state = function() {
+            return copy(xg, {});
+          };
+        }
+        return prng;
+      }
+      if (module2 && module2.exports) {
+        module2.exports = impl;
+      } else if (define2 && define2.amd) {
+        define2(function() {
+          return impl;
+        });
+      } else {
+        this.tychei = impl;
+      }
+    })(exports, typeof module == "object" && module, typeof define == "function" && define);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js
+var require_seedrandom3 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js"(exports, module) {
+    (function(global2, pool3, math) {
+      var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;
+      function seedrandom5(seed, options, callback) {
+        var key = [];
+        options = options == true ? { entropy: true } : options || {};
+        var shortseed = mixkey(flatten4(options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed, 3), key);
+        var arc4 = new ARC4(key);
+        var prng = function() {
+          var n = arc4.g(chunks), d = startdenom, x = 0;
+          while (n < significance) {
+            n = (n + x) * width;
+            d *= width;
+            x = arc4.g(1);
+          }
+          while (n >= overflow) {
+            n /= 2;
+            d /= 2;
+            x >>>= 1;
+          }
+          return (n + x) / d;
+        };
+        prng.int32 = function() {
+          return arc4.g(4) | 0;
+        };
+        prng.quick = function() {
+          return arc4.g(4) / 4294967296;
+        };
+        prng.double = prng;
+        mixkey(tostring(arc4.S), pool3);
+        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {
+          if (state) {
+            if (state.S) {
+              copy(state, arc4);
+            }
+            prng2.state = function() {
+              return copy(arc4, {});
+            };
+          }
+          if (is_math_call) {
+            math[rngname] = prng2;
+            return seed2;
+          } else
+            return prng2;
+        })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
+      }
+      function ARC4(key) {
+        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];
+        if (!keylen) {
+          key = [keylen++];
+        }
+        while (i2 < width) {
+          s2[i2] = i2++;
+        }
+        for (i2 = 0; i2 < width; i2++) {
+          s2[i2] = s2[j = mask & j + key[i2 % keylen] + (t2 = s2[i2])];
+          s2[j] = t2;
+        }
+        (me.g = function(count2) {
+          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;
+          while (count2--) {
+            t3 = s3[i3 = mask & i3 + 1];
+            r2 = r2 * width + s3[mask & (s3[i3] = s3[j2 = mask & j2 + t3]) + (s3[j2] = t3)];
+          }
+          me.i = i3;
+          me.j = j2;
+          return r2;
+        })(width);
+      }
+      function copy(f, t2) {
+        t2.i = f.i;
+        t2.j = f.j;
+        t2.S = f.S.slice();
+        return t2;
+      }
+      ;
+      function flatten4(obj, depth) {
+        var result = [], typ = typeof obj, prop;
+        if (depth && typ == "object") {
+          for (prop in obj) {
+            try {
+              result.push(flatten4(obj[prop], depth - 1));
+            } catch (e2) {
+            }
+          }
+        }
+        return result.length ? result : typ == "string" ? obj : obj + "\0";
+      }
+      function mixkey(seed, key) {
+        var stringseed = seed + "", smear, j = 0;
+        while (j < stringseed.length) {
+          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
+        }
+        return tostring(key);
+      }
+      function autoseed() {
+        try {
+          var out;
+          if (nodecrypto && (out = nodecrypto.randomBytes)) {
+            out = out(width);
+          } else {
+            out = new Uint8Array(width);
+            (global2.crypto || global2.msCrypto).getRandomValues(out);
+          }
+          return tostring(out);
+        } catch (e2) {
+          var browser = global2.navigator, plugins = browser && browser.plugins;
+          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
+        }
+      }
+      function tostring(a6) {
+        return String.fromCharCode.apply(0, a6);
+      }
+      mixkey(math.random(), pool3);
+      if (typeof module == "object" && module.exports) {
+        module.exports = seedrandom5;
+        try {
+          nodecrypto = require_crypto();
+        } catch (ex) {
+        }
+      } else if (typeof define == "function" && define.amd) {
+        define(function() {
+          return seedrandom5;
+        });
+      } else {
+        math["seed" + rngname] = seedrandom5;
+      }
+    })(typeof self !== "undefined" ? self : exports, [], Math);
+  }
+});
+
+// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js
+var require_seedrandom4 = __commonJS({
+  "node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js"(exports, module) {
+    var alea5 = require_alea2();
+    var xor128 = require_xor1282();
+    var xorwow = require_xorwow2();
+    var xorshift7 = require_xorshift72();
+    var xor4096 = require_xor40962();
+    var tychei = require_tychei2();
+    var sr = require_seedrandom3();
+    sr.alea = alea5;
+    sr.xor128 = xor128;
+    sr.xorwow = xorwow;
+    sr.xorshift7 = xorshift7;
+    sr.xor4096 = xor4096;
+    sr.tychei = tychei;
+    module.exports = sr;
+  }
+});
+
+// (disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js
+var require_string_decoder = __commonJS({
+  "(disabled):node_modules/.pnpm/string_decoder@1.1.1/node_modules/string_decoder/lib/string_decoder.js"() {
+  }
+});
+
+// (disabled):fs
+var require_fs = __commonJS({
+  "(disabled):fs"() {
+  }
+});
+
+// (disabled):path
+var require_path = __commonJS({
+  "(disabled):path"() {
+  }
+});
+
+// (disabled):worker_threads
+var require_worker_threads = __commonJS({
+  "(disabled):worker_threads"() {
+  }
+});
+
+// (disabled):perf_hooks
+var require_perf_hooks = __commonJS({
+  "(disabled):perf_hooks"() {
+  }
+});
+
+// (disabled):os
+var require_os = __commonJS({
+  "(disabled):os"() {
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
+var require_tfjs_backend_wasm_threaded_simd = __commonJS({
+  "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(exports, module) {
+    var WasmBackendModuleThreadedSimd2 = (() => {
+      var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
+      if (typeof __filename !== "undefined")
+        _scriptDir = _scriptDir || __filename;
+      return function(WasmBackendModuleThreadedSimd3) {
+        WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {};
+        function GROWABLE_HEAP_I8() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAP8;
+        }
+        function GROWABLE_HEAP_U8() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPU8;
+        }
+        function GROWABLE_HEAP_I16() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAP16;
+        }
+        function GROWABLE_HEAP_U16() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPU16;
+        }
+        function GROWABLE_HEAP_I32() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAP32;
+        }
+        function GROWABLE_HEAP_F32() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPF32;
+        }
+        function GROWABLE_HEAP_F64() {
+          if (wasmMemory.buffer != buffer2) {
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+          }
+          return HEAPF64;
+        }
+        var Module = typeof WasmBackendModuleThreadedSimd3 !== "undefined" ? WasmBackendModuleThreadedSimd3 : {};
+        var readyPromiseResolve, readyPromiseReject;
+        Module["ready"] = new Promise(function(resolve, reject) {
+          readyPromiseResolve = resolve;
+          readyPromiseReject = reject;
+        });
+        var beforeListeners;
+        if (typeof process !== "undefined" && process.listeners) {
+          beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") };
+        }
+        var moduleOverrides = Object.assign({}, Module);
+        var arguments_ = [];
+        var thisProgram = "./this.program";
+        var quit_ = (status, toThrow) => {
+          throw toThrow;
+        };
+        var ENVIRONMENT_IS_WEB = typeof window === "object";
+        var ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
+        var ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
+        var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false;
+        var scriptDirectory = "";
+        function locateFile(path) {
+          if (Module["locateFile"]) {
+            return Module["locateFile"](path, scriptDirectory);
+          }
+          return scriptDirectory + path;
+        }
+        var read_, readAsync, readBinary, setWindowTitle;
+        function logExceptionOnExit(e2) {
+          if (e2 instanceof ExitStatus)
+            return;
+          let toLog = e2;
+          err("exiting due to exception: " + toLog);
+        }
+        var fs;
+        var nodePath;
+        var requireNodeFS;
+        if (ENVIRONMENT_IS_NODE) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = require_path().dirname(scriptDirectory) + "/";
+          } else {
+            scriptDirectory = __dirname + "/";
+          }
+          requireNodeFS = () => {
+            if (!nodePath) {
+              fs = require_fs();
+              nodePath = require_path();
+            }
+          };
+          read_ = function shell_read(filename, binary) {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            return fs.readFileSync(filename, binary ? void 0 : "utf8");
+          };
+          readBinary = (filename) => {
+            var ret = read_(filename, true);
+            if (!ret.buffer) {
+              ret = new Uint8Array(ret);
+            }
+            return ret;
+          };
+          readAsync = (filename, onload, onerror) => {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            fs.readFile(filename, function(err2, data) {
+              if (err2)
+                onerror(err2);
+              else
+                onload(data.buffer);
+            });
+          };
+          if (process["argv"].length > 1) {
+            thisProgram = process["argv"][1].replace(/\\/g, "/");
+          }
+          arguments_ = process["argv"].slice(2);
+          process["on"]("uncaughtException", function(ex) {
+            if (!(ex instanceof ExitStatus)) {
+              throw ex;
+            }
+          });
+          process["on"]("unhandledRejection", function(reason) {
+            throw reason;
+          });
+          quit_ = (status, toThrow) => {
+            if (keepRuntimeAlive()) {
+              process["exitCode"] = status;
+              throw toThrow;
+            }
+            logExceptionOnExit(toThrow);
+            process["exit"](status);
+          };
+          Module["inspect"] = function() {
+            return "[Emscripten Module object]";
+          };
+          let nodeWorkerThreads;
+          try {
+            nodeWorkerThreads = require_worker_threads();
+          } catch (e2) {
+            console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');
+            throw e2;
+          }
+          global.Worker = nodeWorkerThreads.Worker;
+        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = self.location.href;
+          } else if (typeof document !== "undefined" && document.currentScript) {
+            scriptDirectory = document.currentScript.src;
+          }
+          if (typeof _scriptDir !== "undefined" && _scriptDir) {
+            scriptDirectory = _scriptDir;
+          }
+          if (scriptDirectory.indexOf("blob:") !== 0) {
+            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
+          } else {
+            scriptDirectory = "";
+          }
+          if (!ENVIRONMENT_IS_NODE) {
+            read_ = (url) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, false);
+              xhr.send(null);
+              return xhr.responseText;
+            };
+            if (ENVIRONMENT_IS_WORKER) {
+              readBinary = (url) => {
+                var xhr = new XMLHttpRequest();
+                xhr.open("GET", url, false);
+                xhr.responseType = "arraybuffer";
+                xhr.send(null);
+                return new Uint8Array(xhr.response);
+              };
+            }
+            readAsync = (url, onload, onerror) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, true);
+              xhr.responseType = "arraybuffer";
+              xhr.onload = () => {
+                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
+                  onload(xhr.response);
+                  return;
+                }
+                onerror();
+              };
+              xhr.onerror = onerror;
+              xhr.send(null);
+            };
+          }
+          setWindowTitle = (title) => document.title = title;
+        } else {
+        }
+        if (ENVIRONMENT_IS_NODE) {
+          if (typeof performance === "undefined") {
+            global.performance = require_perf_hooks().performance;
+          }
+        }
+        var defaultPrint = console.log.bind(console);
+        var defaultPrintErr = console.warn.bind(console);
+        if (ENVIRONMENT_IS_NODE) {
+          requireNodeFS();
+          defaultPrint = (str) => fs.writeSync(1, str + "\n");
+          defaultPrintErr = (str) => fs.writeSync(2, str + "\n");
+        }
+        var out = Module["print"] || defaultPrint;
+        var err = Module["printErr"] || defaultPrintErr;
+        Object.assign(Module, moduleOverrides);
+        moduleOverrides = null;
+        if (Module["arguments"])
+          arguments_ = Module["arguments"];
+        if (Module["thisProgram"])
+          thisProgram = Module["thisProgram"];
+        if (Module["quit"])
+          quit_ = Module["quit"];
+        var POINTER_SIZE = 4;
+        function warnOnce(text) {
+          if (!warnOnce.shown)
+            warnOnce.shown = {};
+          if (!warnOnce.shown[text]) {
+            warnOnce.shown[text] = 1;
+            err(text);
+          }
+        }
+        function convertJsFunctionToWasm(func2, sig) {
+          if (typeof WebAssembly.Function === "function") {
+            var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
+            var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
+            for (var i2 = 1; i2 < sig.length; ++i2) {
+              type.parameters.push(typeNames[sig[i2]]);
+            }
+            return new WebAssembly.Function(type, func2);
+          }
+          var typeSection = [1, 0, 1, 96];
+          var sigRet = sig.slice(0, 1);
+          var sigParam = sig.slice(1);
+          var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
+          typeSection.push(sigParam.length);
+          for (var i2 = 0; i2 < sigParam.length; ++i2) {
+            typeSection.push(typeCodes[sigParam[i2]]);
+          }
+          if (sigRet == "v") {
+            typeSection.push(0);
+          } else {
+            typeSection = typeSection.concat([1, typeCodes[sigRet]]);
+          }
+          typeSection[1] = typeSection.length - 2;
+          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));
+          var module2 = new WebAssembly.Module(bytes);
+          var instance = new WebAssembly.Instance(module2, { "e": { "f": func2 } });
+          var wrappedFunc = instance.exports["f"];
+          return wrappedFunc;
+        }
+        var freeTableIndexes = [];
+        var functionsInTableMap;
+        function getEmptyTableSlot() {
+          if (freeTableIndexes.length) {
+            return freeTableIndexes.pop();
+          }
+          try {
+            wasmTable.grow(1);
+          } catch (err2) {
+            if (!(err2 instanceof RangeError)) {
+              throw err2;
+            }
+            throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
+          }
+          return wasmTable.length - 1;
+        }
+        function updateTableMap(offset, count2) {
+          for (var i2 = offset; i2 < offset + count2; i2++) {
+            var item = getWasmTableEntry(i2);
+            if (item) {
+              functionsInTableMap.set(item, i2);
+            }
+          }
+        }
+        var tempRet0 = 0;
+        var setTempRet0 = (value) => {
+          tempRet0 = value;
+        };
+        var Atomics_load = Atomics.load;
+        var Atomics_store = Atomics.store;
+        var Atomics_compareExchange = Atomics.compareExchange;
+        var wasmBinary;
+        if (Module["wasmBinary"])
+          wasmBinary = Module["wasmBinary"];
+        var noExitRuntime = Module["noExitRuntime"] || true;
+        if (typeof WebAssembly !== "object") {
+          abort("no native wasm support detected");
+        }
+        var wasmMemory;
+        var wasmModule;
+        var ABORT = false;
+        var EXITSTATUS;
+        function assert3(condition, text) {
+          if (!condition) {
+            abort(text);
+          }
+        }
+        function getCFunc(ident) {
+          var func2 = Module["_" + ident];
+          return func2;
+        }
+        function ccall(ident, returnType, argTypes, args, opts) {
+          var toC = { "string": function(str) {
+            var ret2 = 0;
+            if (str !== null && str !== void 0 && str !== 0) {
+              var len = (str.length << 2) + 1;
+              ret2 = stackAlloc(len);
+              stringToUTF8(str, ret2, len);
+            }
+            return ret2;
+          }, "array": function(arr) {
+            var ret2 = stackAlloc(arr.length);
+            writeArrayToMemory(arr, ret2);
+            return ret2;
+          } };
+          function convertReturnValue(ret2) {
+            if (returnType === "string")
+              return UTF8ToString(ret2);
+            if (returnType === "boolean")
+              return Boolean(ret2);
+            return ret2;
+          }
+          var func2 = getCFunc(ident);
+          var cArgs = [];
+          var stack2 = 0;
+          if (args) {
+            for (var i2 = 0; i2 < args.length; i2++) {
+              var converter = toC[argTypes[i2]];
+              if (converter) {
+                if (stack2 === 0)
+                  stack2 = stackSave();
+                cArgs[i2] = converter(args[i2]);
+              } else {
+                cArgs[i2] = args[i2];
+              }
+            }
+          }
+          var ret = func2.apply(null, cArgs);
+          function onDone(ret2) {
+            if (stack2 !== 0)
+              stackRestore(stack2);
+            return convertReturnValue(ret2);
+          }
+          ret = onDone(ret);
+          return ret;
+        }
+        function cwrap(ident, returnType, argTypes, opts) {
+          argTypes = argTypes || [];
+          var numericArgs = argTypes.every(function(type) {
+            return type === "number";
+          });
+          var numericRet = returnType !== "string";
+          if (numericRet && numericArgs && !opts) {
+            return getCFunc(ident);
+          }
+          return function() {
+            return ccall(ident, returnType, argTypes, arguments, opts);
+          };
+        }
+        var ALLOC_STACK = 1;
+        function TextDecoderWrapper(encoding) {
+          var textDecoder = new TextDecoder(encoding);
+          this.decode = (data) => {
+            if (data.buffer instanceof SharedArrayBuffer) {
+              data = new Uint8Array(data);
+            }
+            return textDecoder.decode.call(textDecoder, data);
+          };
+        }
+        var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoderWrapper("utf8") : void 0;
+        function UTF8ArrayToString(heap, idx, maxBytesToRead) {
+          var endIdx = idx + maxBytesToRead;
+          var endPtr = idx;
+          while (heap[endPtr] && !(endPtr >= endIdx))
+            ++endPtr;
+          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {
+            return UTF8Decoder.decode(heap.subarray(idx, endPtr));
+          } else {
+            var str = "";
+            while (idx < endPtr) {
+              var u0 = heap[idx++];
+              if (!(u0 & 128)) {
+                str += String.fromCharCode(u0);
+                continue;
+              }
+              var u1 = heap[idx++] & 63;
+              if ((u0 & 224) == 192) {
+                str += String.fromCharCode((u0 & 31) << 6 | u1);
+                continue;
+              }
+              var u2 = heap[idx++] & 63;
+              if ((u0 & 240) == 224) {
+                u0 = (u0 & 15) << 12 | u1 << 6 | u2;
+              } else {
+                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
+              }
+              if (u0 < 65536) {
+                str += String.fromCharCode(u0);
+              } else {
+                var ch = u0 - 65536;
+                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
+              }
+            }
+          }
+          return str;
+        }
+        function UTF8ToString(ptr, maxBytesToRead) {
+          return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : "";
+        }
+        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
+          if (!(maxBytesToWrite > 0))
+            return 0;
+          var startIdx = outIdx;
+          var endIdx = outIdx + maxBytesToWrite - 1;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343) {
+              var u1 = str.charCodeAt(++i2);
+              u = 65536 + ((u & 1023) << 10) | u1 & 1023;
+            }
+            if (u <= 127) {
+              if (outIdx >= endIdx)
+                break;
+              heap[outIdx++] = u;
+            } else if (u <= 2047) {
+              if (outIdx + 1 >= endIdx)
+                break;
+              heap[outIdx++] = 192 | u >> 6;
+              heap[outIdx++] = 128 | u & 63;
+            } else if (u <= 65535) {
+              if (outIdx + 2 >= endIdx)
+                break;
+              heap[outIdx++] = 224 | u >> 12;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            } else {
+              if (outIdx + 3 >= endIdx)
+                break;
+              heap[outIdx++] = 240 | u >> 18;
+              heap[outIdx++] = 128 | u >> 12 & 63;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            }
+          }
+          heap[outIdx] = 0;
+          return outIdx - startIdx;
+        }
+        function stringToUTF8(str, outPtr, maxBytesToWrite) {
+          return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);
+        }
+        function lengthBytesUTF8(str) {
+          var len = 0;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343)
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+            if (u <= 127)
+              ++len;
+            else if (u <= 2047)
+              len += 2;
+            else if (u <= 65535)
+              len += 3;
+            else
+              len += 4;
+          }
+          return len;
+        }
+        var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoderWrapper("utf-16le") : void 0;
+        function writeArrayToMemory(array2, buffer3) {
+          GROWABLE_HEAP_I8().set(array2, buffer3);
+        }
+        function writeAsciiToMemory(str, buffer3, dontAddNull) {
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i2);
+          }
+          if (!dontAddNull)
+            GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;
+        }
+        function alignUp(x, multiple) {
+          if (x % multiple > 0) {
+            x += multiple - x % multiple;
+          }
+          return x;
+        }
+        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
+        if (ENVIRONMENT_IS_PTHREAD) {
+          buffer2 = Module["buffer"];
+        }
+        function updateGlobalBufferAndViews(buf) {
+          buffer2 = buf;
+          Module["HEAP8"] = HEAP8 = new Int8Array(buf);
+          Module["HEAP16"] = HEAP16 = new Int16Array(buf);
+          Module["HEAP32"] = HEAP32 = new Int32Array(buf);
+          Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
+          Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
+          Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
+          Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
+          Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
+        }
+        var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
+        if (ENVIRONMENT_IS_PTHREAD) {
+          wasmMemory = Module["wasmMemory"];
+          buffer2 = Module["buffer"];
+        } else {
+          if (Module["wasmMemory"]) {
+            wasmMemory = Module["wasmMemory"];
+          } else {
+            wasmMemory = new WebAssembly.Memory({ "initial": INITIAL_MEMORY / 65536, "maximum": 2147483648 / 65536, "shared": true });
+            if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {
+              err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag");
+              if (ENVIRONMENT_IS_NODE) {
+                console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)");
+              }
+              throw Error("bad memory");
+            }
+          }
+        }
+        if (wasmMemory) {
+          buffer2 = wasmMemory.buffer;
+        }
+        INITIAL_MEMORY = buffer2.byteLength;
+        updateGlobalBufferAndViews(buffer2);
+        var wasmTable;
+        var __ATPRERUN__ = [];
+        var __ATINIT__ = [];
+        var __ATEXIT__ = [];
+        var __ATPOSTRUN__ = [];
+        var runtimeInitialized = false;
+        var runtimeExited = false;
+        var runtimeKeepaliveCounter = 0;
+        function keepRuntimeAlive() {
+          return noExitRuntime || runtimeKeepaliveCounter > 0;
+        }
+        function preRun() {
+          if (Module["preRun"]) {
+            if (typeof Module["preRun"] == "function")
+              Module["preRun"] = [Module["preRun"]];
+            while (Module["preRun"].length) {
+              addOnPreRun(Module["preRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPRERUN__);
+        }
+        function initRuntime() {
+          runtimeInitialized = true;
+          if (ENVIRONMENT_IS_PTHREAD)
+            return;
+          callRuntimeCallbacks(__ATINIT__);
+        }
+        function exitRuntime() {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return;
+          PThread.terminateAllThreads();
+          runtimeExited = true;
+        }
+        function postRun() {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return;
+          if (Module["postRun"]) {
+            if (typeof Module["postRun"] == "function")
+              Module["postRun"] = [Module["postRun"]];
+            while (Module["postRun"].length) {
+              addOnPostRun(Module["postRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPOSTRUN__);
+        }
+        function addOnPreRun(cb) {
+          __ATPRERUN__.unshift(cb);
+        }
+        function addOnInit(cb) {
+          __ATINIT__.unshift(cb);
+        }
+        function addOnPostRun(cb) {
+          __ATPOSTRUN__.unshift(cb);
+        }
+        var runDependencies = 0;
+        var runDependencyWatcher = null;
+        var dependenciesFulfilled = null;
+        function addRunDependency(id) {
+          runDependencies++;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+        }
+        function removeRunDependency(id) {
+          runDependencies--;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+          if (runDependencies == 0) {
+            if (runDependencyWatcher !== null) {
+              clearInterval(runDependencyWatcher);
+              runDependencyWatcher = null;
+            }
+            if (dependenciesFulfilled) {
+              var callback = dependenciesFulfilled;
+              dependenciesFulfilled = null;
+              callback();
+            }
+          }
+        }
+        Module["preloadedImages"] = {};
+        Module["preloadedAudios"] = {};
+        function abort(what) {
+          if (ENVIRONMENT_IS_PTHREAD) {
+            postMessage({ "cmd": "onAbort", "arg": what });
+          } else {
+            if (Module["onAbort"]) {
+              Module["onAbort"](what);
+            }
+          }
+          what = "Aborted(" + what + ")";
+          err(what);
+          ABORT = true;
+          EXITSTATUS = 1;
+          what += ". Build with -s ASSERTIONS=1 for more info.";
+          var e2 = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e2);
+          throw e2;
+        }
+        var dataURIPrefix = "data:application/octet-stream;base64,";
+        function isDataURI(filename) {
+          return filename.startsWith(dataURIPrefix);
+        }
+        function isFileURI(filename) {
+          return filename.startsWith("file://");
+        }
+        var wasmBinaryFile;
+        wasmBinaryFile = "tfjs-backend-wasm-threaded-simd.wasm";
+        if (!isDataURI(wasmBinaryFile)) {
+          wasmBinaryFile = locateFile(wasmBinaryFile);
+        }
+        function getBinary(file) {
+          try {
+            if (file == wasmBinaryFile && wasmBinary) {
+              return new Uint8Array(wasmBinary);
+            }
+            if (readBinary) {
+              return readBinary(file);
+            } else {
+              throw "both async and sync fetching of the wasm failed";
+            }
+          } catch (err2) {
+            abort(err2);
+          }
+        }
+        function getBinaryPromise() {
+          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
+            if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                if (!response["ok"]) {
+                  throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
+                }
+                return response["arrayBuffer"]();
+              }).catch(function() {
+                return getBinary(wasmBinaryFile);
+              });
+            } else {
+              if (readAsync) {
+                return new Promise(function(resolve, reject) {
+                  readAsync(wasmBinaryFile, function(response) {
+                    resolve(new Uint8Array(response));
+                  }, reject);
+                });
+              }
+            }
+          }
+          return Promise.resolve().then(function() {
+            return getBinary(wasmBinaryFile);
+          });
+        }
+        function createWasm() {
+          var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg };
+          function receiveInstance(instance, module2) {
+            var exports3 = instance.exports;
+            Module["asm"] = exports3;
+            registerTlsInit(Module["asm"]["emscripten_tls_init"]);
+            wasmTable = Module["asm"]["__indirect_function_table"];
+            addOnInit(Module["asm"]["__wasm_call_ctors"]);
+            wasmModule = module2;
+            if (!ENVIRONMENT_IS_PTHREAD) {
+              var numWorkersToLoad = PThread.unusedWorkers.length;
+              PThread.unusedWorkers.forEach(function(w) {
+                PThread.loadWasmModuleToWorker(w, function() {
+                  if (!--numWorkersToLoad)
+                    removeRunDependency("wasm-instantiate");
+                });
+              });
+            }
+          }
+          if (!ENVIRONMENT_IS_PTHREAD) {
+            addRunDependency("wasm-instantiate");
+          }
+          function receiveInstantiationResult(result) {
+            receiveInstance(result["instance"], result["module"]);
+          }
+          function instantiateArrayBuffer(receiver) {
+            return getBinaryPromise().then(function(binary) {
+              return WebAssembly.instantiate(binary, info);
+            }).then(function(instance) {
+              return instance;
+            }).then(receiver, function(reason) {
+              err("failed to asynchronously prepare wasm: " + reason);
+              abort(reason);
+            });
+          }
+          function instantiateAsync() {
+            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                var result = WebAssembly.instantiateStreaming(response, info);
+                return result.then(receiveInstantiationResult, function(reason) {
+                  err("wasm streaming compile failed: " + reason);
+                  err("falling back to ArrayBuffer instantiation");
+                  return instantiateArrayBuffer(receiveInstantiationResult);
+                });
+              });
+            } else {
+              return instantiateArrayBuffer(receiveInstantiationResult);
+            }
+          }
+          if (Module["instantiateWasm"]) {
+            try {
+              var exports2 = Module["instantiateWasm"](info, receiveInstance);
+              return exports2;
+            } catch (e2) {
+              err("Module.instantiateWasm callback failed with error: " + e2);
+              return false;
+            }
+          }
+          instantiateAsync().catch(readyPromiseReject);
+          return {};
+        }
+        var tempDouble;
+        var tempI64;
+        var ASM_CONSTS = {};
+        function callRuntimeCallbacks(callbacks2) {
+          while (callbacks2.length > 0) {
+            var callback = callbacks2.shift();
+            if (typeof callback == "function") {
+              callback(Module);
+              continue;
+            }
+            var func2 = callback.func;
+            if (typeof func2 === "number") {
+              if (callback.arg === void 0) {
+                getWasmTableEntry(func2)();
+              } else {
+                getWasmTableEntry(func2)(callback.arg);
+              }
+            } else {
+              func2(callback.arg === void 0 ? null : callback.arg);
+            }
+          }
+        }
+        function withStackSave(f) {
+          var stack2 = stackSave();
+          var ret = f();
+          stackRestore(stack2);
+          return ret;
+        }
+        function demangle(func2) {
+          return func2;
+        }
+        function demangleAll(text) {
+          var regex = /\b_Z[\w\d_]+/g;
+          return text.replace(regex, function(x) {
+            var y = demangle(x);
+            return x === y ? x : y + " [" + x + "]";
+          });
+        }
+        function killThread(pthread_ptr) {
+          GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;
+          var pthread = PThread.pthreads[pthread_ptr];
+          delete PThread.pthreads[pthread_ptr];
+          pthread.worker.terminate();
+          __emscripten_thread_free_data(pthread_ptr);
+          PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);
+          pthread.worker.pthread = void 0;
+        }
+        function cancelThread(pthread_ptr) {
+          var pthread = PThread.pthreads[pthread_ptr];
+          pthread.worker.postMessage({ "cmd": "cancel" });
+        }
+        function cleanupThread(pthread_ptr) {
+          var pthread = PThread.pthreads[pthread_ptr];
+          if (pthread) {
+            GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;
+            var worker = pthread.worker;
+            PThread.returnWorkerToPool(worker);
+          }
+        }
+        function _exit(status) {
+          exit(status);
+        }
+        function handleException(e2) {
+          if (e2 instanceof ExitStatus || e2 == "unwind") {
+            return EXITSTATUS;
+          }
+          quit_(1, e2);
+        }
+        var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
+          if (ENVIRONMENT_IS_PTHREAD) {
+            PThread.initWorker();
+          } else {
+            PThread.initMainThread();
+          }
+        }, initMainThread: function() {
+          var pthreadPoolSize = 8;
+          for (var i2 = 0; i2 < pthreadPoolSize; ++i2) {
+            PThread.allocateUnusedWorker();
+          }
+        }, initWorker: function() {
+          noExitRuntime = false;
+        }, pthreads: {}, setExitStatus: function(status) {
+          EXITSTATUS = status;
+        }, terminateAllThreads: function() {
+          for (var t2 in PThread.pthreads) {
+            var pthread = PThread.pthreads[t2];
+            if (pthread && pthread.worker) {
+              PThread.returnWorkerToPool(pthread.worker);
+            }
+          }
+          for (var i2 = 0; i2 < PThread.unusedWorkers.length; ++i2) {
+            var worker = PThread.unusedWorkers[i2];
+            worker.terminate();
+          }
+          PThread.unusedWorkers = [];
+        }, returnWorkerToPool: function(worker) {
+          PThread.runWithoutMainThreadQueuedCalls(function() {
+            delete PThread.pthreads[worker.pthread.threadInfoStruct];
+            PThread.unusedWorkers.push(worker);
+            PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);
+            __emscripten_thread_free_data(worker.pthread.threadInfoStruct);
+            worker.pthread = void 0;
+          });
+        }, runWithoutMainThreadQueuedCalls: function(func2) {
+          GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0;
+          try {
+            func2();
+          } finally {
+            GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1;
+          }
+        }, receiveObjectTransfer: function(data) {
+        }, threadInit: function() {
+          for (var i2 in PThread.tlsInitFunctions) {
+            PThread.tlsInitFunctions[i2]();
+          }
+        }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {
+          worker.onmessage = (e2) => {
+            var d = e2["data"];
+            var cmd = d["cmd"];
+            if (worker.pthread)
+              PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;
+            if (d["targetThread"] && d["targetThread"] != _pthread_self()) {
+              var thread = PThread.pthreads[d.targetThread];
+              if (thread) {
+                thread.worker.postMessage(d, d["transferList"]);
+              } else {
+                err('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!");
+              }
+              PThread.currentProxiedOperationCallerThread = void 0;
+              return;
+            }
+            if (cmd === "processQueuedMainThreadWork") {
+              _emscripten_main_thread_process_queued_calls();
+            } else if (cmd === "spawnThread") {
+              spawnThread(d);
+            } else if (cmd === "cleanupThread") {
+              cleanupThread(d["thread"]);
+            } else if (cmd === "killThread") {
+              killThread(d["thread"]);
+            } else if (cmd === "cancelThread") {
+              cancelThread(d["thread"]);
+            } else if (cmd === "loaded") {
+              worker.loaded = true;
+              if (onFinishedLoading)
+                onFinishedLoading(worker);
+              if (worker.runPthread) {
+                worker.runPthread();
+                delete worker.runPthread;
+              }
+            } else if (cmd === "print") {
+              out("Thread " + d["threadId"] + ": " + d["text"]);
+            } else if (cmd === "printErr") {
+              err("Thread " + d["threadId"] + ": " + d["text"]);
+            } else if (cmd === "alert") {
+              alert("Thread " + d["threadId"] + ": " + d["text"]);
+            } else if (d.target === "setimmediate") {
+              worker.postMessage(d);
+            } else if (cmd === "onAbort") {
+              if (Module["onAbort"]) {
+                Module["onAbort"](d["arg"]);
+              }
+            } else {
+              err("worker sent an unknown command " + cmd);
+            }
+            PThread.currentProxiedOperationCallerThread = void 0;
+          };
+          worker.onerror = (e2) => {
+            var message = "worker sent an error!";
+            err(message + " " + e2.filename + ":" + e2.lineno + ": " + e2.message);
+            throw e2;
+          };
+          if (ENVIRONMENT_IS_NODE) {
+            worker.on("message", function(data) {
+              worker.onmessage({ data });
+            });
+            worker.on("error", function(e2) {
+              worker.onerror(e2);
+            });
+            worker.on("detachedExit", function() {
+            });
+          }
+          worker.postMessage({ "cmd": "load", "urlOrBlob": Module["mainScriptUrlOrBlob"] || _scriptDir, "wasmMemory": wasmMemory, "wasmModule": wasmModule });
+        }, allocateUnusedWorker: function() {
+          var pthreadMainJs = locateFile("tfjs-backend-wasm-threaded-simd.worker.js");
+          PThread.unusedWorkers.push(new Worker(pthreadMainJs));
+        }, getNewWorker: function() {
+          if (PThread.unusedWorkers.length == 0) {
+            PThread.allocateUnusedWorker();
+            PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);
+          }
+          return PThread.unusedWorkers.pop();
+        } };
+        function establishStackSpace() {
+          var pthread_ptr = _pthread_self();
+          var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2];
+          var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2];
+          var stackMax = stackTop - stackSize;
+          _emscripten_stack_set_limits(stackTop, stackMax);
+          stackRestore(stackTop);
+        }
+        Module["establishStackSpace"] = establishStackSpace;
+        function exitOnMainThread(returnCode) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);
+          try {
+            _exit(returnCode);
+          } catch (e2) {
+            handleException(e2);
+          }
+        }
+        var wasmTableMirror = [];
+        function getWasmTableEntry(funcPtr) {
+          var func2 = wasmTableMirror[funcPtr];
+          if (!func2) {
+            if (funcPtr >= wasmTableMirror.length)
+              wasmTableMirror.length = funcPtr + 1;
+            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);
+          }
+          return func2;
+        }
+        function invokeEntryPoint(ptr, arg) {
+          return getWasmTableEntry(ptr)(arg);
+        }
+        Module["invokeEntryPoint"] = invokeEntryPoint;
+        function jsStackTrace() {
+          var error = new Error();
+          if (!error.stack) {
+            try {
+              throw new Error();
+            } catch (e2) {
+              error = e2;
+            }
+            if (!error.stack) {
+              return "(no stack trace available)";
+            }
+          }
+          return error.stack.toString();
+        }
+        function registerTlsInit(tlsInitFunc, moduleExports, metadata) {
+          PThread.tlsInitFunctions.push(tlsInitFunc);
+        }
+        function setWasmTableEntry(idx, func2) {
+          wasmTable.set(idx, func2);
+          wasmTableMirror[idx] = func2;
+        }
+        var _emscripten_get_now;
+        if (ENVIRONMENT_IS_NODE) {
+          _emscripten_get_now = () => {
+            var t2 = process["hrtime"]();
+            return t2[0] * 1e3 + t2[1] / 1e6;
+          };
+        } else if (ENVIRONMENT_IS_PTHREAD) {
+          _emscripten_get_now = () => performance.now() - Module["__performance_now_clock_drift"];
+        } else
+          _emscripten_get_now = () => performance.now();
+        var _emscripten_get_now_is_monotonic = true;
+        function setErrNo(value) {
+          GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;
+          return value;
+        }
+        function _clock_gettime(clk_id, tp) {
+          var now2;
+          if (clk_id === 0) {
+            now2 = Date.now();
+          } else if ((clk_id === 1 || clk_id === 4) && _emscripten_get_now_is_monotonic) {
+            now2 = _emscripten_get_now();
+          } else {
+            setErrNo(28);
+            return -1;
+          }
+          GROWABLE_HEAP_I32()[tp >> 2] = now2 / 1e3 | 0;
+          GROWABLE_HEAP_I32()[tp + 4 >> 2] = now2 % 1e3 * 1e3 * 1e3 | 0;
+          return 0;
+        }
+        function ___clock_gettime(a0, a12) {
+          return _clock_gettime(a0, a12);
+        }
+        function ___emscripten_init_main_thread_js(tb) {
+          __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);
+          PThread.threadInit();
+        }
+        function ___emscripten_thread_cleanup(thread) {
+          if (!ENVIRONMENT_IS_PTHREAD)
+            cleanupThread(thread);
+          else
+            postMessage({ "cmd": "cleanupThread", "thread": thread });
+        }
+        function spawnThread(threadParams) {
+          var worker = PThread.getNewWorker();
+          if (!worker) {
+            return 6;
+          }
+          PThread.runningWorkers.push(worker);
+          var pthread = PThread.pthreads[threadParams.pthread_ptr] = { worker, threadInfoStruct: threadParams.pthread_ptr };
+          worker.pthread = pthread;
+          var msg = { "cmd": "run", "start_routine": threadParams.startRoutine, "arg": threadParams.arg, "threadInfoStruct": threadParams.pthread_ptr };
+          worker.runPthread = () => {
+            msg.time = performance.now();
+            worker.postMessage(msg, threadParams.transferList);
+          };
+          if (worker.loaded) {
+            worker.runPthread();
+            delete worker.runPthread;
+          }
+          return 0;
+        }
+        function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) {
+          if (typeof SharedArrayBuffer === "undefined") {
+            err("Current environment does not support SharedArrayBuffer, pthreads are not available!");
+            return 6;
+          }
+          var transferList = [];
+          var error = 0;
+          if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {
+            return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);
+          }
+          if (error)
+            return error;
+          var threadParams = { startRoutine: start_routine, pthread_ptr, arg, transferList };
+          if (ENVIRONMENT_IS_PTHREAD) {
+            threadParams.cmd = "spawnThread";
+            postMessage(threadParams, transferList);
+            return 0;
+          }
+          return spawnThread(threadParams);
+        }
+        function __emscripten_default_pthread_stack_size() {
+          return 2097152;
+        }
+        function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {
+          if (targetThreadId == mainThreadId) {
+            postMessage({ "cmd": "processQueuedMainThreadWork" });
+          } else if (ENVIRONMENT_IS_PTHREAD) {
+            postMessage({ "targetThread": targetThreadId, "cmd": "processThreadQueue" });
+          } else {
+            var pthread = PThread.pthreads[targetThreadId];
+            var worker = pthread && pthread.worker;
+            if (!worker) {
+              return;
+            }
+            worker.postMessage({ "cmd": "processThreadQueue" });
+          }
+          return 1;
+        }
+        function _abort() {
+          abort("");
+        }
+        function _emscripten_check_blocking_allowed() {
+          if (ENVIRONMENT_IS_NODE)
+            return;
+          if (ENVIRONMENT_IS_WORKER)
+            return;
+          warnOnce("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
+        }
+        function _emscripten_get_heap_max() {
+          return 2147483648;
+        }
+        function _emscripten_memcpy_big(dest, src, num) {
+          GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);
+        }
+        function _emscripten_num_logical_cores() {
+          if (ENVIRONMENT_IS_NODE)
+            return require_os().cpus().length;
+          return navigator["hardwareConcurrency"];
+        }
+        function _emscripten_proxy_to_main_thread_js(index, sync) {
+          var numCallArgs = arguments.length - 2;
+          var outerArgs = arguments;
+          return withStackSave(function() {
+            var serializedNumCallArgs = numCallArgs;
+            var args = stackAlloc(serializedNumCallArgs * 8);
+            var b = args >> 3;
+            for (var i2 = 0; i2 < numCallArgs; i2++) {
+              var arg = outerArgs[2 + i2];
+              GROWABLE_HEAP_F64()[b + i2] = arg;
+            }
+            return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);
+          });
+        }
+        var _emscripten_receive_on_main_thread_js_callArgs = [];
+        function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {
+          _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
+          var b = args >> 3;
+          for (var i2 = 0; i2 < numCallArgs; i2++) {
+            _emscripten_receive_on_main_thread_js_callArgs[i2] = GROWABLE_HEAP_F64()[b + i2];
+          }
+          var isEmAsmConst = index < 0;
+          var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];
+          return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);
+        }
+        function emscripten_realloc_buffer(size) {
+          try {
+            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+            return 1;
+          } catch (e2) {
+          }
+        }
+        function _emscripten_resize_heap(requestedSize) {
+          var oldSize = GROWABLE_HEAP_U8().length;
+          requestedSize = requestedSize >>> 0;
+          if (requestedSize <= oldSize) {
+            return false;
+          }
+          var maxHeapSize = _emscripten_get_heap_max();
+          if (requestedSize > maxHeapSize) {
+            return false;
+          }
+          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
+            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
+            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
+            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
+            var replacement = emscripten_realloc_buffer(newSize);
+            if (replacement) {
+              return true;
+            }
+          }
+          return false;
+        }
+        var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {
+          for (var i2 = JSEvents.eventHandlers.length - 1; i2 >= 0; --i2) {
+            JSEvents._removeHandler(i2);
+          }
+          JSEvents.eventHandlers = [];
+          JSEvents.deferredCalls = [];
+        }, registerRemoveEventListeners: function() {
+          if (!JSEvents.removeEventListenersRegistered) {
+            __ATEXIT__.push(JSEvents.removeAllEventListeners);
+            JSEvents.removeEventListenersRegistered = true;
+          }
+        }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {
+          function arraysHaveEqualContent(arrA, arrB) {
+            if (arrA.length != arrB.length)
+              return false;
+            for (var i3 in arrA) {
+              if (arrA[i3] != arrB[i3])
+                return false;
+            }
+            return true;
+          }
+          for (var i2 in JSEvents.deferredCalls) {
+            var call = JSEvents.deferredCalls[i2];
+            if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
+              return;
+            }
+          }
+          JSEvents.deferredCalls.push({ targetFunction, precedence, argsList });
+          JSEvents.deferredCalls.sort(function(x, y) {
+            return x.precedence < y.precedence;
+          });
+        }, removeDeferredCalls: function(targetFunction) {
+          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
+            if (JSEvents.deferredCalls[i2].targetFunction == targetFunction) {
+              JSEvents.deferredCalls.splice(i2, 1);
+              --i2;
+            }
+          }
+        }, canPerformEventHandlerRequests: function() {
+          return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;
+        }, runDeferredCalls: function() {
+          if (!JSEvents.canPerformEventHandlerRequests()) {
+            return;
+          }
+          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
+            var call = JSEvents.deferredCalls[i2];
+            JSEvents.deferredCalls.splice(i2, 1);
+            --i2;
+            call.targetFunction.apply(null, call.argsList);
+          }
+        }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {
+          for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
+            if (JSEvents.eventHandlers[i2].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i2].eventTypeString)) {
+              JSEvents._removeHandler(i2--);
+            }
+          }
+        }, _removeHandler: function(i2) {
+          var h = JSEvents.eventHandlers[i2];
+          h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
+          JSEvents.eventHandlers.splice(i2, 1);
+        }, registerOrRemoveHandler: function(eventHandler) {
+          var jsEventHandler = function jsEventHandler2(event) {
+            ++JSEvents.inEventHandler;
+            JSEvents.currentEventHandler = eventHandler;
+            JSEvents.runDeferredCalls();
+            eventHandler.handlerFunc(event);
+            JSEvents.runDeferredCalls();
+            --JSEvents.inEventHandler;
+          };
+          if (eventHandler.callbackfunc) {
+            eventHandler.eventListenerFunc = jsEventHandler;
+            eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);
+            JSEvents.eventHandlers.push(eventHandler);
+            JSEvents.registerRemoveEventListeners();
+          } else {
+            for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
+              if (JSEvents.eventHandlers[i2].target == eventHandler.target && JSEvents.eventHandlers[i2].eventTypeString == eventHandler.eventTypeString) {
+                JSEvents._removeHandler(i2--);
+              }
+            }
+          }
+        }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {
+          withStackSave(function() {
+            var varargs = stackAlloc(12);
+            GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;
+            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;
+            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;
+            _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);
+          });
+        }, getTargetThreadForEventCallback: function(targetThread) {
+          switch (targetThread) {
+            case 1:
+              return 0;
+            case 2:
+              return PThread.currentProxiedOperationCallerThread;
+            default:
+              return targetThread;
+          }
+        }, getNodeNameForTarget: function(target) {
+          if (!target)
+            return "";
+          if (target == window)
+            return "#window";
+          if (target == screen)
+            return "#screen";
+          return target && target.nodeName ? target.nodeName : "";
+        }, fullscreenEnabled: function() {
+          return document.fullscreenEnabled || document.webkitFullscreenEnabled;
+        } };
+        function stringToNewUTF8(jsString) {
+          var length = lengthBytesUTF8(jsString) + 1;
+          var cString = _malloc(length);
+          stringToUTF8(jsString, cString, length);
+          return cString;
+        }
+        function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {
+          withStackSave(function() {
+            var varargs = stackAlloc(12);
+            var targetCanvasPtr = 0;
+            if (targetCanvas) {
+              targetCanvasPtr = stringToNewUTF8(targetCanvas);
+            }
+            GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;
+            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;
+            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;
+            _emscripten_dispatch_to_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs);
+          });
+        }
+        function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {
+          targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : "";
+          _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);
+        }
+        function maybeCStringToJsString(cString) {
+          return cString > 2 ? UTF8ToString(cString) : cString;
+        }
+        var specialHTMLTargets = [0, typeof document !== "undefined" ? document : 0, typeof window !== "undefined" ? window : 0];
+        function findEventTarget(target) {
+          target = maybeCStringToJsString(target);
+          var domElement = specialHTMLTargets[target] || (typeof document !== "undefined" ? document.querySelector(target) : void 0);
+          return domElement;
+        }
+        function findCanvasEventTarget(target) {
+          return findEventTarget(target);
+        }
+        function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {
+          var canvas = findCanvasEventTarget(target);
+          if (!canvas)
+            return -4;
+          if (canvas.canvasSharedPtr) {
+            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width;
+            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height;
+          }
+          if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) {
+            if (canvas.offscreenCanvas)
+              canvas = canvas.offscreenCanvas;
+            var autoResizeViewport = false;
+            if (canvas.GLctxObject && canvas.GLctxObject.GLctx) {
+              var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978);
+              autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height;
+            }
+            canvas.width = width;
+            canvas.height = height;
+            if (autoResizeViewport) {
+              canvas.GLctxObject.GLctx.viewport(0, 0, width, height);
+            }
+          } else if (canvas.canvasSharedPtr) {
+            var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2];
+            _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);
+            return 1;
+          } else {
+            return -4;
+          }
+          return 0;
+        }
+        function _emscripten_set_canvas_element_size_main_thread(target, width, height) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);
+          return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
+        }
+        function _emscripten_set_canvas_element_size(target, width, height) {
+          var canvas = findCanvasEventTarget(target);
+          if (canvas) {
+            return _emscripten_set_canvas_element_size_calling_thread(target, width, height);
+          } else {
+            return _emscripten_set_canvas_element_size_main_thread(target, width, height);
+          }
+        }
+        function _emscripten_unwind_to_js_event_loop() {
+          throw "unwind";
+        }
+        function __webgl_enable_ANGLE_instanced_arrays(ctx) {
+          var ext = ctx.getExtension("ANGLE_instanced_arrays");
+          if (ext) {
+            ctx["vertexAttribDivisor"] = function(index, divisor) {
+              ext["vertexAttribDivisorANGLE"](index, divisor);
+            };
+            ctx["drawArraysInstanced"] = function(mode, first, count2, primcount) {
+              ext["drawArraysInstancedANGLE"](mode, first, count2, primcount);
+            };
+            ctx["drawElementsInstanced"] = function(mode, count2, type, indices, primcount) {
+              ext["drawElementsInstancedANGLE"](mode, count2, type, indices, primcount);
+            };
+            return 1;
+          }
+        }
+        function __webgl_enable_OES_vertex_array_object(ctx) {
+          var ext = ctx.getExtension("OES_vertex_array_object");
+          if (ext) {
+            ctx["createVertexArray"] = function() {
+              return ext["createVertexArrayOES"]();
+            };
+            ctx["deleteVertexArray"] = function(vao) {
+              ext["deleteVertexArrayOES"](vao);
+            };
+            ctx["bindVertexArray"] = function(vao) {
+              ext["bindVertexArrayOES"](vao);
+            };
+            ctx["isVertexArray"] = function(vao) {
+              return ext["isVertexArrayOES"](vao);
+            };
+            return 1;
+          }
+        }
+        function __webgl_enable_WEBGL_draw_buffers(ctx) {
+          var ext = ctx.getExtension("WEBGL_draw_buffers");
+          if (ext) {
+            ctx["drawBuffers"] = function(n, bufs) {
+              ext["drawBuffersWEBGL"](n, bufs);
+            };
+            return 1;
+          }
+        }
+        function __webgl_enable_WEBGL_multi_draw(ctx) {
+          return !!(ctx.multiDrawWebgl = ctx.getExtension("WEBGL_multi_draw"));
+        }
+        var GL = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) {
+          if (!GL.lastError) {
+            GL.lastError = errorCode;
+          }
+        }, getNewId: function(table) {
+          var ret = GL.counter++;
+          for (var i2 = table.length; i2 < ret; i2++) {
+            table[i2] = null;
+          }
+          return ret;
+        }, getSource: function(shader, count2, string2, length) {
+          var source = "";
+          for (var i2 = 0; i2 < count2; ++i2) {
+            var len = length ? GROWABLE_HEAP_I32()[length + i2 * 4 >> 2] : -1;
+            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i2 * 4 >> 2], len < 0 ? void 0 : len);
+          }
+          return source;
+        }, createContext: function(canvas, webGLContextAttributes) {
+          if (!canvas.getContextSafariWebGL2Fixed) {
+            canvas.getContextSafariWebGL2Fixed = canvas.getContext;
+            canvas.getContext = function(ver, attrs) {
+              var gl = canvas.getContextSafariWebGL2Fixed(ver, attrs);
+              return ver == "webgl" == gl instanceof WebGLRenderingContext ? gl : null;
+            };
+          }
+          var ctx = canvas.getContext("webgl", webGLContextAttributes);
+          if (!ctx)
+            return 0;
+          var handle = GL.registerContext(ctx, webGLContextAttributes);
+          return handle;
+        }, registerContext: function(ctx, webGLContextAttributes) {
+          var handle = _malloc(8);
+          GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();
+          var context = { handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx };
+          if (ctx.canvas)
+            ctx.canvas.GLctxObject = context;
+          GL.contexts[handle] = context;
+          if (typeof webGLContextAttributes.enableExtensionsByDefault === "undefined" || webGLContextAttributes.enableExtensionsByDefault) {
+            GL.initExtensions(context);
+          }
+          return handle;
+        }, makeContextCurrent: function(contextHandle) {
+          GL.currentContext = GL.contexts[contextHandle];
+          Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;
+          return !(contextHandle && !GLctx);
+        }, getContext: function(contextHandle) {
+          return GL.contexts[contextHandle];
+        }, deleteContext: function(contextHandle) {
+          if (GL.currentContext === GL.contexts[contextHandle])
+            GL.currentContext = null;
+          if (typeof JSEvents === "object")
+            JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);
+          if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)
+            GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;
+          _free(GL.contexts[contextHandle].handle);
+          GL.contexts[contextHandle] = null;
+        }, initExtensions: function(context) {
+          if (!context)
+            context = GL.currentContext;
+          if (context.initExtensionsDone)
+            return;
+          context.initExtensionsDone = true;
+          var GLctx2 = context.GLctx;
+          __webgl_enable_ANGLE_instanced_arrays(GLctx2);
+          __webgl_enable_OES_vertex_array_object(GLctx2);
+          __webgl_enable_WEBGL_draw_buffers(GLctx2);
+          {
+            GLctx2.disjointTimerQueryExt = GLctx2.getExtension("EXT_disjoint_timer_query");
+          }
+          __webgl_enable_WEBGL_multi_draw(GLctx2);
+          var exts = GLctx2.getSupportedExtensions() || [];
+          exts.forEach(function(ext) {
+            if (!ext.includes("lose_context") && !ext.includes("debug")) {
+              GLctx2.getExtension(ext);
+            }
+          });
+        } };
+        var __emscripten_webgl_power_preferences = ["default", "low-power", "high-performance"];
+        function _emscripten_webgl_do_create_context(target, attributes) {
+          var a6 = attributes >> 2;
+          var powerPreference = GROWABLE_HEAP_I32()[a6 + (24 >> 2)];
+          var contextAttributes = { "alpha": !!GROWABLE_HEAP_I32()[a6 + (0 >> 2)], "depth": !!GROWABLE_HEAP_I32()[a6 + (4 >> 2)], "stencil": !!GROWABLE_HEAP_I32()[a6 + (8 >> 2)], "antialias": !!GROWABLE_HEAP_I32()[a6 + (12 >> 2)], "premultipliedAlpha": !!GROWABLE_HEAP_I32()[a6 + (16 >> 2)], "preserveDrawingBuffer": !!GROWABLE_HEAP_I32()[a6 + (20 >> 2)], "powerPreference": __emscripten_webgl_power_preferences[powerPreference], "failIfMajorPerformanceCaveat": !!GROWABLE_HEAP_I32()[a6 + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a6 + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a6 + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a6 + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a6 + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a6 + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a6 + (52 >> 2)] };
+          var canvas = findCanvasEventTarget(target);
+          if (!canvas) {
+            return 0;
+          }
+          if (contextAttributes.explicitSwapControl) {
+            return 0;
+          }
+          var contextHandle = GL.createContext(canvas, contextAttributes);
+          return contextHandle;
+        }
+        function _emscripten_webgl_create_context(a0, a12) {
+          return _emscripten_webgl_do_create_context(a0, a12);
+        }
+        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
+          var buffer3 = SYSCALLS.buffers[stream];
+          if (curr === 0 || curr === 10) {
+            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));
+            buffer3.length = 0;
+          } else {
+            buffer3.push(curr);
+          }
+        }, varargs: void 0, get: function() {
+          SYSCALLS.varargs += 4;
+          var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];
+          return ret;
+        }, getStr: function(ptr) {
+          var ret = UTF8ToString(ptr);
+          return ret;
+        }, get64: function(low, high) {
+          return low;
+        } };
+        function _fd_close(fd) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(3, 1, fd);
+          return 0;
+        }
+        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);
+        }
+        function _fd_write(fd, iov, iovcnt, pnum) {
+          if (ENVIRONMENT_IS_PTHREAD)
+            return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);
+          var num = 0;
+          for (var i2 = 0; i2 < iovcnt; i2++) {
+            var ptr = GROWABLE_HEAP_I32()[iov >> 2];
+            var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];
+            iov += 8;
+            for (var j = 0; j < len; j++) {
+              SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);
+            }
+            num += len;
+          }
+          GROWABLE_HEAP_I32()[pnum >> 2] = num;
+          return 0;
+        }
+        function _setTempRet0(val) {
+          setTempRet0(val);
+        }
+        PThread.init();
+        var GLctx;
+        var proxiedFunctionTable = [null, exitOnMainThread, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write];
+        var ASSERTIONS = false;
+        var asmLibraryArg = { "__clock_gettime": ___clock_gettime, "__emscripten_init_main_thread_js": ___emscripten_init_main_thread_js, "__emscripten_thread_cleanup": ___emscripten_thread_cleanup, "__pthread_create_js": ___pthread_create_js, "_emscripten_default_pthread_stack_size": __emscripten_default_pthread_stack_size, "_emscripten_notify_thread_queue": __emscripten_notify_thread_queue, "abort": _abort, "emscripten_check_blocking_allowed": _emscripten_check_blocking_allowed, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_get_now": _emscripten_get_now, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_num_logical_cores": _emscripten_num_logical_cores, "emscripten_receive_on_main_thread_js": _emscripten_receive_on_main_thread_js, "emscripten_resize_heap": _emscripten_resize_heap, "emscripten_set_canvas_element_size": _emscripten_set_canvas_element_size, "emscripten_unwind_to_js_event_loop": _emscripten_unwind_to_js_event_loop, "emscripten_webgl_create_context": _emscripten_webgl_create_context, "exit": _exit, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "memory": wasmMemory || Module["wasmMemory"], "setTempRet0": _setTempRet0 };
+        var asm = createWasm();
+        var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
+          return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments);
+        };
+        var _init = Module["_init"] = function() {
+          return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments);
+        };
+        var _init_with_threads_count = Module["_init_with_threads_count"] = function() {
+          return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments);
+        };
+        var _get_threads_count = Module["_get_threads_count"] = function() {
+          return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments);
+        };
+        var _register_tensor = Module["_register_tensor"] = function() {
+          return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments);
+        };
+        var _dispose_data = Module["_dispose_data"] = function() {
+          return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments);
+        };
+        var _dispose = Module["_dispose"] = function() {
+          return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments);
+        };
+        var _Abs = Module["_Abs"] = function() {
+          return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments);
+        };
+        var _Add = Module["_Add"] = function() {
+          return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments);
+        };
+        var _AddN = Module["_AddN"] = function() {
+          return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments);
+        };
+        var _All = Module["_All"] = function() {
+          return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments);
+        };
+        var _Any = Module["_Any"] = function() {
+          return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments);
+        };
+        var _ArgMax = Module["_ArgMax"] = function() {
+          return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments);
+        };
+        var _AvgPool = Module["_AvgPool"] = function() {
+          return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments);
+        };
+        var _BatchMatMul = Module["_BatchMatMul"] = function() {
+          return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments);
+        };
+        var _Ceil = Module["_Ceil"] = function() {
+          return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments);
+        };
+        var _ClipByValue = Module["_ClipByValue"] = function() {
+          return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments);
+        };
+        var _Conv2D = Module["_Conv2D"] = function() {
+          return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments);
+        };
+        var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
+          return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments);
+        };
+        var _Cos = Module["_Cos"] = function() {
+          return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments);
+        };
+        var _Cosh = Module["_Cosh"] = function() {
+          return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments);
+        };
+        var _CropAndResize = Module["_CropAndResize"] = function() {
+          return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments);
+        };
+        var _Cumprod = Module["_Cumprod"] = function() {
+          return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments);
+        };
+        var _Cumsum = Module["_Cumsum"] = function() {
+          return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments);
+        };
+        var _DepthToSpace = Module["_DepthToSpace"] = function() {
+          return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments);
+        };
+        var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
+          return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments);
+        };
+        var _Elu = Module["_Elu"] = function() {
+          return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments);
+        };
+        var _Equal = Module["_Equal"] = function() {
+          return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments);
+        };
+        var _Exp = Module["_Exp"] = function() {
+          return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments);
+        };
+        var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
+          return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments);
+        };
+        var _Floor = Module["_Floor"] = function() {
+          return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments);
+        };
+        var _FloorDiv = Module["_FloorDiv"] = function() {
+          return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments);
+        };
+        var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
+          return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments);
+        };
+        var _FusedConv2D = Module["_FusedConv2D"] = function() {
+          return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments);
+        };
+        var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
+          return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments);
+        };
+        var _Gather = Module["_Gather"] = function() {
+          return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments);
+        };
+        var _GatherNd = Module["_GatherNd"] = function() {
+          return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments);
+        };
+        var _Greater = Module["_Greater"] = function() {
+          return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments);
+        };
+        var _GreaterEqual = Module["_GreaterEqual"] = function() {
+          return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments);
+        };
+        var _LeakyRelu = Module["_LeakyRelu"] = function() {
+          return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments);
+        };
+        var _Less = Module["_Less"] = function() {
+          return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments);
+        };
+        var _LessEqual = Module["_LessEqual"] = function() {
+          return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments);
+        };
+        var _Log = Module["_Log"] = function() {
+          return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments);
+        };
+        var _LogicalAnd = Module["_LogicalAnd"] = function() {
+          return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments);
+        };
+        var _Max = Module["_Max"] = function() {
+          return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments);
+        };
+        var _MaxPool = Module["_MaxPool"] = function() {
+          return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments);
+        };
+        var _Maximum = Module["_Maximum"] = function() {
+          return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments);
+        };
+        var _Mean = Module["_Mean"] = function() {
+          return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments);
+        };
+        var _Min = Module["_Min"] = function() {
+          return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments);
+        };
+        var _Minimum = Module["_Minimum"] = function() {
+          return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments);
+        };
+        var _MirrorPad = Module["_MirrorPad"] = function() {
+          return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments);
+        };
+        var _Multiply = Module["_Multiply"] = function() {
+          return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments);
+        };
+        var _Neg = Module["_Neg"] = function() {
+          return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
+          return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
+          return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
+          return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments);
+        };
+        var _NotEqual = Module["_NotEqual"] = function() {
+          return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments);
+        };
+        var _OneHot = Module["_OneHot"] = function() {
+          return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments);
+        };
+        var _PadV2 = Module["_PadV2"] = function() {
+          return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments);
+        };
+        var _Pow = Module["_Pow"] = function() {
+          return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments);
+        };
+        var _Prelu = Module["_Prelu"] = function() {
+          return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments);
+        };
+        var _Prod = Module["_Prod"] = function() {
+          return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments);
+        };
+        var _RealDiv = Module["_RealDiv"] = function() {
+          return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments);
+        };
+        var _Relu = Module["_Relu"] = function() {
+          return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments);
+        };
+        var _Relu6 = Module["_Relu6"] = function() {
+          return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments);
+        };
+        var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
+          return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments);
+        };
+        var _Reverse = Module["_Reverse"] = function() {
+          return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments);
+        };
+        var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
+          return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments);
+        };
+        var _Round = Module["_Round"] = function() {
+          return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments);
+        };
+        var _Rsqrt = Module["_Rsqrt"] = function() {
+          return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments);
+        };
+        var _ScatterNd = Module["_ScatterNd"] = function() {
+          return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments);
+        };
+        var _SelectV2 = Module["_SelectV2"] = function() {
+          return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments);
+        };
+        var _Sigmoid = Module["_Sigmoid"] = function() {
+          return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments);
+        };
+        var _Sin = Module["_Sin"] = function() {
+          return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments);
+        };
+        var _Softmax = Module["_Softmax"] = function() {
+          return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments);
+        };
+        var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() {
+          return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments);
+        };
+        var _SparseReshape = Module["_SparseReshape"] = function() {
+          return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments);
+        };
+        var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() {
+          return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments);
+        };
+        var _Sqrt = Module["_Sqrt"] = function() {
+          return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments);
+        };
+        var _Square = Module["_Square"] = function() {
+          return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments);
+        };
+        var _SquaredDifference = Module["_SquaredDifference"] = function() {
+          return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments);
+        };
+        var _Step = Module["_Step"] = function() {
+          return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments);
+        };
+        var _StridedSlice = Module["_StridedSlice"] = function() {
+          return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments);
+        };
+        var _Sub = Module["_Sub"] = function() {
+          return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments);
+        };
+        var _Sum = Module["_Sum"] = function() {
+          return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments);
+        };
+        var _Tan = Module["_Tan"] = function() {
+          return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments);
+        };
+        var _Tanh = Module["_Tanh"] = function() {
+          return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments);
+        };
+        var _Tile = Module["_Tile"] = function() {
+          return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments);
+        };
+        var _TopK = Module["_TopK"] = function() {
+          return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments);
+        };
+        var _Transform = Module["_Transform"] = function() {
+          return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments);
+        };
+        var _Transpose = Module["_Transpose"] = function() {
+          return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments);
+        };
+        var __FusedMatMul = Module["__FusedMatMul"] = function() {
+          return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments);
+        };
+        var _malloc = Module["_malloc"] = function() {
+          return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments);
+        };
+        var _free = Module["_free"] = function() {
+          return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments);
+        };
+        var _emscripten_tls_init = Module["_emscripten_tls_init"] = function() {
+          return (_emscripten_tls_init = Module["_emscripten_tls_init"] = Module["asm"]["emscripten_tls_init"]).apply(null, arguments);
+        };
+        var ___errno_location = Module["___errno_location"] = function() {
+          return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments);
+        };
+        var _pthread_self = Module["_pthread_self"] = function() {
+          return (_pthread_self = Module["_pthread_self"] = Module["asm"]["pthread_self"]).apply(null, arguments);
+        };
+        var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() {
+          return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments);
+        };
+        var __emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = function() {
+          return (__emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = Module["asm"]["_emscripten_thread_crashed"]).apply(null, arguments);
+        };
+        var __emscripten_thread_init = Module["__emscripten_thread_init"] = function() {
+          return (__emscripten_thread_init = Module["__emscripten_thread_init"] = Module["asm"]["_emscripten_thread_init"]).apply(null, arguments);
+        };
+        var _emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = function() {
+          return (_emscripten_current_thread_process_queued_calls = Module["_emscripten_current_thread_process_queued_calls"] = Module["asm"]["emscripten_current_thread_process_queued_calls"]).apply(null, arguments);
+        };
+        var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = function() {
+          return (_emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = Module["asm"]["emscripten_main_browser_thread_id"]).apply(null, arguments);
+        };
+        var _emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = function() {
+          return (_emscripten_sync_run_in_main_thread_2 = Module["_emscripten_sync_run_in_main_thread_2"] = Module["asm"]["emscripten_sync_run_in_main_thread_2"]).apply(null, arguments);
+        };
+        var _emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = function() {
+          return (_emscripten_sync_run_in_main_thread_4 = Module["_emscripten_sync_run_in_main_thread_4"] = Module["asm"]["emscripten_sync_run_in_main_thread_4"]).apply(null, arguments);
+        };
+        var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = function() {
+          return (_emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = Module["asm"]["emscripten_run_in_main_runtime_thread_js"]).apply(null, arguments);
+        };
+        var _emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = function() {
+          return (_emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = Module["asm"]["emscripten_dispatch_to_thread_"]).apply(null, arguments);
+        };
+        var __emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = function() {
+          return (__emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = Module["asm"]["_emscripten_thread_free_data"]).apply(null, arguments);
+        };
+        var __emscripten_thread_exit = Module["__emscripten_thread_exit"] = function() {
+          return (__emscripten_thread_exit = Module["__emscripten_thread_exit"] = Module["asm"]["_emscripten_thread_exit"]).apply(null, arguments);
+        };
+        var _memalign = Module["_memalign"] = function() {
+          return (_memalign = Module["_memalign"] = Module["asm"]["memalign"]).apply(null, arguments);
+        };
+        var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() {
+          return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["emscripten_stack_set_limits"]).apply(null, arguments);
+        };
+        var stackSave = Module["stackSave"] = function() {
+          return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments);
+        };
+        var stackRestore = Module["stackRestore"] = function() {
+          return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments);
+        };
+        var stackAlloc = Module["stackAlloc"] = function() {
+          return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments);
+        };
+        var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() {
+          return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments);
+        };
+        var dynCall_jiji = Module["dynCall_jiji"] = function() {
+          return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments);
+        };
+        var __emscripten_allow_main_runtime_queued_calls = Module["__emscripten_allow_main_runtime_queued_calls"] = 21464;
+        Module["cwrap"] = cwrap;
+        Module["keepRuntimeAlive"] = keepRuntimeAlive;
+        Module["PThread"] = PThread;
+        Module["PThread"] = PThread;
+        Module["wasmMemory"] = wasmMemory;
+        Module["ExitStatus"] = ExitStatus;
+        var calledRun;
+        function ExitStatus(status) {
+          this.name = "ExitStatus";
+          this.message = "Program terminated with exit(" + status + ")";
+          this.status = status;
+        }
+        dependenciesFulfilled = function runCaller() {
+          if (!calledRun)
+            run();
+          if (!calledRun)
+            dependenciesFulfilled = runCaller;
+        };
+        function run(args) {
+          args = args || arguments_;
+          if (runDependencies > 0) {
+            return;
+          }
+          if (ENVIRONMENT_IS_PTHREAD) {
+            readyPromiseResolve(Module);
+            initRuntime();
+            postMessage({ "cmd": "loaded" });
+            return;
+          }
+          preRun();
+          if (runDependencies > 0) {
+            return;
+          }
+          function doRun() {
+            if (calledRun)
+              return;
+            calledRun = true;
+            Module["calledRun"] = true;
+            if (ABORT)
+              return;
+            initRuntime();
+            readyPromiseResolve(Module);
+            if (Module["onRuntimeInitialized"])
+              Module["onRuntimeInitialized"]();
+            postRun();
+          }
+          if (Module["setStatus"]) {
+            Module["setStatus"]("Running...");
+            setTimeout(function() {
+              setTimeout(function() {
+                Module["setStatus"]("");
+              }, 1);
+              doRun();
+            }, 1);
+          } else {
+            doRun();
+          }
+        }
+        Module["run"] = run;
+        function exit(status, implicit) {
+          EXITSTATUS = status;
+          if (!implicit) {
+            if (ENVIRONMENT_IS_PTHREAD) {
+              exitOnMainThread(status);
+              throw "unwind";
+            } else {
+            }
+          }
+          if (keepRuntimeAlive()) {
+          } else {
+            exitRuntime();
+          }
+          procExit(status);
+        }
+        function procExit(code) {
+          EXITSTATUS = code;
+          if (!keepRuntimeAlive()) {
+            PThread.terminateAllThreads();
+            if (Module["onExit"])
+              Module["onExit"](code);
+            ABORT = true;
+          }
+          quit_(code, new ExitStatus(code));
+        }
+        if (Module["preInit"]) {
+          if (typeof Module["preInit"] == "function")
+            Module["preInit"] = [Module["preInit"]];
+          while (Module["preInit"].length > 0) {
+            Module["preInit"].pop()();
+          }
+        }
+        run();
+        var listenersAdded;
+        if (beforeListeners) {
+          listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) {
+            return !beforeListeners.uncaughtException.indexOf(listener) > -1;
+          }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) {
+            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;
+          }) };
+        }
+        var actualModule;
+        if (typeof WasmBackendModule !== "undefined") {
+          actualModule = WasmBackendModule;
+        } else if (typeof WasmBackendModuleThreadedSimd3 !== "undefined") {
+          actualModule = WasmBackendModuleThreadedSimd3;
+        } else {
+          throw new Error("Could not find wasm module in post.js");
+        }
+        if (listenersAdded) {
+          var tmpDispose = actualModule["_dispose"];
+          actualModule["_dispose"] = function() {
+            tmpDispose();
+            listenersAdded.uncaughtException.forEach(function(listener) {
+              process.removeListener("uncaughtException", listener);
+            });
+            listenersAdded.unhandledRejection.forEach(function(listener) {
+              process.removeListener("unhandledRejection", listener);
+            });
+          };
+        }
+        return WasmBackendModuleThreadedSimd3.ready;
+      };
+    })();
+    if (typeof exports === "object" && typeof module === "object")
+      module.exports = WasmBackendModuleThreadedSimd2;
+    else if (typeof define === "function" && define["amd"])
+      define([], function() {
+        return WasmBackendModuleThreadedSimd2;
+      });
+    else if (typeof exports === "object")
+      exports["WasmBackendModuleThreadedSimd"] = WasmBackendModuleThreadedSimd2;
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js
+var require_tfjs_backend_wasm = __commonJS({
+  "node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(exports, module) {
+    var WasmBackendModule2 = (() => {
+      var _scriptDir = typeof document !== "undefined" && document.currentScript ? document.currentScript.src : void 0;
+      if (typeof __filename !== "undefined")
+        _scriptDir = _scriptDir || __filename;
+      return function(WasmBackendModule3) {
+        WasmBackendModule3 = WasmBackendModule3 || {};
+        var Module = typeof WasmBackendModule3 !== "undefined" ? WasmBackendModule3 : {};
+        var readyPromiseResolve, readyPromiseReject;
+        Module["ready"] = new Promise(function(resolve, reject) {
+          readyPromiseResolve = resolve;
+          readyPromiseReject = reject;
+        });
+        var beforeListeners;
+        if (typeof process !== "undefined" && process.listeners) {
+          beforeListeners = { uncaughtException: process.listeners("uncaughtException"), unhandledRejection: process.listeners("unhandledRejection") };
+        }
+        var moduleOverrides = Object.assign({}, Module);
+        var arguments_ = [];
+        var thisProgram = "./this.program";
+        var quit_ = (status, toThrow) => {
+          throw toThrow;
+        };
+        var ENVIRONMENT_IS_WEB = typeof window === "object";
+        var ENVIRONMENT_IS_WORKER = typeof importScripts === "function";
+        var ENVIRONMENT_IS_NODE = typeof process === "object" && typeof process.versions === "object" && typeof process.versions.node === "string";
+        var scriptDirectory = "";
+        function locateFile(path) {
+          if (Module["locateFile"]) {
+            return Module["locateFile"](path, scriptDirectory);
+          }
+          return scriptDirectory + path;
+        }
+        var read_, readAsync, readBinary, setWindowTitle;
+        function logExceptionOnExit(e2) {
+          if (e2 instanceof ExitStatus)
+            return;
+          let toLog = e2;
+          err("exiting due to exception: " + toLog);
+        }
+        var fs;
+        var nodePath;
+        var requireNodeFS;
+        if (ENVIRONMENT_IS_NODE) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = require_path().dirname(scriptDirectory) + "/";
+          } else {
+            scriptDirectory = __dirname + "/";
+          }
+          requireNodeFS = () => {
+            if (!nodePath) {
+              fs = require_fs();
+              nodePath = require_path();
+            }
+          };
+          read_ = function shell_read(filename, binary) {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            return fs.readFileSync(filename, binary ? void 0 : "utf8");
+          };
+          readBinary = (filename) => {
+            var ret = read_(filename, true);
+            if (!ret.buffer) {
+              ret = new Uint8Array(ret);
+            }
+            return ret;
+          };
+          readAsync = (filename, onload, onerror) => {
+            requireNodeFS();
+            filename = nodePath["normalize"](filename);
+            fs.readFile(filename, function(err2, data) {
+              if (err2)
+                onerror(err2);
+              else
+                onload(data.buffer);
+            });
+          };
+          if (process["argv"].length > 1) {
+            thisProgram = process["argv"][1].replace(/\\/g, "/");
+          }
+          arguments_ = process["argv"].slice(2);
+          process["on"]("uncaughtException", function(ex) {
+            if (!(ex instanceof ExitStatus)) {
+              throw ex;
+            }
+          });
+          process["on"]("unhandledRejection", function(reason) {
+            throw reason;
+          });
+          quit_ = (status, toThrow) => {
+            if (keepRuntimeAlive()) {
+              process["exitCode"] = status;
+              throw toThrow;
+            }
+            logExceptionOnExit(toThrow);
+            process["exit"](status);
+          };
+          Module["inspect"] = function() {
+            return "[Emscripten Module object]";
+          };
+        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
+          if (ENVIRONMENT_IS_WORKER) {
+            scriptDirectory = self.location.href;
+          } else if (typeof document !== "undefined" && document.currentScript) {
+            scriptDirectory = document.currentScript.src;
+          }
+          if (_scriptDir) {
+            scriptDirectory = _scriptDir;
+          }
+          if (scriptDirectory.indexOf("blob:") !== 0) {
+            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
+          } else {
+            scriptDirectory = "";
+          }
+          {
+            read_ = (url) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, false);
+              xhr.send(null);
+              return xhr.responseText;
+            };
+            if (ENVIRONMENT_IS_WORKER) {
+              readBinary = (url) => {
+                var xhr = new XMLHttpRequest();
+                xhr.open("GET", url, false);
+                xhr.responseType = "arraybuffer";
+                xhr.send(null);
+                return new Uint8Array(xhr.response);
+              };
+            }
+            readAsync = (url, onload, onerror) => {
+              var xhr = new XMLHttpRequest();
+              xhr.open("GET", url, true);
+              xhr.responseType = "arraybuffer";
+              xhr.onload = () => {
+                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
+                  onload(xhr.response);
+                  return;
+                }
+                onerror();
+              };
+              xhr.onerror = onerror;
+              xhr.send(null);
+            };
+          }
+          setWindowTitle = (title) => document.title = title;
+        } else {
+        }
+        var out = Module["print"] || console.log.bind(console);
+        var err = Module["printErr"] || console.warn.bind(console);
+        Object.assign(Module, moduleOverrides);
+        moduleOverrides = null;
+        if (Module["arguments"])
+          arguments_ = Module["arguments"];
+        if (Module["thisProgram"])
+          thisProgram = Module["thisProgram"];
+        if (Module["quit"])
+          quit_ = Module["quit"];
+        var POINTER_SIZE = 4;
+        function warnOnce(text) {
+          if (!warnOnce.shown)
+            warnOnce.shown = {};
+          if (!warnOnce.shown[text]) {
+            warnOnce.shown[text] = 1;
+            err(text);
+          }
+        }
+        function convertJsFunctionToWasm(func2, sig) {
+          if (typeof WebAssembly.Function === "function") {
+            var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
+            var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
+            for (var i2 = 1; i2 < sig.length; ++i2) {
+              type.parameters.push(typeNames[sig[i2]]);
+            }
+            return new WebAssembly.Function(type, func2);
+          }
+          var typeSection = [1, 0, 1, 96];
+          var sigRet = sig.slice(0, 1);
+          var sigParam = sig.slice(1);
+          var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
+          typeSection.push(sigParam.length);
+          for (var i2 = 0; i2 < sigParam.length; ++i2) {
+            typeSection.push(typeCodes[sigParam[i2]]);
+          }
+          if (sigRet == "v") {
+            typeSection.push(0);
+          } else {
+            typeSection = typeSection.concat([1, typeCodes[sigRet]]);
+          }
+          typeSection[1] = typeSection.length - 2;
+          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));
+          var module2 = new WebAssembly.Module(bytes);
+          var instance = new WebAssembly.Instance(module2, { "e": { "f": func2 } });
+          var wrappedFunc = instance.exports["f"];
+          return wrappedFunc;
+        }
+        var freeTableIndexes = [];
+        var functionsInTableMap;
+        function getEmptyTableSlot() {
+          if (freeTableIndexes.length) {
+            return freeTableIndexes.pop();
+          }
+          try {
+            wasmTable.grow(1);
+          } catch (err2) {
+            if (!(err2 instanceof RangeError)) {
+              throw err2;
+            }
+            throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
+          }
+          return wasmTable.length - 1;
+        }
+        function updateTableMap(offset, count2) {
+          for (var i2 = offset; i2 < offset + count2; i2++) {
+            var item = getWasmTableEntry(i2);
+            if (item) {
+              functionsInTableMap.set(item, i2);
+            }
+          }
+        }
+        var tempRet0 = 0;
+        var setTempRet0 = (value) => {
+          tempRet0 = value;
+        };
+        var wasmBinary;
+        if (Module["wasmBinary"])
+          wasmBinary = Module["wasmBinary"];
+        var noExitRuntime = Module["noExitRuntime"] || true;
+        if (typeof WebAssembly !== "object") {
+          abort("no native wasm support detected");
+        }
+        var wasmMemory;
+        var ABORT = false;
+        var EXITSTATUS;
+        function assert3(condition, text) {
+          if (!condition) {
+            abort(text);
+          }
+        }
+        function getCFunc(ident) {
+          var func2 = Module["_" + ident];
+          return func2;
+        }
+        function ccall(ident, returnType, argTypes, args, opts) {
+          var toC = { "string": function(str) {
+            var ret2 = 0;
+            if (str !== null && str !== void 0 && str !== 0) {
+              var len = (str.length << 2) + 1;
+              ret2 = stackAlloc(len);
+              stringToUTF8(str, ret2, len);
+            }
+            return ret2;
+          }, "array": function(arr) {
+            var ret2 = stackAlloc(arr.length);
+            writeArrayToMemory(arr, ret2);
+            return ret2;
+          } };
+          function convertReturnValue(ret2) {
+            if (returnType === "string")
+              return UTF8ToString(ret2);
+            if (returnType === "boolean")
+              return Boolean(ret2);
+            return ret2;
+          }
+          var func2 = getCFunc(ident);
+          var cArgs = [];
+          var stack2 = 0;
+          if (args) {
+            for (var i2 = 0; i2 < args.length; i2++) {
+              var converter = toC[argTypes[i2]];
+              if (converter) {
+                if (stack2 === 0)
+                  stack2 = stackSave();
+                cArgs[i2] = converter(args[i2]);
+              } else {
+                cArgs[i2] = args[i2];
+              }
+            }
+          }
+          var ret = func2.apply(null, cArgs);
+          function onDone(ret2) {
+            if (stack2 !== 0)
+              stackRestore(stack2);
+            return convertReturnValue(ret2);
+          }
+          ret = onDone(ret);
+          return ret;
+        }
+        function cwrap(ident, returnType, argTypes, opts) {
+          argTypes = argTypes || [];
+          var numericArgs = argTypes.every(function(type) {
+            return type === "number";
+          });
+          var numericRet = returnType !== "string";
+          if (numericRet && numericArgs && !opts) {
+            return getCFunc(ident);
+          }
+          return function() {
+            return ccall(ident, returnType, argTypes, arguments, opts);
+          };
+        }
+        var ALLOC_STACK = 1;
+        var UTF8Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf8") : void 0;
+        function UTF8ArrayToString(heap, idx, maxBytesToRead) {
+          var endIdx = idx + maxBytesToRead;
+          var endPtr = idx;
+          while (heap[endPtr] && !(endPtr >= endIdx))
+            ++endPtr;
+          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {
+            return UTF8Decoder.decode(heap.subarray(idx, endPtr));
+          } else {
+            var str = "";
+            while (idx < endPtr) {
+              var u0 = heap[idx++];
+              if (!(u0 & 128)) {
+                str += String.fromCharCode(u0);
+                continue;
+              }
+              var u1 = heap[idx++] & 63;
+              if ((u0 & 224) == 192) {
+                str += String.fromCharCode((u0 & 31) << 6 | u1);
+                continue;
+              }
+              var u2 = heap[idx++] & 63;
+              if ((u0 & 240) == 224) {
+                u0 = (u0 & 15) << 12 | u1 << 6 | u2;
+              } else {
+                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;
+              }
+              if (u0 < 65536) {
+                str += String.fromCharCode(u0);
+              } else {
+                var ch = u0 - 65536;
+                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
+              }
+            }
+          }
+          return str;
+        }
+        function UTF8ToString(ptr, maxBytesToRead) {
+          return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : "";
+        }
+        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
+          if (!(maxBytesToWrite > 0))
+            return 0;
+          var startIdx = outIdx;
+          var endIdx = outIdx + maxBytesToWrite - 1;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343) {
+              var u1 = str.charCodeAt(++i2);
+              u = 65536 + ((u & 1023) << 10) | u1 & 1023;
+            }
+            if (u <= 127) {
+              if (outIdx >= endIdx)
+                break;
+              heap[outIdx++] = u;
+            } else if (u <= 2047) {
+              if (outIdx + 1 >= endIdx)
+                break;
+              heap[outIdx++] = 192 | u >> 6;
+              heap[outIdx++] = 128 | u & 63;
+            } else if (u <= 65535) {
+              if (outIdx + 2 >= endIdx)
+                break;
+              heap[outIdx++] = 224 | u >> 12;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            } else {
+              if (outIdx + 3 >= endIdx)
+                break;
+              heap[outIdx++] = 240 | u >> 18;
+              heap[outIdx++] = 128 | u >> 12 & 63;
+              heap[outIdx++] = 128 | u >> 6 & 63;
+              heap[outIdx++] = 128 | u & 63;
+            }
+          }
+          heap[outIdx] = 0;
+          return outIdx - startIdx;
+        }
+        function stringToUTF8(str, outPtr, maxBytesToWrite) {
+          return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);
+        }
+        function lengthBytesUTF8(str) {
+          var len = 0;
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            var u = str.charCodeAt(i2);
+            if (u >= 55296 && u <= 57343)
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+            if (u <= 127)
+              ++len;
+            else if (u <= 2047)
+              len += 2;
+            else if (u <= 65535)
+              len += 3;
+            else
+              len += 4;
+          }
+          return len;
+        }
+        var UTF16Decoder = typeof TextDecoder !== "undefined" ? new TextDecoder("utf-16le") : void 0;
+        function writeArrayToMemory(array2, buffer3) {
+          HEAP8.set(array2, buffer3);
+        }
+        function writeAsciiToMemory(str, buffer3, dontAddNull) {
+          for (var i2 = 0; i2 < str.length; ++i2) {
+            HEAP8[buffer3++ >> 0] = str.charCodeAt(i2);
+          }
+          if (!dontAddNull)
+            HEAP8[buffer3 >> 0] = 0;
+        }
+        function alignUp(x, multiple) {
+          if (x % multiple > 0) {
+            x += multiple - x % multiple;
+          }
+          return x;
+        }
+        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
+        function updateGlobalBufferAndViews(buf) {
+          buffer2 = buf;
+          Module["HEAP8"] = HEAP8 = new Int8Array(buf);
+          Module["HEAP16"] = HEAP16 = new Int16Array(buf);
+          Module["HEAP32"] = HEAP32 = new Int32Array(buf);
+          Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
+          Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
+          Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
+          Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
+          Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
+        }
+        var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 16777216;
+        var wasmTable;
+        var __ATPRERUN__ = [];
+        var __ATINIT__ = [];
+        var __ATPOSTRUN__ = [];
+        var runtimeInitialized = false;
+        var runtimeExited = false;
+        var runtimeKeepaliveCounter = 0;
+        function keepRuntimeAlive() {
+          return noExitRuntime || runtimeKeepaliveCounter > 0;
+        }
+        function preRun() {
+          if (Module["preRun"]) {
+            if (typeof Module["preRun"] == "function")
+              Module["preRun"] = [Module["preRun"]];
+            while (Module["preRun"].length) {
+              addOnPreRun(Module["preRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPRERUN__);
+        }
+        function initRuntime() {
+          runtimeInitialized = true;
+          callRuntimeCallbacks(__ATINIT__);
+        }
+        function exitRuntime() {
+          runtimeExited = true;
+        }
+        function postRun() {
+          if (Module["postRun"]) {
+            if (typeof Module["postRun"] == "function")
+              Module["postRun"] = [Module["postRun"]];
+            while (Module["postRun"].length) {
+              addOnPostRun(Module["postRun"].shift());
+            }
+          }
+          callRuntimeCallbacks(__ATPOSTRUN__);
+        }
+        function addOnPreRun(cb) {
+          __ATPRERUN__.unshift(cb);
+        }
+        function addOnInit(cb) {
+          __ATINIT__.unshift(cb);
+        }
+        function addOnPostRun(cb) {
+          __ATPOSTRUN__.unshift(cb);
+        }
+        var runDependencies = 0;
+        var runDependencyWatcher = null;
+        var dependenciesFulfilled = null;
+        function addRunDependency(id) {
+          runDependencies++;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+        }
+        function removeRunDependency(id) {
+          runDependencies--;
+          if (Module["monitorRunDependencies"]) {
+            Module["monitorRunDependencies"](runDependencies);
+          }
+          if (runDependencies == 0) {
+            if (runDependencyWatcher !== null) {
+              clearInterval(runDependencyWatcher);
+              runDependencyWatcher = null;
+            }
+            if (dependenciesFulfilled) {
+              var callback = dependenciesFulfilled;
+              dependenciesFulfilled = null;
+              callback();
+            }
+          }
+        }
+        Module["preloadedImages"] = {};
+        Module["preloadedAudios"] = {};
+        function abort(what) {
+          {
+            if (Module["onAbort"]) {
+              Module["onAbort"](what);
+            }
+          }
+          what = "Aborted(" + what + ")";
+          err(what);
+          ABORT = true;
+          EXITSTATUS = 1;
+          what += ". Build with -s ASSERTIONS=1 for more info.";
+          var e2 = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e2);
+          throw e2;
+        }
+        var dataURIPrefix = "data:application/octet-stream;base64,";
+        function isDataURI(filename) {
+          return filename.startsWith(dataURIPrefix);
+        }
+        function isFileURI(filename) {
+          return filename.startsWith("file://");
+        }
+        var wasmBinaryFile;
+        wasmBinaryFile = "tfjs-backend-wasm.wasm";
+        if (!isDataURI(wasmBinaryFile)) {
+          wasmBinaryFile = locateFile(wasmBinaryFile);
+        }
+        function getBinary(file) {
+          try {
+            if (file == wasmBinaryFile && wasmBinary) {
+              return new Uint8Array(wasmBinary);
+            }
+            if (readBinary) {
+              return readBinary(file);
+            } else {
+              throw "both async and sync fetching of the wasm failed";
+            }
+          } catch (err2) {
+            abort(err2);
+          }
+        }
+        function getBinaryPromise() {
+          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
+            if (typeof fetch === "function" && !isFileURI(wasmBinaryFile)) {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                if (!response["ok"]) {
+                  throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
+                }
+                return response["arrayBuffer"]();
+              }).catch(function() {
+                return getBinary(wasmBinaryFile);
+              });
+            } else {
+              if (readAsync) {
+                return new Promise(function(resolve, reject) {
+                  readAsync(wasmBinaryFile, function(response) {
+                    resolve(new Uint8Array(response));
+                  }, reject);
+                });
+              }
+            }
+          }
+          return Promise.resolve().then(function() {
+            return getBinary(wasmBinaryFile);
+          });
+        }
+        function createWasm() {
+          var info = { "env": asmLibraryArg, "wasi_snapshot_preview1": asmLibraryArg };
+          function receiveInstance(instance, module2) {
+            var exports3 = instance.exports;
+            Module["asm"] = exports3;
+            wasmMemory = Module["asm"]["memory"];
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+            wasmTable = Module["asm"]["__indirect_function_table"];
+            addOnInit(Module["asm"]["__wasm_call_ctors"]);
+            removeRunDependency("wasm-instantiate");
+          }
+          addRunDependency("wasm-instantiate");
+          function receiveInstantiationResult(result) {
+            receiveInstance(result["instance"]);
+          }
+          function instantiateArrayBuffer(receiver) {
+            return getBinaryPromise().then(function(binary) {
+              return WebAssembly.instantiate(binary, info);
+            }).then(function(instance) {
+              return instance;
+            }).then(receiver, function(reason) {
+              err("failed to asynchronously prepare wasm: " + reason);
+              abort(reason);
+            });
+          }
+          function instantiateAsync() {
+            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === "function" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === "function") {
+              return fetch(wasmBinaryFile, { credentials: "same-origin" }).then(function(response) {
+                var result = WebAssembly.instantiateStreaming(response, info);
+                return result.then(receiveInstantiationResult, function(reason) {
+                  err("wasm streaming compile failed: " + reason);
+                  err("falling back to ArrayBuffer instantiation");
+                  return instantiateArrayBuffer(receiveInstantiationResult);
+                });
+              });
+            } else {
+              return instantiateArrayBuffer(receiveInstantiationResult);
+            }
+          }
+          if (Module["instantiateWasm"]) {
+            try {
+              var exports2 = Module["instantiateWasm"](info, receiveInstance);
+              return exports2;
+            } catch (e2) {
+              err("Module.instantiateWasm callback failed with error: " + e2);
+              return false;
+            }
+          }
+          instantiateAsync().catch(readyPromiseReject);
+          return {};
+        }
+        var tempDouble;
+        var tempI64;
+        function callRuntimeCallbacks(callbacks2) {
+          while (callbacks2.length > 0) {
+            var callback = callbacks2.shift();
+            if (typeof callback == "function") {
+              callback(Module);
+              continue;
+            }
+            var func2 = callback.func;
+            if (typeof func2 === "number") {
+              if (callback.arg === void 0) {
+                getWasmTableEntry(func2)();
+              } else {
+                getWasmTableEntry(func2)(callback.arg);
+              }
+            } else {
+              func2(callback.arg === void 0 ? null : callback.arg);
+            }
+          }
+        }
+        function demangle(func2) {
+          return func2;
+        }
+        function demangleAll(text) {
+          var regex = /\b_Z[\w\d_]+/g;
+          return text.replace(regex, function(x) {
+            var y = demangle(x);
+            return x === y ? x : y + " [" + x + "]";
+          });
+        }
+        var wasmTableMirror = [];
+        function getWasmTableEntry(funcPtr) {
+          var func2 = wasmTableMirror[funcPtr];
+          if (!func2) {
+            if (funcPtr >= wasmTableMirror.length)
+              wasmTableMirror.length = funcPtr + 1;
+            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);
+          }
+          return func2;
+        }
+        function jsStackTrace() {
+          var error = new Error();
+          if (!error.stack) {
+            try {
+              throw new Error();
+            } catch (e2) {
+              error = e2;
+            }
+            if (!error.stack) {
+              return "(no stack trace available)";
+            }
+          }
+          return error.stack.toString();
+        }
+        function setWasmTableEntry(idx, func2) {
+          wasmTable.set(idx, func2);
+          wasmTableMirror[idx] = func2;
+        }
+        function _abort() {
+          abort("");
+        }
+        function _emscripten_get_heap_max() {
+          return 2147483648;
+        }
+        function _emscripten_memcpy_big(dest, src, num) {
+          HEAPU8.copyWithin(dest, src, src + num);
+        }
+        function emscripten_realloc_buffer(size) {
+          try {
+            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);
+            updateGlobalBufferAndViews(wasmMemory.buffer);
+            return 1;
+          } catch (e2) {
+          }
+        }
+        function _emscripten_resize_heap(requestedSize) {
+          var oldSize = HEAPU8.length;
+          requestedSize = requestedSize >>> 0;
+          var maxHeapSize = _emscripten_get_heap_max();
+          if (requestedSize > maxHeapSize) {
+            return false;
+          }
+          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
+            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);
+            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
+            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
+            var replacement = emscripten_realloc_buffer(newSize);
+            if (replacement) {
+              return true;
+            }
+          }
+          return false;
+        }
+        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {
+          var buffer3 = SYSCALLS.buffers[stream];
+          if (curr === 0 || curr === 10) {
+            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));
+            buffer3.length = 0;
+          } else {
+            buffer3.push(curr);
+          }
+        }, varargs: void 0, get: function() {
+          SYSCALLS.varargs += 4;
+          var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];
+          return ret;
+        }, getStr: function(ptr) {
+          var ret = UTF8ToString(ptr);
+          return ret;
+        }, get64: function(low, high) {
+          return low;
+        } };
+        function _fd_close(fd) {
+          return 0;
+        }
+        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
+        }
+        function _fd_write(fd, iov, iovcnt, pnum) {
+          var num = 0;
+          for (var i2 = 0; i2 < iovcnt; i2++) {
+            var ptr = HEAP32[iov >> 2];
+            var len = HEAP32[iov + 4 >> 2];
+            iov += 8;
+            for (var j = 0; j < len; j++) {
+              SYSCALLS.printChar(fd, HEAPU8[ptr + j]);
+            }
+            num += len;
+          }
+          HEAP32[pnum >> 2] = num;
+          return 0;
+        }
+        function _setTempRet0(val) {
+          setTempRet0(val);
+        }
+        var ASSERTIONS = false;
+        var asmLibraryArg = { "abort": _abort, "emscripten_get_heap_max": _emscripten_get_heap_max, "emscripten_memcpy_big": _emscripten_memcpy_big, "emscripten_resize_heap": _emscripten_resize_heap, "fd_close": _fd_close, "fd_seek": _fd_seek, "fd_write": _fd_write, "setTempRet0": _setTempRet0 };
+        var asm = createWasm();
+        var ___wasm_call_ctors = Module["___wasm_call_ctors"] = function() {
+          return (___wasm_call_ctors = Module["___wasm_call_ctors"] = Module["asm"]["__wasm_call_ctors"]).apply(null, arguments);
+        };
+        var _init = Module["_init"] = function() {
+          return (_init = Module["_init"] = Module["asm"]["init"]).apply(null, arguments);
+        };
+        var _init_with_threads_count = Module["_init_with_threads_count"] = function() {
+          return (_init_with_threads_count = Module["_init_with_threads_count"] = Module["asm"]["init_with_threads_count"]).apply(null, arguments);
+        };
+        var _get_threads_count = Module["_get_threads_count"] = function() {
+          return (_get_threads_count = Module["_get_threads_count"] = Module["asm"]["get_threads_count"]).apply(null, arguments);
+        };
+        var _register_tensor = Module["_register_tensor"] = function() {
+          return (_register_tensor = Module["_register_tensor"] = Module["asm"]["register_tensor"]).apply(null, arguments);
+        };
+        var _dispose_data = Module["_dispose_data"] = function() {
+          return (_dispose_data = Module["_dispose_data"] = Module["asm"]["dispose_data"]).apply(null, arguments);
+        };
+        var _dispose = Module["_dispose"] = function() {
+          return (_dispose = Module["_dispose"] = Module["asm"]["dispose"]).apply(null, arguments);
+        };
+        var _Abs = Module["_Abs"] = function() {
+          return (_Abs = Module["_Abs"] = Module["asm"]["Abs"]).apply(null, arguments);
+        };
+        var _Add = Module["_Add"] = function() {
+          return (_Add = Module["_Add"] = Module["asm"]["Add"]).apply(null, arguments);
+        };
+        var _AddN = Module["_AddN"] = function() {
+          return (_AddN = Module["_AddN"] = Module["asm"]["AddN"]).apply(null, arguments);
+        };
+        var _All = Module["_All"] = function() {
+          return (_All = Module["_All"] = Module["asm"]["All"]).apply(null, arguments);
+        };
+        var _Any = Module["_Any"] = function() {
+          return (_Any = Module["_Any"] = Module["asm"]["Any"]).apply(null, arguments);
+        };
+        var _ArgMax = Module["_ArgMax"] = function() {
+          return (_ArgMax = Module["_ArgMax"] = Module["asm"]["ArgMax"]).apply(null, arguments);
+        };
+        var _AvgPool = Module["_AvgPool"] = function() {
+          return (_AvgPool = Module["_AvgPool"] = Module["asm"]["AvgPool"]).apply(null, arguments);
+        };
+        var _BatchMatMul = Module["_BatchMatMul"] = function() {
+          return (_BatchMatMul = Module["_BatchMatMul"] = Module["asm"]["BatchMatMul"]).apply(null, arguments);
+        };
+        var _Ceil = Module["_Ceil"] = function() {
+          return (_Ceil = Module["_Ceil"] = Module["asm"]["Ceil"]).apply(null, arguments);
+        };
+        var _ClipByValue = Module["_ClipByValue"] = function() {
+          return (_ClipByValue = Module["_ClipByValue"] = Module["asm"]["ClipByValue"]).apply(null, arguments);
+        };
+        var _Conv2D = Module["_Conv2D"] = function() {
+          return (_Conv2D = Module["_Conv2D"] = Module["asm"]["Conv2D"]).apply(null, arguments);
+        };
+        var _Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = function() {
+          return (_Conv2DBackpropInput = Module["_Conv2DBackpropInput"] = Module["asm"]["Conv2DBackpropInput"]).apply(null, arguments);
+        };
+        var _Cos = Module["_Cos"] = function() {
+          return (_Cos = Module["_Cos"] = Module["asm"]["Cos"]).apply(null, arguments);
+        };
+        var _Cosh = Module["_Cosh"] = function() {
+          return (_Cosh = Module["_Cosh"] = Module["asm"]["Cosh"]).apply(null, arguments);
+        };
+        var _CropAndResize = Module["_CropAndResize"] = function() {
+          return (_CropAndResize = Module["_CropAndResize"] = Module["asm"]["CropAndResize"]).apply(null, arguments);
+        };
+        var _Cumprod = Module["_Cumprod"] = function() {
+          return (_Cumprod = Module["_Cumprod"] = Module["asm"]["Cumprod"]).apply(null, arguments);
+        };
+        var _Cumsum = Module["_Cumsum"] = function() {
+          return (_Cumsum = Module["_Cumsum"] = Module["asm"]["Cumsum"]).apply(null, arguments);
+        };
+        var _DepthToSpace = Module["_DepthToSpace"] = function() {
+          return (_DepthToSpace = Module["_DepthToSpace"] = Module["asm"]["DepthToSpace"]).apply(null, arguments);
+        };
+        var _DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = function() {
+          return (_DepthwiseConv2dNative = Module["_DepthwiseConv2dNative"] = Module["asm"]["DepthwiseConv2dNative"]).apply(null, arguments);
+        };
+        var _Elu = Module["_Elu"] = function() {
+          return (_Elu = Module["_Elu"] = Module["asm"]["Elu"]).apply(null, arguments);
+        };
+        var _Equal = Module["_Equal"] = function() {
+          return (_Equal = Module["_Equal"] = Module["asm"]["Equal"]).apply(null, arguments);
+        };
+        var _Exp = Module["_Exp"] = function() {
+          return (_Exp = Module["_Exp"] = Module["asm"]["Exp"]).apply(null, arguments);
+        };
+        var _FlipLeftRight = Module["_FlipLeftRight"] = function() {
+          return (_FlipLeftRight = Module["_FlipLeftRight"] = Module["asm"]["FlipLeftRight"]).apply(null, arguments);
+        };
+        var _Floor = Module["_Floor"] = function() {
+          return (_Floor = Module["_Floor"] = Module["asm"]["Floor"]).apply(null, arguments);
+        };
+        var _FloorDiv = Module["_FloorDiv"] = function() {
+          return (_FloorDiv = Module["_FloorDiv"] = Module["asm"]["FloorDiv"]).apply(null, arguments);
+        };
+        var _FusedBatchNorm = Module["_FusedBatchNorm"] = function() {
+          return (_FusedBatchNorm = Module["_FusedBatchNorm"] = Module["asm"]["FusedBatchNorm"]).apply(null, arguments);
+        };
+        var _FusedConv2D = Module["_FusedConv2D"] = function() {
+          return (_FusedConv2D = Module["_FusedConv2D"] = Module["asm"]["FusedConv2D"]).apply(null, arguments);
+        };
+        var _FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = function() {
+          return (_FusedDepthwiseConv2D = Module["_FusedDepthwiseConv2D"] = Module["asm"]["FusedDepthwiseConv2D"]).apply(null, arguments);
+        };
+        var _Gather = Module["_Gather"] = function() {
+          return (_Gather = Module["_Gather"] = Module["asm"]["Gather"]).apply(null, arguments);
+        };
+        var _GatherNd = Module["_GatherNd"] = function() {
+          return (_GatherNd = Module["_GatherNd"] = Module["asm"]["GatherNd"]).apply(null, arguments);
+        };
+        var _Greater = Module["_Greater"] = function() {
+          return (_Greater = Module["_Greater"] = Module["asm"]["Greater"]).apply(null, arguments);
+        };
+        var _GreaterEqual = Module["_GreaterEqual"] = function() {
+          return (_GreaterEqual = Module["_GreaterEqual"] = Module["asm"]["GreaterEqual"]).apply(null, arguments);
+        };
+        var _LeakyRelu = Module["_LeakyRelu"] = function() {
+          return (_LeakyRelu = Module["_LeakyRelu"] = Module["asm"]["LeakyRelu"]).apply(null, arguments);
+        };
+        var _Less = Module["_Less"] = function() {
+          return (_Less = Module["_Less"] = Module["asm"]["Less"]).apply(null, arguments);
+        };
+        var _LessEqual = Module["_LessEqual"] = function() {
+          return (_LessEqual = Module["_LessEqual"] = Module["asm"]["LessEqual"]).apply(null, arguments);
+        };
+        var _Log = Module["_Log"] = function() {
+          return (_Log = Module["_Log"] = Module["asm"]["Log"]).apply(null, arguments);
+        };
+        var _LogicalAnd = Module["_LogicalAnd"] = function() {
+          return (_LogicalAnd = Module["_LogicalAnd"] = Module["asm"]["LogicalAnd"]).apply(null, arguments);
+        };
+        var _Max = Module["_Max"] = function() {
+          return (_Max = Module["_Max"] = Module["asm"]["Max"]).apply(null, arguments);
+        };
+        var _MaxPool = Module["_MaxPool"] = function() {
+          return (_MaxPool = Module["_MaxPool"] = Module["asm"]["MaxPool"]).apply(null, arguments);
+        };
+        var _Maximum = Module["_Maximum"] = function() {
+          return (_Maximum = Module["_Maximum"] = Module["asm"]["Maximum"]).apply(null, arguments);
+        };
+        var _Mean = Module["_Mean"] = function() {
+          return (_Mean = Module["_Mean"] = Module["asm"]["Mean"]).apply(null, arguments);
+        };
+        var _Min = Module["_Min"] = function() {
+          return (_Min = Module["_Min"] = Module["asm"]["Min"]).apply(null, arguments);
+        };
+        var _Minimum = Module["_Minimum"] = function() {
+          return (_Minimum = Module["_Minimum"] = Module["asm"]["Minimum"]).apply(null, arguments);
+        };
+        var _MirrorPad = Module["_MirrorPad"] = function() {
+          return (_MirrorPad = Module["_MirrorPad"] = Module["asm"]["MirrorPad"]).apply(null, arguments);
+        };
+        var _Multiply = Module["_Multiply"] = function() {
+          return (_Multiply = Module["_Multiply"] = Module["asm"]["Multiply"]).apply(null, arguments);
+        };
+        var _Neg = Module["_Neg"] = function() {
+          return (_Neg = Module["_Neg"] = Module["asm"]["Neg"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = function() {
+          return (_NonMaxSuppressionV3 = Module["_NonMaxSuppressionV3"] = Module["asm"]["NonMaxSuppressionV3"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = function() {
+          return (_NonMaxSuppressionV4 = Module["_NonMaxSuppressionV4"] = Module["asm"]["NonMaxSuppressionV4"]).apply(null, arguments);
+        };
+        var _NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = function() {
+          return (_NonMaxSuppressionV5 = Module["_NonMaxSuppressionV5"] = Module["asm"]["NonMaxSuppressionV5"]).apply(null, arguments);
+        };
+        var _NotEqual = Module["_NotEqual"] = function() {
+          return (_NotEqual = Module["_NotEqual"] = Module["asm"]["NotEqual"]).apply(null, arguments);
+        };
+        var _OneHot = Module["_OneHot"] = function() {
+          return (_OneHot = Module["_OneHot"] = Module["asm"]["OneHot"]).apply(null, arguments);
+        };
+        var _PadV2 = Module["_PadV2"] = function() {
+          return (_PadV2 = Module["_PadV2"] = Module["asm"]["PadV2"]).apply(null, arguments);
+        };
+        var _Pow = Module["_Pow"] = function() {
+          return (_Pow = Module["_Pow"] = Module["asm"]["Pow"]).apply(null, arguments);
+        };
+        var _Prelu = Module["_Prelu"] = function() {
+          return (_Prelu = Module["_Prelu"] = Module["asm"]["Prelu"]).apply(null, arguments);
+        };
+        var _Prod = Module["_Prod"] = function() {
+          return (_Prod = Module["_Prod"] = Module["asm"]["Prod"]).apply(null, arguments);
+        };
+        var _RealDiv = Module["_RealDiv"] = function() {
+          return (_RealDiv = Module["_RealDiv"] = Module["asm"]["RealDiv"]).apply(null, arguments);
+        };
+        var _Relu = Module["_Relu"] = function() {
+          return (_Relu = Module["_Relu"] = Module["asm"]["Relu"]).apply(null, arguments);
+        };
+        var _Relu6 = Module["_Relu6"] = function() {
+          return (_Relu6 = Module["_Relu6"] = Module["asm"]["Relu6"]).apply(null, arguments);
+        };
+        var _ResizeBilinear = Module["_ResizeBilinear"] = function() {
+          return (_ResizeBilinear = Module["_ResizeBilinear"] = Module["asm"]["ResizeBilinear"]).apply(null, arguments);
+        };
+        var _Reverse = Module["_Reverse"] = function() {
+          return (_Reverse = Module["_Reverse"] = Module["asm"]["Reverse"]).apply(null, arguments);
+        };
+        var _RotateWithOffset = Module["_RotateWithOffset"] = function() {
+          return (_RotateWithOffset = Module["_RotateWithOffset"] = Module["asm"]["RotateWithOffset"]).apply(null, arguments);
+        };
+        var _Round = Module["_Round"] = function() {
+          return (_Round = Module["_Round"] = Module["asm"]["Round"]).apply(null, arguments);
+        };
+        var _Rsqrt = Module["_Rsqrt"] = function() {
+          return (_Rsqrt = Module["_Rsqrt"] = Module["asm"]["Rsqrt"]).apply(null, arguments);
+        };
+        var _ScatterNd = Module["_ScatterNd"] = function() {
+          return (_ScatterNd = Module["_ScatterNd"] = Module["asm"]["ScatterNd"]).apply(null, arguments);
+        };
+        var _SelectV2 = Module["_SelectV2"] = function() {
+          return (_SelectV2 = Module["_SelectV2"] = Module["asm"]["SelectV2"]).apply(null, arguments);
+        };
+        var _Sigmoid = Module["_Sigmoid"] = function() {
+          return (_Sigmoid = Module["_Sigmoid"] = Module["asm"]["Sigmoid"]).apply(null, arguments);
+        };
+        var _Sin = Module["_Sin"] = function() {
+          return (_Sin = Module["_Sin"] = Module["asm"]["Sin"]).apply(null, arguments);
+        };
+        var _Softmax = Module["_Softmax"] = function() {
+          return (_Softmax = Module["_Softmax"] = Module["asm"]["Softmax"]).apply(null, arguments);
+        };
+        var _SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = function() {
+          return (_SparseFillEmptyRows = Module["_SparseFillEmptyRows"] = Module["asm"]["SparseFillEmptyRows"]).apply(null, arguments);
+        };
+        var _SparseReshape = Module["_SparseReshape"] = function() {
+          return (_SparseReshape = Module["_SparseReshape"] = Module["asm"]["SparseReshape"]).apply(null, arguments);
+        };
+        var _SparseSegmentReduction = Module["_SparseSegmentReduction"] = function() {
+          return (_SparseSegmentReduction = Module["_SparseSegmentReduction"] = Module["asm"]["SparseSegmentReduction"]).apply(null, arguments);
+        };
+        var _Sqrt = Module["_Sqrt"] = function() {
+          return (_Sqrt = Module["_Sqrt"] = Module["asm"]["Sqrt"]).apply(null, arguments);
+        };
+        var _Square = Module["_Square"] = function() {
+          return (_Square = Module["_Square"] = Module["asm"]["Square"]).apply(null, arguments);
+        };
+        var _SquaredDifference = Module["_SquaredDifference"] = function() {
+          return (_SquaredDifference = Module["_SquaredDifference"] = Module["asm"]["SquaredDifference"]).apply(null, arguments);
+        };
+        var _Step = Module["_Step"] = function() {
+          return (_Step = Module["_Step"] = Module["asm"]["Step"]).apply(null, arguments);
+        };
+        var _StridedSlice = Module["_StridedSlice"] = function() {
+          return (_StridedSlice = Module["_StridedSlice"] = Module["asm"]["StridedSlice"]).apply(null, arguments);
+        };
+        var _Sub = Module["_Sub"] = function() {
+          return (_Sub = Module["_Sub"] = Module["asm"]["Sub"]).apply(null, arguments);
+        };
+        var _Sum = Module["_Sum"] = function() {
+          return (_Sum = Module["_Sum"] = Module["asm"]["Sum"]).apply(null, arguments);
+        };
+        var _Tan = Module["_Tan"] = function() {
+          return (_Tan = Module["_Tan"] = Module["asm"]["Tan"]).apply(null, arguments);
+        };
+        var _Tanh = Module["_Tanh"] = function() {
+          return (_Tanh = Module["_Tanh"] = Module["asm"]["Tanh"]).apply(null, arguments);
+        };
+        var _Tile = Module["_Tile"] = function() {
+          return (_Tile = Module["_Tile"] = Module["asm"]["Tile"]).apply(null, arguments);
+        };
+        var _TopK = Module["_TopK"] = function() {
+          return (_TopK = Module["_TopK"] = Module["asm"]["TopK"]).apply(null, arguments);
+        };
+        var _Transform = Module["_Transform"] = function() {
+          return (_Transform = Module["_Transform"] = Module["asm"]["Transform"]).apply(null, arguments);
+        };
+        var _Transpose = Module["_Transpose"] = function() {
+          return (_Transpose = Module["_Transpose"] = Module["asm"]["Transpose"]).apply(null, arguments);
+        };
+        var __FusedMatMul = Module["__FusedMatMul"] = function() {
+          return (__FusedMatMul = Module["__FusedMatMul"] = Module["asm"]["_FusedMatMul"]).apply(null, arguments);
+        };
+        var _malloc = Module["_malloc"] = function() {
+          return (_malloc = Module["_malloc"] = Module["asm"]["malloc"]).apply(null, arguments);
+        };
+        var _free = Module["_free"] = function() {
+          return (_free = Module["_free"] = Module["asm"]["free"]).apply(null, arguments);
+        };
+        var ___errno_location = Module["___errno_location"] = function() {
+          return (___errno_location = Module["___errno_location"] = Module["asm"]["__errno_location"]).apply(null, arguments);
+        };
+        var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = function() {
+          return (_emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = Module["asm"]["emscripten_main_thread_process_queued_calls"]).apply(null, arguments);
+        };
+        var stackSave = Module["stackSave"] = function() {
+          return (stackSave = Module["stackSave"] = Module["asm"]["stackSave"]).apply(null, arguments);
+        };
+        var stackRestore = Module["stackRestore"] = function() {
+          return (stackRestore = Module["stackRestore"] = Module["asm"]["stackRestore"]).apply(null, arguments);
+        };
+        var stackAlloc = Module["stackAlloc"] = function() {
+          return (stackAlloc = Module["stackAlloc"] = Module["asm"]["stackAlloc"]).apply(null, arguments);
+        };
+        var dynCall_iijjiiii = Module["dynCall_iijjiiii"] = function() {
+          return (dynCall_iijjiiii = Module["dynCall_iijjiiii"] = Module["asm"]["dynCall_iijjiiii"]).apply(null, arguments);
+        };
+        var dynCall_jiji = Module["dynCall_jiji"] = function() {
+          return (dynCall_jiji = Module["dynCall_jiji"] = Module["asm"]["dynCall_jiji"]).apply(null, arguments);
+        };
+        Module["cwrap"] = cwrap;
+        var calledRun;
+        function ExitStatus(status) {
+          this.name = "ExitStatus";
+          this.message = "Program terminated with exit(" + status + ")";
+          this.status = status;
+        }
+        dependenciesFulfilled = function runCaller() {
+          if (!calledRun)
+            run();
+          if (!calledRun)
+            dependenciesFulfilled = runCaller;
+        };
+        function run(args) {
+          args = args || arguments_;
+          if (runDependencies > 0) {
+            return;
+          }
+          preRun();
+          if (runDependencies > 0) {
+            return;
+          }
+          function doRun() {
+            if (calledRun)
+              return;
+            calledRun = true;
+            Module["calledRun"] = true;
+            if (ABORT)
+              return;
+            initRuntime();
+            readyPromiseResolve(Module);
+            if (Module["onRuntimeInitialized"])
+              Module["onRuntimeInitialized"]();
+            postRun();
+          }
+          if (Module["setStatus"]) {
+            Module["setStatus"]("Running...");
+            setTimeout(function() {
+              setTimeout(function() {
+                Module["setStatus"]("");
+              }, 1);
+              doRun();
+            }, 1);
+          } else {
+            doRun();
+          }
+        }
+        Module["run"] = run;
+        function procExit(code) {
+          EXITSTATUS = code;
+          if (!keepRuntimeAlive()) {
+            if (Module["onExit"])
+              Module["onExit"](code);
+            ABORT = true;
+          }
+          quit_(code, new ExitStatus(code));
+        }
+        if (Module["preInit"]) {
+          if (typeof Module["preInit"] == "function")
+            Module["preInit"] = [Module["preInit"]];
+          while (Module["preInit"].length > 0) {
+            Module["preInit"].pop()();
+          }
+        }
+        run();
+        var listenersAdded;
+        if (beforeListeners) {
+          listenersAdded = { uncaughtException: process.listeners("uncaughtException").filter(function(listener) {
+            return !beforeListeners.uncaughtException.indexOf(listener) > -1;
+          }), unhandledRejection: process.listeners("unhandledRejection").filter(function(listener) {
+            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;
+          }) };
+        }
+        var actualModule;
+        if (typeof WasmBackendModule3 !== "undefined") {
+          actualModule = WasmBackendModule3;
+        } else if (typeof WasmBackendModuleThreadedSimd !== "undefined") {
+          actualModule = WasmBackendModuleThreadedSimd;
+        } else {
+          throw new Error("Could not find wasm module in post.js");
+        }
+        if (listenersAdded) {
+          var tmpDispose = actualModule["_dispose"];
+          actualModule["_dispose"] = function() {
+            tmpDispose();
+            listenersAdded.uncaughtException.forEach(function(listener) {
+              process.removeListener("uncaughtException", listener);
+            });
+            listenersAdded.unhandledRejection.forEach(function(listener) {
+              process.removeListener("unhandledRejection", listener);
+            });
+          };
+        }
+        return WasmBackendModule3.ready;
+      };
+    })();
+    if (typeof exports === "object" && typeof module === "object")
+      module.exports = WasmBackendModule2;
+    else if (typeof define === "function" && define["amd"])
+      define([], function() {
+        return WasmBackendModule2;
+      });
+    else if (typeof exports === "object")
+      exports["WasmBackendModule"] = WasmBackendModule2;
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
+var EPSILON_FLOAT32 = 1e-7;
+var EPSILON_FLOAT16 = 1e-4;
+var DataStorage = class {
+  constructor(backend2, dataMover) {
+    this.backend = backend2;
+    this.dataMover = dataMover;
+    this.data = /* @__PURE__ */ new WeakMap();
+    this.dataIdsCount = 0;
+  }
+  get(dataId) {
+    if (!this.data.has(dataId)) {
+      this.dataMover.moveData(this.backend, dataId);
+    }
+    return this.data.get(dataId);
+  }
+  set(dataId, value) {
+    this.dataIdsCount++;
+    this.data.set(dataId, value);
+  }
+  has(dataId) {
+    return this.data.has(dataId);
+  }
+  delete(dataId) {
+    this.dataIdsCount--;
+    return this.data.delete(dataId);
+  }
+  numDataIds() {
+    return this.dataIdsCount;
+  }
+};
+var KernelBackend = class {
+  refCount(dataId) {
+    return notYetImplemented("refCount");
+  }
+  incRef(dataId) {
+    return notYetImplemented("incRef");
+  }
+  timerAvailable() {
+    return true;
+  }
+  time(f) {
+    return notYetImplemented("time");
+  }
+  read(dataId) {
+    return notYetImplemented("read");
+  }
+  readSync(dataId) {
+    return notYetImplemented("readSync");
+  }
+  readToGPU(dataId, options) {
+    return notYetImplemented("readToGPU");
+  }
+  numDataIds() {
+    return notYetImplemented("numDataIds");
+  }
+  disposeData(dataId, force) {
+    return notYetImplemented("disposeData");
+  }
+  write(values, shape, dtype) {
+    return notYetImplemented("write");
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    return notYetImplemented("move");
+  }
+  memory() {
+    return notYetImplemented("memory");
+  }
+  floatPrecision() {
+    return notYetImplemented("floatPrecision");
+  }
+  epsilon() {
+    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
+  }
+  dispose() {
+    return notYetImplemented("dispose");
+  }
+};
+function notYetImplemented(kernelName) {
+  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js
+function shuffle(array2) {
+  let counter = array2.length;
+  let index = 0;
+  while (counter > 0) {
+    index = Math.random() * counter | 0;
+    counter--;
+    swap(array2, counter, index);
+  }
+}
+function shuffleCombo(array2, array22) {
+  if (array2.length !== array22.length) {
+    throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);
+  }
+  let counter = array2.length;
+  let index = 0;
+  while (counter > 0) {
+    index = Math.random() * counter | 0;
+    counter--;
+    swap(array2, counter, index);
+    swap(array22, counter, index);
+  }
+}
+function clamp(min7, x, max7) {
+  return Math.max(min7, Math.min(x, max7));
+}
+function nearestLargerEven(val) {
+  return val % 2 === 0 ? val : val + 1;
+}
+function swap(object, left, right) {
+  const temp = object[left];
+  object[left] = object[right];
+  object[right] = temp;
+}
+function sum(arr) {
+  let sum7 = 0;
+  for (let i2 = 0; i2 < arr.length; i2++) {
+    sum7 += arr[i2];
+  }
+  return sum7;
+}
+function randUniform(a6, b) {
+  const r2 = Math.random();
+  return b * r2 + (1 - r2) * a6;
+}
+function distSquared(a6, b) {
+  let result = 0;
+  for (let i2 = 0; i2 < a6.length; i2++) {
+    const diff = Number(a6[i2]) - Number(b[i2]);
+    result += diff * diff;
+  }
+  return result;
+}
+function assert(expr, msg) {
+  if (!expr) {
+    throw new Error(typeof msg === "string" ? msg : msg());
+  }
+}
+function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
+  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+}
+function assertNonNull(a6) {
+  assert(a6 != null, () => `The input to the tensor constructor must be a non-null value.`);
+}
+function flatten(arr, result = [], skipTypedArray = false) {
+  if (result == null) {
+    result = [];
+  }
+  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
+    for (let i2 = 0; i2 < arr.length; ++i2) {
+      flatten(arr[i2], result, skipTypedArray);
+    }
+  } else {
+    result.push(arr);
+  }
+  return result;
+}
+function sizeFromShape(shape) {
+  if (shape.length === 0) {
+    return 1;
+  }
+  let size = shape[0];
+  for (let i2 = 1; i2 < shape.length; i2++) {
+    size *= shape[i2];
+  }
+  return size;
+}
+function isScalarShape(shape) {
+  return shape.length === 0;
+}
+function arraysEqual(n1, n2) {
+  if (n1 === n2) {
+    return true;
+  }
+  if (n1 == null || n2 == null) {
+    return false;
+  }
+  if (n1.length !== n2.length) {
+    return false;
+  }
+  for (let i2 = 0; i2 < n1.length; i2++) {
+    if (n1[i2] !== n2[i2]) {
+      return false;
+    }
+  }
+  return true;
+}
+function isInt(a6) {
+  return a6 % 1 === 0;
+}
+function tanh(x) {
+  if (Math.tanh != null) {
+    return Math.tanh(x);
+  }
+  if (x === Infinity) {
+    return 1;
+  } else if (x === -Infinity) {
+    return -1;
+  } else {
+    const e2x = Math.exp(2 * x);
+    return (e2x - 1) / (e2x + 1);
+  }
+}
+function sizeToSquarishShape(size) {
+  const width = Math.ceil(Math.sqrt(size));
+  return [width, Math.ceil(size / width)];
+}
+function createShuffledIndices(n) {
+  const shuffledIndices = new Uint32Array(n);
+  for (let i2 = 0; i2 < n; ++i2) {
+    shuffledIndices[i2] = i2;
+  }
+  shuffle(shuffledIndices);
+  return shuffledIndices;
+}
+function rightPad(a6, size) {
+  if (size <= a6.length) {
+    return a6;
+  }
+  return a6 + " ".repeat(size - a6.length);
+}
+function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
+  return new Promise((resolve, reject) => {
+    let tryCount = 0;
+    const tryFn = () => {
+      if (checkFn()) {
+        resolve();
+        return;
+      }
+      tryCount++;
+      const nextBackoff = delayFn(tryCount);
+      if (maxCounter != null && tryCount >= maxCounter) {
+        reject();
+        return;
+      }
+      setTimeout(tryFn, nextBackoff);
+    };
+    tryFn();
+  });
+}
+function inferFromImplicitShape(shape, size) {
+  let shapeProd = 1;
+  let implicitIdx = -1;
+  for (let i2 = 0; i2 < shape.length; ++i2) {
+    if (shape[i2] >= 0) {
+      shapeProd *= shape[i2];
+    } else if (shape[i2] === -1) {
+      if (implicitIdx !== -1) {
+        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i2}`);
+      }
+      implicitIdx = i2;
+    } else if (shape[i2] < 0) {
+      throw Error(`Shapes can not be < 0. Found ${shape[i2]} at dim ${i2}`);
+    }
+  }
+  if (implicitIdx === -1) {
+    if (size > 0 && size !== shapeProd) {
+      throw Error(`Size(${size}) must match the product of shape ${shape}`);
+    }
+    return shape;
+  }
+  if (shapeProd === 0) {
+    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);
+  }
+  if (size % shapeProd !== 0) {
+    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);
+  }
+  const newShape = shape.slice();
+  newShape[implicitIdx] = size / shapeProd;
+  return newShape;
+}
+function parseAxisParam(axis, shape) {
+  const rank = shape.length;
+  axis = axis == null ? shape.map((s2, i2) => i2) : [].concat(axis);
+  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
+  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
+  return axis.map((a6) => a6 < 0 ? rank + a6 : a6);
+}
+function squeezeShape(shape, axis) {
+  const newShape = [];
+  const keptDims = [];
+  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
+  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
+  let j = 0;
+  for (let i2 = 0; i2 < shape.length; ++i2) {
+    if (axes != null) {
+      if (axes[j] === i2 && shape[i2] !== 1) {
+        throw new Error(`Can't squeeze axis ${i2} since its dim '${shape[i2]}' is not 1`);
+      }
+      if ((axes[j] == null || axes[j] > i2) && shape[i2] === 1) {
+        newShape.push(shape[i2]);
+        keptDims.push(i2);
+      }
+      if (axes[j] <= i2) {
+        j++;
+      }
+    }
+    if (shape[i2] !== 1) {
+      newShape.push(shape[i2]);
+      keptDims.push(i2);
+    }
+  }
+  return { newShape, keptDims };
+}
+function getTypedArrayFromDType(dtype, size) {
+  let values = null;
+  if (dtype == null || dtype === "float32") {
+    values = new Float32Array(size);
+  } else if (dtype === "int32") {
+    values = new Int32Array(size);
+  } else if (dtype === "bool") {
+    values = new Uint8Array(size);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+  return values;
+}
+function getArrayFromDType(dtype, size) {
+  let values = null;
+  if (dtype == null || dtype === "float32") {
+    values = new Float32Array(size);
+  } else if (dtype === "int32") {
+    values = new Int32Array(size);
+  } else if (dtype === "bool") {
+    values = new Uint8Array(size);
+  } else if (dtype === "string") {
+    values = new Array(size);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+  return values;
+}
+function checkConversionForErrors(vals, dtype) {
+  for (let i2 = 0; i2 < vals.length; i2++) {
+    const num = vals[i2];
+    if (isNaN(num) || !isFinite(num)) {
+      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
+    }
+  }
+}
+function isValidDtype(dtype) {
+  return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
+}
+function hasEncodingLoss(oldType, newType) {
+  if (newType === "complex64") {
+    return false;
+  }
+  if (newType === "float32" && oldType !== "complex64") {
+    return false;
+  }
+  if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") {
+    return false;
+  }
+  if (newType === "bool" && oldType === "bool") {
+    return false;
+  }
+  return true;
+}
+function isTypedArray(a6) {
+  return a6 instanceof Float32Array || a6 instanceof Int32Array || a6 instanceof Uint8Array || a6 instanceof Uint8ClampedArray;
+}
+function bytesPerElement(dtype) {
+  if (dtype === "float32" || dtype === "int32") {
+    return 4;
+  } else if (dtype === "complex64") {
+    return 8;
+  } else if (dtype === "bool") {
+    return 1;
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+function bytesFromStringArray(arr) {
+  if (arr == null) {
+    return 0;
+  }
+  let bytes = 0;
+  arr.forEach((x) => bytes += x.length);
+  return bytes;
+}
+function isString(value) {
+  return typeof value === "string" || value instanceof String;
+}
+function isBoolean(value) {
+  return typeof value === "boolean";
+}
+function isNumber(value) {
+  return typeof value === "number";
+}
+function inferDtype(values) {
+  if (Array.isArray(values)) {
+    return inferDtype(values[0]);
+  }
+  if (values instanceof Float32Array) {
+    return "float32";
+  } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {
+    return "int32";
+  } else if (isNumber(values)) {
+    return "float32";
+  } else if (isString(values)) {
+    return "string";
+  } else if (isBoolean(values)) {
+    return "bool";
+  }
+  return "float32";
+}
+function isFunction(f) {
+  return !!(f && f.constructor && f.call && f.apply);
+}
+function nearestDivisor(size, start) {
+  for (let i2 = start; i2 < size; ++i2) {
+    if (size % i2 === 0) {
+      return i2;
+    }
+  }
+  return size;
+}
+function computeStrides(shape) {
+  const rank = shape.length;
+  if (rank < 2) {
+    return [];
+  }
+  const strides = new Array(rank - 1);
+  strides[rank - 2] = shape[rank - 1];
+  for (let i2 = rank - 3; i2 >= 0; --i2) {
+    strides[i2] = strides[i2 + 1] * shape[i2 + 1];
+  }
+  return strides;
+}
+function createNestedArray(offset, shape, a6, isComplex = false) {
+  const ret = new Array();
+  if (shape.length === 1) {
+    const d = shape[0] * (isComplex ? 2 : 1);
+    for (let i2 = 0; i2 < d; i2++) {
+      ret[i2] = a6[offset + i2];
+    }
+  } else {
+    const d = shape[0];
+    const rest = shape.slice(1);
+    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
+    for (let i2 = 0; i2 < d; i2++) {
+      ret[i2] = createNestedArray(offset + i2 * len, rest, a6, isComplex);
+    }
+  }
+  return ret;
+}
+function toNestedArray(shape, a6, isComplex = false) {
+  if (shape.length === 0) {
+    return a6[0];
+  }
+  const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
+  if (size === 0) {
+    return [];
+  }
+  if (size !== a6.length) {
+    throw new Error(`[${shape}] does not match the input size ${a6.length}${isComplex ? " for a complex tensor" : ""}.`);
+  }
+  return createNestedArray(0, shape, a6, isComplex);
+}
+function makeOnesTypedArray(size, dtype) {
+  const array2 = makeZerosTypedArray(size, dtype);
+  for (let i2 = 0; i2 < array2.length; i2++) {
+    array2[i2] = 1;
+  }
+  return array2;
+}
+function makeZerosTypedArray(size, dtype) {
+  if (dtype == null || dtype === "float32" || dtype === "complex64") {
+    return new Float32Array(size);
+  } else if (dtype === "int32") {
+    return new Int32Array(size);
+  } else if (dtype === "bool") {
+    return new Uint8Array(size);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+}
+function makeZerosNestedTypedArray(shape, dtype) {
+  const size = shape.reduce((prev, curr) => prev * curr, 1);
+  if (dtype == null || dtype === "float32") {
+    return toNestedArray(shape, new Float32Array(size));
+  } else if (dtype === "int32") {
+    return toNestedArray(shape, new Int32Array(size));
+  } else if (dtype === "bool") {
+    return toNestedArray(shape, new Uint8Array(size));
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+}
+function assertNonNegativeIntegerDimensions(shape) {
+  shape.forEach((dimSize) => {
+    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);
+  });
+}
+function locToIndex(locs, rank, strides) {
+  if (rank === 0) {
+    return 0;
+  } else if (rank === 1) {
+    return locs[0];
+  }
+  let index = locs[locs.length - 1];
+  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+    index += strides[i2] * locs[i2];
+  }
+  return index;
+}
+function indexToLoc(index, rank, strides) {
+  if (rank === 0) {
+    return [];
+  } else if (rank === 1) {
+    return [index];
+  }
+  const locs = new Array(rank);
+  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+    locs[i2] = Math.floor(index / strides[i2]);
+    index -= locs[i2] * strides[i2];
+  }
+  locs[locs.length - 1] = index;
+  return locs;
+}
+function isPromise(object) {
+  return object && object.then && typeof object.then === "function";
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/environment.js
+var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
+var Environment = class {
+  constructor(global2) {
+    this.global = global2;
+    this.flags = {};
+    this.flagRegistry = {};
+    this.urlFlags = {};
+    this.getQueryParams = getQueryParams;
+    this.populateURLFlags();
+  }
+  setPlatform(platformName, platform) {
+    if (this.platform != null) {
+      if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);
+      }
+    }
+    this.platformName = platformName;
+    this.platform = platform;
+  }
+  registerFlag(flagName, evaluationFn, setHook) {
+    this.flagRegistry[flagName] = { evaluationFn, setHook };
+    if (this.urlFlags[flagName] != null) {
+      const flagValue = this.urlFlags[flagName];
+      if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
+      }
+      this.set(flagName, flagValue);
+    }
+  }
+  async getAsync(flagName) {
+    if (flagName in this.flags) {
+      return this.flags[flagName];
+    }
+    this.flags[flagName] = await this.evaluateFlag(flagName);
+    return this.flags[flagName];
+  }
+  get(flagName) {
+    if (flagName in this.flags) {
+      return this.flags[flagName];
+    }
+    const flagValue = this.evaluateFlag(flagName);
+    if (isPromise(flagValue)) {
+      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);
+    }
+    this.flags[flagName] = flagValue;
+    return this.flags[flagName];
+  }
+  getNumber(flagName) {
+    return this.get(flagName);
+  }
+  getBool(flagName) {
+    return this.get(flagName);
+  }
+  getFlags() {
+    return this.flags;
+  }
+  get features() {
+    return this.flags;
+  }
+  set(flagName, value) {
+    if (this.flagRegistry[flagName] == null) {
+      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
+    }
+    this.flags[flagName] = value;
+    if (this.flagRegistry[flagName].setHook != null) {
+      this.flagRegistry[flagName].setHook(value);
+    }
+  }
+  evaluateFlag(flagName) {
+    if (this.flagRegistry[flagName] == null) {
+      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
+    }
+    return this.flagRegistry[flagName].evaluationFn();
+  }
+  setFlags(flags) {
+    this.flags = Object.assign({}, flags);
+  }
+  reset() {
+    this.flags = {};
+    this.urlFlags = {};
+    this.populateURLFlags();
+  }
+  populateURLFlags() {
+    if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") {
+      return;
+    }
+    const urlParams = this.getQueryParams(this.global.location.search);
+    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
+      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
+      keyValues.forEach((keyValue) => {
+        const [key, value] = keyValue.split(":");
+        this.urlFlags[key] = parseValue(key, value);
+      });
+    }
+  }
+};
+function getQueryParams(queryString) {
+  const params = {};
+  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s2, ...t2) => {
+    decodeParam(params, t2[0], t2[1]);
+    return t2.join("=");
+  });
+  return params;
+}
+function decodeParam(params, name, value) {
+  params[decodeURIComponent(name)] = decodeURIComponent(value || "");
+}
+function parseValue(flagName, value) {
+  value = value.toLowerCase();
+  if (value === "true" || value === "false") {
+    return value === "true";
+  } else if (`${+value}` === value) {
+    return +value;
+  }
+  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);
+}
+function env() {
+  return ENV;
+}
+var ENV = null;
+function setEnvironmentGlobal(environment) {
+  ENV = environment;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js
+var globalNameSpace;
+function getGlobalNamespace() {
+  if (globalNameSpace == null) {
+    let ns;
+    if (typeof window !== "undefined") {
+      ns = window;
+    } else if (typeof global !== "undefined") {
+      ns = global;
+    } else if (typeof process !== "undefined") {
+      ns = process;
+    } else if (typeof self !== "undefined") {
+      ns = self;
+    } else {
+      throw new Error("Could not find a global object");
+    }
+    globalNameSpace = ns;
+  }
+  return globalNameSpace;
+}
+function getGlobalMap() {
+  const ns = getGlobalNamespace();
+  if (ns._tfGlobals == null) {
+    ns._tfGlobals = /* @__PURE__ */ new Map();
+  }
+  return ns._tfGlobals;
+}
+function getGlobal(key, init2) {
+  const globalMap = getGlobalMap();
+  if (globalMap.has(key)) {
+    return globalMap.get(key);
+  } else {
+    const singleton = init2();
+    globalMap.set(key, singleton);
+    return globalMap.get(key);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
+var Abs = "Abs";
+var Acos = "Acos";
+var Acosh = "Acosh";
+var Add = "Add";
+var AddN = "AddN";
+var All = "All";
+var Any = "Any";
+var ArgMax = "ArgMax";
+var ArgMin = "ArgMin";
+var Asin = "Asin";
+var Asinh = "Asinh";
+var Atan = "Atan";
+var Atanh = "Atanh";
+var Atan2 = "Atan2";
+var AvgPool = "AvgPool";
+var AvgPoolGrad = "AvgPoolGrad";
+var AvgPool3D = "AvgPool3D";
+var AvgPool3DGrad = "AvgPool3DGrad";
+var BatchMatMul = "BatchMatMul";
+var BatchToSpaceND = "BatchToSpaceND";
+var Bincount = "Bincount";
+var BroadcastTo = "BroadcastTo";
+var BroadcastArgs = "BroadcastArgs";
+var Cast = "Cast";
+var Ceil = "Ceil";
+var ClipByValue = "ClipByValue";
+var Complex = "Complex";
+var ComplexAbs = "ComplexAbs";
+var Concat = "Concat";
+var Conv2D = "Conv2D";
+var Conv2DBackpropFilter = "Conv2DBackpropFilter";
+var Conv2DBackpropInput = "Conv2DBackpropInput";
+var Conv3D = "Conv3D";
+var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
+var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
+var Cos = "Cos";
+var Cosh = "Cosh";
+var Cumprod = "Cumprod";
+var Cumsum = "Cumsum";
+var CropAndResize = "CropAndResize";
+var DenseBincount = "DenseBincount";
+var DepthToSpace = "DepthToSpace";
+var DepthwiseConv2dNative = "DepthwiseConv2dNative";
+var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
+var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
+var Diag = "Diag";
+var Dilation2D = "Dilation2D";
+var Dilation2DBackpropInput = "Dilation2DBackpropInput";
+var Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
+var RealDiv = "RealDiv";
+var Einsum = "Einsum";
+var Elu = "Elu";
+var EluGrad = "EluGrad";
+var Erf = "Erf";
+var Equal = "Equal";
+var Exp = "Exp";
+var ExpandDims = "ExpandDims";
+var Expm1 = "Expm1";
+var FFT = "FFT";
+var Fill = "Fill";
+var FlipLeftRight = "FlipLeftRight";
+var Floor = "Floor";
+var FloorDiv = "FloorDiv";
+var FusedBatchNorm = "FusedBatchNorm";
+var GatherV2 = "GatherV2";
+var GatherNd = "GatherNd";
+var Greater = "Greater";
+var GreaterEqual = "GreaterEqual";
+var Identity = "Identity";
+var IFFT = "IFFT";
+var Imag = "Imag";
+var IsFinite = "IsFinite";
+var IsInf = "IsInf";
+var IsNan = "IsNan";
+var LeakyRelu = "LeakyRelu";
+var Less = "Less";
+var LessEqual = "LessEqual";
+var LinSpace = "LinSpace";
+var Log = "Log";
+var Log1p = "Log1p";
+var LogicalAnd = "LogicalAnd";
+var LogicalNot = "LogicalNot";
+var LogicalOr = "LogicalOr";
+var LogSoftmax = "LogSoftmax";
+var LowerBound = "LowerBound";
+var LRN = "LRN";
+var LRNGrad = "LRNGrad";
+var Max = "Max";
+var Maximum = "Maximum";
+var MaxPool = "MaxPool";
+var MaxPoolGrad = "MaxPoolGrad";
+var MaxPool3D = "MaxPool3D";
+var MaxPool3DGrad = "MaxPool3DGrad";
+var MaxPoolWithArgmax = "MaxPoolWithArgmax";
+var Mean = "Mean";
+var Min = "Min";
+var Minimum = "Minimum";
+var MirrorPad = "MirrorPad";
+var Mod = "Mod";
+var Multinomial = "Multinomial";
+var Multiply = "Multiply";
+var Neg = "Neg";
+var NotEqual = "NotEqual";
+var NonMaxSuppressionV3 = "NonMaxSuppressionV3";
+var NonMaxSuppressionV4 = "NonMaxSuppressionV4";
+var NonMaxSuppressionV5 = "NonMaxSuppressionV5";
+var OnesLike = "OnesLike";
+var OneHot = "OneHot";
+var Pack = "Pack";
+var PadV2 = "PadV2";
+var Pool = "Pool";
+var Pow = "Pow";
+var Prelu = "Prelu";
+var Prod = "Prod";
+var Range = "Range";
+var Real = "Real";
+var Reciprocal = "Reciprocal";
+var Relu = "Relu";
+var Reshape = "Reshape";
+var ResizeNearestNeighbor = "ResizeNearestNeighbor";
+var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
+var ResizeBilinear = "ResizeBilinear";
+var ResizeBilinearGrad = "ResizeBilinearGrad";
+var Relu6 = "Relu6";
+var Reverse = "Reverse";
+var Round = "Round";
+var Rsqrt = "Rsqrt";
+var ScatterNd = "ScatterNd";
+var SearchSorted = "SearchSorted";
+var Select = "Select";
+var Selu = "Selu";
+var Slice = "Slice";
+var Sin = "Sin";
+var Sinh = "Sinh";
+var Sign = "Sign";
+var Sigmoid = "Sigmoid";
+var Softplus = "Softplus";
+var Sqrt = "Sqrt";
+var Sum = "Sum";
+var SpaceToBatchND = "SpaceToBatchND";
+var SplitV = "SplitV";
+var Softmax = "Softmax";
+var SparseFillEmptyRows = "SparseFillEmptyRows";
+var SparseReshape = "SparseReshape";
+var SparseSegmentMean = "SparseSegmentMean";
+var SparseSegmentSum = "SparseSegmentSum";
+var SparseToDense = "SparseToDense";
+var SquaredDifference = "SquaredDifference";
+var Square = "Square";
+var StridedSlice = "StridedSlice";
+var StringNGrams = "StringNGrams";
+var StringSplit = "StringSplit";
+var StringToHashBucketFast = "StringToHashBucketFast";
+var Sub = "Sub";
+var Tan = "Tan";
+var Tanh = "Tanh";
+var Tile = "Tile";
+var TopK = "TopK";
+var Transform = "Transform";
+var Transpose = "Transpose";
+var Unique = "Unique";
+var Unpack = "Unpack";
+var UnsortedSegmentSum = "UnsortedSegmentSum";
+var UpperBound = "UpperBound";
+var ZerosLike = "ZerosLike";
+var Step = "Step";
+var FromPixels = "FromPixels";
+var RotateWithOffset = "RotateWithOffset";
+var _FusedMatMul = "_FusedMatMul";
+var FusedConv2D = "FusedConv2D";
+var FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/log.js
+function warn(...msg) {
+  if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+    console.warn(...msg);
+  }
+}
+function log(...msg) {
+  if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
+    console.log(...msg);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
+var kernelRegistry = getGlobal("kernelRegistry", () => /* @__PURE__ */ new Map());
+var gradRegistry = getGlobal("gradRegistry", () => /* @__PURE__ */ new Map());
+function getKernel(kernelName, backendName) {
+  const key = makeKey(kernelName, backendName);
+  return kernelRegistry.get(key);
+}
+function getGradient(kernelName) {
+  return gradRegistry.get(kernelName);
+}
+function getKernelsForBackend(backendName) {
+  const it = kernelRegistry.entries();
+  const result = [];
+  while (true) {
+    const { done, value } = it.next();
+    if (done) {
+      break;
+    }
+    const [key, config] = value;
+    const [backend2] = key.split("_");
+    if (backend2 === backendName) {
+      result.push(config);
+    }
+  }
+  return result;
+}
+function registerKernel(config) {
+  const { kernelName, backendName } = config;
+  const key = makeKey(kernelName, backendName);
+  if (kernelRegistry.has(key)) {
+    warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
+  }
+  kernelRegistry.set(key, config);
+}
+function registerGradient(config) {
+  const { kernelName } = config;
+  if (gradRegistry.has(kernelName)) {
+    if (env().getBool("DEBUG")) {
+      warn(`Overriding the gradient for '${kernelName}'`);
+    }
+  }
+  gradRegistry.set(kernelName, config);
+}
+function unregisterKernel(kernelName, backendName) {
+  const key = makeKey(kernelName, backendName);
+  if (!kernelRegistry.has(key)) {
+    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);
+  }
+  kernelRegistry.delete(key);
+}
+function unregisterGradient(kernelName) {
+  if (!gradRegistry.has(kernelName)) {
+    throw new Error(`The gradient '${kernelName}' for backend is not registered`);
+  }
+  gradRegistry.delete(kernelName);
+}
+function copyRegisteredKernels(registeredBackendName, newBackendName) {
+  const kernels = getKernelsForBackend(registeredBackendName);
+  kernels.forEach((kernelConfig) => {
+    const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });
+    registerKernel(newKernelConfig);
+  });
+}
+function makeKey(kernelName, backendName) {
+  return `${backendName}_${kernelName}`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/util.js
+var util_exports = {};
+__export(util_exports, {
+  arraysEqual: () => arraysEqual,
+  assert: () => assert,
+  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,
+  assertNonNull: () => assertNonNull,
+  assertShapesMatch: () => assertShapesMatch,
+  bytesFromStringArray: () => bytesFromStringArray,
+  bytesPerElement: () => bytesPerElement,
+  checkConversionForErrors: () => checkConversionForErrors,
+  clamp: () => clamp,
+  computeStrides: () => computeStrides,
+  createScalarValue: () => createScalarValue,
+  createShuffledIndices: () => createShuffledIndices,
+  decodeString: () => decodeString,
+  distSquared: () => distSquared,
+  encodeString: () => encodeString,
+  fetch: () => fetch3,
+  fingerPrint64: () => fingerPrint64,
+  flatten: () => flatten,
+  getArrayFromDType: () => getArrayFromDType,
+  getTypedArrayFromDType: () => getTypedArrayFromDType,
+  hasEncodingLoss: () => hasEncodingLoss,
+  hexToLong: () => hexToLong,
+  indexToLoc: () => indexToLoc,
+  inferDtype: () => inferDtype,
+  inferFromImplicitShape: () => inferFromImplicitShape,
+  isBoolean: () => isBoolean,
+  isFunction: () => isFunction,
+  isInt: () => isInt,
+  isNumber: () => isNumber,
+  isPromise: () => isPromise,
+  isScalarShape: () => isScalarShape,
+  isString: () => isString,
+  isTypedArray: () => isTypedArray,
+  isValidDtype: () => isValidDtype,
+  locToIndex: () => locToIndex,
+  makeOnesTypedArray: () => makeOnesTypedArray,
+  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,
+  makeZerosTypedArray: () => makeZerosTypedArray,
+  nearestDivisor: () => nearestDivisor,
+  nearestLargerEven: () => nearestLargerEven,
+  now: () => now,
+  parseAxisParam: () => parseAxisParam,
+  randUniform: () => randUniform,
+  repeatedTry: () => repeatedTry,
+  rightPad: () => rightPad,
+  shuffle: () => shuffle,
+  shuffleCombo: () => shuffleCombo,
+  sizeFromShape: () => sizeFromShape,
+  sizeToSquarishShape: () => sizeToSquarishShape,
+  squeezeShape: () => squeezeShape,
+  sum: () => sum,
+  swap: () => swap,
+  tanh: () => tanh,
+  toNestedArray: () => toNestedArray,
+  toTypedArray: () => toTypedArray
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/hash_util.js
+var LongExports = __toESM(require_long());
+var Long = LongExports.default || LongExports;
+function hexToLong(hex) {
+  return Long.fromString(hex, true, 16);
+}
+var k0 = hexToLong("c3a5c85c97cb3127");
+var k1 = hexToLong("b492b66fbe98f273");
+var k2 = hexToLong("9ae16a3b2f90404f");
+function shiftMix(val) {
+  return val.xor(val.shru(47));
+}
+function fetch2(s2, offset, numBytes) {
+  const bytes = s2.slice(offset, offset + numBytes);
+  return Long.fromBytes(Array.from(bytes), true, true);
+}
+function fetch64(s2, offset) {
+  return fetch2(s2, offset, 8);
+}
+function fetch32(s2, offset) {
+  return fetch2(s2, offset, 4);
+}
+function rotate64(val, shift) {
+  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
+}
+function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) {
+  let a6 = u.xor(v).mul(mul2);
+  a6 = a6.xor(a6.shru(47));
+  let b = v.xor(a6).mul(mul2);
+  b = b.xor(b.shru(47));
+  b = b.mul(mul2);
+  return b;
+}
+function weakHashLen32WithSeeds(w, x, y, z, a6, b) {
+  a6 = a6.add(w);
+  b = rotate64(b.add(a6).add(z), 21);
+  const c = a6;
+  a6 = a6.add(x);
+  a6 = a6.add(y);
+  b = b.add(rotate64(a6, 44));
+  return [a6.add(z), b.add(c)];
+}
+function weakHashLen32WithSeedsStr(s2, offset, a6, b) {
+  return weakHashLen32WithSeeds(fetch64(s2, offset), fetch64(s2, offset + 8), fetch64(s2, offset + 16), fetch64(s2, offset + 24), a6, b);
+}
+function hashLen0to16(s2, len = s2.length) {
+  if (len >= 8) {
+    const mul2 = k2.add(len * 2);
+    const a6 = fetch64(s2, 0).add(k2);
+    const b = fetch64(s2, len - 8);
+    const c = rotate64(b, 37).mul(mul2).add(a6);
+    const d = rotate64(a6, 25).add(b).mul(mul2);
+    return hashLen16(c, d, mul2);
+  }
+  if (len >= 4) {
+    const mul2 = k2.add(len * 2);
+    const a6 = fetch32(s2, 0);
+    return hashLen16(a6.shl(3).add(len), fetch32(s2, len - 4), mul2);
+  }
+  if (len > 0) {
+    const a6 = s2[0];
+    const b = s2[len >> 1];
+    const c = s2[len - 1];
+    const y = a6 + (b << 8);
+    const z = len + (c << 2);
+    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
+  }
+  return k2;
+}
+function hashLen17to32(s2, len = s2.length) {
+  const mul2 = k2.add(len * 2);
+  const a6 = fetch64(s2, 0).mul(k1);
+  const b = fetch64(s2, 8);
+  const c = fetch64(s2, len - 8).mul(mul2);
+  const d = fetch64(s2, len - 16).mul(k2);
+  return hashLen16(rotate64(a6.add(b), 43).add(rotate64(c, 30)).add(d), a6.add(rotate64(b.add(k2), 18)).add(c), mul2);
+}
+function hashLen33to64(s2, len = s2.length) {
+  const mul2 = k2.add(len * 2);
+  const a6 = fetch64(s2, 0).mul(k2);
+  const b = fetch64(s2, 8);
+  const c = fetch64(s2, len - 8).mul(mul2);
+  const d = fetch64(s2, len - 16).mul(k2);
+  const y = rotate64(a6.add(b), 43).add(rotate64(c, 30)).add(d);
+  const z = hashLen16(y, a6.add(rotate64(b.add(k2), 18)).add(c), mul2);
+  const e2 = fetch64(s2, 16).mul(mul2);
+  const f = fetch64(s2, 24);
+  const g = y.add(fetch64(s2, len - 32)).mul(mul2);
+  const h = z.add(fetch64(s2, len - 24)).mul(mul2);
+  return hashLen16(rotate64(e2.add(f), 43).add(rotate64(g, 30)).add(h), e2.add(rotate64(f.add(a6), 18)).add(g), mul2);
+}
+function fingerPrint64(s2, len = s2.length) {
+  const seed = Long.fromNumber(81, true);
+  if (len <= 32) {
+    if (len <= 16) {
+      return hashLen0to16(s2, len);
+    } else {
+      return hashLen17to32(s2, len);
+    }
+  } else if (len <= 64) {
+    return hashLen33to64(s2, len);
+  }
+  let x = seed;
+  let y = seed.mul(k1).add(113);
+  let z = shiftMix(y.mul(k2).add(113)).mul(k2);
+  let v = [Long.UZERO, Long.UZERO];
+  let w = [Long.UZERO, Long.UZERO];
+  x = x.mul(k2).add(fetch64(s2, 0));
+  let offset = 0;
+  const end = (len - 1 >> 6) * 64;
+  const last64 = end + (len - 1 & 63) - 63;
+  do {
+    x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(k1);
+    y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(k1);
+    x = x.xor(w[1]);
+    y = y.add(v[0]).add(fetch64(s2, offset + 40));
+    z = rotate64(z.add(w[0]), 33).mul(k1);
+    v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(k1), x.add(w[0]));
+    w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+    [z, x] = [x, z];
+    offset += 64;
+  } while (offset !== end);
+  const mul2 = k1.add(z.and(255).shl(1));
+  offset = last64;
+  w[0] = w[0].add(len - 1 & 63);
+  v[0] = v[0].add(w[0]);
+  w[0] = w[0].add(v[0]);
+  x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(mul2);
+  y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(mul2);
+  x = x.xor(w[1].mul(9));
+  y = y.add(v[0].mul(9).add(fetch64(s2, offset + 40)));
+  z = rotate64(z.add(w[0]), 33).mul(mul2);
+  v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(mul2), x.add(w[0]));
+  w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+  [z, x] = [x, z];
+  return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/util.js
+function createScalarValue(value, dtype) {
+  if (dtype === "string") {
+    return encodeString(value);
+  }
+  return toTypedArray([value], dtype);
+}
+function noConversionNeeded(a6, dtype) {
+  return a6 instanceof Float32Array && dtype === "float32" || a6 instanceof Int32Array && dtype === "int32" || a6 instanceof Uint8Array && dtype === "bool";
+}
+function toTypedArray(a6, dtype) {
+  if (dtype === "string") {
+    throw new Error("Cannot convert a string[] to a TypedArray");
+  }
+  if (Array.isArray(a6)) {
+    a6 = flatten(a6);
+  }
+  if (env().getBool("DEBUG")) {
+    checkConversionForErrors(a6, dtype);
+  }
+  if (noConversionNeeded(a6, dtype)) {
+    return a6;
+  }
+  if (dtype == null || dtype === "float32" || dtype === "complex64") {
+    return new Float32Array(a6);
+  } else if (dtype === "int32") {
+    return new Int32Array(a6);
+  } else if (dtype === "bool") {
+    const bool = new Uint8Array(a6.length);
+    for (let i2 = 0; i2 < bool.length; ++i2) {
+      if (Math.round(a6[i2]) !== 0) {
+        bool[i2] = 1;
+      }
+    }
+    return bool;
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+}
+function now() {
+  return env().platform.now();
+}
+function fetch3(path, requestInits) {
+  return env().platform.fetch(path, requestInits);
+}
+function encodeString(s2, encoding = "utf-8") {
+  encoding = encoding || "utf-8";
+  return env().platform.encode(s2, encoding);
+}
+function decodeString(bytes, encoding = "utf-8") {
+  encoding = encoding || "utf-8";
+  return env().platform.decode(bytes, encoding);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js
+var Profiler = class {
+  constructor(backendTimer, logger) {
+    this.backendTimer = backendTimer;
+    this.logger = logger;
+    if (logger == null) {
+      this.logger = new Logger();
+    }
+  }
+  profileKernel(kernelName, inputs, f) {
+    let outputs;
+    const holdResultWrapperFn = () => {
+      outputs = f();
+    };
+    let timer;
+    const start = now();
+    if (this.backendTimer.timerAvailable()) {
+      timer = this.backendTimer.time(holdResultWrapperFn);
+    } else {
+      holdResultWrapperFn();
+      for (const output of outputs) {
+        output.dataSync();
+      }
+      timer = Promise.resolve({ kernelMs: now() - start });
+    }
+    if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) {
+      for (let i2 = 0; i2 < outputs.length; i2++) {
+        const output = outputs[i2];
+        output.data().then((tensorVals) => {
+          checkComputationForErrors(tensorVals, output.dtype, kernelName);
+        });
+      }
+    }
+    const kernelProfile = {
+      kernelName,
+      outputs,
+      inputs,
+      timeMs: timer.then((timing) => timing.kernelMs),
+      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
+    };
+    return kernelProfile;
+  }
+  logKernelProfile(kernelProfile) {
+    const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;
+    outputs.forEach((result) => {
+      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {
+        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
+      });
+    });
+  }
+};
+function checkComputationForErrors(vals, dtype, kernelName) {
+  if (dtype !== "float32") {
+    return false;
+  }
+  for (let i2 = 0; i2 < vals.length; i2++) {
+    const num = vals[i2];
+    if (isNaN(num) || !isFinite(num)) {
+      console.warn(`Found ${num} in the result of '${kernelName}'`);
+      return true;
+    }
+  }
+  return false;
+}
+var Logger = class {
+  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
+    const time2 = typeof timeMs === "number" ? rightPad(`${timeMs}ms`, 9) : timeMs["error"];
+    const paddedName = rightPad(name, 25);
+    const rank = result.rank;
+    const size = result.size;
+    const shape = rightPad(result.shape.toString(), 14);
+    let inputShapesDescription = "";
+    for (const name2 in inputs) {
+      const input2 = inputs[name2];
+      if (input2 != null) {
+        const inputShape = input2.shape || result.shape;
+        const inputRank = inputShape.length;
+        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
+      }
+    }
+    console.log(`%c${paddedName}	%c${time2}	%c${rank}D ${shape}	%c${size}	%c${inputShapesDescription}	%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tape.js
+function getFilteredNodesXToY(tape, xs, y) {
+  const tensorsFromX = {};
+  const nodesFromX = {};
+  for (let i2 = 0; i2 < xs.length; i2++) {
+    tensorsFromX[xs[i2].id] = true;
+  }
+  for (let i2 = 0; i2 < tape.length; i2++) {
+    const node = tape[i2];
+    const nodeInputs = node.inputs;
+    for (const inputName in nodeInputs) {
+      const input2 = nodeInputs[inputName];
+      let anyInputFromX = false;
+      for (let j = 0; j < xs.length; j++) {
+        if (tensorsFromX[input2.id]) {
+          node.outputs.forEach((output) => tensorsFromX[output.id] = true);
+          anyInputFromX = true;
+          nodesFromX[node.id] = true;
+          break;
+        }
+      }
+      if (anyInputFromX) {
+        break;
+      }
+    }
+  }
+  const tensorsLeadToY = {};
+  tensorsLeadToY[y.id] = true;
+  const nodesToY = {};
+  for (let i2 = tape.length - 1; i2 >= 0; i2--) {
+    const node = tape[i2];
+    const nodeInputs = node.inputs;
+    for (let j = 0; j < node.outputs.length; j++) {
+      if (tensorsLeadToY[node.outputs[j].id]) {
+        for (const inputName in nodeInputs) {
+          tensorsLeadToY[nodeInputs[inputName].id] = true;
+          nodesToY[node.id] = true;
+        }
+        break;
+      }
+    }
+  }
+  const filteredTape = [];
+  for (let i2 = 0; i2 < tape.length; i2++) {
+    const node = tape[i2];
+    if (nodesFromX[node.id] && nodesToY[node.id]) {
+      const prunedInputs = {};
+      for (const inputName in node.inputs) {
+        const nodeInput = node.inputs[inputName];
+        if (tensorsFromX[nodeInput.id]) {
+          prunedInputs[inputName] = nodeInput;
+        }
+      }
+      const prunedNode = Object.assign({}, node);
+      prunedNode.inputs = prunedInputs;
+      prunedNode.outputs = node.outputs;
+      filteredTape.push(prunedNode);
+    }
+  }
+  return filteredTape;
+}
+function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {
+  for (let i2 = filteredTape.length - 1; i2 >= 0; i2--) {
+    const node = filteredTape[i2];
+    const dys = [];
+    node.outputs.forEach((o) => {
+      const gradTensor = tensorAccumulatedGradientMap[o.id];
+      if (gradTensor != null) {
+        dys.push(gradTensor);
+      } else {
+        dys.push(null);
+      }
+    });
+    if (node.gradient == null) {
+      throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);
+    }
+    const inputGradients = node.gradient(dys);
+    for (const inputName in node.inputs) {
+      if (!(inputName in inputGradients)) {
+        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);
+      }
+      const dx = tidy2(() => inputGradients[inputName]());
+      if (dx.dtype !== "float32") {
+        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
+      }
+      const x = node.inputs[inputName];
+      if (!arraysEqual(dx.shape, x.shape)) {
+        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);
+      }
+      if (tensorAccumulatedGradientMap[x.id] == null) {
+        tensorAccumulatedGradientMap[x.id] = dx;
+      } else {
+        const curGradient = tensorAccumulatedGradientMap[x.id];
+        tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);
+        curGradient.dispose();
+      }
+    }
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
+var FORMAT_LIMIT_NUM_VALS = 20;
+var FORMAT_NUM_FIRST_LAST_VALS = 3;
+var FORMAT_NUM_SIG_DIGITS = 7;
+function tensorToString(vals, shape, dtype, verbose) {
+  const strides = computeStrides(shape);
+  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
+  const rank = shape.length;
+  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
+  const lines = ["Tensor"];
+  if (verbose) {
+    lines.push(`  dtype: ${dtype}`);
+    lines.push(`  rank: ${rank}`);
+    lines.push(`  shape: [${shape}]`);
+    lines.push(`  values:`);
+  }
+  lines.push(valsLines.map((l3) => "    " + l3).join("\n"));
+  return lines.join("\n");
+}
+function computeMaxSizePerColumn(vals, shape, dtype, strides) {
+  const n = sizeFromShape(shape);
+  const numCols = strides[strides.length - 1];
+  const padPerCol = new Array(numCols).fill(0);
+  const rank = shape.length;
+  const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
+  if (rank > 1) {
+    for (let row = 0; row < n / numCols; row++) {
+      const offset = row * numCols;
+      for (let j = 0; j < numCols; j++) {
+        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
+      }
+    }
+  }
+  return padPerCol;
+}
+function valToString(val, pad3, dtype) {
+  let valStr;
+  if (Array.isArray(val)) {
+    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
+  } else if (isString(val)) {
+    valStr = `'${val}'`;
+  } else if (dtype === "bool") {
+    valStr = boolNumToString(val);
+  } else {
+    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
+  }
+  return rightPad(valStr, pad3);
+}
+function boolNumToString(v) {
+  return v === 0 ? "false" : "true";
+}
+function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
+  const storagePerElement = dtype === "complex64" ? 2 : 1;
+  const size = shape[0];
+  const rank = shape.length;
+  if (rank === 0) {
+    if (dtype === "complex64") {
+      const complexTuple = createComplexTuples(vals);
+      return [valToString(complexTuple[0], 0, dtype)];
+    }
+    if (dtype === "bool") {
+      return [boolNumToString(vals[0])];
+    }
+    return [vals[0].toString()];
+  }
+  if (rank === 1) {
+    if (size > FORMAT_LIMIT_NUM_VALS) {
+      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
+      let firstVals = Array.from(vals.slice(0, firstValsSize));
+      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
+      if (dtype === "complex64") {
+        firstVals = createComplexTuples(firstVals);
+        lastVals = createComplexTuples(lastVals);
+      }
+      return [
+        "[" + firstVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + ", ..., " + lastVals.map((x, i2) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i2], dtype)).join(", ") + "]"
+      ];
+    }
+    const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
+    return [
+      "[" + displayVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + "]"
+    ];
+  }
+  const subshape = shape.slice(1);
+  const substrides = strides.slice(1);
+  const stride = strides[0] * storagePerElement;
+  const lines = [];
+  if (size > FORMAT_LIMIT_NUM_VALS) {
+    for (let i2 = 0; i2 < FORMAT_NUM_FIRST_LAST_VALS; i2++) {
+      const start = i2 * stride;
+      const end = start + stride;
+      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
+    }
+    lines.push("...");
+    for (let i2 = size - FORMAT_NUM_FIRST_LAST_VALS; i2 < size; i2++) {
+      const start = i2 * stride;
+      const end = start + stride;
+      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));
+    }
+  } else {
+    for (let i2 = 0; i2 < size; i2++) {
+      const start = i2 * stride;
+      const end = start + stride;
+      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));
+    }
+  }
+  const sep = rank === 2 ? "," : "";
+  lines[0] = "[" + lines[0] + sep;
+  for (let i2 = 1; i2 < lines.length - 1; i2++) {
+    lines[i2] = " " + lines[i2] + sep;
+  }
+  let newLineSep = ",\n";
+  for (let i2 = 2; i2 < rank; i2++) {
+    newLineSep += "\n";
+  }
+  lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
+  return lines;
+}
+function createComplexTuples(vals) {
+  const complexTuples = [];
+  for (let i2 = 0; i2 < vals.length; i2 += 2) {
+    complexTuples.push([vals[i2], vals[i2 + 1]]);
+  }
+  return complexTuples;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js
+var TensorBuffer = class {
+  constructor(shape, dtype, values) {
+    this.dtype = dtype;
+    this.shape = shape.slice();
+    this.size = sizeFromShape(shape);
+    if (values != null) {
+      const n = values.length;
+      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
+    }
+    if (dtype === "complex64") {
+      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
+    }
+    this.values = values || getArrayFromDType(dtype, this.size);
+    this.strides = computeStrides(shape);
+  }
+  set(value, ...locs) {
+    if (locs.length === 0) {
+      locs = [0];
+    }
+    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);
+    const index = this.locToIndex(locs);
+    this.values[index] = value;
+  }
+  get(...locs) {
+    if (locs.length === 0) {
+      locs = [0];
+    }
+    let i2 = 0;
+    for (const loc of locs) {
+      if (loc < 0 || loc >= this.shape[i2]) {
+        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
+        throw new Error(msg);
+      }
+      i2++;
+    }
+    let index = locs[locs.length - 1];
+    for (let i3 = 0; i3 < locs.length - 1; ++i3) {
+      index += this.strides[i3] * locs[i3];
+    }
+    return this.values[index];
+  }
+  locToIndex(locs) {
+    if (this.rank === 0) {
+      return 0;
+    } else if (this.rank === 1) {
+      return locs[0];
+    }
+    let index = locs[locs.length - 1];
+    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+      index += this.strides[i2] * locs[i2];
+    }
+    return index;
+  }
+  indexToLoc(index) {
+    if (this.rank === 0) {
+      return [];
+    } else if (this.rank === 1) {
+      return [index];
+    }
+    const locs = new Array(this.shape.length);
+    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+      locs[i2] = Math.floor(index / this.strides[i2]);
+      index -= locs[i2] * this.strides[i2];
+    }
+    locs[locs.length - 1] = index;
+    return locs;
+  }
+  get rank() {
+    return this.shape.length;
+  }
+  toTensor() {
+    return trackerFn().makeTensor(this.values, this.shape, this.dtype);
+  }
+};
+var trackerFn = null;
+var opHandler = null;
+var deprecationWarningFn = null;
+function setTensorTracker(fn) {
+  trackerFn = fn;
+}
+function setOpHandler(handler) {
+  opHandler = handler;
+}
+function setDeprecationWarningFn(fn) {
+  deprecationWarningFn = fn;
+}
+var Tensor = class {
+  constructor(shape, dtype, dataId, id) {
+    this.kept = false;
+    this.isDisposedInternal = false;
+    this.shape = shape.slice();
+    this.dtype = dtype || "float32";
+    this.size = sizeFromShape(shape);
+    this.strides = computeStrides(shape);
+    this.dataId = dataId;
+    this.id = id;
+    this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
+  }
+  get rank() {
+    return this.shape.length;
+  }
+  async buffer() {
+    const vals = await this.data();
+    return opHandler.buffer(this.shape, this.dtype, vals);
+  }
+  bufferSync() {
+    return opHandler.buffer(this.shape, this.dtype, this.dataSync());
+  }
+  async array() {
+    const vals = await this.data();
+    return toNestedArray(this.shape, vals, this.dtype === "complex64");
+  }
+  arraySync() {
+    return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64");
+  }
+  async data() {
+    this.throwIfDisposed();
+    const data = trackerFn().read(this.dataId);
+    if (this.dtype === "string") {
+      const bytes = await data;
+      try {
+        return bytes.map((b) => decodeString(b));
+      } catch (_a) {
+        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
+      }
+    }
+    return data;
+  }
+  dataToGPU(options) {
+    this.throwIfDisposed();
+    return trackerFn().readToGPU(this.dataId, options);
+  }
+  dataSync() {
+    this.throwIfDisposed();
+    const data = trackerFn().readSync(this.dataId);
+    if (this.dtype === "string") {
+      try {
+        return data.map((b) => decodeString(b));
+      } catch (_a) {
+        throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
+      }
+    }
+    return data;
+  }
+  async bytes() {
+    this.throwIfDisposed();
+    const data = await trackerFn().read(this.dataId);
+    if (this.dtype === "string") {
+      return data;
+    } else {
+      return new Uint8Array(data.buffer);
+    }
+  }
+  dispose() {
+    if (this.isDisposed) {
+      return;
+    }
+    trackerFn().disposeTensor(this);
+    this.isDisposedInternal = true;
+  }
+  get isDisposed() {
+    return this.isDisposedInternal;
+  }
+  throwIfDisposed() {
+    if (this.isDisposed) {
+      throw new Error(`Tensor is disposed.`);
+    }
+  }
+  print(verbose = false) {
+    return opHandler.print(this, verbose);
+  }
+  clone() {
+    this.throwIfDisposed();
+    return opHandler.clone(this);
+  }
+  toString(verbose = false) {
+    const vals = this.dataSync();
+    return tensorToString(vals, this.shape, this.dtype, verbose);
+  }
+  cast(dtype) {
+    this.throwIfDisposed();
+    return opHandler.cast(this, dtype);
+  }
+  variable(trainable = true, name, dtype) {
+    this.throwIfDisposed();
+    return trackerFn().makeVariable(this, trainable, name, dtype);
+  }
+};
+Object.defineProperty(Tensor, Symbol.hasInstance, {
+  value: (instance) => {
+    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
+  }
+});
+function getGlobalTensorClass() {
+  return getGlobal("Tensor", () => {
+    return Tensor;
+  });
+}
+getGlobalTensorClass();
+var Variable = class extends Tensor {
+  constructor(initialValue, trainable, name, tensorId) {
+    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
+    this.trainable = trainable;
+    this.name = name;
+  }
+  assign(newValue) {
+    if (newValue.dtype !== this.dtype) {
+      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);
+    }
+    if (!arraysEqual(newValue.shape, this.shape)) {
+      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);
+    }
+    trackerFn().disposeTensor(this);
+    this.dataId = newValue.dataId;
+    trackerFn().incRef(this, null);
+  }
+  dispose() {
+    trackerFn().disposeVariable(this);
+    this.isDisposedInternal = true;
+  }
+};
+Object.defineProperty(Variable, Symbol.hasInstance, {
+  value: (instance) => {
+    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
+var tensor_util_exports = {};
+__export(tensor_util_exports, {
+  assertTypesMatch: () => assertTypesMatch,
+  getTensorsInContainer: () => getTensorsInContainer,
+  isTensorInList: () => isTensorInList,
+  makeTypesMatch: () => makeTypesMatch
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/types.js
+var Rank;
+(function(Rank2) {
+  Rank2["R0"] = "R0";
+  Rank2["R1"] = "R1";
+  Rank2["R2"] = "R2";
+  Rank2["R3"] = "R3";
+  Rank2["R4"] = "R4";
+  Rank2["R5"] = "R5";
+  Rank2["R6"] = "R6";
+})(Rank || (Rank = {}));
+var UpcastInt32AndMap;
+(function(UpcastInt32AndMap2) {
+  UpcastInt32AndMap2["float32"] = "float32";
+  UpcastInt32AndMap2["int32"] = "int32";
+  UpcastInt32AndMap2["bool"] = "int32";
+  UpcastInt32AndMap2["complex64"] = "complex64";
+})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
+var UpcastBoolAndMap;
+(function(UpcastBoolAndMap2) {
+  UpcastBoolAndMap2["float32"] = "float32";
+  UpcastBoolAndMap2["int32"] = "int32";
+  UpcastBoolAndMap2["bool"] = "bool";
+  UpcastBoolAndMap2["complex64"] = "complex64";
+})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
+var UpcastFloat32AndMap;
+(function(UpcastFloat32AndMap2) {
+  UpcastFloat32AndMap2["float32"] = "float32";
+  UpcastFloat32AndMap2["int32"] = "float32";
+  UpcastFloat32AndMap2["bool"] = "float32";
+  UpcastFloat32AndMap2["complex64"] = "complex64";
+})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
+var UpcastComplex64AndMap;
+(function(UpcastComplex64AndMap2) {
+  UpcastComplex64AndMap2["float32"] = "complex64";
+  UpcastComplex64AndMap2["int32"] = "complex64";
+  UpcastComplex64AndMap2["bool"] = "complex64";
+  UpcastComplex64AndMap2["complex64"] = "complex64";
+})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
+var upcastTypeMap = {
+  "float32": UpcastFloat32AndMap,
+  "int32": UpcastInt32AndMap,
+  "bool": UpcastBoolAndMap,
+  "complex64": UpcastComplex64AndMap
+};
+function upcastType(typeA, typeB) {
+  if (typeA === "string" || typeB === "string") {
+    if (typeA === "string" && typeB === "string") {
+      return "string";
+    }
+    throw new Error(`Can not upcast ${typeA} with ${typeB}`);
+  }
+  return upcastTypeMap[typeA][typeB];
+}
+function sumOutType(type) {
+  return upcastType(type, "int32");
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
+function makeTypesMatch(a6, b) {
+  if (a6.dtype === b.dtype) {
+    return [a6, b];
+  }
+  const dtype = upcastType(a6.dtype, b.dtype);
+  return [a6.cast(dtype), b.cast(dtype)];
+}
+function assertTypesMatch(a6, b) {
+  assert(a6.dtype === b.dtype, () => `The dtypes of the first(${a6.dtype}) and second(${b.dtype}) input must match`);
+}
+function isTensorInList(tensor2, tensorList) {
+  return tensorList.some((x) => x.id === tensor2.id);
+}
+function getTensorsInContainer(result) {
+  const list = [];
+  const seen = /* @__PURE__ */ new Set();
+  walkTensorContainer(result, list, seen);
+  return list;
+}
+function walkTensorContainer(container, list, seen) {
+  if (container == null) {
+    return;
+  }
+  if (container instanceof Tensor) {
+    list.push(container);
+    return;
+  }
+  if (!isIterable(container)) {
+    return;
+  }
+  const iterable = container;
+  for (const k in iterable) {
+    const val = iterable[k];
+    if (!seen.has(val)) {
+      seen.add(val);
+      walkTensorContainer(val, list, seen);
+    }
+  }
+}
+function isIterable(obj) {
+  return Array.isArray(obj) || typeof obj === "object";
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/engine.js
+function isRegisteredKernelInvocation(kernelInvocation) {
+  return kernelInvocation.kernelName != null;
+}
+var EngineState = class {
+  constructor() {
+    this.registeredVariables = {};
+    this.nextTapeNodeId = 0;
+    this.numBytes = 0;
+    this.numTensors = 0;
+    this.numStringTensors = 0;
+    this.numDataBuffers = 0;
+    this.gradientDepth = 0;
+    this.kernelDepth = 0;
+    this.scopeStack = [];
+    this.numDataMovesStack = [];
+    this.nextScopeId = 0;
+    this.tensorInfo = /* @__PURE__ */ new WeakMap();
+    this.profiling = false;
+    this.activeProfile = {
+      newBytes: 0,
+      newTensors: 0,
+      peakBytes: 0,
+      kernels: [],
+      result: null,
+      get kernelNames() {
+        return Array.from(new Set(this.kernels.map((k) => k.name)));
+      }
+    };
+  }
+  dispose() {
+    for (const variableName in this.registeredVariables) {
+      this.registeredVariables[variableName].dispose();
+    }
+  }
+};
+var Engine = class {
+  constructor(ENV8) {
+    this.ENV = ENV8;
+    this.registry = {};
+    this.registryFactory = {};
+    this.pendingBackendInitId = 0;
+    this.state = new EngineState();
+  }
+  async ready() {
+    if (this.pendingBackendInit != null) {
+      return this.pendingBackendInit.then(() => {
+      });
+    }
+    if (this.backendInstance != null) {
+      return;
+    }
+    const sortedBackends = this.getSortedBackends();
+    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
+      const backendName = sortedBackends[i2];
+      const success = await this.initializeBackend(backendName).success;
+      if (success) {
+        await this.setBackend(backendName);
+        return;
+      }
+    }
+    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
+  }
+  get backend() {
+    if (this.pendingBackendInit != null) {
+      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
+    }
+    if (this.backendInstance == null) {
+      const { name, asyncInit } = this.initializeBackendsAndReturnBest();
+      if (asyncInit) {
+        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
+      }
+      this.setBackend(name);
+    }
+    return this.backendInstance;
+  }
+  backendNames() {
+    return Object.keys(this.registryFactory);
+  }
+  findBackend(backendName) {
+    if (!(backendName in this.registry)) {
+      if (backendName in this.registryFactory) {
+        const { asyncInit } = this.initializeBackend(backendName);
+        if (asyncInit) {
+          return null;
+        }
+      } else {
+        return null;
+      }
+    }
+    return this.registry[backendName];
+  }
+  findBackendFactory(backendName) {
+    if (!(backendName in this.registryFactory)) {
+      return null;
+    }
+    return this.registryFactory[backendName].factory;
+  }
+  registerBackend(backendName, factory, priority = 1) {
+    if (backendName in this.registryFactory) {
+      warn(`${backendName} backend was already registered. Reusing existing backend factory.`);
+      return false;
+    }
+    this.registryFactory[backendName] = { factory, priority };
+    return true;
+  }
+  async setBackend(backendName) {
+    if (this.registryFactory[backendName] == null) {
+      throw new Error(`Backend name '${backendName}' not found in registry`);
+    }
+    this.backendName = backendName;
+    if (this.registry[backendName] == null) {
+      this.backendInstance = null;
+      const { success, asyncInit } = this.initializeBackend(backendName);
+      const result = asyncInit ? await success : success;
+      if (!result) {
+        return false;
+      }
+    }
+    this.backendInstance = this.registry[backendName];
+    this.setupRegisteredKernels();
+    this.profiler = new Profiler(this.backendInstance);
+    return true;
+  }
+  setupRegisteredKernels() {
+    const kernels = getKernelsForBackend(this.backendName);
+    kernels.forEach((kernel) => {
+      if (kernel.setupFunc != null) {
+        kernel.setupFunc(this.backendInstance);
+      }
+    });
+  }
+  disposeRegisteredKernels(backendName) {
+    const kernels = getKernelsForBackend(backendName);
+    kernels.forEach((kernel) => {
+      if (kernel.disposeFunc != null) {
+        kernel.disposeFunc(this.registry[backendName]);
+      }
+    });
+  }
+  initializeBackend(backendName) {
+    const registryFactoryEntry = this.registryFactory[backendName];
+    if (registryFactoryEntry == null) {
+      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
+    }
+    try {
+      const backend2 = registryFactoryEntry.factory();
+      if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") {
+        const promiseId = ++this.pendingBackendInitId;
+        const success = backend2.then((backendInstance) => {
+          if (promiseId < this.pendingBackendInitId) {
+            return false;
+          }
+          this.registry[backendName] = backendInstance;
+          this.pendingBackendInit = null;
+          return true;
+        }).catch((err) => {
+          if (promiseId < this.pendingBackendInitId) {
+            return false;
+          }
+          this.pendingBackendInit = null;
+          warn(`Initialization of backend ${backendName} failed`);
+          warn(err.stack || err.message);
+          return false;
+        });
+        this.pendingBackendInit = success;
+        return { success, asyncInit: true };
+      } else {
+        this.registry[backendName] = backend2;
+        return { success: true, asyncInit: false };
+      }
+    } catch (err) {
+      warn(`Initialization of backend ${backendName} failed`);
+      warn(err.stack || err.message);
+      return { success: false, asyncInit: false };
+    }
+  }
+  removeBackend(backendName) {
+    if (!(backendName in this.registryFactory)) {
+      throw new Error(`${backendName} backend not found in registry`);
+    }
+    if (this.backendName === backendName && this.pendingBackendInit != null) {
+      this.pendingBackendInitId++;
+    }
+    if (backendName in this.registry) {
+      this.disposeRegisteredKernels(backendName);
+      this.registry[backendName].dispose();
+      delete this.registry[backendName];
+    }
+    delete this.registryFactory[backendName];
+    if (this.backendName === backendName) {
+      this.pendingBackendInit = null;
+      this.backendName = null;
+      this.backendInstance = null;
+    }
+  }
+  getSortedBackends() {
+    if (Object.keys(this.registryFactory).length === 0) {
+      throw new Error("No backend found in registry.");
+    }
+    return Object.keys(this.registryFactory).sort((a6, b) => {
+      return this.registryFactory[b].priority - this.registryFactory[a6].priority;
+    });
+  }
+  initializeBackendsAndReturnBest() {
+    const sortedBackends = this.getSortedBackends();
+    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
+      const backendName = sortedBackends[i2];
+      const { success, asyncInit } = this.initializeBackend(backendName);
+      if (asyncInit || success) {
+        return { name: backendName, asyncInit };
+      }
+    }
+    throw new Error(`Could not initialize any backends, all backend initializations failed.`);
+  }
+  moveData(backend2, dataId) {
+    const info = this.state.tensorInfo.get(dataId);
+    const srcBackend = info.backend;
+    const values = this.readSync(dataId);
+    const refCount = srcBackend.refCount(dataId);
+    srcBackend.disposeData(dataId, true);
+    info.backend = backend2;
+    backend2.move(dataId, values, info.shape, info.dtype, refCount);
+    if (this.shouldCheckForMemLeaks()) {
+      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
+    }
+  }
+  tidy(nameOrFn, fn) {
+    let name = null;
+    if (fn == null) {
+      if (typeof nameOrFn !== "function") {
+        throw new Error("Please provide a function to tidy()");
+      }
+      fn = nameOrFn;
+    } else {
+      if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) {
+        throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
+      }
+      if (typeof fn !== "function") {
+        throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
+      }
+      name = nameOrFn;
+    }
+    let result;
+    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {
+      result = fn();
+      if (result instanceof Promise) {
+        console.error("Cannot return a Promise inside of tidy.");
+      }
+      return result;
+    });
+  }
+  scopedRun(start, end, f) {
+    start();
+    try {
+      const res = f();
+      end();
+      return res;
+    } catch (ex) {
+      end();
+      throw ex;
+    }
+  }
+  nextTensorId() {
+    return Engine.nextTensorId++;
+  }
+  nextVariableId() {
+    return Engine.nextVariableId++;
+  }
+  clone(x) {
+    const y = ENGINE.runKernel(Identity, { x });
+    const inputs = { x };
+    const grad2 = (dy) => ({
+      x: () => {
+        const dtype = "float32";
+        const gradInputs = { x: dy };
+        const attrs = { dtype };
+        return ENGINE.runKernel(Cast, gradInputs, attrs);
+      }
+    });
+    const saved = [];
+    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});
+    return y;
+  }
+  runKernel(kernelName, inputs, attrs) {
+    if (this.backendName == null) {
+      this.backend;
+    }
+    const hasKernel = getKernel(kernelName, this.backendName) != null;
+    if (!hasKernel) {
+      throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);
+    }
+    return this.runKernelFunc({ kernelName, inputs, attrs });
+  }
+  shouldCheckForMemLeaks() {
+    return this.ENV.getBool("IS_TEST");
+  }
+  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
+    const numDataIdsAfter = this.backend.numDataIds();
+    let numOutputDataIds = 0;
+    outInfos.forEach((info) => {
+      numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
+    });
+    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
+    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
+    if (dataIdsLeaked > 0) {
+      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);
+    }
+  }
+  runKernelFunc(kernelParams) {
+    let outputs;
+    let saved = [];
+    const isTapeOn = this.isTapeOn();
+    const startingBytecount = this.state.numBytes;
+    const startingNumTensors = this.state.numTensors;
+    if (this.shouldCheckForMemLeaks()) {
+      this.state.numDataMovesStack.push(0);
+    }
+    let kernelFunc3;
+    if (this.backendName == null) {
+      this.backend;
+    }
+    let out;
+    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
+    if (isRegisteredKernelInvocation(kernelParams)) {
+      const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;
+      if (this.backendName == null) {
+        this.backend;
+      }
+      const kernel = getKernel(kernelName, this.backendName);
+      assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);
+      kernelFunc3 = () => {
+        const numDataIdsBefore = this.backend.numDataIds();
+        out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });
+        const outInfos = Array.isArray(out) ? out : [out];
+        if (this.shouldCheckForMemLeaks()) {
+          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
+        }
+        const outTensors = outInfos.map((outInfo) => {
+          if (outInfo.rank != null) {
+            return outInfo;
+          }
+          return this.makeTensorFromTensorInfo(outInfo);
+        });
+        if (isTapeOn) {
+          const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);
+          saved = this.saveTensorsForBackwardMode(tensorsToSave);
+        }
+        return outTensors;
+      };
+    } else {
+      const { forwardFunc } = kernelParams;
+      const saveFunc = (tensors) => {
+        if (!isTapeOn) {
+          return;
+        }
+        saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));
+      };
+      kernelFunc3 = () => {
+        const numDataIdsBefore = this.backend.numDataIds();
+        out = this.tidy(() => forwardFunc(this.backend, saveFunc));
+        const outs = Array.isArray(out) ? out : [out];
+        if (this.shouldCheckForMemLeaks()) {
+          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
+        }
+        return outs;
+      };
+    }
+    const { inputs, attrs } = kernelParams;
+    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;
+    let kernelProfile;
+    this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
+      if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
+        outputs = kernelFunc3();
+      } else {
+        kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());
+        if (this.ENV.getBool("DEBUG")) {
+          this.profiler.logKernelProfile(kernelProfile);
+        }
+        outputs = kernelProfile.outputs;
+      }
+    });
+    if (isTapeOn) {
+      this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
+    }
+    if (this.state.profiling) {
+      this.state.activeProfile.kernels.push({
+        name: kernelOrScopeName,
+        bytesAdded: this.state.numBytes - startingBytecount,
+        totalBytesSnapshot: this.state.numBytes,
+        tensorsAdded: this.state.numTensors - startingNumTensors,
+        totalTensorsSnapshot: this.state.numTensors,
+        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),
+        outputShapes: outputs.map((item) => item.shape),
+        kernelTimeMs: kernelProfile.timeMs,
+        extraInfo: kernelProfile.extraInfo
+      });
+    }
+    return Array.isArray(out) ? outputs : outputs[0];
+  }
+  saveTensorsForBackwardMode(tensors) {
+    const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));
+    return saved;
+  }
+  getTensorsForGradient(kernelName, inputs, outputs) {
+    const gradConfig = getGradient(kernelName);
+    if (gradConfig != null) {
+      const inputsToSave = gradConfig.inputsToSave || [];
+      const outputsToSave = gradConfig.outputsToSave || [];
+      let inputTensorsToSave;
+      if (gradConfig.saveAllInputs) {
+        assert(Array.isArray(inputs), () => "saveAllInputs is true, expected inputs to be an array.");
+        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);
+      } else {
+        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
+      }
+      const outputTensorsToSave = outputs.filter((_, i2) => outputsToSave[i2]);
+      return inputTensorsToSave.concat(outputTensorsToSave);
+    }
+    return [];
+  }
+  makeTensor(values, shape, dtype, backend2) {
+    if (values == null) {
+      throw new Error("Values passed to engine.makeTensor() are null");
+    }
+    dtype = dtype || "float32";
+    backend2 = backend2 || this.backend;
+    let backendVals = values;
+    if (dtype === "string" && isString(values[0])) {
+      backendVals = values.map((d) => encodeString(d));
+    }
+    const dataId = backend2.write(backendVals, shape, dtype);
+    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());
+    this.trackTensor(t2, backend2);
+    if (dtype === "string") {
+      const info = this.state.tensorInfo.get(dataId);
+      const newBytes = bytesFromStringArray(backendVals);
+      this.state.numBytes += newBytes - info.bytes;
+      info.bytes = newBytes;
+    }
+    return t2;
+  }
+  makeTensorFromDataId(dataId, shape, dtype, backend2) {
+    dtype = dtype || "float32";
+    const tensorInfo = { dataId, shape, dtype };
+    return this.makeTensorFromTensorInfo(tensorInfo, backend2);
+  }
+  makeTensorFromTensorInfo(tensorInfo, backend2) {
+    const { dataId, shape, dtype } = tensorInfo;
+    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());
+    this.trackTensor(t2, backend2);
+    return t2;
+  }
+  makeVariable(initialValue, trainable = true, name, dtype) {
+    name = name || this.nextVariableId().toString();
+    if (dtype != null && dtype !== initialValue.dtype) {
+      initialValue = initialValue.cast(dtype);
+    }
+    const v = new Variable(initialValue, trainable, name, this.nextTensorId());
+    if (this.state.registeredVariables[v.name] != null) {
+      throw new Error(`Variable with name ${v.name} was already registered`);
+    }
+    this.state.registeredVariables[v.name] = v;
+    this.incRef(v, this.backend);
+    return v;
+  }
+  trackTensor(a6, backend2) {
+    this.state.numTensors++;
+    if (a6.dtype === "string") {
+      this.state.numStringTensors++;
+    }
+    let bytes = 0;
+    if (a6.dtype !== "complex64" && a6.dtype !== "string") {
+      bytes = a6.size * bytesPerElement(a6.dtype);
+    }
+    this.state.numBytes += bytes;
+    if (!this.state.tensorInfo.has(a6.dataId)) {
+      this.state.numDataBuffers++;
+      this.state.tensorInfo.set(a6.dataId, {
+        backend: backend2 || this.backend,
+        dtype: a6.dtype,
+        shape: a6.shape,
+        bytes
+      });
+    }
+    if (!(a6 instanceof Variable)) {
+      this.track(a6);
+    }
+  }
+  incRef(a6, backend2) {
+    this.trackTensor(a6, backend2);
+    this.backend.incRef(a6.dataId);
+  }
+  removeDataId(dataId, backend2) {
+    if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {
+      this.state.tensorInfo.delete(dataId);
+      this.state.numDataBuffers--;
+    }
+  }
+  disposeTensor(a6) {
+    if (!this.state.tensorInfo.has(a6.dataId)) {
+      return;
+    }
+    const info = this.state.tensorInfo.get(a6.dataId);
+    this.state.numTensors--;
+    if (a6.dtype === "string") {
+      this.state.numStringTensors--;
+      this.state.numBytes -= info.bytes;
+    }
+    if (a6.dtype !== "complex64" && a6.dtype !== "string") {
+      const bytes = a6.size * bytesPerElement(a6.dtype);
+      this.state.numBytes -= bytes;
+    }
+    if (info.backend.disposeData(a6.dataId)) {
+      this.removeDataId(a6.dataId, info.backend);
+    }
+  }
+  disposeVariables() {
+    for (const varName in this.state.registeredVariables) {
+      const v = this.state.registeredVariables[varName];
+      this.disposeVariable(v);
+    }
+  }
+  disposeVariable(v) {
+    this.disposeTensor(v);
+    if (this.state.registeredVariables[v.name] != null) {
+      delete this.state.registeredVariables[v.name];
+    }
+  }
+  memory() {
+    const info = this.backend.memory();
+    info.numTensors = this.state.numTensors;
+    info.numDataBuffers = this.state.numDataBuffers;
+    info.numBytes = this.state.numBytes;
+    if (this.state.numStringTensors > 0) {
+      info.unreliable = true;
+      if (info.reasons == null) {
+        info.reasons = [];
+      }
+      info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
+    }
+    return info;
+  }
+  async profile(query) {
+    this.state.profiling = true;
+    const startBytes = this.state.numBytes;
+    const startNumTensors = this.state.numTensors;
+    this.state.activeProfile.kernels = [];
+    this.state.activeProfile.result = await query();
+    this.state.profiling = false;
+    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));
+    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
+    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
+    for (const kernel of this.state.activeProfile.kernels) {
+      kernel.kernelTimeMs = await kernel.kernelTimeMs;
+      kernel.extraInfo = await kernel.extraInfo;
+    }
+    return this.state.activeProfile;
+  }
+  isTapeOn() {
+    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
+  }
+  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
+    const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };
+    const gradConfig = getGradient(kernelName);
+    if (gradConfig != null) {
+      gradientsFunc = gradConfig.gradFunc;
+    }
+    if (gradientsFunc != null) {
+      tapeNode.gradient = (dys) => {
+        dys = dys.map((dy, i2) => {
+          if (dy == null) {
+            const output = outputs[i2];
+            const vals = makeZerosTypedArray(output.size, output.dtype);
+            return this.makeTensor(vals, output.shape, output.dtype);
+          }
+          return dy;
+        });
+        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
+      };
+    }
+    this.state.activeTape.push(tapeNode);
+  }
+  keep(result) {
+    result.kept = true;
+    return result;
+  }
+  startTape() {
+    if (this.state.gradientDepth === 0) {
+      this.state.activeTape = [];
+    }
+    this.state.gradientDepth++;
+  }
+  endTape() {
+    this.state.gradientDepth--;
+  }
+  startScope(name) {
+    const scopeInfo = {
+      track: [],
+      name: "unnamed scope",
+      id: this.state.nextScopeId++
+    };
+    if (name) {
+      scopeInfo.name = name;
+    }
+    this.state.scopeStack.push(scopeInfo);
+    this.state.activeScope = scopeInfo;
+  }
+  endScope(result) {
+    const tensorsToTrackInParent = getTensorsInContainer(result);
+    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t2) => t2.id));
+    for (let i2 = 0; i2 < this.state.activeScope.track.length; i2++) {
+      const tensor2 = this.state.activeScope.track[i2];
+      if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {
+        tensor2.dispose();
+      }
+    }
+    const oldScope = this.state.scopeStack.pop();
+    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
+    tensorsToTrackInParent.forEach((tensor2) => {
+      if (!tensor2.kept && tensor2.scopeId === oldScope.id) {
+        this.track(tensor2);
+      }
+    });
+  }
+  gradients(f, xs, dy, allowNoGradients = false) {
+    assert(xs.length > 0, () => "gradients() received an empty list of xs.");
+    if (dy != null && dy.dtype !== "float32") {
+      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
+    }
+    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", f));
+    assert(y instanceof Tensor, () => "The result y returned by f() must be a tensor.");
+    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);
+    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
+      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
+    }
+    return this.tidy("backward", () => {
+      const accumulatedGradientMap = {};
+      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
+      backpropagateGradients(accumulatedGradientMap, filteredTape, (f2) => this.tidy(f2), add);
+      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);
+      if (this.state.gradientDepth === 0) {
+        this.state.activeTape.forEach((node) => {
+          for (const tensor2 of node.saved) {
+            tensor2.dispose();
+          }
+        });
+        this.state.activeTape = null;
+      }
+      return { value: y, grads: grads2 };
+    });
+  }
+  customGrad(f) {
+    assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
+    return (...inputs) => {
+      assert(inputs.every((t2) => t2 instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
+      let res;
+      const inputMap = {};
+      inputs.forEach((input2, i2) => {
+        inputMap[i2] = input2;
+      });
+      const forwardFunc = (_, save) => {
+        res = f(...[...inputs, save]);
+        assert(res.value instanceof Tensor, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
+        assert(isFunction(res.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
+        return res.value;
+      };
+      const backwardsFunc = (dy, saved) => {
+        const gradRes = res.gradFunc(dy, saved);
+        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
+        assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
+        assert(grads2.every((t2) => t2 instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
+        const gradMap = {};
+        grads2.forEach((grad2, i2) => {
+          gradMap[i2] = () => grad2;
+        });
+        return gradMap;
+      };
+      return this.runKernelFunc({
+        forwardFunc,
+        backwardsFunc,
+        inputs: inputMap
+      });
+    };
+  }
+  readSync(dataId) {
+    const info = this.state.tensorInfo.get(dataId);
+    return info.backend.readSync(dataId);
+  }
+  read(dataId) {
+    const info = this.state.tensorInfo.get(dataId);
+    return info.backend.read(dataId);
+  }
+  readToGPU(dataId, options) {
+    const info = this.state.tensorInfo.get(dataId);
+    return info.backend.readToGPU(dataId, options);
+  }
+  async time(query) {
+    const start = now();
+    const timingInfo = await this.backend.time(query);
+    timingInfo.wallMs = now() - start;
+    return timingInfo;
+  }
+  track(result) {
+    if (this.state.activeScope != null) {
+      result.scopeId = this.state.activeScope.id;
+      this.state.activeScope.track.push(result);
+    }
+    return result;
+  }
+  get registeredVariables() {
+    return this.state.registeredVariables;
+  }
+  reset() {
+    this.pendingBackendInitId++;
+    this.state.dispose();
+    this.ENV.reset();
+    this.state = new EngineState();
+    for (const backendName in this.registry) {
+      this.disposeRegisteredKernels(backendName);
+      this.registry[backendName].dispose();
+      delete this.registry[backendName];
+    }
+    this.backendName = null;
+    this.backendInstance = null;
+    this.pendingBackendInit = null;
+  }
+};
+Engine.nextTensorId = 0;
+Engine.nextVariableId = 0;
+function ones(shape) {
+  const values = makeOnesTypedArray(sizeFromShape(shape), "float32");
+  return ENGINE.makeTensor(values, shape, "float32");
+}
+function getOrMakeEngine() {
+  const ns = getGlobalNamespace();
+  if (ns._tfengine == null) {
+    const environment = new Environment(ns);
+    ns._tfengine = new Engine(environment);
+  }
+  setEnvironmentGlobal(ns._tfengine.ENV);
+  setTensorTracker(() => ns._tfengine);
+  return ns._tfengine;
+}
+var ENGINE = getOrMakeEngine();
+function add(a6, b) {
+  const inputs = { a: a6, b };
+  return ENGINE.runKernel(Add, inputs);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js
+var device_util_exports = {};
+__export(device_util_exports, {
+  isBrowser: () => isBrowser,
+  isMobile: () => isMobile,
+  mockIsMobile: () => mockIsMobile
+});
+function _isNavigatorDefined() {
+  return typeof navigator !== "undefined" && navigator != null;
+}
+var isMobileMockValue;
+function mockIsMobile(value) {
+  isMobileMockValue = value;
+}
+function isMobile(nav) {
+  if (isMobileMockValue !== void 0) {
+    return isMobileMockValue;
+  }
+  if (nav || _isNavigatorDefined()) {
+    if (!nav) {
+      nav = navigator;
+    }
+    if (nav.product === "ReactNative") {
+      return true;
+    }
+    const a6 = nav.userAgent || nav.vendor || (typeof window !== "undefined" ? window.opera : "");
+    if (!a6) {
+      const navAny = nav;
+      return navAny.userAgentData && navAny.userAgentData.mobile;
+    }
+    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a6) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a6.substr(0, 4));
+  }
+  return false;
+}
+function isBrowser() {
+  return typeof window !== "undefined" && window.document != null || typeof WorkerGlobalScope !== "undefined";
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/flags.js
+var ENV2 = env();
+ENV2.registerFlag("DEBUG", () => false, (debugValue) => {
+  if (debugValue) {
+    console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
+  }
+});
+ENV2.registerFlag("IS_BROWSER", () => isBrowser());
+ENV2.registerFlag("IS_NODE", () => typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
+ENV2.registerFlag("IS_CHROME", () => typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
+ENV2.registerFlag("PROD", () => false);
+ENV2.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => ENV2.getBool("DEBUG"));
+ENV2.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
+ENV2.registerFlag("IS_TEST", () => false);
+ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
+ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
+ENV2.registerFlag("ENGINE_COMPILE_ONLY", () => false);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
+function inferShape(val, dtype) {
+  let firstElem = val;
+  if (isTypedArray(val)) {
+    return dtype === "string" ? [] : [val.length];
+  }
+  if (!Array.isArray(val)) {
+    return [];
+  }
+  const shape = [];
+  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== "string") {
+    shape.push(firstElem.length);
+    firstElem = firstElem[0];
+  }
+  if (Array.isArray(val) && env().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) {
+    deepAssertShapeConsistency(val, shape, []);
+  }
+  return shape;
+}
+function deepAssertShapeConsistency(val, shape, indices) {
+  indices = indices || [];
+  if (!Array.isArray(val) && !isTypedArray(val)) {
+    assert(shape.length === 0, () => `Element arr[${indices.join("][")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);
+    return;
+  }
+  assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
+  assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
+  const subShape = shape.slice(1);
+  for (let i2 = 0; i2 < val.length; ++i2) {
+    deepAssertShapeConsistency(val[i2], subShape, indices.concat(i2));
+  }
+}
+function assertDtype(expectedDtype, actualDType, argName, functionName) {
+  if (expectedDtype === "string_or_numeric") {
+    return;
+  }
+  if (expectedDtype == null) {
+    throw new Error(`Expected dtype cannot be null.`);
+  }
+  if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
+    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);
+  }
+}
+function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
+  if (x instanceof Tensor) {
+    assertDtype(parseAsDtype, x.dtype, argName, functionName);
+    return x;
+  }
+  let inferredDtype = inferDtype(x);
+  if (inferredDtype !== "string" && ["bool", "int32", "float32"].indexOf(parseAsDtype) >= 0) {
+    inferredDtype = parseAsDtype;
+  }
+  assertDtype(parseAsDtype, inferredDtype, argName, functionName);
+  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
+    const type = x == null ? "null" : x.constructor.name;
+    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);
+  }
+  const inferredShape = inferShape(x, inferredDtype);
+  if (!isTypedArray(x) && !Array.isArray(x)) {
+    x = [x];
+  }
+  const skipTypedArray = true;
+  const values = inferredDtype !== "string" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);
+  return ENGINE.makeTensor(values, inferredShape, inferredDtype);
+}
+function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
+  if (!Array.isArray(arg)) {
+    throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
+  }
+  const tensors = arg;
+  return tensors.map((t2, i2) => convertToTensor(t2, `${argName}[${i2}]`, functionName, parseAsDtype));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
+var OP_SCOPE_SUFFIX = "__op";
+function op(f) {
+  const keys = Object.keys(f);
+  if (keys.length !== 1) {
+    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);
+  }
+  let opName = keys[0];
+  const fn = f[opName];
+  if (opName.endsWith("_")) {
+    opName = opName.substring(0, opName.length - 1);
+  }
+  opName = opName + OP_SCOPE_SUFFIX;
+  const f2 = (...args) => {
+    ENGINE.startScope(opName);
+    try {
+      const result = fn(...args);
+      if (isPromise(result)) {
+        console.error("Cannot return a Promise inside of tidy.");
+      }
+      ENGINE.endScope(result);
+      return result;
+    } catch (ex) {
+      ENGINE.endScope(null);
+      throw ex;
+    }
+  };
+  Object.defineProperty(f2, "name", { value: opName, configurable: true });
+  return f2;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
+function complex_(real5, imag5) {
+  const $real = convertToTensor(real5, "real", "complex");
+  const $imag = convertToTensor(imag5, "imag", "complex");
+  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
+  const inputs = { real: $real, imag: $imag };
+  return ENGINE.runKernel(Complex, inputs);
+}
+var complex = op({ complex_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
+function makeTensor(values, shape, inferredShape, dtype) {
+  if (dtype == null) {
+    dtype = inferDtype(values);
+  }
+  if (dtype === "complex64") {
+    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
+  }
+  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
+    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
+  }
+  if (shape != null) {
+    assertNonNegativeIntegerDimensions(shape);
+    const providedSize = sizeFromShape(shape);
+    const inferredSize = sizeFromShape(inferredShape);
+    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
+    for (let i2 = 0; i2 < inferredShape.length; ++i2) {
+      const inferred = inferredShape[i2];
+      const flatDimsDontMatch = i2 === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i2)) : true;
+      assert(inferredShape[i2] === shape[i2] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
+    }
+  }
+  if (!isTypedArray(values) && !Array.isArray(values)) {
+    values = [values];
+  }
+  shape = shape || inferredShape;
+  values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
+function tensor(values, shape, dtype) {
+  const inferredShape = inferShape(values, dtype);
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js
+var DTYPE_VALUE_SIZE_MAP = {
+  "float32": 4,
+  "float16": 2,
+  "int32": 4,
+  "uint16": 2,
+  "uint8": 1,
+  "bool": 1,
+  "complex64": 8
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
+var NUM_BYTES_STRING_LENGTH = 4;
+async function encodeWeights(tensors, group) {
+  const specs = [];
+  const dataPromises = [];
+  const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);
+  for (let i2 = 0; i2 < names.length; ++i2) {
+    const name = names[i2];
+    const t2 = Array.isArray(tensors) ? tensors[i2].tensor : tensors[name];
+    if (t2.dtype !== "float32" && t2.dtype !== "int32" && t2.dtype !== "bool" && t2.dtype !== "string" && t2.dtype !== "complex64") {
+      throw new Error(`Unsupported dtype in weight '${name}': ${t2.dtype}`);
+    }
+    const spec = { name, shape: t2.shape, dtype: t2.dtype };
+    if (t2.dtype === "string") {
+      const utf8bytes = new Promise(async (resolve) => {
+        const vals = await t2.bytes();
+        const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
+        const bytes = new Uint8Array(totalNumBytes);
+        let offset = 0;
+        for (let i3 = 0; i3 < vals.length; i3++) {
+          const val = vals[i3];
+          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
+          bytes.set(bytesOfLength, offset);
+          offset += NUM_BYTES_STRING_LENGTH;
+          bytes.set(val, offset);
+          offset += val.length;
+        }
+        resolve(bytes);
+      });
+      dataPromises.push(utf8bytes);
+    } else {
+      dataPromises.push(t2.data());
+    }
+    if (group != null) {
+      spec.group = group;
+    }
+    specs.push(spec);
+  }
+  const tensorValues = await Promise.all(dataPromises);
+  return { data: concatenateTypedArrays(tensorValues), specs };
+}
+function decodeWeights(buffer2, specs) {
+  const out = {};
+  let float16Decode;
+  let offset = 0;
+  for (const spec of specs) {
+    const name = spec.name;
+    const dtype = spec.dtype;
+    const shape = spec.shape;
+    const size = sizeFromShape(shape);
+    let values;
+    if ("quantization" in spec) {
+      const quantization = spec.quantization;
+      if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
+        if (!("min" in quantization && "scale" in quantization)) {
+          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);
+        }
+      } else if (quantization.dtype === "float16") {
+        if (dtype !== "float32") {
+          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);
+        }
+      } else {
+        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
+      }
+      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];
+      const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor);
+      const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
+      if (dtype === "float32") {
+        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
+          values = new Float32Array(quantizedArray.length);
+          for (let i2 = 0; i2 < quantizedArray.length; i2++) {
+            const v = quantizedArray[i2];
+            values[i2] = v * quantization.scale + quantization.min;
+          }
+        } else if (quantization.dtype === "float16") {
+          if (float16Decode === void 0) {
+            float16Decode = getFloat16Decoder();
+          }
+          values = float16Decode(quantizedArray);
+        } else {
+          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);
+        }
+      } else if (dtype === "int32") {
+        if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") {
+          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
+        }
+        values = new Int32Array(quantizedArray.length);
+        for (let i2 = 0; i2 < quantizedArray.length; i2++) {
+          const v = quantizedArray[i2];
+          values[i2] = Math.round(v * quantization.scale + quantization.min);
+        }
+      } else {
+        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
+      }
+      offset += size * quantizationSizeFactor;
+    } else if (dtype === "string") {
+      const size2 = sizeFromShape(spec.shape);
+      values = [];
+      for (let i2 = 0; i2 < size2; i2++) {
+        const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
+        offset += NUM_BYTES_STRING_LENGTH;
+        const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));
+        values.push(bytes);
+        offset += byteLength;
+      }
+    } else {
+      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
+      const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor);
+      if (dtype === "float32") {
+        values = new Float32Array(byteBuffer);
+      } else if (dtype === "int32") {
+        values = new Int32Array(byteBuffer);
+      } else if (dtype === "bool") {
+        values = new Uint8Array(byteBuffer);
+      } else if (dtype === "complex64") {
+        values = new Float32Array(byteBuffer);
+        const real5 = new Float32Array(values.length / 2);
+        const image2 = new Float32Array(values.length / 2);
+        for (let i2 = 0; i2 < real5.length; i2++) {
+          real5[i2] = values[i2 * 2];
+          image2[i2] = values[i2 * 2 + 1];
+        }
+        const realTensor = tensor(real5, shape, "float32");
+        const imageTensor = tensor(image2, shape, "float32");
+        out[name] = complex(realTensor, imageTensor);
+        realTensor.dispose();
+        imageTensor.dispose();
+      } else {
+        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
+      }
+      offset += size * dtypeFactor;
+    }
+    if (dtype !== "complex64") {
+      out[name] = tensor(values, shape, dtype);
+    }
+  }
+  return out;
+}
+function concatenateTypedArrays(xs) {
+  if (xs === null) {
+    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
+  }
+  let totalByteLength = 0;
+  const normalizedXs = [];
+  xs.forEach((x) => {
+    totalByteLength += x.byteLength;
+    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
+    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {
+      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
+    }
+  });
+  const y = new Uint8Array(totalByteLength);
+  let offset = 0;
+  normalizedXs.forEach((x) => {
+    y.set(new Uint8Array(x.buffer), offset);
+    offset += x.byteLength;
+  });
+  return y.buffer;
+}
+var useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
+function stringByteLength(str) {
+  if (useNodeBuffer) {
+    return Buffer.byteLength(str);
+  }
+  return new Blob([str]).size;
+}
+function arrayBufferToBase64String(buffer2) {
+  if (useNodeBuffer) {
+    return Buffer.from(buffer2).toString("base64");
+  }
+  const buf = new Uint8Array(buffer2);
+  let s2 = "";
+  for (let i2 = 0, l3 = buf.length; i2 < l3; i2++) {
+    s2 += String.fromCharCode(buf[i2]);
+  }
+  return btoa(s2);
+}
+function base64StringToArrayBuffer(str) {
+  if (useNodeBuffer) {
+    const buf = Buffer.from(str, "base64");
+    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
+  }
+  const s2 = atob(str);
+  const buffer2 = new Uint8Array(s2.length);
+  for (let i2 = 0; i2 < s2.length; ++i2) {
+    buffer2.set([s2.charCodeAt(i2)], i2);
+  }
+  return buffer2.buffer;
+}
+function concatenateArrayBuffers(buffers) {
+  if (buffers.length === 1) {
+    return buffers[0];
+  }
+  let totalByteLength = 0;
+  buffers.forEach((buffer2) => {
+    totalByteLength += buffer2.byteLength;
+  });
+  const temp = new Uint8Array(totalByteLength);
+  let offset = 0;
+  buffers.forEach((buffer2) => {
+    temp.set(new Uint8Array(buffer2), offset);
+    offset += buffer2.byteLength;
+  });
+  return temp.buffer;
+}
+function basename(path) {
+  const SEPARATOR = "/";
+  path = path.trim();
+  while (path.endsWith(SEPARATOR)) {
+    path = path.slice(0, path.length - 1);
+  }
+  const items = path.split(SEPARATOR);
+  return items[items.length - 1];
+}
+function getModelJSONForModelArtifacts(artifacts, manifest) {
+  const result = {
+    modelTopology: artifacts.modelTopology,
+    format: artifacts.format,
+    generatedBy: artifacts.generatedBy,
+    convertedBy: artifacts.convertedBy,
+    weightsManifest: manifest
+  };
+  if (artifacts.signature != null) {
+    result.signature = artifacts.signature;
+  }
+  if (artifacts.userDefinedMetadata != null) {
+    result.userDefinedMetadata = artifacts.userDefinedMetadata;
+  }
+  if (artifacts.modelInitializer != null) {
+    result.modelInitializer = artifacts.modelInitializer;
+  }
+  if (artifacts.trainingConfig != null) {
+    result.trainingConfig = artifacts.trainingConfig;
+  }
+  return result;
+}
+async function getModelArtifactsForJSON(modelJSON, loadWeights2) {
+  const modelArtifacts = {
+    modelTopology: modelJSON.modelTopology,
+    format: modelJSON.format,
+    generatedBy: modelJSON.generatedBy,
+    convertedBy: modelJSON.convertedBy
+  };
+  if (modelJSON.trainingConfig != null) {
+    modelArtifacts.trainingConfig = modelJSON.trainingConfig;
+  }
+  if (modelJSON.weightsManifest != null) {
+    const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);
+    modelArtifacts.weightSpecs = weightSpecs;
+    modelArtifacts.weightData = weightData;
+  }
+  if (modelJSON.signature != null) {
+    modelArtifacts.signature = modelJSON.signature;
+  }
+  if (modelJSON.userDefinedMetadata != null) {
+    modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
+  }
+  if (modelJSON.modelInitializer != null) {
+    modelArtifacts.modelInitializer = modelJSON.modelInitializer;
+  }
+  return modelArtifacts;
+}
+function getModelArtifactsInfoForJSON(modelArtifacts) {
+  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+    throw new Error("Expected JSON model topology, received ArrayBuffer.");
+  }
+  return {
+    dateSaved: new Date(),
+    modelTopologyType: "JSON",
+    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
+    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
+    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength
+  };
+}
+function computeFloat16MantisaTable() {
+  const convertMantissa = (i2) => {
+    let m = i2 << 13;
+    let e2 = 0;
+    while ((m & 8388608) === 0) {
+      e2 -= 8388608;
+      m <<= 1;
+    }
+    m &= ~8388608;
+    e2 += 947912704;
+    return m | e2;
+  };
+  const mantisaTable = new Uint32Array(2048);
+  mantisaTable[0] = 0;
+  for (let i2 = 1; i2 < 1024; i2++) {
+    mantisaTable[i2] = convertMantissa(i2);
+  }
+  for (let i2 = 1024; i2 < 2048; i2++) {
+    mantisaTable[i2] = 939524096 + (i2 - 1024 << 13);
+  }
+  return mantisaTable;
+}
+function computeFloat16ExponentTable() {
+  const exponentTable = new Uint32Array(64);
+  exponentTable[0] = 0;
+  exponentTable[31] = 1199570944;
+  exponentTable[32] = 2147483648;
+  exponentTable[63] = 3347054592;
+  for (let i2 = 1; i2 < 31; i2++) {
+    exponentTable[i2] = i2 << 23;
+  }
+  for (let i2 = 33; i2 < 63; i2++) {
+    exponentTable[i2] = 2147483648 + (i2 - 32 << 23);
+  }
+  return exponentTable;
+}
+function computeFloat16OffsetTable() {
+  const offsetTable = new Uint32Array(64);
+  for (let i2 = 0; i2 < 64; i2++) {
+    offsetTable[i2] = 1024;
+  }
+  offsetTable[0] = offsetTable[32] = 0;
+  return offsetTable;
+}
+function getFloat16Decoder() {
+  const mantisaTable = computeFloat16MantisaTable();
+  const exponentTable = computeFloat16ExponentTable();
+  const offsetTable = computeFloat16OffsetTable();
+  return (quantizedArray) => {
+    const buffer2 = new ArrayBuffer(4 * quantizedArray.length);
+    const bufferUint32View = new Uint32Array(buffer2);
+    for (let index = 0; index < quantizedArray.length; index++) {
+      const float16Bits = quantizedArray[index];
+      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];
+      bufferUint32View[index] = float32Bits;
+    }
+    return new Float32Array(buffer2);
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
+var IORouterRegistry = class {
+  constructor() {
+    this.saveRouters = [];
+    this.loadRouters = [];
+  }
+  static getInstance() {
+    if (IORouterRegistry.instance == null) {
+      IORouterRegistry.instance = new IORouterRegistry();
+    }
+    return IORouterRegistry.instance;
+  }
+  static registerSaveRouter(saveRouter) {
+    IORouterRegistry.getInstance().saveRouters.push(saveRouter);
+  }
+  static registerLoadRouter(loadRouter) {
+    IORouterRegistry.getInstance().loadRouters.push(loadRouter);
+  }
+  static getSaveHandlers(url) {
+    return IORouterRegistry.getHandlers(url, "save");
+  }
+  static getLoadHandlers(url, loadOptions) {
+    return IORouterRegistry.getHandlers(url, "load", loadOptions);
+  }
+  static getHandlers(url, handlerType, loadOptions) {
+    const validHandlers = [];
+    const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
+    routers.forEach((router) => {
+      const handler = router(url, loadOptions);
+      if (handler !== null) {
+        validHandlers.push(handler);
+      }
+    });
+    return validHandlers;
+  }
+};
+var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
+var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
+var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
+var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
+var DATABASE_NAME = "tensorflowjs";
+var DATABASE_VERSION = 1;
+var MODEL_STORE_NAME = "models_store";
+var INFO_STORE_NAME = "model_info_store";
+function getIndexedDBFactory() {
+  if (!env().getBool("IS_BROWSER")) {
+    throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
+  }
+  const theWindow = typeof window === "undefined" ? self : window;
+  const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;
+  if (factory == null) {
+    throw new Error("The current browser does not appear to support IndexedDB.");
+  }
+  return factory;
+}
+function setUpDatabase(openRequest) {
+  const db = openRequest.result;
+  db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" });
+  db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" });
+}
+var BrowserIndexedDB = class {
+  constructor(modelPath) {
+    this.indexedDB = getIndexedDBFactory();
+    if (modelPath == null || !modelPath) {
+      throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
+    }
+    this.modelPath = modelPath;
+  }
+  async save(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
+    }
+    return this.databaseAction(this.modelPath, modelArtifacts);
+  }
+  async load() {
+    return this.databaseAction(this.modelPath);
+  }
+  databaseAction(modelPath, modelArtifacts) {
+    return new Promise((resolve, reject) => {
+      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
+      openRequest.onsuccess = () => {
+        const db = openRequest.result;
+        if (modelArtifacts == null) {
+          const modelTx = db.transaction(MODEL_STORE_NAME, "readonly");
+          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+          const getRequest = modelStore.get(this.modelPath);
+          getRequest.onsuccess = () => {
+            if (getRequest.result == null) {
+              db.close();
+              return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
+            } else {
+              resolve(getRequest.result.modelArtifacts);
+            }
+          };
+          getRequest.onerror = (error) => {
+            db.close();
+            return reject(getRequest.error);
+          };
+          modelTx.oncomplete = () => db.close();
+        } else {
+          const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
+          const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
+          let infoStore = infoTx.objectStore(INFO_STORE_NAME);
+          const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });
+          let modelTx;
+          putInfoRequest.onsuccess = () => {
+            modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
+            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+            const putModelRequest = modelStore.put({
+              modelPath: this.modelPath,
+              modelArtifacts,
+              modelArtifactsInfo
+            });
+            putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });
+            putModelRequest.onerror = (error) => {
+              infoStore = infoTx.objectStore(INFO_STORE_NAME);
+              const deleteInfoRequest = infoStore.delete(this.modelPath);
+              deleteInfoRequest.onsuccess = () => {
+                db.close();
+                return reject(putModelRequest.error);
+              };
+              deleteInfoRequest.onerror = (error2) => {
+                db.close();
+                return reject(putModelRequest.error);
+              };
+            };
+          };
+          putInfoRequest.onerror = (error) => {
+            db.close();
+            return reject(putInfoRequest.error);
+          };
+          infoTx.oncomplete = () => {
+            if (modelTx == null) {
+              db.close();
+            } else {
+              modelTx.oncomplete = () => db.close();
+            }
+          };
+        }
+      };
+      openRequest.onerror = (error) => reject(openRequest.error);
+    });
+  }
+};
+BrowserIndexedDB.URL_SCHEME = "indexeddb://";
+var indexedDBRouter = (url) => {
+  if (!env().getBool("IS_BROWSER")) {
+    return null;
+  } else {
+    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
+      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
+    } else {
+      return null;
+    }
+  }
+};
+IORouterRegistry.registerSaveRouter(indexedDBRouter);
+IORouterRegistry.registerLoadRouter(indexedDBRouter);
+function browserIndexedDB(modelPath) {
+  return new BrowserIndexedDB(modelPath);
+}
+function maybeStripScheme(key) {
+  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;
+}
+var BrowserIndexedDBManager = class {
+  constructor() {
+    this.indexedDB = getIndexedDBFactory();
+  }
+  async listModels() {
+    return new Promise((resolve, reject) => {
+      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
+      openRequest.onsuccess = () => {
+        const db = openRequest.result;
+        const tx = db.transaction(INFO_STORE_NAME, "readonly");
+        const store = tx.objectStore(INFO_STORE_NAME);
+        const getAllInfoRequest = store.getAll();
+        getAllInfoRequest.onsuccess = () => {
+          const out = {};
+          for (const item of getAllInfoRequest.result) {
+            out[item.modelPath] = item.modelArtifactsInfo;
+          }
+          resolve(out);
+        };
+        getAllInfoRequest.onerror = (error) => {
+          db.close();
+          return reject(getAllInfoRequest.error);
+        };
+        tx.oncomplete = () => db.close();
+      };
+      openRequest.onerror = (error) => reject(openRequest.error);
+    });
+  }
+  async removeModel(path) {
+    path = maybeStripScheme(path);
+    return new Promise((resolve, reject) => {
+      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
+      openRequest.onsuccess = () => {
+        const db = openRequest.result;
+        const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
+        const infoStore = infoTx.objectStore(INFO_STORE_NAME);
+        const getInfoRequest = infoStore.get(path);
+        let modelTx;
+        getInfoRequest.onsuccess = () => {
+          if (getInfoRequest.result == null) {
+            db.close();
+            return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));
+          } else {
+            const deleteInfoRequest = infoStore.delete(path);
+            const deleteModelData = () => {
+              modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
+              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+              const deleteModelRequest = modelStore.delete(path);
+              deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);
+              deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);
+            };
+            deleteInfoRequest.onsuccess = deleteModelData;
+            deleteInfoRequest.onerror = (error) => {
+              deleteModelData();
+              db.close();
+              return reject(getInfoRequest.error);
+            };
+          }
+        };
+        getInfoRequest.onerror = (error) => {
+          db.close();
+          return reject(getInfoRequest.error);
+        };
+        infoTx.oncomplete = () => {
+          if (modelTx == null) {
+            db.close();
+          } else {
+            modelTx.oncomplete = () => db.close();
+          }
+        };
+      };
+      openRequest.onerror = (error) => reject(openRequest.error);
+    });
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js
+var PATH_SEPARATOR = "/";
+var PATH_PREFIX = "tensorflowjs_models";
+var INFO_SUFFIX = "info";
+var MODEL_TOPOLOGY_SUFFIX = "model_topology";
+var WEIGHT_SPECS_SUFFIX = "weight_specs";
+var WEIGHT_DATA_SUFFIX = "weight_data";
+var MODEL_METADATA_SUFFIX = "model_metadata";
+function getModelKeys(path) {
+  return {
+    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
+    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
+    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
+    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
+    modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
+  };
+}
+function removeItems(keys) {
+  for (const key of Object.values(keys)) {
+    window.localStorage.removeItem(key);
+  }
+}
+function getModelPathFromKey(key) {
+  const items = key.split(PATH_SEPARATOR);
+  if (items.length < 3) {
+    throw new Error(`Invalid key format: ${key}`);
+  }
+  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
+}
+function maybeStripScheme2(key) {
+  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;
+}
+var BrowserLocalStorage = class {
+  constructor(modelPath) {
+    if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") {
+      throw new Error("The current environment does not support local storage.");
+    }
+    this.LS = window.localStorage;
+    if (modelPath == null || !modelPath) {
+      throw new Error("For local storage, modelPath must not be null, undefined or empty.");
+    }
+    this.modelPath = modelPath;
+    this.keys = getModelKeys(this.modelPath);
+  }
+  async save(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
+    } else {
+      const topology = JSON.stringify(modelArtifacts.modelTopology);
+      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
+      const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
+      try {
+        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
+        this.LS.setItem(this.keys.topology, topology);
+        this.LS.setItem(this.keys.weightSpecs, weightSpecs);
+        this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));
+        const metadata = {
+          format: modelArtifacts.format,
+          generatedBy: modelArtifacts.generatedBy,
+          convertedBy: modelArtifacts.convertedBy,
+          signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,
+          userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,
+          modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,
+          trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0
+        };
+        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));
+        return { modelArtifactsInfo };
+      } catch (err) {
+        removeItems(this.keys);
+        throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);
+      }
+    }
+  }
+  async load() {
+    const info = JSON.parse(this.LS.getItem(this.keys.info));
+    if (info == null) {
+      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
+    }
+    if (info.modelTopologyType !== "JSON") {
+      throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
+    }
+    const out = {};
+    const topology = JSON.parse(this.LS.getItem(this.keys.topology));
+    if (topology == null) {
+      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
+    }
+    out.modelTopology = topology;
+    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
+    if (weightSpecs == null) {
+      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
+    }
+    out.weightSpecs = weightSpecs;
+    const metadataString = this.LS.getItem(this.keys.modelMetadata);
+    if (metadataString != null) {
+      const metadata = JSON.parse(metadataString);
+      out.format = metadata.format;
+      out.generatedBy = metadata.generatedBy;
+      out.convertedBy = metadata.convertedBy;
+      if (metadata.signature != null) {
+        out.signature = metadata.signature;
+      }
+      if (metadata.userDefinedMetadata != null) {
+        out.userDefinedMetadata = metadata.userDefinedMetadata;
+      }
+      if (metadata.modelInitializer != null) {
+        out.modelInitializer = metadata.modelInitializer;
+      }
+      if (metadata.trainingConfig != null) {
+        out.trainingConfig = metadata.trainingConfig;
+      }
+    }
+    const weightDataBase64 = this.LS.getItem(this.keys.weightData);
+    if (weightDataBase64 == null) {
+      throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
+    }
+    out.weightData = base64StringToArrayBuffer(weightDataBase64);
+    return out;
+  }
+};
+BrowserLocalStorage.URL_SCHEME = "localstorage://";
+var localStorageRouter = (url) => {
+  if (!env().getBool("IS_BROWSER")) {
+    return null;
+  } else {
+    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
+      return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
+    } else {
+      return null;
+    }
+  }
+};
+IORouterRegistry.registerSaveRouter(localStorageRouter);
+IORouterRegistry.registerLoadRouter(localStorageRouter);
+function browserLocalStorage(modelPath) {
+  return new BrowserLocalStorage(modelPath);
+}
+var BrowserLocalStorageManager = class {
+  constructor() {
+    assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser");
+    assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage");
+    this.LS = window.localStorage;
+  }
+  async listModels() {
+    const out = {};
+    const prefix = PATH_PREFIX + PATH_SEPARATOR;
+    const suffix = PATH_SEPARATOR + INFO_SUFFIX;
+    for (let i2 = 0; i2 < this.LS.length; ++i2) {
+      const key = this.LS.key(i2);
+      if (key.startsWith(prefix) && key.endsWith(suffix)) {
+        const modelPath = getModelPathFromKey(key);
+        out[modelPath] = JSON.parse(this.LS.getItem(key));
+      }
+    }
+    return out;
+  }
+  async removeModel(path) {
+    path = maybeStripScheme2(path);
+    const keys = getModelKeys(path);
+    if (this.LS.getItem(keys.info) == null) {
+      throw new Error(`Cannot find model at path '${path}'`);
+    }
+    const info = JSON.parse(this.LS.getItem(keys.info));
+    removeItems(keys);
+    return info;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
+var URL_SCHEME_SUFFIX = "://";
+var ModelStoreManagerRegistry = class {
+  constructor() {
+    this.managers = {};
+  }
+  static getInstance() {
+    if (ModelStoreManagerRegistry.instance == null) {
+      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
+    }
+    return ModelStoreManagerRegistry.instance;
+  }
+  static registerManager(scheme, manager) {
+    assert(scheme != null, () => "scheme must not be undefined or null.");
+    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
+      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
+    }
+    assert(scheme.length > 0, () => "scheme must not be an empty string.");
+    const registry = ModelStoreManagerRegistry.getInstance();
+    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);
+    registry.managers[scheme] = manager;
+  }
+  static getManager(scheme) {
+    const manager = this.getInstance().managers[scheme];
+    if (manager == null) {
+      throw new Error(`Cannot find model manager for scheme '${scheme}'`);
+    }
+    return manager;
+  }
+  static getSchemes() {
+    return Object.keys(this.getInstance().managers);
+  }
+};
+function parseURL(url) {
+  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
+    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(",")}`);
+  }
+  return {
+    scheme: url.split(URL_SCHEME_SUFFIX)[0],
+    path: url.split(URL_SCHEME_SUFFIX)[1]
+  };
+}
+async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
+  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);
+  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
+  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);
+  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);
+  const loadHandler = loadHandlers[0];
+  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
+  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);
+  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);
+  const saveHandler = saveHandlers[0];
+  const sourceScheme = parseURL(sourceURL).scheme;
+  const sourcePath = parseURL(sourceURL).path;
+  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
+  const modelArtifacts = await loadHandler.load();
+  if (deleteSource && sameMedium) {
+    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
+  }
+  const saveResult = await saveHandler.save(modelArtifacts);
+  if (deleteSource && !sameMedium) {
+    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
+  }
+  return saveResult.modelArtifactsInfo;
+}
+async function listModels() {
+  const schemes = ModelStoreManagerRegistry.getSchemes();
+  const out = {};
+  for (const scheme of schemes) {
+    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
+    for (const path in schemeOut) {
+      const url = scheme + URL_SCHEME_SUFFIX + path;
+      out[url] = schemeOut[path];
+    }
+  }
+  return out;
+}
+async function removeModel(url) {
+  const schemeAndPath = parseURL(url);
+  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
+  return manager.removeModel(schemeAndPath.path);
+}
+async function copyModel(sourceURL, destURL) {
+  const deleteSource = false;
+  return cloneModelInternal(sourceURL, destURL, deleteSource);
+}
+async function moveModel(sourceURL, destURL) {
+  const deleteSource = true;
+  return cloneModelInternal(sourceURL, destURL, deleteSource);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js
+var PlatformBrowser = class {
+  fetch(path, init2) {
+    return fetch(path, init2);
+  }
+  now() {
+    return performance.now();
+  }
+  encode(text, encoding) {
+    if (encoding !== "utf-8" && encoding !== "utf8") {
+      throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
+    }
+    if (this.textEncoder == null) {
+      this.textEncoder = new TextEncoder();
+    }
+    return this.textEncoder.encode(text);
+  }
+  decode(bytes, encoding) {
+    return new TextDecoder(encoding).decode(bytes);
+  }
+};
+if (env().get("IS_BROWSER")) {
+  env().setPlatform("browser", new PlatformBrowser());
+  try {
+    ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
+  } catch (err) {
+  }
+  try {
+    ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
+  } catch (err) {
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js
+var getNodeFetch = {
+  importFetch: () => require_browser()
+};
+var systemFetch;
+var PlatformNode = class {
+  constructor() {
+    this.util = require_util();
+    this.textEncoder = new this.util.TextEncoder();
+  }
+  fetch(path, requestInits) {
+    if (env().global.fetch != null) {
+      return env().global.fetch(path, requestInits);
+    }
+    if (systemFetch == null) {
+      systemFetch = getNodeFetch.importFetch();
+    }
+    return systemFetch(path, requestInits);
+  }
+  now() {
+    const time2 = process.hrtime();
+    return time2[0] * 1e3 + time2[1] / 1e6;
+  }
+  encode(text, encoding) {
+    if (encoding !== "utf-8" && encoding !== "utf8") {
+      throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);
+    }
+    return this.textEncoder.encode(text);
+  }
+  decode(bytes, encoding) {
+    if (bytes.length === 0) {
+      return "";
+    }
+    return new this.util.TextDecoder(encoding).decode(bytes);
+  }
+};
+if (env().get("IS_NODE") && !env().get("IS_BROWSER")) {
+  env().setPlatform("node", new PlatformNode());
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
+function buffer(shape, dtype = "float32", values) {
+  dtype = dtype || "float32";
+  assertNonNegativeIntegerDimensions(shape);
+  return new TensorBuffer(shape, dtype, values);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
+function cast_(x, dtype) {
+  const $x = convertToTensor(x, "x", "cast");
+  if (!isValidDtype(dtype)) {
+    throw new Error(`Failed to cast to unknown dtype ${dtype}`);
+  }
+  if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") {
+    throw new Error("Only strings can be casted to strings");
+  }
+  const inputs = { x: $x };
+  const attrs = { dtype };
+  return ENGINE.runKernel(Cast, inputs, attrs);
+}
+var cast = op({ cast_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
+function clone_(x) {
+  const $x = convertToTensor(x, "x", "clone", "string_or_numeric");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Identity, inputs);
+}
+var clone = op({ clone_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js
+function print(x, verbose = false) {
+  console.log(x.toString(verbose));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
+getOrMakeEngine();
+var opHandler2 = {
+  buffer,
+  cast,
+  clone,
+  print
+};
+setOpHandler(opHandler2);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js
+var io_exports = {};
+__export(io_exports, {
+  browserFiles: () => browserFiles,
+  browserHTTPRequest: () => browserHTTPRequest,
+  concatenateArrayBuffers: () => concatenateArrayBuffers,
+  copyModel: () => copyModel,
+  decodeWeights: () => decodeWeights,
+  encodeWeights: () => encodeWeights,
+  fromMemory: () => fromMemory,
+  fromMemorySync: () => fromMemorySync,
+  getLoadHandlers: () => getLoadHandlers,
+  getModelArtifactsForJSON: () => getModelArtifactsForJSON,
+  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
+  getSaveHandlers: () => getSaveHandlers,
+  http: () => http,
+  isHTTPScheme: () => isHTTPScheme,
+  listModels: () => listModels,
+  loadWeights: () => loadWeights,
+  moveModel: () => moveModel,
+  registerLoadRouter: () => registerLoadRouter,
+  registerSaveRouter: () => registerSaveRouter,
+  removeModel: () => removeModel,
+  weightsLoaderFactory: () => weightsLoaderFactory,
+  withSaveHandler: () => withSaveHandler,
+  withSaveHandlerSync: () => withSaveHandlerSync
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
+var DEFAULT_FILE_NAME_PREFIX = "model";
+var DEFAULT_JSON_EXTENSION_NAME = ".json";
+var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
+function defer(f) {
+  return new Promise((resolve) => setTimeout(resolve)).then(f);
+}
+var BrowserDownloads = class {
+  constructor(fileNamePrefix) {
+    if (!env().getBool("IS_BROWSER")) {
+      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
+    }
+    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
+      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
+    }
+    if (fileNamePrefix == null || fileNamePrefix.length === 0) {
+      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
+    }
+    this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
+    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
+  }
+  async save(modelArtifacts) {
+    if (typeof document === "undefined") {
+      throw new Error("Browser downloads are not supported in this environment since `document` is not present");
+    }
+    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: "application/octet-stream" }));
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
+    } else {
+      const weightsManifest = [{
+        paths: ["./" + this.weightDataFileName],
+        weights: modelArtifacts.weightSpecs
+      }];
+      const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
+      const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" }));
+      const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
+      jsonAnchor.download = this.modelJsonFileName;
+      jsonAnchor.href = modelJsonURL;
+      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent("click")));
+      if (modelArtifacts.weightData != null) {
+        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
+        weightDataAnchor.download = this.weightDataFileName;
+        weightDataAnchor.href = weightsURL;
+        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent("click")));
+      }
+      return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };
+    }
+  }
+};
+BrowserDownloads.URL_SCHEME = "downloads://";
+var BrowserFiles = class {
+  constructor(files) {
+    if (files == null || files.length < 1) {
+      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
+    }
+    this.jsonFile = files[0];
+    this.weightsFiles = files.slice(1);
+  }
+  async load() {
+    return new Promise((resolve, reject) => {
+      const jsonReader = new FileReader();
+      jsonReader.onload = (event) => {
+        const modelJSON = JSON.parse(event.target.result);
+        const modelTopology = modelJSON.modelTopology;
+        if (modelTopology == null) {
+          reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
+          return;
+        }
+        const weightsManifest = modelJSON.weightsManifest;
+        if (weightsManifest == null) {
+          reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
+          return;
+        }
+        if (this.weightsFiles.length === 0) {
+          resolve({ modelTopology });
+          return;
+        }
+        const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));
+        resolve(modelArtifactsPromise);
+      };
+      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
+      jsonReader.readAsText(this.jsonFile);
+    });
+  }
+  loadWeights(weightsManifest) {
+    const weightSpecs = [];
+    const paths = [];
+    for (const entry of weightsManifest) {
+      weightSpecs.push(...entry.weights);
+      paths.push(...entry.paths);
+    }
+    const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);
+    const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));
+    return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]);
+  }
+  loadWeightsFile(path, file) {
+    return new Promise((resolve, reject) => {
+      const weightFileReader = new FileReader();
+      weightFileReader.onload = (event) => {
+        const weightData = event.target.result;
+        resolve(weightData);
+      };
+      weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
+      weightFileReader.readAsArrayBuffer(file);
+    });
+  }
+  checkManifestAndWeightFiles(manifest) {
+    const basenames = [];
+    const fileNames = this.weightsFiles.map((file) => basename(file.name));
+    const pathToFile = {};
+    for (const group of manifest) {
+      group.paths.forEach((path) => {
+        const pathBasename = basename(path);
+        if (basenames.indexOf(pathBasename) !== -1) {
+          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
+        }
+        basenames.push(pathBasename);
+        if (fileNames.indexOf(pathBasename) === -1) {
+          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
+        } else {
+          pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];
+        }
+      });
+    }
+    if (basenames.length !== this.weightsFiles.length) {
+      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
+    }
+    return pathToFile;
+  }
+};
+var browserDownloadsRouter = (url) => {
+  if (!env().getBool("IS_BROWSER")) {
+    return null;
+  } else {
+    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
+      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
+    } else {
+      return null;
+    }
+  }
+};
+IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
+function browserDownloads(fileNamePrefix = "model") {
+  return new BrowserDownloads(fileNamePrefix);
+}
+function browserFiles(files) {
+  return new BrowserFiles(files);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js
+function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
+  checkPromises(promises);
+  startFraction = startFraction == null ? 0 : startFraction;
+  endFraction = endFraction == null ? 1 : endFraction;
+  checkFraction(startFraction, endFraction);
+  let resolvedPromise = 0;
+  const registerMonitor = (promise) => {
+    promise.then((value) => {
+      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
+      onProgress(fraction);
+      return value;
+    });
+    return promise;
+  };
+  function checkPromises(promises2) {
+    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => "promises must be a none empty array");
+  }
+  function checkFraction(startFraction2, endFraction2) {
+    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);
+    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);
+    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);
+  }
+  return Promise.all(promises.map(registerMonitor));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
+async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
+  if (loadOptions == null) {
+    loadOptions = {};
+  }
+  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;
+  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));
+  const fetchStartFraction = 0;
+  const fetchEndFraction = 0.5;
+  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
+  const bufferPromises = responses.map((response) => response.arrayBuffer());
+  const bufferStartFraction = 0.5;
+  const bufferEndFraction = 1;
+  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
+  return buffers;
+}
+async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
+  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });
+  const loadWeights2 = weightsLoaderFactory(fetchWeights);
+  return loadWeights2(manifest, filePathPrefix, weightNames);
+}
+function weightsLoaderFactory(fetchWeightsFunction) {
+  return async (manifest, filePathPrefix = "", weightNames) => {
+    const groupIndicesToFetchMap = manifest.map(() => false);
+    const groupWeightsToFetch = {};
+    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];
+    const allManifestWeightNames = [];
+    manifest.forEach((manifestGroupConfig, groupIndex) => {
+      let groupOffset = 0;
+      manifestGroupConfig.weights.forEach((weightsEntry) => {
+        const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
+        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);
+        const enqueueWeightsForFetchingFn = () => {
+          groupIndicesToFetchMap[groupIndex] = true;
+          if (groupWeightsToFetch[groupIndex] == null) {
+            groupWeightsToFetch[groupIndex] = [];
+          }
+          groupWeightsToFetch[groupIndex].push({
+            manifestEntry: weightsEntry,
+            groupOffset,
+            sizeBytes: weightsBytes
+          });
+        };
+        if (weightNames != null) {
+          weightNames.forEach((weightName, weightIndex) => {
+            if (weightName === weightsEntry.name) {
+              enqueueWeightsForFetchingFn();
+              weightsFound[weightIndex] = true;
+            }
+          });
+        } else {
+          enqueueWeightsForFetchingFn();
+        }
+        allManifestWeightNames.push(weightsEntry.name);
+        groupOffset += weightsBytes;
+      });
+    });
+    if (!weightsFound.every((found) => found)) {
+      const weightsNotFound = weightNames.filter((_, i2) => !weightsFound[i2]);
+      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
+Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
+    }
+    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i2) => {
+      if (shouldFetch) {
+        accumulator.push(i2);
+      }
+      return accumulator;
+    }, []);
+    const fetchUrls = [];
+    groupIndicesToFetch.forEach((i2) => {
+      manifest[i2].paths.forEach((filepath) => {
+        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
+        fetchUrls.push(fetchUrl);
+      });
+    });
+    const buffers = await fetchWeightsFunction(fetchUrls);
+    const weightsTensorMap = {};
+    let bufferIndexOffset = 0;
+    groupIndicesToFetch.forEach((i2) => {
+      const numBuffers = manifest[i2].paths.length;
+      let groupBytes = 0;
+      for (let i3 = 0; i3 < numBuffers; i3++) {
+        groupBytes += buffers[bufferIndexOffset + i3].byteLength;
+      }
+      const groupBuffer = new ArrayBuffer(groupBytes);
+      const groupByteBuffer = new Uint8Array(groupBuffer);
+      let groupBufferOffset = 0;
+      for (let i3 = 0; i3 < numBuffers; i3++) {
+        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i3]);
+        groupByteBuffer.set(buffer2, groupBufferOffset);
+        groupBufferOffset += buffer2.byteLength;
+      }
+      const weightsEntries = groupWeightsToFetch[i2];
+      weightsEntries.forEach((weightsEntry) => {
+        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
+        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
+        for (const name in nameToTensorMap) {
+          weightsTensorMap[name] = nameToTensorMap[name];
+        }
+      });
+      bufferIndexOffset += numBuffers;
+    });
+    return weightsTensorMap;
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js
+var OCTET_STREAM_MIME_TYPE = "application/octet-stream";
+var JSON_TYPE = "application/json";
+var HTTPRequest = class {
+  constructor(path, loadOptions) {
+    this.DEFAULT_METHOD = "POST";
+    if (loadOptions == null) {
+      loadOptions = {};
+    }
+    this.weightPathPrefix = loadOptions.weightPathPrefix;
+    this.onProgress = loadOptions.onProgress;
+    this.weightUrlConverter = loadOptions.weightUrlConverter;
+    if (loadOptions.fetchFunc != null) {
+      assert(typeof loadOptions.fetchFunc === "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
+      this.fetch = loadOptions.fetchFunc;
+    } else {
+      this.fetch = env().platform.fetch;
+    }
+    assert(path != null && path.length > 0, () => "URL path for http must not be null, undefined or empty.");
+    if (Array.isArray(path)) {
+      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);
+    }
+    this.path = path;
+    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {
+      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
+    }
+    this.requestInit = loadOptions.requestInit || {};
+  }
+  async save(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
+    }
+    const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
+    init2.body = new FormData();
+    const weightsManifest = [{
+      paths: ["./model.weights.bin"],
+      weights: modelArtifacts.weightSpecs
+    }];
+    const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
+    init2.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json");
+    if (modelArtifacts.weightData != null) {
+      init2.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin");
+    }
+    const response = await this.fetch(this.path, init2);
+    if (response.ok) {
+      return {
+        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
+        responses: [response]
+      };
+    } else {
+      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);
+    }
+  }
+  async load() {
+    const modelConfigRequest = await this.fetch(this.path, this.requestInit);
+    if (!modelConfigRequest.ok) {
+      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);
+    }
+    let modelJSON;
+    try {
+      modelJSON = await modelConfigRequest.json();
+    } catch (e2) {
+      let message = `Failed to parse model JSON of response from ${this.path}.`;
+      if (this.path.endsWith(".pb")) {
+        message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
+      } else {
+        message += " Please make sure the server is serving valid JSON for this request.";
+      }
+      throw new Error(message);
+    }
+    const modelTopology = modelJSON.modelTopology;
+    const weightsManifest = modelJSON.weightsManifest;
+    if (modelTopology == null && weightsManifest == null) {
+      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
+    }
+    return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));
+  }
+  async loadWeights(weightsManifest) {
+    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
+    const [prefix, suffix] = parseUrl(weightPath);
+    const pathPrefix = this.weightPathPrefix || prefix;
+    const weightSpecs = [];
+    for (const entry of weightsManifest) {
+      weightSpecs.push(...entry.weights);
+    }
+    const fetchURLs = [];
+    const urlPromises = [];
+    for (const weightsGroup of weightsManifest) {
+      for (const path of weightsGroup.paths) {
+        if (this.weightUrlConverter != null) {
+          urlPromises.push(this.weightUrlConverter(path));
+        } else {
+          fetchURLs.push(pathPrefix + path + suffix);
+        }
+      }
+    }
+    if (this.weightUrlConverter) {
+      fetchURLs.push(...await Promise.all(urlPromises));
+    }
+    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
+      requestInit: this.requestInit,
+      fetchFunc: this.fetch,
+      onProgress: this.onProgress
+    });
+    return [weightSpecs, concatenateArrayBuffers(buffers)];
+  }
+};
+HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
+function parseUrl(url) {
+  const lastSlash = url.lastIndexOf("/");
+  const lastSearchParam = url.lastIndexOf("?");
+  const prefix = url.substring(0, lastSlash);
+  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
+  return [prefix + "/", suffix];
+}
+function isHTTPScheme(url) {
+  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
+}
+var httpRouter = (url, loadOptions) => {
+  if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) {
+    return null;
+  } else {
+    let isHTTP = true;
+    if (Array.isArray(url)) {
+      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));
+    } else {
+      isHTTP = isHTTPScheme(url);
+    }
+    if (isHTTP) {
+      return http(url, loadOptions);
+    }
+  }
+  return null;
+};
+IORouterRegistry.registerSaveRouter(httpRouter);
+IORouterRegistry.registerLoadRouter(httpRouter);
+function http(path, loadOptions) {
+  return new HTTPRequest(path, loadOptions);
+}
+function browserHTTPRequest(path, loadOptions) {
+  return http(path, loadOptions);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
+var PassthroughLoader = class {
+  constructor(modelArtifacts) {
+    this.modelArtifacts = modelArtifacts;
+  }
+  load() {
+    return this.modelArtifacts;
+  }
+};
+var PassthroughSaver = class {
+  constructor(saveHandler) {
+    this.saveHandler = saveHandler;
+  }
+  save(modelArtifacts) {
+    return this.saveHandler(modelArtifacts);
+  }
+};
+var PassthroughAsync = class {
+  constructor(handler) {
+    if (handler.load) {
+      this.load = () => Promise.resolve(handler.load());
+    }
+    if (handler.save) {
+      this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));
+    }
+  }
+};
+function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
+  const args = arguments;
+  return new PassthroughAsync(fromMemorySync(...args));
+}
+function fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {
+  if (arguments.length === 1) {
+    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
+    if (isModelArtifacts) {
+      return new PassthroughLoader(modelArtifacts);
+    } else {
+      console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
+      return new PassthroughLoader({ modelTopology: modelArtifacts });
+    }
+  } else {
+    console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
+    return new PassthroughLoader({
+      modelTopology: modelArtifacts,
+      weightSpecs,
+      weightData,
+      trainingConfig
+    });
+  }
+}
+function withSaveHandler(saveHandler) {
+  return new PassthroughSaver(saveHandler);
+}
+function withSaveHandlerSync(saveHandler) {
+  return new PassthroughSaver(saveHandler);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/math.js
+var math_exports = {};
+__export(math_exports, {
+  confusionMatrix: () => confusionMatrix
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
+function matMul_(a6, b, transposeA = false, transposeB = false) {
+  let $a = convertToTensor(a6, "a", "matMul");
+  let $b = convertToTensor(b, "b", "matMul");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  const attrs = { transposeA, transposeB };
+  return ENGINE.runKernel(BatchMatMul, inputs, attrs);
+}
+var matMul = op({ matMul_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
+function oneHot_(indices, depth, onValue = 1, offValue = 0) {
+  if (depth < 2) {
+    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
+  }
+  const $indices = convertToTensor(indices, "indices", "oneHot", "int32");
+  const inputs = { indices: $indices };
+  const attrs = { depth, onValue, offValue };
+  return ENGINE.runKernel(OneHot, inputs, attrs);
+}
+var oneHot = op({ oneHot_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/globals.js
+function enableProdMode() {
+  env().set("PROD", true);
+}
+function enableDebugMode() {
+  env().set("DEBUG", true);
+}
+function disableDeprecationWarnings() {
+  env().set("DEPRECATION_WARNINGS_ENABLED", false);
+  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
+}
+function deprecationWarn(msg) {
+  if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
+    console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
+  }
+}
+setDeprecationWarningFn(deprecationWarn);
+function disposeVariables() {
+  ENGINE.disposeVariables();
+}
+function engine() {
+  return ENGINE;
+}
+function memory() {
+  return ENGINE.memory();
+}
+function profile(f) {
+  return ENGINE.profile(f);
+}
+function tidy(nameOrFn, fn) {
+  return ENGINE.tidy(nameOrFn, fn);
+}
+function dispose(container) {
+  const tensors = getTensorsInContainer(container);
+  tensors.forEach((tensor2) => tensor2.dispose());
+}
+function keep(result) {
+  return ENGINE.keep(result);
+}
+function time(f) {
+  return ENGINE.time(f);
+}
+function setBackend(backendName) {
+  return ENGINE.setBackend(backendName);
+}
+function ready() {
+  return ENGINE.ready();
+}
+function getBackend() {
+  return ENGINE.backendName;
+}
+function removeBackend(name) {
+  ENGINE.removeBackend(name);
+}
+function findBackend(name) {
+  return ENGINE.findBackend(name);
+}
+function findBackendFactory(name) {
+  return ENGINE.findBackendFactory(name);
+}
+function registerBackend(name, factory, priority = 1) {
+  return ENGINE.registerBackend(name, factory, priority);
+}
+function backend() {
+  return ENGINE.backend;
+}
+function setPlatform(platformName, platform) {
+  env().setPlatform(platformName, platform);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
+function imag_(input2) {
+  const $input = convertToTensor(input2, "input", "imag");
+  const inputs = { input: $input };
+  return ENGINE.runKernel(Imag, inputs);
+}
+var imag = op({ imag_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
+function neg_(x) {
+  const $x = convertToTensor(x, "x", "neg");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Neg, inputs);
+}
+var neg = op({ neg_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js
+function real_(input2) {
+  const $input = convertToTensor(input2, "input", "real");
+  const inputs = { input: $input };
+  return ENGINE.runKernel(Real, inputs);
+}
+var real = op({ real_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
+function transpose_(x, perm, conjugate) {
+  const $x = convertToTensor(x, "x", "transpose");
+  if (perm == null) {
+    perm = $x.shape.map((s2, i2) => i2).reverse();
+  }
+  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
+  perm.forEach((axis) => {
+    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);
+  });
+  if ($x.rank <= 1) {
+    return $x.clone();
+  }
+  const inputs = { x: $x };
+  const attrs = { perm };
+  if ($x.dtype === "complex64") {
+    return tidy(() => {
+      let $real = real($x);
+      let $imag = imag($x);
+      $real = ENGINE.runKernel(Transpose, { x: $real }, attrs);
+      $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);
+      if (conjugate) {
+        $imag = neg($imag);
+      }
+      return complex($real, $imag);
+    });
+  }
+  return ENGINE.runKernel(Transpose, inputs, attrs);
+}
+var transpose = op({ transpose_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
+function confusionMatrix_(labels, predictions, numClasses) {
+  const $labels = convertToTensor(labels, "labels", "confusionMatrix");
+  const $predictions = convertToTensor(predictions, "predictions", "confusionMatrix");
+  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);
+  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);
+  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);
+  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);
+  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);
+  const oneHotLabels = oneHot(cast($labels, "int32"), numClasses);
+  const oneHotPredictions = oneHot(cast($predictions, "int32"), numClasses);
+  const oneHotLabelsT = transpose(oneHotLabels);
+  const product = matMul(oneHotLabelsT, oneHotPredictions);
+  return cast(product, "int32");
+}
+var confusionMatrix = op({ confusionMatrix_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
+var broadcast_util_exports = {};
+__export(broadcast_util_exports, {
+  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
+  getBroadcastDims: () => getBroadcastDims,
+  getReductionAxes: () => getReductionAxes
+});
+function getBroadcastDims(inShape, outShape) {
+  const inRank = inShape.length;
+  const dims = [];
+  for (let i2 = 0; i2 < inRank; i2++) {
+    const dim = inRank - 1 - i2;
+    const a6 = inShape[dim] || 1;
+    const b = outShape[outShape.length - 1 - i2] || 1;
+    if (b > 1 && a6 === 1) {
+      dims.unshift(dim);
+    }
+  }
+  return dims;
+}
+function getReductionAxes(inShape, outShape) {
+  const result = [];
+  for (let i2 = 0; i2 < outShape.length; i2++) {
+    const inDim = inShape[inShape.length - i2 - 1];
+    const outAxis = outShape.length - i2 - 1;
+    const outDim = outShape[outAxis];
+    if (inDim == null || inDim === 1 && outDim > 1) {
+      result.unshift(outAxis);
+    }
+  }
+  return result;
+}
+function assertAndGetBroadcastShape(shapeA, shapeB) {
+  const result = [];
+  const l3 = Math.max(shapeA.length, shapeB.length);
+  for (let i2 = 0; i2 < l3; i2++) {
+    let a6 = shapeA[shapeA.length - i2 - 1];
+    if (a6 == null) {
+      a6 = 1;
+    }
+    let b = shapeB[shapeB.length - i2 - 1];
+    if (b == null) {
+      b = 1;
+    }
+    if (a6 === 1) {
+      result.unshift(b);
+    } else if (b === 1) {
+      result.unshift(a6);
+    } else if (a6 !== b) {
+      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;
+      throw Error(errMsg);
+    } else {
+      result.unshift(a6);
+    }
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
+var browser_exports = {};
+__export(browser_exports, {
+  fromPixels: () => fromPixels,
+  fromPixelsAsync: () => fromPixelsAsync,
+  toPixels: () => toPixels
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
+function tensor3d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 3) {
+    throw new Error("tensor3d() requires shape to have three numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 3 && inferredShape.length !== 1) {
+    throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
+var fromPixels2DContext;
+function fromPixels_(pixels, numChannels = 3) {
+  if (numChannels > 4) {
+    throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
+  }
+  if (pixels == null) {
+    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
+  }
+  let isPixelData2 = false;
+  let isImageData = false;
+  let isVideo = false;
+  let isImage = false;
+  let isCanvasLike = false;
+  let isImageBitmap = false;
+  if (pixels.data instanceof Uint8Array) {
+    isPixelData2 = true;
+  } else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) {
+    isImageData = true;
+  } else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) {
+    isVideo = true;
+  } else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) {
+    isImage = true;
+  } else if (pixels.getContext != null) {
+    isCanvasLike = true;
+  } else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) {
+    isImageBitmap = true;
+  } else {
+    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);
+  }
+  if (isVideo) {
+    const HAVE_CURRENT_DATA_READY_STATE = 2;
+    if (isVideo && pixels.readyState < HAVE_CURRENT_DATA_READY_STATE) {
+      throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
+    }
+  }
+  const kernel = getKernel(FromPixels, ENGINE.backendName);
+  if (kernel != null) {
+    const inputs = { pixels };
+    const attrs = { numChannels };
+    return ENGINE.runKernel(FromPixels, inputs, attrs);
+  }
+  const [width, height] = isVideo ? [
+    pixels.videoWidth,
+    pixels.videoHeight
+  ] : [pixels.width, pixels.height];
+  let vals;
+  if (isCanvasLike) {
+    vals = pixels.getContext("2d").getImageData(0, 0, width, height).data;
+  } else if (isImageData || isPixelData2) {
+    vals = pixels.data;
+  } else if (isImage || isVideo || isImageBitmap) {
+    if (fromPixels2DContext == null) {
+      if (typeof document === "undefined") {
+        if (typeof OffscreenCanvas !== "undefined" && typeof OffscreenCanvasRenderingContext2D !== "undefined") {
+          fromPixels2DContext = new OffscreenCanvas(1, 1).getContext("2d");
+        } else {
+          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
+        }
+      } else {
+        fromPixels2DContext = document.createElement("canvas").getContext("2d");
+      }
+    }
+    fromPixels2DContext.canvas.width = width;
+    fromPixels2DContext.canvas.height = height;
+    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
+    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
+  }
+  let values;
+  if (numChannels === 4) {
+    values = new Int32Array(vals);
+  } else {
+    const numPixels = width * height;
+    values = new Int32Array(numPixels * numChannels);
+    for (let i2 = 0; i2 < numPixels; i2++) {
+      for (let channel = 0; channel < numChannels; ++channel) {
+        values[i2 * numChannels + channel] = vals[i2 * 4 + channel];
+      }
+    }
+  }
+  const outShape = [height, width, numChannels];
+  return tensor3d(values, outShape, "int32");
+}
+function isPixelData(pixels) {
+  return pixels != null && pixels.data instanceof Uint8Array;
+}
+function isImageBitmapFullySupported() {
+  return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap");
+}
+function isNonEmptyPixels(pixels) {
+  return pixels != null && pixels.width !== 0 && pixels.height !== 0;
+}
+function canWrapPixelsToImageBitmap(pixels) {
+  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);
+}
+async function fromPixelsAsync(pixels, numChannels = 3) {
+  let inputs = null;
+  if (env().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) {
+    let imageBitmap;
+    try {
+      imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" });
+    } catch (e2) {
+      imageBitmap = null;
+    }
+    if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {
+      inputs = imageBitmap;
+    } else {
+      inputs = pixels;
+    }
+  } else {
+    inputs = pixels;
+  }
+  return fromPixels_(inputs, numChannels);
+}
+async function toPixels(img, canvas) {
+  let $img = convertToTensor(img, "img", "toPixels");
+  if (!(img instanceof Tensor)) {
+    const originalImgTensor = $img;
+    $img = cast(originalImgTensor, "int32");
+    originalImgTensor.dispose();
+  }
+  if ($img.rank !== 2 && $img.rank !== 3) {
+    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);
+  }
+  const [height, width] = $img.shape.slice(0, 2);
+  const depth = $img.rank === 2 ? 1 : $img.shape[2];
+  if (depth > 4 || depth === 2) {
+    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);
+  }
+  if ($img.dtype !== "float32" && $img.dtype !== "int32") {
+    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);
+  }
+  const data = await $img.data();
+  const multiplier = $img.dtype === "float32" ? 255 : 1;
+  const bytes = new Uint8ClampedArray(width * height * 4);
+  for (let i2 = 0; i2 < height * width; ++i2) {
+    const rgba = [0, 0, 0, 255];
+    for (let d = 0; d < depth; d++) {
+      const value = data[i2 * depth + d];
+      if ($img.dtype === "float32") {
+        if (value < 0 || value > 1) {
+          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
+        }
+      } else if ($img.dtype === "int32") {
+        if (value < 0 || value > 255) {
+          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);
+        }
+      }
+      if (depth === 1) {
+        rgba[0] = value * multiplier;
+        rgba[1] = value * multiplier;
+        rgba[2] = value * multiplier;
+      } else {
+        rgba[d] = value * multiplier;
+      }
+    }
+    const j = i2 * 4;
+    bytes[j + 0] = Math.round(rgba[0]);
+    bytes[j + 1] = Math.round(rgba[1]);
+    bytes[j + 2] = Math.round(rgba[2]);
+    bytes[j + 3] = Math.round(rgba[3]);
+  }
+  if (canvas != null) {
+    canvas.width = width;
+    canvas.height = height;
+    const ctx = canvas.getContext("2d");
+    const imageData = new ImageData(bytes, width, height);
+    ctx.putImageData(imageData, 0, 0);
+  }
+  if ($img !== img) {
+    $img.dispose();
+  }
+  return bytes;
+}
+var fromPixels = op({ fromPixels_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
+var gather_nd_util_exports = {};
+__export(gather_nd_util_exports, {
+  prepareAndValidate: () => prepareAndValidate
+});
+function prepareAndValidate(tensor2, indices) {
+  const tensorRank = tensor2.shape.length;
+  const indicesRank = indices.shape.length;
+  if (tensorRank < 1) {
+    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);
+  }
+  if (indicesRank < 1) {
+    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);
+  }
+  if (indices.dtype !== "int32") {
+    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);
+  }
+  if (indices.shape[indicesRank - 1] > tensorRank) {
+    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);
+  }
+  if (sizeFromShape(tensor2.shape) === 0) {
+    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);
+  }
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  let nResult = 1;
+  for (let i2 = 0; i2 < indicesShape.length - 1; ++i2) {
+    nResult *= indicesShape[i2];
+  }
+  const inputShape = tensor2.shape;
+  const resultShape = indicesShape.slice();
+  resultShape.pop();
+  let sliceSize = 1;
+  for (let i2 = sliceRank; i2 < tensorRank; ++i2) {
+    sliceSize *= inputShape[i2];
+    resultShape.push(inputShape[i2]);
+  }
+  const strides = [
+    ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),
+    1
+  ].slice(0, sliceRank);
+  return [resultShape, nResult, sliceSize, strides];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
+var scatter_nd_util_exports = {};
+__export(scatter_nd_util_exports, {
+  calculateShapes: () => calculateShapes,
+  validateInput: () => validateInput,
+  validateUpdateShape: () => validateUpdateShape
+});
+function validateUpdateShape(shape, indices, updates) {
+  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
+  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
+  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
+  if (updates.rank < batchDim) {
+    throw new Error(shapeError + ` update.rank < ${batchDim}. `);
+  }
+  if (shape.length < sliceDim + (updates.rank - batchDim)) {
+    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
+  }
+  if (updates.rank !== batchDim + shape.length - sliceDim) {
+    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
+  }
+  for (let d = 0; d < batchDim; ++d) {
+    if (updates.shape[d] !== indices.shape[d]) {
+      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
+    }
+  }
+  for (let d = 0; d < updates.rank - batchDim; ++d) {
+    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
+      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
+    }
+  }
+}
+function validateInput(updates, indices, shape) {
+  if (indices.rank < 1) {
+    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);
+  }
+  if (updates.rank < 1) {
+    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);
+  }
+  if (indices.dtype !== "int32") {
+    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
+  }
+  if (shape.length < 1) {
+    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
+  }
+  if (shape.length === 0) {
+    if (indices.size === 0) {
+      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
+    }
+    if (updates.size === 0) {
+      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
+    }
+  }
+  validateUpdateShape(shape, indices, updates);
+}
+function calculateShapes(updates, indices, shape) {
+  const indicesRank = indices.shape.length;
+  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
+  const totalNd = shape.length;
+  let sliceSize = 1;
+  for (let i2 = sliceRank; i2 < totalNd; ++i2) {
+    sliceSize *= shape[i2];
+  }
+  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
+  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
+  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
+  const outputSize = sizeFromShape(shape);
+  return { sliceRank, numUpdates, sliceSize, strides, outputSize };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
+var slice_util_exports = {};
+__export(slice_util_exports, {
+  assertParamsValid: () => assertParamsValid,
+  computeFlatOffset: () => computeFlatOffset,
+  computeOutShape: () => computeOutShape,
+  getNormalizedAxes: () => getNormalizedAxes,
+  isSliceContinous: () => isSliceContinous,
+  maskToAxes: () => maskToAxes,
+  parseSliceParams: () => parseSliceParams,
+  sliceInfo: () => sliceInfo,
+  startForAxis: () => startForAxis,
+  startIndicesWithElidedDims: () => startIndicesWithElidedDims,
+  stopForAxis: () => stopForAxis,
+  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
+  stridesForAxis: () => stridesForAxis,
+  stridesWithElidedDims: () => stridesWithElidedDims
+});
+var NEW_AXIS = -2;
+var SHRINK_AXIS = -1;
+function assertParamsValid(input2, begin, size) {
+  const inputRank = input2.shape.length;
+  assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);
+  assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);
+  for (let i2 = 0; i2 < inputRank; ++i2) {
+    assert(begin[i2] + size[i2] <= input2.shape[i2], () => `Error in slice${inputRank}D: begin[${i2}] + size[${i2}] (${begin[i2] + size[i2]}) would overflow input.shape[${i2}] (${input2.shape[i2]})`);
+  }
+}
+function maskToAxes(mask) {
+  const axes = [];
+  let axis = 0;
+  while (mask > 0) {
+    if (mask & 1) {
+      axes.push(axis);
+    }
+    mask /= 2;
+    axis++;
+  }
+  return axes;
+}
+function computeOutShape(begin, end, strides) {
+  const size = [];
+  for (let axis = 0; axis < begin.length; axis++) {
+    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
+  }
+  return size;
+}
+function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
+  const newStrides = [...strides];
+  for (let i2 = newStrides.length; i2 < inputShape.length; i2++) {
+    newStrides.push(1);
+  }
+  for (let i2 = 0; i2 < numElidedAxes; i2++) {
+    if (i2 === 0) {
+      newStrides[ellipsisInsertionIndex] = 1;
+    } else {
+      newStrides.splice(ellipsisInsertionIndex, 0, 1);
+      newStrides.pop();
+    }
+  }
+  return newStrides;
+}
+function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
+  if (normalizedAxis <= ellipsisInsertionIndex) {
+    return normalizedAxis;
+  }
+  return normalizedAxis - (numElidedAxes - 1);
+}
+function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
+  const elidedAxes = [];
+  for (let i2 = 0; i2 < numElidedAxes; i2++) {
+    elidedAxes.push(ellipsisInsertionIndex + i2);
+  }
+  return elidedAxes;
+}
+function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
+  const inputRank = inputShape.length;
+  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
+  if (ellipsisAxes.length && numInterpolatedAxes > 0) {
+    const fullIndex = ellipsisAxes[0];
+    const numElidedAxes = numInterpolatedAxes + 1;
+    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
+    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
+    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
+  } else {
+    for (let axis = 0; axis < inputRank; axis++) {
+      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
+      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
+      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
+    }
+  }
+  return {
+    begin: normalizedBegin,
+    end: normalizedEnd,
+    strides: normalizedStrides
+  };
+}
+function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
+  const newIndices = [...inputShape];
+  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
+  for (let axis = 0; axis < newIndices.length; axis++) {
+    if (elidedAxes.indexOf(axis) > -1) {
+      newIndices[axis] = 0;
+    } else {
+      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
+      let originalValue = originalBegin[originalAxis];
+      if (beginMask & 1 << originalAxis) {
+        originalValue = 0;
+      }
+      newIndices[axis] = originalValue;
+    }
+  }
+  return newIndices;
+}
+function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
+  const newIndices = [...inputShape];
+  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
+  for (let axis = 0; axis < newIndices.length; axis++) {
+    if (elidedAxes.indexOf(axis) > -1) {
+      newIndices[axis] = Number.MAX_SAFE_INTEGER;
+    } else {
+      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
+      let originalValue = originalEnd[originalAxis];
+      if (endMask & 1 << originalAxis) {
+        originalValue = Number.MAX_SAFE_INTEGER;
+      }
+      newIndices[axis] = originalValue;
+    }
+  }
+  for (let i2 = 0; i2 < newIndices.length; i2++) {
+    const axisSize = inputShape[i2];
+    if (newIndices[i2] < 0) {
+      newIndices[i2] += axisSize;
+    }
+    newIndices[i2] = clamp(0, newIndices[i2], inputShape[i2]);
+  }
+  return newIndices;
+}
+function stridesForAxis(strides, axis, ellipsisMask) {
+  let stride = strides[axis];
+  if (ellipsisMask & 1 << axis || stride == null) {
+    stride = 1;
+  }
+  return stride;
+}
+function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
+  let start = startIndices[axis];
+  const stride = strides[axis] || 1;
+  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
+    if (stride > 0) {
+      start = Number.MIN_SAFE_INTEGER;
+    } else {
+      start = Number.MAX_SAFE_INTEGER;
+    }
+  }
+  const axisSize = inputShape[axis];
+  if (start < 0) {
+    start += axisSize;
+  }
+  start = clamp(0, start, axisSize - 1);
+  return start;
+}
+function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
+  let stop = stopIndices[axis];
+  const stride = strides[axis] || 1;
+  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
+    if (stride > 0) {
+      stop = Number.MAX_SAFE_INTEGER;
+    } else {
+      stop = Number.MIN_SAFE_INTEGER;
+    }
+  }
+  const axisSize = inputShape[axis];
+  if (stop < 0) {
+    stop += axisSize;
+  }
+  if (stride > 0) {
+    stop = clamp(0, stop, axisSize);
+  } else {
+    stop = clamp(-1, stop, axisSize - 1);
+  }
+  return stop;
+}
+function isSliceContinous(shape, begin, size) {
+  let firstNonOneAxis = size.length;
+  for (let i2 = 0; i2 < size.length; i2++) {
+    if (size[i2] > 1) {
+      firstNonOneAxis = i2;
+      break;
+    }
+  }
+  for (let i2 = firstNonOneAxis + 1; i2 < size.length; i2++) {
+    if (begin[i2] > 0 || size[i2] !== shape[i2]) {
+      return false;
+    }
+  }
+  return true;
+}
+function computeFlatOffset(begin, strides) {
+  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
+  for (let i2 = 0; i2 < begin.length - 1; i2++) {
+    flatOffset += begin[i2] * strides[i2];
+  }
+  return flatOffset;
+}
+function parseSliceParams(x, begin, size) {
+  let begin_;
+  const xRank = x.shape.length;
+  if (typeof begin === "number") {
+    begin_ = [begin, ...new Array(xRank - 1).fill(0)];
+  } else if (begin.length < xRank) {
+    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
+  } else {
+    begin_ = begin.slice();
+  }
+  begin_.forEach((d) => {
+    assert(d !== -1, () => "slice() does not support negative begin indexing.");
+  });
+  let size_;
+  if (size == null) {
+    size_ = new Array(xRank).fill(-1);
+  } else if (typeof size === "number") {
+    size_ = [size, ...new Array(xRank - 1).fill(-1)];
+  } else if (size.length < xRank) {
+    size_ = size.concat(new Array(xRank - size.length).fill(-1));
+  } else {
+    size_ = size;
+  }
+  size_ = size_.map((d, i2) => {
+    if (d >= 0) {
+      return d;
+    } else {
+      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i2}.`);
+      return x.shape[i2] - begin_[i2];
+    }
+  });
+  return [begin_, size_];
+}
+function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+  let stridesNonNull;
+  if (strides == null) {
+    stridesNonNull = new Array(begin.length);
+    stridesNonNull.fill(1);
+  } else {
+    stridesNonNull = strides;
+  }
+  if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {
+    throw new Error("Multiple ellipses in slice is not allowed.");
+  }
+  let ellipsisSeen = false;
+  const sparseSpec = {
+    dims: stridesNonNull.length,
+    numAddAxisAfterEllipsis: 0,
+    begin: begin.slice(),
+    end: end.slice(),
+    strides: stridesNonNull.slice(),
+    beginMask,
+    endMask,
+    ellipsisMask,
+    newAxisMask,
+    shrinkAxisMask
+  };
+  for (let i2 = 0; i2 < sparseSpec.dims; i2++) {
+    if (ellipsisSeen && (1 << i2 & newAxisMask) !== 0) {
+      sparseSpec.numAddAxisAfterEllipsis++;
+    }
+    if (1 << i2 & ellipsisMask) {
+      ellipsisSeen = true;
+    }
+  }
+  if (!ellipsisSeen) {
+    sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;
+    sparseSpec.dims++;
+  }
+  const denseSpec = {
+    dims: xShape.length,
+    beginMask: 0,
+    endMask: 0,
+    beginValid: false,
+    endValid: false
+  };
+  buildDenseSpec(sparseSpec, denseSpec);
+  let isIdentity = true;
+  let sliceDim0 = true;
+  let isSimpleSlice = true;
+  const processingShape = [];
+  const finalShape = [];
+  for (let i2 = 0; i2 < xShape.length; ++i2) {
+    if (denseSpec.strides[i2] === 0) {
+      throw Error(`strides[${i2}] must be non-zero`);
+    }
+    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i2);
+    const dimI = xShape[i2];
+    if (dimI === -1) {
+      processingShape.push(shrinkI ? 1 : -1);
+      continue;
+    }
+    const masks = [denseSpec.beginMask & 1 << i2, denseSpec.endMask & 1 << i2];
+    const validRange = [
+      denseSpec.strides[i2] > 0 ? 0 : -1,
+      denseSpec.strides[i2] > 0 ? dimI : dimI - 1
+    ];
+    if (shrinkI && denseSpec.strides[i2] <= 0) {
+      throw Error("only stride 1 allowed on non-range indexing.");
+    }
+    isSimpleSlice = isSimpleSlice && denseSpec.strides[i2] === 1;
+    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i2 && denseSpec.endMask & 1 << i2);
+    if (denseSpec.beginValid && denseSpec.endValid) {
+      if (shrinkI) {
+        const xFwd = denseSpec.begin[i2] < 0 ? dimI + denseSpec.begin[i2] : denseSpec.begin[i2];
+        denseSpec.begin[i2] = xFwd;
+        denseSpec.end[i2] = denseSpec.begin[i2] + 1;
+        if (xFwd < 0 || xFwd >= dimI) {
+          throw Error(`slice index ${denseSpec.begin[i2]} of dimension ${i2} out of bounds.`);
+        }
+      } else {
+        denseSpec.begin[i2] = canonical(denseSpec.begin[i2], 0, denseSpec.strides[i2], dimI, masks, validRange);
+        denseSpec.end[i2] = canonical(denseSpec.end[i2], 1, denseSpec.strides[i2], dimI, masks, validRange);
+      }
+      const takeAllInDimension = denseSpec.strides[i2] === 1 && denseSpec.begin[i2] === 0 && denseSpec.end[i2] === dimI;
+      isIdentity = isIdentity && takeAllInDimension;
+      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || takeAllInDimension);
+    } else {
+      isIdentity = isIdentity && (denseSpec.strides[i2] === 1 && beginAndEndMasked);
+      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || beginAndEndMasked);
+    }
+    let intervalLength;
+    let knownInterval = false;
+    if (denseSpec.beginValid && denseSpec.endValid) {
+      intervalLength = denseSpec.end[i2] - denseSpec.begin[i2];
+      knownInterval = true;
+    } else if (shrinkI) {
+      intervalLength = 1;
+      knownInterval = true;
+    } else if (beginAndEndMasked) {
+      if (dimI >= 0) {
+        if (denseSpec.strides[i2] < 0) {
+          intervalLength = -dimI;
+        } else {
+          intervalLength = dimI;
+        }
+        knownInterval = true;
+      }
+    }
+    if (knownInterval) {
+      let sizeI;
+      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i2] < 0) {
+        sizeI = 0;
+      } else {
+        sizeI = Math.trunc(intervalLength / denseSpec.strides[i2]) + (intervalLength % denseSpec.strides[i2] !== 0 ? 1 : 0);
+      }
+      processingShape.push(sizeI);
+    } else {
+      processingShape.push(-1);
+    }
+  }
+  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {
+    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];
+    if (gatherIndex >= 0) {
+      finalShape.push(processingShape[gatherIndex]);
+    } else if (gatherIndex === NEW_AXIS) {
+      finalShape.push(1);
+    }
+  }
+  const finalShapeSparse = finalShape.filter((dim, i2) => denseSpec.finalShapeGatherIndices[i2] !== NEW_AXIS);
+  return {
+    finalShapeSparse,
+    finalShape,
+    isIdentity,
+    sliceDim0,
+    isSimpleSlice,
+    begin: denseSpec.begin,
+    end: denseSpec.end,
+    strides: denseSpec.strides
+  };
+}
+function buildDenseSpec(sparse2, dense2) {
+  dense2.beginMask = 0;
+  dense2.endMask = 0;
+  dense2.shrinkAxisMask = 0;
+  let fullIndex = 0;
+  dense2.beginValid = sparse2.begin != null;
+  dense2.endValid = sparse2.end != null;
+  dense2.begin = new Array(dense2.dims);
+  dense2.end = new Array(dense2.dims);
+  dense2.strides = new Array(dense2.dims);
+  dense2.finalShapeGatherIndices = [];
+  dense2.finalShapeGatherIndicesSparse = [];
+  dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);
+  for (let i2 = 0; i2 < sparse2.dims; i2++) {
+    if (1 << i2 & sparse2.ellipsisMask) {
+      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i2) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);
+      for (; fullIndex < nextIndex; fullIndex++) {
+        dense2.begin[fullIndex] = 0;
+        dense2.end[fullIndex] = 0;
+        dense2.strides[fullIndex] = 1;
+        dense2.beginMask |= 1 << fullIndex;
+        dense2.endMask |= 1 << fullIndex;
+        dense2.finalShapeGatherIndices.push(fullIndex);
+        dense2.finalShapeGatherIndicesSparse.push(-1);
+        dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+      }
+    } else if (1 << i2 & sparse2.newAxisMask) {
+      dense2.finalShapeGatherIndices.push(NEW_AXIS);
+      dense2.finalShapeGatherIndicesSparse.push(-1);
+    } else {
+      if (fullIndex === dense2.begin.length) {
+        throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);
+      }
+      if (sparse2.begin != null) {
+        dense2.begin[fullIndex] = sparse2.begin[i2];
+      }
+      if (sparse2.end != null) {
+        dense2.end[fullIndex] = sparse2.end[i2];
+      }
+      dense2.strides[fullIndex] = sparse2.strides[i2];
+      if (sparse2.beginMask & 1 << i2) {
+        dense2.beginMask |= 1 << fullIndex;
+      }
+      if (sparse2.endMask & 1 << i2) {
+        dense2.endMask |= 1 << fullIndex;
+      }
+      if (sparse2.shrinkAxisMask & 1 << i2) {
+        dense2.finalShapeGatherIndices.push(SHRINK_AXIS);
+        dense2.finalShapeGatherIndicesSparse.push(-1);
+        dense2.shrinkAxisMask |= 1 << fullIndex;
+      } else {
+        dense2.finalShapeGatherIndices.push(fullIndex);
+        dense2.finalShapeGatherIndicesSparse.push(i2);
+      }
+      dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+      fullIndex++;
+    }
+  }
+}
+function canonical(x, c, strideI, dimI, masks, validRange) {
+  if (masks[c]) {
+    return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];
+  } else {
+    const xFwd = x < 0 ? dimI + x : x;
+    return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js
+var serialization_exports = {};
+__export(serialization_exports, {
+  Serializable: () => Serializable,
+  SerializationMap: () => SerializationMap,
+  registerClass: () => registerClass
+});
+var Serializable = class {
+  getClassName() {
+    return this.constructor.className;
+  }
+  static fromConfig(cls, config) {
+    return new cls(config);
+  }
+};
+var SerializationMap = class {
+  constructor() {
+    this.classNameMap = {};
+  }
+  static getMap() {
+    if (SerializationMap.instance == null) {
+      SerializationMap.instance = new SerializationMap();
+    }
+    return SerializationMap.instance;
+  }
+  static register(cls) {
+    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];
+  }
+};
+function registerClass(cls) {
+  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);
+  assert(typeof cls.className === "string", () => `className is required to be a string, but got type ` + typeof cls.className);
+  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);
+  SerializationMap.register(cls);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js
+var test_util_exports = {};
+__export(test_util_exports, {
+  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,
+  encodeStrings: () => encodeStrings,
+  expectArrayBuffersEqual: () => expectArrayBuffersEqual,
+  expectArraysClose: () => expectArraysClose,
+  expectArraysEqual: () => expectArraysEqual,
+  expectNumbersClose: () => expectNumbersClose,
+  expectPromiseToFail: () => expectPromiseToFail,
+  expectValuesInRange: () => expectValuesInRange,
+  testEpsilon: () => testEpsilon
+});
+var TEST_EPSILON_FLOAT32 = 1e-3;
+var TEST_EPSILON_FLOAT16 = 0.1;
+function expectArraysClose(actual, expected, epsilon3) {
+  if (epsilon3 == null) {
+    epsilon3 = testEpsilon();
+  }
+  return expectArraysPredicate(actual, expected, (a6, b) => areClose(a6, b, epsilon3));
+}
+function testEpsilon() {
+  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
+}
+function expectArraysPredicate(actual, expected, predicate) {
+  let checkClassType = true;
+  if (isTypedArray(actual) || isTypedArray(expected)) {
+    checkClassType = false;
+  }
+  if (isTypedArray(actual) && isTypedArray(expected)) {
+    checkClassType = true;
+  }
+  if (checkClassType) {
+    const aType = actual.constructor.name;
+    const bType = expected.constructor.name;
+    if (aType !== bType) {
+      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);
+    }
+  }
+  if (Array.isArray(actual) && Array.isArray(expected)) {
+    const actualShape = inferShape(actual);
+    const expectedShape = inferShape(expected);
+    if (!arraysEqual(actualShape, expectedShape)) {
+      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);
+    }
+  }
+  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);
+  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);
+  if (actualFlat.length !== expectedFlat.length) {
+    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);
+  }
+  for (let i2 = 0; i2 < expectedFlat.length; ++i2) {
+    const a6 = actualFlat[i2];
+    const e2 = expectedFlat[i2];
+    if (!predicate(a6, e2)) {
+      throw new Error(`Arrays differ: actual[${i2}] = ${a6}, expected[${i2}] = ${e2}.
+Actual:   ${actualFlat}.
+Expected: ${expectedFlat}.`);
+    }
+  }
+}
+function expectPromiseToFail(fn, done) {
+  fn().then(() => done.fail(), () => done());
+}
+function expectArraysEqual(actual, expected) {
+  const exp5 = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [expected] : expected;
+  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {
+    return expectArraysPredicate(actual, exp5, (a6, b) => a6 == b);
+  }
+  return expectArraysPredicate(actual, expected, (a6, b) => areClose(a6, b, 0));
+}
+function expectNumbersClose(a6, e2, epsilon3) {
+  if (epsilon3 == null) {
+    epsilon3 = testEpsilon();
+  }
+  if (!areClose(a6, e2, epsilon3)) {
+    throw new Error(`Numbers differ: actual === ${a6}, expected === ${e2}`);
+  }
+}
+function areClose(a6, e2, epsilon3) {
+  if (!isFinite(a6) && !isFinite(e2)) {
+    return true;
+  }
+  if (isNaN(a6) || isNaN(e2) || Math.abs(a6 - e2) > epsilon3) {
+    return false;
+  }
+  return true;
+}
+function expectValuesInRange(actual, low, high) {
+  for (let i2 = 0; i2 < actual.length; i2++) {
+    if (actual[i2] < low || actual[i2] > high) {
+      throw new Error(`Value out of range:${actual[i2]} low: ${low}, high: ${high}`);
+    }
+  }
+}
+function expectArrayBuffersEqual(actual, expected) {
+  const actualArray = new Float32Array(actual);
+  const expectedArray = new Float32Array(expected);
+  if (actualArray.length !== expectedArray.length) {
+    throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);
+  }
+  for (let i2 = 0; i2 < expectedArray.length; i2++) {
+    if (actualArray[i2] !== expectedArray[i2]) {
+      throw new Error(`Expected ArrayBuffer value at ${i2} to be ${expectedArray[i2]} but got ${actualArray[i2]} instead`);
+    }
+  }
+}
+function encodeStrings(a6) {
+  for (let i2 = 0; i2 < a6.length; i2++) {
+    const val = a6[i2];
+    if (Array.isArray(val)) {
+      encodeStrings(val);
+    } else {
+      a6[i2] = encodeString(val);
+    }
+  }
+  return a6;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/version.js
+var version = "3.18.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js
+function add_(a6, b) {
+  let $a = convertToTensor(a6, "a", "add");
+  let $b = convertToTensor(b, "b", "add");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Add, inputs);
+}
+var add2 = op({ add_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
+function floorDiv_(a6, b) {
+  let $a = convertToTensor(a6, "a", "floorDiv");
+  let $b = convertToTensor(b, "b", "floorDiv");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(FloorDiv, inputs);
+}
+var floorDiv = op({ floorDiv_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js
+function div_(a6, b) {
+  let $a = convertToTensor(a6, "a", "div");
+  let $b = convertToTensor(b, "b", "div");
+  [$a, $b] = makeTypesMatch($a, $b);
+  if ($a.dtype === "int32" && $b.dtype === "int32") {
+    return floorDiv($a, $b);
+  }
+  const inputs = { a: $a, b: $b };
+  const attrs = {};
+  return ENGINE.runKernel(RealDiv, inputs, attrs);
+}
+var div = op({ div_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
+function mul_(a6, b) {
+  let $a = convertToTensor(a6, "a", "mul");
+  let $b = convertToTensor(b, "b", "mul");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Multiply, inputs);
+}
+var mul = op({ mul_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
+function abs_(x) {
+  const $x = convertToTensor(x, "x", "abs");
+  if ($x.dtype === "complex64") {
+    const inputs = { x: $x };
+    return ENGINE.runKernel(ComplexAbs, inputs);
+  } else {
+    const inputs = { x: $x };
+    return ENGINE.runKernel(Abs, inputs);
+  }
+}
+var abs = op({ abs_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
+function acos_(x) {
+  const $x = convertToTensor(x, "x", "acos");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Acos, inputs);
+}
+var acos = op({ acos_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
+function acosh_(x) {
+  const $x = convertToTensor(x, "x", "acosh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Acosh, inputs);
+}
+var acosh = op({ acosh_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js
+function addN_(tensors) {
+  assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
+  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
+  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "addN"));
+  const firstTensor = $tensors[0];
+  $tensors.forEach((t2) => {
+    if (t2.dtype !== firstTensor.dtype) {
+      throw new Error("All tensors passed to tf.addN() must have the same dtype");
+    }
+  });
+  $tensors.forEach((t2) => {
+    if (!arraysEqual(t2.shape, firstTensor.shape)) {
+      throw new Error("All tensors passed to tf.addN() must have the same shape");
+    }
+  });
+  const inputs = $tensors;
+  return ENGINE.runKernel(AddN, inputs);
+}
+var addN = op({ addN_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js
+function all_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "all", "bool");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(All, inputs, attrs);
+}
+var all = op({ all_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js
+function any_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "any", "bool");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Any, inputs, attrs);
+}
+var any = op({ any_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
+function argMax_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "argMax");
+  const inputs = { x: $x };
+  const attrs = { axis };
+  return ENGINE.runKernel(ArgMax, inputs, attrs);
+}
+var argMax = op({ argMax_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
+function argMin_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "argMin");
+  const inputs = { x: $x };
+  const attrs = { axis };
+  return ENGINE.runKernel(ArgMin, inputs, attrs);
+}
+var argMin = op({ argMin_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
+function asin_(x) {
+  const $x = convertToTensor(x, "x", "asin");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Asin, inputs);
+}
+var asin = op({ asin_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
+function asinh_(x) {
+  const $x = convertToTensor(x, "x", "asinh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Asinh, inputs);
+}
+var asinh = op({ asinh_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
+function atan_(x) {
+  const $x = convertToTensor(x, "x", "atan");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Atan, inputs);
+}
+var atan = op({ atan_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
+function atan2_(a6, b) {
+  let $a = convertToTensor(a6, "a", "atan2");
+  let $b = convertToTensor(b, "b", "atan2");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Atan2, inputs);
+}
+var atan2 = op({ atan2_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
+function atanh_(x) {
+  const $x = convertToTensor(x, "x", "atanh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Atanh, inputs);
+}
+var atanh = op({ atanh_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
+function computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = "NHWC", dilations) {
+  const inputChannels = inputShape[3];
+  const $filterShape = [...filterShape, inputChannels];
+  const $dataFormat = convertConv2DDataFormat(dataFormat);
+  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat);
+}
+function computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "channelsLast") {
+  const [filterHeight, filterWidth] = parseTupleParam(filterSize);
+  let filterShape;
+  if (dataFormat === "channelsLast") {
+    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
+  } else if (dataFormat === "channelsFirst") {
+    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat);
+}
+function computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = "NDHWC") {
+  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
+  let filterShape;
+  let $dataFormat;
+  if (dataFormat === "NDHWC") {
+    $dataFormat = "channelsLast";
+    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];
+  } else if (dataFormat === "NCDHW") {
+    $dataFormat = "channelsFirst";
+    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode);
+}
+function computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = "channelsLast") {
+  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];
+  if (dataFormat === "channelsLast") {
+    [batchSize, inHeight, inWidth, inChannels] = inShape;
+  } else if (dataFormat === "channelsFirst") {
+    [batchSize, inChannels, inHeight, inWidth] = inShape;
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  const [filterHeight, filterWidth, , filterChannels] = filterShape;
+  const [strideHeight, strideWidth] = parseTupleParam(strides);
+  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
+  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+  const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
+  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+  let outShape;
+  if (dataFormat === "channelsFirst") {
+    outShape = [batchSize, outChannels, outHeight, outWidth];
+  } else if (dataFormat === "channelsLast") {
+    outShape = [batchSize, outHeight, outWidth, outChannels];
+  }
+  return {
+    batchSize,
+    dataFormat,
+    inHeight,
+    inWidth,
+    inChannels,
+    outHeight,
+    outWidth,
+    outChannels,
+    padInfo,
+    strideHeight,
+    strideWidth,
+    filterHeight,
+    filterWidth,
+    effectiveFilterHeight,
+    effectiveFilterWidth,
+    dilationHeight,
+    dilationWidth,
+    inShape,
+    outShape,
+    filterShape
+  };
+}
+function computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = "channelsLast", roundingMode) {
+  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];
+  if (dataFormat === "channelsLast") {
+    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
+  } else if (dataFormat === "channelsFirst") {
+    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
+  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
+  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
+  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
+  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+  const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
+  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+  let outShape;
+  if (dataFormat === "channelsFirst") {
+    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
+  } else if (dataFormat === "channelsLast") {
+    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
+  }
+  return {
+    batchSize,
+    dataFormat,
+    inDepth,
+    inHeight,
+    inWidth,
+    inChannels,
+    outDepth,
+    outHeight,
+    outWidth,
+    outChannels,
+    padInfo,
+    strideDepth,
+    strideHeight,
+    strideWidth,
+    filterDepth,
+    filterHeight,
+    filterWidth,
+    effectiveFilterDepth,
+    effectiveFilterHeight,
+    effectiveFilterWidth,
+    dilationDepth,
+    dilationHeight,
+    dilationWidth,
+    inShape,
+    outShape,
+    filterShape
+  };
+}
+function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
+  if (zeroPad == null) {
+    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+  }
+  const inputRows = inShape[0];
+  const inputCols = inShape[1];
+  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  return [outputRows, outputCols];
+}
+function computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {
+  if (zeroPad == null) {
+    zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+  }
+  const inputDepth = inShape[0];
+  const inputRows = inShape[1];
+  const inputCols = inShape[2];
+  const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+  return [outputDepths, outputRows, outputCols, outChannels];
+}
+function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
+  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
+  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
+}
+function parseTupleParam(param) {
+  if (typeof param === "number") {
+    return [param, param, param];
+  }
+  if (param.length === 2) {
+    return [param[0], param[1], 1];
+  }
+  return param;
+}
+function parse3TupleParam(param) {
+  return typeof param === "number" ? [param, param, param] : param;
+}
+function getEffectiveFilterSize(filterSize, dilation) {
+  if (dilation <= 1) {
+    return filterSize;
+  }
+  return filterSize + (filterSize - 1) * (dilation - 1);
+}
+function getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
+  let padInfo;
+  let outHeight;
+  let outWidth;
+  if (typeof pad3 === "number") {
+    const padType = pad3 === 0 ? "VALID" : "NUMBER";
+    padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType };
+    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);
+    outHeight = outShape[0];
+    outWidth = outShape[1];
+  } else if (pad3 === "same") {
+    outHeight = Math.ceil(inHeight / strideHeight);
+    outWidth = Math.ceil(inWidth / strideWidth);
+    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
+    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
+    const top = Math.floor(padAlongHeight / 2);
+    const bottom = padAlongHeight - top;
+    const left = Math.floor(padAlongWidth / 2);
+    const right = padAlongWidth - left;
+    padInfo = { top, bottom, left, right, type: "SAME" };
+  } else if (pad3 === "valid") {
+    padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: "VALID" };
+    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+  } else if (typeof pad3 === "object") {
+    const top = dataFormat === "channelsLast" ? pad3[1][0] : pad3[2][0];
+    const bottom = dataFormat === "channelsLast" ? pad3[1][1] : pad3[2][1];
+    const left = dataFormat === "channelsLast" ? pad3[2][0] : pad3[3][0];
+    const right = dataFormat === "channelsLast" ? pad3[2][1] : pad3[3][1];
+    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
+    padInfo = { top, bottom, left, right, type: padType };
+    outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
+    outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
+  } else {
+    throw Error(`Unknown padding parameter: ${pad3}`);
+  }
+  return { padInfo, outHeight, outWidth };
+}
+function get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
+  let padInfo;
+  let outDepth;
+  let outHeight;
+  let outWidth;
+  if (typeof pad3 === "number") {
+    const padType = pad3 === 0 ? "VALID" : "NUMBER";
+    padInfo = {
+      top: pad3,
+      bottom: pad3,
+      left: pad3,
+      right: pad3,
+      front: pad3,
+      back: pad3,
+      type: padType
+    };
+    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad3, roundingMode);
+    outDepth = outShape[0];
+    outHeight = outShape[1];
+    outWidth = outShape[2];
+  } else if (pad3 === "same") {
+    outDepth = Math.ceil(inDepth / strideDepth);
+    outHeight = Math.ceil(inHeight / strideHeight);
+    outWidth = Math.ceil(inWidth / strideWidth);
+    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
+    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+    const front = Math.floor(padAlongDepth / 2);
+    const back = padAlongDepth - front;
+    const top = Math.floor(padAlongHeight / 2);
+    const bottom = padAlongHeight - top;
+    const left = Math.floor(padAlongWidth / 2);
+    const right = padAlongWidth - left;
+    padInfo = { top, bottom, left, right, front, back, type: "SAME" };
+  } else if (pad3 === "valid") {
+    padInfo = {
+      top: 0,
+      bottom: 0,
+      left: 0,
+      right: 0,
+      front: 0,
+      back: 0,
+      type: "VALID"
+    };
+    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
+    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+  } else {
+    throw Error(`Unknown padding parameter: ${pad3}`);
+  }
+  return { padInfo, outDepth, outHeight, outWidth };
+}
+function round(value, roundingMode) {
+  if (!roundingMode) {
+    return Math.trunc(value);
+  }
+  switch (roundingMode) {
+    case "round":
+      return Math.round(value);
+    case "ceil":
+      return Math.ceil(value);
+    case "floor":
+      return Math.floor(value);
+    default:
+      throw new Error(`Unknown roundingMode ${roundingMode}`);
+  }
+}
+function tupleValuesAreOne(param) {
+  const [dimA, dimB, dimC] = parseTupleParam(param);
+  return dimA === 1 && dimB === 1 && dimC === 1;
+}
+function eitherStridesOrDilationsAreOne(strides, dilations) {
+  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
+}
+function convertConv2DDataFormat(dataFormat) {
+  if (dataFormat === "NHWC") {
+    return "channelsLast";
+  } else if (dataFormat === "NCHW") {
+    return "channelsFirst";
+  } else {
+    throw new Error(`Unknown dataFormat ${dataFormat}`);
+  }
+}
+function checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) {
+  if (dimRoundingMode != null) {
+    if (typeof pad3 === "string") {
+      throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
+    } else if (typeof pad3 === "number") {
+      assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);
+    } else if (typeof pad3 === "object") {
+      pad3.forEach((p2) => {
+        p2.forEach((v) => {
+          assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);
+        });
+      });
+    } else {
+      throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`);
+    }
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
+function reshape_(x, shape) {
+  const $x = convertToTensor(x, "x", "reshape", "string_or_numeric");
+  const inputs = { x: $x };
+  const attrs = { shape };
+  return ENGINE.runKernel(Reshape, inputs, attrs);
+}
+var reshape = op({ reshape_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
+function avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "avgPool", "float32");
+  const dilations = 1;
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
+  checkPadOnDimRoundingMode("avgPool", pad3, dimRoundingMode);
+  const inputs = { x: x4D };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };
+  let res = ENGINE.runKernel(AvgPool, inputs, attrs);
+  res = cast(res, $x.dtype);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var avgPool = op({ avgPool_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
+function avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = "NDHWC") {
+  const $x = convertToTensor(x, "x", "avgPool3d", "float32");
+  let x5D = $x;
+  let reshapedTo5D = false;
+  if ($x.rank === 4) {
+    reshapedTo5D = true;
+    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
+  assert(dataFormat === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
+  checkPadOnDimRoundingMode("avgPool3d", pad3, dimRoundingMode);
+  const inputs = { x: x5D };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };
+  let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);
+  res = cast(res, x5D.dtype);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var avgPool3d = op({ avgPool3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
+function concat_(tensors, axis = 0) {
+  assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
+  const $tensors = convertToTensorArray(tensors, "tensors", "concat", "string_or_numeric");
+  if ($tensors[0].dtype === "complex64") {
+    $tensors.forEach((tensor2) => {
+      if (tensor2.dtype !== "complex64") {
+        throw new Error(`Cannot concatenate complex64 tensors with a tensor
+          with dtype ${tensor2.dtype}. `);
+      }
+    });
+  }
+  if ($tensors.length === 1) {
+    return clone($tensors[0]);
+  }
+  const inputs = $tensors;
+  const attr = { axis };
+  return ENGINE.runKernel(Concat, inputs, attr);
+}
+var concat = op({ concat_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
+function sigmoid_(x) {
+  const $x = convertToTensor(x, "x", "sigmoid", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sigmoid, inputs);
+}
+var sigmoid = op({ sigmoid_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
+function slice_(x, begin, size) {
+  const $x = convertToTensor(x, "x", "slice", "string_or_numeric");
+  if ($x.rank === 0) {
+    throw new Error("Slicing scalar is not possible");
+  }
+  const inputs = { x: $x };
+  const attrs = { begin, size };
+  return ENGINE.runKernel(Slice, inputs, attrs);
+}
+var slice = op({ slice_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
+function tanh_(x) {
+  const $x = convertToTensor(x, "x", "tanh", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Tanh, inputs);
+}
+var tanh2 = op({ tanh_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js
+function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
+  const $forgetBias = convertToTensor(forgetBias, "forgetBias", "basicLSTMCell");
+  const $lstmKernel = convertToTensor(lstmKernel, "lstmKernel", "basicLSTMCell");
+  const $lstmBias = convertToTensor(lstmBias, "lstmBias", "basicLSTMCell");
+  const $data = convertToTensor(data, "data", "basicLSTMCell");
+  const $c = convertToTensor(c, "c", "basicLSTMCell");
+  const $h = convertToTensor(h, "h", "basicLSTMCell");
+  const combined = concat([$data, $h], 1);
+  const weighted = matMul(combined, $lstmKernel);
+  const res = add2(weighted, $lstmBias);
+  const batchSize = res.shape[0];
+  const sliceCols = res.shape[1] / 4;
+  const sliceSize = [batchSize, sliceCols];
+  const i2 = slice(res, [0, 0], sliceSize);
+  const j = slice(res, [0, sliceCols], sliceSize);
+  const f = slice(res, [0, sliceCols * 2], sliceSize);
+  const o = slice(res, [0, sliceCols * 3], sliceSize);
+  const newC = add2(mul(sigmoid(i2), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
+  const newH = mul(tanh2(newC), sigmoid(o));
+  return [newC, newH];
+}
+var basicLSTMCell = op({ basicLSTMCell_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
+function batchToSpaceND_(x, blockShape, crops) {
+  const $x = convertToTensor(x, "x", "batchToSpaceND");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
+  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
+  assert($x.shape[0] % prod6 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod6}`);
+  const inputs = { x: $x };
+  const attrs = { blockShape, crops };
+  return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);
+}
+var batchToSpaceND = op({ batchToSpaceND_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
+function xAs4D(x) {
+  let x4D;
+  if (x.rank === 0 || x.rank === 1) {
+    x4D = reshape(x, [1, 1, 1, x.size]);
+  } else if (x.rank === 2) {
+    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);
+  } else if (x.rank === 3) {
+    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+  } else {
+    x4D = x;
+  }
+  return x4D;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
+function batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {
+  if (varianceEpsilon == null) {
+    varianceEpsilon = 1e-3;
+  }
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale2 != null) {
+    $scale = convertToTensor(scale2, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($mean.rank === $variance.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
+  assert($offset == null || $mean.rank === $offset.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
+  assert($scale == null || $mean.rank === $scale.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  const x4D = xAs4D($x);
+  const inputs = {
+    x: x4D,
+    scale: $scale,
+    offset: $offset,
+    mean: $mean,
+    variance: $variance
+  };
+  const attrs = { varianceEpsilon };
+  const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);
+  return reshape(res, $x.shape);
+}
+var batchNorm = op({ batchNorm_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
+function batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale2 != null) {
+    $scale = convertToTensor(scale2, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);
+  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);
+  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);
+  if ($scale != null) {
+    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);
+  }
+  if ($offset != null) {
+    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);
+  }
+  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+}
+var batchNorm2d = op({ batchNorm2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
+function batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale2 != null) {
+    $scale = convertToTensor(scale2, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);
+  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);
+  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);
+  if ($scale != null) {
+    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);
+  }
+  if ($offset != null) {
+    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);
+  }
+  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+}
+var batchNorm3d = op({ batchNorm3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
+function batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {
+  const $x = convertToTensor(x, "x", "batchNorm");
+  const $mean = convertToTensor(mean5, "mean", "batchNorm");
+  const $variance = convertToTensor(variance, "variance", "batchNorm");
+  let $scale;
+  if (scale2 != null) {
+    $scale = convertToTensor(scale2, "scale", "batchNorm");
+  }
+  let $offset;
+  if (offset != null) {
+    $offset = convertToTensor(offset, "offset", "batchNorm");
+  }
+  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);
+  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);
+  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);
+  if ($scale != null) {
+    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);
+  }
+  if ($offset != null) {
+    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);
+  }
+  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);
+}
+var batchNorm4d = op({ batchNorm4d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js
+function bincount_(x, weights, size) {
+  const $x = convertToTensor(x, "x", "bincount");
+  const $weights = convertToTensor(weights, "weights", "bincount");
+  assert($x.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);
+  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
+  assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
+  const inputs = { x: $x, weights: $weights };
+  const attrs = { size };
+  return ENGINE.runKernel(Bincount, inputs, attrs);
+}
+var bincount = op({ bincount_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_args.js
+function broadcastArgs_(s0, s1) {
+  const shape1Input = convertToTensor(s0, "s0", "broadcastArgs", "int32");
+  const shape2Input = convertToTensor(s1, "s1", "broadcastArgs", "int32");
+  if (shape1Input.rank !== 1) {
+    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`);
+  }
+  if (shape2Input.rank !== 1) {
+    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`);
+  }
+  const inputs = { s0: shape1Input, s1: shape2Input };
+  return ENGINE.runKernel(BroadcastArgs, inputs);
+}
+var broadcastArgs = op({ broadcastArgs_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
+function broadcastTo_(x, shape) {
+  let input2 = convertToTensor(x, "broadcastTo", "x");
+  const xShape = input2.shape;
+  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
+    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);
+  }
+  if (shape.length < input2.rank) {
+    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);
+  }
+  if (shape.length > input2.rank) {
+    const newShape = input2.shape.slice();
+    while (newShape.length < shape.length) {
+      newShape.unshift(1);
+    }
+    input2 = reshape(input2, newShape);
+  }
+  const inputShape = input2.shape;
+  const reps = Array.from(shape);
+  for (let i2 = shape.length - 1; i2 >= 0; i2--) {
+    if (inputShape[i2] === shape[i2]) {
+      reps[i2] = 1;
+    } else if (input2.shape[i2] !== 1) {
+      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
+    }
+  }
+  const axes = reps.map((n, i2) => n > 1 ? i2 : -1).filter((i2) => i2 >= 0);
+  if (axes.length === 0) {
+    return clone(input2);
+  }
+  const inputs = { x: input2 };
+  const attrs = { reps };
+  return ENGINE.runKernel(Tile, inputs, attrs);
+}
+var broadcastTo = op({ broadcastTo_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
+function ceil_(x) {
+  const $x = convertToTensor(x, "x", "ceil", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Ceil, inputs);
+}
+var ceil = op({ ceil_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
+function clipByValue_(x, clipValueMin, clipValueMax) {
+  const $x = convertToTensor(x, "x", "clipByValue");
+  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);
+  const inputs = { x: $x };
+  const attrs = { clipValueMin, clipValueMax };
+  return ENGINE.runKernel(ClipByValue, inputs, attrs);
+}
+var clipByValue = op({ clipByValue_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
+function concat1d_(tensors) {
+  return concat(tensors, 0);
+}
+var concat1d = op({ concat1d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
+function concat2d_(tensors, axis) {
+  return concat(tensors, axis);
+}
+var concat2d = op({ concat2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
+function concat3d_(tensors, axis) {
+  return concat(tensors, axis);
+}
+var concat3d = op({ concat3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
+function concat4d_(tensors, axis) {
+  return concat(tensors, axis);
+}
+var concat4d = op({ concat4d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
+function conv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "conv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "conv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  checkPadOnDimRoundingMode("conv2d", pad3, dimRoundingMode);
+  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
+  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const inputs = { x: x4D, filter: $filter };
+  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };
+  const res = ENGINE.runKernel(Conv2D, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var conv2d = op({ conv2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
+function conv1d_(x, filter, stride, pad3, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "conv1d");
+  const $filter = convertToTensor(filter, "filter", "conv1d");
+  let x3D = $x;
+  let reshapedTo3D = false;
+  if ($x.rank === 2) {
+    reshapedTo3D = true;
+    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
+  }
+  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
+  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);
+  checkPadOnDimRoundingMode("conv1d", pad3, dimRoundingMode);
+  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);
+  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);
+  assert(dataFormat === "NWC", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
+  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
+  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
+  const strides = [1, stride];
+  const dilations = [1, dilation];
+  const conv2dDataFormat = "NHWC";
+  const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode);
+  if (reshapedTo3D) {
+    return reshape(res, [res.shape[2], res.shape[3]]);
+  }
+  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
+}
+var conv1d = op({ conv1d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
+function conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = "NHWC", dimRoundingMode) {
+  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
+  let xShape4D = xShape;
+  let dy4D = dy;
+  let reshapedTo4D = false;
+  if (dy.rank === 3) {
+    reshapedTo4D = true;
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+    xShape4D = [1, xShape[0], xShape[1], xShape[2]];
+  }
+  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);
+  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);
+  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);
+  const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
+  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
+  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);
+  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);
+  checkPadOnDimRoundingMode("conv2dDerInput", pad3, dimRoundingMode);
+  const inputs = { dy: dy4D, filter };
+  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D };
+  const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var conv2DBackpropInput = op({ conv2DBackpropInput_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
+function conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "conv2dTranspose");
+  const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
+  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, "NHWC", dimRoundingMode);
+}
+var conv2dTranspose = op({ conv2dTranspose_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
+function conv3d_(x, filter, strides, pad3, dataFormat = "NDHWC", dilations = [1, 1, 1]) {
+  const $x = convertToTensor(x, "x", "conv3d");
+  const $filter = convertToTensor(filter, "filter", "conv3d");
+  let x5D = $x;
+  let reshapedTo5D = false;
+  if ($x.rank === 4) {
+    reshapedTo5D = true;
+    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
+  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);
+  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  assert(dataFormat === "NDHWC", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
+  const inputs = { x: x5D, filter: $filter };
+  const attrs = { strides, pad: pad3, dataFormat, dilations };
+  const res = ENGINE.runKernel(Conv3D, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var conv3d = op({ conv3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
+function conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {
+  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);
+  let xShape5D = xShape;
+  let dy5D = dy;
+  let reshapedTo5D = false;
+  if (dy.rank === 4) {
+    reshapedTo5D = true;
+    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
+    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
+  }
+  const inDepth = xShape5D[4];
+  const outDepth = dy5D.shape[4];
+  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);
+  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);
+  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);
+  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);
+  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);
+  const inputs = { dy: dy5D, filter };
+  const attrs = { pad: pad3, strides, inputShape: xShape5D };
+  const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var conv3DBackpropInput = op({ conv3DBackpropInput_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
+function conv3dTranspose_(x, filter, outputShape, strides, pad3) {
+  const $x = convertToTensor(x, "x", "conv3dTranspose");
+  const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
+  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3);
+}
+var conv3dTranspose = op({ conv3dTranspose_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
+function cos_(x) {
+  const $x = convertToTensor(x, "x", "cos", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Cos, inputs);
+}
+var cos = op({ cos_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
+function cosh_(x) {
+  const $x = convertToTensor(x, "x", "cosh", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Cosh, inputs);
+}
+var cosh = op({ cosh_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js
+function cumprod_(x, axis = 0, exclusive = false, reverse5 = false) {
+  const $x = convertToTensor(x, "x", "cumprod");
+  const inputs = { x: $x };
+  const attrs = { axis, exclusive, reverse: reverse5 };
+  return ENGINE.runKernel(Cumprod, inputs, attrs);
+}
+var cumprod = op({ cumprod_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
+function cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {
+  const $x = convertToTensor(x, "x", "cumsum");
+  const inputs = { x: $x };
+  const attrs = { axis, exclusive, reverse: reverse5 };
+  return ENGINE.runKernel(Cumsum, inputs, attrs);
+}
+var cumsum = op({ cumsum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js
+function denseBincount_(x, weights, size, binaryOutput = false) {
+  const $x = convertToTensor(x, "x", "denseBincount");
+  const $weights = convertToTensor(weights, "weights", "denseBincount");
+  assert($x.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);
+  assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);
+  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
+  assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
+  const inputs = { x: $x, weights: $weights };
+  const attrs = { size, binaryOutput };
+  return ENGINE.runKernel(DenseBincount, inputs, attrs);
+}
+var denseBincount = op({ denseBincount_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
+function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
+  const $x = convertToTensor(x, "x", "depthToSpace", "float32");
+  const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
+  assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
+  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
+    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
+    ${$x.shape}`);
+  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying
+    ${inputWidth} and ${blockSize} for depthToSpace with input shape
+        ${$x.shape}`);
+  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
+  const inputs = { x: $x };
+  const attrs = { blockSize, dataFormat };
+  return ENGINE.runKernel(DepthToSpace, inputs, attrs);
+}
+var depthToSpace = op({ depthToSpace_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
+function depthwiseConv2d_(x, filter, strides, pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  assert(x4D.shape[3] === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
+  checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode);
+  const inputs = { x: x4D, filter: $filter };
+  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };
+  const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var depthwiseConv2d = op({ depthwiseConv2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js
+function diag_(x) {
+  const $x = convertToTensor(x, "x", "diag");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Diag, inputs);
+}
+var diag = op({ diag_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
+function dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = "NHWC") {
+  const $x = convertToTensor(x, "x", "dilation2d");
+  const $filter = convertToTensor(filter, "filter", "dilation2d");
+  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);
+  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);
+  assert(dataFormat === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+    reshapedTo4D = true;
+  }
+  const inputs = { x: x4D, filter: $filter };
+  const attrs = { strides, pad: pad3, dilations };
+  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var dilation2d = op({ dilation2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
+function equal_(a6, b) {
+  let $a = convertToTensor(a6, "a", "equal", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "equal", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Equal, inputs);
+}
+var equal = op({ equal_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js
+function where_(condition, a6, b) {
+  const $a = convertToTensor(a6, "a", "where");
+  const $b = convertToTensor(b, "b", "where");
+  const $condition = convertToTensor(condition, "condition", "where", "bool");
+  const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);
+  const $broadcastedCondition = broadcastTo($condition, broadcastShape);
+  const $broadcastedA = broadcastTo($a, broadcastShape);
+  const $broadcastedB = broadcastTo($b, broadcastShape);
+  const inputs = {
+    condition: $broadcastedCondition,
+    t: $broadcastedA,
+    e: $broadcastedB
+  };
+  return ENGINE.runKernel(Select, inputs);
+}
+var where = op({ where_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
+function zerosLike_(x) {
+  const $x = convertToTensor(x, "x", "zerosLike");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(ZerosLike, inputs);
+}
+var zerosLike = op({ zerosLike_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
+function divNoNan_(a6, b) {
+  let $a = convertToTensor(a6, "a", "div");
+  let $b = convertToTensor(b, "b", "div");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const divResult = div($a, $b);
+  const zeros4 = zerosLike(divResult);
+  const bEqualsZero = equal($b, zeros4);
+  return where(bEqualsZero, zeros4, divResult);
+}
+var divNoNan = op({ divNoNan_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
+function dot_(t1, t2) {
+  const $t1 = convertToTensor(t1, "t1", "dot");
+  const $t2 = convertToTensor(t2, "t2", "dot");
+  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);
+  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
+  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
+  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);
+  if ($t1.rank === 1 && $t2.rank === 1) {
+    const t12D = reshape($t1, [1, -1]);
+    const t22D = reshape($t2, [-1, 1]);
+    const t1t2 = matMul(t12D, t22D);
+    return reshape(t1t2, []);
+  } else if ($t1.rank === 1 && $t2.rank === 2) {
+    const t12D = reshape($t1, [1, -1]);
+    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+    const t1t2 = matMul(t12D, t22D);
+    return reshape(t1t2, [t1t2.size]);
+  } else if ($t1.rank === 2 && $t2.rank === 1) {
+    const t22D = reshape($t2, [-1, 1]);
+    const t1t2 = matMul($t1, t22D);
+    return reshape(t1t2, [t1t2.size]);
+  } else {
+    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);
+    const t1t2 = matMul($t1, t22D);
+    return t1t2;
+  }
+}
+var dot = op({ dot_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js
+function einsum_(equation, ...tensors) {
+  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "einsum"));
+  const attrs = { equation };
+  return ENGINE.runKernel(Einsum, $tensors, attrs);
+}
+var einsum = op({ einsum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
+function elu_(x) {
+  const $x = convertToTensor(x, "x", "elu", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Elu, inputs);
+}
+var elu = op({ elu_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
+function erf_(x) {
+  let $x = convertToTensor(x, "x", "erf");
+  assert($x.dtype === "int32" || $x.dtype === "float32", () => "Input dtype must be `int32` or `float32`.");
+  if ($x.dtype === "int32") {
+    $x = cast($x, "float32");
+  }
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Erf, inputs);
+}
+var erf = op({ erf_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
+function axesAreInnerMostDims(axes, rank) {
+  for (let i2 = 0; i2 < axes.length; ++i2) {
+    if (axes[axes.length - i2 - 1] !== rank - 1 - i2) {
+      return false;
+    }
+  }
+  return true;
+}
+function combineLocations(outputLoc, reduceLoc, axes) {
+  const rank = outputLoc.length + reduceLoc.length;
+  const loc = [];
+  let outIdx = 0;
+  let reduceIdx = 0;
+  for (let dim = 0; dim < rank; dim++) {
+    if (axes.indexOf(dim) === -1) {
+      loc.push(outputLoc[outIdx++]);
+    } else {
+      loc.push(reduceLoc[reduceIdx++]);
+    }
+  }
+  return loc;
+}
+function computeOutAndReduceShapes(aShape, axes) {
+  const outShape = [];
+  const rank = aShape.length;
+  for (let dim = 0; dim < rank; dim++) {
+    if (axes.indexOf(dim) === -1) {
+      outShape.push(aShape[dim]);
+    }
+  }
+  const reduceShape = axes.map((dim) => aShape[dim]);
+  return [outShape, reduceShape];
+}
+function expandShapeToKeepDim(shape, axes) {
+  const reduceSubShape = axes.map((x) => 1);
+  return combineLocations(shape, reduceSubShape, axes);
+}
+function assertAxesAreInnerMostDims(msg, axes, rank) {
+  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);
+}
+function getAxesPermutation(axes, rank) {
+  if (axesAreInnerMostDims(axes, rank)) {
+    return null;
+  }
+  const result = [];
+  for (let i2 = 0; i2 < rank; ++i2) {
+    if (axes.indexOf(i2) === -1) {
+      result.push(i2);
+    }
+  }
+  axes.forEach((axis) => result.push(axis));
+  return result;
+}
+function getUndoAxesPermutation(axes) {
+  return axes.map((axis, i2) => [i2, axis]).sort((a6, b) => a6[1] - b[1]).map((x) => x[0]);
+}
+function getInnerMostAxes(numAxes, rank) {
+  const res = [];
+  for (let i2 = rank - numAxes; i2 < rank; ++i2) {
+    res.push(i2);
+  }
+  return res;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js
+function max_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "max");
+  const inputs = { x: $x };
+  const attrs = { reductionIndices: axis, keepDims };
+  return ENGINE.runKernel(Max, inputs, attrs);
+}
+var max = op({ max_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js
+function min_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "min");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Min, inputs, attrs);
+}
+var min = op({ min_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
+function pow_(base, exp5) {
+  let $base = convertToTensor(base, "base", "pow");
+  let $exp = convertToTensor(exp5, "exp", "pow");
+  [$base, $exp] = makeTypesMatch($base, $exp);
+  const inputs = { a: $base, b: $exp };
+  return ENGINE.runKernel(Pow, inputs);
+}
+var pow = op({ pow_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
+function scalar(value, dtype) {
+  if ((isTypedArray(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") {
+    throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
+  }
+  if (dtype === "string" && isTypedArray(value) && !(value instanceof Uint8Array)) {
+    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
+  }
+  const shape = [];
+  const inferredShape = [];
+  return makeTensor(value, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
+function sqrt_(x) {
+  const $x = convertToTensor(x, "x", "sqrt", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sqrt, inputs);
+}
+var sqrt = op({ sqrt_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js
+function square_(x) {
+  const $x = convertToTensor(x, "x", "square");
+  const attrs = {};
+  return ENGINE.runKernel("Square", { x: $x }, attrs);
+}
+var square = op({ square_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
+function sum_(x, axis = null, keepDims = false) {
+  let $x = convertToTensor(x, "x", "sum");
+  if ($x.dtype === "bool") {
+    $x = cast($x, "int32");
+  }
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Sum, inputs, attrs);
+}
+var sum2 = op({ sum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
+function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
+  x = convertToTensor(x, "x", "norm");
+  const norm2 = normImpl(x, ord, axis);
+  let keepDimsShape = norm2.shape;
+  if (keepDims) {
+    const axes = parseAxisParam(axis, x.shape);
+    keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);
+  }
+  return reshape(norm2, keepDimsShape);
+}
+function normImpl(x, p2, axis = null) {
+  if (x.rank === 0) {
+    return abs(x);
+  }
+  if (x.rank !== 1 && axis === null) {
+    return normImpl(reshape(x, [-1]), p2, axis);
+  }
+  if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
+    if (p2 === 1) {
+      return sum2(abs(x), axis);
+    }
+    if (p2 === Infinity) {
+      return max(abs(x), axis);
+    }
+    if (p2 === -Infinity) {
+      return min(abs(x), axis);
+    }
+    if (p2 === "euclidean" || p2 === 2) {
+      return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
+    }
+    throw new Error(`Error in norm: invalid ord value: ${p2}`);
+  }
+  if (Array.isArray(axis) && axis.length === 2) {
+    if (p2 === 1) {
+      return max(sum2(abs(x), axis[0]), axis[1] - 1);
+    }
+    if (p2 === Infinity) {
+      return max(sum2(abs(x), axis[1]), axis[0]);
+    }
+    if (p2 === -Infinity) {
+      return min(sum2(abs(x), axis[1]), axis[0]);
+    }
+    if (p2 === "fro" || p2 === "euclidean") {
+      return sqrt(sum2(square(x), axis));
+    }
+    throw new Error(`Error in norm: invalid ord value: ${p2}`);
+  }
+  throw new Error(`Error in norm: invalid axis: ${axis}`);
+}
+var norm = op({ norm_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js
+function euclideanNorm_(x, axis = null, keepDims = false) {
+  return norm(x, "euclidean", axis, keepDims);
+}
+var euclideanNorm = op({ euclideanNorm_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
+function exp_(x) {
+  const $x = convertToTensor(x, "x", "exp");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Exp, inputs);
+}
+var exp = op({ exp_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
+function expandDims_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "expandDims", "string_or_numeric");
+  assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
+  const inputs = { input: $x };
+  const attrs = { dim: axis };
+  return ENGINE.runKernel(ExpandDims, inputs, attrs);
+}
+var expandDims = op({ expandDims_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
+function expm1_(x) {
+  const $x = convertToTensor(x, "x", "expm1");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Expm1, inputs);
+}
+var expm1 = op({ expm1_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
+function tile_(x, reps) {
+  const $x = convertToTensor(x, "x", "tile", "string_or_numeric");
+  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
+  const inputs = { x: $x };
+  const attrs = { reps };
+  return ENGINE.runKernel(Tile, inputs, attrs);
+}
+var tile = op({ tile_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
+function eye_(numRows, numColumns, batchShape, dtype = "float32") {
+  if (numColumns == null) {
+    numColumns = numRows;
+  }
+  const buff = buffer([numRows, numColumns], dtype);
+  const n = numRows <= numColumns ? numRows : numColumns;
+  for (let i2 = 0; i2 < n; ++i2) {
+    buff.set(1, i2, i2);
+  }
+  const out = reshape(buff.toTensor(), [numRows, numColumns]);
+  if (batchShape == null) {
+    return out;
+  } else {
+    if (batchShape.length === 1) {
+      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
+    } else if (batchShape.length === 2) {
+      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
+    } else if (batchShape.length === 3) {
+      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [
+        batchShape[0],
+        batchShape[1],
+        batchShape[2],
+        1,
+        1
+      ]);
+    } else {
+      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);
+    }
+  }
+}
+var eye = op({ eye_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
+function fill(shape, value, dtype) {
+  const attrs = { shape, value, dtype };
+  return ENGINE.runKernel(Fill, {}, attrs);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
+function floor_(x) {
+  const $x = convertToTensor(x, "x", "floor", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Floor, inputs);
+}
+var floor = op({ floor_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
+function gather_(x, indices, axis = 0, batchDims = 0) {
+  const $x = convertToTensor(x, "x", "gather");
+  const $indices = convertToTensor(indices, "indices", "gather", "int32");
+  const inputs = { x: $x, indices: $indices };
+  const attrs = { axis, batchDims };
+  return ENGINE.runKernel(GatherV2, inputs, attrs);
+}
+var gather = op({ gather_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
+function greater_(a6, b) {
+  let $a = convertToTensor(a6, "a", "greater", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "greater", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Greater, inputs);
+}
+var greater = op({ greater_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
+function greaterEqual_(a6, b) {
+  let $a = convertToTensor(a6, "a", "greaterEqual", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "greaterEqual", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(GreaterEqual, inputs);
+}
+var greaterEqual = op({ greaterEqual_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
+function isFinite_(x) {
+  const $x = convertToTensor(x, "x", "isFinite");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(IsFinite, inputs);
+}
+var isFinite2 = op({ isFinite_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
+function isInf_(x) {
+  const $x = convertToTensor(x, "x", "isInf");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(IsInf, inputs);
+}
+var isInf = op({ isInf_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
+function isNaN_(x) {
+  const $x = convertToTensor(x, "x", "isNaN");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(IsNan, inputs);
+}
+var isNaN2 = op({ isNaN_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
+function leakyRelu_(x, alpha = 0.2) {
+  const $x = convertToTensor(x, "x", "leakyRelu");
+  const inputs = { x: $x };
+  const attrs = { alpha };
+  return ENGINE.runKernel(LeakyRelu, inputs, attrs);
+}
+var leakyRelu = op({ leakyRelu_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js
+function less_(a6, b) {
+  let $a = convertToTensor(a6, "a", "less", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "less", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Less, inputs);
+}
+var less = op({ less_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
+function lessEqual_(a6, b) {
+  let $a = convertToTensor(a6, "a", "lessEqual", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "lessEqual", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(LessEqual, inputs);
+}
+var lessEqual = op({ lessEqual_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js
+function linspace(start, stop, num) {
+  if (num <= 0) {
+    throw new Error("The number of values should be positive.");
+  }
+  const attrs = { start, stop, num };
+  return ENGINE.runKernel(LinSpace, {}, attrs);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
+function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
+  const $x = convertToTensor(x, "x", "localResponseNormalization");
+  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
+               rank ${$x.rank}.`);
+  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  const inputs = { x: x4D };
+  const attrs = { depthRadius, bias, alpha, beta };
+  const res = ENGINE.runKernel(LRN, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  } else {
+    return res;
+  }
+}
+var localResponseNormalization = op({ localResponseNormalization_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js
+function log_(x) {
+  const $x = convertToTensor(x, "x", "log", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Log, inputs);
+}
+var log2 = op({ log_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
+function log1p_(x) {
+  const $x = convertToTensor(x, "x", "log1p");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Log1p, inputs);
+}
+var log1p = op({ log1p_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js
+function grad(f) {
+  assert(isFunction(f), () => "The f passed in grad(f) must be a function");
+  return (x, dy) => {
+    const $x = convertToTensor(x, "x", "tf.grad", "string_or_numeric");
+    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grad") : null;
+    return ENGINE.tidy(() => {
+      const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy);
+      if ($dy != null) {
+        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
+      }
+      checkGrads(grads2);
+      return grads2[0];
+    });
+  };
+}
+function grads(f) {
+  assert(isFunction(f), () => "The f passed in grads(f) must be a function");
+  return (args, dy) => {
+    assert(Array.isArray(args), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
+    const $args = convertToTensorArray(args, "args", "tf.grads", "string_or_numeric");
+    const $dy = dy != null ? convertToTensor(dy, "dy", "tf.grads") : null;
+    return ENGINE.tidy(() => {
+      const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy);
+      if ($dy != null) {
+        assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
+      }
+      checkGrads(grads2);
+      return grads2;
+    });
+  };
+}
+function valueAndGrad(f) {
+  assert(isFunction(f), () => "The f passed in valueAndGrad(f) must be a function");
+  return (x, dy) => {
+    assert(x instanceof Tensor, () => "The x passed in valueAndGrad(f)(x) must be a tensor");
+    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
+    const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy);
+    checkGrads(grads2);
+    return { grad: grads2[0], value };
+  };
+}
+function valueAndGrads(f) {
+  assert(isFunction(f), () => "The f passed in valueAndGrads(f) must be a function");
+  return (args, dy) => {
+    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => "The args passed in valueAndGrads(f)(args) must be array of tensors");
+    assert(dy == null || dy instanceof Tensor, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
+    const res = ENGINE.gradients(() => f(...args), args, dy);
+    if (dy != null) {
+      assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
+    }
+    checkGrads(res.grads);
+    return res;
+  };
+}
+function variableGrads(f, varList) {
+  assert(isFunction(f), () => "The f passed in variableGrads(f) must be a function");
+  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
+  const specifiedVarList = varList != null;
+  if (!specifiedVarList) {
+    varList = [];
+    for (const varName in ENGINE.registeredVariables) {
+      varList.push(ENGINE.registeredVariables[varName]);
+    }
+  }
+  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;
+  const originalVarCount = varList.length;
+  varList = varList.filter((variable2) => variable2.trainable);
+  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
+  const allowNoGradients = true;
+  const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients);
+  assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
+  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
+  const namedGrads = {};
+  varList.forEach((v, i2) => {
+    if (grads2[i2] != null) {
+      namedGrads[v.name] = grads2[i2];
+    }
+  });
+  if (specifiedNonTrainable != null) {
+    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
+  }
+  return { value, grads: namedGrads };
+}
+function customGrad(f) {
+  return ENGINE.customGrad(f);
+}
+function checkGrads(grads2) {
+  const numNullGradients = grads2.filter((g) => g == null).length;
+  if (numNullGradients > 0) {
+    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
+    the f you passed encloses all operations that lead from x to y.`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
+function softplus_(x) {
+  const $x = convertToTensor(x, "x", "softplus");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Softplus, inputs);
+}
+var softplus = op({ softplus_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
+function logSigmoid_(x) {
+  const $x = convertToTensor(x, "x", "logSigmoid");
+  const customOp = customGrad((x2) => {
+    const value = neg(softplus(neg(x2)));
+    const gradFunc = (dy) => {
+      const derX = mul(dy, sigmoid(neg(x2)));
+      return derX;
+    };
+    return { value, gradFunc };
+  });
+  return customOp($x);
+}
+var logSigmoid = op({ logSigmoid_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
+function sub_(a6, b) {
+  let $a = convertToTensor(a6, "a", "sub");
+  let $b = convertToTensor(b, "b", "sub");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Sub, inputs);
+}
+var sub = op({ sub_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
+function logSoftmax_(logits, axis = -1) {
+  const $logits = convertToTensor(logits, "logits", "logSoftmax");
+  if (axis === -1) {
+    axis = $logits.rank - 1;
+  }
+  if (axis !== $logits.rank - 1) {
+    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);
+  }
+  const customOp = customGrad((logits2, save) => {
+    const keepDims = true;
+    const xMax = max(logits2, axis, true);
+    const shifted = sub(logits2, xMax);
+    const value = sub(cast(shifted, "float32"), log2(sum2(exp(shifted), axis, keepDims)));
+    save([value]);
+    const gradFunc = (dy, saved) => {
+      const [value2] = saved;
+      const keepDims2 = true;
+      const softmax7 = exp(value2);
+      return sub(dy, mul(sum2(dy, axis, keepDims2), softmax7));
+    };
+    return { value, gradFunc };
+  });
+  return customOp($logits);
+}
+var logSoftmax = op({ logSoftmax_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
+function logSumExp_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "logSumExp");
+  const axes = parseAxisParam(axis, $x.shape);
+  const xMax = max($x, axes, true);
+  const a6 = sub($x, xMax);
+  const b = exp(a6);
+  const c = sum2(b, axes);
+  const d = log2(c);
+  const res = add2(reshape(xMax, d.shape), d);
+  if (keepDims) {
+    const newShape = expandShapeToKeepDim(res.shape, axes);
+    return reshape(res, newShape);
+  }
+  return res;
+}
+var logSumExp = op({ logSumExp_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
+function logicalAnd_(a6, b) {
+  const $a = convertToTensor(a6, "a", "logicalAnd", "bool");
+  const $b = convertToTensor(b, "b", "logicalAnd", "bool");
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(LogicalAnd, inputs);
+}
+var logicalAnd = op({ logicalAnd_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
+function logicalNot_(x) {
+  const $x = convertToTensor(x, "x", "logicalNot", "bool");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(LogicalNot, inputs);
+}
+var logicalNot = op({ logicalNot_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
+function logicalOr_(a6, b) {
+  const $a = convertToTensor(a6, "a", "logicalOr", "bool");
+  const $b = convertToTensor(b, "b", "logicalOr", "bool");
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(LogicalOr, inputs);
+}
+var logicalOr = op({ logicalOr_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
+function logicalXor_(a6, b) {
+  const $a = convertToTensor(a6, "a", "logicalXor", "bool");
+  const $b = convertToTensor(b, "b", "logicalXor", "bool");
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  return logicalAnd(logicalOr(a6, b), logicalNot(logicalAnd(a6, b)));
+}
+var logicalXor = op({ logicalXor_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/search_sorted.js
+var INT32_MAX = 2147483648;
+function searchSorted_(sortedSequence, values, side = "left") {
+  const $sortedSequence = convertToTensor(sortedSequence, "sortedSequence", "searchSorted");
+  const $values = convertToTensor(values, "values", "searchSorted");
+  const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];
+  const valuesSize = $values.shape[$values.shape.length - 1];
+  const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);
+  const $values2D = reshape($values, [-1, valuesSize]);
+  if ($sortedSequence2D.rank < 2) {
+    throw new Error(`Sorted input argument must be at least 2-dimensional`);
+  }
+  if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {
+    throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);
+  }
+  if (sizeFromShape($values2D.shape) >= INT32_MAX) {
+    throw new Error(`values tensor size must less than ${INT32_MAX}`);
+  }
+  if ($sortedSequence2D.shape[1] >= INT32_MAX) {
+    throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);
+  }
+  const inputs = {
+    sortedSequence: $sortedSequence2D,
+    values: $values2D
+  };
+  const attrs = { side };
+  return ENGINE.runKernel(SearchSorted, inputs, attrs);
+}
+var searchSorted = op({ searchSorted_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/lower_bound.js
+function lowerBound(sortedSequence, values) {
+  return searchSorted(sortedSequence, values, "left");
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
+function maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {
+  const $x = convertToTensor(x, "x", "maxPool");
+  const dilations = 1;
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  checkPadOnDimRoundingMode("maxPool", pad3, dimRoundingMode);
+  const inputs = { x: x4D };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };
+  const res = ENGINE.runKernel(MaxPool, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var maxPool = op({ maxPool_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
+function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = "NDHWC") {
+  const $x = convertToTensor(x, "x", "maxPool3d");
+  let x5D = $x;
+  let reshapedTo5D = false;
+  if ($x.rank === 4) {
+    reshapedTo5D = true;
+    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
+  assert(dataFormat === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);
+  checkPadOnDimRoundingMode("maxPool3d", pad3, dimRoundingMode);
+  const inputs = { x: x5D };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };
+  const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var maxPool3d = op({ maxPool3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js
+function maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) {
+  const $x = convertToTensor(x, "x", "maxPoolWithArgmax");
+  const inputs = { x: $x };
+  const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex };
+  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);
+  return { result: result[0], indexes: result[1] };
+}
+var maxPoolWithArgmax = op({ maxPoolWithArgmax_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
+function maximum_(a6, b) {
+  let $a = convertToTensor(a6, "a", "maximum");
+  let $b = convertToTensor(b, "b", "maximum");
+  [$a, $b] = makeTypesMatch($a, $b);
+  if ($a.dtype === "bool") {
+    $a = cast($a, "int32");
+    $b = cast($b, "int32");
+  }
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Maximum, inputs);
+}
+var maximum = op({ maximum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
+function mean_(x, axis = null, keepDims = false) {
+  const $x = convertToTensor(x, "x", "mean");
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Mean, inputs, attrs);
+}
+var mean = op({ mean_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
+function zeros(shape, dtype = "float32") {
+  if (dtype === "complex64") {
+    const real5 = zeros(shape, "float32");
+    const imag5 = zeros(shape, "float32");
+    return complex(real5, imag5);
+  }
+  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
+function ones2(shape, dtype = "float32") {
+  if (dtype === "complex64") {
+    const real5 = ones2(shape, "float32");
+    const imag5 = zeros(shape, "float32");
+    return complex(real5, imag5);
+  }
+  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/meshgrid.js
+function meshgrid(x, y, { indexing = "xy" } = {}) {
+  if (indexing !== "xy" && indexing !== "ij") {
+    throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);
+  }
+  if (x === void 0) {
+    return [];
+  }
+  let $x = convertToTensor(x, "x", "meshgrid", x instanceof Tensor ? x.dtype : "float32");
+  if (y === void 0) {
+    return [$x];
+  }
+  let $y = convertToTensor(y, "y", "meshgrid", y instanceof Tensor ? y.dtype : "float32");
+  const w = sizeFromShape($x.shape);
+  const h = sizeFromShape($y.shape);
+  if (indexing === "xy") {
+    $x = reshape($x, [1, -1]);
+    $y = reshape($y, [-1, 1]);
+    return [
+      matMul(ones2([h, 1], $x.dtype), $x),
+      matMul($y, ones2([1, w], $y.dtype))
+    ];
+  }
+  $x = reshape($x, [-1, 1]);
+  $y = reshape($y, [1, -1]);
+  return [
+    matMul($x, ones2([1, h], $x.dtype)),
+    matMul(ones2([w, 1], $y.dtype), $y)
+  ];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
+function minimum_(a6, b) {
+  let $a = convertToTensor(a6, "a", "minimum");
+  let $b = convertToTensor(b, "b", "minimum");
+  [$a, $b] = makeTypesMatch($a, $b);
+  if ($a.dtype === "bool") {
+    $a = cast($a, "int32");
+    $b = cast($b, "int32");
+  }
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Minimum, inputs);
+}
+var minimum = op({ minimum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js
+function mirrorPad_(x, paddings, mode) {
+  assert(mode === "reflect" || mode === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);
+  const $x = convertToTensor(x, "x", "mirrorPad");
+  if ($x.rank === 0) {
+    throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
+  }
+  assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);
+  const shapeOffset = mode === "reflect" ? 1 : 0;
+  for (let i2 = 0; i2 < $x.rank; i2++) {
+    assert(paddings[i2].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
+    assert(paddings[i2][0] >= 0 && paddings[i2][0] <= $x.shape[i2] - shapeOffset && paddings[i2][1] >= 0 && paddings[i2][1] <= $x.shape[i2] - shapeOffset, () => `Padding in dimension ${i2} cannot be greater than or equal to ${$x.shape[i2] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
+  }
+  const attrs = { paddings, mode };
+  const inputs = { x: $x };
+  return ENGINE.runKernel(MirrorPad, inputs, attrs);
+}
+var mirrorPad = op({ mirrorPad_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
+function mod_(a6, b) {
+  let $a = convertToTensor(a6, "a", "mod");
+  let $b = convertToTensor(b, "b", "mod");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(Mod, inputs);
+}
+var mod = op({ mod_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
+function moments_(x, axis = null, keepDims = false) {
+  x = convertToTensor(x, "x", "moments");
+  const axes = parseAxisParam(axis, x.shape);
+  const xMean = mean(x, axes, keepDims);
+  let keepDimsShape = xMean.shape;
+  if (!keepDims) {
+    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);
+  }
+  const devSquared = square(sub(cast(x, "float32"), reshape(xMean, keepDimsShape)));
+  const variance = mean(devSquared, axes, keepDims);
+  return { mean: xMean, variance };
+}
+var moments = op({ moments_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js
+function multiRNNCell_(lstmCells, data, c, h) {
+  const $data = convertToTensor(data, "data", "multiRNNCell");
+  const $c = convertToTensorArray(c, "c", "multiRNNCell");
+  const $h = convertToTensorArray(h, "h", "multiRNNCell");
+  let input2 = $data;
+  const newStates = [];
+  for (let i2 = 0; i2 < lstmCells.length; i2++) {
+    const output = lstmCells[i2](input2, $c[i2], $h[i2]);
+    newStates.push(output[0]);
+    newStates.push(output[1]);
+    input2 = output[1];
+  }
+  const newC = [];
+  const newH = [];
+  for (let i2 = 0; i2 < newStates.length; i2 += 2) {
+    newC.push(newStates[i2]);
+    newH.push(newStates[i2 + 1]);
+  }
+  return [newC, newH];
+}
+var multiRNNCell = op({ multiRNNCell_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js
+function multinomial_(logits, numSamples, seed, normalized = false) {
+  const $logits = convertToTensor(logits, "logits", "multinomial");
+  const numOutcomes = $logits.size;
+  const origRank = $logits.rank;
+  if (numOutcomes < 2) {
+    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);
+  }
+  if (origRank > 2) {
+    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
+  }
+  seed = seed || Math.random();
+  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;
+  const inputs = { logits: logits2D };
+  const attrs = { numSamples, seed, normalized };
+  const res = ENGINE.runKernel(Multinomial, inputs, attrs);
+  return origRank === 1 ? reshape(res, [res.size]) : res;
+}
+var multinomial = op({ multinomial_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
+function notEqual_(a6, b) {
+  let $a = convertToTensor(a6, "a", "notEqual", "string_or_numeric");
+  let $b = convertToTensor(b, "b", "notEqual", "string_or_numeric");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  return ENGINE.runKernel(NotEqual, inputs);
+}
+var notEqual = op({ notEqual_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
+function onesLike_(x) {
+  const $x = convertToTensor(x, "x", "onesLike");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(OnesLike, inputs);
+}
+var onesLike = op({ onesLike_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js
+function outerProduct_(v1, v2) {
+  const $v1 = convertToTensor(v1, "v1", "outerProduct");
+  const $v2 = convertToTensor(v2, "v2", "outerProduct");
+  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);
+  const v12D = reshape($v1, [-1, 1]);
+  const v22D = reshape($v2, [1, -1]);
+  return matMul(v12D, v22D);
+}
+var outerProduct = op({ outerProduct_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
+function pad_(x, paddings, constantValue = 0) {
+  const $x = convertToTensor(x, "x", "pad");
+  if ($x.rank === 0) {
+    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
+  }
+  const attrs = { paddings, constantValue };
+  const inputs = { x: $x };
+  return ENGINE.runKernel(PadV2, inputs, attrs);
+}
+var pad = op({ pad_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js
+function pad1d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 2, () => "Invalid number of paddings. Must be length of 2.");
+  return pad(x, [paddings], constantValue);
+}
+var pad1d = op({ pad1d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js
+function pad2d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
+  return pad(x, paddings, constantValue);
+}
+var pad2d = op({ pad2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js
+function pad3d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
+  return pad(x, paddings, constantValue);
+}
+var pad3d = op({ pad3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js
+function pad4d_(x, paddings, constantValue = 0) {
+  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each.");
+  return pad(x, paddings, constantValue);
+}
+var pad4d = op({ pad4d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
+function spaceToBatchND_(x, blockShape, paddings) {
+  const $x = convertToTensor(x, "x", "spaceToBatchND");
+  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
+  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
+  assert($x.shape.reduce((a6, b, i2) => {
+    if (i2 > 0 && i2 <= blockShape.length) {
+      return a6 && (b + paddings[i2 - 1][0] + paddings[i2 - 1][1]) % blockShape[i2 - 1] === 0;
+    }
+    return a6;
+  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
+  const inputs = { x: $x };
+  const attrs = { blockShape, paddings };
+  return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);
+}
+var spaceToBatchND = op({ spaceToBatchND_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
+function pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) {
+  if (dilations == null) {
+    dilations = [1, 1];
+  }
+  if (strides == null) {
+    strides = 1;
+  }
+  if (pad3 === 0) {
+    pad3 = "valid";
+  }
+  const $x = convertToTensor(input2, "x", "maxPool");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3);
+  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
+  let basePadding;
+  if (pad3 === "same") {
+    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
+  } else {
+    basePadding = [[0, 0], [0, 0]];
+  }
+  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
+  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
+  const convertedPad = isDilationOne ? pad3 : "valid";
+  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
+  const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);
+  const y = forwardOp();
+  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
+  const padStart = basePadding.map((b) => b[0]);
+  const origPadEnd = basePadding.map((b) => b[1]);
+  const fullInputShape = inputShape.concat(padStart, origPadEnd);
+  const padEndExtra = blockShape.map((b, i2) => (b - fullInputShape[i2] % b) % b);
+  const padEnd = origPadEnd.map((s2, i2) => s2 + padEndExtra[i2]);
+  const paddings = blockShape.map((_, i2) => [padStart[i2], padEnd[i2]]);
+  const crops = blockShape.map((_, i2) => [0, padEndExtra[i2]]);
+  return [paddings, crops];
+}
+function withSpaceToBatchBasePaddings(filterShape, dilation) {
+  const dilatedFilterShape = filterShape.map((s2, i2) => {
+    return s2 + (s2 - 1) * (dilation[i2] - 1);
+  });
+  const padExtraShape = dilatedFilterShape.map((s2) => s2 - 1);
+  const padExtraStart = padExtraShape.map((s2) => Math.floor(s2 / 2));
+  const padExtraEnd = padExtraShape.map((s2, i2) => s2 - padExtraStart[i2]);
+  return padExtraShape.map((_, i2) => {
+    return [padExtraStart[i2], padExtraEnd[i2]];
+  });
+}
+var pool = op({ pool_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
+function prelu_(x, alpha) {
+  const $x = convertToTensor(x, "x", "prelu");
+  const $alpha = convertToTensor(alpha, "alpha", "prelu");
+  const inputs = { x: $x, alpha: $alpha };
+  return ENGINE.runKernel(Prelu, inputs);
+}
+var prelu = op({ prelu_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
+function prod_(x, axis = null, keepDims = false) {
+  let $x = convertToTensor(x, "x", "prod");
+  if ($x.dtype === "bool") {
+    $x = cast($x, "int32");
+  }
+  const inputs = { x: $x };
+  const attrs = { axis, keepDims };
+  return ENGINE.runKernel(Prod, inputs, attrs);
+}
+var prod = op({ prod_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
+function rand_(shape, randFunction, dtype) {
+  const size = sizeFromShape(shape);
+  let values = null;
+  if (dtype == null || dtype === "float32") {
+    values = new Float32Array(size);
+  } else if (dtype === "int32") {
+    values = new Int32Array(size);
+  } else if (dtype === "bool") {
+    values = new Uint8Array(size);
+  } else {
+    throw new Error(`Unknown data type ${dtype}`);
+  }
+  for (let i2 = 0; i2 < size; i2++) {
+    values[i2] = randFunction();
+  }
+  return ENGINE.makeTensor(values, shape, dtype);
+}
+var rand = op({ rand_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
+var seedrandom = __toESM(require_seedrandom2());
+var MPRandGauss = class {
+  constructor(mean5, stdDeviation, dtype, truncated, seed) {
+    this.mean = mean5;
+    this.stdDev = stdDeviation;
+    this.dtype = dtype;
+    this.nextVal = NaN;
+    this.truncated = truncated;
+    if (this.truncated) {
+      this.upper = this.mean + this.stdDev * 2;
+      this.lower = this.mean - this.stdDev * 2;
+    }
+    const seedValue = seed ? seed : Math.random();
+    this.random = seedrandom.alea(seedValue.toString());
+  }
+  nextValue() {
+    if (!isNaN(this.nextVal)) {
+      const value = this.nextVal;
+      this.nextVal = NaN;
+      return value;
+    }
+    let resultX, resultY;
+    let isValid = false;
+    while (!isValid) {
+      let v1, v2, s2;
+      do {
+        v1 = 2 * this.random() - 1;
+        v2 = 2 * this.random() - 1;
+        s2 = v1 * v1 + v2 * v2;
+      } while (s2 >= 1 || s2 === 0);
+      const mul2 = Math.sqrt(-2 * Math.log(s2) / s2);
+      resultX = this.mean + this.stdDev * v1 * mul2;
+      resultY = this.mean + this.stdDev * v2 * mul2;
+      if (!this.truncated || this.isValidTruncated(resultX)) {
+        isValid = true;
+      }
+    }
+    if (!this.truncated || this.isValidTruncated(resultY)) {
+      this.nextVal = this.convertValue(resultY);
+    }
+    return this.convertValue(resultX);
+  }
+  convertValue(value) {
+    if (this.dtype == null || this.dtype === "float32") {
+      return value;
+    }
+    return Math.round(value);
+  }
+  isValidTruncated(value) {
+    return value <= this.upper && value >= this.lower;
+  }
+};
+var RandGamma = class {
+  constructor(alpha, beta, dtype, seed) {
+    this.alpha = alpha;
+    this.beta = 1 / beta;
+    this.dtype = dtype;
+    const seedValue = seed ? seed : Math.random();
+    this.randu = seedrandom.alea(seedValue.toString());
+    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
+    if (alpha < 1) {
+      this.d = alpha + 2 / 3;
+    } else {
+      this.d = alpha - 1 / 3;
+    }
+    this.c = 1 / Math.sqrt(9 * this.d);
+  }
+  nextValue() {
+    let x2, v0, v1, x, u, v;
+    while (true) {
+      do {
+        x = this.randn.nextValue();
+        v = 1 + this.c * x;
+      } while (v <= 0);
+      v *= v * v;
+      x2 = x * x;
+      v0 = 1 - 0.331 * x2 * x2;
+      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
+      u = this.randu();
+      if (u < v0 || Math.log(u) < v1) {
+        break;
+      }
+    }
+    v = 1 / this.beta * this.d * v;
+    if (this.alpha < 1) {
+      v *= Math.pow(this.randu(), 1 / this.alpha);
+    }
+    return this.convertValue(v);
+  }
+  convertValue(value) {
+    if (this.dtype === "float32") {
+      return value;
+    }
+    return Math.round(value);
+  }
+};
+var UniformRandom = class {
+  constructor(min7 = 0, max7 = 1, dtype, seed) {
+    this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
+    this.min = min7;
+    this.range = max7 - min7;
+    this.dtype = dtype;
+    if (seed == null) {
+      seed = Math.random();
+    }
+    if (typeof seed === "number") {
+      seed = seed.toString();
+    }
+    if (!this.canReturnFloat() && this.range <= 1) {
+      throw new Error(`The difference between ${min7} - ${max7} <= 1 and dtype is not float`);
+    }
+    this.random = seedrandom.alea(seed);
+  }
+  convertValue(value) {
+    if (this.canReturnFloat()) {
+      return value;
+    }
+    return Math.round(value);
+  }
+  nextValue() {
+    return this.convertValue(this.min + this.range * this.random());
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js
+function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
+  if (beta == null) {
+    beta = 1;
+  }
+  if (dtype == null) {
+    dtype = "float32";
+  }
+  if (dtype !== "float32" && dtype !== "int32") {
+    throw new Error(`Unsupported data type ${dtype}`);
+  }
+  const rgamma = new RandGamma(alpha, beta, dtype, seed);
+  const res = buffer(shape, dtype);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = rgamma.nextValue();
+  }
+  return res.toTensor();
+}
+var randomGamma = op({ randomGamma_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
+function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
+  if (dtype != null && dtype === "bool") {
+    throw new Error(`Unsupported data type ${dtype}`);
+  }
+  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
+  const res = buffer(shape, dtype);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = randGauss.nextValue();
+  }
+  return res.toTensor();
+}
+var randomNormal = op({ randomNormal_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
+function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
+  const res = buffer(shape, dtype);
+  const random = new UniformRandom(minval, maxval, null, seed);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = random.nextValue();
+  }
+  return res.toTensor();
+}
+var randomUniform = op({ randomUniform_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js
+function range(start, stop, step5 = 1, dtype = "float32") {
+  if (step5 === 0) {
+    throw new Error("Cannot have a step of zero");
+  }
+  const attrs = { start, stop, step: step5, dtype };
+  return ENGINE.runKernel(Range, {}, attrs);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
+function reciprocal_(x) {
+  const $x = convertToTensor(x, "x", "reciprocal");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Reciprocal, inputs);
+}
+var reciprocal = op({ reciprocal_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
+function relu_(x) {
+  const $x = convertToTensor(x, "x", "relu");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Relu, inputs);
+}
+var relu = op({ relu_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
+function relu6_(x) {
+  const $x = convertToTensor(x, "x", "relu6");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Relu6, inputs);
+}
+var relu6 = op({ relu6_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
+function reverse_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  const inputs = { x: $x };
+  const attrs = { dims: axis };
+  return ENGINE.runKernel(Reverse, inputs, attrs);
+}
+var reverse = op({ reverse_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js
+function reverse1d_(x) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
+  return reverse($x, 0);
+}
+var reverse1d = op({ reverse1d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js
+function reverse2d_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
+  return reverse($x, axis);
+}
+var reverse2d = op({ reverse2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js
+function reverse3d_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
+  return reverse($x, axis);
+}
+var reverse3d = op({ reverse3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js
+function reverse4d_(x, axis) {
+  const $x = convertToTensor(x, "x", "reverse");
+  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
+  return reverse($x, axis);
+}
+var reverse4d = op({ reverse4d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js
+function round_(x) {
+  const $x = convertToTensor(x, "x", "round");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Round, inputs);
+}
+var round2 = op({ round_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
+function rsqrt_(x) {
+  const $x = convertToTensor(x, "x", "rsqrt", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Rsqrt, inputs);
+}
+var rsqrt = op({ rsqrt_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
+function selu_(x) {
+  const $x = convertToTensor(x, "x", "selu");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Selu, inputs);
+}
+var selu = op({ selu_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
+function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = "NHWC") {
+  const $x = convertToTensor(x, "x", "separableConv2d");
+  const $depthwiseFilter = convertToTensor(depthwiseFilter, "depthwiseFilter", "separableConv2d");
+  const $pointwiseFilter = convertToTensor(pointwiseFilter, "pointwiseFilter", "separableConv2d");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  if (dataFormat === "NCHW") {
+    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
+  }
+  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
+  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);
+  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);
+  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
+  const inChannels = $depthwiseFilter.shape[2];
+  const channelMultiplier = $depthwiseFilter.shape[3];
+  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);
+  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation);
+  const pointwiseStride = 1;
+  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var separableConv2d = op({ separableConv2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js
+async function setdiff1dAsync_(x, y) {
+  const $x = convertToTensor(x, "x", "setdiff1d");
+  const $y = convertToTensor(y, "y", "setdiff1d");
+  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
+  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);
+  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);
+  const xVals = await $x.data();
+  const yVals = await $y.data();
+  const ySet = new Set(yVals);
+  let outputSize = 0;
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    if (!ySet.has(xVals[i2])) {
+      outputSize++;
+    }
+  }
+  const buffer2 = new TensorBuffer([outputSize], $x.dtype);
+  const indices = new TensorBuffer([outputSize], "int32");
+  for (let i2 = 0, p2 = 0; i2 < xVals.length; i2++) {
+    if (!ySet.has(xVals[i2])) {
+      buffer2.values[p2] = xVals[i2];
+      indices.values[p2] = i2;
+      p2++;
+    }
+  }
+  return [buffer2.toTensor(), indices.toTensor()];
+}
+var setdiff1dAsync = setdiff1dAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
+function sign_(x) {
+  const $x = convertToTensor(x, "x", "sign");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sign, inputs);
+}
+var sign = op({ sign_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
+function sin_(x) {
+  const $x = convertToTensor(x, "x", "sin", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sin, inputs);
+}
+var sin = op({ sin_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
+function sinh_(x) {
+  const $x = convertToTensor(x, "x", "sinh");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Sinh, inputs);
+}
+var sinh = op({ sinh_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
+function slice1d_(x, begin, size) {
+  const $x = convertToTensor(x, "x", "slice1d");
+  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, [begin], [size]);
+}
+var slice1d = op({ slice1d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
+function slice2d_(x, begin, size) {
+  const $x = convertToTensor(x, "x", "slice2d");
+  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, begin, size);
+}
+var slice2d = op({ slice2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
+function slice3d_(x, begin, size) {
+  const $x = convertToTensor(x, "x", "slice3d");
+  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, begin, size);
+}
+var slice3d = op({ slice3d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
+function slice4d_(x, begin, size) {
+  const $x = convertToTensor(x, "x", "slice4d");
+  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
+  return slice($x, begin, size);
+}
+var slice4d = op({ slice4d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
+function softmax_(logits, dim = -1) {
+  const $logits = convertToTensor(logits, "logits", "softmax", "float32");
+  if (dim === -1) {
+    dim = $logits.rank - 1;
+  }
+  if (dim !== $logits.rank - 1) {
+    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);
+  }
+  const inputs = { logits: $logits };
+  const attrs = { dim };
+  return ENGINE.runKernel(Softmax, inputs, attrs);
+}
+var softmax = op({ softmax_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
+function fft_(input2) {
+  assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);
+  const inputs = { input: input2 };
+  return ENGINE.runKernel(FFT, inputs);
+}
+var fft = op({ fft_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
+function ifft_(input2) {
+  assert(input2.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);
+  const inputs = { input: input2 };
+  return ENGINE.runKernel(IFFT, inputs);
+}
+var ifft = op({ ifft_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
+function irfft_(input2) {
+  const innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = input2.size / innerDimensionSize;
+  let ret;
+  if (innerDimensionSize <= 2) {
+    const complexInput = reshape(input2, [batch, innerDimensionSize]);
+    ret = ifft(complexInput);
+  } else {
+    const outputShape = [batch, 2 * (innerDimensionSize - 1)];
+    const realInput = reshape(real(input2), [batch, innerDimensionSize]);
+    const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);
+    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
+    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
+    const r2 = concat([realInput, realConjugate], 1);
+    const i2 = concat([imagInput, imagConjugate], 1);
+    const complexInput = reshape(complex(r2, i2), [outputShape[0], outputShape[1]]);
+    ret = ifft(complexInput);
+  }
+  ret = real(ret);
+  if (input2.rank === 3 && input2.shape[0] !== 0) {
+    const temp = ret;
+    const batch2 = input2.shape[0];
+    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);
+    temp.dispose();
+  }
+  return ret;
+}
+var irfft = op({ irfft_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js
+function split_(x, numOrSizeSplits, axis = 0) {
+  const $x = convertToTensor(x, "x", "split");
+  const inputs = { x: $x };
+  const attr = { numOrSizeSplits, axis };
+  return ENGINE.runKernel(SplitV, inputs, attr);
+}
+var split = op({ split_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
+function rfft_(input2, fftLength) {
+  assert(input2.dtype === "float32", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);
+  let innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = input2.size / innerDimensionSize;
+  let adjustedInput;
+  if (fftLength != null && fftLength < innerDimensionSize) {
+    const begin = input2.shape.map((v) => 0);
+    const size = input2.shape.map((v) => v);
+    size[input2.shape.length - 1] = fftLength;
+    adjustedInput = slice(input2, begin, size);
+    innerDimensionSize = fftLength;
+  } else if (fftLength != null && fftLength > innerDimensionSize) {
+    const zerosShape = input2.shape.map((v) => v);
+    zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;
+    adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);
+    innerDimensionSize = fftLength;
+  } else {
+    adjustedInput = input2;
+  }
+  const zerosInput = zerosLike(adjustedInput);
+  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);
+  const ret = fft(complexInput);
+  const half = Math.floor(innerDimensionSize / 2) + 1;
+  const realValues = real(ret);
+  const imagValues = imag(ret);
+  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);
+  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);
+  const outputShape = adjustedInput.shape.slice();
+  outputShape[adjustedInput.shape.length - 1] = half;
+  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
+}
+var rfft = op({ rfft_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
+function squaredDifference_(a6, b) {
+  let $a = convertToTensor(a6, "a", "squaredDifference");
+  let $b = convertToTensor(b, "b", "squaredDifference");
+  [$a, $b] = makeTypesMatch($a, $b);
+  assertAndGetBroadcastShape($a.shape, $b.shape);
+  const inputs = { a: $a, b: $b };
+  const attrs = {};
+  return ENGINE.runKernel(SquaredDifference, inputs, attrs);
+}
+var squaredDifference = op({ squaredDifference_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
+function squeeze_(x, axis) {
+  const $x = convertToTensor(x, "x", "squeeze");
+  return reshape($x, squeezeShape($x.shape, axis).newShape);
+}
+var squeeze = op({ squeeze_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
+function stack_(tensors, axis = 0) {
+  const $tensors = convertToTensorArray(tensors, "tensors", "stack", "string_or_numeric");
+  assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
+  if ($tensors.length > 0) {
+    assert(axis <= $tensors[0].rank, () => "Axis must be <= rank of the tensor");
+  }
+  const inputs = $tensors;
+  const attrs = { axis };
+  return ENGINE.runKernel(Pack, inputs, attrs);
+}
+var stack = op({ stack_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js
+function step_(x, alpha = 0) {
+  const $x = convertToTensor(x, "x", "step");
+  const inputs = { x: $x };
+  const attrs = { alpha };
+  return ENGINE.runKernel(Step, inputs, attrs);
+}
+var step = op({ step_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
+function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
+  const $x = convertToTensor(x, "x", "stridedSlice", "string_or_numeric");
+  const inputs = { x: $x };
+  const attrs = {
+    begin,
+    end,
+    strides,
+    beginMask,
+    endMask,
+    ellipsisMask,
+    newAxisMask,
+    shrinkAxisMask
+  };
+  return ENGINE.runKernel(StridedSlice, inputs, attrs);
+}
+var stridedSlice = op({ stridedSlice_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
+function tan_(x) {
+  const $x = convertToTensor(x, "x", "tan", "float32");
+  const inputs = { x: $x };
+  return ENGINE.runKernel(Tan, inputs);
+}
+var tan = op({ tan_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
+function tensor1d(values, dtype) {
+  assertNonNull(values);
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 1) {
+    throw new Error("tensor1d() requires values to be a flat/TypedArray");
+  }
+  const shape = null;
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
+function tensor2d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 2) {
+    throw new Error("tensor2d() requires shape to have two numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 2 && inferredShape.length !== 1) {
+    throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js
+function tensor4d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 4) {
+    throw new Error("tensor4d() requires shape to have four numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 4 && inferredShape.length !== 1) {
+    throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js
+function tensor5d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 5) {
+    throw new Error("tensor5d() requires shape to have five numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 5 && inferredShape.length !== 1) {
+    throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
+  }
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js
+function tensor6d(values, shape, dtype) {
+  assertNonNull(values);
+  if (shape != null && shape.length !== 6) {
+    throw new Error("tensor6d() requires shape to have six numbers");
+  }
+  const inferredShape = inferShape(values, dtype);
+  if (inferredShape.length !== 6 && inferredShape.length !== 1) {
+    throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
+  }
+  if (inferredShape.length === 1 && shape == null) {
+    throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
+  }
+  shape = shape || inferredShape;
+  return makeTensor(values, shape, inferredShape, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
+function topk_(x, k = 1, sorted = true) {
+  const $x = convertToTensor(x, "x", "topk");
+  if ($x.rank === 0) {
+    throw new Error("topk() expects the input to be of rank 1 or higher");
+  }
+  const lastDim = $x.shape[$x.shape.length - 1];
+  if (k < 0) {
+    throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);
+  }
+  if (k > lastDim) {
+    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
+  }
+  const inputs = { x: $x };
+  const attrs = { k, sorted };
+  const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);
+  return { values, indices };
+}
+var topk = op({ topk_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
+function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
+  if (dtype != null && dtype === "bool") {
+    throw new Error(`Unsupported data type $ { dtype }`);
+  }
+  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
+  const res = buffer(shape, dtype);
+  for (let i2 = 0; i2 < res.values.length; i2++) {
+    res.values[i2] = randGauss.nextValue();
+  }
+  return res.toTensor();
+}
+var truncatedNormal = op({ truncatedNormal_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js
+function unique_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "unique", "string_or_numeric");
+  assert($x.rank > 0, () => "The input tensor must be at least 1D");
+  const inputs = { x: $x };
+  const attrs = { axis };
+  const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);
+  return { values, indices };
+}
+var unique = op({ unique_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
+function unsortedSegmentSum_(x, segmentIds, numSegments) {
+  const $x = convertToTensor(x, "x", "unsortedSegmentSum");
+  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
+  assert(isInt(numSegments), () => "numSegments must be of dtype int");
+  const inputs = { x: $x, segmentIds: $segmentIds };
+  const attrs = { numSegments };
+  return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);
+}
+var unsortedSegmentSum = op({ unsortedSegmentSum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
+function unstack_(x, axis = 0) {
+  const $x = convertToTensor(x, "x", "unstack", "string_or_numeric");
+  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
+  const inputs = { value: $x };
+  const attrs = { axis };
+  return ENGINE.runKernel(Unpack, inputs, attrs);
+}
+var unstack = op({ unstack_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/upper_bound.js
+function upperBound(sortedSequence, values) {
+  return searchSorted(sortedSequence, values, "right");
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
+function variable(initialValue, trainable = true, name, dtype) {
+  return ENGINE.makeVariable(initialValue, trainable, name, dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
+function whereImpl(condShape, condVals) {
+  const indices = [];
+  for (let i2 = 0; i2 < condVals.length; i2++) {
+    if (condVals[i2]) {
+      indices.push(i2);
+    }
+  }
+  const inBuffer = buffer(condShape, "int32");
+  const out = buffer([indices.length, condShape.length], "int32");
+  for (let i2 = 0; i2 < indices.length; i2++) {
+    const loc = inBuffer.indexToLoc(indices[i2]);
+    const offset = i2 * condShape.length;
+    out.values.set(loc, offset);
+  }
+  return out.toTensor();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js
+async function whereAsync_(condition) {
+  const $condition = convertToTensor(condition, "condition", "whereAsync", "bool");
+  const vals = await $condition.data();
+  const res = whereImpl($condition.shape, vals);
+  if (condition !== $condition) {
+    $condition.dispose();
+  }
+  return res;
+}
+var whereAsync = whereAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js
+async function booleanMaskAsync_(tensor2, mask, axis) {
+  const $tensor = convertToTensor(tensor2, "tensor", "boolMask");
+  const $mask = convertToTensor(mask, "mask", "boolMask", "bool");
+  const axisFrom = axis == null ? 0 : axis;
+  const maskDim = $mask.rank;
+  const tensorShape = $tensor.shape;
+  assert(maskDim > 0, () => "mask cannot be scalar");
+  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
+  let leadingSize = 1;
+  for (let i2 = axisFrom; i2 < axisFrom + maskDim; i2++) {
+    leadingSize *= tensorShape[i2];
+  }
+  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
+  const reshapedTensor = reshape($tensor, targetTensorShape);
+  const reshapedMask = reshape($mask, [-1]);
+  const positivePositions = await whereAsync(reshapedMask);
+  const indices = squeeze(positivePositions, [1]);
+  const res = gather(reshapedTensor, indices, axisFrom);
+  if (tensor2 !== $tensor) {
+    $tensor.dispose();
+  }
+  if (mask !== $mask) {
+    $mask.dispose();
+  }
+  indices.dispose();
+  reshapedTensor.dispose();
+  reshapedMask.dispose();
+  positivePositions.dispose();
+  return res;
+}
+var booleanMaskAsync = booleanMaskAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
+function movingAverage_(v, x, decay, step5, zeroDebias = true) {
+  const $v = convertToTensor(v, "v", "movingAverage");
+  const $x = convertToTensor(x, "x", "movingAverage");
+  const $decay = convertToTensor(decay, "decay", "movingAverage");
+  assertTypesMatch($v, $x);
+  assert(arraysEqual($v.shape, $x.shape), () => "Shape mismatch in v and x");
+  const one = scalar(1);
+  const oneMinusDecay = sub(one, $decay);
+  let update = mul(sub($x, $v), oneMinusDecay);
+  if (zeroDebias) {
+    assert(step5 != null, () => "When using zeroDebias: true, step is required.");
+    const $step = convertToTensor(step5, "step", "movingAverage");
+    update = div(update, sub(one, pow($decay, $step)));
+  }
+  return add2($v, update);
+}
+var movingAverage = op({ movingAverage_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
+function scatterND_(indices, updates, shape) {
+  const $indices = convertToTensor(indices, "indices", "scatterND", "int32");
+  const $updates = convertToTensor(updates, "updates", "scatterND");
+  validateInput($updates, $indices, shape);
+  const inputs = { indices: $indices, updates: $updates };
+  const attrs = { shape };
+  return ENGINE.runKernel(ScatterNd, inputs, attrs);
+}
+var scatterND = op({ scatterND_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
+function validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {
+  if (sparseIndices.dtype !== "int32") {
+    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);
+  }
+  if (sparseIndices.rank > 2) {
+    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);
+  }
+  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
+  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
+  if (outputShape.length !== numDims) {
+    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);
+  }
+  const numValues = sparseValues.size;
+  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {
+    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);
+  }
+  if (sparseValues.dtype !== defaultValues.dtype) {
+    throw new Error("sparseValues.dtype must match defaultValues.dtype");
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
+function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
+  const $sparseIndices = convertToTensor(sparseIndices, "sparseIndices", "sparseToDense", "int32");
+  const $sparseValues = convertToTensor(sparseValues, "sparseValues", "sparseToDense", "string_or_numeric");
+  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
+  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);
+  const inputs = {
+    sparseIndices: $sparseIndices,
+    sparseValues: $sparseValues,
+    defaultValue: $defaultValue
+  };
+  const attrs = { outputShape };
+  return ENGINE.runKernel(SparseToDense, inputs, attrs);
+}
+var sparseToDense = op({ sparseToDense_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
+function gatherND_(x, indices) {
+  const $indices = convertToTensor(indices, "indices", "gatherND", "int32");
+  const $x = convertToTensor(x, "x", "gatherND", "string_or_numeric");
+  const inputs = { params: $x, indices: $indices };
+  return ENGINE.runKernel(GatherNd, inputs);
+}
+var gatherND = op({ gatherND_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
+function getNoiseShape(x, noiseShape) {
+  if (noiseShape == null) {
+    return x.shape.slice();
+  }
+  if (arraysEqual(x.shape, noiseShape)) {
+    return noiseShape;
+  }
+  if (x.shape.length === noiseShape.length) {
+    const newDimension = [];
+    for (let i2 = 0; i2 < x.shape.length; i2++) {
+      if (noiseShape[i2] == null && x.shape[i2] != null) {
+        newDimension.push(x.shape[i2]);
+      } else {
+        newDimension.push(noiseShape[i2]);
+      }
+    }
+    return newDimension;
+  }
+  return noiseShape;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
+function dropout_(x, rate, noiseShape, seed) {
+  const $x = convertToTensor(x, "x", "dropout");
+  assert($x.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);
+  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);
+  if (rate === 0) {
+    return x instanceof Tensor ? $x.clone() : $x;
+  }
+  const $noiseShape = getNoiseShape($x, noiseShape);
+  const keepProb = 1 - rate;
+  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
+  return mul($x, multiplier);
+}
+var dropout = op({ dropout_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js
+function enclosingPowerOfTwo(value) {
+  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));
+}
+function cosineWindow(windowLength, a6, b) {
+  const even = 1 - windowLength % 2;
+  const newValues = new Float32Array(windowLength);
+  for (let i2 = 0; i2 < windowLength; ++i2) {
+    const cosArg = 2 * Math.PI * i2 / (windowLength + even - 1);
+    newValues[i2] = a6 - b * Math.cos(cosArg);
+  }
+  return tensor1d(newValues, "float32");
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js
+async function inTopKAsync_(predictions, targets, k = 1) {
+  const $predictions = convertToTensor(predictions, "predictions", "inTopK");
+  const $targets = convertToTensor(targets, "targets", "inTopK");
+  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);
+  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);
+  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);
+  const lastDim = $predictions.shape[$predictions.shape.length - 1];
+  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);
+  const predictionsVals = await $predictions.data();
+  const targetsVals = await $targets.data();
+  const [batch, size] = [predictionsVals.length / lastDim, lastDim];
+  const precision3 = getTypedArrayFromDType("bool", batch);
+  for (let b = 0; b < batch; b++) {
+    const offset = b * size;
+    const vals = predictionsVals.subarray(offset, offset + size);
+    const valAndInd = [];
+    for (let i2 = 0; i2 < vals.length; i2++) {
+      valAndInd.push({ value: vals[i2], index: i2 });
+    }
+    valAndInd.sort((a6, b2) => b2.value - a6.value);
+    precision3[b] = 0;
+    for (let i2 = 0; i2 < k; i2++) {
+      if (valAndInd[i2].index === targetsVals[b]) {
+        precision3[b] = 1;
+        break;
+      }
+    }
+  }
+  if (predictions !== $predictions) {
+    $predictions.dispose();
+  }
+  if (targets !== $targets) {
+    $targets.dispose();
+  }
+  return tensor(precision3, $targets.shape, "bool");
+}
+var inTopKAsync = inTopKAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
+var fused_ops_exports = {};
+__export(fused_ops_exports, {
+  conv2d: () => conv2d2,
+  depthwiseConv2d: () => depthwiseConv2d2,
+  matMul: () => matMul2
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
+function conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = "NHWC", dimRoundingMode) {
+  let x4D = x;
+  if (x.rank === 3) {
+    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+  }
+  let dy4D = dy;
+  if (dy4D.rank === 3) {
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);
+  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);
+  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);
+  const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
+  const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
+  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);
+  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);
+  checkPadOnDimRoundingMode("conv2dDerFilter", pad3, dimRoundingMode);
+  const inputs = { x: x4D, dy: dy4D };
+  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape };
+  return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);
+}
+var conv2DBackpropFilter = op({ conv2DBackpropFilter_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
+function getFusedDyActivation(dy, y, activation2) {
+  if (activation2 == null || activation2 === "linear") {
+    return dy;
+  }
+  if (activation2 === "relu") {
+    return mul(dy, step(y));
+  }
+  throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);
+}
+function getFusedBiasGradient(bias, dyActivation) {
+  let res = dyActivation;
+  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);
+  if (reduceAxes.length > 0) {
+    res = sum2(res, reduceAxes);
+  }
+  return reshape(res, bias.shape);
+}
+function applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {
+  if (activation2 === "linear") {
+    return x;
+  } else if (activation2 === "relu") {
+    return relu(x);
+  } else if (activation2 === "elu") {
+    return elu(x);
+  } else if (activation2 === "relu6") {
+    return relu6(x);
+  } else if (activation2 === "prelu") {
+    return prelu(x, preluActivationWeights);
+  } else if (activation2 === "leakyrelu") {
+    return leakyRelu(x, leakyreluAlpha);
+  } else if (activation2 === "sigmoid") {
+    return sigmoid(x);
+  }
+  throw new Error(`Unknown fused activation ${activation2}.`);
+}
+var shouldFuse = (gradientDepth, activation2) => {
+  const gradientMode = gradientDepth > 0;
+  return !gradientMode || activation2 === "linear";
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
+function fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) {
+  activation2 = activation2 || "linear";
+  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
+    assert(dataFormat === "NHWC", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
+    let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
+    if (bias != null) {
+      result = add2(result, bias);
+    }
+    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
+  }
+  const $x = convertToTensor(x, "x", "conv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "conv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  checkPadOnDimRoundingMode("fused conv2d", pad3, dimRoundingMode);
+  const inputChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
+  assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`);
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode);
+  let $bias;
+  if (bias != null) {
+    $bias = convertToTensor(bias, "bias", "fused conv2d");
+    [$bias] = makeTypesMatch($bias, $x);
+    if (dataFormat === "NHWC") {
+      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
+    } else {
+      assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);
+      assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);
+    }
+  }
+  let $preluActivationWeights;
+  if (preluActivationWeights != null) {
+    const alphaShape = preluActivationWeights.shape;
+    assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);
+    if (alphaShape.length === 1) {
+      assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);
+    } else if (alphaShape.length === 3) {
+      try {
+        assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
+      } catch (e2) {
+        const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;
+        throw Error(errMsg);
+      }
+    }
+    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
+  }
+  const grad2 = (dy, saved) => {
+    assert(dataFormat === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
+    const [$filter2, x4D2, y, $bias2] = saved;
+    const dyActivation = getFusedDyActivation(dy, y, activation2);
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
+    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3);
+    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3);
+    const der = [xDer, filterDer];
+    if ($bias2 != null) {
+      const biasDer = getFusedBiasGradient($bias2, dyActivation);
+      der.push(biasDer);
+    }
+    return der;
+  };
+  const inputs = {
+    x: x4D,
+    filter: $filter,
+    bias: $bias,
+    preluActivationWeights: $preluActivationWeights
+  };
+  const attrs = {
+    strides,
+    pad: pad3,
+    dataFormat,
+    dilations,
+    dimRoundingMode,
+    activation: activation2,
+    leakyreluAlpha
+  };
+  if (bias == null) {
+    const customOp = customGrad((x4D2, filter2, save) => {
+      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
+      save([filter2, x4D2, res]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOp(x4D, $filter);
+  } else {
+    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
+      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
+      save([filter2, x4D2, res, bias2]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOpWithBias(x4D, $filter, $bias);
+  }
+}
+var conv2d2 = op({ fusedConv2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
+function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) {
+  let x4D = x;
+  if (x.rank === 3) {
+    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);
+  }
+  let dy4D = dy;
+  if (dy4D.rank === 3) {
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+  }
+  const inputs = { x: x4D, dy: dy4D };
+  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape };
+  return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);
+}
+var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
+function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) {
+  let dy4D = dy;
+  let reshapedTo4D = false;
+  if (dy.rank === 3) {
+    reshapedTo4D = true;
+    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
+  }
+  const inputs = { dy: dy4D, filter };
+  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape };
+  const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
+function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) {
+  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
+    let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
+    if (bias != null) {
+      result = add2(result, bias);
+    }
+    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
+  }
+  const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32");
+  const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32");
+  let x4D = $x;
+  let reshapedTo4D = false;
+  if ($x.rank === 3) {
+    reshapedTo4D = true;
+    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
+  }
+  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);
+  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);
+  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
+  if (dilations == null) {
+    dilations = [1, 1];
+  }
+  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  checkPadOnDimRoundingMode("fused depthwiseConv2d", pad3, dimRoundingMode);
+  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode, true);
+  let $bias;
+  if (bias != null) {
+    $bias = convertToTensor(bias, "bias", "fused conv2d");
+    [$bias] = makeTypesMatch($bias, $x);
+    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
+  }
+  let $preluActivationWeights;
+  if (preluActivationWeights != null) {
+    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
+  }
+  const grad2 = (dy, saved) => {
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
+    const [$filter2, x4D2, y, bias2] = saved;
+    const dyActivation = getFusedDyActivation(dy, y, activation2);
+    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode);
+    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode);
+    if (bias2 != null) {
+      const biasDer = getFusedBiasGradient($bias, dyActivation);
+      return [xDer, filterDer, biasDer];
+    }
+    return [xDer, filterDer];
+  };
+  const inputs = {
+    x: x4D,
+    filter: $filter,
+    bias: $bias,
+    preluActivationWeights: $preluActivationWeights
+  };
+  const attrs = {
+    strides,
+    pad: pad3,
+    dataFormat,
+    dilations,
+    dimRoundingMode,
+    activation: activation2,
+    leakyreluAlpha
+  };
+  if (bias == null) {
+    const customOp = customGrad((x4D2, filter2, save) => {
+      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
+      save([filter2, x4D2, res]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOp(x4D, $filter);
+  } else {
+    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
+      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
+      save([filter2, x4D2, res, bias2]);
+      if (reshapedTo4D) {
+        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+      }
+      return { value: res, gradFunc: grad2 };
+    });
+    return customOpWithBias(x4D, $filter, $bias);
+  }
+}
+var depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
+function fusedMatMul_({ a: a6, b, transposeA = false, transposeB = false, bias, activation: activation2 = "linear", preluActivationWeights, leakyreluAlpha }) {
+  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {
+    let result = matMul(a6, b, transposeA, transposeB);
+    if (bias != null) {
+      result = add2(result, bias);
+    }
+    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);
+  }
+  let $a = convertToTensor(a6, "a", "fused matMul");
+  let $b = convertToTensor(b, "b", "fused matMul");
+  [$a, $b] = makeTypesMatch($a, $b);
+  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
+  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
+  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
+  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
+  const outerDimsA = $a.shape.slice(0, -2);
+  const outerDimsB = $b.shape.slice(0, -2);
+  const batchDimA = sizeFromShape(outerDimsA);
+  const batchDimB = sizeFromShape(outerDimsB);
+  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
+  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
+  let $bias;
+  if (bias != null) {
+    $bias = convertToTensor(bias, "bias", "fused matMul");
+    [$bias] = makeTypesMatch($bias, $a);
+    assertAndGetBroadcastShape(outShape, $bias.shape);
+  }
+  let $preluActivationWeights;
+  if (preluActivationWeights != null) {
+    $preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused matMul");
+  }
+  const grad2 = (dy, saved) => {
+    const [a3D2, b3D2, y, $bias2] = saved;
+    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);
+    let aDer;
+    let bDer;
+    if (!transposeA && !transposeB) {
+      aDer = matMul(dyActivation, b3D2, false, true);
+      bDer = matMul(a3D2, dyActivation, true, false);
+    } else if (!transposeA && transposeB) {
+      aDer = matMul(dyActivation, b3D2, false, false);
+      bDer = matMul(dyActivation, a3D2, true, false);
+    } else if (transposeA && !transposeB) {
+      aDer = matMul(b3D2, dyActivation, false, true);
+      bDer = matMul(a3D2, dyActivation, false, false);
+    } else {
+      aDer = matMul(b3D2, dyActivation, true, true);
+      bDer = matMul(dyActivation, a3D2, true, true);
+    }
+    if (bias != null) {
+      const biasDer = getFusedBiasGradient($bias2, dyActivation);
+      return [aDer, bDer, biasDer];
+    } else {
+      return [aDer, bDer];
+    }
+  };
+  const inputs = {
+    a: a3D,
+    b: b3D,
+    bias: $bias,
+    preluActivationWeights: $preluActivationWeights
+  };
+  const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha };
+  if (bias == null) {
+    const customOp = customGrad((a3D2, b3D2, save) => {
+      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
+      save([a3D2, b3D2, res]);
+      return { value: reshape(res, outShape), gradFunc: grad2 };
+    });
+    return customOp(a3D, b3D);
+  } else {
+    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
+      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
+      save([a3D2, b3D2, res, $bias2]);
+      return { value: reshape(res, outShape), gradFunc: grad2 };
+    });
+    return customOpWithBias(a3D, b3D, $bias);
+  }
+}
+var matMul2 = op({ fusedMatMul_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js
+function hammingWindow_(windowLength) {
+  return cosineWindow(windowLength, 0.54, 0.46);
+}
+var hammingWindow = op({ hammingWindow_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js
+function hannWindow_(windowLength) {
+  return cosineWindow(windowLength, 0.5, 0.5);
+}
+var hannWindow = op({ hannWindow_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js
+function frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {
+  let start = 0;
+  const output = [];
+  while (start + frameLength <= signal2.size) {
+    output.push(slice(signal2, start, frameLength));
+    start += frameStep;
+  }
+  if (padEnd) {
+    while (start < signal2.size) {
+      const padLen = start + frameLength - signal2.size;
+      const pad3 = concat([
+        slice(signal2, start, frameLength - padLen),
+        fill([padLen], padValue)
+      ]);
+      output.push(pad3);
+      start += frameStep;
+    }
+  }
+  if (output.length === 0) {
+    return tensor2d([], [0, frameLength]);
+  }
+  return reshape(concat(output), [output.length, frameLength]);
+}
+var frame = op({ frame_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js
+function stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {
+  if (fftLength == null) {
+    fftLength = enclosingPowerOfTwo(frameLength);
+  }
+  const framedSignal = frame(signal2, frameLength, frameStep);
+  const windowedSignal = mul(framedSignal, windowFn(frameLength));
+  return rfft(windowedSignal, fftLength);
+}
+var stft = op({ stft_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
+function cropAndResize_(image2, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) {
+  const $image = convertToTensor(image2, "image", "cropAndResize");
+  const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
+  const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
+  const numBoxes = $boxes.shape[0];
+  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);
+  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);
+  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);
+  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);
+  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);
+  assert(method === "bilinear" || method === "nearest", () => `method must be bilinear or nearest, but was ${method}`);
+  const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };
+  const attrs = { method, extrapolationValue, cropSize };
+  const res = ENGINE.runKernel(CropAndResize, inputs, attrs);
+  return res;
+}
+var cropAndResize = op({ cropAndResize_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
+function flipLeftRight_(image2) {
+  const $image = convertToTensor(image2, "image", "flipLeftRight", "float32");
+  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);
+  const inputs = { image: $image };
+  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});
+  return res;
+}
+var flipLeftRight = op({ flipLeftRight_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js
+function grayscaleToRGB_(image2) {
+  const $image = convertToTensor(image2, "image", "grayscaleToRGB");
+  const lastDimsIdx = $image.rank - 1;
+  const lastDims = $image.shape[lastDimsIdx];
+  assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);
+  assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);
+  const reps = new Array($image.rank);
+  reps.fill(1, 0, lastDimsIdx);
+  reps[lastDimsIdx] = 3;
+  return tile($image, reps);
+}
+var grayscaleToRGB = op({ grayscaleToRGB_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
+function rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {
+  const $image = convertToTensor(image2, "image", "rotateWithOffset", "float32");
+  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
+  const inputs = { image: $image };
+  const attrs = { radians, fillValue, center };
+  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
+  return res;
+}
+var rotateWithOffset = op({ rotateWithOffset_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
+function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+  if (iouThreshold == null) {
+    iouThreshold = 0.5;
+  }
+  if (scoreThreshold == null) {
+    scoreThreshold = Number.NEGATIVE_INFINITY;
+  }
+  if (softNmsSigma == null) {
+    softNmsSigma = 0;
+  }
+  const numBoxes = boxes.shape[0];
+  maxOutputSize = Math.min(maxOutputSize, numBoxes);
+  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
+  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
+  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
+  assert(scores.rank === 1, () => "scores must be a 1D tensor");
+  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);
+  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
+  return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
+function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression", "float32");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression", "float32");
+  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+  maxOutputSize = inputs.maxOutputSize;
+  iouThreshold = inputs.iouThreshold;
+  scoreThreshold = inputs.scoreThreshold;
+  const attrs = { maxOutputSize, iouThreshold, scoreThreshold };
+  return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);
+}
+var nonMaxSuppression = op({ nonMaxSuppression_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js
+function binaryInsert(arr, element, comparator) {
+  const index = binarySearch(arr, element, comparator);
+  const insertionPoint = index < 0 ? -(index + 1) : index;
+  arr.splice(insertionPoint, 0, element);
+}
+function binarySearch(arr, target, comparator) {
+  return binarySearch_(arr, target, comparator || defaultComparator);
+}
+function defaultComparator(a6, b) {
+  return a6 > b ? 1 : a6 < b ? -1 : 0;
+}
+function binarySearch_(arr, target, comparator) {
+  let left = 0;
+  let right = arr.length;
+  let middle = 0;
+  let found = false;
+  while (left < right) {
+    middle = left + (right - left >>> 1);
+    const compareResult = comparator(target, arr[middle]);
+    if (compareResult > 0) {
+      left = middle + 1;
+    } else {
+      right = middle;
+      found = !compareResult;
+    }
+  }
+  return found ? left : -left - 1;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
+function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);
+}
+function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
+  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0, false, padToMaxOutputSize, true);
+}
+function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
+  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
+}
+function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
+  const candidates = [];
+  for (let i2 = 0; i2 < scores.length; i2++) {
+    if (scores[i2] > scoreThreshold) {
+      candidates.push({ score: scores[i2], boxIndex: i2, suppressBeginIndex: 0 });
+    }
+  }
+  candidates.sort(ascendingComparator);
+  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;
+  const selectedIndices = [];
+  const selectedScores = [];
+  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {
+    const candidate = candidates.pop();
+    const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;
+    if (originalScore < scoreThreshold) {
+      break;
+    }
+    let ignoreCandidate = false;
+    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {
+      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
+      if (iou >= iouThreshold) {
+        ignoreCandidate = true;
+        break;
+      }
+      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou);
+      if (candidate.score <= scoreThreshold) {
+        break;
+      }
+    }
+    candidate.suppressBeginIndex = selectedIndices.length;
+    if (!ignoreCandidate) {
+      if (candidate.score === originalScore) {
+        selectedIndices.push(boxIndex);
+        selectedScores.push(candidate.score);
+      } else if (candidate.score > scoreThreshold) {
+        binaryInsert(candidates, candidate, ascendingComparator);
+      }
+    }
+  }
+  const validOutputs = selectedIndices.length;
+  const elemsToPad = maxOutputSize - validOutputs;
+  if (padToMaxOutputSize && elemsToPad > 0) {
+    selectedIndices.push(...new Array(elemsToPad).fill(0));
+    selectedScores.push(...new Array(elemsToPad).fill(0));
+  }
+  const result = { selectedIndices };
+  if (returnScoresTensor) {
+    result["selectedScores"] = selectedScores;
+  }
+  if (returnValidOutputs) {
+    result["validOutputs"] = validOutputs;
+  }
+  return result;
+}
+function intersectionOverUnion(boxes, i2, j) {
+  const iCoord = boxes.subarray(i2 * 4, i2 * 4 + 4);
+  const jCoord = boxes.subarray(j * 4, j * 4 + 4);
+  const yminI = Math.min(iCoord[0], iCoord[2]);
+  const xminI = Math.min(iCoord[1], iCoord[3]);
+  const ymaxI = Math.max(iCoord[0], iCoord[2]);
+  const xmaxI = Math.max(iCoord[1], iCoord[3]);
+  const yminJ = Math.min(jCoord[0], jCoord[2]);
+  const xminJ = Math.min(jCoord[1], jCoord[3]);
+  const ymaxJ = Math.max(jCoord[0], jCoord[2]);
+  const xmaxJ = Math.max(jCoord[1], jCoord[3]);
+  const areaI = (ymaxI - yminI) * (xmaxI - xminI);
+  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+  if (areaI <= 0 || areaJ <= 0) {
+    return 0;
+  }
+  const intersectionYmin = Math.max(yminI, yminJ);
+  const intersectionXmin = Math.max(xminI, xminJ);
+  const intersectionYmax = Math.min(ymaxI, ymaxJ);
+  const intersectionXmax = Math.min(xmaxI, xmaxJ);
+  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);
+  return intersectionArea / (areaI + areaJ - intersectionArea);
+}
+function suppressWeight(iouThreshold, scale2, iou) {
+  const weight = Math.exp(scale2 * iou * iou);
+  return iou <= iouThreshold ? weight : 0;
+}
+function ascendingComparator(c1, c2) {
+  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
+async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
+  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+  maxOutputSize = inputs.maxOutputSize;
+  iouThreshold = inputs.iouThreshold;
+  scoreThreshold = inputs.scoreThreshold;
+  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
+  const boxesVals = boxesAndScores[0];
+  const scoresVals = boxesAndScores[1];
+  const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  if ($boxes !== boxes) {
+    $boxes.dispose();
+  }
+  if ($scores !== scores) {
+    $scores.dispose();
+  }
+  return tensor1d(selectedIndices, "int32");
+}
+var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
+function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  maxOutputSize = params.maxOutputSize;
+  iouThreshold = params.iouThreshold;
+  scoreThreshold = params.scoreThreshold;
+  softNmsSigma = params.softNmsSigma;
+  const inputs = { boxes: $boxes, scores: $scores };
+  const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };
+  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);
+  return { selectedIndices: result[0], selectedScores: result[1] };
+}
+var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
+async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  maxOutputSize = params.maxOutputSize;
+  iouThreshold = params.iouThreshold;
+  scoreThreshold = params.scoreThreshold;
+  softNmsSigma = params.softNmsSigma;
+  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);
+  const boxesVals = boxesAndScores[0];
+  const scoresVals = boxesAndScores[1];
+  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  if ($boxes !== boxes) {
+    $boxes.dispose();
+  }
+  if ($scores !== scores) {
+    $scores.dispose();
+  }
+  return {
+    selectedIndices: tensor1d(selectedIndices, "int32"),
+    selectedScores: tensor1d(selectedScores)
+  };
+}
+var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
+function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppression");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
+  const $maxOutputSize = params.maxOutputSize;
+  const $iouThreshold = params.iouThreshold;
+  const $scoreThreshold = params.scoreThreshold;
+  const inputs = { boxes: $boxes, scores: $scores };
+  const attrs = {
+    maxOutputSize: $maxOutputSize,
+    iouThreshold: $iouThreshold,
+    scoreThreshold: $scoreThreshold,
+    padToMaxOutputSize
+  };
+  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);
+  return { selectedIndices: result[0], validOutputs: result[1] };
+}
+var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
+async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
+  const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppressionAsync");
+  const $scores = convertToTensor(scores, "scores", "nonMaxSuppressionAsync");
+  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
+  const $maxOutputSize = params.maxOutputSize;
+  const $iouThreshold = params.iouThreshold;
+  const $scoreThreshold = params.scoreThreshold;
+  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);
+  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
+  if ($boxes !== boxes) {
+    $boxes.dispose();
+  }
+  if ($scores !== scores) {
+    $scores.dispose();
+  }
+  return {
+    selectedIndices: tensor1d(selectedIndices, "int32"),
+    validOutputs: scalar(validOutputs, "int32")
+  };
+}
+var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
+function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {
+  const $images = convertToTensor(images, "images", "resizeBilinear");
+  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);
+  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);
+  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);
+  let batchImages = $images;
+  let reshapedTo4D = false;
+  if ($images.rank === 3) {
+    reshapedTo4D = true;
+    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
+  }
+  const [] = size;
+  const inputs = { images: batchImages };
+  const attrs = { alignCorners, halfPixelCenters, size };
+  const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var resizeBilinear = op({ resizeBilinear_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
+function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {
+  const $images = convertToTensor(images, "images", "resizeNearestNeighbor");
+  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);
+  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);
+  assert($images.dtype === "float32" || $images.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype");
+  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);
+  let batchImages = $images;
+  let reshapedTo4D = false;
+  if ($images.rank === 3) {
+    reshapedTo4D = true;
+    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);
+  }
+  const [] = size;
+  const inputs = { images: batchImages };
+  const attrs = { alignCorners, halfPixelCenters, size };
+  const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var resizeNearestNeighbor = op({ resizeNearestNeighbor_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js
+function threshold_(image2, method = "binary", inverted = false, threshValue = 0.5) {
+  const $image = convertToTensor(image2, "image", "threshold");
+  const RED_INTENCITY_COEF = 0.2989;
+  const GREEN_INTENCITY_COEF = 0.587;
+  const BLUE_INTENCITY_COEF = 0.114;
+  const totalPixelsInImage = $image.shape[0] * $image.shape[1];
+  let $threshold = mul(tensor1d([threshValue]), 255);
+  let r2, g, b, grayscale;
+  assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);
+  assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);
+  assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);
+  assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`);
+  if ($image.shape[2] === 3) {
+    [r2, g, b] = split($image, [1, 1, 1], -1);
+    const $r = mul(r2, RED_INTENCITY_COEF);
+    const $g = mul(g, GREEN_INTENCITY_COEF);
+    const $b = mul(b, BLUE_INTENCITY_COEF);
+    grayscale = add2(add2($r, $g), $b);
+  } else {
+    grayscale = image2;
+  }
+  if (method === "otsu") {
+    const $histogram = bincount(cast(round2(grayscale), "int32"), tensor([]), 256);
+    $threshold = otsu($histogram, totalPixelsInImage);
+  }
+  const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);
+  const result = cast(mul(invCondition, 255), "int32");
+  return result;
+}
+function otsu(histogram, total) {
+  let bestThresh = tensor1d([-1]);
+  let bestInBetVar = tensor1d([0]);
+  let cInBetVar = tensor1d([0]);
+  let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;
+  for (let index = 0; index < histogram.size - 1; index++) {
+    classFirst = slice(histogram, 0, index + 1);
+    classSecond = slice(histogram, index + 1);
+    weightForeground = div(sum2(classFirst), total);
+    weightBack = div(sum2(classSecond), total);
+    const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));
+    meanFirst = div(meanFirstDivA, sum2(classFirst));
+    const meanSecFill = fill(classSecond.shape, classFirst.size);
+    const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);
+    const meanSecMul = mul(classSecond, meanSecAdd);
+    meanSec = div(sum2(meanSecMul), sum2(classSecond));
+    const cInBetVarSubA = sub(meanFirst, meanSec);
+    const cInBetVarSubB = sub(meanFirst, meanSec);
+    const cInBetVarMul = mul(weightForeground, weightBack);
+    cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);
+    const condition = greater(cInBetVar, bestInBetVar);
+    bestInBetVar = where(condition, cInBetVar, bestInBetVar);
+    bestThresh = where(condition, tensor1d([index]), bestThresh);
+  }
+  return bestThresh;
+}
+var threshold = op({ threshold_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js
+function transform_(image2, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) {
+  const $image = convertToTensor(image2, "image", "transform", "float32");
+  const $transforms = convertToTensor(transforms, "transforms", "transform", "float32");
+  assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);
+  assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);
+  assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);
+  const inputs = { image: $image, transforms: $transforms };
+  const attrs = { interpolation, fillMode, fillValue, outputShape };
+  return ENGINE.runKernel(Transform, inputs, attrs);
+}
+var transform = op({ transform_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
+function bandPart_(a6, numLower, numUpper) {
+  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
+  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
+  const $a = convertToTensor(a6, "a", "bandPart");
+  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
+  const shape = $a.shape;
+  const [M, N] = $a.shape.slice(-2);
+  if (!(numLower <= M)) {
+    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);
+  }
+  if (!(numUpper <= N)) {
+    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);
+  }
+  if (numLower < 0) {
+    numLower = M;
+  }
+  if (numUpper < 0) {
+    numUpper = N;
+  }
+  const i2 = reshape(range(0, M, 1, "int32"), [-1, 1]);
+  const j = range(0, N, 1, "int32");
+  const ij = sub(i2, j);
+  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
+  const zero = zeros([M, N], $a.dtype);
+  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
+}
+var bandPart = op({ bandPart_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
+function gramSchmidt_(xs) {
+  let inputIsTensor2D;
+  if (Array.isArray(xs)) {
+    inputIsTensor2D = false;
+    assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
+    const dim = xs[0].shape[0];
+    for (let i2 = 1; i2 < xs.length; ++i2) {
+      assert(xs[i2].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i2].shape[0]} vs. ${dim})`);
+    }
+  } else {
+    inputIsTensor2D = true;
+    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));
+  }
+  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
+  const ys = [];
+  const xs1d = xs;
+  for (let i2 = 0; i2 < xs.length; ++i2) {
+    ys.push(ENGINE.tidy(() => {
+      let x = xs1d[i2];
+      if (i2 > 0) {
+        for (let j = 0; j < i2; ++j) {
+          const proj = mul(sum2(mul(ys[j], x)), ys[j]);
+          x = sub(x, proj);
+        }
+      }
+      return div(x, norm(x, "euclidean"));
+    }));
+  }
+  if (inputIsTensor2D) {
+    return stack(ys, 0);
+  } else {
+    return ys;
+  }
+}
+var gramSchmidt = op({ gramSchmidt_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
+function qr_(x, fullMatrices = false) {
+  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
+  if (x.rank === 2) {
+    return qr2d(x, fullMatrices);
+  } else {
+    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);
+    const x2ds = unstack(reshape(x, [
+      outerDimsProd,
+      x.shape[x.shape.length - 2],
+      x.shape[x.shape.length - 1]
+    ]), 0);
+    const q2ds = [];
+    const r2ds = [];
+    x2ds.forEach((x2d) => {
+      const [q2d, r2d] = qr2d(x2d, fullMatrices);
+      q2ds.push(q2d);
+      r2ds.push(r2d);
+    });
+    const q = reshape(stack(q2ds, 0), x.shape);
+    const r2 = reshape(stack(r2ds, 0), x.shape);
+    return [q, r2];
+  }
+}
+function qr2d(x, fullMatrices = false) {
+  return ENGINE.tidy(() => {
+    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
+    const m = x.shape[0];
+    const n = x.shape[1];
+    let q = eye(m);
+    let r2 = clone(x);
+    const one2D = tensor2d([[1]], [1, 1]);
+    let w = clone(one2D);
+    const iters = m >= n ? n : m;
+    for (let j = 0; j < iters; ++j) {
+      const rTemp = r2;
+      const wTemp = w;
+      const qTemp = q;
+      [w, r2, q] = ENGINE.tidy(() => {
+        const rjEnd1 = slice(r2, [j, j], [m - j, 1]);
+        const normX = norm(rjEnd1);
+        const rjj = slice(r2, [j, j], [1, 1]);
+        const s2 = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
+        const u1 = sub(rjj, mul(s2, normX));
+        const wPre = div(rjEnd1, u1);
+        if (wPre.shape[0] === 1) {
+          w = clone(one2D);
+        } else {
+          w = concat([
+            one2D,
+            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
+          ], 0);
+        }
+        const tau = neg(div(matMul(s2, u1), normX));
+        const rjEndAll = slice(r2, [j, 0], [m - j, n]);
+        const tauTimesW = mul(tau, w);
+        const wT = transpose(w);
+        if (j === 0) {
+          r2 = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+        } else {
+          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+          r2 = concat([slice(r2, [0, 0], [j, n]), rTimesTau], 0);
+        }
+        const tawTimesWT = transpose(tauTimesW);
+        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
+        if (j === 0) {
+          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
+        } else {
+          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
+          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
+        }
+        return [w, r2, q];
+      });
+      dispose([rTemp, wTemp, qTemp]);
+    }
+    if (!fullMatrices && m > n) {
+      q = slice(q, [0, 0], [m, n]);
+      r2 = slice(r2, [0, 0], [n, n]);
+    }
+    return [q, r2];
+  });
+}
+var qr = op({ qr_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js
+var Reduction;
+(function(Reduction2) {
+  Reduction2[Reduction2["NONE"] = 0] = "NONE";
+  Reduction2[Reduction2["MEAN"] = 1] = "MEAN";
+  Reduction2[Reduction2["SUM"] = 2] = "SUM";
+  Reduction2[Reduction2["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
+})(Reduction || (Reduction = {}));
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js
+function computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $losses = convertToTensor(losses2, "losses", "computeWeightedLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "computeWeightedLoss");
+  }
+  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);
+  if (reduction === Reduction.NONE) {
+    return weightedLoss;
+  }
+  if (reduction === Reduction.SUM) {
+    return sum2(weightedLoss);
+  }
+  if (reduction === Reduction.MEAN) {
+    if ($weights == null) {
+      return mean(weightedLoss);
+    } else {
+      const broadcastFactor = $losses.size / $weights.size;
+      const result = div(sum2(weightedLoss), sum2($weights));
+      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;
+    }
+  }
+  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
+    if ($weights == null) {
+      return div(sum2(weightedLoss), scalar($losses.size));
+    } else {
+      const broadcastedWeights = mul($weights, ones2($losses.shape));
+      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), "float32");
+      return div(sum2(weightedLoss), numNonZeros);
+    }
+  }
+  throw Error(`Unknown reduction: ${reduction}`);
+}
+var computeWeightedLoss = op({ computeWeightedLoss_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js
+function absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels, "labels", "absoluteDifference");
+  const $predictions = convertToTensor(predictions, "predictions", "absoluteDifference");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "absoluteDifference");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
+  const losses2 = abs(sub($labels, $predictions));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var absoluteDifference = op({ absoluteDifference_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js
+function cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels, "labels", "cosineDistance");
+  const $predictions = convertToTensor(predictions, "predictions", "cosineDistance");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "cosineDistance");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in cosineDistance: ");
+  const one = scalar(1);
+  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var cosineDistance = op({ cosineDistance_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js
+function hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  let $labels = convertToTensor(labels, "labels", "hingeLoss");
+  const $predictions = convertToTensor(predictions, "predictions", "hingeLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "hingeLoss");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in hingeLoss: ");
+  const one = scalar(1);
+  $labels = sub(mul(scalar(2), $labels), one);
+  const losses2 = relu(sub(one, mul($labels, $predictions)));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var hingeLoss = op({ hingeLoss_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js
+function huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels, "labels", "huberLoss");
+  const $predictions = convertToTensor(predictions, "predictions", "huberLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "huberLoss");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in huberLoss: ");
+  const deltaScalar = scalar(delta);
+  const error = abs(sub($predictions, $labels));
+  const quadratic = minimum(error, deltaScalar);
+  const linear = sub(error, quadratic);
+  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var huberLoss = op({ huberLoss_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js
+function logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels, "labels", "logLoss");
+  const $predictions = convertToTensor(predictions, "predictions", "logLoss");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "logLoss");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in logLoss: ");
+  const one = scalar(1);
+  const epsilonScalar = scalar(epsilon3);
+  const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar))));
+  const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar)));
+  const losses2 = sub(l13, l23);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var logLoss = op({ logLoss_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js
+function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  const $labels = convertToTensor(labels, "labels", "meanSquaredError");
+  const $predictions = convertToTensor(predictions, "predictions", "meanSquaredError");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "meanSquaredError");
+  }
+  assertShapesMatch($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
+  const losses2 = squaredDifference($labels, $predictions);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var meanSquaredError = op({ meanSquaredError_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js
+function sigmoidCrossEntropyWithLogits_(labels, logits) {
+  const $labels = convertToTensor(labels, "labels", "sigmoidCrossEntropyWithLogits");
+  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropyWithLogits");
+  assertShapesMatch($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
+  const maxOutput = relu($logits);
+  const outputXTarget = mul($logits, $labels);
+  const sigmoidOutput = log1p(exp(neg(abs($logits))));
+  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);
+}
+function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  let $multiClassLabels = convertToTensor(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
+  const $logits = convertToTensor(logits, "logits", "sigmoidCrossEntropy");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "sigmoidCrossEntropy");
+  }
+  assertShapesMatch($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
+  if (labelSmoothing > 0) {
+    const labelSmoothingScalar = scalar(labelSmoothing);
+    const one = scalar(1);
+    const half = scalar(0.5);
+    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));
+  }
+  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js
+function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
+  if (dim === -1) {
+    dim = logits.rank - 1;
+  }
+  if (dim !== logits.rank - 1) {
+    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);
+  }
+  const customOp = customGrad((labels2, logits2, save) => {
+    const keepDims = true;
+    const lse = logSumExp(logits2, [dim], keepDims);
+    const logResult = sub(cast(logits2, "float32"), lse);
+    save([labels2, logResult]);
+    const costVector = neg(mul(logResult, labels2));
+    const value = sum2(costVector, [dim]);
+    const gradFunc = (dy, saved) => {
+      const [labels3, logResult2] = saved;
+      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);
+      return [
+        mul(reshape(dy, dyShape), sub(cast(labels3, "float32"), exp(logResult2))),
+        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, "float32")))
+      ];
+    };
+    return { value, gradFunc };
+  });
+  return customOp(labels, logits);
+}
+function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
+  let $onehotLabels = convertToTensor(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
+  const $logits = convertToTensor(logits, "logits", "softmaxCrossEntropy");
+  let $weights = null;
+  if (weights != null) {
+    $weights = convertToTensor(weights, "weights", "softmaxCrossEntropy");
+  }
+  assertShapesMatch($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
+  if (labelSmoothing > 0) {
+    const labelSmoothingScalar = scalar(labelSmoothing);
+    const one = scalar(1);
+    const numClasses = scalar($onehotLabels.shape[1]);
+    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));
+  }
+  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
+  return computeWeightedLoss(losses2, $weights, reduction);
+}
+var softmaxCrossEntropy = op({ softmaxCrossEntropy_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows.js
+function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {
+  const $indices = convertToTensor(indices, "indices", "sparseFillEmptyRows", "int32");
+  const $values = convertToTensor(values, "values", "sparseFillEmptyRows");
+  const $denseShape = convertToTensor(denseShape, "denseShape", "sparseFillEmptyRows", "int32");
+  const $defaultValue = convertToTensor(defaultValue, "defaultValue", "sparseFillEmptyRows", $values.dtype);
+  if ($indices.rank !== 2) {
+    throw new Error(`Indices should be Tensor2D but received shape
+        ${$indices.shape}`);
+  }
+  if ($values.rank !== 1) {
+    throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);
+  }
+  if ($denseShape.rank !== 1) {
+    throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);
+  }
+  if ($defaultValue.rank !== 0) {
+    throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);
+  }
+  const inputs = {
+    indices: $indices,
+    values: $values,
+    denseShape: $denseShape,
+    defaultValue: $defaultValue
+  };
+  const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);
+  return {
+    outputIndices: result[0],
+    outputValues: result[1],
+    emptyRowIndicator: result[2],
+    reverseIndexMap: result[3]
+  };
+}
+var sparseFillEmptyRows = op({ sparseFillEmptyRows_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape.js
+function sparseReshape_(inputIndices, inputShape, newShape) {
+  const $inputIndices = convertToTensor(inputIndices, "inputIndices", "sparseReshape", "int32");
+  const $inputShape = convertToTensor(inputShape, "inputShape", "sparseReshape", "int32");
+  const $newShape = convertToTensor(newShape, "newShape", "sparseReshape", "int32");
+  if ($inputIndices.rank !== 2) {
+    throw new Error(`Input indices should be Tensor2D but received shape
+        ${$inputIndices.shape}`);
+  }
+  if ($inputShape.rank !== 1) {
+    throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);
+  }
+  if ($newShape.rank !== 1) {
+    throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);
+  }
+  const inputs = {
+    inputIndices: $inputIndices,
+    inputShape: $inputShape,
+    newShape: $newShape
+  };
+  const result = ENGINE.runKernel(SparseReshape, inputs);
+  return { outputIndices: result[0], outputShape: result[1] };
+}
+var sparseReshape = op({ sparseReshape_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_mean.js
+function sparseSegmentMean_(data, indices, segmentIds) {
+  const $data = convertToTensor(data, "data", "sparseSegmentMean");
+  const $indices = convertToTensor(indices, "indices", "sparseSegmentMean", "int32");
+  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentMean", "int32");
+  if ($data.rank < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if ($indices.rank !== 1) {
+    throw new Error(`Indices should be Tensor1D but received shape
+          ${$indices.shape}`);
+  }
+  if ($segmentIds.rank !== 1) {
+    throw new Error(`Segment ids should be Tensor1D but received shape
+          ${$segmentIds.shape}`);
+  }
+  const inputs = {
+    data: $data,
+    indices: $indices,
+    segmentIds: $segmentIds
+  };
+  return ENGINE.runKernel(SparseSegmentMean, inputs);
+}
+var sparseSegmentMean = op({ sparseSegmentMean_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum.js
+function sparseSegmentSum_(data, indices, segmentIds) {
+  const $data = convertToTensor(data, "data", "sparseSegmentSum");
+  const $indices = convertToTensor(indices, "indices", "sparseSegmentSum", "int32");
+  const $segmentIds = convertToTensor(segmentIds, "segmentIds", "sparseSegmentSum", "int32");
+  if ($data.rank < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if ($indices.rank !== 1) {
+    throw new Error(`Indices should be Tensor1D but received shape
+         ${$indices.shape}`);
+  }
+  if ($segmentIds.rank !== 1) {
+    throw new Error(`Segment ids should be Tensor1D but received shape
+         ${$segmentIds.shape}`);
+  }
+  const inputs = {
+    data: $data,
+    indices: $indices,
+    segmentIds: $segmentIds
+  };
+  return ENGINE.runKernel(SparseSegmentSum, inputs);
+}
+var sparseSegmentSum = op({ sparseSegmentSum_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_n_grams.js
+function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
+  const $data = convertToTensor(data, "data", "stringNGrams", "string");
+  if ($data.dtype !== "string") {
+    throw new Error("Data must be of datatype string");
+  }
+  if ($data.shape.length !== 1) {
+    throw new Error(`Data must be a vector, saw: ${$data.shape}`);
+  }
+  const $dataSplits = convertToTensor(dataSplits, "dataSplits", "stringNGrams");
+  if ($dataSplits.dtype !== "int32") {
+    throw new Error("Data splits must be of datatype int32");
+  }
+  const attrs = {
+    separator,
+    nGramWidths,
+    leftPad,
+    rightPad: rightPad2,
+    padWidth,
+    preserveShortSequences
+  };
+  const inputs = { data: $data, dataSplits: $dataSplits };
+  const result = ENGINE.runKernel(StringNGrams, inputs, attrs);
+  return { nGrams: result[0], nGramsSplits: result[1] };
+}
+var stringNGrams = op({ stringNGrams_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_split.js
+function stringSplit_(input2, delimiter, skipEmpty = true) {
+  const $input = convertToTensor(input2, "input", "stringSplit", "string");
+  const $delimiter = convertToTensor(delimiter, "delimiter", "stringSplit", "string");
+  if ($input.rank !== 1) {
+    throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);
+  }
+  if ($delimiter.rank !== 0) {
+    throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);
+  }
+  const attrs = { skipEmpty };
+  const inputs = { input: $input, delimiter: $delimiter };
+  const result = ENGINE.runKernel(StringSplit, inputs, attrs);
+  return { indices: result[0], values: result[1], shape: result[2] };
+}
+var stringSplit = op({ stringSplit_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_to_hash_bucket_fast.js
+function stringToHashBucketFast_(input2, numBuckets) {
+  const $input = convertToTensor(input2, "input", "stringToHashBucketFast", "string");
+  const attrs = { numBuckets };
+  if (numBuckets <= 0) {
+    throw new Error(`Number of buckets must be at least 1`);
+  }
+  const inputs = { input: $input };
+  return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);
+}
+var stringToHashBucketFast = op({ stringToHashBucketFast_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
+var spectral = {
+  fft,
+  ifft,
+  rfft,
+  irfft
+};
+var signal = {
+  hammingWindow,
+  hannWindow,
+  frame,
+  stft
+};
+var image = {
+  flipLeftRight,
+  grayscaleToRGB,
+  resizeNearestNeighbor,
+  resizeBilinear,
+  rotateWithOffset,
+  cropAndResize,
+  nonMaxSuppression,
+  nonMaxSuppressionAsync,
+  nonMaxSuppressionWithScore,
+  nonMaxSuppressionWithScoreAsync,
+  nonMaxSuppressionPadded,
+  nonMaxSuppressionPaddedAsync,
+  threshold,
+  transform
+};
+var linalg = {
+  bandPart,
+  gramSchmidt,
+  qr
+};
+var losses = {
+  absoluteDifference,
+  computeWeightedLoss,
+  cosineDistance,
+  hingeLoss,
+  huberLoss,
+  logLoss,
+  meanSquaredError,
+  sigmoidCrossEntropy,
+  softmaxCrossEntropy
+};
+var sparse = {
+  sparseFillEmptyRows,
+  sparseReshape,
+  sparseSegmentMean,
+  sparseSegmentSum
+};
+var string = {
+  stringNGrams,
+  stringSplit,
+  stringToHashBucketFast
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
+var Optimizer = class extends Serializable {
+  minimize(f, returnCost = false, varList) {
+    const { value, grads: grads2 } = this.computeGradients(f, varList);
+    if (varList != null) {
+      const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] }));
+      this.applyGradients(gradArray);
+    } else {
+      this.applyGradients(grads2);
+    }
+    dispose(grads2);
+    if (returnCost) {
+      return value;
+    } else {
+      value.dispose();
+      return null;
+    }
+  }
+  get iterations() {
+    if (this.iterations_ == null) {
+      this.iterations_ = 0;
+    }
+    return this.iterations_;
+  }
+  incrementIterations() {
+    this.iterations_ = this.iterations + 1;
+  }
+  computeGradients(f, varList) {
+    return variableGrads(f, varList);
+  }
+  dispose() {
+    if (this.iterations_ != null) {
+      dispose(this.iterations_);
+    }
+  }
+  async saveIterations() {
+    if (this.iterations_ == null) {
+      this.iterations_ = 0;
+    }
+    return {
+      name: "iter",
+      tensor: scalar(this.iterations_, "int32")
+    };
+  }
+  async getWeights() {
+    throw new Error("getWeights() is not implemented for this optimizer yet.");
+  }
+  async setWeights(weightValues) {
+    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
+  }
+  async extractIterations(weightValues) {
+    this.iterations_ = (await weightValues[0].tensor.data())[0];
+    return weightValues.slice(1);
+  }
+};
+Object.defineProperty(Optimizer, Symbol.hasInstance, {
+  value: (instance) => {
+    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
+var AdadeltaOptimizer = class extends Optimizer {
+  constructor(learningRate, rho, epsilon3 = null) {
+    super();
+    this.learningRate = learningRate;
+    this.rho = rho;
+    this.epsilon = epsilon3;
+    this.accumulatedGrads = [];
+    this.accumulatedUpdates = [];
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      const trainable = false;
+      if (this.accumulatedGrads[i2] == null) {
+        this.accumulatedGrads[i2] = {
+          originalName: `${name}/accum_grad`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      if (this.accumulatedUpdates[i2] == null) {
+        this.accumulatedUpdates[i2] = {
+          originalName: `${name}/accum_var`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const accumulatedGrad = this.accumulatedGrads[i2].variable;
+      const accumulatedUpdate = this.accumulatedUpdates[i2].variable;
+      tidy(() => {
+        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
+        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
+        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
+        accumulatedGrad.assign(newAccumulatedGrad);
+        accumulatedUpdate.assign(newAccumulatedUpdate);
+        const newValue = add2(mul(updates, -this.learningRate), value);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    if (this.accumulatedUpdates != null) {
+      dispose(this.accumulatedGrads.map((v) => v.variable));
+      dispose(this.accumulatedUpdates.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
+    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const variableCount = weightValues.length / 2;
+    const trainable = false;
+    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "rho": this.rho,
+      "epsilon": this.epsilon
+    };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"], config["rho"], config["epsilon"]);
+  }
+};
+AdadeltaOptimizer.className = "Adadelta";
+registerClass(AdadeltaOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
+var AdagradOptimizer = class extends Optimizer {
+  constructor(learningRate, initialAccumulatorValue = 0.1) {
+    super();
+    this.learningRate = learningRate;
+    this.initialAccumulatorValue = initialAccumulatorValue;
+    this.accumulatedGrads = [];
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      if (this.accumulatedGrads[i2] == null) {
+        const trainable = false;
+        this.accumulatedGrads[i2] = {
+          originalName: `${name}/accumulator`,
+          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
+        };
+      }
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const accumulatedGrad = this.accumulatedGrads[i2].variable;
+      tidy(() => {
+        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
+        accumulatedGrad.assign(newAccumulatedGrad);
+        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    if (this.accumulatedGrads != null) {
+      dispose(this.accumulatedGrads.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const trainable = false;
+    this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "initialAccumulatorValue": this.initialAccumulatorValue
+    };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"], config["initialAccumulatorValue"]);
+  }
+};
+AdagradOptimizer.className = "Adagrad";
+registerClass(AdagradOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
+var AdamOptimizer = class extends Optimizer {
+  constructor(learningRate, beta1, beta2, epsilon3 = null) {
+    super();
+    this.learningRate = learningRate;
+    this.beta1 = beta1;
+    this.beta2 = beta2;
+    this.epsilon = epsilon3;
+    this.accumulatedFirstMoment = [];
+    this.accumulatedSecondMoment = [];
+    tidy(() => {
+      this.accBeta1 = scalar(beta1).variable();
+      this.accBeta2 = scalar(beta2).variable();
+    });
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+  }
+  applyGradients(variableGradients) {
+    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
+    tidy(() => {
+      const oneMinusAccBeta1 = sub(1, this.accBeta1);
+      const oneMinusAccBeta2 = sub(1, this.accBeta2);
+      varNames.forEach((name, i2) => {
+        const value = ENGINE.registeredVariables[name];
+        const trainable = false;
+        if (this.accumulatedFirstMoment[i2] == null) {
+          this.accumulatedFirstMoment[i2] = {
+            originalName: `${name}/m`,
+            variable: tidy(() => zerosLike(value).variable(trainable))
+          };
+        }
+        if (this.accumulatedSecondMoment[i2] == null) {
+          this.accumulatedSecondMoment[i2] = {
+            originalName: `${name}/v`,
+            variable: tidy(() => zerosLike(value).variable(trainable))
+          };
+        }
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        if (gradient == null) {
+          return;
+        }
+        const firstMoment = this.accumulatedFirstMoment[i2].variable;
+        const secondMoment = this.accumulatedSecondMoment[i2].variable;
+        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
+        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
+        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
+        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
+        firstMoment.assign(newFirstMoment);
+        secondMoment.assign(newSecondMoment);
+        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
+        value.assign(newValue);
+      });
+      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
+      this.accBeta2.assign(mul(this.accBeta2, this.beta2));
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    this.accBeta1.dispose();
+    this.accBeta2.dispose();
+    if (this.accumulatedFirstMoment != null) {
+      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
+    }
+    if (this.accumulatedSecondMoment != null) {
+      dispose(this.accumulatedSecondMoment.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
+    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    tidy(() => {
+      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
+      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
+    });
+    const variableCount = weightValues.length / 2;
+    const trainable = false;
+    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "beta1": this.beta1,
+      "beta2": this.beta2,
+      "epsilon": this.epsilon
+    };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"]);
+  }
+};
+AdamOptimizer.className = "Adam";
+registerClass(AdamOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
+var AdamaxOptimizer = class extends Optimizer {
+  constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {
+    super();
+    this.learningRate = learningRate;
+    this.beta1 = beta1;
+    this.beta2 = beta2;
+    this.epsilon = epsilon3;
+    this.decay = decay;
+    this.accumulatedFirstMoment = [];
+    this.accumulatedWeightedInfNorm = [];
+    tidy(() => {
+      this.iteration = scalar(0).variable();
+      this.accBeta1 = scalar(beta1).variable();
+    });
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    tidy(() => {
+      const oneMinusAccBeta1 = sub(1, this.accBeta1);
+      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
+      variableNames.forEach((name, i2) => {
+        const value = ENGINE.registeredVariables[name];
+        const trainable = false;
+        if (this.accumulatedFirstMoment[i2] == null) {
+          this.accumulatedFirstMoment[i2] = {
+            originalName: `${name}/m`,
+            variable: zerosLike(value).variable(trainable)
+          };
+        }
+        if (this.accumulatedWeightedInfNorm[i2] == null) {
+          this.accumulatedWeightedInfNorm[i2] = {
+            originalName: `${name}/v`,
+            variable: zerosLike(value).variable(trainable)
+          };
+        }
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        if (gradient == null) {
+          return;
+        }
+        const firstMoment = this.accumulatedFirstMoment[i2].variable;
+        const weightedInfNorm = this.accumulatedWeightedInfNorm[i2].variable;
+        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
+        const ut0 = mul(weightedInfNorm, this.beta2);
+        const ut1 = abs(gradient);
+        const newWeightedInfNorm = maximum(ut0, ut1);
+        firstMoment.assign(newFirstMoment);
+        weightedInfNorm.assign(newWeightedInfNorm);
+        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);
+        value.assign(newValue);
+      });
+      this.iteration.assign(add2(this.iteration, 1));
+      this.accBeta1.assign(mul(this.accBeta1, this.beta1));
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    this.accBeta1.dispose();
+    this.iteration.dispose();
+    if (this.accumulatedFirstMoment != null) {
+      dispose(this.accumulatedFirstMoment.map((v) => v.variable));
+    }
+    if (this.accumulatedWeightedInfNorm != null) {
+      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    throw new Error("getWeights() is not implemented for Adamax yet.");
+  }
+  async setWeights(weightValues) {
+    throw new Error("setWeights() is not implemented for Adamax yet.");
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "beta1": this.beta1,
+      "beta2": this.beta2,
+      "epsilon": this.epsilon,
+      "decay": this.decay
+    };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"], config["decay"]);
+  }
+};
+AdamaxOptimizer.className = "Adamax";
+registerClass(AdamaxOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
+var SGDOptimizer = class extends Optimizer {
+  constructor(learningRate) {
+    super();
+    this.learningRate = learningRate;
+    this.setLearningRate(learningRate);
+  }
+  applyGradients(variableGradients) {
+    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
+    varNames.forEach((name, i2) => {
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const value = ENGINE.registeredVariables[name];
+      tidy(() => {
+        const newValue = add2(mul(this.c, gradient), value);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  setLearningRate(learningRate) {
+    this.learningRate = learningRate;
+    if (this.c != null) {
+      this.c.dispose();
+    }
+    this.c = keep(scalar(-learningRate));
+  }
+  dispose() {
+    this.c.dispose();
+  }
+  async getWeights() {
+    return [await this.saveIterations()];
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    if (weightValues.length !== 0) {
+      throw new Error("SGD optimizer does not have settable weights.");
+    }
+  }
+  getConfig() {
+    return { "learningRate": this.learningRate };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"]);
+  }
+};
+SGDOptimizer.className = "SGD";
+registerClass(SGDOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
+var MomentumOptimizer = class extends SGDOptimizer {
+  constructor(learningRate, momentum, useNesterov = false) {
+    super(learningRate);
+    this.learningRate = learningRate;
+    this.momentum = momentum;
+    this.useNesterov = useNesterov;
+    this.accumulations = [];
+    this.m = scalar(this.momentum);
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      if (this.accumulations[i2] == null) {
+        const trainable = false;
+        this.accumulations[i2] = {
+          originalName: `${name}/momentum`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      const accumulation = this.accumulations[i2].variable;
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      tidy(() => {
+        let newValue;
+        const newAccumulation = add2(mul(this.m, accumulation), gradient);
+        if (this.useNesterov) {
+          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);
+        } else {
+          newValue = add2(mul(this.c, newAccumulation), value);
+        }
+        accumulation.assign(newAccumulation);
+        value.assign(newValue);
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    this.m.dispose();
+    if (this.accumulations != null) {
+      dispose(this.accumulations.map((v) => v.variable));
+    }
+  }
+  setMomentum(momentum) {
+    this.momentum = momentum;
+  }
+  async getWeights() {
+    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const trainable = false;
+    this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "momentum": this.momentum,
+      "useNesterov": this.useNesterov
+    };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"], config["momentum"], config["useNesterov"]);
+  }
+};
+MomentumOptimizer.className = "Momentum";
+registerClass(MomentumOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
+var RMSPropOptimizer = class extends Optimizer {
+  constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {
+    super();
+    this.learningRate = learningRate;
+    this.decay = decay;
+    this.momentum = momentum;
+    this.epsilon = epsilon3;
+    this.accumulatedMeanSquares = [];
+    this.accumulatedMoments = [];
+    this.accumulatedMeanGrads = [];
+    this.centered = centered;
+    if (epsilon3 == null) {
+      this.epsilon = ENGINE.backend.epsilon();
+    }
+    if (learningRate == null) {
+      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
+    }
+  }
+  applyGradients(variableGradients) {
+    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
+    variableNames.forEach((name, i2) => {
+      const value = ENGINE.registeredVariables[name];
+      const trainable = false;
+      if (this.accumulatedMeanSquares[i2] == null) {
+        this.accumulatedMeanSquares[i2] = {
+          originalName: `${name}/rms`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      if (this.accumulatedMoments[i2] == null) {
+        this.accumulatedMoments[i2] = {
+          originalName: `${name}/momentum`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      if (this.accumulatedMeanGrads[i2] == null && this.centered) {
+        this.accumulatedMeanGrads[i2] = {
+          originalName: `${name}/mg`,
+          variable: tidy(() => zerosLike(value).variable(trainable))
+        };
+      }
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      if (gradient == null) {
+        return;
+      }
+      const accumulatedMeanSquare = this.accumulatedMeanSquares[i2].variable;
+      const accumulatedMoments = this.accumulatedMoments[i2].variable;
+      tidy(() => {
+        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
+        if (this.centered) {
+          const accumulatedMeanGrad = this.accumulatedMeanGrads[i2].variable;
+          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
+          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
+          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
+          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
+          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
+          accumulatedMoments.assign(newAccumulatedMoments);
+          const newValue = sub(value, newAccumulatedMoments);
+          value.assign(newValue);
+        } else {
+          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
+          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));
+          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);
+          accumulatedMoments.assign(newAccumulatedMoments);
+          const newValue = sub(value, newAccumulatedMoments);
+          value.assign(newValue);
+        }
+      });
+    });
+    this.incrementIterations();
+  }
+  dispose() {
+    if (this.accumulatedMeanSquares != null) {
+      dispose(this.accumulatedMeanSquares.map((v) => v.variable));
+    }
+    if (this.accumulatedMeanGrads != null && this.centered) {
+      dispose(this.accumulatedMeanGrads.map((v) => v.variable));
+    }
+    if (this.accumulatedMoments != null) {
+      dispose(this.accumulatedMoments.map((v) => v.variable));
+    }
+  }
+  async getWeights() {
+    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
+    if (this.centered) {
+      variables.push(...this.accumulatedMeanGrads);
+    }
+    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));
+  }
+  async setWeights(weightValues) {
+    weightValues = await this.extractIterations(weightValues);
+    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
+    const trainable = false;
+    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({
+      originalName: v.name,
+      variable: v.tensor.variable(trainable)
+    }));
+    if (this.centered) {
+      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({
+        originalName: v.name,
+        variable: v.tensor.variable(trainable)
+      }));
+    }
+  }
+  getConfig() {
+    return {
+      "learningRate": this.learningRate,
+      "decay": this.decay,
+      "momentum": this.momentum,
+      "epsilon": this.epsilon,
+      "centered": this.centered
+    };
+  }
+  static fromConfig(cls, config) {
+    return new cls(config["learningRate"], config["decay"], config["momentum"], config["epsilon"], config["centered"]);
+  }
+};
+RMSPropOptimizer.className = "RMSProp";
+registerClass(RMSPropOptimizer);
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
+var OptimizerConstructors = class {
+  static sgd(learningRate) {
+    return new SGDOptimizer(learningRate);
+  }
+  static momentum(learningRate, momentum, useNesterov = false) {
+    return new MomentumOptimizer(learningRate, momentum, useNesterov);
+  }
+  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {
+    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);
+  }
+  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {
+    return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);
+  }
+  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {
+    return new AdadeltaOptimizer(learningRate, rho, epsilon3);
+  }
+  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {
+    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);
+  }
+  static adagrad(learningRate, initialAccumulatorValue = 0.1) {
+    return new AdagradOptimizer(learningRate, initialAccumulatorValue);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/train.js
+var train = {
+  sgd: OptimizerConstructors.sgd,
+  momentum: OptimizerConstructors.momentum,
+  adadelta: OptimizerConstructors.adadelta,
+  adagrad: OptimizerConstructors.adagrad,
+  rmsprop: OptimizerConstructors.rmsprop,
+  adamax: OptimizerConstructors.adamax,
+  adam: OptimizerConstructors.adam
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js
+var delayCallback = (() => {
+  if (typeof requestAnimationFrame !== "undefined") {
+    return requestAnimationFrame;
+  } else if (typeof setImmediate !== "undefined") {
+    return setImmediate;
+  }
+  return (f) => f();
+})();
+function nextFrame() {
+  return new Promise((resolve) => delayCallback(() => resolve()));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
+var backend_util_exports = {};
+__export(backend_util_exports, {
+  ERF_A1: () => ERF_A1,
+  ERF_A2: () => ERF_A2,
+  ERF_A3: () => ERF_A3,
+  ERF_A4: () => ERF_A4,
+  ERF_A5: () => ERF_A5,
+  ERF_P: () => ERF_P,
+  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,
+  SELU_SCALE: () => SELU_SCALE,
+  SELU_SCALEALPHA: () => SELU_SCALEALPHA,
+  applyActivation: () => applyActivation,
+  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
+  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,
+  assertParamsConsistent: () => assertParamsConsistent,
+  assignToTypedArray: () => assignToTypedArray,
+  axesAreInnerMostDims: () => axesAreInnerMostDims,
+  calculateShapes: () => calculateShapes,
+  checkEinsumDimSizes: () => checkEinsumDimSizes,
+  checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,
+  combineLocations: () => combineLocations,
+  complexWithEvenIndex: () => complexWithEvenIndex,
+  complexWithOddIndex: () => complexWithOddIndex,
+  computeConv2DInfo: () => computeConv2DInfo,
+  computeConv3DInfo: () => computeConv3DInfo,
+  computeDefaultPad: () => computeDefaultPad,
+  computeDilation2DInfo: () => computeDilation2DInfo,
+  computeOptimalWindowSize: () => computeOptimalWindowSize,
+  computeOutAndReduceShapes: () => computeOutAndReduceShapes,
+  computeOutShape: () => computeOutShape2,
+  computePool2DInfo: () => computePool2DInfo,
+  computePool3DInfo: () => computePool3DInfo,
+  convertConv2DDataFormat: () => convertConv2DDataFormat,
+  decodeEinsumEquation: () => decodeEinsumEquation,
+  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
+  expandShapeToKeepDim: () => expandShapeToKeepDim,
+  exponent: () => exponent,
+  exponents: () => exponents,
+  fromStringArrayToUint8: () => fromStringArrayToUint8,
+  fromUint8ToStringArray: () => fromUint8ToStringArray,
+  getAxesPermutation: () => getAxesPermutation,
+  getBroadcastDims: () => getBroadcastDims,
+  getComplexWithIndex: () => getComplexWithIndex,
+  getEinsumComputePath: () => getEinsumComputePath,
+  getEinsumPermutation: () => getEinsumPermutation,
+  getFusedBiasGradient: () => getFusedBiasGradient,
+  getFusedDyActivation: () => getFusedDyActivation,
+  getImageCenter: () => getImageCenter,
+  getInnerMostAxes: () => getInnerMostAxes,
+  getPermuted: () => getPermuted,
+  getReductionAxes: () => getReductionAxes,
+  getReshaped: () => getReshaped,
+  getReshapedPermuted: () => getReshapedPermuted,
+  getSliceBeginCoords: () => getSliceBeginCoords,
+  getSliceSize: () => getSliceSize,
+  getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,
+  getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,
+  getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,
+  getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,
+  getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,
+  getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,
+  getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,
+  getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,
+  getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,
+  getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,
+  getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,
+  getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,
+  getUndoAxesPermutation: () => getUndoAxesPermutation,
+  isIdentityPermutation: () => isIdentityPermutation,
+  log: () => log,
+  mergeRealAndImagArrays: () => mergeRealAndImagArrays,
+  prepareAndValidate: () => prepareAndValidate,
+  prepareSplitSize: () => prepareSplitSize,
+  segment_util: () => segment_util_exports,
+  shouldFuse: () => shouldFuse,
+  slice_util: () => slice_util_exports,
+  splitRealAndImagArrays: () => splitRealAndImagArrays,
+  tupleValuesAreOne: () => tupleValuesAreOne,
+  upcastType: () => upcastType,
+  validateInput: () => validateInput,
+  validateUpdateShape: () => validateUpdateShape,
+  warn: () => warn
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
+function assertParamsConsistent(shapes, axis) {
+  const rank = shapes[0].length;
+  shapes.forEach((shape, i2) => {
+    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i2}] must be the same as the rank of the rest (${rank})`);
+  });
+  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
+  const firstShape = shapes[0];
+  shapes.forEach((shape, i2) => {
+    for (let r2 = 0; r2 < rank; r2++) {
+      assert(r2 === axis || shape[r2] === firstShape[r2], () => `Error in concat${rank}D: Shape of tensors[${i2}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i2}.`);
+    }
+  });
+}
+function computeOutShape2(shapes, axis) {
+  const outputShape = shapes[0].slice();
+  for (let i2 = 1; i2 < shapes.length; i2++) {
+    outputShape[axis] += shapes[i2][axis];
+  }
+  return outputShape;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
+var PARALLELIZE_THRESHOLD = 30;
+function computeOptimalWindowSize(inSize) {
+  if (inSize <= PARALLELIZE_THRESHOLD) {
+    return inSize;
+  }
+  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
+function getImageCenter(center, imageHeight, imageWidth) {
+  const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
+  const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
+  return [centerX, centerY];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
+function getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {
+  let reshaped = [];
+  if (batchToSpace) {
+    reshaped = reshaped.concat(blockShape.slice(0));
+    reshaped.push(inputShape[0] / prod6);
+    reshaped = reshaped.concat(inputShape.slice(1));
+  } else {
+    reshaped = reshaped.concat(inputShape[0]);
+    const spatialLength = blockShape.length;
+    for (let i2 = 0; i2 < spatialLength; ++i2) {
+      reshaped = reshaped.concat([inputShape[i2 + 1] / blockShape[i2], blockShape[i2]]);
+    }
+    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
+  }
+  return reshaped;
+}
+function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
+  const permuted = [];
+  if (batchToSpace) {
+    permuted.push(blockShapeRank);
+    for (let i2 = blockShapeRank + 1; i2 < reshapedRank; ++i2) {
+      if (i2 <= 2 * blockShapeRank) {
+        permuted.push(i2);
+        permuted.push(i2 - (blockShapeRank + 1));
+      } else {
+        permuted.push(i2);
+      }
+    }
+  } else {
+    const permutedBeforeBatch = [];
+    const permutedAfterBatch = [];
+    for (let i2 = 1; i2 < reshapedRank; ++i2) {
+      if (i2 >= blockShapeRank * 2 + 1 || i2 % 2 === 1) {
+        permutedAfterBatch.push(i2);
+      } else {
+        permutedBeforeBatch.push(i2);
+      }
+    }
+    permuted.push(...permutedBeforeBatch);
+    permuted.push(0);
+    permuted.push(...permutedAfterBatch);
+  }
+  return permuted;
+}
+function getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true) {
+  const reshapedPermuted = [];
+  if (batchToSpace) {
+    reshapedPermuted.push(inputShape[0] / prod6);
+  } else {
+    reshapedPermuted.push(inputShape[0] * prod6);
+  }
+  for (let i2 = 1; i2 < inputShape.length; ++i2) {
+    if (i2 <= blockShape.length) {
+      if (batchToSpace) {
+        reshapedPermuted.push(blockShape[i2 - 1] * inputShape[i2]);
+      } else {
+        reshapedPermuted.push(inputShape[i2] / blockShape[i2 - 1]);
+      }
+    } else {
+      reshapedPermuted.push(inputShape[i2]);
+    }
+  }
+  return reshapedPermuted;
+}
+function getSliceBeginCoords(crops, blockShape) {
+  const sliceBeginCoords = [0];
+  for (let i2 = 0; i2 < blockShape; ++i2) {
+    sliceBeginCoords.push(crops[i2][0]);
+  }
+  return sliceBeginCoords;
+}
+function getSliceSize(uncroppedShape, crops, blockShape) {
+  const sliceSize = uncroppedShape.slice(0, 1);
+  for (let i2 = 0; i2 < blockShape; ++i2) {
+    sliceSize.push(uncroppedShape[i2 + 1] - crops[i2][0] - crops[i2][1]);
+  }
+  return sliceSize;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
+var SELU_SCALEALPHA = 1.7580993408473768;
+var SELU_SCALE = 1.0507009873554805;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
+var ERF_P = 0.3275911;
+var ERF_A1 = 0.254829592;
+var ERF_A2 = -0.284496736;
+var ERF_A3 = 1.421413741;
+var ERF_A4 = -1.453152027;
+var ERF_A5 = 1.061405429;
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
+function mergeRealAndImagArrays(real5, imag5) {
+  if (real5.length !== imag5.length) {
+    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);
+  }
+  const result = new Float32Array(real5.length * 2);
+  for (let i2 = 0; i2 < result.length; i2 += 2) {
+    result[i2] = real5[i2 / 2];
+    result[i2 + 1] = imag5[i2 / 2];
+  }
+  return result;
+}
+function splitRealAndImagArrays(complex5) {
+  const real5 = new Float32Array(complex5.length / 2);
+  const imag5 = new Float32Array(complex5.length / 2);
+  for (let i2 = 0; i2 < complex5.length; i2 += 2) {
+    real5[i2 / 2] = complex5[i2];
+    imag5[i2 / 2] = complex5[i2 + 1];
+  }
+  return { real: real5, imag: imag5 };
+}
+function complexWithEvenIndex(complex5) {
+  const len = Math.ceil(complex5.length / 4);
+  const real5 = new Float32Array(len);
+  const imag5 = new Float32Array(len);
+  for (let i2 = 0; i2 < complex5.length; i2 += 4) {
+    real5[Math.floor(i2 / 4)] = complex5[i2];
+    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  }
+  return { real: real5, imag: imag5 };
+}
+function complexWithOddIndex(complex5) {
+  const len = Math.floor(complex5.length / 4);
+  const real5 = new Float32Array(len);
+  const imag5 = new Float32Array(len);
+  for (let i2 = 2; i2 < complex5.length; i2 += 4) {
+    real5[Math.floor(i2 / 4)] = complex5[i2];
+    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  }
+  return { real: real5, imag: imag5 };
+}
+function getComplexWithIndex(complex5, index) {
+  const real5 = complex5[index * 2];
+  const imag5 = complex5[index * 2 + 1];
+  return { real: real5, imag: imag5 };
+}
+function assignToTypedArray(data, real5, imag5, index) {
+  data[index * 2] = real5;
+  data[index * 2 + 1] = imag5;
+}
+function exponents(n, inverse) {
+  const real5 = new Float32Array(n / 2);
+  const imag5 = new Float32Array(n / 2);
+  for (let i2 = 0; i2 < Math.ceil(n / 2); i2++) {
+    const x = (inverse ? 2 : -2) * Math.PI * (i2 / n);
+    real5[i2] = Math.cos(x);
+    imag5[i2] = Math.sin(x);
+  }
+  return { real: real5, imag: imag5 };
+}
+function exponent(k, n, inverse) {
+  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
+  const real5 = Math.cos(x);
+  const imag5 = Math.sin(x);
+  return { real: real5, imag: imag5 };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js
+var ARROW = "->";
+var ARROW_REGEX = /->/g;
+var COMMA = ",";
+var ELLIPSIS = "...";
+function decodeEinsumEquation(equation, numTensors) {
+  equation = equation.replace(/\s/g, "");
+  const numArrows = (equation.length - equation.replace(ARROW_REGEX, "").length) / ARROW.length;
+  if (numArrows < 1) {
+    throw new Error("Equations without an arrow are not supported.");
+  } else if (numArrows > 1) {
+    throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`);
+  }
+  const [inputString, outputString] = equation.split(ARROW);
+  assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`);
+  const inputTerms = inputString.split(COMMA);
+  const numInputs = inputTerms.length;
+  if (numTensors !== numInputs) {
+    throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);
+  }
+  if (numInputs > 2) {
+    throw new Error("Support for more than 2 input tensors is not implemented yet.");
+  }
+  const allDims = [];
+  for (let i2 = 0; i2 < outputString.length; ++i2) {
+    const dimName = outputString[i2];
+    if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {
+      throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);
+    }
+    if (allDims.indexOf(dimName) === -1) {
+      allDims.push(dimName);
+    }
+  }
+  for (let i2 = 0; i2 < inputString.length; ++i2) {
+    const dimName = inputString[i2];
+    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
+      allDims.push(dimName);
+    }
+  }
+  const idDims = new Array(inputTerms.length);
+  for (let i2 = 0; i2 < numInputs; ++i2) {
+    if (new Set(inputTerms[i2].split("")).size !== inputTerms[i2].length) {
+      throw new Error(`Found duplicate axes in input component ${inputTerms[i2]}. Support for duplicate axes in input is not implemented yet.`);
+    }
+    idDims[i2] = [];
+    for (let j = 0; j < inputTerms[i2].length; ++j) {
+      idDims[i2].push(allDims.indexOf(inputTerms[i2][j]));
+    }
+  }
+  const numDims = allDims.length;
+  const numOutDims = outputString.length;
+  const summedDims = [];
+  for (let i2 = numOutDims; i2 < numDims; ++i2) {
+    summedDims.push(i2);
+  }
+  return { allDims, summedDims, idDims };
+}
+function getEinsumPermutation(nDims, idDims) {
+  let permutationIndices = new Array(nDims);
+  permutationIndices.fill(-1);
+  for (let i2 = 0; i2 < idDims.length; ++i2) {
+    permutationIndices[idDims[i2]] = i2;
+  }
+  const expandDims7 = [];
+  for (let i2 = 0; i2 < nDims; ++i2) {
+    if (permutationIndices[i2] === -1) {
+      expandDims7.push(i2);
+    }
+  }
+  permutationIndices = permutationIndices.filter((d) => d !== -1);
+  return { permutationIndices, expandDims: expandDims7 };
+}
+function checkEinsumDimSizes(nDims, idDims, tensors) {
+  const dimSizes = new Array(nDims);
+  for (let i2 = 0; i2 < tensors.length; ++i2) {
+    const shape = tensors[i2].shape;
+    for (let j = 0; j < idDims[i2].length; ++j) {
+      if (dimSizes[idDims[i2][j]] === void 0) {
+        dimSizes[idDims[i2][j]] = shape[j];
+      } else {
+        assert(dimSizes[idDims[i2][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i2][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
+      }
+    }
+  }
+}
+function getEinsumComputePath(summedDims, idDims) {
+  const path = summedDims;
+  const steps = [];
+  let nSteps = 0;
+  if (summedDims.length === 0) {
+    path.push(-1);
+  }
+  nSteps = summedDims.length + 1;
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    steps.push([]);
+  }
+  const computedTermIndices = [];
+  for (let i2 = 0; i2 < path.length; ++i2) {
+    const summedDim = path[i2];
+    const termIndices = findTermsWithDim(idDims, summedDim);
+    for (const termIndex of termIndices) {
+      if (computedTermIndices.indexOf(termIndex) === -1) {
+        steps[i2].push(termIndex);
+        computedTermIndices.push(termIndex);
+      }
+    }
+  }
+  return { path, steps };
+}
+function isIdentityPermutation(perm) {
+  return perm.every((dim, index) => dim === index);
+}
+function findTermsWithDim(idDims, dim) {
+  const termIndices = [];
+  for (let i2 = 0; i2 < idDims.length; ++i2) {
+    if (idDims[i2].length === 0 || idDims[i2].indexOf(dim) !== -1 || dim === -1) {
+      termIndices.push(i2);
+    }
+  }
+  return termIndices;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
+function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
+  let splitSizes = [];
+  if (typeof numOrSizeSplits === "number") {
+    assert(x.shape[axis] % numOrSizeSplits === 0, () => "Number of splits must evenly divide the axis.");
+    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
+  } else {
+    const numOfNegs = numOrSizeSplits.reduce((count2, value) => {
+      if (value === -1) {
+        count2 += 1;
+      }
+      return count2;
+    }, 0);
+    assert(numOfNegs <= 1, () => "There should be only one negative value in split array.");
+    const negIndex = numOrSizeSplits.indexOf(-1);
+    if (negIndex !== -1) {
+      const total = numOrSizeSplits.reduce((a6, b) => b > 0 ? a6 + b : a6);
+      numOrSizeSplits[negIndex] = x.shape[axis] - total;
+    }
+    assert(x.shape[axis] === numOrSizeSplits.reduce((a6, b) => a6 + b), () => "The sum of sizes must match the size of the axis dimension.");
+    splitSizes = numOrSizeSplits;
+  }
+  return splitSizes;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js
+function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {
+  return `Received SparseTensor with denseShape[0] = 0 but
+  indices.shape[0] = ${indicesLength}`;
+}
+function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {
+  return `indices(${index}, 0) is invalid: ${value} < 0`;
+}
+function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {
+  return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js
+function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {
+  return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;
+}
+function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {
+  return `size ${dim} must be non-negative, not ${value}`;
+}
+function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {
+  return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
+}
+function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {
+  const inputSize = sizeFromShape(inputShape);
+  const outputSize = sizeFromShape(outputShape);
+  return `Input to reshape is a SparseTensor with ${inputSize}
+  dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;
+}
+function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {
+  const inputSize = sizeFromShape(inputShape);
+  const outputSize = sizeFromShape(outputShape);
+  return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js
+function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {
+  return `segment ids must be >= 0`;
+}
+function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {
+  return `segment ids are not increasing`;
+}
+function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {
+  return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;
+}
+function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {
+  return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
+var segment_util_exports = {};
+__export(segment_util_exports, {
+  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
+  computeOutShape: () => computeOutShape3,
+  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
+});
+function segOpComputeOptimalWindowSize(inSize, numSegments) {
+  let done = false;
+  let res;
+  if (inSize <= PARALLELIZE_THRESHOLD) {
+    res = inSize;
+    done = true;
+  } else {
+    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+  }
+  while (!done) {
+    if (res > numSegments || res === inSize) {
+      done = true;
+    } else {
+      res = nearestDivisor(inSize, res + 1);
+    }
+  }
+  return res;
+}
+function computeOutShape3(aShape, axis, numSegments) {
+  const outShape = [];
+  const rank = aShape.length;
+  for (let dim = 0; dim < rank; dim++) {
+    if (dim !== axis) {
+      outShape.push(aShape[dim]);
+    } else {
+      outShape.push(numSegments);
+    }
+  }
+  return outShape;
+}
+function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
+  const indicesRank = indices.shape.length;
+  const xRank = x.shape.length;
+  if (batchDims !== 0) {
+    if (batchDims < -indicesRank || batchDims > indicesRank) {
+      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);
+    }
+  }
+  if (batchDims < 0) {
+    batchDims += indicesRank;
+  }
+  if (batchDims > xRank) {
+    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
+    ${xRank}).`);
+  }
+  if (axis < batchDims) {
+    throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
+  }
+  for (let i2 = 0; i2 < batchDims; ++i2) {
+    if (x.shape[i2] !== indices.shape[i2]) {
+      throw new Error(`x.shape[${i2}]: ${x.shape[i2]} should be equal to indices.shape[${i2}]: ${indices.shape[i2]}.`);
+    }
+  }
+  const dimSize = x.shape[axis];
+  const outputShape = [];
+  let batchSize = 1;
+  let outerSize = 1;
+  let sliceSize = 1;
+  for (let i2 = 0; i2 < batchDims; ++i2) {
+    outputShape.push(x.shape[i2]);
+    batchSize *= x.shape[i2];
+  }
+  for (let i2 = batchDims; i2 < axis; i2++) {
+    outputShape.push(x.shape[i2]);
+    outerSize *= x.shape[i2];
+  }
+  for (let i2 = batchDims; i2 < indicesRank; i2++) {
+    outputShape.push(indices.shape[i2]);
+  }
+  for (let i2 = axis + 1; i2 < xRank; i2++) {
+    outputShape.push(x.shape[i2]);
+    sliceSize *= x.shape[i2];
+  }
+  return { batchSize, sliceSize, outerSize, dimSize, outputShape };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
+function fromUint8ToStringArray(vals) {
+  try {
+    return vals.map((val) => decodeString(val));
+  } catch (err) {
+    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);
+  }
+}
+function fromStringArrayToUint8(strings) {
+  return strings.map((s2) => encodeString(s2));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
+var kernel_impls_exports = {};
+__export(kernel_impls_exports, {
+  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
+  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
+  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
+  whereImpl: () => whereImpl
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
+var absGradConfig = {
+  kernelName: Abs,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, step(cast(x, "float32"), -1)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
+var acosGradConfig = {
+  kernelName: Acos,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const a6 = square(cast(x, "float32"));
+        const b = sqrt(sub(scalar(1), a6));
+        return neg(div(dy, b));
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
+var acoshGradConfig = {
+  kernelName: Acosh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const a6 = sqrt(sub(square(cast(x, "float32")), 1));
+        return div(dy, a6);
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
+var addGradConfig = {
+  kernelName: Add,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, a6.shape);
+    };
+    const derB = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, b.shape);
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
+var addNGradConfig = {
+  kernelName: AddN,
+  saveAllInputs: true,
+  gradFunc: (dy, saved) => {
+    const ders = {};
+    saved.forEach((_, i2) => {
+      ders[i2] = () => dy.clone();
+    });
+    return ders;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
+var argMaxGradConfig = {
+  kernelName: ArgMax,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => zerosLike(x) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
+var argMinGradConfig = {
+  kernelName: ArgMin,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => zerosLike(x) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
+var asinGradConfig = {
+  kernelName: Asin,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32"))))) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
+var asinhGradConfig = {
+  kernelName: Asinh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const a6 = sqrt(add2(scalar(1), square(cast(x, "float32"))));
+        return div(dy, a6);
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
+var atan2GradConfig = {
+  kernelName: Atan2,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const d = add2(square(a6), square(b));
+      let res = mul(dy, div(b, d));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, a6.shape);
+    };
+    const derB = () => {
+      const d = add2(square(a6), square(b));
+      let res = neg(mul(dy, div(a6, d)));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, b.shape);
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
+var atanGradConfig = {
+  kernelName: Atan,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, add2(square(cast(x, "float32")), 1)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
+var atanhGradConfig = {
+  kernelName: Atanh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, sub(scalar(1), square(cast(x, "float32")))) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js
+function avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) {
+  const $dy = convertToTensor(dy, "dy", "avgPool3dGrad");
+  const $input = convertToTensor(input2, "input", "avgPool3dGrad");
+  let dy5D = $dy;
+  let input5D = $input;
+  let reshapedTo5D = false;
+  if ($input.rank === 4) {
+    reshapedTo5D = true;
+    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
+    input5D = reshape($input, [
+      1,
+      $input.shape[0],
+      $input.shape[1],
+      $input.shape[2],
+      $input.shape[3]
+    ]);
+  }
+  assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
+  assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
+  checkPadOnDimRoundingMode("avgPool3dGrad", pad3, dimRoundingMode);
+  const inputs = { dy: dy5D, input: input5D };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };
+  const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var avgPool3dGrad = op({ avgPool3dGrad_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
+var avgPool3DGradConfig = {
+  kernelName: AvgPool3D,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+    return {
+      x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js
+function avgPoolGrad_(dy, input2, filterSize, strides, pad3) {
+  const $dy = convertToTensor(dy, "dy", "avgPoolGrad");
+  const $input = convertToTensor(input2, "input", "avgPoolGrad");
+  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
+  let input4D = $input;
+  let dy4D = $dy;
+  let reshapedTo4D = false;
+  if ($input.rank === 3) {
+    reshapedTo4D = true;
+    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
+    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
+  }
+  assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);
+  assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);
+  const inputs = { dy: dy4D, input: input4D };
+  const attrs = { filterSize, strides, pad: pad3 };
+  const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
+  if (reshapedTo4D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
+  }
+  return res;
+}
+var avgPoolGrad = op({ avgPoolGrad_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
+var avgPoolGradConfig = {
+  kernelName: AvgPool,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { filterSize, strides, pad: pad3 } = attrs;
+    return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
+var batchMatMulGradConfig = {
+  kernelName: BatchMatMul,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved, attrs) => {
+    const [a6, b] = saved;
+    const { transposeA, transposeB } = attrs;
+    if (!transposeA && !transposeB) {
+      return {
+        a: () => matMul(dy, b, false, true),
+        b: () => matMul(a6, dy, true, false)
+      };
+    } else if (!transposeA && transposeB) {
+      return {
+        a: () => matMul(dy, b, false, false),
+        b: () => matMul(dy, a6, true, false)
+      };
+    } else if (transposeA && !transposeB) {
+      return {
+        a: () => matMul(b, dy, false, true),
+        b: () => matMul(a6, dy, false, false)
+      };
+    } else {
+      return {
+        a: () => matMul(b, dy, true, true),
+        b: () => matMul(dy, a6, true, true)
+      };
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
+var batchToSpaceNDGradConfig = {
+  kernelName: BatchToSpaceND,
+  gradFunc: (dy, saved, attrs) => {
+    const { blockShape, crops } = attrs;
+    return { x: () => spaceToBatchND(dy, blockShape, crops) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
+var broadcastToGradConfig = {
+  kernelName: BroadcastTo,
+  gradFunc: (dy, saved, attrs) => {
+    const broadCastToAttrs = attrs;
+    const inputShape = broadCastToAttrs.inputShape;
+    const outputShape = broadCastToAttrs.shape;
+    const reps = Array.from(outputShape);
+    for (let i2 = inputShape.length - 1; i2 >= 0; i2--) {
+      if (inputShape[i2] === outputShape[i2]) {
+        reps[i2] = 1;
+      } else if (inputShape[i2] !== 1) {
+        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
+      }
+    }
+    const axes = [];
+    for (let i2 = 0; i2 < reps.length; i2++) {
+      if (reps[i2] > 1) {
+        axes.push(i2);
+      }
+    }
+    return { x: () => sum2(dy, axes, true) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
+var castGradConfig = {
+  kernelName: Cast,
+  gradFunc: (dy) => {
+    return { x: () => dy.clone() };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
+var ceilGradConfig = {
+  kernelName: Ceil,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
+var clipByValueGradConfig = {
+  kernelName: ClipByValue,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { clipValueMin, clipValueMax } = attrs;
+    return {
+      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js
+var complexAbsGradConfig = {
+  kernelName: ComplexAbs,
+  inputsToSave: ["x"],
+  gradFunc: absGradConfig.gradFunc
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
+var concatGradConfig = {
+  kernelName: Concat,
+  saveAllInputs: true,
+  gradFunc: (dy, saved, attrs) => {
+    const shapes = saved.map((t2) => t2.shape);
+    const { axis } = attrs;
+    const $axis = parseAxisParam(axis, saved[0].shape)[0];
+    const sizeSplits = shapes.map((s2) => s2[$axis]);
+    const derTensors = split(dy, sizeSplits, $axis);
+    return derTensors.map((t2) => () => t2);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
+var conv2DGradConfig = {
+  kernelName: Conv2D,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x4D, $filter] = saved;
+    const { dilations, strides, pad: pad3, dataFormat } = attrs;
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
+    return {
+      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat),
+      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
+var conv2DBackpropInputGradConfig = {
+  kernelName: Conv2DBackpropInput,
+  inputsToSave: ["dy", "filter"],
+  gradFunc: (ddx, saved, attrs) => {
+    const [dy, filter] = saved;
+    const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+    return {
+      dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode),
+      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
+function conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {
+  let x5D = x;
+  if (x.rank === 4) {
+    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
+  }
+  let dy5D = dy;
+  if (dy5D.rank === 4) {
+    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
+  }
+  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
+  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
+  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
+  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
+  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
+  const inputs = { x: x5D, dy: dy5D };
+  const attrs = { strides, pad: pad3, filterShape };
+  return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);
+}
+var conv3DBackpropFilter = op({ conv3DBackpropFilter_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
+var conv3DGradConfig = {
+  kernelName: Conv3D,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const { dilations, strides, pad: pad3 } = attrs;
+    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
+    const [x5D, $filter] = saved;
+    return {
+      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3),
+      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
+var cosGradConfig = {
+  kernelName: Cos,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(neg(sin(cast(x, "float32"))), dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
+var coshGradConfig = {
+  kernelName: Cosh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(sinh(cast(x, "float32")), dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
+var cumsumGradConfig = {
+  kernelName: Cumsum,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { axis, exclusive, reverse: reverse5 } = attrs;
+    return {
+      x: () => {
+        const permutation = getAxesPermutation([axis], x.rank);
+        let out = cumsum(dy, axis, exclusive, !reverse5);
+        if (permutation != null) {
+          out = transpose(out, permutation);
+        }
+        return out;
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
+var depthwiseConv2dNativeGradConfig = {
+  kernelName: DepthwiseConv2dNative,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const { dilations, strides, pad: pad3, dimRoundingMode } = attrs;
+    const $dilations = dilations == null ? [1, 1] : dilations;
+    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
+    const [x, filter] = saved;
+    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
+    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
+    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
+    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);
+    checkPadOnDimRoundingMode("depthwiseConv2d", pad3, dimRoundingMode);
+    return {
+      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode),
+      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
+var dilation2dGradConfig = {
+  kernelName: Dilation2D,
+  inputsToSave: ["x", "filter"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, filter] = saved;
+    const inputInputs = { x, filter, dy };
+    const filterInputs = { x, filter, dy };
+    return {
+      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
+      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
+var eluGradConfig = {
+  kernelName: Elu,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    const inputs = { dy, y };
+    return { x: () => ENGINE.runKernel(EluGrad, inputs) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
+var erfGradConfig = {
+  kernelName: Erf,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    const a6 = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
+    return { x: () => mul(dy, a6) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
+var expGradConfig = {
+  kernelName: Exp,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    return { x: () => mul(dy, y) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js
+var expandDimsGradConfig = {
+  kernelName: ExpandDims,
+  inputsToSave: ["input"],
+  gradFunc: (dy, saved) => {
+    const [input2] = saved;
+    return { input: () => reshape(dy, input2.shape) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
+var expm1GradConfig = {
+  kernelName: Expm1,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, exp(x)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
+var floorGradConfig = {
+  kernelName: Floor,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
+var floorDivGradConfig = {
+  kernelName: FloorDiv,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const res = div(dy, cast(b, "float32"));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), a6.shape);
+      }
+      return res;
+    };
+    const derB = () => {
+      let res = mul(dy, cast(a6, "float32"));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = reshape(sum2(res, reduceAxes), b.shape);
+      }
+      const tmp = square(b);
+      return neg(div(res, cast(tmp, "float32")));
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
+var fusedBatchNormGradConfig = {
+  kernelName: FusedBatchNorm,
+  inputsToSave: ["x", "mean", "variance", "scale"],
+  gradFunc: (dy, saved, attrs) => {
+    const { varianceEpsilon } = attrs;
+    const [x, mean5, variance, scale2] = saved;
+    const scaleValue = scale2 == null ? scalar(1) : scale2;
+    const reductionAxes = getReductionAxes(mean5.shape, x.shape);
+    const tileShape = [];
+    if (mean5.rank === 1) {
+      for (let i2 = 0; i2 < x.shape.length - 1; ++i2) {
+        tileShape.push(x.shape[i2]);
+      }
+      tileShape.push(1);
+    }
+    const xMinusMean = sub(x, mean5);
+    const dyTimesScaleValue = mul(dy, scaleValue);
+    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
+    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
+    const derX = () => {
+      if (mean5.rank === 1) {
+        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);
+      } else {
+        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
+      }
+    };
+    const derMean = () => {
+      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
+      if (mean5.rank === 1) {
+        meanDer = sum2(meanDer, reductionAxes);
+      }
+      return reshape(meanDer, mean5.shape);
+    };
+    const derVariance = () => {
+      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
+      if (mean5.rank === 1) {
+        varianceDer = sum2(varianceDer, reductionAxes);
+      }
+      return reshape(varianceDer, mean5.shape);
+    };
+    const derScale = () => {
+      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
+      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
+      if (mean5.rank === 1) {
+        scaleDer = sum2(scaleDer, reductionAxes);
+      }
+      return reshape(scaleDer, mean5.shape);
+    };
+    const derOffset = () => {
+      let offsetDer = dy;
+      if (mean5.rank === 1) {
+        offsetDer = sum2(offsetDer, reductionAxes);
+      }
+      return reshape(offsetDer, mean5.shape);
+    };
+    return {
+      x: derX,
+      mean: derMean,
+      variance: derVariance,
+      scale: derScale,
+      offset: derOffset
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
+var gatherGradConfig = {
+  kernelName: GatherV2,
+  inputsToSave: ["x", "indices"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, indices] = saved;
+    const { axis } = attrs;
+    const parsedAxis = parseAxisParam(axis, x.shape)[0];
+    const derX = () => {
+      const paramsShape = x.shape;
+      const indicesSize = indices.size;
+      const outerShape = paramsShape.slice(0, parsedAxis);
+      const outerDims = outerShape.length;
+      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
+      const innerDims = innerShape.length;
+      const outerAxesIndices = arrayRange(0, outerDims);
+      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
+      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
+      const values = reshape(dy, valuesShape);
+      const reshapedIndices = reshape(indices, [indicesSize]);
+      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
+      const valuesTranspose = transpose(values, transposeDims);
+      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
+      const invertTransposeDims = getUndoAxesPermutation(transposeDims);
+      paramsGrad = transpose(paramsGrad, invertTransposeDims);
+      return paramsGrad;
+    };
+    return { x: derX, indices: () => indices };
+  }
+};
+function arrayRange(start, stop) {
+  const result = [];
+  for (let i2 = start; i2 < stop; ++i2) {
+    result.push(i2);
+  }
+  return result;
+}
+function arrayConcat(arrays) {
+  const result = [];
+  for (let i2 = 0; i2 < arrays.length; ++i2) {
+    for (let j = 0; j < arrays[i2].length; ++j) {
+      result.push(arrays[i2][j]);
+    }
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
+var greaterEqualGradConfig = {
+  kernelName: GreaterEqual,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    return { a: () => zerosLike(a6), b: () => zerosLike(b) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
+var identityGradConfig = {
+  kernelName: Identity,
+  gradFunc: (dy) => {
+    return { x: () => cast(dy, "float32") };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
+var isFiniteGradConfig = {
+  kernelName: IsFinite,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
+var isInfGradConfig = {
+  kernelName: IsInf,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
+var isNanGradConfig = {
+  kernelName: IsNan,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js
+var leakyReluGradConfig = {
+  kernelName: LeakyRelu,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { alpha } = attrs;
+    const mask = greater(x, 0);
+    return { x: () => where(mask, dy, mul(dy, alpha)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
+var log1pGradConfig = {
+  kernelName: Log1p,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, add2(x, 1)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
+var logGradConfig = {
+  kernelName: Log,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, cast(x, "float32")) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
+var logSoftmaxGradConfig = {
+  kernelName: LogSoftmax,
+  inputsToSave: [],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [value] = saved;
+    const { axis } = attrs;
+    return {
+      logits: () => {
+        const keepDims = true;
+        const softmax7 = exp(value);
+        return sub(dy, mul(sum2(dy, axis, keepDims), softmax7));
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
+function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
+  const inputs = { x, y, dy };
+  const attrs = { depthRadius, bias, alpha, beta };
+  return ENGINE.runKernel(LRNGrad, inputs, attrs);
+}
+var localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
+var lrnGradConfig = {
+  kernelName: LRN,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, y] = saved;
+    const { depthRadius, bias, alpha, beta } = attrs;
+    return {
+      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
+function gradForMinAndMax(dy, y, xOrig, origAxes) {
+  if (y.rank < xOrig.rank) {
+    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
+  }
+  if (dy.rank < xOrig.rank) {
+    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
+  }
+  return {
+    x: () => {
+      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
+      return dx;
+    }
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
+var maxGradConfig = {
+  kernelName: Max,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const maxAttrs = attrs;
+    const { reductionIndices } = maxAttrs;
+    const x = saved[0];
+    const y = saved[1];
+    const origAxes = parseAxisParam(reductionIndices, x.shape);
+    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);
+    return {
+      x: () => {
+        return maxGrad["x"]();
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
+var maximumGradConfig = {
+  kernelName: Maximum,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const derA = () => mul(dy, cast(greaterEqual(a6, b), "float32"));
+    const derB = () => mul(dy, cast(less(a6, b), "float32"));
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js
+function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {
+  const $dy = convertToTensor(dy, "dy", "maxPool3dGrad");
+  const $input = convertToTensor(input2, "input", "maxPool3dGrad");
+  const $output = convertToTensor(output, "output", "maxPool3dGrad");
+  let dy5D = $dy;
+  let input5D = $input;
+  let output5D = $output;
+  let reshapedTo5D = false;
+  if ($input.rank === 4) {
+    reshapedTo5D = true;
+    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
+    input5D = reshape($input, [
+      1,
+      $input.shape[0],
+      $input.shape[1],
+      $input.shape[2],
+      $input.shape[3]
+    ]);
+    output5D = reshape($output, [
+      1,
+      $output.shape[0],
+      $output.shape[1],
+      $output.shape[2],
+      $output.shape[3]
+    ]);
+  }
+  assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
+  assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
+  assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);
+  checkPadOnDimRoundingMode("maxPool3dGrad", pad3, dimRoundingMode);
+  const inputs = { dy: dy5D, input: input5D, output: output5D };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };
+  const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);
+  if (reshapedTo5D) {
+    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
+  }
+  return res;
+}
+var maxPool3dGrad = op({ maxPool3dGrad_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
+var maxPool3DGradConfig = {
+  kernelName: MaxPool3D,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, y] = saved;
+    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+    return {
+      x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js
+function maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {
+  const $dy = convertToTensor(dy, "dy", "maxPoolGrad");
+  const $input = convertToTensor(input2, "input", "maxPoolGrad");
+  const $output = convertToTensor(output, "output", "maxPoolGrad");
+  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
+  assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);
+  assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);
+  checkPadOnDimRoundingMode("maxPoolGrad", pad3, dimRoundingMode);
+  const inputs = { dy: $dy, input: $input, output: $output };
+  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };
+  return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);
+}
+var maxPoolGrad = op({ maxPoolGrad_ });
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
+var maxPoolGradConfig = {
+  kernelName: MaxPool,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [x, y] = saved;
+    const { filterSize, strides, pad: pad3 } = attrs;
+    return {
+      x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3)
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js
+var meanGradConfig = {
+  kernelName: Mean,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { axis } = attrs;
+    const axes = parseAxisParam(axis, x.shape);
+    const shapes = computeOutAndReduceShapes(x.shape, axes);
+    const reduceShape = shapes[1];
+    const reduceSize = sizeFromShape(reduceShape);
+    const derX = () => {
+      const expandedDyShape = x.shape.slice();
+      axes.forEach((axis2) => {
+        expandedDyShape[axis2] = 1;
+      });
+      const expandedDy = reshape(dy, expandedDyShape);
+      const res = div(mul(expandedDy, ones2(x.shape, "float32")), reduceSize);
+      return res;
+    };
+    return { x: derX };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
+var minGradConfig = {
+  kernelName: Min,
+  inputsToSave: ["x"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const minAttrs = attrs;
+    const { axis } = minAttrs;
+    const [x, y] = saved;
+    const origAxes = parseAxisParam(axis, x.shape);
+    const minGrad = gradForMinAndMax(dy, y, x, origAxes);
+    return {
+      x: () => {
+        return minGrad["x"]();
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
+var minimumGradConfig = {
+  kernelName: Minimum,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const derA = () => mul(dy, cast(lessEqual(a6, b), "float32"));
+    const derB = () => mul(dy, cast(greater(a6, b), "float32"));
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js
+var mirrorPadGradConfig = {
+  kernelName: MirrorPad,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const x = saved[0];
+    const { paddings } = attrs;
+    const begin = paddings.map((p2) => p2[0]);
+    return { x: () => slice(dy, begin, x.shape) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
+var modGradConfig = {
+  kernelName: Mod,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(dy, reduceAxes), a6.shape);
+      }
+      return dy;
+    };
+    const derB = () => {
+      const res = mul(dy, neg(floor(div(a6, b))));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), b.shape);
+      }
+      return res;
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
+var multiplyGradConfig = {
+  kernelName: Multiply,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const res = mul(dy, cast(b, "float32"));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), a6.shape);
+      }
+      return res;
+    };
+    const derB = () => {
+      const res = mul(dy, cast(a6, "float32"));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), b.shape);
+      }
+      return res;
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js
+var negGradConfig = {
+  kernelName: Neg,
+  gradFunc: (dy) => {
+    return { x: () => neg(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
+var oneHotGradConfig = {
+  kernelName: OneHot,
+  inputsToSave: ["indices"],
+  gradFunc: (dy, saved) => {
+    const indices = saved[0];
+    return { indices: () => zeros(indices.shape, "float32") };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
+var onesLikeGradConfig = {
+  kernelName: OnesLike,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js
+var packGradConfig = {
+  kernelName: Pack,
+  saveAllInputs: true,
+  gradFunc: (dy, saved, attrs) => {
+    const { axis } = attrs;
+    const derTensors = unstack(dy, axis);
+    return derTensors.map((t2) => () => t2);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
+var padV2GradConfig = {
+  kernelName: PadV2,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const x = saved[0];
+    const { paddings } = attrs;
+    const begin = paddings.map((p2) => p2[0]);
+    return { x: () => slice(dy, begin, x.shape) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
+var powGradConfig = {
+  kernelName: Pow,
+  inputsToSave: ["a", "b"],
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [a6, b, y] = saved;
+    const base = a6;
+    const exp5 = b;
+    const outShape = assertAndGetBroadcastShape(base.shape, exp5.shape);
+    const derBase = () => {
+      const expFloat = cast(exp5, "float32");
+      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
+      const reduceAxes = getReductionAxes(base.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, base.shape);
+    };
+    const derExp = () => {
+      const condition = greater(base, 0);
+      const logBase = where(condition, log2(base), zerosLike(base));
+      let res = mul(dy, mul(y, logBase));
+      const reduceAxes = getReductionAxes(exp5.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, exp5.shape);
+    };
+    return { a: derBase, b: derExp };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
+var preluGradConfig = {
+  kernelName: Prelu,
+  inputsToSave: ["x", "alpha"],
+  gradFunc: (dy, saved) => {
+    const [x, alpha] = saved;
+    const mask = greater(x, 0);
+    return {
+      x: () => where(mask, dy, mul(dy, alpha)),
+      alpha: () => {
+        let res = where(mask, zerosLike(dy), mul(dy, x));
+        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
+        if (reduceAxes.length > 0) {
+          res = sum2(res, reduceAxes);
+        }
+        return reshape(res, alpha.shape);
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js
+function prodGradFn_(x, dy, axis) {
+  const expandedYShape = x.shape.slice();
+  expandedYShape[axis] = 1;
+  const expandedDy = reshape(dy, expandedYShape);
+  const xCumProd = cumprod(x, axis, true, false);
+  const xCumRevProd = cumprod(x, axis, true, true);
+  const dx = mul(xCumProd, xCumRevProd);
+  return mul(expandedDy, dx);
+}
+function prodsGradFn_(x, dy, axis) {
+  const xRank = x.shape.length;
+  const finalProdAxis = xRank - axis.length;
+  const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);
+  let permutedX = x;
+  if (xPermutation != null) {
+    permutedX = transpose(x, xPermutation);
+  }
+  const newShape = permutedX.shape.slice();
+  const removedShape = newShape.splice(xRank - axis.length, axis.length);
+  const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);
+  newShape.push(endPartShape);
+  const reshapedPermutedX = permutedX.reshape(newShape);
+  let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);
+  prodGrad = prodGrad.reshape(permutedX.shape);
+  if (xPermutation != null) {
+    const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);
+    prodGrad = transpose(prodGrad, undoPermutation);
+  }
+  return prodGrad;
+}
+var prodGradConfig = {
+  kernelName: Prod,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { axis } = attrs;
+    let axisArr = [];
+    if (axis === void 0 || axis === null) {
+      axisArr = x.shape.map((_, i2) => i2);
+    } else if (typeof axis === "number") {
+      axisArr = [axis];
+    } else {
+      axisArr = axis;
+    }
+    return { x: () => prodsGradFn_(x, dy, axisArr) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js
+var divGradConfig = {
+  kernelName: RealDiv,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      const res = div(dy, cast(b, "float32"));
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        return reshape(sum2(res, reduceAxes), a6.shape);
+      }
+      return res;
+    };
+    const derB = () => {
+      let res = mul(dy, cast(a6, "float32"));
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = reshape(sum2(res, reduceAxes), b.shape);
+      }
+      const tmp = square(b);
+      return neg(div(res, cast(tmp, "float32")));
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
+var reciprocalGradConfig = {
+  kernelName: Reciprocal,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, neg(square(x))) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
+var relu6GradConfig = {
+  kernelName: Relu6,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    const mask = mul(lessEqual(x, 6), step(x));
+    return { x: () => mul(dy, cast(mask, "float32")) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
+var reluGradConfig = {
+  kernelName: Relu,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, cast(step(x), "float32")) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
+var reshapeGradConfig = {
+  kernelName: Reshape,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => reshape(dy, x.shape) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
+var resizeBilinearGradConfig = {
+  kernelName: ResizeBilinear,
+  inputsToSave: ["images"],
+  gradFunc: (dy, saved, attrs) => {
+    const [images] = saved;
+    const inputs = { dy, images };
+    const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);
+    return { images: imagesDer };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
+var resizeNearestNeighborGradConfig = {
+  kernelName: ResizeNearestNeighbor,
+  inputsToSave: ["images"],
+  gradFunc: (dy, saved, attrs) => {
+    const [images] = saved;
+    const inputs = { dy, images };
+    const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);
+    return { images: imagesDer };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
+var reverseGradConfig = {
+  kernelName: Reverse,
+  gradFunc: (dy, saved, attrs) => {
+    const { dims } = attrs;
+    const axes = parseAxisParam(dims, dy.shape);
+    return { x: () => reverse(dy, axes) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
+var roundGradConfig = {
+  kernelName: Round,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
+var rsqrtGradConfig = {
+  kernelName: Rsqrt,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js
+var selectGradConfig = {
+  kernelName: Select,
+  inputsToSave: ["condition"],
+  gradFunc: (dy, saved) => {
+    const [condition] = saved;
+    return {
+      condition: () => cast(zerosLike(condition), "float32"),
+      t: () => mul(dy, cast(condition, dy.dtype)),
+      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
+var seluGradConfig = {
+  kernelName: Selu,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return {
+      x: () => {
+        const mask = greater(x, scalar(0));
+        const scaleAlpha2 = scalar(SELU_SCALEALPHA);
+        const scale2 = scalar(SELU_SCALE);
+        const greaterThanZeroDer = mul(dy, scale2);
+        const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, "float32")));
+        return where(mask, greaterThanZeroDer, lessEqualZeroDer);
+      }
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
+var sigmoidGradConfig = {
+  kernelName: Sigmoid,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
+var signGradConfig = {
+  kernelName: Sign,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
+var sinGradConfig = {
+  kernelName: Sin,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(cos(cast(x, "float32")), dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
+var sinhGradConfig = {
+  kernelName: Sinh,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(cosh(cast(x, "float32")), dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
+var sliceGradConfig = {
+  kernelName: Slice,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { begin, size } = attrs;
+    const inputShape = x.shape;
+    const [begin_, size_] = parseSliceParams(x, begin, size);
+    const paddings = [];
+    for (let i2 = 0; i2 < dy.rank; i2++) {
+      paddings.push([begin_[i2], inputShape[i2] - begin_[i2] - size_[i2]]);
+    }
+    return { x: () => pad(dy, paddings) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
+var softmaxGradConfig = {
+  kernelName: Softmax,
+  outputsToSave: [true],
+  gradFunc: (dy, saved, attrs) => {
+    const [y] = saved;
+    const { dim } = attrs;
+    const keepDims = true;
+    const dyTimesY = mul(dy, y);
+    return {
+      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
+    };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
+var softplusGradConfig = {
+  kernelName: Softplus,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, sigmoid(x)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
+var spaceToBatchNDGradConfig = {
+  kernelName: SpaceToBatchND,
+  gradFunc: (dy, saved, attrs) => {
+    const { blockShape, paddings } = attrs;
+    return { x: () => batchToSpaceND(dy, blockShape, paddings) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
+var splitVGradConfig = {
+  kernelName: SplitV,
+  gradFunc: (dy, saved, attrs) => {
+    const { axis } = attrs;
+    return { x: () => concat(dy, axis) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
+var sqrtGradConfig = {
+  kernelName: Sqrt,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, mul(sqrt(cast(x, "float32")), 2)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
+var squareGradConfig = {
+  kernelName: Square,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => mul(dy, mul(cast(x, "float32"), 2)) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
+var squaredDifferenceGradConfig = {
+  kernelName: SquaredDifference,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const two = scalar(2);
+    const derA = () => mul(dy, mul(two, sub(a6, b)));
+    const derB = () => mul(dy, mul(two, sub(b, a6)));
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
+var stepGradConfig = {
+  kernelName: Step,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
+var subGradConfig = {
+  kernelName: Sub,
+  inputsToSave: ["a", "b"],
+  gradFunc: (dy, saved) => {
+    const [a6, b] = saved;
+    const outShape = assertAndGetBroadcastShape(a6.shape, b.shape);
+    const derA = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(a6.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(res, a6.shape);
+    };
+    const derB = () => {
+      let res = dy;
+      const reduceAxes = getReductionAxes(b.shape, outShape);
+      if (reduceAxes.length > 0) {
+        res = sum2(res, reduceAxes);
+      }
+      return reshape(neg(res), b.shape);
+    };
+    return { a: derA, b: derB };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
+var sumGradConfig = {
+  kernelName: Sum,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const expandedDyShape = x.shape.slice();
+    const { axis } = attrs;
+    const axes = parseAxisParam(axis, x.shape);
+    axes.forEach((axis2) => {
+      expandedDyShape[axis2] = 1;
+    });
+    const expandedDy = reshape(dy, expandedDyShape);
+    const derX = mul(expandedDy, ones2(x.shape, "float32"));
+    return { x: () => derX };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
+var tanGradConfig = {
+  kernelName: Tan,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved) => {
+    const [x] = saved;
+    return { x: () => div(dy, square(cos(x))) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
+var tanhGradConfig = {
+  kernelName: Tanh,
+  outputsToSave: [true],
+  gradFunc: (dy, saved) => {
+    const [y] = saved;
+    return { x: () => mul(sub(scalar(1), square(y)), dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
+var tileGradConfig = {
+  kernelName: Tile,
+  inputsToSave: ["x"],
+  gradFunc: (dy, saved, attrs) => {
+    const [x] = saved;
+    const { reps } = attrs;
+    const derX = () => {
+      let xGrad = zerosLike(x);
+      if (x.rank === 1) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0]], [x.shape[0]]));
+        }
+      } else if (x.rank === 2) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          for (let j = 0; j < reps[1]; ++j) {
+            xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1]], [
+              x.shape[0],
+              x.shape[1]
+            ]));
+          }
+        }
+      } else if (x.rank === 3) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          for (let j = 0; j < reps[1]; ++j) {
+            for (let k = 0; k < reps[2]; ++k) {
+              xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
+            }
+          }
+        }
+      } else if (x.rank === 4) {
+        for (let i2 = 0; i2 < reps[0]; ++i2) {
+          for (let j = 0; j < reps[1]; ++j) {
+            for (let k = 0; k < reps[2]; ++k) {
+              for (let l3 = 0; l3 < reps[3]; ++l3) {
+                xGrad = add2(xGrad, slice(dy, [
+                  i2 * x.shape[0],
+                  j * x.shape[1],
+                  k * x.shape[2],
+                  l3 * x.shape[3]
+                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
+              }
+            }
+          }
+        }
+      } else {
+        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
+      }
+      return xGrad;
+    };
+    return { x: derX };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
+var transposeGradConfig = {
+  kernelName: Transpose,
+  gradFunc: (dy, saved, attrs) => {
+    const transposeAttrs = attrs;
+    const { perm } = transposeAttrs;
+    const undoPerm = getUndoAxesPermutation(perm);
+    return { x: () => transpose(dy, undoPerm) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
+var unpackGradConfig = {
+  kernelName: Unpack,
+  gradFunc: (dy, saved, attrs) => {
+    const unpackAttrs = attrs;
+    const { axis } = unpackAttrs;
+    return { value: () => stack(dy, axis) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
+var unsortedSegmentSumGradConfig = {
+  kernelName: UnsortedSegmentSum,
+  inputsToSave: ["segmentIds"],
+  gradFunc: (dy, saved) => {
+    const [segmentIds] = saved;
+    const derX = () => {
+      return gatherDropNegatives(dy, segmentIds);
+    };
+    return { x: derX };
+  }
+};
+function gatherDropNegatives(x, indices) {
+  const zeroClippedIndices = maximum(indices, zerosLike(indices));
+  const gathered = gather(x, zeroClippedIndices);
+  let isPositive = greaterEqual(indices, scalar(0, "int32"));
+  const numIters = gathered.rank - isPositive.rank;
+  for (let i2 = 0; i2 < numIters; ++i2) {
+    isPositive = expandDims(isPositive, i2 + 1);
+  }
+  isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
+  const zeroSlice = zerosLike(gathered);
+  return where(isPositive, gathered, zeroSlice);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
+var zerosLikeGradConfig = {
+  kernelName: ZerosLike,
+  gradFunc: (dy) => {
+    return { x: () => zerosLike(dy) };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
+var gradConfigs = [
+  absGradConfig,
+  acosGradConfig,
+  acoshGradConfig,
+  addGradConfig,
+  addNGradConfig,
+  argMaxGradConfig,
+  argMinGradConfig,
+  asinGradConfig,
+  asinhGradConfig,
+  atan2GradConfig,
+  atanGradConfig,
+  atanhGradConfig,
+  avgPool3DGradConfig,
+  avgPoolGradConfig,
+  batchMatMulGradConfig,
+  batchToSpaceNDGradConfig,
+  broadcastToGradConfig,
+  castGradConfig,
+  ceilGradConfig,
+  clipByValueGradConfig,
+  complexAbsGradConfig,
+  concatGradConfig,
+  conv2DBackpropInputGradConfig,
+  conv2DGradConfig,
+  conv3DGradConfig,
+  cosGradConfig,
+  coshGradConfig,
+  cumsumGradConfig,
+  depthwiseConv2dNativeGradConfig,
+  dilation2dGradConfig,
+  divGradConfig,
+  eluGradConfig,
+  erfGradConfig,
+  expGradConfig,
+  expandDimsGradConfig,
+  expm1GradConfig,
+  floorDivGradConfig,
+  floorGradConfig,
+  fusedBatchNormGradConfig,
+  gatherGradConfig,
+  greaterEqualGradConfig,
+  identityGradConfig,
+  isFiniteGradConfig,
+  isInfGradConfig,
+  isNanGradConfig,
+  leakyReluGradConfig,
+  log1pGradConfig,
+  logGradConfig,
+  logSoftmaxGradConfig,
+  lrnGradConfig,
+  maxGradConfig,
+  maxGradConfig,
+  maximumGradConfig,
+  maxPool3DGradConfig,
+  maxPoolGradConfig,
+  meanGradConfig,
+  minGradConfig,
+  minimumGradConfig,
+  mirrorPadGradConfig,
+  modGradConfig,
+  multiplyGradConfig,
+  negGradConfig,
+  oneHotGradConfig,
+  onesLikeGradConfig,
+  packGradConfig,
+  padV2GradConfig,
+  padV2GradConfig,
+  powGradConfig,
+  preluGradConfig,
+  prodGradConfig,
+  reciprocalGradConfig,
+  relu6GradConfig,
+  reluGradConfig,
+  reshapeGradConfig,
+  resizeBilinearGradConfig,
+  resizeNearestNeighborGradConfig,
+  reverseGradConfig,
+  roundGradConfig,
+  rsqrtGradConfig,
+  selectGradConfig,
+  seluGradConfig,
+  sigmoidGradConfig,
+  signGradConfig,
+  sinGradConfig,
+  sinhGradConfig,
+  sliceGradConfig,
+  softmaxGradConfig,
+  softplusGradConfig,
+  spaceToBatchNDGradConfig,
+  spaceToBatchNDGradConfig,
+  splitVGradConfig,
+  splitVGradConfig,
+  sqrtGradConfig,
+  squaredDifferenceGradConfig,
+  squareGradConfig,
+  stepGradConfig,
+  subGradConfig,
+  sumGradConfig,
+  tanGradConfig,
+  tanhGradConfig,
+  tileGradConfig,
+  transposeGradConfig,
+  unpackGradConfig,
+  unsortedSegmentSumGradConfig,
+  zerosLikeGradConfig
+];
+for (const gradientConfig of gradConfigs) {
+  registerGradient(gradientConfig);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js
+getGlobalTensorClass().prototype.abs = function() {
+  this.throwIfDisposed();
+  return abs(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js
+getGlobalTensorClass().prototype.acos = function() {
+  this.throwIfDisposed();
+  return acos(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js
+getGlobalTensorClass().prototype.acosh = function() {
+  this.throwIfDisposed();
+  return acosh(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js
+getGlobalTensorClass().prototype.add = function(b) {
+  this.throwIfDisposed();
+  return add2(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js
+getGlobalTensorClass().prototype.all = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return all(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js
+getGlobalTensorClass().prototype.any = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return any(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js
+getGlobalTensorClass().prototype.argMax = function(axis) {
+  this.throwIfDisposed();
+  return argMax(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js
+getGlobalTensorClass().prototype.argMin = function(axis) {
+  this.throwIfDisposed();
+  return argMin(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js
+getGlobalTensorClass().prototype.asScalar = function() {
+  this.throwIfDisposed();
+  assert(this.size === 1, () => "The array must have only 1 element.");
+  return reshape(this, []);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js
+getGlobalTensorClass().prototype.asType = function(dtype) {
+  this.throwIfDisposed();
+  return cast(this, dtype);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js
+getGlobalTensorClass().prototype.as1D = function() {
+  this.throwIfDisposed();
+  return reshape(this, [this.size]);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js
+getGlobalTensorClass().prototype.as2D = function(rows, columns) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns]);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js
+getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns, depth]);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js
+getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns, depth, depth2]);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js
+getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {
+  this.throwIfDisposed();
+  return reshape(this, [rows, columns, depth, depth2, depth3]);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js
+getGlobalTensorClass().prototype.asin = function() {
+  this.throwIfDisposed();
+  return asin(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js
+getGlobalTensorClass().prototype.asinh = function() {
+  this.throwIfDisposed();
+  return asinh(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js
+getGlobalTensorClass().prototype.atan = function() {
+  this.throwIfDisposed();
+  return atan(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js
+getGlobalTensorClass().prototype.atan2 = function(b) {
+  this.throwIfDisposed();
+  return atan2(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js
+getGlobalTensorClass().prototype.atanh = function() {
+  this.throwIfDisposed();
+  return atanh(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js
+getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) {
+  this.throwIfDisposed();
+  return avgPool(this, filterSize, strides, pad3, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js
+getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {
+  this.throwIfDisposed();
+  return batchToSpaceND(this, blockShape, crops);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js
+getGlobalTensorClass().prototype.batchNorm = function(mean5, variance, offset, scale2, varianceEpsilon) {
+  this.throwIfDisposed();
+  return batchNorm(this, mean5, variance, offset, scale2, varianceEpsilon);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js
+getGlobalTensorClass().prototype.broadcastTo = function(shape) {
+  this.throwIfDisposed();
+  return broadcastTo(this, shape);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js
+getGlobalTensorClass().prototype.cast = function(dtype) {
+  this.throwIfDisposed();
+  return cast(this, dtype);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js
+getGlobalTensorClass().prototype.ceil = function() {
+  this.throwIfDisposed();
+  return ceil(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js
+getGlobalTensorClass().prototype.clipByValue = function(min7, max7) {
+  this.throwIfDisposed();
+  return clipByValue(this, min7, max7);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js
+getGlobalTensorClass().prototype.concat = function(x, axis) {
+  this.throwIfDisposed();
+  if (x instanceof Tensor) {
+    x = [x];
+  }
+  return concat([this, ...x], axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js
+getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {
+  this.throwIfDisposed();
+  return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js
+getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) {
+  this.throwIfDisposed();
+  return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js
+getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {
+  this.throwIfDisposed();
+  return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js
+getGlobalTensorClass().prototype.cos = function() {
+  this.throwIfDisposed();
+  return cos(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js
+getGlobalTensorClass().prototype.cosh = function() {
+  this.throwIfDisposed();
+  return cosh(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js
+getGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) {
+  this.throwIfDisposed();
+  return cumprod(this, axis, exclusive, reverse5);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js
+getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {
+  this.throwIfDisposed();
+  return cumsum(this, axis, exclusive, reverse5);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js
+getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {
+  this.throwIfDisposed();
+  return depthToSpace(this, blockSize, dataFormat);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js
+getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {
+  this.throwIfDisposed();
+  return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js
+getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) {
+  this.throwIfDisposed();
+  return dilation2d(this, filter, strides, pad3, dilations, dataFormat);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js
+getGlobalTensorClass().prototype.divNoNan = function(b) {
+  this.throwIfDisposed();
+  return divNoNan(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js
+getGlobalTensorClass().prototype.div = function(b) {
+  this.throwIfDisposed();
+  return div(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js
+getGlobalTensorClass().prototype.dot = function(b) {
+  this.throwIfDisposed();
+  return dot(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js
+getGlobalTensorClass().prototype.elu = function() {
+  this.throwIfDisposed();
+  return elu(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js
+getGlobalTensorClass().prototype.equal = function(b) {
+  this.throwIfDisposed();
+  return equal(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js
+getGlobalTensorClass().prototype.erf = function() {
+  this.throwIfDisposed();
+  return erf(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js
+getGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return euclideanNorm(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js
+getGlobalTensorClass().prototype.exp = function() {
+  this.throwIfDisposed();
+  return exp(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js
+getGlobalTensorClass().prototype.expandDims = function(axis) {
+  this.throwIfDisposed();
+  return expandDims(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js
+getGlobalTensorClass().prototype.expm1 = function() {
+  this.throwIfDisposed();
+  return expm1(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js
+getGlobalTensorClass().prototype.fft = function() {
+  this.throwIfDisposed();
+  return fft(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js
+getGlobalTensorClass().prototype.flatten = function() {
+  this.throwIfDisposed();
+  return reshape(this, [this.size]);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js
+getGlobalTensorClass().prototype.floor = function() {
+  this.throwIfDisposed();
+  return floor(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js
+getGlobalTensorClass().prototype.floorDiv = function(b) {
+  this.throwIfDisposed();
+  return floorDiv(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js
+getGlobalTensorClass().prototype.gather = function(indices, axis) {
+  this.throwIfDisposed();
+  return gather(this, indices, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js
+getGlobalTensorClass().prototype.greaterEqual = function(b) {
+  this.throwIfDisposed();
+  return greaterEqual(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js
+getGlobalTensorClass().prototype.greater = function(b) {
+  this.throwIfDisposed();
+  return greater(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js
+getGlobalTensorClass().prototype.ifft = function() {
+  this.throwIfDisposed();
+  return ifft(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js
+getGlobalTensorClass().prototype.irfft = function() {
+  this.throwIfDisposed();
+  return irfft(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js
+getGlobalTensorClass().prototype.isFinite = function() {
+  this.throwIfDisposed();
+  return isFinite2(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js
+getGlobalTensorClass().prototype.isInf = function() {
+  this.throwIfDisposed();
+  return isInf(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js
+getGlobalTensorClass().prototype.isNaN = function() {
+  this.throwIfDisposed();
+  return isNaN2(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js
+getGlobalTensorClass().prototype.leakyRelu = function(alpha) {
+  this.throwIfDisposed();
+  return leakyRelu(this, alpha);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js
+getGlobalTensorClass().prototype.lessEqual = function(b) {
+  this.throwIfDisposed();
+  return lessEqual(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js
+getGlobalTensorClass().prototype.less = function(b) {
+  this.throwIfDisposed();
+  return less(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js
+getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {
+  this.throwIfDisposed();
+  return localResponseNormalization(this, depthRadius, bias, alpha, beta);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js
+getGlobalTensorClass().prototype.logSigmoid = function() {
+  this.throwIfDisposed();
+  return logSigmoid(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js
+getGlobalTensorClass().prototype.logSoftmax = function(axis) {
+  this.throwIfDisposed();
+  return logSoftmax(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js
+getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return logSumExp(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js
+getGlobalTensorClass().prototype.log = function() {
+  this.throwIfDisposed();
+  return log2(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js
+getGlobalTensorClass().prototype.log1p = function() {
+  this.throwIfDisposed();
+  return log1p(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js
+getGlobalTensorClass().prototype.logicalAnd = function(b) {
+  this.throwIfDisposed();
+  return logicalAnd(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js
+getGlobalTensorClass().prototype.logicalNot = function() {
+  this.throwIfDisposed();
+  return logicalNot(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js
+getGlobalTensorClass().prototype.logicalOr = function(b) {
+  this.throwIfDisposed();
+  return logicalOr(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js
+getGlobalTensorClass().prototype.logicalXor = function(b) {
+  this.throwIfDisposed();
+  return logicalXor(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js
+getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {
+  this.throwIfDisposed();
+  return matMul(this, b, transposeA, transposeB);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js
+getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) {
+  this.throwIfDisposed();
+  return maxPool(this, filterSize, strides, pad3, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js
+getGlobalTensorClass().prototype.max = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return max(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js
+getGlobalTensorClass().prototype.maximum = function(b) {
+  this.throwIfDisposed();
+  return maximum(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js
+getGlobalTensorClass().prototype.mean = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return mean(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js
+getGlobalTensorClass().prototype.min = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return min(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js
+getGlobalTensorClass().prototype.minimum = function(b) {
+  this.throwIfDisposed();
+  return minimum(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js
+getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {
+  this.throwIfDisposed();
+  return mirrorPad(this, paddings, mode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js
+getGlobalTensorClass().prototype.mod = function(b) {
+  this.throwIfDisposed();
+  return mod(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js
+getGlobalTensorClass().prototype.mul = function(b) {
+  this.throwIfDisposed();
+  return mul(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js
+getGlobalTensorClass().prototype.neg = function() {
+  this.throwIfDisposed();
+  return neg(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js
+getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {
+  this.throwIfDisposed();
+  return norm(this, ord, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js
+getGlobalTensorClass().prototype.notEqual = function(b) {
+  this.throwIfDisposed();
+  return notEqual(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js
+getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {
+  this.throwIfDisposed();
+  return oneHot(this, depth, onValue, offValue);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js
+getGlobalTensorClass().prototype.onesLike = function() {
+  this.throwIfDisposed();
+  return onesLike(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js
+getGlobalTensorClass().prototype.pad = function(paddings, constantValue) {
+  this.throwIfDisposed();
+  return pad(this, paddings, constantValue);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js
+getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {
+  this.throwIfDisposed();
+  return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js
+getGlobalTensorClass().prototype.pow = function(exp5) {
+  this.throwIfDisposed();
+  return pow(this, exp5);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js
+getGlobalTensorClass().prototype.prelu = function(alpha) {
+  this.throwIfDisposed();
+  return prelu(this, alpha);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js
+getGlobalTensorClass().prototype.prod = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return prod(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js
+getGlobalTensorClass().prototype.reciprocal = function() {
+  this.throwIfDisposed();
+  return reciprocal(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js
+getGlobalTensorClass().prototype.relu = function() {
+  this.throwIfDisposed();
+  return relu(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js
+getGlobalTensorClass().prototype.relu6 = function() {
+  this.throwIfDisposed();
+  return relu6(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js
+getGlobalTensorClass().prototype.reshapeAs = function(x) {
+  this.throwIfDisposed();
+  return reshape(this, x.shape);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js
+getGlobalTensorClass().prototype.reshape = function(shape) {
+  this.throwIfDisposed();
+  return reshape(this, shape);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js
+getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {
+  this.throwIfDisposed();
+  return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js
+getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {
+  this.throwIfDisposed();
+  return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js
+getGlobalTensorClass().prototype.reverse = function(axis) {
+  this.throwIfDisposed();
+  return reverse(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js
+getGlobalTensorClass().prototype.rfft = function() {
+  this.throwIfDisposed();
+  return rfft(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js
+getGlobalTensorClass().prototype.round = function() {
+  this.throwIfDisposed();
+  return round2(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js
+getGlobalTensorClass().prototype.rsqrt = function() {
+  this.throwIfDisposed();
+  return rsqrt(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js
+getGlobalTensorClass().prototype.selu = function() {
+  this.throwIfDisposed();
+  return selu(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js
+getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) {
+  this.throwIfDisposed();
+  return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js
+getGlobalTensorClass().prototype.sigmoid = function() {
+  this.throwIfDisposed();
+  return sigmoid(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js
+getGlobalTensorClass().prototype.sign = function() {
+  this.throwIfDisposed();
+  return sign(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js
+getGlobalTensorClass().prototype.sin = function() {
+  this.throwIfDisposed();
+  return sin(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js
+getGlobalTensorClass().prototype.sinh = function() {
+  this.throwIfDisposed();
+  return sinh(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js
+getGlobalTensorClass().prototype.slice = function(begin, size) {
+  this.throwIfDisposed();
+  return slice(this, begin, size);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js
+getGlobalTensorClass().prototype.softmax = function(dim) {
+  this.throwIfDisposed();
+  return softmax(this, dim);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js
+getGlobalTensorClass().prototype.softplus = function() {
+  this.throwIfDisposed();
+  return softplus(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js
+getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {
+  this.throwIfDisposed();
+  return spaceToBatchND(this, blockShape, paddings);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js
+getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {
+  this.throwIfDisposed();
+  return split(this, numOrSizeSplits, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js
+getGlobalTensorClass().prototype.sqrt = function() {
+  this.throwIfDisposed();
+  return sqrt(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js
+getGlobalTensorClass().prototype.square = function() {
+  this.throwIfDisposed();
+  return square(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js
+getGlobalTensorClass().prototype.squaredDifference = function(b) {
+  this.throwIfDisposed();
+  return squaredDifference(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js
+getGlobalTensorClass().prototype.squeeze = function(axis) {
+  this.throwIfDisposed();
+  return squeeze(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js
+getGlobalTensorClass().prototype.stack = function(x, axis) {
+  this.throwIfDisposed();
+  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];
+  return stack(tensorsToBeStacked, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js
+getGlobalTensorClass().prototype.step = function(alpha) {
+  this.throwIfDisposed();
+  return step(this, alpha);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js
+getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+  this.throwIfDisposed();
+  return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js
+getGlobalTensorClass().prototype.sub = function(b) {
+  this.throwIfDisposed();
+  return sub(this, b);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js
+getGlobalTensorClass().prototype.sum = function(axis, keepDims) {
+  this.throwIfDisposed();
+  return sum2(this, axis, keepDims);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js
+getGlobalTensorClass().prototype.tan = function() {
+  this.throwIfDisposed();
+  return tan(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js
+getGlobalTensorClass().prototype.tanh = function() {
+  this.throwIfDisposed();
+  return tanh2(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js
+getGlobalTensorClass().prototype.tile = function(reps) {
+  this.throwIfDisposed();
+  return tile(this, reps);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js
+getGlobalTensorClass().prototype.toBool = function() {
+  this.throwIfDisposed();
+  return cast(this, "bool");
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js
+getGlobalTensorClass().prototype.toFloat = function() {
+  this.throwIfDisposed();
+  return cast(this, "float32");
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js
+getGlobalTensorClass().prototype.toInt = function() {
+  this.throwIfDisposed();
+  return cast(this, "int32");
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js
+getGlobalTensorClass().prototype.topk = function(k, sorted) {
+  this.throwIfDisposed();
+  return topk(this, k, sorted);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js
+getGlobalTensorClass().prototype.transpose = function(perm) {
+  this.throwIfDisposed();
+  return transpose(this, perm);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js
+getGlobalTensorClass().prototype.unique = function(axis) {
+  this.throwIfDisposed();
+  return unique(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js
+getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {
+  this.throwIfDisposed();
+  return unsortedSegmentSum(this, segmentIds, numSegments);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js
+getGlobalTensorClass().prototype.unstack = function(axis) {
+  this.throwIfDisposed();
+  return unstack(this, axis);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js
+getGlobalTensorClass().prototype.where = function(condition, x) {
+  this.throwIfDisposed();
+  return where(condition, this, x);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js
+getGlobalTensorClass().prototype.zerosLike = function() {
+  this.throwIfDisposed();
+  return zerosLike(this);
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/errors.js
+var AttributeError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, AttributeError.prototype);
+  }
+};
+var RuntimeError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, RuntimeError.prototype);
+  }
+};
+var ValueError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, ValueError.prototype);
+  }
+};
+var NotImplementedError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, NotImplementedError.prototype);
+  }
+};
+var AssertionError = class extends Error {
+  constructor(message) {
+    super(message);
+    Object.setPrototypeOf(this, AssertionError.prototype);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js
+var LruCache = class {
+  constructor(maxEntries) {
+    this.maxEntries = maxEntries || 100;
+    this.cache = /* @__PURE__ */ new Map();
+  }
+  get(key) {
+    let entry;
+    if (this.cache.has(key)) {
+      entry = this.cache.get(key);
+      this.cache.delete(key);
+      this.cache.set(key, entry);
+    }
+    return entry;
+  }
+  put(key, value) {
+    if (this.cache.has(key)) {
+      this.cache.delete(key);
+    } else if (this.cache.size >= this.maxEntries) {
+      const keyToDelete = this.cache.keys().next().value;
+      this.cache.delete(keyToDelete);
+    }
+    this.cache.set(key, value);
+  }
+  getMaxEntries() {
+    return this.maxEntries;
+  }
+  setMaxEntries(maxEntries) {
+    if (maxEntries < 0) {
+      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);
+    }
+    if (this.maxEntries > maxEntries) {
+      for (let i2 = 0; i2 < this.maxEntries - maxEntries; i2++) {
+        const keyToDelete = this.cache.keys().next().value;
+        this.cache.delete(keyToDelete);
+      }
+    }
+    this.maxEntries = maxEntries;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js
+function pyListRepeat(value, numValues) {
+  if (Array.isArray(value)) {
+    let newArray = [];
+    for (let i2 = 0; i2 < numValues; i2++) {
+      newArray = newArray.concat(value);
+    }
+    return newArray;
+  } else {
+    const newArray = new Array(numValues);
+    newArray.fill(value);
+    return newArray;
+  }
+}
+function assert2(val, message) {
+  if (!val) {
+    throw new AssertionError(message);
+  }
+}
+function count(array2, refernce) {
+  let counter = 0;
+  for (const item of array2) {
+    if (item === refernce) {
+      counter++;
+    }
+  }
+  return counter;
+}
+function singletonOrArray(xs) {
+  if (xs.length === 1) {
+    return xs[0];
+  }
+  return xs;
+}
+function toList(x) {
+  if (Array.isArray(x)) {
+    return x;
+  }
+  return [x];
+}
+function toSnakeCase(name) {
+  const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, "$1_$2");
+  const insecure = intermediate.replace(/([a-z])([A-Z])/g, "$1_$2").toLowerCase();
+  if (insecure[0] !== "_") {
+    return insecure;
+  }
+  return "private" + insecure;
+}
+function toCamelCase(identifier) {
+  if (identifier.length <= 1) {
+    return identifier;
+  }
+  if (identifier.indexOf("_") === -1) {
+    return identifier;
+  }
+  return identifier.replace(/[_]+(\w|$)/g, (m, p1) => p1.toUpperCase());
+}
+var _GLOBAL_CUSTOM_OBJECTS = {};
+function serializeKerasObject(instance) {
+  if (instance === null || instance === void 0) {
+    return null;
+  }
+  const dict = {};
+  dict["className"] = instance.getClassName();
+  dict["config"] = instance.getConfig();
+  return dict;
+}
+function convertNDArrayScalarsInConfig(config) {
+  if (config == null || typeof config !== "object") {
+    return;
+  } else if (Array.isArray(config)) {
+    config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));
+  } else {
+    const fields = Object.keys(config);
+    for (const field of fields) {
+      const value = config[field];
+      if (value != null && typeof value === "object") {
+        if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") {
+          config[field] = value["value"];
+        } else {
+          convertNDArrayScalarsInConfig(value);
+        }
+      }
+    }
+  }
+}
+function deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = "object", fastWeightInit = false) {
+  if (typeof identifier === "string") {
+    const functionName = identifier;
+    let fn;
+    if (functionName in customObjects) {
+      fn = customObjects[functionName];
+    } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {
+      fn = _GLOBAL_CUSTOM_OBJECTS[functionName];
+    } else {
+      fn = moduleObjects[functionName];
+      if (fn == null) {
+        throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
+      }
+    }
+    return fn;
+  } else {
+    const config = identifier;
+    if (config["className"] == null || config["config"] == null) {
+      throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
+'className' and 'config' must set.`);
+    }
+    const className = config["className"];
+    let cls, fromConfig;
+    if (className in customObjects) {
+      [cls, fromConfig] = customObjects[className];
+    } else if (className in _GLOBAL_CUSTOM_OBJECTS) {
+      [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS["className"];
+    } else if (className in moduleObjects) {
+      [cls, fromConfig] = moduleObjects[className];
+    }
+    if (cls == null) {
+      throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:
+1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.
+2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
+    }
+    if (fromConfig != null) {
+      const customObjectsCombined = {};
+      for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {
+        customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];
+      }
+      for (const key of Object.keys(customObjects)) {
+        customObjectsCombined[key] = customObjects[key];
+      }
+      const nestedConfig = config["config"];
+      nestedConfig["customObjects"] = customObjectsCombined;
+      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
+      for (const key of Object.keys(customObjects)) {
+        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
+      }
+      convertNDArrayScalarsInConfig(config["config"]);
+      const returnObj = fromConfig(cls, config["config"], customObjects, fastWeightInit);
+      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
+      return returnObj;
+    } else {
+      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);
+      for (const key of Object.keys(customObjects)) {
+        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];
+      }
+      const returnObj = new cls(config["config"]);
+      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);
+      return returnObj;
+    }
+  }
+}
+function numberCompare(a6, b) {
+  return a6 < b ? -1 : a6 > b ? 1 : 0;
+}
+function reverseNumberCompare(a6, b) {
+  return -1 * numberCompare(a6, b);
+}
+function unique2(xs) {
+  if (xs == null) {
+    return xs;
+  }
+  const out = [];
+  for (const x of xs) {
+    if (out.indexOf(x) === -1) {
+      out.push(x);
+    }
+  }
+  return out;
+}
+function isObjectEmpty(obj) {
+  if (obj == null) {
+    throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);
+  }
+  for (const key in obj) {
+    if (obj.hasOwnProperty(key)) {
+      return false;
+    }
+  }
+  return true;
+}
+function checkStringTypeUnionValue(values, label, value) {
+  if (value == null) {
+    return;
+  }
+  if (values.indexOf(value) < 0) {
+    throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);
+  }
+}
+function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {
+  assert2(minLength >= 0);
+  assert2(maxLength >= minLength);
+  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e2) => typeof e2 === expectedType);
+}
+function assertPositiveInteger(value, name) {
+  if (Array.isArray(value)) {
+    util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);
+    value.forEach((v, i2) => assertPositiveInteger(v, `element ${i2 + 1} of ${name}`));
+  } else {
+    util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);
+  }
+}
+function formatAsFriendlyString(value) {
+  if (value === null) {
+    return "null";
+  } else if (Array.isArray(value)) {
+    return "[" + value.map((v) => formatAsFriendlyString(v)).join(",") + "]";
+  } else if (typeof value === "string") {
+    return `"${value}"`;
+  } else {
+    return `${value}`;
+  }
+}
+function debounce(f, waitMs, nowFunc) {
+  let lastTime = nowFunc != null ? nowFunc() : util_exports.now();
+  let lastResult;
+  const f2 = (...args) => {
+    const now2 = nowFunc != null ? nowFunc() : util_exports.now();
+    if (now2 - lastTime < waitMs) {
+      return lastResult;
+    }
+    lastTime = now2;
+    lastResult = f(...args);
+    return lastResult;
+  };
+  return f2;
+}
+function mapActivationToFusedKernel(activationName) {
+  if (activationName === "relu") {
+    return "relu";
+  }
+  if (activationName === "linear") {
+    return "linear";
+  }
+  if (activationName === "elu") {
+    return "elu";
+  }
+  return null;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js
+var _nextUniqueTensorId = 0;
+function getNextUniqueTensorId() {
+  return _nextUniqueTensorId++;
+}
+var _uidPrefixes = {};
+function getUid(prefix = "") {
+  if (!(prefix in _uidPrefixes)) {
+    _uidPrefixes[prefix] = 0;
+  }
+  _uidPrefixes[prefix] += 1;
+  return prefix + _uidPrefixes[prefix].toString();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js
+var VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"];
+var VALID_INTERPOLATION_FORMAT_VALUES = ["nearest", "bilinear"];
+var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"];
+var VALID_POOL_MODE_VALUES = ["max", "avg"];
+var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"];
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/common.js
+var nameMap = /* @__PURE__ */ new Map();
+function checkDataFormat(value) {
+  checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value);
+}
+function checkInterpolationFormat(value) {
+  checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, "InterpolationFormat", value);
+}
+function checkPaddingMode(value) {
+  checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value);
+}
+function checkPoolMode(value) {
+  checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value);
+}
+var _nameScopeStack = [];
+var _nameScopeDivider = "/";
+function nameScope(name, fn) {
+  _nameScopeStack.push(name);
+  try {
+    const val = fn();
+    _nameScopeStack.pop();
+    return val;
+  } catch (e2) {
+    _nameScopeStack.pop();
+    throw e2;
+  }
+}
+function currentNameScopePrefix() {
+  if (_nameScopeStack.length === 0) {
+    return "";
+  } else {
+    return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;
+  }
+}
+function getScopedTensorName(tensorName) {
+  if (!isValidTensorName(tensorName)) {
+    throw new Error("Not a valid tensor name: '" + tensorName + "'");
+  }
+  return currentNameScopePrefix() + tensorName;
+}
+function getUniqueTensorName(scopedName) {
+  if (!isValidTensorName(scopedName)) {
+    throw new Error("Not a valid tensor name: '" + scopedName + "'");
+  }
+  if (!nameMap.has(scopedName)) {
+    nameMap.set(scopedName, 0);
+  }
+  const index = nameMap.get(scopedName);
+  nameMap.set(scopedName, nameMap.get(scopedName) + 1);
+  if (index > 0) {
+    const result = `${scopedName}_${index}`;
+    nameMap.set(result, 1);
+    return result;
+  } else {
+    return scopedName;
+  }
+}
+var tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
+function isValidTensorName(name) {
+  return !!name.match(tensorNameRegex);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js
+function isInteger(x) {
+  return x === parseInt(x.toString(), 10);
+}
+function arrayProd(array2, begin, end) {
+  if (begin == null) {
+    begin = 0;
+  }
+  if (end == null) {
+    end = array2.length;
+  }
+  let prod6 = 1;
+  for (let i2 = begin; i2 < end; ++i2) {
+    prod6 *= array2[i2];
+  }
+  return prod6;
+}
+function min2(array2) {
+  if (array2.length === 0) {
+    return Number.NaN;
+  }
+  let min7 = Number.POSITIVE_INFINITY;
+  for (let i2 = 0; i2 < array2.length; i2++) {
+    const value = array2[i2];
+    if (value < min7) {
+      min7 = value;
+    }
+  }
+  return min7;
+}
+function max2(array2) {
+  if (array2.length === 0) {
+    return Number.NaN;
+  }
+  let max7 = Number.NEGATIVE_INFINITY;
+  for (let i2 = 0; i2 < array2.length; i2++) {
+    const value = array2[i2];
+    if (value > max7) {
+      max7 = value;
+    }
+  }
+  return max7;
+}
+function range2(begin, end) {
+  if (end < begin) {
+    throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
+  }
+  const out = [];
+  for (let i2 = begin; i2 < end; ++i2) {
+    out.push(i2);
+  }
+  return out;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js
+var _epsilon;
+function epsilon() {
+  if (_epsilon == null) {
+    _epsilon = backend().epsilon();
+  }
+  return _epsilon;
+}
+function imageDataFormat() {
+  return "channelsLast";
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js
+function cast2(x, dtype) {
+  return cast(x, dtype);
+}
+function expandDims2(x, axis = -1) {
+  const outShape = x.shape.slice();
+  if (axis < 0) {
+    axis = outShape.length + axis + 1;
+  }
+  outShape.splice(axis, 0, 1);
+  return reshape(x, outShape);
+}
+function repeat(x, n) {
+  return tidy(() => {
+    if (x.shape.length !== 2) {
+      throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);
+    }
+    const y = expandDims2(x, 1);
+    return tile2(y, [1, n, 1]);
+  });
+}
+function flatten2(x) {
+  const newShape = [arrayProd(x.shape)];
+  return reshape(x, newShape);
+}
+function batchFlatten(x) {
+  if (x.rank <= 1) {
+    throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);
+  }
+  const newShape = [x.shape[0], arrayProd(x.shape, 1)];
+  return reshape(x, newShape);
+}
+function sliceAlongFirstAxis(array2, start, size) {
+  return tidy(() => {
+    switch (array2.rank) {
+      case 1:
+        return slice1d(array2, start, size);
+      case 2:
+        return slice2d(array2, [start, 0], [size, array2.shape[1]]);
+      case 3:
+        return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);
+      case 4:
+        return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);
+      case 5:
+        return slice(array2, [start, 0, 0, 0, 0], [
+          size,
+          array2.shape[1],
+          array2.shape[2],
+          array2.shape[3],
+          array2.shape[4]
+        ]);
+      case 6:
+        return slice(array2, [start, 0, 0, 0, 0, 0], [
+          size,
+          array2.shape[1],
+          array2.shape[2],
+          array2.shape[3],
+          array2.shape[4],
+          array2.shape[5]
+        ]);
+      default:
+        throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);
+    }
+  });
+}
+function sliceAlongLastAxis(array2, start, size) {
+  return tidy(() => {
+    switch (array2.rank) {
+      case 1:
+        return slice1d(array2, start, size);
+      case 2:
+        return slice2d(array2, [0, start], [array2.shape[0], size]);
+      case 3:
+        return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);
+      case 4:
+        return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);
+      default:
+        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
+    }
+  });
+}
+function sliceAlongAxis(array2, start, size, axis) {
+  return tidy(() => {
+    switch (array2.rank) {
+      case 1:
+        return slice1d(array2, start, size);
+      case 2:
+        switch (axis) {
+          case 1:
+            return sliceAlongFirstAxis(array2, start, size);
+          case 2:
+            return sliceAlongLastAxis(array2, start, size);
+          default:
+            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
+        }
+      case 3:
+        switch (axis) {
+          case 1:
+            return sliceAlongFirstAxis(array2, start, size);
+          case 2:
+            return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);
+          case 3:
+            return sliceAlongLastAxis(array2, start, size);
+          default:
+            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
+        }
+      case 4:
+        switch (axis) {
+          case 1:
+            return sliceAlongFirstAxis(array2, start, size);
+          case 2:
+            return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);
+          case 3:
+            return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);
+          case 4:
+            return sliceAlongLastAxis(array2, start, size);
+          default:
+            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);
+        }
+      default:
+        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);
+    }
+  });
+}
+function concatenate(tensors, axis = -1) {
+  let rank;
+  if (axis < 0) {
+    rank = tensors[0].rank;
+    if (rank !== 0) {
+      axis = rank;
+    } else {
+      axis = 0;
+    }
+  }
+  if (axis === tensors[0].rank) {
+    axis = -1;
+  }
+  return concat(tensors, axis);
+}
+function concatAlongFirstAxis(a6, b) {
+  switch (a6.rank) {
+    case 1:
+      return concat1d([a6, b]);
+    case 2:
+      return concat2d([a6, b], 0);
+    case 3:
+      return concat3d([a6, b], 0);
+    case 4:
+      return concat4d([a6, b], 0);
+    default:
+      throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a6.rank}`);
+  }
+}
+function tile2(x, n) {
+  if (!Array.isArray(n)) {
+    n = [n];
+  }
+  if (x.rank !== n.length) {
+    throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
+  }
+  return tile(x, n);
+}
+function randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {
+  return randomNormal(shape, mean5, stddev, dtype, seed);
+}
+function dot2(a6, b, activation2, bias) {
+  if (a6.rank < 2 || b.rank < 2) {
+    throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a6.shape} and y shape = ${b.shape}`);
+  }
+  if (b.rank >= 3) {
+    const xLastDim = a6.shape.slice(-1)[0];
+    const ySecondLastDim = b.shape.slice(-2)[0];
+    if (xLastDim !== ySecondLastDim) {
+      throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a6.shape} and  y shape = ${b.shape}`);
+    }
+  }
+  if (a6.rank === 2 && b.rank === 2) {
+    const transposeA = false;
+    const transposeB = false;
+    return fused_ops_exports.matMul({
+      a: a6,
+      b,
+      transposeA,
+      transposeB,
+      bias: bias ? reshapeBias(a6.rank, bias, imageDataFormat()) : null,
+      activation: activation2
+    });
+  } else {
+    const aFirstDims = a6.shape.slice();
+    const aLastDim = aFirstDims.pop();
+    a6 = reshape(a6, [-1, aLastDim]);
+    const bShape = b.shape.slice();
+    const bLastDim = bShape.pop();
+    const ySecondLastDim = bShape.pop();
+    const yOtherDims = [...bShape, bLastDim];
+    const perm = Array.from({ length: b.rank }, (_, i2) => {
+      if (i2 === 0) {
+        return b.rank - 2;
+      } else if (i2 <= b.rank - 2) {
+        return i2 - 1;
+      }
+      return i2;
+    });
+    b = reshape(transpose(b, perm), [ySecondLastDim, -1]);
+    const outputShape = [...aFirstDims, ...yOtherDims];
+    const transposeA = false;
+    const transposeB = false;
+    return reshape(fused_ops_exports.matMul({
+      a: a6,
+      b,
+      transposeA,
+      transposeB,
+      bias: bias ? reshapeBias(a6.rank, bias, imageDataFormat()) : null,
+      activation: activation2
+    }), outputShape);
+  }
+}
+function gather2(reference, indices, axis) {
+  return tidy(() => {
+    if (Array.isArray(indices)) {
+      indices = tensor1d(indices, "int32");
+    } else {
+      indices = cast(indices, "int32");
+    }
+    return gather(reference, indices, axis);
+  });
+}
+function square2(x) {
+  return mul(x, x);
+}
+function reshapeBias(xRank, bias, dataFormat) {
+  const biasShape = bias.shape;
+  if (bias.rank !== 1 && bias.rank !== xRank) {
+    throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);
+  }
+  if (xRank === 5) {
+    if (dataFormat === "channelsFirst") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, biasShape[0], 1, 1, 1]);
+      } else {
+        return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
+      }
+    } else if (dataFormat === "channelsLast") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, 1, 1, 1, biasShape[0]]);
+      } else {
+        return reshape(bias, [1].concat(biasShape));
+      }
+    }
+  } else if (xRank === 4) {
+    if (dataFormat === "channelsFirst") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, biasShape[0], 1, 1]);
+      } else {
+        return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);
+      }
+    } else if (dataFormat === "channelsLast") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, 1, 1, biasShape[0]]);
+      } else {
+        return reshape(bias, [1].concat(biasShape));
+      }
+    }
+  } else if (xRank === 3) {
+    if (dataFormat === "channelsFirst") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, biasShape[0], 1]);
+      } else {
+        return reshape(bias, [1, biasShape[1], biasShape[0]]);
+      }
+    } else if (dataFormat === "channelsLast") {
+      if (biasShape.length === 1) {
+        return reshape(bias, [1, 1, biasShape[0]]);
+      } else {
+        return reshape(bias, [1].concat(biasShape));
+      }
+    }
+  } else if (xRank < 3) {
+    return bias;
+  }
+  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
+}
+function biasAdd(x, bias, dataFormat) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    return add2(x, reshapeBias(x.rank, bias, dataFormat));
+  });
+}
+function elu2(x, alpha = 1) {
+  if (alpha !== 1) {
+    throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);
+  }
+  return elu(x);
+}
+function softsign(x) {
+  return tidy(() => div(x, add2(abs(x), 1)));
+}
+function dropout2(x, level, noiseShape, seed) {
+  return tidy(() => dropout(x, level, noiseShape, seed));
+}
+function hardSigmoid(x) {
+  return tidy(() => {
+    const y = add2(0.5, mul(0.2, x));
+    return clipByValue(y, 0, 1);
+  });
+}
+function inTrainPhase(x, alt, training = false) {
+  return training ? x() : alt();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js
+var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"];
+var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"];
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/initializers.js
+function checkFanMode(value) {
+  checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value);
+}
+function checkDistribution(value) {
+  checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value);
+}
+var Initializer = class extends serialization_exports.Serializable {
+  fromConfigUsesCustomObjects() {
+    return false;
+  }
+  getConfig() {
+    return {};
+  }
+};
+var Zeros = class extends Initializer {
+  apply(shape, dtype) {
+    return zeros(shape, dtype);
+  }
+};
+Zeros.className = "Zeros";
+serialization_exports.registerClass(Zeros);
+var Ones = class extends Initializer {
+  apply(shape, dtype) {
+    return ones2(shape, dtype);
+  }
+};
+Ones.className = "Ones";
+serialization_exports.registerClass(Ones);
+var Constant = class extends Initializer {
+  constructor(args) {
+    super();
+    if (typeof args !== "object") {
+      throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);
+    }
+    if (args.value === void 0) {
+      throw new ValueError(`config must have value set but got ${args}`);
+    }
+    this.value = args.value;
+  }
+  apply(shape, dtype) {
+    return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));
+  }
+  getConfig() {
+    return {
+      value: this.value
+    };
+  }
+};
+Constant.className = "Constant";
+serialization_exports.registerClass(Constant);
+var RandomUniform = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_MINVAL = -0.05;
+    this.DEFAULT_MAXVAL = 0.05;
+    this.minval = args.minval || this.DEFAULT_MINVAL;
+    this.maxval = args.maxval || this.DEFAULT_MAXVAL;
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    return randomUniform(shape, this.minval, this.maxval, dtype);
+  }
+  getConfig() {
+    return { minval: this.minval, maxval: this.maxval, seed: this.seed };
+  }
+};
+RandomUniform.className = "RandomUniform";
+serialization_exports.registerClass(RandomUniform);
+var RandomNormal = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_MEAN = 0;
+    this.DEFAULT_STDDEV = 0.05;
+    this.mean = args.mean || this.DEFAULT_MEAN;
+    this.stddev = args.stddev || this.DEFAULT_STDDEV;
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    dtype = dtype || "float32";
+    if (dtype !== "float32" && dtype !== "int32") {
+      throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);
+    }
+    return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);
+  }
+  getConfig() {
+    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
+  }
+};
+RandomNormal.className = "RandomNormal";
+serialization_exports.registerClass(RandomNormal);
+var TruncatedNormal = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_MEAN = 0;
+    this.DEFAULT_STDDEV = 0.05;
+    this.mean = args.mean || this.DEFAULT_MEAN;
+    this.stddev = args.stddev || this.DEFAULT_STDDEV;
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    dtype = dtype || "float32";
+    if (dtype !== "float32" && dtype !== "int32") {
+      throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);
+    }
+    return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);
+  }
+  getConfig() {
+    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
+  }
+};
+TruncatedNormal.className = "TruncatedNormal";
+serialization_exports.registerClass(TruncatedNormal);
+var Identity2 = class extends Initializer {
+  constructor(args) {
+    super();
+    this.gain = args.gain != null ? args.gain : 1;
+  }
+  apply(shape, dtype) {
+    return tidy(() => {
+      if (shape.length !== 2 || shape[0] !== shape[1]) {
+        throw new ValueError("Identity matrix initializer can only be used for 2D square matrices.");
+      } else {
+        return mul(this.gain, eye(shape[0]));
+      }
+    });
+  }
+  getConfig() {
+    return { gain: this.gain };
+  }
+};
+Identity2.className = "Identity";
+serialization_exports.registerClass(Identity2);
+function computeFans(shape, dataFormat = "channelsLast") {
+  let fanIn;
+  let fanOut;
+  checkDataFormat(dataFormat);
+  if (shape.length === 2) {
+    fanIn = shape[0];
+    fanOut = shape[1];
+  } else if ([3, 4, 5].indexOf(shape.length) !== -1) {
+    if (dataFormat === "channelsFirst") {
+      const receptiveFieldSize = arrayProd(shape, 2);
+      fanIn = shape[1] * receptiveFieldSize;
+      fanOut = shape[0] * receptiveFieldSize;
+    } else if (dataFormat === "channelsLast") {
+      const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);
+      fanIn = shape[shape.length - 2] * receptiveFieldSize;
+      fanOut = shape[shape.length - 1] * receptiveFieldSize;
+    }
+  } else {
+    const shapeProd = arrayProd(shape);
+    fanIn = Math.sqrt(shapeProd);
+    fanOut = Math.sqrt(shapeProd);
+  }
+  return [fanIn, fanOut];
+}
+var VarianceScaling = class extends Initializer {
+  constructor(args) {
+    super();
+    if (args.scale < 0) {
+      throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);
+    }
+    this.scale = args.scale == null ? 1 : args.scale;
+    this.mode = args.mode == null ? "fanIn" : args.mode;
+    checkFanMode(this.mode);
+    this.distribution = args.distribution == null ? "normal" : args.distribution;
+    checkDistribution(this.distribution);
+    this.seed = args.seed;
+  }
+  apply(shape, dtype) {
+    const fans = computeFans(shape);
+    const fanIn = fans[0];
+    const fanOut = fans[1];
+    let scale2 = this.scale;
+    if (this.mode === "fanIn") {
+      scale2 /= Math.max(1, fanIn);
+    } else if (this.mode === "fanOut") {
+      scale2 /= Math.max(1, fanOut);
+    } else {
+      scale2 /= Math.max(1, (fanIn + fanOut) / 2);
+    }
+    if (this.distribution === "normal") {
+      const stddev = Math.sqrt(scale2);
+      dtype = dtype || "float32";
+      if (dtype !== "float32" && dtype !== "int32") {
+        throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);
+      }
+      return truncatedNormal(shape, 0, stddev, dtype, this.seed);
+    } else {
+      const limit = Math.sqrt(3 * scale2);
+      return randomUniform(shape, -limit, limit, dtype);
+    }
+  }
+  getConfig() {
+    return {
+      scale: this.scale,
+      mode: this.mode,
+      distribution: this.distribution,
+      seed: this.seed
+    };
+  }
+};
+VarianceScaling.className = "VarianceScaling";
+serialization_exports.registerClass(VarianceScaling);
+var GlorotUniform = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanAvg",
+      distribution: "uniform",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+GlorotUniform.className = "GlorotUniform";
+serialization_exports.registerClass(GlorotUniform);
+var GlorotNormal = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanAvg",
+      distribution: "normal",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+GlorotNormal.className = "GlorotNormal";
+serialization_exports.registerClass(GlorotNormal);
+var HeNormal = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 2,
+      mode: "fanIn",
+      distribution: "normal",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+HeNormal.className = "HeNormal";
+serialization_exports.registerClass(HeNormal);
+var HeUniform = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 2,
+      mode: "fanIn",
+      distribution: "uniform",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+HeUniform.className = "HeUniform";
+serialization_exports.registerClass(HeUniform);
+var LeCunNormal = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanIn",
+      distribution: "normal",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+LeCunNormal.className = "LeCunNormal";
+serialization_exports.registerClass(LeCunNormal);
+var LeCunUniform = class extends VarianceScaling {
+  constructor(args) {
+    super({
+      scale: 1,
+      mode: "fanIn",
+      distribution: "uniform",
+      seed: args == null ? null : args.seed
+    });
+  }
+  getClassName() {
+    return VarianceScaling.className;
+  }
+};
+LeCunUniform.className = "LeCunNormal";
+serialization_exports.registerClass(LeCunUniform);
+var Orthogonal = class extends Initializer {
+  constructor(args) {
+    super();
+    this.DEFAULT_GAIN = 1;
+    this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;
+    this.seed = args.seed;
+    if (this.seed != null) {
+      throw new NotImplementedError("Random seed is not implemented for Orthogonal Initializer yet.");
+    }
+  }
+  apply(shape, dtype) {
+    return tidy(() => {
+      if (shape.length < 2) {
+        throw new NotImplementedError("Shape must be at least 2D.");
+      }
+      if (shape[0] * shape[1] > 2e3) {
+        console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);
+      }
+      const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;
+      const a6 = randomNormal2(normalizedShape, 0, 1, "float32");
+      let q = linalg.gramSchmidt(a6);
+      if (shape[0] > shape[1]) {
+        q = transpose(q);
+      }
+      return mul(this.gain, q);
+    });
+  }
+  getConfig() {
+    return {
+      gain: this.gain,
+      seed: this.seed
+    };
+  }
+};
+Orthogonal.className = "Orthogonal";
+serialization_exports.registerClass(Orthogonal);
+var INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
+  "constant": "Constant",
+  "glorotNormal": "GlorotNormal",
+  "glorotUniform": "GlorotUniform",
+  "heNormal": "HeNormal",
+  "heUniform": "HeUniform",
+  "identity": "Identity",
+  "leCunNormal": "LeCunNormal",
+  "leCunUniform": "LeCunUniform",
+  "ones": "Ones",
+  "orthogonal": "Orthogonal",
+  "randomNormal": "RandomNormal",
+  "randomUniform": "RandomUniform",
+  "truncatedNormal": "TruncatedNormal",
+  "varianceScaling": "VarianceScaling",
+  "zeros": "Zeros"
+};
+function deserializeInitializer(config, customObjects = {}) {
+  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "initializer");
+}
+function serializeInitializer(initializer) {
+  return serializeKerasObject(initializer);
+}
+function getInitializer(identifier) {
+  if (typeof identifier === "string") {
+    const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
+    if (className === "GlorotNormal") {
+      return new GlorotNormal();
+    } else if (className === "GlorotUniform") {
+      return new GlorotUniform();
+    } else if (className === "HeNormal") {
+      return new HeNormal();
+    } else if (className === "HeUniform") {
+      return new HeUniform();
+    } else if (className === "LeCunNormal") {
+      return new LeCunNormal();
+    } else if (className === "LeCunUniform") {
+      return new LeCunUniform();
+    } else {
+      const config = {};
+      config["className"] = className;
+      config["config"] = {};
+      return deserializeInitializer(config);
+    }
+  } else if (identifier instanceof Initializer) {
+    return identifier;
+  } else {
+    return deserializeInitializer(identifier);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js
+function isArrayOfShapes(x) {
+  return Array.isArray(x) && Array.isArray(x[0]);
+}
+function normalizeShapeList(x) {
+  if (x.length === 0) {
+    return [];
+  }
+  if (!Array.isArray(x[0])) {
+    return [x];
+  }
+  return x;
+}
+function getExactlyOneTensor(xs) {
+  let x;
+  if (Array.isArray(xs)) {
+    if (xs.length !== 1) {
+      throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);
+    }
+    x = xs[0];
+  } else {
+    x = xs;
+  }
+  return x;
+}
+function getExactlyOneShape(shapes) {
+  if (Array.isArray(shapes) && Array.isArray(shapes[0])) {
+    if (shapes.length === 1) {
+      shapes = shapes;
+      return shapes[0];
+    } else {
+      throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);
+    }
+  } else {
+    return shapes;
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js
+function countParamsInWeights(weights) {
+  let count2 = 0;
+  for (const weight of weights) {
+    if (weight.shape.length === 0) {
+      count2 += 1;
+    } else {
+      count2 += weight.shape.reduce((a6, b) => a6 * b);
+    }
+  }
+  return count2;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/variables.js
+var DEFAULT_VARIABLE_NAME_PREFIX = "Variable";
+var LayerVariable = class {
+  constructor(val, dtype = "float32", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {
+    this.dtype = dtype == null ? "float32" : dtype;
+    this.shape = val.shape;
+    this.id = getNextUniqueTensorId();
+    name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;
+    this.originalName = getScopedTensorName(name);
+    this.name = getUniqueTensorName(this.originalName);
+    this.trainable_ = trainable;
+    this.constraint = constraint;
+    this.val = variable(val, this.trainable_, this.name, this.dtype);
+  }
+  read() {
+    this.assertNotDisposed();
+    return this.val;
+  }
+  write(newVal) {
+    this.assertNotDisposed();
+    checkShapesMatch(this.val, newVal);
+    if (this.val.id !== newVal.id) {
+      this.val.assign(newVal);
+      if (this.constraint != null) {
+        this.val.assign(this.constraint.apply(this.val));
+      }
+    }
+    return this;
+  }
+  dispose() {
+    this.assertNotDisposed();
+    this.val.dispose();
+  }
+  assertNotDisposed() {
+    if (this.val.isDisposed) {
+      throw new Error(`LayersVariable ${this.name} is already disposed.`);
+    }
+  }
+  get trainable() {
+    return this.trainable_;
+  }
+  set trainable(trainable) {
+    this.trainable_ = trainable;
+    this.val.trainable = trainable;
+  }
+};
+function checkShapesMatch(x, y) {
+  if (x.shape.toString() !== y.shape.toString()) {
+    throw new Error("Shape mismatch: " + JSON.stringify(x.shape) + " vs. " + JSON.stringify(y.shape));
+  }
+}
+function batchGetValue(xs) {
+  return xs.map((x) => x.read());
+}
+function batchSetValue(variablesAndValues) {
+  variablesAndValues.forEach((variableAndValue) => {
+    const variable2 = variableAndValue[0];
+    variable2.write(variableAndValue[1]);
+  });
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js
+var InputSpec = class {
+  constructor(args) {
+    this.dtype = args.dtype;
+    this.shape = args.shape;
+    if (args.shape != null) {
+      this.ndim = args.shape.length;
+    } else {
+      this.ndim = args.ndim;
+    }
+    this.maxNDim = args.maxNDim;
+    this.minNDim = args.minNDim;
+    this.axes = args.axes || {};
+  }
+};
+var SymbolicTensor = class {
+  constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {
+    this.dtype = dtype;
+    this.shape = shape;
+    this.sourceLayer = sourceLayer;
+    this.inputs = inputs;
+    this.callArgs = callArgs;
+    this.outputTensorIndex = outputTensorIndex;
+    this.id = getNextUniqueTensorId();
+    if (name != null) {
+      this.originalName = getScopedTensorName(name);
+      this.name = getUniqueTensorName(this.originalName);
+    }
+    this.rank = shape.length;
+  }
+};
+var _nextNodeID = 0;
+var Node = class {
+  constructor(args, callArgs) {
+    this.callArgs = callArgs;
+    this.id = _nextNodeID++;
+    this.outboundLayer = args.outboundLayer;
+    this.inboundLayers = args.inboundLayers;
+    this.nodeIndices = args.nodeIndices;
+    this.tensorIndices = args.tensorIndices;
+    this.inputTensors = args.inputTensors;
+    this.outputTensors = args.outputTensors;
+    this.inputMasks = args.inputMasks;
+    this.outputMasks = args.outputMasks;
+    this.inputShapes = args.inputShapes;
+    this.outputShapes = args.outputShapes;
+    for (const layer of args.inboundLayers) {
+      if (layer != null) {
+        layer.outboundNodes.push(this);
+      }
+    }
+    args.outboundLayer.inboundNodes.push(this);
+  }
+  getConfig() {
+    const inboundNames = [];
+    for (const layer of this.inboundLayers) {
+      if (layer != null) {
+        inboundNames.push(layer.name);
+      } else {
+        inboundNames.push(null);
+      }
+    }
+    return {
+      outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,
+      inboundLayers: inboundNames,
+      nodeIndices: this.nodeIndices,
+      tensorIndices: this.tensorIndices
+    };
+  }
+};
+var _nextLayerID = 0;
+var Layer = class extends serialization_exports.Serializable {
+  constructor(args = {}) {
+    super();
+    this._callHook = null;
+    this._addedWeightNames = [];
+    this._stateful = false;
+    this.id = _nextLayerID++;
+    this.activityRegularizer = null;
+    this.inputSpec = null;
+    this.supportsMasking = false;
+    this._trainableWeights = [];
+    this._nonTrainableWeights = [];
+    this._losses = [];
+    this._updates = [];
+    this._built = false;
+    this.inboundNodes = [];
+    this.outboundNodes = [];
+    let name = args.name;
+    if (!name) {
+      const prefix = this.getClassName();
+      name = toSnakeCase(prefix) + "_" + getUid(prefix);
+    }
+    this.name = name;
+    this.trainable_ = args.trainable == null ? true : args.trainable;
+    if (args.inputShape != null || args.batchInputShape != null) {
+      let batchInputShape;
+      if (args.batchInputShape != null) {
+        batchInputShape = args.batchInputShape;
+      } else if (args.inputShape != null) {
+        let batchSize = null;
+        if (args.batchSize != null) {
+          batchSize = args.batchSize;
+        }
+        batchInputShape = [batchSize].concat(args.inputShape);
+      }
+      this.batchInputShape = batchInputShape;
+      let dtype = args.dtype;
+      if (dtype == null) {
+        dtype = args.inputDType;
+      }
+      if (dtype == null) {
+        dtype = "float32";
+      }
+      this.dtype = dtype;
+    }
+    if (args.weights != null) {
+      this.initialWeights = args.weights;
+    } else {
+      this.initialWeights = null;
+    }
+    this._refCount = null;
+    this.fastWeightInitDuringBuild = false;
+  }
+  static nodeKey(layer, nodeIndex) {
+    return layer.name + "_ib-" + nodeIndex.toString();
+  }
+  getNodeAtIndex(nodeIndex, attrName) {
+    if (this.inboundNodes.length === 0) {
+      throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);
+    }
+    if (this.inboundNodes.length <= nodeIndex) {
+      throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);
+    }
+    return this.inboundNodes[nodeIndex];
+  }
+  getInputAt(nodeIndex) {
+    return singletonOrArray(this.getNodeAtIndex(nodeIndex, "input").inputTensors);
+  }
+  getOutputAt(nodeIndex) {
+    return singletonOrArray(this.getNodeAtIndex(nodeIndex, "output").outputTensors);
+  }
+  get input() {
+    if (this.inboundNodes.length > 1) {
+      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);
+    } else if (this.inboundNodes.length === 0) {
+      throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);
+    }
+    return singletonOrArray(this.getNodeAtIndex(0, "input").inputTensors);
+  }
+  get output() {
+    if (this.inboundNodes.length === 0) {
+      throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);
+    }
+    if (this.inboundNodes.length > 1) {
+      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);
+    }
+    return singletonOrArray(this.getNodeAtIndex(0, "output").outputTensors);
+  }
+  get losses() {
+    return this._losses;
+  }
+  calculateLosses() {
+    return this.losses.map((lossFn) => lossFn());
+  }
+  get updates() {
+    return this._updates;
+  }
+  get built() {
+    return this._built;
+  }
+  set built(built) {
+    this._built = built;
+  }
+  get trainable() {
+    return this.trainable_;
+  }
+  set trainable(trainable) {
+    this._trainableWeights.forEach((w) => w.trainable = trainable);
+    this.trainable_ = trainable;
+  }
+  get trainableWeights() {
+    if (this.trainable_) {
+      return this._trainableWeights.filter((w) => w.trainable);
+    } else {
+      return [];
+    }
+  }
+  set trainableWeights(weights) {
+    this._trainableWeights = weights;
+  }
+  get nonTrainableWeights() {
+    if (this.trainable) {
+      return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);
+    } else {
+      return this._trainableWeights.concat(this._nonTrainableWeights);
+    }
+  }
+  set nonTrainableWeights(weights) {
+    this._nonTrainableWeights = weights;
+  }
+  get weights() {
+    return this.trainableWeights.concat(this.nonTrainableWeights);
+  }
+  get stateful() {
+    return this._stateful;
+  }
+  resetStates() {
+    if (!this.stateful) {
+      throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.");
+    }
+  }
+  assertInputCompatibility(inputs) {
+    inputs = toList(inputs);
+    if (this.inputSpec == null || this.inputSpec.length === 0) {
+      return;
+    }
+    const inputSpec = toList(this.inputSpec);
+    if (inputs.length !== inputSpec.length) {
+      throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);
+    }
+    for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {
+      const x = inputs[inputIndex];
+      const spec = inputSpec[inputIndex];
+      if (spec == null) {
+        continue;
+      }
+      const ndim = x.rank;
+      if (spec.ndim != null) {
+        if (ndim !== spec.ndim) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);
+        }
+      }
+      if (spec.maxNDim != null) {
+        if (ndim > spec.maxNDim) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);
+        }
+      }
+      if (spec.minNDim != null) {
+        if (ndim < spec.minNDim) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);
+        }
+      }
+      if (spec.dtype != null) {
+        if (x.dtype !== spec.dtype) {
+          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);
+        }
+      }
+      if (spec.axes) {
+        const xShape = x.shape;
+        for (const key in spec.axes) {
+          const axis = Number(key);
+          const value = spec.axes[key];
+          const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];
+          if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {
+            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);
+          }
+        }
+      }
+      if (spec.shape != null) {
+        for (let i2 = 0; i2 < spec.shape.length; ++i2) {
+          const specDim = spec.shape[i2];
+          const dim = x.shape[i2];
+          if (specDim != null && dim != null) {
+            if (specDim !== dim) {
+              throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);
+            }
+          }
+        }
+      }
+    }
+  }
+  call(inputs, kwargs) {
+    return inputs;
+  }
+  invokeCallHook(inputs, kwargs) {
+    if (this._callHook != null) {
+      this._callHook(inputs, kwargs);
+    }
+  }
+  setCallHook(callHook) {
+    this._callHook = callHook;
+  }
+  clearCallHook() {
+    this._callHook = null;
+  }
+  apply(inputs, kwargs) {
+    kwargs = kwargs || {};
+    this.assertNotDisposed();
+    const inputsList = toList(inputs);
+    let allAreSymbolic = true;
+    for (const input2 of inputsList) {
+      if (!(input2 instanceof SymbolicTensor)) {
+        allAreSymbolic = false;
+        break;
+      }
+    }
+    let noneAreSymbolic = true;
+    for (const input2 of inputsList) {
+      if (input2 instanceof SymbolicTensor) {
+        noneAreSymbolic = false;
+        break;
+      }
+    }
+    if (allAreSymbolic === noneAreSymbolic) {
+      throw new ValueError("Arguments to apply() must be all SymbolicTensors or all Tensors");
+    }
+    return nameScope(this.name, () => {
+      if (!this.built) {
+        this.assertInputCompatibility(inputs);
+        const inputShapes = [];
+        for (const xElem of toList(inputs)) {
+          inputShapes.push(xElem.shape);
+        }
+        this.build(singletonOrArray(inputShapes));
+        this.built = true;
+        if (this.initialWeights) {
+          this.setWeights(this.initialWeights);
+        }
+        if (this._refCount === null && noneAreSymbolic) {
+          this._refCount = 1;
+        }
+      }
+      this.assertInputCompatibility(inputs);
+      if (noneAreSymbolic) {
+        let output = this.call(inputs, kwargs);
+        const outputList = toList(output);
+        const outputListCopy = [];
+        for (let x of outputList) {
+          if (inputsList.indexOf(x) !== -1) {
+            x = x.clone();
+          }
+          outputListCopy.push(x);
+        }
+        output = singletonOrArray(outputListCopy);
+        if (this.activityRegularizer != null) {
+          throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
+        }
+        return output;
+      } else {
+        const inputShape = collectInputShape(inputs);
+        const outputShape = this.computeOutputShape(inputShape);
+        let output;
+        const outputDType = guessOutputDType(inputs);
+        this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);
+        if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {
+          output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));
+        } else {
+          output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);
+        }
+        this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);
+        this._refCount++;
+        if (this.activityRegularizer != null) {
+          throw new NotImplementedError("Layer invocation in the presence of activity regularizer(s) is not supported yet.");
+        }
+        return output;
+      }
+    });
+  }
+  warnOnIncompatibleInputShape(inputShape) {
+    if (this.batchInputShape == null) {
+      return;
+    } else if (inputShape.length !== this.batchInputShape.length) {
+      console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
+    } else {
+      let dimMismatch = false;
+      this.batchInputShape.forEach((dimension, i2) => {
+        if (dimension != null && inputShape[i2] != null && inputShape[i2] !== dimension) {
+          dimMismatch = true;
+        }
+      });
+      if (dimMismatch) {
+        console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);
+      }
+    }
+  }
+  get outputShape() {
+    if (this.inboundNodes == null || this.inboundNodes.length === 0) {
+      throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);
+    }
+    const allOutputShapes = [];
+    for (const node of this.inboundNodes) {
+      const shapeString = JSON.stringify(node.outputShapes);
+      if (allOutputShapes.indexOf(shapeString) === -1) {
+        allOutputShapes.push(shapeString);
+      }
+    }
+    if (allOutputShapes.length === 1) {
+      const outputShapes = this.inboundNodes[0].outputShapes;
+      if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {
+        return outputShapes[0];
+      } else {
+        return outputShapes;
+      }
+    } else {
+      throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`);
+    }
+  }
+  countParams() {
+    if (!this.built) {
+      throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
+    }
+    return countParamsInWeights(this.weights);
+  }
+  build(inputShape) {
+    this.built = true;
+  }
+  getWeights(trainableOnly = false) {
+    return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);
+  }
+  setWeights(weights) {
+    tidy(() => {
+      const params = this.weights;
+      if (params.length !== weights.length) {
+        throw new ValueError(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);
+      }
+      if (params.length === 0) {
+        return;
+      }
+      const weightValueTuples = [];
+      const paramValues = batchGetValue(params);
+      for (let i2 = 0; i2 < paramValues.length; ++i2) {
+        const pv = paramValues[i2];
+        const p2 = params[i2];
+        const w = weights[i2];
+        if (!util_exports.arraysEqual(pv.shape, w.shape)) {
+          throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);
+        }
+        weightValueTuples.push([p2, w]);
+      }
+      batchSetValue(weightValueTuples);
+    });
+  }
+  addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {
+    if (this._addedWeightNames.indexOf(name) !== -1) {
+      throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);
+    }
+    this._addedWeightNames.push(name);
+    if (dtype == null) {
+      dtype = "float32";
+    }
+    if (this.fastWeightInitDuringBuild) {
+      initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer("zeros");
+    }
+    const initValue = initializer.apply(shape, dtype);
+    const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);
+    initValue.dispose();
+    if (regularizer != null) {
+      this.addLoss(() => regularizer.apply(weight.read()));
+    }
+    if (trainable == null) {
+      trainable = true;
+    }
+    if (trainable) {
+      this._trainableWeights.push(weight);
+    } else {
+      this._nonTrainableWeights.push(weight);
+    }
+    return weight;
+  }
+  setFastWeightInitDuringBuild(value) {
+    this.fastWeightInitDuringBuild = value;
+  }
+  addLoss(losses2) {
+    if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) {
+      return;
+    }
+    losses2 = toList(losses2);
+    if (this._losses !== void 0 && this._losses !== null) {
+      this.losses.push(...losses2);
+    }
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  computeMask(inputs, mask) {
+    if (!this.supportsMasking) {
+      if (mask != null) {
+        if (Array.isArray(mask)) {
+          mask.forEach((maskElement) => {
+            if (maskElement != null) {
+              throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
+            }
+          });
+        } else {
+          throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);
+        }
+      }
+      return null;
+    }
+    return mask;
+  }
+  addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {
+    const inputTensorList = toList(inputTensors);
+    outputTensors = toList(outputTensors);
+    inputMasks = toList(inputMasks);
+    outputMasks = toList(outputMasks);
+    inputShapes = normalizeShapeList(inputShapes);
+    outputShapes = normalizeShapeList(outputShapes);
+    const inboundLayers = [];
+    const nodeIndices = [];
+    const tensorIndices = [];
+    for (const x of inputTensorList) {
+      inboundLayers.push(x.sourceLayer);
+      nodeIndices.push(x.nodeIndex);
+      tensorIndices.push(x.tensorIndex);
+    }
+    new Node({
+      outboundLayer: this,
+      inboundLayers,
+      nodeIndices,
+      tensorIndices,
+      inputTensors: inputTensorList,
+      outputTensors,
+      inputMasks,
+      outputMasks,
+      inputShapes,
+      outputShapes
+    }, kwargs);
+    for (let i2 = 0; i2 < outputTensors.length; i2++) {
+      outputTensors[i2].sourceLayer = this;
+      outputTensors[i2].nodeIndex = this.inboundNodes.length - 1;
+      outputTensors[i2].tensorIndex = i2;
+    }
+  }
+  getConfig() {
+    const config = { name: this.name, trainable: this.trainable };
+    if (this.batchInputShape != null) {
+      config["batchInputShape"] = this.batchInputShape;
+    }
+    if (this.dtype != null) {
+      config["dtype"] = this.dtype;
+    }
+    return config;
+  }
+  disposeWeights() {
+    this.weights.forEach((weight) => weight.dispose());
+    return this.weights.length;
+  }
+  assertNotDisposed() {
+    if (this._refCount === 0) {
+      throw new Error(`Layer '${this.name}' is already disposed.`);
+    }
+  }
+  dispose() {
+    if (!this.built) {
+      throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);
+    }
+    if (this._refCount === null) {
+      throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);
+    }
+    this.assertNotDisposed();
+    let numDisposedVariables = 0;
+    if (--this._refCount === 0) {
+      numDisposedVariables = this.disposeWeights();
+    }
+    return { refCountAfterDispose: this._refCount, numDisposedVariables };
+  }
+};
+function collectInputShape(inputTensors) {
+  inputTensors = toList(inputTensors);
+  const shapes = [];
+  for (const x of inputTensors) {
+    shapes.push(x.shape);
+  }
+  return singletonOrArray(shapes);
+}
+function guessOutputDType(inputTensors) {
+  return "float32";
+}
+function getSourceInputs(tensor2, layer, nodeIndex) {
+  if (layer == null || nodeIndex != null && nodeIndex > 0) {
+    layer = tensor2.sourceLayer;
+    nodeIndex = tensor2.nodeIndex;
+  }
+  if (layer.inboundNodes.length === 0) {
+    return [tensor2];
+  } else {
+    const node = layer.inboundNodes[nodeIndex];
+    if (node.inboundLayers.length === 0) {
+      return node.inputTensors;
+    } else {
+      const sourceTensors = [];
+      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {
+        const x = node.inputTensors[i2];
+        const layer2 = node.inboundLayers[i2];
+        const nodeIndex2 = node.nodeIndices[i2];
+        const previousSources = getSourceInputs(x, layer2, nodeIndex2);
+        for (const x2 of previousSources) {
+          if (sourceTensors.indexOf(x2) === -1) {
+            sourceTensors.push(x2);
+          }
+        }
+      }
+      return sourceTensors;
+    }
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js
+var InputLayer = class extends Layer {
+  constructor(args) {
+    super({
+      dtype: args.dtype,
+      name: args.name != null ? args.name : getUid("input").toString()
+    });
+    if (args.batchSize == null) {
+      args.batchSize = null;
+    }
+    if (args.sparse == null) {
+      args.sparse = false;
+    }
+    this.trainable = false;
+    this.built = true;
+    this.sparse = args.sparse;
+    if (args.inputShape != null && args.batchInputShape != null) {
+      throw new ValueError("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
+    }
+    let batchInputShape = args.batchInputShape;
+    if (batchInputShape == null) {
+      if (args.inputShape == null) {
+        throw new ValueError("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
+      } else {
+        batchInputShape = [args.batchSize].concat(args.inputShape);
+      }
+    } else {
+      if (args.batchSize != null) {
+        throw new ValueError("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");
+      }
+    }
+    const dtype = args.dtype || "float32";
+    this.batchInputShape = batchInputShape;
+    this.dtype = dtype;
+    this.inputSpec = [{ shape: batchInputShape }];
+    const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);
+    inputTensor.nodeIndex = 0;
+    inputTensor.tensorIndex = 0;
+    new Node({
+      outboundLayer: this,
+      inboundLayers: [],
+      nodeIndices: [],
+      tensorIndices: [],
+      inputTensors: [inputTensor],
+      outputTensors: [inputTensor],
+      inputMasks: [null],
+      outputMasks: [null],
+      inputShapes: [batchInputShape],
+      outputShapes: [batchInputShape]
+    });
+  }
+  apply(inputs, kwargs) {
+    throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
+  }
+  dispose() {
+    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };
+  }
+  getConfig() {
+    return {
+      batchInputShape: this.batchInputShape,
+      dtype: this.dtype,
+      sparse: this.sparse,
+      name: this.name
+    };
+  }
+};
+InputLayer.className = "InputLayer";
+serialization_exports.registerClass(InputLayer);
+function Input(config) {
+  if (config.batchShape == null && config.shape == null) {
+    throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
+  }
+  if (config.batchShape != null && config.shape != null) {
+    throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
+  }
+  let batchShape = config.batchShape;
+  if (config.shape != null && batchShape == null) {
+    batchShape = [null].concat(config.shape);
+  }
+  let dtype = config.dtype;
+  if (dtype == null) {
+    dtype = "float32";
+  }
+  const inputLayer2 = new InputLayer({
+    batchInputShape: batchShape,
+    name: config.name,
+    dtype,
+    sparse: config.sparse
+  });
+  const outputs = inputLayer2.inboundNodes[0].outputTensors;
+  return outputs[0];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js
+function assertFeedCompatibility(key, val) {
+  if (key.dtype == null || key.dtype === val.dtype) {
+    return val;
+  }
+  try {
+    return cast(val, key.dtype);
+  } catch (err) {
+    throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);
+  }
+}
+var FeedDict = class {
+  constructor(feeds) {
+    this.id2Value = {};
+    this.id2Mask = {};
+    this.name2Id = {};
+    if (feeds instanceof FeedDict) {
+      for (const id in feeds.id2Value) {
+        this.id2Value[id] = feeds.id2Value[id];
+        if (id in feeds.id2Mask) {
+          this.id2Mask[id] = feeds.id2Mask[id];
+        }
+      }
+    } else {
+      if (feeds == null) {
+        return;
+      }
+      for (const feed of feeds) {
+        this.add(feed.key, feed.value);
+      }
+    }
+  }
+  add(key, value, mask) {
+    if (this.id2Value[key.id] == null) {
+      this.id2Value[key.id] = assertFeedCompatibility(key, value);
+      this.name2Id[key.name] = key.id;
+      if (mask != null) {
+        this.id2Mask[key.id] = mask;
+      }
+    } else {
+      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);
+    }
+    return this;
+  }
+  addFeed(feed) {
+    this.add(feed.key, feed.value);
+  }
+  hasKey(key) {
+    return this.id2Value[key.id] != null;
+  }
+  names() {
+    return Object.keys(this.name2Id);
+  }
+  getValue(key) {
+    if (key instanceof SymbolicTensor) {
+      if (this.id2Value[key.id] == null) {
+        throw new ValueError(`Nonexistent key: ${key.name}`);
+      } else {
+        return this.id2Value[key.id];
+      }
+    } else {
+      const id = this.name2Id[key];
+      if (id == null) {
+        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
+      }
+      return this.id2Value[id];
+    }
+  }
+  getMask(key) {
+    if (key instanceof SymbolicTensor) {
+      if (this.id2Value[key.id] == null) {
+        throw new ValueError(`Nonexistent key: ${key.name}`);
+      } else {
+        return this.id2Mask[key.id];
+      }
+    } else {
+      const id = this.name2Id[key];
+      if (id == null) {
+        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);
+      }
+      return this.id2Mask[id];
+    }
+  }
+  disposeMasks() {
+    if (this.id2Mask != null) {
+      dispose(this.id2Mask);
+    }
+  }
+};
+var cachedSorted = new LruCache();
+var cachedRecipientCounts = new LruCache();
+function updateCacheMaxEntries(maxEntries) {
+  if (cachedSorted != null) {
+    cachedSorted.setMaxEntries(maxEntries);
+  }
+  if (cachedRecipientCounts != null) {
+    cachedRecipientCounts.setMaxEntries(maxEntries);
+  }
+}
+function execute(fetches, feedDict, kwargs, probe) {
+  const training = kwargs == null ? false : kwargs["training"];
+  const arrayFetches = Array.isArray(fetches);
+  const fetchArray = arrayFetches ? fetches : [fetches];
+  const outputNames = fetchArray.map((t2) => t2.name);
+  const finalOutputs = [];
+  const feedNames = feedDict.names();
+  for (const outputName of outputNames) {
+    if (feedNames.indexOf(outputName) !== -1) {
+      finalOutputs.push(feedDict.getValue(outputName));
+    } else {
+      finalOutputs.push(null);
+    }
+  }
+  if (probe != null) {
+    probe.maxNumTensors = -Infinity;
+    probe.minNumTensors = Infinity;
+  }
+  const fetchAndFeedKey = outputNames.join(",") + "|" + feedDict.names().sort().join(",");
+  let sorted = cachedSorted.get(fetchAndFeedKey);
+  let recipientCounts;
+  if (sorted == null) {
+    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
+    sorted = out.sorted;
+    recipientCounts = out.recipientCounts;
+    cachedSorted.put(fetchAndFeedKey, sorted);
+    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);
+  }
+  recipientCounts = {};
+  if (!training) {
+    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));
+  }
+  const internalFeedDict = new FeedDict(feedDict);
+  for (let i2 = 0; i2 < sorted.length; ++i2) {
+    if (probe != null) {
+      const numTensors = memory().numTensors;
+      if (numTensors > probe.maxNumTensors) {
+        probe.maxNumTensors = numTensors;
+      }
+      if (numTensors < probe.minNumTensors) {
+        probe.minNumTensors = numTensors;
+      }
+    }
+    const symbolic = sorted[i2];
+    const srcLayer = symbolic.sourceLayer;
+    if (srcLayer instanceof InputLayer) {
+      continue;
+    }
+    const inputValues = [];
+    const inputMasks = [];
+    const tensorsToDispose = [];
+    let maskExists = false;
+    for (const input2 of symbolic.inputs) {
+      const value = internalFeedDict.getValue(input2);
+      const mask = internalFeedDict.getMask(input2);
+      inputValues.push(value);
+      inputMasks.push(mask);
+      if (mask != null) {
+        maskExists = true;
+      }
+      if (!training) {
+        recipientCounts[input2.name]--;
+        if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {
+          tensorsToDispose.push(value);
+        }
+      }
+    }
+    if (maskExists) {
+      kwargs = kwargs || {};
+      kwargs["mask"] = inputMasks[0];
+    }
+    const outputTensors = toList(srcLayer.apply(inputValues, kwargs));
+    let outputMask = null;
+    if (srcLayer.supportsMasking) {
+      outputMask = srcLayer.computeMask(inputValues, inputMasks);
+    }
+    const layerOutputs = getNodeOutputs(symbolic);
+    const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
+    for (let i3 = 0; i3 < outputSymbolicTensors.length; ++i3) {
+      if (!internalFeedDict.hasKey(outputSymbolicTensors[i3])) {
+        internalFeedDict.add(outputSymbolicTensors[i3], outputTensors[i3], Array.isArray(outputMask) ? outputMask[0] : outputMask);
+      }
+      const index = outputNames.indexOf(outputSymbolicTensors[i3].name);
+      if (index !== -1) {
+        finalOutputs[index] = outputTensors[i3];
+      }
+    }
+    if (!training) {
+      dispose(tensorsToDispose);
+    }
+  }
+  internalFeedDict.disposeMasks();
+  return arrayFetches ? finalOutputs : finalOutputs[0];
+}
+function getTopologicalSortAndRecipientCounts(fetches, feedDict) {
+  util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);
+  let finalSorted = [];
+  let finalRecipientMap = {};
+  if (fetches.length === 1) {
+    const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);
+    finalSorted = out.sorted;
+    finalRecipientMap = out.recipientMap;
+  } else {
+    const visited = /* @__PURE__ */ new Set();
+    for (const fetch4 of fetches) {
+      const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict);
+      for (const symbolicTensor of sorted) {
+        if (!visited.has(symbolicTensor.name)) {
+          finalSorted.push(symbolicTensor);
+          visited.add(symbolicTensor.name);
+        }
+      }
+      for (const name in recipientMap) {
+        if (finalRecipientMap[name] == null) {
+          finalRecipientMap[name] = /* @__PURE__ */ new Set();
+        }
+        recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));
+      }
+    }
+  }
+  return {
+    sorted: finalSorted,
+    recipientCounts: recipientMap2Counts(finalRecipientMap)
+  };
+}
+function recipientMap2Counts(recipientMap) {
+  const recipientCounts = {};
+  for (const name in recipientMap) {
+    recipientCounts[name] = recipientMap[name].size;
+  }
+  return recipientCounts;
+}
+function getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) {
+  const visited = /* @__PURE__ */ new Set();
+  const sorted = [];
+  const recipientMap = {};
+  for (const key of feedDict.names()) {
+    visited.add(key);
+  }
+  const stack2 = [];
+  const marks = [];
+  stack2.push(fetch4);
+  while (stack2.length > 0) {
+    const top = stack2[stack2.length - 1];
+    if (visited.has(top.name)) {
+      stack2.pop();
+      continue;
+    }
+    const topIsMarked = marks[marks.length - 1] === stack2.length - 1;
+    if (top.inputs.length === 0 || topIsMarked) {
+      stack2.pop();
+      sorted.push(top);
+      visited.add(top.name);
+      if (topIsMarked) {
+        marks.pop();
+      }
+    } else {
+      marks.push(stack2.length - 1);
+      for (const input2 of top.inputs) {
+        if (recipientMap[input2.name] == null) {
+          recipientMap[input2.name] = /* @__PURE__ */ new Set();
+        }
+        recipientMap[input2.name].add(top.name);
+        if (visited.has(input2.name)) {
+          continue;
+        }
+        stack2.push(input2);
+      }
+    }
+  }
+  return { sorted, recipientMap };
+}
+function getNodeOutputs(fetch4) {
+  let layerOutputs;
+  if (fetch4.sourceLayer.inboundNodes.length === 1) {
+    layerOutputs = fetch4.sourceLayer.output;
+  } else {
+    let nodeIndex = null;
+    for (let i2 = 0; i2 < fetch4.sourceLayer.inboundNodes.length; ++i2) {
+      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i2].outputTensors) {
+        if (outputTensor.id === fetch4.id) {
+          nodeIndex = i2;
+          break;
+        }
+      }
+    }
+    layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);
+  }
+  return layerOutputs;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js
+var ENV3 = env();
+ENV3.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, updateCacheMaxEntries);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
+var exports_constraints_exports = {};
+__export(exports_constraints_exports, {
+  maxNorm: () => maxNorm,
+  minMaxNorm: () => minMaxNorm,
+  nonNeg: () => nonNeg,
+  unitNorm: () => unitNorm
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/constraints.js
+function calcL2Norms(w, axis) {
+  return tidy(() => sqrt(sum2(mul(w, w), axis, true)));
+}
+var Constraint = class extends serialization_exports.Serializable {
+  getConfig() {
+    return {};
+  }
+};
+var MaxNorm = class extends Constraint {
+  constructor(args) {
+    super();
+    this.defaultMaxValue = 2;
+    this.defaultAxis = 0;
+    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
+    this.axis = args.axis != null ? args.axis : this.defaultAxis;
+  }
+  apply(w) {
+    return tidy(() => {
+      const norms = calcL2Norms(w, this.axis);
+      const desired = clipByValue(norms, 0, this.maxValue);
+      return mul(w, div(desired, add2(epsilon(), norms)));
+    });
+  }
+  getConfig() {
+    return { maxValue: this.maxValue, axis: this.axis };
+  }
+};
+MaxNorm.className = "MaxNorm";
+serialization_exports.registerClass(MaxNorm);
+var UnitNorm = class extends Constraint {
+  constructor(args) {
+    super();
+    this.defaultAxis = 0;
+    this.axis = args.axis != null ? args.axis : this.defaultAxis;
+  }
+  apply(w) {
+    return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));
+  }
+  getConfig() {
+    return { axis: this.axis };
+  }
+};
+UnitNorm.className = "UnitNorm";
+serialization_exports.registerClass(UnitNorm);
+var NonNeg = class extends Constraint {
+  apply(w) {
+    return relu(w);
+  }
+};
+NonNeg.className = "NonNeg";
+serialization_exports.registerClass(NonNeg);
+var MinMaxNorm = class extends Constraint {
+  constructor(args) {
+    super();
+    this.defaultMinValue = 0;
+    this.defaultMaxValue = 1;
+    this.defaultRate = 1;
+    this.defaultAxis = 0;
+    this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;
+    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;
+    this.rate = args.rate != null ? args.rate : this.defaultRate;
+    this.axis = args.axis != null ? args.axis : this.defaultAxis;
+  }
+  apply(w) {
+    return tidy(() => {
+      const norms = calcL2Norms(w, this.axis);
+      const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));
+      return mul(w, div(desired, add2(epsilon(), norms)));
+    });
+  }
+  getConfig() {
+    return {
+      minValue: this.minValue,
+      maxValue: this.maxValue,
+      rate: this.rate,
+      axis: this.axis
+    };
+  }
+};
+MinMaxNorm.className = "MinMaxNorm";
+serialization_exports.registerClass(MinMaxNorm);
+var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
+  "maxNorm": "MaxNorm",
+  "minMaxNorm": "MinMaxNorm",
+  "nonNeg": "NonNeg",
+  "unitNorm": "UnitNorm"
+};
+function serializeConstraint(constraint) {
+  return serializeKerasObject(constraint);
+}
+function deserializeConstraint(config, customObjects = {}) {
+  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "constraint");
+}
+function getConstraint(identifier) {
+  if (identifier == null) {
+    return null;
+  }
+  if (typeof identifier === "string") {
+    const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
+    const config = { className, config: {} };
+    return deserializeConstraint(config);
+  } else if (identifier instanceof Constraint) {
+    return identifier;
+  } else {
+    return deserializeConstraint(identifier);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
+function maxNorm(args) {
+  return new MaxNorm(args);
+}
+function unitNorm(args) {
+  return new UnitNorm(args);
+}
+function nonNeg() {
+  return new NonNeg();
+}
+function minMaxNorm(config) {
+  return new MinMaxNorm(config);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
+var exports_initializers_exports = {};
+__export(exports_initializers_exports, {
+  constant: () => constant,
+  glorotNormal: () => glorotNormal,
+  glorotUniform: () => glorotUniform,
+  heNormal: () => heNormal,
+  heUniform: () => heUniform,
+  identity: () => identity,
+  leCunNormal: () => leCunNormal,
+  leCunUniform: () => leCunUniform,
+  ones: () => ones3,
+  orthogonal: () => orthogonal,
+  randomNormal: () => randomNormal3,
+  randomUniform: () => randomUniform2,
+  truncatedNormal: () => truncatedNormal2,
+  varianceScaling: () => varianceScaling,
+  zeros: () => zeros2
+});
+function zeros2() {
+  return new Zeros();
+}
+function ones3() {
+  return new Ones();
+}
+function constant(args) {
+  return new Constant(args);
+}
+function randomUniform2(args) {
+  return new RandomUniform(args);
+}
+function randomNormal3(args) {
+  return new RandomNormal(args);
+}
+function truncatedNormal2(args) {
+  return new TruncatedNormal(args);
+}
+function identity(args) {
+  return new Identity2(args);
+}
+function varianceScaling(config) {
+  return new VarianceScaling(config);
+}
+function glorotUniform(args) {
+  return new GlorotUniform(args);
+}
+function glorotNormal(args) {
+  return new GlorotNormal(args);
+}
+function heNormal(args) {
+  return new HeNormal(args);
+}
+function heUniform(args) {
+  return new HeUniform(args);
+}
+function leCunNormal(args) {
+  return new LeCunNormal(args);
+}
+function leCunUniform(args) {
+  return new LeCunUniform(args);
+}
+function orthogonal(args) {
+  return new Orthogonal(args);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
+var exports_layers_exports = {};
+__export(exports_layers_exports, {
+  Layer: () => Layer,
+  RNN: () => RNN,
+  RNNCell: () => RNNCell,
+  activation: () => activation,
+  add: () => add3,
+  alphaDropout: () => alphaDropout,
+  average: () => average,
+  averagePooling1d: () => averagePooling1d,
+  averagePooling2d: () => averagePooling2d,
+  averagePooling3d: () => averagePooling3d,
+  avgPool1d: () => avgPool1d,
+  avgPool2d: () => avgPool2d,
+  avgPool3d: () => avgPool3d2,
+  avgPooling1d: () => avgPooling1d,
+  avgPooling2d: () => avgPooling2d,
+  avgPooling3d: () => avgPooling3d,
+  batchNormalization: () => batchNormalization2,
+  bidirectional: () => bidirectional,
+  concatenate: () => concatenate2,
+  conv1d: () => conv1d2,
+  conv2d: () => conv2d3,
+  conv2dTranspose: () => conv2dTranspose2,
+  conv3d: () => conv3d2,
+  conv3dTranspose: () => conv3dTranspose2,
+  convLstm2d: () => convLstm2d,
+  convLstm2dCell: () => convLstm2dCell,
+  cropping2D: () => cropping2D,
+  dense: () => dense,
+  depthwiseConv2d: () => depthwiseConv2d4,
+  dot: () => dot3,
+  dropout: () => dropout3,
+  elu: () => elu3,
+  embedding: () => embedding,
+  flatten: () => flatten3,
+  gaussianDropout: () => gaussianDropout,
+  gaussianNoise: () => gaussianNoise,
+  globalAveragePooling1d: () => globalAveragePooling1d,
+  globalAveragePooling2d: () => globalAveragePooling2d,
+  globalMaxPool1d: () => globalMaxPool1d,
+  globalMaxPool2d: () => globalMaxPool2d,
+  globalMaxPooling1d: () => globalMaxPooling1d,
+  globalMaxPooling2d: () => globalMaxPooling2d,
+  gru: () => gru,
+  gruCell: () => gruCell,
+  input: () => input,
+  inputLayer: () => inputLayer,
+  layerNormalization: () => layerNormalization,
+  leakyReLU: () => leakyReLU,
+  lstm: () => lstm,
+  lstmCell: () => lstmCell,
+  masking: () => masking,
+  maxPool1d: () => maxPool1d,
+  maxPool2d: () => maxPool2d,
+  maxPooling1d: () => maxPooling1d,
+  maxPooling2d: () => maxPooling2d,
+  maxPooling3d: () => maxPooling3d,
+  maximum: () => maximum2,
+  minimum: () => minimum2,
+  multiply: () => multiply,
+  permute: () => permute,
+  prelu: () => prelu2,
+  reLU: () => reLU,
+  repeatVector: () => repeatVector,
+  reshape: () => reshape2,
+  rnn: () => rnn2,
+  separableConv2d: () => separableConv2d2,
+  simpleRNN: () => simpleRNN,
+  simpleRNNCell: () => simpleRNNCell,
+  softmax: () => softmax2,
+  spatialDropout1d: () => spatialDropout1d,
+  stackedRNNCells: () => stackedRNNCells,
+  thresholdedReLU: () => thresholdedReLU,
+  timeDistributed: () => timeDistributed,
+  upSampling2d: () => upSampling2d,
+  zeroPadding2d: () => zeroPadding2d
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/logs.js
+async function resolveScalarsInLogs(logs) {
+  if (logs == null) {
+    return;
+  }
+  const promises = [];
+  const keys = [];
+  const scalarsToDispose = [];
+  for (const key in logs) {
+    const value = logs[key];
+    if (typeof value !== "number") {
+      const valueScalar = value;
+      promises.push(valueScalar.data());
+      keys.push(key);
+      scalarsToDispose.push(valueScalar);
+    }
+  }
+  if (promises.length > 0) {
+    const values = await Promise.all(promises);
+    for (let i2 = 0; i2 < values.length; ++i2) {
+      logs[keys[i2]] = values[i2][0];
+    }
+    dispose(scalarsToDispose);
+  }
+}
+function disposeTensorsInLogs(logs) {
+  if (logs == null) {
+    return;
+  }
+  for (const key in logs) {
+    const value = logs[key];
+    if (typeof value !== "number") {
+      value.dispose();
+    }
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js
+var ModelLoggingVerbosity;
+(function(ModelLoggingVerbosity2) {
+  ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT";
+  ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE";
+})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));
+var DEFAULT_YIELD_EVERY_MS = 125;
+var BaseCallback = class {
+  constructor() {
+    this.validationData = null;
+  }
+  setParams(params) {
+    this.params = params;
+  }
+  async onEpochBegin(epoch, logs) {
+  }
+  async onEpochEnd(epoch, logs) {
+  }
+  async onBatchBegin(batch, logs) {
+  }
+  async onBatchEnd(batch, logs) {
+  }
+  async onTrainBegin(logs) {
+  }
+  async onTrainEnd(logs) {
+  }
+  setModel(model2) {
+  }
+};
+var CallbackList = class {
+  constructor(callbacks2, queueLength = 10) {
+    if (callbacks2 == null) {
+      callbacks2 = [];
+    }
+    this.callbacks = callbacks2;
+    this.queueLength = queueLength;
+  }
+  append(callback) {
+    this.callbacks.push(callback);
+  }
+  setParams(params) {
+    for (const callback of this.callbacks) {
+      callback.setParams(params);
+    }
+  }
+  setModel(model2) {
+    for (const callback of this.callbacks) {
+      callback.setModel(model2);
+    }
+  }
+  async onEpochBegin(epoch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onEpochBegin(epoch, logs);
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onEpochEnd(epoch, logs);
+    }
+  }
+  async onBatchBegin(batch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onBatchBegin(batch, logs);
+    }
+  }
+  async onBatchEnd(batch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onBatchEnd(batch, logs);
+    }
+  }
+  async onTrainBegin(logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onTrainBegin(logs);
+    }
+  }
+  async onTrainEnd(logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    for (const callback of this.callbacks) {
+      await callback.onTrainEnd(logs);
+    }
+  }
+};
+var BaseLogger = class extends BaseCallback {
+  constructor() {
+    super();
+  }
+  async onEpochBegin(epoch) {
+    this.seen = 0;
+    this.totals = {};
+  }
+  async onBatchEnd(batch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    const batchSize = logs["size"] == null ? 0 : logs["size"];
+    this.seen += batchSize;
+    for (const key in logs) {
+      const value = logs[key];
+      if (typeof value === "number") {
+        if (!this.totals.hasOwnProperty(key)) {
+          this.totals[key] = 0;
+        }
+        this.totals[key] = this.totals[key] + value * batchSize;
+      } else {
+        let oldTotalsToDispose;
+        if (key in this.totals) {
+          oldTotalsToDispose = this.totals[key];
+        } else {
+          this.totals[key] = 0;
+        }
+        const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));
+        this.totals[key] = total;
+        if (oldTotalsToDispose != null) {
+          oldTotalsToDispose.dispose();
+        }
+      }
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    if (logs != null) {
+      for (const key of this.params["metrics"]) {
+        if (this.totals[key] == null) {
+          continue;
+        }
+        if (typeof this.totals[key] === "number") {
+          logs[key] = this.totals[key] / this.seen;
+        } else {
+          tidy(() => {
+            const log6 = mul(div(1, this.seen), this.totals[key]);
+            logs[key] = log6;
+            this.totals[key].dispose();
+            keep(logs[key]);
+          });
+        }
+      }
+    }
+  }
+};
+var History = class extends BaseCallback {
+  async onTrainBegin(logs) {
+    this.epoch = [];
+    this.history = {};
+  }
+  async onEpochEnd(epoch, logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    this.epoch.push(epoch);
+    for (const key in logs) {
+      if (this.history[key] == null) {
+        this.history[key] = [];
+      }
+      this.history[key].push(logs[key]);
+    }
+  }
+  async syncData() {
+    const promises = [];
+    const keys = [];
+    const indices = [];
+    for (const key in this.history) {
+      const valueArray = this.history[key];
+      for (let i2 = 0; i2 < valueArray.length; ++i2) {
+        if (typeof valueArray[i2] !== "number") {
+          const valueScalar = valueArray[i2];
+          promises.push(valueScalar.data());
+          keys.push(key);
+          indices.push(i2);
+        }
+      }
+    }
+    const values = await Promise.all(promises);
+    for (let n = 0; n < values.length; ++n) {
+      const tensorToDispose = this.history[keys[n]][indices[n]];
+      tensorToDispose.dispose();
+      this.history[keys[n]][indices[n]] = values[n][0];
+    }
+  }
+};
+var CustomCallback = class extends BaseCallback {
+  constructor(args, yieldEvery) {
+    super();
+    this.currentEpoch = 0;
+    this.nowFunc = args.nowFunc;
+    this.nextFrameFunc = args.nextFrameFunc || nextFrame;
+    this.yieldEvery = yieldEvery || "auto";
+    if (this.yieldEvery === "auto") {
+      this.yieldEvery = DEFAULT_YIELD_EVERY_MS;
+    }
+    if (this.yieldEvery === "never" && args.onYield != null) {
+      throw new Error("yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback");
+    }
+    if (util_exports.isNumber(this.yieldEvery)) {
+      this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc);
+    }
+    this.trainBegin = args.onTrainBegin;
+    this.trainEnd = args.onTrainEnd;
+    this.epochBegin = args.onEpochBegin;
+    this.epochEnd = args.onEpochEnd;
+    this.batchBegin = args.onBatchBegin;
+    this.batchEnd = args.onBatchEnd;
+    this.yield = args.onYield;
+  }
+  async maybeWait(epoch, batch, logs) {
+    const ps = [];
+    if (this.yield != null) {
+      await resolveScalarsInLogs(logs);
+      ps.push(this.yield(epoch, batch, logs));
+    }
+    ps.push(this.nextFrameFunc());
+    await Promise.all(ps);
+  }
+  async onEpochBegin(epoch, logs) {
+    this.currentEpoch = epoch;
+    if (this.epochBegin != null) {
+      await resolveScalarsInLogs(logs);
+      await this.epochBegin(epoch, logs);
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    const ps = [];
+    if (this.epochEnd != null) {
+      await resolveScalarsInLogs(logs);
+      ps.push(this.epochEnd(epoch, logs));
+    }
+    if (this.yieldEvery === "epoch") {
+      ps.push(this.nextFrameFunc());
+    }
+    await Promise.all(ps);
+  }
+  async onBatchBegin(batch, logs) {
+    if (this.batchBegin != null) {
+      await resolveScalarsInLogs(logs);
+      await this.batchBegin(batch, logs);
+    }
+  }
+  async onBatchEnd(batch, logs) {
+    const ps = [];
+    if (this.batchEnd != null) {
+      await resolveScalarsInLogs(logs);
+      ps.push(this.batchEnd(batch, logs));
+    }
+    if (this.yieldEvery === "batch") {
+      ps.push(this.nextFrameFunc());
+    } else if (util_exports.isNumber(this.yieldEvery)) {
+      ps.push(this.maybeWait(this.currentEpoch, batch, logs));
+    }
+    await Promise.all(ps);
+  }
+  async onTrainBegin(logs) {
+    if (this.trainBegin != null) {
+      await resolveScalarsInLogs(logs);
+      await this.trainBegin(logs);
+    }
+  }
+  async onTrainEnd(logs) {
+    if (this.trainEnd != null) {
+      await resolveScalarsInLogs(logs);
+      await this.trainEnd(logs);
+    }
+  }
+};
+function standardizeCallbacks(callbacks2, yieldEvery) {
+  if (callbacks2 == null) {
+    callbacks2 = {};
+  }
+  if (callbacks2 instanceof BaseCallback) {
+    return [callbacks2];
+  }
+  if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {
+    return callbacks2;
+  }
+  const callbackConfigs = toList(callbacks2);
+  return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));
+}
+var CallbackConstructorRegistry = class {
+  constructor() {
+  }
+  static registerCallbackConstructor(verbosityLevel, callbackConstructor) {
+    util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);
+    CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);
+    if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {
+      CallbackConstructorRegistry.constructors[verbosityLevel] = [];
+    }
+    CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);
+  }
+  static checkForDuplicate(callbackConstructor) {
+    for (const levelName in CallbackConstructorRegistry.constructors) {
+      const constructors = CallbackConstructorRegistry.constructors[+levelName];
+      constructors.forEach((ctor) => {
+        if (ctor === callbackConstructor) {
+          throw new ValueError("Duplicate callback constructor.");
+        }
+      });
+    }
+  }
+  static clear() {
+    CallbackConstructorRegistry.constructors = {};
+  }
+  static createCallbacks(verbosityLevel) {
+    const constructors = [];
+    for (const levelName in CallbackConstructorRegistry.constructors) {
+      const level = +levelName;
+      if (verbosityLevel >= level) {
+        constructors.push(...CallbackConstructorRegistry.constructors[level]);
+      }
+    }
+    return constructors.map((ctor) => new ctor());
+  }
+};
+CallbackConstructorRegistry.constructors = {};
+function configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {
+  const history = new History();
+  const actualCallbacks = [
+    new BaseLogger(),
+    ...CallbackConstructorRegistry.createCallbacks(verbose)
+  ];
+  if (callbacks2 != null) {
+    actualCallbacks.push(...callbacks2);
+  }
+  actualCallbacks.push(history);
+  const callbackList = new CallbackList(actualCallbacks);
+  callbackList.setParams({
+    epochs,
+    initialEpoch,
+    samples: numTrainSamples,
+    steps: stepsPerEpoch,
+    batchSize,
+    verbose,
+    doValidation,
+    metrics: callbackMetrics
+  });
+  return { callbackList, history };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js
+function deserialize(config, customObjects = {}, fastWeightInit = false) {
+  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/losses.js
+function l2Normalize(x, axis) {
+  return tidy(() => {
+    if (x.dtype !== "float32") {
+      x = cast(x, "float32");
+    }
+    const squareSum = sum2(square2(x), axis, true);
+    const epsilonTensor = fill(squareSum.shape, epsilon());
+    const norm2 = sqrt(maximum(squareSum, epsilonTensor));
+    return div(x, norm2);
+  });
+}
+function meanSquaredError2(yTrue, yPred) {
+  return tidy(() => mean(square2(sub(yPred, yTrue)), -1));
+}
+function meanAbsoluteError(yTrue, yPred) {
+  return tidy(() => mean(abs(sub(yPred, yTrue)), -1));
+}
+function meanAbsolutePercentageError(yTrue, yPred) {
+  return tidy(() => {
+    const diff = sub(yTrue, yPred);
+    const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);
+    const absResult = abs(div(diff, clippedTrue));
+    return mul(100, mean(absResult, -1));
+  });
+}
+function meanSquaredLogarithmicError(yTrue, yPred) {
+  return tidy(() => {
+    const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);
+    const firstLog = log2(add2(1, clippedPred));
+    const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
+    const secondLog = log2(add2(1, clippedTrue));
+    return mean(square2(sub(firstLog, secondLog)), -1);
+  });
+}
+function squaredHinge(yTrue, yPred) {
+  return tidy(() => {
+    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
+    return mean(square2(maxResult), -1);
+  });
+}
+function hinge(yTrue, yPred) {
+  return tidy(() => {
+    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
+    return mean(maxResult, -1);
+  });
+}
+function categoricalHinge(yTrue, yPred) {
+  return tidy(() => {
+    const pos = sum2(mul(yTrue, yPred), -1);
+    const neg5 = max(mul(sub(1, yTrue), yPred), -1);
+    return maximum(0, add2(1, sub(neg5, pos)));
+  });
+}
+function logcosh(yTrue, yPred) {
+  return tidy(() => {
+    const log22 = Math.log(2);
+    const predictionDiff = sub(yPred, yTrue);
+    const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);
+    return mean(logcoshResult, -1);
+  });
+}
+function categoricalCrossentropy(target, output, fromLogits = false) {
+  return tidy(() => {
+    if (fromLogits) {
+      output = softmax(output);
+    } else {
+      const outputSum = sum2(output, output.shape.length - 1, true);
+      output = div(output, outputSum);
+    }
+    output = clipByValue(output, epsilon(), 1 - epsilon());
+    return neg(sum2(mul(cast(target, "float32"), log2(output)), output.shape.length - 1));
+  });
+}
+function sparseCategoricalCrossentropy(target, output, fromLogits = false) {
+  return tidy(() => {
+    const flatTarget = cast(floor(flatten2(target)), "int32");
+    output = clipByValue(output, epsilon(), 1 - epsilon());
+    const outputShape = output.shape;
+    const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);
+    return categoricalCrossentropy(oneHotTarget, output, fromLogits);
+  });
+}
+function sigmoidCrossEntropyWithLogits(labels, logits) {
+  if (!util_exports.arraysEqual(labels.shape, logits.shape)) {
+    throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);
+  }
+  return tidy(() => {
+    const reluLogits = relu(logits);
+    const negAbsLogits = neg(abs(logits));
+    return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits)));
+  });
+}
+function binaryCrossentropy(yTrue, yPred) {
+  return tidy(() => {
+    let y;
+    y = clipByValue(yPred, epsilon(), 1 - epsilon());
+    y = log2(div(y, sub(1, y)));
+    return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);
+  });
+}
+function kullbackLeiblerDivergence(yTrue, yPred) {
+  return tidy(() => {
+    const clippedTrue = clipByValue(yTrue, epsilon(), 1);
+    const clippedPred = clipByValue(yPred, epsilon(), 1);
+    return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1);
+  });
+}
+function poisson(yTrue, yPred) {
+  return tidy(() => {
+    const logPred = log2(add2(epsilon(), yPred));
+    return mean(sub(yPred, mul(yTrue, logPred)), -1);
+  });
+}
+function cosineProximity(yTrue, yPred) {
+  return tidy(() => {
+    const trueNormalized = l2Normalize(yTrue, -1);
+    const predNormalized = l2Normalize(yPred, -1);
+    const trueXPred = mul(trueNormalized, predNormalized);
+    return neg(sum2(trueXPred, -1));
+  });
+}
+var lossesMap = {
+  meanSquaredError: meanSquaredError2,
+  meanAbsoluteError,
+  meanAbsolutePercentageError,
+  meanSquaredLogarithmicError,
+  squaredHinge,
+  hinge,
+  categoricalHinge,
+  logcosh,
+  categoricalCrossentropy,
+  sparseCategoricalCrossentropy,
+  binaryCrossentropy,
+  kullbackLeiblerDivergence,
+  poisson,
+  cosineProximity
+};
+function get(identifierOrFn) {
+  if (typeof identifierOrFn === "string") {
+    if (identifierOrFn in lossesMap) {
+      return lossesMap[identifierOrFn];
+    }
+    let errMsg = `Unknown loss ${identifierOrFn}`;
+    if (identifierOrFn.toLowerCase().includes("softmaxcrossentropy")) {
+      errMsg = `Unknown loss ${identifierOrFn}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`;
+    }
+    throw new ValueError(errMsg);
+  } else {
+    return identifierOrFn;
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/metrics.js
+function binaryAccuracy(yTrue, yPred) {
+  return tidy(() => {
+    const threshold3 = mul(0.5, onesLike(yPred));
+    const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);
+    return mean(equal(yTrue, yPredThresholded), -1);
+  });
+}
+function categoricalAccuracy(yTrue, yPred) {
+  return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32"));
+}
+function truePositives(yTrue, yPred) {
+  return tidy(() => {
+    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), "float32");
+  });
+}
+function falseNegatives(yTrue, yPred) {
+  return tidy(() => {
+    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), "float32");
+  });
+}
+function falsePositives(yTrue, yPred) {
+  return tidy(() => {
+    return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), "float32");
+  });
+}
+function precision(yTrue, yPred) {
+  return tidy(() => {
+    const tp = truePositives(yTrue, yPred);
+    const fp = falsePositives(yTrue, yPred);
+    const denominator = add2(tp, fp);
+    return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32");
+  });
+}
+function recall(yTrue, yPred) {
+  return tidy(() => {
+    const tp = truePositives(yTrue, yPred);
+    const fn = falseNegatives(yTrue, yPred);
+    const denominator = add2(tp, fn);
+    return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32");
+  });
+}
+function binaryCrossentropy2(yTrue, yPred) {
+  return binaryCrossentropy(yTrue, yPred);
+}
+function sparseCategoricalAccuracy(yTrue, yPred) {
+  if (yTrue.rank === yPred.rank) {
+    yTrue = squeeze(yTrue, [yTrue.rank - 1]);
+  }
+  yPred = argMax(yPred, -1);
+  if (yPred.dtype !== yTrue.dtype) {
+    yPred = cast(yPred, yTrue.dtype);
+  }
+  return cast(equal(yTrue, yPred), "float32");
+}
+var mse = meanSquaredError2;
+var MSE = meanSquaredError2;
+var mae = meanAbsoluteError;
+var MAE = meanAbsoluteError;
+var mape = meanAbsolutePercentageError;
+var MAPE = meanAbsolutePercentageError;
+var categoricalCrossentropy2 = categoricalCrossentropy;
+var cosine = cosineProximity;
+var sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;
+var metricsMap = {
+  binaryAccuracy,
+  categoricalAccuracy,
+  precision,
+  categoricalCrossentropy: categoricalCrossentropy2,
+  sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,
+  mse,
+  MSE,
+  mae,
+  MAE,
+  mape,
+  MAPE,
+  cosine
+};
+function get2(identifier) {
+  if (typeof identifier === "string" && identifier in metricsMap) {
+    return metricsMap[identifier];
+  } else if (typeof identifier !== "string" && identifier != null) {
+    return identifier;
+  } else {
+    throw new ValueError(`Unknown metric ${identifier}`);
+  }
+}
+function getLossOrMetricName(fn) {
+  assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);
+  if (typeof fn === "string") {
+    return fn;
+  } else {
+    let fnName;
+    for (const key of Object.keys(lossesMap)) {
+      if (lossesMap[key] === fn) {
+        fnName = key;
+        break;
+      }
+    }
+    if (fnName !== void 0) {
+      return fnName;
+    }
+    for (const key of Object.keys(metricsMap)) {
+      if (metricsMap[key] === fn) {
+        fnName = key;
+        break;
+      }
+    }
+    if (fnName !== void 0) {
+      return fnName;
+    }
+    return fn.name;
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js
+function getOptimizer(identifier) {
+  const optimizerMap = {
+    "Adagrad": () => train.adagrad(0.01),
+    "Adadelta": () => train.adadelta(1, 0.95, epsilon()),
+    "Adam": () => train.adam(1e-3, 0.9, 0.999, epsilon()),
+    "Adamax": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),
+    "RMSProp": () => train.rmsprop(1e-3, 0.9, 0, epsilon()),
+    "SGD": () => train.sgd(0.01)
+  };
+  optimizerMap["adagrad"] = optimizerMap["Adagrad"];
+  optimizerMap["adadelta"] = optimizerMap["Adadelta"];
+  optimizerMap["adam"] = optimizerMap["Adam"];
+  optimizerMap["adamax"] = optimizerMap["Adamax"];
+  optimizerMap["rmsprop"] = optimizerMap["RMSProp"];
+  optimizerMap["sgd"] = optimizerMap["SGD"];
+  if (identifier in optimizerMap) {
+    return optimizerMap[identifier]();
+  }
+  throw new ValueError(`Unknown Optimizer ${identifier}`);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js
+var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;
+function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {
+  if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {
+    throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
+  }
+  if (checkSize) {
+    const out = JSON.stringify(userDefinedMetadata);
+    if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {
+      console.warn(`User-defined metadata of model "${modelName}" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);
+    }
+  }
+}
+function plainObjectCheck(x) {
+  if (x === null) {
+    return true;
+  } else if (typeof x === "object") {
+    if (Object.getPrototypeOf(x) === Object.prototype) {
+      const keys = Object.keys(x);
+      for (const key of keys) {
+        if (typeof key !== "string") {
+          return false;
+        }
+        if (!plainObjectCheck(x[key])) {
+          return false;
+        }
+      }
+      return true;
+    } else {
+      if (Array.isArray(x)) {
+        for (const item of x) {
+          if (!plainObjectCheck(item)) {
+            return false;
+          }
+        }
+        return true;
+      } else {
+        return false;
+      }
+    }
+  } else {
+    const xType = typeof x;
+    return xType === "string" || xType === "number" || xType === "boolean";
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js
+function printSummary(model2, lineLength, positions, printFn = console.log) {
+  const sequentialLike = isModelSequentialLike(model2);
+  const toDisplay = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
+  if (sequentialLike) {
+    lineLength = lineLength || 90;
+    positions = positions || [0.32, 0.61, 0.89, 1];
+  } else {
+    lineLength = lineLength || 115;
+    positions = positions || [0.24, 0.48, 0.7, 0.8, 1];
+  }
+  if (positions[positions.length - 1] <= 1) {
+    positions = positions.map((p2) => Math.floor(lineLength * p2));
+  }
+  let relevantNodes;
+  if (!sequentialLike) {
+    toDisplay.push("Receives inputs");
+    relevantNodes = [];
+    for (const depth in model2.nodesByDepth) {
+      relevantNodes.push(...model2.nodesByDepth[depth]);
+    }
+  }
+  printFn("_".repeat(lineLength));
+  printRow(toDisplay, positions, printFn);
+  printFn("=".repeat(lineLength));
+  const layers = model2.layers;
+  for (let i2 = 0; i2 < layers.length; ++i2) {
+    if (sequentialLike) {
+      printLayerSummary(layers[i2], positions, printFn);
+    } else {
+      printLayerSummaryWithConnections(layers[i2], positions, relevantNodes, printFn);
+    }
+    printFn((i2 === layers.length - 1 ? "=" : "_").repeat(lineLength));
+  }
+  model2.checkTrainableWeightsConsistency();
+  const trainableCount = countTrainableParams(model2);
+  const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);
+  printFn(`Total params: ${trainableCount + nonTrainableCount}`);
+  printFn(`Trainable params: ${trainableCount}`);
+  printFn(`Non-trainable params: ${nonTrainableCount}`);
+  printFn("_".repeat(lineLength));
+}
+function countTrainableParams(model2) {
+  let trainableCount;
+  if (model2.collectedTrainableWeights != null) {
+    trainableCount = countParamsInWeights(model2.collectedTrainableWeights);
+  } else {
+    trainableCount = countParamsInWeights(model2.trainableWeights);
+  }
+  return trainableCount;
+}
+function isModelSequentialLike(model2) {
+  let sequentialLike = true;
+  const nodesByDepth = [];
+  const nodes = [];
+  for (const depth in model2.nodesByDepth) {
+    nodesByDepth.push(model2.nodesByDepth[depth]);
+  }
+  for (const depthNodes of nodesByDepth) {
+    if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {
+      sequentialLike = false;
+      break;
+    }
+    nodes.push(...depthNodes);
+  }
+  if (sequentialLike) {
+    for (const layer of model2.layers) {
+      let flag = false;
+      for (const node of layer.inboundNodes) {
+        if (nodes.indexOf(node) !== -1) {
+          if (flag) {
+            sequentialLike = false;
+            break;
+          } else {
+            flag = true;
+          }
+        }
+      }
+      if (!sequentialLike) {
+        break;
+      }
+    }
+  }
+  return sequentialLike;
+}
+function printRow(fields, positions, printFn = console.log) {
+  let line = "";
+  for (let i2 = 0; i2 < fields.length; ++i2) {
+    if (i2 > 0) {
+      line = line.slice(0, line.length - 1) + " ";
+    }
+    line += fields[i2];
+    line = line.slice(0, positions[i2]);
+    line += " ".repeat(positions[i2] - line.length);
+  }
+  printFn(line);
+}
+function printLayerSummary(layer, positions, printFn) {
+  let outputShape;
+  let inputShape;
+  try {
+    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(",");
+  } catch (err) {
+    inputShape = "multiple";
+  }
+  try {
+    outputShape = JSON.stringify(layer.outputShape);
+  } catch (err) {
+    outputShape = "multiple";
+  }
+  const name = layer.name;
+  const className = layer.getClassName();
+  const fields = [
+    `${name} (${className})`,
+    inputShape,
+    outputShape,
+    layer.countParams().toString()
+  ];
+  printRow(fields, positions, printFn);
+}
+function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {
+  let outputShape;
+  let inputShape;
+  try {
+    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(",");
+  } catch (err) {
+    inputShape = "multiple";
+  }
+  try {
+    outputShape = JSON.stringify(layer.outputShape);
+  } catch (err) {
+    outputShape = "multiple";
+  }
+  const connections = [];
+  for (const node of layer.inboundNodes) {
+    if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {
+      continue;
+    }
+    for (let i2 = 0; i2 < node.inboundLayers.length; ++i2) {
+      const inboundLayer = node.inboundLayers[i2].name;
+      const inboundLayerIndex = node.nodeIndices[i2];
+      const inboundTensorIndex = node.tensorIndices[i2];
+      connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
+    }
+  }
+  const name = layer.name;
+  const className = layer.getClassName();
+  const firstConnection = connections.length === 0 ? "" : connections[0];
+  const fields = [
+    `${name} (${className})`,
+    inputShape,
+    outputShape,
+    layer.countParams().toString(),
+    firstConnection
+  ];
+  printRow(fields, positions, printFn);
+  for (let i2 = 1; i2 < connections.length; ++i2) {
+    printRow(["", "", "", "", connections[i2]], positions, printFn);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js
+function isArrayItemInputOrOutputName(key, index, value) {
+  return (key === "inboundNodes" || key === "outputLayers" || key === "inputLayers") && index === 0 && typeof value === "string";
+}
+function convertPythonicToTs(pythonicConfig, key) {
+  if (pythonicConfig === null) {
+    return null;
+  } else if (typeof pythonicConfig === "string") {
+    return toCamelCase(pythonicConfig);
+  } else if (typeof pythonicConfig === "number" || typeof pythonicConfig === "boolean") {
+    return pythonicConfig;
+  } else if (pythonicConfig instanceof Array) {
+    const tsArray = [];
+    const arrayLength = pythonicConfig.length;
+    for (let i2 = 0; i2 < arrayLength; ++i2) {
+      const item = pythonicConfig[i2];
+      if (isArrayItemInputOrOutputName(key, i2, item)) {
+        tsArray.push(item);
+      } else {
+        tsArray.push(convertPythonicToTs(item, key));
+      }
+    }
+    return tsArray;
+  } else {
+    const tsDict = {};
+    for (const pythonicKey of Object.keys(pythonicConfig)) {
+      const pythonicValue = pythonicConfig[pythonicKey];
+      if (pythonicKey === "name" && typeof pythonicValue === "string") {
+        tsDict[pythonicKey] = pythonicValue;
+      } else {
+        const tsKey = toCamelCase(pythonicKey);
+        tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);
+      }
+    }
+    return tsDict;
+  }
+}
+function convertTsToPythonic(tsConfig, key) {
+  if (tsConfig === null || tsConfig === void 0) {
+    return null;
+  } else if (typeof tsConfig === "string") {
+    return toSnakeCase(tsConfig);
+  } else if (typeof tsConfig === "number" || typeof tsConfig === "boolean") {
+    return tsConfig;
+  } else if (tsConfig instanceof Array) {
+    const pyArray = [];
+    const arrayLength = tsConfig.length;
+    for (let i2 = 0; i2 < arrayLength; ++i2) {
+      const item = tsConfig[i2];
+      if (isArrayItemInputOrOutputName(key, i2, item)) {
+        pyArray.push(item);
+      } else {
+        pyArray.push(convertTsToPythonic(item, key));
+      }
+    }
+    return pyArray;
+  } else {
+    const pyDict = {};
+    for (const tsKey of Object.keys(tsConfig)) {
+      const tsValue = tsConfig[tsKey];
+      const pyKey = toSnakeCase(tsKey);
+      if ((tsKey === "name" || tsKey === "className") && typeof tsValue === "string") {
+        pyDict[pyKey] = tsValue;
+      } else {
+        pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);
+      }
+    }
+    return pyDict;
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/version.js
+var version2 = "3.18.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js
+var Container = class extends Layer {
+  constructor(args) {
+    super({});
+    this.containerNodes = /* @__PURE__ */ new Set();
+    this.name = args.name;
+    if (this.name == null) {
+      const prefix = this.getClassName().toLowerCase();
+      this.name = getUid(prefix);
+    }
+    this.supportsMasking = false;
+    this.trainable_ = true;
+    if (Array.isArray(args.inputs)) {
+      this.inputs = args.inputs.slice();
+    } else {
+      this.inputs = [args.inputs];
+    }
+    if (Array.isArray(args.outputs)) {
+      this.outputs = args.outputs.slice();
+    } else {
+      this.outputs = [args.outputs];
+    }
+    if (unique2(this.inputs).length !== this.inputs.length) {
+      throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);
+    }
+    if (unique2(this.outputs).length !== this.outputs.length) {
+      console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);
+    }
+    this.inputLayers = [];
+    this.inputLayersNodeIndices = [];
+    this.inputLayersTensorIndices = [];
+    this.outputLayers = [];
+    this.outputLayersNodeIndices = [];
+    this.outputLayersTensorIndices = [];
+    this.layers = [];
+    this.internalContainerRefs = [];
+    for (const x of this.outputs) {
+      const layer = x.sourceLayer;
+      const nodeIndex = x.nodeIndex;
+      const tensorIndex = x.tensorIndex;
+      this.outputLayers.push(layer);
+      this.outputLayersNodeIndices.push(nodeIndex);
+      this.outputLayersTensorIndices.push(tensorIndex);
+    }
+    for (const x of this.inputs) {
+      const layer = x.sourceLayer;
+      const nodeIndex = x.nodeIndex;
+      const tensorIndex = x.tensorIndex;
+      assert2(nodeIndex === 0, "input layer has >1 nodes");
+      assert2(tensorIndex === 0, "input layer has >1 tensors");
+      this.inputLayers.push(layer);
+      this.inputLayersNodeIndices.push(nodeIndex);
+      this.inputLayersTensorIndices.push(tensorIndex);
+    }
+    this.inputNames = [];
+    this.outputNames = [];
+    this.feedInputShapes = [];
+    this.feedInputNames = [];
+    this.feedOutputNames = [];
+    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
+      const layer = this.inputLayers[i2];
+      if (!(layer instanceof InputLayer)) {
+        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i2} (0-based) originates from layer type ${layer.getClassName()}.`);
+      }
+      this.inputNames.push(layer.name);
+      this.feedInputShapes.push(layer.batchInputShape);
+      this.feedInputNames.push(layer.name);
+    }
+    for (const layer of this.outputLayers) {
+      this.outputNames.push(layer.name);
+    }
+    this.internalInputShapes = this.inputs.map((x) => x.shape);
+    this.internalOutputShapes = this.outputs.map((x) => x.shape);
+    const nodesDepths = {};
+    const nodeIDToNode = {};
+    const layersDepths = {};
+    const layerIDToLayer = {};
+    const layerIndices = {};
+    const nodesInDecreasingDepth = [];
+    const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {
+      if (layer == null || nodeIndex == null || tensorIndex == null) {
+        layer = tensor2.sourceLayer;
+        nodeIndex = tensor2.nodeIndex;
+        tensorIndex = tensor2.tensorIndex;
+      }
+      const node = layer.inboundNodes[nodeIndex];
+      if (nodesInProgress2.indexOf(node) !== -1) {
+        throw new RuntimeError(`The tensor ${tensor2.name} at layer "${layer.name}" is part of a cycle.`);
+      }
+      if (finishedNodes2.indexOf(node) !== -1) {
+        return;
+      }
+      this.containerNodes.add(Container.nodeKey(layer, nodeIndex));
+      if (!(layer.id in layerIndices)) {
+        layerIndices[layer.id] = Object.keys(layerIndices).length;
+      }
+      if (nodesInProgress2.indexOf(node) === -1) {
+        nodesInProgress2.push(node);
+      }
+      const numInboundLayers = node.inboundLayers.length;
+      for (let i2 = 0; i2 < numInboundLayers; i2++) {
+        const x = node.inputTensors[i2];
+        const layer2 = node.inboundLayers[i2];
+        const nodeIndex2 = node.nodeIndices[i2];
+        const tensorIndex2 = node.tensorIndices[i2];
+        buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);
+      }
+      finishedNodes2.push(node);
+      while (nodesInProgress2.indexOf(node) >= 0) {
+        nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1);
+      }
+      nodesInDecreasingDepth.push(node);
+    };
+    const finishedNodes = [];
+    const nodesInProgress = [];
+    for (const x of this.outputs) {
+      buildMapOfGraph(x, finishedNodes, nodesInProgress);
+    }
+    const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();
+    for (const node of reversedNodesInDecreasingDepth) {
+      nodeIDToNode[node.id] = node;
+      if (!(node.id in nodesDepths)) {
+        nodesDepths[node.id] = 0;
+      }
+      let depth = nodesDepths[node.id];
+      const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id];
+      depth = Math.max(depth, previousDepth);
+      layersDepths[node.outboundLayer.id] = depth;
+      layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;
+      nodesDepths[node.id] = depth;
+      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {
+        const inboundLayer = node.inboundLayers[i2];
+        const nodeIndex = node.nodeIndices[i2];
+        const inboundNode = inboundLayer.inboundNodes[nodeIndex];
+        const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];
+        nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);
+        nodeIDToNode[inboundNode.id] = inboundNode;
+      }
+    }
+    const nodesByDepth = {};
+    for (const nodeID in nodesDepths) {
+      const depth = nodesDepths[nodeID];
+      if (!(depth in nodesByDepth)) {
+        nodesByDepth[depth] = [];
+      }
+      nodesByDepth[depth].push(nodeIDToNode[nodeID]);
+    }
+    const layersByDepth = {};
+    for (const layerID in layersDepths) {
+      const depth = layersDepths[layerID];
+      if (!(depth in layersByDepth)) {
+        layersByDepth[depth] = [];
+      }
+      layersByDepth[depth].push(layerIDToLayer[layerID]);
+    }
+    let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    this.layers = [];
+    for (const depth of depthKeys) {
+      const layersForDepth = layersByDepth[depth];
+      layersForDepth.sort((a6, b) => {
+        const aIndex = layerIndices[a6.id];
+        const bIndex = layerIndices[b.id];
+        if (aIndex < bIndex) {
+          return -1;
+        }
+        if (aIndex > bIndex) {
+          return 1;
+        }
+        return 0;
+      });
+      for (const layer of layersForDepth) {
+        if (layer instanceof Container) {
+          this.internalContainerRefs.push(layer);
+        }
+        this.layers.push(layer);
+      }
+    }
+    this.layersByDepth = layersByDepth;
+    depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    const computableTensors = this.inputs.slice();
+    const layersWithCompleteInput = [];
+    for (const depth of depthKeys) {
+      for (const node of nodesByDepth[depth]) {
+        const layer = node.outboundLayer;
+        if (layer != null) {
+          for (const x of node.inputTensors) {
+            if (computableTensors.indexOf(x) === -1) {
+              throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${layer.name}". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);
+            }
+          }
+          for (const x of node.outputTensors) {
+            computableTensors.push(x);
+          }
+          layersWithCompleteInput.push(layer.name);
+        }
+      }
+    }
+    this.nodesByDepth = nodesByDepth;
+    const allNames = this.layers.map((x) => x.name);
+    for (const name of allNames) {
+      const numOccurrences = allNames.filter((x) => x === name).length;
+      if (numOccurrences !== 1) {
+        throw new RuntimeError(`The name "${name}" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));
+      }
+    }
+    this.outboundNodes = [];
+    this.inboundNodes = [];
+    new Node({
+      outboundLayer: this,
+      inboundLayers: [],
+      nodeIndices: [],
+      tensorIndices: [],
+      inputTensors: this.inputs,
+      outputTensors: this.outputs,
+      inputMasks: this.inputs.map((x) => null),
+      outputMasks: this.outputs.map((x) => null),
+      inputShapes: this.inputs.map((x) => x.shape),
+      outputShapes: this.outputs.map((x) => x.shape)
+    });
+    this.built = true;
+    this._refCount = 1;
+  }
+  assertNotDisposed() {
+    if (this._refCount === 0) {
+      throw new Error(`Container '${this.name}' is already disposed.`);
+    }
+  }
+  dispose() {
+    this.assertNotDisposed();
+    const result = { refCountAfterDispose: null, numDisposedVariables: 0 };
+    if (--this._refCount === 0) {
+      for (const layer of this.layers) {
+        result.numDisposedVariables += layer.dispose().numDisposedVariables;
+      }
+      for (const container of this.internalContainerRefs) {
+        result.numDisposedVariables += container.dispose().numDisposedVariables;
+      }
+    }
+    result.refCountAfterDispose = this._refCount;
+    return result;
+  }
+  get trainable() {
+    return this.trainable_;
+  }
+  set trainable(trainable) {
+    this.layers.forEach((layer) => {
+      layer._trainableWeights.forEach((w) => w.trainable = trainable);
+    });
+    this.trainable_ = trainable;
+  }
+  get trainableWeights() {
+    if (this._trainableWeights.length > 0) {
+      throw new ValueError("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");
+    }
+    if (!this.trainable) {
+      return [];
+    }
+    let weights = [];
+    for (const layer of this.layers) {
+      weights = weights.concat(layer.trainableWeights);
+    }
+    return weights;
+  }
+  get nonTrainableWeights() {
+    const weights = [];
+    for (const layer of this.layers) {
+      weights.push(...layer.nonTrainableWeights);
+    }
+    if (!this.trainable) {
+      const trainableWeights = [];
+      for (const layer of this.layers) {
+        trainableWeights.push(...layer.trainableWeights);
+      }
+      return trainableWeights.concat(weights);
+    }
+    return weights;
+  }
+  get weights() {
+    return this.trainableWeights.concat(this.nonTrainableWeights);
+  }
+  loadWeights(weights, strict = true) {
+    const nameToWeight = {};
+    let totalWeightsCount = 0;
+    for (const layer of this.layers) {
+      for (const weight of layer.weights) {
+        if (nameToWeight[weight.originalName] != null) {
+          throw new ValueError(`Duplicate weight name: ${weight.originalName}`);
+        }
+        nameToWeight[weight.originalName] = weight;
+        totalWeightsCount++;
+      }
+    }
+    const weightValueTuples = [];
+    for (const name in weights) {
+      let validatedName = name;
+      if (nameToWeight[name] == null) {
+        const tokens = name.split("/");
+        const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);
+        validatedName = shortenNameArray.join("/");
+      }
+      if (nameToWeight[validatedName] != null) {
+        weightValueTuples.push([nameToWeight[validatedName], weights[name]]);
+      } else if (strict) {
+        throw new ValueError(`Provided weight data has no target variable: ${name}`);
+      }
+      delete nameToWeight[validatedName];
+    }
+    if (strict) {
+      const unsetNames = [];
+      for (const name in nameToWeight) {
+        unsetNames.push(name);
+      }
+      if (unsetNames.length > 0) {
+        throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);
+      }
+    }
+    batchSetValue(weightValueTuples);
+  }
+  updatedConfig() {
+    const theConfig = this.getConfig();
+    const modelConfig = {};
+    modelConfig["className"] = this.getClassName();
+    modelConfig["config"] = theConfig;
+    modelConfig["kerasVersion"] = `tfjs-layers ${version2}`;
+    modelConfig["backend"] = "TensorFlow.js";
+    return modelConfig;
+  }
+  toJSON(unused, returnString = true) {
+    const modelConfig = convertTsToPythonic(this.updatedConfig());
+    return returnString ? JSON.stringify(modelConfig) : modelConfig;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = toList(inputs);
+      const feedDict = new FeedDict();
+      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+        feedDict.add(this.inputs[i2], inputs[i2]);
+      }
+      return execute(this.outputs, feedDict, kwargs);
+    });
+  }
+  computeMask(inputs, mask) {
+    return tidy(() => {
+      inputs = toList(inputs);
+      let masks;
+      if (mask == null) {
+        masks = pyListRepeat(null, inputs.length);
+      } else {
+        masks = toList(mask);
+      }
+      return this.runInternalGraph(inputs, masks)[1];
+    });
+  }
+  computeOutputShape(inputShape) {
+    const inputShapes = normalizeShapeList(inputShape);
+    if (inputShapes.length !== this.inputLayers.length) {
+      throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);
+    }
+    const layersToOutputShapes = {};
+    for (let i2 = 0; i2 < inputShapes.length; i2++) {
+      const layer = this.inputLayers[i2];
+      const inputShape2 = inputShapes[i2];
+      const shapeKey = layer.name + "_0_0";
+      layersToOutputShapes[shapeKey] = inputShape2;
+    }
+    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    if (depthKeys.length > 1) {
+      for (const depth of depthKeys) {
+        const nodes = this.nodesByDepth[depth];
+        for (const node of nodes) {
+          const layer = node.outboundLayer;
+          if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {
+            continue;
+          }
+          const inputShapes2 = [];
+          for (let j = 0; j < node.inboundLayers.length; j++) {
+            const inboundLayer = node.inboundLayers[j];
+            const nodeIndex2 = node.nodeIndices[j];
+            const tensorIndex = node.tensorIndices[j];
+            const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;
+            const inputShape2 = layersToOutputShapes[shapeKey];
+            inputShapes2.push(inputShape2);
+          }
+          const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));
+          const outputShapes2 = normalizeShapeList(outputShape);
+          const nodeIndex = layer.inboundNodes.indexOf(node);
+          for (let j = 0; j < outputShapes2.length; j++) {
+            const shapeKey = `${layer.name}_${nodeIndex}_${j}`;
+            layersToOutputShapes[shapeKey] = outputShapes2[j];
+          }
+        }
+      }
+    }
+    const outputShapes = [];
+    const outputShapeKeys = [];
+    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
+      const layer = this.outputLayers[i2];
+      const nodeIndex = this.outputLayersNodeIndices[i2];
+      const tensorIndex = this.outputLayersTensorIndices[i2];
+      const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
+      outputShapeKeys.push(shapeKey);
+    }
+    for (let i2 = 0; i2 < outputShapeKeys.length; i2++) {
+      const key = outputShapeKeys[i2];
+      assert2(key in layersToOutputShapes);
+      outputShapes.push(layersToOutputShapes[key]);
+    }
+    return singletonOrArray(outputShapes);
+  }
+  runInternalGraph(inputs, masks) {
+    if (masks == null) {
+      masks = pyListRepeat(null, inputs.length);
+    }
+    const tensorMap = {};
+    for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+      const x = this.inputs[i2];
+      const y = inputs[i2];
+      const mask = masks[i2];
+      tensorMap[x.id] = [y, mask];
+    }
+    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
+    for (const depth of depthKeys) {
+      const nodes = this.nodesByDepth[depth];
+      for (const node of nodes) {
+        const layer = node.outboundLayer;
+        const referenceInputTensors = node.inputTensors;
+        const referenceOutputTensors = node.outputTensors;
+        const computedData = new Array();
+        for (const x of referenceInputTensors) {
+          if (x.id in tensorMap) {
+            computedData.push(tensorMap[x.id]);
+          }
+        }
+        if (computedData.length === referenceInputTensors.length) {
+          let kwargs = {};
+          let computedTensors;
+          let computedMasks;
+          let outputTensors2;
+          let outputMasks2;
+          if (node.callArgs != null) {
+            kwargs = node.callArgs;
+          }
+          if (computedData.length === 1) {
+            const [computedTensor, computedMask] = computedData[0];
+            if (kwargs["mask"] == null) {
+              kwargs["mask"] = computedMask;
+            }
+            outputTensors2 = toList(layer.call(computedTensor, kwargs));
+            outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));
+            computedTensors = [computedTensor];
+            computedMasks = [computedMask];
+          } else {
+            computedTensors = computedData.map((x) => x[0]);
+            computedMasks = computedData.map((x) => x[1]);
+            if (kwargs["mask"] == null) {
+              kwargs["mask"] = computedMasks;
+            }
+            outputTensors2 = toList(layer.call(computedTensors, kwargs));
+            outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));
+          }
+          if (layer.activityRegularizer) {
+            throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
+          }
+          for (let i2 = 0; i2 < referenceOutputTensors.length; ++i2) {
+            const x = referenceOutputTensors[i2];
+            const y = outputTensors2[i2];
+            const mask = outputMasks2[i2];
+            tensorMap[x.id] = [y, mask];
+          }
+        }
+      }
+    }
+    const outputTensors = [];
+    const outputMasks = [];
+    const outputShapes = [];
+    for (const x of this.outputs) {
+      assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);
+      const [tensor2, mask] = tensorMap[x.id];
+      outputShapes.push(tensor2.shape);
+      outputTensors.push(tensor2);
+      outputMasks.push(mask);
+    }
+    return [outputTensors, outputMasks, outputShapes];
+  }
+  buildNodeConversionMap(layers) {
+    const nodeConversionMap = {};
+    let keptNodes;
+    for (const layer of this.layers) {
+      keptNodes = layer instanceof Container ? 1 : 0;
+      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
+        const nodeKey = Container.nodeKey(layer, originalNodeIndex);
+        if (this.containerNodes.has(nodeKey)) {
+          nodeConversionMap[nodeKey] = keptNodes;
+          keptNodes += 1;
+        }
+      }
+    }
+    return nodeConversionMap;
+  }
+  getLayer(name, index) {
+    if (index != null) {
+      if (this.layers.length <= index) {
+        throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);
+      } else {
+        return this.layers[index];
+      }
+    } else {
+      if (name == null) {
+        throw new ValueError("Provide either a layer name or layer index");
+      }
+    }
+    for (const layer of this.layers) {
+      if (layer.name === name) {
+        return layer;
+      }
+    }
+    throw new ValueError(`No such layer: ${name}`);
+  }
+  calculateLosses() {
+    return tidy(() => {
+      const losses2 = [];
+      for (const layer of this.layers) {
+        for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {
+          const nodeKey = Container.nodeKey(layer, nodeIndex);
+          if (this.containerNodes.has(nodeKey)) {
+            losses2.push(...layer.calculateLosses());
+          }
+        }
+      }
+      return losses2;
+    });
+  }
+  getConfig() {
+    const config = { name: this.name };
+    const nodeConversionMap = this.buildNodeConversionMap(this.layers);
+    const layerConfigs = [];
+    for (const layer of this.layers) {
+      const layerClassName = layer.getClassName();
+      const layerConfig = layer.getConfig();
+      const filteredInboundNodes = [];
+      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {
+        const node = layer.inboundNodes[originalNodeIndex];
+        const nodeKey = Container.nodeKey(layer, originalNodeIndex);
+        let kwargs = {};
+        if (this.containerNodes.has(nodeKey)) {
+          if (node.callArgs) {
+            try {
+              JSON.stringify(node.callArgs);
+              kwargs = node.callArgs;
+            } catch (err) {
+              console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);
+              kwargs = {};
+            }
+          }
+          if (node.inboundLayers.length > 0) {
+            const nodeData = [];
+            for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {
+              const inboundLayer = node.inboundLayers[i2];
+              const nodeIndex = node.nodeIndices[i2];
+              const tensorIndex = node.tensorIndices[i2];
+              const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);
+              let newNodeIndex = nodeConversionMap[nodeKey2];
+              if (newNodeIndex == null) {
+                newNodeIndex = 0;
+              }
+              nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);
+            }
+            filteredInboundNodes.push(nodeData);
+          }
+        }
+      }
+      const dict = {};
+      dict["name"] = layer.name;
+      dict["className"] = layerClassName;
+      dict["config"] = layerConfig;
+      dict["inboundNodes"] = filteredInboundNodes;
+      layerConfigs.push(dict);
+    }
+    config["layers"] = layerConfigs;
+    const modelInputs = [];
+    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
+      const layer = this.inputLayers[i2];
+      const nodeIndex = this.inputLayersNodeIndices[i2];
+      const nodeKey = Container.nodeKey(layer, nodeIndex);
+      if (!this.containerNodes.has(nodeKey)) {
+        continue;
+      }
+      let newNodeIndex = nodeConversionMap[nodeKey];
+      if (newNodeIndex === null || newNodeIndex === void 0) {
+        newNodeIndex = 0;
+      }
+      const tensorIndex = this.inputLayersTensorIndices[i2];
+      modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
+    }
+    config["inputLayers"] = modelInputs;
+    const modelOutputs = [];
+    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
+      const layer = this.outputLayers[i2];
+      const nodeIndex = this.outputLayersNodeIndices[i2];
+      const nodeKey = Container.nodeKey(layer, nodeIndex);
+      if (!this.containerNodes.has(nodeKey)) {
+        continue;
+      }
+      let newNodeIndex = nodeConversionMap[nodeKey];
+      if (newNodeIndex === null || newNodeIndex === void 0) {
+        newNodeIndex = 0;
+      }
+      const tensorIndex = this.outputLayersTensorIndices[i2];
+      modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
+    }
+    config["outputLayers"] = modelOutputs;
+    return config;
+  }
+  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {
+    const createdLayers = {};
+    const unprocessedNodes = {};
+    function addUnprocessedNode(layer, nodeData) {
+      if (!(layer.name in unprocessedNodes)) {
+        unprocessedNodes[layer.name] = [nodeData];
+      } else {
+        unprocessedNodes[layer.name].push(nodeData);
+      }
+    }
+    function processNode(layer, nodeData) {
+      const inputTensors2 = [];
+      let kwargs;
+      for (const inputData of nodeData) {
+        const inboundLayerName = inputData[0];
+        const inboundNodeIndex = inputData[1];
+        const inboundTensorIndex = inputData[2];
+        kwargs = inputData[3] == null ? {} : inputData[3];
+        if (!(inboundLayerName in createdLayers)) {
+          addUnprocessedNode(layer, nodeData);
+          return;
+        }
+        const inboundLayer = createdLayers[inboundLayerName];
+        if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {
+          addUnprocessedNode(layer, nodeData);
+          return;
+        }
+        const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];
+        inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);
+      }
+      if (inputTensors2.length > 0) {
+        layer.apply(singletonOrArray(inputTensors2), kwargs);
+      }
+    }
+    function processLayer(layerData) {
+      const layerName = layerData["name"];
+      const layer = deserialize(layerData, config["customObjects"] != null ? config["customObjects"] : {});
+      layer.setFastWeightInitDuringBuild(fastWeightInit);
+      createdLayers[layerName] = layer;
+      const inboundNodesData = layerData["inboundNodes"];
+      inboundNodesData.forEach((nodeData) => {
+        if (!(nodeData instanceof Array)) {
+          throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);
+        }
+        addUnprocessedNode(layer, nodeData);
+      });
+    }
+    const name = config["name"];
+    const layersFromConfig = config["layers"];
+    for (const layerData of layersFromConfig) {
+      processLayer(layerData);
+    }
+    while (!isObjectEmpty(unprocessedNodes)) {
+      for (const layerData of layersFromConfig) {
+        const layer = createdLayers[layerData["name"]];
+        if (layer.name in unprocessedNodes) {
+          const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];
+          delete unprocessedNodes[layer.name];
+          for (const nodeData of currentUnprocessedNodesForLayer) {
+            processNode(layer, nodeData);
+          }
+        }
+      }
+    }
+    const inputTensors = [];
+    const outputTensors = [];
+    const inputLayersFromConfig = config["inputLayers"];
+    for (const layerData of inputLayersFromConfig) {
+      const layerName = layerData[0];
+      const nodeIndex = layerData[1];
+      const tensorIndex = layerData[2];
+      assert2(layerName in createdLayers);
+      const layer = createdLayers[layerName];
+      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
+      inputTensors.push(layerOutputTensors[tensorIndex]);
+    }
+    const outputLayersFromConfig = config["outputLayers"];
+    for (const layerData of outputLayersFromConfig) {
+      const layerName = layerData[0];
+      const nodeIndex = layerData[1];
+      const tensorIndex = layerData[2];
+      assert2(layerName in createdLayers);
+      const layer = createdLayers[layerName];
+      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;
+      outputTensors.push(layerOutputTensors[tensorIndex]);
+    }
+    return new cls({ inputs: inputTensors, outputs: outputTensors, name });
+  }
+  get stateful() {
+    if (this._stateful) {
+      throw new ValueError("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");
+    }
+    for (const layer of this.layers) {
+      if (layer.stateful) {
+        return true;
+      }
+    }
+    return false;
+  }
+  resetStates() {
+    tidy(() => {
+      this.layers.forEach((layer) => {
+        if (layer.stateful) {
+          layer.resetStates();
+        }
+      });
+    });
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js
+function standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {
+  const numOutputs = outputNames.length;
+  if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {
+    return outputNames.map((name) => null);
+  }
+  if (numOutputs === 1) {
+    if (Array.isArray(xWeight) && xWeight.length === 1) {
+      return xWeight;
+    } else if (typeof xWeight === "object" && outputNames[0] in xWeight) {
+      return [xWeight[outputNames[0]]];
+    } else {
+      return [xWeight];
+    }
+  }
+  if (Array.isArray(xWeight)) {
+    if (xWeight.length !== numOutputs) {
+      throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);
+    }
+    return xWeight;
+  } else if (typeof xWeight === "object" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === "object") {
+    const output = [];
+    outputNames.forEach((outputName) => {
+      if (outputName in xWeight) {
+        output.push(xWeight[outputName]);
+      } else {
+        output.push(null);
+      }
+    });
+    return output;
+  } else {
+    throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);
+  }
+}
+function standardizeClassWeights(classWeight, outputNames) {
+  return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight");
+}
+async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {
+  if (sampleWeight != null || sampleWeightMode != null) {
+    throw new Error("Support sampleWeight is not implemented yet");
+  }
+  if (classWeight != null) {
+    const yClasses = tidy(() => {
+      if (y.shape.length === 1) {
+        return clone(y);
+      } else if (y.shape.length === 2) {
+        if (y.shape[1] > 1) {
+          const axis = 1;
+          return argMax(y, axis);
+        } else if (y.shape[1] === 1) {
+          return reshape(y, [y.shape[0]]);
+        } else {
+          throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
+        }
+      } else {
+        throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
+      }
+    });
+    const yClassIndices = Array.from(await yClasses.data());
+    dispose(yClasses);
+    const classSampleWeight = [];
+    yClassIndices.forEach((classIndex) => {
+      if (classWeight[classIndex] == null) {
+        throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);
+      } else {
+        classSampleWeight.push(classWeight[classIndex]);
+      }
+    });
+    return tensor1d(classSampleWeight, "float32");
+  } else {
+    return null;
+  }
+}
+function computeWeightedLoss2(losses2, sampleWeights) {
+  return mul(losses2, sampleWeights);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js
+var DEFAULT_VALIDATION_BATCH_SIZE = 32;
+function standardizeDataIteratorOutput(model2, iteratorOut) {
+  let xs;
+  let ys;
+  const iteratorOutObj = iteratorOut;
+  xs = iteratorOutObj["xs"];
+  ys = iteratorOutObj["ys"];
+  util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);
+  const flattenedXs = flattenTensorOrArrayOrMap("input", model2.inputNames, xs);
+  const flattenedYs = flattenTensorOrArrayOrMap("output", model2.outputNames, ys);
+  const batchSize = flattenedXs[0].shape[0];
+  util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`);
+  util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);
+  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
+    util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);
+  }
+  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
+    util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);
+  }
+  return { xs: flattenedXs, ys: flattenedYs };
+}
+function flattenTensorOrArrayOrMap(inputOrOutput, names, values) {
+  if (values instanceof Tensor) {
+    return [values];
+  } else if (Array.isArray(values)) {
+    util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);
+    return values;
+  } else {
+    const result = [];
+    for (const name of names) {
+      if (values[name] == null) {
+        throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);
+      }
+      result.push(values[name]);
+    }
+    return result;
+  }
+}
+function standardizeTensorValidationData(data) {
+  if (data.length === 3) {
+    throw new NotImplementedError("Validation with sample weights is not implemented yet.");
+  }
+  return { xs: data[0], ys: data[1] };
+}
+async function fitDataset(model2, dataset, args) {
+  const hasBatchesPerEpoch = args.batchesPerEpoch != null;
+  util_exports.assert(model2.optimizer != null, () => "You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).");
+  util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);
+  util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);
+  util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);
+  util_exports.assert(args["validationSplit"] == null, () => "`validationSplit` is not supported by `fitDataset()`. Use validationData instead.");
+  if (model2.isTraining) {
+    throw new Error("Cannot start training because another fit() call is ongoing.");
+  }
+  model2.isTraining = true;
+  try {
+    const doValidation = args.validationData != null;
+    let valXs;
+    let valYs;
+    if (doValidation) {
+      if (isDatasetObject(args.validationData)) {
+        util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);
+      } else {
+        const validationData = standardizeTensorValidationData(args.validationData);
+        valXs = validationData.xs;
+        valYs = validationData.ys;
+      }
+    }
+    const trainFunction = model2.makeTrainFunction();
+    const outLabels = model2.getDedupedMetricsNames();
+    let callbackMetrics;
+    if (doValidation) {
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
+    } else {
+      callbackMetrics = outLabels.slice();
+    }
+    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
+    const verbose = args.verbose == null ? 1 : args.verbose;
+    const { callbackList, history } = configureCallbacks(callbacks2, verbose, args.epochs, null, null, getStepsPerEpoch(dataset, args), null, doValidation, callbackMetrics);
+    callbackList.setModel(model2);
+    model2.history = history;
+    await callbackList.onTrainBegin();
+    model2.stopTraining_ = false;
+    let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;
+    let dataIterator = await dataset.iterator();
+    while (epoch < args.epochs) {
+      const epochLogs = {};
+      await callbackList.onEpochBegin(epoch);
+      let stepsDone = 0;
+      let batchIndex = 0;
+      if (!hasBatchesPerEpoch) {
+        dataIterator = await dataset.iterator();
+      }
+      while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {
+        const iteratorOut = await dataIterator.next();
+        if (hasBatchesPerEpoch && iteratorOut.done) {
+          console.warn(`You provided \`batchesPerEpoch\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);
+          break;
+        }
+        if (iteratorOut.value != null) {
+          const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);
+          const batchLogs = {};
+          batchLogs["batch"] = batchIndex;
+          batchLogs["size"] = xs[0].shape[0];
+          await callbackList.onBatchBegin(batchIndex, batchLogs);
+          const sampleWeights = [];
+          if (args.classWeight != null) {
+            const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);
+            for (let i2 = 0; i2 < standardClassWeights.length; ++i2) {
+              sampleWeights.push(await standardizeWeights(ys[i2], null, standardClassWeights[i2]));
+            }
+          }
+          const ins = xs.concat(ys).concat(sampleWeights);
+          const outs = trainFunction(ins);
+          dispose(ins);
+          for (let i2 = 0; i2 < outLabels.length; ++i2) {
+            const label = outLabels[i2];
+            const out = outs[i2];
+            batchLogs[label] = out;
+            keep(out);
+          }
+          await callbackList.onBatchEnd(batchIndex, batchLogs);
+          disposeTensorsInLogs(batchLogs);
+          batchIndex++;
+          stepsDone++;
+        }
+        if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {
+          if (doValidation) {
+            let valOuts;
+            if (isDatasetObject(args.validationData)) {
+              valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches }));
+            } else {
+              valOuts = toList(model2.evaluate(valXs, valYs, {
+                batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,
+                verbose: 0
+              }));
+            }
+            for (let i2 = 0; i2 < model2.metricsNames.length; ++i2) {
+              epochLogs[`val_${model2.metricsNames[i2]}`] = valOuts[i2];
+            }
+          }
+          break;
+        }
+        if (model2.stopTraining_) {
+          break;
+        }
+      }
+      await callbackList.onEpochEnd(epoch, epochLogs);
+      epoch++;
+      if (model2.stopTraining_) {
+        break;
+      }
+    }
+    await callbackList.onTrainEnd();
+    await model2.history.syncData();
+    return model2.history;
+  } finally {
+    model2.isTraining = false;
+  }
+}
+function getStepsPerEpoch(dataset, args) {
+  let stepsPerEpoch = null;
+  if (args.batchesPerEpoch != null) {
+    stepsPerEpoch = args.batchesPerEpoch;
+  } else if (Number.isFinite(dataset.size)) {
+    stepsPerEpoch = dataset.size;
+  }
+  return stepsPerEpoch;
+}
+function isDatasetObject(dataset) {
+  return typeof dataset.iterator === "function";
+}
+function isLazyIteratorObject(iterator) {
+  return typeof iterator.next === "function";
+}
+async function evaluateDataset(model2, dataset, args) {
+  args = args || {};
+  const hasBatches = args.batches != null;
+  const f = model2.testFunction;
+  let outs = [];
+  if (args.verbose > 0) {
+    throw new NotImplementedError("Verbose mode is not implemented yet.");
+  }
+  util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);
+  const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();
+  let numExamples = 0;
+  let batch = 0;
+  while (hasBatches ? batch < args.batches : true) {
+    const iteratorOut = await dataIterator.next();
+    outs = tidy(() => {
+      if (iteratorOut.value) {
+        const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);
+        const xsAndYs = xs.concat(ys);
+        const batchOuts = tidy(() => f(xsAndYs));
+        dispose(xsAndYs);
+        if (batch === 0) {
+          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+            outs.push(scalar(0));
+          }
+        }
+        const batchSize = xsAndYs[0].shape[0];
+        for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+          const batchOut = batchOuts[i2];
+          const oldScalar = outs[i2];
+          outs[i2] = tidy(() => add2(outs[i2], mul(batchSize, batchOut)));
+          if (batch > 0) {
+            dispose(oldScalar);
+          }
+        }
+        dispose(batchOuts);
+        numExamples += batchSize;
+        ++batch;
+      }
+      return outs;
+    });
+    if (iteratorOut.done) {
+      if (hasBatches) {
+        console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);
+      }
+      break;
+    }
+  }
+  for (let i2 = 0; i2 < outs.length; ++i2) {
+    const oldScalar = outs[i2];
+    outs[i2] = div(outs[i2], numExamples);
+    dispose(oldScalar);
+  }
+  return singletonOrArray(outs);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js
+function checkBatchSize(batchSize) {
+  util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);
+}
+function sliceArrays(arrays, start, stop) {
+  if (arrays == null) {
+    return [null];
+  } else if (Array.isArray(arrays)) {
+    return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));
+  } else {
+    return sliceAlongFirstAxis(arrays, start, stop - start);
+  }
+}
+function sliceArraysByIndices(arrays, indices) {
+  return tidy(() => {
+    if (arrays == null) {
+      return null;
+    } else if (Array.isArray(arrays)) {
+      return arrays.map((array2) => sliceArraysByIndices(array2, indices));
+    } else {
+      return gather2(arrays, indices.dtype === "int32" ? indices : cast(indices, "int32"));
+    }
+  });
+}
+function makeBatches(size, batchSize) {
+  const output = [];
+  let batchStart = 0;
+  let batchEnd = null;
+  while (batchStart < size) {
+    batchEnd = batchStart + batchSize;
+    if (batchEnd >= size) {
+      batchEnd = size;
+    }
+    output.push([batchStart, batchEnd]);
+    batchStart = batchEnd;
+  }
+  return output;
+}
+async function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {
+  if (batchSize == null) {
+    batchSize = 32;
+  }
+  if (epochs == null) {
+    epochs = 1;
+  }
+  if (shuffle2 == null) {
+    shuffle2 = true;
+  }
+  if (initialEpoch == null) {
+    initialEpoch = 0;
+  }
+  let doValidation = false;
+  if (valF != null && valIns != null) {
+    doValidation = true;
+  }
+  if (validationSteps != null) {
+    doValidation = true;
+    if (stepsPerEpoch == null) {
+      throw new ValueError("Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.");
+    }
+  }
+  const numTrainSamples = model2.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch");
+  let indexArray;
+  if (numTrainSamples != null) {
+    indexArray = range2(0, numTrainSamples);
+  }
+  if (verbose == null) {
+    verbose = 1;
+  }
+  const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);
+  callbackList.setModel(model2);
+  model2.history = history;
+  await callbackList.onTrainBegin();
+  model2.stopTraining_ = false;
+  for (let epoch = initialEpoch; epoch < epochs; ++epoch) {
+    await callbackList.onEpochBegin(epoch);
+    const epochLogs = {};
+    if (stepsPerEpoch != null) {
+      throw new NotImplementedError("stepsPerEpoch mode is not implemented yet.");
+    } else {
+      if (shuffle2 === "batch") {
+        throw new NotImplementedError("batch shuffling is not implemneted yet");
+      } else if (shuffle2) {
+        util_exports.shuffle(indexArray);
+      }
+      const epochIndexArray1D = tensor1d(indexArray);
+      const batches = makeBatches(numTrainSamples, batchSize);
+      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
+        const batchLogs = {};
+        await callbackList.onBatchBegin(batchIndex, batchLogs);
+        tidy(() => {
+          const batchStart = batches[batchIndex][0];
+          const batchEnd = batches[batchIndex][1];
+          const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);
+          batchLogs["batch"] = batchIndex;
+          batchLogs["size"] = batchEnd - batchStart;
+          const insBatch = sliceArraysByIndices(ins, batchIds);
+          const outs = f(insBatch);
+          for (let i2 = 0; i2 < outLabels.length; ++i2) {
+            const label = outLabels[i2];
+            const out = outs[i2];
+            batchLogs[label] = out;
+            keep(out);
+          }
+          if (batchIndex === batches.length - 1) {
+            if (doValidation) {
+              const valOuts = model2.testLoop(valF, valIns, batchSize);
+              for (let i2 = 0; i2 < outLabels.length; ++i2) {
+                const label = outLabels[i2];
+                const out = valOuts[i2];
+                keep(out);
+                epochLogs["val_" + label] = out;
+              }
+            }
+          }
+        });
+        await callbackList.onBatchEnd(batchIndex, batchLogs);
+        disposeTensorsInLogs(batchLogs);
+        if (model2.stopTraining_) {
+          break;
+        }
+      }
+      epochIndexArray1D.dispose();
+    }
+    await callbackList.onEpochEnd(epoch, epochLogs);
+    if (model2.stopTraining_) {
+      break;
+    }
+  }
+  await callbackList.onTrainEnd();
+  await model2.history.syncData();
+  return model2.history;
+}
+async function fitTensors(model2, x, y, args = {}) {
+  if (model2.isTraining) {
+    throw new Error("Cannot start training because another fit() call is ongoing.");
+  }
+  model2.isTraining = true;
+  let inputs;
+  let targets;
+  let originalInputs;
+  let originalTargets;
+  let inputValX;
+  let inputValY;
+  let valX;
+  let valY;
+  let sampleWeights;
+  try {
+    const batchSize = args.batchSize == null ? 32 : args.batchSize;
+    checkBatchSize(batchSize);
+    const checkBatchAxis = false;
+    const standardizedOuts = await model2.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);
+    inputs = standardizedOuts[0];
+    targets = standardizedOuts[1];
+    sampleWeights = standardizedOuts[2];
+    let doValidation = false;
+    let valIns;
+    if (args.validationData != null && args.validationData.length > 0) {
+      doValidation = true;
+      if (args.validationData.length === 2) {
+        inputValX = args.validationData[0];
+        inputValY = args.validationData[1];
+      } else if (args.validationData.length === 3) {
+        throw new NotImplementedError("validationData including sample weights is not supported yet.");
+      } else {
+        throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);
+      }
+      const checkBatchAxis2 = true;
+      const valStandardized = await model2.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);
+      valX = valStandardized[0];
+      valY = valStandardized[1];
+      valIns = valX.concat(valY);
+    } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {
+      doValidation = true;
+      const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));
+      const originalBatchSize = inputs[0].shape[0];
+      valX = sliceArrays(inputs, splitAt, originalBatchSize);
+      originalInputs = inputs;
+      inputs = sliceArrays(inputs, 0, splitAt);
+      valY = sliceArrays(targets, splitAt, originalBatchSize);
+      originalTargets = targets;
+      targets = sliceArrays(targets, 0, splitAt);
+      valIns = valX.concat(valY);
+    } else if (args.validationSteps != null) {
+      doValidation = true;
+    }
+    const ins = inputs.concat(targets).concat(sampleWeights);
+    model2.checkTrainableWeightsConsistency();
+    const trainFunction = model2.makeTrainFunction();
+    const outLabels = model2.getDedupedMetricsNames();
+    let valFunction;
+    let callbackMetrics;
+    if (doValidation) {
+      model2.makeTestFunction();
+      valFunction = model2.testFunction;
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
+    } else {
+      valFunction = null;
+      valIns = [];
+      callbackMetrics = outLabels.slice();
+    }
+    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
+    const out = await fitLoop(model2, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);
+    return out;
+  } finally {
+    model2.isTraining = false;
+    disposeNewTensors(inputs, x);
+    disposeNewTensors(targets, y);
+    disposeNewTensors(originalInputs, x);
+    disposeNewTensors(originalTargets, y);
+    disposeNewTensors(valX, inputValX);
+    disposeNewTensors(valY, inputValY);
+    if (sampleWeights != null) {
+      dispose(sampleWeights);
+    }
+  }
+}
+function ensureTensorsRank2OrHigher(tensors) {
+  const outs = [];
+  if (tensors instanceof Tensor) {
+    tensors = [tensors];
+  }
+  for (let i2 = 0; i2 < tensors.length; ++i2) {
+    const tensor2 = tensors[i2];
+    if (tensor2.rank === 1) {
+      outs.push(expandDims2(tensor2, 1));
+    } else if (tensor2.rank === 0) {
+      throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
+    } else {
+      outs.push(tensor2);
+    }
+  }
+  return outs;
+}
+function disposeNewTensors(tensors, refTensors) {
+  if (tensors == null) {
+    return;
+  }
+  const oldTensorIds = [];
+  if (refTensors instanceof Tensor) {
+    oldTensorIds.push(refTensors.id);
+  } else if (Array.isArray(refTensors)) {
+    refTensors.forEach((t2) => oldTensorIds.push(t2.id));
+  } else if (refTensors != null) {
+    for (const name in refTensors) {
+      const oldTensor = refTensors[name];
+      oldTensorIds.push(oldTensor.id);
+    }
+  }
+  const tensorsToDispose = [];
+  if (tensors instanceof Tensor) {
+    if (oldTensorIds.indexOf(tensors.id) === -1) {
+      tensorsToDispose.push(tensors);
+    }
+  } else if (Array.isArray(tensors)) {
+    tensors.forEach((t2) => {
+      if (oldTensorIds.indexOf(t2.id) === -1) {
+        tensorsToDispose.push(t2);
+      }
+    });
+  } else if (tensors != null) {
+    for (const name in tensors) {
+      const tensor2 = tensors[name];
+      if (oldTensorIds.indexOf(tensor2.id) === -1) {
+        tensorsToDispose.push(tensor2);
+      }
+    }
+  }
+  tensorsToDispose.forEach((t2) => {
+    if (!t2.isDisposed) {
+      t2.dispose();
+    }
+  });
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js
+function isDataTensor(x) {
+  return x instanceof Tensor;
+}
+function isDataArray(x) {
+  return Array.isArray(x);
+}
+function isDataDict(x) {
+  return !isDataTensor(x) && !isDataArray(x);
+}
+function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
+  if (names == null || names.length === 0) {
+    if (data != null) {
+      let gotUnexpectedData = false;
+      if (isDataArray(data) && data.length > 0) {
+        gotUnexpectedData = true;
+      } else if (isDataDict(data)) {
+        for (const key in data) {
+          if (data.hasOwnProperty(key)) {
+            gotUnexpectedData = true;
+            break;
+          }
+        }
+      } else {
+        gotUnexpectedData = true;
+      }
+      if (gotUnexpectedData) {
+        throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);
+      }
+    }
+    return [];
+  }
+  if (data == null) {
+    return names.map((name) => null);
+  }
+  let arrays;
+  if (isDataDict(data)) {
+    data = data;
+    arrays = [];
+    for (const name of names) {
+      if (data[name] == null) {
+        throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);
+      }
+      arrays.push(data[name]);
+    }
+  } else if (isDataArray(data)) {
+    data = data;
+    if (data.length !== names.length) {
+      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);
+    }
+    arrays = data;
+  } else {
+    data = data;
+    if (names.length > 1) {
+      throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);
+    }
+    arrays = [data];
+  }
+  arrays = ensureTensorsRank2OrHigher(arrays);
+  if (shapes != null) {
+    for (let i2 = 0; i2 < names.length; ++i2) {
+      if (shapes[i2] == null) {
+        continue;
+      }
+      const array2 = arrays[i2];
+      if (array2.shape.length !== shapes[i2].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s). but got array with shape ${array2.shape}`);
+      }
+      for (let j = 0; j < shapes[i2].length; ++j) {
+        if (j === 0 && !checkBatchAxis) {
+          continue;
+        }
+        const dim = array2.shape[j];
+        const refDim = shapes[i2][j];
+        if (refDim != null && refDim >= 0 && dim !== refDim) {
+          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i2].slice(1, shapes[i2].length)}] (i.e.,tensor shape [*,${shapes[i2].slice(1, shapes[i2].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
+        }
+      }
+    }
+  }
+  return arrays;
+}
+function checkArrayLengths(inputs, targets, weights) {
+  const setX = unique2(inputs.map((input2) => input2.shape[0]));
+  setX.sort();
+  const setY = unique2(targets.map((target) => target.shape[0]));
+  setY.sort();
+  if (setX.length > 1) {
+    throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);
+  }
+  if (setY.length > 1) {
+    throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);
+  }
+  if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {
+    throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);
+  }
+}
+function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {
+  const keyLosses = [
+    meanSquaredError2,
+    binaryCrossentropy,
+    categoricalCrossentropy
+  ];
+  for (let i2 = 0; i2 < targets.length; ++i2) {
+    const y = targets[i2];
+    const loss = lossFns[i2];
+    const shape = outputShapes[i2];
+    if (loss == null) {
+      continue;
+    }
+    if (loss === categoricalCrossentropy) {
+      if (y.shape[y.shape.length - 1] === 1) {
+        throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
+      }
+    }
+    if (keyLosses.indexOf(loss) !== -1) {
+      const slicedYShape = y.shape.slice(1);
+      const slicedShape = shape.slice(1);
+      for (let j = 0; j < slicedYShape.length; ++j) {
+        const targetDim = slicedYShape[j];
+        const outDim = slicedShape[j];
+        if (outDim != null && targetDim !== outDim) {
+          throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);
+        }
+      }
+    }
+  }
+}
+function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
+  let arrays;
+  if (Array.isArray(data)) {
+    if (data.length !== names.length) {
+      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);
+    }
+    arrays = data;
+  } else {
+    if (names.length > 1) {
+      throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);
+    }
+    arrays = [data];
+  }
+  if (shapes != null) {
+    for (let i2 = 0; i2 < names.length; ++i2) {
+      if (shapes[i2] == null) {
+        continue;
+      }
+      const array2 = arrays[i2];
+      if (array2.shape.length !== shapes[i2].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
+      }
+      for (let j = 0; j < shapes[i2].length; ++j) {
+        if (j === 0 && !checkBatchAxis) {
+          continue;
+        }
+        const dim = array2.shape[j];
+        const refDim = shapes[i2][j];
+        if (refDim != null) {
+          if (refDim !== dim) {
+            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have shape ${JSON.stringify(shapes[i2])} but got array with shape ${JSON.stringify(array2.shape)}.`);
+          }
+        }
+      }
+    }
+  }
+}
+function collectMetrics(metrics, outputNames) {
+  if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {
+    return outputNames.map((name) => []);
+  }
+  let wrappedMetrics;
+  if (typeof metrics === "string" || typeof metrics === "function") {
+    wrappedMetrics = [metrics];
+  } else if (Array.isArray(metrics) || typeof metrics === "object") {
+    wrappedMetrics = metrics;
+  } else {
+    throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);
+  }
+  if (Array.isArray(wrappedMetrics)) {
+    return outputNames.map((name) => wrappedMetrics);
+  } else {
+    const nestedMetrics = [];
+    for (const name of outputNames) {
+      let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];
+      if (!Array.isArray(outputMetrics)) {
+        outputMetrics = [outputMetrics];
+      }
+      nestedMetrics.push(outputMetrics);
+    }
+    return nestedMetrics;
+  }
+}
+var LAYERS_MODEL_FORMAT_NAME = "layers-model";
+var LayersModel = class extends Container {
+  constructor(args) {
+    super(args);
+    this.isTraining = false;
+  }
+  summary(lineLength, positions, printFn = console.log) {
+    if (!this.built) {
+      throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);
+    }
+    printSummary(this, lineLength, positions, printFn);
+  }
+  compile(args) {
+    if (args.loss == null) {
+      args.loss = [];
+    }
+    this.loss = args.loss;
+    if (typeof args.optimizer === "string") {
+      this.optimizer_ = getOptimizer(args.optimizer);
+      this.isOptimizerOwned = true;
+    } else {
+      if (!(args.optimizer instanceof Optimizer)) {
+        throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);
+      }
+      this.optimizer_ = args.optimizer;
+      this.isOptimizerOwned = false;
+    }
+    let lossFunctions = [];
+    if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") {
+      args.loss = args.loss;
+      for (const name in args.loss) {
+        if (this.outputNames.indexOf(name) === -1) {
+          throw new ValueError(`Unknown entry in loss dictionary: "${name}". Only expected the following keys: ${this.outputNames}`);
+        }
+      }
+      for (const name of this.outputNames) {
+        if (args.loss[name] == null) {
+          console.warn(`Output "${name}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);
+        }
+        lossFunctions.push(get(args.loss[name]));
+      }
+    } else if (Array.isArray(args.loss)) {
+      if (args.loss.length !== this.outputs.length) {
+        throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);
+      }
+      const theLosses = args.loss;
+      lossFunctions = theLosses.map((l3) => get(l3));
+    } else {
+      const lossFunction = get(args.loss);
+      this.outputs.forEach((_) => {
+        lossFunctions.push(lossFunction);
+      });
+    }
+    this.lossFunctions = lossFunctions;
+    this.feedOutputNames = [];
+    this.feedOutputShapes = [];
+    this.feedLossFns = [];
+    for (let i2 = 0; i2 < this.outputs.length; ++i2) {
+      const shape = this.internalOutputShapes[i2];
+      const name = this.outputNames[i2];
+      this.feedOutputNames.push(name);
+      this.feedOutputShapes.push(shape);
+      this.feedLossFns.push(this.lossFunctions[i2]);
+    }
+    const skipTargetIndices = [];
+    this.metrics = args.metrics;
+    this.metricsNames = ["loss"];
+    this.metricsTensors = [];
+    nameScope("loss", () => {
+      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
+        if (skipTargetIndices.indexOf(i2) !== -1) {
+          continue;
+        }
+        const weightedLoss = this.lossFunctions[i2];
+        if (this.outputs.length > 1) {
+          this.metricsTensors.push([weightedLoss, i2]);
+          this.metricsNames.push(this.outputNames[i2] + "_loss");
+        }
+      }
+    });
+    const nestedMetrics = collectMetrics(args.metrics, this.outputNames);
+    const appendMetric = (outputIndex, metricName, metricTensor) => {
+      if (this.outputNames.length > 1) {
+        metricName = this.outputNames[outputIndex] + "_" + metricName;
+      }
+      this.metricsNames.push(metricName);
+      this.metricsTensors.push([metricTensor, outputIndex]);
+    };
+    nameScope("metric", () => {
+      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
+        if (skipTargetIndices.indexOf(i2) !== -1) {
+          continue;
+        }
+        const outputMetrics = nestedMetrics[i2];
+        const handleMetrics = (metrics) => {
+          const metricNamePrefix = "";
+          let metricName;
+          let accFn;
+          let weightedMetricFn;
+          for (const metric of metrics) {
+            if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) {
+              const outputShape = this.internalOutputShapes[i2];
+              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i2] === binaryCrossentropy) {
+                if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                  accFn = binaryAccuracy;
+                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                  accFn = binaryCrossentropy2;
+                }
+              } else if (this.lossFunctions[i2] === sparseCategoricalCrossentropy) {
+                if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                  accFn = sparseCategoricalAccuracy;
+                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                  accFn = sparseCategoricalCrossentropy2;
+                }
+              } else {
+                if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                  accFn = categoricalAccuracy;
+                } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                  accFn = categoricalCrossentropy2;
+                }
+              }
+              let suffix;
+              if (["accuracy", "acc"].indexOf(metric) !== -1) {
+                suffix = "acc";
+              } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
+                suffix = "ce";
+              }
+              weightedMetricFn = accFn;
+              metricName = metricNamePrefix + suffix;
+            } else {
+              const metricFn = get2(metric);
+              weightedMetricFn = metricFn;
+              metricName = metricNamePrefix + getLossOrMetricName(metric);
+            }
+            let metricResult;
+            nameScope(metricName, () => {
+              metricResult = weightedMetricFn;
+            });
+            appendMetric(i2, metricName, metricResult);
+          }
+        };
+        handleMetrics(outputMetrics);
+      }
+    });
+    this.collectedTrainableWeights = this.trainableWeights;
+  }
+  checkTrainableWeightsConsistency() {
+    if (this.collectedTrainableWeights == null) {
+      return;
+    }
+    if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {
+      console.warn("Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?");
+    }
+  }
+  evaluate(x, y, args = {}) {
+    const batchSize = args.batchSize == null ? 32 : args.batchSize;
+    checkBatchSize(batchSize);
+    const checkBatchAxis = true;
+    const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
+    try {
+      const ins = standardizedOuts[0].concat(standardizedOuts[1]);
+      this.makeTestFunction();
+      const f = this.testFunction;
+      const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);
+      return singletonOrArray(testOuts);
+    } finally {
+      disposeNewTensors(standardizedOuts[0], x);
+      disposeNewTensors(standardizedOuts[1], y);
+    }
+  }
+  async evaluateDataset(dataset, args) {
+    this.makeTestFunction();
+    return evaluateDataset(this, dataset, args);
+  }
+  checkNumSamples(ins, batchSize, steps, stepsName = "steps") {
+    let numSamples;
+    if (steps != null) {
+      numSamples = null;
+      if (batchSize != null) {
+        throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);
+      }
+    } else if (ins != null) {
+      if (Array.isArray(ins)) {
+        numSamples = ins[0].shape[0];
+      } else {
+        numSamples = ins.shape[0];
+      }
+    } else {
+      throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);
+    }
+    return numSamples;
+  }
+  execute(inputs, outputs) {
+    if (Array.isArray(outputs) && outputs.length === 0) {
+      throw new ValueError("`outputs` is an empty Array, which is not allowed.");
+    }
+    const outputsIsArray = Array.isArray(outputs);
+    const outputNames = outputsIsArray ? outputs : [outputs];
+    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);
+    const feedDict = new FeedDict();
+    if (inputs instanceof Tensor) {
+      inputs = [inputs];
+    }
+    if (Array.isArray(inputs)) {
+      if (inputs.length !== this.inputs.length) {
+        throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
+      }
+      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+        feedDict.add(this.inputs[i2], inputs[i2]);
+      }
+    } else {
+      for (const input2 of this.inputs) {
+        const tensorValue = inputs[input2.name];
+        if (tensorValue == null) {
+          throw new ValueError(`No value is provided for the model's input ${input2.name}`);
+        }
+        feedDict.add(input2, tensorValue);
+      }
+    }
+    const executeOutputs = execute(outputSymbolicTensors, feedDict);
+    return outputsIsArray ? executeOutputs : executeOutputs[0];
+  }
+  retrieveSymbolicTensors(symbolicTensorNames) {
+    const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);
+    let outputsRemaining = symbolicTensorNames.length;
+    for (const layer of this.layers) {
+      const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];
+      const layerOutputNames = layerOutputs.map((output) => output.name);
+      for (let i2 = 0; i2 < symbolicTensorNames.length; ++i2) {
+        const index = layerOutputNames.indexOf(symbolicTensorNames[i2]);
+        if (index !== -1) {
+          outputSymbolicTensors[i2] = layerOutputs[index];
+          outputsRemaining--;
+        }
+        if (outputsRemaining === 0) {
+          break;
+        }
+      }
+      if (outputsRemaining === 0) {
+        break;
+      }
+    }
+    if (outputsRemaining > 0) {
+      const remainingNames = [];
+      outputSymbolicTensors.forEach((tensor2, i2) => {
+        if (tensor2 == null) {
+          remainingNames.push(symbolicTensorNames[i2]);
+        }
+      });
+      throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);
+    }
+    return outputSymbolicTensors;
+  }
+  predictLoop(ins, batchSize = 32, verbose = false) {
+    return tidy(() => {
+      const numSamples = this.checkNumSamples(ins);
+      if (verbose) {
+        throw new NotImplementedError("Verbose predictLoop() is not implemented yet.");
+      }
+      const batches = makeBatches(numSamples, batchSize);
+      const outsBatches = this.outputs.map((output) => []);
+      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
+        const batchOuts = tidy(() => {
+          const batchStart = batches[batchIndex][0];
+          const batchEnd = batches[batchIndex][1];
+          const insBatch = sliceArrays(ins, batchStart, batchEnd);
+          const feeds = [];
+          if (Array.isArray(insBatch)) {
+            for (let i2 = 0; i2 < insBatch.length; ++i2) {
+              feeds.push({ key: this.inputs[i2], value: insBatch[i2] });
+            }
+          } else {
+            feeds.push({ key: this.inputs[0], value: insBatch });
+          }
+          const feedDict = new FeedDict(feeds);
+          return execute(this.outputs, feedDict);
+        });
+        batchOuts.forEach((batchOut, i2) => outsBatches[i2].push(batchOut));
+      }
+      return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));
+    });
+  }
+  predict(x, args = {}) {
+    const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);
+    checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);
+    try {
+      const batchSize = args.batchSize == null ? 32 : args.batchSize;
+      checkBatchSize(batchSize);
+      return this.predictLoop(xsRank2OrHigher, batchSize);
+    } finally {
+      disposeNewTensors(xsRank2OrHigher, x);
+    }
+  }
+  predictOnBatch(x) {
+    checkInputData(x, this.inputNames, this.feedInputShapes, true);
+    const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];
+    return this.predictLoop(x, batchSize);
+  }
+  standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {
+    if (this.optimizer_ == null) {
+      throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
+    }
+    const outputShapes = [];
+    for (let i2 = 0; i2 < this.feedOutputShapes.length; ++i2) {
+      const outputShape = this.feedOutputShapes[i2];
+      const lossFn = this.feedLossFns[i2];
+      if (lossFn === sparseCategoricalCrossentropy) {
+        outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));
+      } else {
+        outputShapes.push(outputShape);
+      }
+    }
+    x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, "input");
+    y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, "target");
+    checkArrayLengths(x, y, null);
+    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);
+    if (this.stateful && batchSize != null && batchSize > 0) {
+      if (x[0].shape[0] % batchSize !== 0) {
+        throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);
+      }
+    }
+    return [x, y];
+  }
+  async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {
+    const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);
+    if (sampleWeight != null) {
+      throw new Error("sample weight is not supported yet.");
+    }
+    let standardSampleWeights = null;
+    if (classWeight != null) {
+      const classWeights = standardizeClassWeights(classWeight, this.outputNames);
+      standardSampleWeights = [];
+      for (let i2 = 0; i2 < classWeights.length; ++i2) {
+        standardSampleWeights.push(await standardizeWeights(standardYs[i2], null, classWeights[i2]));
+      }
+    }
+    return [standardXs, standardYs, standardSampleWeights];
+  }
+  testLoop(f, ins, batchSize, verbose = 0, steps) {
+    return tidy(() => {
+      const numSamples = this.checkNumSamples(ins, batchSize, steps, "steps");
+      const outs = [];
+      if (verbose > 0) {
+        throw new NotImplementedError("Verbose mode is not implemented yet.");
+      }
+      if (steps != null) {
+        throw new NotImplementedError("steps mode in testLoop() is not implemented yet");
+      } else {
+        const batches = makeBatches(numSamples, batchSize);
+        const indexArray = tensor1d(range2(0, numSamples));
+        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {
+          const batchStart = batches[batchIndex][0];
+          const batchEnd = batches[batchIndex][1];
+          const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);
+          const insBatch = sliceArraysByIndices(ins, batchIds);
+          const batchOuts = f(insBatch);
+          if (batchIndex === 0) {
+            for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+              outs.push(scalar(0));
+            }
+          }
+          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+            const batchOut = batchOuts[i2];
+            outs[i2] = add2(outs[i2], mul(batchEnd - batchStart, batchOut));
+          }
+        }
+        for (let i2 = 0; i2 < outs.length; ++i2) {
+          outs[i2] = div(outs[i2], numSamples);
+        }
+      }
+      return outs;
+    });
+  }
+  getDedupedMetricsNames() {
+    const outLabels = this.metricsNames;
+    const dedupedOutLabels = [];
+    for (let i2 = 0; i2 < outLabels.length; ++i2) {
+      const label = outLabels[i2];
+      let newLabel = label;
+      if (count(outLabels, label) > 1) {
+        const dupIndex = count(outLabels.slice(0, i2), label);
+        newLabel += `_${dupIndex}`;
+      }
+      dedupedOutLabels.push(newLabel);
+    }
+    return dedupedOutLabels;
+  }
+  makeTrainFunction() {
+    return (data) => {
+      const lossValues = [];
+      const inputs = data.slice(0, this.inputs.length);
+      const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
+      const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);
+      const metricsValues = [];
+      const totalLossFunction = () => {
+        const feeds = [];
+        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        }
+        const feedDict = new FeedDict(feeds);
+        const outputs = execute(this.outputs, feedDict, { "training": true });
+        let totalLoss;
+        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
+          const lossFunction = this.lossFunctions[i2];
+          let loss = lossFunction(targets[i2], outputs[i2]);
+          if (sampleWeights[i2] != null) {
+            loss = computeWeightedLoss2(loss, sampleWeights[i2]);
+          }
+          const meanLoss = mean(loss);
+          lossValues.push(meanLoss);
+          if (i2 === 0) {
+            totalLoss = loss;
+          } else {
+            totalLoss = add2(totalLoss, loss);
+          }
+        }
+        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
+          let weightedMetric;
+          if (this.outputs.length > 1 && i2 < this.outputs.length) {
+            weightedMetric = lossValues[i2];
+          } else {
+            const metric = this.metricsTensors[i2][0];
+            const outputIndex = this.metricsTensors[i2][1];
+            weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
+          }
+          keep(weightedMetric);
+          metricsValues.push(weightedMetric);
+        }
+        totalLoss = mean(totalLoss);
+        this.calculateLosses().forEach((regularizerLoss) => {
+          totalLoss = add2(totalLoss, regularizerLoss);
+        });
+        return totalLoss;
+      };
+      const variables = this.collectedTrainableWeights.map((param) => param.read());
+      const returnCost = true;
+      const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);
+      return [totalLossValue].concat(metricsValues);
+    };
+  }
+  makeTestFunction() {
+    this.testFunction = (data) => {
+      return tidy(() => {
+        const valOutputs = [];
+        let totalLoss;
+        const inputs = data.slice(0, this.inputs.length);
+        const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
+        const feeds = [];
+        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
+          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        }
+        const feedDict = new FeedDict(feeds);
+        const outputs = execute(this.outputs, feedDict);
+        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
+          const lossFunction = this.lossFunctions[i2];
+          const loss = mean(lossFunction(targets[i2], outputs[i2]));
+          if (i2 === 0) {
+            totalLoss = loss;
+          } else {
+            totalLoss = add2(totalLoss, loss);
+          }
+          valOutputs.push(totalLoss);
+        }
+        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
+          const metric = this.metricsTensors[i2][0];
+          const outputIndex = this.metricsTensors[i2][1];
+          const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
+          valOutputs.push(meanMetric);
+        }
+        return valOutputs;
+      });
+    };
+  }
+  async fit(x, y, args = {}) {
+    return fitTensors(this, x, y, args);
+  }
+  async fitDataset(dataset, args) {
+    return fitDataset(this, dataset, args);
+  }
+  async trainOnBatch(x, y) {
+    const standardizeOut = await this.standardizeUserData(x, y);
+    const inputs = standardizeOut[0];
+    const targets = standardizeOut[1];
+    const trainFunction = this.makeTrainFunction();
+    const losses2 = trainFunction(inputs.concat(targets));
+    const lossValues = [];
+    for (const loss of losses2) {
+      const v = await loss.data();
+      lossValues.push(v[0]);
+    }
+    dispose(losses2);
+    disposeNewTensors(standardizeOut[0], x);
+    disposeNewTensors(standardizeOut[1], y);
+    return singletonOrArray(lossValues);
+  }
+  getNamedWeights(config) {
+    const namedWeights = [];
+    const trainableOnly = config != null && config.trainableOnly;
+    const weights = trainableOnly ? this.trainableWeights : this.weights;
+    const weightValues = this.getWeights(trainableOnly);
+    for (let i2 = 0; i2 < weights.length; ++i2) {
+      if (trainableOnly && !weights[i2].trainable) {
+        continue;
+      }
+      namedWeights.push({ name: weights[i2].originalName, tensor: weightValues[i2] });
+    }
+    return namedWeights;
+  }
+  set stopTraining(stop) {
+    this.stopTraining_ = stop;
+  }
+  get stopTraining() {
+    return this.stopTraining_;
+  }
+  get optimizer() {
+    return this.optimizer_;
+  }
+  set optimizer(optimizer) {
+    if (this.optimizer_ !== optimizer) {
+      this.optimizer_ = optimizer;
+      this.isOptimizerOwned = false;
+    }
+  }
+  dispose() {
+    const result = super.dispose();
+    if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
+      const numTensorsBeforeOptmizerDisposal = memory().numTensors;
+      this.optimizer_.dispose();
+      result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;
+    }
+    return result;
+  }
+  getLossIdentifiers() {
+    let lossNames;
+    if (typeof this.loss === "string") {
+      lossNames = toSnakeCase(this.loss);
+    } else if (Array.isArray(this.loss)) {
+      for (const loss of this.loss) {
+        if (typeof loss !== "string") {
+          throw new Error("Serialization of non-string loss is not supported.");
+        }
+      }
+      lossNames = this.loss.map((name) => toSnakeCase(name));
+    } else {
+      const outputNames = Object.keys(this.loss);
+      lossNames = {};
+      const losses2 = this.loss;
+      for (const outputName of outputNames) {
+        if (typeof losses2[outputName] === "string") {
+          lossNames[outputName] = toSnakeCase(losses2[outputName]);
+        } else {
+          throw new Error("Serialization of non-string loss is not supported.");
+        }
+      }
+    }
+    return lossNames;
+  }
+  getMetricIdentifiers() {
+    if (typeof this.metrics === "string" || typeof this.metrics === "function") {
+      return [toSnakeCase(getLossOrMetricName(this.metrics))];
+    } else if (Array.isArray(this.metrics)) {
+      return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));
+    } else {
+      const metricsIdentifiers = {};
+      for (const key in this.metrics) {
+        metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));
+      }
+      return metricsIdentifiers;
+    }
+  }
+  getTrainingConfig() {
+    return {
+      loss: this.getLossIdentifiers(),
+      metrics: this.getMetricIdentifiers(),
+      optimizer_config: {
+        class_name: this.optimizer.getClassName(),
+        config: this.optimizer.getConfig()
+      }
+    };
+  }
+  loadTrainingConfig(trainingConfig) {
+    if (trainingConfig.weighted_metrics != null) {
+      throw new Error("Loading weight_metrics is not supported yet.");
+    }
+    if (trainingConfig.loss_weights != null) {
+      throw new Error("Loading loss_weights is not supported yet.");
+    }
+    if (trainingConfig.sample_weight_mode != null) {
+      throw new Error("Loading sample_weight_mode is not supported yet.");
+    }
+    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);
+    const optimizer = deserialize(tsConfig);
+    let loss;
+    if (typeof trainingConfig.loss === "string") {
+      loss = toCamelCase(trainingConfig.loss);
+    } else if (Array.isArray(trainingConfig.loss)) {
+      loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));
+    } else if (trainingConfig.loss != null) {
+      loss = {};
+      for (const key in trainingConfig.loss) {
+        loss[key] = toCamelCase(trainingConfig.loss[key]);
+      }
+    }
+    let metrics;
+    if (Array.isArray(trainingConfig.metrics)) {
+      metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));
+    } else if (trainingConfig.metrics != null) {
+      metrics = {};
+      for (const key in trainingConfig.metrics) {
+        metrics[key] = toCamelCase(trainingConfig.metrics[key]);
+      }
+    }
+    this.compile({ loss, metrics, optimizer });
+  }
+  async save(handlerOrURL, config) {
+    if (typeof handlerOrURL === "string") {
+      const handlers = io_exports.getSaveHandlers(handlerOrURL);
+      if (handlers.length === 0) {
+        throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);
+      } else if (handlers.length > 1) {
+        throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
+      }
+      handlerOrURL = handlers[0];
+    }
+    if (handlerOrURL.save == null) {
+      throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
+    }
+    const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));
+    const returnString = false;
+    const unusedArg = null;
+    const modelConfig = this.toJSON(unusedArg, returnString);
+    const modelArtifacts = {
+      modelTopology: modelConfig,
+      format: LAYERS_MODEL_FORMAT_NAME,
+      generatedBy: `TensorFlow.js tfjs-layers v${version2}`,
+      convertedBy: null
+    };
+    const includeOptimizer = config == null ? false : config.includeOptimizer;
+    if (includeOptimizer && this.optimizer != null) {
+      modelArtifacts.trainingConfig = this.getTrainingConfig();
+      const weightType = "optimizer";
+      const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);
+      weightDataAndSpecs.specs.push(...optimizerWeightSpecs);
+      weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);
+    }
+    if (this.userDefinedMetadata != null) {
+      const checkSize = true;
+      checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);
+      modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;
+    }
+    modelArtifacts.weightData = weightDataAndSpecs.data;
+    modelArtifacts.weightSpecs = weightDataAndSpecs.specs;
+    return handlerOrURL.save(modelArtifacts);
+  }
+  setUserDefinedMetadata(userDefinedMetadata) {
+    checkUserDefinedMetadata(userDefinedMetadata, this.name);
+    this.userDefinedMetadata = userDefinedMetadata;
+  }
+  getUserDefinedMetadata() {
+    return this.userDefinedMetadata;
+  }
+};
+LayersModel.className = "Model";
+serialization_exports.registerClass(LayersModel);
+var Functional = class extends LayersModel {
+};
+Functional.className = "Functional";
+serialization_exports.registerClass(Functional);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/models.js
+async function modelFromJSON(modelAndWeightsConfig, customObjects) {
+  if (!("modelTopology" in modelAndWeightsConfig)) {
+    modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };
+  }
+  modelAndWeightsConfig = modelAndWeightsConfig;
+  let modelTopology = modelAndWeightsConfig.modelTopology;
+  if (modelTopology["model_config"] != null) {
+    modelTopology = modelTopology["model_config"];
+  }
+  const tsConfig = convertPythonicToTs(modelTopology);
+  const model2 = deserialize(tsConfig, customObjects);
+  if (modelAndWeightsConfig.weightsManifest != null) {
+    const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));
+    const uniqueWeightValues = {};
+    for (const weight of model2.weights) {
+      uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];
+    }
+    model2.loadWeights(uniqueWeightValues);
+    dispose(weightValues);
+  }
+  return model2;
+}
+async function loadLayersModelInternal(pathOrIOHandler, options) {
+  if (options == null) {
+    options = {};
+  }
+  if (typeof pathOrIOHandler === "string") {
+    const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);
+    if (handlers.length === 0) {
+      handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));
+    } else if (handlers.length > 1) {
+      throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);
+    }
+    pathOrIOHandler = handlers[0];
+  }
+  return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);
+}
+async function loadLayersModelFromIOHandler(handler, customObjects, options) {
+  if (options == null) {
+    options = {};
+  }
+  if (handler.load == null) {
+    throw new ValueError("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
+  }
+  const artifacts = await handler.load();
+  let modelTopology = artifacts.modelTopology;
+  if (modelTopology["model_config"] != null) {
+    modelTopology = modelTopology["model_config"];
+  }
+  const strict = options.strict == null ? true : options.strict;
+  const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;
+  const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);
+  const trainingConfig = artifacts.trainingConfig;
+  if (trainingConfig != null) {
+    model2.loadTrainingConfig(trainingConfig);
+  }
+  if (artifacts.userDefinedMetadata != null) {
+    model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);
+  }
+  if (artifacts.weightData != null) {
+    if (artifacts.weightSpecs == null) {
+      throw new ValueError("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
+    }
+    const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);
+    model2.loadWeights(modelWeights, strict);
+    if (model2.optimizer != null && optimizerWeights.length > 0) {
+      await model2.optimizer.setWeights(optimizerWeights);
+    }
+    dispose(modelWeights);
+    dispose(optimizerWeights.map((w) => w.tensor));
+  }
+  return model2;
+}
+function decodeModelAndOptimizerWeights(buffer2, specs) {
+  const name2Tensor = io_exports.decodeWeights(buffer2, specs);
+  const modelWeights = {};
+  const optimizerWeights = [];
+  specs.forEach((spec) => {
+    if (spec.group === "optimizer") {
+      optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });
+    } else {
+      modelWeights[spec.name] = name2Tensor[spec.name];
+    }
+  });
+  return { modelWeights, optimizerWeights };
+}
+var Sequential = class extends LayersModel {
+  constructor(args) {
+    super({ inputs: [], outputs: [] });
+    args = args || {};
+    this.trainable = true;
+    this.built = false;
+    this.name = args.name != null ? args.name : getUid("sequential_");
+    if (args.layers != null) {
+      for (const layer of args.layers) {
+        this.add(layer);
+      }
+    }
+  }
+  checkShape(layer) {
+    const shape = layer.inboundNodes[0].outputTensors[0].shape;
+    if (shape.some((x) => x < 0)) {
+      throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);
+    }
+  }
+  add(layer) {
+    const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;
+    let modelLayer;
+    if (isLayerModelInstance) {
+      modelLayer = layer;
+      if (modelLayer.outputs.length !== 1) {
+        throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
+      }
+      if (modelLayer.inputs.length !== 1) {
+        throw new ValueError("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.");
+      }
+    }
+    if (this.outputs.length === 0) {
+      if (layer.inboundNodes.length === 0) {
+        if (layer.batchInputShape == null) {
+          throw new ValueError("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
+        }
+        const x = Input({
+          batchShape: layer.batchInputShape,
+          dtype: layer.dtype,
+          name: layer.name + "_input"
+        });
+        layer.apply(x);
+      }
+      if (isLayerModelInstance) {
+        this.outputs = modelLayer.outputs;
+        this.inputs = modelLayer.inputs;
+      } else {
+        if (layer.inboundNodes.length !== 1) {
+          throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);
+        }
+        if (layer.inboundNodes[0].outputTensors.length !== 1) {
+          throw new ValueError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
+        }
+        this.checkShape(layer);
+        this.outputs = [layer.inboundNodes[0].outputTensors[0]];
+        this.inputs = getSourceInputs(this.outputs[0]);
+      }
+      this.inboundNodes = [];
+      new Node({
+        outboundLayer: this,
+        inboundLayers: [],
+        nodeIndices: [],
+        tensorIndices: [],
+        inputTensors: this.inputs,
+        outputTensors: this.outputs,
+        inputMasks: pyListRepeat(null, this.inputs.length),
+        outputMasks: [null],
+        inputShapes: this.inputs.map((x) => x.shape),
+        outputShapes: this.outputs[0].shape
+      });
+    } else {
+      const outputTensor = layer.apply(this.outputs[0]);
+      if (Array.isArray(outputTensor)) {
+        throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
+      }
+      this.checkShape(layer);
+      this.outputs = [outputTensor];
+      this.inboundNodes[0].outputTensors = this.outputs;
+      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
+    }
+    this.layers.push(layer);
+    this.built = false;
+  }
+  pop() {
+    if (this.layers.length === 0) {
+      throw new TypeError("There are no layers in the model.");
+    }
+    this.layers.pop();
+    if (this.layers.length === 0) {
+      this.outputs = [];
+      this.inboundNodes = [];
+      this.outboundNodes = [];
+    } else {
+      const lastLayerIndex = this.layers.length - 1;
+      this.layers[lastLayerIndex].outboundNodes = [];
+      this.outputs = [this.layers[lastLayerIndex].output];
+      this.inboundNodes[0].outputTensors = this.outputs;
+      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];
+    }
+  }
+  call(inputs, kwargs) {
+    if (this.model == null) {
+      this.build();
+    }
+    return this.model.call(inputs, kwargs);
+  }
+  build(inputShape) {
+    getExactlyOneShape(inputShape);
+    if (this.inputs.length === 0 || this.outputs.length === 0) {
+      throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");
+    }
+    this.model = new LayersModel({
+      inputs: this.inputs,
+      outputs: this.outputs[0],
+      name: this.name + "_model"
+    });
+    this.model.trainable = this.trainable;
+    this.supportsMasking = this.model.supportsMasking;
+    this.inputLayers = this.model.inputLayers;
+    this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;
+    this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;
+    this.outputLayers = this.model.outputLayers;
+    this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;
+    this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;
+    this.nodesByDepth = this.model.nodesByDepth;
+    this.containerNodes = this.model.containerNodes;
+    this.outputNames = this.model.outputNames;
+    this.inputNames = this.model.inputNames;
+    this.built = true;
+  }
+  countParams() {
+    if (!this.built) {
+      this.build();
+    }
+    return super.countParams();
+  }
+  summary(lineLength, positions, printFn = console.log) {
+    if (!this.built) {
+      this.build();
+    }
+    super.summary(lineLength, positions, printFn);
+  }
+  setWeights(weights) {
+    if (this.model == null) {
+      this.build();
+    }
+    this.model.setWeights(weights);
+  }
+  evaluate(x, y, args = {}) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.evaluate(x, y, args);
+  }
+  async evaluateDataset(dataset, args) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.evaluateDataset(dataset, args);
+  }
+  predict(x, args = {}) {
+    if (this.model == null) {
+      this.build();
+    }
+    return this.model.predict(x, args);
+  }
+  predictOnBatch(x) {
+    if (this.model == null) {
+      this.build();
+    }
+    return this.model.predictOnBatch(x);
+  }
+  compile(args) {
+    this.build();
+    this.model.compile(args);
+    this.optimizer_ = this.model.optimizer;
+    this.isOptimizerOwned = this.model.isOptimizerOwned;
+    this.loss = this.model.loss;
+    this.metrics = this.model.metrics;
+    this.metricsTensors = this.model.metricsTensors;
+    this.metricsNames = this.model.metricsNames;
+  }
+  get optimizer() {
+    return this.model == null ? void 0 : this.model.optimizer;
+  }
+  set optimizer(optimizer) {
+    this.model.optimizer = optimizer;
+  }
+  async fit(x, y, args = {}) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.fit(x, y, args);
+  }
+  async fitDataset(dataset, args) {
+    if (!this.built) {
+      throw new RuntimeError("The model needs to be compiled before being used.");
+    }
+    return this.model.fitDataset(dataset, args);
+  }
+  async trainOnBatch(x, y) {
+    return this.model.trainOnBatch(x, y);
+  }
+  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {
+    let configArray;
+    let extraModelConfig = {};
+    if (config instanceof Array) {
+      if (!(config[0].className != null) || config[0]["className"] === "Merge") {
+        throw new ValueError("Legacy serialization format not supported yet.");
+      }
+      configArray = config;
+    } else {
+      util_exports.assert(config["layers"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);
+      configArray = config["layers"];
+      delete config["layers"];
+      extraModelConfig = config;
+    }
+    const model2 = new cls(extraModelConfig);
+    if (!(model2 instanceof Sequential)) {
+      throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);
+    }
+    for (const conf of configArray) {
+      const customObjects2 = void 0;
+      const layer = deserialize(conf, customObjects2, fastWeightInit);
+      if (fastWeightInit) {
+        layer.setFastWeightInitDuringBuild(true);
+      }
+      model2.add(layer);
+    }
+    return model2;
+  }
+  set stopTraining(stop) {
+    if (this.model == null) {
+      throw new ValueError("Cannot set the stopTraining property of a sequential model before it is compiled.");
+    }
+    this.model.stopTraining = stop;
+  }
+  get stopTraining() {
+    if (this.model == null) {
+      throw new ValueError("Cannot get the stopTraining property of a sequential model before it is compiled.");
+    }
+    return this.model.stopTraining;
+  }
+  getConfig() {
+    const layers = [];
+    for (const layer of this.layers) {
+      const dict = {};
+      dict["className"] = layer.getClassName();
+      dict["config"] = layer.getConfig();
+      layers.push(dict);
+    }
+    return { name: this.name, layers };
+  }
+};
+Sequential.className = "Sequential";
+serialization_exports.registerClass(Sequential);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports.js
+function model(args) {
+  return new LayersModel(args);
+}
+function sequential(config) {
+  return new Sequential(config);
+}
+function loadLayersModel(pathOrIOHandler, options) {
+  if (options == null) {
+    options = {};
+  }
+  return loadLayersModelInternal(pathOrIOHandler, options);
+}
+function input(config) {
+  return Input(config);
+}
+function registerCallbackConstructor(verbosityLevel, callbackConstructor) {
+  CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/activations.js
+var Activation = class extends serialization_exports.Serializable {
+  getConfig() {
+    return {};
+  }
+};
+var Elu2 = class extends Activation {
+  apply(x, alpha = 1) {
+    return elu2(x, alpha);
+  }
+};
+Elu2.className = "elu";
+serialization_exports.registerClass(Elu2);
+var Selu2 = class extends Activation {
+  apply(x) {
+    return selu(x);
+  }
+};
+Selu2.className = "selu";
+serialization_exports.registerClass(Selu2);
+var Relu2 = class extends Activation {
+  apply(x) {
+    return relu(x);
+  }
+};
+Relu2.className = "relu";
+serialization_exports.registerClass(Relu2);
+var Relu62 = class extends Activation {
+  apply(x) {
+    return tidy(() => minimum(6, relu(x)));
+  }
+};
+Relu62.className = "relu6";
+serialization_exports.registerClass(Relu62);
+var Linear = class extends Activation {
+  apply(x) {
+    return x;
+  }
+};
+Linear.className = "linear";
+serialization_exports.registerClass(Linear);
+var Sigmoid2 = class extends Activation {
+  apply(x) {
+    return sigmoid(x);
+  }
+};
+Sigmoid2.className = "sigmoid";
+serialization_exports.registerClass(Sigmoid2);
+var HardSigmoid = class extends Activation {
+  apply(x) {
+    return hardSigmoid(x);
+  }
+};
+HardSigmoid.className = "hardSigmoid";
+serialization_exports.registerClass(HardSigmoid);
+var Softplus2 = class extends Activation {
+  apply(x) {
+    return softplus(x);
+  }
+};
+Softplus2.className = "softplus";
+serialization_exports.registerClass(Softplus2);
+var Softsign = class extends Activation {
+  apply(x) {
+    return softsign(x);
+  }
+};
+Softsign.className = "softsign";
+serialization_exports.registerClass(Softsign);
+var Tanh2 = class extends Activation {
+  apply(x) {
+    return tanh2(x);
+  }
+};
+Tanh2.className = "tanh";
+serialization_exports.registerClass(Tanh2);
+var Softmax2 = class extends Activation {
+  apply(x, axis = -1) {
+    return softmax(x, axis);
+  }
+};
+Softmax2.className = "softmax";
+serialization_exports.registerClass(Softmax2);
+var LogSoftmax2 = class extends Activation {
+  apply(x, axis = -1) {
+    return logSoftmax(x, axis);
+  }
+};
+LogSoftmax2.className = "logSoftmax";
+serialization_exports.registerClass(LogSoftmax2);
+var Swish = class extends Activation {
+  apply(x, alpha = 1) {
+    return tidy(() => mul(sigmoid(mul(x, alpha)), x));
+  }
+};
+Swish.className = "swish";
+serialization_exports.registerClass(Swish);
+var Mish = class extends Activation {
+  apply(x) {
+    return tidy(() => mul(x, tanh2(softplus(x))));
+  }
+};
+Mish.className = "mish";
+serialization_exports.registerClass(Mish);
+function serializeActivation(activation2) {
+  return activation2.getClassName();
+}
+function deserializeActivation(config, customObjects = {}) {
+  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation");
+}
+function getActivation(identifier) {
+  if (identifier == null) {
+    const config = {};
+    config["className"] = "linear";
+    config["config"] = {};
+    return deserializeActivation(config);
+  }
+  if (typeof identifier === "string") {
+    const config = {};
+    config["className"] = identifier;
+    config["config"] = {};
+    return deserializeActivation(config);
+  } else if (identifier instanceof Activation) {
+    return identifier;
+  } else {
+    return deserializeActivation(identifier);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js
+function assertObjectArgs(args) {
+  if (args != null && typeof args !== "object") {
+    throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);
+  }
+}
+var Regularizer = class extends serialization_exports.Serializable {
+};
+var L1L2 = class extends Regularizer {
+  constructor(args) {
+    super();
+    assertObjectArgs(args);
+    this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;
+    this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;
+    this.hasL1 = this.l1 !== 0;
+    this.hasL2 = this.l2 !== 0;
+  }
+  apply(x) {
+    return tidy(() => {
+      let regularization = zeros([1]);
+      if (this.hasL1) {
+        regularization = add2(regularization, sum2(mul(this.l1, abs(x))));
+      }
+      if (this.hasL2) {
+        regularization = add2(regularization, sum2(mul(this.l2, square2(x))));
+      }
+      return reshape(regularization, []);
+    });
+  }
+  getConfig() {
+    return { "l1": this.l1, "l2": this.l2 };
+  }
+  static fromConfig(cls, config) {
+    return new cls({ l1: config["l1"], l2: config["l2"] });
+  }
+};
+L1L2.className = "L1L2";
+serialization_exports.registerClass(L1L2);
+function l1(args) {
+  assertObjectArgs(args);
+  return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });
+}
+function l2(args) {
+  assertObjectArgs(args);
+  return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });
+}
+var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
+  "l1l2": "L1L2"
+};
+function serializeRegularizer(constraint) {
+  return serializeKerasObject(constraint);
+}
+function deserializeRegularizer(config, customObjects = {}) {
+  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "regularizer");
+}
+function getRegularizer(identifier) {
+  if (identifier == null) {
+    return null;
+  }
+  if (typeof identifier === "string") {
+    const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;
+    const config = { className, config: {} };
+    return deserializeRegularizer(config);
+  } else if (identifier instanceof Regularizer) {
+    return identifier;
+  } else {
+    return deserializeRegularizer(identifier);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js
+var ReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.supportsMasking = true;
+    if (args != null) {
+      this.maxValue = args.maxValue;
+    }
+  }
+  call(inputs, kwargs) {
+    inputs = getExactlyOneTensor(inputs);
+    let output = relu(inputs);
+    if (this.maxValue != null) {
+      output = clipByValue(output, 0, this.maxValue);
+    }
+    return output;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config = { maxValue: this.maxValue };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+ReLU.className = "ReLU";
+serialization_exports.registerClass(ReLU);
+var LeakyReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_ALPHA = 0.3;
+    if (args == null) {
+      args = {};
+    }
+    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return leakyRelu(x, this.alpha);
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config = { alpha: this.alpha };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+LeakyReLU.className = "LeakyReLU";
+serialization_exports.registerClass(LeakyReLU);
+var PReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_ALPHA_INITIALIZER = "zeros";
+    if (args == null) {
+      args = {};
+    }
+    this.supportsMasking = true;
+    this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);
+    this.alphaRegularizer = getRegularizer(args.alphaRegularizer);
+    this.alphaConstraint = getConstraint(args.alphaConstraint);
+    if (args.sharedAxes == null) {
+      this.sharedAxes = null;
+    } else if (Array.isArray(args.sharedAxes)) {
+      this.sharedAxes = args.sharedAxes;
+    } else if (typeof args.sharedAxes === "number") {
+      this.sharedAxes = [args.sharedAxes];
+    } else {
+      throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);
+    }
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const paramShape = inputShape.slice(1);
+    if (this.sharedAxes != null) {
+      for (const i2 of this.sharedAxes) {
+        paramShape[i2 - 1] = 1;
+      }
+    }
+    this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
+    const axes = {};
+    if (this.sharedAxes != null) {
+      for (let i2 = 1; i2 < inputShape.length; ++i2) {
+        axes[i2] = inputShape[i2];
+      }
+    }
+    this.inputSpec = [new InputSpec({
+      ndim: inputShape.length,
+      axes
+    })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    inputs = getExactlyOneTensor(inputs);
+    return prelu(inputs, this.alpha.read());
+  }
+  getConfig() {
+    const config = {
+      alphaInitializer: serializeInitializer(this.alphaInitializer),
+      alphaRegularizer: serializeRegularizer(this.alphaRegularizer),
+      alphaConstraint: serializeConstraint(this.alphaConstraint),
+      sharedAxes: this.sharedAxes
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+PReLU.className = "PReLU";
+serialization_exports.registerClass(PReLU);
+var ELU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_ALPHA = 1;
+    if (args == null) {
+      args = {};
+    }
+    if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {
+      throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);
+    }
+    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return elu(x);
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config = { alpha: this.alpha };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+ELU.className = "ELU";
+serialization_exports.registerClass(ELU);
+var ThresholdedReLU = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_THETA = 1;
+    if (args == null) {
+      args = {};
+    }
+    this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return mul(x, cast(greater(x, this.theta), "float32"));
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config = { theta: this.theta };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+ThresholdedReLU.className = "ThresholdedReLU";
+serialization_exports.registerClass(ThresholdedReLU);
+var Softmax3 = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.DEFAULT_AXIS = 1;
+    if (args == null) {
+      args = {};
+    }
+    this.softmax = new Softmax2().apply;
+    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
+  }
+  call(inputs, kwargs) {
+    const x = getExactlyOneTensor(inputs);
+    return this.softmax(x, this.axis);
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const config = { axis: this.axis };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Softmax3.className = "Softmax";
+serialization_exports.registerClass(Softmax3);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js
+function normalizeArray(value, n, name) {
+  if (typeof value === "number") {
+    return pyListRepeat(value, n);
+  } else {
+    if (value.length !== n) {
+      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);
+    }
+    for (let i2 = 0; i2 < n; ++i2) {
+      const singleValue = value[i2];
+      if (!isInteger(singleValue)) {
+        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
+      }
+    }
+    return value;
+  }
+}
+function convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {
+  if (inputLength == null) {
+    return inputLength;
+  }
+  const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);
+  let outputLength;
+  if (padding === "same") {
+    outputLength = inputLength;
+  } else {
+    outputLength = inputLength - dilatedFilterSize + 1;
+  }
+  return Math.floor((outputLength + stride - 1) / stride);
+}
+function deconvLength(dimSize, strideSize, kernelSize, padding) {
+  if (dimSize == null) {
+    return null;
+  }
+  if (padding === "valid") {
+    dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);
+  } else if (padding === "same") {
+    dimSize = dimSize * strideSize;
+  } else {
+    throw new ValueError(`Unsupport padding mode: ${padding}.`);
+  }
+  return dimSize;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js
+function preprocessConv2DInput(x, dataFormat) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    if (dataFormat === "channelsFirst") {
+      return transpose(x, [0, 2, 3, 1]);
+    } else {
+      return x;
+    }
+  });
+}
+function preprocessConv3DInput(x, dataFormat) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    if (dataFormat === "channelsFirst") {
+      return transpose(x, [0, 2, 3, 4, 1]);
+    } else {
+      return x;
+    }
+  });
+}
+function conv1dWithBias(x, kernel, bias, strides = 1, padding = "valid", dataFormat, dilationRate = 1) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    if (x.shape.length !== 3) {
+      throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);
+    }
+    if (kernel.shape.length !== 3) {
+      throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);
+    }
+    if (bias != null && bias.shape.length !== 1) {
+      throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);
+    }
+    if (dataFormat === "channelsFirst") {
+      x = transpose(x, [0, 2, 1]);
+    }
+    if (padding === "causal") {
+      throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
+    }
+    let y = conv1d(x, kernel, strides, padding === "same" ? "same" : "valid", "NWC", dilationRate);
+    if (bias != null) {
+      y = biasAdd(y, bias);
+    }
+    return y;
+  });
+}
+function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = "valid", dataFormat, dilationRate, activation2 = null) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    if (x.rank !== 3 && x.rank !== 4) {
+      throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);
+    }
+    if (kernel.rank !== 3 && kernel.rank !== 4) {
+      throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);
+    }
+    let y = preprocessConv2DInput(x, dataFormat);
+    if (padding === "causal") {
+      throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
+    }
+    y = fused_ops_exports.conv2d({
+      x: y,
+      filter: kernel,
+      strides,
+      pad: padding === "same" ? "same" : "valid",
+      dilations: dilationRate,
+      dataFormat: "NHWC",
+      bias,
+      activation: activation2
+    });
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 3, 1, 2]);
+    }
+    return y;
+  });
+}
+function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = "valid", dataFormat, dilationRate) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    if (x.rank !== 4 && x.rank !== 5) {
+      throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);
+    }
+    if (kernel.rank !== 4 && kernel.rank !== 5) {
+      throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);
+    }
+    let y = preprocessConv3DInput(x, dataFormat);
+    if (padding === "causal") {
+      throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
+    }
+    y = conv3d(y, kernel, strides, padding === "same" ? "same" : "valid", "NDHWC", dilationRate);
+    if (bias != null) {
+      y = biasAdd(y, bias);
+    }
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 4, 1, 2, 3]);
+    }
+    return y;
+  });
+}
+var BaseConv = class extends Layer {
+  constructor(rank, args) {
+    super(args);
+    this.bias = null;
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    BaseConv.verifyArgs(args);
+    this.rank = rank;
+    assertPositiveInteger(this.rank, "rank");
+    if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {
+      throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
+    }
+    this.kernelSize = normalizeArray(args.kernelSize, rank, "kernelSize");
+    this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    checkPaddingMode(this.padding);
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    this.activation = getActivation(args.activation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.activityRegularizer = getRegularizer(args.activityRegularizer);
+    this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, "dilationRate");
+    if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {
+      throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
+    } else if (this.rank === 2) {
+      if (typeof this.dilationRate === "number") {
+        this.dilationRate = [this.dilationRate, this.dilationRate];
+      } else if (this.dilationRate.length !== 2) {
+        throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);
+      }
+    } else if (this.rank === 3) {
+      if (typeof this.dilationRate === "number") {
+        this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];
+      } else if (this.dilationRate.length !== 3) {
+        throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);
+      }
+    }
+  }
+  static verifyArgs(args) {
+    assert2("kernelSize" in args, `required key 'kernelSize' not in config`);
+    if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 3)) {
+      throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);
+    }
+  }
+  getConfig() {
+    const config = {
+      kernelSize: this.kernelSize,
+      strides: this.strides,
+      padding: this.padding,
+      dataFormat: this.dataFormat,
+      dilationRate: this.dilationRate,
+      activation: serializeActivation(this.activation),
+      useBias: this.useBias,
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      biasConstraint: serializeConstraint(this.biasConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+var Conv = class extends BaseConv {
+  constructor(rank, args) {
+    super(rank, args);
+    this.kernel = null;
+    Conv.verifyArgs(args);
+    this.filters = args.filters;
+    assertPositiveInteger(this.filters, "filters");
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const kernelShape = this.kernelSize.concat([inputDim, this.filters]);
+    this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      let outputs;
+      const biasValue = this.bias == null ? null : this.bias.read();
+      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
+      if (fusedActivationName != null && this.rank === 2) {
+        outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);
+      } else {
+        if (this.rank === 1) {
+          outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
+        } else if (this.rank === 2) {
+          outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
+        } else if (this.rank === 3) {
+          outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);
+        } else {
+          throw new NotImplementedError("convolutions greater than 3D are not implemented yet.");
+        }
+        if (this.activation != null) {
+          outputs = this.activation.apply(outputs);
+        }
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const newSpace = [];
+    const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);
+    for (let i2 = 0; i2 < space.length; ++i2) {
+      const newDim = convOutputLength(space[i2], this.kernelSize[i2], this.padding, this.strides[i2], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i2]);
+      newSpace.push(newDim);
+    }
+    let outputShape = [inputShape[0]];
+    if (this.dataFormat === "channelsLast") {
+      outputShape = outputShape.concat(newSpace);
+      outputShape.push(this.filters);
+    } else {
+      outputShape.push(this.filters);
+      outputShape = outputShape.concat(newSpace);
+    }
+    return outputShape;
+  }
+  getConfig() {
+    const config = {
+      filters: this.filters,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  static verifyArgs(args) {
+    if (!("filters" in args) || typeof args.filters !== "number" || args.filters < 1) {
+      throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);
+    }
+  }
+};
+var Conv2D2 = class extends Conv {
+  constructor(args) {
+    super(2, args);
+    Conv2D2.verifyArgs(args);
+  }
+  getConfig() {
+    const config = super.getConfig();
+    delete config["rank"];
+    return config;
+  }
+  static verifyArgs(args) {
+    if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 2)) {
+      throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);
+    }
+  }
+};
+Conv2D2.className = "Conv2D";
+serialization_exports.registerClass(Conv2D2);
+var Conv3D2 = class extends Conv {
+  constructor(args) {
+    super(3, args);
+    Conv3D2.verifyArgs(args);
+  }
+  getConfig() {
+    const config = super.getConfig();
+    delete config["rank"];
+    return config;
+  }
+  static verifyArgs(args) {
+    if (typeof args.kernelSize !== "number") {
+      if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {
+        throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);
+      }
+    }
+  }
+};
+Conv3D2.className = "Conv3D";
+serialization_exports.registerClass(Conv3D2);
+var Conv2DTranspose = class extends Conv2D2 {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+    if (this.padding !== "same" && this.padding !== "valid") {
+      throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
+    }
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length !== 4) {
+      throw new ValueError("Input should have rank 4; Received input shape: " + JSON.stringify(inputShape));
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
+    }
+    const inputDim = inputShape[channelAxis];
+    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
+    this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      let input2 = getExactlyOneTensor(inputs);
+      if (input2.shape.length !== 4) {
+        throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
+      }
+      const inputShape = input2.shape;
+      const batchSize = inputShape[0];
+      let hAxis;
+      let wAxis;
+      if (this.dataFormat === "channelsFirst") {
+        hAxis = 2;
+        wAxis = 3;
+      } else {
+        hAxis = 1;
+        wAxis = 2;
+      }
+      const height = inputShape[hAxis];
+      const width = inputShape[wAxis];
+      const kernelH = this.kernelSize[0];
+      const kernelW = this.kernelSize[1];
+      const strideH = this.strides[0];
+      const strideW = this.strides[1];
+      const outHeight = deconvLength(height, strideH, kernelH, this.padding);
+      const outWidth = deconvLength(width, strideW, kernelW, this.padding);
+      const outputShape = [batchSize, outHeight, outWidth, this.filters];
+      if (this.dataFormat !== "channelsLast") {
+        input2 = transpose(input2, [0, 2, 3, 1]);
+      }
+      let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
+      if (this.dataFormat !== "channelsLast") {
+        outputs = transpose(outputs, [0, 3, 1, 2]);
+      }
+      if (this.bias != null) {
+        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation != null) {
+        outputs = this.activation.apply(outputs);
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    let channelAxis;
+    let heightAxis;
+    let widthAxis;
+    if (this.dataFormat === "channelsFirst") {
+      channelAxis = 1;
+      heightAxis = 2;
+      widthAxis = 3;
+    } else {
+      channelAxis = 3;
+      heightAxis = 1;
+      widthAxis = 2;
+    }
+    const kernelH = this.kernelSize[0];
+    const kernelW = this.kernelSize[1];
+    const strideH = this.strides[0];
+    const strideW = this.strides[1];
+    outputShape[channelAxis] = this.filters;
+    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
+    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
+    return outputShape;
+  }
+  getConfig() {
+    const config = super.getConfig();
+    delete config["dilationRate"];
+    return config;
+  }
+};
+Conv2DTranspose.className = "Conv2DTranspose";
+serialization_exports.registerClass(Conv2DTranspose);
+var Conv3DTranspose = class extends Conv3D2 {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 5 })];
+    if (this.padding !== "same" && this.padding !== "valid") {
+      throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
+    }
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length !== 5) {
+      throw new ValueError("Input should have rank 5; Received input shape: " + JSON.stringify(inputShape));
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
+    }
+    const inputDim = inputShape[channelAxis];
+    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
+    this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      let input2 = getExactlyOneTensor(inputs);
+      if (input2.shape.length !== 5) {
+        throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
+      }
+      const inputShape = input2.shape;
+      const batchSize = inputShape[0];
+      let hAxis;
+      let wAxis;
+      let dAxis;
+      if (this.dataFormat === "channelsFirst") {
+        dAxis = 2;
+        hAxis = 3;
+        wAxis = 4;
+      } else {
+        dAxis = 1;
+        hAxis = 2;
+        wAxis = 3;
+      }
+      const depth = inputShape[dAxis];
+      const height = inputShape[hAxis];
+      const width = inputShape[wAxis];
+      const kernelD = this.kernelSize[0];
+      const kernelH = this.kernelSize[1];
+      const kernelW = this.kernelSize[2];
+      const strideD = this.strides[0];
+      const strideH = this.strides[1];
+      const strideW = this.strides[2];
+      const outDepth = deconvLength(depth, strideD, kernelD, this.padding);
+      const outHeight = deconvLength(height, strideH, kernelH, this.padding);
+      const outWidth = deconvLength(width, strideW, kernelW, this.padding);
+      const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];
+      if (this.dataFormat !== "channelsLast") {
+        input2 = transpose(input2, [0, 2, 3, 4, 1]);
+      }
+      let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
+      if (this.dataFormat !== "channelsLast") {
+        outputs = transpose(outputs, [0, 4, 1, 2, 3]);
+      }
+      if (this.bias !== null) {
+        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation !== null) {
+        outputs = this.activation.apply(outputs);
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    let channelAxis;
+    let depthAxis;
+    let heightAxis;
+    let widthAxis;
+    if (this.dataFormat === "channelsFirst") {
+      channelAxis = 1;
+      depthAxis = 2;
+      heightAxis = 3;
+      widthAxis = 4;
+    } else {
+      channelAxis = 4;
+      depthAxis = 1;
+      heightAxis = 2;
+      widthAxis = 3;
+    }
+    const kernelD = this.kernelSize[0];
+    const kernelH = this.kernelSize[1];
+    const kernelW = this.kernelSize[2];
+    const strideD = this.strides[0];
+    const strideH = this.strides[1];
+    const strideW = this.strides[2];
+    outputShape[channelAxis] = this.filters;
+    outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);
+    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
+    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
+    return outputShape;
+  }
+  getConfig() {
+    const config = super.getConfig();
+    delete config["dilationRate"];
+    return config;
+  }
+};
+Conv3DTranspose.className = "Conv3DTranspose";
+serialization_exports.registerClass(Conv3DTranspose);
+var SeparableConv = class extends Conv {
+  constructor(rank, config) {
+    super(rank, config);
+    this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform";
+    this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform";
+    this.depthwiseKernel = null;
+    this.pointwiseKernel = null;
+    if (config.filters == null) {
+      throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");
+    }
+    if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {
+      throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
+    }
+    if (config.padding != null && config.padding !== "same" && config.padding !== "valid") {
+      throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);
+    }
+    this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;
+    this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);
+    this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);
+    this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);
+    this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);
+    this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);
+    this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length < this.rank + 2) {
+      throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
+      throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);
+    const pointwiseKernelShape = [];
+    for (let i2 = 0; i2 < this.rank; ++i2) {
+      pointwiseKernelShape.push(1);
+    }
+    pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
+    const trainable = true;
+    this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);
+    this.pointwiseKernel = this.addWeight("pointwise_kernel", pointwiseKernelShape, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      let output;
+      if (this.rank === 1) {
+        throw new NotImplementedError("1D separable convolution is not implemented yet.");
+      } else if (this.rank === 2) {
+        if (this.dataFormat === "channelsFirst") {
+          inputs = transpose(inputs, [0, 2, 3, 1]);
+        }
+        output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC");
+      }
+      if (this.useBias) {
+        output = biasAdd(output, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation != null) {
+        output = this.activation.apply(output);
+      }
+      if (this.dataFormat === "channelsFirst") {
+        output = transpose(output, [0, 3, 1, 2]);
+      }
+      return output;
+    });
+  }
+  getConfig() {
+    const config = super.getConfig();
+    delete config["rank"];
+    delete config["kernelInitializer"];
+    delete config["kernelRegularizer"];
+    delete config["kernelConstraint"];
+    config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
+    config["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer);
+    config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
+    config["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer);
+    config["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint);
+    config["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint);
+    return config;
+  }
+};
+SeparableConv.className = "SeparableConv";
+var SeparableConv2D = class extends SeparableConv {
+  constructor(args) {
+    super(2, args);
+  }
+};
+SeparableConv2D.className = "SeparableConv2D";
+serialization_exports.registerClass(SeparableConv2D);
+var Conv1D = class extends Conv {
+  constructor(args) {
+    super(1, args);
+    Conv1D.verifyArgs(args);
+    this.inputSpec = [{ ndim: 3 }];
+  }
+  getConfig() {
+    const config = super.getConfig();
+    delete config["rank"];
+    delete config["dataFormat"];
+    return config;
+  }
+  static verifyArgs(args) {
+    if (typeof args.kernelSize !== "number" && !checkArrayTypeAndLength(args.kernelSize, "number", 1, 1)) {
+      throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);
+    }
+  }
+};
+Conv1D.className = "Conv1D";
+serialization_exports.registerClass(Conv1D);
+var Cropping2D = class extends Layer {
+  constructor(args) {
+    super(args);
+    if (typeof args.cropping === "number") {
+      this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];
+    } else if (typeof args.cropping[0] === "number") {
+      this.cropping = [
+        [args.cropping[0], args.cropping[0]],
+        [args.cropping[1], args.cropping[1]]
+      ];
+    } else {
+      this.cropping = args.cropping;
+    }
+    this.dataFormat = args.dataFormat === void 0 ? "channelsLast" : args.dataFormat;
+    this.inputSpec = [{ ndim: 4 }];
+  }
+  computeOutputShape(inputShape) {
+    if (this.dataFormat === "channelsFirst") {
+      return [
+        inputShape[0],
+        inputShape[1],
+        inputShape[2] - this.cropping[0][0] - this.cropping[0][1],
+        inputShape[3] - this.cropping[1][0] - this.cropping[1][1]
+      ];
+    } else {
+      return [
+        inputShape[0],
+        inputShape[1] - this.cropping[0][0] - this.cropping[0][1],
+        inputShape[2] - this.cropping[1][0] - this.cropping[1][1],
+        inputShape[3]
+      ];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsLast") {
+        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
+        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
+      } else {
+        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
+        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
+      }
+    });
+  }
+  getConfig() {
+    const config = { cropping: this.cropping, dataFormat: this.dataFormat };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Cropping2D.className = "Cropping2D";
+serialization_exports.registerClass(Cropping2D);
+var UpSampling2D = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_SIZE = [2, 2];
+    this.inputSpec = [{ ndim: 4 }];
+    this.size = args.size == null ? this.DEFAULT_SIZE : args.size;
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    this.interpolation = args.interpolation == null ? "nearest" : args.interpolation;
+    checkInterpolationFormat(this.interpolation);
+  }
+  computeOutputShape(inputShape) {
+    if (this.dataFormat === "channelsFirst") {
+      const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];
+      const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];
+      return [inputShape[0], inputShape[1], height, width];
+    } else {
+      const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];
+      const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];
+      return [inputShape[0], height, width, inputShape[3]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      let input2 = getExactlyOneTensor(inputs);
+      const inputShape = input2.shape;
+      if (this.dataFormat === "channelsFirst") {
+        input2 = transpose(input2, [0, 2, 3, 1]);
+        const height = this.size[0] * inputShape[2];
+        const width = this.size[1] * inputShape[3];
+        const resized = this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);
+        return transpose(resized, [0, 3, 1, 2]);
+      } else {
+        const height = this.size[0] * inputShape[1];
+        const width = this.size[1] * inputShape[2];
+        return this.interpolation === "nearest" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);
+      }
+    });
+  }
+  getConfig() {
+    const config = {
+      size: this.size,
+      dataFormat: this.dataFormat,
+      interpolation: this.interpolation
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+UpSampling2D.className = "UpSampling2D";
+serialization_exports.registerClass(UpSampling2D);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js
+function depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = "valid", dataFormat, dilationRate) {
+  return tidy(() => {
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    checkDataFormat(dataFormat);
+    let y = preprocessConv2DInput(x, dataFormat);
+    if (x.rank !== 4) {
+      throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);
+    }
+    if (depthwiseKernel.rank !== 4) {
+      throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);
+    }
+    y = depthwiseConv2d(y, depthwiseKernel, strides, padding === "same" ? "same" : "valid", "NHWC", dilationRate);
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 3, 1, 2]);
+    }
+    return y;
+  });
+}
+var DepthwiseConv2D = class extends BaseConv {
+  constructor(args) {
+    super(2, args);
+    this.depthwiseKernel = null;
+    this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;
+    this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);
+    this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length < 4) {
+      throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);
+    }
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : 3;
+    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {
+      throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const depthwiseKernelShape = [
+      this.kernelSize[0],
+      this.kernelSize[1],
+      inputDim,
+      this.depthMultiplier
+    ];
+    this.depthwiseKernel = this.addWeight("depthwise_kernel", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
+      if (this.useBias) {
+        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);
+      }
+      if (this.activation != null) {
+        outputs = this.activation.apply(outputs);
+      }
+      return outputs;
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
+    const cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
+    const outFilters = this.dataFormat === "channelsFirst" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;
+    const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);
+    const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);
+    if (this.dataFormat === "channelsFirst") {
+      return [inputShape[0], outFilters, outRows, outCols];
+    } else {
+      return [inputShape[0], outRows, outCols, outFilters];
+    }
+  }
+  getConfig() {
+    const config = super.getConfig();
+    config["depthMultiplier"] = this.depthMultiplier;
+    config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
+    config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
+    config["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer);
+    return config;
+  }
+};
+DepthwiseConv2D.className = "DepthwiseConv2D";
+serialization_exports.registerClass(DepthwiseConv2D);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js
+function standardizeArgs(inputs, initialState, constants, numConstants) {
+  if (Array.isArray(inputs)) {
+    if (initialState != null || constants != null) {
+      throw new ValueError("When inputs is an array, neither initialState or constants should be provided");
+    }
+    if (numConstants != null) {
+      constants = inputs.slice(inputs.length - numConstants, inputs.length);
+      inputs = inputs.slice(0, inputs.length - numConstants);
+    }
+    if (inputs.length > 1) {
+      initialState = inputs.slice(1, inputs.length);
+    }
+    inputs = inputs[0];
+  }
+  function toListOrNull(x) {
+    if (x == null || Array.isArray(x)) {
+      return x;
+    } else {
+      return [x];
+    }
+  }
+  initialState = toListOrNull(initialState);
+  constants = toListOrNull(constants);
+  return { inputs, initialState, constants };
+}
+function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {
+  return tidy(() => {
+    const ndim = inputs.shape.length;
+    if (ndim < 3) {
+      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);
+    }
+    const axes = [1, 0].concat(range2(2, ndim));
+    inputs = transpose(inputs, axes);
+    if (constants != null) {
+      throw new NotImplementedError("The rnn() functoin of the deeplearn.js backend does not support constants yet.");
+    }
+    if (unroll) {
+      console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.");
+    }
+    if (mask != null) {
+      mask = cast(cast(mask, "bool"), "float32");
+      if (mask.rank === ndim - 1) {
+        mask = expandDims(mask, -1);
+      }
+      mask = transpose(mask, axes);
+    }
+    if (goBackwards) {
+      inputs = reverse(inputs, 0);
+      if (mask != null) {
+        mask = reverse(mask, 0);
+      }
+    }
+    const perStepOutputs = [];
+    let lastOutput;
+    let states = initialStates;
+    const timeSteps = inputs.shape[0];
+    const perStepInputs = unstack(inputs);
+    let perStepMasks;
+    if (mask != null) {
+      perStepMasks = unstack(mask);
+    }
+    for (let t2 = 0; t2 < timeSteps; ++t2) {
+      const currentInput = perStepInputs[t2];
+      const stepOutputs = tidy(() => stepFunction(currentInput, states));
+      if (mask == null) {
+        lastOutput = stepOutputs[0];
+        states = stepOutputs[1];
+      } else {
+        const maskedOutputs = tidy(() => {
+          const stepMask = perStepMasks[t2];
+          const negStepMask = sub(onesLike(stepMask), stepMask);
+          const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));
+          const newStates = states.map((state, i2) => {
+            return add2(mul(stepOutputs[1][i2], stepMask), mul(state, negStepMask));
+          });
+          return { output, newStates };
+        });
+        lastOutput = maskedOutputs.output;
+        states = maskedOutputs.newStates;
+      }
+      if (needPerStepOutputs) {
+        perStepOutputs.push(lastOutput);
+      }
+    }
+    let outputs;
+    if (needPerStepOutputs) {
+      const axis = 1;
+      outputs = stack(perStepOutputs, axis);
+    }
+    return [lastOutput, outputs, states];
+  });
+}
+var RNN = class extends Layer {
+  constructor(args) {
+    super(args);
+    let cell;
+    if (args.cell == null) {
+      throw new ValueError("cell property is missing for the constructor of RNN.");
+    } else if (Array.isArray(args.cell)) {
+      cell = new StackedRNNCells({ cells: args.cell });
+    } else {
+      cell = args.cell;
+    }
+    if (cell.stateSize == null) {
+      throw new ValueError("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
+    }
+    this.cell = cell;
+    this.returnSequences = args.returnSequences == null ? false : args.returnSequences;
+    this.returnState = args.returnState == null ? false : args.returnState;
+    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;
+    this._stateful = args.stateful == null ? false : args.stateful;
+    this.unroll = args.unroll == null ? false : args.unroll;
+    this.supportsMasking = true;
+    this.inputSpec = [new InputSpec({ ndim: 3 })];
+    this.stateSpec = null;
+    this.states_ = null;
+    this.numConstants = null;
+    this.keptStates = [];
+  }
+  getStates() {
+    if (this.states_ == null) {
+      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
+      return range2(0, numStates).map((x) => null);
+    } else {
+      return this.states_;
+    }
+  }
+  setStates(states) {
+    this.states_ = states;
+  }
+  computeOutputShape(inputShape) {
+    if (isArrayOfShapes(inputShape)) {
+      inputShape = inputShape[0];
+    }
+    inputShape = inputShape;
+    let stateSize = this.cell.stateSize;
+    if (!Array.isArray(stateSize)) {
+      stateSize = [stateSize];
+    }
+    const outputDim = stateSize[0];
+    let outputShape;
+    if (this.returnSequences) {
+      outputShape = [inputShape[0], inputShape[1], outputDim];
+    } else {
+      outputShape = [inputShape[0], outputDim];
+    }
+    if (this.returnState) {
+      const stateShape = [];
+      for (const dim of stateSize) {
+        stateShape.push([inputShape[0], dim]);
+      }
+      return [outputShape].concat(stateShape);
+    } else {
+      return outputShape;
+    }
+  }
+  computeMask(inputs, mask) {
+    return tidy(() => {
+      if (Array.isArray(mask)) {
+        mask = mask[0];
+      }
+      const outputMask = this.returnSequences ? mask : null;
+      if (this.returnState) {
+        const stateMask = this.states.map((s2) => null);
+        return [outputMask].concat(stateMask);
+      } else {
+        return outputMask;
+      }
+    });
+  }
+  get states() {
+    if (this.states_ == null) {
+      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
+      const output = [];
+      for (let i2 = 0; i2 < numStates; ++i2) {
+        output.push(null);
+      }
+      return output;
+    } else {
+      return this.states_;
+    }
+  }
+  set states(s2) {
+    this.states_ = s2;
+  }
+  build(inputShape) {
+    const constantShape = null;
+    if (this.numConstants != null) {
+      throw new NotImplementedError("Constants support is not implemented in RNN yet.");
+    }
+    if (isArrayOfShapes(inputShape)) {
+      inputShape = inputShape[0];
+    }
+    inputShape = inputShape;
+    const batchSize = this.stateful ? inputShape[0] : null;
+    const inputDim = inputShape.slice(2);
+    this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });
+    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));
+    if (constantShape != null) {
+      throw new NotImplementedError("Constants support is not implemented in RNN yet.");
+    } else {
+      this.cell.build(stepInputShape);
+    }
+    let stateSize;
+    if (Array.isArray(this.cell.stateSize)) {
+      stateSize = this.cell.stateSize;
+    } else {
+      stateSize = [this.cell.stateSize];
+    }
+    if (this.stateSpec != null) {
+      if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {
+        throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
+      }
+    } else {
+      this.stateSpec = stateSize.map((dim) => new InputSpec({ shape: [null, dim] }));
+    }
+    if (this.stateful) {
+      this.resetStates();
+    }
+  }
+  resetStates(states, training = false) {
+    tidy(() => {
+      if (!this.stateful) {
+        throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
+      }
+      const batchSize = this.inputSpec[0].shape[0];
+      if (batchSize == null) {
+        throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
+      }
+      if (this.states_ == null) {
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
+        } else {
+          this.states_ = [zeros([batchSize, this.cell.stateSize])];
+        }
+      } else if (states == null) {
+        dispose(this.states_);
+        if (this.keptStates != null) {
+          dispose(this.keptStates);
+          this.keptStates = [];
+        }
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));
+        } else {
+          this.states_[0] = zeros([batchSize, this.cell.stateSize]);
+        }
+      } else {
+        if (!Array.isArray(states)) {
+          states = [states];
+        }
+        if (states.length !== this.states_.length) {
+          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
+        }
+        if (training === true) {
+          this.keptStates.push(this.states_.slice());
+        } else {
+          dispose(this.states_);
+        }
+        for (let index = 0; index < this.states_.length; ++index) {
+          const value = states[index];
+          const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;
+          const expectedShape = [batchSize, dim];
+          if (!util_exports.arraysEqual(value.shape, expectedShape)) {
+            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
+          }
+          this.states_[index] = value;
+        }
+      }
+      this.states_ = this.states_.map((state) => keep(state.clone()));
+    });
+  }
+  apply(inputs, kwargs) {
+    let initialState = kwargs == null ? null : kwargs["initialState"];
+    let constants = kwargs == null ? null : kwargs["constants"];
+    if (kwargs == null) {
+      kwargs = {};
+    }
+    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
+    inputs = standardized.inputs;
+    initialState = standardized.initialState;
+    constants = standardized.constants;
+    let additionalInputs = [];
+    let additionalSpecs = [];
+    if (initialState != null) {
+      kwargs["initialState"] = initialState;
+      additionalInputs = additionalInputs.concat(initialState);
+      this.stateSpec = [];
+      for (const state of initialState) {
+        this.stateSpec.push(new InputSpec({ shape: state.shape }));
+      }
+      additionalSpecs = additionalSpecs.concat(this.stateSpec);
+    }
+    if (constants != null) {
+      kwargs["constants"] = constants;
+      additionalInputs = additionalInputs.concat(constants);
+      this.numConstants = constants.length;
+    }
+    const isTensor = additionalInputs[0] instanceof SymbolicTensor;
+    if (isTensor) {
+      const fullInput = [inputs].concat(additionalInputs);
+      const fullInputSpec = this.inputSpec.concat(additionalSpecs);
+      const originalInputSpec = this.inputSpec;
+      this.inputSpec = fullInputSpec;
+      const output = super.apply(fullInput, kwargs);
+      this.inputSpec = originalInputSpec;
+      return output;
+    } else {
+      return super.apply(inputs, kwargs);
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const mask = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      let initialState = kwargs == null ? null : kwargs["initialState"];
+      inputs = getExactlyOneTensor(inputs);
+      if (initialState == null) {
+        if (this.stateful) {
+          initialState = this.states_;
+        } else {
+          initialState = this.getInitialState(inputs);
+        }
+      }
+      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
+      if (initialState.length !== numStates) {
+        throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);
+      }
+      if (this.unroll) {
+        console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");
+      }
+      const cellCallKwargs = { training };
+      const step5 = (inputs2, states2) => {
+        const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);
+        return [outputs2[0], outputs2.slice(1)];
+      };
+      const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);
+      const lastOutput = rnnOutputs[0];
+      const outputs = rnnOutputs[1];
+      const states = rnnOutputs[2];
+      if (this.stateful) {
+        this.resetStates(states, training);
+      }
+      const output = this.returnSequences ? outputs : lastOutput;
+      if (this.returnState) {
+        return [output].concat(states);
+      } else {
+        return output;
+      }
+    });
+  }
+  getInitialState(inputs) {
+    return tidy(() => {
+      let initialState = zeros(inputs.shape);
+      initialState = sum2(initialState, [1, 2]);
+      initialState = expandDims2(initialState);
+      if (Array.isArray(this.cell.stateSize)) {
+        return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);
+      } else {
+        return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];
+      }
+    });
+  }
+  get trainableWeights() {
+    if (!this.trainable) {
+      return [];
+    }
+    return this.cell.trainableWeights;
+  }
+  get nonTrainableWeights() {
+    if (!this.trainable) {
+      return this.cell.weights;
+    }
+    return this.cell.nonTrainableWeights;
+  }
+  setFastWeightInitDuringBuild(value) {
+    super.setFastWeightInitDuringBuild(value);
+    if (this.cell != null) {
+      this.cell.setFastWeightInitDuringBuild(value);
+    }
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = {
+      returnSequences: this.returnSequences,
+      returnState: this.returnState,
+      goBackwards: this.goBackwards,
+      stateful: this.stateful,
+      unroll: this.unroll
+    };
+    if (this.numConstants != null) {
+      config["numConstants"] = this.numConstants;
+    }
+    const cellConfig = this.cell.getConfig();
+    if (this.getClassName() === RNN.className) {
+      config["cell"] = {
+        "className": this.cell.getClassName(),
+        "config": cellConfig
+      };
+    }
+    return Object.assign({}, cellConfig, baseConfig, config);
+  }
+  static fromConfig(cls, config, customObjects = {}) {
+    const cellConfig = config["cell"];
+    const cell = deserialize(cellConfig, customObjects);
+    return new cls(Object.assign(config, { cell }));
+  }
+};
+RNN.className = "RNN";
+serialization_exports.registerClass(RNN);
+var RNNCell = class extends Layer {
+};
+var SimpleRNNCell = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_ACTIVATION = "tanh";
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    this.units = args.units;
+    assertPositiveInteger(this.units, `units`);
+    this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
+    this.recurrentDropout = min2([
+      1,
+      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
+    ]);
+    this.dropoutFunc = args.dropoutFunc;
+    this.stateSize = this.units;
+    this.dropoutMask = null;
+    this.recurrentDropoutMask = null;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    this.kernel = this.addWeight("kernel", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      if (inputs.length !== 2) {
+        throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);
+      }
+      let prevOutput = inputs[1];
+      inputs = inputs[0];
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(inputs),
+          rate: this.dropout,
+          training,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(prevOutput),
+          rate: this.recurrentDropout,
+          training,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      let h;
+      const dpMask = this.dropoutMask;
+      const recDpMask = this.recurrentDropoutMask;
+      if (dpMask != null) {
+        h = dot2(mul(inputs, dpMask), this.kernel.read());
+      } else {
+        h = dot2(inputs, this.kernel.read());
+      }
+      if (this.bias != null) {
+        h = biasAdd(h, this.bias.read());
+      }
+      if (recDpMask != null) {
+        prevOutput = mul(prevOutput, recDpMask);
+      }
+      let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));
+      if (this.activation != null) {
+        output = this.activation.apply(output);
+      }
+      return [output, output];
+    });
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint),
+      dropout: this.dropout,
+      recurrentDropout: this.recurrentDropout
+    };
+    return Object.assign({}, baseConfig, config);
+  }
+};
+SimpleRNNCell.className = "SimpleRNNCell";
+serialization_exports.registerClass(SimpleRNNCell);
+var SimpleRNN = class extends RNN {
+  constructor(args) {
+    args.cell = new SimpleRNNCell(args);
+    super(args);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      const mask = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask, training, initialState });
+    });
+  }
+  static fromConfig(cls, config) {
+    return new cls(config);
+  }
+};
+SimpleRNN.className = "SimpleRNN";
+serialization_exports.registerClass(SimpleRNN);
+var GRUCell = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_ACTIVATION = "tanh";
+    this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    if (args.resetAfter) {
+      throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);
+    }
+    this.units = args.units;
+    assertPositiveInteger(this.units, "units");
+    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
+    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
+    this.recurrentDropout = min2([
+      1,
+      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
+    ]);
+    this.dropoutFunc = args.dropoutFunc;
+    this.implementation = args.implementation;
+    this.stateSize = this.units;
+    this.dropoutMask = null;
+    this.recurrentDropoutMask = null;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const inputDim = inputShape[inputShape.length - 1];
+    this.kernel = this.addWeight("kernel", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    if (this.useBias) {
+      this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      if (inputs.length !== 2) {
+        throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);
+      }
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      let hTMinus1 = inputs[1];
+      inputs = inputs[0];
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(inputs),
+          rate: this.dropout,
+          training,
+          count: 3,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(hTMinus1),
+          rate: this.recurrentDropout,
+          training,
+          count: 3,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const dpMask = this.dropoutMask;
+      const recDpMask = this.recurrentDropoutMask;
+      let z;
+      let r2;
+      let hh;
+      if (0 < this.dropout && this.dropout < 1) {
+        inputs = mul(inputs, dpMask[0]);
+      }
+      let matrixX = dot2(inputs, this.kernel.read());
+      if (this.useBias) {
+        matrixX = biasAdd(matrixX, this.bias.read());
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
+        hTMinus1 = mul(hTMinus1, recDpMask[0]);
+      }
+      const recurrentKernelValue = this.recurrentKernel.read();
+      const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);
+      const matrixInner = dot2(hTMinus1, rk1);
+      const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);
+      const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);
+      z = this.recurrentActivation.apply(add2(xZ, recurrentZ));
+      r2 = this.recurrentActivation.apply(add2(xR, recurrentR));
+      const recurrentH = dot2(mul(r2, hTMinus1), rk2);
+      hh = this.activation.apply(add2(xH, recurrentH));
+      const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));
+      return [h, h];
+    });
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      recurrentActivation: serializeActivation(this.recurrentActivation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint),
+      dropout: this.dropout,
+      recurrentDropout: this.recurrentDropout,
+      implementation: this.implementation,
+      resetAfter: false
+    };
+    return Object.assign({}, baseConfig, config);
+  }
+};
+GRUCell.className = "GRUCell";
+serialization_exports.registerClass(GRUCell);
+var GRU = class extends RNN {
+  constructor(args) {
+    if (args.implementation === 0) {
+      console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
+    }
+    args.cell = new GRUCell(args);
+    super(args);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      const mask = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask, training, initialState });
+    });
+  }
+  static fromConfig(cls, config) {
+    if (config["implmentation"] === 0) {
+      config["implementation"] = 1;
+    }
+    return new cls(config);
+  }
+};
+GRU.className = "GRU";
+serialization_exports.registerClass(GRU);
+var LSTMCell = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_ACTIVATION = "tanh";
+    this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid";
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    this.units = args.units;
+    assertPositiveInteger(this.units, "units");
+    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);
+    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);
+    this.useBias = args.useBias == null ? true : args.useBias;
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.unitForgetBias = args.unitForgetBias;
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.recurrentConstraint = getConstraint(args.recurrentConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
+    this.recurrentDropout = min2([
+      1,
+      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
+    ]);
+    this.dropoutFunc = args.dropoutFunc;
+    this.implementation = args.implementation;
+    this.stateSize = [this.units, this.units];
+    this.dropoutMask = null;
+    this.recurrentDropoutMask = null;
+  }
+  build(inputShape) {
+    var _a;
+    inputShape = getExactlyOneShape(inputShape);
+    const inputDim = inputShape[inputShape.length - 1];
+    this.kernel = this.addWeight("kernel", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    let biasInitializer;
+    if (this.useBias) {
+      if (this.unitForgetBias) {
+        const capturedBiasInit = this.biasInitializer;
+        const capturedUnits = this.units;
+        biasInitializer = new (_a = class CustomInit extends Initializer {
+          apply(shape, dtype) {
+            const bI = capturedBiasInit.apply([capturedUnits]);
+            const bF = new Ones().apply([capturedUnits]);
+            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);
+            return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);
+          }
+        }, _a.className = "CustomInit", _a)();
+      } else {
+        biasInitializer = this.biasInitializer;
+      }
+      this.bias = this.addWeight("bias", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    } else {
+      this.bias = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      inputs = inputs;
+      if (inputs.length !== 3) {
+        throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
+      }
+      let hTMinus1 = inputs[1];
+      const cTMinus1 = inputs[2];
+      inputs = inputs[0];
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(inputs),
+          rate: this.dropout,
+          training,
+          count: 4,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(hTMinus1),
+          rate: this.recurrentDropout,
+          training,
+          count: 4,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const dpMask = this.dropoutMask;
+      const recDpMask = this.recurrentDropoutMask;
+      let i2;
+      let f;
+      let c;
+      let o;
+      if (0 < this.dropout && this.dropout < 1) {
+        inputs = mul(inputs, dpMask[0]);
+      }
+      let z = dot2(inputs, this.kernel.read());
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
+        hTMinus1 = mul(hTMinus1, recDpMask[0]);
+      }
+      z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));
+      if (this.useBias) {
+        z = biasAdd(z, this.bias.read());
+      }
+      const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);
+      i2 = this.recurrentActivation.apply(z0);
+      f = this.recurrentActivation.apply(z1);
+      c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(z2)));
+      o = this.recurrentActivation.apply(z3);
+      const h = mul(o, this.activation.apply(c));
+      return [h, h, c];
+    });
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      recurrentActivation: serializeActivation(this.recurrentActivation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      recurrentInitializer: serializeInitializer(this.recurrentInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      unitForgetBias: this.unitForgetBias,
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      recurrentConstraint: serializeConstraint(this.recurrentConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint),
+      dropout: this.dropout,
+      recurrentDropout: this.recurrentDropout,
+      implementation: this.implementation
+    };
+    return Object.assign({}, baseConfig, config);
+  }
+};
+LSTMCell.className = "LSTMCell";
+serialization_exports.registerClass(LSTMCell);
+var LSTM = class extends RNN {
+  constructor(args) {
+    if (args.implementation === 0) {
+      console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.");
+    }
+    args.cell = new LSTMCell(args);
+    super(args);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      const mask = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask, training, initialState });
+    });
+  }
+  static fromConfig(cls, config) {
+    if (config["implmentation"] === 0) {
+      config["implementation"] = 1;
+    }
+    return new cls(config);
+  }
+};
+LSTM.className = "LSTM";
+serialization_exports.registerClass(LSTM);
+var StackedRNNCells = class extends RNNCell {
+  constructor(args) {
+    super(args);
+    this.cells = args.cells;
+  }
+  get stateSize() {
+    const stateSize = [];
+    for (const cell of this.cells.slice().reverse()) {
+      if (Array.isArray(cell.stateSize)) {
+        stateSize.push(...cell.stateSize);
+      } else {
+        stateSize.push(cell.stateSize);
+      }
+    }
+    return stateSize;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      let states = inputs.slice(1);
+      const nestedStates = [];
+      for (const cell of this.cells.slice().reverse()) {
+        if (Array.isArray(cell.stateSize)) {
+          nestedStates.push(states.splice(0, cell.stateSize.length));
+        } else {
+          nestedStates.push(states.splice(0, 1));
+        }
+      }
+      nestedStates.reverse();
+      const newNestedStates = [];
+      let callInputs;
+      for (let i2 = 0; i2 < this.cells.length; ++i2) {
+        const cell = this.cells[i2];
+        states = nestedStates[i2];
+        if (i2 === 0) {
+          callInputs = [inputs[0]].concat(states);
+        } else {
+          callInputs = [callInputs[0]].concat(states);
+        }
+        callInputs = cell.call(callInputs, kwargs);
+        newNestedStates.push(callInputs.slice(1));
+      }
+      states = [];
+      for (const cellStates of newNestedStates.slice().reverse()) {
+        states.push(...cellStates);
+      }
+      return [callInputs[0]].concat(states);
+    });
+  }
+  build(inputShape) {
+    if (isArrayOfShapes(inputShape)) {
+      inputShape = inputShape[0];
+    }
+    inputShape = inputShape;
+    let outputDim;
+    this.cells.forEach((cell, i2) => {
+      nameScope(`RNNCell_${i2}`, () => {
+        cell.build(inputShape);
+        if (Array.isArray(cell.stateSize)) {
+          outputDim = cell.stateSize[0];
+        } else {
+          outputDim = cell.stateSize;
+        }
+        inputShape = [inputShape[0], outputDim];
+      });
+    });
+    this.built = true;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const getCellConfig = (cell) => {
+      return {
+        "className": cell.getClassName(),
+        "config": cell.getConfig()
+      };
+    };
+    const cellConfigs = this.cells.map(getCellConfig);
+    const config = { "cells": cellConfigs };
+    return Object.assign({}, baseConfig, config);
+  }
+  static fromConfig(cls, config, customObjects = {}) {
+    const cells = [];
+    for (const cellConfig of config["cells"]) {
+      cells.push(deserialize(cellConfig, customObjects));
+    }
+    return new cls({ cells });
+  }
+  get trainableWeights() {
+    if (!this.trainable) {
+      return [];
+    }
+    const weights = [];
+    for (const cell of this.cells) {
+      weights.push(...cell.trainableWeights);
+    }
+    return weights;
+  }
+  get nonTrainableWeights() {
+    const weights = [];
+    for (const cell of this.cells) {
+      weights.push(...cell.nonTrainableWeights);
+    }
+    if (!this.trainable) {
+      const trainableWeights = [];
+      for (const cell of this.cells) {
+        trainableWeights.push(...cell.trainableWeights);
+      }
+      return trainableWeights.concat(weights);
+    }
+    return weights;
+  }
+  getWeights() {
+    const weights = [];
+    for (const cell of this.cells) {
+      weights.push(...cell.weights);
+    }
+    return batchGetValue(weights);
+  }
+  setWeights(weights) {
+    const tuples = [];
+    for (const cell of this.cells) {
+      const numParams = cell.weights.length;
+      const inputWeights = weights.splice(numParams);
+      for (let i2 = 0; i2 < cell.weights.length; ++i2) {
+        tuples.push([cell.weights[i2], inputWeights[i2]]);
+      }
+    }
+    batchSetValue(tuples);
+  }
+};
+StackedRNNCells.className = "StackedRNNCells";
+serialization_exports.registerClass(StackedRNNCells);
+function generateDropoutMask(args) {
+  const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args;
+  const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate);
+  const createMask = () => inTrainPhase(droppedInputs, ones4, training);
+  if (!count2 || count2 <= 1) {
+    return keep(createMask().clone());
+  }
+  const masks = Array(count2).fill(void 0).map(createMask);
+  return masks.map((m) => keep(m.clone()));
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
+var __rest = function(s2, e2) {
+  var t2 = {};
+  for (var p2 in s2)
+    if (Object.prototype.hasOwnProperty.call(s2, p2) && e2.indexOf(p2) < 0)
+      t2[p2] = s2[p2];
+  if (s2 != null && typeof Object.getOwnPropertySymbols === "function")
+    for (var i2 = 0, p2 = Object.getOwnPropertySymbols(s2); i2 < p2.length; i2++) {
+      if (e2.indexOf(p2[i2]) < 0 && Object.prototype.propertyIsEnumerable.call(s2, p2[i2]))
+        t2[p2[i2]] = s2[p2[i2]];
+    }
+  return t2;
+};
+var ConvRNN2D = class extends RNN {
+  constructor(args) {
+    if (args.unroll) {
+      throw new NotImplementedError("Unrolling is not possible with convolutional RNNs.");
+    }
+    if (Array.isArray(args.cell)) {
+      throw new NotImplementedError("It is not possible at the moment to stack convolutional cells.");
+    }
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 5 })];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.cell.dropoutMask != null) {
+        dispose(this.cell.dropoutMask);
+        this.cell.dropoutMask = null;
+      }
+      if (this.cell.recurrentDropoutMask != null) {
+        dispose(this.cell.recurrentDropoutMask);
+        this.cell.recurrentDropoutMask = null;
+      }
+      if (kwargs && kwargs["constants"]) {
+        throw new ValueError("ConvRNN2D cell does not support constants");
+      }
+      const mask = kwargs == null ? null : kwargs["mask"];
+      const training = kwargs == null ? null : kwargs["training"];
+      const initialState = kwargs == null ? null : kwargs["initialState"];
+      return super.call(inputs, { mask, training, initialState });
+    });
+  }
+  computeOutputShape(inputShape) {
+    let outShape = this.computeSingleOutputShape(inputShape);
+    if (!this.returnSequences) {
+      outShape = [outShape[0], ...outShape.slice(2)];
+    }
+    if (this.returnState) {
+      outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];
+    }
+    return outShape;
+  }
+  getInitialState(inputs) {
+    return tidy(() => {
+      const { stateSize } = this.cell;
+      const inputShape = inputs.shape;
+      const outputShape = this.computeSingleOutputShape(inputShape);
+      const stateShape = [outputShape[0], ...outputShape.slice(2)];
+      const initialState = zeros(stateShape);
+      if (Array.isArray(stateSize)) {
+        return Array(stateSize.length).fill(initialState);
+      }
+      return [initialState];
+    });
+  }
+  resetStates(states, training = false) {
+    tidy(() => {
+      if (!this.stateful) {
+        throw new AttributeError("Cannot call resetStates() on an RNN Layer that is not stateful.");
+      }
+      const inputShape = this.inputSpec[0].shape;
+      const outputShape = this.computeSingleOutputShape(inputShape);
+      const stateShape = [outputShape[0], ...outputShape.slice(2)];
+      const batchSize = inputShape[0];
+      if (batchSize == null) {
+        throw new ValueError("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
+      }
+      if (this.getStates() == null) {
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
+        } else {
+          this.states_ = [zeros(stateShape)];
+        }
+      } else if (states == null) {
+        dispose(this.states_);
+        if (this.keptStates != null) {
+          dispose(this.keptStates);
+          this.keptStates = [];
+        }
+        if (Array.isArray(this.cell.stateSize)) {
+          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));
+        } else {
+          this.states_[0] = zeros(stateShape);
+        }
+      } else {
+        if (!Array.isArray(states)) {
+          states = [states];
+        }
+        if (states.length !== this.states_.length) {
+          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);
+        }
+        if (training) {
+          this.keptStates.push(this.states_.slice());
+        } else {
+          dispose(this.states_);
+        }
+        for (let index = 0; index < this.states_.length; ++index) {
+          const value = states[index];
+          const expectedShape = stateShape;
+          if (!util_exports.arraysEqual(value.shape, expectedShape)) {
+            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);
+          }
+          this.states_[index] = value;
+        }
+      }
+      this.states_ = this.states_.map((state) => keep(state.clone()));
+    });
+  }
+  computeSingleOutputShape(inputShape) {
+    const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;
+    const isChannelsFirst = dataFormat === "channelsFirst";
+    const h = inputShape[isChannelsFirst ? 3 : 2];
+    const w = inputShape[isChannelsFirst ? 4 : 3];
+    const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);
+    const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);
+    const outShape = [
+      ...inputShape.slice(0, 2),
+      ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]
+    ];
+    return outShape;
+  }
+};
+ConvRNN2D.className = "ConvRNN2D";
+var ConvLSTM2DCell = class extends LSTMCell {
+  constructor(args) {
+    const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args;
+    super(Object.assign({}, args, { units: filters }));
+    this.filters = filters;
+    assertPositiveInteger(this.filters, "filters");
+    this.kernelSize = normalizeArray(kernelSize, 2, "kernelSize");
+    this.kernelSize.forEach((size) => assertPositiveInteger(size, "kernelSize"));
+    this.strides = normalizeArray(strides || 1, 2, "strides");
+    this.strides.forEach((stride) => assertPositiveInteger(stride, "strides"));
+    this.padding = padding || "valid";
+    checkPaddingMode(this.padding);
+    this.dataFormat = dataFormat || "channelsLast";
+    checkDataFormat(this.dataFormat);
+    this.dilationRate = normalizeArray(dilationRate || 1, 2, "dilationRate");
+    this.dilationRate.forEach((rate) => assertPositiveInteger(rate, "dilationRate"));
+  }
+  build(inputShape) {
+    var _a;
+    inputShape = getExactlyOneShape(inputShape);
+    const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
+    if (inputShape[channelAxis] == null) {
+      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);
+    }
+    const inputDim = inputShape[channelAxis];
+    const numOfKernels = 4;
+    const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);
+    this.kernel = this.addWeight("kernel", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+    const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);
+    this.recurrentKernel = this.addWeight("recurrent_kernel", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
+    if (this.useBias) {
+      let biasInitializer;
+      if (this.unitForgetBias) {
+        const init2 = this.biasInitializer;
+        const filters = this.filters;
+        biasInitializer = new (_a = class CustomInit extends Initializer {
+          apply(shape, dtype) {
+            const biasI = init2.apply([filters]);
+            const biasF = ones2([filters]);
+            const biasCAndO = init2.apply([filters * 2]);
+            return concatenate([biasI, biasF, biasCAndO]);
+          }
+        }, _a.className = "CustomInit", _a)();
+      } else {
+        biasInitializer = this.biasInitializer;
+      }
+      this.bias = this.addWeight("bias", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (inputs.length !== 3) {
+        throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);
+      }
+      const training = kwargs["training"] || false;
+      const x = inputs[0];
+      const hTMinus1 = inputs[1];
+      const cTMinus1 = inputs[2];
+      const numOfKernels = 4;
+      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
+        this.dropoutMask = generateDropoutMask({
+          ones: () => onesLike(x),
+          rate: this.dropout,
+          training,
+          count: numOfKernels,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const dropoutMask = this.dropoutMask;
+      const applyDropout = (x2, mask, index) => {
+        if (!mask || !mask[index]) {
+          return x2;
+        }
+        return mul(mask[index], x2);
+      };
+      let xI = applyDropout(x, dropoutMask, 0);
+      let xF = applyDropout(x, dropoutMask, 1);
+      let xC = applyDropout(x, dropoutMask, 2);
+      let xO = applyDropout(x, dropoutMask, 3);
+      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
+        this.recurrentDropoutMask = generateDropoutMask({
+          ones: () => onesLike(hTMinus1),
+          rate: this.recurrentDropout,
+          training,
+          count: numOfKernels,
+          dropoutFunc: this.dropoutFunc
+        });
+      }
+      const recDropoutMask = this.recurrentDropoutMask;
+      let hI = applyDropout(hTMinus1, recDropoutMask, 0);
+      let hF = applyDropout(hTMinus1, recDropoutMask, 1);
+      let hC = applyDropout(hTMinus1, recDropoutMask, 2);
+      let hO = applyDropout(hTMinus1, recDropoutMask, 3);
+      const kernelChannelAxis = 3;
+      const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);
+      const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];
+      xI = this.inputConv(xI, kernelI, biasI, this.padding);
+      xF = this.inputConv(xF, kernelF, biasF, this.padding);
+      xC = this.inputConv(xC, kernelC, biasC, this.padding);
+      xO = this.inputConv(xO, kernelO, biasO, this.padding);
+      const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);
+      hI = this.recurrentConv(hI, recKernelI);
+      hF = this.recurrentConv(hF, recKernelF);
+      hC = this.recurrentConv(hC, recKernelC);
+      hO = this.recurrentConv(hO, recKernelO);
+      const i2 = this.recurrentActivation.apply(add2(xI, hI));
+      const f = this.recurrentActivation.apply(add2(xF, hF));
+      const c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(add2(xC, hC))));
+      const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));
+      return [h, h, c];
+    });
+  }
+  getConfig() {
+    const _a = super.getConfig(), { "units": _ } = _a, baseConfig = __rest(_a, ["units"]);
+    const config = {
+      filters: this.filters,
+      kernelSize: this.kernelSize,
+      padding: this.padding,
+      dataFormat: this.dataFormat,
+      dilationRate: this.dilationRate,
+      strides: this.strides
+    };
+    return Object.assign({}, baseConfig, config);
+  }
+  inputConv(x, w, b, padding) {
+    const out = conv2d(x, w, this.strides, padding || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
+    if (b) {
+      return biasAdd(out, b, this.dataFormat);
+    }
+    return out;
+  }
+  recurrentConv(x, w) {
+    const strides = 1;
+    return conv2d(x, w, strides, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
+  }
+};
+ConvLSTM2DCell.className = "ConvLSTM2DCell";
+serialization_exports.registerClass(ConvLSTM2DCell);
+var ConvLSTM2D = class extends ConvRNN2D {
+  constructor(args) {
+    const cell = new ConvLSTM2DCell(args);
+    super(Object.assign({}, args, { cell }));
+  }
+  static fromConfig(cls, config) {
+    return new cls(config);
+  }
+};
+ConvLSTM2D.className = "ConvLSTM2D";
+serialization_exports.registerClass(ConvLSTM2D);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js
+var Dropout = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.rate = Math.max(Math.min(args.rate, 1), 0);
+    this.noiseShape = args.noiseShape;
+    this.seed = args.seed;
+    this.supportsMasking = true;
+  }
+  getNoiseShape(input2) {
+    if (this.noiseShape == null) {
+      return this.noiseShape;
+    }
+    const inputShape = input2.shape;
+    const noiseShape = [];
+    for (let i2 = 0; i2 < this.noiseShape.length; ++i2) {
+      noiseShape.push(this.noiseShape[i2] == null ? inputShape[i2] : this.noiseShape[i2]);
+    }
+    return noiseShape;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      if (0 < this.rate && this.rate < 1) {
+        const training = kwargs["training"] == null ? false : kwargs["training"];
+        const noiseShape = this.getNoiseShape(input2);
+        const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);
+        return output;
+      }
+      return inputs;
+    });
+  }
+  getConfig() {
+    const config = {
+      rate: this.rate,
+      noiseShape: this.noiseShape,
+      seed: this.seed
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  dispose() {
+    return super.dispose();
+  }
+};
+Dropout.className = "Dropout";
+serialization_exports.registerClass(Dropout);
+var SpatialDropout1D = class extends Dropout {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [{ ndim: 3 }];
+  }
+  getNoiseShape(input2) {
+    const inputShape = input2.shape;
+    return [inputShape[0], 1, inputShape[2]];
+  }
+};
+SpatialDropout1D.className = "SpatialDropout1D";
+serialization_exports.registerClass(SpatialDropout1D);
+var Dense = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.activation = null;
+    this.useBias = true;
+    this.kernel = null;
+    this.bias = null;
+    this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
+    this.DEFAULT_BIAS_INITIALIZER = "zeros";
+    if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {
+      let batchSize = null;
+      if (args.batchSize != null) {
+        batchSize = args.batchSize;
+      }
+      this.batchInputShape = [batchSize, args.inputDim];
+    }
+    this.units = args.units;
+    assertPositiveInteger(this.units, "units");
+    this.activation = getActivation(args.activation);
+    if (args.useBias != null) {
+      this.useBias = args.useBias;
+    }
+    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
+    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
+    this.kernelConstraint = getConstraint(args.kernelConstraint);
+    this.biasConstraint = getConstraint(args.biasConstraint);
+    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
+    this.biasRegularizer = getRegularizer(args.biasRegularizer);
+    this.activityRegularizer = getRegularizer(args.activityRegularizer);
+    this.supportsMasking = true;
+    this.inputSpec = [{ minNDim: 2 }];
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const inputLastDim = inputShape[inputShape.length - 1];
+    if (this.kernel == null) {
+      this.kernel = this.addWeight("kernel", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
+      if (this.useBias) {
+        this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
+      }
+    }
+    this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];
+    this.built = true;
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    outputShape[outputShape.length - 1] = this.units;
+    return outputShape;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());
+      let output;
+      if (fusedActivationName != null) {
+        output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);
+      } else {
+        output = dot2(input2, this.kernel.read());
+        if (this.bias != null) {
+          output = biasAdd(output, this.bias.read());
+        }
+        if (this.activation != null) {
+          output = this.activation.apply(output);
+        }
+      }
+      return output;
+    });
+  }
+  getConfig() {
+    const config = {
+      units: this.units,
+      activation: serializeActivation(this.activation),
+      useBias: this.useBias,
+      kernelInitializer: serializeInitializer(this.kernelInitializer),
+      biasInitializer: serializeInitializer(this.biasInitializer),
+      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
+      biasRegularizer: serializeRegularizer(this.biasRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      kernelConstraint: serializeConstraint(this.kernelConstraint),
+      biasConstraint: serializeConstraint(this.biasConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Dense.className = "Dense";
+serialization_exports.registerClass(Dense);
+var Flatten = class extends Layer {
+  constructor(args) {
+    args = args || {};
+    super(args);
+    this.inputSpec = [{ minNDim: 3 }];
+    this.dataFormat = args.dataFormat;
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    for (const dim of inputShape.slice(1)) {
+      if (dim == null) {
+        throw new ValueError(`The shape of the input to "Flatten" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);
+      }
+    }
+    return [inputShape[0], arrayProd(inputShape, 1)];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      let input2 = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsFirst" && input2.rank > 1) {
+        const permutation = [0];
+        for (let i2 = 2; i2 < input2.rank; ++i2) {
+          permutation.push(i2);
+        }
+        permutation.push(1);
+        input2 = transpose(input2, permutation);
+      }
+      return batchFlatten(input2);
+    });
+  }
+  getConfig() {
+    const config = {};
+    if (this.dataFormat != null) {
+      config["dataFormat"] = this.dataFormat;
+    }
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Flatten.className = "Flatten";
+serialization_exports.registerClass(Flatten);
+var Activation2 = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.activation = getActivation(args.activation);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      return this.activation.apply(input2);
+    });
+  }
+  getConfig() {
+    const config = { activation: serializeActivation(this.activation) };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Activation2.className = "Activation";
+serialization_exports.registerClass(Activation2);
+var RepeatVector = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.n = args.n;
+    this.inputSpec = [{ ndim: 2 }];
+  }
+  computeOutputShape(inputShape) {
+    return [inputShape[0], this.n, inputShape[1]];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      return repeat(inputs, this.n);
+    });
+  }
+  getConfig() {
+    const config = {
+      n: this.n
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+RepeatVector.className = "RepeatVector";
+serialization_exports.registerClass(RepeatVector);
+var Reshape2 = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.targetShape = args.targetShape;
+    for (let i2 = 0; i2 < this.targetShape.length; ++i2) {
+      if (this.isUnknown(this.targetShape[i2])) {
+        this.targetShape[i2] = null;
+      }
+    }
+  }
+  isUnknown(dim) {
+    return dim < 0 || dim == null;
+  }
+  fixUnknownDimension(inputShape, outputShape) {
+    const errorMsg = "Total size of new array must be unchanged.";
+    const finalShape = outputShape.slice();
+    let known = 1;
+    let unknown = null;
+    for (let i2 = 0; i2 < finalShape.length; ++i2) {
+      const dim = finalShape[i2];
+      if (this.isUnknown(dim)) {
+        if (unknown === null) {
+          unknown = i2;
+        } else {
+          throw new ValueError("Can only specifiy one unknown dimension.");
+        }
+      } else {
+        known *= dim;
+      }
+    }
+    const originalSize = arrayProd(inputShape);
+    if (unknown !== null) {
+      if (known === 0 || originalSize % known !== 0) {
+        throw new ValueError(errorMsg);
+      }
+      finalShape[unknown] = originalSize / known;
+    } else if (originalSize !== known) {
+      throw new ValueError(errorMsg);
+    }
+    return finalShape;
+  }
+  computeOutputShape(inputShape) {
+    let anyUnknownDims = false;
+    for (let i2 = 0; i2 < inputShape.length; ++i2) {
+      if (this.isUnknown(inputShape[i2])) {
+        anyUnknownDims = true;
+        break;
+      }
+    }
+    if (anyUnknownDims) {
+      return inputShape.slice(0, 1).concat(this.targetShape);
+    } else {
+      return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const inputShape = input2.shape;
+      const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));
+      return reshape(input2, outputShape);
+    });
+  }
+  getConfig() {
+    const config = {
+      targetShape: this.targetShape
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Reshape2.className = "Reshape";
+serialization_exports.registerClass(Reshape2);
+var Permute = class extends Layer {
+  constructor(args) {
+    super(args);
+    if (args.dims == null) {
+      throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
+    }
+    if (!Array.isArray(args.dims)) {
+      throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${args.dims} instead.`);
+    }
+    const expectedSortedIndices = range2(1, args.dims.length + 1);
+    if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {
+      throw new Error("Invalid permutation `dims`: " + JSON.stringify(args.dims) + " `dims` must contain consecutive integers starting from 1.");
+    }
+    this.dims = args.dims;
+    this.dimsIncludingBatch = [0].concat(this.dims);
+    this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const outputShape = inputShape.slice();
+    this.dims.forEach((dim, i2) => {
+      outputShape[i2 + 1] = inputShape[dim];
+    });
+    return outputShape;
+  }
+  call(inputs, kwargs) {
+    return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);
+  }
+  getConfig() {
+    const config = {
+      dims: this.dims
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Permute.className = "Permute";
+serialization_exports.registerClass(Permute);
+var Masking = class extends Layer {
+  constructor(args) {
+    super(args == null ? {} : args);
+    this.supportsMasking = true;
+    if (args != null) {
+      this.maskValue = args.maskValue == null ? 0 : args.maskValue;
+    } else {
+      this.maskValue = 0;
+    }
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = { maskValue: this.maskValue };
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  computeMask(inputs, mask) {
+    const input2 = getExactlyOneTensor(inputs);
+    const axis = -1;
+    return any(notEqual(input2, this.maskValue), axis);
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const axis = -1;
+      const keepDims = true;
+      const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);
+      const output = mul(input2, cast(booleanMask, input2.dtype));
+      return output;
+    });
+  }
+};
+Masking.className = "Masking";
+serialization_exports.registerClass(Masking);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js
+var Embedding = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.embeddings = null;
+    this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform";
+    if (args.batchInputShape == null && args.inputShape == null) {
+      let batchSize = null;
+      if (args.batchSize != null) {
+        batchSize = args.batchSize;
+      }
+      if (args.inputLength == null) {
+        this.batchInputShape = [batchSize, null];
+      } else {
+        this.batchInputShape = [batchSize].concat(toList(args.inputLength));
+      }
+    }
+    this.inputDim = args.inputDim;
+    assertPositiveInteger(this.inputDim, "inputDim");
+    this.outputDim = args.outputDim;
+    assertPositiveInteger(this.outputDim, "outputDim");
+    this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);
+    this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);
+    this.activityRegularizer = getRegularizer(args.activityRegularizer);
+    this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);
+    this.maskZero = args.maskZero;
+    this.supportsMasking = args.maskZero;
+    this.inputLength = args.inputLength;
+  }
+  build(inputShape) {
+    this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);
+    this.built = true;
+  }
+  warnOnIncompatibleInputShape(inputShape) {
+  }
+  computeMask(inputs, mask) {
+    return tidy(() => {
+      if (!this.maskZero) {
+        return null;
+      } else {
+        inputs = getExactlyOneTensor(inputs);
+        return notEqual(inputs, zerosLike(inputs));
+      }
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (this.inputLength == null) {
+      return [...inputShape, this.outputDim];
+    }
+    const inLens = toList(this.inputLength);
+    if (inLens.length !== inputShape.length - 1) {
+      throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
+    } else {
+      let i2 = 0;
+      for (let k = 0; k < inLens.length; ++k) {
+        const s1 = inLens[k];
+        const s2 = inputShape[k + 1];
+        if (s1 != null && s2 != null && s1 !== s2) {
+          throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
+        } else if (s1 == null) {
+          inLens[i2] = s2;
+        }
+        i2++;
+      }
+    }
+    return [inputShape[0], ...inLens, this.outputDim];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      let input2 = getExactlyOneTensor(inputs);
+      if (input2.dtype !== "int32") {
+        input2 = cast2(input2, "int32");
+      }
+      const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));
+      return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));
+    });
+  }
+  getConfig() {
+    const config = {
+      inputDim: this.inputDim,
+      outputDim: this.outputDim,
+      embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),
+      embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),
+      activityRegularizer: serializeRegularizer(this.activityRegularizer),
+      embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),
+      maskZero: this.maskZero,
+      inputLength: this.inputLength
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Embedding.className = "Embedding";
+serialization_exports.registerClass(Embedding);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js
+var Merge = class extends Layer {
+  constructor(args) {
+    super(args || {});
+    this.supportsMasking = true;
+  }
+  mergeFunction(inputs) {
+    throw new NotImplementedError();
+  }
+  computeElementwiseOpOutputShape(shape1, shape2) {
+    if (shape1 == null || shape2 == null) {
+      return null;
+    } else if (shape1.length < shape2.length) {
+      return this.computeElementwiseOpOutputShape(shape2, shape1);
+    } else if (shape2.length === 0) {
+      return shape1;
+    }
+    const outputShape = shape1.slice(0, shape1.length - shape2.length);
+    for (let k = 0; k < shape2.length; ++k) {
+      const i2 = shape1[shape1.length - shape2.length + k];
+      const j = shape2[k];
+      if (i2 == null || j == null || i2 < 0 || j < 0) {
+        outputShape.push(null);
+      } else if (i2 === 1) {
+        outputShape.push(j);
+      } else if (j === 1) {
+        outputShape.push(i2);
+      } else {
+        if (i2 !== j) {
+          throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2));
+        }
+        outputShape.push(i2);
+      }
+    }
+    return outputShape;
+  }
+  build(inputShape) {
+    if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {
+      inputShape = [getExactlyOneShape(inputShape)];
+    }
+    inputShape = inputShape;
+    if (inputShape.length < 2) {
+      throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);
+    }
+    let batchSizes = [];
+    for (const shape of inputShape) {
+      if (shape != null && shape[0] !== null) {
+        batchSizes.push(shape[0]);
+      }
+    }
+    batchSizes = unique2(batchSizes);
+    if (batchSizes.length > 1) {
+      throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
+    }
+    let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);
+    for (let i2 = 1; i2 < inputShape.length; ++i2) {
+      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
+    }
+    const allRanks = inputShape.map((shape) => shape.length);
+    if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {
+      this.reshapeRequired = false;
+    } else {
+      this.reshapeRequired = true;
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = inputs;
+      if (this.reshapeRequired) {
+        const reshapedInputs = [];
+        const inputDims = inputs.map((input2) => input2.rank);
+        if (inputDims.indexOf(null) === -1) {
+          const maxNDim = max2(inputDims);
+          for (let x of inputs) {
+            const xNDim = x.rank;
+            for (let k = 0; k < maxNDim - xNDim; ++k) {
+              x = expandDims2(x, 1);
+            }
+            reshapedInputs.push(x);
+          }
+          return this.mergeFunction(reshapedInputs);
+        } else {
+          let transposed = false;
+          for (const x of inputs) {
+            const xNDim = x.rank;
+            if (xNDim == null) {
+              const xShape = x.shape;
+              const batchSize = xShape[0];
+              const newShape = xShape.slice(1).concat([batchSize]);
+              let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));
+              xTransposed = transpose(xTransposed, [1, 0]);
+              xTransposed = reshape(xTransposed, newShape);
+              reshapedInputs.push(xTransposed);
+              transposed = true;
+            } else if (xNDim > 1) {
+              const dims = range2(1, xNDim).concat([0]);
+              reshapedInputs.push(transpose(x, dims));
+              transposed = true;
+            } else {
+              reshapedInputs.push(x);
+            }
+          }
+          let y = this.mergeFunction(reshapedInputs);
+          const yNDim = y.rank;
+          if (transposed) {
+            if (yNDim == null) {
+              const yShape = y.shape;
+              const yNDim2 = yShape.length;
+              const batchSize = yShape[yNDim2 - 1];
+              const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));
+              y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);
+            } else if (yNDim > 1) {
+              const dims = [yNDim - 1].concat(range2(0, yNDim - 1));
+              y = transpose(y, dims);
+            }
+          }
+          return y;
+        }
+      } else {
+        return this.mergeFunction(inputs);
+      }
+    });
+  }
+  computeOutputShape(inputShape) {
+    inputShape = inputShape;
+    let outputShape;
+    if (inputShape[0] == null) {
+      outputShape = null;
+    } else {
+      outputShape = inputShape[0].slice(1);
+    }
+    for (let i2 = 1; i2 < inputShape.length; ++i2) {
+      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
+    }
+    let batchSizes = [];
+    for (const shape of inputShape) {
+      if (shape != null && shape[0] !== null) {
+        batchSizes.push(shape[0]);
+      }
+    }
+    batchSizes = unique2(batchSizes);
+    if (batchSizes.length === 1) {
+      outputShape = batchSizes.concat(outputShape);
+    } else {
+      outputShape = [null].concat(outputShape);
+    }
+    return outputShape;
+  }
+  computeMask(inputs, mask) {
+    return tidy(() => {
+      if (mask == null) {
+        return null;
+      }
+      if (!Array.isArray(mask)) {
+        throw new ValueError("`mask` should be an Array");
+      }
+      if (!Array.isArray(inputs)) {
+        throw new ValueError("`inputs` should be an Array");
+      }
+      if (mask.length !== inputs.length) {
+        throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);
+      }
+      if (mask.every((m) => m == null)) {
+        return null;
+      }
+      mask = mask.map((m) => m == null ? m : expandDims(m, 0));
+      let output = mask[0];
+      for (let i2 = 1; i2 < mask.length - 1; ++i2) {
+        output = logicalAnd(output, mask[i2]);
+      }
+      return output;
+    });
+  }
+};
+var Add2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0].clone();
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = add2(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Add2.className = "Add";
+serialization_exports.registerClass(Add2);
+var Multiply2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0].clone();
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = mul(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Multiply2.className = "Multiply";
+serialization_exports.registerClass(Multiply2);
+var Average = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0].clone();
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = add2(output, inputs[i2]);
+      }
+      return mul(1 / inputs.length, output);
+    });
+  }
+};
+Average.className = "Average";
+serialization_exports.registerClass(Average);
+var Maximum2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0];
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = maximum(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Maximum2.className = "Maximum";
+serialization_exports.registerClass(Maximum2);
+var Minimum2 = class extends Merge {
+  constructor(args) {
+    super(args);
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      let output = inputs[0];
+      for (let i2 = 1; i2 < inputs.length; ++i2) {
+        output = minimum(output, inputs[i2]);
+      }
+      return output;
+    });
+  }
+};
+Minimum2.className = "Minimum";
+serialization_exports.registerClass(Minimum2);
+var Concatenate = class extends Merge {
+  constructor(args) {
+    super(args);
+    this.DEFAULT_AXIS = -1;
+    if (args == null) {
+      args = {};
+    }
+    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;
+    this.supportsMasking = true;
+    this.reshapeRequired = false;
+  }
+  build(inputShape) {
+    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {
+      throw new ValueError("A `Concatenate` layer should be called on a list of at least 2 inputs");
+    }
+    inputShape = inputShape;
+    let allNoneShape = true;
+    for (const shape of inputShape) {
+      if (shape != null) {
+        allNoneShape = false;
+        break;
+      }
+    }
+    if (allNoneShape) {
+      return;
+    }
+    const shapeSet = [];
+    for (let i2 = 0; i2 < inputShape.length; ++i2) {
+      const shapeWithoutConcatAxis = inputShape[i2].slice();
+      shapeWithoutConcatAxis.splice(this.axis, 1);
+      let exists = false;
+      for (const shape of shapeSet) {
+        if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {
+          exists = true;
+          break;
+        }
+      }
+      if (!exists) {
+        shapeSet.push(shapeWithoutConcatAxis);
+      }
+    }
+    if (shapeSet.length > 1) {
+      throw new ValueError("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(inputShape));
+    }
+  }
+  mergeFunction(inputs) {
+    return tidy(() => {
+      return concatenate(inputs, this.axis);
+    });
+  }
+  computeOutputShape(inputShape) {
+    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {
+      throw new ValueError("A `Concatenate` layer should be called on a list of inputs.");
+    }
+    const inputShapes = inputShape;
+    const outputShape = inputShapes[0].slice();
+    const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;
+    for (const shape of inputShapes.slice(1)) {
+      if (outputShape[axis] == null || shape[axis] == null) {
+        outputShape[axis] = null;
+        break;
+      }
+      outputShape[axis] += shape[axis];
+    }
+    return outputShape;
+  }
+  computeMask(inputs, mask) {
+    if (mask == null) {
+      return null;
+    }
+    if (!Array.isArray(mask)) {
+      throw new ValueError("`mask` should be an array for Concatenate");
+    }
+    if (!Array.isArray(inputs)) {
+      throw new ValueError("`inputs` should be an array for Concatenate");
+    }
+    if (mask.length !== inputs.length) {
+      throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);
+    }
+    return tidy(() => {
+      let allNullMasks = true;
+      mask.forEach((m) => {
+        if (m != null) {
+          allNullMasks = false;
+          return;
+        }
+      });
+      if (allNullMasks) {
+        return null;
+      }
+      const outputMasks = [];
+      for (let i2 = 0; i2 < inputs.length; ++i2) {
+        if (mask[i2] == null) {
+          outputMasks.push(cast(onesLike(inputs[i2]), "bool"));
+        } else if (mask[i2].rank < inputs[i2].rank) {
+          outputMasks.push(expandDims(mask[i2], -1));
+        } else {
+          outputMasks.push(mask[i2]);
+        }
+      }
+      const concatenatedMasks = concat(outputMasks, this.axis);
+      return all(concatenatedMasks, -1, false);
+    });
+  }
+  getConfig() {
+    const config = {
+      "axis": this.axis
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Concatenate.className = "Concatenate";
+serialization_exports.registerClass(Concatenate);
+function interpretAxis(axis, dim) {
+  while (axis < 0) {
+    axis += dim;
+  }
+  return axis;
+}
+function batchDot(x, y, axes) {
+  if (x.shape.length > 3 || y.shape.length > 3) {
+    throw new NotImplementedError("batchDot is not implemented for tensors of 4D or higher rank yet");
+  }
+  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);
+  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);
+  if (typeof axes === "number") {
+    axes = [axes, axes];
+  }
+  if (x.dtype === "complex64" || y.dtype === "complex64") {
+    throw new NotImplementedError("batchDot is not implemented for complex64-type Tensors yet.");
+  }
+  const xNDim = x.shape.length;
+  const yNDim = y.shape.length;
+  if (axes == null) {
+    axes = [xNDim - 1, yNDim - 2];
+  }
+  const axesArray = axes;
+  return tidy(() => {
+    let diff;
+    if (xNDim > yNDim) {
+      diff = xNDim - yNDim;
+      const diffShape = [];
+      for (let i2 = 0; i2 < diff; ++i2) {
+        diffShape.push(1);
+      }
+      y = reshape(y, y.shape.concat(diffShape));
+    } else if (yNDim > xNDim) {
+      diff = yNDim - xNDim;
+      const diffShape = [];
+      for (let i2 = 0; i2 < diff; ++i2) {
+        diffShape.push(1);
+      }
+      x = reshape(x, x.shape.concat(diffShape));
+    } else {
+      diff = 0;
+    }
+    let out;
+    if (x.shape.length === 2 && y.shape.length === 2) {
+      if (axesArray[0] === axesArray[1]) {
+        out = sum2(mul(x, y), axesArray[0]);
+      } else {
+        out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);
+      }
+    } else {
+      const adjX = axesArray[0] !== x.shape.length - 1;
+      const adjY = axesArray[1] === y.shape.length - 1;
+      out = matMul(x, y, adjX, adjY);
+    }
+    if (diff > 0) {
+      let idx;
+      if (xNDim > yNDim) {
+        idx = xNDim + yNDim - 3;
+      } else {
+        idx = xNDim - 1;
+      }
+      const squeezeAxes = [];
+      for (let i2 = idx; i2 < idx + diff; ++i2) {
+        squeezeAxes.push(i2);
+      }
+      out = squeeze(out, squeezeAxes);
+    }
+    if (out.shape.length === 1) {
+      out = expandDims(out, 1);
+    }
+    return out;
+  });
+}
+var Dot = class extends Merge {
+  constructor(args) {
+    super(args);
+    this.axes = args.axes;
+    this.normalize = args.normalize == null ? false : args.normalize;
+    this.supportsMasking = true;
+    this.reshapeRequired = false;
+  }
+  build(inputShape) {
+    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
+    const shape1 = inputShape[0];
+    const shape2 = inputShape[1];
+    if (shape1.length > 3 || shape2.length > 3) {
+      throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
+    }
+    const axes = this.interpretAxes(shape1, shape2);
+    if (shape1[axes[0]] !== shape2[axes[1]]) {
+      throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);
+    }
+  }
+  mergeFunction(inputs) {
+    if (inputs.length !== 2) {
+      throw new ValueError(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);
+    }
+    let x1 = inputs[0];
+    let x2 = inputs[1];
+    let axes;
+    if (!Array.isArray(this.axes)) {
+      axes = [
+        interpretAxis(this.axes, x1.shape.length),
+        interpretAxis(this.axes, x2.shape.length)
+      ];
+    } else {
+      axes = this.axes.map((axis, i2) => interpretAxis(axis, inputs[i2].shape.length));
+    }
+    if (this.normalize) {
+      x1 = l2Normalize(x1, axes[0]);
+      x2 = l2Normalize(x2, axes[1]);
+    }
+    return batchDot(x1, x2, axes);
+  }
+  interpretAxes(shape1, shape2) {
+    let axes;
+    if (!Array.isArray(this.axes)) {
+      axes = [
+        interpretAxis(this.axes, shape1.length),
+        interpretAxis(this.axes, shape2.length)
+      ];
+    } else {
+      axes = this.axes;
+    }
+    return axes;
+  }
+  computeOutputShape(inputShape) {
+    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
+    const shape1 = inputShape[0].slice();
+    const shape2 = inputShape[1].slice();
+    if (shape1.length > 3 || shape2.length > 3) {
+      throw new NotImplementedError("Dot layer does not support tensors of 4D or higher rank yet.");
+    }
+    const axes = this.interpretAxes(shape1, shape2);
+    shape1.splice(axes[0], 1);
+    shape2.splice(axes[1], 1);
+    shape2.splice(0, 1);
+    const outputShape = shape1.concat(shape2);
+    if (outputShape.length === 1) {
+      outputShape.push(1);
+    }
+    return outputShape;
+  }
+  computeMask(inputs, mask) {
+    return null;
+  }
+  getConfig() {
+    const config = {
+      "axes": this.axes,
+      "normalize": this.normalize
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+Dot.className = "Dot";
+serialization_exports.registerClass(Dot);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js
+var GaussianNoise = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.stddev = args.stddev;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = { stddev: this.stddev };
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);
+      const output = inTrainPhase(noised, () => input2, kwargs["training"] || false);
+      return output;
+    });
+  }
+};
+GaussianNoise.className = "GaussianNoise";
+serialization_exports.registerClass(GaussianNoise);
+var GaussianDropout = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.rate = args.rate;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = { rate: this.rate };
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      const input2 = getExactlyOneTensor(inputs);
+      if (this.rate > 0 && this.rate < 1) {
+        const noised = () => {
+          const stddev = Math.sqrt(this.rate / (1 - this.rate));
+          return mul(input2, randomNormal2(input2.shape, 1, stddev));
+        };
+        return inTrainPhase(noised, () => input2, kwargs["training"] || false);
+      }
+      return input2;
+    });
+  }
+};
+GaussianDropout.className = "GaussianDropout";
+serialization_exports.registerClass(GaussianDropout);
+var AlphaDropout = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+    this.rate = args.rate;
+    this.noiseShape = args.noiseShape;
+  }
+  _getNoiseShape(inputs) {
+    return this.noiseShape || getExactlyOneTensor(inputs).shape;
+  }
+  computeOutputShape(inputShape) {
+    return inputShape;
+  }
+  getConfig() {
+    const baseConfig = super.getConfig();
+    const config = { rate: this.rate };
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      if (this.rate < 1 && this.rate > 0) {
+        const noiseShape = this._getNoiseShape(inputs);
+        const droppedInputs = () => {
+          const input2 = getExactlyOneTensor(inputs);
+          const alpha = 1.6732632423543772;
+          const scale2 = 1.0507009873554805;
+          const alphaP = -alpha * scale2;
+          let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);
+          keptIdx = cast2(keptIdx, "float32");
+          const a6 = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;
+          const b = -a6 * alphaP * this.rate;
+          const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));
+          return add2(mul(x, a6), b);
+        };
+        return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false);
+      }
+      return inputs;
+    });
+  }
+};
+AlphaDropout.className = "AlphaDropout";
+serialization_exports.registerClass(AlphaDropout);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js
+function batchNormalization(x, mean5, variance, beta, gamma, epsilon3 = 1e-3) {
+  let out;
+  if (x.rank === 2) {
+    out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon3);
+  } else if (x.rank === 3) {
+    out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon3);
+  } else if (x.rank === 4) {
+    out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon3);
+  } else {
+    throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);
+  }
+  return out;
+}
+function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
+  return tidy(() => {
+    const meanAndVariance = moments(x, reductionAxes);
+    const mean5 = meanAndVariance.mean;
+    const variance = meanAndVariance.variance;
+    const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon3);
+    return [normed, mean5, variance];
+  });
+}
+function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
+  return tidy(() => {
+    const meanAndVariance = moments(x, reductionAxes);
+    const mean5 = meanAndVariance.mean;
+    const variance = meanAndVariance.variance;
+    const targetShape = [];
+    for (const axis of range2(0, x.rank)) {
+      if (reductionAxes.indexOf(axis) !== -1) {
+        targetShape.push(1);
+      } else {
+        targetShape.push(x.shape[axis]);
+      }
+    }
+    const broadcastMean = reshape(mean5, targetShape);
+    const broadcastVariance = reshape(variance, targetShape);
+    const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);
+    const broadcastBeta = beta == null ? null : reshape(beta, targetShape);
+    const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);
+    return [normed, mean5, variance];
+  });
+}
+function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
+  if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {
+    return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);
+  } else {
+    return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);
+  }
+}
+var BatchNormalization = class extends Layer {
+  constructor(args) {
+    if (args == null) {
+      args = {};
+    }
+    super(args);
+    this.supportsMasking = true;
+    this.axis = args.axis == null ? -1 : args.axis;
+    this.momentum = args.momentum == null ? 0.99 : args.momentum;
+    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
+    this.center = args.center == null ? true : args.center;
+    this.scale = args.scale == null ? true : args.scale;
+    this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
+    this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
+    this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || "zeros");
+    this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || "ones");
+    this.betaConstraint = getConstraint(args.betaConstraint);
+    this.gammaConstraint = getConstraint(args.gammaConstraint);
+    this.betaRegularizer = getRegularizer(args.betaRegularizer);
+    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;
+    const dim = inputShape[axis];
+    if (dim == null) {
+      throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);
+    }
+    this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];
+    const shape = [dim];
+    if (this.scale) {
+      this.gamma = this.addWeight("gamma", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);
+    }
+    if (this.center) {
+      this.beta = this.addWeight("beta", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);
+    }
+    this.movingMean = this.addWeight("moving_mean", shape, null, this.movingMeanInitializer, null, false);
+    this.movingVariance = this.addWeight("moving_variance", shape, null, this.movingVarianceInitializer, null, false);
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const training = kwargs["training"] == null ? false : kwargs["training"];
+      const input2 = getExactlyOneTensor(inputs);
+      const inputShape = input2.shape;
+      const ndim = inputShape.length;
+      const reductionAxes = range2(0, ndim);
+      const axis = this.axis >= 0 ? this.axis : this.axis + ndim;
+      reductionAxes.splice(axis, 1);
+      const broadcastShape = pyListRepeat(1, ndim);
+      broadcastShape[axis] = inputShape[axis];
+      const sortedReductionAxes = reductionAxes.slice();
+      sortedReductionAxes.sort();
+      const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));
+      const normalizeInference = () => {
+        if (needsBroadcasting) {
+          const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);
+          const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);
+          const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;
+          const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;
+          return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);
+        } else {
+          return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
+        }
+      };
+      if (!training) {
+        return normalizeInference();
+      }
+      const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);
+      const doMovingAverage = (variable2, value, momentum) => {
+        tidy(() => {
+          const decay = 1 - momentum;
+          const origValue = variable2.read();
+          const updateDelta = mul(sub(origValue, value), decay);
+          variable2.write(sub(origValue, updateDelta));
+        });
+      };
+      const updateMovingMeanAndVariance = () => {
+        doMovingAverage(this.movingMean, mean5, this.momentum);
+        doMovingAverage(this.movingVariance, variance, this.momentum);
+      };
+      updateMovingMeanAndVariance();
+      return normedTraining;
+    });
+  }
+  getConfig() {
+    const config = {
+      axis: this.axis,
+      momentum: this.momentum,
+      epsilon: this.epsilon,
+      center: this.center,
+      scale: this.scale,
+      betaInitializer: serializeInitializer(this.betaInitializer),
+      gammaInitializer: serializeInitializer(this.gammaInitializer),
+      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),
+      movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),
+      betaRegularizer: serializeRegularizer(this.betaRegularizer),
+      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),
+      betaConstraint: serializeConstraint(this.betaConstraint),
+      gammaConstraint: serializeConstraint(this.gammaConstraint)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+BatchNormalization.className = "BatchNormalization";
+serialization_exports.registerClass(BatchNormalization);
+var LayerNormalization = class extends Layer {
+  constructor(args) {
+    if (args == null) {
+      args = {};
+    }
+    super(args);
+    this.axis = args.axis == null ? -1 : args.axis;
+    if (typeof this.axis === "number") {
+      if (!Number.isInteger(this.axis)) {
+        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
+      }
+    } else if (Array.isArray(this.axis)) {
+      for (const axis of this.axis) {
+        if (!Number.isInteger(axis)) {
+          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);
+        }
+      }
+    } else {
+      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);
+    }
+    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;
+    this.center = args.center == null ? true : args.center;
+    this.scale = args.scale == null ? true : args.scale;
+    this.betaInitializer = getInitializer(args.betaInitializer || "zeros");
+    this.gammaInitializer = getInitializer(args.gammaInitializer || "ones");
+    this.betaRegularizer = getRegularizer(args.betaRegularizer);
+    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);
+    this.supportsMasking = true;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const nDims = inputShape.length;
+    if (typeof this.axis === "number") {
+      this.axis = [this.axis];
+    }
+    for (let i2 = 0; i2 < this.axis.length; ++i2) {
+      if (this.axis[i2] < 0) {
+        this.axis[i2] += nDims;
+      }
+    }
+    for (const axis of this.axis) {
+      if (axis < 0 || axis >= nDims) {
+        throw new Error(`Invalid axis: ${axis}`);
+      }
+    }
+    if (this.axis.length !== unique2(this.axis).length) {
+      throw new Error(`Found duplicate axes in: ${this.axis}`);
+    }
+    const paramShape = this.axis.map((axis) => inputShape[axis]);
+    const trainable = true;
+    if (this.scale) {
+      this.gamma = this.addWeight("gamma", paramShape, "float32", this.gammaInitializer, this.gammaRegularizer, trainable);
+    } else {
+      this.gamma = null;
+    }
+    if (this.center) {
+      this.beta = this.addWeight("beta", paramShape, "float32", this.betaInitializer, this.betaRegularizer, trainable);
+    } else {
+      this.beta = null;
+    }
+    this.built = true;
+  }
+  call(inputs, kwargs) {
+    const input2 = getExactlyOneTensor(inputs);
+    const inputShape = input2.shape;
+    const nDims = inputShape.length;
+    return tidy(() => {
+      const keepDims = true;
+      let { mean: mean5, variance } = moments(input2, this.axis, keepDims);
+      const broadcastShape = pyListRepeat(1, nDims);
+      for (const dim of this.axis) {
+        broadcastShape[dim] = inputShape[dim];
+      }
+      const broadcast = (v) => {
+        if (v != null && v.shape.length !== nDims) {
+          return reshape(v, broadcastShape);
+        } else {
+          return v;
+        }
+      };
+      let scale2 = this.scale ? broadcast(this.gamma.read()) : null;
+      let offset = this.center ? broadcast(this.beta.read()) : null;
+      const momentsTiling = [];
+      const scaleOffsetTiling = [];
+      for (let i2 = 0; i2 < nDims; ++i2) {
+        if (this.axis.indexOf(i2) !== -1) {
+          momentsTiling.push(inputShape[i2]);
+          scaleOffsetTiling.push(1);
+        } else {
+          momentsTiling.push(1);
+          scaleOffsetTiling.push(inputShape[i2]);
+        }
+      }
+      mean5 = tile(mean5, momentsTiling);
+      variance = tile(variance, momentsTiling);
+      if (scale2 != null) {
+        scale2 = tile(scale2, scaleOffsetTiling);
+      }
+      if (offset != null) {
+        offset = tile(offset, scaleOffsetTiling);
+      }
+      return batchNormalization(input2, mean5, variance, offset, scale2, this.epsilon);
+    });
+  }
+  getConfig() {
+    const config = {
+      axis: this.axis,
+      epsilon: this.epsilon,
+      center: this.center,
+      scale: this.scale,
+      betaInitializer: serializeInitializer(this.betaInitializer),
+      gammaInitializer: serializeInitializer(this.gammaInitializer),
+      betaRegularizer: serializeRegularizer(this.betaRegularizer),
+      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+LayerNormalization.className = "LayerNormalization";
+serialization_exports.registerClass(LayerNormalization);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js
+function spatial2dPadding(x, padding, dataFormat) {
+  return tidy(() => {
+    if (x.rank !== 4) {
+      throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);
+    }
+    if (padding == null) {
+      padding = [[1, 1], [1, 1]];
+    }
+    if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) {
+      throw new ValueError("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
+    }
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    if (dataFormat !== "channelsLast" && dataFormat !== "channelsFirst") {
+      throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
+    }
+    let pattern;
+    if (dataFormat === "channelsFirst") {
+      pattern = [[0, 0], [0, 0], padding[0], padding[1]];
+    } else {
+      pattern = [[0, 0], padding[0], padding[1], [0, 0]];
+    }
+    return pad(x, pattern);
+  });
+}
+var ZeroPadding2D = class extends Layer {
+  constructor(args) {
+    if (args == null) {
+      args = {};
+    }
+    super(args);
+    this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;
+    if (args.padding == null) {
+      this.padding = [[1, 1], [1, 1]];
+    } else if (typeof args.padding === "number") {
+      this.padding = [[args.padding, args.padding], [args.padding, args.padding]];
+    } else {
+      args.padding = args.padding;
+      if (args.padding.length !== 2) {
+        throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);
+      }
+      let heightPadding;
+      let widthPadding;
+      if (typeof args.padding[0] === "number") {
+        heightPadding = [args.padding[0], args.padding[0]];
+        widthPadding = [args.padding[1], args.padding[1]];
+      } else {
+        args.padding = args.padding;
+        if (args.padding[0].length !== 2) {
+          throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);
+        }
+        heightPadding = args.padding[0];
+        if (args.padding[1].length !== 2) {
+          throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);
+        }
+        widthPadding = args.padding[1];
+      }
+      this.padding = [heightPadding, widthPadding];
+    }
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    let rows;
+    let cols;
+    if (this.dataFormat === "channelsFirst") {
+      if (inputShape[2] != null && inputShape[2] >= 0) {
+        rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];
+      } else {
+        rows = null;
+      }
+      if (inputShape[3] != null && inputShape[3] >= 0) {
+        cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];
+      } else {
+        cols = null;
+      }
+      return [inputShape[0], inputShape[1], rows, cols];
+    } else {
+      if (inputShape[1] != null && inputShape[1] >= 0) {
+        rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];
+      } else {
+        rows = null;
+      }
+      if (inputShape[2] != null && inputShape[2] >= 0) {
+        cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];
+      } else {
+        cols = null;
+      }
+      return [inputShape[0], rows, cols, inputShape[3]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));
+  }
+  getConfig() {
+    const config = {
+      padding: this.padding,
+      dataFormat: this.dataFormat
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+ZeroPadding2D.className = "ZeroPadding2D";
+serialization_exports.registerClass(ZeroPadding2D);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js
+function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    checkPoolMode(poolMode);
+    checkPaddingMode(padding);
+    if (strides == null) {
+      strides = [1, 1];
+    }
+    if (padding == null) {
+      padding = "valid";
+    }
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    if (poolMode == null) {
+      poolMode = "max";
+    }
+    x = preprocessConv2DInput(x, dataFormat);
+    let y;
+    const paddingString = padding === "same" ? "same" : "valid";
+    if (poolMode === "max") {
+      y = maxPool(x, poolSize, strides, paddingString);
+    } else {
+      y = avgPool(x, poolSize, strides, paddingString);
+    }
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 3, 1, 2]);
+    }
+    return y;
+  });
+}
+function pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {
+  return tidy(() => {
+    checkDataFormat(dataFormat);
+    checkPoolMode(poolMode);
+    checkPaddingMode(padding);
+    if (strides == null) {
+      strides = [1, 1, 1];
+    }
+    if (padding == null) {
+      padding = "valid";
+    }
+    if (dataFormat == null) {
+      dataFormat = imageDataFormat();
+    }
+    if (poolMode == null) {
+      poolMode = "max";
+    }
+    x = preprocessConv3DInput(x, dataFormat);
+    let y;
+    const paddingString = padding === "same" ? "same" : "valid";
+    if (poolMode === "max") {
+      y = maxPool3d(x, poolSize, strides, paddingString);
+    } else {
+      y = avgPool3d(x, poolSize, strides, paddingString);
+    }
+    if (dataFormat === "channelsFirst") {
+      y = transpose(y, [0, 4, 1, 2, 3]);
+    }
+    return y;
+  });
+}
+var Pooling1D = class extends Layer {
+  constructor(args) {
+    if (args.poolSize == null) {
+      args.poolSize = 2;
+    }
+    super(args);
+    if (typeof args.poolSize === "number") {
+      this.poolSize = [args.poolSize];
+    } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === "number") {
+      this.poolSize = args.poolSize;
+    } else {
+      throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);
+    }
+    assertPositiveInteger(this.poolSize, "poolSize");
+    if (args.strides == null) {
+      this.strides = this.poolSize;
+    } else {
+      if (typeof args.strides === "number") {
+        this.strides = [args.strides];
+      } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === "number") {
+        this.strides = args.strides;
+      } else {
+        throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);
+      }
+    }
+    assertPositiveInteger(this.strides, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    checkPaddingMode(this.padding);
+    this.inputSpec = [new InputSpec({ ndim: 3 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);
+    return [inputShape[0], length, inputShape[2]];
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      inputs = expandDims2(getExactlyOneTensor(inputs), 2);
+      const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
+      return squeeze(output, [2]);
+    });
+  }
+  getConfig() {
+    const config = {
+      poolSize: this.poolSize,
+      padding: this.padding,
+      strides: this.strides
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+var MaxPooling1D = class extends Pooling1D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding);
+    return pool2d(inputs, poolSize, strides, padding, dataFormat, "max");
+  }
+};
+MaxPooling1D.className = "MaxPooling1D";
+serialization_exports.registerClass(MaxPooling1D);
+var AveragePooling1D = class extends Pooling1D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding);
+    return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg");
+  }
+};
+AveragePooling1D.className = "AveragePooling1D";
+serialization_exports.registerClass(AveragePooling1D);
+var Pooling2D = class extends Layer {
+  constructor(args) {
+    if (args.poolSize == null) {
+      args.poolSize = [2, 2];
+    }
+    super(args);
+    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];
+    if (args.strides == null) {
+      this.strides = this.poolSize;
+    } else if (Array.isArray(args.strides)) {
+      if (args.strides.length !== 2) {
+        throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);
+      }
+      this.strides = args.strides;
+    } else {
+      this.strides = [args.strides, args.strides];
+    }
+    assertPositiveInteger(this.poolSize, "poolSize");
+    assertPositiveInteger(this.strides, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    checkPaddingMode(this.padding);
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    let rows = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
+    let cols = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
+    rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);
+    cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);
+    if (this.dataFormat === "channelsFirst") {
+      return [inputShape[0], inputShape[1], rows, cols];
+    } else {
+      return [inputShape[0], rows, cols, inputShape[3]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
+    });
+  }
+  getConfig() {
+    const config = {
+      poolSize: this.poolSize,
+      padding: this.padding,
+      strides: this.strides,
+      dataFormat: this.dataFormat
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+var MaxPooling2D = class extends Pooling2D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding);
+    return pool2d(inputs, poolSize, strides, padding, dataFormat, "max");
+  }
+};
+MaxPooling2D.className = "MaxPooling2D";
+serialization_exports.registerClass(MaxPooling2D);
+var AveragePooling2D = class extends Pooling2D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding);
+    return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg");
+  }
+};
+AveragePooling2D.className = "AveragePooling2D";
+serialization_exports.registerClass(AveragePooling2D);
+var Pooling3D = class extends Layer {
+  constructor(args) {
+    if (args.poolSize == null) {
+      args.poolSize = [2, 2, 2];
+    }
+    super(args);
+    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];
+    if (args.strides == null) {
+      this.strides = this.poolSize;
+    } else if (Array.isArray(args.strides)) {
+      if (args.strides.length !== 3) {
+        throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);
+      }
+      this.strides = args.strides;
+    } else {
+      this.strides = [args.strides, args.strides, args.strides];
+    }
+    assertPositiveInteger(this.poolSize, "poolSize");
+    assertPositiveInteger(this.strides, "strides");
+    this.padding = args.padding == null ? "valid" : args.padding;
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    checkPaddingMode(this.padding);
+    this.inputSpec = [new InputSpec({ ndim: 5 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    let depths = this.dataFormat === "channelsFirst" ? inputShape[2] : inputShape[1];
+    let rows = this.dataFormat === "channelsFirst" ? inputShape[3] : inputShape[2];
+    let cols = this.dataFormat === "channelsFirst" ? inputShape[4] : inputShape[3];
+    depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);
+    rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);
+    cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);
+    if (this.dataFormat === "channelsFirst") {
+      return [inputShape[0], inputShape[1], depths, rows, cols];
+    } else {
+      return [inputShape[0], depths, rows, cols, inputShape[4]];
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      this.invokeCallHook(inputs, kwargs);
+      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);
+    });
+  }
+  getConfig() {
+    const config = {
+      poolSize: this.poolSize,
+      padding: this.padding,
+      strides: this.strides,
+      dataFormat: this.dataFormat
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+var MaxPooling3D = class extends Pooling3D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding);
+    return pool3d(inputs, poolSize, strides, padding, dataFormat, "max");
+  }
+};
+MaxPooling3D.className = "MaxPooling3D";
+serialization_exports.registerClass(MaxPooling3D);
+var AveragePooling3D = class extends Pooling3D {
+  constructor(args) {
+    super(args);
+  }
+  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
+    checkDataFormat(dataFormat);
+    checkPaddingMode(padding);
+    return pool3d(inputs, poolSize, strides, padding, dataFormat, "avg");
+  }
+};
+AveragePooling3D.className = "AveragePooling3D";
+serialization_exports.registerClass(AveragePooling3D);
+var GlobalPooling1D = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.inputSpec = [new InputSpec({ ndim: 3 })];
+  }
+  computeOutputShape(inputShape) {
+    return [inputShape[0], inputShape[2]];
+  }
+  call(inputs, kwargs) {
+    throw new NotImplementedError();
+  }
+};
+var GlobalAveragePooling1D = class extends GlobalPooling1D {
+  constructor(args) {
+    super(args || {});
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      return mean(input2, 1);
+    });
+  }
+};
+GlobalAveragePooling1D.className = "GlobalAveragePooling1D";
+serialization_exports.registerClass(GlobalAveragePooling1D);
+var GlobalMaxPooling1D = class extends GlobalPooling1D {
+  constructor(args) {
+    super(args || {});
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      return max(input2, 1);
+    });
+  }
+};
+GlobalMaxPooling1D.className = "GlobalMaxPooling1D";
+serialization_exports.registerClass(GlobalMaxPooling1D);
+var GlobalPooling2D = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.dataFormat = args.dataFormat == null ? "channelsLast" : args.dataFormat;
+    checkDataFormat(this.dataFormat);
+    this.inputSpec = [new InputSpec({ ndim: 4 })];
+  }
+  computeOutputShape(inputShape) {
+    inputShape = inputShape;
+    if (this.dataFormat === "channelsLast") {
+      return [inputShape[0], inputShape[3]];
+    } else {
+      return [inputShape[0], inputShape[1]];
+    }
+  }
+  call(inputs, kwargs) {
+    throw new NotImplementedError();
+  }
+  getConfig() {
+    const config = { dataFormat: this.dataFormat };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+};
+var GlobalAveragePooling2D = class extends GlobalPooling2D {
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsLast") {
+        return mean(input2, [1, 2]);
+      } else {
+        return mean(input2, [2, 3]);
+      }
+    });
+  }
+};
+GlobalAveragePooling2D.className = "GlobalAveragePooling2D";
+serialization_exports.registerClass(GlobalAveragePooling2D);
+var GlobalMaxPooling2D = class extends GlobalPooling2D {
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const input2 = getExactlyOneTensor(inputs);
+      if (this.dataFormat === "channelsLast") {
+        return max(input2, [1, 2]);
+      } else {
+        return max(input2, [2, 3]);
+      }
+    });
+  }
+};
+GlobalMaxPooling2D.className = "GlobalMaxPooling2D";
+serialization_exports.registerClass(GlobalMaxPooling2D);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js
+var Wrapper = class extends Layer {
+  constructor(args) {
+    super(args);
+    this.layer = args.layer;
+  }
+  build(inputShape) {
+    this.built = true;
+  }
+  get trainable() {
+    if (this.layer != null) {
+      return this.layer.trainable;
+    } else {
+      return false;
+    }
+  }
+  set trainable(value) {
+    if (this.layer != null) {
+      this.layer.trainable = value;
+    }
+  }
+  get trainableWeights() {
+    return this.layer.trainableWeights;
+  }
+  get nonTrainableWeights() {
+    return this.layer.nonTrainableWeights;
+  }
+  get updates() {
+    return this.layer._updates;
+  }
+  get losses() {
+    return this.layer.losses;
+  }
+  getWeights() {
+    return this.layer.getWeights();
+  }
+  setWeights(weights) {
+    this.layer.setWeights(weights);
+  }
+  getConfig() {
+    const config = {
+      "layer": {
+        "className": this.layer.getClassName(),
+        "config": this.layer.getConfig()
+      }
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  setFastWeightInitDuringBuild(value) {
+    super.setFastWeightInitDuringBuild(value);
+    if (this.layer != null) {
+      this.layer.setFastWeightInitDuringBuild(value);
+    }
+  }
+  static fromConfig(cls, config, customObjects = {}) {
+    const layerConfig = config["layer"];
+    const layer = deserialize(layerConfig, customObjects);
+    delete config["layer"];
+    const newConfig = { layer };
+    Object.assign(newConfig, config);
+    return new cls(newConfig);
+  }
+};
+var TimeDistributed = class extends Wrapper {
+  constructor(args) {
+    super(args);
+    this.supportsMasking = true;
+  }
+  build(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    if (inputShape.length < 3) {
+      throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);
+    }
+    this.inputSpec = [{ shape: inputShape }];
+    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
+    if (!this.layer.built) {
+      this.layer.build(childInputShape);
+      this.layer.built = true;
+    }
+    super.build(inputShape);
+  }
+  computeOutputShape(inputShape) {
+    inputShape = getExactlyOneShape(inputShape);
+    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));
+    const childOutputShape = this.layer.computeOutputShape(childInputShape);
+    const timesteps = inputShape[1];
+    return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      inputs = getExactlyOneTensor(inputs);
+      const step5 = (inputs2, states) => {
+        const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));
+        return [output, []];
+      };
+      const rnnOutputs = rnn(step5, inputs, [], false, null, null, false, true);
+      const y = rnnOutputs[1];
+      return y;
+    });
+  }
+};
+TimeDistributed.className = "TimeDistributed";
+serialization_exports.registerClass(TimeDistributed);
+function checkBidirectionalMergeMode(value) {
+  checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, "BidirectionalMergeMode", value);
+}
+var DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat";
+var Bidirectional = class extends Wrapper {
+  constructor(args) {
+    super(args);
+    const layerConfig = args.layer.getConfig();
+    const forwDict = {};
+    forwDict["className"] = args.layer.getClassName();
+    forwDict["config"] = layerConfig;
+    this.forwardLayer = deserialize(forwDict);
+    layerConfig["goBackwards"] = layerConfig["goBackwards"] === true ? false : true;
+    const backDict = {};
+    backDict["className"] = args.layer.getClassName();
+    backDict["config"] = layerConfig;
+    this.backwardLayer = deserialize(backDict);
+    this.forwardLayer.name = "forward_" + this.forwardLayer.name;
+    this.backwardLayer.name = "backward_" + this.backwardLayer.name;
+    this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;
+    checkBidirectionalMergeMode(this.mergeMode);
+    if (args.weights) {
+      throw new NotImplementedError("weights support is not implemented for Bidirectional layer yet.");
+    }
+    this._stateful = args.layer.stateful;
+    this.returnSequences = args.layer.returnSequences;
+    this.returnState = args.layer.returnState;
+    this.supportsMasking = true;
+    this._trainable = true;
+    this.inputSpec = args.layer.inputSpec;
+    this.numConstants = null;
+  }
+  get trainable() {
+    return this._trainable;
+  }
+  set trainable(value) {
+    this._trainable = value;
+    if (this.forwardLayer != null) {
+      this.forwardLayer.trainable = value;
+    }
+    if (this.backwardLayer != null) {
+      this.backwardLayer.trainable = value;
+    }
+  }
+  getWeights() {
+    return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());
+  }
+  setWeights(weights) {
+    const numWeights = weights.length;
+    const numeightsOver2 = Math.floor(numWeights / 2);
+    this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));
+    this.backwardLayer.setWeights(weights.slice(numeightsOver2));
+  }
+  computeOutputShape(inputShape) {
+    let layerShapes = this.forwardLayer.computeOutputShape(inputShape);
+    if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {
+      layerShapes = [layerShapes];
+    }
+    layerShapes = layerShapes;
+    let outputShape;
+    let outputShapes;
+    let stateShape;
+    if (this.returnState) {
+      stateShape = layerShapes.slice(1);
+      outputShape = layerShapes[0];
+    } else {
+      outputShape = layerShapes[0];
+    }
+    outputShape = outputShape;
+    if (this.mergeMode === "concat") {
+      outputShape[outputShape.length - 1] *= 2;
+      outputShapes = [outputShape];
+    } else if (this.mergeMode == null) {
+      outputShapes = [outputShape, outputShape.slice()];
+    } else {
+      outputShapes = [outputShape];
+    }
+    if (this.returnState) {
+      if (this.mergeMode == null) {
+        return outputShapes.concat(stateShape).concat(stateShape.slice());
+      }
+      return [outputShape].concat(stateShape).concat(stateShape.slice());
+    }
+    return singletonOrArray(outputShapes);
+  }
+  apply(inputs, kwargs) {
+    let initialState = kwargs == null ? null : kwargs["initialState"];
+    let constants = kwargs == null ? null : kwargs["constants"];
+    if (kwargs == null) {
+      kwargs = {};
+    }
+    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
+    inputs = standardized.inputs;
+    initialState = standardized.initialState;
+    constants = standardized.constants;
+    if (Array.isArray(inputs)) {
+      initialState = inputs.slice(1);
+      inputs = inputs[0];
+    }
+    if ((initialState == null || initialState.length === 0) && constants == null) {
+      return super.apply(inputs, kwargs);
+    }
+    const additionalInputs = [];
+    const additionalSpecs = [];
+    if (initialState != null) {
+      const numStates = initialState.length;
+      if (numStates % 2 > 0) {
+        throw new ValueError("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
+      }
+      kwargs["initialState"] = initialState;
+      additionalInputs.push(...initialState);
+      const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape }));
+      this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);
+      this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);
+      additionalSpecs.push(...stateSpecs);
+    }
+    if (constants != null) {
+      throw new NotImplementedError("Support for constants in Bidirectional layers is not implemented yet.");
+    }
+    const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;
+    for (const tensor2 of additionalInputs) {
+      if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {
+        throw new ValueError("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");
+      }
+    }
+    if (isSymbolicTensor) {
+      const fullInput = [inputs].concat(additionalInputs);
+      const fullInputSpec = this.inputSpec.concat(additionalSpecs);
+      const originalInputSpec = this.inputSpec;
+      this.inputSpec = fullInputSpec;
+      const output = super.apply(fullInput, kwargs);
+      this.inputSpec = originalInputSpec;
+      return output;
+    } else {
+      return super.apply(inputs, kwargs);
+    }
+  }
+  call(inputs, kwargs) {
+    return tidy(() => {
+      const initialState = kwargs["initialState"];
+      let y;
+      let yRev;
+      if (initialState == null) {
+        y = this.forwardLayer.call(inputs, kwargs);
+        yRev = this.backwardLayer.call(inputs, kwargs);
+      } else {
+        const forwardState = initialState.slice(0, initialState.length / 2);
+        const backwardState = initialState.slice(initialState.length / 2);
+        y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));
+        yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));
+      }
+      let states;
+      if (this.returnState) {
+        if (Array.isArray(y)) {
+          states = y.slice(1).concat(yRev.slice(1));
+        } else {
+        }
+        y = y[0];
+        yRev = yRev[0];
+      }
+      if (this.returnSequences) {
+        yRev = reverse(yRev, 1);
+      }
+      let output;
+      if (this.mergeMode === "concat") {
+        output = concatenate([y, yRev]);
+      } else if (this.mergeMode === "sum") {
+        output = add2(y, yRev);
+      } else if (this.mergeMode === "ave") {
+        output = mul(0.5, add2(y, yRev));
+      } else if (this.mergeMode === "mul") {
+        output = mul(y, yRev);
+      } else if (this.mergeMode == null) {
+        output = [y, yRev];
+      }
+      if (this.returnState) {
+        if (this.mergeMode == null) {
+          return output.concat(states);
+        }
+        return [output].concat(states);
+      }
+      return output;
+    });
+  }
+  resetStates(states) {
+    this.forwardLayer.resetStates();
+    this.backwardLayer.resetStates();
+  }
+  build(inputShape) {
+    nameScope(this.forwardLayer.name, () => {
+      this.forwardLayer.build(inputShape);
+    });
+    nameScope(this.backwardLayer.name, () => {
+      this.backwardLayer.build(inputShape);
+    });
+    this.built = true;
+  }
+  computeMask(inputs, mask) {
+    if (Array.isArray(mask)) {
+      mask = mask[0];
+    }
+    let outputMask;
+    if (this.returnSequences) {
+      if (this.mergeMode == null) {
+        outputMask = [mask, mask];
+      } else {
+        outputMask = mask;
+      }
+    } else {
+      if (this.mergeMode == null) {
+        outputMask = [null, null];
+      } else {
+        outputMask = null;
+      }
+    }
+    if (this.returnState) {
+      const states = this.forwardLayer.states;
+      const stateMask = states.map((state) => null);
+      if (Array.isArray(outputMask)) {
+        return outputMask.concat(stateMask).concat(stateMask);
+      } else {
+        return [outputMask].concat(stateMask).concat(stateMask);
+      }
+    } else {
+      return outputMask;
+    }
+  }
+  get trainableWeights() {
+    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);
+  }
+  get nonTrainableWeights() {
+    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);
+  }
+  setFastWeightInitDuringBuild(value) {
+    super.setFastWeightInitDuringBuild(value);
+    if (this.forwardLayer != null) {
+      this.forwardLayer.setFastWeightInitDuringBuild(value);
+    }
+    if (this.backwardLayer != null) {
+      this.backwardLayer.setFastWeightInitDuringBuild(value);
+    }
+  }
+  getConfig() {
+    const config = {
+      "mergeMode": this.mergeMode
+    };
+    const baseConfig = super.getConfig();
+    Object.assign(config, baseConfig);
+    return config;
+  }
+  static fromConfig(cls, config) {
+    const rnnLayer = deserialize(config["layer"]);
+    delete config["layer"];
+    if (config["numConstants"] != null) {
+      throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);
+    }
+    const newConfig = config;
+    newConfig["layer"] = rnnLayer;
+    return new cls(newConfig);
+  }
+};
+Bidirectional.className = "Bidirectional";
+serialization_exports.registerClass(Bidirectional);
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
+function inputLayer(args) {
+  return new InputLayer(args);
+}
+function elu3(args) {
+  return new ELU(args);
+}
+function reLU(args) {
+  return new ReLU(args);
+}
+function leakyReLU(args) {
+  return new LeakyReLU(args);
+}
+function prelu2(args) {
+  return new PReLU(args);
+}
+function softmax2(args) {
+  return new Softmax3(args);
+}
+function thresholdedReLU(args) {
+  return new ThresholdedReLU(args);
+}
+function conv1d2(args) {
+  return new Conv1D(args);
+}
+function conv2d3(args) {
+  return new Conv2D2(args);
+}
+function conv2dTranspose2(args) {
+  return new Conv2DTranspose(args);
+}
+function conv3d2(args) {
+  return new Conv3D2(args);
+}
+function conv3dTranspose2(args) {
+  return new Conv3DTranspose(args);
+}
+function separableConv2d2(args) {
+  return new SeparableConv2D(args);
+}
+function cropping2D(args) {
+  return new Cropping2D(args);
+}
+function upSampling2d(args) {
+  return new UpSampling2D(args);
+}
+function depthwiseConv2d4(args) {
+  return new DepthwiseConv2D(args);
+}
+function activation(args) {
+  return new Activation2(args);
+}
+function dense(args) {
+  return new Dense(args);
+}
+function dropout3(args) {
+  return new Dropout(args);
+}
+function spatialDropout1d(args) {
+  return new SpatialDropout1D(args);
+}
+function flatten3(args) {
+  return new Flatten(args);
+}
+function repeatVector(args) {
+  return new RepeatVector(args);
+}
+function reshape2(args) {
+  return new Reshape2(args);
+}
+function permute(args) {
+  return new Permute(args);
+}
+function embedding(args) {
+  return new Embedding(args);
+}
+function add3(args) {
+  return new Add2(args);
+}
+function average(args) {
+  return new Average(args);
+}
+function concatenate2(args) {
+  return new Concatenate(args);
+}
+function maximum2(args) {
+  return new Maximum2(args);
+}
+function minimum2(args) {
+  return new Minimum2(args);
+}
+function multiply(args) {
+  return new Multiply2(args);
+}
+function dot3(args) {
+  return new Dot(args);
+}
+function batchNormalization2(args) {
+  return new BatchNormalization(args);
+}
+function layerNormalization(args) {
+  return new LayerNormalization(args);
+}
+function zeroPadding2d(args) {
+  return new ZeroPadding2D(args);
+}
+function averagePooling1d(args) {
+  return new AveragePooling1D(args);
+}
+function avgPool1d(args) {
+  return averagePooling1d(args);
+}
+function avgPooling1d(args) {
+  return averagePooling1d(args);
+}
+function averagePooling2d(args) {
+  return new AveragePooling2D(args);
+}
+function avgPool2d(args) {
+  return averagePooling2d(args);
+}
+function avgPooling2d(args) {
+  return averagePooling2d(args);
+}
+function averagePooling3d(args) {
+  return new AveragePooling3D(args);
+}
+function avgPool3d2(args) {
+  return averagePooling3d(args);
+}
+function avgPooling3d(args) {
+  return averagePooling3d(args);
+}
+function globalAveragePooling1d(args) {
+  return new GlobalAveragePooling1D(args);
+}
+function globalAveragePooling2d(args) {
+  return new GlobalAveragePooling2D(args);
+}
+function globalMaxPooling1d(args) {
+  return new GlobalMaxPooling1D(args);
+}
+function globalMaxPooling2d(args) {
+  return new GlobalMaxPooling2D(args);
+}
+function maxPooling1d(args) {
+  return new MaxPooling1D(args);
+}
+function maxPooling2d(args) {
+  return new MaxPooling2D(args);
+}
+function maxPooling3d(args) {
+  return new MaxPooling3D(args);
+}
+function gru(args) {
+  return new GRU(args);
+}
+function gruCell(args) {
+  return new GRUCell(args);
+}
+function lstm(args) {
+  return new LSTM(args);
+}
+function lstmCell(args) {
+  return new LSTMCell(args);
+}
+function simpleRNN(args) {
+  return new SimpleRNN(args);
+}
+function simpleRNNCell(args) {
+  return new SimpleRNNCell(args);
+}
+function convLstm2d(args) {
+  return new ConvLSTM2D(args);
+}
+function convLstm2dCell(args) {
+  return new ConvLSTM2DCell(args);
+}
+function rnn2(args) {
+  return new RNN(args);
+}
+function stackedRNNCells(args) {
+  return new StackedRNNCells(args);
+}
+function bidirectional(args) {
+  return new Bidirectional(args);
+}
+function timeDistributed(args) {
+  return new TimeDistributed(args);
+}
+var globalMaxPool1d = globalMaxPooling1d;
+var globalMaxPool2d = globalMaxPooling2d;
+var maxPool1d = maxPooling1d;
+var maxPool2d = maxPooling2d;
+function gaussianNoise(args) {
+  return new GaussianNoise(args);
+}
+function gaussianDropout(args) {
+  return new GaussianDropout(args);
+}
+function alphaDropout(args) {
+  return new AlphaDropout(args);
+}
+function masking(args) {
+  return new Masking(args);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js
+var exports_metrics_exports = {};
+__export(exports_metrics_exports, {
+  MAPE: () => MAPE2,
+  MSE: () => MSE2,
+  binaryAccuracy: () => binaryAccuracy2,
+  binaryCrossentropy: () => binaryCrossentropy3,
+  categoricalAccuracy: () => categoricalAccuracy2,
+  categoricalCrossentropy: () => categoricalCrossentropy3,
+  cosineProximity: () => cosineProximity2,
+  mape: () => mape2,
+  meanAbsoluteError: () => meanAbsoluteError2,
+  meanAbsolutePercentageError: () => meanAbsolutePercentageError2,
+  meanSquaredError: () => meanSquaredError3,
+  mse: () => mse2,
+  precision: () => precision2,
+  recall: () => recall2,
+  sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2
+});
+function binaryAccuracy2(yTrue, yPred) {
+  return binaryAccuracy(yTrue, yPred);
+}
+function binaryCrossentropy3(yTrue, yPred) {
+  return binaryCrossentropy2(yTrue, yPred);
+}
+function sparseCategoricalAccuracy2(yTrue, yPred) {
+  return sparseCategoricalAccuracy(yTrue, yPred);
+}
+function categoricalAccuracy2(yTrue, yPred) {
+  return categoricalAccuracy(yTrue, yPred);
+}
+function categoricalCrossentropy3(yTrue, yPred) {
+  return categoricalCrossentropy2(yTrue, yPred);
+}
+function precision2(yTrue, yPred) {
+  return precision(yTrue, yPred);
+}
+function recall2(yTrue, yPred) {
+  return recall(yTrue, yPred);
+}
+function cosineProximity2(yTrue, yPred) {
+  return cosineProximity(yTrue, yPred);
+}
+function meanAbsoluteError2(yTrue, yPred) {
+  return meanAbsoluteError(yTrue, yPred);
+}
+function meanAbsolutePercentageError2(yTrue, yPred) {
+  return meanAbsolutePercentageError(yTrue, yPred);
+}
+function MAPE2(yTrue, yPred) {
+  return meanAbsolutePercentageError(yTrue, yPred);
+}
+function mape2(yTrue, yPred) {
+  return meanAbsolutePercentageError(yTrue, yPred);
+}
+function meanSquaredError3(yTrue, yPred) {
+  return meanSquaredError2(yTrue, yPred);
+}
+function MSE2(yTrue, yPred) {
+  return meanSquaredError2(yTrue, yPred);
+}
+function mse2(yTrue, yPred) {
+  return meanSquaredError2(yTrue, yPred);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js
+var exports_models_exports = {};
+__export(exports_models_exports, {
+  modelFromJSON: () => modelFromJSON
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js
+var exports_regularizers_exports = {};
+__export(exports_regularizers_exports, {
+  l1: () => l12,
+  l1l2: () => l1l2,
+  l2: () => l22
+});
+function l1l2(config) {
+  return new L1L2(config);
+}
+function l12(config) {
+  return l1(config);
+}
+function l22(config) {
+  return l2(config);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js
+var Callback = class extends BaseCallback {
+  constructor() {
+    super(...arguments);
+    this.model = null;
+  }
+  setModel(model2) {
+    if (!(model2 instanceof LayersModel)) {
+      throw new Error("model must be a LayersModel, not some other Container");
+    }
+    this.model = model2;
+  }
+};
+function less2(currVal, prevVal) {
+  return currVal < prevVal;
+}
+function greater2(currVal, prevVal) {
+  return currVal > prevVal;
+}
+var EarlyStopping = class extends Callback {
+  constructor(args) {
+    super();
+    if (args == null) {
+      args = {};
+    }
+    if (args.restoreBestWeights) {
+      throw new NotImplementedError("restoreBestWeights = True is not implemented in EarlyStopping yet.");
+    }
+    this.monitor = args.monitor || "val_loss";
+    this.minDelta = Math.abs(args.minDelta || 0);
+    this.patience = args.patience || 0;
+    this.verbose = args.verbose || 0;
+    this.mode = args.mode || "auto";
+    this.baseline = args.baseline;
+    if (["auto", "min", "max"].indexOf(this.mode) === -1) {
+      console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);
+      this.mode = "auto";
+    }
+    if (this.mode === "min") {
+      this.monitorFunc = less2;
+    } else if (this.mode === "max") {
+      this.monitorFunc = greater2;
+    } else {
+      if (this.monitor.indexOf("acc") !== -1) {
+        this.monitorFunc = greater2;
+      } else {
+        this.monitorFunc = less2;
+      }
+    }
+    if (this.monitorFunc === less2) {
+      this.minDelta *= -1;
+    }
+  }
+  async onTrainBegin(logs) {
+    this.wait = 0;
+    this.stoppedEpoch = 0;
+    if (this.baseline != null) {
+      this.best = this.baseline;
+    } else {
+      this.best = this.monitorFunc === less2 ? Infinity : -Infinity;
+    }
+  }
+  async onEpochEnd(epoch, logs) {
+    await resolveScalarsInLogs(logs);
+    const current = this.getMonitorValue(logs);
+    if (current == null) {
+      return;
+    }
+    if (this.monitorFunc(current - this.minDelta, this.best)) {
+      this.best = current;
+      this.wait = 0;
+    } else {
+      this.wait++;
+      if (this.wait >= this.patience) {
+        this.stoppedEpoch = epoch;
+        this.model.stopTraining = true;
+      }
+    }
+  }
+  async onTrainEnd(logs) {
+    if (this.stoppedEpoch > 0 && this.verbose) {
+      console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);
+    }
+  }
+  getMonitorValue(logs) {
+    if (logs == null) {
+      logs = {};
+    }
+    const monitorValue = logs[this.monitor];
+    if (monitorValue == null) {
+      console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);
+    }
+    return monitorValue;
+  }
+};
+function earlyStopping(args) {
+  return new EarlyStopping(args);
+}
+var callbacks = { earlyStopping };
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/flags.js
+var ENV4 = env();
+ENV4.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (debugValue) => {
+  if (debugValue) {
+    console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js
+var DataType;
+(function(DataType2) {
+  DataType2[DataType2["DT_INVALID"] = 0] = "DT_INVALID";
+  DataType2[DataType2["DT_FLOAT"] = 1] = "DT_FLOAT";
+  DataType2[DataType2["DT_DOUBLE"] = 2] = "DT_DOUBLE";
+  DataType2[DataType2["DT_INT32"] = 3] = "DT_INT32";
+  DataType2[DataType2["DT_UINT8"] = 4] = "DT_UINT8";
+  DataType2[DataType2["DT_INT16"] = 5] = "DT_INT16";
+  DataType2[DataType2["DT_INT8"] = 6] = "DT_INT8";
+  DataType2[DataType2["DT_STRING"] = 7] = "DT_STRING";
+  DataType2[DataType2["DT_COMPLEX64"] = 8] = "DT_COMPLEX64";
+  DataType2[DataType2["DT_INT64"] = 9] = "DT_INT64";
+  DataType2[DataType2["DT_BOOL"] = 10] = "DT_BOOL";
+  DataType2[DataType2["DT_QINT8"] = 11] = "DT_QINT8";
+  DataType2[DataType2["DT_QUINT8"] = 12] = "DT_QUINT8";
+  DataType2[DataType2["DT_QINT32"] = 13] = "DT_QINT32";
+  DataType2[DataType2["DT_BFLOAT16"] = 14] = "DT_BFLOAT16";
+  DataType2[DataType2["DT_QINT16"] = 15] = "DT_QINT16";
+  DataType2[DataType2["DT_QUINT16"] = 16] = "DT_QUINT16";
+  DataType2[DataType2["DT_UINT16"] = 17] = "DT_UINT16";
+  DataType2[DataType2["DT_COMPLEX128"] = 18] = "DT_COMPLEX128";
+  DataType2[DataType2["DT_HALF"] = 19] = "DT_HALF";
+  DataType2[DataType2["DT_RESOURCE"] = 20] = "DT_RESOURCE";
+  DataType2[DataType2["DT_VARIANT"] = 21] = "DT_VARIANT";
+  DataType2[DataType2["DT_UINT32"] = 22] = "DT_UINT32";
+  DataType2[DataType2["DT_UINT64"] = 23] = "DT_UINT64";
+  DataType2[DataType2["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF";
+  DataType2[DataType2["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF";
+  DataType2[DataType2["DT_INT32_REF"] = 103] = "DT_INT32_REF";
+  DataType2[DataType2["DT_UINT8_REF"] = 104] = "DT_UINT8_REF";
+  DataType2[DataType2["DT_INT16_REF"] = 105] = "DT_INT16_REF";
+  DataType2[DataType2["DT_INT8_REF"] = 106] = "DT_INT8_REF";
+  DataType2[DataType2["DT_STRING_REF"] = 107] = "DT_STRING_REF";
+  DataType2[DataType2["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF";
+  DataType2[DataType2["DT_INT64_REF"] = 109] = "DT_INT64_REF";
+  DataType2[DataType2["DT_BOOL_REF"] = 110] = "DT_BOOL_REF";
+  DataType2[DataType2["DT_QINT8_REF"] = 111] = "DT_QINT8_REF";
+  DataType2[DataType2["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF";
+  DataType2[DataType2["DT_QINT32_REF"] = 113] = "DT_QINT32_REF";
+  DataType2[DataType2["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF";
+  DataType2[DataType2["DT_QINT16_REF"] = 115] = "DT_QINT16_REF";
+  DataType2[DataType2["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF";
+  DataType2[DataType2["DT_UINT16_REF"] = 117] = "DT_UINT16_REF";
+  DataType2[DataType2["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF";
+  DataType2[DataType2["DT_HALF_REF"] = 119] = "DT_HALF_REF";
+  DataType2[DataType2["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF";
+  DataType2[DataType2["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF";
+  DataType2[DataType2["DT_UINT32_REF"] = 122] = "DT_UINT32_REF";
+  DataType2[DataType2["DT_UINT64_REF"] = 123] = "DT_UINT64_REF";
+})(DataType || (DataType = {}));
+var SaverDef;
+(function(SaverDef2) {
+  let CheckpointFormatVersion;
+  (function(CheckpointFormatVersion2) {
+    CheckpointFormatVersion2[CheckpointFormatVersion2["LEGACY"] = 0] = "LEGACY";
+    CheckpointFormatVersion2[CheckpointFormatVersion2["V1"] = 1] = "V1";
+    CheckpointFormatVersion2[CheckpointFormatVersion2["V2"] = 2] = "V2";
+  })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));
+})(SaverDef || (SaverDef = {}));
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js
+var CUSTOM_OPS = {};
+function registerOp(name, opFunc) {
+  const opMapper = {
+    tfOpName: name,
+    category: "custom",
+    inputs: [],
+    attrs: [],
+    customExecutor: opFunc
+  };
+  CUSTOM_OPS[name] = opMapper;
+}
+function getRegisteredOp(name) {
+  return CUSTOM_OPS[name];
+}
+function deregisterOp(name) {
+  delete CUSTOM_OPS[name];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js
+function getParamValue(paramName, node, tensorMap, context, resourceManager) {
+  const inputParam = node.inputParams[paramName];
+  if (inputParam && inputParam.inputIndexStart !== void 0) {
+    const start = inputParam.inputIndexStart;
+    const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;
+    if (inputParam.type === "tensor") {
+      return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);
+    }
+    if (inputParam.type === "tensors") {
+      const inputs = node.inputNames.slice(start, end);
+      return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));
+    }
+    const tensor2 = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);
+    const data = tensor2.dataSync();
+    return inputParam.type === "number" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);
+  }
+  const attrParam = node.attrParams[paramName];
+  return attrParam && attrParam.value;
+}
+function getTensor(name, tensorsMap, context, resourceManager) {
+  const [nodeName, index] = parseNodeName(name);
+  if (resourceManager != null) {
+    const tensor2 = resourceManager.getHashTableHandleByName(nodeName);
+    if (tensor2 != null) {
+      return tensor2;
+    }
+  }
+  const contextId = context.currentContextIds.find((contextId2) => {
+    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];
+  });
+  return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;
+}
+function getTensorsForCurrentContenxt(name, tensorsMap, context) {
+  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
+}
+function getNodeNameAndIndex(inputName, context) {
+  const [nodeName, index, outputName] = parseNodeName(inputName);
+  return [
+    getNodeNameWithContextId(nodeName, context && context.currentContextId),
+    index,
+    outputName
+  ];
+}
+function getNodeNameWithContextId(name, contextId) {
+  return !!contextId ? `${name}-${contextId}` : name;
+}
+function parseNodeName(name) {
+  const parts = name.split(":");
+  if (parts.length === 1) {
+    return [name, 0, void 0];
+  }
+  const nodeName = parts[0];
+  const outputName = parts.length === 3 ? parts[1] : void 0;
+  const index = Number(parts[parts.length - 1]);
+  return [nodeName, index, outputName];
+}
+function getPadding(node, tensorMap, context) {
+  let pad3 = getParamValue("pad", node, tensorMap, context);
+  if (pad3 === "explicit") {
+    pad3 = getParamValue("explicitPaddings", node, tensorMap, context);
+    const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+    for (let i2 = 0; i2 < 4; i2++) {
+      explicitPadding[i2][0] = pad3[i2 * 2];
+      explicitPadding[i2][1] = pad3[i2 * 2 + 1];
+    }
+    return explicitPadding;
+  }
+  return pad3;
+}
+function cloneTensor(tensor2) {
+  return tensor2.kept ? tensor2 : clone(tensor2);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js
+var arithmetic_exports = {};
+__export(arithmetic_exports, {
+  json: () => json
+});
+var json = [
+  {
+    "tfOpName": "Add",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "AddV2",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "AddN",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "BiasAdd",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sub",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "RealDiv",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Div",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "DivNoNan",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FloorDiv",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Mul",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Maximum",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Minimum",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Pow",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "SquaredDifference",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Mod",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FloorMod",
+    "category": "arithmetic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js
+var basic_math_exports = {};
+__export(basic_math_exports, {
+  json: () => json2
+});
+var json2 = [
+  {
+    "tfOpName": "Abs",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Acos",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Asin",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Atan",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Atan2",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "y",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Ceil",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ClipByValue",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "clipValueMin",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "clipValueMax",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Complex",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "real",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "imag",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ComplexAbs",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cos",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cosh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Elu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Exp",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Floor",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Log",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Imag",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "outputType",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Neg",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Real",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "outputType",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Prelu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "alpha",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Relu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Relu6",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Selu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sigmoid",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sin",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sinh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sqrt",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Rsqrt",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Square",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Tan",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Tanh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sign",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Round",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Expm1",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Log1p",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Reciprocal",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Softplus",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Asinh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Acosh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Atanh",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Erf",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Prod",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axes",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool",
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LeakyRelu",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "alpha",
+        "name": "alpha",
+        "type": "number",
+        "defaultValue": 0.2
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "IsNan",
+    "category": "basic_math",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js
+var control_exports = {};
+__export(control_exports, {
+  json: () => json3
+});
+var json3 = [
+  {
+    "tfOpName": "EmptyTensorList",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 1,
+        "name": "maxNumElements",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LoopCond",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "pred",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Switch",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "pred",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Merge",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Enter",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "frame_name",
+        "name": "frameName",
+        "type": "string"
+      },
+      {
+        "tfName": "is_constant",
+        "name": "isConstant",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Exit",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "NextIteration",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "size",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfName": "dynamic_size",
+        "name": "dynamicSize",
+        "type": "bool"
+      },
+      {
+        "tfName": "clear_after_read",
+        "name": "clearAfterRead",
+        "type": "bool"
+      },
+      {
+        "tfName": "identical_element_shapes",
+        "name": "identicalElementShapes",
+        "type": "bool"
+      },
+      {
+        "tfName": "tensor_array_name",
+        "name": "name",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayWriteV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayReadV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayGatherV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayScatterV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayConcatV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "element_shape_except0",
+        "name": "elementShapeExcept0",
+        "type": "shape",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArraySplitV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "lengths",
+        "type": "number[]"
+      },
+      {
+        "start": 3,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArraySizeV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "flowIn",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorArrayCloseV3",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorArrayId",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "StatelessIf",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "cond",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "then_branch",
+        "name": "thenBranch",
+        "type": "func"
+      },
+      {
+        "tfName": "else_branch",
+        "name": "elseBranch",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "If",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "cond",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "then_branch",
+        "name": "thenBranch",
+        "type": "func"
+      },
+      {
+        "tfName": "else_branch",
+        "name": "elseBranch",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "StatelessWhile",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "cond",
+        "name": "cond",
+        "type": "func"
+      },
+      {
+        "tfName": "body",
+        "name": "body",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "While",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "cond",
+        "name": "cond",
+        "type": "func"
+      },
+      {
+        "tfName": "body",
+        "name": "body",
+        "type": "func"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListScatter",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListScatterV2",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 3,
+        "name": "numElements",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListGather",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListGetItem",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListSetItem",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "index",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListReserve",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 1,
+        "name": "numElements",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListFromTensor",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListStack",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      },
+      {
+        "tfName": "num_elements",
+        "name": "numElements",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListSplit",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "start": 2,
+        "name": "lengths",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListConcat",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListConcatV2",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_shape",
+        "name": "elementShape",
+        "type": "shape"
+      },
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListPopBack",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "elementShape",
+        "type": "shape"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListPushBack",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "element_dtype",
+        "name": "elementDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListLength",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TensorListResize",
+    "category": "control",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensorListId",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js
+var convolution_exports = {};
+__export(convolution_exports, {
+  json: () => json4
+});
+var json4 = [
+  {
+    "tfOpName": "AvgPool",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MaxPool",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": [],
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MaxPoolWithArgmax",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "include_batch_in_index",
+        "name": "includeBatchInIndex",
+        "type": "bool"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "AvgPool3D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MaxPool3D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "ksize",
+        "name": "kernelSize",
+        "type": "number[]"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv1D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "stride",
+        "name": "stride",
+        "type": "number"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NWC"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "dilation",
+        "name": "dilation",
+        "type": "number",
+        "defaultValue": 1
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv2D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "useCudnnOnGpu",
+        "name": "useCudnnOnGpu",
+        "type": "bool"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "_FusedConv2D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_args",
+        "name": "numArgs",
+        "type": "number"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "use_cudnn_on_gpu",
+        "name": "useCudnnOnGpu",
+        "type": "bool",
+        "defaultValue": true
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]",
+        "defaultValue": [
+          1,
+          1,
+          1,
+          1
+        ]
+      },
+      {
+        "tfName": "fused_ops",
+        "name": "fusedOps",
+        "type": "string[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-4
+      },
+      {
+        "tfName": "leakyrelu_alpha",
+        "name": "leakyreluAlpha",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv2DBackpropInput",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 2,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      },
+      {
+        "start": 0,
+        "name": "outputShape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "DepthwiseConv2d",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "DepthwiseConv2dNative",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedDepthwiseConv2dNative",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_args",
+        "name": "numArgs",
+        "type": "number"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]",
+        "defaultValue": [
+          1,
+          1,
+          1,
+          1
+        ]
+      },
+      {
+        "tfName": "fused_ops",
+        "name": "fusedOps",
+        "type": "string[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "explicit_paddings",
+        "name": "explicitPaddings",
+        "type": "number[]",
+        "defaultValue": []
+      }
+    ]
+  },
+  {
+    "tfOpName": "Conv3D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "defaultValue": "NHWC"
+      },
+      {
+        "tfName": "dilations",
+        "name": "dilations",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Dilation2D",
+    "category": "convolution",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "filter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "strides",
+        "name": "strides",
+        "type": "number[]"
+      },
+      {
+        "tfName": "rates",
+        "name": "dilations",
+        "type": "number[]"
+      },
+      {
+        "tfName": "padding",
+        "name": "pad",
+        "type": "string"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js
+var creation_exports = {};
+__export(creation_exports, {
+  json: () => json5
+});
+var json5 = [
+  {
+    "tfOpName": "Fill",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      },
+      {
+        "start": 1,
+        "name": "value",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LinSpace",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "start",
+        "type": "number"
+      },
+      {
+        "start": 1,
+        "name": "stop",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "num",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "OneHot",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "depth",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "onValue",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "start": 3,
+        "name": "offValue",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "name": "axis",
+        "type": "number",
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Ones",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "OnesLike",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "RandomUniform",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "minval",
+        "name": "minval",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "maxval",
+        "name": "maxval",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "seed",
+        "name": "seed",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "seed2",
+        "name": "seed2",
+        "type": "number",
+        "defaultValue": 0,
+        "notSupported": true
+      },
+      {
+        "tfName": "T",
+        "name": "T",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Range",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "start",
+        "type": "number"
+      },
+      {
+        "start": 1,
+        "name": "stop",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "step",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tidx",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TruncatedNormal",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "means",
+        "name": "mean",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "stddev",
+        "name": "stdDev",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "seed",
+        "name": "seed",
+        "type": "number"
+      },
+      {
+        "tfName": "seed2",
+        "name": "seed2",
+        "type": "number",
+        "defaultValue": 0,
+        "notSupported": true
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "T",
+        "name": "T",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Zeros",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ZerosLike",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Multinomial",
+    "category": "creation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "logits",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "numSamples",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "seed",
+        "name": "seed",
+        "type": "number"
+      },
+      {
+        "tfName": "seed2",
+        "name": "seed2",
+        "type": "number"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      },
+      {
+        "tfName": "output_dtype",
+        "name": "output_dtype",
+        "type": "dtype"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js
+var dynamic_exports = {};
+__export(dynamic_exports, {
+  json: () => json6
+});
+var json6 = [
+  {
+    "tfOpName": "NonMaxSuppressionV2",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "NonMaxSuppressionV3",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      },
+      {
+        "start": 4,
+        "name": "scoreThreshold",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "NonMaxSuppressionV4",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      },
+      {
+        "start": 4,
+        "name": "scoreThreshold",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "T_threshold",
+        "name": "threshold",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "pad_to_max_output_size",
+        "name": "padToMaxOutputSize",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "NonMaxSuppressionV5",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scores",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "maxOutputSize",
+        "type": "number"
+      },
+      {
+        "start": 3,
+        "name": "iouThreshold",
+        "type": "number"
+      },
+      {
+        "start": 4,
+        "name": "scoreThreshold",
+        "type": "number"
+      },
+      {
+        "start": 5,
+        "name": "softNmsSigma",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Where",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "condition",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ListDiff",
+    "category": "dynamic",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "y",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js
+var evaluation_exports = {};
+__export(evaluation_exports, {
+  json: () => json7
+});
+var json7 = [
+  {
+    "tfOpName": "LowerBound",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sortedSequence",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "TopKV2",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "k",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "sorted",
+        "name": "sorted",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "UpperBound",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sortedSequence",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Unique",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "UniqueV2",
+    "category": "evaluation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js
+var graph_exports = {};
+__export(graph_exports, {
+  json: () => json8
+});
+var json8 = [
+  {
+    "tfOpName": "PlaceholderWithDefault",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "default",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "shape",
+        "name": "shape",
+        "type": "shape"
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Placeholder",
+    "category": "graph",
+    "attrs": [
+      {
+        "tfName": "shape",
+        "name": "shape",
+        "type": "shape"
+      },
+      {
+        "tfName": "dtype",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Const",
+    "category": "graph"
+  },
+  {
+    "tfOpName": "Identity",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "IdentityN",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "x",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Snapshot",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Rank",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Size",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Shape",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ShapeN",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "x",
+        "type": "tensors"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Print",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "data",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "message",
+        "name": "message",
+        "type": "string"
+      },
+      {
+        "tfName": "first_n",
+        "name": "firstN",
+        "type": "number",
+        "notSupported": true
+      },
+      {
+        "tfName": "summarize",
+        "name": "summarize",
+        "type": "number",
+        "defaultValue": 3
+      }
+    ]
+  },
+  {
+    "tfOpName": "NoOp",
+    "category": "graph",
+    "inputs": []
+  },
+  {
+    "tfOpName": "StopGradient",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "FakeQuantWithMinMaxVars",
+    "category": "graph",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "min",
+        "name": "min",
+        "type": "number"
+      },
+      {
+        "tfName": "max",
+        "name": "max",
+        "type": "number"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js
+var hash_table_exports = {};
+__export(hash_table_exports, {
+  json: () => json9
+});
+var json9 = [
+  {
+    "tfOpName": "HashTable",
+    "category": "hash_table",
+    "inputs": [],
+    "attrs": [
+      {
+        "tfName": "shared_name",
+        "name": "sharedName",
+        "type": "string"
+      },
+      {
+        "tfName": "use_node_name_sharing",
+        "name": "useNodeNameSharing",
+        "type": "bool"
+      },
+      {
+        "tfName": "key_dtype",
+        "name": "keyDType",
+        "type": "dtype"
+      },
+      {
+        "tfName": "value_dtype",
+        "name": "valueDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "HashTableV2",
+    "category": "hash_table",
+    "inputs": [],
+    "attrs": [
+      {
+        "tfName": "shared_name",
+        "name": "sharedName",
+        "type": "string"
+      },
+      {
+        "tfName": "use_node_name_sharing",
+        "name": "useNodeNameSharing",
+        "type": "bool"
+      },
+      {
+        "tfName": "key_dtype",
+        "name": "keyDType",
+        "type": "dtype"
+      },
+      {
+        "tfName": "value_dtype",
+        "name": "valueDType",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableImport",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "values",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableImportV2",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "values",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableFind",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableFindV2",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "keys",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "Tin",
+        "name": "tIn",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "Tout",
+        "name": "tOut",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableSize",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LookupTableSizeV2",
+    "category": "hash_table",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tableHandle",
+        "type": "tensor"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js
+var image_exports = {};
+__export(image_exports, {
+  json: () => json10
+});
+var json10 = [
+  {
+    "tfOpName": "ResizeBilinear",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "images",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "align_corners",
+        "name": "alignCorners",
+        "type": "bool"
+      },
+      {
+        "tfName": "half_pixel_centers",
+        "name": "halfPixelCenters",
+        "type": "bool"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "ResizeNearestNeighbor",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "images",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "align_corners",
+        "name": "alignCorners",
+        "type": "bool"
+      },
+      {
+        "tfName": "half_pixel_centers",
+        "name": "halfPixelCenters",
+        "type": "bool"
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "CropAndResize",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "image",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "boxes",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "boxInd",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "cropSize",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "method",
+        "name": "method",
+        "type": "string"
+      },
+      {
+        "tfName": "extrapolation_value",
+        "name": "extrapolationValue",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ImageProjectiveTransformV3",
+    "category": "image",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "images",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "transforms",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "outputShape",
+        "type": "number[]"
+      },
+      {
+        "start": 3,
+        "name": "fillValue",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "interpolation",
+        "name": "interpolation",
+        "type": "string"
+      },
+      {
+        "tfName": "fill_mode",
+        "name": "fillMode",
+        "type": "string"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js
+var logical_exports = {};
+__export(logical_exports, {
+  json: () => json11
+});
+var json11 = [
+  {
+    "tfOpName": "Equal",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "NotEqual",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Greater",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "GreaterEqual",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Less",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LessEqual",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogicalAnd",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogicalNot",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogicalOr",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Select",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "condition",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "SelectV2",
+    "category": "logical",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "condition",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js
+var matrices_exports = {};
+__export(matrices_exports, {
+  json: () => json12
+});
+var json12 = [
+  {
+    "tfOpName": "_FusedMatMul",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "end": 0,
+        "name": "args",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_args",
+        "name": "numArgs",
+        "type": "number"
+      },
+      {
+        "tfName": "fused_ops",
+        "name": "fusedOps",
+        "type": "string[]",
+        "defaultValue": []
+      },
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-4
+      },
+      {
+        "tfName": "transpose_a",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "transpose_b",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "MatMul",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "transpose_a",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "transpose_b",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "BatchMatMul",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "adj_x",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "adj_y",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "BatchMatMulV2",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "a",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "b",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "adj_x",
+        "name": "transposeA",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "adj_y",
+        "name": "transposeB",
+        "type": "bool",
+        "defaultValue": false
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Transpose",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "perm",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Einsum",
+    "category": "matrices",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "equation",
+        "name": "equation",
+        "type": "string"
+      },
+      {
+        "tfName": "N",
+        "name": "n",
+        "type": "number",
+        "defaultValue": 2
+      },
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js
+var normalization_exports = {};
+__export(normalization_exports, {
+  json: () => json13
+});
+var json13 = [
+  {
+    "tfOpName": "EuclideanNorm",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool",
+        "defaultValue": false
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedBatchNorm",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scale",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "offset",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "mean",
+        "type": "tensor"
+      },
+      {
+        "start": 4,
+        "name": "variance",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-3
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedBatchNormV2",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scale",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "offset",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "mean",
+        "type": "tensor"
+      },
+      {
+        "start": 4,
+        "name": "variance",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-3
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "FusedBatchNormV3",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "scale",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "offset",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "mean",
+        "type": "tensor"
+      },
+      {
+        "start": 4,
+        "name": "variance",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "epsilon",
+        "name": "epsilon",
+        "type": "number",
+        "defaultValue": 1e-3
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "LRN",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "depth_radius",
+        "name": "radius",
+        "type": "number",
+        "defaultValue": 5
+      },
+      {
+        "tfName": "bias",
+        "name": "bias",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "alpha",
+        "name": "alpha",
+        "type": "number",
+        "defaultValue": 1
+      },
+      {
+        "tfName": "beta",
+        "name": "beta",
+        "type": "number",
+        "defaultValue": 0.5
+      }
+    ]
+  },
+  {
+    "tfOpName": "Softmax",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "LogSoftmax",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseToDense",
+    "category": "normalization",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sparseIndices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "outputShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "sparseValues",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "validate_indices",
+        "name": "validateIndices",
+        "type": "bool",
+        "defaultValue": true,
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js
+var reduction_exports = {};
+__export(reduction_exports, {
+  json: () => json14
+});
+var json14 = [
+  {
+    "tfOpName": "Bincount",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "weights",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "DenseBincount",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "size",
+        "type": "number"
+      },
+      {
+        "start": 2,
+        "name": "weights",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "binary_output",
+        "name": "binaryOutput",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Max",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Mean",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Min",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Sum",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "All",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Any",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ArgMax",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ArgMin",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Prod",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "keep_dims",
+        "name": "keepDims",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cumprod",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "exclusive",
+        "name": "exclusive",
+        "type": "bool"
+      },
+      {
+        "tfName": "reverse",
+        "name": "reverse",
+        "type": "bool"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Cumsum",
+    "category": "reduction",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "exclusive",
+        "name": "exclusive",
+        "type": "bool"
+      },
+      {
+        "tfName": "reverse",
+        "name": "reverse",
+        "type": "bool"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js
+var slice_join_exports = {};
+__export(slice_join_exports, {
+  json: () => json15
+});
+var json15 = [
+  {
+    "tfOpName": "ConcatV2",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "end": -1,
+        "name": "tensors",
+        "type": "tensors"
+      },
+      {
+        "start": -1,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "N",
+        "name": "n",
+        "type": "number",
+        "defaultValue": 2
+      }
+    ]
+  },
+  {
+    "tfOpName": "Concat",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 1,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      },
+      {
+        "start": 0,
+        "name": "axis",
+        "type": "number"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "N",
+        "name": "n",
+        "type": "number",
+        "defaultValue": 2
+      }
+    ]
+  },
+  {
+    "tfOpName": "GatherV2",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "batch_dims",
+        "name": "batchDims",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Gather",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "validate_indices",
+        "name": "validateIndices",
+        "type": "bool",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Reverse",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "dims",
+        "type": "bool[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ReverseV2",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Slice",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "begin",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "size",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "StridedSlice",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "begin",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "end",
+        "type": "number[]"
+      },
+      {
+        "start": 3,
+        "name": "strides",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "begin_mask",
+        "name": "beginMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "end_mask",
+        "name": "endMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "new_axis_mask",
+        "name": "newAxisMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "ellipsis_mask",
+        "name": "ellipsisMask",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "shrink_axis_mask",
+        "name": "shrinkAxisMask",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Pack",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "end": 0,
+        "name": "tensors",
+        "type": "tensors"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Unpack",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "tensor",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "tfName": "num",
+        "name": "num",
+        "type": "number",
+        "defaultValue": 0,
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "Tile",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "reps",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Split",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      },
+      {
+        "start": 1,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_split",
+        "name": "numOrSizeSplits",
+        "type": "number",
+        "defaultValue": 1
+      }
+    ]
+  },
+  {
+    "tfOpName": "SplitV",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "numOrSizeSplits",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "axis",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "ScatterNd",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "GatherNd",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseToDense",
+    "category": "slice_join",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "sparseIndices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "outputShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "sparseValues",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "validate_indices",
+        "name": "validateIndices",
+        "type": "bool",
+        "defaultValue": false,
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js
+var sparse_exports = {};
+__export(sparse_exports, {
+  json: () => json16
+});
+var json16 = [
+  {
+    "tfOpName": "SparseFillEmptyRows",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "values",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "denseShape",
+        "type": "tensor"
+      },
+      {
+        "start": 3,
+        "name": "defaultValue",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseReshape",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "inputIndices",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "inputShape",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "newShape",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "T",
+        "name": "dtype",
+        "type": "dtype",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseSegmentMean",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "segmentIds",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SparseSegmentSum",
+    "category": "sparse",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "indices",
+        "type": "tensor"
+      },
+      {
+        "start": 2,
+        "name": "segmentIds",
+        "type": "tensor"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js
+var spectral_exports = {};
+__export(spectral_exports, {
+  json: () => json17
+});
+var json17 = [
+  {
+    "tfOpName": "FFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "IFFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ]
+  },
+  {
+    "tfOpName": "RFFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "fft_length",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  },
+  {
+    "tfOpName": "IRFFT",
+    "category": "spectral",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "fft_length",
+        "type": "number",
+        "notSupported": true
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js
+var string_exports = {};
+__export(string_exports, {
+  json: () => json18
+});
+var json18 = [
+  {
+    "tfOpName": "StringNGrams",
+    "category": "string",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "data",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "dataSplits",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "separator",
+        "name": "separator",
+        "type": "string"
+      },
+      {
+        "tfName": "ngram_widths",
+        "name": "nGramWidths",
+        "type": "number[]"
+      },
+      {
+        "tfName": "left_pad",
+        "name": "leftPad",
+        "type": "string"
+      },
+      {
+        "tfName": "right_pad",
+        "name": "rightPad",
+        "type": "string"
+      },
+      {
+        "tfName": "pad_width",
+        "name": "padWidth",
+        "type": "number"
+      },
+      {
+        "tfName": "preserve_short_sequences",
+        "name": "preserveShortSequences",
+        "type": "bool"
+      }
+    ],
+    "outputs": [
+      "ngrams",
+      "ngrams_splits"
+    ]
+  },
+  {
+    "tfOpName": "StringSplit",
+    "category": "string",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "delimiter",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "skip_empty",
+        "name": "skipEmpty",
+        "type": "bool"
+      }
+    ],
+    "outputs": [
+      "indices",
+      "values",
+      "shape"
+    ]
+  },
+  {
+    "tfOpName": "StringToHashBucketFast",
+    "category": "string",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "input",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "num_buckets",
+        "name": "numBuckets",
+        "type": "number"
+      }
+    ]
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js
+var transformation_exports = {};
+__export(transformation_exports, {
+  json: () => json19
+});
+var json19 = [
+  {
+    "tfOpName": "Cast",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "SrcT",
+        "name": "sdtype",
+        "type": "dtype",
+        "notSupported": true
+      },
+      {
+        "tfName": "DstT",
+        "name": "dtype",
+        "type": "dtype"
+      }
+    ]
+  },
+  {
+    "tfOpName": "ExpandDims",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "axis",
+        "type": "number"
+      }
+    ]
+  },
+  {
+    "tfOpName": "MirrorPad",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "padding",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "mode",
+        "name": "mode",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Pad",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "padding",
+        "type": "number[]"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "constant_value",
+        "name": "constantValue",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "PadV2",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "padding",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "constantValue",
+        "type": "number",
+        "defaultValue": 0
+      }
+    ]
+  },
+  {
+    "tfOpName": "Reshape",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "Squeeze",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "axis",
+        "tfDeprecatedName": "squeeze_dims",
+        "name": "axis",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "SpaceToBatchND",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "blockShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "paddings",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "BatchToSpaceND",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "blockShape",
+        "type": "number[]"
+      },
+      {
+        "start": 2,
+        "name": "crops",
+        "type": "number[]"
+      }
+    ]
+  },
+  {
+    "tfOpName": "DepthToSpace",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      }
+    ],
+    "attrs": [
+      {
+        "tfName": "block_size",
+        "name": "blockSize",
+        "type": "number"
+      },
+      {
+        "tfName": "data_format",
+        "name": "dataFormat",
+        "type": "string"
+      }
+    ]
+  },
+  {
+    "tfOpName": "BroadcastTo",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "x",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "shape",
+        "type": "number[]"
+      }
+    ],
+    "attrs": []
+  },
+  {
+    "tfOpName": "BroadcastArgs",
+    "category": "transformation",
+    "inputs": [
+      {
+        "start": 0,
+        "name": "s0",
+        "type": "tensor"
+      },
+      {
+        "start": 1,
+        "name": "s1",
+        "type": "tensor"
+      }
+    ],
+    "attrs": []
+  }
+];
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js
+var OperationMapper = class {
+  static get Instance() {
+    return this._instance || (this._instance = new this());
+  }
+  constructor() {
+    const ops = [
+      arithmetic_exports,
+      basic_math_exports,
+      control_exports,
+      convolution_exports,
+      creation_exports,
+      dynamic_exports,
+      evaluation_exports,
+      graph_exports,
+      hash_table_exports,
+      image_exports,
+      logical_exports,
+      matrices_exports,
+      normalization_exports,
+      reduction_exports,
+      slice_join_exports,
+      sparse_exports,
+      spectral_exports,
+      string_exports,
+      transformation_exports
+    ];
+    const mappersJson = [].concat(...ops.map((op2) => op2.json));
+    this.opMappers = mappersJson.reduce((map, mapper) => {
+      map[mapper.tfOpName] = mapper;
+      return map;
+    }, {});
+  }
+  transformGraph(graph, signature = {}) {
+    const tfNodes = graph.node;
+    const placeholders = [];
+    const weights = [];
+    const initNodes = [];
+    const nodes = tfNodes.reduce((map, node) => {
+      map[node.name] = this.mapNode(node);
+      if (node.op.startsWith("Placeholder")) {
+        placeholders.push(map[node.name]);
+      } else if (node.op === "Const") {
+        weights.push(map[node.name]);
+      } else if (node.input == null || node.input.length === 0) {
+        initNodes.push(map[node.name]);
+      }
+      return map;
+    }, {});
+    let inputs = [];
+    const outputs = [];
+    let inputNodeNameToKey = {};
+    let outputNodeNameToKey = {};
+    if (signature != null) {
+      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
+      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
+    }
+    const allNodes = Object.keys(nodes);
+    allNodes.forEach((key) => {
+      const node = nodes[key];
+      node.inputNames.forEach((name, index) => {
+        const [nodeName, , outputName] = getNodeNameAndIndex(name);
+        const inputNode = nodes[nodeName];
+        if (inputNode.outputs != null) {
+          const outputIndex = inputNode.outputs.indexOf(outputName);
+          if (outputIndex !== -1) {
+            const inputName = `${nodeName}:${outputIndex}`;
+            node.inputNames[index] = inputName;
+          }
+        }
+        node.inputs.push(inputNode);
+        inputNode.children.push(node);
+      });
+    });
+    if (Object.keys(outputNodeNameToKey).length === 0) {
+      allNodes.forEach((key) => {
+        const node = nodes[key];
+        if (node.children.length === 0) {
+          outputs.push(node);
+        }
+      });
+    } else {
+      Object.keys(outputNodeNameToKey).forEach((name) => {
+        const [nodeName] = getNodeNameAndIndex(name);
+        const node = nodes[nodeName];
+        if (node != null) {
+          node.signatureKey = outputNodeNameToKey[name];
+          outputs.push(node);
+        }
+      });
+    }
+    if (Object.keys(inputNodeNameToKey).length > 0) {
+      Object.keys(inputNodeNameToKey).forEach((name) => {
+        const [nodeName] = getNodeNameAndIndex(name);
+        const node = nodes[nodeName];
+        if (node) {
+          node.signatureKey = inputNodeNameToKey[name];
+          inputs.push(node);
+        }
+      });
+    } else {
+      inputs = placeholders;
+    }
+    let functions = {};
+    if (graph.library != null && graph.library.function != null) {
+      functions = graph.library.function.reduce((functions2, func2) => {
+        functions2[func2.signature.name] = this.mapFunction(func2);
+        return functions2;
+      }, {});
+    }
+    const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };
+    if (initNodes.length > 0) {
+      result.initNodes = initNodes;
+    }
+    return result;
+  }
+  mapSignatureEntries(entries) {
+    return Object.keys(entries || {}).reduce((prev, curr) => {
+      prev[entries[curr].name] = curr;
+      return prev;
+    }, {});
+  }
+  mapNode(node) {
+    const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};
+    if (node.attr == null) {
+      node.attr = {};
+    }
+    const newNode = {
+      name: node.name,
+      op: node.op,
+      category: mapper.category,
+      inputNames: (node.input || []).map((input2) => input2.startsWith("^") ? input2.slice(1) : input2),
+      inputs: [],
+      children: [],
+      inputParams: {},
+      attrParams: {},
+      rawAttrs: node.attr,
+      outputs: mapper.outputs
+    };
+    if (mapper.inputs != null) {
+      newNode.inputParams = mapper.inputs.reduce((map, param) => {
+        map[param.name] = {
+          type: param.type,
+          inputIndexStart: param.start,
+          inputIndexEnd: param.end
+        };
+        return map;
+      }, {});
+    }
+    if (mapper.attrs != null) {
+      newNode.attrParams = mapper.attrs.reduce((map, param) => {
+        const type = param.type;
+        let value = void 0;
+        switch (param.type) {
+          case "string":
+            value = getStringParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "string[]":
+            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "number":
+            value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "number[]":
+            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "bool":
+            value = getBoolParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "bool[]":
+            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "shape":
+            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "shape[]":
+            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "dtype":
+            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "dtype[]":
+            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "func":
+            value = getFuncParam(node.attr, param.tfName, param.defaultValue);
+            if (value === void 0 && !!param.tfDeprecatedName) {
+              value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);
+            }
+            break;
+          case "tensor":
+          case "tensors":
+            break;
+          default:
+            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);
+        }
+        map[param.name] = { value, type };
+        return map;
+      }, {});
+    }
+    return newNode;
+  }
+  mapFunction(functionDef) {
+    const tfNodes = functionDef.nodeDef;
+    const placeholders = [];
+    const weights = [];
+    let nodes = {};
+    if (tfNodes != null) {
+      nodes = tfNodes.reduce((map, node) => {
+        map[node.name] = this.mapNode(node);
+        if (node.op === "Const") {
+          weights.push(map[node.name]);
+        }
+        return map;
+      }, {});
+    }
+    const inputs = [];
+    const outputs = [];
+    functionDef.signature.inputArg.forEach((arg) => {
+      const [nodeName] = getNodeNameAndIndex(arg.name);
+      const node = {
+        name: nodeName,
+        op: "Placeholder",
+        inputs: [],
+        inputNames: [],
+        category: "graph",
+        inputParams: {},
+        attrParams: { dtype: { value: parseDtypeParam(arg.type), type: "dtype" } },
+        children: []
+      };
+      node.signatureKey = arg.name;
+      inputs.push(node);
+      nodes[nodeName] = node;
+    });
+    const allNodes = Object.keys(nodes);
+    allNodes.forEach((key) => {
+      const node = nodes[key];
+      node.inputNames.forEach((name, index) => {
+        const [nodeName, , outputName] = getNodeNameAndIndex(name);
+        const inputNode = nodes[nodeName];
+        if (inputNode.outputs != null) {
+          const outputIndex = inputNode.outputs.indexOf(outputName);
+          if (outputIndex !== -1) {
+            const inputName = `${nodeName}:${outputIndex}`;
+            node.inputNames[index] = inputName;
+          }
+        }
+        node.inputs.push(inputNode);
+        inputNode.children.push(node);
+      });
+    });
+    const returnNodeMap = functionDef.ret;
+    functionDef.signature.outputArg.forEach((output) => {
+      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);
+      const node = nodes[nodeName];
+      if (node != null) {
+        node.defaultOutput = index;
+        outputs.push(node);
+      }
+    });
+    const signature = this.mapArgsToSignature(functionDef);
+    return { nodes, inputs, outputs, weights, placeholders, signature };
+  }
+  mapArgsToSignature(functionDef) {
+    return {
+      methodName: functionDef.signature.name,
+      inputs: functionDef.signature.inputArg.reduce((map, arg) => {
+        map[arg.name] = this.mapArgToTensorInfo(arg);
+        return map;
+      }, {}),
+      outputs: functionDef.signature.outputArg.reduce((map, arg) => {
+        map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);
+        return map;
+      }, {})
+    };
+  }
+  mapArgToTensorInfo(arg, nameMap2) {
+    let name = arg.name;
+    if (nameMap2 != null) {
+      name = nameMap2[name];
+    }
+    return { name, dtype: arg.type };
+  }
+};
+function decodeBase64(text) {
+  const global2 = env().global;
+  if (typeof global2.atob !== "undefined") {
+    return global2.atob(text);
+  } else if (typeof Buffer !== "undefined") {
+    return new Buffer(text, "base64").toString();
+  } else {
+    throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
+  }
+}
+function parseStringParam(s2, keepCase) {
+  const value = Array.isArray(s2) ? String.fromCharCode.apply(null, s2) : decodeBase64(s2);
+  return keepCase ? value : value.toLowerCase();
+}
+function getStringParam(attrs, name, def, keepCase = false) {
+  const param = attrs[name];
+  if (param != null) {
+    return parseStringParam(param.s, keepCase);
+  }
+  return def;
+}
+function getBoolParam(attrs, name, def) {
+  const param = attrs[name];
+  return param ? param.b : def;
+}
+function getNumberParam(attrs, name, def) {
+  const param = attrs[name] || {};
+  const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def;
+  return typeof value === "number" ? value : parseInt(value, 10);
+}
+function parseDtypeParam(value) {
+  if (typeof value === "string") {
+    value = DataType[value];
+  }
+  switch (value) {
+    case DataType.DT_FLOAT:
+    case DataType.DT_HALF:
+      return "float32";
+    case DataType.DT_INT32:
+    case DataType.DT_INT64:
+    case DataType.DT_INT8:
+    case DataType.DT_UINT8:
+      return "int32";
+    case DataType.DT_BOOL:
+      return "bool";
+    case DataType.DT_DOUBLE:
+      return "float32";
+    case DataType.DT_STRING:
+      return "string";
+    default:
+      return null;
+  }
+}
+function getFuncParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.func) {
+    return param.func.name;
+  }
+  return def;
+}
+function getDtypeParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.type) {
+    return parseDtypeParam(param.type);
+  }
+  return def;
+}
+function getDtypeArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.list && param.list.type) {
+    return param.list.type.map((v) => parseDtypeParam(v));
+  }
+  return def;
+}
+function parseTensorShapeParam(shape) {
+  if (shape.unknownRank) {
+    return void 0;
+  }
+  if (shape.dim != null) {
+    return shape.dim.map((dim) => typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10));
+  }
+  return [];
+}
+function getTensorShapeParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.shape) {
+    return parseTensorShapeParam(param.shape);
+  }
+  return def;
+}
+function getNumericArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param) {
+    return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === "number" ? v : parseInt(v, 10));
+  }
+  return def;
+}
+function getStringArrayParam(attrs, name, def, keepCase = false) {
+  const param = attrs[name];
+  if (param && param.list && param.list.s) {
+    return param.list.s.map((v) => {
+      return parseStringParam(v, keepCase);
+    });
+  }
+  return def;
+}
+function getTensorShapeArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.list && param.list.shape) {
+    return param.list.shape.map((v) => {
+      return parseTensorShapeParam(v);
+    });
+  }
+  return def;
+}
+function getBoolArrayParam(attrs, name, def) {
+  const param = attrs[name];
+  if (param && param.list && param.list.b) {
+    return param.list.b;
+  }
+  return def;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js
+var NodeValueImpl = class {
+  constructor(node, tensorMap, context) {
+    this.node = node;
+    this.tensorMap = tensorMap;
+    this.context = context;
+    this.inputs = [];
+    this.attrs = {};
+    this.inputs = node.inputNames.map((name) => this.getInput(name));
+    if (node.rawAttrs != null) {
+      this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => {
+        attrs[key] = this.getAttr(key);
+        return attrs;
+      }, {});
+    }
+  }
+  getInput(name) {
+    return getTensor(name, this.tensorMap, this.context);
+  }
+  getAttr(name, defaultValue) {
+    const value = this.node.rawAttrs[name];
+    if (value.tensor != null) {
+      return getTensor(name, this.tensorMap, this.context);
+    }
+    if (value.i != null || value.f != null) {
+      return getNumberParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.s != null) {
+      return getStringParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.b != null) {
+      return getBoolParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.shape != null) {
+      return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.type != null) {
+      return getDtypeParam(this.node.rawAttrs, name, defaultValue);
+    }
+    if (value.list != null) {
+      if (value.list.i != null || value.list.f != null) {
+        return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.s != null) {
+        return getStringArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.shape != null) {
+        return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.b != null) {
+        return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+      if (value.list.type != null) {
+        return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);
+      }
+    }
+    return defaultValue;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js
+var executeOp = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "BiasAdd":
+    case "AddV2":
+    case "Add": {
+      return [add2(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "AddN": {
+      return [addN(getParamValue("tensors", node, tensorMap, context))];
+    }
+    case "FloorMod":
+    case "Mod":
+      return [mod(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    case "Mul":
+      return [mul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    case "RealDiv":
+    case "Div": {
+      return [div(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "DivNoNan": {
+      return [divNoNan(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "FloorDiv": {
+      return [floorDiv(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Sub": {
+      return [sub(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Minimum": {
+      return [minimum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Maximum": {
+      return [maximum(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Pow": {
+      return [pow(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "SquaredDifference": {
+      return [squaredDifference(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js
+var executeOp2 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Abs":
+    case "ComplexAbs":
+      return [abs(getParamValue("x", node, tensorMap, context))];
+    case "Acos":
+      return [acos(getParamValue("x", node, tensorMap, context))];
+    case "Acosh":
+      return [acosh(getParamValue("x", node, tensorMap, context))];
+    case "Asin":
+      return [asin(getParamValue("x", node, tensorMap, context))];
+    case "Asinh":
+      return [asinh(getParamValue("x", node, tensorMap, context))];
+    case "Atan":
+      return [atan(getParamValue("x", node, tensorMap, context))];
+    case "Atan2":
+      return [atan2(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context))];
+    case "Atanh":
+      return [atanh(getParamValue("x", node, tensorMap, context))];
+    case "Ceil":
+      return [ceil(getParamValue("x", node, tensorMap, context))];
+    case "Complex":
+      return [complex(getParamValue("real", node, tensorMap, context), getParamValue("imag", node, tensorMap, context))];
+    case "Cos":
+      return [cos(getParamValue("x", node, tensorMap, context))];
+    case "Cosh":
+      return [cosh(getParamValue("x", node, tensorMap, context))];
+    case "Elu":
+      return [elu(getParamValue("x", node, tensorMap, context))];
+    case "Erf":
+      return [erf(getParamValue("x", node, tensorMap, context))];
+    case "Exp":
+      return [exp(getParamValue("x", node, tensorMap, context))];
+    case "Expm1": {
+      return [expm1(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Floor":
+      return [floor(getParamValue("x", node, tensorMap, context))];
+    case "Log":
+      return [log2(getParamValue("x", node, tensorMap, context))];
+    case "Log1p": {
+      return [log1p(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Imag":
+      return [imag(getParamValue("x", node, tensorMap, context))];
+    case "Neg":
+      return [neg(getParamValue("x", node, tensorMap, context))];
+    case "Reciprocal": {
+      return [reciprocal(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Real":
+      return [real(getParamValue("x", node, tensorMap, context))];
+    case "Relu":
+      return [relu(getParamValue("x", node, tensorMap, context))];
+    case "Round": {
+      return [round2(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Selu":
+      return [selu(getParamValue("x", node, tensorMap, context))];
+    case "Sigmoid":
+      return [sigmoid(getParamValue("x", node, tensorMap, context))];
+    case "Sin":
+      return [sin(getParamValue("x", node, tensorMap, context))];
+    case "Sign": {
+      return [sign(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Sinh": {
+      return [sinh(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Softplus": {
+      return [softplus(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Sqrt": {
+      return [sqrt(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Square": {
+      return [square(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Tanh": {
+      return [tanh2(getParamValue("x", node, tensorMap, context))];
+    }
+    case "Tan":
+      return [tan(getParamValue("x", node, tensorMap, context))];
+    case "ClipByValue":
+      return [clipByValue(getParamValue("x", node, tensorMap, context), getParamValue("clipValueMin", node, tensorMap, context), getParamValue("clipValueMax", node, tensorMap, context))];
+    case "Relu6":
+      return [relu6(getParamValue("x", node, tensorMap, context))];
+    case "Rsqrt":
+      return [rsqrt(getTensor(node.inputNames[0], tensorMap, context))];
+    case "Prod":
+      return [prod(getParamValue("x", node, tensorMap, context), getParamValue("axes", node, tensorMap, context))];
+    case "LeakyRelu":
+      return [leakyRelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))];
+    case "Prelu":
+      return [prelu(getParamValue("x", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context))];
+    case "IsNan":
+      return [isNaN2(getTensor(node.inputNames[0], tensorMap, context))];
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js
+function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") {
+  if (typeof shapeA === "number" || typeof shapeB === "number") {
+    return;
+  }
+  util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+  for (let i2 = 0; i2 < shapeA.length; i2++) {
+    const dim0 = shapeA[i2];
+    const dim1 = shapeB[i2];
+    util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
+  }
+}
+function fullDefinedShape(elementShape) {
+  if (typeof elementShape === "number" || elementShape.some((dim) => dim < 0)) {
+    return false;
+  }
+  return true;
+}
+function inferElementShape(listElementShape, tensors, elementShape) {
+  let partialShape = mergeElementShape(listElementShape, elementShape);
+  const notfullDefinedShape = !fullDefinedShape(partialShape);
+  if (notfullDefinedShape && tensors.length === 0) {
+    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);
+  }
+  if (notfullDefinedShape) {
+    tensors.forEach((tensor2) => {
+      partialShape = mergeElementShape(tensor2.shape, partialShape);
+    });
+  }
+  if (!fullDefinedShape(partialShape)) {
+    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);
+  }
+  return partialShape;
+}
+function mergeElementShape(elementShapeA, elementShapeB) {
+  if (typeof elementShapeA === "number") {
+    return elementShapeB;
+  }
+  if (typeof elementShapeB === "number") {
+    return elementShapeA;
+  }
+  if (elementShapeA.length !== elementShapeB.length) {
+    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);
+  }
+  const result = [];
+  for (let i2 = 0; i2 < elementShapeA.length; ++i2) {
+    const dim0 = elementShapeA[i2];
+    const dim1 = elementShapeB[i2];
+    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
+      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);
+    }
+    result[i2] = dim0 >= 0 ? dim0 : dim1;
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js
+var TensorArray = class {
+  constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {
+    this.name = name;
+    this.dtype = dtype;
+    this.maxSize = maxSize;
+    this.elementShape = elementShape;
+    this.identicalElementShapes = identicalElementShapes;
+    this.dynamicSize = dynamicSize;
+    this.clearAfterRead = clearAfterRead;
+    this.tensors = [];
+    this.closed_ = false;
+    this.idTensor = scalar(0);
+    keep(this.idTensor);
+  }
+  get id() {
+    return this.idTensor.id;
+  }
+  get closed() {
+    return this.closed_;
+  }
+  clearAndClose(keepIds) {
+    this.tensors.forEach((tensor2) => {
+      if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {
+        tensor2.tensor.dispose();
+      }
+    });
+    this.tensors = [];
+    this.closed_ = true;
+    this.idTensor.dispose();
+  }
+  size() {
+    return this.tensors.length;
+  }
+  read(index) {
+    if (this.closed_) {
+      throw new Error(`TensorArray ${this.name} has already been closed.`);
+    }
+    if (index < 0 || index >= this.size()) {
+      throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);
+    }
+    const tensorWithState = this.tensors[index];
+    if (tensorWithState.cleared) {
+      throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);
+    }
+    if (this.clearAfterRead) {
+      tensorWithState.cleared = true;
+    }
+    tensorWithState.read = true;
+    return tensorWithState.tensor;
+  }
+  readMany(indices) {
+    return indices.map((index) => this.read(index));
+  }
+  write(index, tensor2) {
+    if (this.closed_) {
+      throw new Error(`TensorArray ${this.name} has already been closed.`);
+    }
+    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {
+      throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);
+    }
+    const t2 = this.tensors[index] || {};
+    if (tensor2.dtype !== this.dtype) {
+      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
+          because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);
+    }
+    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {
+      this.elementShape = tensor2.shape;
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);
+    if (t2.read) {
+      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);
+    }
+    if (t2.written) {
+      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);
+    }
+    t2.tensor = tensor2;
+    keep(tensor2);
+    t2.written = true;
+    this.tensors[index] = t2;
+  }
+  writeMany(indices, tensors) {
+    if (indices.length !== tensors.length) {
+      throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
+    }
+    indices.forEach((i2, index) => this.write(i2, tensors[index]));
+  }
+  gather(indices, dtype) {
+    if (!!dtype && dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);
+    }
+    if (!indices) {
+      indices = [];
+      for (let i2 = 0; i2 < this.size(); i2++) {
+        indices.push(i2);
+      }
+    } else {
+      indices = indices.slice(0, this.size());
+    }
+    if (indices.length === 0) {
+      return tensor([], [0].concat(this.elementShape));
+    }
+    const tensors = this.readMany(indices);
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: ");
+    return stack(tensors, 0);
+  }
+  concat(dtype) {
+    if (!!dtype && dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);
+    }
+    if (this.size() === 0) {
+      return tensor([], [0].concat(this.elementShape));
+    }
+    const indices = [];
+    for (let i2 = 0; i2 < this.size(); i2++) {
+      indices.push(i2);
+    }
+    const tensors = this.readMany(indices);
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
+    return concat(tensors, 0);
+  }
+  scatter(indices, tensor2) {
+    if (tensor2.dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);
+    }
+    if (indices.length !== tensor2.shape[0]) {
+      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);
+    }
+    const maxIndex = Math.max(...indices);
+    if (!this.dynamicSize && maxIndex >= this.maxSize) {
+      throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);
+    }
+    this.writeMany(indices, unstack(tensor2, 0));
+  }
+  split(length, tensor2) {
+    if (tensor2.dtype !== this.dtype) {
+      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);
+    }
+    let totalLength = 0;
+    const cumulativeLengths = length.map((len) => {
+      totalLength += len;
+      return totalLength;
+    });
+    if (totalLength !== tensor2.shape[0]) {
+      throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);
+    }
+    if (!this.dynamicSize && length.length !== this.maxSize) {
+      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);
+    }
+    const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;
+    const tensors = [];
+    tidy(() => {
+      tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
+      for (let i2 = 0; i2 < length.length; ++i2) {
+        const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+        const indices2 = [0, previousLength, 0];
+        const sizes = [1, length[i2], elementPerRow];
+        tensors[i2] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
+      }
+      return tensors;
+    });
+    const indices = [];
+    for (let i2 = 0; i2 < length.length; i2++) {
+      indices[i2] = i2;
+    }
+    this.writeMany(indices, tensors);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js
+var TensorList = class {
+  constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {
+    this.tensors = tensors;
+    this.elementShape = elementShape;
+    this.elementDtype = elementDtype;
+    if (tensors != null) {
+      tensors.forEach((tensor2) => {
+        if (elementDtype !== tensor2.dtype) {
+          throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);
+        }
+        assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, "TensorList shape mismatch: ");
+        keep(tensor2);
+      });
+    }
+    this.idTensor = scalar(0);
+    this.maxNumElements = maxNumElements;
+    keep(this.idTensor);
+  }
+  get id() {
+    return this.idTensor.id;
+  }
+  copy() {
+    return new TensorList([...this.tensors], this.elementShape, this.elementDtype);
+  }
+  clearAndClose(keepIds) {
+    this.tensors.forEach((tensor2) => {
+      if (keepIds == null || !keepIds.has(tensor2.id)) {
+        tensor2.dispose();
+      }
+    });
+    this.tensors.length = 0;
+    this.idTensor.dispose();
+  }
+  size() {
+    return this.tensors.length;
+  }
+  stack(elementShape, elementDtype, numElements = -1) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    if (numElements !== -1 && this.tensors.length !== numElements) {
+      throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);
+    }
+    assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, "TensorList shape mismatch: ");
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    return tidy(() => {
+      const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));
+      return stack(reshapedTensors, 0);
+    });
+  }
+  popBack(elementShape, elementDtype) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    if (this.size() === 0) {
+      throw new Error("Trying to pop from an empty list.");
+    }
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    const tensor2 = this.tensors.pop();
+    assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, "TensorList shape mismatch: ");
+    return reshape(tensor2, outputElementShape);
+  }
+  pushBack(tensor2) {
+    if (tensor2.dtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);
+    }
+    assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, "TensorList shape mismatch: ");
+    if (this.maxNumElements === this.size()) {
+      throw new Error(`Trying to push element into a full list.`);
+    }
+    keep(tensor2);
+    this.tensors.push(tensor2);
+  }
+  resize(size) {
+    if (size < 0) {
+      throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);
+    }
+    if (this.maxNumElements !== -1 && size > this.maxNumElements) {
+      throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);
+    }
+    const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);
+    destTensorList.tensors.length = size;
+    for (let i2 = 0; i2 < Math.min(this.tensors.length, size); ++i2) {
+      destTensorList.tensors[i2] = this.tensors[i2];
+    }
+    return destTensorList;
+  }
+  getItem(elementIndex, elementShape, elementDtype) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    if (elementIndex < 0 || elementIndex > this.tensors.length) {
+      throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);
+    }
+    if (this.tensors[elementIndex] == null) {
+      throw new Error(`element at index ${elementIndex} is null.`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: ");
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    return reshape(this.tensors[elementIndex], outputElementShape);
+  }
+  setItem(elementIndex, tensor2) {
+    if (tensor2.dtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);
+    }
+    if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {
+      throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, "TensorList shape mismatch: ");
+    keep(tensor2);
+    this.tensors[elementIndex] = tensor2;
+  }
+  gather(indices, elementDtype, elementShape) {
+    if (elementDtype !== this.elementDtype) {
+      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
+    indices = indices.slice(0, this.size());
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    if (indices.length === 0) {
+      return tensor([], [0].concat(outputElementShape));
+    }
+    return tidy(() => {
+      const tensors = indices.map((i2) => reshape(this.tensors[i2], outputElementShape));
+      return stack(tensors, 0);
+    });
+  }
+  concat(elementDtype, elementShape) {
+    if (!!elementDtype && elementDtype !== this.elementDtype) {
+      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);
+    }
+    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, "TensorList shape mismatch: ");
+    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);
+    if (this.size() === 0) {
+      return tensor([], [0].concat(outputElementShape));
+    }
+    return tidy(() => {
+      const tensors = this.tensors.map((t2) => reshape(t2, outputElementShape));
+      return concat(tensors, 0);
+    });
+  }
+};
+function fromTensor(tensor2, elementShape, elementDtype) {
+  const dtype = tensor2.dtype;
+  if (tensor2.shape.length < 1) {
+    throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);
+  }
+  if (tensor2.dtype !== elementDtype) {
+    throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);
+  }
+  const tensorElementShape = tensor2.shape.slice(1);
+  assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, "TensorList shape mismatch: ");
+  const tensorList = unstack(tensor2);
+  return new TensorList(tensorList, elementShape, dtype);
+}
+function reserve(elementShape, elementDtype, numElements) {
+  return new TensorList([], elementShape, elementDtype, numElements);
+}
+function scatter(tensor2, indices, elementShape, numElements) {
+  if (indices.length !== tensor2.shape[0]) {
+    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);
+  }
+  const maxIndex = Math.max(...indices);
+  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {
+    throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);
+  }
+  const list = new TensorList([], elementShape, tensor2.dtype, numElements);
+  const tensors = unstack(tensor2, 0);
+  indices.forEach((value, index) => {
+    list.setItem(value, tensors[index]);
+  });
+  return list;
+}
+function split2(tensor2, length, elementShape) {
+  let totalLength = 0;
+  const cumulativeLengths = length.map((len) => {
+    totalLength += len;
+    return totalLength;
+  });
+  if (totalLength !== tensor2.shape[0]) {
+    throw new Error(`Expected sum of lengths to be equal to
+          tensor.shape[0], but sum of lengths is
+        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);
+  }
+  const shapeWithoutFirstDim = tensor2.shape.slice(1);
+  const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);
+  const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;
+  const tensors = tidy(() => {
+    const tensors2 = [];
+    tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
+    for (let i2 = 0; i2 < length.length; ++i2) {
+      const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+      const indices = [0, previousLength, 0];
+      const sizes = [1, length[i2], elementPerRow];
+      tensors2[i2] = reshape(slice(tensor2, indices, sizes), outputElementShape);
+    }
+    tensor2.dispose();
+    return tensors2;
+  });
+  const list = new TensorList([], elementShape, tensor2.dtype, length.length);
+  for (let i2 = 0; i2 < tensors.length; i2++) {
+    list.setItem(i2, tensors[i2]);
+  }
+  return list;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js
+var executeOp3 = async (node, tensorMap, context) => {
+  switch (node.op) {
+    case "If":
+    case "StatelessIf": {
+      const thenFunc = getParamValue("thenBranch", node, tensorMap, context);
+      const elseFunc = getParamValue("elseBranch", node, tensorMap, context);
+      const cond = getParamValue("cond", node, tensorMap, context);
+      const args = getParamValue("args", node, tensorMap, context);
+      const condValue = await cond.data();
+      if (condValue[0]) {
+        return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
+      } else {
+        return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
+      }
+    }
+    case "While":
+    case "StatelessWhile": {
+      const bodyFunc = getParamValue("body", node, tensorMap, context);
+      const condFunc = getParamValue("cond", node, tensorMap, context);
+      const args = getParamValue("args", node, tensorMap, context);
+      const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
+      const argIds = args.map((tensor2) => tensor2.id);
+      let condValue = await condResult[0].data();
+      condResult.forEach((tensor2) => {
+        if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {
+          tensor2.dispose();
+        }
+      });
+      let result = args;
+      while (condValue[0]) {
+        const origResult = result;
+        result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
+        const resultIds = result.map((tensor2) => tensor2.id);
+        origResult.forEach((tensor2) => {
+          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {
+            tensor2.dispose();
+          }
+        });
+        const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
+        condValue = await condResult2[0].data();
+        condResult2.forEach((tensor2) => {
+          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {
+            tensor2.dispose();
+          }
+        });
+      }
+      return result;
+    }
+    case "LoopCond": {
+      const pred = getParamValue("pred", node, tensorMap, context);
+      return [cloneTensor(pred)];
+    }
+    case "Switch": {
+      const pred = getParamValue("pred", node, tensorMap, context);
+      let data = getParamValue("data", node, tensorMap, context);
+      if (!data.kept) {
+        data = cloneTensor(data);
+      }
+      return (await pred.data())[0] ? [void 0, data] : [data, void 0];
+    }
+    case "Merge": {
+      const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);
+      if (inputName) {
+        const data = getTensor(inputName, tensorMap, context);
+        return [cloneTensor(data)];
+      }
+      return void 0;
+    }
+    case "Enter": {
+      const frameId = getParamValue("frameName", node, tensorMap, context);
+      const data = getParamValue("tensor", node, tensorMap, context);
+      context.enterFrame(frameId);
+      return [cloneTensor(data)];
+    }
+    case "Exit": {
+      const data = getParamValue("tensor", node, tensorMap, context);
+      context.exitFrame();
+      return [cloneTensor(data)];
+    }
+    case "NextIteration": {
+      const data = getParamValue("tensor", node, tensorMap, context);
+      context.nextIteration();
+      return [cloneTensor(data)];
+    }
+    case "TensorArrayV3": {
+      const size = getParamValue("size", node, tensorMap, context);
+      const dtype = getParamValue("dtype", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const dynamicSize = getParamValue("dynamicSize", node, tensorMap, context);
+      const clearAfterRead = getParamValue("clearAfterRead", node, tensorMap, context);
+      const identicalElementShapes = getParamValue("identicalElementShapes", node, tensorMap, context);
+      const name = getParamValue("name", node, tensorMap, context);
+      const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
+      context.addTensorArray(tensorArray);
+      return [tensorArray.idTensor, scalar(1)];
+    }
+    case "TensorArrayWriteV3": {
+      const id = getParamValue("tensorArrayId", node, tensorMap, context);
+      const index = getParamValue("index", node, tensorMap, context);
+      const writeTensor = getParamValue("tensor", node, tensorMap, context);
+      const writeTensorArray = context.getTensorArray(id.id);
+      writeTensorArray.write(index, writeTensor);
+      return [writeTensorArray.idTensor];
+    }
+    case "TensorArrayReadV3": {
+      const readId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const readIndex = getParamValue("index", node, tensorMap, context);
+      const readTensorArray = context.getTensorArray(readId.id);
+      return [readTensorArray.read(readIndex)];
+    }
+    case "TensorArrayGatherV3": {
+      const gatherId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const gatherIndices = getParamValue("indices", node, tensorMap, context);
+      const gatherDtype = getParamValue("dtype", node, tensorMap, context);
+      const gatherTensorArray = context.getTensorArray(gatherId.id);
+      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];
+    }
+    case "TensorArrayScatterV3": {
+      const scatterId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const scatterIndices = getParamValue("indices", node, tensorMap, context);
+      const scatterTensor = getParamValue("tensor", node, tensorMap, context);
+      const scatterTensorArray = context.getTensorArray(scatterId.id);
+      scatterTensorArray.scatter(scatterIndices, scatterTensor);
+      return [scatterTensorArray.idTensor];
+    }
+    case "TensorArrayConcatV3": {
+      const concatId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const concatTensorArray = context.getTensorArray(concatId.id);
+      const concatDtype = getParamValue("dtype", node, tensorMap, context);
+      return [concatTensorArray.concat(concatDtype)];
+    }
+    case "TensorArraySplitV3": {
+      const splitId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const splitTensor = getParamValue("tensor", node, tensorMap, context);
+      const lengths = getParamValue("lengths", node, tensorMap, context);
+      const splitTensorArray = context.getTensorArray(splitId.id);
+      splitTensorArray.split(lengths, splitTensor);
+      return [splitTensorArray.idTensor];
+    }
+    case "TensorArraySizeV3": {
+      const sizeId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const sizeTensorArray = context.getTensorArray(sizeId.id);
+      return [scalar(sizeTensorArray.size(), "int32")];
+    }
+    case "TensorArrayCloseV3": {
+      const closeId = getParamValue("tensorArrayId", node, tensorMap, context);
+      const closeTensorArray = context.getTensorArray(closeId.id);
+      closeTensorArray.clearAndClose();
+      return [closeTensorArray.idTensor];
+    }
+    case "TensorListSetItem": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const index = getParamValue("index", node, tensorMap, context);
+      const writeTensor = getParamValue("tensor", node, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      tensorList.setItem(index, writeTensor);
+      return [tensorList.idTensor];
+    }
+    case "TensorListGetItem": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const readIndex = getParamValue("index", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const elementDType = getParamValue("elementDType", node, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [tensorList.getItem(readIndex, elementShape, elementDType)];
+    }
+    case "TensorListScatterV2":
+    case "TensorListScatter": {
+      const scatterIndices = getParamValue("indices", node, tensorMap, context);
+      const scatterTensor = getParamValue("tensor", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const numElements = getParamValue("numElements", node, tensorMap, context);
+      const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListReserve":
+    case "EmptyTensorList": {
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node, tensorMap, context);
+      let numElementsParam;
+      if (node.op === "TensorListReserve") {
+        numElementsParam = "numElements";
+      } else {
+        numElementsParam = "maxNumElements";
+      }
+      const numElements = getParamValue(numElementsParam, node, tensorMap, context);
+      const tensorList = reserve(elementShape, elementDtype, numElements);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListGather": {
+      const gatherId = getParamValue("tensorListId", node, tensorMap, context);
+      const gatherIndices = getParamValue("indices", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node, tensorMap, context);
+      const tensorList = context.getTensorList(gatherId.id);
+      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];
+    }
+    case "TensorListStack": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node, tensorMap, context);
+      const numElements = getParamValue("numElements", node, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [tensorList.stack(elementShape, elementDtype, numElements)];
+    }
+    case "TensorListFromTensor": {
+      const tensor2 = getParamValue("tensor", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const elementDtype = getParamValue("elementDType", node, tensorMap, context);
+      const tensorList = fromTensor(tensor2, elementShape, elementDtype);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListConcat":
+    case "TensorListConcatV2": {
+      const concatId = getParamValue("tensorListId", node, tensorMap, context);
+      const tensorList = context.getTensorList(concatId.id);
+      const concatDtype = getParamValue("dtype", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      return [tensorList.concat(concatDtype, elementShape)];
+    }
+    case "TensorListPushBack": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const writeTensor = getParamValue("tensor", node, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      tensorList.pushBack(writeTensor);
+      return [tensorList.idTensor];
+    }
+    case "TensorListPopBack": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const elementDType = getParamValue("elementDType", node, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [tensorList.popBack(elementShape, elementDType)];
+    }
+    case "TensorListSplit": {
+      const splitTensor = getParamValue("tensor", node, tensorMap, context);
+      const elementShape = getParamValue("elementShape", node, tensorMap, context);
+      const lengths = getParamValue("lengths", node, tensorMap, context);
+      const tensorList = split2(splitTensor, lengths, elementShape);
+      context.addTensorList(tensorList);
+      return [tensorList.idTensor];
+    }
+    case "TensorListLength": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const tensorList = context.getTensorList(idTensor.id);
+      return [scalar(tensorList.size(), "int32")];
+    }
+    case "TensorListResize": {
+      const idTensor = getParamValue("tensorListId", node, tensorMap, context);
+      const size = getParamValue("size", node, tensorMap, context);
+      const srcTensorList = context.getTensorList(idTensor.id);
+      const destTensorList = srcTensorList.resize(size);
+      context.addTensorList(destTensorList);
+      return [destTensorList.idTensor];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js
+function fusedConvAndDepthWiseParams(node, tensorMap, context) {
+  const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context);
+  const isBiasAdd = extraOp === "biasadd";
+  const noBiasAdd = !isBiasAdd;
+  const isPrelu = activationFunc === "prelu";
+  const isBatchNorm = extraOp === "fusedbatchnorm";
+  const numArgs = getParamValue("numArgs", node, tensorMap, context);
+  if (isBiasAdd) {
+    if (isPrelu && numArgs !== 2) {
+      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
+    }
+    if (!isPrelu && isBiasAdd && numArgs !== 1) {
+      throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
+    }
+  }
+  if (isBatchNorm) {
+    throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
+  }
+  const stride = getParamValue("strides", node, tensorMap, context);
+  const pad3 = getPadding(node, tensorMap, context);
+  const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
+  const dilations = getParamValue("dilations", node, tensorMap, context);
+  let [biasArg, preluArg] = getParamValue("args", node, tensorMap, context);
+  if (noBiasAdd) {
+    preluArg = biasArg;
+    biasArg = void 0;
+  }
+  const leakyreluAlpha = getParamValue("leakyreluAlpha", node, tensorMap, context);
+  return {
+    stride,
+    pad: pad3,
+    dataFormat,
+    dilations,
+    biasArg,
+    preluArg,
+    activationFunc,
+    leakyreluAlpha
+  };
+}
+var executeOp4 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Conv1D": {
+      const stride = getParamValue("stride", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
+      const dilation = getParamValue("dilation", node, tensorMap, context);
+      return [conv1d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), stride, pad3, dataFormat, dilation)];
+    }
+    case "Conv2D": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getPadding(node, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
+      const dilations = getParamValue("dilations", node, tensorMap, context);
+      return [conv2d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];
+    }
+    case "_FusedConv2D": {
+      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);
+      return [fused_ops_exports.conv2d({
+        x: getParamValue("x", node, tensorMap, context),
+        filter: getParamValue("filter", node, tensorMap, context),
+        strides: [stride[1], stride[2]],
+        pad: pad3,
+        dataFormat,
+        dilations: [dilations[1], dilations[2]],
+        bias: biasArg,
+        activation: activationFunc,
+        preluActivationWeights: preluArg,
+        leakyreluAlpha
+      })];
+    }
+    case "FusedDepthwiseConv2dNative": {
+      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);
+      return [fused_ops_exports.depthwiseConv2d({
+        x: getParamValue("x", node, tensorMap, context),
+        filter: getParamValue("filter", node, tensorMap, context),
+        strides: [stride[1], stride[2]],
+        pad: pad3,
+        dataFormat,
+        dilations: [dilations[1], dilations[2]],
+        bias: biasArg,
+        activation: activationFunc,
+        preluActivationWeights: preluArg,
+        leakyreluAlpha
+      })];
+    }
+    case "Conv2DBackpropInput":
+    case "Conv2dTranspose": {
+      const shape = getParamValue("outputShape", node, tensorMap, context);
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getPadding(node, tensorMap, context);
+      return [conv2dTranspose(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)];
+    }
+    case "DepthwiseConv2dNative":
+    case "DepthwiseConv2d": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getPadding(node, tensorMap, context);
+      const dilations = getParamValue("dilations", node, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
+      return [depthwiseConv2d(getParamValue("input", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];
+    }
+    case "Conv3D": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
+      const dilations = getParamValue("dilations", node, tensorMap, context);
+      return [conv3d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];
+    }
+    case "AvgPool": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
+      return [avgPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];
+    }
+    case "MaxPool": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
+      return [maxPool(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];
+    }
+    case "MaxPoolWithArgmax": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
+      const includeBatchInIndex = getParamValue("includeBatchInIndex", node, tensorMap, context);
+      const { result, indexes } = maxPoolWithArgmax(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);
+      return [result, indexes];
+    }
+    case "AvgPool3D": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
+      return [avgPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];
+    }
+    case "MaxPool3D": {
+      const stride = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const kernelSize = getParamValue("kernelSize", node, tensorMap, context);
+      return [maxPool3d(getParamValue("x", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];
+    }
+    case "Dilation2D": {
+      const strides = getParamValue("strides", node, tensorMap, context);
+      const pad3 = getParamValue("pad", node, tensorMap, context);
+      const dilations = getParamValue("dilations", node, tensorMap, context);
+      const strideHeight = strides[1];
+      const strideWidth = strides[2];
+      const dilationHeight = dilations[1];
+      const dilationWidth = dilations[2];
+      return [dilation2d(getParamValue("x", node, tensorMap, context), getParamValue("filter", node, tensorMap, context), [strideHeight, strideWidth], pad3, [dilationHeight, dilationWidth], "NHWC")];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js
+var executeOp5 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Fill": {
+      const shape = getParamValue("shape", node, tensorMap, context);
+      const dtype = getParamValue("dtype", node, tensorMap, context);
+      const value = getParamValue("value", node, tensorMap, context);
+      return [fill(shape, value, dtype)];
+    }
+    case "LinSpace": {
+      const start = getParamValue("start", node, tensorMap, context);
+      const stop = getParamValue("stop", node, tensorMap, context);
+      const num = getParamValue("num", node, tensorMap, context);
+      return [linspace(start, stop, num)];
+    }
+    case "Multinomial": {
+      const logits = getParamValue("logits", node, tensorMap, context);
+      const numSamples = getParamValue("numSamples", node, tensorMap, context);
+      const seed = getParamValue("seed", node, tensorMap, context);
+      return [multinomial(logits, numSamples, seed)];
+    }
+    case "OneHot": {
+      const indices = getParamValue("indices", node, tensorMap, context);
+      const depth = getParamValue("depth", node, tensorMap, context);
+      const onValue = getParamValue("onValue", node, tensorMap, context);
+      const offValue = getParamValue("offValue", node, tensorMap, context);
+      return [oneHot(indices, depth, onValue, offValue)];
+    }
+    case "Ones": {
+      return [ones2(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
+    }
+    case "OnesLike": {
+      return [onesLike(getParamValue("x", node, tensorMap, context))];
+    }
+    case "RandomUniform": {
+      return [randomUniform(getParamValue("shape", node, tensorMap, context), getParamValue("minval", node, tensorMap, context), getParamValue("maxval", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
+    }
+    case "Range": {
+      const start = getParamValue("start", node, tensorMap, context);
+      const stop = getParamValue("stop", node, tensorMap, context);
+      const step5 = getParamValue("step", node, tensorMap, context);
+      return [range(start, stop, step5, getParamValue("dtype", node, tensorMap, context))];
+    }
+    case "TruncatedNormal": {
+      const shape = getParamValue("shape", node, tensorMap, context);
+      const mean5 = getParamValue("mean", node, tensorMap, context);
+      const stdDev = getParamValue("stdDev", node, tensorMap, context);
+      const seed = getParamValue("seed", node, tensorMap, context);
+      return [truncatedNormal(shape, mean5, stdDev, getParamValue("dtype", node, tensorMap, context), seed)];
+    }
+    case "Zeros": {
+      return [zeros(getParamValue("shape", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
+    }
+    case "ZerosLike": {
+      return [zerosLike(getParamValue("x", node, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js
+function nmsParams(node, tensorMap, context) {
+  const boxes = getParamValue("boxes", node, tensorMap, context);
+  const scores = getParamValue("scores", node, tensorMap, context);
+  const maxOutputSize = getParamValue("maxOutputSize", node, tensorMap, context);
+  const iouThreshold = getParamValue("iouThreshold", node, tensorMap, context);
+  const scoreThreshold = getParamValue("scoreThreshold", node, tensorMap, context);
+  const softNmsSigma = getParamValue("softNmsSigma", node, tensorMap, context);
+  return {
+    boxes,
+    scores,
+    maxOutputSize,
+    iouThreshold,
+    scoreThreshold,
+    softNmsSigma
+  };
+}
+var executeOp6 = async (node, tensorMap, context) => {
+  switch (node.op) {
+    case "NonMaxSuppressionV5": {
+      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);
+      const result = await image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+      return [result.selectedIndices, result.selectedScores];
+    }
+    case "NonMaxSuppressionV4": {
+      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);
+      const padToMaxOutputSize = getParamValue("padToMaxOutputSize", node, tensorMap, context);
+      const result = await image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+      return [result.selectedIndices, result.validOutputs];
+    }
+    case "NonMaxSuppressionV3":
+    case "NonMaxSuppressionV2": {
+      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);
+      return [await image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
+    }
+    case "Where": {
+      const condition = cast(getParamValue("condition", node, tensorMap, context), "bool");
+      const result = [await whereAsync(condition)];
+      condition.dispose();
+      return result;
+    }
+    case "ListDiff": {
+      return setdiff1dAsync(getParamValue("x", node, tensorMap, context), getParamValue("y", node, tensorMap, context));
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js
+var executeOp7 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "LowerBound": {
+      const sortedSequence = getParamValue("sortedSequence", node, tensorMap, context);
+      const values = getParamValue("values", node, tensorMap, context);
+      return [lowerBound(sortedSequence, values)];
+    }
+    case "TopKV2": {
+      const x = getParamValue("x", node, tensorMap, context);
+      const k = getParamValue("k", node, tensorMap, context);
+      const sorted = getParamValue("sorted", node, tensorMap, context);
+      const result = topk(x, k, sorted);
+      return [result.values, result.indices];
+    }
+    case "UpperBound": {
+      const sortedSequence = getParamValue("sortedSequence", node, tensorMap, context);
+      const values = getParamValue("values", node, tensorMap, context);
+      return [upperBound(sortedSequence, values)];
+    }
+    case "Unique": {
+      const x = getParamValue("x", node, tensorMap, context);
+      const result = unique(x);
+      return [result.values, result.indices];
+    }
+    case "UniqueV2": {
+      const x = getParamValue("x", node, tensorMap, context);
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const result = unique(x, axis);
+      return [result.values, result.indices];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js
+var executeOp8 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Const": {
+      return tensorMap[node.name];
+    }
+    case "PlaceholderWithDefault":
+      const def = getParamValue("default", node, tensorMap, context);
+      return [getTensor(node.name, tensorMap, context) || def];
+    case "Placeholder":
+      return [getTensor(node.name, tensorMap, context)];
+    case "Identity":
+    case "StopGradient":
+    case "FakeQuantWithMinMaxVars": {
+      const data2 = getParamValue("x", node, tensorMap, context);
+      return [cloneTensor(data2)];
+    }
+    case "IdentityN":
+      return getParamValue("x", node, tensorMap, context).map((t2) => cloneTensor(t2));
+    case "Snapshot":
+      const snapshot = getParamValue("x", node, tensorMap, context);
+      return [cloneTensor(snapshot)];
+    case "Shape":
+      return [tensor1d(getParamValue("x", node, tensorMap, context).shape, "int32")];
+    case "ShapeN":
+      return getParamValue("x", node, tensorMap, context).map((t2) => tensor1d(t2.shape));
+    case "Size":
+      return [scalar(getParamValue("x", node, tensorMap, context).size, "int32")];
+    case "Rank":
+      return [scalar(getParamValue("x", node, tensorMap, context).rank, "int32")];
+    case "NoOp":
+      return [scalar(1)];
+    case "Print":
+      const input2 = getParamValue("x", node, tensorMap, context);
+      const data = getParamValue("data", node, tensorMap, context);
+      const message = getParamValue("message", node, tensorMap, context);
+      const summarize = getParamValue("summarize", node, tensorMap, context);
+      console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
+      console.log(message);
+      for (let i2 = 0; i2 < data.length; i2++) {
+        console.log(Array.prototype.slice.call(data[i2].dataSync()).slice(0, summarize));
+      }
+      return [input2];
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js
+var HashTable = class {
+  constructor(keyDType, valueDType) {
+    this.keyDType = keyDType;
+    this.valueDType = valueDType;
+    this.handle = scalar(0);
+    this.tensorMap = /* @__PURE__ */ new Map();
+    keep(this.handle);
+  }
+  get id() {
+    return this.handle.id;
+  }
+  clearAndClose() {
+    this.tensorMap.forEach((value) => value.dispose());
+    this.tensorMap.clear();
+    this.handle.dispose();
+  }
+  size() {
+    return this.tensorMap.size;
+  }
+  tensorSize() {
+    return scalar(this.size(), "int32");
+  }
+  async import(keys, values) {
+    this.checkKeyAndValueTensor(keys, values);
+    const $keys = await keys.data();
+    this.tensorMap.forEach((value) => value.dispose());
+    this.tensorMap.clear();
+    return tidy(() => {
+      const $values = unstack(values);
+      const keysLength = $keys.length;
+      const valuesLength = $values.length;
+      util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);
+      for (let i2 = 0; i2 < keysLength; i2++) {
+        const key = $keys[i2];
+        const value = $values[i2];
+        keep(value);
+        this.tensorMap.set(key, value);
+      }
+      return this.handle;
+    });
+  }
+  async find(keys, defaultValue) {
+    this.checkKeyAndValueTensor(keys, defaultValue);
+    const $keys = await keys.data();
+    return tidy(() => {
+      const result = [];
+      for (let i2 = 0; i2 < $keys.length; i2++) {
+        const key = $keys[i2];
+        const value = this.findWithDefault(key, defaultValue);
+        result.push(value);
+      }
+      return stack(result);
+    });
+  }
+  findWithDefault(key, defaultValue) {
+    const result = this.tensorMap.get(key);
+    return result != null ? result : defaultValue;
+  }
+  checkKeyAndValueTensor(key, value) {
+    if (key.dtype !== this.keyDType) {
+      throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);
+    }
+    if (value.dtype !== this.valueDType) {
+      throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js
+var executeOp9 = async (node, tensorMap, context, resourceManager) => {
+  switch (node.op) {
+    case "HashTable":
+    case "HashTableV2": {
+      const keyDType = getParamValue("keyDType", node, tensorMap, context);
+      const valueDType = getParamValue("valueDType", node, tensorMap, context);
+      const hashTable = new HashTable(keyDType, valueDType);
+      resourceManager.addHashTable(node.name, hashTable);
+      return [hashTable.handle];
+    }
+    case "LookupTableImport":
+    case "LookupTableImportV2": {
+      const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager);
+      const keys = getParamValue("keys", node, tensorMap, context);
+      const values = getParamValue("values", node, tensorMap, context);
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [await hashTable.import(keys, values)];
+    }
+    case "LookupTableFind":
+    case "LookupTableFindV2": {
+      const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager);
+      const keys = getParamValue("keys", node, tensorMap, context);
+      const defaultValue = getParamValue("defaultValue", node, tensorMap, context);
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [await hashTable.find(keys, defaultValue)];
+    }
+    case "LookupTableSize":
+    case "LookupTableSizeV2": {
+      const handle = getParamValue("tableHandle", node, tensorMap, context, resourceManager);
+      const hashTable = resourceManager.getHashTableById(handle.id);
+      return [hashTable.tensorSize()];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js
+var executeOp10 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "ResizeBilinear": {
+      const images = getParamValue("images", node, tensorMap, context);
+      const size = getParamValue("size", node, tensorMap, context);
+      const alignCorners = getParamValue("alignCorners", node, tensorMap, context);
+      const halfPixelCenters = getParamValue("halfPixelCenters", node, tensorMap, context);
+      return [image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
+    }
+    case "ResizeNearestNeighbor": {
+      const images = getParamValue("images", node, tensorMap, context);
+      const size = getParamValue("size", node, tensorMap, context);
+      const alignCorners = getParamValue("alignCorners", node, tensorMap, context);
+      const halfPixelCenters = getParamValue("halfPixelCenters", node, tensorMap, context);
+      return [image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
+    }
+    case "CropAndResize": {
+      const image2 = getParamValue("image", node, tensorMap, context);
+      const boxes = getParamValue("boxes", node, tensorMap, context);
+      const boxInd = getParamValue("boxInd", node, tensorMap, context);
+      const cropSize = getParamValue("cropSize", node, tensorMap, context);
+      const method = getParamValue("method", node, tensorMap, context);
+      const extrapolationValue = getParamValue("extrapolationValue", node, tensorMap, context);
+      return [image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)];
+    }
+    case "ImageProjectiveTransformV3": {
+      const images = getParamValue("images", node, tensorMap, context);
+      const transforms = getParamValue("transforms", node, tensorMap, context);
+      const outputShape = getParamValue("outputShape", node, tensorMap, context);
+      const fillValue = getParamValue("fillValue", node, tensorMap, context);
+      const interpolation = getParamValue("interpolation", node, tensorMap, context);
+      const fillMode = getParamValue("fillMode", node, tensorMap, context);
+      return [image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js
+var executeOp11 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Equal": {
+      return [equal(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "NotEqual": {
+      return [notEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Greater": {
+      return [greater(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "GreaterEqual": {
+      return [greaterEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Less": {
+      return [less(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "LessEqual": {
+      return [lessEqual(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "LogicalAnd": {
+      return [logicalAnd(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "LogicalNot": {
+      return [logicalNot(getParamValue("a", node, tensorMap, context))];
+    }
+    case "LogicalOr": {
+      return [logicalOr(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    case "Select":
+    case "SelectV2": {
+      return [where(getParamValue("condition", node, tensorMap, context), getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js
+var executeOp12 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "BatchMatMul":
+    case "BatchMatMulV2":
+    case "MatMul":
+      return [matMul(getParamValue("a", node, tensorMap, context), getParamValue("b", node, tensorMap, context), getParamValue("transposeA", node, tensorMap, context), getParamValue("transposeB", node, tensorMap, context))];
+    case "Einsum":
+      return [einsum(getParamValue("equation", node, tensorMap, context), ...getParamValue("tensors", node, tensorMap, context))];
+    case "Transpose":
+      return [transpose(getParamValue("x", node, tensorMap, context), getParamValue("perm", node, tensorMap, context))];
+    case "_FusedMatMul":
+      const [extraOp, activationFunc] = getParamValue("fusedOps", node, tensorMap, context);
+      const isBiasAdd = extraOp === "biasadd";
+      const isPrelu = activationFunc === "prelu";
+      const numArgs = getParamValue("numArgs", node, tensorMap, context);
+      const leakyreluAlpha = getParamValue("leakyreluAlpha", node, tensorMap, context);
+      if (isBiasAdd) {
+        if (isPrelu && numArgs !== 2) {
+          throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
+        }
+        if (!isPrelu && numArgs !== 1) {
+          throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
+        }
+      }
+      const [biasArg, preluArg] = getParamValue("args", node, tensorMap, context);
+      return [fused_ops_exports.matMul({
+        a: getParamValue("a", node, tensorMap, context),
+        b: getParamValue("b", node, tensorMap, context),
+        transposeA: getParamValue("transposeA", node, tensorMap, context),
+        transposeB: getParamValue("transposeB", node, tensorMap, context),
+        bias: biasArg,
+        activation: activationFunc,
+        preluActivationWeights: preluArg,
+        leakyreluAlpha
+      })];
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js
+var executeOp13 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "EuclideanNorm":
+      return [euclideanNorm(getParamValue("x", node, tensorMap, context), getParamValue("axis", node, tensorMap, context), getParamValue("keepDims", node, tensorMap, context))];
+    case "FusedBatchNorm":
+    case "FusedBatchNormV2": {
+      return [batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))];
+    }
+    case "FusedBatchNormV3": {
+      return [batchNorm(getParamValue("x", node, tensorMap, context), getParamValue("mean", node, tensorMap, context), getParamValue("variance", node, tensorMap, context), getParamValue("offset", node, tensorMap, context), getParamValue("scale", node, tensorMap, context), getParamValue("epsilon", node, tensorMap, context))];
+    }
+    case "LRN": {
+      return [localResponseNormalization(getParamValue("x", node, tensorMap, context), getParamValue("radius", node, tensorMap, context), getParamValue("bias", node, tensorMap, context), getParamValue("alpha", node, tensorMap, context), getParamValue("beta", node, tensorMap, context))];
+    }
+    case "Softmax": {
+      return [softmax(getParamValue("x", node, tensorMap, context))];
+    }
+    case "LogSoftmax": {
+      return [logSoftmax(getParamValue("x", node, tensorMap, context))];
+    }
+    case "SparseToDense": {
+      return [sparseToDense(getParamValue("sparseIndices", node, tensorMap, context), getParamValue("outputShape", node, tensorMap, context), getParamValue("sparseValues", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js
+var executeOp14 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Max": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [max(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "Mean": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [mean(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "Min": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [min(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "Sum": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [sum2(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "All": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [all(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "Any": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [any(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "ArgMax": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      return [argMax(getParamValue("x", node, tensorMap, context), axis)];
+    }
+    case "ArgMin": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      return [argMin(getParamValue("x", node, tensorMap, context), axis)];
+    }
+    case "Prod": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const keepDims = getParamValue("keepDims", node, tensorMap, context);
+      return [prod(getParamValue("x", node, tensorMap, context), axis, keepDims)];
+    }
+    case "Cumprod": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const exclusive = getParamValue("exclusive", node, tensorMap, context);
+      const reverse5 = getParamValue("reverse", node, tensorMap, context);
+      return [cumprod(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse5)];
+    }
+    case "Cumsum": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const exclusive = getParamValue("exclusive", node, tensorMap, context);
+      const reverse5 = getParamValue("reverse", node, tensorMap, context);
+      return [cumsum(getParamValue("x", node, tensorMap, context), axis, exclusive, reverse5)];
+    }
+    case "Bincount":
+      const x = getParamValue("x", node, tensorMap, context);
+      const weights = getParamValue("weights", node, tensorMap, context);
+      const size = getParamValue("size", node, tensorMap, context);
+      return [bincount(x, weights, size)];
+    case "DenseBincount": {
+      const x2 = getParamValue("x", node, tensorMap, context);
+      const weights2 = getParamValue("weights", node, tensorMap, context);
+      const size2 = getParamValue("size", node, tensorMap, context);
+      const binaryOutput = getParamValue("binaryOutput", node, tensorMap, context);
+      return [denseBincount(x2, weights2, size2, binaryOutput)];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js
+var executeOp15 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "ConcatV2":
+    case "Concat": {
+      const n = getParamValue("n", node, tensorMap, context);
+      const axis = getParamValue("axis", node, tensorMap, context);
+      let inputs = getParamValue("tensors", node, tensorMap, context);
+      inputs = inputs.slice(0, n);
+      return [concat(inputs, axis)];
+    }
+    case "Gather": {
+      const input2 = getParamValue("x", node, tensorMap, context);
+      const indices = getParamValue("indices", node, tensorMap, context);
+      return [gather(input2, cast(indices, "int32"), 0)];
+    }
+    case "GatherV2": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const batchDims = getParamValue("batchDims", node, tensorMap, context);
+      const input2 = getParamValue("x", node, tensorMap, context);
+      const indices = getParamValue("indices", node, tensorMap, context);
+      return [gather(input2, cast(indices, "int32"), axis, batchDims)];
+    }
+    case "Reverse": {
+      const dims = getParamValue("dims", node, tensorMap, context);
+      const axis = [];
+      for (let i2 = 0; i2 < dims.length; i2++) {
+        if (dims[i2]) {
+          axis.push(i2);
+        }
+      }
+      const input2 = getParamValue("x", node, tensorMap, context);
+      return [reverse(input2, axis)];
+    }
+    case "ReverseV2": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const input2 = getParamValue("x", node, tensorMap, context);
+      return [reverse(input2, axis)];
+    }
+    case "Slice": {
+      const begin = getParamValue("begin", node, tensorMap, context);
+      const size = getParamValue("size", node, tensorMap, context);
+      return [slice(getParamValue("x", node, tensorMap, context), begin, size)];
+    }
+    case "StridedSlice": {
+      const begin = getParamValue("begin", node, tensorMap, context);
+      const end = getParamValue("end", node, tensorMap, context);
+      const strides = getParamValue("strides", node, tensorMap, context);
+      const beginMask = getParamValue("beginMask", node, tensorMap, context);
+      const endMask = getParamValue("endMask", node, tensorMap, context);
+      const ellipsisMask = getParamValue("ellipsisMask", node, tensorMap, context);
+      const newAxisMask = getParamValue("newAxisMask", node, tensorMap, context);
+      const shrinkAxisMask = getParamValue("shrinkAxisMask", node, tensorMap, context);
+      const tensor2 = getParamValue("x", node, tensorMap, context);
+      return [stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];
+    }
+    case "Pack": {
+      return tidy(() => {
+        const axis = getParamValue("axis", node, tensorMap, context);
+        const tensors = getParamValue("tensors", node, tensorMap, context);
+        const shape = tensors[0].shape;
+        const squeezedShape = squeeze(tensors[0]).shape;
+        const mapped = tensors.map((tensor2) => {
+          const sameShape = util_exports.arraysEqual(tensor2.shape, shape);
+          if (!sameShape && !util_exports.arraysEqual(squeeze(tensor2).shape, squeezedShape)) {
+            throw new Error("the input tensors shape does not match");
+          }
+          return sameShape ? tensor2 : reshape(tensor2, shape);
+        });
+        return [stack(mapped, axis)];
+      });
+    }
+    case "Unpack": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const tensor2 = getParamValue("tensor", node, tensorMap, context);
+      return unstack(tensor2, axis);
+    }
+    case "Tile": {
+      const reps = getParamValue("reps", node, tensorMap, context);
+      return [tile(getParamValue("x", node, tensorMap, context), reps)];
+    }
+    case "Split":
+    case "SplitV": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      const numOrSizeSplits = getParamValue("numOrSizeSplits", node, tensorMap, context);
+      const tensor2 = getParamValue("x", node, tensorMap, context);
+      return split(tensor2, numOrSizeSplits, axis);
+    }
+    case "ScatterNd": {
+      const indices = getParamValue("indices", node, tensorMap, context);
+      const values = getParamValue("values", node, tensorMap, context);
+      const shape = getParamValue("shape", node, tensorMap, context);
+      return [scatterND(indices, values, shape)];
+    }
+    case "GatherNd": {
+      const x = getParamValue("x", node, tensorMap, context);
+      const indices = getParamValue("indices", node, tensorMap, context);
+      return [gatherND(x, indices)];
+    }
+    case "SparseToDense": {
+      const indices = getParamValue("sparseIndices", node, tensorMap, context);
+      const shape = getParamValue("outputShape", node, tensorMap, context);
+      const sparseValues = getParamValue("sparseValues", node, tensorMap, context);
+      const defaultValue = getParamValue("defaultValue", node, tensorMap, context);
+      return [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : cast(defaultValue, sparseValues.dtype))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js
+var executeOp16 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "SparseFillEmptyRows": {
+      const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = sparse.sparseFillEmptyRows(getParamValue("indices", node, tensorMap, context), getParamValue("values", node, tensorMap, context), getParamValue("denseShape", node, tensorMap, context), getParamValue("defaultValue", node, tensorMap, context));
+      return [
+        outputIndices,
+        outputValues,
+        emptyRowIndicator,
+        reverseIndexMap
+      ];
+    }
+    case "SparseReshape": {
+      const { outputIndices, outputShape } = sparse.sparseReshape(getParamValue("inputIndices", node, tensorMap, context), getParamValue("inputShape", node, tensorMap, context), getParamValue("newShape", node, tensorMap, context));
+      return [outputIndices, outputShape];
+    }
+    case "SparseSegmentMean": {
+      const outputData = sparse.sparseSegmentMean(getParamValue("data", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("segmentIds", node, tensorMap, context));
+      return [outputData];
+    }
+    case "SparseSegmentSum": {
+      const outputData = sparse.sparseSegmentSum(getParamValue("data", node, tensorMap, context), getParamValue("indices", node, tensorMap, context), getParamValue("segmentIds", node, tensorMap, context));
+      return [outputData];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js
+var executeOp17 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "FFT": {
+      return [fft(getParamValue("x", node, tensorMap, context))];
+    }
+    case "IFFT": {
+      return [ifft(getParamValue("x", node, tensorMap, context))];
+    }
+    case "RFFT": {
+      return [rfft(getParamValue("x", node, tensorMap, context))];
+    }
+    case "IRFFT": {
+      return [irfft(getParamValue("x", node, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js
+var executeOp18 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "StringNGrams": {
+      const { nGrams, nGramsSplits } = string.stringNGrams(getParamValue("data", node, tensorMap, context), getParamValue("dataSplits", node, tensorMap, context), getParamValue("separator", node, tensorMap, context), getParamValue("nGramWidths", node, tensorMap, context), getParamValue("leftPad", node, tensorMap, context), getParamValue("rightPad", node, tensorMap, context), getParamValue("padWidth", node, tensorMap, context), getParamValue("preserveShortSequences", node, tensorMap, context));
+      return [nGrams, nGramsSplits];
+    }
+    case "StringSplit": {
+      const { indices, values, shape } = string.stringSplit(getParamValue("input", node, tensorMap, context), getParamValue("delimiter", node, tensorMap, context), getParamValue("skipEmpty", node, tensorMap, context));
+      return [indices, values, shape];
+    }
+    case "StringToHashBucketFast": {
+      const output = string.stringToHashBucketFast(getParamValue("input", node, tensorMap, context), getParamValue("numBuckets", node, tensorMap, context));
+      return [output];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js
+var executeOp19 = (node, tensorMap, context) => {
+  switch (node.op) {
+    case "Cast": {
+      return [cast(getParamValue("x", node, tensorMap, context), getParamValue("dtype", node, tensorMap, context))];
+    }
+    case "ExpandDims": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      return [expandDims(getParamValue("x", node, tensorMap, context), axis)];
+    }
+    case "Squeeze": {
+      const axis = getParamValue("axis", node, tensorMap, context);
+      return [squeeze(getParamValue("x", node, tensorMap, context), axis)];
+    }
+    case "Reshape": {
+      return [reshape(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))];
+    }
+    case "MirrorPad": {
+      return [mirrorPad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("mode", node, tensorMap, context))];
+    }
+    case "PadV2":
+    case "Pad": {
+      return [pad(getParamValue("x", node, tensorMap, context), getParamValue("padding", node, tensorMap, context), getParamValue("constantValue", node, tensorMap, context))];
+    }
+    case "SpaceToBatchND": {
+      const blockShape = getParamValue("blockShape", node, tensorMap, context);
+      const paddings = getParamValue("paddings", node, tensorMap, context);
+      return [spaceToBatchND(getParamValue("x", node, tensorMap, context), blockShape, paddings)];
+    }
+    case "BatchToSpaceND": {
+      const blockShape = getParamValue("blockShape", node, tensorMap, context);
+      const crops = getParamValue("crops", node, tensorMap, context);
+      return [batchToSpaceND(getParamValue("x", node, tensorMap, context), blockShape, crops)];
+    }
+    case "DepthToSpace": {
+      const blockSize = getParamValue("blockSize", node, tensorMap, context);
+      const dataFormat = getParamValue("dataFormat", node, tensorMap, context).toUpperCase();
+      return [depthToSpace(getParamValue("x", node, tensorMap, context), blockSize, dataFormat)];
+    }
+    case "BroadcastTo": {
+      return [broadcastTo(getParamValue("x", node, tensorMap, context), getParamValue("shape", node, tensorMap, context))];
+    }
+    case "BroadcastArgs": {
+      return [broadcastArgs(getParamValue("s0", node, tensorMap, context), getParamValue("s1", node, tensorMap, context))];
+    }
+    default:
+      throw TypeError(`Node type ${node.op} is not implemented`);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js
+function executeOp20(node, tensorMap, context, resourceManager) {
+  const value = ((node2, tensorMap2, context2) => {
+    switch (node2.category) {
+      case "arithmetic":
+        return tidy(() => executeOp(node2, tensorMap2, context2));
+      case "basic_math":
+        return tidy(() => executeOp2(node2, tensorMap2, context2));
+      case "control":
+        return executeOp3(node2, tensorMap2, context2);
+      case "convolution":
+        return tidy(() => executeOp4(node2, tensorMap2, context2));
+      case "creation":
+        return tidy(() => executeOp5(node2, tensorMap2, context2));
+      case "dynamic":
+        return executeOp6(node2, tensorMap2, context2);
+      case "evaluation":
+        return tidy(() => executeOp7(node2, tensorMap2, context2));
+      case "image":
+        return tidy(() => executeOp10(node2, tensorMap2, context2));
+      case "graph":
+        return tidy(() => executeOp8(node2, tensorMap2, context2));
+      case "logical":
+        return tidy(() => executeOp11(node2, tensorMap2, context2));
+      case "matrices":
+        return tidy(() => executeOp12(node2, tensorMap2, context2));
+      case "normalization":
+        return tidy(() => executeOp13(node2, tensorMap2, context2));
+      case "reduction":
+        return tidy(() => executeOp14(node2, tensorMap2, context2));
+      case "slice_join":
+        return tidy(() => executeOp15(node2, tensorMap2, context2));
+      case "sparse":
+        return tidy(() => executeOp16(node2, tensorMap2, context2));
+      case "spectral":
+        return tidy(() => executeOp17(node2, tensorMap2, context2));
+      case "string":
+        return tidy(() => executeOp18(node2, tensorMap2, context2));
+      case "transformation":
+        return tidy(() => executeOp19(node2, tensorMap2, context2));
+      case "hash_table":
+        return executeOp9(node2, tensorMap2, context2, resourceManager);
+      case "custom":
+        const opMapper = getRegisteredOp(node2.op);
+        if (opMapper && opMapper.customExecutor) {
+          return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2));
+        } else {
+          throw TypeError(`Custom op ${node2.op} is not registered.`);
+        }
+      default:
+        throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);
+    }
+  })(node, tensorMap, context);
+  if (util_exports.isPromise(value)) {
+    return value.then((data) => [].concat(data));
+  }
+  return [].concat(value);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js
+var ExecutionContext = class {
+  constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {
+    this.weightMap = weightMap;
+    this.tensorArrayMap = tensorArrayMap;
+    this.tensorListMap = tensorListMap;
+    this.functionMap = functionMap;
+    this.rootContext = { id: 0, frameName: "", iterationId: 0 };
+    this.contexts = [this.rootContext];
+    this.lastId = 0;
+    this.generateCurrentContextIds();
+  }
+  newFrame(id, frameName) {
+    return { id, frameName, iterationId: 0 };
+  }
+  set currentContext(contexts2) {
+    if (this.contexts !== contexts2) {
+      this.contexts = contexts2;
+      this.generateCurrentContextIds();
+    }
+  }
+  get currentContext() {
+    return this.contexts;
+  }
+  get currentContextId() {
+    return this._currentContextIds[0];
+  }
+  get currentContextIds() {
+    return this._currentContextIds;
+  }
+  generateCurrentContextIds() {
+    const names = [];
+    for (let i2 = 0; i2 < this.contexts.length - 1; i2++) {
+      const contexts2 = this.contexts.slice(0, this.contexts.length - i2);
+      names.push(this.contextIdforContexts(contexts2));
+    }
+    names.push("");
+    this._currentContextIds = names;
+  }
+  contextIdforContexts(contexts2) {
+    return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : "";
+  }
+  enterFrame(frameId) {
+    if (this.contexts) {
+      this.lastId++;
+      this.contexts = this.contexts.slice();
+      this.contexts.push(this.newFrame(this.lastId, frameId));
+      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
+    }
+  }
+  exitFrame() {
+    if (this.contexts && this.contexts.length > 1) {
+      this.contexts = this.contexts.slice();
+      this.contexts.splice(-1);
+      this.currentContextIds.shift();
+    } else {
+      throw new Error("Cannot exit frame, the context is empty");
+    }
+  }
+  nextIteration() {
+    if (this.contexts && this.contexts.length > 0) {
+      this.contexts = this.contexts.slice();
+      this.lastId++;
+      const context = Object.assign({}, this.contexts[this.contexts.length - 1]);
+      context.iterationId += 1;
+      context.id = this.lastId;
+      this.contexts.splice(-1, 1, context);
+      this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
+    } else {
+      throw new Error("Cannot increase frame iteration, the context is empty");
+    }
+  }
+  getWeight(name) {
+    return this.weightMap[name];
+  }
+  addTensorArray(tensorArray) {
+    this.tensorArrayMap[tensorArray.id] = tensorArray;
+  }
+  getTensorArray(id) {
+    return this.tensorArrayMap[id];
+  }
+  addTensorList(tensorList) {
+    this.tensorListMap[tensorList.id] = tensorList;
+  }
+  getTensorList(id) {
+    return this.tensorListMap[id];
+  }
+  dispose(keepIds) {
+    for (const key in this.tensorArrayMap) {
+      this.tensorArrayMap[key].clearAndClose(keepIds);
+    }
+    for (const key in this.tensorListMap) {
+      this.tensorListMap[key].clearAndClose(keepIds);
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js
+function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {
+  const usedNodes = /* @__PURE__ */ new Set();
+  const missingInputs = [];
+  let dynamicNode = null;
+  let syncInputs = null;
+  const seen = /* @__PURE__ */ new Set();
+  const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);
+  let initNodeNames = [];
+  if (initNodes != null) {
+    initNodeNames = initNodes.map((node) => parseNodeName(node.name)[0]);
+  }
+  const frontier = [...outputs];
+  while (frontier.length > 0) {
+    const node = frontier.pop();
+    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {
+      if (dynamicNode == null) {
+        dynamicNode = node;
+        syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));
+      }
+    }
+    usedNodes.add(node.name);
+    if (weightMap[node.name] != null) {
+      continue;
+    }
+    if (inputNodeNames.indexOf(node.name) !== -1) {
+      continue;
+    }
+    if (initNodeNames.indexOf(node.name) !== -1) {
+      continue;
+    }
+    if (node.inputs.length === 0) {
+      missingInputs.push(node.name);
+      continue;
+    }
+    node.inputs.forEach((input2) => {
+      if (seen.has(input2.name)) {
+        return;
+      }
+      seen.add(input2.name);
+      frontier.push(input2);
+    });
+  }
+  return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };
+}
+function getNodesInTopologicalOrder(graph, weightMap, executionInfo) {
+  const { usedNodes, inputs } = executionInfo;
+  const frontier = [];
+  const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]);
+  const initNodes = graph.initNodes;
+  inputNodes.forEach((input2) => {
+    if (usedNodes.has(input2.name)) {
+      frontier.push(input2);
+    }
+  });
+  graph.weights.forEach((weight) => {
+    if (usedNodes.has(weight.name)) {
+      frontier.push(weight);
+    }
+  });
+  if (initNodes != null) {
+    initNodes.forEach((node) => {
+      if (usedNodes.has(node.name)) {
+        frontier.push(node);
+      }
+    });
+  }
+  const seen = /* @__PURE__ */ new Set();
+  const orderedNodes = [];
+  while (frontier.length > 0) {
+    const node = frontier.pop();
+    seen.add(node.name);
+    if (!weightMap[node.name]) {
+      orderedNodes.push(node);
+    }
+    node.children.forEach((child) => {
+      if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {
+        frontier.push(child);
+      }
+    });
+  }
+  return orderedNodes;
+}
+var CONTROL_FLOW_OPS = [
+  "Switch",
+  "Merge",
+  "Enter",
+  "Exit",
+  "NextIteration",
+  "StatelessIf",
+  "StatelessWhile",
+  "if",
+  "While"
+];
+var DYNAMIC_SHAPE_OPS = [
+  "NonMaxSuppressionV2",
+  "NonMaxSuppressionV3",
+  "NonMaxSuppressionV5",
+  "Where"
+];
+var HASH_TABLE_OPS = [
+  "HashTable",
+  "HashTableV2",
+  "LookupTableImport",
+  "LookupTableImportV2",
+  "LookupTableFind",
+  "LookupTableFindV2",
+  "LookupTableSize",
+  "LookupTableSizeV2"
+];
+function isControlFlow(node) {
+  return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;
+}
+function isDynamicShape(node) {
+  return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;
+}
+function isHashTable(node) {
+  return HASH_TABLE_OPS.indexOf(node.op) >= 0;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js
+var GraphExecutor = class {
+  constructor(graph, parent) {
+    this.graph = graph;
+    this.parent = parent;
+    this.compiledMap = /* @__PURE__ */ new Map();
+    this._weightMap = {};
+    this.SEPERATOR = ",";
+    this._functions = {};
+    this._functionExecutorMap = {};
+    this.intermediateTensors = {};
+    this.keepTensorForDebug = false;
+    this._outputs = graph.outputs;
+    this._inputs = graph.inputs;
+    this._initNodes = graph.initNodes;
+    this._signature = graph.signature;
+    this._functions = graph.functions;
+    if (graph.functions != null) {
+      Object.keys(graph.functions).forEach((name) => {
+        this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this);
+      });
+    }
+  }
+  get weightIds() {
+    return this.parent ? this.parent.weightIds : this._weightIds;
+  }
+  get functionExecutorMap() {
+    return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;
+  }
+  get weightMap() {
+    return this.parent ? this.parent.weightMap : this._weightMap;
+  }
+  set weightMap(weightMap) {
+    const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));
+    this._weightIds = [].concat(...weightIds);
+    this._weightMap = weightMap;
+  }
+  set resourceManager(resourceManager) {
+    this._resourceManager = resourceManager;
+  }
+  get inputs() {
+    return this._inputs.map((node) => {
+      return {
+        name: node.name,
+        shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0,
+        dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0
+      };
+    });
+  }
+  get outputs() {
+    return this._outputs.map((node) => {
+      return {
+        name: node.name,
+        shape: node.attrParams["shape"] ? node.attrParams["shape"].value : void 0,
+        dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : void 0
+      };
+    });
+  }
+  get inputNodes() {
+    return this._inputs.map((node) => node.signatureKey || node.name);
+  }
+  get outputNodes() {
+    return this._outputs.map((node) => {
+      const name = node.signatureKey || node.name;
+      return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;
+    });
+  }
+  get functions() {
+    return Object.keys(this._functions).reduce((map, key) => {
+      map[key] = this._functions[key].signature;
+      return map;
+    }, {});
+  }
+  getCompilationKey(inputs, outputs) {
+    const sortedInputs = inputs.map((node) => node.name).sort();
+    const sortedOutputs = outputs.map((node) => node.name).sort();
+    return sortedInputs.join(this.SEPERATOR) + "--" + sortedOutputs.join(this.SEPERATOR);
+  }
+  compile(inputs, outputs) {
+    const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);
+    const { missingInputs, dynamicNode, syncInputs } = executionInfo;
+    if (dynamicNode != null) {
+      throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);
+    }
+    if (missingInputs.length > 0) {
+      const outNames = outputs.map((n) => n.name);
+      const inNames = Object.keys(inputs);
+      throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);
+    }
+    return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);
+  }
+  execute(inputs, outputs) {
+    inputs = this.mapInputs(inputs);
+    const names = Object.keys(inputs).sort();
+    this.checkInputs(inputs);
+    this.checkInputShapeAndType(inputs);
+    outputs = this.mapOutputs(outputs);
+    this.checkOutputs(outputs);
+    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
+    const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);
+    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);
+    this.resetIntermediateTensors();
+    if (outputNodes.length === 0) {
+      outputNodes = this._outputs;
+    }
+    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);
+    let orderedNodes = this.compiledMap.get(compilationKey);
+    if (orderedNodes == null) {
+      orderedNodes = this.compile(inputs, outputNodes);
+      this.compiledMap.set(compilationKey, orderedNodes);
+    }
+    const tensorArrayMap = {};
+    const tensorListMap = {};
+    return tidy(() => {
+      const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
+      const tensorsMap = Object.assign({}, this.weightMap);
+      Object.keys(inputs).forEach((name) => {
+        const [nodeName, index] = parseNodeName(name);
+        const tensors = [];
+        tensors[index] = inputs[name];
+        tensorsMap[nodeName] = tensors;
+      });
+      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
+      const intermediateTensorConsumerCount = {};
+      for (let i2 = 0; i2 < orderedNodes.length; i2++) {
+        const node = orderedNodes[i2];
+        if (!tensorsMap[node.name]) {
+          const tensors = executeOp20(node, tensorsMap, context, this._resourceManager);
+          if (util_exports.isPromise(tensors)) {
+            throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);
+          }
+          tensorsMap[node.name] = tensors;
+          this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);
+        }
+      }
+      if (this.parent == null) {
+        context.dispose(tensorsToKeep);
+      }
+      return outputs.map((name) => getTensor(name, tensorsMap, context));
+    });
+  }
+  getFrozenTensorIds(tensorMap) {
+    const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));
+    return new Set(ids);
+  }
+  checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {
+    if (node.category === "control" || outputNames.indexOf(nodeName) !== -1) {
+      return;
+    }
+    tensorMap[nodeName].forEach((tensor2) => {
+      if (tensor2 != null) {
+        intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length;
+      }
+    });
+    node.inputs.forEach((input2) => {
+      if (input2.category !== "control") {
+        const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);
+        if (tensors != null) {
+          tensors.forEach((tensor2) => {
+            if (tensor2 && !tensor2.kept && !tensorsToKeep.has(tensor2.id)) {
+              const count2 = intermediateTensorConsumerCount[tensor2.id];
+              if (count2 === 1) {
+                if (!this.keepTensorForDebug) {
+                  tensor2.dispose();
+                } else {
+                  const [nodeName2, index] = getNodeNameAndIndex(node.name, context);
+                  if (this.intermediateTensors[nodeName2]) {
+                    this.intermediateTensors[nodeName2][index] = tensor2;
+                  } else {
+                    this.intermediateTensors[nodeName2] = [];
+                    this.intermediateTensors[nodeName2][index] = tensor2;
+                  }
+                }
+                delete intermediateTensorConsumerCount[tensor2.id];
+              } else if (count2 != null) {
+                intermediateTensorConsumerCount[tensor2.id]--;
+              }
+            }
+          });
+        }
+      }
+    });
+  }
+  async executeAsync(inputs, outputs) {
+    return this._executeAsync(inputs, outputs);
+  }
+  disposeIntermediateTensors() {
+    if (!this.intermediateTensors) {
+      return;
+    }
+    Object.keys(this.intermediateTensors).forEach((key) => this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose()));
+    this.disposeTensorsMap();
+  }
+  disposeTensorsMap() {
+    if (!this.tensorsMap) {
+      return;
+    }
+    Object.keys(this.tensorsMap).forEach((key) => {
+      const tensorArray = this.tensorsMap[key];
+      tensorArray.forEach((tensor2) => {
+        if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !this.keepIds.has(tensor2.id)) {
+          tensor2.dispose();
+        }
+      });
+    });
+  }
+  getIntermediateTensors() {
+    return this.tensorsMap;
+  }
+  resetIntermediateTensors() {
+    for (const key in this.intermediateTensors) {
+      this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose());
+      delete this.intermediateTensors[key];
+    }
+  }
+  async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {
+    if (!isFunctionExecution) {
+      inputs = this.mapInputs(inputs);
+      this.checkInputs(inputs);
+      this.checkInputShapeAndType(inputs);
+      outputs = this.mapOutputs(outputs);
+      this.checkOutputs(outputs);
+    }
+    try {
+      this.keepTensorForDebug = env().getBool("KEEP_INTERMEDIATE_TENSORS");
+    } catch (e2) {
+      console.warn(e2.message);
+    }
+    this.resetIntermediateTensors();
+    const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
+    this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
+    const results = outputs.map((name) => getTensor(name, this.tensorsMap, context));
+    const outputIds = results.map((t2) => t2.id);
+    const inputIds = Object.keys(inputs).map((name) => inputs[name].id);
+    this.keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);
+    if (!this.keepTensorForDebug) {
+      this.disposeTensorsMap();
+    }
+    if (this.parent == null) {
+      context.dispose(this.keepIds);
+    }
+    return results;
+  }
+  async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {
+    const mappedInputs = inputs.reduce((map, tensor2, index) => {
+      map[this.inputs[index].name] = tensor2;
+      return map;
+    }, {});
+    return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
+  }
+  async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {
+    const names = Object.keys(inputs);
+    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
+    const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);
+    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);
+    if (outputNodes.length === 0) {
+      outputNodes = this._outputs;
+    }
+    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);
+    const stack2 = [
+      ...inputNodes,
+      ...this.graph.weights,
+      ...this._initNodes || []
+    ].map((node) => {
+      return { node, contexts: context.currentContext };
+    });
+    const tensorsMap = Object.assign({}, this.weightMap);
+    Object.keys(inputs).forEach((name) => {
+      const [nodeName, index] = parseNodeName(name);
+      const tensors = [];
+      tensors[index] = inputs[name];
+      tensorsMap[nodeName] = tensors;
+    });
+    const intermediateTensorConsumerCount = {};
+    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
+    const added = {};
+    while (stack2.length > 0) {
+      const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);
+      await Promise.all(promises);
+    }
+    if (dynamicNode == null && !isFunctionExecution) {
+      console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);
+    }
+    const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name);
+    if (missingOutputs.length > 0) {
+      let alternativeMsg = "";
+      if (dynamicNode != null) {
+        alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;
+      }
+      throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);
+    }
+    return tensorsMap;
+  }
+  processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {
+    const promises = [];
+    while (stack2.length > 0) {
+      const item = stack2.pop();
+      context.currentContext = item.contexts;
+      let nodeName = "";
+      if (item.node.op === "Enter" && getParamValue("isConstant", item.node, tensorMap, context)) {
+        [nodeName] = getNodeNameAndIndex(item.node.name, context);
+      }
+      if (tensorMap[item.node.name] == null) {
+        const tensors = executeOp20(item.node, tensorMap, context, this._resourceManager);
+        if (!nodeName) {
+          [nodeName] = getNodeNameAndIndex(item.node.name, context);
+        }
+        const currentContext = context.currentContext;
+        if (util_exports.isPromise(tensors)) {
+          promises.push(tensors.then((t2) => {
+            tensorMap[nodeName] = t2;
+            context.currentContext = currentContext;
+            this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
+            this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
+            return t2;
+          }));
+        } else {
+          tensorMap[nodeName] = tensors;
+          this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
+          this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
+        }
+      } else {
+        this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
+      }
+    }
+    return promises;
+  }
+  processChildNodes(node, stack2, context, tensorMap, added, usedNodes) {
+    node.children.forEach((childNode) => {
+      const [nodeName] = getNodeNameAndIndex(childNode.name, context);
+      if (added[nodeName] || !usedNodes.has(childNode.name)) {
+        return;
+      }
+      if (childNode.op === "Merge") {
+        if (childNode.inputNames.some((name) => {
+          return !!getTensor(name, tensorMap, context);
+        })) {
+          added[nodeName] = true;
+          stack2.push({ contexts: context.currentContext, node: childNode });
+        }
+      } else if (childNode.inputNames.every((name) => {
+        return !!getTensor(name, tensorMap, context);
+      })) {
+        added[nodeName] = true;
+        stack2.push({ contexts: context.currentContext, node: childNode });
+      }
+    });
+  }
+  dispose() {
+    Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));
+  }
+  checkInputShapeAndType(inputs) {
+    Object.keys(inputs).forEach((name) => {
+      const input2 = inputs[name];
+      const [nodeName] = parseNodeName(name);
+      const node = this.graph.nodes[nodeName];
+      if (node.attrParams["shape"] && node.attrParams["shape"].value) {
+        const shape = node.attrParams["shape"].value;
+        const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);
+        util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);
+      }
+      if (node.attrParams["dtype"] && node.attrParams["dtype"].value) {
+        util_exports.assert(input2.dtype === node.attrParams["dtype"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams["dtype"].value}, but was ${input2.dtype}`);
+      }
+    });
+  }
+  mapInputs(inputs) {
+    const result = {};
+    for (const inputName in inputs) {
+      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {
+        const tensor2 = this._signature.inputs[inputName];
+        result[tensor2.name] = inputs[inputName];
+      } else {
+        result[inputName] = inputs[inputName];
+      }
+    }
+    return result;
+  }
+  checkInputs(inputs) {
+    const notInGraph = Object.keys(inputs).filter((name) => {
+      const [nodeName] = parseNodeName(name);
+      return this.graph.nodes[nodeName] == null;
+    });
+    if (notInGraph.length > 0) {
+      throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);
+    }
+  }
+  mapOutputs(outputs) {
+    return outputs.map((name) => {
+      if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {
+        const tensor2 = this._signature.outputs[name];
+        return tensor2.name;
+      }
+      return name;
+    }, {});
+  }
+  checkOutputs(outputs) {
+    outputs.forEach((name) => {
+      const [normalizedName] = parseNodeName(name);
+      if (!this.graph.nodes[normalizedName]) {
+        throw new Error(`The output '${name}' is not found in the graph`);
+      }
+    });
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js
+var ResourceManager = class {
+  constructor(hashTableNameToHandle = {}, hashTableMap = {}) {
+    this.hashTableNameToHandle = hashTableNameToHandle;
+    this.hashTableMap = hashTableMap;
+  }
+  addHashTable(name, hashTable) {
+    this.hashTableNameToHandle[name] = hashTable.handle;
+    this.hashTableMap[hashTable.id] = hashTable;
+  }
+  getHashTableHandleByName(name) {
+    return this.hashTableNameToHandle[name];
+  }
+  getHashTableById(id) {
+    return this.hashTableMap[id];
+  }
+  dispose() {
+    for (const key in this.hashTableMap) {
+      this.hashTableMap[key].clearAndClose();
+      delete this.hashTableMap[key];
+    }
+    for (const name in this.hashTableNameToHandle) {
+      this.hashTableNameToHandle[name].dispose();
+      delete this.hashTableNameToHandle[name];
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js
+var TFHUB_SEARCH_PARAM = "?tfjs-format=file";
+var DEFAULT_MODEL_NAME = "model.json";
+var GraphModel = class {
+  constructor(modelUrl, loadOptions = {}) {
+    this.modelUrl = modelUrl;
+    this.loadOptions = loadOptions;
+    this.version = "n/a";
+    if (loadOptions == null) {
+      this.loadOptions = {};
+    }
+    this.resourceManager = new ResourceManager();
+  }
+  get modelVersion() {
+    return this.version;
+  }
+  get inputNodes() {
+    return this.executor.inputNodes;
+  }
+  get outputNodes() {
+    return this.executor.outputNodes;
+  }
+  get inputs() {
+    return this.executor.inputs;
+  }
+  get outputs() {
+    return this.executor.outputs;
+  }
+  get weights() {
+    return this.executor.weightMap;
+  }
+  get metadata() {
+    return this.artifacts.userDefinedMetadata;
+  }
+  get modelSignature() {
+    return this.signature;
+  }
+  findIOHandler() {
+    const path = this.modelUrl;
+    if (path.load != null) {
+      this.handler = path;
+    } else if (this.loadOptions.requestInit != null) {
+      this.handler = io_exports.browserHTTPRequest(path, this.loadOptions);
+    } else {
+      const handlers = io_exports.getLoadHandlers(path, this.loadOptions);
+      if (handlers.length === 0) {
+        handlers.push(io_exports.browserHTTPRequest(path, this.loadOptions));
+      } else if (handlers.length > 1) {
+        throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);
+      }
+      this.handler = handlers[0];
+    }
+  }
+  load() {
+    this.findIOHandler();
+    if (this.handler.load == null) {
+      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
+    }
+    const loadResult = this.handler.load();
+    if (util_exports.isPromise(loadResult)) {
+      return loadResult.then((artifacts) => this.loadSync(artifacts));
+    }
+    return this.loadSync(loadResult);
+  }
+  loadSync(artifacts) {
+    this.artifacts = artifacts;
+    const graph = this.artifacts.modelTopology;
+    let signature;
+    if (this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null) {
+      signature = this.artifacts.userDefinedMetadata.signature;
+    } else {
+      signature = this.artifacts.signature;
+    }
+    this.signature = signature;
+    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;
+    const weightMap = io_exports.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
+    this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));
+    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
+    this.executor.resourceManager = this.resourceManager;
+    if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {
+      const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);
+      this.initializer = new GraphExecutor(initializer);
+      this.initializer.weightMap = this.executor.weightMap;
+      this.initializer.resourceManager = this.resourceManager;
+      this.initializer.executeAsync({}, []);
+    }
+    return true;
+  }
+  async save(handlerOrURL, config) {
+    if (typeof handlerOrURL === "string") {
+      const handlers = io_exports.getSaveHandlers(handlerOrURL);
+      if (handlers.length === 0) {
+        throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);
+      } else if (handlers.length > 1) {
+        throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);
+      }
+      handlerOrURL = handlers[0];
+    }
+    if (handlerOrURL.save == null) {
+      throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
+    }
+    return handlerOrURL.save(this.artifacts);
+  }
+  predict(inputs, config) {
+    return this.execute(inputs, this.outputNodes);
+  }
+  normalizeInputs(inputs) {
+    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {
+      return inputs;
+    }
+    inputs = Array.isArray(inputs) ? inputs : [inputs];
+    if (inputs.length !== this.inputNodes.length) {
+      throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);
+    }
+    return this.inputNodes.reduce((map, inputName, i2) => {
+      map[inputName] = inputs[i2];
+      return map;
+    }, {});
+  }
+  normalizeOutputs(outputs) {
+    outputs = outputs || this.outputNodes;
+    return !Array.isArray(outputs) ? [outputs] : outputs;
+  }
+  execute(inputs, outputs) {
+    inputs = this.normalizeInputs(inputs);
+    outputs = this.normalizeOutputs(outputs);
+    const result = this.executor.execute(inputs, outputs);
+    return result.length > 1 ? result : result[0];
+  }
+  async executeAsync(inputs, outputs) {
+    inputs = this.normalizeInputs(inputs);
+    outputs = this.normalizeOutputs(outputs);
+    const result = await this.executor.executeAsync(inputs, outputs);
+    return result.length > 1 ? result : result[0];
+  }
+  getIntermediateTensors() {
+    return this.executor.getIntermediateTensors();
+  }
+  disposeIntermediateTensors() {
+    this.executor.disposeIntermediateTensors();
+  }
+  convertTensorMapToTensorsMap(map) {
+    return Object.keys(map).reduce((newMap, key) => {
+      newMap[key] = [map[key]];
+      return newMap;
+    }, {});
+  }
+  dispose() {
+    this.executor.dispose();
+    if (this.initializer) {
+      this.initializer.dispose();
+    }
+    this.resourceManager.dispose();
+  }
+};
+async function loadGraphModel(modelUrl, options = {}) {
+  if (modelUrl == null) {
+    throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
+  }
+  if (options == null) {
+    options = {};
+  }
+  if (options.fromTFHub && typeof modelUrl === "string") {
+    modelUrl = getTFHubUrl(modelUrl);
+  }
+  const model2 = new GraphModel(modelUrl, options);
+  await model2.load();
+  return model2;
+}
+function loadGraphModelSync(modelSource) {
+  if (modelSource == null) {
+    throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model");
+  }
+  if (!modelSource.load) {
+    throw new Error(`modelUrl IO Handler ${modelSource} has no load function`);
+  }
+  const model2 = new GraphModel(modelSource);
+  model2.load();
+  return model2;
+}
+function getTFHubUrl(modelUrl) {
+  if (!modelUrl.endsWith("/")) {
+    modelUrl = modelUrl + "/";
+  }
+  return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/version.js
+var version3 = "3.18.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/index.js
+var dist_exports2 = {};
+__export(dist_exports2, {
+  CSVDataset: () => CSVDataset,
+  Dataset: () => Dataset,
+  FileDataSource: () => FileDataSource,
+  TextLineDataset: () => TextLineDataset,
+  URLDataSource: () => URLDataSource,
+  array: () => array,
+  csv: () => csv,
+  func: () => func,
+  generator: () => generator,
+  microphone: () => microphone,
+  version_data: () => version4,
+  webcam: () => webcam,
+  zip: () => zip
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/dataset.js
+var seedrandom3 = __toESM(require_seedrandom4());
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
+var seedrandom2 = __toESM(require_seedrandom4());
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js
+function deepMap(input2, mapFn) {
+  return deepMapInternal(input2, mapFn);
+}
+function deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {
+  if (input2 == null) {
+    return null;
+  }
+  if (typeof Blob === "function" && input2 instanceof Blob) {
+    return input2.slice();
+  }
+  if (containedIn.has(input2)) {
+    throw new Error("Circular references are not supported.");
+  }
+  if (seen.has(input2)) {
+    return seen.get(input2);
+  }
+  const result = mapFn(input2);
+  if (result.recurse && result.value !== null) {
+    throw new Error("A deep map function may not return both a value and recurse=true.");
+  }
+  if (!result.recurse) {
+    seen.set(input2, result.value);
+    return result.value;
+  } else if (isIterable2(input2)) {
+    const mappedIterable = Array.isArray(input2) ? [] : {};
+    containedIn.add(input2);
+    for (const k in input2) {
+      const child = input2[k];
+      const childResult = deepMapInternal(child, mapFn, seen, containedIn);
+      mappedIterable[k] = childResult;
+    }
+    containedIn.delete(input2);
+    if (input2.__proto__) {
+      mappedIterable.__proto__ = input2.__proto__;
+    }
+    return mappedIterable;
+  } else {
+    throw new Error(`Can't recurse into non-iterable type: ${input2}`);
+  }
+}
+function deepZip(inputs, zipFn = zipToList) {
+  return deepZipInternal(inputs, zipFn);
+}
+function deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) {
+  const input2 = inputs[0];
+  if (containedIn.has(input2)) {
+    throw new Error("Circular references are not supported.");
+  }
+  const result = zipFn(inputs);
+  if (result.recurse && result.value !== null) {
+    throw new Error("A deep zip function may not return both a value and recurse=true.");
+  }
+  if (!result.recurse) {
+    return result.value;
+  } else if (isIterable2(input2)) {
+    const mappedIterable = Array.isArray(input2) ? [] : {};
+    containedIn.add(input2);
+    for (const k in input2) {
+      const children = inputs.map((x) => x[k]);
+      const childResult = deepZipInternal(children, zipFn, containedIn);
+      mappedIterable[k] = childResult;
+    }
+    containedIn.delete(input2);
+    return mappedIterable;
+  } else {
+    throw new Error(`Can't recurse into non-iterable type: ${input2}`);
+  }
+}
+function zipToList(x) {
+  if (x === null) {
+    return null;
+  }
+  if (isIterable2(x[0])) {
+    return { value: null, recurse: true };
+  } else {
+    return { value: x, recurse: false };
+  }
+}
+async function deepMapAndAwaitAll(input2, mapFn) {
+  const seen = /* @__PURE__ */ new Map();
+  deepMapInternal(input2, mapFn, seen);
+  for (const key of Array.from(seen.keys())) {
+    const value = seen.get(key);
+    if (util_exports.isPromise(value)) {
+      const mappedValue = await value;
+      seen.set(key, mappedValue);
+    }
+  }
+  const result = deepMapInternal(input2, mapFn, seen);
+  return result;
+}
+function isIterable2(obj) {
+  let isTextDecoder = false;
+  if (env().get("IS_BROWSER")) {
+    isTextDecoder = obj instanceof TextDecoder;
+  } else {
+    const { StringDecoder } = require_string_decoder();
+    isTextDecoder = obj instanceof StringDecoder;
+  }
+  return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);
+}
+function canTensorify(obj) {
+  return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === "object" && obj instanceof Tensor || util_exports.isTypedArray(obj);
+}
+function isPrimitive(value) {
+  return value === null || typeof value !== "object" && typeof value !== "function";
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js
+function deepClone(container) {
+  return deepMap(container, cloneIfTensor);
+}
+function cloneIfTensor(item) {
+  if (item instanceof Tensor) {
+    return { value: item.clone(), recurse: false };
+  } else if (isIterable2(item)) {
+    return { value: null, recurse: true };
+  } else {
+    return { value: item, recurse: false };
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js
+var RingBuffer = class {
+  constructor(capacity) {
+    this.capacity = capacity;
+    this.begin = 0;
+    this.end = 0;
+    if (capacity == null) {
+      throw new RangeError("Can't create a ring buffer of unknown capacity.");
+    }
+    if (capacity < 1) {
+      throw new RangeError("Can't create ring buffer of capacity < 1.");
+    }
+    this.data = new Array(capacity);
+    this.doubledCapacity = 2 * capacity;
+  }
+  wrap(index) {
+    while (index < 0) {
+      index += this.doubledCapacity;
+    }
+    return index % this.doubledCapacity;
+  }
+  get(index) {
+    if (index < 0) {
+      throw new RangeError("Can't get item at a negative index.");
+    }
+    return this.data[index % this.capacity];
+  }
+  set(index, value) {
+    if (index < 0) {
+      throw new RangeError("Can't set item at a negative index.");
+    }
+    this.data[index % this.capacity] = value;
+  }
+  length() {
+    let length = this.end - this.begin;
+    if (length < 0) {
+      length = this.doubledCapacity + length;
+    }
+    return length;
+  }
+  isFull() {
+    return this.length() === this.capacity;
+  }
+  isEmpty() {
+    return this.length() === 0;
+  }
+  push(value) {
+    if (this.isFull()) {
+      throw new RangeError("Ring buffer is full.");
+    }
+    this.set(this.end, value);
+    this.end = this.wrap(this.end + 1);
+  }
+  pushAll(values) {
+    for (const value of values) {
+      this.push(value);
+    }
+  }
+  pop() {
+    if (this.isEmpty()) {
+      throw new RangeError("Ring buffer is empty.");
+    }
+    this.end = this.wrap(this.end - 1);
+    const result = this.get(this.end);
+    this.set(this.end, void 0);
+    return result;
+  }
+  unshift(value) {
+    if (this.isFull()) {
+      throw new RangeError("Ring buffer is full.");
+    }
+    this.begin = this.wrap(this.begin - 1);
+    this.set(this.begin, value);
+  }
+  shift() {
+    if (this.isEmpty()) {
+      throw new RangeError("Ring buffer is empty.");
+    }
+    const result = this.get(this.begin);
+    this.set(this.begin, void 0);
+    this.begin = this.wrap(this.begin + 1);
+    return result;
+  }
+  shuffleExcise(relativeIndex) {
+    if (this.isEmpty()) {
+      throw new RangeError("Ring buffer is empty.");
+    }
+    const index = this.wrap(this.begin + relativeIndex);
+    const result = this.get(index);
+    this.set(index, this.pop());
+    return result;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js
+var GrowingRingBuffer = class extends RingBuffer {
+  constructor() {
+    super(GrowingRingBuffer.INITIAL_CAPACITY);
+  }
+  isFull() {
+    return false;
+  }
+  push(value) {
+    if (super.isFull()) {
+      this.expand();
+    }
+    super.push(value);
+  }
+  unshift(value) {
+    if (super.isFull()) {
+      this.expand();
+    }
+    super.unshift(value);
+  }
+  expand() {
+    const newCapacity = this.capacity * 2;
+    const newData = new Array(newCapacity);
+    const len = this.length();
+    for (let i2 = 0; i2 < len; i2++) {
+      newData[i2] = this.get(this.wrap(this.begin + i2));
+    }
+    this.data = newData;
+    this.capacity = newCapacity;
+    this.doubledCapacity = 2 * this.capacity;
+    this.begin = 0;
+    this.end = len;
+  }
+};
+GrowingRingBuffer.INITIAL_CAPACITY = 32;
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
+function iteratorFromItems(items) {
+  return new ArrayIterator(items);
+}
+function iteratorFromFunction(func2) {
+  return new FunctionCallIterator(func2);
+}
+function iteratorFromConcatenated(baseIterators, baseErrorHandler) {
+  return new ChainedIterator(baseIterators, baseErrorHandler);
+}
+function iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {
+  return new ZipIterator(iterators, mismatchMode);
+}
+var LazyIterator = class {
+  async toArray() {
+    const result = [];
+    let x = await this.next();
+    while (!x.done) {
+      result.push(x.value);
+      x = await this.next();
+    }
+    return result;
+  }
+  async toArrayForTest() {
+    const stream = this.prefetch(100);
+    const result = [];
+    let x = await stream.next();
+    while (!x.done) {
+      result.push(x.value);
+      x = await stream.next();
+    }
+    return result;
+  }
+  async resolveFully() {
+    let x = await this.next();
+    while (!x.done) {
+      x = await this.next();
+    }
+  }
+  async resolveWhile(predicate) {
+    let x = await this.next();
+    let shouldContinue = predicate(x.value);
+    while (!x.done && shouldContinue) {
+      x = await this.next();
+      shouldContinue = predicate(x.value);
+    }
+  }
+  handleErrors(handler) {
+    return new ErrorHandlingLazyIterator(this, handler);
+  }
+  filter(predicate) {
+    return new FilterIterator(this, predicate);
+  }
+  map(transform6) {
+    return new MapIterator(this, transform6);
+  }
+  mapAsync(transform6) {
+    return new AsyncMapIterator(this, transform6);
+  }
+  serialMapAsync(transform6) {
+    return new AsyncMapIterator(this, transform6).serial();
+  }
+  flatmap(transform6) {
+    return new FlatmapIterator(this, transform6);
+  }
+  async forEachAsync(f) {
+    return this.map(f).resolveFully();
+  }
+  async serialForEach(f) {
+    return this.serialMapAsync(f).resolveWhile((x) => x === true);
+  }
+  rowMajorBatch(batchSize, smallLastBatch = true) {
+    return new RowMajorBatchIterator(this, batchSize, smallLastBatch);
+  }
+  columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {
+    const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);
+    return rowBatches.map((x) => deepZip(x, zipFn));
+  }
+  concatenate(iterator, baseErrorHandler) {
+    return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);
+  }
+  take(count2) {
+    if (count2 < 0 || count2 == null) {
+      return this;
+    }
+    return new TakeIterator(this, count2);
+  }
+  skip(count2) {
+    if (count2 < 0 || count2 == null) {
+      return this;
+    }
+    return new SkipIterator(this, count2);
+  }
+  prefetch(bufferSize) {
+    return new PrefetchIterator(this, bufferSize);
+  }
+  shuffle(windowSize, seed) {
+    return new ShuffleIterator(this, windowSize, seed);
+  }
+  serial() {
+    return new SerialIterator(this);
+  }
+};
+var ArrayIterator = class extends LazyIterator {
+  constructor(items) {
+    super();
+    this.items = items;
+    this.trav = 0;
+  }
+  summary() {
+    return `Array of ${this.items.length} items`;
+  }
+  async next() {
+    if (this.trav >= this.items.length) {
+      return { value: null, done: true };
+    }
+    const item = this.items[this.trav];
+    this.trav++;
+    return { value: deepClone(item), done: false };
+  }
+};
+var FunctionCallIterator = class extends LazyIterator {
+  constructor(nextFn) {
+    super();
+    this.nextFn = nextFn;
+  }
+  summary() {
+    return `Function call`;
+  }
+  async next() {
+    try {
+      return this.nextFn();
+    } catch (e2) {
+      e2.message = `Error thrown while iterating through a dataset: ${e2.message}`;
+      throw e2;
+    }
+  }
+};
+var SerialIterator = class extends LazyIterator {
+  constructor(upstream) {
+    super();
+    this.upstream = upstream;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Serial`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    return this.upstream.next();
+  }
+};
+var SkipIterator = class extends LazyIterator {
+  constructor(upstream, maxCount) {
+    super();
+    this.upstream = upstream;
+    this.maxCount = maxCount;
+    this.count = 0;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Skip`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (this.count++ < this.maxCount) {
+      const skipped = await this.upstream.next();
+      if (skipped.done) {
+        return skipped;
+      }
+      dispose(skipped.value);
+    }
+    return this.upstream.next();
+  }
+};
+var TakeIterator = class extends LazyIterator {
+  constructor(upstream, maxCount) {
+    super();
+    this.upstream = upstream;
+    this.maxCount = maxCount;
+    this.count = 0;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Take`;
+  }
+  async next() {
+    if (this.count++ >= this.maxCount) {
+      return { value: null, done: true };
+    }
+    return this.upstream.next();
+  }
+};
+var RowMajorBatchIterator = class extends LazyIterator {
+  constructor(upstream, batchSize, enableSmallLastBatch = true) {
+    super();
+    this.upstream = upstream;
+    this.batchSize = batchSize;
+    this.enableSmallLastBatch = enableSmallLastBatch;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> RowMajorBatch`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    const batch = [];
+    while (batch.length < this.batchSize) {
+      const item = await this.upstream.next();
+      if (item.done) {
+        if (this.enableSmallLastBatch && batch.length > 0) {
+          return { value: batch, done: false };
+        }
+        return { value: null, done: true };
+      }
+      batch.push(item.value);
+    }
+    return { value: batch, done: false };
+  }
+};
+var FilterIterator = class extends LazyIterator {
+  constructor(upstream, predicate) {
+    super();
+    this.upstream = upstream;
+    this.predicate = predicate;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Filter`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (true) {
+      const item = await this.upstream.next();
+      if (item.done || this.predicate(item.value)) {
+        return item;
+      }
+      dispose(item.value);
+    }
+  }
+};
+var MapIterator = class extends LazyIterator {
+  constructor(upstream, transform6) {
+    super();
+    this.upstream = upstream;
+    this.transform = transform6;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Map`;
+  }
+  async next() {
+    const item = await this.upstream.next();
+    if (item.done) {
+      return { value: null, done: true };
+    }
+    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
+    const mapped = this.transform(item.value);
+    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
+    for (const t2 of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
+        t2.dispose();
+      }
+    }
+    return { value: mapped, done: false };
+  }
+};
+var ErrorHandlingLazyIterator = class extends LazyIterator {
+  constructor(upstream, handler) {
+    super();
+    this.upstream = upstream;
+    this.handler = handler;
+    this.count = 0;
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  summary() {
+    return `${this.upstream.summary()} -> handleErrors`;
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (true) {
+      try {
+        return await this.upstream.next();
+      } catch (e2) {
+        if (!this.handler(e2)) {
+          return { value: null, done: true };
+        }
+      }
+    }
+  }
+};
+var AsyncMapIterator = class extends LazyIterator {
+  constructor(upstream, transform6) {
+    super();
+    this.upstream = upstream;
+    this.transform = transform6;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> AsyncMap`;
+  }
+  async next() {
+    const item = await this.upstream.next();
+    if (item.done) {
+      return { value: null, done: true };
+    }
+    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
+    const mapped = await this.transform(item.value);
+    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
+    for (const t2 of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
+        t2.dispose();
+      }
+    }
+    return { value: mapped, done: false };
+  }
+};
+var OneToManyIterator = class extends LazyIterator {
+  constructor() {
+    super();
+    this.outputQueue = new GrowingRingBuffer();
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  async serialNext() {
+    while (this.outputQueue.length() === 0) {
+      if (!await this.pump()) {
+        return { value: null, done: true };
+      }
+    }
+    return { value: this.outputQueue.shift(), done: false };
+  }
+};
+var FlatmapIterator = class extends OneToManyIterator {
+  constructor(upstream, transform6) {
+    super();
+    this.upstream = upstream;
+    this.transform = transform6;
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Flatmap`;
+  }
+  async pump() {
+    const item = await this.upstream.next();
+    if (item.done) {
+      return false;
+    }
+    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
+    const mappedArray = this.transform(item.value);
+    const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);
+    this.outputQueue.pushAll(mappedArray);
+    for (const t2 of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
+        t2.dispose();
+      }
+    }
+    return true;
+  }
+};
+var ChainedIterator = class extends LazyIterator {
+  constructor(iterators, baseErrorHandler) {
+    super();
+    this.baseErrorHandler = baseErrorHandler;
+    this.lastRead = null;
+    this.iterator = null;
+    this.moreIterators = iterators;
+  }
+  summary() {
+    const upstreamSummaries = "TODO: fill in upstream of chained summaries";
+    return `${upstreamSummaries} -> Chained`;
+  }
+  async next() {
+    this.lastRead = this.readFromChain(this.lastRead);
+    return this.lastRead;
+  }
+  async readFromChain(lastRead) {
+    await lastRead;
+    if (this.iterator == null) {
+      const iteratorResult = await this.moreIterators.next();
+      if (iteratorResult.done) {
+        return { value: null, done: true };
+      }
+      this.iterator = iteratorResult.value;
+      if (this.baseErrorHandler != null) {
+        this.iterator = this.iterator.handleErrors(this.baseErrorHandler);
+      }
+    }
+    const itemResult = await this.iterator.next();
+    if (itemResult.done) {
+      this.iterator = null;
+      return this.readFromChain(lastRead);
+    }
+    return itemResult;
+  }
+};
+var ZipMismatchMode;
+(function(ZipMismatchMode2) {
+  ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL";
+  ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST";
+  ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST";
+})(ZipMismatchMode || (ZipMismatchMode = {}));
+var ZipIterator = class extends LazyIterator {
+  constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {
+    super();
+    this.iterators = iterators;
+    this.mismatchMode = mismatchMode;
+    this.count = 0;
+    this.currentPromise = null;
+  }
+  summary() {
+    const upstreamSummaries = "TODO: fill in upstream of zip summaries";
+    return `{${upstreamSummaries}} -> Zip`;
+  }
+  async nextState(afterState) {
+    await afterState;
+    let numIterators = 0;
+    let iteratorsDone = 0;
+    function getNext(container) {
+      if (container instanceof LazyIterator) {
+        const result = container.next();
+        return {
+          value: result.then((x) => {
+            numIterators++;
+            if (x.done) {
+              iteratorsDone++;
+            }
+            return x.value;
+          }),
+          recurse: false
+        };
+      } else {
+        return { value: null, recurse: true };
+      }
+    }
+    const mapped = await deepMapAndAwaitAll(this.iterators, getNext);
+    if (numIterators === iteratorsDone) {
+      return { value: null, done: true };
+    }
+    if (iteratorsDone > 0) {
+      switch (this.mismatchMode) {
+        case ZipMismatchMode.FAIL:
+          throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);
+        case ZipMismatchMode.SHORTEST:
+          return { value: null, done: true };
+        case ZipMismatchMode.LONGEST:
+        default:
+      }
+    }
+    this.count++;
+    return { value: mapped, done: false };
+  }
+  async next() {
+    this.currentPromise = this.nextState(this.currentPromise);
+    return this.currentPromise;
+  }
+};
+var PrefetchIterator = class extends LazyIterator {
+  constructor(upstream, bufferSize) {
+    super();
+    this.upstream = upstream;
+    this.bufferSize = bufferSize;
+    this.buffer = new RingBuffer(bufferSize);
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Prefetch`;
+  }
+  refill() {
+    while (!this.buffer.isFull()) {
+      const v = this.upstream.next();
+      this.buffer.push(v);
+    }
+  }
+  next() {
+    this.refill();
+    return this.buffer.shift();
+  }
+};
+var ShuffleIterator = class extends PrefetchIterator {
+  constructor(upstream, windowSize, seed) {
+    super(upstream, windowSize);
+    this.upstream = upstream;
+    this.windowSize = windowSize;
+    this.upstreamExhausted = false;
+    this.random = seedrandom2.alea(seed || util_exports.now().toString());
+    this.lastRead = Promise.resolve({ value: null, done: false });
+  }
+  async next() {
+    this.lastRead = this.lastRead.then(() => this.serialNext());
+    return this.lastRead;
+  }
+  randomInt(max7) {
+    return Math.floor(this.random() * max7);
+  }
+  chooseIndex() {
+    return this.randomInt(this.buffer.length());
+  }
+  async serialNext() {
+    if (!this.upstreamExhausted) {
+      this.refill();
+    }
+    while (!this.buffer.isEmpty()) {
+      const chosenIndex = this.chooseIndex();
+      const result = await this.buffer.shuffleExcise(chosenIndex);
+      if (result.done) {
+        this.upstreamExhausted = true;
+      } else {
+        this.refill();
+        return result;
+      }
+    }
+    return { value: null, done: true };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/dataset.js
+var Dataset = class {
+  constructor() {
+    this.size = null;
+  }
+  batch(batchSize, smallLastBatch = true) {
+    const base = this;
+    util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is
+      ${batchSize}`);
+    let size;
+    if (this.size === Infinity || this.size == null) {
+      size = this.size;
+    } else if (smallLastBatch) {
+      size = Math.ceil(this.size / batchSize);
+    } else {
+      size = Math.floor(this.size / batchSize);
+    }
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);
+    }, size);
+  }
+  concatenate(dataset) {
+    const base = this;
+    let size;
+    if (this.size === Infinity || dataset.size === Infinity) {
+      size = Infinity;
+    } else if (this.size != null && dataset.size != null) {
+      size = this.size + dataset.size;
+    } else {
+      size = null;
+    }
+    return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);
+  }
+  filter(predicate) {
+    const base = this;
+    let size;
+    if (this.size === Infinity) {
+      size = Infinity;
+    } else {
+      size = null;
+    }
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).filter((x) => tidy(() => predicate(x)));
+    }, size);
+  }
+  async forEachAsync(f) {
+    return (await this.iterator()).forEachAsync(f);
+  }
+  map(transform6) {
+    const base = this;
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).map((x) => tidy(() => transform6(x)));
+    }, this.size);
+  }
+  mapAsync(transform6) {
+    const base = this;
+    return datasetFromIteratorFn(async () => {
+      return (await base.iterator()).mapAsync(transform6);
+    }, this.size);
+  }
+  prefetch(bufferSize) {
+    if (bufferSize == null) {
+      throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");
+    }
+    const base = this;
+    return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);
+  }
+  repeat(count2) {
+    const base = this;
+    let size;
+    if (this.size != null && count2 > 0) {
+      size = this.size * count2;
+    } else if (count2 === 0) {
+      size = 0;
+    } else if (this.size != null && (count2 === void 0 || count2 < 0)) {
+      size = Infinity;
+    } else {
+      size = null;
+    }
+    return datasetFromIteratorFn(async () => {
+      const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));
+      return iteratorFromConcatenated(iteratorIterator.take(count2));
+    }, size);
+  }
+  skip(count2) {
+    const base = this;
+    let size;
+    if (this.size != null && count2 >= 0 && this.size >= count2) {
+      size = this.size - count2;
+    } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {
+      size = 0;
+    } else {
+      size = null;
+    }
+    return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size);
+  }
+  shuffle(bufferSize, seed, reshuffleEachIteration = true) {
+    if (bufferSize == null || bufferSize < 0) {
+      if (this.size == null) {
+        throw new RangeError("`Dataset.shuffle()` requires bufferSize to be specified.");
+      } else {
+        throw new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);
+      }
+    }
+    const base = this;
+    const random = seedrandom3.alea(seed || util_exports.now().toString());
+    return datasetFromIteratorFn(async () => {
+      let seed2 = random.int32();
+      if (reshuffleEachIteration) {
+        seed2 += random.int32();
+      }
+      return (await base.iterator()).shuffle(bufferSize, seed2.toString());
+    }, this.size);
+  }
+  take(count2) {
+    const base = this;
+    let size;
+    if (this.size != null && this.size > count2) {
+      size = count2;
+    } else if (this.size != null && this.size <= count2) {
+      size = this.size;
+    } else {
+      size = null;
+    }
+    return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size);
+  }
+  async toArray() {
+    if (this.size === Infinity) {
+      throw new Error("Can not convert infinite data stream to array.");
+    }
+    return (await this.iterator()).toArray();
+  }
+  async toArrayForTest() {
+    if (this.size === Infinity) {
+      throw new Error("Can not convert infinite data stream to array.");
+    }
+    return (await this.iterator()).toArrayForTest();
+  }
+};
+Dataset.MAX_BUFFER_SIZE = 1e4;
+function datasetFromIteratorFn(iteratorFn, size = null) {
+  return new class extends Dataset {
+    constructor() {
+      super(...arguments);
+      this.size = size;
+    }
+    async iterator() {
+      return iteratorFn();
+    }
+  }();
+}
+function array(items) {
+  return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);
+}
+function zip(datasets) {
+  if (!isIterable2(datasets)) {
+    throw new Error("The argument to zip() must be an object or array.");
+  }
+  let size;
+  if (Array.isArray(datasets)) {
+    for (let i2 = 0; i2 < datasets.length; i2++) {
+      size = size == null ? datasets[i2].size : Math.min(size, datasets[i2].size);
+    }
+  } else if (datasets instanceof Object) {
+    for (const ds in datasets) {
+      size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);
+    }
+  }
+  return datasetFromIteratorFn(async () => {
+    const streams = await deepMapAndAwaitAll(datasets, (d) => {
+      if (d instanceof Dataset) {
+        return { value: d.iterator(), recurse: false };
+      } else if (isIterable2(d)) {
+        return { value: null, recurse: true };
+      } else {
+        throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
+      }
+    });
+    return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);
+  }, size);
+}
+function deepBatchConcat(rows) {
+  if (rows === null) {
+    return null;
+  }
+  const exampleRow = rows[0];
+  if (canTensorify(exampleRow)) {
+    const value = batchConcat(rows);
+    return { value, recurse: false };
+  }
+  return { value: null, recurse: true };
+}
+function batchConcat(arrays) {
+  if (arrays.length === 0) {
+    throw new Error("Can't make a batch of zero elements.");
+  }
+  if (arrays[0] instanceof Tensor) {
+    return stack(arrays);
+  } else {
+    return tensor(arrays);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js
+var TextLineDataset = class extends Dataset {
+  constructor(input2) {
+    super();
+    this.input = input2;
+  }
+  async iterator() {
+    const inputIterator = await this.input.iterator();
+    const utf8Iterator = inputIterator.decodeUTF8();
+    const lineIterator = utf8Iterator.split("\n").map((line) => {
+      if (line.endsWith("\r")) {
+        line = line.slice(0, -1);
+      }
+      return line;
+    });
+    return lineIterator;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js
+var CODE_QUOTE = '"';
+var STATE_OUT = Symbol("out");
+var STATE_FIELD = Symbol("field");
+var STATE_QUOTE = Symbol("quote");
+var STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote");
+var STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote");
+var CSVDataset = class extends Dataset {
+  constructor(input2, csvConfig) {
+    super();
+    this.input = input2;
+    this.hasHeader = true;
+    this.fullColumnNames = null;
+    this.columnNamesValidated = false;
+    this.columnConfigs = null;
+    this.configuredColumnsOnly = false;
+    this.delimiter = ",";
+    this.delimWhitespace = false;
+    this.base = new TextLineDataset(input2);
+    if (!csvConfig) {
+      csvConfig = {};
+    }
+    this.hasHeader = csvConfig.hasHeader === false ? false : true;
+    this.fullColumnNames = csvConfig.columnNames;
+    this.columnConfigs = csvConfig.columnConfigs;
+    this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;
+    if (csvConfig.delimWhitespace) {
+      util_exports.assert(csvConfig.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true.");
+      this.delimWhitespace = true;
+      this.delimiter = " ";
+    } else {
+      this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : ",";
+    }
+  }
+  async columnNames() {
+    if (!this.columnNamesValidated) {
+      await this.setColumnNames();
+    }
+    return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;
+  }
+  async setColumnNames() {
+    const columnNamesFromFile = await this.maybeReadHeaderLine();
+    if (!this.fullColumnNames && !columnNamesFromFile) {
+      throw new Error("Column names must be provided if there is no header line.");
+    } else if (this.fullColumnNames && columnNamesFromFile) {
+      util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => "The length of provided columnNames (" + this.fullColumnNames.length.toString() + ") does not match the length of the header line read from file (" + columnNamesFromFile.length.toString() + ").");
+    }
+    if (!this.fullColumnNames) {
+      this.fullColumnNames = columnNamesFromFile;
+    }
+    const counts = this.fullColumnNames.reduce((countAcc, name) => {
+      countAcc[name] = countAcc[name] + 1 || 1;
+      return countAcc;
+    }, {});
+    const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);
+    util_exports.assert(duplicateNames.length === 0, () => "Duplicate column names found: " + duplicateNames.toString());
+    if (this.columnConfigs) {
+      for (const key of Object.keys(this.columnConfigs)) {
+        const index = this.fullColumnNames.indexOf(key);
+        if (index === -1) {
+          throw new Error('The key "' + key + '" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + ").");
+        }
+      }
+    }
+    this.columnNamesValidated = true;
+  }
+  async maybeReadHeaderLine() {
+    if (this.hasHeader) {
+      const iter = await this.base.iterator();
+      const firstElement = await iter.next();
+      if (firstElement.done) {
+        throw new Error("No data was found for CSV parsing.");
+      }
+      const firstLine = firstElement.value;
+      const headers = this.parseRow(firstLine, false);
+      return headers;
+    } else {
+      return null;
+    }
+  }
+  async iterator() {
+    if (!this.columnNamesValidated) {
+      await this.setColumnNames();
+    }
+    let lines = await this.base.iterator();
+    if (this.hasHeader) {
+      lines = lines.skip(1);
+    }
+    return lines.map((x) => this.makeDataElement(x));
+  }
+  makeDataElement(line) {
+    const values = this.parseRow(line);
+    const features = {};
+    const labels = {};
+    for (let i2 = 0; i2 < this.fullColumnNames.length; i2++) {
+      const key = this.fullColumnNames[i2];
+      const config = this.columnConfigs ? this.columnConfigs[key] : null;
+      if (this.configuredColumnsOnly && !config) {
+        continue;
+      } else {
+        const value = values[i2];
+        let parsedValue = null;
+        if (value === "") {
+          if (config && config.default !== void 0) {
+            parsedValue = config.default;
+          } else if (config && (config.required || config.isLabel)) {
+            throw new Error(`Required column ${key} is empty in this line: ${line}`);
+          } else {
+            parsedValue = void 0;
+          }
+        } else {
+          const valueAsNum = Number(value);
+          if (isNaN(valueAsNum)) {
+            if (config && config.dtype === "bool") {
+              parsedValue = this.getBoolean(value);
+            } else {
+              parsedValue = value;
+            }
+          } else if (!config || !config.dtype) {
+            parsedValue = valueAsNum;
+          } else {
+            switch (config.dtype) {
+              case "float32":
+                parsedValue = valueAsNum;
+                break;
+              case "int32":
+                parsedValue = Math.floor(valueAsNum);
+                break;
+              case "bool":
+                parsedValue = this.getBoolean(value);
+                break;
+              default:
+                parsedValue = valueAsNum;
+            }
+          }
+        }
+        config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;
+      }
+    }
+    if (Object.keys(labels).length === 0) {
+      return features;
+    } else {
+      return { xs: features, ys: labels };
+    }
+  }
+  getBoolean(value) {
+    if (value === "1" || value.toLowerCase() === "true") {
+      return 1;
+    } else {
+      return 0;
+    }
+  }
+  parseRow(line, validateElementCount = true) {
+    const result = [];
+    let readOffset = 0;
+    const readLength = line.length;
+    let currentState = STATE_OUT;
+    for (let i2 = 0; i2 < readLength; i2++) {
+      switch (currentState) {
+        case STATE_OUT:
+          switch (line.charAt(i2)) {
+            case CODE_QUOTE:
+              readOffset = i2 + 1;
+              currentState = STATE_QUOTE;
+              break;
+            case this.delimiter:
+              readOffset = i2 + 1;
+              if (this.delimiter === " " && this.delimWhitespace) {
+                break;
+              }
+              result.push("");
+              currentState = STATE_OUT;
+              break;
+            default:
+              currentState = STATE_FIELD;
+              readOffset = i2;
+              break;
+          }
+          break;
+        case STATE_FIELD:
+          switch (line.charAt(i2)) {
+            case this.delimiter:
+              result.push(line.substring(readOffset, i2));
+              currentState = STATE_OUT;
+              readOffset = i2 + 1;
+              break;
+            default:
+          }
+          break;
+        case STATE_QUOTE:
+          switch (line.charAt(i2)) {
+            case CODE_QUOTE:
+              currentState = STATE_QUOTE_AFTER_QUOTE;
+              break;
+            default:
+          }
+          break;
+        case STATE_QUOTE_AFTER_QUOTE:
+          switch (line.charAt(i2)) {
+            case this.delimiter:
+              result.push(line.substring(readOffset, i2 - 1));
+              currentState = STATE_OUT;
+              readOffset = i2 + 1;
+              break;
+            case CODE_QUOTE:
+              currentState = STATE_QUOTE;
+              break;
+            default:
+              currentState = STATE_WITHIN_QUOTE_IN_QUOTE;
+              break;
+          }
+          break;
+        case STATE_WITHIN_QUOTE_IN_QUOTE:
+          switch (line.charAt(i2)) {
+            case CODE_QUOTE:
+              currentState = STATE_QUOTE;
+              break;
+            default:
+          }
+          break;
+        default:
+      }
+    }
+    if (currentState === STATE_QUOTE_AFTER_QUOTE) {
+      result.push(line.substring(readOffset, readLength - 1));
+    } else {
+      result.push(line.substring(readOffset));
+    }
+    if (validateElementCount && result.length !== this.fullColumnNames.length) {
+      throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);
+    }
+    return result;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js
+var MicrophoneIterator = class extends LazyIterator {
+  constructor(microphoneConfig) {
+    super();
+    this.microphoneConfig = microphoneConfig;
+    this.isClosed = false;
+    this.fftSize = microphoneConfig.fftSize || 1024;
+    const fftSizeLog2 = Math.log2(this.fftSize);
+    if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {
+      throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);
+    }
+    this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;
+    this.sampleRateHz = microphoneConfig.sampleRateHz;
+    this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;
+    this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;
+    this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;
+    this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;
+    this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;
+    if (!this.includeSpectrogram && !this.includeWaveform) {
+      throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.");
+    }
+  }
+  summary() {
+    return `microphone`;
+  }
+  static async create(microphoneConfig = {}) {
+    if (!env().get("IS_BROWSER")) {
+      throw new Error("microphone API is only supported in browser environment.");
+    }
+    const microphoneIterator = new MicrophoneIterator(microphoneConfig);
+    await microphoneIterator.start();
+    return microphoneIterator;
+  }
+  async start() {
+    try {
+      this.stream = await navigator.mediaDevices.getUserMedia({
+        audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,
+        video: false
+      });
+    } catch (e2) {
+      throw new Error(`Error thrown while initializing video stream: ${e2.message}`);
+    }
+    if (!this.stream) {
+      throw new Error("Could not obtain audio from microphone.");
+    }
+    const ctxConstructor = window.AudioContext || window.webkitAudioContext;
+    this.audioContext = new ctxConstructor();
+    if (!this.sampleRateHz) {
+      this.sampleRateHz = this.audioContext.sampleRate;
+    } else if (this.audioContext.sampleRate !== this.sampleRateHz) {
+      throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);
+    }
+    const streamSource = this.audioContext.createMediaStreamSource(this.stream);
+    this.analyser = this.audioContext.createAnalyser();
+    this.analyser.fftSize = this.fftSize * 2;
+    this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;
+    streamSource.connect(this.analyser);
+    this.freqData = new Float32Array(this.fftSize);
+    this.timeData = new Float32Array(this.fftSize);
+    return;
+  }
+  async next() {
+    if (this.isClosed) {
+      return { value: null, done: true };
+    }
+    let spectrogramTensor;
+    let waveformTensor;
+    const audioDataQueue = await this.getAudioData();
+    if (this.includeSpectrogram) {
+      const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);
+      spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);
+    }
+    if (this.includeWaveform) {
+      const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);
+      waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);
+    }
+    return {
+      value: { "spectrogram": spectrogramTensor, "waveform": waveformTensor },
+      done: false
+    };
+  }
+  async capture() {
+    return (await this.next()).value;
+  }
+  async getAudioData() {
+    const freqDataQueue = [];
+    const timeDataQueue = [];
+    let currentFrames = 0;
+    return new Promise((resolve) => {
+      const intervalID = setInterval(() => {
+        if (this.includeSpectrogram) {
+          this.analyser.getFloatFrequencyData(this.freqData);
+          if (this.freqData[0] === -Infinity) {
+            resolve({ freqDataQueue, timeDataQueue });
+          }
+          freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));
+        }
+        if (this.includeWaveform) {
+          this.analyser.getFloatTimeDomainData(this.timeData);
+          timeDataQueue.push(this.timeData.slice());
+        }
+        if (++currentFrames === this.numFrames) {
+          clearInterval(intervalID);
+          resolve({ freqDataQueue, timeDataQueue });
+        }
+      }, this.fftSize / this.sampleRateHz * 1e3);
+    });
+  }
+  stop() {
+    if (!this.isClosed) {
+      this.isClosed = true;
+      this.analyser.disconnect();
+      this.audioContext.close();
+      if (this.stream != null && this.stream.getTracks().length > 0) {
+        this.stream.getTracks()[0].stop();
+      }
+    }
+  }
+  toArray() {
+    throw new Error("Can not convert infinite audio stream to array.");
+  }
+  getSampleRate() {
+    return this.sampleRateHz;
+  }
+  flattenQueue(queue) {
+    const frameSize = queue[0].length;
+    const freqData = new Float32Array(queue.length * frameSize);
+    queue.forEach((data, i2) => freqData.set(data, i2 * frameSize));
+    return freqData;
+  }
+  getTensorFromAudioDataArray(freqData, shape) {
+    const vals = new Float32Array(util_exports.sizeFromShape(shape));
+    vals.set(freqData, vals.length - freqData.length);
+    return tensor(vals, shape);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js
+var WebcamIterator = class extends LazyIterator {
+  constructor(webcamVideoElement, webcamConfig) {
+    super();
+    this.webcamVideoElement = webcamVideoElement;
+    this.webcamConfig = webcamConfig;
+    this.isClosed = true;
+    this.resize = false;
+    if (this.needToResize()) {
+      this.resize = true;
+      this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];
+      this.cropBoxInd = tensor1d([0], "int32");
+      if (this.webcamConfig.centerCrop) {
+        const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;
+        const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;
+        const widthCropStart = (1 - widthCroppingRatio) / 2;
+        const heightCropStart = (1 - heightCroppingRatio) / 2;
+        const widthCropEnd = widthCropStart + widthCroppingRatio;
+        const heightCropEnd = heightCroppingRatio + heightCropStart;
+        this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);
+      } else {
+        this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);
+      }
+    }
+  }
+  summary() {
+    return `webcam`;
+  }
+  static async create(webcamVideoElement, webcamConfig = {}) {
+    if (!env().get("IS_BROWSER")) {
+      throw new Error("tf.data.webcam is only supported in browser environment.");
+    }
+    if (!webcamVideoElement) {
+      webcamVideoElement = document.createElement("video");
+      if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {
+        throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
+      }
+      webcamVideoElement.width = webcamConfig.resizeWidth;
+      webcamVideoElement.height = webcamConfig.resizeHeight;
+    }
+    const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);
+    await webcamIterator.start();
+    return webcamIterator;
+  }
+  async start() {
+    if (this.webcamConfig.facingMode) {
+      util_exports.assert(this.webcamConfig.facingMode === "user" || this.webcamConfig.facingMode === "environment", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);
+    }
+    try {
+      this.stream = await navigator.mediaDevices.getUserMedia({
+        video: {
+          deviceId: this.webcamConfig.deviceId,
+          facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : "user",
+          width: this.webcamVideoElement.width,
+          height: this.webcamVideoElement.height
+        }
+      });
+    } catch (e2) {
+      e2.message = `Error thrown while initializing video stream: ${e2.message}`;
+      throw e2;
+    }
+    if (!this.stream) {
+      throw new Error("Could not obtain video from webcam.");
+    }
+    try {
+      this.webcamVideoElement.srcObject = this.stream;
+    } catch (error) {
+      console.log(error);
+      this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);
+    }
+    this.webcamVideoElement.play();
+    this.isClosed = false;
+    return new Promise((resolve) => {
+      this.webcamVideoElement.onloadedmetadata = () => {
+        resolve();
+      };
+    });
+  }
+  async next() {
+    if (this.isClosed) {
+      return { value: null, done: true };
+    }
+    let img;
+    try {
+      img = browser_exports.fromPixels(this.webcamVideoElement);
+    } catch (e2) {
+      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e2)}`);
+    }
+    if (this.resize) {
+      try {
+        return { value: this.cropAndResizeFrame(img), done: false };
+      } catch (e2) {
+        throw new Error(`Error thrown cropping the video: ${e2.message}`);
+      } finally {
+        img.dispose();
+      }
+    } else {
+      return { value: img, done: false };
+    }
+  }
+  needToResize() {
+    if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {
+      return true;
+    }
+    return false;
+  }
+  cropAndResizeFrame(img) {
+    return tidy(() => {
+      const expandedImage = expandDims(cast(img, "float32"), 0);
+      let resizedImage;
+      resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
+      const shape = resizedImage.shape;
+      return reshape(resizedImage, shape.slice(1));
+    });
+  }
+  async capture() {
+    return (await this.next()).value;
+  }
+  stop() {
+    const tracks = this.stream.getTracks();
+    tracks.forEach((track) => track.stop());
+    try {
+      this.webcamVideoElement.srcObject = null;
+    } catch (error) {
+      console.log(error);
+      this.webcamVideoElement.src = null;
+    }
+    this.isClosed = true;
+  }
+  toArray() {
+    throw new Error("Can not convert infinite video stream to array.");
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/datasource.js
+var DataSource = class {
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js
+var StringIterator = class extends LazyIterator {
+  split(separator) {
+    return new SplitIterator(this, separator);
+  }
+};
+var SplitIterator = class extends StringIterator {
+  constructor(upstream, separator) {
+    super();
+    this.upstream = upstream;
+    this.impl = new SplitIteratorImpl(upstream, separator);
+  }
+  summary() {
+    return this.impl.summary();
+  }
+  async next() {
+    return this.impl.next();
+  }
+};
+var SplitIteratorImpl = class extends OneToManyIterator {
+  constructor(upstream, separator) {
+    super();
+    this.upstream = upstream;
+    this.separator = separator;
+    this.carryover = "";
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Split('${this.separator}')`;
+  }
+  async pump() {
+    const chunkResult = await this.upstream.next();
+    if (chunkResult.done) {
+      if (this.carryover === "") {
+        return false;
+      }
+      this.outputQueue.push(this.carryover);
+      this.carryover = "";
+      return true;
+    }
+    const lines = chunkResult.value.split(this.separator);
+    lines[0] = this.carryover + lines[0];
+    for (const line of lines.slice(0, -1)) {
+      this.outputQueue.push(line);
+    }
+    this.carryover = lines[lines.length - 1];
+    return true;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js
+var ByteChunkIterator = class extends LazyIterator {
+  decodeUTF8() {
+    return new Utf8Iterator(this);
+  }
+};
+var Utf8Iterator = class extends StringIterator {
+  constructor(upstream) {
+    super();
+    this.upstream = upstream;
+    this.impl = new Utf8IteratorImpl(upstream);
+  }
+  summary() {
+    return this.impl.summary();
+  }
+  async next() {
+    return this.impl.next();
+  }
+};
+var Utf8IteratorImpl = class extends OneToManyIterator {
+  constructor(upstream) {
+    super();
+    this.upstream = upstream;
+    if (env().get("IS_BROWSER")) {
+      this.decoder = new TextDecoder("utf-8");
+    } else {
+      const { StringDecoder } = require_string_decoder();
+      this.decoder = new StringDecoder("utf8");
+    }
+  }
+  summary() {
+    return `${this.upstream.summary()} -> Utf8`;
+  }
+  async pump() {
+    const chunkResult = await this.upstream.next();
+    let chunk;
+    if (chunkResult.done) {
+      return false;
+    } else {
+      chunk = chunkResult.value;
+    }
+    let text;
+    if (env().get("IS_BROWSER")) {
+      text = this.decoder.decode(chunk, { stream: true });
+    } else {
+      text = this.decoder.write(Buffer.from(chunk.buffer));
+    }
+    this.outputQueue.push(text);
+    return true;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js
+var FileChunkIterator = class extends ByteChunkIterator {
+  constructor(file, options = {}) {
+    super();
+    this.file = file;
+    this.options = options;
+    util_exports.assert(file instanceof Uint8Array || (env().get("IS_BROWSER") ? file instanceof File || file instanceof Blob : false), () => "FileChunkIterator only supports File, Blob and Uint8Array right now.");
+    this.offset = options.offset || 0;
+    this.chunkSize = options.chunkSize || 1024 * 1024;
+  }
+  summary() {
+    return `FileChunks ${this.file}`;
+  }
+  async next() {
+    if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {
+      return { value: null, done: true };
+    }
+    const chunk = new Promise((resolve, reject) => {
+      const end = this.offset + this.chunkSize;
+      if (this.file instanceof Uint8Array) {
+        resolve(new Uint8Array(this.file.slice(this.offset, end)));
+      } else {
+        const fileReader = new FileReader();
+        fileReader.onload = (event) => {
+          let data = fileReader.result;
+          if (data instanceof ArrayBuffer) {
+            data = new Uint8Array(data);
+          }
+          if (!(data instanceof Uint8Array)) {
+            return reject(new TypeError("FileReader returned unknown type."));
+          }
+          resolve(data);
+        };
+        fileReader.onabort = (event) => {
+          return reject(new Error("Aborted"));
+        };
+        fileReader.onerror = (event) => {
+          return reject(new Error(event.type));
+        };
+        const slice6 = this.file.slice(this.offset, end);
+        fileReader.readAsArrayBuffer(slice6);
+      }
+      this.offset = end;
+    });
+    return { value: await chunk, done: false };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js
+async function urlChunkIterator(url, options = {}, fetchFunc) {
+  let urlString;
+  let requestInit;
+  if (typeof url === "string") {
+    urlString = url;
+  } else {
+    urlString = url.url;
+    requestInit = getRequestInitFromRequest(url);
+  }
+  const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit);
+  if (response.ok) {
+    const uint8Array = new Uint8Array(await response.arrayBuffer());
+    return new FileChunkIterator(uint8Array, options);
+  } else {
+    throw new Error(response.statusText);
+  }
+}
+var getRequestInitFromRequest = (request) => {
+  const init2 = {
+    method: request.method,
+    headers: request.headers,
+    body: request.body,
+    mode: request.mode,
+    credentials: request.credentials,
+    cache: request.cache,
+    redirect: request.redirect,
+    referrer: request.referrer,
+    integrity: request.integrity
+  };
+  return init2;
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js
+function isLocalPath(source) {
+  return typeof source === "string" && source.slice(0, 7) === "file://";
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js
+var FileDataSource = class extends DataSource {
+  constructor(input2, options = {}) {
+    super();
+    this.input = input2;
+    this.options = options;
+  }
+  async iterator() {
+    if (isLocalPath(this.input) && env().get("IS_NODE")) {
+      const fs = require_fs();
+      this.input = fs.readFileSync(this.input.slice(7));
+    }
+    return new FileChunkIterator(this.input, this.options);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js
+var URLDataSource = class extends DataSource {
+  constructor(url, fileOptions = {}) {
+    super();
+    this.url = url;
+    this.fileOptions = fileOptions;
+  }
+  async iterator() {
+    if (isLocalPath(this.url)) {
+      return new FileDataSource(this.url, this.fileOptions).iterator();
+    } else {
+      return urlChunkIterator(this.url, this.fileOptions);
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/readers.js
+function csv(source, csvConfig = {}) {
+  return new CSVDataset(new URLDataSource(source), csvConfig);
+}
+function func(f) {
+  const iter = iteratorFromFunction(f);
+  return datasetFromIteratorFn(async () => iter);
+}
+function generator(generator2) {
+  return datasetFromIteratorFn(async () => {
+    const gen = await generator2();
+    return iteratorFromFunction(() => gen.next());
+  });
+}
+async function webcam(webcamVideoElement, webcamConfig) {
+  return WebcamIterator.create(webcamVideoElement, webcamConfig);
+}
+async function microphone(microphoneConfig) {
+  return MicrophoneIterator.create(microphoneConfig);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.18.0_ct4ioau627xna4dkbmwj6slhxi/node_modules/@tensorflow/tfjs-data/dist/version.js
+var version4 = "3.18.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js
+function assertNotComplex(tensor2, opName) {
+  if (!Array.isArray(tensor2)) {
+    tensor2 = [tensor2];
+  }
+  tensor2.forEach((t2) => {
+    if (t2 != null) {
+      util_exports.assert(t2.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`);
+    }
+  });
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js
+var whereImpl2 = kernel_impls_exports.whereImpl;
+var MathBackendCPU = class extends KernelBackend {
+  constructor() {
+    super();
+    this.blockSize = 48;
+    this.firstUse = true;
+    this.data = new DataStorage(this, engine());
+  }
+  nextDataId() {
+    return MathBackendCPU.nextDataId++;
+  }
+  write(values, shape, dtype) {
+    if (this.firstUse) {
+      this.firstUse = false;
+      if (env().get("IS_NODE")) {
+        backend_util_exports.warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================");
+      }
+    }
+    const dataId = { id: this.nextDataId() };
+    this.data.set(dataId, { values, dtype, refCount: 1 });
+    return dataId;
+  }
+  makeTensorInfo(shape, dtype, values) {
+    let outId;
+    if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
+      const encodedValues = values.map((d) => util_exports.encodeString(d));
+      outId = this.write(encodedValues, shape, dtype);
+    } else {
+      outId = this.write(values, shape, dtype);
+    }
+    return { dataId: outId, shape, dtype };
+  }
+  refCount(dataId) {
+    if (this.data.has(dataId)) {
+      const tensorData = this.data.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const tensorData = this.data.get(dataId);
+    tensorData.refCount++;
+  }
+  decRef(dataId) {
+    if (this.data.has(dataId)) {
+      const tensorData = this.data.get(dataId);
+      tensorData.refCount--;
+    }
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    this.data.set(dataId, { values, dtype, refCount });
+  }
+  numDataIds() {
+    return this.data.numDataIds();
+  }
+  async read(dataId) {
+    return this.readSync(dataId);
+  }
+  readSync(dataId) {
+    const { dtype, complexTensorInfos } = this.data.get(dataId);
+    if (dtype === "complex64") {
+      const realValues = this.readSync(complexTensorInfos.real.dataId);
+      const imagValues = this.readSync(complexTensorInfos.imag.dataId);
+      return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    }
+    return this.data.get(dataId).values;
+  }
+  bufferSync(t2) {
+    const data = this.readSync(t2.dataId);
+    if (t2.dtype === "string") {
+      try {
+        const strings = data.map((d) => util_exports.decodeString(d));
+        return buffer(t2.shape, t2.dtype, strings);
+      } catch (_a) {
+        throw new Error("Failed to decode encoded string bytes into utf-8");
+      }
+    }
+    return buffer(t2.shape, t2.dtype, data);
+  }
+  makeOutput(values, shape, dtype) {
+    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
+  }
+  disposeData(dataId, force = false) {
+    if (this.data.has(dataId)) {
+      this.data.get(dataId).refCount--;
+      if (!force && this.data.get(dataId).refCount > 0) {
+        return false;
+      }
+      const { complexTensorInfos } = this.data.get(dataId);
+      if (complexTensorInfos != null) {
+        this.disposeData(complexTensorInfos.real.dataId, true);
+        this.disposeData(complexTensorInfos.imag.dataId, true);
+      }
+      this.data.delete(dataId);
+    }
+    return true;
+  }
+  disposeIntermediateTensorInfo(tensorInfo) {
+    this.disposeData(tensorInfo.dataId);
+  }
+  async time(f) {
+    const start = util_exports.now();
+    f();
+    const kernelMs = util_exports.now() - start;
+    return { kernelMs };
+  }
+  memory() {
+    return {
+      unreliable: true,
+      reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]
+    };
+  }
+  where(condition) {
+    assertNotComplex([condition], "where");
+    const condVals = this.readSync(condition.dataId);
+    return whereImpl2(condition.shape, condVals);
+  }
+  dispose() {
+  }
+  floatPrecision() {
+    return 32;
+  }
+  epsilon() {
+    return super.epsilon();
+  }
+};
+MathBackendCPU.nextDataId = 0;
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
+var shared_exports = {};
+__export(shared_exports, {
+  addImpl: () => addImpl,
+  bincountImpl: () => bincountImpl,
+  bincountReduceImpl: () => bincountReduceImpl,
+  ceilImpl: () => ceilImpl,
+  concatImpl: () => concatImpl,
+  equalImpl: () => equalImpl,
+  expImpl: () => expImpl,
+  expm1Impl: () => expm1Impl,
+  floorImpl: () => floorImpl,
+  gatherNdImpl: () => gatherNdImpl,
+  gatherV2Impl: () => gatherV2Impl,
+  greaterEqualImpl: () => greaterEqualImpl,
+  greaterImpl: () => greaterImpl,
+  lessEqualImpl: () => lessEqualImpl,
+  lessImpl: () => lessImpl,
+  linSpaceImpl: () => linSpaceImpl,
+  logImpl: () => logImpl,
+  maxImpl: () => maxImpl,
+  maximumImpl: () => maximumImpl,
+  minimumImpl: () => minimumImpl,
+  multiplyImpl: () => multiplyImpl,
+  negImpl: () => negImpl,
+  notEqualImpl: () => notEqualImpl,
+  prodImpl: () => prodImpl,
+  rangeImpl: () => rangeImpl,
+  rsqrtImpl: () => rsqrtImpl,
+  scatterImpl: () => scatterImpl,
+  sigmoidImpl: () => sigmoidImpl,
+  simpleAbsImpl: () => simpleAbsImpl,
+  sliceImpl: () => sliceImpl,
+  sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,
+  sparseReshapeImpl: () => sparseReshapeImpl,
+  sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,
+  sqrtImpl: () => sqrtImpl,
+  squaredDifferenceImpl: () => squaredDifferenceImpl,
+  stridedSliceImpl: () => stridedSliceImpl,
+  stringNGramsImpl: () => stringNGramsImpl,
+  stringSplitImpl: () => stringSplitImpl,
+  stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,
+  subImpl: () => subImpl,
+  tileImpl: () => tileImpl,
+  topKImpl: () => topKImpl,
+  transposeImpl: () => transposeImpl,
+  uniqueImpl: () => uniqueImpl
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js
+function simpleAbsImpl(vals) {
+  const resultValues = new Float32Array(vals.length);
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    resultValues[i2] = Math.abs(vals[i2]);
+  }
+  return resultValues;
+}
+var abs2 = (args) => {
+  const { x } = args.inputs;
+  const cpuBackend = args.backend;
+  assertNotComplex(x, "abs");
+  let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
+  const values = cpuBackend.data.get(x.dataId).values;
+  resultValues = simpleAbsImpl(values);
+  return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);
+};
+var absConfig = {
+  kernelName: Abs,
+  backendName: "cpu",
+  kernelFunc: abs2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js
+function createSimpleBinaryKernelImpl(op2) {
+  return (aShape, bShape, aVals, bVals, dtype) => {
+    const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    const resultRank = newShape.length;
+    const resultStrides = util_exports.computeStrides(newShape);
+    const resultSize = util_exports.sizeFromShape(newShape);
+    const result = util_exports.getTypedArrayFromDType(dtype, resultSize);
+    const aRank = aShape.length;
+    const bRank = bShape.length;
+    const aStrides = util_exports.computeStrides(aShape);
+    const bStrides = util_exports.computeStrides(bShape);
+    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);
+    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);
+    if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+      for (let i2 = 0; i2 < result.length; ++i2) {
+        result[i2] = op2(aVals[i2 % aVals.length], bVals[i2 % bVals.length]);
+      }
+    } else {
+      for (let i2 = 0; i2 < result.length; ++i2) {
+        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+        const aLoc = loc.slice(-aRank);
+        aBroadcastDims.forEach((d) => aLoc[d] = 0);
+        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
+        const bLoc = loc.slice(-bRank);
+        bBroadcastDims.forEach((d) => bLoc[d] = 0);
+        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
+        result[i2] = op2(aVals[aIndex], bVals[bIndex]);
+      }
+    }
+    return [result, newShape];
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js
+function complex2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { real: real5, imag: imag5 } = inputs;
+  const realVals = backend2.data.get(real5.dataId).values;
+  const imagVals = backend2.data.get(imag5.dataId).values;
+  const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64");
+  const complex5 = backend2.data.get(complexInfo.dataId);
+  complex5.complexTensorInfos = {
+    real: backend2.makeTensorInfo(real5.shape, "float32", realVals),
+    imag: backend2.makeTensorInfo(imag5.shape, "float32", imagVals)
+  };
+  return complexInfo;
+}
+var complexConfig = {
+  kernelName: Complex,
+  backendName: "cpu",
+  kernelFunc: complex2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js
+function zeros3(backend2, shape, dtype = "float32") {
+  if (dtype === "complex64") {
+    const real5 = zeros3(backend2, shape, "float32");
+    const imag5 = zeros3(backend2, shape, "float32");
+    return complex2({ inputs: { real: real5, imag: imag5 }, backend: backend2 });
+  }
+  const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);
+  return backend2.makeTensorInfo(shape, dtype, values);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js
+function identity2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  backend2.incRef(x.dataId);
+  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+}
+var identityConfig = {
+  kernelName: Identity,
+  backendName: "cpu",
+  kernelFunc: identity2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js
+function real2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const real5 = backend2.data.get(input2.dataId).complexTensorInfos.real;
+  const realVal = backend2.data.get(real5.dataId).values;
+  return backend2.makeTensorInfo(real5.shape, real5.dtype, realVal);
+}
+var realConfig = {
+  kernelName: Real,
+  backendName: "cpu",
+  kernelFunc: real2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js
+function cast3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dtype } = attrs;
+  if (dtype === "complex64") {
+    if (x.dtype === "complex64") {
+      return identity2({ inputs: { x }, backend: backend2 });
+    }
+    const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);
+    const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+    const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
+    backend2.disposeIntermediateTensorInfo(floatX);
+    return result;
+  }
+  if (x.dtype === "complex64") {
+    const realPart = real2({ inputs: { input: x }, backend: backend2 });
+    const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    return result;
+  }
+  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
+    const result = identity2({ inputs: { x }, backend: backend2 });
+    return { dataId: result.dataId, shape: result.shape, dtype };
+  }
+  if (dtype === "int32") {
+    const values = backend2.data.get(x.dataId).values;
+    const resultValues = Int32Array.from(values);
+    return backend2.makeTensorInfo(x.shape, "int32", resultValues);
+  }
+  if (dtype === "bool") {
+    const xVals = backend2.data.get(x.dataId).values;
+    const zero = util_exports.toTypedArray([0], x.dtype);
+    const [resultData, resultShape] = createSimpleBinaryKernelImpl((a6, b) => a6 !== b ? 1 : 0)(x.shape, [], xVals, zero, "bool");
+    return backend2.makeTensorInfo(resultShape, "bool", resultData);
+  }
+  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
+}
+var castConfig = {
+  kernelName: Cast,
+  backendName: "cpu",
+  kernelFunc: cast3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js
+function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {
+  if (complexImpl == null) {
+    return ({ inputs, backend: backend2 }) => {
+      const { a: a6, b } = inputs;
+      const cpuBackend = backend2;
+      assertNotComplex([a6, b], name);
+      const aVals = cpuBackend.data.get(a6.dataId).values;
+      const bVals = cpuBackend.data.get(b.dataId).values;
+      const decodedAVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;
+      const decodedBVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;
+      const $dtype = dtype || a6.dtype;
+      const [resultData, resultShape] = simpleImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);
+      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
+    };
+  }
+  return ({ inputs, backend: backend2 }) => {
+    const { a: a6, b } = inputs;
+    const cpuBackend = backend2;
+    if (a6.dtype === "complex64" || b.dtype === "complex64") {
+      const $aComplex = cast3({ inputs: { x: a6 }, backend: cpuBackend, attrs: { dtype: "complex64" } });
+      const $aComplexVals = cpuBackend.data.get($aComplex.dataId);
+      const aReal = $aComplexVals.complexTensorInfos.real;
+      const aImag = $aComplexVals.complexTensorInfos.imag;
+      const aRealVals = cpuBackend.data.get(aReal.dataId).values;
+      const aImagVals = cpuBackend.data.get(aImag.dataId).values;
+      const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: "complex64" } });
+      const $bComplexVals = cpuBackend.data.get($bComplex.dataId);
+      const bReal = $bComplexVals.complexTensorInfos.real;
+      const bImag = $bComplexVals.complexTensorInfos.imag;
+      const bRealVals = cpuBackend.data.get(bReal.dataId).values;
+      const bImagVals = cpuBackend.data.get(bImag.dataId).values;
+      const [resultRealData, resultImagData, resultShape] = complexImpl(a6.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);
+      const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData);
+      const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData);
+      const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });
+      cpuBackend.disposeIntermediateTensorInfo($aComplex);
+      cpuBackend.disposeIntermediateTensorInfo($bComplex);
+      cpuBackend.disposeIntermediateTensorInfo(resultReal);
+      cpuBackend.disposeIntermediateTensorInfo(resultImag);
+      return result;
+    } else {
+      const aVals = cpuBackend.data.get(a6.dataId).values;
+      const bVals = cpuBackend.data.get(b.dataId).values;
+      const $dtype = dtype || a6.dtype;
+      const [resultData, resultShape] = simpleImpl(a6.shape, b.shape, aVals, bVals, $dtype);
+      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
+    }
+  };
+}
+function createComplexBinaryKernelImpl(op2) {
+  return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {
+    const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    const resultSize = util_exports.sizeFromShape(resultShape);
+    const resultRank = resultShape.length;
+    const resultStrides = util_exports.computeStrides(resultShape);
+    const resultRealVals = util_exports.getTypedArrayFromDType("float32", resultSize);
+    const resultImagVals = util_exports.getTypedArrayFromDType("float32", resultSize);
+    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);
+    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);
+    const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);
+    const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);
+    const aRank = aShape.length;
+    const aStrides = util_exports.computeStrides(aShape);
+    const bRank = bShape.length;
+    const bStrides = util_exports.computeStrides(bShape);
+    if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
+        const aIdx = i2 % aVals.length;
+        const bIdx = i2 % bVals.length;
+        const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
+        resultRealVals[i2] = result.real;
+        resultImagVals[i2] = result.imag;
+      }
+    } else {
+      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
+        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+        const aLoc = loc.slice(-aRank);
+        aBroadcastDims.forEach((d) => aLoc[d] = 0);
+        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
+        const bLoc = loc.slice(-bRank);
+        bBroadcastDims.forEach((d) => bLoc[d] = 0);
+        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
+        const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
+        resultRealVals[i2] = opResult.real;
+        resultImagVals[i2] = opResult.imag;
+      }
+    }
+    return [resultRealVals, resultImagVals, resultShape];
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js
+var addImpl = createSimpleBinaryKernelImpl((a6, b) => a6 + b);
+var addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
+  return { real: aReal + bReal, imag: aImag + bImag };
+});
+var add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);
+var addConfig = {
+  kernelName: Add,
+  backendName: "cpu",
+  kernelFunc: add4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js
+function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
+  const weightsSize = util_exports.sizeFromShape(weightsShape);
+  const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    const value = xVals[i2];
+    if (value < 0) {
+      throw new Error("Input x must be non-negative!");
+    }
+    if (value >= size) {
+      continue;
+    }
+    if (weightsSize > 0) {
+      outVals[value] += weightsVals[i2];
+    } else {
+      outVals[value] += 1;
+    }
+  }
+  return outVals;
+}
+function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
+  const numRows = xBuf.shape[0];
+  const numCols = xBuf.shape[1];
+  const outBuf = buffer([numRows, size], weightsBuf.dtype);
+  for (let i2 = 0; i2 < numRows; i2++) {
+    for (let j = 0; j < numCols; j++) {
+      const value = xBuf.get(i2, j);
+      if (value < 0) {
+        throw new Error("Input x must be non-negative!");
+      }
+      if (value >= size) {
+        continue;
+      }
+      if (binaryOutput) {
+        outBuf.set(1, i2, value);
+      } else {
+        if (weightsBuf.size > 0) {
+          outBuf.set(outBuf.get(i2, value) + weightsBuf.get(i2, j), i2, value);
+        } else {
+          outBuf.set(outBuf.get(i2, value) + 1, i2, value);
+        }
+      }
+    }
+  }
+  return outBuf;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js
+function createSimpleUnaryImpl(op2) {
+  return (values, dtype, attrs) => {
+    const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);
+    for (let i2 = 0; i2 < values.length; ++i2) {
+      newValues[i2] = op2(values[i2], attrs);
+    }
+    return newValues;
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js
+function unaryKernelFunc(name, op2, dtype) {
+  return ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    assertNotComplex(x, name);
+    if (x.dtype === "string" || dtype === "string") {
+      throw new Error("unaryKernelFunc does not support string input/output");
+    }
+    const cpuBackend = backend2;
+    const values = cpuBackend.data.get(x.dataId).values;
+    const xSize = util_exports.sizeFromShape(x.shape);
+    const $dtype = dtype || x.dtype;
+    const newValues = util_exports.getArrayFromDType($dtype, xSize);
+    for (let i2 = 0; i2 < xSize; ++i2) {
+      newValues[i2] = op2(values[i2], attrs);
+    }
+    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
+  };
+}
+function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
+  return ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    assertNotComplex(x, name);
+    if (x.dtype === "string" || dtype === "string") {
+      throw new Error("unaryKernelFunc does not support string input/output");
+    }
+    const cpuBackend = backend2;
+    const values = cpuBackend.data.get(x.dataId).values;
+    const $dtype = dtype || x.dtype;
+    const newValues = unaryImpl(values, $dtype, attrs);
+    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js
+var ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));
+var ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);
+var ceilConfig = {
+  kernelName: Ceil,
+  backendName: "cpu",
+  kernelFunc: ceil2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js
+function concatImpl(inputs, outShape, dtype, simplyConcat) {
+  const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
+  if (simplyConcat && dtype !== "string") {
+    let offset = 0;
+    inputs.forEach((input2) => {
+      const size = util_exports.sizeFromShape(input2.shape);
+      outVals.set(input2.vals, offset);
+      offset += size;
+    });
+  } else {
+    let colOffset = 0;
+    inputs.forEach((input2) => {
+      const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;
+      let tIdx = 0;
+      for (let row = 0; row < input2.shape[0]; ++row) {
+        const resIdx = row * outShape[1] + colOffset;
+        for (let col = 0; col < input2.shape[1]; ++col) {
+          outVals[resIdx + col] = decodedData[tIdx++];
+        }
+      }
+      colOffset += input2.shape[1];
+    });
+  }
+  return outVals;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js
+var equalImpl = createSimpleBinaryKernelImpl((a6, b) => a6 === b ? 1 : 0);
+var equal2 = binaryKernelFunc(Equal, equalImpl, null, "bool");
+var equalConfig = {
+  kernelName: Equal,
+  backendName: "cpu",
+  kernelFunc: equal2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js
+var expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));
+var exp2 = unaryKernelFuncFromImpl(Exp, expImpl, "float32");
+var expConfig = {
+  kernelName: Exp,
+  backendName: "cpu",
+  kernelFunc: exp2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js
+var expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));
+var expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);
+var expm1Config = {
+  kernelName: Expm1,
+  backendName: "cpu",
+  kernelFunc: expm12
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js
+var floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));
+var floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);
+var floorConfig = {
+  kernelName: Floor,
+  backendName: "cpu",
+  kernelFunc: floor2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js
+function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {
+  const outBuf = buffer([numSlices, sliceSize], dtype);
+  for (let i2 = 0; i2 < numSlices; i2++) {
+    const index = [];
+    let flattenIndex = 0;
+    for (let j = 0; j < sliceRank; j++) {
+      const dim = indicesData[i2 * sliceRank + j];
+      flattenIndex += dim * strides[j];
+      index.push(dim);
+    }
+    if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {
+      throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);
+    }
+    for (let k = 0; k < sliceSize; k++) {
+      outBuf.values[i2 * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
+    }
+  }
+  return outBuf;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js
+function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
+  const outBuf = buffer(flattenOutputShape, xBuf.dtype);
+  for (let i2 = 0; i2 < outBuf.size; ++i2) {
+    const newLoc = outBuf.indexToLoc(i2);
+    const originalLoc = newLoc.slice();
+    const batchIdx = originalLoc[0];
+    const indicesIdx = originalLoc[2];
+    const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
+    originalLoc[2] = indicesBuf.values[indicesIndex];
+    const originalIndex = xBuf.locToIndex(originalLoc);
+    if (0 <= originalIndex && originalIndex < xBuf.values.length) {
+      outBuf.values[i2] = xBuf.values[originalIndex];
+    }
+  }
+  return outBuf;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js
+var greaterImpl = createSimpleBinaryKernelImpl((a6, b) => a6 > b ? 1 : 0);
+var greater3 = binaryKernelFunc(Greater, greaterImpl, null, "bool");
+var greaterConfig = {
+  kernelName: Greater,
+  backendName: "cpu",
+  kernelFunc: greater3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js
+var greaterEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 >= b ? 1 : 0);
+var greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, "bool");
+var greaterEqualConfig = {
+  kernelName: GreaterEqual,
+  backendName: "cpu",
+  kernelFunc: greaterEqual2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js
+var lessImpl = createSimpleBinaryKernelImpl((a6, b) => a6 < b ? 1 : 0);
+var less3 = binaryKernelFunc(Less, lessImpl, null, "bool");
+var lessConfig = {
+  kernelName: Less,
+  backendName: "cpu",
+  kernelFunc: less3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js
+var lessEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 <= b ? 1 : 0);
+var lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, "bool");
+var lessEqualConfig = {
+  kernelName: LessEqual,
+  backendName: "cpu",
+  kernelFunc: lessEqual2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js
+function linSpaceImpl(start, stop, num) {
+  const step5 = (stop - start) / (num - 1);
+  const values = util_exports.makeZerosTypedArray(num, "float32");
+  values[0] = start;
+  for (let i2 = 1; i2 < values.length; i2++) {
+    values[i2] = values[i2 - 1] + step5;
+  }
+  return values;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js
+var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));
+var log3 = unaryKernelFuncFromImpl(Log, logImpl);
+var logConfig = {
+  kernelName: Log,
+  backendName: "cpu",
+  kernelFunc: log3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js
+function maxImpl(aVals, reduceSize, outShape, dtype) {
+  const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let max7 = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (Number.isNaN(value) || value > max7) {
+        max7 = value;
+      }
+    }
+    vals[i2] = max7;
+  }
+  return vals;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js
+var maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));
+var maximum3 = binaryKernelFunc(Maximum, maximumImpl);
+var maximumConfig = {
+  kernelName: Maximum,
+  backendName: "cpu",
+  kernelFunc: maximum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js
+var minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));
+var minimum3 = binaryKernelFunc(Minimum, minimumImpl);
+var minimumConfig = {
+  kernelName: Minimum,
+  backendName: "cpu",
+  kernelFunc: minimum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js
+var multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);
+var multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
+  return {
+    real: aReal * bReal - aImag * bImag,
+    imag: aReal * bImag + aImag * bReal
+  };
+});
+var multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);
+var multiplyConfig = {
+  kernelName: Multiply,
+  backendName: "cpu",
+  kernelFunc: multiply2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js
+function negImpl(xVals, xShape, xDtype) {
+  const minusOne = util_exports.createScalarValue(-1, xDtype);
+  return multiplyImpl([], xShape, minusOne, xVals, xDtype);
+}
+function neg2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  assertNotComplex(x, "neg");
+  const xVals = backend2.data.get(x.dataId).values;
+  const [res, newShape] = negImpl(xVals, x.shape, x.dtype);
+  return backend2.makeTensorInfo(newShape, x.dtype, res);
+}
+var negConfig = {
+  kernelName: Neg,
+  backendName: "cpu",
+  kernelFunc: neg2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js
+var notEqualImpl = createSimpleBinaryKernelImpl((a6, b) => a6 !== b ? 1 : 0);
+var notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, "bool");
+var notEqualConfig = {
+  kernelName: NotEqual,
+  backendName: "cpu",
+  kernelFunc: notEqual2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js
+function transposeImpl(xVals, xShape, dtype, perm, newShape) {
+  const xRank = xShape.length;
+  const xSize = util_exports.sizeFromShape(xShape);
+  const xStrides = util_exports.computeStrides(xShape);
+  const newStrides = util_exports.computeStrides(newShape);
+  const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));
+  for (let i2 = 0; i2 < xSize; ++i2) {
+    const loc = util_exports.indexToLoc(i2, xRank, xStrides);
+    const newLoc = new Array(loc.length);
+    for (let i3 = 0; i3 < newLoc.length; i3++) {
+      newLoc[i3] = loc[perm[i3]];
+    }
+    const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);
+    result[newIndex] = xVals[i2];
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js
+function transpose2(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x } = inputs;
+  const { perm } = attrs;
+  assertNotComplex(x, "transpose");
+  const xRank = x.shape.length;
+  const newShape = new Array(xRank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[perm[i2]];
+  }
+  const values = backend2.data.get(x.dataId).values;
+  const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
+  const dataId = backend2.write(result, newShape, x.dtype);
+  return { dataId, shape: newShape, dtype: x.dtype };
+}
+var transposeConfig = {
+  kernelName: Transpose,
+  backendName: "cpu",
+  kernelFunc: transpose2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js
+function prodImpl(xShape, xDtype, xVals, reductionAxes) {
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);
+  const outDtype = upcastType(xDtype, "int32");
+  const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  for (let i2 = 0; i2 < outVals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let prod6 = 1;
+    for (let j = 0; j < reduceSize; ++j) {
+      prod6 *= xVals[offset + j];
+    }
+    outVals[i2] = prod6;
+  }
+  return { outVals, outShape, outDtype };
+}
+function prod2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "prod");
+  const xRank = x.shape.length;
+  const axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
+  let reductionAxes = axes;
+  let permutedX = x;
+  const intermediateTensorInfos = [];
+  if (permutation != null) {
+    permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+    intermediateTensorInfos.push(permutedX);
+    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
+  }
+  const xVals = backend2.data.get(permutedX.dataId).values;
+  const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);
+  let resultShape = outShape;
+  if (keepDims) {
+    resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);
+  }
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return backend2.makeTensorInfo(resultShape, outDtype, outVals);
+}
+var prodConfig = {
+  kernelName: Prod,
+  backendName: "cpu",
+  kernelFunc: prod2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js
+function rangeImpl(start, stop, step5, dtype) {
+  const sameStartStop = start === stop;
+  const increasingRangeNegativeStep = start < stop && step5 < 0;
+  const decreasingRangePositiveStep = stop < start && step5 > 1;
+  if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
+    return util_exports.makeZerosTypedArray(0, dtype);
+  }
+  const numElements = Math.abs(Math.ceil((stop - start) / step5));
+  const values = util_exports.makeZerosTypedArray(numElements, dtype);
+  if (stop < start && step5 === 1) {
+    step5 = -1;
+  }
+  values[0] = start;
+  for (let i2 = 1; i2 < values.length; i2++) {
+    values[i2] = values[i2 - 1] + step5;
+  }
+  return values;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js
+var rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));
+var rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);
+var rsqrtConfig = {
+  kernelName: Rsqrt,
+  backendName: "cpu",
+  kernelFunc: rsqrt2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js
+function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
+  const flattenShape = [outputSize / sliceSize, sliceSize];
+  const indicesData = indices.values;
+  const updatesData = updates.values;
+  if (outputSize === 0) {
+    return buffer(shape, updates.dtype);
+  }
+  const outBuf = buffer(flattenShape, updates.dtype);
+  if (typeof defaultValue === "string") {
+    outBuf.values.fill(defaultValue);
+  } else if (typeof defaultValue === "number") {
+    outBuf.values.fill(defaultValue);
+  } else if (typeof defaultValue === "boolean") {
+    outBuf.values.fill(+defaultValue);
+  }
+  for (let i2 = 0; i2 < numUpdates; i2++) {
+    const index = [];
+    let flattenIndex = 0;
+    for (let j = 0; j < sliceRank; j++) {
+      const dim = indicesData[i2 * sliceRank + j];
+      index.push(dim);
+      flattenIndex += dim * strides[j];
+    }
+    if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
+      throw new Error(`Invalid indices: ${index} does not index into ${shape}`);
+    }
+    for (let k = 0; k < sliceSize; k++) {
+      if (sumDupeIndices) {
+        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i2 * sliceSize + k];
+      } else {
+        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i2 * sliceSize + k];
+      }
+    }
+  }
+  return outBuf;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js
+var sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));
+var sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));
+var sigmoidConfig = {
+  kernelName: Sigmoid,
+  backendName: "cpu",
+  kernelFunc: sigmoid2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js
+function sliceImpl(vals, begin, size, shape, dtype) {
+  const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);
+  const length = util_exports.sizeFromShape(size);
+  const xStrides = util_exports.computeStrides(shape);
+  if (isContinous) {
+    const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);
+    if (dtype === "string") {
+      return vals.slice(flatOffset, flatOffset + length);
+    }
+    return vals.subarray(flatOffset, flatOffset + length);
+  }
+  const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
+  const inBuf = buffer(shape, dtype, decodedData);
+  const outBuf = buffer(size, dtype);
+  for (let i2 = 0; i2 < outBuf.size; ++i2) {
+    const outLoc = outBuf.indexToLoc(i2);
+    const inLoc = outLoc.map((idx, j) => idx + begin[j]);
+    outBuf.set(inBuf.get(...inLoc), ...outLoc);
+  }
+  if (dtype === "string") {
+    return backend_util_exports.fromStringArrayToUint8(outBuf.values);
+  }
+  return outBuf.values;
+}
+function slice2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, size } = attrs;
+  assertNotComplex(x, "slice");
+  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
+  slice_util_exports.assertParamsValid(x, $begin, $size);
+  const vals = backend2.data.get(x.dataId).values;
+  const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
+  return backend2.makeTensorInfo($size, x.dtype, outVals);
+}
+var sliceConfig = {
+  kernelName: Slice,
+  backendName: "cpu",
+  kernelFunc: slice2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js
+function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {
+  const indicesCount = indicesShape[0];
+  const denseRows = denseShape[0];
+  const emptyRowIndicator = new Array(denseRows);
+  const reverseIndexMap = new Array(indicesCount);
+  const rank = indicesShape[1];
+  if (denseRows === 0) {
+    if (indicesCount !== 0) {
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));
+    }
+    const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);
+    const outputValues = util_exports.getArrayFromDType(valuesDType, 0);
+    return [
+      outputIndices,
+      [0, rank],
+      outputValues,
+      emptyRowIndicator,
+      reverseIndexMap
+    ];
+  }
+  let rowsAreOrdered = true;
+  let lastIndicesRow = 0;
+  const csrOffset = new Array(denseRows).fill(0);
+  for (let i2 = 0; i2 < indicesCount; ++i2) {
+    const row = indices[i2 * rank];
+    if (row < 0) {
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i2, row));
+    }
+    if (row >= denseRows) {
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i2, row, denseRows));
+    }
+    ++csrOffset[row];
+    rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;
+    lastIndicesRow = row;
+  }
+  let allRowsFull = true;
+  for (let row = 0; row < denseRows; ++row) {
+    const rowEmpty = csrOffset[row] === 0;
+    emptyRowIndicator[row] = rowEmpty;
+    allRowsFull = allRowsFull && !rowEmpty;
+    csrOffset[row] = Math.max(csrOffset[row], 1);
+    if (row > 0) {
+      csrOffset[row] += csrOffset[row - 1];
+    }
+  }
+  if (allRowsFull && rowsAreOrdered) {
+    const outputIndices = indices;
+    const outputValues = values;
+    for (let i2 = 0; i2 < indicesCount; ++i2) {
+      reverseIndexMap[i2] = i2;
+    }
+    return [
+      outputIndices,
+      [indicesCount, rank],
+      outputValues,
+      emptyRowIndicator,
+      reverseIndexMap
+    ];
+  } else {
+    const fullIndicesCount = csrOffset[denseRows - 1];
+    const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);
+    const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);
+    const filledCount = new Array(denseRows).fill(0);
+    for (let i2 = 0; i2 < indicesCount; ++i2) {
+      const row = indices[i2 * rank];
+      const offset = filledCount[row];
+      const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;
+      filledCount[row]++;
+      for (let j = 0; j < rank; ++j) {
+        outputIndices[outputI * rank + j] = indices[i2 * rank + j];
+      }
+      outputValues[outputI] = values[i2];
+      reverseIndexMap[i2] = outputI;
+    }
+    for (let row = 0; row < denseRows; ++row) {
+      const rowCount = filledCount[row];
+      if (rowCount === 0) {
+        const startingIndex = row === 0 ? 0 : csrOffset[row - 1];
+        outputIndices[startingIndex * rank + 0] = row;
+        for (let col = 1; col < rank; ++col) {
+          outputIndices[startingIndex * rank + col] = 0;
+        }
+        outputValues[startingIndex] = defaultValue;
+      }
+    }
+    return [
+      outputIndices,
+      [fullIndicesCount, rank],
+      outputValues,
+      emptyRowIndicator,
+      reverseIndexMap
+    ];
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js
+function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {
+  const denseSize = util_exports.sizeFromShape(inputShape);
+  const nnz = inputIndicesShape[0];
+  const outputRank = targetShape.length;
+  const outputShape = [];
+  let product = 1;
+  let unknownIndex = -1;
+  for (let d = 0; d < outputRank; ++d) {
+    const size = targetShape[d];
+    if (size === -1) {
+      if (unknownIndex !== -1) {
+        throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));
+      }
+      unknownIndex = d;
+      outputShape.push(1);
+    } else {
+      if (size < 0) {
+        throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size));
+      }
+      product *= size;
+      outputShape.push(size);
+    }
+  }
+  if (unknownIndex !== -1) {
+    if (product <= 0) {
+      throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
+    }
+    const missing = Math.trunc(denseSize / product);
+    if (product * missing !== denseSize) {
+      throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));
+    }
+    outputShape[unknownIndex] = missing;
+  }
+  const outputSize = util_exports.sizeFromShape(outputShape);
+  if (outputSize !== denseSize) {
+    throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));
+  }
+  const inputRank = inputShape.length;
+  const inputStrides = [];
+  if (inputRank > 0) {
+    inputStrides[inputRank - 1] = 1;
+    for (let d = inputRank - 2; d >= 0; --d) {
+      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
+    }
+  }
+  const outputStrides = [];
+  if (outputRank > 0) {
+    outputStrides[outputRank - 1] = 1;
+    for (let d = outputRank - 2; d >= 0; --d) {
+      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
+    }
+  }
+  const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);
+  for (let i2 = 0; i2 < nnz; ++i2) {
+    let id = 0;
+    for (let j = 0; j < inputRank; ++j) {
+      id += inputIndices[i2 * inputRank + j] * inputStrides[j];
+    }
+    for (let j = 0; j < outputRank; ++j) {
+      newIndices[i2 * outputRank + j] = Math.trunc(id / outputStrides[j]);
+      id %= outputStrides[j];
+    }
+  }
+  return [newIndices, [nnz, outputRank], outputShape];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js
+function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {
+  const numIndices = indices.length;
+  const inputFlat = [inputShape[0], input2.length / inputShape[0]];
+  const numCol = inputFlat[1];
+  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
+  const outputRows = lastSegmentIdPlusOne;
+  if (outputRows < 0) {
+    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+  }
+  const outputShape = inputShape.slice();
+  outputShape[0] = outputRows;
+  const outputLength = outputShape.reduce((product, value) => product * value, 1);
+  const output = util_exports.getArrayFromDType(inputDType, outputLength);
+  if (numIndices === 0) {
+    if (outputRows > 0) {
+      output.fill(defaultValue);
+    }
+    return [output, outputShape];
+  }
+  if (outputRows <= 0) {
+    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+  }
+  let start = 0, end = 1;
+  let uninitializedIndex = 0;
+  let outIndex = segmentIds[start];
+  while (true) {
+    let nextIndex = 0;
+    if (end < numIndices) {
+      nextIndex = segmentIds[end];
+      if (outIndex === nextIndex) {
+        ++end;
+        continue;
+      }
+      if (outIndex >= nextIndex) {
+        throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
+      }
+    }
+    if (outIndex < 0 || outIndex >= outputRows) {
+      throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));
+    }
+    if (outIndex > uninitializedIndex) {
+      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
+    }
+    for (let i2 = start; i2 < end; ++i2) {
+      const index = indices[i2];
+      if (index < 0 || index >= inputFlat[0]) {
+        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i2, indices[i2], inputFlat[0]));
+      }
+      for (let j = 0; j < numCol; j++) {
+        output[outIndex * numCol + j] += input2[index * numCol + j];
+      }
+    }
+    if (isMean) {
+      for (let j = 0; j < numCol; j++) {
+        output[outIndex * numCol + j] /= end - start;
+      }
+    }
+    start = end;
+    ++end;
+    uninitializedIndex = outIndex + 1;
+    outIndex = nextIndex;
+    if (end > numIndices) {
+      break;
+    }
+  }
+  if (uninitializedIndex < outputRows) {
+    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);
+  }
+  return [output, outputShape];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js
+var sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));
+var sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));
+var sqrtConfig = {
+  kernelName: Sqrt,
+  backendName: "cpu",
+  kernelFunc: sqrt2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js
+var squaredDifferenceImpl = createSimpleBinaryKernelImpl((a6, b) => {
+  const diff = a6 - b;
+  return diff * diff;
+});
+var squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);
+var squaredDifferenceConfig = {
+  kernelName: SquaredDifference,
+  backendName: "cpu",
+  kernelFunc: squaredDifference2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js
+function stridedSliceImpl(outShape, xBuf, strides, begin) {
+  const outBuf = buffer(outShape, xBuf.dtype);
+  for (let i2 = 0; i2 < outBuf.size; i2++) {
+    const loc = outBuf.indexToLoc(i2);
+    const newLoc = new Array(loc.length);
+    for (let j = 0; j < newLoc.length; j++) {
+      newLoc[j] = loc[j] * strides[j] + begin[j];
+    }
+    outBuf.set(xBuf.get(...newLoc), ...loc);
+  }
+  return outBuf;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js
+var StringNGramsOp = class {
+  constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
+    this.separator = util_exports.encodeString(separator);
+    this.nGramWidths = nGramWidths;
+    this.leftPad = util_exports.encodeString(leftPad);
+    this.rightPad = util_exports.encodeString(rightPad2);
+    this.padWidth = padWidth;
+    this.preserveShort = preserveShortSequences;
+  }
+  getPadWidth(nGramWidth) {
+    return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);
+  }
+  getNumNGrams(length, nGramWidth) {
+    const padWidth = this.getPadWidth(nGramWidth);
+    return Math.max(0, length + 2 * padWidth - nGramWidth + 1);
+  }
+  createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {
+    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {
+      const padWidth = this.getPadWidth(nGramWidth);
+      const leftPadding = Math.max(0, padWidth - nGramIndex);
+      const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
+      const numTokens = nGramWidth - (leftPadding + rightPadding);
+      const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
+      let nGramSize = 0;
+      nGramSize += leftPadding * this.leftPad.length;
+      for (let n = 0; n < numTokens; ++n) {
+        nGramSize += data[dataStartIndex + n].length;
+      }
+      nGramSize += rightPadding * this.rightPad.length;
+      const numSeparators = leftPadding + rightPadding + numTokens - 1;
+      nGramSize += numSeparators * this.separator.length;
+      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
+      const nGram = output[outputStartIndex + nGramIndex];
+      let nextNGramIndex = 0;
+      const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);
+      for (let n = 0; n < leftPadding; ++n) {
+        appendToNGram(this.leftPad);
+        appendToNGram(this.separator);
+      }
+      for (let n = 0; n < numTokens - 1; ++n) {
+        appendToNGram(data[dataStartIndex + n]);
+        appendToNGram(this.separator);
+      }
+      if (numTokens > 0) {
+        appendToNGram(data[dataStartIndex + numTokens - 1]);
+        for (let n = 0; n < rightPadding; ++n) {
+          appendToNGram(this.separator);
+          appendToNGram(this.rightPad);
+        }
+      } else {
+        for (let n = 0; n < rightPadding - 1; ++n) {
+          appendToNGram(this.rightPad);
+          appendToNGram(this.separator);
+        }
+        appendToNGram(this.rightPad);
+      }
+    }
+  }
+  compute(data, splits) {
+    const inputDataSize = data.length;
+    const splitsSize = splits.length;
+    if (splitsSize > 0) {
+      let prevSplit = splits[0];
+      if (prevSplit !== 0) {
+        throw new Error(`First split value must be 0, got ${prevSplit}`);
+      }
+      for (let i2 = 1; i2 < splitsSize; ++i2) {
+        let validSplits = splits[i2] >= prevSplit;
+        validSplits = validSplits && splits[i2] <= inputDataSize;
+        if (!validSplits) {
+          throw new Error(`Invalid split value ${splits[i2]}, must be in [${prevSplit}, ${inputDataSize}]`);
+        }
+        prevSplit = splits[i2];
+      }
+      if (prevSplit !== inputDataSize) {
+        throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
+      }
+    }
+    const numBatchItems = splitsSize - 1;
+    const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize);
+    if (inputDataSize === 0 || splitsSize === 0) {
+      const empty = new Array(inputDataSize);
+      for (let i2 = 0; i2 <= numBatchItems; ++i2) {
+        nGramsSplits[i2] = 0;
+      }
+      return [empty, nGramsSplits];
+    }
+    nGramsSplits[0] = 0;
+    for (let i2 = 1; i2 <= numBatchItems; ++i2) {
+      const length = splits[i2] - splits[i2 - 1];
+      let numNGrams = 0;
+      this.nGramWidths.forEach((nGramWidth) => {
+        numNGrams += this.getNumNGrams(length, nGramWidth);
+      });
+      if (this.preserveShort && length > 0 && numNGrams === 0) {
+        numNGrams = 1;
+      }
+      nGramsSplits[i2] = nGramsSplits[i2 - 1] + numNGrams;
+    }
+    const nGrams = new Array(nGramsSplits[numBatchItems]);
+    for (let i2 = 0; i2 < numBatchItems; ++i2) {
+      const splitIndex = splits[i2];
+      let outputStartIdx = nGramsSplits[i2];
+      this.nGramWidths.forEach((nGramWidth) => {
+        const length = splits[i2 + 1] - splits[i2];
+        const numNGrams = this.getNumNGrams(length, nGramWidth);
+        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+        outputStartIdx += numNGrams;
+      });
+      if (this.preserveShort && outputStartIdx === nGramsSplits[i2]) {
+        const dataLength = splits[i2 + 1] - splits[i2];
+        if (dataLength === 0) {
+          continue;
+        }
+        const nGramWidth = dataLength + 2 * this.padWidth;
+        const numNGrams = 1;
+        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
+      }
+    }
+    return [nGrams, nGramsSplits];
+  }
+};
+function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
+  return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js
+function split3(str, delimiters, skipEmpty, result) {
+  if (!str.length) {
+    return;
+  }
+  if (delimiters.length === 0) {
+    for (let i2 = 0; i2 < str.length; ++i2) {
+      result.push(str.subarray(i2, i2 + 1));
+    }
+    return;
+  }
+  if (delimiters.length === 1) {
+    const delimiter = delimiters[0];
+    let f = str.indexOf(delimiter);
+    while (f !== -1) {
+      const token = str.subarray(0, f);
+      if (!skipEmpty || token.length !== 0) {
+        result.push(token);
+      }
+      str = str.subarray(f + 1);
+      f = str.indexOf(delimiter);
+    }
+    if (!skipEmpty || str.length !== 0) {
+      result.push(str);
+    }
+    return;
+  }
+  let tokenStart = 0;
+  for (let i2 = 0; i2 < str.length + 1; i2++) {
+    if (i2 === str.length || delimiters.indexOf(str[i2]) !== -1) {
+      const token = str.subarray(tokenStart, i2);
+      if (!skipEmpty || token.length !== 0) {
+        result.push(token);
+      }
+      tokenStart = i2 + 1;
+    }
+  }
+}
+function stringSplitImpl(input2, delimiter, skipEmpty) {
+  const batchSize = input2.length;
+  const tokens = [];
+  let outputSize = 0;
+  let maxNumEntries = 0;
+  const numIndices = new Array(batchSize);
+  for (let i2 = 0; i2 < batchSize; ++i2) {
+    const prevTokensLength = tokens.length;
+    split3(input2[i2], delimiter, skipEmpty, tokens);
+    const nEntries = tokens.length - prevTokensLength;
+    numIndices[i2] = nEntries;
+    outputSize += nEntries;
+    maxNumEntries = Math.max(maxNumEntries, nEntries);
+  }
+  const indices = util_exports.getArrayFromDType("int32", outputSize * 2);
+  const values = new Array(outputSize);
+  const shape = [batchSize, maxNumEntries];
+  let c = 0;
+  for (let i2 = 0; i2 < batchSize; ++i2) {
+    for (let j = 0; j < numIndices[i2]; ++j) {
+      indices[c * 2] = i2;
+      indices[c * 2 + 1] = j;
+      values[c] = tokens[c];
+      ++c;
+    }
+  }
+  return [indices, values, shape];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js
+function stringToHashBucketFastImpl(input2, numBuckets) {
+  const output = util_exports.getArrayFromDType("int32", input2.length);
+  for (let i2 = 0; i2 < input2.length; ++i2) {
+    output[i2] = util_exports.fingerPrint64(input2[i2]).modulo(numBuckets).getLowBitsUnsigned();
+  }
+  return output;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js
+var subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);
+var subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
+  return { real: aReal - bReal, imag: aImag - bImag };
+});
+var sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);
+var subConfig = {
+  kernelName: Sub,
+  backendName: "cpu",
+  kernelFunc: sub2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js
+function tileImpl(xBuf, reps) {
+  const newShape = new Array(xBuf.rank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = xBuf.shape[i2] * reps[i2];
+  }
+  const result = buffer(newShape, xBuf.dtype);
+  for (let i2 = 0; i2 < result.values.length; ++i2) {
+    const newLoc = result.indexToLoc(i2);
+    const originalLoc = new Array(xBuf.rank);
+    for (let j = 0; j < originalLoc.length; j++) {
+      originalLoc[j] = newLoc[j] % xBuf.shape[j];
+    }
+    const originalIndex = xBuf.locToIndex(originalLoc);
+    result.values[i2] = xBuf.values[originalIndex];
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js
+var comparePair = (a6, b) => {
+  const valueDiff = b.value - a6.value;
+  return valueDiff === 0 ? a6.index - b.index : valueDiff;
+};
+function select(array2, k, left = 0, right = array2.length - 1) {
+  while (right > left) {
+    if (right - left > 600) {
+      const n = right - left + 1;
+      const i3 = k - left + 1;
+      const z = Math.log(n);
+      const s2 = 0.5 * Math.exp(2 * z / 3);
+      const sd = 0.5 * Math.sqrt(z * s2 * (n - s2) / n) * Math.sign(i3 - n / 2);
+      const newLeft = Math.max(left, Math.floor(k - i3 * s2 / n + sd));
+      const newRight = Math.min(right, Math.floor(k + (n - i3) * s2 / n + sd));
+      select(array2, k, newLeft, newRight);
+    }
+    const t2 = array2[k];
+    let i2 = left;
+    let j = right;
+    util_exports.swap(array2, left, k);
+    if (comparePair(array2[right], t2) > 0) {
+      util_exports.swap(array2, left, right);
+    }
+    while (i2 < j) {
+      util_exports.swap(array2, i2, j);
+      i2++;
+      j--;
+      while (comparePair(array2[i2], t2) < 0) {
+        i2 = i2 + 1;
+      }
+      while (comparePair(array2[j], t2) > 0) {
+        j = j - 1;
+      }
+    }
+    if (comparePair(array2[left], t2) === 0) {
+      util_exports.swap(array2, left, j);
+    } else {
+      j = j + 1;
+      util_exports.swap(array2, j, right);
+    }
+    if (j <= k) {
+      left = j + 1;
+    }
+    if (k <= j) {
+      right = j - 1;
+    }
+  }
+}
+function topKImpl(x, xShape, xDtype, k, sorted) {
+  const lastDim = xShape[xShape.length - 1];
+  const [batch, size] = [x.length / lastDim, lastDim];
+  const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);
+  const allTopKIndices = util_exports.getTypedArrayFromDType("int32", batch * k);
+  for (let b = 0; b < batch; b++) {
+    const offset = b * size;
+    const vals = x.subarray(offset, offset + size);
+    let valAndInd = new Array(vals.length);
+    vals.forEach((value, index) => valAndInd[index] = { value, index });
+    if (k < valAndInd.length) {
+      select(valAndInd, k);
+      valAndInd = valAndInd.slice(0, k);
+    }
+    if (sorted) {
+      valAndInd.sort(comparePair);
+    }
+    const outOffset = b * k;
+    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
+    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
+    for (let i2 = 0; i2 < k; i2++) {
+      topKVals[i2] = valAndInd[i2].value;
+      topKIndices[i2] = valAndInd[i2].index;
+    }
+  }
+  const outputShape = xShape.slice();
+  outputShape[outputShape.length - 1] = k;
+  return [
+    buffer(outputShape, xDtype, allTopKVals),
+    buffer(outputShape, "int32", allTopKIndices)
+  ];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js
+function uniqueImpl(values, axis, shape, dtype) {
+  const $axis = util_exports.parseAxisParam(axis, shape)[0];
+  const newShape = [1, shape[0], 1];
+  for (let i2 = 0; i2 < $axis; i2++) {
+    newShape[0] *= shape[i2];
+  }
+  newShape[1] = shape[$axis];
+  for (let i2 = $axis + 1; i2 < shape.length; i2++) {
+    newShape[2] *= shape[i2];
+  }
+  const uniqueElements = {};
+  const indices = new Int32Array(shape[$axis]);
+  const inputBuffer = new TensorBuffer(newShape, dtype, values);
+  const uniqueIndices = [];
+  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
+  for (let i2 = 0; i2 < shape[$axis]; i2++) {
+    let element;
+    if (is1DTensor) {
+      element = values[i2].toString();
+    } else {
+      const axisValues = [];
+      for (let m = 0; m < newShape[0]; m++) {
+        for (let n = 0; n < newShape[2]; n++) {
+          axisValues.push(inputBuffer.get(m, i2, n));
+        }
+      }
+      element = axisValues.join(",");
+    }
+    if (uniqueElements[element] !== void 0) {
+      indices[i2] = uniqueElements[element];
+    } else {
+      const uniqueIndex = Object.keys(uniqueElements).length;
+      uniqueElements[element] = uniqueIndex;
+      indices[i2] = uniqueIndex;
+      uniqueIndices.push(i2);
+    }
+  }
+  const outputTmpShape = newShape.slice();
+  outputTmpShape[1] = Object.keys(uniqueElements).length;
+  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
+  uniqueIndices.forEach((uniqueElementIndex, i2) => {
+    for (let m = 0; m < newShape[0]; m++) {
+      for (let n = 0; n < newShape[2]; n++) {
+        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i2, n);
+      }
+    }
+  });
+  const outputShape = shape.slice();
+  outputShape[$axis] = outputTmpShape[1];
+  return {
+    outputValues: outputBuffer.values,
+    outputShape,
+    indices
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js
+registerBackend("cpu", () => new MathBackendCPU(), 1);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js
+var elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);
+var eluConfig = {
+  kernelName: Elu,
+  backendName: "cpu",
+  kernelFunc: elu4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js
+function leakyRelu2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { alpha } = attrs;
+  assertNotComplex([x], "leakyRelu");
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const xVals = backend2.data.get(x.dataId).values;
+  const outVals = util_exports.getTypedArrayFromDType("float32", xSize);
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    outVals[i2] = xVals[i2] < 0 ? alpha * xVals[i2] : xVals[i2];
+  }
+  return backend2.makeTensorInfo(x.shape, "float32", outVals);
+}
+var leakyReluConfig = {
+  kernelName: LeakyRelu,
+  backendName: "cpu",
+  kernelFunc: leakyRelu2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js
+var preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);
+function prelu3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha } = inputs;
+  assertNotComplex([x, alpha], "prelu");
+  const aVals = backend2.data.get(x.dataId).values;
+  const bVals = backend2.data.get(alpha.dataId).values;
+  const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, "float32");
+  return backend2.makeTensorInfo(resultShape, "float32", resultData);
+}
+var preluConfig = {
+  kernelName: Prelu,
+  backendName: "cpu",
+  kernelFunc: prelu3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js
+var relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));
+var reluConfig = {
+  kernelName: Relu,
+  backendName: "cpu",
+  kernelFunc: relu2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js
+var relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));
+var relu6Config = {
+  kernelName: Relu6,
+  backendName: "cpu",
+  kernelFunc: relu62
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js
+function applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {
+  if (activation2 === "linear") {
+    return identity2({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "relu") {
+    return relu2({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "elu") {
+    return elu4({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "relu6") {
+    return relu62({ inputs: { x }, backend: backend2 });
+  } else if (activation2 === "prelu") {
+    return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });
+  } else if (activation2 === "leakyrelu") {
+    return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });
+  } else if (activation2 === "sigmoid") {
+    return sigmoid2({ inputs: { x }, backend: backend2 });
+  }
+  throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js
+function reshape3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  const $xSize = util_exports.sizeFromShape($shape);
+  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
+  backend2.incRef(x.dataId);
+  const xData = backend2.data.get(x.dataId);
+  if (xData.complexTensorInfos != null) {
+    const real5 = xData.complexTensorInfos.real;
+    const imag5 = xData.complexTensorInfos.imag;
+    real5.shape = $shape;
+    imag5.shape = $shape;
+  }
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig = {
+  kernelName: Reshape,
+  backendName: "cpu",
+  kernelFunc: reshape3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js
+function batchMatMul(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  assertNotComplex([a6, b], "matMul");
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape3({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
+  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
+  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const a3dValues = backend2.data.get(a3d.dataId).values;
+  const b3dValues = backend2.data.get(b3d.dataId).values;
+  const a3dStrides = util_exports.computeStrides(a3d.shape);
+  const b3dStrides = util_exports.computeStrides(b3d.shape);
+  const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];
+  const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];
+  const size = leftDim * rightDim;
+  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);
+  const resVals = result.values;
+  const blockSize = backend2.blockSize;
+  for (let bi = 0; bi < batchDim; bi++) {
+    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {
+      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {
+        for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {
+          const iBlock = Math.min(i0 + blockSize, leftDim);
+          const jBlock = Math.min(j0 + blockSize, rightDim);
+          const kBlock = Math.min(k02 + blockSize, sharedDim);
+          for (let i2 = i0; i2 < iBlock; i2++) {
+            for (let j = j0; j < jBlock; j++) {
+              let sum7 = 0;
+              for (let k = k02; k < kBlock; k++) {
+                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
+                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
+                const aVal = a3dValues[batchOffsetA + i2 * aOuterStep + k * aInnerStep];
+                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
+                sum7 += aVal * bVal;
+              }
+              resVals[bi * size + (i2 * rightDim + j)] += sum7;
+            }
+          }
+        }
+      }
+    }
+  }
+  backend2.disposeIntermediateTensorInfo(a3d);
+  backend2.disposeIntermediateTensorInfo(b3d);
+  return backend2.makeTensorInfo(outShape, result.dtype, result.values);
+}
+var batchMatMulConfig = {
+  kernelName: BatchMatMul,
+  backendName: "cpu",
+  kernelFunc: batchMatMul
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js
+function _fusedMatMul(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  let current;
+  let addRes;
+  let activationRes;
+  const intermediates = [];
+  const matMulRes = batchMatMul({ inputs: { a: a6, b }, attrs: { transposeA, transposeB }, backend: backend2 });
+  current = matMulRes;
+  if (bias) {
+    addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 });
+    intermediates.push(current);
+    current = addRes;
+  }
+  if (activation2) {
+    activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);
+    intermediates.push(current);
+    current = activationRes;
+  }
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return current;
+}
+var _fusedMatMulConfig = {
+  kernelName: _FusedMatMul,
+  backendName: "cpu",
+  kernelFunc: _fusedMatMul
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js
+var acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));
+var acosConfig = {
+  kernelName: Acos,
+  backendName: "cpu",
+  kernelFunc: acos2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js
+var acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));
+var acoshConfig = {
+  kernelName: Acosh,
+  backendName: "cpu",
+  kernelFunc: acosh2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js
+function addN2(args) {
+  const { inputs, backend: backend2 } = args;
+  const tensors = inputs;
+  assertNotComplex(inputs, "addN");
+  const vals = tensors.map((t2) => backend2.data.get(t2.dataId).values);
+  const outBuf = buffer(tensors[0].shape, tensors[0].dtype);
+  const outVals = outBuf.values;
+  for (let i2 = 0; i2 < tensors.length; i2++) {
+    const currVals = vals[i2];
+    for (let j = 0; j < outVals.length; j++) {
+      outVals[j] += currVals[j];
+    }
+  }
+  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+}
+var addNConfig = {
+  kernelName: AddN,
+  backendName: "cpu",
+  kernelFunc: addN2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js
+function all2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "all");
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("all", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let all5 = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      all5 = all5 && value;
+    }
+    vals[i2] = all5;
+  }
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo($x);
+  }
+  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
+  if (keepDims) {
+    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
+    backend2.disposeIntermediateTensorInfo(result);
+    return reshapedResult;
+  }
+  return result;
+}
+var allConfig = {
+  kernelName: All,
+  backendName: "cpu",
+  kernelFunc: all2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js
+function any2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "any");
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("any", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let anyVal = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      anyVal = anyVal || value;
+    }
+    vals[i2] = anyVal;
+  }
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo($x);
+  }
+  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
+  if (keepDims) {
+    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
+    backend2.disposeIntermediateTensorInfo(result);
+    return reshapedResult;
+  }
+  return result;
+}
+var anyConfig = {
+  kernelName: Any,
+  backendName: "cpu",
+  kernelFunc: any2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js
+function argMax2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  assertNotComplex(x, "argMax");
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  axes = [axes[0]];
+  backend_util_exports.assertAxesAreInnerMostDims("argMax", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const outSize = util_exports.sizeFromShape(outShape);
+  const vals = util_exports.makeZerosTypedArray(outSize, "int32");
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let max7 = aVals[offset];
+    let maxIndex = 0;
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (value > max7) {
+        max7 = value;
+        maxIndex = j;
+      }
+    }
+    vals[i2] = maxIndex;
+  }
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return backend2.makeTensorInfo(outShape, "int32", vals);
+}
+var argMaxConfig = {
+  kernelName: ArgMax,
+  backendName: "cpu",
+  kernelFunc: argMax2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js
+function argMin2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  assertNotComplex(x, "argMin");
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  axes = [axes[0]];
+  backend_util_exports.assertAxesAreInnerMostDims("argMin", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const outSize = util_exports.sizeFromShape(outShape);
+  const vals = util_exports.makeZerosTypedArray(outSize, "int32");
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let min7 = aVals[offset];
+    let minIndex = 0;
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (value < min7) {
+        min7 = value;
+        minIndex = j;
+      }
+    }
+    vals[i2] = minIndex;
+  }
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return backend2.makeTensorInfo(outShape, "int32", vals);
+}
+var argMinConfig = {
+  kernelName: ArgMin,
+  backendName: "cpu",
+  kernelFunc: argMin2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js
+var asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));
+var asinConfig = {
+  kernelName: Asin,
+  backendName: "cpu",
+  kernelFunc: asin2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js
+var asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));
+var asinhConfig = {
+  kernelName: Asinh,
+  backendName: "cpu",
+  kernelFunc: asinh2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js
+var atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));
+var atanConfig = {
+  kernelName: Atan,
+  backendName: "cpu",
+  kernelFunc: atan3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js
+var atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));
+var atan22 = binaryKernelFunc(Atan2, atan2Impl);
+var atan2Config = {
+  kernelName: Atan2,
+  backendName: "cpu",
+  kernelFunc: atan22
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js
+var atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));
+var atanhConfig = {
+  kernelName: Atanh,
+  backendName: "cpu",
+  kernelFunc: atanh2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js
+function pool2(xValues, xShape, dtype, strides, convInfo, poolType) {
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
+  const output = buffer(convInfo.outShape, dtype);
+  const outputVals = output.values;
+  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
+  const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
+  const outputColStrides = convInfo.outShape[3];
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const outputBatchOffset = b * outputBatchStrides;
+    const inputBatchOffset = b * strides[0];
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+        const xRCorner = yR * strideHeight - padTop;
+        const xRMin = Math.max(0, xRCorner);
+        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+        const outputRowOffset = outputBatchOffset + yR * outputRowStrides;
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const xCCorner = yC * strideWidth - padLeft;
+          const xCMin = Math.max(0, xCCorner);
+          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+          let minMaxValue = initialValue;
+          let avgValue = 0;
+          let count2 = 0;
+          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
+            const xROffset = inputBatchOffset + xR * strides[1];
+            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
+              const xCOffset = xROffset + xC * strides[2];
+              const pixel = xValues[xCOffset + d];
+              if (poolType === "max" && pixel > minMaxValue) {
+                minMaxValue = pixel;
+              } else if (poolType === "avg") {
+                avgValue += pixel;
+                count2++;
+              }
+            }
+            if (isNaN(minMaxValue)) {
+              break;
+            }
+          }
+          const outputOffset = outputRowOffset + yC * outputColStrides + d;
+          outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue;
+        }
+      }
+    }
+  }
+  return output;
+}
+function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {
+  const maxPositions = buffer(convInfo.outShape, "int32");
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  const xBuf = buffer(xShape, dtype, xValues);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+        const xRCorner = yR * strideHeight - padTop;
+        let xRMin = xRCorner;
+        while (xRMin < 0) {
+          xRMin += dilationHeight;
+        }
+        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const xCCorner = yC * strideWidth - padLeft;
+          let xCMin = xCCorner;
+          while (xCMin < 0) {
+            xCMin += dilationWidth;
+          }
+          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+          let maxValue = Number.NEGATIVE_INFINITY;
+          let maxPosition = -1;
+          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
+            const wR = xR - xRCorner;
+            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
+              const wC = xC - xCCorner;
+              const pixel = xBuf.get(b, xR, xC, d);
+              if (pixel > maxValue) {
+                maxValue = pixel;
+                if (flattenPositions) {
+                  maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
+                } else {
+                  maxPosition = wR * effectiveFilterWidth + wC;
+                }
+              }
+            }
+          }
+          maxPositions.set(maxPosition, b, yR, yC, d);
+        }
+      }
+    }
+  }
+  return maxPositions;
+}
+function pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = convInfo.padInfo.front;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
+  const output = buffer(convInfo.outShape, dtype);
+  const outputVals = output.values;
+  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
+  const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
+  const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
+  const outputColStrides = convInfo.outShape[4];
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    const outputBatchOffset = batch * outputBatchStrides;
+    const inputBatchOffset = batch * strides[0];
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
+        const xDepthCorner = yDepth * strideDepth - padFront;
+        let xDepthMin = xDepthCorner;
+        while (xDepthMin < 0) {
+          xDepthMin += dilationDepth;
+        }
+        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
+        const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;
+        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
+          const xRowCorner = yRow * strideHeight - padTop;
+          let xRowMin = xRowCorner;
+          while (xRowMin < 0) {
+            xRowMin += dilationHeight;
+          }
+          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
+          const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
+          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
+            const xColCorner = yCol * strideWidth - padLeft;
+            let xColMin = xColCorner;
+            while (xColMin < 0) {
+              xColMin += dilationWidth;
+            }
+            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
+            const outputColOffset = outputRowOffset + yCol * outputColStrides;
+            let minMaxValue = initialValue;
+            let avgValue = 0;
+            let count2 = 0;
+            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
+              const xDepthOffset = inputBatchOffset + xDepth * strides[1];
+              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
+                const xRowOffset = xDepthOffset + xRow * strides[2];
+                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
+                  const xColOffset = xRowOffset + xCol * strides[3];
+                  const pixel = xValues[xColOffset + channel];
+                  if (poolType === "max" && pixel > minMaxValue) {
+                    minMaxValue = pixel;
+                  } else if (poolType === "avg") {
+                    avgValue += pixel;
+                    count2++;
+                  }
+                  if (isNaN(minMaxValue)) {
+                    break;
+                  }
+                }
+                if (isNaN(minMaxValue)) {
+                  break;
+                }
+              }
+              if (isNaN(minMaxValue)) {
+                break;
+              }
+            }
+            const outputOffset = outputColOffset + channel;
+            outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue;
+          }
+        }
+      }
+    }
+  }
+  return output;
+}
+function maxPool3dPositions(xBuf, convInfo) {
+  const maxPositions = buffer(convInfo.outShape, "int32");
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = convInfo.padInfo.front;
+  const padTop = convInfo.padInfo.top;
+  const padLeft = convInfo.padInfo.left;
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
+        const xDepthCorner = yDepth * strideDepth - padFront;
+        let xDepthMin = xDepthCorner;
+        while (xDepthMin < 0) {
+          xDepthMin += dilationDepth;
+        }
+        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
+        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
+          const xRowCorner = yRow * strideHeight - padTop;
+          let xRowMin = xRowCorner;
+          while (xRowMin < 0) {
+            xRowMin += dilationHeight;
+          }
+          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
+          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
+            const xColCorner = yCol * strideWidth - padLeft;
+            let xColMin = xColCorner;
+            while (xColMin < 0) {
+              xColMin += dilationWidth;
+            }
+            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
+            let maxValue = Number.NEGATIVE_INFINITY;
+            let maxPosition = -1;
+            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
+              const wDepth = xDepth - xDepthCorner;
+              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
+                const wRow = xRow - xRowCorner;
+                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
+                  const wCol = xCol - xColCorner;
+                  const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);
+                  if (pixel >= maxValue) {
+                    maxValue = pixel;
+                    maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;
+                  }
+                }
+              }
+            }
+            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
+          }
+        }
+      }
+    }
+  }
+  return maxPositions;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js
+function avgPool2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex(x, "avgPool");
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  let res;
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    res = identity2({ inputs: { x }, backend: backend2 });
+  } else {
+    const xValues = backend2.data.get(x.dataId).values;
+    const strides2 = util_exports.computeStrides(x.shape);
+    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "avg");
+    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
+  }
+  return res;
+}
+var avgPoolConfig = {
+  kernelName: AvgPool,
+  backendName: "cpu",
+  kernelFunc: avgPool2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js
+function avgPool3D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  assertNotComplex(x, "avgPool3d");
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);
+  const xValues = backend2.data.get(x.dataId).values;
+  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "avg");
+  return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
+}
+var avgPool3DConfig = {
+  kernelName: AvgPool3D,
+  backendName: "cpu",
+  kernelFunc: avgPool3D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js
+function avgPool3DGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  assertNotComplex([dy, input2], "avgPool3DGrad");
+  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const filterDepth = convInfo.filterDepth;
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(input2.shape, "float32");
+  const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
+  const dyBuf = backend2.bufferSync(dy);
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
+        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
+          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
+            const dyDepthCorner = dxDepth - padFront;
+            const dyRowCorner = dxRow - padTop;
+            const dyColCorner = dxCol - padLeft;
+            let dotProd = 0;
+            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
+              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
+              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
+                continue;
+              }
+              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
+                const dyRow = (dyRowCorner + wRow) / strideHeight;
+                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
+                  continue;
+                }
+                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
+                  const dyCol = (dyColCorner + wCol) / strideWidth;
+                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
+                    continue;
+                  }
+                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                  dotProd += pixel;
+                }
+              }
+            }
+            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var avgPool3DGradConfig2 = {
+  kernelName: AvgPool3DGrad,
+  backendName: "cpu",
+  kernelFunc: avgPool3DGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js
+function avgPoolGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  assertNotComplex([dy, input2], "avgPoolGrad");
+  const { filterSize, strides, pad: pad3 } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(x.shape, "float32");
+  const avgMultiplier = 1 / (filterHeight * filterWidth);
+  const dyData = backend2.data.get(dy.dataId).values;
+  const dyBuf = buffer(dy.shape, "float32", dyData);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+          const dyRCorner = dxR - padTop;
+          const dyCCorner = dxC - padLeft;
+          let dotProd = 0;
+          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+            const dyR = (dyRCorner + wR) / strideHeight;
+            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
+              continue;
+            }
+            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+              const dyC = (dyCCorner + wC) / strideWidth;
+              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
+                continue;
+              }
+              const pixel = dyBuf.get(b, dyR, dyC, d);
+              dotProd += pixel;
+            }
+          }
+          dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var avgPoolGradConfig2 = {
+  kernelName: AvgPoolGrad,
+  backendName: "cpu",
+  kernelFunc: avgPoolGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js
+function batchNorm2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, scale: scale2, offset, mean: mean5, variance } = inputs;
+  util_exports.assert(mean5.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
+  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
+  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  assertNotComplex([x, mean5, variance, scale2, offset], "batchNorm");
+  let { varianceEpsilon } = attrs;
+  if (varianceEpsilon == null) {
+    varianceEpsilon = 1e-3;
+  }
+  const xVals = backend2.data.get(x.dataId).values;
+  const mVals = backend2.data.get(mean5.dataId).values;
+  const varVals = backend2.data.get(variance.dataId).values;
+  const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]);
+  const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);
+  const outVals = new Float32Array(xVals.length);
+  const offValsLength = offVals.length;
+  const sValsLength = sVals.length;
+  const varValsLength = varVals.length;
+  const mValsLength = mVals.length;
+  let offi = 0;
+  let mi = 0;
+  let si = 0;
+  let vi = 0;
+  for (let i2 = 0; i2 < xVals.length; ++i2) {
+    outVals[i2] = offVals[offi++] + (xVals[i2] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
+    if (offi >= offValsLength) {
+      offi = 0;
+    }
+    if (mi >= mValsLength) {
+      mi = 0;
+    }
+    if (si >= sValsLength) {
+      si = 0;
+    }
+    if (vi >= varValsLength) {
+      vi = 0;
+    }
+  }
+  return backend2.makeTensorInfo(x.shape, x.dtype, outVals);
+}
+var batchNormConfig = {
+  kernelName: FusedBatchNorm,
+  backendName: "cpu",
+  kernelFunc: batchNorm2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js
+function batchToSpaceND2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  assertNotComplex([x], "batchToSpaceND");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });
+  const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });
+  const result = slice2({
+    inputs: { x: xTransposedReshaped },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  backend2.disposeIntermediateTensorInfo(xReshaped);
+  backend2.disposeIntermediateTensorInfo(xTransposed);
+  backend2.disposeIntermediateTensorInfo(xTransposedReshaped);
+  return result;
+}
+var batchToSpaceNDConfig = {
+  kernelName: BatchToSpaceND,
+  backendName: "cpu",
+  kernelFunc: batchToSpaceND2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js
+function bincount2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size } = attrs;
+  const xVals = backend2.data.get(x.dataId).values;
+  const weightsVals = backend2.data.get(weights.dataId).values;
+  const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
+  return backend2.makeTensorInfo([size], weights.dtype, outVals);
+}
+var bincountConfig = {
+  kernelName: Bincount,
+  backendName: "cpu",
+  kernelFunc: bincount2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js
+function broadcastArgs2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { s0, s1 } = inputs;
+  const s0Vals = backend2.data.get(s0.dataId).values;
+  const s1Vals = backend2.data.get(s1.dataId).values;
+  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
+  return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape));
+}
+var broadcastArgsConfig = {
+  kernelName: BroadcastArgs,
+  backendName: "cpu",
+  kernelFunc: broadcastArgs2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js
+var clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {
+  const clipAttrs = attrs;
+  if (xi > clipAttrs.clipValueMax) {
+    return clipAttrs.clipValueMax;
+  }
+  return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
+});
+var clipByValueConfig = {
+  kernelName: ClipByValue,
+  backendName: "cpu",
+  kernelFunc: clipByValue2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js
+var complexAbs = (args) => {
+  const { x } = args.inputs;
+  const cpuBackend = args.backend;
+  const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
+  const complexVals = cpuBackend.data.get(x.dataId);
+  const real5 = complexVals.complexTensorInfos.real;
+  const imag5 = complexVals.complexTensorInfos.imag;
+  const realVals = cpuBackend.data.get(real5.dataId).values;
+  const imagVals = cpuBackend.data.get(imag5.dataId).values;
+  for (let i2 = 0; i2 < realVals.length; i2++) {
+    const real6 = realVals[i2];
+    const imag6 = imagVals[i2];
+    resultValues[i2] = Math.hypot(real6, imag6);
+  }
+  return cpuBackend.makeOutput(resultValues, x.shape, "float32");
+};
+var complexAbsConfig = {
+  kernelName: ComplexAbs,
+  backendName: "cpu",
+  kernelFunc: complexAbs
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js
+function imag2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const imag5 = backend2.data.get(input2.dataId).complexTensorInfos.imag;
+  const imagVal = backend2.data.get(imag5.dataId).values;
+  return backend2.makeTensorInfo(imag5.shape, imag5.dtype, imagVal);
+}
+var imagConfig = {
+  kernelName: Imag,
+  backendName: "cpu",
+  kernelFunc: imag2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js
+function concat2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
+  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
+  }
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, $axis);
+  if ($inputs[0].dtype === "complex64") {
+    const reals = $inputs.map((t2) => real2({ inputs: { input: t2 }, backend: backend2 }));
+    const imags = $inputs.map((t2) => imag2({ inputs: { input: t2 }, backend: backend2 }));
+    const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });
+    const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });
+    const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
+    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    backend2.disposeIntermediateTensorInfo(realConcated);
+    backend2.disposeIntermediateTensorInfo(imagConcated);
+    return result;
+  }
+  const inputs2D = $inputs.map((t2) => {
+    const innerSize = util_exports.sizeFromShape(t2.shape.slice($axis));
+    const shape = [-1, innerSize];
+    return reshape3({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+  });
+  const inputsValShapes = inputs2D.map((t2) => {
+    return { vals: backend2.data.get(t2.dataId).values, shape: t2.shape };
+  });
+  outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);
+  const simplyConcat = inputs2D[0].shape[0] === 1;
+  const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
+  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), $axis);
+  const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
+  inputs2D.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return outInfo;
+}
+var concatConfig = {
+  kernelName: Concat,
+  backendName: "cpu",
+  kernelFunc: concat2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js
+function conv2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;
+  assertNotComplex([x, filter], "conv2d");
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const padLeft = convInfo.padInfo.left;
+  const padTop = convInfo.padInfo.top;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const y = new TensorBuffer(convInfo.outShape, x.dtype);
+  const xStrides = util_exports.computeStrides(x.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const xBatchStride = xStrides[0];
+  const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
+  const xColStride = isChannelsLast ? xStrides[2] : 1;
+  const xChannelStride = isChannelsLast ? 1 : xStrides[1];
+  const yBatchStride = y.strides[0];
+  const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
+  const yColStride = isChannelsLast ? y.strides[2] : 1;
+  const yChannelStride = isChannelsLast ? 1 : y.strides[1];
+  const xVals = backend2.data.get(x.dataId).values;
+  const wVals = backend2.data.get(filter.dataId).values;
+  const yVals = y.values;
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const xOffset1 = b * xBatchStride;
+    const yOffset1 = b * yBatchStride;
+    for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+      const yOffset2 = yOffset1 + yR * yRowStride;
+      const xRCorner = yR * convInfo.strideHeight - padTop;
+      for (let wR = 0; wR < filterHeight; ++wR) {
+        const xR = xRCorner + wR * dilationHeight;
+        if (xR < 0 || xR >= convInfo.inHeight) {
+          continue;
+        }
+        const wOffset1 = wR * filterStrides[0];
+        const xOffset2 = xOffset1 + xR * xRowStride;
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const yOffset3 = yOffset2 + yC * yColStride;
+          const xCCorner = yC * convInfo.strideWidth - padLeft;
+          for (let wC = 0; wC < filterWidth; ++wC) {
+            const xC = xCCorner + wC * dilationWidth;
+            if (xC < 0 || xC >= convInfo.inWidth) {
+              continue;
+            }
+            const wOffset2 = wOffset1 + wC * filterStrides[1];
+            const xOffset3 = xOffset2 + xC * xColStride;
+            let wOffset3 = wOffset2;
+            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+              const xVal = xVals[xOffset3 + d1 * xChannelStride];
+              for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];
+              }
+              wOffset3 += convInfo.outChannels;
+            }
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, y.dtype, yVals);
+}
+var conv2DConfig = {
+  kernelName: Conv2D,
+  backendName: "cpu",
+  kernelFunc: conv2D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js
+function conv2DBackpropFilter2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;
+  assertNotComplex([x, dy], "conv2dBackpropFilter");
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const dW = new TensorBuffer(convInfo.filterShape, "float32");
+  const leftPad = convInfo.padInfo.left;
+  const topPad = convInfo.padInfo.top;
+  const xVals = backend2.data.get(x.dataId).values;
+  const dyVals = backend2.data.get(dy.dataId).values;
+  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
+  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
+  for (let wR = 0; wR < filterHeight; ++wR) {
+    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+    for (let wC = 0; wC < filterWidth; ++wC) {
+      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+      for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+          let dotProd = 0;
+          for (let b = 0; b < convInfo.batchSize; ++b) {
+            for (let yR = yRMin; yR < yRMax; ++yR) {
+              const xR = wR + yR * strideHeight - topPad;
+              for (let yC = yCMin; yC < yCMax; ++yC) {
+                const xC = wC + yC * strideWidth - leftPad;
+                if (isChannelsLast) {
+                  dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
+                } else {
+                  dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);
+                }
+              }
+            }
+          }
+          dW.set(dotProd, wR, wC, d1, d2);
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
+}
+var conv2DBackpropFilterConfig = {
+  kernelName: Conv2DBackpropFilter,
+  backendName: "cpu",
+  kernelFunc: conv2DBackpropFilter2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js
+function conv2DBackpropInput2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  assertNotComplex([dy, filter], "conv2dBackpropInput");
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const dx = new TensorBuffer(convInfo.inShape, "float32");
+  const dxValues = dx.values;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const fltValues = backend2.data.get(filter.dataId).values;
+  const [fltS0, fltS1, fltS2] = filterStrides;
+  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
+  $dataFormat = convInfo.dataFormat;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  const isChannelsLast = $dataFormat === "channelsLast";
+  const xBatchStride = dx.strides[0];
+  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
+  const xColStride = isChannelsLast ? dx.strides[2] : 1;
+  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
+  const yBatchStride = dyStrides[0];
+  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
+  const yColStride = isChannelsLast ? dyStrides[2] : 1;
+  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
+  for (let b = 0; b < batchSize; ++b) {
+    for (let d1 = 0; d1 < inChannels; ++d1) {
+      for (let xR = 0; xR < inHeight; ++xR) {
+        const xRCorner = xR - topPad;
+        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+        for (let xC = 0; xC < inWidth; ++xC) {
+          const xCCorner = xC - leftPad;
+          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+          let dotProd = 0;
+          for (let yR = xRMin; yR < yRMax; ++yR) {
+            const wR = yR * strideHeight - xRCorner;
+            for (let yC = xCMin; yC < yCMax; ++yC) {
+              const wC = yC * strideWidth - xCCorner;
+              const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
+              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+              for (let d2 = 0; d2 < outChannels; ++d2) {
+                const pixel = dyValues[dyOffset + yChannelStride * d2];
+                const weight = fltValues[fltOffset + d2];
+                dotProd += pixel * weight;
+              }
+            }
+          }
+          const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;
+          dxValues[dxOffset] = dotProd;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var conv2DBackpropInputConfig = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "cpu",
+  kernelFunc: conv2DBackpropInput2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js
+function conv3D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dilations } = attrs;
+  assertNotComplex([x, filter], "conv3d");
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);
+  const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;
+  const padFront = padInfo.front;
+  const padLeft = padInfo.left;
+  const padTop = padInfo.top;
+  const y = new TensorBuffer(convInfo.outShape, x.dtype);
+  const xVals = backend2.data.get(x.dataId).values;
+  const wVals = backend2.data.get(filter.dataId).values;
+  const yVals = y.values;
+  const xStrides = util_exports.computeStrides(x.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const xOffset1 = b * xStrides[0];
+    const yOffset1 = b * y.strides[0];
+    for (let yF = 0; yF < convInfo.outDepth; ++yF) {
+      const yOffset2 = yOffset1 + yF * y.strides[1];
+      const xFCorner = yF * convInfo.strideDepth - padFront;
+      for (let wF = 0; wF < filterDepth; ++wF) {
+        const xF = xFCorner + wF * dilationDepth;
+        if (xF < 0 || xF >= convInfo.inDepth) {
+          continue;
+        }
+        const wOffset1 = wF * filterStrides[0];
+        const xOffset2 = xOffset1 + xF * xStrides[1];
+        for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+          const yOffset3 = yOffset2 + yR * y.strides[2];
+          const xRCorner = yR * convInfo.strideHeight - padTop;
+          for (let wR = 0; wR < filterHeight; ++wR) {
+            const xR = xRCorner + wR * dilationHeight;
+            if (xR < 0 || xR >= convInfo.inHeight) {
+              continue;
+            }
+            const wOffset2 = wOffset1 + wR * filterStrides[1];
+            const xOffset3 = xOffset2 + xR * xStrides[2];
+            for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+              const yOffset4 = yOffset3 + yC * convInfo.outChannels;
+              const xCCorner = yC * convInfo.strideWidth - padLeft;
+              for (let wC = 0; wC < filterWidth; ++wC) {
+                const xC = xCCorner + wC * dilationWidth;
+                if (xC < 0 || xC >= convInfo.inWidth) {
+                  continue;
+                }
+                const wOffset3 = wOffset2 + wC * filterStrides[2];
+                const xOffset4 = xOffset3 + xC * convInfo.inChannels;
+                let wOffset4 = wOffset3;
+                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                  const xVal = xVals[xOffset4 + d1];
+                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
+                  }
+                  wOffset4 += convInfo.outChannels;
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
+}
+var conv3DConfig = {
+  kernelName: Conv3D,
+  backendName: "cpu",
+  kernelFunc: conv3D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js
+function conv3DBackpropFilterV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides, pad: pad3, filterShape } = attrs;
+  assertNotComplex([x, dy], "conv3dBackpropFilterV2");
+  const xStrides = util_exports.computeStrides(x.shape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const filterDepth = convInfo.filterDepth;
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const dw = new TensorBuffer(convInfo.filterShape, "float32");
+  const dwValues = dw.values;
+  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
+  const xValues = backend2.data.get(x.dataId).values;
+  const [xS0, xS1, xS2, xS3] = xStrides;
+  const frontPad = convInfo.padInfo.front;
+  const leftPad = convInfo.padInfo.left;
+  const topPad = convInfo.padInfo.top;
+  for (let wF = 0; wF < filterDepth; ++wF) {
+    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
+    const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
+    const wOffset1 = wF * dwS0;
+    for (let wR = 0; wR < filterHeight; ++wR) {
+      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+      const wOffset2 = wR * dwS1 + wOffset1;
+      for (let wC = 0; wC < filterWidth; ++wC) {
+        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+        const wOffset3 = wC * dwS2 + wOffset2;
+        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+          const wOffset4 = d1 * dwS3 + wOffset3;
+          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+            let dotProd = 0;
+            for (let b = 0; b < convInfo.batchSize; ++b) {
+              const xOffset1 = b * xS0;
+              const yOffset1 = b * dyS0;
+              for (let yF = yFMin; yF < yFMax; ++yF) {
+                const xF = wF + yF * strideDepth - frontPad;
+                const xOffset2 = xF * xS1 + xOffset1;
+                const yOffset2 = yF * dyS1 + yOffset1;
+                for (let yR = yRMin; yR < yRMax; ++yR) {
+                  const xR = wR + yR * strideHeight - topPad;
+                  const xOffset3 = xR * xS2 + xOffset2;
+                  const yOffset3 = yR * dyS2 + yOffset2;
+                  for (let yC = yCMin; yC < yCMax; ++yC) {
+                    const xC = wC + yC * strideWidth - leftPad;
+                    const xOffset4 = xC * xS3 + xOffset3;
+                    const yOffset4 = yC * dyS3 + yOffset3;
+                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
+                  }
+                }
+              }
+            }
+            dwValues[wOffset4 + d2] = dotProd;
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);
+}
+var conv3DBackpropFilterV2Config = {
+  kernelName: Conv3DBackpropFilterV2,
+  backendName: "cpu",
+  kernelFunc: conv3DBackpropFilterV2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js
+function conv3DBackpropInputV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { pad: pad3, strides, inputShape } = attrs;
+  assertNotComplex([dy], "conv3dBackpropInputV2");
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);
+  const dx = new TensorBuffer(convInfo.inShape, "float32");
+  const dxValues = dx.values;
+  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
+  const fltValues = backend2.data.get(filter.dataId).values;
+  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
+  const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;
+  const frontPad = filterDepth - 1 - convInfo.padInfo.front;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  for (let b = 0; b < batchSize; ++b) {
+    for (let d1 = 0; d1 < inChannels; ++d1) {
+      for (let xF = 0; xF < inDepth; ++xF) {
+        const xFCorner = xF - frontPad;
+        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
+        const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
+        for (let xR = 0; xR < inHeight; ++xR) {
+          const xRCorner = xR - topPad;
+          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+          for (let xC = 0; xC < inWidth; ++xC) {
+            const xCCorner = xC - leftPad;
+            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+            let dotProd = 0;
+            for (let yF = xFMin; yF < yFMax; ++yF) {
+              const wF = yF * strideDepth - xFCorner;
+              for (let yR = xRMin; yR < yRMax; ++yR) {
+                const wR = yR * strideHeight - xRCorner;
+                for (let yC = xCMin; yC < yCMax; ++yC) {
+                  const wC = yC * strideWidth - xCCorner;
+                  const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
+                  const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
+                  for (let d2 = 0; d2 < outChannels; ++d2) {
+                    const pixel = dyValues[dyOffset + d2];
+                    const weight = fltValues[fltOffset + d2];
+                    dotProd += pixel * weight;
+                  }
+                }
+              }
+            }
+            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var conv3DBackpropInputV2Config = {
+  kernelName: Conv3DBackpropInputV2,
+  backendName: "cpu",
+  kernelFunc: conv3DBackpropInputV2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js
+var cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));
+var cosConfig = {
+  kernelName: Cos,
+  backendName: "cpu",
+  kernelFunc: cos2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js
+var cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));
+var coshConfig = {
+  kernelName: Cosh,
+  backendName: "cpu",
+  kernelFunc: cosh2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js
+function cropAndResize2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, boxes, boxInd } = inputs;
+  const { cropSize, method, extrapolationValue } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const numBoxes = boxes.shape[0];
+  const [cropHeight, cropWidth] = cropSize;
+  const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], "float32");
+  const boxVals = backend2.data.get(boxes.dataId).values;
+  const boxIndVals = backend2.data.get(boxInd.dataId).values;
+  const imageVals = backend2.data.get(image2.dataId).values;
+  const inStride = util_exports.computeStrides(image2.shape);
+  const outStride = util_exports.computeStrides(output.shape);
+  for (let b = 0; b < numBoxes; b++) {
+    const startInd = b * 4;
+    const y1 = boxVals[startInd];
+    const x1 = boxVals[startInd + 1];
+    const y2 = boxVals[startInd + 2];
+    const x2 = boxVals[startInd + 3];
+    const bInd = boxIndVals[b];
+    if (bInd >= batch) {
+      continue;
+    }
+    const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
+    const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
+    for (let y = 0; y < cropHeight; y++) {
+      const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);
+      if (yInd < 0 || yInd > imageHeight - 1) {
+        for (let x = 0; x < cropWidth; x++) {
+          for (let c = 0; c < numChannels; c++) {
+            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+            output.values[ind] = extrapolationValue;
+          }
+        }
+        continue;
+      }
+      if (method === "bilinear") {
+        const topInd = Math.floor(yInd);
+        const bottomInd = Math.ceil(yInd);
+        const yLerp = yInd - topInd;
+        for (let x = 0; x < cropWidth; x++) {
+          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
+          if (xInd < 0 || xInd > imageWidth - 1) {
+            for (let c = 0; c < numChannels; c++) {
+              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+              output.values[ind] = extrapolationValue;
+            }
+            continue;
+          }
+          const leftInd = Math.floor(xInd);
+          const rightInd = Math.ceil(xInd);
+          const xLerp = xInd - leftInd;
+          for (let c = 0; c < numChannels; c++) {
+            let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
+            const topLeft = imageVals[ind];
+            ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
+            const topRight = imageVals[ind];
+            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
+            const bottomLeft = imageVals[ind];
+            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
+            const bottomRight = imageVals[ind];
+            const top = topLeft + (topRight - topLeft) * xLerp;
+            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
+            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+            output.values[ind] = top + (bottom - top) * yLerp;
+          }
+        }
+      } else {
+        for (let x = 0; x < cropWidth; ++x) {
+          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
+          if (xInd < 0 || xInd > imageWidth - 1) {
+            for (let c = 0; c < numChannels; c++) {
+              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+              output.values[ind] = extrapolationValue;
+            }
+            continue;
+          }
+          const closestX = Math.round(xInd);
+          const closestY = Math.round(yInd);
+          for (let c = 0; c < numChannels; c++) {
+            const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];
+            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+            output.values[outInd] = imageVals[inInd];
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(output.shape, output.dtype, output.values);
+}
+var cropAndResizeConfig = {
+  kernelName: CropAndResize,
+  backendName: "cpu",
+  kernelFunc: cropAndResize2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js
+function cumprod2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  assertNotComplex(x, "cumprod");
+  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
+  let $x = x;
+  if (permutation != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
+  if (permutedAxis !== $x.shape.length - 1) {
+    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
+  }
+  const resultDtype = upcastType($x.dtype, "int32");
+  const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  const finalDim = $x.shape[$x.shape.length - 1];
+  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
+  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+    for (let j = 0; j < finalDim; j++) {
+      const idx = indexAdjuster(i2, j);
+      if (j === 0) {
+        vals[idx] = exclusive ? 1 : aVals[idx];
+      } else {
+        const prevIdx = indexAdjuster(i2, j - 1);
+        vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];
+      }
+    }
+  }
+  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeIntermediateTensorInfo(result);
+    backend2.disposeIntermediateTensorInfo($x);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+var cumprodConfig = {
+  kernelName: Cumprod,
+  backendName: "cpu",
+  kernelFunc: cumprod2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js
+function cumsum2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  assertNotComplex(x, "cumsum");
+  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
+  let $x = x;
+  if (permutation != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
+  if (permutedAxis !== $x.shape.length - 1) {
+    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
+  }
+  const resultDtype = upcastType($x.dtype, "int32");
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  const finalDim = $x.shape[$x.shape.length - 1];
+  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
+  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+    for (let j = 0; j < finalDim; j++) {
+      const idx = indexAdjuster(i2, j);
+      if (j === 0) {
+        vals[idx] = exclusive ? 0 : aVals[idx];
+      } else {
+        const prevIdx = indexAdjuster(i2, j - 1);
+        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
+      }
+    }
+  }
+  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeIntermediateTensorInfo(result);
+    backend2.disposeIntermediateTensorInfo($x);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+var cumsumConfig = {
+  kernelName: Cumsum,
+  backendName: "cpu",
+  kernelFunc: cumsum2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js
+function denseBincount2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size, binaryOutput } = attrs;
+  if (x.shape.length === 1) {
+    const xVals = backend2.data.get(x.dataId).values;
+    const weightsVals = backend2.data.get(weights.dataId).values;
+    const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
+    return backend2.makeTensorInfo([size], weights.dtype, outVals);
+  } else if (x.shape.length === 2) {
+    const xBuf = backend2.bufferSync(x);
+    const weightsBuf = backend2.bufferSync(weights);
+    const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);
+    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
+  }
+  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
+}
+var denseBincountConfig = {
+  kernelName: DenseBincount,
+  backendName: "cpu",
+  kernelFunc: denseBincount2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js
+function depthToSpace2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  util_exports.assert(dataFormat === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);
+  const batchSize = x.shape[0];
+  const inputHeight = x.shape[1];
+  const inputWidth = x.shape[2];
+  const inputDepth = x.shape[3];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const xValues = backend2.data.get(x.dataId).values;
+  const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
+  let outputIdx = 0;
+  for (let b = 0; b < batchSize; ++b) {
+    for (let h = 0; h < outputHeight; ++h) {
+      const inH = Math.floor(h / blockSize);
+      const offsetH = h % blockSize;
+      for (let w = 0; w < outputWidth; ++w) {
+        const inW = Math.floor(w / blockSize);
+        const offsetW = w % blockSize;
+        const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
+        for (let d = 0; d < outputDepth; ++d) {
+          const inD = d + offsetD;
+          const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
+          result[outputIdx++] = xValues[inputIdx];
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
+}
+var depthToSpaceConfig = {
+  kernelName: DepthToSpace,
+  backendName: "cpu",
+  kernelFunc: depthToSpace2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js
+function depthwiseConv2dNative(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;
+  assertNotComplex([x, filter], "depthwiseConv2DNative");
+  const xStrides = util_exports.computeStrides(x.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
+  const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;
+  const padLeft = padInfo.left;
+  const padTop = padInfo.top;
+  const chMul = convInfo.outChannels / convInfo.inChannels;
+  const y = new TensorBuffer(convInfo.outShape, x.dtype);
+  const xVals = backend2.data.get(x.dataId).values;
+  const wVals = backend2.data.get(filter.dataId).values;
+  const yVals = y.values;
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    const xOffset1 = b * xStrides[0];
+    const yOffset1 = b * y.strides[0];
+    for (let yR = 0; yR < convInfo.outHeight; ++yR) {
+      const yOffset2 = yOffset1 + yR * y.strides[1];
+      const xRCorner = yR * convInfo.strideHeight - padTop;
+      for (let wR = 0; wR < filterHeight; ++wR) {
+        const xR = xRCorner + wR * dilationHeight;
+        if (xR < 0 || xR >= convInfo.inHeight) {
+          continue;
+        }
+        const wOffset1 = wR * filterStrides[0];
+        const xOffset2 = xOffset1 + xR * xStrides[1];
+        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
+          const yOffset3 = yOffset2 + yC * y.strides[2];
+          const xCCorner = yC * convInfo.strideWidth - padLeft;
+          for (let wC = 0; wC < filterWidth; ++wC) {
+            const xC = xCCorner + wC * dilationWidth;
+            if (xC < 0 || xC >= convInfo.inWidth) {
+              continue;
+            }
+            const wOffset2 = wOffset1 + wC * filterStrides[1];
+            const xOffset3 = xOffset2 + xC * convInfo.inChannels;
+            let yOffset4 = yOffset3;
+            let wOffset3 = wOffset2;
+            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
+              const xVal = xVals[xOffset3 + d1];
+              for (let q = 0; q < chMul; ++q) {
+                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
+              }
+              yOffset4 += chMul;
+              wOffset3 += chMul;
+            }
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
+}
+var depthwiseConv2dNativeConfig = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "cpu",
+  kernelFunc: depthwiseConv2dNative
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
+function depthwiseConv2dNativeBackpropFilter2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;
+  assertNotComplex([x, dy], "depthwiseConv2dNativeBackpropFilter");
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);
+  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
+  const dW = new TensorBuffer(convInfo.filterShape, "float32");
+  const leftPad = convInfo.padInfo.left;
+  const topPad = convInfo.padInfo.top;
+  const chMul = convInfo.outChannels / convInfo.inChannels;
+  const xVals = backend2.data.get(x.dataId).values;
+  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
+  const dyVals = backend2.data.get(dy.dataId).values;
+  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
+  for (let wR = 0; wR < filterHeight; ++wR) {
+    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+    for (let wC = 0; wC < filterWidth; ++wC) {
+      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+      for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
+        const d1 = Math.trunc(d2 / chMul);
+        const dm = d2 % chMul;
+        let dotProd = 0;
+        for (let b = 0; b < convInfo.batchSize; ++b) {
+          for (let yR = yRMin; yR < yRMax; ++yR) {
+            const xR = wR + yR * strideHeight - topPad;
+            for (let yC = yCMin; yC < yCMax; ++yC) {
+              const xC = wC + yC * strideWidth - leftPad;
+              dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
+            }
+          }
+        }
+        dW.set(dotProd, wR, wC, d1, dm);
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
+}
+var depthwiseConv2dNativeBackpropFilterConfig = {
+  kernelName: DepthwiseConv2dNativeBackpropFilter,
+  backendName: "cpu",
+  kernelFunc: depthwiseConv2dNativeBackpropFilter2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
+function depthwiseConv2dNativeBackpropInput2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;
+  assertNotComplex([dy, filter], "depthwiseConv2DNativeBackpropInput");
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const filterStrides = util_exports.computeStrides(filter.shape);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);
+  const dx = new TensorBuffer(convInfo.inShape, "float32");
+  const dxValues = dx.values;
+  const [dxS0, dxS1, dxS2] = dx.strides;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const [dyS0, dyS1, dyS2] = dyStrides;
+  const fltValues = backend2.data.get(filter.dataId).values;
+  const [fltS0, fltS1, fltS2] = filterStrides;
+  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  const chMul = outChannels / inChannels;
+  for (let b = 0; b < batchSize; ++b) {
+    for (let d1 = 0; d1 < inChannels; ++d1) {
+      for (let xR = 0; xR < inHeight; ++xR) {
+        const xRCorner = xR - topPad;
+        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+        for (let xC = 0; xC < inWidth; ++xC) {
+          const xCCorner = xC - leftPad;
+          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+          let dotProd = 0;
+          for (let yR = xRMin; yR < yRMax; ++yR) {
+            const wR = yR * strideHeight - xRCorner;
+            for (let yC = xCMin; yC < yCMax; ++yC) {
+              const wC = yC * strideWidth - xCCorner;
+              const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+              for (let dm = 0; dm < chMul; ++dm) {
+                const d2 = d1 * chMul + dm;
+                const pixel = dyValues[dyOffset + d2];
+                const weight = fltValues[fltOffset + dm];
+                dotProd += pixel * weight;
+              }
+            }
+          }
+          dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var depthwiseConv2dNativeBackpropInputConfig = {
+  kernelName: DepthwiseConv2dNativeBackpropInput,
+  backendName: "cpu",
+  kernelFunc: depthwiseConv2dNativeBackpropInput2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js
+function diag2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const xVals = backend2.data.get(x.dataId).values;
+  const outBuf = buffer([xSize, xSize], x.dtype);
+  const vals = outBuf.values;
+  for (let i2 = 0; i2 < xVals.length; i2++) {
+    vals[i2 * xSize + i2] = xVals[i2];
+  }
+  const outShape = [...x.shape, ...x.shape];
+  return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
+}
+var diagConfig = {
+  kernelName: Diag,
+  backendName: "cpu",
+  kernelFunc: diag2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js
+var dilation2DConfig = {
+  kernelName: Dilation2D,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2, attrs }) => {
+    const { x, filter } = inputs;
+    const { strides, pad: pad3, dilations } = attrs;
+    const cpuBackend = backend2;
+    const xVals = cpuBackend.data.get(x.dataId).values;
+    const xRank = x.shape.length;
+    const filterVals = cpuBackend.data.get(filter.dataId).values;
+    const filterRank = filter.shape.length;
+    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
+    const outSize = util_exports.sizeFromShape(outShape);
+    const outRank = outShape.length;
+    const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);
+    for (let b = 0; b < batchSize; ++b) {
+      for (let hOut = 0; hOut < outHeight; ++hOut) {
+        const hBeg = hOut * strideHeight - padInfo.top;
+        for (let wOut = 0; wOut < outWidth; ++wOut) {
+          const wBeg = wOut * strideWidth - padInfo.left;
+          for (let d = 0; d < inChannels; ++d) {
+            let curVal = Number.MIN_SAFE_INTEGER;
+            for (let h = 0; h < filterHeight; ++h) {
+              const hIn = hBeg + h * dilationHeight;
+              if (hIn >= 0 && hIn < inHeight) {
+                for (let w = 0; w < filterWidth; ++w) {
+                  const wIn = wBeg + w * dilationWidth;
+                  if (wIn >= 0 && wIn < inWidth) {
+                    const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));
+                    const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));
+                    const val = xVals[xIndex] + filterVals[filterIndex];
+                    if (val > curVal) {
+                      curVal = val;
+                    }
+                  }
+                }
+              }
+            }
+            const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));
+            outputVals[outputIndex] = curVal;
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
+    return { dataId, shape: outShape, dtype: x.dtype };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js
+var dilation2DBackpropFilterConfig = {
+  kernelName: Dilation2DBackpropFilter,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2, attrs }) => {
+    const { x, filter, dy } = inputs;
+    const { strides, pad: pad3, dilations } = attrs;
+    const cpuBackend = backend2;
+    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
+    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
+    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
+    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
+    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
+    const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);
+    for (let b = 0; b < batchSize; ++b) {
+      for (let hOut = 0; hOut < outHeight; ++hOut) {
+        const hBeg = hOut * strideHeight - padInfo.top;
+        for (let wOut = 0; wOut < outWidth; ++wOut) {
+          const wBeg = wOut * strideWidth - padInfo.left;
+          for (let d = 0; d < inChannels; ++d) {
+            let curVal = Number.MIN_SAFE_INTEGER;
+            let hMax = 0;
+            let wMax = 0;
+            for (let h = 0; h < filterHeight; ++h) {
+              const hIn = hBeg + h * dilationHeight;
+              if (hIn >= 0 && hIn < inHeight) {
+                for (let w = 0; w < filterWidth; ++w) {
+                  const wIn = wBeg + w * dilationWidth;
+                  if (wIn >= 0 && wIn < inWidth) {
+                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
+                    if (val > curVal) {
+                      curVal = val;
+                      hMax = h;
+                      wMax = w;
+                    }
+                  }
+                }
+              }
+            }
+            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
+    return { dataId, shape: filter.shape, dtype: filter.dtype };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js
+var dilation2DBackpropInputConfig = {
+  kernelName: Dilation2DBackpropInput,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2, attrs }) => {
+    const { x, filter, dy } = inputs;
+    const { strides, pad: pad3, dilations } = attrs;
+    const cpuBackend = backend2;
+    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
+    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
+    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
+    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
+    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
+    const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);
+    for (let b = 0; b < batchSize; ++b) {
+      for (let hOut = 0; hOut < outHeight; ++hOut) {
+        const hBeg = hOut * strideHeight - padInfo.top;
+        for (let wOut = 0; wOut < outWidth; ++wOut) {
+          const wBeg = wOut * strideWidth - padInfo.left;
+          for (let d = 0; d < inChannels; ++d) {
+            let curVal = Number.MIN_SAFE_INTEGER;
+            let hInMax = hBeg < 0 ? 0 : hBeg;
+            let wInMax = wBeg < 0 ? 0 : wBeg;
+            for (let h = 0; h < filterHeight; ++h) {
+              const hIn = hBeg + h * dilationHeight;
+              if (hIn >= 0 && hIn < inHeight) {
+                for (let w = 0; w < filterWidth; ++w) {
+                  const wIn = wBeg + w * dilationWidth;
+                  if (wIn >= 0 && wIn < inWidth) {
+                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
+                    if (val > curVal) {
+                      curVal = val;
+                      hInMax = hIn;
+                      wInMax = wIn;
+                    }
+                  }
+                }
+              }
+            }
+            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
+    return { dataId, shape: x.shape, dtype: x.dtype };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js
+function sum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "sum");
+  let $x;
+  if (x.dtype === "bool") {
+    $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "int32" } });
+  } else {
+    $x = identity2({ inputs: { x }, backend: backend2 });
+  }
+  const xRank = $x.shape.length;
+  const axes = util_exports.parseAxisParam(axis, $x.shape);
+  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
+  let reductionAxes = axes;
+  let permutedX = $x;
+  if (permutation != null) {
+    permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });
+    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);
+  const resultDtype = backend_util_exports.upcastType(permutedX.dtype, "int32");
+  let result = zeros3(backend2, outShape, resultDtype);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = backend2.data.get(result.dataId).values;
+  const aVals = backend2.data.get(permutedX.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let sum7 = 0;
+    for (let j = 0; j < reduceSize; ++j) {
+      sum7 += aVals[offset + j];
+    }
+    vals[i2] = sum7;
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);
+    const oldResult = result;
+    result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });
+    backend2.disposeIntermediateTensorInfo(oldResult);
+  }
+  backend2.disposeIntermediateTensorInfo($x);
+  if (permutation != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return result;
+}
+var sumConfig = {
+  kernelName: Sum,
+  backendName: "cpu",
+  kernelFunc: sum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js
+function einsum2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { equation } = attrs;
+  const tensors = inputs;
+  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
+  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
+  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
+  const nSteps = steps.length;
+  let out = null;
+  let numDimsRemaining = allDims.length;
+  const tensorsToDispose = [];
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    for (const idTerm of steps[i2]) {
+      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
+      let x;
+      if (backend_util_exports.isIdentityPermutation(perm)) {
+        x = tensors[idTerm];
+      } else {
+        x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
+        tensorsToDispose.push(x);
+      }
+      const targetShape = x.shape.slice();
+      for (let k = 0; k < dimsToExpand.length; ++k) {
+        targetShape.splice(dimsToExpand[k], 0, 1);
+      }
+      if (!util_exports.arraysEqual(x.shape, targetShape)) {
+        x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
+        tensorsToDispose.push(x);
+      }
+      if (out === null) {
+        out = x;
+      } else {
+        out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });
+        tensorsToDispose.push(out);
+      }
+    }
+    if (i2 < nSteps - 1) {
+      if (path[i2] >= 0) {
+        out = sum3({
+          inputs: { x: out },
+          backend: backend2,
+          attrs: {
+            axis: path[i2] - (allDims.length - numDimsRemaining),
+            keepDims: false
+          }
+        });
+        tensorsToDispose.push(out);
+      }
+      numDimsRemaining--;
+    }
+  }
+  for (const tensorInfo of tensorsToDispose) {
+    if (tensorInfo === out) {
+      continue;
+    }
+    backend2.disposeIntermediateTensorInfo(tensorInfo);
+  }
+  return out;
+}
+var einsumConfig = {
+  kernelName: Einsum,
+  backendName: "cpu",
+  kernelFunc: einsum2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js
+function eluGrad(args) {
+  const { inputs, backend: backend2 } = args;
+  const { dy, y } = inputs;
+  assertNotComplex([dy, y], "eluGrad");
+  const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));
+  const values = backend2.data.get(y.dataId).values;
+  const dyValues = backend2.data.get(dy.dataId).values;
+  for (let i2 = 0; i2 < values.length; ++i2) {
+    const v = values[i2];
+    if (v >= 1) {
+      resultValues[i2] = dyValues[i2];
+    } else {
+      resultValues[i2] = dyValues[i2] * (v + 1);
+    }
+  }
+  return backend2.makeTensorInfo(y.shape, "float32", resultValues);
+}
+var eluGradConfig2 = {
+  kernelName: EluGrad,
+  backendName: "cpu",
+  kernelFunc: eluGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js
+var p = backend_util_exports.ERF_P;
+var a1 = backend_util_exports.ERF_A1;
+var a2 = backend_util_exports.ERF_A2;
+var a3 = backend_util_exports.ERF_A3;
+var a4 = backend_util_exports.ERF_A4;
+var a5 = backend_util_exports.ERF_A5;
+var erf2 = unaryKernelFunc(Erf, (xi) => {
+  const sign4 = Math.sign(xi);
+  const v = Math.abs(xi);
+  const t2 = 1 / (1 + p * v);
+  return sign4 * (1 - ((((a5 * t2 + a4) * t2 + a3) * t2 + a2) * t2 + a1) * t2 * Math.exp(-v * v));
+});
+var erfConfig = {
+  kernelName: Erf,
+  backendName: "cpu",
+  kernelFunc: erf2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js
+function expandDims3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { input: input2 } = inputs;
+  const { dim } = attrs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig = {
+  kernelName: ExpandDims,
+  backendName: "cpu",
+  kernelFunc: expandDims3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js
+var realDivImpl = createSimpleBinaryKernelImpl((a6, b) => a6 / b);
+var div2 = binaryKernelFunc(RealDiv, realDivImpl);
+var realDivConfig = {
+  kernelName: RealDiv,
+  backendName: "cpu",
+  kernelFunc: div2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js
+function fftBatch(input2, inverse, cpuBackend) {
+  const inputShape = input2.shape;
+  const batch = inputShape[0];
+  const innerDim = inputShape[1];
+  const inputVals = cpuBackend.data.get(input2.dataId);
+  const real2D = inputVals.complexTensorInfos.real;
+  const imag2D = inputVals.complexTensorInfos.imag;
+  const resultShape = [batch, innerDim];
+  const resultSize = util_exports.sizeFromShape(resultShape);
+  const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize);
+  const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize);
+  for (let b = 0; b < batch; b++) {
+    const r2 = slice2({
+      inputs: { x: real2D },
+      backend: cpuBackend,
+      attrs: { begin: [b, 0], size: [1, innerDim] }
+    });
+    const i2 = slice2({
+      inputs: { x: imag2D },
+      backend: cpuBackend,
+      attrs: { begin: [b, 0], size: [1, innerDim] }
+    });
+    const input3 = complex2({ inputs: { real: r2, imag: i2 }, backend: cpuBackend });
+    const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);
+    const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);
+    for (let d = 0; d < innerDim; d++) {
+      const c = backend_util_exports.getComplexWithIndex(res, d);
+      resultReal[b * innerDim + d] = c.real;
+      resultImag[b * innerDim + d] = c.imag;
+    }
+    cpuBackend.disposeIntermediateTensorInfo(r2);
+    cpuBackend.disposeIntermediateTensorInfo(i2);
+    cpuBackend.disposeIntermediateTensorInfo(input3);
+  }
+  const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
+  const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag);
+  const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });
+  cpuBackend.disposeIntermediateTensorInfo($realInfo);
+  cpuBackend.disposeIntermediateTensorInfo($imagInfo);
+  return result;
+}
+function fftImpl(input2, inverse, cpuBackend) {
+  const inputSize = util_exports.sizeFromShape(input2.shape);
+  const inputVals = cpuBackend.data.get(input2.dataId);
+  const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;
+  const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;
+  if (isExponentOf2(inputSize)) {
+    const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);
+    const resultShape = [input2.shape[0], input2.shape[1]];
+    if (inverse) {
+      const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real);
+      const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag);
+      const sizeInfo = cpuBackend.makeTensorInfo([], "float32", util_exports.createScalarValue(inputSize, "float32"));
+      const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend });
+      const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });
+      const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });
+      const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;
+      const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;
+      cpuBackend.disposeIntermediateTensorInfo(realInfo);
+      cpuBackend.disposeIntermediateTensorInfo(imagInfo);
+      cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
+      cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
+      cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
+      cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
+      return { real: divRealVals, imag: divImagVals };
+    }
+    return result;
+  } else {
+    const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
+    const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);
+    return backend_util_exports.splitRealAndImagArrays(rawOutput);
+  }
+}
+function isExponentOf2(size) {
+  return (size & size - 1) === 0;
+}
+function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
+  if (size === 1) {
+    return { real: realVals, imag: imagVals };
+  }
+  const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
+  const half = size / 2;
+  const evenComplex = backend_util_exports.complexWithEvenIndex(data);
+  const evenRealVals = evenComplex.real;
+  const evenImagVals = evenComplex.imag;
+  const evenShape = [evenRealVals.length];
+  const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals);
+  const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals);
+  const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });
+  const oddComplex = backend_util_exports.complexWithOddIndex(data);
+  const oddRealVals = oddComplex.real;
+  const oddImagVals = oddComplex.imag;
+  const oddShape = [oddRealVals.length];
+  const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals);
+  const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals);
+  const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });
+  const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);
+  const $evenRealVals = $evenComplex.real;
+  const $evenImagVals = $evenComplex.imag;
+  const $evenShape = [$evenRealVals.length];
+  const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals);
+  const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals);
+  const $evenTensorInfo = complex2({
+    inputs: { real: $evenRealInfo, imag: $evenImagInfo },
+    backend: cpuBackend
+  });
+  const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);
+  const $oddRealVals = $oddComplex.real;
+  const $oddImagVals = $oddComplex.imag;
+  const $oddShape = [$oddRealVals.length];
+  const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
+  const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
+  const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });
+  const e2 = backend_util_exports.exponents(size, inverse);
+  const eShape = [e2.real.length];
+  const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.real);
+  const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.imag);
+  const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });
+  const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });
+  const addPart = add4({
+    inputs: { a: $evenTensorInfo, b: exponentInfo },
+    backend: cpuBackend
+  });
+  const subPart = sub2({
+    inputs: { a: $evenTensorInfo, b: exponentInfo },
+    backend: cpuBackend
+  });
+  const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });
+  const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });
+  const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });
+  const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });
+  const $real = concat2({
+    inputs: [addPartReal, subPartReal],
+    backend: cpuBackend,
+    attrs: { axis: 0 }
+  });
+  const $imag = concat2({
+    inputs: [addPartImag, subPartImag],
+    backend: cpuBackend,
+    attrs: { axis: 0 }
+  });
+  const $realVals = cpuBackend.data.get($real.dataId).values;
+  const $imagVals = cpuBackend.data.get($imag.dataId).values;
+  cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
+  cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
+  cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
+  cpuBackend.disposeIntermediateTensorInfo(complexInfo);
+  cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
+  cpuBackend.disposeIntermediateTensorInfo(addPart);
+  cpuBackend.disposeIntermediateTensorInfo(subPart);
+  cpuBackend.disposeIntermediateTensorInfo(addPartReal);
+  cpuBackend.disposeIntermediateTensorInfo(addPartImag);
+  cpuBackend.disposeIntermediateTensorInfo(subPartReal);
+  cpuBackend.disposeIntermediateTensorInfo(subPartImag);
+  cpuBackend.disposeIntermediateTensorInfo($real);
+  cpuBackend.disposeIntermediateTensorInfo($imag);
+  return { real: $realVals, imag: $imagVals };
+}
+function fourierTransformByMatmul(data, size, inverse) {
+  const ret = new Float32Array(size * 2);
+  for (let r2 = 0; r2 < size; r2++) {
+    let real5 = 0;
+    let imag5 = 0;
+    for (let c = 0; c < size; c++) {
+      const e2 = backend_util_exports.exponent(r2 * c, size, inverse);
+      const term = backend_util_exports.getComplexWithIndex(data, c);
+      real5 += term.real * e2.real - term.imag * e2.imag;
+      imag5 += term.real * e2.imag + term.imag * e2.real;
+    }
+    if (inverse) {
+      real5 /= size;
+      imag5 /= size;
+    }
+    backend_util_exports.assignToTypedArray(ret, real5, imag5, r2);
+  }
+  return ret;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js
+function fft2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputSize = util_exports.sizeFromShape(input2.shape);
+  const innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = inputSize / innerDimensionSize;
+  const input2D = reshape3({
+    inputs: { x: input2 },
+    backend: backend2,
+    attrs: { shape: [batch, innerDimensionSize] }
+  });
+  const result = fftBatch(input2D, false, backend2);
+  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });
+  backend2.disposeIntermediateTensorInfo(input2D);
+  backend2.disposeIntermediateTensorInfo(result);
+  return resultReshaped;
+}
+var fftConfig = {
+  kernelName: FFT,
+  backendName: "cpu",
+  kernelFunc: fft2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js
+function fill2(args) {
+  const { backend: backend2, attrs } = args;
+  const { shape, value, dtype } = attrs;
+  const $dtype = dtype || util_exports.inferDtype(value);
+  const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));
+  fillValues(values, value, $dtype);
+  return backend2.makeTensorInfo(shape, $dtype, values);
+}
+var fillConfig = {
+  kernelName: Fill,
+  backendName: "cpu",
+  kernelFunc: fill2
+};
+function fillValues(values, value, dtype) {
+  if (dtype === "string") {
+    values.fill(value);
+  } else {
+    values.fill(value);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js
+var flipLeftRightConfig = {
+  kernelName: FlipLeftRight,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const cpuBackend = backend2;
+    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));
+    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+    const imageVals = cpuBackend.data.get(image2.dataId).values;
+    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
+      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
+      for (let row = 0; row < imageHeight; row++) {
+        const rowOffset = row * (imageWidth * numChannels);
+        for (let col = 0; col < imageWidth; col++) {
+          const colOffset = col * numChannels;
+          for (let channel = 0; channel < numChannels; channel++) {
+            const coordX = Math.round(imageWidth - col - 1);
+            const outIdx = batchOffset + rowOffset + colOffset + channel;
+            let outputValue = imageVals[outIdx];
+            if (coordX >= 0 && coordX < imageWidth) {
+              const rotatedColOffset = coordX * numChannels;
+              const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;
+              outputValue = imageVals[imageIdx];
+            }
+            output[outIdx] = outputValue;
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);
+    return { dataId, shape: image2.shape, dtype: image2.dtype };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js
+var floorDivImpl = createSimpleBinaryKernelImpl((a6, b) => Math.floor(a6 / b));
+var floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, "int32");
+var floorDivConfig = {
+  kernelName: FloorDiv,
+  backendName: "cpu",
+  kernelFunc: floorDiv2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js
+function fusedConv2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  let result = conv2D({
+    inputs: { x, filter },
+    backend: backend2,
+    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }
+  });
+  if (bias) {
+    const resultOld = result;
+    if (dataFormat === "NCHW" && bias.shape.length === 1 && bias.shape[0] !== 1) {
+      const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });
+      result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 });
+      backend2.disposeIntermediateTensorInfo(reshapedBias);
+    } else {
+      result = add4({ inputs: { a: result, b: bias }, backend: backend2 });
+    }
+    backend2.disposeIntermediateTensorInfo(resultOld);
+  }
+  if (activation2) {
+    const resultOld = result;
+    if (dataFormat === "NCHW" && activation2 === "prelu" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {
+      const reshapedAlpha = reshape3({
+        inputs: { x: preluActivationWeights },
+        backend: backend2,
+        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }
+      });
+      result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha);
+      backend2.disposeIntermediateTensorInfo(reshapedAlpha);
+    } else {
+      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);
+    }
+    backend2.disposeIntermediateTensorInfo(resultOld);
+  }
+  return result;
+}
+var fusedConv2DConfig = {
+  kernelName: FusedConv2D,
+  backendName: "cpu",
+  kernelFunc: fusedConv2D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js
+function fusedDepthwiseConv2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  let result = depthwiseConv2dNative({
+    inputs: { x, filter },
+    backend: backend2,
+    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }
+  });
+  if (bias) {
+    const oldResult = result;
+    result = add4({ inputs: { a: result, b: bias }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(oldResult);
+  }
+  if (activation2) {
+    const oldResult = result;
+    result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);
+    backend2.disposeIntermediateTensorInfo(oldResult);
+  }
+  return result;
+}
+var fusedDepthwiseConv2DConfig = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "cpu",
+  kernelFunc: fusedDepthwiseConv2D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js
+function gatherNd(args) {
+  const { inputs, backend: backend2 } = args;
+  const { params, indices } = inputs;
+  const paramsSize = util_exports.sizeFromShape(params.shape);
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
+  if (numSlices === 0) {
+    return backend2.makeTensorInfo(resultShape, params.dtype, []);
+  }
+  const indicesData = backend2.data.get(indices.dataId).values;
+  const paramsBuf = backend2.bufferSync(params);
+  const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
+  return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);
+}
+var gatherNdConfig = {
+  kernelName: GatherNd,
+  backendName: "cpu",
+  kernelFunc: gatherNd
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js
+function gatherV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  assertNotComplex([x, indices], "gatherV2");
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const indicesVals = backend2.data.get(indices.dataId).values;
+  const axisDim = x.shape[parsedAxis];
+  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
+    const index = indicesVals[i2];
+    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
+  }
+  let $batchDims = batchDims;
+  if (batchDims == null) {
+    $batchDims = 0;
+  }
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);
+  const flattenX = reshape3({
+    inputs: { x },
+    backend: backend2,
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    }
+  });
+  const flattenIndex = reshape3({
+    inputs: { x: indices },
+    backend: backend2,
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+  });
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  const indicesBuf = backend2.bufferSync(flattenIndex);
+  const xBuf = backend2.bufferSync(flattenX);
+  const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);
+  backend2.disposeIntermediateTensorInfo(flattenX);
+  backend2.disposeIntermediateTensorInfo(flattenIndex);
+  return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+}
+var gatherV2Config = {
+  kernelName: GatherV2,
+  backendName: "cpu",
+  kernelFunc: gatherV2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js
+function ifft2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputSize = util_exports.sizeFromShape(input2.shape);
+  const innerDimensionSize = input2.shape[input2.shape.length - 1];
+  const batch = inputSize / innerDimensionSize;
+  const input2D = reshape3({
+    inputs: { x: input2 },
+    backend: backend2,
+    attrs: { shape: [batch, innerDimensionSize] }
+  });
+  const result = fftBatch(input2D, true, backend2);
+  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });
+  backend2.disposeIntermediateTensorInfo(input2D);
+  backend2.disposeIntermediateTensorInfo(result);
+  return resultReshaped;
+}
+var ifftConfig = {
+  kernelName: IFFT,
+  backendName: "cpu",
+  kernelFunc: ifft2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js
+var isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, "bool");
+var isFiniteConfig = {
+  kernelName: IsFinite,
+  backendName: "cpu",
+  kernelFunc: isFinite3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js
+var isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, "bool");
+var isInfConfig = {
+  kernelName: IsInf,
+  backendName: "cpu",
+  kernelFunc: isInf2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js
+var isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, "bool");
+var isNaNConfig = {
+  kernelName: IsNan,
+  backendName: "cpu",
+  kernelFunc: isNaN3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js
+function linSpace(args) {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, num } = attrs;
+  const outVals = linSpaceImpl(start, stop, num);
+  return backend2.makeTensorInfo([outVals.length], "float32", outVals);
+}
+var linSpaceConfig = {
+  kernelName: LinSpace,
+  backendName: "cpu",
+  kernelFunc: linSpace
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js
+var log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));
+var log1pConfig = {
+  kernelName: Log1p,
+  backendName: "cpu",
+  kernelFunc: log1p2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js
+var logicalAndImpl = createSimpleBinaryKernelImpl((a6, b) => a6 && b);
+var logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, "bool");
+var logicalAndConfig = {
+  kernelName: LogicalAnd,
+  backendName: "cpu",
+  kernelFunc: logicalAnd2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js
+var logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, "bool");
+var logicalNotConfig = {
+  kernelName: LogicalNot,
+  backendName: "cpu",
+  kernelFunc: logicalNot2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js
+var logicalOrImpl = createSimpleBinaryKernelImpl((a6, b) => a6 || b);
+var logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, "bool");
+var logicalOrConfig = {
+  kernelName: LogicalOr,
+  backendName: "cpu",
+  kernelFunc: logicalOr2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js
+function lRN(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { depthRadius, bias, alpha, beta } = attrs;
+  assertNotComplex(x, "LRN");
+  const channels = x.shape[3];
+  const maxD = channels - 1;
+  const xValues = backend2.data.get(x.dataId).values;
+  const size = util_exports.sizeFromShape(x.shape);
+  const result = new Float32Array(size);
+  function sumAcrossChannels(offset) {
+    const currentChannel = offset % channels;
+    let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+    const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
+    let sum7 = 0;
+    for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
+      const z = xValues[beginSumOffset];
+      sum7 += z * z;
+    }
+    return sum7;
+  }
+  for (let offset = 0; offset < size; offset++) {
+    const sum7 = sumAcrossChannels(offset);
+    const val = xValues[offset] * Math.pow(bias + alpha * sum7, -beta);
+    result[offset] = val;
+  }
+  return backend2.makeTensorInfo(x.shape, x.dtype, result);
+}
+var LRNConfig = {
+  kernelName: LRN,
+  backendName: "cpu",
+  kernelFunc: lRN
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js
+function lRNGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, y, dy } = inputs;
+  const { depthRadius, bias, alpha, beta } = attrs;
+  assertNotComplex(dy, "LRNGrad");
+  const dySize = util_exports.sizeFromShape(dy.shape);
+  const channels = dy.shape[3];
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const xValues = backend2.data.get(x.dataId).values;
+  const yValues = backend2.data.get(y.dataId).values;
+  const result = new Float32Array(dySize);
+  const size = dySize;
+  for (let offset = 0; offset < size; offset++) {
+    const currentChannel = offset % channels;
+    const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+    const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);
+    let norm2 = 0;
+    for (let k = depthBegin; k < depthEnd; k++) {
+      norm2 += Math.pow(xValues[k], 2);
+    }
+    norm2 = alpha * norm2 + bias;
+    for (let k = depthBegin; k < depthEnd; k++) {
+      let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;
+      if (offset === k) {
+        dyi += Math.pow(norm2, -beta);
+      }
+      dyi *= dyValues[offset];
+      result[k] += dyi;
+    }
+  }
+  return backend2.makeTensorInfo(dy.shape, x.dtype, result);
+}
+var LRNGradConfig = {
+  kernelName: LRNGrad,
+  backendName: "cpu",
+  kernelFunc: lRNGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js
+function max3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reductionIndices, keepDims } = attrs;
+  const cpuBackend = backend2;
+  let xShape = x.shape;
+  const xRank = xShape.length;
+  const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let xVals = cpuBackend.data.get(x.dataId).values;
+  if (permutedAxes != null) {
+    const newShape = new Array(xRank);
+    for (let i2 = 0; i2 < newShape.length; i2++) {
+      newShape[i2] = xShape[permutedAxes[i2]];
+    }
+    xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    xShape = newShape;
+  }
+  assertNotComplex(x, "max");
+  backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
+  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);
+  const dataId = cpuBackend.write(result, maxOutShape, x.dtype);
+  let outShape = maxOutShape;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
+    outShape = newShape;
+  }
+  return { dataId, shape: outShape, dtype: x.dtype };
+}
+var maxConfig = {
+  kernelName: Max,
+  backendName: "cpu",
+  kernelFunc: max3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js
+function maxPool2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex(x, "maxPool");
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  let res;
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    res = identity2({ inputs: { x }, backend: backend2 });
+  } else {
+    const xValues = backend2.data.get(x.dataId).values;
+    const strides2 = util_exports.computeStrides(x.shape);
+    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "max");
+    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
+  }
+  return res;
+}
+var maxPoolConfig = {
+  kernelName: MaxPool,
+  backendName: "cpu",
+  kernelFunc: maxPool2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js
+function maxPool3D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  assertNotComplex(x, "maxPool3d");
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);
+  const xValues = backend2.data.get(x.dataId).values;
+  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "max");
+  return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
+}
+var maxPool3DConfig = {
+  kernelName: MaxPool3D,
+  backendName: "cpu",
+  kernelFunc: maxPool3D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js
+function maxPool3DGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  assertNotComplex([dy, input2], "maxPool3DGrad");
+  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);
+  const inputBuf = backend2.bufferSync(input2);
+  const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);
+  const strideDepth = convInfo.strideDepth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationDepth = convInfo.dilationDepth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(input2.shape, "float32");
+  const dyBuf = backend2.bufferSync(dy);
+  for (let batch = 0; batch < convInfo.batchSize; ++batch) {
+    for (let channel = 0; channel < convInfo.inChannels; ++channel) {
+      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
+        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
+          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
+            const dyDepthCorner = dxDepth - padFront;
+            const dyRowCorner = dxRow - padTop;
+            const dyColCorner = dxCol - padLeft;
+            let dotProd = 0;
+            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
+              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
+              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
+                continue;
+              }
+              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
+                const dyRow = (dyRowCorner + wRow) / strideHeight;
+                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
+                  continue;
+                }
+                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
+                  const dyCol = (dyColCorner + wCol) / strideWidth;
+                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
+                    continue;
+                  }
+                  const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                  const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;
+                  const mask = maxPos === curPos ? 1 : 0;
+                  if (mask === 0) {
+                    continue;
+                  }
+                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
+                  dotProd += pixel * mask;
+                }
+              }
+            }
+            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
+          }
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var maxPool3DGradConfig2 = {
+  kernelName: MaxPool3DGrad,
+  backendName: "cpu",
+  kernelFunc: maxPool3DGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js
+function maxPoolGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2, output } = inputs;
+  const x = input2;
+  assertNotComplex([input2, output], "maxPoolGrad");
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
+  const xValues = backend2.data.get(x.dataId).values;
+  const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+  const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+  const dx = buffer(x.shape, "float32");
+  const dyData = backend2.data.get(dy.dataId).values;
+  const dyBuf = buffer(dy.shape, "float32", dyData);
+  for (let b = 0; b < convInfo.batchSize; ++b) {
+    for (let d = 0; d < convInfo.inChannels; ++d) {
+      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+          const dyRCorner = dxR - padTop;
+          const dyCCorner = dxC - padLeft;
+          let dotProd = 0;
+          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+            const dyR = (dyRCorner + wR) / strideHeight;
+            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
+              continue;
+            }
+            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+              const dyC = (dyCCorner + wC) / strideWidth;
+              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
+                continue;
+              }
+              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);
+              const curPos = wR * effectiveFilterWidth + wC;
+              const mask = maxPos === curPos ? 1 : 0;
+              if (mask === 0) {
+                continue;
+              }
+              const pixel = dyBuf.get(b, dyR, dyC, d);
+              dotProd += pixel * mask;
+            }
+          }
+          dx.set(dotProd, b, dxR, dxC, d);
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
+}
+var maxPoolGradConfig2 = {
+  kernelName: MaxPoolGrad,
+  backendName: "cpu",
+  kernelFunc: maxPoolGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js
+function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {
+  const strides = util_exports.computeStrides(xShape);
+  const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, "max");
+  const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
+  return [maxPools.values, maxPositions.values];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js
+var maxPoolWithArgmaxConfig = {
+  kernelName: MaxPoolWithArgmax,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;
+    const cpuBackend = backend2;
+    assertNotComplex(x, "MaxPoolWithArgmax");
+    const values = cpuBackend.data.get(x.dataId).values;
+    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);
+    const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);
+    const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);
+    const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);
+    return [
+      { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },
+      { dataId: indexesDataId, shape: convInfo.outShape, dtype: "int32" }
+    ];
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js
+function mean2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const axes = util_exports.parseAxisParam(axis, x.shape);
+  const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);
+  const reduceShape = shapes[1];
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const toDispose = [];
+  const reduceSizeScalar = backend2.makeTensorInfo([], "float32", new Float32Array([reduceSize]));
+  toDispose.push(reduceSizeScalar);
+  const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+  toDispose.push($x);
+  const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });
+  toDispose.push(res);
+  const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var meanConfig = {
+  kernelName: Mean,
+  backendName: "cpu",
+  kernelFunc: mean2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js
+function min3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  assertNotComplex(x, "min");
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  if (permutedAxes != null) {
+    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("min", axes, $x.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
+  const aVals = backend2.data.get($x.dataId).values;
+  for (let i2 = 0; i2 < vals.length; ++i2) {
+    const offset = i2 * reduceSize;
+    let min7 = aVals[offset];
+    for (let j = 0; j < reduceSize; ++j) {
+      const value = aVals[offset + j];
+      if (Number.isNaN(value) || value < min7) {
+        min7 = value;
+      }
+    }
+    vals[i2] = min7;
+  }
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo($x);
+  }
+  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
+  if (keepDims) {
+    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
+    backend2.disposeIntermediateTensorInfo(result);
+    return reshapedResult;
+  }
+  return result;
+}
+var minConfig = {
+  kernelName: Min,
+  backendName: "cpu",
+  kernelFunc: min3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js
+function mirrorPad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, mode } = attrs;
+  assertNotComplex(x, "mirrorPad");
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const start = paddings.map((p2) => p2[0]);
+  const end = paddings.map((p2, i2) => p2[0] + x.shape[i2]);
+  const offset = mode === "reflect" ? 0 : 1;
+  const xVals = backend2.data.get(x.dataId).values;
+  const xRank = x.shape.length;
+  const xStrides = util_exports.computeStrides(x.shape);
+  const resultSize = util_exports.sizeFromShape(outShape);
+  const resultRank = outShape.length;
+  const resultStrides = util_exports.computeStrides(outShape);
+  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
+  for (let i2 = 0; i2 < resultSize; i2++) {
+    let coords3 = util_exports.indexToLoc(i2, resultRank, resultStrides);
+    for (let i3 = 0; i3 < resultRank; i3++) {
+      if (coords3[i3] < start[i3]) {
+        coords3[i3] = start[i3] * 2 - coords3[i3] - offset;
+      } else if (coords3[i3] >= end[i3]) {
+        coords3[i3] = (end[i3] - 1) * 2 - coords3[i3] + offset;
+      }
+    }
+    coords3 = coords3.map((c, i3) => c - start[i3]);
+    const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);
+    resVals[i2] = xVals[inIndex];
+  }
+  const outId = backend2.write(resVals, outShape, x.dtype);
+  return { dataId: outId, shape: outShape, dtype: x.dtype };
+}
+var mirrorPadConfig = {
+  kernelName: MirrorPad,
+  backendName: "cpu",
+  kernelFunc: mirrorPad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js
+var modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {
+  const rem = aValue % bValue;
+  if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {
+    return rem;
+  } else {
+    return (rem + bValue) % bValue;
+  }
+});
+var mod2 = binaryKernelFunc(Mod, modImpl);
+var modConfig = {
+  kernelName: Mod,
+  backendName: "cpu",
+  kernelFunc: mod2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
+var seedrandom4 = __toESM(require_seedrandom2());
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js
+function softmax3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { dim } = attrs;
+  const logitsRank = logits.shape.length;
+  let $dim = dim;
+  if ($dim === -1) {
+    $dim = logitsRank - 1;
+  }
+  if ($dim !== logitsRank - 1) {
+    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);
+  }
+  const axes = util_exports.parseAxisParam([$dim], logits.shape);
+  const maxLogit = max3({
+    inputs: { x: logits },
+    backend: backend2,
+    attrs: { reductionIndices: axes, keepDims: false }
+  });
+  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
+  const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
+  const a6 = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });
+  const b = exp2({ inputs: { x: a6 }, backend: backend2 });
+  const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
+  const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
+  const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(maxLogit);
+  backend2.disposeIntermediateTensorInfo(maxLogitReshaped);
+  backend2.disposeIntermediateTensorInfo(a6);
+  backend2.disposeIntermediateTensorInfo(b);
+  backend2.disposeIntermediateTensorInfo(sumExp);
+  backend2.disposeIntermediateTensorInfo(sumReshaped);
+  return result;
+}
+var softmaxConfig = {
+  kernelName: Softmax,
+  backendName: "cpu",
+  kernelFunc: softmax3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
+function multinomial2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { numSamples, seed, normalized } = attrs;
+  assertNotComplex(logits, "multinomial");
+  const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });
+  const batchSize = probabilities.shape[0];
+  const numEvents = probabilities.shape[1];
+  const probVals = backend2.data.get(probabilities.dataId).values;
+  const resShape = [batchSize, numSamples];
+  const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), "int32");
+  for (let b = 0; b < batchSize; ++b) {
+    const offset = b * numEvents;
+    const cdf = new Float32Array(numEvents - 1);
+    cdf[0] = probVals[offset];
+    for (let event = 1; event < cdf.length; ++event) {
+      cdf[event] = cdf[event - 1] + probVals[offset + event];
+    }
+    const random = seedrandom4.alea(seed.toString());
+    const outOffset = b * numSamples;
+    for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
+      const r2 = random();
+      resVals[outOffset + sampleId] = cdf.length;
+      for (let event = 0; event < cdf.length; event++) {
+        if (r2 < cdf[event]) {
+          resVals[outOffset + sampleId] = event;
+          break;
+        }
+      }
+    }
+  }
+  if (!normalized) {
+    backend2.disposeIntermediateTensorInfo(probabilities);
+  }
+  return backend2.makeTensorInfo(resShape, "int32", resVals);
+}
+var multinomialConfig = {
+  kernelName: Multinomial,
+  backendName: "cpu",
+  kernelFunc: multinomial2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js
+var nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;
+function nonMaxSuppressionV3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
+  assertNotComplex(boxes, "NonMaxSuppression");
+  const boxesVals = backend2.data.get(boxes.dataId).values;
+  const scoresVals = backend2.data.get(scores.dataId).values;
+  const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
+}
+var nonMaxSuppressionV3Config = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "cpu",
+  kernelFunc: nonMaxSuppressionV3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js
+var nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;
+function nonMaxSuppressionV4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
+  assertNotComplex(boxes, "NonMaxSuppressionPadded");
+  const boxesVals = backend2.data.get(boxes.dataId).values;
+  const scoresVals = backend2.data.get(scores.dataId).values;
+  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
+  ];
+}
+var nonMaxSuppressionV4Config = {
+  kernelName: NonMaxSuppressionV4,
+  backendName: "cpu",
+  kernelFunc: nonMaxSuppressionV4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js
+var nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;
+function nonMaxSuppressionV5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
+  assertNotComplex(boxes, "NonMaxSuppressionWithScore");
+  const boxesVals = backend2.data.get(boxes.dataId).values;
+  const scoresVals = backend2.data.get(scores.dataId).values;
+  const maxOutputSizeVal = maxOutputSize;
+  const iouThresholdVal = iouThreshold;
+  const scoreThresholdVal = scoreThreshold;
+  const softNmsSigmaVal = softNmsSigma;
+  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
+  ];
+}
+var nonMaxSuppressionV5Config = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "cpu",
+  kernelFunc: nonMaxSuppressionV5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js
+function oneHot2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices } = inputs;
+  const { depth, onValue, offValue } = attrs;
+  assertNotComplex(indices, "oneHot");
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const res = new Float32Array(indicesSize * depth);
+  res.fill(offValue);
+  const indicesVal = backend2.data.get(indices.dataId).values;
+  for (let event = 0; event < indicesSize; ++event) {
+    if (indicesVal[event] >= 0 && indicesVal[event] < depth) {
+      res[event * depth + indicesVal[event]] = onValue;
+    }
+  }
+  return backend2.makeTensorInfo([...indices.shape, depth], "int32", res);
+}
+var oneHotConfig = {
+  kernelName: OneHot,
+  backendName: "cpu",
+  kernelFunc: oneHot2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js
+function zerosLike2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("zerosLike is not supported for string tensors");
+  } else if (x.dtype === "complex64") {
+    const realPart = real2({ inputs: { input: x }, backend: backend2 });
+    const r2 = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });
+  }
+}
+var zerosLikeConfig = {
+  kernelName: ZerosLike,
+  backendName: "cpu",
+  kernelFunc: zerosLike2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js
+function onesLike2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("onesLike is not supported for string tensors");
+  } else if (x.dtype === "complex64") {
+    const realPart = real2({ inputs: { input: x }, backend: backend2 });
+    const r2 = onesLike2({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });
+  }
+}
+var onesLikeConfig = {
+  kernelName: OnesLike,
+  backendName: "cpu",
+  kernelFunc: onesLike2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js
+function pack(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims3({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var packConfig = {
+  kernelName: Pack,
+  backendName: "cpu",
+  kernelFunc: pack
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js
+function padV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, constantValue } = attrs;
+  assertNotComplex(x, "pad");
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const start = paddings.map((p2) => p2[0]);
+  const xVals = backend2.data.get(x.dataId).values;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const xRank = x.shape.length;
+  const xStrides = util_exports.computeStrides(x.shape);
+  const resultSize = util_exports.sizeFromShape(outShape);
+  const resultRank = outShape.length;
+  const resultStrides = util_exports.computeStrides(outShape);
+  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
+  if (constantValue !== 0) {
+    resVals.fill(constantValue);
+  }
+  for (let i2 = 0; i2 < xSize; i2++) {
+    const coords3 = util_exports.indexToLoc(i2, xRank, xStrides);
+    const outCoords = coords3.map((c, i3) => c + start[i3]);
+    const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);
+    resVals[outIndex] = xVals[i2];
+  }
+  const outId = backend2.write(resVals, outShape, x.dtype);
+  return { dataId: outId, shape: outShape, dtype: x.dtype };
+}
+var padV2Config = {
+  kernelName: PadV2,
+  backendName: "cpu",
+  kernelFunc: padV2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js
+var powImpl = createSimpleBinaryKernelImpl((a6, b) => Math.pow(a6, b));
+var pow2 = binaryKernelFunc(Pow, powImpl);
+var powConfig = {
+  kernelName: Pow,
+  backendName: "cpu",
+  kernelFunc: pow2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js
+function range3(args) {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, dtype, step: step5 } = attrs;
+  const values = rangeImpl(start, stop, step5, dtype);
+  return backend2.makeTensorInfo([values.length], dtype, values);
+}
+var rangeConfig = {
+  kernelName: Range,
+  backendName: "cpu",
+  kernelFunc: range3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js
+var reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);
+var reciprocalConfig = {
+  kernelName: Reciprocal,
+  backendName: "cpu",
+  kernelFunc: reciprocal2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js
+function resizeBilinear2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size } = attrs;
+  assertNotComplex(images, "resizeBilinear");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const [newHeight, newWidth] = size;
+  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
+  const xValues = backend2.data.get(images.dataId).values;
+  const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));
+  const effectiveInputSize = [
+    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+  ];
+  const effectiveOutputSize = [
+    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+  ];
+  let outputIdx = 0;
+  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+  for (let b = 0; b < batch; b++) {
+    for (let r2 = 0; r2 < newHeight; r2++) {
+      let sourceFracRow;
+      if (halfPixelCenters) {
+        sourceFracRow = effectiveRowSizeRatio * (r2 + 0.5) - 0.5;
+      } else {
+        sourceFracRow = effectiveRowSizeRatio * r2;
+      }
+      const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
+      const rowFrac = sourceFracRow - sourceRowFloor;
+      const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
+      const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
+      const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
+      for (let c = 0; c < newWidth; c++) {
+        let sourceFracCol;
+        if (halfPixelCenters) {
+          sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
+        } else {
+          sourceFracCol = effectiveColSizeRatio * c;
+        }
+        const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
+        const colFrac = sourceFracCol - sourceColFloor;
+        const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
+        const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
+        const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
+        const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
+        const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
+        for (let d = 0; d < numChannels; d++) {
+          const topLeft = xValues[topLeftOffest + d];
+          const bottomLeft = xValues[botLeftOffset + d];
+          const topRight = xValues[topRightOffset + d];
+          const bottomRight = xValues[botRightOffest + d];
+          const top = topLeft + (topRight - topLeft) * colFrac;
+          const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
+          const newValue = top + (bottom - top) * rowFrac;
+          result[outputIdx++] = newValue;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], "float32", result);
+}
+var resizeBilinearConfig = {
+  kernelName: ResizeBilinear,
+  backendName: "cpu",
+  kernelFunc: resizeBilinear2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js
+function resizeBilinearGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  assertNotComplex([dy, images], "resizeBilinearGrad");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const [batch, xHeight, xWidth, depth] = images.shape;
+  const [, yHeight, yWidth] = dy.shape;
+  const output = new Float32Array(batch * xHeight * xWidth * depth);
+  const effectiveXSize = [
+    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+  ];
+  const effectiveYSize = [
+    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+  ];
+  const heightScale = effectiveXSize[0] / effectiveYSize[0];
+  const widthScale = effectiveXSize[1] / effectiveYSize[1];
+  const dyValues = backend2.data.get(dy.dataId).values;
+  let offset = 0;
+  for (let b = 0; b < batch; b++) {
+    const bOffset = b * imagesStrides[0];
+    for (let r2 = 0; r2 < yHeight; r2++) {
+      const dxR = r2 * heightScale;
+      const topDxRIndex = Math.floor(dxR);
+      const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
+      const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
+      const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
+      const dxRLerp = dxR - topDxRIndex;
+      const inverseDxRLerp = 1 - dxRLerp;
+      for (let c = 0; c < yWidth; c++) {
+        const dxC = c * widthScale;
+        const leftDxCIndex = Math.floor(dxC);
+        const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
+        const dxCLerp = dxC - leftDxCIndex;
+        const inverseDxCLerp = 1 - dxCLerp;
+        const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
+        const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];
+        const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];
+        const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];
+        const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
+        const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
+        const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
+        const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
+        for (let d = 0; d < depth; d++) {
+          const dyVal = dyValues[offset++];
+          output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;
+          output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
+          output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
+          output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], "float32", output);
+}
+var resizeBilinearGradConfig2 = {
+  kernelName: ResizeBilinearGrad,
+  backendName: "cpu",
+  kernelFunc: resizeBilinearGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js
+function resizeNearestNeighbor2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size } = attrs;
+  assertNotComplex(images, "resizeNearestNeighbor");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const [newHeight, newWidth] = size;
+  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
+  const xValues = backend2.data.get(images.dataId).values;
+  const output = new Float32Array(batch * newHeight * newWidth * numChannels);
+  const effectiveInputSize = [
+    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+  ];
+  const effectiveOutputSize = [
+    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+  ];
+  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+  let outputOffset = 0;
+  for (let b = 0; b < batch; b++) {
+    const batchOffset = b * imagesStrides[0];
+    for (let r2 = 0; r2 < newHeight; r2++) {
+      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r2 + 0.5) : effectiveRowSizeRatio * r2;
+      let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
+      if (halfPixelCenters) {
+        sourceNearestRow = Math.max(0, sourceNearestRow);
+      }
+      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
+      for (let c = 0; c < newWidth; c++) {
+        const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;
+        let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
+        if (halfPixelCenters) {
+          sourceNearestCol = Math.max(0, sourceNearestCol);
+        }
+        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
+        for (let d = 0; d < numChannels; d++) {
+          const newVal = xValues[colOffset + d];
+          output[outputOffset++] = newVal;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);
+}
+var resizeNearestNeighborConfig = {
+  kernelName: ResizeNearestNeighbor,
+  backendName: "cpu",
+  kernelFunc: resizeNearestNeighbor2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js
+function resizeNearestNeighborGrad(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  assertNotComplex([dy, images], "resizeNearestNeighborGrad");
+  const imagesStrides = util_exports.computeStrides(images.shape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const [batch, xHeight, xWidth, depth] = images.shape;
+  const [, yHeight, yWidth] = dy.shape;
+  const output = new Float32Array(batch * xHeight * xWidth * depth);
+  const dyValues = backend2.data.get(dy.dataId).values;
+  const effectiveXSize = [
+    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+  ];
+  const effectiveYSize = [
+    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+  ];
+  const heightScale = effectiveXSize[0] / effectiveYSize[0];
+  const widthScale = effectiveXSize[1] / effectiveYSize[1];
+  const invHeightScale = 1 / heightScale;
+  const invWidthScale = 1 / widthScale;
+  const winHeight = Math.ceil(invHeightScale) * 2 + 2;
+  const winWidth = Math.ceil(invWidthScale) * 2 + 2;
+  for (let b = 0; b < batch; b++) {
+    const batchOffset = b * imagesStrides[0];
+    for (let r2 = 0; r2 < xHeight; r2++) {
+      const rowOffset = batchOffset + r2 * imagesStrides[1];
+      const startRLerp = Math.floor(r2 * invHeightScale);
+      const startDyR = Math.floor(startRLerp - winHeight / 2);
+      for (let c = 0; c < xWidth; c++) {
+        const colOffset = rowOffset + c * imagesStrides[2];
+        const startCLerp = Math.floor(c * invWidthScale);
+        const startDyC = Math.floor(startCLerp - winWidth / 2);
+        for (let d = 0; d < depth; d++) {
+          let accum = 0;
+          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
+            const dyR = dyRIndex + startDyR;
+            if (dyR < 0 || dyR >= yHeight) {
+              continue;
+            }
+            const dyROffset = batchOffset + dyR * dyStrides[1];
+            const sourceFracRow = dyR * heightScale;
+            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
+            if (r2 !== sourceNearestRow) {
+              continue;
+            }
+            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
+              const dyC = dyCIndex + startDyC;
+              if (dyC < 0 || dyC >= yWidth) {
+                continue;
+              }
+              const dyCOffset = dyROffset + dyC * dyStrides[2];
+              const sourceFracCol = dyC * widthScale;
+              const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
+              if (c === sourceNearestCol) {
+                accum += dyValues[dyCOffset + d];
+              }
+            }
+          }
+          output[colOffset + d] = accum;
+        }
+      }
+    }
+  }
+  return backend2.makeTensorInfo(images.shape, images.dtype, output);
+}
+var resizeNearestNeighborGradConfig2 = {
+  kernelName: ResizeNearestNeighborGrad,
+  backendName: "cpu",
+  kernelFunc: resizeNearestNeighborGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js
+function reverse2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dims } = attrs;
+  assertNotComplex(x, "reverse");
+  const xRank = x.shape.length;
+  const $dims = util_exports.parseAxisParam(dims, x.shape);
+  if (xRank === 0) {
+    return identity2({ inputs: { x }, backend: backend2 });
+  }
+  const outBuf = new TensorBuffer(x.shape, x.dtype);
+  const xBuf = backend2.bufferSync(x);
+  for (let i2 = 0; i2 < outBuf.size; i2++) {
+    const outLoc = outBuf.indexToLoc(i2);
+    const inLoc = outLoc.slice();
+    $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
+    outBuf.set(xBuf.get(...inLoc), ...outLoc);
+  }
+  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+}
+var reverseConfig = {
+  kernelName: Reverse,
+  backendName: "cpu",
+  kernelFunc: reverse2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js
+var rotateWithOffsetConfig = {
+  kernelName: RotateWithOffset,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const { radians, fillValue, center } = attrs;
+    const cpuBackend = backend2;
+    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));
+    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+    const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
+    const fullOpacityValue = 255;
+    const sinFactor = Math.sin(radians);
+    const cosFactor = Math.cos(radians);
+    const imageVals = cpuBackend.data.get(image2.dataId).values;
+    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
+      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
+      for (let row = 0; row < imageHeight; row++) {
+        const rowOffset = row * (imageWidth * numChannels);
+        for (let col = 0; col < imageWidth; col++) {
+          const colOffset = col * numChannels;
+          for (let channel = 0; channel < numChannels; channel++) {
+            const coords3 = [batch, row, col, channel];
+            const x = coords3[2];
+            const y = coords3[1];
+            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
+            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
+            coordX = Math.round(coordX + centerX);
+            coordY = Math.round(coordY + centerY);
+            let outputValue = fillValue;
+            if (typeof fillValue !== "number") {
+              if (channel === 3) {
+                outputValue = fullOpacityValue;
+              } else {
+                outputValue = fillValue[channel];
+              }
+            }
+            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {
+              const rotatedRowOffset = coordY * (imageWidth * numChannels);
+              const rotatedColOffset = coordX * numChannels;
+              const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
+              outputValue = imageVals[imageIdx];
+            }
+            const outIdx = batchOffset + rowOffset + colOffset + channel;
+            output[outIdx] = outputValue;
+          }
+        }
+      }
+    }
+    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);
+    return { dataId, shape: image2.shape, dtype: image2.dtype };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js
+var round3 = unaryKernelFunc(Round, (xi) => {
+  const base = Math.floor(xi);
+  if (xi - base < 0.5) {
+    return Math.floor(xi);
+  } else if (xi - base > 0.5) {
+    return Math.ceil(xi);
+  } else {
+    if (base % 2 === 0) {
+      return base;
+    } else {
+      return base + 1;
+    }
+  }
+});
+var roundConfig = {
+  kernelName: Round,
+  backendName: "cpu",
+  kernelFunc: round3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js
+function scatterNd(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);
+  const sumDupeIndices = true;
+  const indicesBuf = backend2.bufferSync(indices);
+  const updatesBuf = backend2.bufferSync(updates);
+  const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);
+  return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
+}
+var scatterNdConfig = {
+  kernelName: ScatterNd,
+  backendName: "cpu",
+  kernelFunc: scatterNd
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js
+function lowerBound2(array2, value) {
+  let left = 0;
+  let right = array2.length;
+  let mid = 0;
+  while (left < right) {
+    mid = Math.floor((left + right) / 2);
+    if (array2[mid] < value) {
+      left = mid + 1;
+    } else {
+      right = mid;
+    }
+  }
+  return right;
+}
+function upperBound2(array2, value) {
+  let left = 0;
+  let right = array2.length;
+  let mid = 0;
+  while (left < right) {
+    mid = Math.floor((left + right) / 2);
+    if (array2[mid] <= value) {
+      left = mid + 1;
+    } else {
+      right = mid;
+    }
+  }
+  return right;
+}
+function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {
+  const output = util_exports.getArrayFromDType("int32", batchSize * numValues);
+  for (let b = 0; b < batchSize; ++b) {
+    const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
+    const outputOffset = b * numValues;
+    for (let i2 = 0; i2 < numValues; ++i2) {
+      output[outputOffset + i2] = side === "left" ? lowerBound2(sortedInputsSlice, values[i2 + outputOffset]) : upperBound2(sortedInputsSlice, values[i2 + outputOffset]);
+    }
+  }
+  return output;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js
+function searchSorted2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sortedSequence, values } = inputs;
+  const { side } = attrs;
+  const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;
+  const $values = backend2.data.get(values.dataId).values;
+  const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
+  return backend2.makeTensorInfo(values.shape, "int32", output);
+}
+var searchSortedConfig = {
+  kernelName: SearchSorted,
+  backendName: "cpu",
+  kernelFunc: searchSorted2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js
+function select2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  assertNotComplex([condition, t2, e2], "select");
+  const conditionRank = condition.shape.length;
+  const values = backend2.data.get(condition.dataId).values;
+  const tValues = backend2.data.get(t2.dataId).values;
+  const eValues = backend2.data.get(e2.dataId).values;
+  const resultDtype = upcastType(t2.dtype, e2.dtype);
+  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t2.shape), resultDtype);
+  let index = 0;
+  const offset = conditionRank === 0 || conditionRank > 1 || t2.shape.length === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
+  for (let i2 = 0; i2 < values.length; i2++) {
+    for (let j = 0; j < offset; j++) {
+      if (values[i2] === 1) {
+        newValues[index++] = tValues[i2];
+      } else {
+        newValues[index++] = eValues[i2];
+      }
+    }
+  }
+  return backend2.makeTensorInfo(t2.shape, resultDtype, newValues);
+}
+var selectConfig = {
+  kernelName: Select,
+  backendName: "cpu",
+  kernelFunc: select2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js
+var scaleAlpha = backend_util_exports.SELU_SCALEALPHA;
+var scale = backend_util_exports.SELU_SCALE;
+var selu2 = unaryKernelFunc(Selu, (xi) => {
+  if (xi >= 0) {
+    return scale * xi;
+  } else {
+    return scaleAlpha * (Math.exp(xi) - 1);
+  }
+});
+var seluConfig = {
+  kernelName: Selu,
+  backendName: "cpu",
+  kernelFunc: selu2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js
+var sign2 = unaryKernelFunc(Sign, (xi) => {
+  if (xi < 0) {
+    return -1;
+  } else if (xi > 0) {
+    return 1;
+  } else {
+    return 0;
+  }
+});
+var signConfig = {
+  kernelName: Sign,
+  backendName: "cpu",
+  kernelFunc: sign2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js
+var sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));
+var sinConfig = {
+  kernelName: Sin,
+  backendName: "cpu",
+  kernelFunc: sin2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js
+var sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));
+var sinhConfig = {
+  kernelName: Sinh,
+  backendName: "cpu",
+  kernelFunc: sinh2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js
+var epsilon2 = 11920928955078125e-23;
+var threshold2 = Math.log(epsilon2) + 2;
+var softplus2 = unaryKernelFunc(Softplus, (xi) => {
+  const tooLarge = xi > -threshold2;
+  const tooSmall = xi < threshold2;
+  const expX = Math.exp(xi);
+  let result;
+  if (tooSmall) {
+    result = expX;
+  } else if (tooLarge) {
+    result = xi;
+  } else {
+    result = Math.log(1 + expX);
+  }
+  return result;
+});
+var softplusConfig = {
+  kernelName: Softplus,
+  backendName: "cpu",
+  kernelFunc: softplus2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js
+function spaceToBatchND2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  assertNotComplex([x], "spaceToBatchND");
+  const prod6 = util_exports.sizeFromShape(blockShape);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const paddedX = padV2Config.kernelFunc({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapeInputs = { x: paddedX };
+  const reshapeAttrs = { shape: reshapedPaddedShape };
+  const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });
+  const transposeInputs = { x: paddedXReshaped };
+  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };
+  const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });
+  const resultReshapeInputs = { x: paddedXT };
+  const resultReshapeAttrs = { shape: flattenShape };
+  const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });
+  backend2.disposeIntermediateTensorInfo(paddedX);
+  backend2.disposeIntermediateTensorInfo(paddedXReshaped);
+  backend2.disposeIntermediateTensorInfo(paddedXT);
+  return result;
+}
+var spaceToBatchNDConfig = {
+  kernelName: SpaceToBatchND,
+  backendName: "cpu",
+  kernelFunc: spaceToBatchND2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js
+function sparseFillEmptyRows2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { indices, values, denseShape, defaultValue } = inputs;
+  if (denseShape.shape.length !== 1) {
+    throw new Error(`Dense shape must be a vector, saw:
+        ${denseShape.shape}`);
+  }
+  if (indices.shape.length !== 2) {
+    throw new Error(`Indices must be a matrix, saw:
+        ${indices.shape}`);
+  }
+  if (values.shape.length !== 1) {
+    throw new Error(`Values must be a vector, saw:
+        ${values.shape}`);
+  }
+  if (defaultValue.shape.length !== 0) {
+    throw new Error(`Default value must be a scalar, saw:
+        ${defaultValue.shape}`);
+  }
+  const $indices = backend2.data.get(indices.dataId).values;
+  const $values = backend2.data.get(values.dataId).values;
+  const $denseShape = backend2.data.get(denseShape.dataId).values;
+  const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
+  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
+  return [
+    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
+    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
+    backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),
+    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))
+  ];
+}
+var sparseFillEmptyRowsConfig = {
+  kernelName: SparseFillEmptyRows,
+  backendName: "cpu",
+  kernelFunc: sparseFillEmptyRows2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js
+function sparseReshape2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { inputIndices, inputShape, newShape } = inputs;
+  if (inputIndices.shape.length !== 2) {
+    throw new Error(`Input indices should be a matrix but received shape
+        ${inputIndices.shape}`);
+  }
+  if (inputShape.shape.length !== 1) {
+    throw new Error(`Input shape should be a vector but received shape
+        ${inputShape.shape}`);
+  }
+  if (newShape.shape.length !== 1) {
+    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
+  }
+  const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);
+  const $inputIndices = backend2.data.get(inputIndices.dataId).values;
+  const targetShape = Array.from(backend2.data.get(newShape.dataId).values);
+  const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
+  return [
+    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
+    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))
+  ];
+}
+var sparseReshapeConfig = {
+  kernelName: SparseReshape,
+  backendName: "cpu",
+  kernelFunc: sparseReshape2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js
+function sparseSegmentMean2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+          ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+          ${segmentIds.shape}`);
+  }
+  if (indices.shape[0] !== segmentIds.shape[0]) {
+    throw new Error(`segmentIds and indices should have same size.`);
+  }
+  const $data = backend2.data.get(data.dataId).values;
+  const $indices = backend2.data.get(indices.dataId).values;
+  const $segmentIds = backend2.data.get(segmentIds.dataId).values;
+  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentMeanConfig = {
+  kernelName: SparseSegmentMean,
+  backendName: "cpu",
+  kernelFunc: sparseSegmentMean2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js
+function sparseSegmentSum2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+         ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+         ${segmentIds.shape}`);
+  }
+  if (indices.shape[0] !== segmentIds.shape[0]) {
+    throw new Error(`segmentIds and indices should have same size.`);
+  }
+  const $data = backend2.data.get(data.dataId).values;
+  const $indices = backend2.data.get(indices.dataId).values;
+  const $segmentIds = backend2.data.get(segmentIds.dataId).values;
+  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentSumConfig = {
+  kernelName: SparseSegmentSum,
+  backendName: "cpu",
+  kernelFunc: sparseSegmentSum2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js
+function sparseToDense2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sparseIndices, sparseValues, defaultValue } = inputs;
+  const { outputShape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
+  const sumDupeIndices = false;
+  const indicesBuf = backend2.bufferSync(sparseIndices);
+  let outBuf;
+  switch (sparseValues.dtype) {
+    case "bool": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+      break;
+    }
+    case "float32": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+      break;
+    }
+    case "int32": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+      break;
+    }
+    case "string": {
+      const updatesBuf = backend2.bufferSync(sparseValues);
+      const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);
+      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+      break;
+    }
+    default:
+      throw new Error(`Unsupported type ${sparseValues.dtype}`);
+  }
+  return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+}
+var sparseToDenseConfig = {
+  kernelName: SparseToDense,
+  backendName: "cpu",
+  kernelFunc: sparseToDense2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js
+function splitV(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const begin = new Array(x.shape.length).fill(0);
+  const size = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const sliceSize = [...size];
+    sliceSize[$axis] = s2;
+    const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
+    begin[$axis] += s2;
+    return sliceT;
+  });
+}
+var splitVConfig = {
+  kernelName: SplitV,
+  backendName: "cpu",
+  kernelFunc: splitV
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js
+var squareConfig = {
+  kernelName: Square,
+  backendName: "cpu",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const cpuBackend = backend2;
+    assertNotComplex(x, "square");
+    const values = cpuBackend.data.get(x.dataId).values;
+    const newValues = new Float32Array(values.length);
+    for (let i2 = 0; i2 < values.length; ++i2) {
+      const value = values[i2];
+      newValues[i2] = value * value;
+    }
+    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
+    return { dataId, shape: x.shape, dtype: x.dtype };
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js
+var step2 = unaryKernelFunc(Step, (xi, attrs) => {
+  const stepAttrs = attrs;
+  if (isNaN(xi)) {
+    return NaN;
+  } else {
+    return xi > 0 ? 1 : stepAttrs.alpha;
+  }
+});
+var stepConfig = {
+  kernelName: Step,
+  backendName: "cpu",
+  kernelFunc: step2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js
+function stridedSlice2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  assertNotComplex(x, "stridedSlice");
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
+    result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeIntermediateTensorInfo(sliced);
+  } else {
+    const xBuf = backend2.bufferSync(x);
+    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);
+    result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);
+  }
+  return result;
+}
+var stridedSliceConfig = {
+  kernelName: StridedSlice,
+  backendName: "cpu",
+  kernelFunc: stridedSlice2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js
+function stringNGrams2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
+  const { data, dataSplits } = inputs;
+  const $data = backend2.data.get(data.dataId).values;
+  const $dataSplits = backend2.data.get(dataSplits.dataId).values;
+  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
+  return [
+    backend2.makeTensorInfo([nGrams.length], "string", nGrams),
+    backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
+  ];
+}
+var stringNGramsConfig = {
+  kernelName: StringNGrams,
+  backendName: "cpu",
+  kernelFunc: stringNGrams2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js
+function stringSplit2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { skipEmpty } = attrs;
+  const { input: input2, delimiter } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (input2.shape.length !== 1) {
+    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);
+  }
+  if (delimiter.shape.length !== 0) {
+    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
+  }
+  const $input = backend2.data.get(input2.dataId).values;
+  const $delimiter = backend2.data.get(delimiter.dataId).values[0];
+  const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);
+  const outputSize = values.length;
+  return [
+    backend2.makeTensorInfo([outputSize, 2], "int32", indices),
+    backend2.makeTensorInfo([outputSize], "string", values),
+    backend2.makeTensorInfo([2], "int32", new Int32Array(shape))
+  ];
+}
+var stringSplitConfig = {
+  kernelName: StringSplit,
+  backendName: "cpu",
+  kernelFunc: stringSplit2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js
+function stringToHashBucketFast2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { numBuckets } = attrs;
+  const { input: input2 } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (numBuckets <= 0) {
+    throw new Error(`Number of buckets must be at least 1`);
+  }
+  const $input = backend2.data.get(input2.dataId).values;
+  const output = stringToHashBucketFastImpl($input, numBuckets);
+  return backend2.makeTensorInfo(input2.shape, "int32", output);
+}
+var stringToHashBucketFastConfig = {
+  kernelName: StringToHashBucketFast,
+  backendName: "cpu",
+  kernelFunc: stringToHashBucketFast2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js
+var tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));
+var tanConfig = {
+  kernelName: Tan,
+  backendName: "cpu",
+  kernelFunc: tan2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js
+var tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));
+var tanhConfig = {
+  kernelName: Tanh,
+  backendName: "cpu",
+  kernelFunc: tanh3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js
+function tile3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reps } = attrs;
+  assertNotComplex(x, "tile");
+  const outBuf = tileImpl(backend2.bufferSync(x), reps);
+  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+}
+var tileConfig = {
+  kernelName: Tile,
+  backendName: "cpu",
+  kernelFunc: tile3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js
+function topK(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  assertNotComplex(x, "topk");
+  const xVals = backend2.data.get(x.dataId).values;
+  const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);
+  return [
+    backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+    backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+  ];
+}
+var topKConfig = {
+  kernelName: TopK,
+  backendName: "cpu",
+  kernelFunc: topK
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js
+function transform2(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [batch, outHeight, outWidth, numChannels];
+  const strides = util_exports.computeStrides(image2.shape);
+  const batchStride = strides[0];
+  const rowStride = strides[1];
+  const colStride = strides[2];
+  const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape));
+  outVals.fill(fillValue);
+  const imageVals = backend2.data.get(image2.dataId).values;
+  const transformVals = backend2.data.get(transforms.dataId).values;
+  for (let b = 0; b < batch; ++b) {
+    const transform6 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);
+    for (let outY = 0; outY < outHeight; ++outY) {
+      for (let outX = 0; outX < outWidth; ++outX) {
+        for (let channel = 0; channel < numChannels; ++channel) {
+          let val;
+          const projection = transform6[6] * outX + transform6[7] * outY + 1;
+          if (projection === 0) {
+            continue;
+          }
+          const inX = (transform6[0] * outX + transform6[1] * outY + transform6[2]) / projection;
+          const inY = (transform6[3] * outX + transform6[4] * outY + transform6[5]) / projection;
+          const x = mapCoord(inX, imageWidth, fillMode);
+          const y = mapCoord(inY, imageHeight, fillMode);
+          switch (interpolation) {
+            case "nearest":
+              val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
+              break;
+            case "bilinear":
+              val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
+              break;
+            default:
+              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);
+          }
+          const ind = b * batchStride + outY * rowStride + outX * colStride + channel;
+          outVals[ind] = val;
+        }
+      }
+    }
+    return backend2.makeTensorInfo(outShape, image2.dtype, outVals);
+  }
+  const dataId = backend2.write(outVals, outShape, image2.dtype);
+  return { dataId, shape: image2.shape, dtype: image2.dtype };
+}
+var transformConfig = {
+  kernelName: Transform,
+  backendName: "cpu",
+  kernelFunc: transform2
+};
+function mapCoord(outCoord, len, mode) {
+  switch (mode) {
+    case "reflect":
+      return mapCoordReflect(outCoord, len);
+    case "wrap":
+      return mapCoordWrap(outCoord, len);
+    case "nearest":
+      return mapCoordNearest(outCoord, len);
+    case "constant":
+    default:
+      return mapCoordConstant(outCoord, len);
+  }
+}
+function mapCoordReflect(outCoord, len) {
+  let inCoord = outCoord;
+  if (inCoord < 0) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz2 = 2 * len;
+      if (inCoord < sz2) {
+        inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
+      }
+      inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
+    }
+  } else if (inCoord > len - 1) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz2 = 2 * len;
+      inCoord -= sz2 * Math.trunc(inCoord / sz2);
+      if (inCoord >= len) {
+        inCoord = sz2 - inCoord - 1;
+      }
+    }
+  }
+  return util_exports.clamp(0, inCoord, len - 1);
+}
+function mapCoordWrap(outCoord, len) {
+  let inCoord = outCoord;
+  if (inCoord < 0) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz = len - 1;
+      inCoord += len * (Math.trunc(-inCoord / sz) + 1);
+    }
+  } else if (inCoord > len - 1) {
+    if (len <= 1) {
+      inCoord = 0;
+    } else {
+      const sz = len - 1;
+      inCoord -= len * Math.trunc(inCoord / sz);
+    }
+  }
+  return util_exports.clamp(0, inCoord, len - 1);
+}
+function mapCoordConstant(outCoord, len) {
+  return outCoord;
+}
+function mapCoordNearest(outCoord, len) {
+  return util_exports.clamp(0, outCoord, len - 1);
+}
+function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+  const ind = batch * batchStride + y * rowStride + x * colStride + channel;
+  if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
+    return imageVals[ind];
+  } else {
+    return fillValue;
+  }
+}
+function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+  const $y = Math.round(y);
+  const $x = Math.round(x);
+  return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
+}
+function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
+  const yFloor = Math.floor(y);
+  const xFloor = Math.floor(x);
+  const yCeil = yFloor + 1;
+  const xCeil = xFloor + 1;
+  const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
+  const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
+  return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js
+function unique3(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { axis } = attrs;
+  const { x } = inputs;
+  assertNotComplex(x, "unique");
+  const values = backend2.data.get(x.dataId).values;
+  const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);
+  return [
+    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
+    backend2.makeTensorInfo([indices.length], "int32", indices)
+  ];
+}
+var uniqueConfig = {
+  kernelName: Unique,
+  backendName: "cpu",
+  kernelFunc: unique3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js
+function unpack(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const valueRank = value.shape.length;
+  const num = value.shape[axis];
+  const outShape = new Array(valueRank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < valueRank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = value.shape[i2];
+    }
+  }
+  const begin = new Array(valueRank).fill(0);
+  const size = value.shape.slice();
+  size[axis] = 1;
+  const res = new Array(num);
+  for (let i2 = 0; i2 < res.length; i2++) {
+    begin[axis] = i2;
+    const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });
+    res[i2] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
+    backend2.disposeIntermediateTensorInfo(tempRes);
+  }
+  return res;
+}
+var unpackConfig = {
+  kernelName: Unpack,
+  backendName: "cpu",
+  kernelFunc: unpack
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js
+function unsortedSegmentSum2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, segmentIds } = inputs;
+  const { numSegments } = attrs;
+  assertNotComplex(x, "unsortedSegmentSum");
+  const xRank = x.shape.length;
+  const segmentIdsRank = segmentIds.shape.length;
+  const res = [];
+  const intermediates = [];
+  const numIters = xRank - segmentIdsRank;
+  let $segmentIds = segmentIds;
+  for (let i2 = 0; i2 < numIters; ++i2) {
+    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i2 + 1 } });
+    $segmentIds = expanded;
+    intermediates.push(expanded);
+  }
+  for (let i2 = 0; i2 < numSegments; ++i2) {
+    const scalarValue = util_exports.createScalarValue(i2, "int32");
+    const segmentId = backend2.makeTensorInfo([], "int32", scalarValue);
+    const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });
+    const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: "float32" } });
+    const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 });
+    const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });
+    res.push(sumTensorInfo);
+    intermediates.push(segmentId);
+    intermediates.push(mask);
+    intermediates.push(maskCasted);
+    intermediates.push(mul2);
+    intermediates.push(sumTensorInfo);
+  }
+  const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });
+  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var unsortedSegmentSumConfig = {
+  kernelName: UnsortedSegmentSum,
+  backendName: "cpu",
+  kernelFunc: unsortedSegmentSum2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js
+var kernelConfigs = [
+  _fusedMatMulConfig,
+  absConfig,
+  acosConfig,
+  acoshConfig,
+  addConfig,
+  addNConfig,
+  allConfig,
+  anyConfig,
+  argMaxConfig,
+  argMinConfig,
+  asinConfig,
+  asinhConfig,
+  atanConfig,
+  atan2Config,
+  atanhConfig,
+  avgPoolConfig,
+  avgPool3DConfig,
+  avgPool3DGradConfig2,
+  avgPoolGradConfig2,
+  batchMatMulConfig,
+  batchNormConfig,
+  batchToSpaceNDConfig,
+  bincountConfig,
+  broadcastArgsConfig,
+  castConfig,
+  ceilConfig,
+  clipByValueConfig,
+  complexConfig,
+  complexAbsConfig,
+  concatConfig,
+  conv2DConfig,
+  conv2DBackpropFilterConfig,
+  conv2DBackpropInputConfig,
+  conv3DConfig,
+  conv3DBackpropFilterV2Config,
+  conv3DBackpropInputV2Config,
+  cosConfig,
+  coshConfig,
+  cropAndResizeConfig,
+  cumprodConfig,
+  cumsumConfig,
+  denseBincountConfig,
+  depthToSpaceConfig,
+  depthwiseConv2dNativeConfig,
+  depthwiseConv2dNativeBackpropFilterConfig,
+  depthwiseConv2dNativeBackpropInputConfig,
+  diagConfig,
+  dilation2DConfig,
+  dilation2DBackpropFilterConfig,
+  dilation2DBackpropInputConfig,
+  einsumConfig,
+  eluConfig,
+  eluGradConfig2,
+  equalConfig,
+  erfConfig,
+  expConfig,
+  expandDimsConfig,
+  expm1Config,
+  fftConfig,
+  fillConfig,
+  flipLeftRightConfig,
+  floorConfig,
+  floorDivConfig,
+  fusedConv2DConfig,
+  fusedDepthwiseConv2DConfig,
+  gatherNdConfig,
+  gatherV2Config,
+  greaterConfig,
+  greaterEqualConfig,
+  identityConfig,
+  ifftConfig,
+  imagConfig,
+  isFiniteConfig,
+  isInfConfig,
+  isNaNConfig,
+  leakyReluConfig,
+  lessConfig,
+  lessEqualConfig,
+  linSpaceConfig,
+  logConfig,
+  log1pConfig,
+  logicalAndConfig,
+  logicalNotConfig,
+  logicalOrConfig,
+  LRNConfig,
+  LRNGradConfig,
+  maxConfig,
+  maximumConfig,
+  maxPoolConfig,
+  maxPool3DConfig,
+  maxPool3DGradConfig2,
+  maxPoolGradConfig2,
+  maxPoolWithArgmaxConfig,
+  meanConfig,
+  minConfig,
+  minimumConfig,
+  mirrorPadConfig,
+  modConfig,
+  multinomialConfig,
+  multiplyConfig,
+  negConfig,
+  nonMaxSuppressionV3Config,
+  nonMaxSuppressionV4Config,
+  nonMaxSuppressionV5Config,
+  notEqualConfig,
+  oneHotConfig,
+  onesLikeConfig,
+  packConfig,
+  padV2Config,
+  powConfig,
+  preluConfig,
+  prodConfig,
+  rangeConfig,
+  realConfig,
+  realDivConfig,
+  reciprocalConfig,
+  reluConfig,
+  relu6Config,
+  reshapeConfig,
+  resizeBilinearConfig,
+  resizeBilinearGradConfig2,
+  resizeNearestNeighborConfig,
+  resizeNearestNeighborGradConfig2,
+  reverseConfig,
+  rotateWithOffsetConfig,
+  roundConfig,
+  rsqrtConfig,
+  scatterNdConfig,
+  searchSortedConfig,
+  selectConfig,
+  seluConfig,
+  sigmoidConfig,
+  signConfig,
+  sinConfig,
+  sinhConfig,
+  sliceConfig,
+  softmaxConfig,
+  softplusConfig,
+  spaceToBatchNDConfig,
+  sparseFillEmptyRowsConfig,
+  sparseReshapeConfig,
+  sparseSegmentMeanConfig,
+  sparseSegmentSumConfig,
+  sparseToDenseConfig,
+  splitVConfig,
+  sqrtConfig,
+  squareConfig,
+  squaredDifferenceConfig,
+  stepConfig,
+  stridedSliceConfig,
+  stringNGramsConfig,
+  stringSplitConfig,
+  stringToHashBucketFastConfig,
+  subConfig,
+  sumConfig,
+  tanConfig,
+  tanhConfig,
+  tileConfig,
+  topKConfig,
+  transformConfig,
+  transposeConfig,
+  uniqueConfig,
+  unpackConfig,
+  unsortedSegmentSumConfig,
+  zerosLikeConfig
+];
+for (const kernelConfig of kernelConfigs) {
+  registerKernel(kernelConfig);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js
+var webgl_util_exports = {};
+__export(webgl_util_exports, {
+  assertNotComplex: () => assertNotComplex2,
+  bindCanvasToFramebuffer: () => bindCanvasToFramebuffer,
+  bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer,
+  bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler,
+  bindTextureUnit: () => bindTextureUnit,
+  bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute,
+  callAndCheck: () => callAndCheck,
+  canBeRepresented: () => canBeRepresented,
+  createFragmentShader: () => createFragmentShader,
+  createFramebuffer: () => createFramebuffer,
+  createProgram: () => createProgram,
+  createStaticIndexBuffer: () => createStaticIndexBuffer,
+  createStaticVertexBuffer: () => createStaticVertexBuffer,
+  createTexture: () => createTexture,
+  createVertexShader: () => createVertexShader,
+  getBatchDim: () => getBatchDim,
+  getExtensionOrThrow: () => getExtensionOrThrow,
+  getFramebufferErrorMessage: () => getFramebufferErrorMessage,
+  getMaxTexturesInShader: () => getMaxTexturesInShader,
+  getNumChannels: () => getNumChannels,
+  getProgramUniformLocation: () => getProgramUniformLocation,
+  getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow,
+  getRowsCols: () => getRowsCols,
+  getShapeAs3D: () => getShapeAs3D,
+  getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape,
+  getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion,
+  getWebGLErrorMessage: () => getWebGLErrorMessage,
+  getWebGLMaxTextureSize: () => getWebGLMaxTextureSize,
+  hasExtension: () => hasExtension,
+  isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture,
+  isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled,
+  isReshapeFree: () => isReshapeFree,
+  isWebGLFenceEnabled: () => isWebGLFenceEnabled,
+  isWebGLVersionEnabled: () => isWebGLVersionEnabled,
+  linkProgram: () => linkProgram,
+  logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog,
+  resetMaxTextureSize: () => resetMaxTextureSize,
+  resetMaxTexturesInShader: () => resetMaxTexturesInShader,
+  unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer,
+  unbindTextureUnit: () => unbindTextureUnit,
+  validateFramebuffer: () => validateFramebuffer,
+  validateProgram: () => validateProgram,
+  validateTextureSize: () => validateTextureSize
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js
+var contexts = {};
+var WEBGL_ATTRIBUTES = {
+  alpha: false,
+  antialias: false,
+  premultipliedAlpha: false,
+  preserveDrawingBuffer: false,
+  depth: false,
+  stencil: false,
+  failIfMajorPerformanceCaveat: true
+};
+function setWebGLContext(webGLVersion, gl) {
+  contexts[webGLVersion] = gl;
+}
+function getWebGLContext(webGLVersion, customCanvas) {
+  if (!(webGLVersion in contexts) || customCanvas != null) {
+    const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);
+    if (newCtx !== null) {
+      contexts[webGLVersion] = newCtx;
+    } else {
+      console.log("Could not get context for WebGL version", webGLVersion);
+      return null;
+    }
+  }
+  const gl = contexts[webGLVersion];
+  if (gl == null || gl.isContextLost()) {
+    delete contexts[webGLVersion];
+    return getWebGLContext(webGLVersion);
+  }
+  gl.disable(gl.DEPTH_TEST);
+  gl.disable(gl.STENCIL_TEST);
+  gl.disable(gl.BLEND);
+  gl.disable(gl.DITHER);
+  gl.disable(gl.POLYGON_OFFSET_FILL);
+  gl.disable(gl.SAMPLE_COVERAGE);
+  gl.enable(gl.SCISSOR_TEST);
+  gl.enable(gl.CULL_FACE);
+  gl.cullFace(gl.BACK);
+  return contexts[webGLVersion];
+}
+function createCanvas(webGLVersion) {
+  if (typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) {
+    return new OffscreenCanvas(300, 150);
+  } else if (typeof document !== "undefined") {
+    return document.createElement("canvas");
+  } else {
+    throw new Error("Cannot create a canvas in this context");
+  }
+}
+function getWebGLRenderingContext(webGLVersion, customCanvas) {
+  if (webGLVersion !== 1 && webGLVersion !== 2) {
+    throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
+  }
+  const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;
+  canvas.addEventListener("webglcontextlost", (ev) => {
+    ev.preventDefault();
+    delete contexts[webGLVersion];
+  }, false);
+  if (webGLVersion === 1) {
+    return canvas.getContext("webgl", WEBGL_ATTRIBUTES) || canvas.getContext("experimental-webgl", WEBGL_ATTRIBUTES);
+  }
+  return canvas.getContext("webgl2", WEBGL_ATTRIBUTES);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js
+var PackingScheme;
+(function(PackingScheme2) {
+  PackingScheme2[PackingScheme2["DENSE"] = 0] = "DENSE";
+  PackingScheme2[PackingScheme2["SHARED_BATCH"] = 1] = "SHARED_BATCH";
+})(PackingScheme || (PackingScheme = {}));
+var TextureUsage;
+(function(TextureUsage2) {
+  TextureUsage2[TextureUsage2["RENDER"] = 0] = "RENDER";
+  TextureUsage2[TextureUsage2["UPLOAD"] = 1] = "UPLOAD";
+  TextureUsage2[TextureUsage2["PIXELS"] = 2] = "PIXELS";
+  TextureUsage2[TextureUsage2["DOWNLOAD"] = 3] = "DOWNLOAD";
+})(TextureUsage || (TextureUsage = {}));
+var PhysicalTextureType;
+(function(PhysicalTextureType2) {
+  PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
+  PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
+  PhysicalTextureType2[PhysicalTextureType2["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
+  PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
+  PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
+})(PhysicalTextureType || (PhysicalTextureType = {}));
+function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
+  return [columns, rows];
+}
+function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
+  return matrixSize * channelsPerTexture;
+}
+function getDenseTexShape(shape) {
+  const size = util_exports.sizeFromShape(shape);
+  const texelsNeeded = Math.ceil(size / 4);
+  return util_exports.sizeToSquarishShape(texelsNeeded);
+}
+function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
+  return [
+    Math.max(1, Math.ceil(columns / 2)),
+    Math.max(1, Math.ceil(rows / 2))
+  ];
+}
+function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
+  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+  return w * h * 4;
+}
+function getTextureConfig(gl, textureHalfFloatExtension) {
+  const glany = gl;
+  let internalFormatFloat;
+  let internalFormatHalfFloat;
+  let internalFormatPackedHalfFloat;
+  let internalFormatPackedFloat;
+  let textureFormatFloat;
+  let downloadTextureFormat;
+  let downloadUnpackNumChannels;
+  let defaultNumChannels;
+  let textureTypeHalfFloat;
+  let textureTypeFloat;
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    internalFormatFloat = glany.R32F;
+    internalFormatHalfFloat = glany.R16F;
+    internalFormatPackedHalfFloat = glany.RGBA16F;
+    internalFormatPackedFloat = glany.RGBA32F;
+    textureFormatFloat = glany.RED;
+    downloadUnpackNumChannels = 4;
+    defaultNumChannels = 1;
+    textureTypeHalfFloat = glany.HALF_FLOAT;
+    textureTypeFloat = glany.FLOAT;
+    downloadTextureFormat = glany.RGBA8;
+  } else {
+    internalFormatFloat = gl.RGBA;
+    internalFormatHalfFloat = gl.RGBA;
+    internalFormatPackedHalfFloat = gl.RGBA;
+    internalFormatPackedFloat = glany.RGBA;
+    textureFormatFloat = gl.RGBA;
+    downloadUnpackNumChannels = 4;
+    defaultNumChannels = 4;
+    textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;
+    textureTypeFloat = gl.FLOAT;
+    downloadTextureFormat = gl.RGBA;
+  }
+  return {
+    internalFormatFloat,
+    internalFormatHalfFloat,
+    internalFormatPackedHalfFloat,
+    internalFormatPackedFloat,
+    textureFormatFloat,
+    downloadTextureFormat,
+    downloadUnpackNumChannels,
+    defaultNumChannels,
+    textureTypeHalfFloat,
+    textureTypeFloat
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js
+function callAndCheck(gl, func2) {
+  const returnValue = func2();
+  if (env().getBool("DEBUG")) {
+    checkWebGLError(gl);
+  }
+  return returnValue;
+}
+function checkWebGLError(gl) {
+  const error = gl.getError();
+  if (error !== gl.NO_ERROR) {
+    throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error));
+  }
+}
+var MIN_FLOAT16 = 596e-10;
+var MAX_FLOAT16 = 65504;
+function canBeRepresented(num) {
+  if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {
+    return true;
+  }
+  return false;
+}
+function getWebGLErrorMessage(gl, status) {
+  switch (status) {
+    case gl.NO_ERROR:
+      return "NO_ERROR";
+    case gl.INVALID_ENUM:
+      return "INVALID_ENUM";
+    case gl.INVALID_VALUE:
+      return "INVALID_VALUE";
+    case gl.INVALID_OPERATION:
+      return "INVALID_OPERATION";
+    case gl.INVALID_FRAMEBUFFER_OPERATION:
+      return "INVALID_FRAMEBUFFER_OPERATION";
+    case gl.OUT_OF_MEMORY:
+      return "OUT_OF_MEMORY";
+    case gl.CONTEXT_LOST_WEBGL:
+      return "CONTEXT_LOST_WEBGL";
+    default:
+      return `Unknown error code ${status}`;
+  }
+}
+function getExtensionOrThrow(gl, extensionName) {
+  return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.');
+}
+function createVertexShader(gl, vertexShaderSource) {
+  const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
+  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));
+  callAndCheck(gl, () => gl.compileShader(vertexShader));
+  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
+    console.log(gl.getShaderInfoLog(vertexShader));
+    throw new Error("Failed to compile vertex shader.");
+  }
+  return vertexShader;
+}
+function createFragmentShader(gl, fragmentShaderSource) {
+  const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
+  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));
+  callAndCheck(gl, () => gl.compileShader(fragmentShader));
+  if (env().get("ENGINE_COMPILE_ONLY")) {
+    return fragmentShader;
+  }
+  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
+    logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
+    throw new Error("Failed to compile fragment shader.");
+  }
+  return fragmentShader;
+}
+var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
+function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
+  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
+  if (lineNumberRegexResult == null) {
+    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);
+    console.log(shaderSource);
+    return;
+  }
+  const lineNumber = +lineNumberRegexResult[1];
+  const shaderLines = shaderSource.split("\n");
+  const pad3 = shaderLines.length.toString().length + 2;
+  const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);
+  let maxLineLength = 0;
+  for (let i2 = 0; i2 < linesWithLineNumbers.length; i2++) {
+    maxLineLength = Math.max(linesWithLineNumbers[i2].length, maxLineLength);
+  }
+  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
+  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
+  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);
+  console.log(beforeErrorLines.join("\n"));
+  console.log(shaderInfoLog.split("\n")[0]);
+  console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717");
+  console.log(afterErrorLines.join("\n"));
+}
+function createProgram(gl) {
+  return throwIfNull(gl, () => gl.createProgram(), "Unable to create WebGLProgram.");
+}
+function linkProgram(gl, program) {
+  callAndCheck(gl, () => gl.linkProgram(program));
+  if (env().get("ENGINE_COMPILE_ONLY")) {
+    return;
+  }
+  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
+    console.log(gl.getProgramInfoLog(program));
+    throw new Error("Failed to link vertex and fragment shaders.");
+  }
+}
+function validateProgram(gl, program) {
+  callAndCheck(gl, () => gl.validateProgram(program));
+  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
+    console.log(gl.getProgramInfoLog(program));
+    throw new Error("Shader program validation failed.");
+  }
+}
+function createStaticVertexBuffer(gl, data) {
+  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
+  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
+  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));
+  return buffer2;
+}
+function createStaticIndexBuffer(gl, data) {
+  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
+  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));
+  callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));
+  return buffer2;
+}
+function getNumChannels() {
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    return 1;
+  }
+  return 4;
+}
+function createTexture(gl) {
+  return throwIfNull(gl, () => gl.createTexture(), "Unable to create WebGLTexture.");
+}
+function validateTextureSize(width, height) {
+  const maxTextureSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
+  if (width <= 0 || height <= 0) {
+    const requested = `[${width}x${height}]`;
+    throw new Error("Requested texture size " + requested + " is invalid.");
+  }
+  if (width > maxTextureSize || height > maxTextureSize) {
+    const requested = `[${width}x${height}]`;
+    const max7 = `[${maxTextureSize}x${maxTextureSize}]`;
+    throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max7 + ".");
+  }
+}
+function createFramebuffer(gl) {
+  return throwIfNull(gl, () => gl.createFramebuffer(), "Unable to create WebGLFramebuffer.");
+}
+function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
+  const loc = gl.getAttribLocation(program, attribute);
+  if (loc === -1) {
+    return false;
+  }
+  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
+  callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));
+  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));
+  return true;
+}
+function bindTextureUnit(gl, texture, textureUnit) {
+  validateTextureUnit(gl, textureUnit);
+  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
+}
+function unbindTextureUnit(gl, textureUnit) {
+  validateTextureUnit(gl, textureUnit);
+  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+}
+function getProgramUniformLocationOrThrow(gl, program, uniformName) {
+  return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.');
+}
+function getProgramUniformLocation(gl, program, uniformName) {
+  return gl.getUniformLocation(program, uniformName);
+}
+function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {
+  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));
+  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));
+}
+function bindCanvasToFramebuffer(gl) {
+  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
+  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));
+  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));
+}
+function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
+  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
+  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));
+}
+function unbindColorTextureFromFramebuffer(gl, framebuffer) {
+  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
+  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));
+}
+function validateFramebuffer(gl) {
+  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
+  if (status !== gl.FRAMEBUFFER_COMPLETE) {
+    throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status));
+  }
+}
+function getFramebufferErrorMessage(gl, status) {
+  switch (status) {
+    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
+      return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
+    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
+      return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
+    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
+      return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
+    case gl.FRAMEBUFFER_UNSUPPORTED:
+      return "FRAMEBUFFER_UNSUPPORTED";
+    default:
+      return `unknown error ${status}`;
+  }
+}
+function throwIfNull(gl, returnTOrNull, failureMessage) {
+  const tOrNull = callAndCheck(gl, () => returnTOrNull());
+  if (tOrNull == null) {
+    throw new Error(failureMessage);
+  }
+  return tOrNull;
+}
+function validateTextureUnit(gl, textureUnit) {
+  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
+  const glTextureUnit = textureUnit + gl.TEXTURE0;
+  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
+    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;
+    throw new Error(`textureUnit must be in ${textureUnitRange}.`);
+  }
+}
+function getBatchDim(shape, dimsToSkip = 2) {
+  return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
+}
+function getRowsCols(shape) {
+  if (shape.length === 0) {
+    throw Error("Cannot get rows and columns of an empty shape array.");
+  }
+  return [
+    shape.length > 1 ? shape[shape.length - 2] : 1,
+    shape[shape.length - 1]
+  ];
+}
+function getShapeAs3D(shape) {
+  let shapeAs3D = [1, 1, 1];
+  const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;
+  if (!isScalar) {
+    shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];
+  }
+  return shapeAs3D;
+}
+function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
+  let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
+  if (isPacked) {
+    maxTexSize = maxTexSize * 2;
+    logShape = logShape.map((d, i2) => i2 >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i2]) : logShape[i2]);
+    if (logShape.length === 1) {
+      logShape = [2, logShape[0]];
+    }
+  }
+  if (logShape.length !== 2) {
+    const squeezeResult = util_exports.squeezeShape(logShape);
+    logShape = squeezeResult.newShape;
+  }
+  let size = util_exports.sizeFromShape(logShape);
+  if (logShape.length <= 1 && size <= maxTexSize) {
+    return [1, size];
+  } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {
+    return logShape;
+  } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {
+    return [logShape[0] * logShape[1], logShape[2]];
+  } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {
+    return [logShape[0], logShape[1] * logShape[2]];
+  } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {
+    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
+  } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
+    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
+  } else {
+    if (isPacked) {
+      const batchDim = getBatchDim(logShape);
+      let rows = 2, cols = 2;
+      if (logShape.length) {
+        [rows, cols] = getRowsCols(logShape);
+      }
+      size = batchDim * (rows / 2) * (cols / 2);
+      return util_exports.sizeToSquarishShape(size).map((d) => d * 2);
+    }
+    return util_exports.sizeToSquarishShape(size);
+  }
+}
+function isEven(n) {
+  return n % 2 === 0;
+}
+function isReshapeFree(shape1, shape2) {
+  shape1 = shape1.slice(-2);
+  shape2 = shape2.slice(-2);
+  if (util_exports.arraysEqual(shape1, shape2)) {
+    return true;
+  }
+  if (!shape1.length || !shape2.length) {
+    return true;
+  }
+  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {
+    return true;
+  }
+  if (shape1.length !== shape2.length) {
+    const shape1Cols = shape1.slice(-1)[0];
+    const shape2Cols = shape2.slice(-1)[0];
+    if (shape1Cols === shape2Cols) {
+      return true;
+    }
+    if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {
+      return true;
+    }
+  }
+  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
+}
+var MAX_TEXTURE_SIZE;
+var MAX_TEXTURES_IN_SHADER;
+function getWebGLMaxTextureSize(webGLVersion) {
+  if (MAX_TEXTURE_SIZE == null) {
+    const gl = getWebGLContext(webGLVersion);
+    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
+  }
+  return MAX_TEXTURE_SIZE;
+}
+function resetMaxTextureSize() {
+  MAX_TEXTURE_SIZE = null;
+}
+function resetMaxTexturesInShader() {
+  MAX_TEXTURES_IN_SHADER = null;
+}
+function getMaxTexturesInShader(webGLVersion) {
+  if (MAX_TEXTURES_IN_SHADER == null) {
+    const gl = getWebGLContext(webGLVersion);
+    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
+  }
+  return Math.min(16, MAX_TEXTURES_IN_SHADER);
+}
+function getWebGLDisjointQueryTimerVersion(webGLVersion) {
+  if (webGLVersion === 0) {
+    return 0;
+  }
+  let queryTimerVersion;
+  const gl = getWebGLContext(webGLVersion);
+  if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) {
+    queryTimerVersion = 2;
+  } else if (hasExtension(gl, "EXT_disjoint_timer_query")) {
+    queryTimerVersion = 1;
+  } else {
+    queryTimerVersion = 0;
+  }
+  return queryTimerVersion;
+}
+function hasExtension(gl, extensionName) {
+  const ext = gl.getExtension(extensionName);
+  return ext != null;
+}
+function isWebGLVersionEnabled(webGLVersion) {
+  try {
+    const gl = getWebGLContext(webGLVersion);
+    if (gl != null) {
+      return true;
+    }
+  } catch (e2) {
+    console.log("Error when getting WebGL context: ", e2);
+    return false;
+  }
+  return false;
+}
+function isCapableOfRenderingToFloatTexture(webGLVersion) {
+  if (webGLVersion === 0) {
+    return false;
+  }
+  const gl = getWebGLContext(webGLVersion);
+  if (webGLVersion === 1) {
+    if (!hasExtension(gl, "OES_texture_float")) {
+      return false;
+    }
+  } else {
+    if (!hasExtension(gl, "EXT_color_buffer_float")) {
+      return false;
+    }
+  }
+  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
+  return isFrameBufferComplete;
+}
+function isDownloadFloatTextureEnabled(webGLVersion) {
+  if (webGLVersion === 0) {
+    return false;
+  }
+  const gl = getWebGLContext(webGLVersion);
+  if (webGLVersion === 1) {
+    if (!hasExtension(gl, "OES_texture_float")) {
+      return false;
+    }
+    if (!hasExtension(gl, "WEBGL_color_buffer_float")) {
+      return false;
+    }
+  } else {
+    if (hasExtension(gl, "EXT_color_buffer_float")) {
+      return createFloatTextureAndBindToFramebuffer(gl);
+    }
+    const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
+    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
+      const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
+      return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
+    }
+    return false;
+  }
+  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
+  return isFrameBufferComplete;
+}
+function createFloatTextureAndBindToFramebuffer(gl) {
+  const texConfig = getTextureConfig(gl);
+  const texture = gl.createTexture();
+  gl.bindTexture(gl.TEXTURE_2D, texture);
+  const width = 1;
+  const height = 1;
+  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
+  const frameBuffer = gl.createFramebuffer();
+  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+  gl.bindTexture(gl.TEXTURE_2D, null);
+  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+  gl.deleteTexture(texture);
+  gl.deleteFramebuffer(frameBuffer);
+  return isFrameBufferComplete;
+}
+function createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {
+  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);
+  const texture = gl.createTexture();
+  gl.bindTexture(gl.TEXTURE_2D, texture);
+  const width = 1;
+  const height = 1;
+  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
+  const frameBuffer = gl.createFramebuffer();
+  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+  gl.bindTexture(gl.TEXTURE_2D, null);
+  gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+  gl.deleteTexture(texture);
+  gl.deleteFramebuffer(frameBuffer);
+  return isFrameBufferComplete;
+}
+function isWebGLFenceEnabled(webGLVersion) {
+  if (webGLVersion !== 2) {
+    return false;
+  }
+  const gl = getWebGLContext(webGLVersion);
+  const isEnabled = gl.fenceSync != null;
+  return isEnabled;
+}
+function assertNotComplex2(tensor2, opName) {
+  if (!Array.isArray(tensor2)) {
+    tensor2 = [tensor2];
+  }
+  tensor2.forEach((t2) => {
+    if (t2 != null) {
+      util_exports.assert(t2.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`);
+    }
+  });
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js
+var ENV5 = env();
+ENV5.registerFlag("HAS_WEBGL", () => ENV5.getNumber("WEBGL_VERSION") > 0);
+ENV5.registerFlag("WEBGL_VERSION", () => {
+  if (isWebGLVersionEnabled(2)) {
+    return 2;
+  } else if (isWebGLVersionEnabled(1)) {
+    return 1;
+  }
+  return 0;
+});
+ENV5.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
+ENV5.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV5.get("WEBGL_VERSION") === 2);
+ENV5.registerFlag("WEBGL_CPU_FORWARD", () => true);
+ENV5.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
+ENV5.registerFlag("WEBGL_PACK", () => ENV5.getBool("HAS_WEBGL"));
+ENV5.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_CLIP", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_PACK_REDUCE", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_CONV_IM2COL", () => ENV5.getBool("WEBGL_PACK"));
+ENV5.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
+  const webGLVersion = ENV5.getNumber("WEBGL_VERSION");
+  if (webGLVersion === 0) {
+    return 0;
+  }
+  return getWebGLDisjointQueryTimerVersion(webGLVersion);
+});
+ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV5.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile());
+ENV5.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => {
+  return ENV5.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV5.getBool("WEBGL_RENDER_FLOAT32_CAPABLE");
+});
+ENV5.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV5.getNumber("WEBGL_VERSION")));
+ENV5.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => {
+  const useUniforms = ENV5.getBool("WEBGL_RENDER_FLOAT32_ENABLED");
+  return useUniforms ? 4 : 0;
+});
+ENV5.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => {
+  return -1;
+}, (threshold3) => {
+  if (threshold3 < 0 && threshold3 !== -1) {
+    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);
+  }
+});
+ENV5.registerFlag("WEBGL_FLUSH_THRESHOLD", () => {
+  return device_util_exports.isMobile() ? 1 : -1;
+}, (threshold3) => {
+  if (threshold3 < 0 && threshold3 !== -1) {
+    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);
+  }
+});
+ENV5.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
+ENV5.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
+ENV5.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
+ENV5.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js
+function getGlslDifferences() {
+  let version9;
+  let attribute;
+  let varyingVs;
+  let varyingFs;
+  let texture2D;
+  let output;
+  let defineOutput;
+  let defineSpecialNaN;
+  let defineSpecialInf;
+  let defineRound;
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    version9 = "#version 300 es";
+    attribute = "in";
+    varyingVs = "out";
+    varyingFs = "in";
+    texture2D = "texture";
+    output = "outputColor";
+    defineOutput = "out vec4 outputColor;";
+    defineSpecialNaN = `
+      bool isnan_custom(float val) {
+        uint floatToUint = floatBitsToUint(val);
+        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
+      }
+
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan_custom(val.x),
+          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
+      }
+
+      #define isnan(value) isnan_custom(value)
+    `;
+    defineSpecialInf = ``;
+    defineRound = `
+      #define round(value) newRound(value)
+      int newRound(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 newRound(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `;
+  } else {
+    version9 = "";
+    attribute = "attribute";
+    varyingVs = "varying";
+    varyingFs = "varying";
+    texture2D = "texture2D";
+    output = "gl_FragColor";
+    defineOutput = "";
+    defineSpecialNaN = `
+      #define isnan(value) isnan_custom(value)
+      bool isnan_custom(float val) {
+        return (val > 0. || val < 1. || val == 0.) ? false : true;
+      }
+      bvec4 isnan_custom(vec4 val) {
+        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
+      }
+    `;
+    defineSpecialInf = `
+      uniform float INFINITY;
+
+      bool isinf(float val) {
+        return abs(val) == INFINITY;
+      }
+      bvec4 isinf(vec4 val) {
+        return equal(abs(val), vec4(INFINITY));
+      }
+    `;
+    defineRound = `
+      int round(float value) {
+        return int(floor(value + 0.5));
+      }
+
+      ivec4 round(vec4 value) {
+        return ivec4(floor(value + vec4(0.5)));
+      }
+    `;
+  }
+  return {
+    version: version9,
+    attribute,
+    varyingVs,
+    varyingFs,
+    texture2D,
+    output,
+    defineOutput,
+    defineSpecialNaN,
+    defineSpecialInf,
+    defineRound
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js
+function getLogicalCoordinatesFromFlatIndex(coords3, shape, index = "index") {
+  const strides = util_exports.computeStrides(shape);
+  return strides.map((stride, i2) => {
+    const line1 = `int ${coords3[i2]} = ${index} / ${stride}`;
+    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${stride}` : `index -= ${coords3[i2]} * ${stride}`;
+    return `${line1}; ${line2};`;
+  }).join("");
+}
+function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index = "index") {
+  const strides = util_exports.computeStrides(shape);
+  return strides.map((_, i2) => {
+    const line1 = `int ${coords3[i2]} = ${index} / outShapeStrides[${i2}]`;
+    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * outShapeStrides[${i2}]` : `index -= ${coords3[i2]} * outShapeStrides[${i2}]`;
+    return `${line1}; ${line2};`;
+  }).join("");
+}
+function symbolicallyComputeStrides(indicesArr, variableName) {
+  const numCoords = indicesArr.length;
+  const shape = indicesArr.map((d) => `${variableName}[${d}]`);
+  const strides = new Array(numCoords - 1);
+  strides[numCoords - 2] = shape[numCoords - 1];
+  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
+    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;
+  }
+  return strides;
+}
+function getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index = "index") {
+  const indicesArray = coords3.map((_, i2) => i2);
+  const strides = symbolicallyComputeStrides(indicesArray, variableName);
+  return strides.map((_, i2) => {
+    const line1 = `int ${coords3[i2]} = ${index} / ${strides[i2]}`;
+    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${strides[i2]}` : `index -= ${coords3[i2]} * ${strides[i2]}`;
+    return `${line1}; ${line2};`;
+  }).join("");
+}
+function getFlatIndexFrom3D(shape) {
+  const strides = util_exports.computeStrides(shape).map((d) => d.toString());
+  return `
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
+  }
+`;
+}
+function getFlatIndexFrom3DOutput() {
+  return `
+  int getFlatIndex(ivec3 coords) {
+    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
+  }
+`;
+}
+var ENCODE_FLOAT_SNIPPET = `
+  const float FLOAT_MAX = 1.70141184e38;
+  const float FLOAT_MIN = 1.17549435e-38;
+
+  lowp vec4 encode_float(highp float v) {
+    if (isnan(v)) {
+      return vec4(255, 255, 255, 255);
+    }
+
+    highp float av = abs(v);
+
+    if(av < FLOAT_MIN) {
+      return vec4(0.0, 0.0, 0.0, 0.0);
+    } else if(v > FLOAT_MAX) {
+      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
+    } else if(v < -FLOAT_MAX) {
+      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
+    }
+
+    highp vec4 c = vec4(0,0,0,0);
+
+    highp float e = floor(log2(av));
+    highp float m = exp2(fract(log2(av))) - 1.0;
+
+    c[2] = floor(128.0 * m);
+    m -= c[2] / 128.0;
+    c[1] = floor(32768.0 * m);
+    m -= c[1] / 32768.0;
+    c[0] = floor(8388608.0 * m);
+
+    highp float ebias = e + 127.0;
+    c[3] = floor(ebias / 2.0);
+    ebias -= c[3] * 2.0;
+    c[2] += floor(ebias) * 128.0;
+
+    c[3] += 128.0 * step(0.0, -v);
+
+    return c / 255.0;
+  }
+`;
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js
+var { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;
+function makeShader(inputsInfo, outputShape, program) {
+  const prefixSnippets = [];
+  inputsInfo.forEach((x) => {
+    const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);
+    if (x.shapeInfo.isUniform) {
+      prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ""};`);
+    } else {
+      prefixSnippets.push(`uniform sampler2D ${x.name};`);
+      prefixSnippets.push(`uniform int offset${x.name};`);
+    }
+    if (program.enableShapeUniforms) {
+      const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);
+      switch (uniformShape.length) {
+        case 1:
+          prefixSnippets.push(`uniform int ${x.name}Shape;`);
+          break;
+        case 2:
+          prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);
+          break;
+        case 3:
+          prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);
+          break;
+        case 4:
+          prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);
+          break;
+        default:
+          break;
+      }
+      prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);
+    }
+  });
+  if (program.enableShapeUniforms) {
+    switch (outputShape.logicalShape.length) {
+      case 1:
+        prefixSnippets.push(`uniform int outShape;`);
+        break;
+      case 2:
+        prefixSnippets.push(`uniform ivec2 outShape;`);
+        prefixSnippets.push(`uniform int outShapeStrides;`);
+        break;
+      case 3:
+        prefixSnippets.push(`uniform ivec3 outShape;`);
+        prefixSnippets.push(`uniform ivec2 outShapeStrides;`);
+        break;
+      case 4:
+        prefixSnippets.push(`uniform ivec4 outShape;`);
+        prefixSnippets.push(`uniform ivec3 outShapeStrides;`);
+        break;
+      default:
+        break;
+    }
+    prefixSnippets.push(`uniform ivec2 outTexShape;`);
+  }
+  if (program.customUniforms) {
+    program.customUniforms.forEach((d) => {
+      prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
+    });
+  }
+  const inputPrefixSnippet = prefixSnippets.join("\n");
+  const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join("\n");
+  const outTexShape = outputShape.texShape;
+  const glsl = getGlslDifferences();
+  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
+  let outputSamplingSnippet;
+  let floatTextureSetOutputSnippet;
+  let shaderPrefix = getShaderPrefix(glsl);
+  if (outputShape.isPacked) {
+    outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
+    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
+  } else {
+    outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
+    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
+  }
+  if (program.packedInputs) {
+    shaderPrefix += SHADER_PACKED_PREFIX;
+  }
+  const source = [
+    shaderPrefix,
+    floatTextureSampleSnippet,
+    floatTextureSetOutputSnippet,
+    inputPrefixSnippet,
+    outputSamplingSnippet,
+    inputSamplingSnippet,
+    program.userCode
+  ].join("\n");
+  return source;
+}
+function getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {
+  const shape = inInfo.shapeInfo.logicalShape;
+  switch (shape.length) {
+    case 0:
+      return getSamplerScalar(inInfo, enableShapeUniforms);
+    case 1:
+      return getSampler1D(inInfo, enableShapeUniforms);
+    case 2:
+      return getSampler2D(inInfo, enableShapeUniforms);
+    case 3:
+      return getSampler3D(inInfo, enableShapeUniforms);
+    case 4:
+      return getSampler4D(inInfo, enableShapeUniforms);
+    case 5:
+      return getSampler5D(inInfo);
+    case 6:
+      return getSampler6D(inInfo);
+    default:
+      throw new Error(`${shape.length}-D input sampling is not yet supported`);
+  }
+}
+function getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {
+  const shape = inInfo.shapeInfo.logicalShape;
+  switch (shape.length) {
+    case 0:
+      return getPackedSamplerScalar(inInfo);
+    case 1:
+      return getPackedSampler1D(inInfo, enableShapeUniforms);
+    case 2:
+      return getPackedSampler2D(inInfo, enableShapeUniforms);
+    case 3:
+      return getPackedSampler3D(inInfo, enableShapeUniforms);
+    default:
+      return getPackedSamplerND(inInfo, enableShapeUniforms);
+  }
+}
+function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {
+  let res = "";
+  if (usesPackedTextures) {
+    res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);
+  } else {
+    res += getSamplerFromInInfo(inInfo, enableShapeUniforms);
+  }
+  const inShape = inInfo.shapeInfo.logicalShape;
+  const outShape = outShapeInfo.logicalShape;
+  if (inShape.length <= outShape.length) {
+    if (usesPackedTextures) {
+      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
+    } else {
+      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
+    }
+  }
+  return res;
+}
+function getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
+  switch (outShape.length) {
+    case 0:
+      return getOutputScalarCoords();
+    case 1:
+      return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 2:
+      return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 3:
+      return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);
+    default:
+      return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);
+  }
+}
+function getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
+  switch (outShape.length) {
+    case 0:
+      return getOutputScalarCoords();
+    case 1:
+      return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 2:
+      return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 3:
+      return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 4:
+      return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);
+    case 5:
+      return getOutput5DCoords(outShape, outTexShape);
+    case 6:
+      return getOutput6DCoords(outShape, outTexShape);
+    default:
+      throw new Error(`${outShape.length}-D output sampling is not yet supported`);
+  }
+}
+function getFloatTextureSampleSnippet(glsl) {
+  return `
+    float sampleTexture(sampler2D textureSampler, vec2 uv) {
+      return ${glsl.texture2D}(textureSampler, uv).r;
+    }
+  `;
+}
+function getFloatTextureSetRSnippet(glsl) {
+  return `
+    void setOutput(float val) {
+      ${glsl.output} = vec4(val, 0, 0, 0);
+    }
+  `;
+}
+function getFloatTextureSetRGBASnippet(glsl) {
+  return `
+    void setOutput(vec4 val) {
+      ${glsl.output} = val;
+    }
+  `;
+}
+function getShaderPrefix(glsl) {
+  const SHADER_PREFIX = `${glsl.version}
+    precision highp float;
+    precision highp int;
+    precision highp sampler2D;
+    ${glsl.varyingFs} vec2 resultUV;
+    ${glsl.defineOutput}
+    const vec2 halfCR = vec2(0.5, 0.5);
+
+    struct ivec5
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+    };
+
+    struct ivec6
+    {
+      int x;
+      int y;
+      int z;
+      int w;
+      int u;
+      int v;
+    };
+
+    uniform float NAN;
+    ${glsl.defineSpecialNaN}
+    ${glsl.defineSpecialInf}
+    ${glsl.defineRound}
+
+    int imod(int x, int y) {
+      return x - y * (x / y);
+    }
+
+    int idiv(int a, int b, float sign) {
+      int res = a / b;
+      int mod = imod(a, b);
+      if (sign < 0. && mod != 0) {
+        res -= 1;
+      }
+      return res;
+    }
+
+    //Based on the work of Dave Hoskins
+    //https://www.shadertoy.com/view/4djSRW
+    #define HASHSCALE1 443.8975
+    float random(float seed){
+      vec2 p = resultUV * seed;
+      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
+      p3 += dot(p3, p3.yzx + 19.19);
+      return fract((p3.x + p3.y) * p3.z);
+    }
+
+    ${SAMPLE_1D_SNIPPET}
+    ${SAMPLE_2D_SNIPPET}
+    ${SAMPLE_3D_SNIPPET}
+  `;
+  return SHADER_PREFIX;
+}
+var SAMPLE_1D_SNIPPET = `
+vec2 uvFromFlat(int texNumR, int texNumC, int index) {
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
+  int texelIndex = index / 2;
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`;
+var SAMPLE_2D_SNIPPET = `
+vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
+  int texNumC, int row, int col) {
+  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = texelIndex / texNumC;
+  int texC = texelIndex - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`;
+var SAMPLE_3D_SNIPPET = `
+vec2 packedUVfrom3D(int texNumR, int texNumC,
+    int texelsInBatch, int texelsInLogicalRow, int b,
+    int row, int col) {
+  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
+  int texR = index / texNumC;
+  int texC = index - texR * texNumC;
+  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
+}
+`;
+var SHADER_PACKED_PREFIX = `
+  float getChannel(vec4 frag, vec2 innerDims) {
+    vec2 modCoord = mod(innerDims, 2.);
+    return modCoord.x == 0. ?
+      (modCoord.y == 0. ? frag.r : frag.g) :
+      (modCoord.y == 0. ? frag.b : frag.a);
+  }
+  float getChannel(vec4 frag, int dim) {
+    float modCoord = mod(float(dim), 2.);
+    return modCoord == 0. ? frag.r : frag.g;
+  }
+`;
+function getOutputScalarCoords() {
+  return `
+    int getOutputCoords() {
+      return 0;
+    }
+  `;
+}
+function getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  if (packedTexShape[0] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
+      }
+    `;
+  }
+  if (packedTexShape[1] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    int getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
+    }
+  `;
+  }
+  return `
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
+    }
+  `;
+}
+function getOutput1DCoords(shape, texShape, enableShapeUniforms) {
+  if (texShape[0] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return int(resultUV.x * float(outTexShape[1]));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return int(resultUV.x * ${texShape[1]}.0);
+      }
+    `;
+  }
+  if (texShape[1] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      int getOutputCoords() {
+        return int(resultUV.y * float(outTexShape[0]));
+      }
+    `;
+    }
+    return `
+      int getOutputCoords() {
+        return int(resultUV.y * ${texShape[0]}.0);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(outTexShape[0], outTexShape[1]));
+      return resTexRC.x * outTexShape[1] + resTexRC.y;
+    }
+  `;
+  }
+  return `
+    int getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      return resTexRC.x * ${texShape[1]} + resTexRC.y;
+    }
+  `;
+}
+function getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    return `
+    ivec3 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
+      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+
+      int b = index / texelsInBatch;
+      index -= b * texelsInBatch;
+
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const texelsInLogicalRow = Math.ceil(shape[2] / 2);
+  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
+  return `
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
+
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec3(b, r, c);
+    }
+  `;
+}
+function getOutput3DCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], shape);
+    return `
+  ivec3 getOutputCoords() {
+    ivec2 resTexRC = ivec2(resultUV.yx *
+                           vec2(outTexShape[0], outTexShape[1]));
+    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+    ${coordsFromIndexSnippet2}
+    return ivec3(r, c, d);
+  }
+`;
+  }
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
+  return `
+    ivec3 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
+      return ivec3(r, c, d);
+    }
+  `;
+}
+function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    return `
+    ivec4 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+
+      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
+      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
+      int texelsInBatchN = texelsInBatch * outShape[1];
+
+      int b2 = index / texelsInBatchN;
+      index -= b2 * texelsInBatchN;
+
+      int b = index / texelsInBatch;
+      index -= b * texelsInBatch;
+
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec4(b2, b, r, c);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
+  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
+  let texelsInBatchN = texelsInBatch;
+  let batches = ``;
+  let coords3 = "b, r, c";
+  for (let b = 2; b < shape.length - 1; b++) {
+    texelsInBatchN *= shape[shape.length - b - 1];
+    batches = `
+      int b${b} = index / ${texelsInBatchN};
+      index -= b${b} * ${texelsInBatchN};
+    ` + batches;
+    coords3 = `b${b}, ` + coords3;
+  }
+  return `
+    ivec${shape.length} getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+
+      ${batches}
+
+      int b = index / ${texelsInBatch};
+      index -= b * ${texelsInBatch};
+
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec${shape.length}(${coords3});
+    }
+  `;
+}
+function getOutput4DCoords(shape, texShape, enableShapeUniforms) {
+  if (enableShapeUniforms) {
+    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d", "d2"], shape);
+    return `
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(outTexShape[0], outTexShape[1]));
+      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+      ${coordsFromIndexSnippet2}
+      return ivec4(r, c, d, d2);
+    }
+  `;
+  }
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2"], shape);
+  return `
+    ivec4 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      ${coordsFromIndexSnippet}
+      return ivec4(r, c, d, d2);
+    }
+  `;
+}
+function getOutput5DCoords(shape, texShape) {
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3"], shape);
+  return `
+    ivec5 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
+                             ${texShape[1]}));
+
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+
+      ${coordsFromIndexSnippet}
+
+      ivec5 outShape = ivec5(r, c, d, d2, d3);
+      return outShape;
+    }
+  `;
+}
+function getOutput6DCoords(shape, texShape) {
+  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3", "d4"], shape);
+  return `
+    ivec6 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+        vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+
+      ${coordsFromIndexSnippet}
+
+      ivec6 result = ivec6(r, c, d, d2, d3, d4);
+      return result;
+    }
+  `;
+}
+function getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  if (util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+      }
+    `;
+  }
+  const texelsInLogicalRow = Math.ceil(shape[1] / 2);
+  if (enableShapeUniforms) {
+    return `
+    ivec2 getOutputCoords() {
+      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
+      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(packedTexShape[0], packedTexShape[1]));
+
+      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
+      int r = 2 * (index / texelsInLogicalRow);
+      int c = imod(index, texelsInLogicalRow) * 2;
+
+      return ivec2(r, c);
+    }
+  `;
+  }
+  return `
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
+
+      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
+      int r = 2 * (index / ${texelsInLogicalRow});
+      int c = imod(index, ${texelsInLogicalRow}) * 2;
+
+      return ivec2(r, c);
+    }
+  `;
+}
+function getOutput2DCoords(shape, texShape, enableShapeUniforms) {
+  if (util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
+      }
+    `;
+  }
+  if (shape[1] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(outTexShape[0], outTexShape[1]));
+        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+        return ivec2(index, 0);
+      }
+    `;
+  }
+  if (shape[0] === 1) {
+    if (enableShapeUniforms) {
+      return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(outTexShape[0], outTexShape[1]));
+        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `;
+    }
+    return `
+      ivec2 getOutputCoords() {
+        ivec2 resTexRC = ivec2(resultUV.yx *
+                               vec2(${texShape[0]}, ${texShape[1]}));
+        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+        return ivec2(0, index);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(outTexShape[0], outTexShape[1]));
+      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
+      int r = index / outShape[1];
+      int c = index - r * outShape[1];
+      return ivec2(r, c);
+    }
+  `;
+  }
+  return `
+    ivec2 getOutputCoords() {
+      ivec2 resTexRC = ivec2(resultUV.yx *
+                             vec2(${texShape[0]}, ${texShape[1]}));
+      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
+      int r = index / ${shape[1]};
+      int c = index - r * ${shape[1]};
+      return ivec2(r, c);
+    }
+  `;
+}
+function getFlatOffsetUniformName(texName) {
+  return `offset${texName}`;
+}
+function getPackedSamplerScalar(inputInfo) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const glsl = getGlslDifferences();
+  return `
+    vec4 ${funcName}() {
+      return ${glsl.texture2D}(${texName}, halfCR);
+    }
+  `;
+}
+function getSamplerScalar(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  if (inputInfo.shapeInfo.isUniform) {
+    return `float ${funcName}() {return ${texName};}`;
+  }
+  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;
+  if (texNumR === 1 && texNumC === 1) {
+    return `
+      float ${funcName}() {
+        return sampleTexture(${texName}, halfCR);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;
+  return `
+    float ${funcName}() {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getPackedSampler1D(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  const glsl = getGlslDifferences();
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int index) {
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      vec2 uv = packedUVfrom1D(
+        packedTexShape[0], packedTexShape[1], index);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  return `
+    vec4 ${funcName}(int index) {
+      vec2 uv = packedUVfrom1D(
+        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler1D(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int index) {
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const texShape = inputInfo.shapeInfo.texShape;
+  const tNumR = texShape[0];
+  const tNumC = texShape[1];
+  if (tNumC === 1 && tNumR === 1) {
+    return `
+      float ${funcName}(int index) {
+        return sampleTexture(${texName}, halfCR);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (tNumC === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (tNumR === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int index) {
+        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  return `
+    float ${funcName}(int index) {
+      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getPackedSampler2D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const glsl = getGlslDifferences();
+  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+
+        return ${glsl.texture2D}(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      vec4 ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+
+        return ${glsl.texture2D}(${texName}, uv);
+      }
+    `;
+  }
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int row, int col) {
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));
+      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const valuesPerRow = Math.ceil(shape[1] / 2);
+  return `
+    vec4 ${funcName}(int row, int col) {
+      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler2D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col) {
+        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    const texNumR2 = texShape[0];
+    const texNumC2 = texShape[1];
+    return `
+    float ${funcName}(int row, int col) {
+      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  const squeezedShape = newShape;
+  if (squeezedShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+    const params = ["row", "col"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+      float ${funcName}(int row, int col) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col) {
+        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const offset = getFlatOffsetUniformName(texName);
+  if (texNumC === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col) {
+        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
+        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  if (texNumR === 1) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col) {
+        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
+        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+    float ${funcName}(int row, int col) {
+      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
+      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  if (enableShapeUniforms) {
+    return `
+      float ${funcName}(int row, int col) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${texName}Shape[1] + col + ${offset};
+        vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  return `
+  float ${funcName}(int row, int col) {
+    // Explicitly use integer operations as dot() only works on floats.
+    int index = row * ${shape[1]} + col + ${offset};
+    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+    return sampleTexture(${texName}, uv);
+  }
+`;
+}
+function getPackedSampler3D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const texShape = inputInfo.shapeInfo.texShape;
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  if (shape[0] === 1) {
+    const squeezedShape = shape.slice(1);
+    const keptDims = [1, 2];
+    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+    const params = ["b", "row", "col"];
+    return `
+        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+        vec4 ${funcName}(int b, int row, int col) {
+          return ${funcName}(${getSqueezedParams(params, keptDims)});
+        }
+      `;
+  }
+  const glsl = getGlslDifferences();
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int b, int row, int col) {
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));
+      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));
+      vec2 uv = packedUVfrom3D(
+        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const texNumR = packedTexShape[0];
+  const texNumC = packedTexShape[1];
+  const valuesPerRow = Math.ceil(shape[2] / 2);
+  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
+  return `
+    vec4 ${funcName}(int b, int row, int col) {
+      vec2 uv = packedUVfrom3D(
+        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler3D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const stride0 = shape[1] * shape[2];
+  const stride1 = shape[2];
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  const squeezedShape = newShape;
+  if (squeezedShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+    const params = ["row", "col", "depth"];
+    return `
+        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+        float ${funcName}(int row, int col, int depth) {
+          return ${funcName}(${getSqueezedParams(params, keptDims)});
+        }
+      `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth) {
+        int index = round(dot(vec3(row, col, depth),
+                          vec3(${stride0}, ${stride1}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  if (texNumC === stride0 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth) {
+        int stride1 = ${texName}Shape[2];
+        float texR = float(row);
+        float texC = dot(vec2(col, depth), vec2(stride1, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+        float ${funcName}(int row, int col, int depth) {
+          float texR = float(row);
+          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
+          vec2 uv = (vec2(texC, texR) + halfCR) /
+                     vec2(${texNumC}.0, ${texNumR}.0);
+          return sampleTexture(${texName}, uv);
+        }
+      `;
+  }
+  if (texNumC === stride1 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth) {
+        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));
+        float texC = float(depth);
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+    float ${funcName}(int row, int col, int depth) {
+      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
+      float texC = float(depth);
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}(int row, int col, int depth) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];
+      int stride1 = ${texName}Shape[2];
+      int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
+      return sampleTexture(${texName}, uv);
+    }
+    `;
+  }
+  return `
+      float ${funcName}(int row, int col, int depth) {
+        // Explicitly use integer operations as dot() only works on floats.
+        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
+        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+        return sampleTexture(${texName}, uv);
+      }
+  `;
+}
+function getPackedSamplerND(inputInfo, enableShapeUniforms) {
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const glsl = getGlslDifferences();
+  if (enableShapeUniforms) {
+    return `
+    vec4 ${funcName}(int b2, int b, int row, int col) {
+      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));
+      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));
+      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
+      texelsInBatch *= ${texName}Shape[1];
+      index = b2 * texelsInBatch + index;
+      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
+      int texR = index / packedTexShape[1];
+      int texC = index - texR * packedTexShape[1];
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+  }
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const rank = shape.length;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+  const texNumR = packedTexShape[0];
+  const texNumC = packedTexShape[1];
+  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);
+  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
+  let params = `int b, int row, int col`;
+  let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;
+  for (let b = 2; b < rank - 1; b++) {
+    params = `int b${b}, ` + params;
+    texelsInBatch *= shape[rank - b - 1];
+    index = `b${b} * ${texelsInBatch} + ` + index;
+  }
+  return `
+    vec4 ${funcName}(${params}) {
+      int index = ${index};
+      int texR = index / ${texNumC};
+      int texC = index - texR * ${texNumC};
+      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
+      return ${glsl.texture2D}(${texName}, uv);
+    }
+  `;
+}
+function getSampler4D(inputInfo, enableShapeUniforms) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const stride2 = shape[3];
+  const stride1 = shape[2] * stride2;
+  const stride0 = shape[1] * stride1;
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  if (newShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
+    const params = ["row", "col", "depth", "depth2"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        int index = round(dot(vec4(row, col, depth, depth2),
+                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  const stride2Str = `int stride2 = ${texName}Shape[3];`;
+  const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;
+  const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;
+  if (texNumC === stride0 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        ${stride2Str}
+        ${stride1Str}
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(stride1, stride2, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = float(row);
+        float texC =
+            dot(vec3(col, depth, depth2),
+                vec3(${stride1}, ${stride2}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (texNumC === stride2 && flatOffset == null) {
+    if (enableShapeUniforms) {
+      return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+    }
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2) {
+        float texR = dot(vec3(row, col, depth),
+                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));
+        float texC = float(depth2);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  if (enableShapeUniforms) {
+    return `
+    float ${funcName}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      ${stride2Str}
+      ${stride1Str}
+      ${stride0Str}
+      int index = row * stride0 + col * stride1 +
+          depth * stride2 + depth2;
+      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+  }
+  return `
+    float ${funcName}(int row, int col, int depth, int depth2) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} +
+          depth * ${stride2} + depth2;
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getSampler5D(inputInfo) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const stride3 = shape[4];
+  const stride2 = shape[3] * stride3;
+  const stride1 = shape[2] * stride2;
+  const stride0 = shape[1] * stride1;
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  if (newShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
+    const params = ["row", "col", "depth", "depth2", "depth3"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        float index = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
+          depth3;
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  if (texNumC === stride0 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (texNumC === stride3 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+        float texR = dot(
+          vec4(row, col, depth, depth2),
+          vec4(${shape[1] * shape[2] * shape[3]},
+               ${shape[2] * shape[3]}, ${shape[3]}, 1));
+        int texC = depth3;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  return `
+    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getSampler6D(inputInfo) {
+  const shape = inputInfo.shapeInfo.logicalShape;
+  const texName = inputInfo.name;
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  if (newShape.length < shape.length) {
+    const newInputInfo = squeezeInputInfo(inputInfo, newShape);
+    const params = ["row", "col", "depth", "depth2", "depth3", "depth4"];
+    return `
+      ${getSamplerFromInInfo(newInputInfo)}
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        return ${funcName}(${getSqueezedParams(params, keptDims)});
+      }
+    `;
+  }
+  const stride4 = shape[5];
+  const stride3 = shape[4] * stride4;
+  const stride2 = shape[3] * stride3;
+  const stride1 = shape[2] * stride2;
+  const stride0 = shape[1] * stride1;
+  if (inputInfo.shapeInfo.isUniform) {
+    return `
+      float ${funcName}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+        int index = round(dot(
+          vec4(row, col, depth, depth2),
+          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
+          dot(
+            vec2(depth3, depth4),
+            vec2(${stride4}, 1)));
+        ${getUniformSampler(inputInfo)}
+      }
+    `;
+  }
+  const flatOffset = inputInfo.shapeInfo.flatOffset;
+  const texShape = inputInfo.shapeInfo.texShape;
+  const texNumR = texShape[0];
+  const texNumC = texShape[1];
+  if (texNumC === stride0 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        int texR = row;
+        float texC = dot(vec4(col, depth, depth2, depth3),
+          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
+               float(depth4);
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                   vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  if (texNumC === stride4 && flatOffset == null) {
+    return `
+      float ${funcName}(int row, int col, int depth,
+                    int depth2, int depth3, int depth4) {
+        float texR = dot(vec4(row, col, depth, depth2),
+          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},
+               ${shape[2] * shape[3] * shape[4]},
+               ${shape[3] * shape[4]},
+               ${shape[4]})) + float(depth3);
+        int texC = depth4;
+        vec2 uv = (vec2(texC, texR) + halfCR) /
+                  vec2(${texNumC}.0, ${texNumR}.0);
+        return sampleTexture(${texName}, uv);
+      }
+    `;
+  }
+  const offset = getFlatOffsetUniformName(texName);
+  return `
+    float ${funcName}(int row, int col, int depth,
+                  int depth2, int depth3, int depth4) {
+      // Explicitly use integer operations as dot() only works on floats.
+      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
+          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
+      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
+      return sampleTexture(${texName}, uv);
+    }
+  `;
+}
+function getUniformSampler(inputInfo) {
+  const texName = inputInfo.name;
+  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+  if (inSize < 2) {
+    return `return ${texName};`;
+  }
+  return `
+    for (int i = 0; i < ${inSize}; i++) {
+      if (i == index) {
+        return ${texName}[i];
+      }
+    }
+  `;
+}
+function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
+  const texName = inputInfo.name;
+  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+  const funcName = "get" + texFuncSnippet + "AtOutCoords";
+  const inRank = inputInfo.shapeInfo.logicalShape.length;
+  const outRank = outShapeInfo.logicalShape.length;
+  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+  const type = getCoordsDataType(outRank);
+  const rankDiff = outRank - inRank;
+  let coordsSnippet;
+  const fields = ["x", "y", "z", "w", "u", "v"];
+  if (inRank === 0) {
+    coordsSnippet = "";
+  } else if (outRank < 2 && broadcastDims.length >= 1) {
+    coordsSnippet = "coords = 0;";
+  } else {
+    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
+  }
+  let unpackedCoordsSnippet = "";
+  if (outRank < 2 && inRank > 0) {
+    unpackedCoordsSnippet = "coords";
+  } else {
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+  }
+  let output = `return outputValue;`;
+  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+  const isInputScalar = inSize === 1;
+  const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);
+  const isOutputScalar = outSize === 1;
+  if (inRank === 1 && !isInputScalar && !isOutputScalar) {
+    output = `
+      return vec4(outputValue.xy, outputValue.xy);
+    `;
+  } else if (isInputScalar && !isOutputScalar) {
+    if (outRank === 1) {
+      output = `
+        return vec4(outputValue.x, outputValue.x, 0., 0.);
+      `;
+    } else {
+      output = `
+        return vec4(outputValue.x);
+      `;
+    }
+  } else if (broadcastDims.length) {
+    const rows = inRank - 2;
+    const cols = inRank - 1;
+    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
+      output = `return vec4(outputValue.x);`;
+    } else if (broadcastDims.indexOf(rows) > -1) {
+      output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;
+    } else if (broadcastDims.indexOf(cols) > -1) {
+      output = `return vec4(outputValue.xx, outputValue.zz);`;
+    }
+  }
+  return `
+    vec4 ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
+      ${output}
+    }
+  `;
+}
+function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
+  const texName = inputInfo.name;
+  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+  const funcName = "get" + texFuncSnippet + "AtOutCoords";
+  const outTexShape = outShapeInfo.texShape;
+  const inTexShape = inputInfo.shapeInfo.texShape;
+  const inRank = inputInfo.shapeInfo.logicalShape.length;
+  const outRank = outShapeInfo.logicalShape.length;
+  if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {
+    return `
+      float ${funcName}() {
+        return sampleTexture(${texName}, resultUV);
+      }
+    `;
+  }
+  const type = getCoordsDataType(outRank);
+  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+  const rankDiff = outRank - inRank;
+  let coordsSnippet;
+  const fields = ["x", "y", "z", "w", "u", "v"];
+  if (inRank === 0) {
+    coordsSnippet = "";
+  } else if (outRank < 2 && broadcastDims.length >= 1) {
+    coordsSnippet = "coords = 0;";
+  } else {
+    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
+  }
+  let unpackedCoordsSnippet = "";
+  if (outRank < 2 && inRank > 0) {
+    unpackedCoordsSnippet = "coords";
+  } else {
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+  }
+  return `
+    float ${funcName}() {
+      ${type} coords = getOutputCoords();
+      ${coordsSnippet}
+      return get${texFuncSnippet}(${unpackedCoordsSnippet});
+    }
+  `;
+}
+function getCoordsDataType(rank) {
+  if (rank <= 1) {
+    return "int";
+  } else if (rank === 2) {
+    return "ivec2";
+  } else if (rank === 3) {
+    return "ivec3";
+  } else if (rank === 4) {
+    return "ivec4";
+  } else if (rank === 5) {
+    return "ivec5";
+  } else if (rank === 6) {
+    return "ivec6";
+  } else {
+    throw Error(`GPU for rank ${rank} is not yet supported`);
+  }
+}
+function getUniformInfoFromShape(isPacked, shape, texShape) {
+  const { newShape, keptDims } = util_exports.squeezeShape(shape);
+  const rank = shape.length;
+  const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;
+  const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;
+  const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;
+  const uniformShape = useSqueezeShape ? squeezeShape2 : shape;
+  return { useSqueezeShape, uniformShape, keptDims };
+}
+function squeezeInputInfo(inInfo, squeezedShape) {
+  const newInputInfo = JSON.parse(JSON.stringify(inInfo));
+  newInputInfo.shapeInfo.logicalShape = squeezedShape;
+  return newInputInfo;
+}
+function getSqueezedParams(params, keptDims) {
+  return keptDims.map((d) => params[d]).join(", ");
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js
+function compileProgram(gpgpu, program, inputs, output) {
+  const inputInfos = inputs.map((input2, i2) => {
+    const shapeInfo = {
+      logicalShape: input2.shape,
+      texShape: input2.isUniform ? null : input2.texData.texShape,
+      isUniform: input2.isUniform,
+      isPacked: input2.isUniform ? false : input2.texData.isPacked,
+      flatOffset: null
+    };
+    if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {
+      shapeInfo.flatOffset = input2.texData.slice.flatOffset;
+    }
+    return { name: program.variableNames[i2], shapeInfo };
+  });
+  const inShapeInfos = inputInfos.map((x) => x.shapeInfo);
+  const outShapeInfo = {
+    logicalShape: output.shape,
+    texShape: output.texData.texShape,
+    isUniform: false,
+    isPacked: output.texData.isPacked,
+    flatOffset: null
+  };
+  const source = makeShader(inputInfos, outShapeInfo, program);
+  const fragmentShader = createFragmentShader(gpgpu.gl, source);
+  const webGLProgram = gpgpu.createProgram(fragmentShader);
+  if (!env().get("ENGINE_COMPILE_ONLY")) {
+    return Object.assign({
+      program,
+      fragmentShader,
+      source,
+      webGLProgram,
+      inShapeInfos,
+      outShapeInfo
+    }, getUniformLocations(gpgpu, program, webGLProgram));
+  } else {
+    return {
+      program,
+      fragmentShader,
+      source,
+      webGLProgram,
+      inShapeInfos,
+      outShapeInfo,
+      uniformLocations: null,
+      customUniformLocations: null,
+      infLoc: null,
+      nanLoc: null,
+      inShapesLocations: null,
+      inTexShapesLocations: null,
+      outShapeLocation: null,
+      outShapeStridesLocation: null,
+      outTexShapeLocation: null
+    };
+  }
+}
+function getUniformLocations(gpgpu, program, webGLProgram) {
+  const uniformLocations = {};
+  const inShapesLocations = {};
+  const inTexShapesLocations = {};
+  const customUniformLocations = [];
+  let outShapeLocation;
+  let outTexShapeLocation;
+  let outShapeStridesLocation;
+  let infLoc = null;
+  let nanLoc = null;
+  nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
+  if (env().getNumber("WEBGL_VERSION") === 1) {
+    infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
+  }
+  const shouldThrow = false;
+  for (let i2 = 0; i2 < program.variableNames.length; i2++) {
+    const varName = program.variableNames[i2];
+    uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
+    uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);
+    if (program.enableShapeUniforms) {
+      inShapesLocations[`${varName}Shape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);
+      inTexShapesLocations[`${varName}TexShape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);
+    }
+  }
+  if (program.enableShapeUniforms) {
+    outShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outShape", shouldThrow);
+    outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, "outShapeStrides", shouldThrow);
+    outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow);
+  }
+  if (program.customUniforms) {
+    program.customUniforms.forEach((d, i2) => {
+      customUniformLocations[i2] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
+    });
+  }
+  return {
+    uniformLocations,
+    customUniformLocations,
+    infLoc,
+    nanLoc,
+    inShapesLocations,
+    inTexShapesLocations,
+    outShapeLocation,
+    outShapeStridesLocation,
+    outTexShapeLocation
+  };
+}
+function validateBinaryAndProgram(shapeInfos, inputs) {
+  if (shapeInfos.length !== inputs.length) {
+    throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);
+  }
+  shapeInfos.forEach((s2, i2) => {
+    const shapeA = s2.logicalShape;
+    const input2 = inputs[i2];
+    const shapeB = input2.shape;
+    if (!util_exports.arraysEqual(shapeA, shapeB)) {
+      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);
+    }
+    if (s2.isUniform && input2.isUniform) {
+      return;
+    }
+    const texShapeA = s2.texShape;
+    const texShapeB = input2.isUniform ? null : input2.texData.texShape;
+    if (!util_exports.arraysEqual(texShapeA, texShapeB)) {
+      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);
+    }
+  });
+}
+function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
+  if (!binary.program.enableShapeUniforms) {
+    validateBinaryAndProgram(binary.inShapeInfos, inputs);
+    validateBinaryAndProgram([binary.outShapeInfo], [output]);
+  }
+  const outTex = output.texData.texture;
+  const outTexShape = output.texData.texShape;
+  if (output.texData.isPacked) {
+    gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
+  } else {
+    gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
+  }
+  gpgpu.setProgram(binary.webGLProgram);
+  if (env().getNumber("WEBGL_VERSION") === 1) {
+    if (binary.infLoc !== null) {
+      gpgpu.gl.uniform1f(binary.infLoc, Infinity);
+    }
+  }
+  if (binary.nanLoc !== null) {
+    gpgpu.gl.uniform1f(binary.nanLoc, NaN);
+  }
+  inputs.forEach((input2, i2) => {
+    const varName = binary.program.variableNames[i2];
+    const varLoc = binary.uniformLocations[varName];
+    const varOffsetLoc = binary.uniformLocations[`offset${varName}`];
+    const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];
+    const varTexShapeLoc = binary.inTexShapesLocations[`${varName}TexShape`];
+    if (varShapeLoc) {
+      const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);
+      switch (uniformShape.length) {
+        case 1:
+          gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        case 2:
+          gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        case 3:
+          gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        case 4:
+          gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));
+          break;
+        default:
+          break;
+      }
+    }
+    if (varTexShapeLoc) {
+      gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);
+    }
+    if (varLoc == null) {
+      return;
+    }
+    if (input2.isUniform) {
+      if (util_exports.sizeFromShape(input2.shape) < 2) {
+        gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);
+      } else {
+        let vals = input2.uniformValues;
+        if (!(vals instanceof Float32Array)) {
+          vals = new Float32Array(vals);
+        }
+        gpgpu.gl.uniform1fv(varLoc, vals);
+      }
+      return;
+    }
+    if (input2.texData.slice != null && varOffsetLoc != null) {
+      gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);
+    }
+    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i2);
+  });
+  const outShapeLoc = binary.outShapeLocation;
+  if (outShapeLoc) {
+    switch (output.shape.length) {
+      case 1:
+        gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      case 2:
+        gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      case 3:
+        gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      case 4:
+        gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));
+        break;
+      default:
+        break;
+    }
+  }
+  if (binary.outShapeStridesLocation) {
+    const strides = util_exports.computeStrides(output.shape);
+    switch (output.shape.length) {
+      case 2:
+        gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));
+        break;
+      case 3:
+        gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));
+        break;
+      case 4:
+        gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));
+        break;
+      default:
+        break;
+    }
+  }
+  if (binary.outTexShapeLocation) {
+    gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);
+  }
+  if (binary.program.customUniforms && customUniformValues) {
+    binary.program.customUniforms.forEach((d, i2) => {
+      const customLoc = binary.customUniformLocations[i2];
+      const customValue = customUniformValues[i2];
+      if (d.type === "float") {
+        gpgpu.gl.uniform1fv(customLoc, customValue);
+      } else if (d.type === "vec2") {
+        gpgpu.gl.uniform2fv(customLoc, customValue);
+      } else if (d.type === "vec3") {
+        gpgpu.gl.uniform3fv(customLoc, customValue);
+      } else if (d.type === "vec4") {
+        gpgpu.gl.uniform4fv(customLoc, customValue);
+      } else if (d.type === "int") {
+        gpgpu.gl.uniform1iv(customLoc, customValue);
+      } else if (d.type === "ivec2") {
+        gpgpu.gl.uniform2iv(customLoc, customValue);
+      } else if (d.type === "ivec3") {
+        gpgpu.gl.uniform3iv(customLoc, customValue);
+      } else if (d.type === "ivec4") {
+        gpgpu.gl.uniform4iv(customLoc, customValue);
+      } else {
+        throw Error(`uniform type ${d.type} is not supported yet.`);
+      }
+    });
+  }
+  gpgpu.executeProgram();
+}
+function makeShaderKey(program, inputs, output) {
+  let keyInputs = "";
+  inputs.concat(output).forEach((x) => {
+    const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;
+    if (program.enableShapeUniforms && !x.isUniform) {
+      const xTexShape = x.texData.texShape;
+      const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);
+      let rank1 = "", rank2 = "", rank34 = "";
+      if (uniformShape.length === 1 && program.packedInputs) {
+        const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];
+        rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;
+      } else if (uniformShape.length === 2 && !program.packedInputs) {
+        rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;
+      } else if (uniformShape.length > 2 && !program.packedInputs) {
+        const strides = util_exports.computeStrides(uniformShape);
+        rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;
+      }
+      const xRank = x.shape.length;
+      const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);
+      const isScalar = util_exports.sizeFromShape(x.shape) === 1;
+      const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);
+      const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);
+      const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? "" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;
+      keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : ""}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;
+    } else {
+      const texShape = x.isUniform ? "uniform" : x.texData.texShape;
+      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;
+    }
+  });
+  const keyUserCode = program.userCode;
+  let key = program.constructor.name;
+  key += "_" + keyInputs + "_" + keyUserCode + `${env().getNumber("WEBGL_VERSION")}`;
+  return key;
+}
+function useShapeUniforms(rank) {
+  return env().getBool("WEBGL_USE_SHAPES_UNIFORMS") && rank <= 4;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js
+var DecodeMatrixProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    this.outPackingScheme = PackingScheme.DENSE;
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
+        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getA(rc.x, rc.y, rc.z);
+        }
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js
+var DecodeMatrixPackedProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outPackingScheme = PackingScheme.DENSE;
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      ivec3 outCoordsFromFlatIndex(int index) {
+        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
+        return ivec3(r, c, d);
+      }
+
+      void main() {
+        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
+        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
+
+        vec4 result = vec4(0.);
+
+        for (int i=0; i<4; i++) {
+          int flatIndex = index + i;
+          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
+          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
+        }
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js
+var EncodeFloatProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.outTexUsage = TextureUsage.DOWNLOAD;
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.userCode = `
+      ${ENCODE_FLOAT_SNIPPET}
+
+      void main() {
+        float x = getAAtOutCoords();
+        ${glsl.output} = encode_float(x);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js
+var EncodeFloatPackedProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = false;
+    this.outTexUsage = TextureUsage.DOWNLOAD;
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.userCode = `
+      ${ENCODE_FLOAT_SNIPPET}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
+        ${glsl.output} = encode_float(x);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js
+var EncodeMatrixProgram = class {
+  constructor(outputShape, inputIsUnsignedByte = false) {
+    this.variableNames = ["A"];
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    let output = `result`;
+    if (inputIsUnsignedByte) {
+      output = `floor(result * 255. + 0.5)`;
+    }
+    this.userCode = `
+      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 coords = getOutputCoords();
+
+        int flatIndex = getFlatIndex(coords);
+        int offset = imod(flatIndex, 4);
+
+        flatIndex = idiv(flatIndex, 4, 1.);
+
+        int r = flatIndex / texShape[1];
+        int c = imod(flatIndex, texShape[1]);
+        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
+        vec4 values = ${glsl.texture2D}(A, uv);
+
+        float result;
+
+        if(offset == 0) {
+          result = values[0];
+        } else if(offset == 1) {
+          result = values[1];
+        } else if(offset == 2) {
+          result = values[2];
+        } else {
+          result = values[3];
+        }
+
+        ${glsl.output} = vec4(${output}, 0., 0., 0.);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js
+var EncodeMatrixPackedProgram = class {
+  constructor(outputShape, inputIsUnsignedByte = false) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    this.customUniforms = [{ name: "texShape", type: "ivec2" }];
+    const glsl = getGlslDifferences();
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    let mainLoop = "";
+    let output = "result";
+    if (inputIsUnsignedByte) {
+      output = "floor(result * 255. + 0.5)";
+    }
+    for (let row = 0; row <= 1; row++) {
+      for (let col = 0; col <= 1; col++) {
+        const channel = row * 2 + col;
+        mainLoop += `
+          localCoords = coords;
+          if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? "outShape[2]" : `${outputShape[2]}`}) {
+          localCoords[2] += ${col};
+          if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? "outShape[1]" : `${outputShape[1]}`}) {
+            localCoords[1] += ${row};
+
+            flatIndex = getFlatIndex(localCoords);
+            offset = imod(flatIndex, 4);
+
+            flatIndex = idiv(flatIndex, 4, 1.);
+
+            int r = flatIndex / texShape[1];
+            int c = imod(flatIndex, texShape[1]);
+            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
+            values = ${glsl.texture2D}(A, uv);
+
+            if (offset == 0) {
+              result[${channel}] = values[0];
+            } else if (offset == 1) {
+              result[${channel}] = values[1];
+            } else if (offset == 2) {
+              result[${channel}] = values[2];
+            } else {
+              result[${channel}] = values[3];
+            }
+          }
+        }
+        `;
+      }
+    }
+    this.userCode = `
+        ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
+
+        void main() {
+          ivec3 coords = getOutputCoords();
+
+          vec4 result = vec4(0.);
+          int flatIndex, r, c, offset;
+          ivec3 localCoords;
+          vec2 uv;
+          vec4 values;
+
+          ${mainLoop}
+
+          ${glsl.output} = ${output};
+        }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js
+var gpgpu_util_exports = {};
+__export(gpgpu_util_exports, {
+  bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams,
+  createBufferFromOutputTexture: () => createBufferFromOutputTexture,
+  createFloat16MatrixTexture: () => createFloat16MatrixTexture,
+  createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture,
+  createFloat32MatrixTexture: () => createFloat32MatrixTexture,
+  createIndexBuffer: () => createIndexBuffer,
+  createPackedMatrixTexture: () => createPackedMatrixTexture,
+  createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture,
+  createVertexBuffer: () => createVertexBuffer,
+  createVertexShader: () => createVertexShader2,
+  downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture,
+  downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer,
+  downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture,
+  downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer,
+  getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture,
+  getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture,
+  getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture,
+  getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture,
+  getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture,
+  uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture,
+  uploadPixelDataToTexture: () => uploadPixelDataToTexture
+});
+function createVertexShader2(gl) {
+  const glsl = getGlslDifferences();
+  const vertexShaderSource = `${glsl.version}
+    precision highp float;
+    ${glsl.attribute} vec3 clipSpacePos;
+    ${glsl.attribute} vec2 uv;
+    ${glsl.varyingVs} vec2 resultUV;
+
+    void main() {
+      gl_Position = vec4(clipSpacePos, 1);
+      resultUV = uv;
+    }`;
+  return createVertexShader(gl, vertexShaderSource);
+}
+function createVertexBuffer(gl) {
+  const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
+  return createStaticVertexBuffer(gl, vertexArray);
+}
+function createIndexBuffer(gl) {
+  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
+  return createStaticIndexBuffer(gl, triangleVertexIndices);
+}
+function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
+  validateTextureSize(width, height);
+  const texture = createTexture(gl);
+  const tex2d = gl.TEXTURE_2D;
+  callAndCheck(gl, () => gl.bindTexture(tex2d, texture));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
+  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
+  if (env().getNumber("WEBGL_VERSION") === 1) {
+    callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));
+  } else {
+    callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));
+  }
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+  return { texture, texShape: [height, width] };
+}
+function getInternalFormatForFloat32MatrixTexture(textureConfig) {
+  return textureConfig.internalFormatFloat;
+}
+function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);
+}
+function getInternalFormatForFloat16MatrixTexture(textureConfig) {
+  return textureConfig.internalFormatHalfFloat;
+}
+function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
+}
+function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {
+  return textureConfig.downloadTextureFormat;
+}
+function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);
+}
+function getInternalFormatForPackedMatrixTexture(textureConfig) {
+  return textureConfig.internalFormatPackedFloat;
+}
+function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);
+}
+function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {
+  return textureConfig.internalFormatPackedHalfFloat;
+}
+function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
+  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);
+}
+function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
+  const posOffset = 0;
+  const uvOffset = 3 * 4;
+  const stride = 3 * 4 + 2 * 4;
+  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));
+  const success = bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset);
+  return success && bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset);
+}
+function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
+  let dataForUpload, texelDataType, internalFormat;
+  if (data instanceof Uint8Array) {
+    dataForUpload = new Uint8Array(width * height * 4);
+    texelDataType = gl.UNSIGNED_BYTE;
+    internalFormat = gl.RGBA;
+  } else {
+    dataForUpload = new Float32Array(width * height * 4);
+    texelDataType = gl.FLOAT;
+    internalFormat = textureConfig.internalFormatPackedFloat;
+  }
+  dataForUpload.set(data);
+  if (env().getNumber("WEBGL_VERSION") === 2) {
+    callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));
+  } else {
+    callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));
+  }
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+}
+function uploadPixelDataToTexture(gl, texture, pixels) {
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
+  if (pixels.data instanceof Uint8Array) {
+    if (env().getNumber("WEBGL_VERSION") === 2) {
+      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
+    } else {
+      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
+    }
+  } else {
+    if (env().getNumber("WEBGL_VERSION") === 2) {
+      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
+    } else {
+      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
+    }
+  }
+  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
+}
+function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {
+  const buffer2 = gl2.createBuffer();
+  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));
+  const bytesPerFloat = 4;
+  const valuesPerTexel = 4;
+  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
+  callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));
+  callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));
+  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));
+  return buffer2;
+}
+function downloadFloat32MatrixFromBuffer(gl, buffer2, size) {
+  const gl2 = gl;
+  const downloadTarget = new Float32Array(size);
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
+  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
+  return downloadTarget;
+}
+function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+  const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
+  const numChannels = 4;
+  const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
+  callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));
+  return new Float32Array(downloadTarget.buffer);
+}
+function downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+  const gl2 = gl;
+  const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
+  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
+  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
+  return downloadTarget;
+}
+function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
+  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
+  callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));
+  return packedRGBA;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js
+var GPGPUContext = class {
+  constructor(gl) {
+    this.outputTexture = null;
+    this.program = null;
+    this.disposed = false;
+    this.vertexAttrsAreBound = false;
+    this.itemsToPoll = [];
+    const glVersion = env().getNumber("WEBGL_VERSION");
+    if (gl != null) {
+      this.gl = gl;
+      setWebGLContext(glVersion, gl);
+    } else {
+      this.gl = getWebGLContext(glVersion);
+    }
+    let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float";
+    const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
+    this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile");
+    if (env().getNumber("WEBGL_VERSION") === 1) {
+      const TEXTURE_FLOAT = "OES_texture_float";
+      const TEXTURE_HALF_FLOAT = "OES_texture_half_float";
+      this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
+      if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
+        this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
+      } else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
+        throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
+      }
+      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
+      if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
+        this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
+      } else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
+        throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
+      }
+    } else {
+      COLOR_BUFFER_FLOAT = "EXT_color_buffer_float";
+      if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
+        this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
+      } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
+        this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
+      } else {
+        throw new Error("GL context does not support color renderable floats");
+      }
+    }
+    this.vertexBuffer = createVertexBuffer(this.gl);
+    this.indexBuffer = createIndexBuffer(this.gl);
+    this.framebuffer = createFramebuffer(this.gl);
+    this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);
+  }
+  get debug() {
+    return env().getBool("DEBUG");
+  }
+  dispose() {
+    if (this.disposed) {
+      return;
+    }
+    if (this.program != null) {
+      console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.");
+    }
+    if (this.outputTexture != null) {
+      console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");
+    }
+    const gl = this.gl;
+    callAndCheck(gl, () => gl.finish());
+    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
+    callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));
+    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));
+    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));
+    callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));
+    this.disposed = true;
+  }
+  createFloat32MatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  createFloat16MatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  createUnsignedBytesMatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  uploadPixelDataToTexture(texture, pixels) {
+    this.throwIfDisposed();
+    uploadPixelDataToTexture(this.gl, texture, pixels);
+  }
+  uploadDenseMatrixToTexture(texture, width, height, data) {
+    this.throwIfDisposed();
+    uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);
+  }
+  createFloat16PackedMatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  createPackedMatrixTexture(rows, columns) {
+    this.throwIfDisposed();
+    return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+  }
+  deleteMatrixTexture(texture) {
+    this.throwIfDisposed();
+    if (this.outputTexture === texture) {
+      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+      this.outputTexture = null;
+    }
+    callAndCheck(this.gl, () => this.gl.deleteTexture(texture));
+  }
+  downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {
+    return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));
+  }
+  downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {
+    return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
+  }
+  downloadFloat32MatrixFromBuffer(buffer2, size) {
+    return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);
+  }
+  createBufferFromTexture(texture, rows, columns) {
+    this.bindTextureToFrameBuffer(texture);
+    const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);
+    this.unbindTextureToFrameBuffer();
+    return result;
+  }
+  createAndWaitForFence() {
+    const fenceContext = this.createFence(this.gl);
+    return this.pollFence(fenceContext);
+  }
+  createFence(gl) {
+    let query;
+    let isFencePassed;
+    if (env().getBool("WEBGL_FENCE_API_ENABLED")) {
+      const gl2 = gl;
+      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);
+      gl.flush();
+      isFencePassed = () => {
+        const status = gl2.clientWaitSync(sync, 0, 0);
+        return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;
+      };
+      query = sync;
+    } else if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) {
+      query = this.beginQuery();
+      this.endQuery();
+      isFencePassed = () => this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
+    } else {
+      isFencePassed = () => true;
+    }
+    return { query, isFencePassed };
+  }
+  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
+    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
+  }
+  createProgram(fragmentShader) {
+    this.throwIfDisposed();
+    const gl = this.gl;
+    if (this.vertexShader == null) {
+      this.vertexShader = createVertexShader2(gl);
+    }
+    const program = createProgram(gl);
+    callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));
+    callAndCheck(gl, () => gl.attachShader(program, fragmentShader));
+    linkProgram(gl, program);
+    if (this.debug) {
+      validateProgram(gl, program);
+    }
+    if (!this.vertexAttrsAreBound) {
+      this.setProgram(program);
+      this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
+    }
+    return program;
+  }
+  deleteProgram(program) {
+    this.throwIfDisposed();
+    if (program === this.program) {
+      this.program = null;
+    }
+    if (program != null) {
+      callAndCheck(this.gl, () => this.gl.deleteProgram(program));
+    }
+  }
+  setProgram(program) {
+    this.throwIfDisposed();
+    this.program = program;
+    if (this.program != null && this.debug) {
+      validateProgram(this.gl, this.program);
+    }
+    callAndCheck(this.gl, () => this.gl.useProgram(program));
+  }
+  getUniformLocation(program, uniformName, shouldThrow = true) {
+    this.throwIfDisposed();
+    if (shouldThrow) {
+      return getProgramUniformLocationOrThrow(this.gl, program, uniformName);
+    } else {
+      return getProgramUniformLocation(this.gl, program, uniformName);
+    }
+  }
+  getAttributeLocation(program, attribute) {
+    this.throwIfDisposed();
+    return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));
+  }
+  getUniformLocationNoThrow(program, uniformName) {
+    this.throwIfDisposed();
+    return this.gl.getUniformLocation(program, uniformName);
+  }
+  setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {
+    this.throwIfDisposed();
+    this.throwIfNoProgram();
+    bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);
+  }
+  setOutputMatrixTexture(outputMatrixTexture, rows, columns) {
+    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
+  }
+  setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {
+    this.throwIfDisposed();
+    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
+    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
+  }
+  setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
+    this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
+  }
+  setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
+    throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
+  }
+  debugValidate() {
+    if (this.program != null) {
+      validateProgram(this.gl, this.program);
+    }
+    validateFramebuffer(this.gl);
+  }
+  executeProgram() {
+    this.throwIfDisposed();
+    this.throwIfNoProgram();
+    const gl = this.gl;
+    if (this.debug) {
+      this.debugValidate();
+    }
+    callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));
+  }
+  blockUntilAllProgramsCompleted() {
+    this.throwIfDisposed();
+    callAndCheck(this.gl, () => this.gl.finish());
+  }
+  getQueryTimerExtension() {
+    if (this.disjointQueryTimerExtension == null) {
+      this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query");
+    }
+    return this.disjointQueryTimerExtension;
+  }
+  getQueryTimerExtensionWebGL2() {
+    return this.getQueryTimerExtension();
+  }
+  getQueryTimerExtensionWebGL1() {
+    return this.getQueryTimerExtension();
+  }
+  beginQuery() {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
+      const gl2 = this.gl;
+      const ext2 = this.getQueryTimerExtensionWebGL2();
+      const query2 = gl2.createQuery();
+      gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);
+      return query2;
+    }
+    const ext = this.getQueryTimerExtensionWebGL1();
+    const query = ext.createQueryEXT();
+    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
+    return query;
+  }
+  endQuery() {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
+      const gl2 = this.gl;
+      const ext2 = this.getQueryTimerExtensionWebGL2();
+      gl2.endQuery(ext2.TIME_ELAPSED_EXT);
+      return;
+    }
+    const ext = this.getQueryTimerExtensionWebGL1();
+    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
+  }
+  async waitForQueryAndGetTime(query) {
+    await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")));
+    return this.getQueryTime(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
+  }
+  getQueryTime(query, queryTimerVersion) {
+    if (queryTimerVersion === 0) {
+      return null;
+    }
+    if (queryTimerVersion === 2) {
+      const gl2 = this.gl;
+      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
+      return timeElapsedNanos / 1e6;
+    } else {
+      const ext = this.getQueryTimerExtensionWebGL1();
+      const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
+      return timeElapsedNanos / 1e6;
+    }
+  }
+  isQueryAvailable(query, queryTimerVersion) {
+    if (queryTimerVersion === 0) {
+      return true;
+    }
+    if (queryTimerVersion === 2) {
+      const gl2 = this.gl;
+      const ext = this.getQueryTimerExtensionWebGL2();
+      const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
+      if (this.disjoint == null) {
+        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+      }
+      return available && !this.disjoint;
+    } else {
+      const ext = this.getQueryTimerExtensionWebGL1();
+      const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
+      if (this.disjoint == null) {
+        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+      }
+      return available && !this.disjoint;
+    }
+  }
+  pollFence(fenceContext) {
+    return new Promise((resolve) => {
+      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());
+    });
+  }
+  pollItems() {
+    const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));
+    for (let i2 = 0; i2 <= index; ++i2) {
+      const { resolveFn } = this.itemsToPoll[i2];
+      resolveFn();
+    }
+    this.itemsToPoll = this.itemsToPoll.slice(index + 1);
+  }
+  addItemToPoll(isDoneFn, resolveFn) {
+    this.itemsToPoll.push({ isDoneFn, resolveFn });
+    if (this.itemsToPoll.length > 1) {
+      return;
+    }
+    util_exports.repeatedTry(() => {
+      this.pollItems();
+      return this.itemsToPoll.length === 0;
+    });
+  }
+  bindTextureToFrameBuffer(texture) {
+    this.throwIfDisposed();
+    bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
+    if (this.debug) {
+      validateFramebuffer(this.gl);
+    }
+  }
+  unbindTextureToFrameBuffer() {
+    if (this.outputTexture != null) {
+      bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
+      if (this.debug) {
+        validateFramebuffer(this.gl);
+      }
+    } else {
+      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+    }
+  }
+  downloadMatrixDriver(texture, downloadAndDecode) {
+    this.bindTextureToFrameBuffer(texture);
+    const result = downloadAndDecode();
+    this.unbindTextureToFrameBuffer();
+    return result;
+  }
+  setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {
+    this.throwIfDisposed();
+    const gl = this.gl;
+    bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
+    if (this.debug) {
+      validateFramebuffer(gl);
+    }
+    this.outputTexture = outputMatrixTextureMaybePacked;
+    callAndCheck(gl, () => gl.viewport(0, 0, width, height));
+    callAndCheck(gl, () => gl.scissor(0, 0, width, height));
+  }
+  setOutputMatrixWriteRegionDriver(x, y, width, height) {
+    this.throwIfDisposed();
+    callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));
+  }
+  throwIfDisposed() {
+    if (this.disposed) {
+      throw new Error("Attempted to use disposed GPGPUContext.");
+    }
+  }
+  throwIfNoProgram() {
+    if (this.program == null) {
+      throw new Error("No GPU program is currently set.");
+    }
+  }
+};
+function linearSearchLastTrue(arr) {
+  let i2 = 0;
+  for (; i2 < arr.length; ++i2) {
+    const isDone = arr[i2]();
+    if (!isDone) {
+      break;
+    }
+  }
+  return i2 - 1;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js
+var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js
+function getVecChannels(name, rank) {
+  return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`);
+}
+function getChannels(name, rank) {
+  if (rank === 1) {
+    return [name];
+  }
+  return getVecChannels(name, rank);
+}
+function getSourceCoords(rank, dims) {
+  if (rank === 1) {
+    return "rc";
+  }
+  let coords3 = "";
+  for (let i2 = 0; i2 < rank; i2++) {
+    coords3 += dims[i2];
+    if (i2 < rank - 1) {
+      coords3 += ",";
+    }
+  }
+  return coords3;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js
+var PackProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    if (this.rank === 0) {
+      this.userCode = `
+        void main() {
+          setOutput(vec4(getA(), 0., 0., 0.));
+        }
+      `;
+    } else {
+      const channels = getChannels("rc", this.rank);
+      const dtype = getCoordsDataType(this.rank);
+      const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);
+      const setup50 = this.getSetup(channels);
+      const output = this.getOutput(channels);
+      this.userCode = `
+        void main() {
+          ${dtype} rc = getOutputCoords();
+
+          if(${outOfBoundsCondition}) {
+            setOutput(vec4(0));
+          } else {
+            ${setup50}
+
+            setOutput(vec4(${output}));
+          }
+        }
+      `;
+    }
+  }
+  getSourceCoordsArr(dims) {
+    const coords3 = [];
+    for (let row = 0; row <= 1; row++) {
+      for (let col = 0; col <= 1; col++) {
+        let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`;
+        for (let d = 2; d < this.rank; d++) {
+          coord = `${dims[dims.length - 1 - d]},` + coord;
+        }
+        coords3.push(coord);
+      }
+    }
+    return coords3;
+  }
+  getOutOfBoundsCondition(dims) {
+    if (this.rank === 1) {
+      return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
+    }
+    let cond = "";
+    for (let i2 = this.rank - 2; i2 < this.rank; i2++) {
+      cond += `${dims[i2]} >= ${this.enableShapeUniforms ? `outShape[${i2}]` : this.outputShape[i2]}`;
+      if (i2 < this.rank - 1) {
+        cond += "||";
+      }
+    }
+    return cond;
+  }
+  getSetup(dims) {
+    if (this.rank === 1) {
+      return "";
+    }
+    const innerDims = dims.slice(-2);
+    const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];
+    const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];
+    return `
+      int r = ${innerDims[0]};
+      int c = ${innerDims[1]};
+      int rp1 = r + 1;
+      int cp1 = c + 1;
+
+      bool cEdge = cp1 >= ${col};
+      bool rEdge = rp1 >= ${row};
+    `;
+  }
+  getOutput(dims) {
+    const sourceCoords = this.getSourceCoordsArr(dims);
+    if (this.rank === 1) {
+      const outShape = this.enableShapeUniforms ? "outShape" : this.outputShape[0];
+      return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;
+    }
+    return `getA(${sourceCoords[0]}),
+            cEdge ? 0. : getA(${sourceCoords[1]}),
+            rEdge ? 0. : getA(${sourceCoords[2]}),
+            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js
+var ReshapePackedProgram = class {
+  constructor(outputShape, inputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [{ name: "inputShape", type: "ivec3" }];
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    let mainLoop = ``;
+    for (let i2 = 0; i2 < 4; i2++) {
+      let thisRC = `thisRC = rc;`;
+      if (i2 % 2 === 1) {
+        thisRC += `thisRC.z += 1;`;
+      }
+      if (i2 > 1) {
+        thisRC += `thisRC.y += 1;`;
+      }
+      mainLoop += `
+        ${thisRC}
+        ${i2 > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
+          int flatIndex = getFlatIndex(thisRC);
+
+          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
+          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
+
+          result[${i2}] =
+            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
+        ${i2 > 0 ? "}" : ""}
+      `;
+    }
+    this.userCode = `
+      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}
+      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+
+        vec4 result = vec4(0.);
+
+        ivec3 thisRC;
+        int rows = ${this.enableShapeUniforms ? "outShape[1]" : outputShape[1]};
+        int cols = ${this.enableShapeUniforms ? "outShape[2]" : outputShape[2]};
+
+        ${mainLoop}
+
+        setOutput(result);
+      }
+    `;
+  }
+};
+function getReshapedInputCoords(shape, enableShapeUniforms) {
+  const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], "inputShape") : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
+  return `
+    ivec3 inputCoordsFromReshapedOutCoords(int index) {
+      ${coordsFromIndexSnippet}
+      return ivec3(r, c, d);
+    }
+  `;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js
+var TextureManager = class {
+  constructor(gpgpu) {
+    this.gpgpu = gpgpu;
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this._numBytesAllocated = 0;
+    this._numBytesFree = 0;
+    this.freeTextures = {};
+    this.logEnabled = false;
+    this.usedTextures = {};
+  }
+  acquireTexture(shapeRC, usage, isPacked) {
+    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
+    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
+    if (!(shapeKey in this.freeTextures)) {
+      this.freeTextures[shapeKey] = [];
+    }
+    if (!(shapeKey in this.usedTextures)) {
+      this.usedTextures[shapeKey] = [];
+    }
+    const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
+    if (this.freeTextures[shapeKey].length > 0) {
+      this.numFreeTextures--;
+      this.numUsedTextures++;
+      this._numBytesFree -= texBytes;
+      this.log();
+      const newTexture2 = this.freeTextures[shapeKey].shift();
+      this.usedTextures[shapeKey].push(newTexture2);
+      return newTexture2;
+    }
+    let newTexture;
+    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
+      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
+      newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
+      newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
+      newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
+    } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
+      newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
+    }
+    this.usedTextures[shapeKey].push(newTexture);
+    this.numUsedTextures++;
+    this._numBytesAllocated += texBytes;
+    this.log();
+    return newTexture;
+  }
+  releaseTexture(texture, shape, logicalTexType, isPacked) {
+    if (this.freeTextures == null) {
+      return;
+    }
+    const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
+    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
+    if (!(shapeKey in this.freeTextures)) {
+      this.freeTextures[shapeKey] = [];
+    }
+    const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
+    const deleteTexThreshold = env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
+    if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {
+      this.gpgpu.deleteMatrixTexture(texture.texture);
+      this._numBytesAllocated -= texBytes;
+    } else {
+      this.freeTextures[shapeKey].push(texture);
+      this.numFreeTextures++;
+      this._numBytesFree += texBytes;
+    }
+    this.numUsedTextures--;
+    const texList = this.usedTextures[shapeKey];
+    const texIndex = texList.indexOf(texture);
+    if (texIndex < 0) {
+      throw new Error("Cannot release a texture that was never provided by this texture manager");
+    }
+    texList.splice(texIndex, 1);
+    this.log();
+  }
+  log() {
+    if (!this.logEnabled) {
+      return;
+    }
+    const total = this.numFreeTextures + this.numUsedTextures;
+    console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);
+    const freeRatio = this._numBytesFree / this._numBytesAllocated;
+    console.log(`Bytes allocated: ${this._numBytesAllocated}`);
+    console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);
+  }
+  get numBytesAllocated() {
+    return this._numBytesAllocated;
+  }
+  get numBytesFree() {
+    return this._numBytesFree;
+  }
+  getNumUsedTextures() {
+    return this.numUsedTextures;
+  }
+  getNumFreeTextures() {
+    return this.numFreeTextures;
+  }
+  dispose() {
+    if (this.freeTextures == null) {
+      return;
+    }
+    for (const texShape in this.freeTextures) {
+      this.freeTextures[texShape].forEach((tex) => {
+        this.gpgpu.deleteMatrixTexture(tex.texture);
+      });
+    }
+    for (const texShape in this.usedTextures) {
+      this.usedTextures[texShape].forEach((tex) => {
+        this.gpgpu.deleteMatrixTexture(tex.texture);
+      });
+    }
+    this.freeTextures = null;
+    this.usedTextures = null;
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this._numBytesAllocated = 0;
+    this._numBytesFree = 0;
+  }
+};
+function numBytesForInternalFormat(gl, internalFormat) {
+  const glany = gl;
+  if (internalFormat === glany.R32F) {
+    return 4;
+  } else if (internalFormat === glany.R16F) {
+    return 2;
+  } else if (internalFormat === glany.RGBA32F) {
+    return 16;
+  } else if (internalFormat === gl.RGBA) {
+    return 16;
+  } else if (internalFormat === glany.RGBA16F) {
+    return 8;
+  } else if (internalFormat === glany.RGBA8) {
+    return 4;
+  }
+  throw new Error(`Unknown internal format ${internalFormat}`);
+}
+function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {
+  const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);
+  let numElements;
+  if (isPacked) {
+    const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
+    numElements = packedWidth * packedHeight;
+  } else {
+    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
+    numElements = width * height;
+  }
+  const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);
+  return numElements * bytesPerElement2;
+}
+function internalFormatForPhysicalTexType(physicalTexType, textureConfig) {
+  switch (physicalTexType) {
+    case PhysicalTextureType.PACKED_2X2_FLOAT32:
+      return getInternalFormatForPackedMatrixTexture(textureConfig);
+    case PhysicalTextureType.PACKED_2X2_FLOAT16:
+      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);
+    case PhysicalTextureType.UNPACKED_FLOAT32:
+      return getInternalFormatForFloat32MatrixTexture(textureConfig);
+    case PhysicalTextureType.UNPACKED_FLOAT16:
+      return getInternalFormatForFloat16MatrixTexture(textureConfig);
+    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:
+      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);
+    default:
+      throw new Error(`Unknown physical texture type ${physicalTexType}`);
+  }
+}
+function getPhysicalTextureForRendering(isPacked) {
+  if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) {
+    if (isPacked) {
+      return PhysicalTextureType.PACKED_2X2_FLOAT32;
+    }
+    return PhysicalTextureType.UNPACKED_FLOAT32;
+  }
+  if (isPacked) {
+    return PhysicalTextureType.PACKED_2X2_FLOAT16;
+  }
+  return PhysicalTextureType.UNPACKED_FLOAT16;
+}
+function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
+  if (logicalTexType === TextureUsage.UPLOAD) {
+    return PhysicalTextureType.PACKED_2X2_FLOAT32;
+  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
+    return getPhysicalTextureForRendering(isPacked);
+  } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {
+    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
+  }
+  throw new Error(`Unknown logical texture type ${logicalTexType}`);
+}
+function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
+  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js
+var UnaryOpProgram = class {
+  constructor(aShape, opSnippet) {
+    this.variableNames = ["A"];
+    this.outputShape = aShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      float unaryOperation(float x) {
+        ${opSnippet}
+      }
+
+      void main() {
+        float x = getAAtOutCoords();
+        float y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `;
+  }
+};
+var CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;
+var LINEAR = `return x;`;
+var ABS = `return abs(x);`;
+var ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
+var RELU = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : x;
+`;
+var RELU6 = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`;
+var CLONE = "return x;";
+var SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js
+var LINEAR2 = `return x;`;
+var ELU3 = `
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`;
+var RELU2 = `
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var RELU62 = `
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;
+var UnaryOpPackedProgram = class {
+  constructor(aShape, opSnippet) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = aShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      vec4 unaryOperation(vec4 x) {
+        ${opSnippet}
+      }
+
+      void main() {
+        vec4 x = getAAtOutCoords();
+        vec4 y = unaryOperation(x);
+
+        setOutput(y);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js
+var UnpackProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = false;
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const rank = outputShape.length;
+    const channels = getChannels("rc", rank);
+    const dtype = getCoordsDataType(rank);
+    const sourceCoords = getSourceCoords(rank, channels);
+    const innerDims = channels.slice(-2);
+    const coords3 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`;
+    this.userCode = `
+      void main() {
+        ${dtype} rc = getOutputCoords();
+        vec4 packedInput = getA(${sourceCoords});
+
+        setOutput(getChannel(packedInput, ${coords3}));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js
+var whereImpl3 = kernel_impls_exports.whereImpl;
+var EPSILON_FLOAT322 = 1e-7;
+var EPSILON_FLOAT162 = 1e-4;
+var binaryCaches = {};
+function getBinaryCache(webGLVersion) {
+  if (webGLVersion in binaryCaches) {
+    return binaryCaches[webGLVersion];
+  }
+  binaryCaches[webGLVersion] = {};
+  return binaryCaches[webGLVersion];
+}
+var CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
+var BEFORE_PAGING_CONSTANT = 600;
+function numMBBeforeWarning() {
+  if (env().global.screen == null) {
+    return 1024;
+  }
+  return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;
+}
+var MathBackendWebGL = class extends KernelBackend {
+  constructor(gpuResource) {
+    super();
+    this.pendingRead = /* @__PURE__ */ new WeakMap();
+    this.pendingDisposal = /* @__PURE__ */ new WeakSet();
+    this.dataRefCount = /* @__PURE__ */ new WeakMap();
+    this.numBytesInGPU = 0;
+    this.uploadWaitMs = 0;
+    this.downloadWaitMs = 0;
+    this.lastGlFlushTime = 0;
+    this.warnedAboutMemory = false;
+    this.pendingDeletes = 0;
+    this.disposed = false;
+    if (!env().getBool("HAS_WEBGL")) {
+      throw new Error("WebGL is not supported on this device");
+    }
+    let newGPGPU;
+    if (gpuResource != null) {
+      if (gpuResource instanceof GPGPUContext) {
+        newGPGPU = gpuResource;
+      } else {
+        const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"), gpuResource);
+        newGPGPU = new GPGPUContext(gl);
+      }
+      this.binaryCache = {};
+      this.gpgpuCreatedLocally = false;
+    } else {
+      const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"));
+      newGPGPU = new GPGPUContext(gl);
+      this.binaryCache = getBinaryCache(env().getNumber("WEBGL_VERSION"));
+      this.gpgpuCreatedLocally = true;
+    }
+    this.gpgpu = newGPGPU;
+    this.canvas = this.gpgpu.gl.canvas;
+    this.textureManager = new TextureManager(this.gpgpu);
+    this.numMBBeforeWarning = numMBBeforeWarning();
+    this.texData = new DataStorage(this, engine());
+  }
+  nextDataId() {
+    return MathBackendWebGL.nextDataId++;
+  }
+  numDataIds() {
+    return this.texData.numDataIds() - this.pendingDeletes;
+  }
+  write(values, shape, dtype) {
+    if (env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || env().getBool("DEBUG")) {
+      this.checkNumericalProblems(values);
+    }
+    if (dtype === "complex64" && values != null) {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    const dataId = { id: this.nextDataId() };
+    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });
+    return dataId;
+  }
+  refCount(dataId) {
+    if (this.texData.has(dataId)) {
+      const tensorData = this.texData.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const texData = this.texData.get(dataId);
+    texData.refCount++;
+  }
+  decRef(dataId) {
+    if (this.texData.has(dataId)) {
+      const texData = this.texData.get(dataId);
+      texData.refCount--;
+    }
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    if (env().getBool("DEBUG")) {
+      this.checkNumericalProblems(values);
+    }
+    if (dtype === "complex64") {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });
+  }
+  disposeIntermediateTensorInfo(tensorInfo) {
+    this.disposeData(tensorInfo.dataId);
+  }
+  readSync(dataId) {
+    const texData = this.texData.get(dataId);
+    const { values, dtype, complexTensorInfos, slice: slice6, shape, isPacked } = texData;
+    if (slice6 != null) {
+      let program;
+      if (isPacked) {
+        program = new UnaryOpPackedProgram(shape, CLONE);
+      } else {
+        program = new UnaryOpProgram(shape, CLONE);
+      }
+      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
+      const data = this.readSync(res.dataId);
+      this.disposeIntermediateTensorInfo(res);
+      return data;
+    }
+    if (values != null) {
+      return this.convertAndCacheOnCPU(dataId);
+    }
+    if (dtype === "string") {
+      return values;
+    }
+    const shouldTimeProgram = this.activeTimers != null;
+    let start;
+    if (shouldTimeProgram) {
+      start = util_exports.now();
+    }
+    let result;
+    if (dtype === "complex64") {
+      const realValues = this.readSync(complexTensorInfos.real.dataId);
+      const imagValues = this.readSync(complexTensorInfos.imag.dataId);
+      result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    } else {
+      result = this.getValuesFromTexture(dataId);
+    }
+    if (shouldTimeProgram) {
+      this.downloadWaitMs += util_exports.now() - start;
+    }
+    return this.convertAndCacheOnCPU(dataId, result);
+  }
+  async read(dataId) {
+    if (this.pendingRead.has(dataId)) {
+      const subscribers2 = this.pendingRead.get(dataId);
+      return new Promise((resolve) => subscribers2.push(resolve));
+    }
+    const texData = this.texData.get(dataId);
+    const { values, shape, slice: slice6, dtype, complexTensorInfos, isPacked } = texData;
+    if (slice6 != null) {
+      let program;
+      if (isPacked) {
+        program = new UnaryOpPackedProgram(shape, CLONE);
+      } else {
+        program = new UnaryOpProgram(shape, CLONE);
+      }
+      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
+      const data = this.read(res.dataId);
+      this.disposeIntermediateTensorInfo(res);
+      return data;
+    }
+    if (values != null) {
+      return this.convertAndCacheOnCPU(dataId);
+    }
+    if (env().getBool("DEBUG")) {
+      if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) {
+        throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);
+      }
+    }
+    let buffer2 = null;
+    let tmpDownloadTarget;
+    if (dtype !== "complex64" && env().get("WEBGL_BUFFER_SUPPORTED")) {
+      tmpDownloadTarget = this.decode(dataId);
+      const tmpData = this.texData.get(tmpDownloadTarget.dataId);
+      buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));
+    }
+    this.pendingRead.set(dataId, []);
+    if (dtype !== "complex64") {
+      await this.gpgpu.createAndWaitForFence();
+    }
+    let vals;
+    if (dtype === "complex64") {
+      const ps = await Promise.all([
+        this.read(complexTensorInfos.real.dataId),
+        this.read(complexTensorInfos.imag.dataId)
+      ]);
+      const realValues = ps[0];
+      const imagValues = ps[1];
+      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    } else if (buffer2 == null) {
+      vals = this.getValuesFromTexture(dataId);
+    } else {
+      const size = util_exports.sizeFromShape(shape);
+      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);
+    }
+    if (tmpDownloadTarget != null) {
+      this.disposeIntermediateTensorInfo(tmpDownloadTarget);
+    }
+    if (buffer2 != null) {
+      const gl = this.gpgpu.gl;
+      callAndCheck(gl, () => gl.deleteBuffer(buffer2));
+    }
+    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
+    const subscribers = this.pendingRead.get(dataId);
+    this.pendingRead.delete(dataId);
+    subscribers.forEach((resolve) => resolve(dTypeVals));
+    if (this.pendingDisposal.has(dataId)) {
+      this.pendingDisposal.delete(dataId);
+      if (this.disposeData(dataId)) {
+        engine().removeDataId(dataId, this);
+      }
+      this.pendingDeletes--;
+    }
+    return dTypeVals;
+  }
+  readToGPU(dataId, options = {}) {
+    const texData = this.texData.get(dataId);
+    const { values, shape, slice: slice6, dtype, isPacked, texture } = texData;
+    if (dtype === "complex64") {
+      throw new Error("Does not support reading texture for complex64 dtype.");
+    }
+    if (slice6 != null) {
+      let program;
+      if (isPacked) {
+        program = new UnaryOpPackedProgram(shape, CLONE);
+      } else {
+        program = new UnaryOpProgram(shape, CLONE);
+      }
+      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
+      const gpuResouorce = this.readToGPU(res, options);
+      this.disposeIntermediateTensorInfo(res);
+      return gpuResouorce;
+    }
+    if (texture == null) {
+      if (values != null) {
+        throw new Error("Data is not on GPU but on CPU.");
+      } else {
+        throw new Error("There is no data on GPU or CPU.");
+      }
+    }
+    const tmpTarget = this.decode(dataId, options.customTexShape);
+    const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);
+    const tmpData = this.texData.get(tmpTarget.dataId);
+    return Object.assign({ tensorRef }, tmpData.texture);
+  }
+  bufferSync(t2) {
+    const data = this.readSync(t2.dataId);
+    if (t2.dtype === "string") {
+      try {
+        const strings = data.map((d) => util_exports.decodeString(d));
+        return buffer(t2.shape, t2.dtype, strings);
+      } catch (_a) {
+        throw new Error("Failed to decode encoded string bytes into utf-8");
+      }
+    }
+    return buffer(t2.shape, t2.dtype, data);
+  }
+  checkNumericalProblems(values) {
+    if (values == null) {
+      return;
+    }
+    for (let i2 = 0; i2 < values.length; i2++) {
+      const num = values[i2];
+      if (!canBeRepresented(num)) {
+        if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) {
+          throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
+        }
+        throw Error(`The value ${num} cannot be represented on this device.`);
+      }
+    }
+  }
+  getValuesFromTexture(dataId) {
+    const { shape, dtype, isPacked } = this.texData.get(dataId);
+    const size = util_exports.sizeFromShape(shape);
+    if (env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
+      const tmpTarget = this.decode(dataId);
+      const tmpData2 = this.texData.get(tmpTarget.dataId);
+      const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size);
+      this.disposeIntermediateTensorInfo(tmpTarget);
+      return vals2;
+    }
+    const shouldUsePackedProgram = env().getBool("WEBGL_PACK") && isPacked === true;
+    const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;
+    const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);
+    const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], "float32");
+    const tmpData = this.texData.get(output.dataId);
+    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);
+    this.disposeIntermediateTensorInfo(output);
+    return vals;
+  }
+  timerAvailable() {
+    return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
+  }
+  time(f) {
+    const oldActiveTimers = this.activeTimers;
+    const newActiveTimers = [];
+    let outerMostTime = false;
+    if (this.programTimersStack == null) {
+      this.programTimersStack = newActiveTimers;
+      outerMostTime = true;
+    } else {
+      this.activeTimers.push(newActiveTimers);
+    }
+    this.activeTimers = newActiveTimers;
+    f();
+    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);
+    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);
+    this.activeTimers = oldActiveTimers;
+    if (outerMostTime) {
+      this.programTimersStack = null;
+    }
+    const res = {
+      uploadWaitMs: this.uploadWaitMs,
+      downloadWaitMs: this.downloadWaitMs,
+      kernelMs: null,
+      wallMs: null
+    };
+    return (async () => {
+      if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+        const kernelMs = await Promise.all(flattenedActiveTimerQueries);
+        res["kernelMs"] = util_exports.sum(kernelMs);
+        res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+      } else {
+        res["kernelMs"] = {
+          error: "WebGL query timers are not supported in this environment."
+        };
+      }
+      this.uploadWaitMs = 0;
+      this.downloadWaitMs = 0;
+      return res;
+    })();
+  }
+  memory() {
+    return {
+      unreliable: false,
+      numBytesInGPU: this.numBytesInGPU,
+      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
+      numBytesInGPUFree: this.textureManager.numBytesFree
+    };
+  }
+  startTimer() {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+      return this.gpgpu.beginQuery();
+    }
+    return { startMs: util_exports.now(), endMs: null };
+  }
+  endTimer(query) {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+      this.gpgpu.endQuery();
+      return query;
+    }
+    query.endMs = util_exports.now();
+    return query;
+  }
+  async getQueryTime(query) {
+    if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
+      return this.gpgpu.waitForQueryAndGetTime(query);
+    }
+    const timerQuery = query;
+    return timerQuery.endMs - timerQuery.startMs;
+  }
+  disposeData(dataId, force = false) {
+    if (this.pendingDisposal.has(dataId)) {
+      return false;
+    }
+    if (!this.texData.has(dataId)) {
+      return true;
+    }
+    if (force) {
+      this.texData.get(dataId).refCount = 0;
+    } else {
+      this.texData.get(dataId).refCount--;
+    }
+    if (!force && this.texData.get(dataId).refCount > 0) {
+      return false;
+    }
+    if (this.pendingRead.has(dataId)) {
+      this.pendingDisposal.add(dataId);
+      this.pendingDeletes++;
+      return false;
+    }
+    this.releaseGPUData(dataId);
+    const { complexTensorInfos } = this.texData.get(dataId);
+    if (complexTensorInfos != null) {
+      this.disposeData(complexTensorInfos.real.dataId, force);
+      this.disposeData(complexTensorInfos.imag.dataId, force);
+    }
+    this.texData.delete(dataId);
+    return true;
+  }
+  releaseGPUData(dataId) {
+    const { texture, dtype, texShape, usage, isPacked, slice: slice6 } = this.texData.get(dataId);
+    const key = slice6 && slice6.origDataId || dataId;
+    const refCount = this.dataRefCount.get(key);
+    if (refCount > 1) {
+      this.dataRefCount.set(key, refCount - 1);
+    } else {
+      this.dataRefCount.delete(key);
+      if (texture != null) {
+        this.numBytesInGPU -= this.computeBytes(texShape, dtype);
+        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
+      }
+    }
+    const texData = this.texData.get(dataId);
+    texData.texture = null;
+    texData.texShape = null;
+    texData.isPacked = false;
+    texData.slice = null;
+  }
+  getTexture(dataId) {
+    this.uploadToGPU(dataId);
+    return this.texData.get(dataId).texture.texture;
+  }
+  getDataInfo(dataId) {
+    return this.texData.get(dataId);
+  }
+  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {
+    return env().getBool("WEBGL_CPU_FORWARD") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);
+  }
+  getGPGPUContext() {
+    return this.gpgpu;
+  }
+  where(condition) {
+    backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
+    const condVals = condition.dataSync();
+    return whereImpl3(condition.shape, condVals);
+  }
+  packedUnaryOp(x, op2, dtype) {
+    const program = new UnaryOpPackedProgram(x.shape, op2);
+    const outInfo = this.compileAndRun(program, [x], dtype);
+    return engine().makeTensorFromTensorInfo(outInfo);
+  }
+  abs(x) {
+    if (this.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
+      const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);
+      return this.makeOutput(x.shape, x.dtype, outValues);
+    }
+    if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
+      return this.packedUnaryOp(x, ABS, x.dtype);
+    }
+    const program = new UnaryOpProgram(x.shape, ABS);
+    const outInfo = this.compileAndRun(program, [x]);
+    return engine().makeTensorFromTensorInfo(outInfo);
+  }
+  makeTensorInfo(shape, dtype, values) {
+    let dataId;
+    if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
+      const encodedValues = values.map((d) => util_exports.encodeString(d));
+      dataId = this.write(encodedValues, shape, dtype);
+    } else {
+      dataId = this.write(values, shape, dtype);
+    }
+    this.texData.get(dataId).usage = null;
+    return { dataId, shape, dtype };
+  }
+  makeOutput(shape, dtype, values) {
+    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
+  }
+  unpackTensor(input2) {
+    const program = new UnpackProgram(input2.shape);
+    return this.runWebGLProgram(program, [input2], input2.dtype);
+  }
+  packTensor(input2) {
+    const program = new PackProgram(input2.shape);
+    const preventEagerUnpackingOutput = true;
+    return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);
+  }
+  packedReshape(input2, afterShape) {
+    const input3DShape = [
+      getBatchDim(input2.shape),
+      ...getRowsCols(input2.shape)
+    ];
+    const input3D = {
+      dtype: input2.dtype,
+      shape: input3DShape,
+      dataId: input2.dataId
+    };
+    const afterShapeAs3D = [
+      getBatchDim(afterShape),
+      ...getRowsCols(afterShape)
+    ];
+    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
+    const preventEagerUnpackingOfOutput = true;
+    const customValues = [input3DShape];
+    const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);
+    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
+  }
+  decode(dataId, customTexShape) {
+    const texData = this.texData.get(dataId);
+    const { isPacked, shape, dtype } = texData;
+    if (customTexShape != null) {
+      const size = util_exports.sizeFromShape(shape);
+      const texSize = customTexShape[0] * customTexShape[1] * 4;
+      util_exports.assert(size <= texSize, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
+    }
+    const shapeAs3D = getShapeAs3D(shape);
+    let program;
+    if (isPacked) {
+      program = new DecodeMatrixPackedProgram(shapeAs3D);
+    } else {
+      program = new DecodeMatrixProgram(shapeAs3D);
+    }
+    const preventEagerUnpackingOfOutput = true;
+    const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];
+    const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);
+    return { dtype, shape, dataId: out.dataId };
+  }
+  runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {
+    const output = this.makeTensorInfo(program.outputShape, outputDtype);
+    const outData = this.texData.get(output.dataId);
+    if (program.packedOutput) {
+      outData.isPacked = true;
+    }
+    if (program.outPackingScheme === PackingScheme.DENSE) {
+      const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);
+      outData.texShape = texelShape.map((d) => d * 2);
+    }
+    if (program.outTexUsage != null) {
+      outData.usage = program.outTexUsage;
+    }
+    if (util_exports.sizeFromShape(output.shape) === 0) {
+      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
+      return output;
+    }
+    const dataToDispose = [];
+    const inputsData = inputs.map((input2) => {
+      if (input2.dtype === "complex64") {
+        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
+      }
+      let texData = this.texData.get(input2.dataId);
+      if (texData.texture == null) {
+        if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) {
+          return {
+            shape: input2.shape,
+            texData: null,
+            isUniform: true,
+            uniformValues: texData.values
+          };
+        }
+        if (program.packedInputs) {
+          texData.isPacked = true;
+          texData.shape = input2.shape;
+        }
+      }
+      this.uploadToGPU(input2.dataId);
+      if (!!texData.isPacked !== !!program.packedInputs) {
+        input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);
+        dataToDispose.push(input2);
+        texData = this.texData.get(input2.dataId);
+      } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {
+        const savedInput = input2;
+        const targetShape = input2.shape;
+        input2.shape = texData.shape;
+        input2 = this.packedReshape(input2, targetShape);
+        dataToDispose.push(input2);
+        texData = this.texData.get(input2.dataId);
+        savedInput.shape = targetShape;
+      }
+      return { shape: input2.shape, texData, isUniform: false };
+    });
+    this.uploadToGPU(output.dataId);
+    const outputData = { shape: output.shape, texData: outData, isUniform: false };
+    const key = makeShaderKey(program, inputsData, outputData);
+    const binary = this.getAndSaveBinary(key, () => {
+      return compileProgram(this.gpgpu, program, inputsData, outputData);
+    });
+    const shouldTimeProgram = this.activeTimers != null;
+    let query;
+    if (shouldTimeProgram) {
+      query = this.startTimer();
+    }
+    if (!env().get("ENGINE_COMPILE_ONLY")) {
+      runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);
+    }
+    dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));
+    if (shouldTimeProgram) {
+      query = this.endTimer(query);
+      this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
+    }
+    const glFlushThreshold = env().get("WEBGL_FLUSH_THRESHOLD");
+    if (glFlushThreshold > 0) {
+      const time2 = util_exports.now();
+      if (time2 - this.lastGlFlushTime > glFlushThreshold) {
+        this.gpgpu.gl.flush();
+        this.lastGlFlushTime = time2;
+      }
+    }
+    if (!env().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) {
+      const unpacked = this.unpackTensor(output);
+      this.disposeIntermediateTensorInfo(output);
+      return unpacked;
+    }
+    return output;
+  }
+  compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {
+    outputDtype = outputDtype || inputs[0].dtype;
+    const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);
+    return outInfo;
+  }
+  getAndSaveBinary(key, getBinary) {
+    if (!(key in this.binaryCache)) {
+      this.binaryCache[key] = getBinary();
+    }
+    return this.binaryCache[key];
+  }
+  getTextureManager() {
+    return this.textureManager;
+  }
+  dispose() {
+    if (this.disposed) {
+      return;
+    }
+    if (!env().getBool("IS_TEST")) {
+      const allKeys = Object.keys(this.binaryCache);
+      allKeys.forEach((key) => {
+        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
+        delete this.binaryCache[key];
+      });
+    }
+    this.textureManager.dispose();
+    if (this.canvas != null && (typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement)) {
+      this.canvas.remove();
+    } else {
+      this.canvas = null;
+    }
+    if (this.gpgpuCreatedLocally) {
+      this.gpgpu.program = null;
+      this.gpgpu.dispose();
+    }
+    this.disposed = true;
+  }
+  floatPrecision() {
+    if (this.floatPrecisionValue == null) {
+      this.floatPrecisionValue = tidy(() => {
+        if (!env().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
+          const debugFlag = env().getBool("DEBUG");
+          env().set("DEBUG", false);
+          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];
+          env().set("DEBUG", debugFlag);
+          if (underflowCheckValue > 0) {
+            return 32;
+          }
+        }
+        return 16;
+      });
+    }
+    return this.floatPrecisionValue;
+  }
+  epsilon() {
+    return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;
+  }
+  uploadToGPU(dataId) {
+    const texData = this.texData.get(dataId);
+    const { shape, dtype, values, texture, usage, isPacked } = texData;
+    if (texture != null) {
+      return;
+    }
+    const shouldTimeProgram = this.activeTimers != null;
+    let start;
+    if (shouldTimeProgram) {
+      start = util_exports.now();
+    }
+    let texShape = texData.texShape;
+    if (texShape == null) {
+      texShape = getTextureShapeFromLogicalShape(shape, isPacked);
+      texData.texShape = texShape;
+    }
+    if (values != null) {
+      const shapeAs3D = getShapeAs3D(shape);
+      let program;
+      let width = texShape[1], height = texShape[0];
+      const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;
+      if (isPacked || !isByteArray) {
+        [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);
+      }
+      if (isPacked) {
+        program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);
+      } else {
+        program = new EncodeMatrixProgram(shapeAs3D, isByteArray);
+      }
+      const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;
+      const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);
+      const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);
+      if (isByteArray) {
+        tempDenseInputTexData.usage = TextureUsage.PIXELS;
+      } else {
+        tempDenseInputTexData.usage = TextureUsage.UPLOAD;
+      }
+      tempDenseInputTexData.texShape = tempDenseInputTexShape;
+      this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
+      const customValues = [[height, width]];
+      const preventEagerUnpacking = true;
+      const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);
+      const outputTexData = this.texData.get(encodedOutputTarget.dataId);
+      texData.texShape = outputTexData.texShape;
+      texData.isPacked = outputTexData.isPacked;
+      texData.usage = outputTexData.usage;
+      if (!env().get("ENGINE_COMPILE_ONLY")) {
+        texData.texture = outputTexData.texture;
+        texData.values = null;
+        this.texData.delete(encodedOutputTarget.dataId);
+      } else {
+        this.disposeData(encodedOutputTarget.dataId);
+      }
+      this.disposeIntermediateTensorInfo(tempDenseInputHandle);
+      if (shouldTimeProgram) {
+        this.uploadWaitMs += util_exports.now() - start;
+      }
+    } else {
+      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
+      texData.texture = newTexture;
+    }
+  }
+  convertAndCacheOnCPU(dataId, float32Values) {
+    const texData = this.texData.get(dataId);
+    const { dtype } = texData;
+    this.releaseGPUData(dataId);
+    if (float32Values != null) {
+      texData.values = float32ToTypedArray(float32Values, dtype);
+    }
+    return texData.values;
+  }
+  acquireTexture(texShape, texType, dtype, isPacked) {
+    this.numBytesInGPU += this.computeBytes(texShape, dtype);
+    if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
+      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
+      this.warnedAboutMemory = true;
+      console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);
+    }
+    return this.textureManager.acquireTexture(texShape, texType, isPacked);
+  }
+  computeBytes(shape, dtype) {
+    return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);
+  }
+  checkCompileCompletion() {
+    for (const [, binary] of Object.entries(this.binaryCache)) {
+      this.checkCompletion_(binary);
+    }
+  }
+  async checkCompileCompletionAsync() {
+    const ps = [];
+    if (this.gpgpu.parallelCompilationExtension) {
+      for (const [, binary] of Object.entries(this.binaryCache)) {
+        ps.push(this.checkCompletionAsync_(binary));
+      }
+      return Promise.all(ps);
+    } else {
+      for (const [, binary] of Object.entries(this.binaryCache)) {
+        const p2 = new Promise((resolve) => {
+          try {
+            this.checkCompletion_(binary);
+            resolve(true);
+          } catch (error) {
+            throw error;
+          }
+        });
+        ps.push(p2);
+      }
+      return Promise.all(ps);
+    }
+  }
+  async checkCompletionAsync_(binary) {
+    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {
+      return this.checkCompletion_(binary);
+    } else {
+      await nextFrame();
+      return this.checkCompletionAsync_(binary);
+    }
+  }
+  checkCompletion_(binary) {
+    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {
+      console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));
+      if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {
+        logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));
+        throw new Error("Failed to compile fragment shader.");
+      }
+      throw new Error("Failed to link vertex and fragment shaders.");
+    }
+    return true;
+  }
+  getUniformLocations() {
+    for (const [, binary] of Object.entries(this.binaryCache)) {
+      const { uniformLocations, customUniformLocations, infLoc, nanLoc, inShapesLocations, inTexShapesLocations, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);
+      binary.uniformLocations = uniformLocations;
+      binary.customUniformLocations = customUniformLocations;
+      binary.infLoc = infLoc;
+      binary.nanLoc = nanLoc;
+      binary.inShapesLocations = inShapesLocations;
+      binary.inTexShapesLocations = inTexShapesLocations;
+      binary.outShapeLocation = outShapeLocation;
+      binary.outShapeStridesLocation = outShapeStridesLocation;
+      binary.outTexShapeLocation = outTexShapeLocation;
+    }
+  }
+};
+MathBackendWebGL.nextDataId = 0;
+function float32ToTypedArray(a6, dtype) {
+  if (dtype === "float32" || dtype === "complex64") {
+    return a6;
+  } else if (dtype === "int32" || dtype === "bool") {
+    const result = dtype === "int32" ? new Int32Array(a6.length) : new Uint8Array(a6.length);
+    for (let i2 = 0; i2 < result.length; ++i2) {
+      result[i2] = Math.round(a6[i2]);
+    }
+    return result;
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js
+var version6 = "3.18.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl.js
+function forceHalfFloat() {
+  env().set("WEBGL_FORCE_F16_TEXTURES", true);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js
+if (device_util_exports.isBrowser()) {
+  registerBackend("webgl", () => new MathBackendWebGL(), 2);
+}
+var webgl = { forceHalfFloat };
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js
+var CHECK_NAN_SNIPPET2 = `
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`;
+var BinaryOpProgram = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["A", "B"];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    this.userCode = `
+      float binaryOperation(float a, float b) {
+        ${op2}
+      }
+
+      void main() {
+        float a = getAAtOutCoords();
+        float b = getBAtOutCoords();
+        setOutput(binaryOperation(a, b));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js
+var CHECK_NAN_SNIPPET3 = `
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`;
+var BinaryOpPackedProgram = class {
+  constructor(op2, aShape, bShape, checkOutOfBounds = false) {
+    this.variableNames = ["A", "B"];
+    this.supportsBroadcasting = true;
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    const rank = this.outputShape.length;
+    this.enableShapeUniforms = useShapeUniforms(rank);
+    let checkOutOfBoundsString = "";
+    if (checkOutOfBounds) {
+      if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {
+        checkOutOfBoundsString = `
+          result.y = 0.;
+          result.z = 0.;
+          result.w = 0.;
+        `;
+      } else {
+        const dtype = getCoordsDataType(rank);
+        checkOutOfBoundsString = `
+          ${dtype} coords = getOutputCoords();
+        `;
+        if (rank === 1) {
+          if (this.enableShapeUniforms) {
+            checkOutOfBoundsString += `
+            result.y = (coords + 1) >= outShape ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;
+          } else {
+            checkOutOfBoundsString += `
+            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
+            result.z = 0.;
+            result.w = 0.;
+          `;
+          }
+        } else {
+          const channels = getChannels("coords", rank);
+          if (this.enableShapeUniforms) {
+            checkOutOfBoundsString += `
+            bool nextRowOutOfBounds =
+              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];
+            bool nextColOutOfBounds =
+              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `;
+          } else {
+            checkOutOfBoundsString += `
+            bool nextRowOutOfBounds =
+              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};
+            bool nextColOutOfBounds =
+              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};
+            result.y = nextColOutOfBounds ? 0. : result.y;
+            result.z = nextRowOutOfBounds ? 0. : result.z;
+            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
+          `;
+          }
+        }
+      }
+    }
+    this.userCode = `
+      vec4 binaryOperation(vec4 a, vec4 b) {
+        ${op2}
+      }
+
+      void main() {
+        vec4 a = getAAtOutCoords();
+        vec4 b = getBAtOutCoords();
+
+        vec4 result = binaryOperation(a, b);
+        ${checkOutOfBoundsString}
+
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js
+function identity3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  backend2.incRef(x.dataId);
+  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+}
+var identityConfig2 = {
+  kernelName: Identity,
+  backendName: "webgl",
+  kernelFunc: identity3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js
+function complex3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { real: real5, imag: imag5 } = inputs;
+  const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64");
+  const complex5 = backend2.texData.get(complexInfo.dataId);
+  const realTensorInfo = identity3({ inputs: { x: real5 }, backend: backend2 });
+  const imagTensorInfo = identity3({ inputs: { x: imag5 }, backend: backend2 });
+  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };
+  return complexInfo;
+}
+var complexConfig2 = {
+  kernelName: Complex,
+  backendName: "webgl",
+  kernelFunc: complex3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js
+var LEAKYRELU = `return (a < 0.) ? b * a : a;`;
+var LEAKYRELU_PACKED = `
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`;
+function leakyRelu3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { alpha } = attrs;
+  const $alpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(alpha, "float32"));
+  const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);
+  const result = backend2.runWebGLProgram(program, [x, $alpha], "float32");
+  backend2.disposeIntermediateTensorInfo($alpha);
+  return result;
+}
+var leakyReluConfig2 = {
+  kernelName: LeakyRelu,
+  backendName: "webgl",
+  kernelFunc: leakyRelu3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js
+var PRELU = `return (a < 0.) ? b * a : a;`;
+var PRELU_PACKED = `
+  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
+  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
+`;
+function prelu4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha } = inputs;
+  const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);
+  return backend2.runWebGLProgram(program, [x, alpha], "float32");
+}
+var preluConfig2 = {
+  kernelName: Prelu,
+  backendName: "webgl",
+  kernelFunc: prelu4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js
+var CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;
+var CHECK_NAN_SNIPPET_BINARY = `
+  if (isnan(a)) return a;
+  if (isnan(b)) return b;
+`;
+var CHECK_NAN_SNIPPET_BINARY_PACKED = `
+  result.r = isNaN.r > 0. ? NAN : result.r;
+  result.g = isNaN.g > 0. ? NAN : result.g;
+  result.b = isNaN.b > 0. ? NAN : result.b;
+  result.a = isNaN.a > 0. ? NAN : result.a;
+`;
+function unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const webglBackend = backend2;
+    const $dtype = dtype || x.dtype;
+    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
+      const xData = webglBackend.texData.get(x.dataId);
+      const outValues = cpuKernelImpl(xData.values, $dtype);
+      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
+    }
+    const shouldUsePackedProgram = env().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null;
+    let program;
+    if (shouldUsePackedProgram) {
+      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);
+    } else {
+      program = new UnaryOpProgram(x.shape, opSnippet);
+    }
+    return webglBackend.runWebGLProgram(program, [x], $dtype);
+  };
+}
+function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { a: a6, b } = inputs;
+    const webglBackend = backend2;
+    if (supportsComplex && a6.dtype === "complex64") {
+      const aData = webglBackend.texData.get(a6.dataId);
+      const bData = webglBackend.texData.get(b.dataId);
+      const [real5, imag5] = [
+        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
+        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
+      ].map((complexParts) => {
+        const [aPart, bPart] = complexParts;
+        const aHandle = {
+          dataId: aPart.dataId,
+          dtype: aPart.dtype,
+          shape: a6.shape
+        };
+        const bHandle = {
+          dataId: bPart.dataId,
+          dtype: bPart.dtype,
+          shape: b.shape
+        };
+        const program2 = new BinaryOpProgram(opSnippet, a6.shape, b.shape);
+        return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
+      });
+      const complexOutput = complex3({ inputs: { real: real5, imag: imag5 }, backend: webglBackend });
+      webglBackend.disposeIntermediateTensorInfo(real5);
+      webglBackend.disposeIntermediateTensorInfo(imag5);
+      return complexOutput;
+    }
+    const $dtype = dtype || upcastType(a6.dtype, b.dtype);
+    if ((a6.dtype === "string" || b.dtype === "string" || webglBackend.shouldExecuteOnCPU([a6, b])) && cpuKernelImpl != null) {
+      const aVals = webglBackend.texData.get(a6.dataId).values;
+      const bVals = webglBackend.texData.get(b.dataId).values;
+      const decodedAVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;
+      const decodedBVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;
+      const [outValues, outShape] = cpuKernelImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);
+      const out = webglBackend.makeTensorInfo(outShape, $dtype);
+      const outData = webglBackend.texData.get(out.dataId);
+      outData.values = outValues;
+      return out;
+    }
+    const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null;
+    let program;
+    if (shouldUsePackedProgram) {
+      program = new BinaryOpPackedProgram(packedOpSnippet, a6.shape, b.shape, checkOutOfBounds);
+    } else {
+      program = new BinaryOpProgram(opSnippet, a6.shape, b.shape);
+    }
+    return webglBackend.runWebGLProgram(program, [a6, b], $dtype);
+  };
+}
+function mapActivationToShaderProgram(activation2, packed = false) {
+  if (activation2 === "linear") {
+    if (packed) {
+      return LINEAR2;
+    }
+    return LINEAR;
+  } else if (activation2 === "relu") {
+    if (packed) {
+      return RELU2;
+    }
+    return RELU;
+  } else if (activation2 === "elu") {
+    if (packed) {
+      return ELU3;
+    }
+    return ELU2;
+  } else if (activation2 === "relu6") {
+    if (packed) {
+      return RELU62;
+    }
+    return RELU6;
+  } else if (activation2 === "prelu") {
+    if (packed) {
+      return PRELU_PACKED;
+    }
+    return PRELU;
+  } else if (activation2 === "leakyrelu") {
+    if (packed) {
+      return LEAKYRELU_PACKED;
+    }
+    return LEAKYRELU;
+  } else if (activation2 === "sigmoid") {
+    if (packed) {
+      return SIGMOID2;
+    }
+    return SIGMOID;
+  }
+  throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js
+var MatMulPackedProgram = class {
+  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {
+    this.variableNames = ["matrixA", "matrixB"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const sharedDim = transposeA ? aShape[1] : aShape[2];
+    const sharedDimensionPacked = Math.ceil(sharedDim / 2);
+    const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2";
+    const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z";
+    const aSwizzle = transposeA ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"];
+    const bSwizzle = transposeB ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"];
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivation) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyreluActivation) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `vec4 activation(vec4 x) {
+          ${activation2}
+        }`;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyreluActivation) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    let batchASnippet = "rc.x";
+    let batchBSnippet = "rc.x";
+    if (aShape[0] < bShape[0]) {
+      batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;
+    } else if (bShape[0] < aShape[0]) {
+      batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;
+    }
+    this.userCode = `
+      ${activationSnippet}
+      // Don't use uniform for sharedDimensionPacked for performance.
+      const float sharedDimension = ${sharedDimensionPacked}.0;
+
+      vec4 dot2x2ARowBCol(ivec3 rc) {
+        vec4 result = vec4(0);
+        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
+          int batchA = ${batchASnippet};
+          int batchB = ${batchBSnippet};
+          vec4 a = getMatrixA(batchA, ${aSample});
+          vec4 b = getMatrixB(batchB, ${bSample});
+
+          // These swizzled products need to be separately added.
+          // See: https://github.com/tensorflow/tfjs/issues/1735
+          result += (${aSwizzle[0]} * ${bSwizzle[0]});
+          result += (${aSwizzle[1]} * ${bSwizzle[1]});
+        }
+        return result;
+      }
+
+      void main() {
+        ivec3 rc = getOutputCoords();
+        vec4 result = dot2x2ARowBCol(rc);
+
+        ${addBiasSnippet}
+
+        ${applyActivationSnippet}
+
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js
+var COMPLEX_MULTIPLY = {
+  REAL: "return areal * breal - aimag * bimag;",
+  IMAG: "return areal * bimag + aimag * breal;"
+};
+var BinaryOpComplexProgram = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["AReal", "AImag", "BReal", "BImag"];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.userCode = `
+      float binaryOpComplex(
+          float areal, float aimag, float breal, float bimag) {
+        ${op2}
+      }
+
+      void main() {
+        float areal = getARealAtOutCoords();
+        float aimag = getAImagAtOutCoords();
+        float breal = getBRealAtOutCoords();
+        float bimag = getBImagAtOutCoords();
+        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js
+var MUL = "return a * b;";
+function multiply3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { a: a6, b } = inputs;
+  const dtype = backend_util_exports.upcastType(a6.dtype, b.dtype);
+  if (a6.dtype === "complex64") {
+    const aData = backend2.texData.get(a6.dataId);
+    const bData = backend2.texData.get(b.dataId);
+    const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a6.shape, b.shape);
+    const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a6.shape, b.shape);
+    const inputs2 = [
+      {
+        dataId: aData.complexTensorInfos.real.dataId,
+        dtype: aData.complexTensorInfos.real.dtype,
+        shape: a6.shape
+      },
+      {
+        dataId: aData.complexTensorInfos.imag.dataId,
+        dtype: aData.complexTensorInfos.imag.dtype,
+        shape: a6.shape
+      },
+      {
+        dataId: bData.complexTensorInfos.real.dataId,
+        dtype: bData.complexTensorInfos.real.dtype,
+        shape: b.shape
+      },
+      {
+        dataId: bData.complexTensorInfos.imag.dataId,
+        dtype: bData.complexTensorInfos.imag.dtype,
+        shape: b.shape
+      }
+    ];
+    const realPart = backend2.runWebGLProgram(realProgram, inputs2, "float32");
+    const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, "float32");
+    const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    return complexOutput;
+  }
+  if (backend2.shouldExecuteOnCPU([a6, b])) {
+    const aData = backend2.texData.get(a6.dataId);
+    const bData = backend2.texData.get(b.dataId);
+    const [outValues, outShape] = multiplyImplCPU(a6.shape, b.shape, aData.values, bData.values, dtype);
+    const out = backend2.makeTensorInfo(outShape, dtype);
+    const outData = backend2.texData.get(out.dataId);
+    outData.values = outValues;
+    return out;
+  }
+  let program;
+  if (env().getBool("WEBGL_PACK_BINARY_OPERATIONS")) {
+    program = new BinaryOpPackedProgram(MUL, a6.shape, b.shape);
+  } else {
+    program = new BinaryOpProgram(MUL, a6.shape, b.shape);
+  }
+  return backend2.runWebGLProgram(program, [a6, b], dtype);
+}
+var multiplyConfig2 = {
+  kernelName: Multiply,
+  backendName: "webgl",
+  kernelFunc: multiply3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js
+function packedReshape(input2, afterShape, backend2) {
+  const input3DShape = [
+    getBatchDim(input2.shape),
+    ...getRowsCols(input2.shape)
+  ];
+  const input3D = {
+    dtype: input2.dtype,
+    shape: input3DShape,
+    dataId: input2.dataId
+  };
+  const afterShapeAs3D = [
+    getBatchDim(afterShape),
+    ...getRowsCols(afterShape)
+  ];
+  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
+  const preventEagerUnpackingOfOutput = true;
+  const customValues = [input3DShape];
+  const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);
+  return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js
+function reshape4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const webglBackend = backend2;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  const $xSize = util_exports.sizeFromShape($shape);
+  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
+  const xTexData = webglBackend.texData.get(x.dataId);
+  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {
+    return packedReshape(x, $shape, webglBackend);
+  }
+  webglBackend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig2 = {
+  kernelName: Reshape,
+  backendName: "webgl",
+  kernelFunc: reshape4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js
+var MeanProgram = class {
+  constructor(reduceInfo, divisor) {
+    this.variableNames = ["x"];
+    const { windowSize, batchSize, inSize, outSize } = reduceInfo;
+    this.outputShape = [batchSize, outSize];
+    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+    const windowSizeVec4Remainder = windowSize % 4;
+    let updateSnippet = `sumValue += dot(values, ones);`;
+    if (divisor != null) {
+      const denominator = 1 / divisor;
+      updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;
+    }
+    let checkOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return 0.0;
+        }
+      `;
+    }
+    this.userCode = `
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${checkOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder === 1}) {
+          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1), 0.0, 0.0);
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2), 0.0);
+
+          ${updateSnippet}
+        }
+        setOutput(sumValue);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js
+var ReduceProgram = class {
+  constructor(reduceInfo, reduceType) {
+    this.variableNames = ["x"];
+    const { windowSize, batchSize, inSize, outSize } = reduceInfo;
+    this.outputShape = [batchSize, outSize];
+    let initializationValue = "0.0";
+    let compareOp = ``;
+    if (reduceType === "prod") {
+      initializationValue = "1.0";
+    } else if (reduceType === "min") {
+      initializationValue = "1.0 / 1e-20";
+      compareOp = `min`;
+    } else if (reduceType === "max") {
+      initializationValue = "-1.0 / 1e-20";
+      compareOp = `max`;
+    }
+    let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
+    if (reduceType === "sum") {
+      returnValue = `sumValue`;
+    } else if (reduceType === "prod") {
+      returnValue = `prodValue`;
+    } else if (reduceType === "all") {
+      returnValue = `allValue`;
+    } else if (reduceType === "any") {
+      returnValue = `anyValue`;
+    }
+    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+    const windowSizeVec4Remainder = windowSize % 4;
+    let updateSnippet = `
+      if (${reduceType === "sum"}) {
+        sumValue += dot(values, ones);
+      } else if (${reduceType === "prod"}) {
+        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
+        prodValue *= tmp[0] * tmp[1];
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+        if (${reduceType === "min"} || ${reduceType === "max"}) {
+          minMaxValue = ${compareOp}(values, minMaxValue);
+          bvec4 isNaN = isnan(values);
+          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
+            minMaxValue = vec4(NAN);
+          }
+        }
+      }
+    `;
+    let vecType = `vec4`;
+    if (reduceType === "all") {
+      initializationValue = "1.0";
+      updateSnippet = `
+        bool reducedAllValue = all(values);
+        float floatedReducedAllValue = float(reducedAllValue);
+        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
+      `;
+      vecType = `bvec4`;
+    } else if (reduceType === "any") {
+      initializationValue = "0.0";
+      updateSnippet = `
+        bool reducedAnyValue = any(values);
+        float floatedReducedAnyValue = float(reducedAnyValue);
+        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
+      `;
+      vecType = `bvec4`;
+    }
+    let checkOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return initializationValue;
+        }
+      `;
+    }
+    this.userCode = `
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float getValue(int batch, int inIdx) {
+        ${checkOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        vec4 minMaxValue = vec4(${initializationValue});
+        float prodValue = 1.0;
+        float sumValue = 0.0;
+        float allValue = 1.0;
+        float anyValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder === 1}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 2}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 3}) {
+          ${vecType} values = ${vecType}(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          ${updateSnippet}
+        }
+        setOutput(${returnValue});
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js
+function getReductionStages(inShape) {
+  const stages = [];
+  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {
+    const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];
+    const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);
+    stages.push({
+      inSize: outSize,
+      windowSize,
+      outSize: Math.ceil(outSize / windowSize)
+    });
+  }
+  return stages;
+}
+function reduce(x, dtype, reductionType, backend2) {
+  const reductionStages = getReductionStages(x.shape);
+  let result = x;
+  for (let i2 = 0; i2 < reductionStages.length; i2++) {
+    const { inSize, windowSize, outSize } = reductionStages[i2];
+    let program;
+    let previousResult;
+    if (reductionType === "mean") {
+      program = i2 === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
+    } else {
+      program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);
+    }
+    previousResult = result;
+    result = backend2.runWebGLProgram(program, [result], dtype);
+    if (previousResult.dataId !== x.dataId) {
+      backend2.disposeIntermediateTensorInfo(previousResult);
+    }
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js
+var TransposeProgram = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    const dtype = getCoordsDataType(this.rank);
+    const switched = getSwitchedCoords(newDim);
+    this.userCode = `
+    void main() {
+      ${dtype} resRC = getOutputCoords();
+      setOutput(getA(${switched}));
+    }
+    `;
+  }
+};
+function getSwitchedCoords(newDim) {
+  const rank = newDim.length;
+  if (rank > 6) {
+    throw Error(`Transpose for rank ${rank} is not yet supported`);
+  }
+  const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"];
+  const switchedCoords = new Array(rank);
+  for (let i2 = 0; i2 < newDim.length; i2++) {
+    switchedCoords[newDim[i2]] = originalOrder[i2];
+  }
+  return switchedCoords.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js
+var TransposePackedProgram = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    if (this.rank > 6) {
+      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
+    }
+    const dtype = getCoordsDataType(this.rank);
+    const outputOrder = getVecChannels("rc", this.rank);
+    const switchedOrder = new Array(this.rank);
+    for (let i2 = 0; i2 < newDim.length; i2++) {
+      switchedOrder[newDim[i2]] = outputOrder[i2];
+    }
+    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
+    const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
+    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;
+    this.userCode = `
+    void main() {
+      ${dtype} rc = getOutputCoords();
+      vec4 result = vec4(0.);
+      result[0] = ${getc};
+      if(${nextColumn}) {
+        result[1] = ${getc};
+      }
+      --${outputOrder[this.rank - 1]};
+      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {
+        result[2] = ${getc};
+        if(${nextColumn}) {
+          result[3] = ${getc};
+        }
+      }
+      setOutput(result);
+    }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js
+function transposeImpl2(x, perm, backend2) {
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js
+function sumImpl(x, axis, keepDims, backend2) {
+  const reductionIndices = axis;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  const sumInputIsTransposed = permutedAxes != null;
+  let sumInput = x;
+  if (sumInputIsTransposed) {
+    sumInput = transposeImpl2(x, permutedAxes, backend2);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
+  const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);
+  let outShape = sumOutShape;
+  if (keepDims) {
+    outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);
+  }
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const batchSize = xSize / inSize;
+  const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
+  const outType = sumOutType(x.dtype);
+  const reduced = reduce(reshapedInput, outType, "sum", backend2);
+  const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(reshapedInput);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (sumInputIsTransposed) {
+    backend2.disposeIntermediateTensorInfo(sumInput);
+  }
+  return out;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js
+function sum4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return sumImpl(x, axis, keepDims, backend2);
+}
+var sumConfig2 = {
+  kernelName: Sum,
+  backendName: "webgl",
+  kernelFunc: sum4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js
+function transpose3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { perm } = attrs;
+  const webglBackend = backend2;
+  const xRank = x.shape.length;
+  const newShape = new Array(xRank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[perm[i2]];
+  }
+  let out;
+  if (webglBackend.shouldExecuteOnCPU([x])) {
+    const xTexData = webglBackend.texData.get(x.dataId);
+    const values = xTexData.values;
+    const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);
+    out = webglBackend.makeTensorInfo(newShape, x.dtype);
+    const outData = webglBackend.texData.get(out.dataId);
+    outData.values = outValues;
+  } else {
+    out = transposeImpl2(x, perm, webglBackend);
+  }
+  return out;
+}
+var transposeConfig2 = {
+  kernelName: Transpose,
+  backendName: "webgl",
+  kernelFunc: transpose3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js
+var MATMUL_SHARED_DIM_THRESHOLD = 1e3;
+function batchMatMulImpl({ a: a6, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape4({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const intermediates = [a3d, b3d];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const hasLeakyreluAlpha = activation2 === "leakyrelu";
+  const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;
+  const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;
+  let out;
+  if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
+    let aVec = a3d;
+    let bVec = b3d;
+    if (transposeA) {
+      aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });
+      intermediates.push(aVec);
+    }
+    if (transposeB) {
+      bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });
+      intermediates.push(bVec);
+    }
+    const shouldReshapeA = outerShapeB !== 1;
+    const shouldReshapeB = outerShapeB === 1;
+    let aVec3d = aVec;
+    if (shouldReshapeA) {
+      aVec3d = reshape4({
+        inputs: { x: aVec },
+        backend: backend2,
+        attrs: { shape: [batchDim, sharedDim, 1] }
+      });
+      intermediates.push(aVec3d);
+    }
+    const axis = outerShapeB === 1 ? 2 : 1;
+    let bVec3d = bVec;
+    if (shouldReshapeB) {
+      bVec3d = reshape4({
+        inputs: { x: bVec },
+        backend: backend2,
+        attrs: { shape: [batchDim, 1, sharedDim] }
+      });
+      intermediates.push(bVec3d);
+    }
+    const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });
+    out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });
+    intermediates.push(product);
+  } else {
+    const dtype = upcastType(a6.dtype, b.dtype);
+    const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+    const inputs = [a3d, b3d];
+    if (bias != null) {
+      inputs.push(bias);
+    }
+    if (hasPreluActivationWeights) {
+      inputs.push(preluActivationWeights);
+    }
+    if (hasLeakyreluAlpha) {
+      const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+      inputs.push($leakyreluAlpha);
+      intermediates.push($leakyreluAlpha);
+    }
+    out = backend2.runWebGLProgram(program, inputs, dtype);
+  }
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
+  intermediates.push(out);
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return outReshaped;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js
+function _fusedMatMul2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  return batchMatMulImpl({
+    a: a6,
+    b,
+    transposeA,
+    transposeB,
+    backend: backend2,
+    bias,
+    preluActivationWeights,
+    leakyreluAlpha,
+    activation: activation2
+  });
+}
+var _fusedMatMulConfig2 = {
+  kernelName: _FusedMatMul,
+  backendName: "webgl",
+  kernelFunc: _fusedMatMul2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js
+var ABS2 = `return abs(x);`;
+function abs3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
+    const xData = backend2.texData.get(x.dataId);
+    const outValues = simpleAbsImplCPU(xData.values);
+    return backend2.makeTensorInfo(x.shape, x.dtype, outValues);
+  }
+  let program;
+  if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
+    program = new UnaryOpPackedProgram(x.shape, ABS2);
+  } else {
+    program = new UnaryOpProgram(x.shape, ABS2);
+  }
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var absConfig2 = {
+  kernelName: Abs,
+  backendName: "webgl",
+  kernelFunc: abs3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js
+var ACOS = CHECK_NAN_SNIPPET + `
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return acos(x);
+`;
+var acos3 = unaryKernelFunc2({ opSnippet: ACOS });
+var acosConfig2 = {
+  kernelName: Acos,
+  backendName: "webgl",
+  kernelFunc: acos3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js
+var ACOSH = CHECK_NAN_SNIPPET + `
+  if (x < 1.0) return NAN;
+return log(x + sqrt(x * x - 1.0));`;
+var acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });
+var acoshConfig2 = {
+  kernelName: Acosh,
+  backendName: "webgl",
+  kernelFunc: acosh3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js
+var ADD = "return a + b;";
+var addKernelFunc = binaryKernelFunc2({
+  opSnippet: ADD,
+  packedOpSnippet: ADD,
+  supportsComplex: true,
+  cpuKernelImpl: addImplCPU
+});
+var addConfig2 = {
+  kernelName: Add,
+  backendName: "webgl",
+  kernelFunc: addKernelFunc
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js
+var AddNProgram = class {
+  constructor(outputShape, shapes) {
+    this.outputShape = [];
+    this.outputShape = outputShape;
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const snippets = [];
+    this.variableNames.forEach((variable2) => {
+      snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);
+    });
+    const operation = this.variableNames.map((variable2) => {
+      return `v${variable2}`;
+    }).join(" + ");
+    this.userCode = `
+      void main() {
+        ${snippets.join("\n        ")}
+
+        float result = ${operation};
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js
+var AddNPackedProgram = class {
+  constructor(outputShape, shapes) {
+    this.outputShape = [];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = outputShape;
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const snippets = [];
+    this.variableNames.forEach((variable2) => {
+      snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);
+    });
+    const operation = this.variableNames.map((variable2) => {
+      return `v${variable2}`;
+    }).join(" + ");
+    this.userCode = `
+      void main() {
+        ${snippets.join("\n        ")}
+
+        vec4 result = ${operation};
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js
+function addN3(args) {
+  const { inputs, backend: backend2 } = args;
+  const tensors = inputs;
+  if (tensors.length === 1) {
+    return identity3({ inputs: { x: tensors[0] }, backend: backend2 });
+  }
+  if (tensors.length > env().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
+    const midIndex = Math.floor(tensors.length / 2);
+    const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 });
+    const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });
+    return addN3({ inputs: [leftSide, rightSide], backend: backend2 });
+  }
+  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));
+  const shapes = tensors.map((t2) => t2.shape);
+  const usePackedOp = env().getBool("WEBGL_PACK");
+  const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);
+  return backend2.runWebGLProgram(program, tensors, dtype);
+}
+var addNConfig2 = {
+  kernelName: AddN,
+  backendName: "webgl",
+  kernelFunc: addN3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js
+function all3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("all", axes, xRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  const reduced = reduce(a2D, a2D.dtype, "all", backend2);
+  let res;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
+  } else {
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+  }
+  backend2.disposeIntermediateTensorInfo(a2D);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return res;
+}
+var allConfig2 = {
+  kernelName: All,
+  backendName: "webgl",
+  kernelFunc: all3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js
+function any3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("any", axes, xRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  const reduced = reduce(a2D, a2D.dtype, "any", backend2);
+  let res;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
+  } else {
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+  }
+  backend2.disposeIntermediateTensorInfo(a2D);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return res;
+}
+var anyConfig2 = {
+  kernelName: Any,
+  backendName: "webgl",
+  kernelFunc: any3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js
+var ArgMinMaxProgram = class {
+  constructor(reduceInfo, op2, firstPass) {
+    this.variableNames = ["A"];
+    const { windowSize, batchSize, outSize } = reduceInfo;
+    if (!firstPass) {
+      this.variableNames.push("bestIndicesA");
+    }
+    this.outputShape = [batchSize, outSize];
+    const compOp = op2 === "max" ? ">" : "<";
+    const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = outIdx * ${windowSize};
+
+        int bestIndex = inOffset;
+        float bestValue = getA(batch, bestIndex);
+
+        for (int i = 0; i < ${windowSize}; i++) {
+          int inIdx = ${indexSnippet};
+          float candidate = getA(batch, inIdx);
+          if (candidate ${compOp} bestValue) {
+            bestValue = candidate;
+            bestIndex = inIdx;
+          }
+        }
+        setOutput(float(bestIndex));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js
+var ArgMinMaxPackedProgram = class {
+  constructor(shape, windowSize, op2, firstPass) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);
+    const inSize = shape[shape.length - 1];
+    const outSize = Math.ceil(inSize / windowSize);
+    this.outputShape = shape.slice(0, -1);
+    if (outSize > 1) {
+      this.outputShape.push(outSize);
+    }
+    if (!firstPass) {
+      this.variableNames.push("bestIndicesA");
+    }
+    const outShape = this.outputShape;
+    const rank = outShape.length;
+    const dtype = getCoordsDataType(rank);
+    const coords3 = getChannels("coords", rank);
+    let sourceLocSetup;
+    let sourceRank;
+    if (outSize === 1) {
+      sourceRank = rank + 1;
+      const sourceLocDType = getCoordsDataType(sourceRank);
+      sourceLocSetup = `
+        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords3.join()}, 0);
+        ++${coords3[rank - 1]};
+        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords3.join()}, 0);
+        ++${coords3[rank - 2]};
+        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords3.join()}, 0);
+        --${coords3[rank - 1]};
+        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords3.join()}, 0);
+        --${coords3[rank - 2]};`;
+    } else {
+      sourceRank = rank;
+      sourceLocSetup = `
+        ${dtype} sourceLocR = coords;
+        ++${coords3[rank - 1]};
+        ${dtype} sourceLocG = coords;
+        ++${coords3[rank - 2]};
+        ${dtype} sourceLocA = coords;
+        --${coords3[rank - 1]};
+        ${dtype} sourceLocB = coords;
+        --${coords3[rank - 2]};`;
+    }
+    const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank);
+    const inChannel = "." + channels[sourceRank - 1];
+    const intChannels = channels.map((x) => "int " + x);
+    const srcRCoords = getChannels("sourceLocR", sourceRank - 1).concat("inIdx.r");
+    const srcGCoords = getChannels("sourceLocG", sourceRank - 1).concat("inIdx.g");
+    const srcBCoords = getChannels("sourceLocB", sourceRank - 1).concat("inIdx.b");
+    const srcACoords = getChannels("sourceLocA", sourceRank - 1).concat("inIdx.a");
+    const compOp = op2 === "max" ? "greaterThan" : "lessThan";
+    const fetchCandidateIdx = firstPass ? "" : `
+          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
+                             getBestIndicesAChannel(${srcGCoords.join()}),
+                             getBestIndicesAChannel(${srcBCoords.join()}),
+                             getBestIndicesAChannel(${srcACoords.join()})));`;
+    const fetchValue = `vec4(
+            getAChannel(${srcRCoords.join()}),
+            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
+            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
+            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;
+    const getBestIndicesAChannelSnippet = firstPass ? "" : `
+      float getBestIndicesAChannel(${intChannels.join()}) {
+        return getChannel(getBestIndicesA(${channels.join()}),
+                                          vec2(${channels.slice(-2).join()}));
+      }`;
+    this.userCode = `
+      float getAChannel(${intChannels.join()}) {
+        return getChannel(getA(${channels.join()}),
+                               vec2(${channels.slice(-2).join()}));
+      }
+      ${getBestIndicesAChannelSnippet}
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        bool hasNextCol = ${coords3[rank - 1]} < ${outShape[rank - 1] - 1};
+        bool hasNextRow = ${coords3[rank - 2]} < ${outShape[rank - 2] - 1};
+        ${sourceLocSetup}
+        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
+          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
+        ivec4 inIdx = srcIdx;
+        vec4 bestIndex = vec4(inIdx);
+        vec4 bestValue = ${fetchValue};
+
+        for (int i = 0; i < ${windowSize}; i++) {
+          inIdx = srcIdx;
+          ${fetchCandidateIdx}
+          vec4 candidate = ${fetchValue};
+          bvec4 nan = isnan(candidate);
+          bvec4 replace = bvec4(
+            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
+
+          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
+                           replace.y  ? candidate.y : bestValue.y,
+                           replace.z  ? candidate.z : bestValue.z,
+                           replace.w  ? candidate.w : bestValue.w);
+          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
+          srcIdx++;
+        }
+        setOutput(bestIndex);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js
+function argReduce(backend2, x, reduceType, bestIndicesA = null) {
+  let batchSize = x.shape[0];
+  let inSize = x.shape[1];
+  if (bestIndicesA != null) {
+    batchSize = bestIndicesA.shape[0];
+    inSize = bestIndicesA.shape[1];
+  }
+  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
+  const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };
+  const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
+  const inputs = [x];
+  if (bestIndicesA != null) {
+    inputs.push(bestIndicesA);
+  }
+  const output = backend2.runWebGLProgram(program, inputs, "int32");
+  if (output.shape[1] === 1) {
+    return output;
+  }
+  const result = argReduce(backend2, x, reduceType, output);
+  backend2.disposeIntermediateTensorInfo(output);
+  return result;
+}
+function argReducePacked(backend2, x, reduceType, bestIndicesA = null) {
+  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
+  const inSize = inShape[inShape.length - 1];
+  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
+  const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);
+  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
+  const output = backend2.runWebGLProgram(program, inputs, "int32");
+  if (output.shape.length === x.shape.length) {
+    const result = argReducePacked(backend2, x, reduceType, output);
+    backend2.disposeIntermediateTensorInfo(output);
+    return result;
+  }
+  return output;
+}
+function argMinMaxReduce(backend2, x, axis, reduceType) {
+  const axes = [axis];
+  backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);
+  if (!env().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) {
+    const intermediateTensorInfos = [];
+    const xtexData = backend2.texData.get(x.dataId);
+    const xIsPacked = xtexData !== null && xtexData.isPacked;
+    let xUnPacked = x;
+    if (xIsPacked) {
+      xUnPacked = backend2.unpackTensor(x);
+      intermediateTensorInfos.push(xUnPacked);
+    }
+    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);
+    const inSize = util_exports.sizeFromShape(reduceShape);
+    const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });
+    intermediateTensorInfos.push(a2D);
+    const reduced = argReduce(backend2, a2D, reduceType);
+    intermediateTensorInfos.push(reduced);
+    const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+    intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    return reshaped;
+  }
+  return argReducePacked(backend2, x, reduceType);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js
+function argMax3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
+  const out = argMinMaxReduce(backend2, $x, axes[0], "max");
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return out;
+}
+var argMaxConfig2 = {
+  kernelName: ArgMax,
+  backendName: "webgl",
+  kernelFunc: argMax3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js
+function argMin3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
+  const out = argMinMaxReduce(backend2, $x, axes[0], "min");
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return out;
+}
+var argMinConfig2 = {
+  kernelName: ArgMin,
+  backendName: "webgl",
+  kernelFunc: argMin3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js
+var ASIN = CHECK_NAN_SNIPPET + `
+  if (abs(x) > 1.) {
+    return NAN;
+  }
+  return asin(x);
+`;
+var asin3 = unaryKernelFunc2({ opSnippet: ASIN });
+var asinConfig2 = {
+  kernelName: Asin,
+  backendName: "webgl",
+  kernelFunc: asin3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js
+var ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;
+var asinh3 = unaryKernelFunc2({ opSnippet: ASINH });
+var asinhConfig2 = {
+  kernelName: Asinh,
+  backendName: "webgl",
+  kernelFunc: asinh3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js
+var ATAN = CHECK_NAN_SNIPPET + `
+  return atan(x);
+`;
+var atan4 = unaryKernelFunc2({ opSnippet: ATAN });
+var atanConfig2 = {
+  kernelName: Atan,
+  backendName: "webgl",
+  kernelFunc: atan4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js
+var ATAN2 = CHECK_NAN_SNIPPET_BINARY + `
+  return atan(a, b);
+`;
+var ATAN2_PACKED = `
+  vec4 result = atan(a, b);
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  ` + CHECK_NAN_SNIPPET_BINARY_PACKED + `
+  return result;
+`;
+var atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });
+var atan2Config2 = {
+  kernelName: Atan2,
+  backendName: "webgl",
+  kernelFunc: atan23
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js
+var ATANH = CHECK_NAN_SNIPPET + `
+  if ((x < -1.0) || (x > 1.0)) return NAN;
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
+var atanh3 = unaryKernelFunc2({ opSnippet: ATANH });
+var atanhConfig2 = {
+  kernelName: Atanh,
+  backendName: "webgl",
+  kernelFunc: atanh3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js
+var Pool2DProgram = class {
+  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
+    this.variableNames = ["x"];
+    if (poolType === "avg" && computePositions) {
+      throw new Error("Cannot compute positions for average pool.");
+    }
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    this.outputShape = convInfo.outShape;
+    const isAvgPool = poolType === "avg";
+    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
+    const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
+    let initializationValue = "0.0";
+    if (!isAvgPool) {
+      initializationValue = "-1.0 / 1e-20";
+    }
+    if (computePositions) {
+      const compareOp2 = ">=";
+      this.userCode = `
+        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+        const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int batch = coords[0];
+          int d = coords[3];
+
+          ivec2 xRCCorner = coords.yz * strides - pads;
+          int xRCorner = xRCCorner.x;
+          int xCCorner = xRCCorner.y;
+
+          // max/min x(?, ?, d) to get y(yR, yC, d).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+          float avgValue = 0.0;
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              int xC = xCCorner + wC;
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              float value = getX(batch, xR, xC, d);
+
+              // If a min / max value has already been found, use it. If not,
+              // use the current value.
+              float currMinMaxValue = mix(
+                  value, minMaxValue, minMaxValueFound);
+              if (value ${compareOp2} currMinMaxValue) {
+                minMaxValue = value;
+                minMaxValueFound = 1.0;
+                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;
+      return;
+    }
+    const compareOp = "max";
+    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
+    if (poolType === "avg") {
+      returnValue = `avgValue / count`;
+    }
+    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+    const filterWidthVec4Remainder = filterWidth % 4;
+    const updateSnippet = `
+      if (${isAvgPool}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `;
+    this.userCode = `
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xR, int xC, int d) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xR, xC, d);
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d = coords[3];
+
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // max/min x(?, ?, d) to get y(yR, yC, d).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${initializationValue});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          int xR = xRCorner + wR;
+
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
+            );
+
+            ${updateSnippet}
+          }
+
+          int xC = xCCorner + ${filterWidthNearestVec4};
+          if (${filterWidthVec4Remainder === 1}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              initializationValue,
+              initializationValue,
+              initializationValue
+            );
+
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder === 2}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              initializationValue,
+              initializationValue
+            );
+
+            ${updateSnippet}
+          } else if (${filterWidthVec4Remainder === 3}) {
+            vec4 values = vec4(
+              getValue(batch, xR, xC, d),
+              getValue(batch, xR, xC + ${dilationWidth}, d),
+              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
+              initializationValue
+            );
+
+            ${updateSnippet}
+          }
+        }
+        setOutput(${returnValue});
+      }
+    `;
+  }
+};
+var Pool3DProgram = class {
+  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
+    this.variableNames = ["x"];
+    if (poolType === "avg" && computePositions) {
+      throw new Error("Cannot compute positions for average pool.");
+    }
+    const filterWidth = convInfo.filterWidth;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padFront = convInfo.padInfo.front;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    this.outputShape = convInfo.outShape;
+    const isAvgPool = poolType === "avg";
+    let initializationValue = "0.0";
+    if (!isAvgPool) {
+      initializationValue = "-1.0 / 1e-20";
+    }
+    if (computePositions) {
+      const compareOp2 = ">=";
+      this.userCode = `
+        const ivec3 strides =
+            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+        void main() {
+          ivec5 coords = getOutputCoords();
+          int batch = coords.x;
+          int ch = coords.u;
+
+          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+          int xDCorner = xCorner.x;
+          int xRCorner = xCorner.y;
+          int xCCorner = xCorner.z;
+
+          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
+          // ? = to be determined
+          float minMaxValue = 0.0;
+          float minMaxValueFound = 0.0;
+          int minMaxPosition = 0;
+
+          for (int wD = 0; wD < ${effectiveFilterDepth};
+              wD += ${dilationDepth}) {
+            int xD = xDCorner + wD;
+
+            if (xD < 0 || xD >= ${convInfo.inDepth}) {
+              continue;
+            }
+
+            for (int wR = 0; wR < ${effectiveFilterHeight};
+                wR += ${dilationHeight}) {
+              int xR = xRCorner + wR;
+
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
+                continue;
+              }
+
+              for (int wC = 0; wC < ${effectiveFilterWidth};
+                  wC += ${dilationWidth}) {
+                int xC = xCCorner + wC;
+
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                  continue;
+                }
+
+                float value = getX(batch, xD, xR, xC, ch);
+
+                // If a min / max value has already been found, use it. If not,
+                // use the current value.
+                float currMinMaxValue = mix(
+                    value, minMaxValue, minMaxValueFound);
+                if (value ${compareOp2} currMinMaxValue) {
+                  minMaxValue = value;
+                  minMaxValueFound = 1.0;
+                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                      wR * ${effectiveFilterWidth} + wC`};
+                }
+              }
+            }
+          }
+          setOutput(float(minMaxPosition));
+        }
+      `;
+      return;
+    }
+    const compareOp = "max";
+    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
+    if (poolType === "avg") {
+      returnValue = `avgValue / count`;
+    }
+    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+    const filterWidthVec4Remainder = filterWidth % 4;
+    const updateSnippet = `
+      if (${isAvgPool}) {
+        avgValue += dot(values, ones);
+      } else {
+        minMaxValue = ${compareOp}(values, minMaxValue);
+      }
+    `;
+    this.userCode = `
+      const ivec3 strides =
+        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float initializationValue = ${initializationValue};
+      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
+
+      float count = 0.0;
+
+      float getValue(int batch, int xD, int xR, int xC, int ch) {
+        if (xC < 0 || xC >= ${convInfo.inWidth}) {
+          return initializationValue;
+        }
+        count += 1.0;
+        return getX(batch, xD, xR, xC, ch);
+      }
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xDCorner = xCorner.x;
+        int xRCorner = xCorner.y;
+        int xCCorner = xCorner.z;
+
+        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
+        // ? = to be determined
+        vec4 minMaxValue = vec4(${initializationValue});
+        float avgValue = 0.0;
+        count = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          int xD = xDCorner + wD;
+
+          if (xD < 0 || xD >= ${convInfo.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+            int xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
+              int xC = xCCorner + wC * ${dilationWidth};
+
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
+              );
+
+              ${updateSnippet}
+            }
+
+            int xC = xCCorner + ${filterWidthNearestVec4};
+            if (${filterWidthVec4Remainder === 1}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                initializationValue,
+                initializationValue,
+                initializationValue
+              );
+
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder === 2}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                initializationValue,
+                initializationValue
+              );
+
+              ${updateSnippet}
+            } else if (${filterWidthVec4Remainder === 3}) {
+              vec4 values = vec4(
+                getValue(batch, xD, xR, xC, ch),
+                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
+                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
+                initializationValue
+              );
+
+              ${updateSnippet}
+            }
+          }
+          setOutput(${returnValue});
+        }
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js
+function avgPool3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex2(x, "avgPool");
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    return identity3({ inputs: { x }, backend: backend2 });
+  }
+  const avgPoolProgram = new Pool2DProgram(convInfo, "avg", false);
+  return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
+}
+var avgPoolConfig2 = {
+  kernelName: AvgPool,
+  backendName: "webgl",
+  kernelFunc: avgPool3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js
+function avgPool3D2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);
+  const avgPoolProgram = new Pool3DProgram(convInfo, "avg", false);
+  return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
+}
+var avgPool3DConfig2 = {
+  kernelName: AvgPool3D,
+  backendName: "webgl",
+  kernelFunc: avgPool3D2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js
+var AvgPool2DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy"];
+    this.outputShape = convInfo.inShape;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const avgMultiplier = 1 / (filterHeight * filterWidth);
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+            wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${effectiveFilterWidth};
+            wC+= ${dilationWidth}) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+
+            dotProd += dyValue * avgMultiplier;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var AvgPool3DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy"];
+    this.outputShape = convInfo.inShape;
+    const filterDepth = convInfo.filterDepth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
+    this.userCode = `
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+      const float avgMultiplier = float(${avgMultiplier});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+            wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
+
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+
+              dotProd += dyValue * avgMultiplier;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js
+function avgPool3DGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
+  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
+}
+var avgPool3DGradConfig3 = {
+  kernelName: AvgPool3DGrad,
+  backendName: "webgl",
+  kernelFunc: avgPool3DGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js
+function avgPoolGrad3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  assertNotComplex2([dy, input2], "avgPoolGrad");
+  const { filterSize, strides, pad: pad3 } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);
+  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
+  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
+}
+var avgPoolGradConfig3 = {
+  kernelName: AvgPoolGrad,
+  backendName: "webgl",
+  kernelFunc: avgPoolGrad3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js
+function batchMatMul2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  return batchMatMulImpl({ a: a6, b, transposeA, transposeB, backend: backend2 });
+}
+var batchMatMulConfig2 = {
+  kernelName: BatchMatMul,
+  backendName: "webgl",
+  kernelFunc: batchMatMul2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js
+var BatchNormProgram = class {
+  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+    this.outputShape = [];
+    this.variableNames = ["x", "mean", "variance"];
+    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
+    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
+    let offsetSnippet = "0.0";
+    if (offsetShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
+      this.variableNames.push("offset");
+      offsetSnippet = "getOffsetAtOutCoords()";
+    }
+    let scaleSnippet = "1.0";
+    if (scaleShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
+      this.variableNames.push("scale");
+      scaleSnippet = "getScaleAtOutCoords()";
+    }
+    this.outputShape = xShape;
+    this.userCode = `
+      void main() {
+        float x = getXAtOutCoords();
+        float mean = getMeanAtOutCoords();
+        float variance = getVarianceAtOutCoords();
+        float offset = ${offsetSnippet};
+        float scale = ${scaleSnippet};
+        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
+        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js
+var BatchNormPackedProgram = class {
+  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.variableNames = ["x", "mean", "variance"];
+    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
+    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
+    let offsetSnippet = "vec4(0.0)";
+    if (offsetShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
+      this.variableNames.push("offset");
+      offsetSnippet = "getOffsetAtOutCoords()";
+    }
+    let scaleSnippet = "vec4(1.0)";
+    if (scaleShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
+      this.variableNames.push("scale");
+      scaleSnippet = "getScaleAtOutCoords()";
+    }
+    this.outputShape = xShape;
+    this.userCode = `
+      void main() {
+        vec4 offset = ${offsetSnippet};
+        vec4 scale = ${scaleSnippet};
+
+        vec4 x = getXAtOutCoords();
+        vec4 mean = getMeanAtOutCoords();
+        vec4 variance = getVarianceAtOutCoords();
+
+        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
+
+        setOutput((x - mean) * inv + offset);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js
+var batchNorm3 = ({ inputs, backend: backend2, attrs }) => {
+  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;
+  util_exports.assert(mean5.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
+  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
+  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
+  let { varianceEpsilon } = attrs;
+  if (varianceEpsilon == null) {
+    varianceEpsilon = 1e-3;
+  }
+  const finalInputs = [x, mean5, variance];
+  let offsetShape = null;
+  if (offset != null) {
+    offsetShape = offset.shape;
+    finalInputs.push(offset);
+  }
+  let scaleShape = null;
+  if (scale2 != null) {
+    scaleShape = scale2.shape;
+    finalInputs.push(scale2);
+  }
+  const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+  const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
+  return output;
+};
+var batchNormConfig2 = {
+  kernelName: FusedBatchNorm,
+  backendName: "webgl",
+  kernelFunc: batchNorm3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js
+var SliceProgram = class {
+  constructor(destSize) {
+    this.variableNames = ["source"];
+    this.outputShape = destSize;
+    this.rank = destSize.length;
+    const dtype = getCoordsDataType(this.rank);
+    this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
+    const sourceCoords = getCoords(this.rank);
+    let body;
+    const coordSum = destSize.map((_, i2) => {
+      return `sourceLoc.${coords[i2]} = start[${i2}] + coords.${coords[i2]};`;
+    });
+    body = `
+        ${dtype} sourceLoc;
+        ${dtype} coords = getOutputCoords();
+        ${coordSum.join("\n")}
+      `;
+    this.userCode = `
+      void main() {
+        ${body}
+        setOutput(getSource(${sourceCoords}));
+      }
+    `;
+  }
+};
+var coords = ["x", "y", "z", "w", "u", "v"];
+function getCoords(rank) {
+  if (rank === 1) {
+    return "sourceLoc";
+  } else if (rank <= 6) {
+    return coords.slice(0, rank).map((x) => "sourceLoc." + x).join(",");
+  } else {
+    throw Error(`Slicing for rank ${rank} is not yet supported`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js
+var SlicePackedProgram = class {
+  constructor(destSize) {
+    this.variableNames = ["source"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = destSize;
+    this.rank = destSize.length;
+    this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
+    const dtype = getCoordsDataType(this.rank);
+    const coords3 = getChannels("coords", this.rank);
+    const sourceLoc = getChannels("sourceLoc", this.rank);
+    const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`;
+    const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;
+    const upperRow = `
+      result.x = ${getChannel};
+      if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {
+        ++${sourceLoc[this.rank - 1]};
+        result.y = ${getChannel};
+        --${sourceLoc[this.rank - 1]};
+      }
+    `;
+    const lowerRow = this.rank === 1 ? "" : `
+      --${coords3[this.rank - 1]};
+      if (++${coords3[this.rank - 2]} < ${destSize[this.rank - 2]}) {
+        ++${sourceLoc[this.rank - 2]};
+        result.z = ${getChannel};
+        if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {
+          ++${sourceLoc[this.rank - 1]};
+          result.w = ${getChannel};
+        }
+      }
+    `;
+    const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
+            ${dtype}(${destSize.map((_, i2) => `start[${i2}]`).join()});` : destSize.map((_, i2) => `${sourceLoc[i2]} = ${coords3[i2]} + start[${i2}];`).join("\n");
+    this.userCode = `
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        ${dtype} sourceLoc;
+        ${sourceLocSetup}
+        vec4 result = vec4(0.);
+        ${upperRow}
+        ${lowerRow}
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js
+function shallowSlice(x, begin, size, backend2) {
+  const xTexData = backend2.texData.get(x.dataId);
+  const t2 = backend2.makeTensorInfo(size, x.dtype);
+  const newTexData = backend2.texData.get(t2.dataId);
+  Object.assign(newTexData, xTexData);
+  newTexData.refCount = 1;
+  newTexData.shape = size;
+  newTexData.dtype = x.dtype;
+  let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));
+  if (xTexData.slice) {
+    flatOffset += xTexData.slice.flatOffset;
+  }
+  newTexData.slice = {
+    flatOffset,
+    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
+  };
+  const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;
+  backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
+  return t2;
+}
+function slice3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, size } = attrs;
+  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
+  slice_util_exports.assertParamsValid(x, $begin, $size);
+  if (util_exports.sizeFromShape($size) === 0) {
+    return backend2.makeTensorInfo($size, x.dtype, []);
+  }
+  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") {
+    const xTexData = backend2.texData.get(x.dataId);
+    const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);
+    return backend2.makeTensorInfo($size, x.dtype, outValues);
+  }
+  const { isPacked } = backend2.texData.get(x.dataId);
+  const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);
+  if (isPacked || !isContinous) {
+    const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new SlicePackedProgram($size) : new SliceProgram($size);
+    const customValues = [$begin];
+    return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
+  }
+  backend2.uploadToGPU(x.dataId);
+  return shallowSlice(x, $begin, $size, backend2);
+}
+var sliceConfig2 = {
+  kernelName: Slice,
+  backendName: "webgl",
+  kernelFunc: slice3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js
+var batchToSpaceND3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const toDispose = [];
+  const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });
+  const reshapedIntermediate2 = reshape4({
+    inputs: { x: transposedIntermediate },
+    backend: backend2,
+    attrs: { shape: reshapedPermuted }
+  });
+  const sliced = slice3({
+    inputs: { x: reshapedIntermediate2 },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  toDispose.push(reshapedIntermediate);
+  toDispose.push(transposedIntermediate);
+  toDispose.push(reshapedIntermediate2);
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return sliced;
+};
+var batchToSpaceNDConfig2 = {
+  kernelName: BatchToSpaceND,
+  backendName: "webgl",
+  kernelFunc: batchToSpaceND3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js
+function bincount3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size } = attrs;
+  const xVals = backend2.readSync(x.dataId);
+  const weightsVals = backend2.readSync(weights.dataId);
+  const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
+  return backend2.makeTensorInfo([size], weights.dtype, outVals);
+}
+var bincountConfig2 = {
+  kernelName: Bincount,
+  backendName: "webgl",
+  kernelFunc: bincount3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js
+function broadcastArgs3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { s0, s1 } = inputs;
+  const s0Vals = backend2.readSync(s0.dataId);
+  const s1Vals = backend2.readSync(s1.dataId);
+  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
+  return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape));
+}
+var broadcastArgsConfig2 = {
+  kernelName: BroadcastArgs,
+  backendName: "webgl",
+  kernelFunc: broadcastArgs3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js
+var NOT_EQUAL = `return float(a != b);`;
+var notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: "bool" });
+var notEqualConfig2 = {
+  kernelName: NotEqual,
+  backendName: "webgl",
+  kernelFunc: notEqual3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js
+function real3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.texData.get(input2.dataId);
+  return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });
+}
+var realConfig2 = {
+  kernelName: Real,
+  backendName: "webgl",
+  kernelFunc: real3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js
+var TO_INT = `return float(int(x));`;
+function int(input2, backend2) {
+  const program = new UnaryOpProgram(input2.shape, TO_INT);
+  const output = backend2.runWebGLProgram(program, [input2], "int32");
+  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js
+function cast4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dtype } = attrs;
+  if (dtype === "complex64") {
+    if (x.dtype === "complex64") {
+      return identity3({ inputs: { x }, backend: backend2 });
+    }
+    const zerosTensor = zeros(x.shape);
+    const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+    const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });
+    zerosTensor.dispose();
+    backend2.disposeIntermediateTensorInfo(floatX);
+    return result;
+  }
+  if (x.dtype === "complex64") {
+    const realPart = real3({ inputs: { input: x }, backend: backend2 });
+    const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    return result;
+  }
+  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
+    const result = identity3({ inputs: { x }, backend: backend2 });
+    return { dataId: result.dataId, shape: result.shape, dtype };
+  }
+  if (dtype === "int32") {
+    return int(x, backend2);
+  }
+  if (dtype === "bool") {
+    const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1));
+    const binaryInputs = { a: x, b: zerosTensorInfo };
+    const result = notEqual3({ inputs: binaryInputs, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
+    return result;
+  }
+  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
+}
+var castConfig2 = {
+  kernelName: Cast,
+  backendName: "webgl",
+  kernelFunc: cast4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js
+var CEIL = `return ceil(x);`;
+var ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });
+var ceilConfig2 = {
+  kernelName: Ceil,
+  backendName: "webgl",
+  kernelFunc: ceil3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js
+var ClipProgram = class {
+  constructor(aShape) {
+    this.variableNames = ["A"];
+    this.customUniforms = [
+      { name: "minVal", type: "float" },
+      { name: "maxVal", type: "float" }
+    ];
+    this.outputShape = aShape;
+    this.userCode = `
+
+      void main() {
+        float value = getAAtOutCoords();
+        if (isnan(value)) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, minVal, maxVal));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js
+var ClipPackedProgram = class {
+  constructor(aShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [
+      { name: "minVal", type: "float" },
+      { name: "maxVal", type: "float" }
+    ];
+    this.outputShape = aShape;
+    this.userCode = `
+      void main() {
+        vec4 value = getAAtOutCoords();
+
+        if (any(isnan(value))) {
+          setOutput(value);
+          return;
+        }
+
+        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js
+function clipByValue3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { clipValueMin, clipValueMax } = attrs;
+  let program;
+  if (env().getBool("WEBGL_PACK_CLIP")) {
+    program = new ClipPackedProgram(x.shape);
+  } else {
+    program = new ClipProgram(x.shape);
+  }
+  const customValues = [[clipValueMin], [clipValueMax]];
+  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
+}
+var clipByValueConfig2 = {
+  kernelName: ClipByValue,
+  backendName: "webgl",
+  kernelFunc: clipByValue3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js
+var ComplexAbsProgram = class {
+  constructor(shape) {
+    this.variableNames = ["real", "imag"];
+    this.outputShape = shape;
+    this.userCode = `
+      void main() {
+        float re = abs(getRealAtOutCoords());
+        float im = abs(getImagAtOutCoords());
+        float mx = max(re, im);
+
+        // sadly the length function in glsl is not underflow-safe
+        // (at least not on Intel GPUs). So the safe solution is
+        // to ensure underflow-safety in all cases.
+        setOutput(
+          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
+        );
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js
+function makeComplexComponentTensorInfo(complexTensor, complexPart) {
+  return {
+    dataId: complexPart.dataId,
+    dtype: complexPart.dtype,
+    shape: complexTensor.shape
+  };
+}
+function complexAbs2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  const xData = backend2.texData.get(x.dataId);
+  const program = new ComplexAbsProgram(x.shape);
+  const programInputs = [
+    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
+    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)
+  ];
+  return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);
+}
+var complexAbsConfig2 = {
+  kernelName: ComplexAbs,
+  backendName: "webgl",
+  kernelFunc: complexAbs2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js
+var ConcatProgram = class {
+  constructor(shapes) {
+    this.outputShape = [];
+    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const offsets = new Array(shapes.length - 1);
+    offsets[0] = shapes[0][1];
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
+    }
+    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      const shift = offsets[i2 - 1];
+      snippets.push(`else if (yC < ${offsets[i2]}) setOutput(getT${i2}(yR, yC-${shift}));`);
+    }
+    const lastIndex = offsets.length;
+    const lastShift = offsets[offsets.length - 1];
+    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int yR = coords.x;
+        int yC = coords.y;
+
+        ${snippets.join("\n        ")}
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js
+var ConcatPackedProgram = class {
+  constructor(shapes, axis) {
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = [];
+    this.outputShape = backend_util_exports.computeOutShape(shapes, axis);
+    const shape = this.outputShape;
+    const rank = shape.length;
+    const dtype = getCoordsDataType(rank);
+    const coords3 = getChannels("coords", rank);
+    const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank);
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    const offsets = new Array(shapes.length - 1);
+    offsets[0] = shapes[0][axis];
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      offsets[i2] = offsets[i2 - 1] + shapes[i2][axis];
+    }
+    const channel = channels[axis];
+    const lastChannels = channels.slice(-2);
+    const allChannels = channels.join();
+    let getValueSnippet = `if (${channel} < ${offsets[0]}) {
+        return getChannel(
+            getT0(${allChannels}), vec2(${lastChannels.join()}));
+        }`;
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      const shift2 = offsets[i2 - 1];
+      getValueSnippet += `
+        if (${channel} < ${offsets[i2]}  && ${channel} >= ${offsets[i2 - 1]}) {
+          return getChannel(
+            getT${i2}(${shiftedChannels(channels, channel, shift2)}),
+            vec2(${shiftedChannels(lastChannels, channel, shift2)}));
+        }`;
+    }
+    const lastIndex = offsets.length;
+    const shift = offsets[offsets.length - 1];
+    getValueSnippet += `
+        return getChannel(
+          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),
+          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;
+    this.userCode = `
+      float getValue(${channels.map((x) => "int " + x)}) {
+        ${getValueSnippet}
+      }
+
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        vec4 result = vec4(getValue(${coords3}), 0., 0., 0.);
+
+        ${coords3[rank - 1]} = ${coords3[rank - 1]} + 1;
+        if (${coords3[rank - 1]} < ${shape[rank - 1]}) {
+          result.g = getValue(${coords3});
+        }
+
+        ${coords3[rank - 2]} = ${coords3[rank - 2]} + 1;
+        if (${coords3[rank - 2]} < ${shape[rank - 2]}) {
+          result.a = getValue(${coords3});
+        }
+
+        ${coords3[rank - 1]} = ${coords3[rank - 1]} - 1;
+        if (${coords3[rank - 2]} < ${shape[rank - 2]} &&
+            ${coords3[rank - 1]} < ${shape[rank - 1]}) {
+          result.b = getValue(${coords3});
+        }
+        setOutput(result);
+      }
+    `;
+  }
+};
+function shiftedChannels(channels, channel, shift) {
+  const channelIdx = channels.indexOf(channel);
+  const res = channels.map((c, idx) => {
+    if (idx === channelIdx) {
+      return `${c} - ${shift}`;
+    } else {
+      return c;
+    }
+  });
+  return res.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js
+function imag3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.texData.get(input2.dataId);
+  return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });
+}
+var imagConfig2 = {
+  kernelName: Imag,
+  backendName: "webgl",
+  kernelFunc: imag3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js
+function concatImpl2(inputs, axis, backend2) {
+  const dtype = inputs[0].dtype;
+  if (dtype === "complex64") {
+    const reals = inputs.map((t2) => real3({ inputs: { input: t2 }, backend: backend2 }));
+    const imags = inputs.map((t2) => imag3({ inputs: { input: t2 }, backend: backend2 }));
+    const realConcated = concatImpl2(reals, axis, backend2);
+    const imagConcated = concatImpl2(imags, axis, backend2);
+    const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
+    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    backend2.disposeIntermediateTensorInfo(realConcated);
+    backend2.disposeIntermediateTensorInfo(imagConcated);
+    return result2;
+  }
+  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);
+  if (dtype === "string") {
+    runOnCpu = true;
+  }
+  if (runOnCpu) {
+    const tensors2D2 = inputs.map((t2) => {
+      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+      const shape = [-1, innerSize];
+      return reshape4({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    });
+    const inputsValShapes = tensors2D2.map((t2) => {
+      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    });
+    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);
+    const simplyConcat = tensors2D2[0].shape[0] === 1;
+    const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);
+    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
+    tensors2D2.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    return outInfo;
+  }
+  if (inputs.length > env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
+    const midIndex = Math.floor(inputs.length / 2);
+    const leftSide = concatImpl2(inputs.slice(0, midIndex), axis, backend2);
+    const rightSide = concatImpl2(inputs.slice(midIndex), axis, backend2);
+    const result2 = concatImpl2([leftSide, rightSide], axis, backend2);
+    backend2.disposeIntermediateTensorInfo(leftSide);
+    backend2.disposeIntermediateTensorInfo(rightSide);
+    return result2;
+  }
+  if (env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && inputs[0].shape.length > 1) {
+    const program2 = new ConcatPackedProgram(inputs.map((t2) => t2.shape), axis);
+    return backend2.runWebGLProgram(program2, inputs, dtype);
+  }
+  const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);
+  const program = new ConcatProgram(tensors2D.map((t2) => t2.shape));
+  const result = backend2.runWebGLProgram(program, tensors2D, dtype);
+  tensors2D.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+  const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(result);
+  return reshapedResult;
+}
+function computeTensors2D(inputs, axis, backend2) {
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const tensors2D = inputs.map((x) => reshape4({
+    inputs: { x },
+    attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },
+    backend: backend2
+  }));
+  return { tensors2D, outShape };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js
+function concat3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
+  }
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, $axis);
+  return concatImpl2($inputs, $axis, backend2);
+}
+var concatConfig2 = {
+  kernelName: Concat,
+  backendName: "webgl",
+  kernelFunc: concat3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js
+var Conv2DProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {
+    this.variableNames = ["x", "W"];
+    this.outputShape = convInfo.outShape;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+    const inputDepthVec4Remainder = convInfo.inChannels % 4;
+    const isChannelsLast = convInfo.dataFormat === "channelsLast";
+    const rowDim = isChannelsLast ? 1 : 2;
+    const colDim = isChannelsLast ? 2 : 3;
+    const channelDim = isChannelsLast ? 3 : 1;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivationWeights) {
+        activationSnippet = `float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyreluAlpha) {
+        activationSnippet = `float activation(float a) {
+          float b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `
+          float activation(float x) {
+            ${activation2}
+          }
+        `;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyreluAlpha) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    this.userCode = `
+      ${activationSnippet}
+
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d2 = coords[${channelDim}];
+
+        ivec2 xRCCorner =
+            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * ${dilationHeight};
+
+          if (xR < 0 || xR >= ${convInfo.inHeight}) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * ${dilationWidth};
+
+            if (xC < 0 || xC >= ${convInfo.inWidth}) {
+              continue;
+            }
+
+            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
+              vec4 wValues = vec4(
+                getW(wR, wC, d1, d2),
+                getW(wR, wC, d1 + 1, d2),
+                getW(wR, wC, d1 + 2, d2),
+                getW(wR, wC, d1 + 3, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec4 xValues = vec4(
+                  getX(batch, xR, xC, d1),
+                  getX(batch, xR, xC, d1 + 1),
+                  getX(batch, xR, xC, d1 + 2),
+                  getX(batch, xR, xC, d1 + 3)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec4 xValues = vec4(
+                  getX(batch, d1, xR, xC),
+                  getX(batch, d1 + 1, xR, xC),
+                  getX(batch, d1 + 2, xR, xC),
+                  getX(batch, d1 + 3, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+
+            if (${inputDepthVec4Remainder === 1}) {
+
+              if (${isChannelsLast}) {
+                dotProd +=
+                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
+              } else {
+                dotProd +=
+                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
+                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
+              }
+
+            } else if (${inputDepthVec4Remainder === 2}) {
+              vec2 wValues = vec2(
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec2 xValues = vec2(
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec2 xValues = vec2(
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            } else if (${inputDepthVec4Remainder === 3}) {
+              vec3 wValues = vec3(
+                getW(wR, wC, ${inputDepthNearestVec4}, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
+              );
+
+              if (${isChannelsLast}) {
+                vec3 xValues = vec3(
+                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else {
+                vec3 xValues = vec3(
+                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
+                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+
+            }
+          }
+        }
+
+        float result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `;
+  }
+};
+var Conv3DProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "W"];
+    this.outputShape = convInfo.outShape;
+    const padFront = convInfo.padInfo.front;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const filterDepth = convInfo.filterDepth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+    const inputDepthVec4Remainder = convInfo.inChannels % 4;
+    this.userCode = `
+      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d2 = coords.u;
+
+        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
+        int xFCorner = xFRCCorner.x;
+        int xRCorner = xFRCCorner.y;
+        int xCCorner = xFRCCorner.z;
+
+        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
+        // y(yF, yR, yC, d2). ? = to be determined. : = across all
+        // values in that axis.
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          int xF = xFCorner + wF * ${dilationDepth};
+
+          if (xF < 0 || xF >= ${convInfo.inDepth}) {
+            continue;
+          }
+
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            int xR = xRCorner + wR * ${dilationHeight};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              int xC = xCCorner + wC * ${dilationWidth};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
+                vec4 xValues = vec4(
+                  getX(batch, xF, xR, xC, d1),
+                  getX(batch, xF, xR, xC, d1 + 1),
+                  getX(batch, xF, xR, xC, d1 + 2),
+                  getX(batch, xF, xR, xC, d1 + 3)
+                );
+                vec4 wValues = vec4(
+                  getW(wF, wR, wC, d1, d2),
+                  getW(wF, wR, wC, d1 + 1, d2),
+                  getW(wF, wR, wC, d1 + 2, d2),
+                  getW(wF, wR, wC, d1 + 3, d2)
+                );
+
+                dotProd += dot(xValues, wValues);
+              }
+
+              if (${inputDepthVec4Remainder === 1}) {
+                dotProd +=
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
+              } else if (${inputDepthVec4Remainder === 2}) {
+                vec2 xValues = vec2(
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
+                );
+                vec2 wValues = vec2(
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              } else if (${inputDepthVec4Remainder === 3}) {
+                vec3 xValues = vec3(
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
+                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
+                );
+                vec3 wValues = vec3(
+                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
+                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
+                );
+                dotProd += dot(xValues, wValues);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js
+var Im2ColPackedProgram = class {
+  constructor(outputShape, convInfo) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [
+      { name: "inputShape", type: "ivec3" },
+      { name: "pad", type: "ivec2" },
+      { name: "stride", type: "ivec2" },
+      { name: "dilation", type: "ivec2" },
+      { name: "inChannels", type: "int" },
+      { name: "itemsPerBlockRow", type: "int" },
+      { name: "outWidth", type: "int" }
+    ];
+    this.outputShape = outputShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const { dataFormat } = convInfo;
+    const glsl = getGlslDifferences();
+    const isChannelsLast = dataFormat === "channelsLast";
+    const rowDim = isChannelsLast ? 0 : 1;
+    const colDim = isChannelsLast ? 1 : 2;
+    const boundsCheckingSnippet = this.enableShapeUniforms ? "if(blockIndex < outShape[1] && pos < outShape[0]) {" : `if(blockIndex < ${outputShape[1]} && pos < ${outputShape[0]}) {`;
+    let unrolled = ``;
+    for (let row = 0; row <= 1; row++) {
+      for (let col = 0; col <= 1; col++) {
+        unrolled += `
+          blockIndex = rc.y + ${col};
+          pos = rc.x + ${row};
+
+          ${boundsCheckingSnippet}
+            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
+            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
+
+            if(d0 < inputShape[${rowDim}] && d0 >= 0) {
+              // Use custom imod instead mod. On Intel GPU, mod may generate
+              // unexpected value.
+              // https://github.com/tensorflow/tfjs/issues/5447
+              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
+              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
+                  inChannels);
+
+              if(d1 < inputShape[${colDim}] && d1 >= 0) {
+
+                ch = imod(pos, inChannels);
+
+                if (${isChannelsLast}) {
+                  innerDims = vec2(d1, ch);
+                  result[${row * 2 + col}] = getChannel(
+                    getA(d0, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                } else {
+                  innerDims = vec2(d0, d1);
+                  result[${row * 2 + col}] = getChannel(
+                    getA(ch, int(innerDims.x),
+                    int(innerDims.y)), innerDims);
+                }
+              }
+            }
+          }
+        `;
+      }
+    }
+    this.userCode = `
+      void main() {
+        ivec2 rc = getOutputCoords();
+
+        vec4 result = vec4(0);
+
+        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
+        vec2 innerDims;
+
+        ${unrolled}
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js
+function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const xShape = x.shape;
+  const xTexData = backend2.texData.get(x.dataId);
+  const sharedMatMulDim = convInfo.inChannels;
+  const outerShapeX = xShape[0] * xShape[1] * xShape[2];
+  const outerShapeFilter = convInfo.outChannels;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const transposeA = false;
+  const transposeB = false;
+  let out;
+  const intermediates = [];
+  if (preluActivationWeights != null && !isChannelsLast && preluActivationWeights.shape.length === 3) {
+    const preluActivationWeightsInNhwcFormat = transpose3({
+      inputs: { x: preluActivationWeights },
+      backend: backend2,
+      attrs: { perm: [1, 2, 0] }
+    });
+    intermediates.push(preluActivationWeightsInNhwcFormat);
+    preluActivationWeights = preluActivationWeightsInNhwcFormat;
+  }
+  const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
+  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));
+  if (canOptimize) {
+    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);
+    const xReshaped = {
+      dataId: x.dataId,
+      shape: [1, targetShape, convInfo.inChannels],
+      dtype: x.dtype
+    };
+    const originalXTexDataShape = xTexData.shape;
+    xTexData.shape = xTexData.shape.slice();
+    xTexData.shape[xTexData.shape.length - 2]++;
+    util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);
+    const filterReshaped = reshape4({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+    });
+    intermediates.push(filterReshaped);
+    const pointwiseConv = batchMatMulImpl({
+      a: xReshaped,
+      b: filterReshaped,
+      backend: backend2,
+      transposeA,
+      transposeB,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+    const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);
+    util_exports.assert(pointwiseConvTexData.isPacked, () => "batchMatMul result is expected to be packed");
+    xTexData.shape = originalXTexDataShape;
+    pointwiseConvTexData.shape = convInfo.outShape;
+    out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 });
+    out.shape = convInfo.outShape;
+    intermediates.push(pointwiseConv);
+  } else {
+    const xInNhwcFormat = isChannelsLast ? x : transpose3({ inputs: { x }, backend: backend2, attrs: { perm: [0, 2, 3, 1] } });
+    const xInNhwcFormatShape = xInNhwcFormat.shape;
+    const targetShape = xInNhwcFormatShape[0] * xInNhwcFormatShape[1] * xInNhwcFormatShape[2];
+    const xReshaped = reshape4({
+      inputs: { x: xInNhwcFormat },
+      backend: backend2,
+      attrs: { shape: [1, targetShape, convInfo.inChannels] }
+    });
+    const filterReshaped = reshape4({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+    });
+    const result = batchMatMulImpl({
+      a: xReshaped,
+      b: filterReshaped,
+      transposeA,
+      transposeB,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+    const outInNHWCFormatShape = [
+      convInfo.batchSize,
+      convInfo.outHeight,
+      convInfo.outWidth,
+      convInfo.outChannels
+    ];
+    const outInNHWCFormat = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outInNHWCFormatShape } });
+    out = isChannelsLast ? outInNHWCFormat : transpose3({
+      inputs: { x: outInNHWCFormat },
+      backend: backend2,
+      attrs: { perm: [0, 3, 1, 2] }
+    });
+    if (!isChannelsLast) {
+      intermediates.push(xInNhwcFormat);
+      intermediates.push(outInNHWCFormat);
+    }
+    intermediates.push(xReshaped);
+    intermediates.push(filterReshaped);
+    intermediates.push(result);
+  }
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return out;
+}
+function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;
+  const isChannelsLast = dataFormat === "channelsLast";
+  const sharedDim = filterWidth * filterHeight * inChannels;
+  const numCols = outHeight * outWidth;
+  const x2ColShape = [sharedDim, numCols];
+  const transposeA = true;
+  const transposeB = false;
+  const intermediates = [];
+  if (preluActivationWeights != null && !isChannelsLast && preluActivationWeights.shape.length === 3) {
+    const preluActivationWeightsInNhwcFormat = transpose3({
+      inputs: { x: preluActivationWeights },
+      backend: backend2,
+      attrs: { perm: [1, 2, 0] }
+    });
+    intermediates.push(preluActivationWeightsInNhwcFormat);
+    preluActivationWeights = preluActivationWeightsInNhwcFormat;
+  }
+  const xSqueezed = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: x.shape.slice(1) } });
+  const w2Row = reshape4({
+    inputs: { x: filter },
+    backend: backend2,
+    attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }
+  });
+  intermediates.push(xSqueezed);
+  intermediates.push(w2Row);
+  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);
+  const customValues = [
+    xSqueezed.shape,
+    [convInfo.padInfo.top, convInfo.padInfo.left],
+    [convInfo.strideHeight, convInfo.strideWidth],
+    [convInfo.dilationHeight, convInfo.dilationWidth],
+    [convInfo.inChannels],
+    [convInfo.filterWidth * convInfo.inChannels],
+    [convInfo.outWidth]
+  ];
+  const im2Col = backend2.runWebGLProgram(im2ColProgram, [xSqueezed], "float32", customValues);
+  const im2ColReshaped = reshape4({
+    inputs: { x: im2Col },
+    backend: backend2,
+    attrs: { shape: [1, x2ColShape[0], x2ColShape[1]] }
+  });
+  intermediates.push(im2Col);
+  intermediates.push(im2ColReshaped);
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const hasLeakyreluAlpha = activation2 === "leakyrelu";
+  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;
+  const matmulProgram = new MatMulPackedProgram(im2ColReshaped.shape, w2Row.shape, [1, numCols, convInfo.outChannels], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+  const inputs = [im2ColReshaped, w2Row];
+  if (bias) {
+    inputs.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    inputs.push(preluActivationWeights);
+  }
+  if (hasLeakyreluAlpha) {
+    const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+    inputs.push($leakyreluAlpha);
+    intermediates.push($leakyreluAlpha);
+  }
+  const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32");
+  const outInNHWCFormatShape = [1, outHeight, outWidth, convInfo.outChannels];
+  const outInNHWCFormat = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: outInNHWCFormatShape } });
+  const out = isChannelsLast ? outInNHWCFormat : transpose3({ inputs: { x: outInNHWCFormat }, backend: backend2, attrs: { perm: [0, 3, 1, 2] } });
+  if (!isChannelsLast) {
+    intermediates.push(outInNHWCFormat);
+  }
+  intermediates.push(product);
+  for (const i2 of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i2);
+  }
+  return out;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js
+function conv2d4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  let out;
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
+    out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });
+  } else if (env().getBool("WEBGL_CONV_IM2COL") && x.shape[0] === 1) {
+    out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });
+  } else {
+    const program = new Conv2DProgram(convInfo);
+    out = backend2.runWebGLProgram(program, [x, filter], "float32");
+  }
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  backend2.disposeIntermediateTensorInfo(out);
+  return outReshaped;
+}
+var conv2DConfig2 = {
+  kernelName: Conv2D,
+  backendName: "webgl",
+  kernelFunc: conv2d4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js
+var Conv2DDerFilterProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "dy"];
+    this.outputShape = convInfo.filterShape;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const isChannelsLast = convInfo.dataFormat === "channelsLast";
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int d2 = coords.w;
+
+        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              if (${isChannelsLast}) {
+                float dyValue = getDy(b, yR, yC, d2);
+                float xValue = getX(b, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              } else {
+                float dyValue = getDy(b, d2, yR, yC);
+                float xValue = getX(b, d1, xR, xC);
+                dotProd += (xValue * dyValue);
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Conv2DDerInputProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.outputShape = convInfo.inShape;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const isChannelsLast = convInfo.dataFormat === "channelsLast";
+    const padTop = filterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
+    const rowDim = isChannelsLast ? 1 : 2;
+    const colDim = isChannelsLast ? 2 : 3;
+    const channelDim = isChannelsLast ? 3 : 1;
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[${channelDim}];
+
+        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${filterHeight} - 1 - wR;
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${filterWidth} - 1 - wC;
+
+            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
+
+              if (${isChannelsLast}) {
+                float xValue = getDy(batch, idyR, idyC, d2);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              } else {
+                float xValue = getDy(batch, d2, idyR, idyC);
+                float wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Conv3DDerFilterProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "dy"];
+    this.outputShape = convInfo.filterShape;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padFront = convInfo.padInfo.front;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    this.userCode = `
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int wF = coords.x;
+        int wR = coords.y;
+        int wC = coords.z;
+        int d1 = coords.w;
+        int d2 = coords.u;
+
+        float dotProd = 0.0;
+
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
+            int xF = wF + yF * ${strideDepth} - ${padFront};
+
+            if (xF < 0 || xF >= ${convInfo.inDepth}) {
+              continue;
+            }
+
+            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+              int xR = wR + yR * ${strideHeight} - ${padTop};
+
+              if (xR < 0 || xR >= ${convInfo.inHeight}) {
+                continue;
+              }
+
+              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+                int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+                if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                  continue;
+                }
+
+                float dyValue = getDy(b, yF, yR, yC, d2);
+                float xValue = getX(b, xF, xR, xC, d1);
+                dotProd += (xValue * dyValue);
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var Conv3DDerInputProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.outputShape = convInfo.inShape;
+    const filterDepth = convInfo.filterDepth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padFront = filterDepth - 1 - convInfo.padInfo.front;
+    const padTop = filterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
+    this.userCode = `
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.u;
+
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyFCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        float dotProd = 0.0;
+        for (int wF = 0; wF < ${filterDepth}; wF++) {
+          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
+
+          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
+            continue;
+          }
+          int idyF = int(dyF);
+
+          int wFPerm = ${filterDepth} - 1 - wF;
+
+          for (int wR = 0; wR < ${filterHeight}; wR++) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+              fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            int wRPerm = ${filterHeight} - 1 - wR;
+
+            for (int wC = 0; wC < ${filterWidth}; wC++) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              int wCPerm = ${filterWidth} - 1 - wC;
+
+              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
+                float xValue = getDy(batch, idyF, idyR, idyC, d2);
+                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
+                dotProd += xValue * wValue;
+              }
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js
+function conv2DBackpropFilter3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const program = new Conv2DDerFilterProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, dy], "float32");
+}
+var conv2DBackpropFilterConfig2 = {
+  kernelName: Conv2DBackpropFilter,
+  backendName: "webgl",
+  kernelFunc: conv2DBackpropFilter3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js
+function conv2DBackpropInput3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const program = new Conv2DDerInputProgram(convInfo);
+  return backend2.runWebGLProgram(program, [dy, filter], "float32");
+}
+var conv2DBackpropInputConfig2 = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "webgl",
+  kernelFunc: conv2DBackpropInput3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js
+function conv3D2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dilations } = attrs;
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);
+  const program = new Conv3DProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, filter], "float32");
+}
+var conv3DConfig2 = {
+  kernelName: Conv3D,
+  backendName: "webgl",
+  kernelFunc: conv3D2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js
+function conv3DBackpropFilterV22(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides, pad: pad3, filterShape } = attrs;
+  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);
+  const program = new Conv3DDerFilterProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, dy], "float32");
+}
+var conv3DBackpropFilterV2Config2 = {
+  kernelName: Conv3DBackpropFilterV2,
+  backendName: "webgl",
+  kernelFunc: conv3DBackpropFilterV22
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js
+function conv3DBackpropInput2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { pad: pad3, strides, inputShape } = attrs;
+  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);
+  const program = new Conv3DDerInputProgram(convInfo);
+  return backend2.runWebGLProgram(program, [dy, filter], "float32");
+}
+var conv3DBackpropInputConfig = {
+  kernelName: Conv3DBackpropInputV2,
+  backendName: "webgl",
+  kernelFunc: conv3DBackpropInput2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js
+var COS = CHECK_NAN_SNIPPET_UNARY + `
+  return cos(x);
+`;
+var cos3 = unaryKernelFunc2({ opSnippet: COS });
+var cosConfig2 = {
+  kernelName: Cos,
+  backendName: "webgl",
+  kernelFunc: cos3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js
+var COSH = `
+  float e2x = exp(-x);
+  return (e2x + 1.0 / e2x) / 2.0;
+`;
+var cosh3 = unaryKernelFunc2({ opSnippet: COSH });
+var coshConfig2 = {
+  kernelName: Cosh,
+  backendName: "webgl",
+  kernelFunc: cosh3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js
+var CropAndResizeProgram = class {
+  constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {
+    this.variableNames = ["Image", "Boxes", "BoxInd"];
+    this.outputShape = [];
+    const [batch, imageHeight, imageWidth, depth] = imageShape;
+    const [numBoxes] = boxShape;
+    const [cropHeight, cropWidth] = cropSize;
+    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
+    const methodId = method === "bilinear" ? 1 : 0;
+    const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];
+    const [heightRatio, heightScale, inY] = cropHeight > 1 ? [
+      `${(imageHeight - 1) / (cropHeight - 1)}`,
+      "(y2-y1) * height_ratio",
+      `y1*${inputHeightFloat} + float(y)*(height_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (y1+y2) * ${inputHeightFloat}`
+    ];
+    const [widthRatio, widthScale, inX] = cropWidth > 1 ? [
+      `${(imageWidth - 1) / (cropWidth - 1)}`,
+      "(x2-x1) * width_ratio",
+      `x1*${inputWidthFloat} + float(x)*(width_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (x1+x2) * ${inputWidthFloat}`
+    ];
+    this.userCode = `
+      const float height_ratio = float(${heightRatio});
+      const float width_ratio = float(${widthRatio});
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int y = coords[1];
+        int x = coords[2];
+        int d = coords[3];
+
+        // get box vals
+        float y1 = getBoxes(b,0);
+        float x1 = getBoxes(b,1);
+        float y2 = getBoxes(b,2);
+        float x2 = getBoxes(b,3);
+
+        // get image in batch index
+        int bInd = round(getBoxInd(b));
+        if(bInd < 0 || bInd >= ${batch}) {
+          return;
+        }
+
+        float height_scale = ${heightScale};
+        float width_scale = ${widthScale};
+
+        float in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutput(float(${extrapolationValue}));
+          return;
+        }
+        float in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutput(float(${extrapolationValue}));
+          return;
+        }
+
+        vec2 sourceFracIndexCR = vec2(in_x,in_y);
+        if(${methodId} == 1) {
+          // Compute the four integer indices.
+          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
+          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
+
+          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
+          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
+          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
+          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
+
+          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
+
+          float top = topLeft + (topRight - topLeft) * fracCR.x;
+          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          float newValue = top + (bottom - top) * fracCR.y;
+          setOutput(newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          ivec2 sourceNearestCR = ivec2(floor(
+            sourceFracIndexCR + vec2(0.5,0.5)));
+          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutput(newValue);
+        }
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js
+var cropAndResize3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, boxes, boxInd } = inputs;
+  const { cropSize, method, extrapolationValue } = attrs;
+  const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue);
+  return backend2.runWebGLProgram(program, [image2, boxes, boxInd], "float32");
+};
+var cropAndResizeConfig2 = {
+  kernelName: CropAndResize,
+  backendName: "webgl",
+  kernelFunc: cropAndResize3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js
+var CumOpType;
+(function(CumOpType3) {
+  CumOpType3["Prod"] = "*";
+  CumOpType3["Sum"] = "+";
+})(CumOpType || (CumOpType = {}));
+var CumProgram = class {
+  constructor(op2, outputShape, exclusive, reverse5) {
+    this.op = op2;
+    this.outputShape = outputShape;
+    this.variableNames = ["x"];
+    this.customUniforms = [{ name: "index", type: "float" }];
+    const rank = this.outputShape.length;
+    const initVal = this.op === CumOpType.Prod ? "1.0" : "0.0";
+    const val = exclusive ? initVal : `getX(${getCoords2(rank, "coords", this.op)})`;
+    const length = this.outputShape[this.outputShape.length - 1];
+    let condition = "";
+    let idxString = "";
+    if (exclusive) {
+      condition = reverse5 ? `end != ${length - 1}` : "end != 0";
+      idxString = reverse5 ? "end + 1" : "end - 1";
+    } else {
+      condition = reverse5 ? `end + pow2 < ${length}` : "end >= pow2";
+      idxString = reverse5 ? "end + pow2" : "end - pow2";
+    }
+    this.userCode = `
+      void main() {
+        ${getCoordsDataType(rank)} coords = getOutputCoords();
+        int end = ${getFinalCoord(rank, "coords", this.op)};
+        float val = ${val};
+        int pow2 = int(pow(2.0, index));
+        if (${condition}) {
+          int idx = ${idxString};
+          ${getFinalCoord(rank, "coords", this.op)} = idx;
+          val ${this.op}= getX(${getCoords2(rank, "coords", this.op)});
+        }
+        setOutput(val);
+      }
+    `;
+  }
+};
+function getCoords2(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.x, ${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.x, ${name}.y, ${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
+  } else {
+    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+function getFinalCoord(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.w`;
+  } else {
+    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js
+function cumImpl(op2, x, backend2, axis, exclusive, reverse5) {
+  const xRank = x.shape.length;
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  if (permutedAxis !== xRank - 1) {
+    throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);
+  }
+  const size = permutedX.shape[permutedAxis];
+  let result = identity3({ inputs: { x: permutedX }, backend: backend2 });
+  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {
+    const program = new CumProgram(op2, permutedX.shape, false, reverse5);
+    const customValues = [[i2]];
+    const prevResult = result;
+    result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);
+    backend2.disposeIntermediateTensorInfo(prevResult);
+  }
+  if (exclusive) {
+    const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5);
+    const prevResult = result;
+    result = backend2.runWebGLProgram(program, [result], result.dtype);
+    backend2.disposeIntermediateTensorInfo(prevResult);
+  }
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeIntermediateTensorInfo(result);
+    backend2.disposeIntermediateTensorInfo(permutedX);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js
+function cumprod3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5);
+}
+var cumprodConfig2 = {
+  kernelName: Cumprod,
+  backendName: "webgl",
+  kernelFunc: cumprod3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js
+function cumsum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5);
+}
+var cumsumConfig2 = {
+  kernelName: Cumsum,
+  backendName: "webgl",
+  kernelFunc: cumsum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js
+function denseBincount3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, weights } = inputs;
+  const { size, binaryOutput } = attrs;
+  if (x.shape.length === 1) {
+    const xVals = backend2.readSync(x.dataId);
+    const weightsVals = backend2.readSync(weights.dataId);
+    const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
+    return backend2.makeTensorInfo([size], weights.dtype, outVals);
+  } else if (x.shape.length === 2) {
+    const xBuf = backend2.bufferSync(x);
+    const weightsBuf = backend2.bufferSync(weights);
+    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);
+    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
+  }
+  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
+}
+var denseBincountConfig2 = {
+  kernelName: DenseBincount,
+  backendName: "webgl",
+  kernelFunc: denseBincount3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js
+var DepthToSpaceProgram = class {
+  constructor(outputShape, blockSize, dataFormat) {
+    this.variableNames = ["x"];
+    this.outputShape = [];
+    this.outputShape = outputShape;
+    this.blockSize = blockSize;
+    this.dataFormat = dataFormat;
+    this.userCode = `
+    void main() {
+      ivec4 coords = getOutputCoords();
+      int b = coords[0];
+      int h = ${this.getHeightCoordString()};
+      int w = ${this.getWidthCoordString()};
+      int d = ${this.getDepthCoordString()};
+
+      int in_h = h / ${blockSize};
+      int offset_h = imod(h, ${blockSize});
+      int in_w = w / ${blockSize};
+      int offset_w = imod(w, ${blockSize});
+      int offset_d = (offset_h * ${blockSize} + offset_w) *
+        ${this.getOutputDepthSize()};
+      int in_d = d + offset_d;
+
+      float result = ${this.getInputSamplingString()};
+      setOutput(result);
+    }
+  `;
+  }
+  getHeightCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[1]`;
+    } else {
+      return `coords[2]`;
+    }
+  }
+  getWidthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[2]`;
+    } else {
+      return `coords[3]`;
+    }
+  }
+  getDepthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[3]`;
+    } else {
+      return `coords[1]`;
+    }
+  }
+  getOutputDepthSize() {
+    if (this.dataFormat === "NHWC") {
+      return this.outputShape[3];
+    } else {
+      return this.outputShape[1];
+    }
+  }
+  getInputSamplingString() {
+    if (this.dataFormat === "NHWC") {
+      return `getX(b, in_h, in_w, in_d)`;
+    } else {
+      return `getX(b, in_d, in_h, in_w)`;
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js
+function depthToSpace3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  const batchSize = x.shape[0];
+  const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
+  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var depthToSpaceConfig2 = {
+  kernelName: DepthToSpace,
+  backendName: "webgl",
+  kernelFunc: depthToSpace3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js
+var DepthwiseConv2DProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
+    this.variableNames = ["x", "W"];
+    this.customUniforms = [
+      { name: "pads", type: "ivec2" },
+      { name: "strides", type: "ivec2" },
+      { name: "dilations", type: "ivec2" },
+      { name: "inDims", type: "ivec2" }
+    ];
+    this.outputShape = convInfo.outShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivation) {
+        activationSnippet = `float activation(float a) {
+          float b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyReluAlpha) {
+        activationSnippet = `float activation(float a) {
+          float b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `
+          float activation(float x) {
+            ${activation2}
+          }
+        `;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyReluAlpha) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    this.userCode = `
+      ${activationSnippet}
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
+
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          int xR = xRCorner + wR * dilations[0];
+
+          if (xR < 0 || xR >= inDims[0]) {
+            continue;
+          }
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            int xC = xCCorner + wC * dilations[1];
+
+            if (xC < 0 || xC >= inDims[1]) {
+              continue;
+            }
+
+            float xVal = getX(batch, xR, xC, d1);
+            float wVal = getW(wR, wC, d1, q);
+            dotProd += xVal * wVal;
+          }
+        }
+
+        float result = dotProd;
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js
+var DepthwiseConvPacked2DProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
+    this.variableNames = ["x", "W"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [
+      { name: "pads", type: "ivec2" },
+      { name: "strides", type: "ivec2" },
+      { name: "dilations", type: "ivec2" },
+      { name: "inDims", type: "ivec2" }
+    ];
+    this.outputShape = convInfo.outShape;
+    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    const padLeft = convInfo.padInfo.left;
+    const strideWidth = convInfo.strideWidth;
+    const dilationWidth = convInfo.dilationWidth;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const texelsAcross = filterWidth;
+    let mainLoop = `
+      int xR; int xC; int xCOffset;
+      vec4 wTexel; vec4 previous; vec4 final;`;
+    for (let c = 0; c < filterWidth; c++) {
+      mainLoop += `
+          vec4 xTexelC${c * 2};
+          int xTexelC${c * 2}Ready;
+          vec4 xTexelC${c * 2 + 1};
+          int xTexelC${c * 2 + 1}Ready;
+          vec4 xC${c};`;
+    }
+    mainLoop += `
+    for (int r = 0; r < ${filterHeight}; r++) {
+      `;
+    for (let c = 0; c < filterWidth; c++) {
+      mainLoop += `
+          xTexelC${c * 2} = vec4(0.0);
+          xTexelC${c * 2}Ready = 0;
+          xTexelC${c * 2 + 1} = vec4(0.0);
+          xTexelC${c * 2 + 1}Ready = 0;
+          xC${c} = vec4(0.0);`;
+    }
+    mainLoop += `
+        xR = xRCorner + r * dilations[0];
+        if (xR >=0 && xR < inDims[0]) {
+      `;
+    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
+      const colIndex = texelC * 2;
+      mainLoop += `
+          xC = xCCorner + ${colIndex * dilationWidth};
+          `;
+      if (strideWidth === 1) {
+        if (colIndex < filterWidth) {
+          if (padLeft % 2 === 1) {
+            mainLoop += `
+                xCOffset = xC + 1;
+                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
+
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+              `;
+            if (dilationWidth === 1 && colIndex > 0) {
+              mainLoop += `
+                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);
+                `;
+            } else {
+              mainLoop += `
+                  xCOffset = xC + 1 - 2;
+
+                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
+                    previous = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= inDims[1]) {
+                      previous.zw = vec2(0.0);
+                    }
+
+                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
+                  } else {
+                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
+                  }
+                  `;
+            }
+          } else {
+            mainLoop += `
+                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+
+                xC${colIndex} = xTexelC${colIndex};
+                `;
+          }
+          if (colIndex + 1 < filterWidth) {
+            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;
+            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {
+              mainLoop += `
+                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};
+
+                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
+
+                    // Need to manually clear unused channels in case
+                    // we're reading from recycled texture.
+                    if (xCOffset + 1 >= inDims[1]) {
+                      xTexelC${colIndex + 1}.zw = vec2(0.0);
+                    }
+                    xTexelC${colIndex + 1}Ready = 1;
+                  }
+                  `;
+              if (dilationWidth > 1) {
+                mainLoop += `
+                    xCOffset -= 2;
+                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                      xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
+                      xTexelC${colIndex}Ready = 1;
+                    }
+                    `;
+              }
+              mainLoop += `
+                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);
+                  `;
+            } else {
+              if (nextTexelOffset === 1) {
+                mainLoop += `
+                    xC${colIndex + 1} = xTexelC${colIndex};
+                    `;
+              } else {
+                mainLoop += `
+                    xCOffset = xC + ${nextTexelOffset};
+
+                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
+                      if (xCOffset + 1 >= inDims[1]) {
+                        xTexelC${colIndex + 1}.zw = vec2(0.0);
+                      }
+                      xTexelC${colIndex + 1}Ready = 1;
+                    }
+
+                    xC${colIndex + 1} = xTexelC${colIndex + 1};
+                    `;
+              }
+            }
+          }
+        }
+      } else {
+        if (colIndex < filterWidth) {
+          if (padLeft % 2 === 1) {
+            mainLoop += `
+                xCOffset = xC + 1 - strides[1];
+                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+
+                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
+                  // Need to manually clear unused channels in case
+                  // we're reading from recycled texture.
+                  if (xC + 2 >= inDims[1]) {
+                    xTexelC${colIndex + 1}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex + 1}Ready = 1;
+                }
+
+                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
+              `;
+            if (colIndex + 1 < filterWidth) {
+              mainLoop += `
+                  final = vec4(0.0);
+                  xCOffset = xC + 1 + strides[1];
+                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
+                    final = getX(batch, xR, xCOffset, d1);
+                  }
+                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
+                `;
+            }
+          } else {
+            mainLoop += `
+                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
+                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
+                  if (xC + 1 >= inDims[1]) {
+                    xTexelC${colIndex}.zw = vec2(0.0);
+                  }
+                  xTexelC${colIndex}Ready = 1;
+                }
+
+                xCOffset = xC + strides[1];
+                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
+                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
+                  if (xCOffset + 1 >= inDims[1]) {
+                    xTexelC${colIndex + 1}.zw = vec2(0.);
+                  }
+                  xTexelC${colIndex + 1}Ready = 1;
+                }
+
+                xC${colIndex} = vec4(
+                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
+              `;
+            if (colIndex + 1 < filterWidth) {
+              mainLoop += `
+                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
+                `;
+            }
+          }
+        }
+      }
+      if (colIndex < filterWidth) {
+        mainLoop += `
+            wTexel = getW(r, ${colIndex}, d1, q);
+            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);
+          `;
+        if (colIndex + 1 < filterWidth) {
+          mainLoop += `
+              wTexel = getW(r, ${colIndex + 1}, d1, q);
+              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);
+            `;
+        }
+      }
+    }
+    mainLoop += `
+    }
+  `;
+    mainLoop += `
+      }
+    `;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (activation2) {
+      if (hasPreluActivation) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getPreluActivationWeightsAtOutCoords();
+          ${activation2}
+        }`;
+      } else if (hasLeakyReluAlpha) {
+        activationSnippet = `vec4 activation(vec4 a) {
+          vec4 b = getLeakyreluAlphaAtOutCoords();
+          ${activation2}
+        }`;
+      } else {
+        activationSnippet = `vec4 activation(vec4 x) {
+          ${activation2}
+        }`;
+      }
+      applyActivationSnippet = `result = activation(result);`;
+    }
+    const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    if (hasLeakyReluAlpha) {
+      this.variableNames.push("leakyreluAlpha");
+    }
+    this.userCode = `
+      ${activationSnippet}
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        ivec2 xRCCorner = coords.yz * strides - pads;
+        int d2 = coords.w;
+        int d1 = d2 / ${channelMul};
+        int q = d2 - d1 * ${channelMul};
+        int xRCorner = xRCCorner.x;
+        int xCCorner = xRCCorner.y;
+
+        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
+        vec4 dotProd = vec4(0.000000000000001);
+
+        ${mainLoop}
+
+        vec4 result = dotProd - vec4(0.000000000000001);
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js
+function depthwiseConv2dNative2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
+  let program;
+  if (env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {
+    program = new DepthwiseConvPacked2DProgram(convInfo);
+  } else {
+    program = new DepthwiseConv2DProgram(convInfo);
+  }
+  const customValues = [
+    [convInfo.padInfo.top, convInfo.padInfo.left],
+    [convInfo.strideHeight, convInfo.strideWidth],
+    [convInfo.dilationHeight, convInfo.dilationWidth],
+    [convInfo.inHeight, convInfo.inWidth]
+  ];
+  return backend2.runWebGLProgram(program, [x, filter], "float32", customValues);
+}
+var depthwiseConv2dNativeConfig2 = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "webgl",
+  kernelFunc: depthwiseConv2dNative2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js
+var DepthwiseConv2DDerFilterProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "dy"];
+    this.outputShape = convInfo.filterShape;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padTop = convInfo.padInfo.top;
+    const padLeft = convInfo.padInfo.left;
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int wR = coords.x;
+        int wC = coords.y;
+        int d1 = coords.z;
+        int dm = coords.w;
+        int d2 = d1 * ${channelMul} + dm;
+
+        float dotProd = 0.0;
+
+        // TO DO: Vec4 over the batch size
+        for (int b = 0; b < ${convInfo.batchSize}; b++) {
+          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
+            int xR = wR + yR * ${strideHeight} - ${padTop};
+
+            if (xR < 0 || xR >= ${convInfo.inHeight}) {
+              continue;
+            }
+
+            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
+              int xC = wC + yC * ${strideWidth} - ${padLeft};
+
+              if (xC < 0 || xC >= ${convInfo.inWidth}) {
+                continue;
+              }
+
+              float dyValue = getDy(b, yR, yC, d2);
+              float xValue = getX(b, xR, xC, d1);
+              dotProd += (xValue * dyValue);
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var DepthwiseConv2DDerInputProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.outputShape = convInfo.inShape;
+    const filterHeight = convInfo.filterHeight;
+    const filterWidth = convInfo.filterWidth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const padTop = filterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = filterWidth - 1 - convInfo.padInfo.left;
+    const channelMul = convInfo.outChannels / convInfo.inChannels;
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords[0];
+        int d1 = coords[3];
+        ivec2 dyCorner = coords.yz - pads;
+        int dyRCorner = dyCorner.x;
+        int dyCCorner = dyCorner.y;
+
+        float dotProd = 0.0;
+
+        for (int wR = 0; wR < ${filterHeight}; wR++) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          int wRPerm = ${filterHeight} - 1 - wR;
+
+          for (int wC = 0; wC < ${filterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            int wCPerm = ${filterWidth} - 1 - wC;
+
+            // TO DO: Vec4 over the channelMul
+            for (int dm = 0; dm < ${channelMul}; dm++) {
+              int d2 = d1 * ${channelMul} + dm;
+              float xValue = getDy(batch, idyR, idyC, d2);
+              float wValue = getW(wRPerm, wCPerm, d1, dm);
+              dotProd += xValue * wValue;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
+function depthwiseConv2dNativeBackpropFilter3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, dy } = inputs;
+  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);
+  const program = new DepthwiseConv2DDerFilterProgram(convInfo);
+  return backend2.runWebGLProgram(program, [x, dy], "float32");
+}
+var depthwiseConv2dNativeBackpropFilterConfig2 = {
+  kernelName: DepthwiseConv2dNativeBackpropFilter,
+  backendName: "webgl",
+  kernelFunc: depthwiseConv2dNativeBackpropFilter3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
+function depthwiseConv2dNativeBackpropInput3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);
+  const program = new DepthwiseConv2DDerInputProgram(convInfo);
+  return backend2.runWebGLProgram(program, [dy, filter], "float32");
+}
+var depthwiseConv2dNativeBackpropInputConfig2 = {
+  kernelName: DepthwiseConv2dNativeBackpropInput,
+  backendName: "webgl",
+  kernelFunc: depthwiseConv2dNativeBackpropInput3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js
+var DiagProgram = class {
+  constructor(size) {
+    this.variableNames = ["X"];
+    this.outputShape = [size, size];
+    this.userCode = `
+      void main() {
+          ivec2 coords = getOutputCoords();
+          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
+          setOutput(val);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js
+function diag3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  const outShape = [...x.shape, ...x.shape];
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });
+  const program = new DiagProgram(xSize);
+  const res = backend2.runWebGLProgram(program, [flat], flat.dtype);
+  const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
+  backend2.disposeIntermediateTensorInfo(flat);
+  backend2.disposeIntermediateTensorInfo(res);
+  return out;
+}
+var diagConfig2 = {
+  kernelName: Diag,
+  backendName: "webgl",
+  kernelFunc: diag3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js
+var Dilation2DProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "W"];
+    this.outputShape = convInfo.outShape;
+    const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;
+    const { top: padTop, left: padLeft } = padInfo;
+    this.userCode = `
+      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+      const float neg_infinity = -3.4e38;
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int batch = coords.x;
+        int d1 = coords.w;
+        ivec2 outTopLeftCorner =
+            coords.yz * strides - pads;
+        int hBeg = outTopLeftCorner.x;
+        int wBeg = outTopLeftCorner.y;
+
+        float curVal = neg_infinity;
+        for (int h = 0; h < ${filterHeight}; h++) {
+          int hIn = hBeg + h * ${dilationHeight};
+
+          if (hIn >= 0 && hIn < ${inHeight}) {
+            for (int w = 0; w < ${filterWidth}; w++) {
+              int wIn = wBeg + w * ${dilationWidth};
+
+              if (wIn >= 0 && wIn < ${inWidth}) {
+                float xVal = getX(batch, hIn, wIn, d1);
+                float wVal = getW(h, w, d1);
+
+                float val = xVal + wVal;
+                if (val > curVal) {
+                  curVal = val;
+                }
+              }
+            }
+          }
+        }
+
+        float result = curVal;
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js
+function dilation2D(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dilations } = attrs;
+  const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, "NHWC", dilations);
+  let out;
+  const program = new Dilation2DProgram(convInfo);
+  out = backend2.runWebGLProgram(program, [x, filter], "float32");
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  backend2.disposeIntermediateTensorInfo(out);
+  return outReshaped;
+}
+var dilation2DConfig2 = {
+  kernelName: Dilation2D,
+  backendName: "webgl",
+  kernelFunc: dilation2D
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js
+function einsum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { equation } = attrs;
+  const tensors = inputs;
+  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
+  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
+  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
+  const nSteps = steps.length;
+  let out = null;
+  let numDimsRemaining = allDims.length;
+  const tensorsToDispose = [];
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    for (const idTerm of steps[i2]) {
+      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
+      let x;
+      if (backend_util_exports.isIdentityPermutation(perm)) {
+        x = tensors[idTerm];
+      } else {
+        x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
+        tensorsToDispose.push(x);
+      }
+      const targetShape = x.shape.slice();
+      for (let k = 0; k < dimsToExpand.length; ++k) {
+        targetShape.splice(dimsToExpand[k], 0, 1);
+      }
+      if (!util_exports.arraysEqual(x.shape, targetShape)) {
+        x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
+        tensorsToDispose.push(x);
+      }
+      if (out === null) {
+        out = x;
+      } else {
+        out = multiply3({ inputs: { a: x, b: out }, backend: backend2 });
+        tensorsToDispose.push(out);
+      }
+    }
+    if (i2 < nSteps - 1) {
+      if (path[i2] >= 0) {
+        out = sum4({
+          inputs: { x: out },
+          backend: backend2,
+          attrs: {
+            axis: path[i2] - (allDims.length - numDimsRemaining),
+            keepDims: false
+          }
+        });
+        tensorsToDispose.push(out);
+      }
+      numDimsRemaining--;
+    }
+  }
+  for (const tensorInfo of tensorsToDispose) {
+    if (tensorInfo === out) {
+      continue;
+    }
+    backend2.disposeIntermediateTensorInfo(tensorInfo);
+  }
+  return out;
+}
+var einsumConfig2 = {
+  kernelName: Einsum,
+  backendName: "webgl",
+  kernelFunc: einsum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js
+var ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
+var ELU_PACKED = `
+  vec4 result;
+
+  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
+  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
+  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
+  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
+
+  return result;
+`;
+var elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });
+var eluConfig2 = {
+  kernelName: Elu,
+  backendName: "webgl",
+  kernelFunc: elu5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js
+var ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;
+var ELU_DER_PACKED = `
+  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
+  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
+`;
+var eluGrad2 = (args) => {
+  const { inputs, backend: backend2 } = args;
+  const { dy, y } = inputs;
+  const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
+  return backend2.runWebGLProgram(program, [dy, y], dy.dtype);
+};
+var eluGradConfig3 = {
+  kernelName: EluGrad,
+  backendName: "webgl",
+  kernelFunc: eluGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js
+var PACKED_EQUAL = `
+  return vec4(equal(a, b));
+`;
+var EQUAL = `return float(a == b);`;
+var equal3 = binaryKernelFunc2({
+  opSnippet: EQUAL,
+  packedOpSnippet: PACKED_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: equalImplCPU
+});
+var equalConfig2 = {
+  kernelName: Equal,
+  backendName: "webgl",
+  kernelFunc: equal3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js
+var ERF = `
+  // Error function is calculated approximately with elementary function.
+  // See "Handbook of Mathematical Functions with Formulas,
+  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
+  float p = ${backend_util_exports.ERF_P};
+  float a1 = ${backend_util_exports.ERF_A1};
+  float a2 = ${backend_util_exports.ERF_A2};
+  float a3 = ${backend_util_exports.ERF_A3};
+  float a4 = ${backend_util_exports.ERF_A4};
+  float a5 = ${backend_util_exports.ERF_A5};
+
+  float sign = sign(x);
+  x = abs(x);
+  float t = 1.0 / (1.0 + p * x);
+  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
+`;
+var erf3 = unaryKernelFunc2({ opSnippet: ERF });
+var erfConfig2 = {
+  kernelName: Erf,
+  backendName: "webgl",
+  kernelFunc: erf3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js
+var EXP = CHECK_NAN_SNIPPET_UNARY + `
+  return exp(x);
+`;
+var EXP_PACKED = `
+  vec4 result = exp(x);
+  bvec4 isNaN = isnan(x);
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var exp3 = unaryKernelFunc2({
+  opSnippet: EXP,
+  packedOpSnippet: EXP_PACKED,
+  cpuKernelImpl: expImplCPU,
+  dtype: "float32"
+});
+var expConfig2 = {
+  kernelName: Exp,
+  backendName: "webgl",
+  kernelFunc: exp3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js
+function expandDims4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { dim } = attrs;
+  const { input: input2 } = inputs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig2 = {
+  kernelName: ExpandDims,
+  backendName: "webgl",
+  kernelFunc: expandDims4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js
+var EXPM1 = `return exp(x) - 1.0;`;
+var expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });
+var expm1Config2 = {
+  kernelName: Expm1,
+  backendName: "webgl",
+  kernelFunc: expm13
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js
+var FFTProgram = class {
+  constructor(component, inputShape, inverse) {
+    this.variableNames = ["real", "imag"];
+    const innerDim = inputShape[1];
+    this.outputShape = inputShape;
+    const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;
+    const resultDenominator = inverse ? `${innerDim}.0` : "1.0";
+    let opString;
+    if (component === "real") {
+      opString = "return real * expR - imag * expI;";
+    } else if (component === "imag") {
+      opString = "return real * expI + imag * expR;";
+    } else {
+      throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);
+    }
+    this.userCode = `
+      const float exponentMultiplier = ${exponentMultiplierSnippet};
+
+      float unaryOpComplex(float real, float expR, float imag, float expI) {
+        ${opString}
+      }
+
+      float mulMatDFT(int batch, int index) {
+        float indexRatio = float(index) / float(${innerDim});
+        float exponentMultiplierTimesIndexRatio =
+            exponentMultiplier * indexRatio;
+
+        float result = 0.0;
+
+        for (int i = 0; i < ${innerDim}; i++) {
+          // x = (-2|2 * PI / N) * index * i;
+          float x = exponentMultiplierTimesIndexRatio * float(i);
+          float expR = cos(x);
+          float expI = sin(x);
+          float real = getReal(batch, i);
+          float imag = getImag(batch, i);
+
+          result +=
+              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
+        }
+
+        return result;
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        setOutput(mulMatDFT(coords[0], coords[1]));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js
+function fftImpl2(x, inverse, backend2) {
+  const xData = backend2.texData.get(x.dataId);
+  const inputSize = util_exports.sizeFromShape(x.shape);
+  const innerDimensionSize = x.shape[x.shape.length - 1];
+  const batch = inputSize / innerDimensionSize;
+  const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });
+  const xShape = input2D.shape;
+  const realProgram = new FFTProgram("real", xShape, inverse);
+  const imagProgram = new FFTProgram("imag", xShape, inverse);
+  const inputs = [
+    {
+      dataId: xData.complexTensorInfos.real.dataId,
+      dtype: xData.complexTensorInfos.real.dtype,
+      shape: xShape
+    },
+    {
+      dataId: xData.complexTensorInfos.imag.dataId,
+      dtype: xData.complexTensorInfos.imag.dtype,
+      shape: xShape
+    }
+  ];
+  const realPart = backend2.runWebGLProgram(realProgram, inputs, "float32");
+  const imagPart = backend2.runWebGLProgram(imagProgram, inputs, "float32");
+  const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(realPart);
+  backend2.disposeIntermediateTensorInfo(imagPart);
+  const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });
+  backend2.disposeIntermediateTensorInfo(input2D);
+  backend2.disposeIntermediateTensorInfo(complexOutput);
+  return complexOutputReshaped;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js
+function fft3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  return fftImpl2(input2, false, backend2);
+}
+var fftConfig2 = {
+  kernelName: FFT,
+  backendName: "webgl",
+  kernelFunc: fft3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js
+var FillProgram = class {
+  constructor(shape, value) {
+    this.outputShape = [];
+    this.customUniforms = [{ name: "value", type: "float" }];
+    this.variableNames = ["x"];
+    this.outputShape = shape;
+    this.userCode = `
+      void main() {
+        // Input can be obtained from uniform value.
+        setOutput(value);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js
+function fill3(args) {
+  const { backend: backend2, attrs } = args;
+  const { shape, value } = attrs;
+  let { dtype } = attrs;
+  dtype = dtype || util_exports.inferDtype(value);
+  if (dtype === "string") {
+    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));
+    values.fill(value);
+    return backend2.makeTensorInfo(shape, dtype, values);
+  } else {
+    const program = new FillProgram(shape, value);
+    const customValues = [[value]];
+    return backend2.runWebGLProgram(program, [], dtype, customValues);
+  }
+}
+var fillConfig2 = {
+  kernelName: Fill,
+  backendName: "webgl",
+  kernelFunc: fill3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js
+var FlipLeftRightProgram = class {
+  constructor(imageShape) {
+    this.variableNames = ["Image"];
+    this.outputShape = [];
+    const imageWidth = imageShape[2];
+    this.outputShape = imageShape;
+    this.userCode = `
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+
+          int coordX = ${imageWidth} - x - 1;
+          float outputValue;
+          if(coordX >= 0 && coordX < ${imageWidth}) {
+            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
+          } else {
+            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js
+var flipLeftRightConfig2 = {
+  kernelName: FlipLeftRight,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const webglBackend = backend2;
+    const program = new FlipLeftRightProgram(image2.shape);
+    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype);
+    return output;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js
+var FLOOR = `return floor(x);`;
+var floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });
+var floorConfig2 = {
+  kernelName: Floor,
+  backendName: "webgl",
+  kernelFunc: floor3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js
+var INT_DIV = `
+  float s = sign(a) * sign(b);
+  int ia = round(a);
+  int ib = round(b);
+  if (ib != 0) {
+    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+    return float(idiv(ia, ib, s));
+  } else {
+    return NAN;
+  }
+`;
+var INT_DIV_PACKED = `
+  ivec4 ia = round(a);
+  ivec4 ib = round(b);
+  bvec4 cond = notEqual(ib, ivec4(0));
+  ivec4 result = ivec4(0);
+  vec4 s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    result[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    result[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    result[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    result[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4(result);
+`;
+var floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: "int32" });
+var floorDivConfig2 = {
+  kernelName: FloorDiv,
+  backendName: "webgl",
+  kernelFunc: floorDiv3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js
+var FromPixelsProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    const glsl = getGlslDifferences();
+    const [height, width] = outputShape;
+    this.outputShape = outputShape;
+    this.userCode = `
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
+
+        vec4 values = ${glsl.texture2D}(A, uv);
+        float value;
+        if (depth == 0) {
+          value = values.r;
+        } else if (depth == 1) {
+          value = values.g;
+        } else if (depth == 2) {
+          value = values.b;
+        } else if (depth == 3) {
+          value = values.a;
+        }
+
+        setOutput(floor(value * 255.0 + 0.5));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js
+var FromPixelsPackedProgram = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.packedInputs = false;
+    this.packedOutput = true;
+    const glsl = getGlslDifferences();
+    const [height, width] = outputShape;
+    this.outputShape = outputShape;
+    this.userCode = `
+      void main() {
+        ivec3 coords = getOutputCoords();
+        int texR = coords[0];
+        int texC = coords[1];
+        int depth = coords[2];
+
+        vec4 result = vec4(0.);
+
+        for(int row=0; row<=1; row++) {
+          for(int col=0; col<=1; col++) {
+            texC = coords[1] + row;
+            depth = coords[2] + col;
+
+            vec2 uv = (vec2(texC, texR) + halfCR) /
+                       vec2(${width}.0, ${height}.0);
+            vec4 values = ${glsl.texture2D}(A, uv);
+            float value;
+            if (depth == 0) {
+              value = values.r;
+            } else if (depth == 1) {
+              value = values.g;
+            } else if (depth == 2) {
+              value = values.b;
+            } else if (depth == 3) {
+              value = values.a;
+            }
+
+            result[row * 2 + col] = floor(value * 255.0 + 0.5);
+          }
+        }
+
+        ${glsl.output} = result;
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js
+var fromPixelsConfig = {
+  kernelName: FromPixels,
+  backendName: "webgl",
+  kernelFunc: fromPixels2
+};
+var fromPixels2DContext2;
+function fromPixels2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  let { pixels } = inputs;
+  const { numChannels } = attrs;
+  const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
+  const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
+  const [width, height] = isVideo ? [
+    pixels.videoWidth,
+    pixels.videoHeight
+  ] : [pixels.width, pixels.height];
+  const texShape = [height, width];
+  const outShape = [height, width, numChannels];
+  if (isImage || isVideo) {
+    if (fromPixels2DContext2 == null) {
+      fromPixels2DContext2 = document.createElement("canvas").getContext("2d");
+    }
+    fromPixels2DContext2.canvas.width = width;
+    fromPixels2DContext2.canvas.height = height;
+    fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);
+    pixels = fromPixels2DContext2.canvas;
+  }
+  const tempPixelHandle = backend2.makeTensorInfo(texShape, "int32");
+  backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
+  backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);
+  const program = env().getBool("WEBGL_PACK") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);
+  const res = backend2.runWebGLProgram(program, [tempPixelHandle], "int32");
+  backend2.disposeData(tempPixelHandle.dataId);
+  return res;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js
+function fusedConv2d(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  let out;
+  const intermediates = [];
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
+    out = conv2dByMatMul({
+      x,
+      filter,
+      convInfo,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+  } else if (env().getBool("WEBGL_CONV_IM2COL") && x.shape[0] === 1) {
+    out = conv2dWithIm2Row({
+      x,
+      filter,
+      convInfo,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+  } else {
+    const hasBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    const hasLeakyreluAlpha = activation2 === "leakyrelu";
+    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;
+    const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+    const inputs2 = [x, filter];
+    const alignInputWithDataFormat = (input2, dataFormat2) => {
+      if (dataFormat2 === "NCHW" && input2.shape.length === 1 && input2.shape[0] !== 1) {
+        const alignedInput = reshape4({
+          inputs: { x: input2 },
+          backend: backend2,
+          attrs: { shape: [input2.shape[0], 1, 1] }
+        });
+        intermediates.push(alignedInput);
+        return alignedInput;
+      }
+      return input2;
+    };
+    if (hasBias) {
+      inputs2.push(alignInputWithDataFormat(bias, dataFormat));
+    }
+    if (hasPreluActivationWeights) {
+      inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));
+    }
+    if (hasLeakyreluAlpha) {
+      const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+      inputs2.push($leakyreluAlpha);
+      intermediates.push($leakyreluAlpha);
+    }
+    out = backend2.runWebGLProgram(program, inputs2, "float32");
+  }
+  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  intermediates.push(out);
+  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return outReshaped;
+}
+var fusedConv2DConfig2 = {
+  kernelName: FusedConv2D,
+  backendName: "webgl",
+  kernelFunc: fusedConv2d
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js
+function fusedDepthwiseConv2D2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const intermediates = [];
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
+  const shouldPackDepthwiseConv = env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;
+  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;
+  const programInputs = [x, filter];
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const hasLeakyreluAlpha = activation2 === "leakyrelu";
+  if (hasBias) {
+    programInputs.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    programInputs.push(preluActivationWeights);
+  }
+  if (hasLeakyreluAlpha) {
+    const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
+    programInputs.push($leakyreluAlpha);
+    intermediates.push($leakyreluAlpha);
+  }
+  let program;
+  if (shouldPackDepthwiseConv) {
+    program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+  } else {
+    program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
+  }
+  const customValues = [
+    [convInfo.padInfo.top, convInfo.padInfo.left],
+    [convInfo.strideHeight, convInfo.strideWidth],
+    [convInfo.dilationHeight, convInfo.dilationWidth],
+    [convInfo.inHeight, convInfo.inWidth]
+  ];
+  const result = backend2.runWebGLProgram(program, programInputs, "float32", customValues);
+  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var fusedDepthwiseConv2DConfig2 = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "webgl",
+  kernelFunc: fusedDepthwiseConv2D2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js
+var GatherNDProgram = class {
+  constructor(sliceDim, strides, shape) {
+    this.sliceDim = sliceDim;
+    this.strides = strides;
+    this.variableNames = ["x", "indices"];
+    this.outputShape = shape;
+    const stridesType = getCoordsDataType(strides.length);
+    const dtype = getCoordsDataType(shape.length);
+    const strideString = this.sliceDim > 1 ? "strides[j]" : "strides";
+    this.userCode = `
+        ${stridesType} strides = ${stridesType}(${this.strides});
+         void main() {
+          ${dtype} coords = getOutputCoords();
+          int flattenIndex = 0;
+          for (int j = 0; j < ${this.sliceDim}; j++) {
+            int index = round(getIndices(coords[0], j));
+            flattenIndex += index * ${strideString};
+          }
+          setOutput(getX(flattenIndex, coords[1]));
+        }
+      `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js
+function gatherNd2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { params, indices } = inputs;
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const paramsSize = util_exports.sizeFromShape(params.shape);
+  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
+  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });
+  const flattenX = reshape4({
+    inputs: { x: params },
+    backend: backend2,
+    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }
+  });
+  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") {
+    const indicesData = backend2.readSync(indices.dataId);
+    const paramsBuf = backend2.bufferSync(params);
+    const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
+    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);
+  }
+  const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);
+  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });
+  backend2.disposeIntermediateTensorInfo(flattenIndices);
+  backend2.disposeIntermediateTensorInfo(flattenX);
+  backend2.disposeIntermediateTensorInfo(res);
+  return reshaped;
+}
+var gatherNdConfig2 = {
+  kernelName: GatherNd,
+  backendName: "webgl",
+  kernelFunc: gatherNd2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js
+var GatherProgram = class {
+  constructor(aShape, outputShape) {
+    this.variableNames = ["A", "indices"];
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    const dtype = getCoordsDataType(this.rank);
+    const sourceCoords = getSourceCoords2(aShape, 2);
+    this.userCode = `
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        int index = int(getIndices(resRC.x, resRC.z));
+        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;
+        setOutput(inBounds * getA(${sourceCoords}));
+      }
+    `;
+  }
+};
+function getSourceCoords2(aShape, axis) {
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < aShape.length; i2++) {
+    if (i2 === 2) {
+      sourceCoords.push("index");
+    } else {
+      sourceCoords.push(`${currentCoords[i2]}`);
+    }
+  }
+  return sourceCoords.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js
+function gatherV22(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  if (env().get("DEBUG")) {
+    const indicesVals = backend2.readSync(indices.dataId);
+    const axisDim = x.shape[parsedAxis];
+    for (let i2 = 0; i2 < indicesVals.length; ++i2) {
+      const index = indicesVals[i2];
+      util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
+    }
+  }
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const toDispose = [];
+  const flattenX = reshape4({
+    inputs: { x },
+    backend: backend2,
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    }
+  });
+  const flattenIndex = reshape4({
+    inputs: { x: indices },
+    backend: backend2,
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+  });
+  toDispose.push(flattenX);
+  toDispose.push(flattenIndex);
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === "string") {
+    const indicesBuf = backend2.bufferSync(flattenIndex);
+    const xBuf = backend2.bufferSync(flattenX);
+    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
+    toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+  }
+  const program = new GatherProgram(flattenX.shape, flattenOutputShape);
+  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);
+  toDispose.push(res);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return reshaped;
+}
+var gatherV2Config2 = {
+  kernelName: GatherV2,
+  backendName: "webgl",
+  kernelFunc: gatherV22
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js
+var GREATER = `return float(a > b);`;
+var GREATER_PACKED = `
+  return vec4(greaterThan(a, b));
+`;
+var greater4 = binaryKernelFunc2({
+  opSnippet: GREATER,
+  packedOpSnippet: GREATER_PACKED,
+  cpuKernelImpl: greaterImplCPU,
+  dtype: "bool"
+});
+var greaterConfig2 = {
+  kernelName: Greater,
+  backendName: "webgl",
+  kernelFunc: greater4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js
+var GREATER_EQUAL = `return float(a >= b);`;
+var GREATER_EQUAL_PACKED = `
+  return vec4(greaterThanEqual(a, b));
+`;
+var greaterEqual3 = binaryKernelFunc2({
+  opSnippet: GREATER_EQUAL,
+  packedOpSnippet: GREATER_EQUAL_PACKED,
+  dtype: "bool",
+  cpuKernelImpl: greaterEqualImplCPU
+});
+var greaterEqualConfig2 = {
+  kernelName: GreaterEqual,
+  backendName: "webgl",
+  kernelFunc: greaterEqual3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js
+function ifft3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  return fftImpl2(input2, true, backend2);
+}
+var ifftConfig2 = {
+  kernelName: IFFT,
+  backendName: "webgl",
+  kernelFunc: ifft3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js
+var IS_FINITE = `return float(!isnan(x) && !isinf(x));`;
+var isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: "bool" });
+var isFiniteConfig2 = {
+  kernelName: IsFinite,
+  backendName: "webgl",
+  kernelFunc: isFinite4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js
+var IS_INF = `return float(isinf(x));`;
+var isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: "bool" });
+var isInfConfig2 = {
+  kernelName: IsInf,
+  backendName: "webgl",
+  kernelFunc: isInf3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js
+var IS_NAN = `return float(isnan(x));`;
+var isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: "bool" });
+var isNaNConfig2 = {
+  kernelName: IsNan,
+  backendName: "webgl",
+  kernelFunc: isNaN4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js
+var LESS = `return float(a < b);`;
+var LESS_PACKED = `
+  return vec4(lessThan(a, b));
+`;
+var less4 = binaryKernelFunc2({
+  opSnippet: LESS,
+  packedOpSnippet: LESS_PACKED,
+  cpuKernelImpl: lessImplCPU,
+  dtype: "bool"
+});
+var lessConfig2 = {
+  kernelName: Less,
+  backendName: "webgl",
+  kernelFunc: less4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js
+var LESS_EQUAL = `return float(a <= b);`;
+var LESS_EQUAL_PACKED = `
+  return vec4(lessThanEqual(a, b));
+`;
+var lessEqual3 = binaryKernelFunc2({
+  opSnippet: LESS_EQUAL,
+  packedOpSnippet: LESS_EQUAL_PACKED,
+  cpuKernelImpl: lessEqualImplCPU,
+  dtype: "bool"
+});
+var lessEqualConfig2 = {
+  kernelName: LessEqual,
+  backendName: "webgl",
+  kernelFunc: lessEqual3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js
+function linSpace2(args) {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, num } = attrs;
+  const outVals = linSpaceImplCPU(start, stop, num);
+  return backend2.makeTensorInfo([outVals.length], "float32", outVals);
+}
+var linSpaceConfig2 = {
+  kernelName: LinSpace,
+  backendName: "webgl",
+  kernelFunc: linSpace2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js
+var LOG = CHECK_NAN_SNIPPET_UNARY + `
+  return x < 0.0 ? 0./0. : log(x);
+`;
+var LOG_PACKED = `
+  vec4 result = log(x);
+  bvec4 isNaN = isnan(x);
+  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
+  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
+  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
+  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
+  return result;
+`;
+var log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });
+var logConfig2 = {
+  kernelName: Log,
+  backendName: "webgl",
+  kernelFunc: log4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js
+var LOG1P = CHECK_NAN_SNIPPET_UNARY + `
+  return log(1.0 + x);
+`;
+var log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });
+var log1pConfig2 = {
+  kernelName: Log1p,
+  backendName: "webgl",
+  kernelFunc: log1p3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js
+var LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;
+var LOGICAL_AND_PACKED = `
+  return vec4(
+    vec4(greaterThanEqual(a, vec4(1.0))) *
+    vec4(greaterThanEqual(b, vec4(1.0))));
+`;
+var logicalAnd3 = binaryKernelFunc2({
+  opSnippet: LOGICAL_AND,
+  packedOpSnippet: LOGICAL_AND_PACKED,
+  dtype: "bool"
+});
+var logicalAndConfig2 = {
+  kernelName: LogicalAnd,
+  backendName: "webgl",
+  kernelFunc: logicalAnd3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js
+var LOGICAL_NOT = `return float(!(x >= 1.0));`;
+var logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });
+var logicalNotConfig2 = {
+  kernelName: LogicalNot,
+  backendName: "webgl",
+  kernelFunc: logicalNot3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js
+var LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;
+var LOGICAL_OR_PACKED = `
+  return min(
+    vec4(greaterThanEqual(a, vec4(1.0))) +
+    vec4(greaterThanEqual(b, vec4(1.0))),
+    vec4(1.0));
+`;
+var logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: "bool" });
+var logicalOrConfig2 = {
+  kernelName: LogicalOr,
+  backendName: "webgl",
+  kernelFunc: logicalOr3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js
+var LRNProgram = class {
+  constructor(xShape, radius, bias, alpha, beta) {
+    this.variableNames = ["x"];
+    this.outputShape = [];
+    const rad = radius;
+    const maxD = xShape[3] - 1;
+    this.outputShape = xShape;
+    let powOperator;
+    const basis = `float(${bias}) + float(${alpha}) * sum`;
+    if (beta === 0.5) {
+      powOperator = `inversesqrt(${basis})`;
+    } else if (beta === 1) {
+      powOperator = `1.0/(${basis})`;
+    } else {
+      powOperator = `exp(log(${basis}) * float(-${beta}));`;
+    }
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+        int d = coords[3];
+        float x = getX(b, r, c, d);
+        float sum = 0.0;
+        for (int j = -${rad}; j <= ${rad}; j++) {
+          int idx = d + j;
+          if (idx >= 0 && idx <=  ${maxD}) {
+            float z = getX(b, r, c, idx);
+            sum += z * z;
+          }
+        }
+        float val = x * ${powOperator};
+        setOutput(val);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js
+var LRNPackedProgram = class {
+  constructor(xShape, radius, bias, alpha, beta) {
+    this.variableNames = ["x"];
+    this.outputShape = [];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    const rad = radius;
+    const maxD = xShape[3] - 1;
+    this.outputShape = xShape;
+    let powOperator;
+    const basis = `float(${bias}) + float(${alpha}) * sum`;
+    if (beta === 0.5) {
+      powOperator = `inversesqrt(${basis})`;
+    } else if (beta === 1) {
+      powOperator = `1.0/(${basis})`;
+    } else {
+      powOperator = `exp(log(${basis}) * float(-${beta}));`;
+    }
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords.x;
+        int r = coords.y;
+        int c = coords.z;
+        int d = coords.w;
+
+        bool hasNextCol = d < ${this.outputShape[3]};
+        bool hasNextRow = c < ${this.outputShape[2]};
+
+        vec4 sum = vec4(0.);
+        vec4 xFragAtOutputCoords = getX(b, r, c, d);
+
+        vec4 xAtOutputCoords = vec4(
+          getChannel(xFragAtOutputCoords, vec2(c, d)),
+          hasNextCol ?
+            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
+          hasNextRow ?
+            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
+        );
+
+        int firstChannel = d - ${rad};
+        vec2 cache = vec2(0.);
+        if(firstChannel >= 0){
+          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
+          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
+            if(hasNextRow){
+              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
+            }
+        }
+
+        ivec2 depth = ivec2(d, d + 1);
+        for (int j = - ${rad}; j <= ${rad}; j++) {
+          ivec2 idx = depth + j;
+          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
+          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
+
+          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
+          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
+
+          if(depthInRange || depthPlusOneInRange){
+            vec4 z = vec4(0.);
+            vec4 xFragAtCurrentDepth;
+            z.xz = cache.xy;
+            if(depthPlusOneInRange && hasNextCol){
+              xFragAtCurrentDepth = idx.y != d ?
+                getX(b, r, c, idx.y) : xFragAtOutputCoords;
+              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
+              if(hasNextRow){
+                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
+              }
+            }
+            cache.xy = z.yw;
+            sum += z * z;
+          }
+        }
+        vec4 result = xAtOutputCoords * ${powOperator};
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js
+var lrn = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { depthRadius, bias, alpha, beta } = attrs;
+  const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+};
+var LRNConfig2 = {
+  kernelName: LRN,
+  backendName: "webgl",
+  kernelFunc: lrn
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js
+var LRNGradProgram = class {
+  constructor(inputShape, depthRadius, bias, alpha, beta) {
+    this.variableNames = ["inputImage", "outputImage", "dy"];
+    this.outputShape = [];
+    this.outputShape = inputShape;
+    this.depth = inputShape[3];
+    this.depthRadius = depthRadius;
+    this.bias = bias;
+    this.alpha = alpha;
+    this.beta = beta;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int r = coords[1];
+        int c = coords[2];
+
+        float result = 0.0;
+        for (int d = 0; d < ${this.depth}; ++d) {
+          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
+          int depthEnd = int(min(float(${this.depth}),
+              float(d + ${depthRadius} + 1)));
+
+          const int MIN_DEPTH_BEGIN = 0;
+          const int MAX_DEPTH_END = ${this.depth};
+
+          float norm = 0.0;
+          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd) {
+              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
+            }
+            else {
+              break;
+            }
+          }
+
+          norm = float(${alpha}) * norm + float(${bias});
+
+          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
+            if (k < depthBegin){
+              continue;
+            }
+            else if (k >= depthBegin && k < depthEnd){
+              float dyi = -2.0 * float(${alpha})
+                * float(${beta})
+                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
+                / norm;
+              if (k == d) {
+                dyi += pow(norm, -1.0 * ${beta});
+              }
+              if (k == coords[3]) {
+                dyi *= getDy(b, r, c, d);
+                result += dyi;
+              }
+            }
+            else {
+              break;
+            }
+          }
+      }
+      setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js
+var lrnGrad = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, y, dy } = inputs;
+  const { depthRadius, bias, alpha, beta } = attrs;
+  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);
+  return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);
+};
+var LRNGradConfig2 = {
+  kernelName: LRNGrad,
+  backendName: "webgl",
+  kernelFunc: lrnGrad
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js
+function maxImpl2(x, reduceShape, outShape, backend2) {
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const batchSize = xSize / inSize;
+  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
+  const reduced = reduce(reshapedInput, x.dtype, "max", backend2);
+  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(reshapedInput);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  return reshapedOutput;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js
+function max4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reductionIndices, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  const maxInputIsTransposed = permutedAxes != null;
+  const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
+  let maxInput = x;
+  if (maxInputIsTransposed) {
+    if (shouldExecuteOnCPU) {
+      const xTexData = backend2.texData.get(maxInput.dataId);
+      const values = xTexData.values;
+      const newShape = new Array(xRank);
+      for (let i2 = 0; i2 < newShape.length; i2++) {
+        newShape[i2] = x.shape[permutedAxes[i2]];
+      }
+      const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
+      maxInput = backend2.makeTensorInfo(newShape, x.dtype);
+      const maxInputData = backend2.texData.get(maxInput.dataId);
+      maxInputData.values = maxInputValues;
+    } else {
+      maxInput = transposeImpl2(x, permutedAxes, backend2);
+    }
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
+  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);
+  let outShape = maxOutShape;
+  if (keepDims) {
+    outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
+  }
+  let out;
+  if (shouldExecuteOnCPU) {
+    const xTexData = backend2.texData.get(maxInput.dataId);
+    const values = xTexData.values;
+    const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);
+    out = backend2.makeTensorInfo(outShape, x.dtype);
+    const outData = backend2.texData.get(out.dataId);
+    outData.values = outValues;
+  } else {
+    out = maxImpl2(maxInput, reduceShape, outShape, backend2);
+  }
+  if (maxInputIsTransposed) {
+    backend2.disposeIntermediateTensorInfo(maxInput);
+  }
+  return out;
+}
+var maxConfig2 = {
+  kernelName: Max,
+  backendName: "webgl",
+  kernelFunc: max4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js
+var MAXIMUM = CHECK_NAN_SNIPPET2 + `
+  return max(a, b);
+`;
+var MAXIMUM_PACKED = `
+  vec4 result = vec4(max(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var maximum4 = binaryKernelFunc2({
+  opSnippet: MAXIMUM,
+  packedOpSnippet: MAXIMUM_PACKED,
+  cpuKernelImpl: maximumImplCPU
+});
+var maximumConfig2 = {
+  kernelName: Maximum,
+  backendName: "webgl",
+  kernelFunc: maximum4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js
+function maxPool3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  assertNotComplex2(x, "maxPool");
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    return identity3({ inputs: { x }, backend: backend2 });
+  }
+  const maxPoolProgram = new Pool2DProgram(convInfo, "max", false);
+  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
+}
+var maxPoolConfig2 = {
+  kernelName: MaxPool,
+  backendName: "webgl",
+  kernelFunc: maxPool3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js
+function maxPool3d2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);
+  const maxPoolProgram = new Pool3DProgram(convInfo, "max", false);
+  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
+}
+var maxPool3DConfig2 = {
+  kernelName: MaxPool3D,
+  backendName: "webgl",
+  kernelFunc: maxPool3d2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js
+var MaxPool2DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "maxPos"];
+    this.outputShape = convInfo.inShape;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationHeight = convInfo.dilationHeight;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
+    this.userCode = `
+      const ivec2 pads = ivec2(${padTop}, ${padLeft});
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+
+        ivec2 dyRCCorner = coords.yz - pads;
+        int dyRCorner = dyRCCorner.x;
+        int dyCCorner = dyRCCorner.y;
+
+        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+        for (int wR = 0; wR < ${effectiveFilterHeight};
+          wR += ${dilationHeight}) {
+          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
+            continue;
+          }
+          int idyR = int(dyR);
+
+          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
+            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                fract(dyC) > 0.0) {
+              continue;
+            }
+            int idyC = int(dyC);
+
+            float dyValue = getDy(b, idyR, idyC, d);
+            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
+
+            // Get the current value, check it against the value from the
+            // position matrix.
+            int curPosValue = wR * ${effectiveFilterWidth} + wC;
+            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+            dotProd += dyValue * mask;
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+var MaxPool3DBackpropProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "maxPos"];
+    this.outputShape = convInfo.inShape;
+    const strideDepth = convInfo.strideDepth;
+    const strideHeight = convInfo.strideHeight;
+    const strideWidth = convInfo.strideWidth;
+    const dilationDepth = convInfo.dilationDepth;
+    const dilationHeight = convInfo.dilationHeight;
+    const dilationWidth = convInfo.dilationWidth;
+    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
+    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
+    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
+    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
+    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+    const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
+    this.userCode = `
+      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
+
+      void main() {
+        ivec5 coords = getOutputCoords();
+        int batch = coords.x;
+        int ch = coords.u;
+
+        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
+        int dyDCorner = dyCorner.x;
+        int dyRCorner = dyCorner.y;
+        int dyCCorner = dyCorner.z;
+
+        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
+        // dx(xD, xR, xC, ch).
+        // ? = to be determined. : = across all values in that axis.
+        float dotProd = 0.0;
+
+        for (int wD = 0; wD < ${effectiveFilterDepth};
+           wD += ${dilationDepth}) {
+          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
+
+          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
+            continue;
+          }
+          int idyD = int(dyD);
+
+          for (int wR = 0; wR < ${effectiveFilterHeight};
+              wR += ${dilationHeight}) {
+            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
+
+            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
+                fract(dyR) > 0.0) {
+              continue;
+            }
+            int idyR = int(dyR);
+
+            for (int wC = 0; wC < ${effectiveFilterWidth};
+                wC += ${dilationWidth}) {
+              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
+
+              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
+                  fract(dyC) > 0.0) {
+                continue;
+              }
+              int idyC = int(dyC);
+
+              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
+              int maxPosValue = ${lastIndex} -
+                  int(getMaxPos(batch, idyD, idyR, idyC, ch));
+
+              // Get the current value, check it against the value from the
+              // position matrix.
+              int curPosValue =
+                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
+                  wR * ${effectiveFilterWidth} + wC;
+              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
+
+              dotProd += dyValue * mask;
+            }
+          }
+        }
+        setOutput(dotProd);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js
+function maxPool3DGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2 } = inputs;
+  const x = input2;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = [1, 1, 1];
+  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, "max", true);
+  const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);
+  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
+  const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);
+  backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);
+  return result;
+}
+var maxPool3DGradConfig3 = {
+  kernelName: MaxPool3DGrad,
+  backendName: "webgl",
+  kernelFunc: maxPool3DGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js
+function maxPoolGrad3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, input: input2, output } = inputs;
+  const x = input2;
+  assertNotComplex2([input2, output], "maxPoolGrad");
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
+  const getPositions = true;
+  const maxPoolPositionsProgram = new Pool2DProgram(convInfo, "max", getPositions);
+  const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);
+  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
+  const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);
+  backend2.disposeIntermediateTensorInfo(maxPoolPositions2);
+  return result;
+}
+var maxPoolGradConfig3 = {
+  kernelName: MaxPoolGrad,
+  backendName: "webgl",
+  kernelFunc: maxPoolGrad3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js
+function maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {
+  let program = new Pool2DProgram(convInfo, "max", false);
+  const poolOutput = backend2.runWebGLProgram(program, [x], "float32");
+  program = new Pool2DProgram(convInfo, "max", true, true, includeBatchInIndex);
+  const indexOutput = backend2.runWebGLProgram(program, [x], "float32");
+  return [poolOutput, indexOutput];
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js
+var maxPoolWithArgmaxConfig2 = {
+  kernelName: MaxPoolWithArgmax,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;
+    const webglBackend = backend2;
+    util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);
+    const dilations = [1, 1];
+    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
+    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3);
+    const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);
+    return [result, indexes];
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js
+function meanImpl(x, reduceShape, outShape, backend2) {
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const batchSize = xSize / inSize;
+  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
+  const reduced = reduce(reshapedInput, "float32", "mean", backend2);
+  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(reshapedInput);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  return reshapedOutput;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js
+var meanConfig2 = {
+  kernelName: Mean,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { keepDims, axis } = attrs;
+    const webglBackend = backend2;
+    const xRank = x.shape.length;
+    const origAxes = util_exports.parseAxisParam(axis, x.shape);
+    let axes = origAxes;
+    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+    const meanInputIsTransposed = permutedAxes != null;
+    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
+    const intermediates = [];
+    let meanInput = x;
+    if (meanInputIsTransposed) {
+      if (shouldExecuteOnCPU) {
+        const xTexData = webglBackend.texData.get(meanInput.dataId);
+        const values = xTexData.values;
+        const newShape = new Array(xRank);
+        for (let i2 = 0; i2 < newShape.length; i2++) {
+          newShape[i2] = x.shape[permutedAxes[i2]];
+        }
+        const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
+        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
+        const meanInputData = webglBackend.texData.get(meanInput.dataId);
+        meanInputData.values = meanInputValues;
+      } else {
+        meanInput = transposeImpl2(x, permutedAxes, webglBackend);
+      }
+      intermediates.push(meanInput);
+      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    }
+    backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
+    const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);
+    let outShape = meanOutShape;
+    if (keepDims) {
+      outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);
+    }
+    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
+    for (const i2 of intermediates) {
+      webglBackend.disposeIntermediateTensorInfo(i2);
+    }
+    return out;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js
+function min4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("min", axes, xRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+  const inSize = util_exports.sizeFromShape(reduceShape);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  const reduced = reduce(a2D, a2D.dtype, "min", backend2);
+  let res;
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
+  } else {
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+  }
+  backend2.disposeIntermediateTensorInfo(a2D);
+  backend2.disposeIntermediateTensorInfo(reduced);
+  if (permutedAxes != null) {
+    backend2.disposeIntermediateTensorInfo(permutedX);
+  }
+  return res;
+}
+var minConfig2 = {
+  kernelName: Min,
+  backendName: "webgl",
+  kernelFunc: min4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js
+var MINIMUM = CHECK_NAN_SNIPPET2 + `
+  return min(a, b);
+`;
+var MINIMUM_PACKED = `
+  vec4 result = vec4(min(a, b));
+  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var minimum4 = binaryKernelFunc2({
+  opSnippet: MINIMUM,
+  packedOpSnippet: MINIMUM_PACKED,
+  cpuKernelImpl: minimumImplCPU
+});
+var minimumConfig2 = {
+  kernelName: Minimum,
+  backendName: "webgl",
+  kernelFunc: minimum4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js
+var MirrorPadProgram = class {
+  constructor(xShape, paddings, mode) {
+    this.variableNames = ["x"];
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const dtype = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
+    const offset = mode === "reflect" ? 0 : 1;
+    if (rank === 1) {
+      this.userCode = `
+        int start = ${start};
+        int end = ${end};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start) {
+            outC = start * 2 - outC - ${offset};
+          } else if(outC >= end) {
+            outC = (end - 1) * 2 - outC + ${offset};
+          }
+          setOutput(getX(outC - start));
+        }
+      `;
+      return;
+    }
+    this.userCode = `
+      ${dtype} start = ${dtype}(${start});
+      ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outC = getOutputCoords();
+        for (int i = 0; i < ${rank}; i++) {
+          if (outC[i] < start[i]) {
+            outC[i] = start[i] * 2 - outC[i] - ${offset};
+          } else if(outC[i] >= end[i]) {
+            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
+          }
+        }
+        ${dtype} coords = outC - start;
+        setOutput(getX(${unpackedCoords}));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js
+var MirrorPadPackedProgram = class {
+  constructor(xShape, paddings, mode) {
+    this.variableNames = ["x"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const dtype = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const coords3 = getChannels("rc", rank);
+    const source = getChannels("source", rank);
+    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
+    const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
+    const offset = mode === "reflect" ? 0 : 1;
+    let mainLoop = "";
+    if (rank === 1) {
+      const padSetup = `
+        ${dtype} source = rc;
+        if (source < start) {
+          source = start * 2 - source - ${offset};
+        } else if (source >= end) {
+          source = (end - 1) * 2 - source + ${offset};
+        }
+        source -= start;
+      `;
+      mainLoop = `
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords3[rank - 1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+      `;
+    } else {
+      const padSetup = `
+        ${dtype} source = rc;
+        ${dtype} lt = ${dtype}(lessThan(source, start));
+        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
+        ${dtype} orig = 1 - (lt + gte);
+        source = orig * source +
+                lt * (start * 2 - source - ${offset}) +
+                gte * ((end - 1) * 2 - source + ${offset});
+        source -= start;
+      `;
+      mainLoop = `
+        ${dtype} rc = outputLoc;
+        ${padSetup}
+        result[0] = getChannel(getX(${source.join()}), ${innerDims});
+        ${coords3[rank - 1]} += 1;
+        if(${cLimit}) {
+          ${padSetup}
+          result[1] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+        rc = outputLoc;
+        ${coords3[rank - 2]} += 1;
+        if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {
+          ${padSetup}
+          result[2] = getChannel(getX(${source.join()}), ${innerDims});
+          ${coords3[rank - 1]} += 1;
+          if(${cLimit}) {
+            ${padSetup}
+            result[3] = getChannel(getX(${source.join()}), ${innerDims});
+          }
+        }
+      `;
+    }
+    this.userCode = `
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${mainLoop}
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js
+var mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {
+  const { x } = inputs;
+  const { paddings, mode } = attrs;
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);
+  const output = backend2.runWebGLProgram(program, [x], x.dtype);
+  return output;
+};
+var mirrorPadConfig2 = {
+  kernelName: MirrorPad,
+  backendName: "webgl",
+  kernelFunc: mirrorPadKernelFunc
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js
+var MOD = `if (b == 0.0) return NAN;
+  return mod(a, b);`;
+var MOD_PACKED = `
+  vec4 result = mod(a, b);
+  vec4 isNaN = vec4(equal(b, vec4(0.0)));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var mod3 = binaryKernelFunc2({
+  opSnippet: MOD,
+  packedOpSnippet: MOD_PACKED
+});
+var modConfig2 = {
+  kernelName: Mod,
+  backendName: "webgl",
+  kernelFunc: mod3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js
+var MultinomialProgram = class {
+  constructor(batchSize, numOutcomes, numSamples) {
+    this.variableNames = ["probs"];
+    this.customUniforms = [{ name: "seed", type: "float" }];
+    this.outputShape = [batchSize, numSamples];
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+
+        float r = random(seed);
+        float cdf = 0.0;
+
+        for (int i = 0; i < ${numOutcomes - 1}; i++) {
+          cdf += getProbs(batch, i);
+
+          if (r < cdf) {
+            setOutput(float(i));
+            return;
+          }
+        }
+
+        // If no other event happened, last event happened.
+        setOutput(float(${numOutcomes - 1}));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js
+var DIV = `
+if (a == b) {
+  return 1.0;
+};
+return a / b;`;
+var DIV_PACKED = `
+  // vec4 one = vec4(equal(a, b));
+  // return one + (vec4(1.0) - one) * a / b;
+  vec4 result = a / b;
+  if(a.x == b.x) {
+    result.x = 1.;
+  }
+  if(a.y == b.y) {
+    result.y = 1.;
+  }
+  if(a.z == b.z) {
+    result.z = 1.;
+  }
+  if(a.w == b.w) {
+    result.w = 1.;
+  }
+
+  return result;
+`;
+var realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });
+var realDivConfig2 = {
+  kernelName: RealDiv,
+  backendName: "webgl",
+  kernelFunc: realDiv
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js
+var SUB = "return a - b;";
+var sub3 = binaryKernelFunc2({
+  opSnippet: SUB,
+  packedOpSnippet: SUB,
+  supportsComplex: true,
+  cpuKernelImpl: subImplCPU
+});
+var subConfig2 = {
+  kernelName: Sub,
+  backendName: "webgl",
+  kernelFunc: sub3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js
+function softmax4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { dim } = attrs;
+  const axes = util_exports.parseAxisParam([dim], logits.shape);
+  const maxLogit = max4({
+    inputs: { x: logits },
+    backend: backend2,
+    attrs: { reductionIndices: axes, keepDims: false }
+  });
+  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
+  const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
+  const a6 = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });
+  const b = exp3({ inputs: { x: a6 }, backend: backend2 });
+  const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
+  const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
+  const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });
+  backend2.disposeIntermediateTensorInfo(maxLogit);
+  backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);
+  backend2.disposeIntermediateTensorInfo(a6);
+  backend2.disposeIntermediateTensorInfo(b);
+  backend2.disposeIntermediateTensorInfo(sumExp);
+  backend2.disposeIntermediateTensorInfo(sumExpReshaped);
+  return res;
+}
+var softmaxConfig2 = {
+  kernelName: Softmax,
+  backendName: "webgl",
+  kernelFunc: softmax4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js
+function multinomial3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { numSamples, seed, normalized } = attrs;
+  const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });
+  const batchSize = probs.shape[0];
+  const numOutcomes = probs.shape[1];
+  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
+  const customValues = [[seed]];
+  const res = backend2.runWebGLProgram(program, [probs], "int32", customValues);
+  if (!normalized) {
+    backend2.disposeIntermediateTensorInfo(probs);
+  }
+  return res;
+}
+var multinomialConfig2 = {
+  kernelName: Multinomial,
+  backendName: "webgl",
+  kernelFunc: multinomial3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js
+var NEG = CHECK_NAN_SNIPPET + `
+  return -x;
+`;
+var NEG_PACKED = `
+  vec4 result = -x;
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+function neg3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xData = backend2.texData.get(x.dataId);
+    const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);
+    return backend2.makeTensorInfo(newShape, x.dtype, outValues);
+  }
+  let program;
+  if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
+    program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);
+  } else {
+    program = new UnaryOpProgram(x.shape, NEG);
+  }
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var negConfig2 = {
+  kernelName: Neg,
+  backendName: "webgl",
+  kernelFunc: neg3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js
+var nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;
+function nonMaxSuppressionV32(args) {
+  backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
+}
+var nonMaxSuppressionV3Config2 = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "webgl",
+  kernelFunc: nonMaxSuppressionV32
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js
+var nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;
+function nonMaxSuppressionV42(args) {
+  backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
+  ];
+}
+var nonMaxSuppressionV4Config2 = {
+  kernelName: NonMaxSuppressionV4,
+  backendName: "webgl",
+  kernelFunc: nonMaxSuppressionV42
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js
+var nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;
+function nonMaxSuppressionV52(args) {
+  backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const maxOutputSizeVal = maxOutputSize;
+  const iouThresholdVal = iouThreshold;
+  const scoreThresholdVal = scoreThreshold;
+  const softNmsSigmaVal = softNmsSigma;
+  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
+  ];
+}
+var nonMaxSuppressionV5Config2 = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "webgl",
+  kernelFunc: nonMaxSuppressionV52
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js
+var OneHotProgram = class {
+  constructor(numIndices, depth, onValue, offValue) {
+    this.variableNames = ["indices"];
+    this.outputShape = [numIndices, depth];
+    this.userCode = `
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int index = round(getIndices(coords.x));
+        setOutput(mix(float(${offValue}), float(${onValue}),
+                      float(index == coords.y)));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js
+var oneHot3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices } = inputs;
+  const { depth, onValue, offValue } = attrs;
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);
+  const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });
+  const result = backend2.runWebGLProgram(program, [reshaped], indices.dtype);
+  backend2.disposeIntermediateTensorInfo(reshaped);
+  const outShape = [...indices.shape, depth];
+  const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });
+  backend2.disposeIntermediateTensorInfo(result);
+  return out;
+};
+var oneHotConfig2 = {
+  kernelName: OneHot,
+  backendName: "webgl",
+  kernelFunc: oneHot3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js
+function zerosLike3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "complex64") {
+    const realPart = real3({ inputs: { input: x }, backend: backend2 });
+    const r2 = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill3({
+      attrs: {
+        shape: x.shape,
+        dtype: x.dtype,
+        value: x.dtype === "string" ? "" : 0
+      },
+      backend: backend2
+    });
+  }
+}
+var zerosLikeConfig2 = {
+  kernelName: ZerosLike,
+  backendName: "webgl",
+  kernelFunc: zerosLike3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js
+function onesLike3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("onesLike is not supported under string dtype");
+  } else if (x.dtype === "complex64") {
+    const realPart = real3({ inputs: { input: x }, backend: backend2 });
+    const r2 = onesLike3({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeIntermediateTensorInfo(realPart);
+    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(imagPart);
+    backend2.disposeIntermediateTensorInfo(i2);
+    return result;
+  } else {
+    return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
+  }
+}
+var onesLikeConfig2 = {
+  kernelName: OnesLike,
+  backendName: "webgl",
+  kernelFunc: onesLike3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js
+function pack2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims4({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var packConfig2 = {
+  kernelName: Pack,
+  backendName: "webgl",
+  kernelFunc: pack2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js
+var PadProgram = class {
+  constructor(xShape, paddings, constantValue) {
+    this.variableNames = ["x"];
+    this.customUniforms = [{ name: "value", type: "float" }];
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const type = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
+    if (rank === 1) {
+      this.userCode = `
+        int start = ${start};
+        int end = ${end};
+
+        void main() {
+          int outC = getOutputCoords();
+          if (outC < start || outC >= end) {
+            setOutput(value);
+          } else {
+            setOutput(getX(outC - start));
+          }
+        }
+      `;
+      return;
+    }
+    this.userCode = `
+      ${type} start = ${type}(${start});
+      ${type} end = ${type}(${end});
+
+      void main() {
+        ${type} outC = getOutputCoords();
+        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
+          setOutput(value);
+        } else {
+          ${type} coords = outC - start;
+          setOutput(getX(${unpackedCoords}));
+        }
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js
+var PadPackedProgram = class {
+  constructor(xShape, paddings, constantValue) {
+    this.variableNames = ["x"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.customUniforms = [{ name: "value", type: "float" }];
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    const rank = xShape.length;
+    const dtype = getCoordsDataType(rank);
+    const start = paddings.map((p2) => p2[0]).join(",");
+    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const coords3 = getChannels("rc", rank);
+    const source = getChannels("source", rank);
+    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
+    const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
+    const componentSetup = [
+      `${dtype} rc = outputLoc;`,
+      `${coords3[rank - 1]} += 1;
+       if(${cLimit}) {
+      `,
+      rank === 1 ? "" : `}
+       rc = outputLoc;
+       ${coords3[rank - 2]} += 1;
+       if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {`,
+      rank === 1 ? "" : `  ${coords3[rank - 1]} += 1;
+         if(${cLimit}) {`
+    ];
+    const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
+    let mainLoop = "";
+    for (let i2 = 0, j = rank === 1 ? 2 : 4; i2 < j; i2++) {
+      mainLoop += `
+        ${componentSetup[i2]}
+        if (${paddingArea}) {
+          result[${i2}] = float(value);
+        } else {
+          ${dtype} source = rc - start;
+          result[${i2}] = getChannel(getX(${source.join()}), ${innerDims});
+        }
+      `;
+    }
+    mainLoop += rank === 1 ? `} ` : `}}`;
+    this.userCode = `
+      const ${dtype} start = ${dtype}(${start});
+      const ${dtype} end = ${dtype}(${end});
+
+      void main() {
+        ${dtype} outputLoc = getOutputCoords();
+        vec4 result = vec4(0.);
+        ${mainLoop}
+        setOutput(result);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js
+var padV22 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, constantValue } = attrs;
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    return fill3({
+      backend: backend2,
+      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
+    });
+  }
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);
+  const customValues = [[constantValue]];
+  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
+};
+var padV2Config2 = {
+  kernelName: PadV2,
+  backendName: "webgl",
+  kernelFunc: padV22
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js
+var POW = `
+  if(a < 0.0 && floor(b) < b){
+    return NAN;
+  }
+  if (b == 0.0) {
+    return 1.0;
+  }
+  return (round(mod(b, 2.0)) != 1) ?
+      pow(abs(a), b) : sign(a) * pow(abs(a), b);
+`;
+var POW_PACKED = `
+  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
+  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
+  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
+  vec4 result = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  bvec4 isExpZero = equal(b, vec4(0.0));
+  result.r = isExpZero.r ? 1.0 : result.r;
+  result.g = isExpZero.g ? 1.0 : result.g;
+  result.b = isExpZero.b ? 1.0 : result.b;
+  result.a = isExpZero.a ? 1.0 : result.a;
+
+  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
+  ` + CHECK_NAN_SNIPPET3 + `
+  return result;
+`;
+var pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });
+var powConfig2 = {
+  kernelName: Pow,
+  backendName: "webgl",
+  kernelFunc: pow3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js
+function prod3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  const xRank = x.shape.length;
+  const toDispose = [];
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let permutedX = x;
+  if (permutedAxes != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    toDispose.push(permutedX);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("prod", axes, xRank);
+  let res;
+  if (backend2.shouldExecuteOnCPU([permutedX])) {
+    const xVals = backend2.texData.get(permutedX.dataId).values;
+    const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);
+    res = backend2.makeTensorInfo(outShape, outDtype, outVals);
+  } else {
+    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
+    const inSize = util_exports.sizeFromShape(reduceShape);
+    const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+    const outputDType = sumOutType(x.dtype);
+    const reduced = reduce(a2D, outputDType, "prod", backend2);
+    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
+    toDispose.push(a2D);
+    toDispose.push(reduced);
+  }
+  if (keepDims) {
+    toDispose.push(res);
+    const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);
+    res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });
+  }
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return res;
+}
+var prodConfig2 = {
+  kernelName: Prod,
+  backendName: "webgl",
+  kernelFunc: prod3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js
+var range4 = (args) => {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, step: step5, dtype } = attrs;
+  const values = rangeImplCPU(start, stop, step5, dtype);
+  return backend2.makeTensorInfo([values.length], dtype, values);
+};
+var rangeConfig2 = {
+  kernelName: Range,
+  backendName: "webgl",
+  kernelFunc: range4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js
+var RECIPROCAL = `return 1.0 / x;`;
+var reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });
+var reciprocalConfig2 = {
+  kernelName: Reciprocal,
+  backendName: "webgl",
+  kernelFunc: reciprocal3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js
+var RELU3 = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : x;
+`;
+var RELU_PACKED = `
+  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });
+var reluConfig2 = {
+  kernelName: Relu,
+  backendName: "webgl",
+  kernelFunc: relu3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js
+var RELU63 = CHECK_NAN_SNIPPET + `
+  return (x < 0.0) ? 0.0 : min(6.0, x);
+`;
+var RELU6_PACKED = `
+  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });
+var relu6Config2 = {
+  kernelName: Relu6,
+  backendName: "webgl",
+  kernelFunc: relu63
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js
+var ResizeBilinearProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;
+    } else {
+      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the four integer indices.
+        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
+        ivec2 sourceCeilRC = ivec2(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
+        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
+        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
+        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
+
+        float top = topLeft + (topRight - topLeft) * fracRC.y;
+        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+        float newValue = top + (bottom - top) * fracRC.x;
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js
+var ResizeBilinearPackedProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;
+    } else {
+      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the four integer indices.
+        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
+        ivec3 sourceCeilRC = ivec3(
+          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${depth - 1};
+        bool hasNextRow = coords.z < ${newWidth - 1};
+
+        // In parallel, construct four corners for all four components in
+        // packed 2x2 cell.
+        vec4 topLeft = vec4(
+          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 bottomLeft = vec4(
+          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
+
+        vec4 topRight = vec4(
+          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec4 bottomRight = vec4(
+          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
+          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
+
+        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
+
+        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
+        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
+        vec4 newValue = mix(top, bottom, fracRC.x);
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js
+function resizeBilinear3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size } = attrs;
+  const [newHeight, newWidth] = size;
+  const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
+  return backend2.runWebGLProgram(program, [images], "float32");
+}
+var resizeBilinearConfig2 = {
+  kernelName: ResizeBilinear,
+  backendName: "webgl",
+  kernelFunc: resizeBilinear3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js
+var ResizeBilinearBackpropProgram = class {
+  constructor(dyShape, inputShape, alignCorners) {
+    this.variableNames = ["dy"];
+    this.outputShape = [];
+    this.outputShape = inputShape;
+    const [, xHeight, xWidth] = inputShape;
+    const [, yHeight, yWidth] = dyShape;
+    const effectiveXSize = [
+      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+    ];
+    const effectiveYSize = [
+      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+    ];
+    const heightScale = effectiveXSize[0] / effectiveYSize[0];
+    const widthScale = effectiveXSize[1] / effectiveYSize[1];
+    const invHeightScale = 1 / heightScale;
+    const invWidthScale = 1 / widthScale;
+    const winHeight = Math.ceil(invHeightScale) * 2 + 2;
+    const winWidth = Math.ceil(invWidthScale) * 2 + 2;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
+
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
+
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(startRLerp - float(winHeight / 2));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(startCLerp - float(winWidth / 2));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${yHeight}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${yWidth}) {
+              continue;
+            }
+
+            float dxR = float(dyR) * heightScale;
+            int topDxRIndex = int(floor(dxR));
+            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));
+            float dxRLerp = dxR - float(topDxRIndex);
+            float inverseDxRLerp = 1.0 - dxRLerp;
+
+            float dxC = float(dyC) * widthScale;
+            int leftDxCIndex = int(floor(dxC));
+            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));
+            float dxCLerp = dxC - float(leftDxCIndex);
+            float inverseDxCLerp = 1.0 - dxCLerp;
+
+            if (r == topDxRIndex && c == leftDxCIndex) {
+              // topLeft
+              accumulator +=
+                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
+            }
+
+            if (r == topDxRIndex && c == rightDxCIndex) {
+              // topRight
+              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == leftDxCIndex) {
+              // bottomLeft
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
+            }
+
+            if (r == bottomDxRIndex && c == rightDxCIndex) {
+              // bottomRight
+              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js
+function resizeBilinearGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);
+  return backend2.runWebGLProgram(program, [dy], dy.dtype);
+}
+var resizeBilinearGradConfig3 = {
+  kernelName: ResizeBilinearGrad,
+  backendName: "webgl",
+  kernelFunc: resizeBilinearGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js
+var ResizeNearestNeighborProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    const roundBase = alignCorners ? "0.5" : "0.0";
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;
+    } else {
+      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec2 effectiveInputOverOutputRatioRC = vec2(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        ivec2 yRC = coords.yz;
+
+        // Fractional source index.
+        vec2 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec2 sourceNearestRC = ivec2(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
+        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js
+var ResizeNearestNeighborPackedProgram = class {
+  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
+    this.variableNames = ["A"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    this.outputShape = [];
+    const [batch, oldHeight, oldWidth, depth] = inputShape;
+    this.outputShape = [batch, newHeight, newWidth, depth];
+    const effectiveInSize = [
+      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
+      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
+    ];
+    const effectiveOutSize = [
+      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
+      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
+    ];
+    const roundBase = alignCorners ? "0.5" : "0.0";
+    let sourceFracIndexRC;
+    if (halfPixelCenters) {
+      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;
+    } else {
+      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
+    }
+    this.userCode = `
+      const vec3 effectiveInputOverOutputRatioRC = vec3(
+          ${effectiveInSize[0] / effectiveOutSize[0]},
+          ${effectiveInSize[1] / effectiveOutSize[1]},
+          ${effectiveInSize[1] / effectiveOutSize[1]});
+      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
+                                     ${oldWidth}.0);
+
+      float getAValue(int b, int r, int c, int d) {
+        return getChannel(getA(b, r, c, d), vec2(c, d));
+      }
+
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        // Calculate values for next column in yRC.z.
+        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
+
+        // Fractional source index.
+        vec3 sourceFracIndexRC = ${sourceFracIndexRC};
+
+        // Compute the coordinators of nearest neighbor point.
+        ivec3 sourceNearestRC = ivec3(
+          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
+
+        // Should we calculate next column and row elements in 2x2 packed cell.
+        bool hasNextCol = d < ${depth - 1};
+        bool hasNextRow = coords.z < ${newWidth - 1};
+
+        vec4 newValue = vec4(
+          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
+          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
+                     : 0.0,
+          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
+                     : 0.0,
+          (hasNextRow && hasNextCol) ?
+            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
+
+        setOutput(newValue);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js
+function resizeNearestNeighbor3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size } = attrs;
+  const [newHeight, newWidth] = size;
+  const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
+  return backend2.runWebGLProgram(program, [images], images.dtype);
+}
+var resizeNearestNeighborConfig2 = {
+  kernelName: ResizeNearestNeighbor,
+  backendName: "webgl",
+  kernelFunc: resizeNearestNeighbor3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js
+var ResizeNearestNeigborBackpropProgram = class {
+  constructor(dyShape, inputShape, alignCorners) {
+    this.variableNames = ["dy"];
+    this.outputShape = [];
+    this.outputShape = inputShape;
+    const [, xHeight, xWidth] = inputShape;
+    const [, yHeight, yWidth] = dyShape;
+    const effectiveXSize = [
+      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
+      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
+    ];
+    const effectiveYSize = [
+      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
+      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
+    ];
+    const heightScale = effectiveXSize[0] / effectiveYSize[0];
+    const widthScale = effectiveXSize[1] / effectiveYSize[1];
+    const invHeightScale = 1 / heightScale;
+    const invWidthScale = 1 / widthScale;
+    const winHeight = Math.ceil(invHeightScale) * 2 + 2;
+    const winWidth = Math.ceil(invWidthScale) * 2 + 2;
+    this.userCode = `
+      void main() {
+        ivec4 coords = getOutputCoords();
+        int b = coords[0];
+        int d = coords[3];
+        int r = coords[1];
+        int c = coords[2];
+
+        float accumulator = 0.0;
+
+        const float heightScale = float(${heightScale});
+        const float widthScale = float(${widthScale});
+
+        const float invHeightScale = float(${invHeightScale});
+        const float invWidthScale = float(${invWidthScale});
+
+        const int winHeight = int(${winHeight});
+        const int winWidth = int(${winWidth});
+
+        // Compute bounds for where in dy we will look
+        float startRLerp = floor(float(r) * invHeightScale);
+        int startDyR = int(floor(startRLerp - float(winHeight / 2)));
+
+        float startCLerp = floor(float(c) * invWidthScale);
+        int startDyC = int(floor(startCLerp - float(winWidth / 2)));
+
+        // Loop over dy
+        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
+          int dyR = dyROffset + startDyR;
+
+          // Guard against the window exceeding the bounds of dy
+          if (dyR < 0 || dyR >= ${yHeight}) {
+            continue;
+          }
+
+          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
+            int dyC = dyCOffset + startDyC;
+
+            // Guard against the window exceeding the bounds of dy
+            if (dyC < 0 || dyC >= ${yWidth}) {
+              continue;
+            }
+
+            float sourceFracRow =
+              float(${effectiveXSize[0]}) *
+                (float(dyR) / float(${effectiveYSize[0]}));
+
+            float sourceFracCol =
+                float(${effectiveXSize[1]}) *
+                  (float(dyC) / float(${effectiveYSize[1]}));
+
+            int sourceNearestRow = int(min(
+                float(int(${xHeight}) - 1),
+                ${alignCorners} ? float(round(sourceFracRow)) :
+                                  float(floor(sourceFracRow))));
+
+            int sourceNearestCol = int(min(
+                float(int(${xWidth}) - 1),
+                ${alignCorners} ? float(round(sourceFracCol)) :
+                                  float(floor(sourceFracCol))));
+
+            if (r == sourceNearestRow && c == sourceNearestCol) {
+              accumulator += getDy(b, dyR, dyC, d);
+            }
+          }
+        }
+        // End loop over dy
+
+        setOutput(accumulator);
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js
+function resizeNearestNeighborGrad2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images, dy } = inputs;
+  const { alignCorners } = attrs;
+  const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);
+  return backend2.runWebGLProgram(program, [dy], dy.dtype);
+}
+var resizeNearestNeighborGradConfig3 = {
+  kernelName: ResizeNearestNeighborGrad,
+  backendName: "webgl",
+  kernelFunc: resizeNearestNeighborGrad2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js
+var ReverseProgram = class {
+  constructor(xShape, axis) {
+    this.variableNames = ["x"];
+    const rank = xShape.length;
+    if (rank > 4) {
+      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
+    }
+    this.outputShape = xShape;
+    if (rank === 1) {
+      this.userCode = `
+        void main() {
+          int coord = getOutputCoords();
+          setOutput(getX(${xShape[0]} - coord - 1));
+        }
+      `;
+      return;
+    }
+    const getInCoord = (i2) => {
+      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
+        return `${xShape[i2]} - coords[${i2}] - 1`;
+      }
+      return `coords[${i2}]`;
+    };
+    const inCoords = xShape.map((_, i2) => getInCoord(i2)).join(",");
+    const type = getCoordsDataType(rank);
+    this.userCode = `
+      void main() {
+        ${type} coords = getOutputCoords();
+        setOutput(getX(${inCoords}));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js
+var ReversePackedProgram = class {
+  constructor(xShape, axis) {
+    this.variableNames = ["x"];
+    this.packedInputs = true;
+    this.packedOutput = true;
+    const rank = xShape.length;
+    if (rank > 4) {
+      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
+    }
+    this.outputShape = xShape;
+    const channels = getChannels("rc", rank);
+    const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;
+    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;
+    const type = getCoordsDataType(rank);
+    if (rank === 1) {
+      this.userCode = `
+        void main(){
+          int rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = getChannel(getX(${xShape[0]} - rc - 1),
+            ${xShape[0]} - rc - 1);
+          if(${nextColumn}){
+              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
+                ${xShape[0]} - (rc  + 1) - 1);
+          }
+          setOutput(result);
+        }
+      `;
+    } else {
+      this.userCode = `
+        void main() {
+          ${type} rc = getOutputCoords();
+          vec4 result = vec4(0.);
+          result.r = ${getR(channels.slice())};
+          if(${nextColumn}){
+            result.g = ${getG(channels.slice())};
+          }
+          if(${nextRow}) {
+            result.b = ${getB(channels.slice())};
+            if(${nextColumn}) {
+              result.a = ${getA(channels.slice())};
+            }
+          }
+          setOutput(result);
+        }
+    `;
+    }
+    function getR(channels2) {
+      return getChannel(channels2);
+    }
+    function getG(channels2) {
+      channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
+      return getChannel(channels2);
+    }
+    function getB(channels2) {
+      channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
+      return getChannel(channels2);
+    }
+    function getA(channels2) {
+      channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
+      channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
+      return getChannel(channels2);
+    }
+    function getChannel(channels2) {
+      const inCoordsArray = xShape.map((_, i2) => getInCoord(i2, channels2));
+      const inCoords = inCoordsArray.join(",");
+      const innerDims = inCoordsArray.slice(-2).join(",");
+      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
+    }
+    function getInCoord(i2, channels1) {
+      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
+        return `${xShape[i2]} - ${channels1[i2]} - 1`;
+      } else {
+        return `${channels1[i2]}`;
+      }
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js
+function reverse3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dims } = attrs;
+  const xRank = x.shape.length;
+  const $dims = util_exports.parseAxisParam(dims, x.shape);
+  if (xRank === 0) {
+    return identity3({ inputs: { x }, backend: backend2 });
+  }
+  const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var reverseConfig2 = {
+  kernelName: Reverse,
+  backendName: "webgl",
+  kernelFunc: reverse3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js
+var RotateProgram = class {
+  constructor(imageShape, fillValue) {
+    this.variableNames = ["Image"];
+    this.outputShape = [];
+    this.customUniforms = [{ name: "params", type: "vec4" }];
+    const imageHeight = imageShape[1];
+    const imageWidth = imageShape[2];
+    this.outputShape = imageShape;
+    let fillSnippet = "";
+    if (typeof fillValue === "number") {
+      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;
+    } else {
+      fillSnippet = `
+        vec3 fill = vec3(${fillValue.join(",")});
+        float outputValue = fill[coords[3]];`;
+    }
+    this.userCode = `
+        void main() {
+          ivec4 coords = getOutputCoords();
+          int x = coords[2];
+          int y = coords[1];
+          float coordXFloat = (float(x) - params[0]) * params[3] -
+            (float(y) - params[1]) * params[2];
+          float coordYFloat = (float(x) - params[0]) * params[2] +
+            (float(y) - params[1]) * params[3];
+          int coordX = int(round(coordXFloat + params[0]));
+          int coordY = int(round(coordYFloat + params[1]));
+          ${fillSnippet}
+          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
+            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
+          }
+          setOutput(outputValue);
+        }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js
+var rotateWithOffsetConfig2 = {
+  kernelName: RotateWithOffset,
+  backendName: "webgl",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const { radians, fillValue, center } = attrs;
+    const webglBackend = backend2;
+    const program = new RotateProgram(image2.shape, fillValue);
+    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);
+    const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];
+    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues);
+    return output;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js
+var ROUND = `
+  // OpenGL ES does not support round function.
+  // The algorithm is based on banker's rounding.
+  float base = floor(x);
+  if ((x - base) < 0.5) {
+    return floor(x);
+  } else if ((x - base) > 0.5) {
+    return ceil(x);
+  } else {
+    if (mod(base, 2.0) == 0.0) {
+      return base;
+    } else {
+      return base + 1.0;
+    }
+  }
+`;
+var round4 = unaryKernelFunc2({ opSnippet: ROUND });
+var roundConfig2 = {
+  kernelName: Round,
+  backendName: "webgl",
+  kernelFunc: round4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js
+var RSQRT = `return inversesqrt(x);`;
+var rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });
+var rsqrtConfig2 = {
+  kernelName: Rsqrt,
+  backendName: "webgl",
+  kernelFunc: rsqrt3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js
+var ScatterProgram = class {
+  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {
+    this.variableNames = ["updates", "indices", "defaultValue"];
+    this.outputShape = shape;
+    const stridesType = getCoordsDataType(strides.length);
+    const dtype = getCoordsDataType(shape.length);
+    let indicesString = "";
+    if (indicesRank === 1) {
+      indicesString = "i";
+    } else if (indicesRank === 2) {
+      indicesString = "i, j";
+    }
+    const indicesSnippet = `getIndices(${indicesString})`;
+    let updatesString = "";
+    if (updatesRank === 1) {
+      updatesString = "i";
+    } else if (updatesRank === 2) {
+      updatesString = "i, coords[1]";
+    }
+    const updatesSnippet = `getUpdates(${updatesString})`;
+    const strideString = sliceDim > 1 ? "strides[j]" : "strides";
+    this.userCode = `
+        ${stridesType} strides = ${stridesType}(${strides});
+
+        void main() {
+          ${dtype} coords = getOutputCoords();
+          float sum = 0.0;
+          bool found = false;
+          for (int i = 0; i < ${updateSize}; i++) {
+            int flattenedIndex = 0;
+            for (int j = 0; j < ${sliceDim}; j++) {
+              int index = round(${indicesSnippet});
+              flattenedIndex += index * ${strideString};
+            }
+            if (flattenedIndex == coords[0]) {
+              sum += ${updatesSnippet};
+              found = true;
+            }
+          }
+          setOutput(mix(getDefaultValue(), sum, float(found)));
+        }
+      `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js
+function scatterNd2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);
+  const flattenShape = [outputSize / sliceSize, sliceSize];
+  if (outputSize === 0) {
+    return backend2.makeTensorInfo(shape, indices.dtype);
+  }
+  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });
+  const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });
+  const defaultValue = backend2.makeTensorInfo([], "float32", new Float32Array([0]));
+  const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);
+  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } });
+  backend2.disposeIntermediateTensorInfo(flattenIndices);
+  backend2.disposeIntermediateTensorInfo(flattenX);
+  backend2.disposeIntermediateTensorInfo(res);
+  backend2.disposeIntermediateTensorInfo(defaultValue);
+  return reshaped;
+}
+var scatterNdConfig2 = {
+  kernelName: ScatterNd,
+  backendName: "webgl",
+  kernelFunc: scatterNd2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js
+var SearchSortedProgram = class {
+  constructor(batchSize, numInputs, numValues, side) {
+    this.variableNames = ["sortedSequence", "values"];
+    this.customUniforms = [{ name: "numInputs", type: "int" }];
+    this.outputShape = [batchSize, numValues];
+    const webGL2LoopHead = "while (left < right) {";
+    const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;
+    const loopHead = env().getNumber("WEBGL_VERSION") === 2 ? webGL2LoopHead : webGL1LoopHead;
+    const boundComparator = side === "left" ? "<" : "<=";
+    this.userCode = `
+       int findBound(int batch, float value) {
+         int left = 0;
+         int right = numInputs;
+         int mid;
+         ${loopHead}
+           mid = (left + right) / 2;
+           if (getSortedSequence(batch, mid) ${boundComparator} value) {
+             left = mid + 1;
+           } else {
+             right = mid;
+           }
+         }
+         return right;
+       }
+
+       void main() {
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int valueIndex = coords[1];
+
+         float value = getValues(batch, valueIndex);
+
+         setOutput(float(findBound(batch, value)));
+       }
+     `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js
+function searchSorted3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sortedSequence, values } = inputs;
+  const { side } = attrs;
+  const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
+  const customValues = [[sortedSequence.shape[1]]];
+  return backend2.runWebGLProgram(program, [sortedSequence, values], "int32", customValues);
+}
+var searchSortedConfig2 = {
+  kernelName: SearchSorted,
+  backendName: "webgl",
+  kernelFunc: searchSorted3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js
+var SelectProgram = class {
+  constructor(cRank, shape, rank) {
+    this.variableNames = ["c", "a", "b"];
+    this.outputShape = shape;
+    let cCoords;
+    let abCoords;
+    if (rank > 4) {
+      throw Error(`Where for rank ${rank} is not yet supported`);
+    }
+    if (rank === 1) {
+      abCoords = `resRC`;
+      cCoords = `resRC`;
+    } else {
+      const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+      const cCoordVars = [];
+      const abCoordVars = [];
+      for (let i2 = 0; i2 < shape.length; i2++) {
+        abCoordVars.push(`${currentCoords[i2]}`);
+        if (i2 < cRank) {
+          cCoordVars.push(`${currentCoords[i2]}`);
+        }
+      }
+      cCoords = cCoordVars.join();
+      abCoords = abCoordVars.join();
+    }
+    const dtype = getCoordsDataType(rank);
+    this.userCode = `
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        float cVal = getC(${cCoords});
+        if (cVal >= 1.0) {
+          setOutput(getA(${abCoords}));
+        } else {
+          setOutput(getB(${abCoords}));
+        }
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js
+function select3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  const program = new SelectProgram(condition.shape.length, t2.shape, t2.shape.length);
+  return backend2.runWebGLProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+}
+var selectConfig2 = {
+  kernelName: Select,
+  backendName: "webgl",
+  kernelFunc: select3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js
+var SELU = `
+  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
+  // see: https://arxiv.org/abs/1706.02515
+  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
+  float scale = ${backend_util_exports.SELU_SCALE};
+  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
+`;
+var selu3 = unaryKernelFunc2({ opSnippet: SELU });
+var seluConfig2 = {
+  kernelName: Selu,
+  backendName: "webgl",
+  kernelFunc: selu3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js
+var SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `
+  return 1.0 / (1.0 + exp(-1.0 * x));
+`;
+var SIGMOID_PACKED = `
+  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
+  bvec4 isNaN = isnan(x);
+
+  result.r = isNaN.r ? x.r : result.r;
+  result.g = isNaN.g ? x.g : result.g;
+  result.b = isNaN.b ? x.b : result.b;
+  result.a = isNaN.a ? x.a : result.a;
+
+  return result;
+`;
+var sigmoid3 = unaryKernelFunc2({
+  opSnippet: SIGMOID3,
+  packedOpSnippet: SIGMOID_PACKED,
+  cpuKernelImpl: sigmoidImplCPU
+});
+var sigmoidConfig2 = {
+  kernelName: Sigmoid,
+  backendName: "webgl",
+  kernelFunc: sigmoid3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js
+var SIGN = `
+  if (isnan(x)) { return 0.0; }
+  return sign(x);
+`;
+var sign3 = unaryKernelFunc2({ opSnippet: SIGN });
+var signConfig2 = {
+  kernelName: Sign,
+  backendName: "webgl",
+  kernelFunc: sign3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js
+var SIN = CHECK_NAN_SNIPPET_UNARY + `
+  return sin(x);
+`;
+var sin3 = unaryKernelFunc2({ opSnippet: SIN });
+var sinConfig2 = {
+  kernelName: Sin,
+  backendName: "webgl",
+  kernelFunc: sin3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js
+var SINH = `
+  float e2x = exp(x);
+  return (e2x - 1.0 / e2x) / 2.0;
+`;
+var sinh3 = unaryKernelFunc2({ opSnippet: SINH });
+var sinhConfig2 = {
+  kernelName: Sinh,
+  backendName: "webgl",
+  kernelFunc: sinh3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js
+var SOFTPLUS = `
+  float epsilon = 1.1920928955078125e-7;
+  float threshold = log(epsilon) + 2.0;
+
+  bool too_large = x > -threshold;
+  bool too_small = x < threshold;
+
+  float result;
+  float exp_x = exp(x);
+
+  if (too_large){
+    result = x;
+  }
+  else if (too_small){
+    result = exp_x;
+  }
+  else{
+    result = log(exp_x + 1.0);
+  }
+  return result;
+`;
+var softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });
+var softplusConfig2 = {
+  kernelName: Softplus,
+  backendName: "webgl",
+  kernelFunc: softplus3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js
+var spaceToBatchND3 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const toDispose = [];
+  const paddedX = padV22({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });
+  const paddedXT = transpose3({
+    inputs: { x: reshapedPaddedX },
+    backend: backend2,
+    attrs: { perm: permutedReshapedPaddedPermutation }
+  });
+  const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });
+  toDispose.push(paddedX);
+  toDispose.push(reshapedPaddedX);
+  toDispose.push(paddedXT);
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+};
+var spaceToBatchNDConfig2 = {
+  kernelName: SpaceToBatchND,
+  backendName: "webgl",
+  kernelFunc: spaceToBatchND3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js
+function sparseFillEmptyRows3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { indices, values, denseShape, defaultValue } = inputs;
+  if (denseShape.shape.length !== 1) {
+    throw new Error(`Dense shape must be a vector, saw:
+         ${denseShape.shape}`);
+  }
+  if (indices.shape.length !== 2) {
+    throw new Error(`Indices must be a matrix, saw:
+         ${indices.shape}`);
+  }
+  if (values.shape.length !== 1) {
+    throw new Error(`Values must be a vector, saw:
+         ${values.shape}`);
+  }
+  if (defaultValue.shape.length !== 0) {
+    throw new Error(`Default value must be a scalar, saw:
+        ${defaultValue.shape}`);
+  }
+  const $indices = backend2.readSync(indices.dataId);
+  const $values = backend2.readSync(values.dataId);
+  const $denseShape = backend2.readSync(denseShape.dataId);
+  const $defaultValue = backend2.readSync(defaultValue.dataId)[0];
+  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
+  return [
+    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
+    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
+    backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),
+    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))
+  ];
+}
+var sparseFillEmptyRowsConfig2 = {
+  kernelName: SparseFillEmptyRows,
+  backendName: "webgl",
+  kernelFunc: sparseFillEmptyRows3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js
+function sparseReshape3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { inputIndices, inputShape, newShape } = inputs;
+  if (inputIndices.shape.length !== 2) {
+    throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);
+  }
+  if (inputShape.shape.length !== 1) {
+    throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);
+  }
+  if (newShape.shape.length !== 1) {
+    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
+  }
+  const $inputShape = Array.from(backend2.readSync(inputShape.dataId));
+  const $inputIndices = backend2.readSync(inputIndices.dataId);
+  const targetShape = Array.from(backend2.readSync(newShape.dataId));
+  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
+  return [
+    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
+    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))
+  ];
+}
+var sparseReshapeConfig2 = {
+  kernelName: SparseReshape,
+  backendName: "webgl",
+  kernelFunc: sparseReshape3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js
+function sparseSegmentMean3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+              ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+              ${segmentIds.shape}`);
+  }
+  const $data = backend2.readSync(data.dataId);
+  const $indices = backend2.readSync(indices.dataId);
+  const $segmentIds = backend2.readSync(segmentIds.dataId);
+  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentMeanConfig2 = {
+  kernelName: SparseSegmentMean,
+  backendName: "webgl",
+  kernelFunc: sparseSegmentMean3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js
+function sparseSegmentSum3(args) {
+  const { inputs, backend: backend2 } = args;
+  const { data, indices, segmentIds } = inputs;
+  if (data.shape.length < 1) {
+    throw new Error(`Data should be at least 1 dimensional but received scalar`);
+  }
+  if (indices.shape.length !== 1) {
+    throw new Error(`Indices should be a vector but received shape
+             ${indices.shape}`);
+  }
+  if (segmentIds.shape.length !== 1) {
+    throw new Error(`Segment ids should be a vector but received shape
+             ${segmentIds.shape}`);
+  }
+  const $data = backend2.readSync(data.dataId);
+  const $indices = backend2.readSync(indices.dataId);
+  const $segmentIds = backend2.readSync(segmentIds.dataId);
+  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);
+  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
+}
+var sparseSegmentSumConfig2 = {
+  kernelName: SparseSegmentSum,
+  backendName: "webgl",
+  kernelFunc: sparseSegmentSum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js
+function sparseToDense3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sparseIndices, sparseValues, defaultValue } = inputs;
+  const { outputShape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
+  const sumDupeIndices = false;
+  if (sparseValues.dtype === "string") {
+    const indicesBuf = backend2.bufferSync(sparseIndices);
+    const updatesBuf = backend2.bufferSync(sparseValues);
+    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);
+    const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+  }
+  const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);
+  const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);
+  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });
+  backend2.disposeIntermediateTensorInfo(res);
+  return reshaped;
+}
+var sparseToDenseConfig2 = {
+  kernelName: SparseToDense,
+  backendName: "webgl",
+  kernelFunc: sparseToDense3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js
+function splitV2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const xRank = x.shape.length;
+  const begin = new Array(xRank).fill(0);
+  const size = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const sliceSize = [...size];
+    sliceSize[$axis] = s2;
+    const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
+    begin[$axis] += s2;
+    return sliceT;
+  });
+}
+var splitVConfig2 = {
+  kernelName: SplitV,
+  backendName: "webgl",
+  kernelFunc: splitV2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js
+var SQRT = `return sqrt(x);`;
+var sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });
+var sqrtConfig2 = {
+  kernelName: Sqrt,
+  backendName: "webgl",
+  kernelFunc: sqrt3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js
+var SQUARE = `return x * x;`;
+var square3 = unaryKernelFunc2({ opSnippet: SQUARE });
+var squareConfig2 = {
+  kernelName: Square,
+  backendName: "webgl",
+  kernelFunc: square3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js
+var SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
+var squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });
+var squaredDifferenceConfig2 = {
+  kernelName: SquaredDifference,
+  backendName: "webgl",
+  kernelFunc: squaredDifference3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js
+function step3({ inputs, attrs, backend: backend2 }) {
+  const { x } = inputs;
+  const opSnippet = CHECK_NAN_SNIPPET + `
+    return x > 0.0 ? 1.0 : float(${attrs.alpha});
+  `;
+  const program = new UnaryOpProgram(x.shape, opSnippet);
+  return backend2.runWebGLProgram(program, [x], x.dtype);
+}
+var stepConfig2 = {
+  kernelName: Step,
+  backendName: "webgl",
+  kernelFunc: step3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js
+var StridedSliceProgram = class {
+  constructor(begin, strides, size) {
+    this.variableNames = ["x"];
+    this.outputShape = size;
+    const rank = size.length;
+    const inputDtype = getCoordsDataType(size.length);
+    const dtype = getCoordsDataType(size.length);
+    let newCoords = "";
+    if (rank === 1) {
+      newCoords = "coords * strides + begin";
+    } else {
+      let outputAxis = 0;
+      newCoords = size.map((_, i2) => {
+        outputAxis++;
+        return size.length === 1 ? `coords * strides[${i2}] + begin[${i2}]` : `coords[${outputAxis - 1}] * strides[${i2}] + begin[${i2}]`;
+      }).join(",");
+    }
+    this.userCode = `
+      ${inputDtype} begin = ${inputDtype}(${begin});
+      ${inputDtype} strides = ${inputDtype}(${strides});
+
+      void main() {
+        ${dtype} coords = getOutputCoords();
+        setOutput(getX(${newCoords}));
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js
+function stridedSlice3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
+    result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeIntermediateTensorInfo(sliced);
+  } else {
+    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
+    if (shouldExecuteOnCPU) {
+      const values = backend2.readSync(x.dataId);
+      const xBuf = buffer(x.shape, x.dtype, values);
+      const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);
+      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);
+    } else {
+      const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);
+      result = backend2.runWebGLProgram(program, [x], x.dtype);
+    }
+  }
+  const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });
+  backend2.disposeIntermediateTensorInfo(result);
+  return resultReshaped;
+}
+var stridedSliceConfig2 = {
+  kernelName: StridedSlice,
+  backendName: "webgl",
+  kernelFunc: stridedSlice3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js
+function stringNGrams3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
+  const { data, dataSplits } = inputs;
+  const $data = backend2.readSync(data.dataId);
+  const $dataSplits = backend2.readSync(dataSplits.dataId);
+  const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
+  return [
+    backend2.makeTensorInfo([nGrams.length], "string", nGrams),
+    backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
+  ];
+}
+var stringNGramsConfig2 = {
+  kernelName: StringNGrams,
+  backendName: "webgl",
+  kernelFunc: stringNGrams3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js
+function stringSplit3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { skipEmpty } = attrs;
+  const { input: input2, delimiter } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (input2.shape.length !== 1) {
+    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);
+  }
+  if (delimiter.shape.length !== 0) {
+    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
+  }
+  const $input = backend2.readSync(input2.dataId);
+  const $delimiter = backend2.readSync(delimiter.dataId)[0];
+  const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);
+  const outputSize = values.length;
+  return [
+    backend2.makeTensorInfo([outputSize, 2], "int32", indices),
+    backend2.makeTensorInfo([outputSize], "string", values),
+    backend2.makeTensorInfo([2], "int32", new Int32Array(shape))
+  ];
+}
+var stringSplitConfig2 = {
+  kernelName: StringSplit,
+  backendName: "webgl",
+  kernelFunc: stringSplit3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js
+function stringToHashBucketFast3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { numBuckets } = attrs;
+  const { input: input2 } = inputs;
+  if (input2.dtype !== "string") {
+    throw new Error("Input must be of datatype string");
+  }
+  if (numBuckets <= 0) {
+    throw new Error(`Number of buckets must be at least 1`);
+  }
+  const $input = backend2.readSync(input2.dataId);
+  const output = stringToHashBucketFastImplCPU($input, numBuckets);
+  return backend2.makeTensorInfo(input2.shape, "int32", output);
+}
+var stringToHashBucketFastConfig2 = {
+  kernelName: StringToHashBucketFast,
+  backendName: "webgl",
+  kernelFunc: stringToHashBucketFast3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js
+var TAN = `return tan(x);`;
+var tan3 = unaryKernelFunc2({ opSnippet: TAN });
+var tanConfig2 = {
+  kernelName: Tan,
+  backendName: "webgl",
+  kernelFunc: tan3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js
+var TANH = `
+  float e2x = exp(-2.0 * abs(x));
+  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
+`;
+var tanh4 = unaryKernelFunc2({ opSnippet: TANH });
+var tanhConfig2 = {
+  kernelName: Tanh,
+  backendName: "webgl",
+  kernelFunc: tanh4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js
+var TileProgram = class {
+  constructor(aShape, reps) {
+    this.variableNames = ["A"];
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[i2] * reps[i2];
+    }
+    this.outputShape = outputShape;
+    this.rank = outputShape.length;
+    const dtype = getCoordsDataType(this.rank);
+    const sourceCoords = getSourceCoords3(aShape);
+    this.userCode = `
+      void main() {
+        ${dtype} resRC = getOutputCoords();
+        setOutput(getA(${sourceCoords}));
+      }
+    `;
+  }
+};
+function getSourceCoords3(aShape) {
+  const rank = aShape.length;
+  if (rank > 5) {
+    throw Error(`Tile for rank ${rank} is not yet supported`);
+  }
+  if (rank === 1) {
+    return `imod(resRC, ${aShape[0]})`;
+  }
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < aShape.length; i2++) {
+    sourceCoords.push(`imod(${currentCoords[i2]}, ${aShape[i2]})`);
+  }
+  return sourceCoords.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js
+function tile4(params) {
+  const { inputs, backend: backend2, attrs } = params;
+  const { x } = inputs;
+  const { reps } = attrs;
+  if (x.dtype === "string" || x.shape.length > 5) {
+    const data = backend2.readSync(x.dataId);
+    const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data;
+    const buf = buffer(x.shape, x.dtype, value);
+    const outBuf = tileImplCPU(buf, reps);
+    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+  }
+  const program = new TileProgram(x.shape, reps);
+  const output = backend2.runWebGLProgram(program, [x], x.dtype);
+  return output;
+}
+var tileConfig2 = {
+  kernelName: Tile,
+  backendName: "webgl",
+  kernelFunc: tile4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js
+var SwapProgram = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.customUniforms = [
+      { name: "n", type: "int" },
+      { name: "firstPass", type: "int" },
+      { name: "negativeInf", type: "float" },
+      { name: "dir", type: "int" },
+      { name: "inc", type: "int" }
+    ];
+    this.outputShape = shape;
+    this.userCode = `
+       void main() {
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int elemIdx = coords[1];
+
+         // We compare elements pair-wise within a group of size 2 * inc.
+         // The comparing rule for each group alternates between ascending
+         // and descending. Within each group, we compare each pair at
+         // positions i and i+inc. To decide whether an element at position i
+         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
+         // inc, it is in the first half of the group, we denote it as x0,
+         // otherwise we denote it as x1.
+         // For example, as shown in the Bitonic top K paper referenced above,
+         // Figure5(a) shows that element[1] is in the
+         // second half of the group when group size is 2, but it is in the
+         // first half of the group when group size is 4.
+
+         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
+         int i = isFirstInPair ? elemIdx : elemIdx - inc;
+
+         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
+         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
+         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
+         float x1 = i1 < n ? getX(batch, i1) : negativeInf;
+
+         // Denotes which direction indices are in (ascending or descending).
+         bool reverse = imod(elemIdx, 2 * dir) >= dir;
+         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
+         if (reverse == isGreater) { // Elements in opposite order of direction
+           int iTemp = i0;
+           i0 = i1;
+           i1 = iTemp;
+         }
+         if (isFirstInPair) {
+            setOutput(float(i0));
+         } else {
+            setOutput(float(i1));
+         }
+       }
+     `;
+  }
+};
+var MergeProgram = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.customUniforms = [
+      { name: "n", type: "int" },
+      { name: "firstPass", type: "int" },
+      { name: "k", type: "int" }
+    ];
+    this.outputShape = shape;
+    this.userCode = `
+    void main() {
+         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
+         ivec2 coords = getOutputCoords();
+         int batch = coords[0];
+         int elemIdx = coords[1];
+
+         // The output size is half of the previous size.
+         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
+         // we only need to output the indices at positions |, the indices at
+         // positions _ can be thrown away, see Figure5(b) After Phase 2
+         // (Merge phase) in the Bitonic Top K paper referenced above.
+         // For example, the paper shows we only need to output the orange bars.
+         // The output sequence should look like this | | | | | | | |.
+         // Because the sequence is halved, to map the output index back
+         // to the previous sequence to find the corresponding value,
+         // we need to double the index. When we double the index,
+         // we basically interpolate a position, so 2i looks like
+         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
+         // of each 2k positions by - elemIdx % k. E.g. for output at
+         // index 4,5,6,7, we want to get the corresponding element at
+         // original index 8,9,10,11, for output at index 8,9,10,11,
+         // we want to get the corresponding element at original index
+         // 16,17,18,19, so on and so forth.
+
+         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
+         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
+         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
+
+         float x0 = getX(batch, i0);
+         float x1 = i1 < n ? getX(batch, i1) : x0;
+
+         setOutput(x0 >= x1 ? float(i0) : float(i1));
+       }
+     `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js
+function disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {
+  if (tensorInfo !== null) {
+    backend2.disposeIntermediateTensorInfo(tensorInfo);
+  }
+}
+function roundUpToPow2(num) {
+  let pow22 = 1;
+  while (pow22 < num) {
+    pow22 *= 2;
+  }
+  return pow22;
+}
+function topK2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD");
+  const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD");
+  const xShape = x.shape;
+  const lastDim = xShape[xShape.length - 1];
+  if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {
+    const xVals = backend2.readSync(x.dataId);
+    const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);
+    return [
+      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+    ];
+  }
+  if (k === 0) {
+    xShape[xShape.length - 1] = 0;
+    return [
+      backend2.makeTensorInfo(xShape, x.dtype, []),
+      backend2.makeTensorInfo(xShape, "int32", [])
+    ];
+  }
+  if (lastDim === 1) {
+    return [
+      x,
+      fill3({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 })
+    ];
+  }
+  const xtexData = backend2.texData.get(x.dataId);
+  const xIsPacked = xtexData !== null && xtexData.isPacked;
+  const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;
+  const xSize = util_exports.sizeFromShape(xShape);
+  const batch = xSize / lastDim;
+  const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });
+  if (xIsPacked) {
+    disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);
+  }
+  const kPow2 = roundUpToPow2(k);
+  const lastDimPow2 = roundUpToPow2(lastDim);
+  let indices = null;
+  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
+  const runSwap = (dir, inc, shape) => {
+    const inputs2 = getInputs();
+    const program = new SwapProgram(shape);
+    const fistPass = indices === null ? 1 : 0;
+    const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGLProgram(program, inputs2, "int32", customValues);
+    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);
+  };
+  for (let len = 1; len < kPow2; len *= 2) {
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, [batch, lastDimPow2]);
+    }
+  }
+  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
+    const inputs2 = getInputs();
+    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);
+    const firstPass = indices === null ? 1 : 0;
+    const customValues = [[lastDim], [firstPass], [kPow2]];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGLProgram(mergeProgram, inputs2, "int32", customValues);
+    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);
+    const len = kPow2 / 2;
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, indices.shape);
+    }
+  }
+  let prevIndices = indices;
+  indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });
+  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);
+  let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });
+  disposeIntermediateTensorInfoOrNull(backend2, x2D);
+  const newShape = xShape.slice(0, -1);
+  newShape.push(k);
+  prevIndices = indices;
+  indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);
+  const prevValues = values;
+  values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull(backend2, prevValues);
+  return [values, indices];
+}
+var topKConfig2 = {
+  kernelName: TopK,
+  backendName: "webgl",
+  kernelFunc: topK2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js
+var TransformProgram = class {
+  constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {
+    this.variableNames = ["Image", "Transforms"];
+    this.outputShape = outShape;
+    const interpolationModeId = interpolation === "nearest" ? 1 : 2;
+    let fillModeId;
+    switch (fillMode) {
+      case "constant":
+        fillModeId = 1;
+        break;
+      case "reflect":
+        fillModeId = 2;
+        break;
+      case "wrap":
+        fillModeId = 3;
+        break;
+      case "nearest":
+        fillModeId = 4;
+        break;
+      default:
+        fillModeId = 1;
+        break;
+    }
+    this.userCode = `
+            float mapCoord(float outCoord, float len) {
+              float inCoord = outCoord;
+              if(${fillModeId} == 2) {
+                if (inCoord < 0.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz2 = 2.0 * len;
+                    if (inCoord < sz2) {
+                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
+                      inCoord;
+                    }
+                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
+                  }
+                } else if (inCoord > len - 1.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz2 = 2.0 * len;
+                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
+                    if (inCoord >= len) {
+                      inCoord = sz2 - inCoord - 1.0;
+                    }
+                  }
+                }
+                return clamp(inCoord, 0.0, len - 1.0);
+              } else if (${fillModeId} == 3) {
+                if (inCoord < 0.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz = len - 1.0;
+                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
+                  }
+                } else if (inCoord > len - 1.0) {
+                  if (len <= 1.0) {
+                    inCoord = 0.0;
+                  } else {
+                    float sz = len - 1.0;
+                    inCoord -= len * float(int(float(inCoord / sz)));
+                  }
+                }
+                return clamp(inCoord, 0.0, len - 1.0);
+              } else if (${fillModeId} == 4) {
+                return clamp(outCoord, 0.0, len - 1.0);
+              } else {
+                return outCoord;
+              }
+            }
+
+            float readWithFillValue(int batch, int coordY, int coordX,
+              int channel) {
+              float outputValue;
+              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {
+                  outputValue = getImage(batch, coordY, coordX, channel);
+              } else {
+                outputValue = float(${fillValue});
+              }
+              return outputValue;
+            }
+
+            void main() {
+              ivec4 coords = getOutputCoords();
+              float outputValue;
+              int batch = coords[0];
+              int x = coords[2];
+              int y = coords[1];
+              int channel = coords[3];
+              float xf = float(x);
+              float yf = float(y);
+              float a1 = getTransforms(batch, 0);
+              float a2 = getTransforms(batch, 1);
+              float a3 = getTransforms(batch, 2);
+              float b1 = getTransforms(batch, 3);
+              float b2 = getTransforms(batch, 4);
+              float b3 = getTransforms(batch, 5);
+              float c1 = getTransforms(batch, 6);
+              float c2 = getTransforms(batch, 7);
+              float projection = c1 * xf + c2 * yf + 1.0;
+              if (projection == 0.0) {
+                outputValue = float(${fillValue});
+              } else {
+                float inX = (a1 * xf + a2 * yf + a3) / projection;
+                float inY = (b1 * xf + b2 * yf + b3) / projection;
+                float mapX = mapCoord(inX, float(${imageWidth}));
+                float mapY = mapCoord(inY, float(${imageHeight}));
+
+                if (${interpolationModeId} == 1) {
+                  int coordY = int(round(mapY));
+                  int coordX = int(round(mapX));
+                  outputValue = readWithFillValue(batch, coordY, coordX,
+                    channel);
+                } else {
+                  float yFloor = floor(mapY);
+                  float xFloor = floor(mapX);
+                  float yCeil = yFloor + 1.0;
+                  float xCeil = xFloor + 1.0;
+                  float valueYFloor = (xCeil - mapX) *
+                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
+                  float valueYCeil = (xCeil - mapX) *
+                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
+                  outputValue = (yCeil - mapY) * valueYFloor +
+                  (mapY - yFloor) * valueYCeil;
+                }
+              }
+              setOutput(outputValue);
+            }
+        `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js
+function transform3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [
+    batch,
+    outHeight,
+    outWidth,
+    numChannels
+  ];
+  const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
+  return backend2.runWebGLProgram(program, [image2, transforms], "float32");
+}
+var transformConfig2 = {
+  kernelName: Transform,
+  backendName: "webgl",
+  kernelFunc: transform3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js
+function unique4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { axis } = attrs;
+  const { x } = inputs;
+  assertNotComplex2(x, "unique");
+  console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
+  const values = backend2.readSync(x.dataId);
+  const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);
+  return [
+    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
+    backend2.makeTensorInfo([indices.length], "int32", indices)
+  ];
+}
+var uniqueConfig2 = {
+  kernelName: Unique,
+  backendName: "webgl",
+  kernelFunc: unique4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js
+function unpack2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const x = value;
+  const xRank = x.shape.length;
+  const num = value.shape[axis];
+  const outShape = new Array(xRank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < xRank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = x.shape[i2];
+    }
+  }
+  const toDispose = [];
+  const begin = new Array(xRank).fill(0);
+  const size = x.shape.slice();
+  size[axis] = 1;
+  const res = new Array(num);
+  for (let i2 = 0; i2 < res.length; i2++) {
+    begin[axis] = i2;
+    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });
+    const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
+    res[i2] = reshaped;
+    toDispose.push(sliced);
+  }
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return res;
+}
+var unpackConfig2 = {
+  kernelName: Unpack,
+  backendName: "webgl",
+  kernelFunc: unpack2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js
+var SegmentOpProgram = class {
+  constructor(segOpInfo, segOpType) {
+    this.variableNames = ["x", "segmentIds"];
+    const windowSize = segOpInfo.windowSize;
+    const batchSize = segOpInfo.batchSize;
+    const inSize = segOpInfo.inSize;
+    const numSegments = segOpInfo.numSegments;
+    const outSize = numSegments * Math.ceil(inSize / windowSize);
+    this.outputShape = [batchSize, outSize];
+    const initializationValue = "0.0";
+    const returnValue = `sumValue`;
+    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+    const windowSizeVec4Remainder = windowSize % 4;
+    const updateSnippet = `
+        sumValue += dot(values, segFilter);
+    `;
+    let checkValueOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkValueOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return initializationValue;
+        }
+      `;
+    }
+    let checkSegmentIdOutOfBounds = "";
+    if (inSize % windowSize > 0) {
+      checkSegmentIdOutOfBounds = `
+        if (inIdx < 0 || inIdx >= ${inSize}) {
+          return -1.0;
+        }
+      `;
+    }
+    this.userCode = `
+      const float initializationValue = ${initializationValue};
+
+      float getValue(int batch, int inIdx) {
+        ${checkValueOutOfBounds}
+        return getX(batch, inIdx);
+      }
+
+      float getSegmentIdAtIndex(int inIdx) {
+        ${checkSegmentIdOutOfBounds}
+        return getSegmentIds(inIdx);
+      }
+
+      void main() {
+        ivec2 coords = getOutputCoords();
+        int batch = coords[0];
+        int outIdx = coords[1];
+        int inOffset = int(floor(float(outIdx) / float(
+          ${numSegments})) * float(${windowSize}));
+        int currentSeg = int(mod(float(outIdx), float(${numSegments})));
+
+        float sumValue = 0.0;
+
+        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
+          int inIdx = inOffset + i;
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            getValue(batch, inIdx + 3)
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
+          );
+
+          ${updateSnippet}
+        }
+
+        int inIdx = inOffset + ${windowSizeNearestVec4};
+        if (${windowSizeVec4Remainder === 1}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            initializationValue,
+            initializationValue,
+            initializationValue
+          );
+
+          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            0,
+            0,
+            0
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 2}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            initializationValue,
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+              0,
+              0
+          );
+
+          ${updateSnippet}
+        } else if (${windowSizeVec4Remainder === 3}) {
+          vec4 values = vec4(
+            getValue(batch, inIdx),
+            getValue(batch, inIdx + 1),
+            getValue(batch, inIdx + 2),
+            initializationValue
+          );
+
+          vec4 segFilter = vec4(
+            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
+            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
+            0
+          );
+
+          ${updateSnippet}
+        }
+        setOutput(${returnValue});
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js
+function unsortedSegmentSum3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, segmentIds } = inputs;
+  const { numSegments } = attrs;
+  const xRank = x.shape.length;
+  const toDispose = [];
+  let axis = 0;
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation != null) {
+    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+    toDispose.push(permutedX);
+    axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  }
+  const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);
+  const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);
+  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
+  toDispose.push(a2D);
+  const outputDType = sumOutType(x.dtype);
+  const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {
+    const batchSize = x2.shape[0];
+    const inSize2 = x2.shape[1];
+    const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);
+    const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };
+    const program = new SegmentOpProgram(segOpInfo, segOpType);
+    const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);
+    toDispose.push(output);
+    if (output.shape[1] === numSegments2) {
+      return output;
+    }
+    const rangeInfo = range4({
+      backend: backend2,
+      attrs: { start: 0, stop: numSegments2, step: 1, dtype: "float32" }
+    });
+    const tileInfo = tile4({
+      inputs: { x: rangeInfo },
+      backend: backend2,
+      attrs: { reps: [inSize2 / windowSize] }
+    });
+    toDispose.push(rangeInfo);
+    toDispose.push(tileInfo);
+    const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);
+    return result2;
+  };
+  const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments);
+  const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });
+  let result = reshaped;
+  if (permutation != null) {
+    toDispose.push(reshaped);
+    const perm = backend_util_exports.getUndoAxesPermutation(permutation);
+    result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });
+  }
+  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  return result;
+}
+var unsortedSegmentSumConfig2 = {
+  kernelName: UnsortedSegmentSum,
+  backendName: "webgl",
+  kernelFunc: unsortedSegmentSum3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js
+var kernelConfigs2 = [
+  _fusedMatMulConfig2,
+  absConfig2,
+  acosConfig2,
+  acoshConfig2,
+  addConfig2,
+  addNConfig2,
+  allConfig2,
+  anyConfig2,
+  argMaxConfig2,
+  argMinConfig2,
+  asinConfig2,
+  asinhConfig2,
+  atanConfig2,
+  atan2Config2,
+  atanhConfig2,
+  avgPoolConfig2,
+  avgPool3DConfig2,
+  avgPool3DGradConfig3,
+  avgPoolGradConfig3,
+  batchMatMulConfig2,
+  batchNormConfig2,
+  batchToSpaceNDConfig2,
+  bincountConfig2,
+  broadcastArgsConfig2,
+  castConfig2,
+  ceilConfig2,
+  clipByValueConfig2,
+  complexConfig2,
+  complexAbsConfig2,
+  concatConfig2,
+  conv2DConfig2,
+  conv2DBackpropFilterConfig2,
+  conv2DBackpropInputConfig2,
+  conv3DConfig2,
+  conv3DBackpropFilterV2Config2,
+  conv3DBackpropInputConfig,
+  cosConfig2,
+  coshConfig2,
+  cropAndResizeConfig2,
+  cumprodConfig2,
+  cumsumConfig2,
+  denseBincountConfig2,
+  depthToSpaceConfig2,
+  depthwiseConv2dNativeConfig2,
+  depthwiseConv2dNativeBackpropFilterConfig2,
+  depthwiseConv2dNativeBackpropInputConfig2,
+  diagConfig2,
+  dilation2DConfig2,
+  einsumConfig2,
+  eluConfig2,
+  eluGradConfig3,
+  equalConfig2,
+  erfConfig2,
+  expConfig2,
+  expandDimsConfig2,
+  expm1Config2,
+  fftConfig2,
+  fillConfig2,
+  flipLeftRightConfig2,
+  floorConfig2,
+  floorDivConfig2,
+  fromPixelsConfig,
+  fusedConv2DConfig2,
+  fusedDepthwiseConv2DConfig2,
+  gatherNdConfig2,
+  gatherV2Config2,
+  greaterConfig2,
+  greaterEqualConfig2,
+  identityConfig2,
+  ifftConfig2,
+  imagConfig2,
+  isFiniteConfig2,
+  isInfConfig2,
+  isNaNConfig2,
+  leakyReluConfig2,
+  lessConfig2,
+  lessEqualConfig2,
+  linSpaceConfig2,
+  logConfig2,
+  log1pConfig2,
+  logicalAndConfig2,
+  logicalNotConfig2,
+  logicalOrConfig2,
+  LRNConfig2,
+  LRNGradConfig2,
+  maxConfig2,
+  maximumConfig2,
+  maxPoolConfig2,
+  maxPool3DConfig2,
+  maxPool3DGradConfig3,
+  maxPoolGradConfig3,
+  maxPoolWithArgmaxConfig2,
+  meanConfig2,
+  minConfig2,
+  minimumConfig2,
+  mirrorPadConfig2,
+  modConfig2,
+  multinomialConfig2,
+  multiplyConfig2,
+  negConfig2,
+  nonMaxSuppressionV3Config2,
+  nonMaxSuppressionV4Config2,
+  nonMaxSuppressionV5Config2,
+  notEqualConfig2,
+  oneHotConfig2,
+  onesLikeConfig2,
+  packConfig2,
+  padV2Config2,
+  powConfig2,
+  preluConfig2,
+  prodConfig2,
+  rangeConfig2,
+  realConfig2,
+  realDivConfig2,
+  reciprocalConfig2,
+  reluConfig2,
+  relu6Config2,
+  reshapeConfig2,
+  resizeBilinearConfig2,
+  resizeBilinearGradConfig3,
+  resizeNearestNeighborConfig2,
+  resizeNearestNeighborGradConfig3,
+  reverseConfig2,
+  rotateWithOffsetConfig2,
+  roundConfig2,
+  rsqrtConfig2,
+  scatterNdConfig2,
+  searchSortedConfig2,
+  selectConfig2,
+  seluConfig2,
+  sigmoidConfig2,
+  signConfig2,
+  sinConfig2,
+  sinhConfig2,
+  sliceConfig2,
+  softmaxConfig2,
+  softplusConfig2,
+  spaceToBatchNDConfig2,
+  sparseFillEmptyRowsConfig2,
+  sparseReshapeConfig2,
+  sparseSegmentMeanConfig2,
+  sparseSegmentSumConfig2,
+  sparseToDenseConfig2,
+  splitVConfig2,
+  sqrtConfig2,
+  squareConfig2,
+  squaredDifferenceConfig2,
+  stepConfig2,
+  stridedSliceConfig2,
+  stringNGramsConfig2,
+  stringSplitConfig2,
+  stringToHashBucketFastConfig2,
+  subConfig2,
+  sumConfig2,
+  tanConfig2,
+  tanhConfig2,
+  tileConfig2,
+  topKConfig2,
+  transformConfig2,
+  transposeConfig2,
+  uniqueConfig2,
+  unpackConfig2,
+  unsortedSegmentSumConfig2,
+  zerosLikeConfig2
+];
+for (const kernelConfig of kernelConfigs2) {
+  registerKernel(kernelConfig);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js
+var CppDType;
+(function(CppDType2) {
+  CppDType2[CppDType2["float32"] = 0] = "float32";
+  CppDType2[CppDType2["int32"] = 1] = "int32";
+  CppDType2[CppDType2["bool"] = 2] = "bool";
+  CppDType2[CppDType2["string"] = 3] = "string";
+  CppDType2[CppDType2["complex64"] = 4] = "complex64";
+})(CppDType || (CppDType = {}));
+var FusableActivation;
+(function(FusableActivation2) {
+  FusableActivation2[FusableActivation2["linear"] = 0] = "linear";
+  FusableActivation2[FusableActivation2["relu"] = 1] = "relu";
+  FusableActivation2[FusableActivation2["relu6"] = 2] = "relu6";
+  FusableActivation2[FusableActivation2["prelu"] = 3] = "prelu";
+  FusableActivation2[FusableActivation2["leakyrelu"] = 4] = "leakyrelu";
+  FusableActivation2[FusableActivation2["sigmoid"] = 5] = "sigmoid";
+  FusableActivation2[FusableActivation2["elu"] = 6] = "elu";
+})(FusableActivation || (FusableActivation = {}));
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js
+var wasmFusedMatMul;
+function setup(backend2) {
+  wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function fusedBatchMatMul(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  if (a6.dtype !== "float32" || b.dtype !== "float32") {
+    throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);
+  }
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  const aId = backend2.dataIdMap.get(a6.dataId).id;
+  const bId = backend2.dataIdMap.get(b.dataId).id;
+  let biasId = 0;
+  if (bias != null) {
+    const biasData = backend2.dataIdMap.get(bias.dataId);
+    if (biasData.shape.length !== 1) {
+      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);
+    }
+    biasId = biasData.id;
+  }
+  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
+  const fusedActivation = FusableActivation[activation2];
+  if (fusedActivation == null) {
+    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
+  }
+  const leftDim = transposeA ? a6.shape[2] : a6.shape[1];
+  const rightDim = transposeB ? b.shape[1] : b.shape[2];
+  const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a6.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const aShapeBytes = new Uint8Array(new Int32Array(a6.shape).buffer);
+  const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
+  wasmFusedMatMul(aId, aShapeBytes, a6.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);
+  return out;
+}
+var _fusedMatMulConfig3 = {
+  kernelName: _FusedMatMul,
+  backendName: "wasm",
+  setupFunc: setup,
+  kernelFunc: fusedBatchMatMul
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js
+function createUnaryKernelConfig(kernelName, outType) {
+  let wasmFunc9;
+  function setupFunc3(backend2) {
+    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [
+      "number",
+      "number",
+      "number"
+    ]);
+  }
+  function kernelFunc3(args) {
+    const { backend: backend2, inputs: { x } } = args;
+    const xId = backend2.dataIdMap.get(x.dataId).id;
+    const out = backend2.makeOutput(x.shape, outType || x.dtype);
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    if (util_exports.sizeFromShape(out.shape) === 0) {
+      return out;
+    }
+    wasmFunc9(xId, CppDType[x.dtype], outId);
+    return out;
+  }
+  return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js
+var absConfig3 = createUnaryKernelConfig(Abs);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js
+function createBinaryKernelConfig(kernelName, supportsFullBroadcast17, dtype) {
+  let wasmFunc9;
+  function setupFunc3(backend2) {
+    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [
+      "number",
+      "array",
+      "number",
+      "number",
+      "array",
+      "number",
+      "number",
+      "number"
+    ]);
+  }
+  function kernelFunc3(args) {
+    const { backend: backend2, inputs } = args;
+    const { a: a6, b } = inputs;
+    const aId = backend2.dataIdMap.get(a6.dataId).id;
+    const bId = backend2.dataIdMap.get(b.dataId).id;
+    const outputType = dtype != null ? dtype : a6.dtype;
+    const newShape = backend_util_exports.assertAndGetBroadcastShape(a6.shape, b.shape);
+    const out = backend2.makeOutput(newShape, outputType);
+    if (util_exports.sizeFromShape(newShape) === 0) {
+      return out;
+    }
+    const aShapeBytes = new Uint8Array(new Int32Array(a6.shape).buffer);
+    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a6.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a6.dtype], outId);
+    kernelFunc4();
+    return out;
+  }
+  return { kernelName, backendName: "wasm", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js
+var supportsFullBroadcast = true;
+var addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js
+var wasmFunc;
+function setupFunc(backend2) {
+  wasmFunc = backend2.wasm.cwrap(AddN, null, [
+    "array",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function addn(args) {
+  const { inputs, backend: backend2 } = args;
+  const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);
+  if (util_exports.sizeFromShape(out.shape) === 0) {
+    return out;
+  }
+  const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);
+  const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);
+  return out;
+}
+var addNConfig3 = {
+  kernelName: AddN,
+  backendName: "wasm",
+  setupFunc,
+  kernelFunc: addn
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js
+function identity4(args) {
+  const { inputs: { x }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const inVals = backend2.typedArrayFromHeap(x);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.set(inVals);
+  return out;
+}
+var identityConfig3 = {
+  kernelName: Identity,
+  backendName: "wasm",
+  kernelFunc: identity4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js
+var wasmTranspose;
+function setup2(backend2) {
+  wasmTranspose = backend2.wasm.cwrap(Transpose, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number"
+  ]);
+}
+function transpose4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
+  let permIsNoOp = true;
+  for (let i2 = 0; i2 < perm.length; i2++) {
+    if (perm[i2] !== i2) {
+      permIsNoOp = false;
+    }
+  }
+  const outShape = computeOutShape4(inputs.x.shape, attrs.perm);
+  const x = {
+    dataId: inputs.x.dataId,
+    shape: reducedShape,
+    dtype: inputs.x.dtype
+  };
+  if (permIsNoOp) {
+    const cloned = identity4({ inputs, backend: backend2 });
+    cloned.shape = outShape;
+    return cloned;
+  }
+  const out = backend2.makeOutput(outShape, x.dtype);
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const permBytes = new Uint8Array(new Int32Array(perm).buffer);
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);
+  return out;
+}
+function computeOutShape4(inShape, perm) {
+  const outShape = new Array(inShape.length);
+  for (let i2 = 0; i2 < outShape.length; i2++) {
+    outShape[i2] = inShape[perm[i2]];
+  }
+  return outShape;
+}
+function removeOneSizeDims(shape, perm) {
+  const newShape = [];
+  const newPerm = [];
+  for (let i2 = 0; i2 < shape.length; ++i2) {
+    if (shape[i2] !== 1) {
+      newShape.push(shape[i2]);
+    }
+    if (shape[perm[i2]] !== 1) {
+      newPerm.push(perm[i2]);
+    }
+  }
+  for (let i2 = 0; i2 < newPerm.length; ++i2) {
+    let minValIdx = -1;
+    for (let j = 0; j < newPerm.length; ++j) {
+      if (newPerm[j] >= i2 && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
+        minValIdx = j;
+      }
+    }
+    newPerm[minValIdx] = i2;
+  }
+  return [newShape, newPerm];
+}
+var transposeConfig3 = {
+  kernelName: Transpose,
+  backendName: "wasm",
+  kernelFunc: transpose4,
+  setupFunc: setup2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js
+function permuteAxesAndTranspose(x, axis, backend2) {
+  const xShape = x.shape;
+  const xRank = x.shape.length;
+  const originalAxes = util_exports.parseAxisParam(axis, xShape);
+  let axes = originalAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let xTransposed = null;
+  let inputWasTransposed = false;
+  if (permutedAxes != null) {
+    const newShape = new Array(xRank);
+    for (let i2 = 0; i2 < newShape.length; i2++) {
+      newShape[i2] = xShape[permutedAxes[i2]];
+    }
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });
+    const xId = backend2.dataIdMap.get(x.dataId).id;
+    const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;
+    if (transposedId !== xId) {
+      inputWasTransposed = true;
+    }
+  }
+  return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/All.js
+var wasmAll;
+function setup3(backend2) {
+  wasmAll = backend2.wasm.cwrap(All, null, ["number, number, number"]);
+}
+function all4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    input2 = transposed;
+    inputId = transposedId;
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("all", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, x.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmAll(inputId, reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var allConfig3 = {
+  kernelName: All,
+  backendName: "wasm",
+  setupFunc: setup3,
+  kernelFunc: all4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Any.js
+var wasmAny;
+function setup4(backend2) {
+  wasmAny = backend2.wasm.cwrap(Any, null, ["number, number, number"]);
+}
+function any4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    input2 = transposed;
+    inputId = transposedId;
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("any", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, x.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmAny(inputId, reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var anyConfig3 = {
+  kernelName: Any,
+  backendName: "wasm",
+  setupFunc: setup4,
+  kernelFunc: any4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js
+var wasmFunc2;
+function setup5(backend2) {
+  wasmFunc2 = backend2.wasm.cwrap(ArgMax, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function argmax(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+    }
+  }
+  const outShape = input2.shape.slice(0, -1);
+  const out = backend2.makeOutput(outShape, "int32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const outerSize = util_exports.sizeFromShape(out.shape);
+  const innerSize = input2.shape[axes[0]];
+  wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  return out;
+}
+var argMaxConfig3 = {
+  kernelName: ArgMax,
+  backendName: "wasm",
+  kernelFunc: argmax,
+  setupFunc: setup5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js
+var wasmAvgPool;
+function setup6(backend2) {
+  wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function avgPool4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const x = inputs.x;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const channels = convInfo.inChannels;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {
+    throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);
+  return out;
+}
+var avgPoolConfig3 = {
+  kernelName: AvgPool,
+  backendName: "wasm",
+  setupFunc: setup6,
+  kernelFunc: avgPool4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js
+function reshape5(args) {
+  const { inputs, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);
+  args.backend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig3 = {
+  kernelName: Reshape,
+  backendName: "wasm",
+  kernelFunc: reshape5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js
+var wasmBatchMatMul;
+function setup7(backend2) {
+  wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function batchMatMul3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  if (a6.dtype !== "float32" || b.dtype !== "float32") {
+    throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);
+  }
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape5({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const a3dId = backend2.dataIdMap.get(a3d.dataId).id;
+  const b3dId = backend2.dataIdMap.get(b3d.dataId).id;
+  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
+  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);
+  const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);
+  wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);
+  backend2.disposeData(a3d.dataId);
+  backend2.disposeData(b3d.dataId);
+  out.shape = outShape;
+  return out;
+}
+var batchMatMulConfig3 = {
+  kernelName: BatchMatMul,
+  backendName: "wasm",
+  setupFunc: setup7,
+  kernelFunc: batchMatMul3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js
+function slice4(args) {
+  const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args;
+  const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);
+  const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);
+  const xVals = backend2.readSync(x.dataId);
+  const out = backend2.makeOutput(size_, x.dtype);
+  const xStrides = util_exports.computeStrides(x.shape);
+  const outData = backend2.dataIdMap.get(out.dataId);
+  if (isContinous) {
+    const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);
+    if (x.dtype === "string") {
+      outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));
+    } else {
+      const outVals2 = backend2.typedArrayFromHeap(out);
+      outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));
+    }
+    return out;
+  }
+  if (x.dtype === "string") {
+    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);
+    outData.stringBytes = res;
+    return out;
+  }
+  const outVals = backend2.typedArrayFromHeap(out);
+  const rank = x.shape.length;
+  if (rank === 2) {
+    slice2d2(xVals, xStrides[0], outVals, begin_, size_);
+  } else if (rank === 3) {
+    slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);
+  } else if (rank === 4) {
+    slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);
+  } else {
+    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);
+    outVals.set(res);
+  }
+  return out;
+}
+function slice2d2(xVals, xStride, outVals, begin, size) {
+  let outOffset = 0;
+  const beginI = begin[0];
+  const beginJ = begin[1];
+  const endI = beginI + size[0];
+  for (let i2 = beginI; i2 < endI; i2++) {
+    const xOffset = i2 * xStride + beginJ;
+    outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);
+    outOffset += size[1];
+  }
+}
+function slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {
+  let outOffset = 0;
+  const beginI = begin[0];
+  const beginJ = begin[1];
+  const beginK = begin[2];
+  const endI = beginI + size[0];
+  const endJ = beginJ + size[1];
+  for (let i2 = beginI; i2 < endI; i2++) {
+    for (let j = beginJ; j < endJ; j++) {
+      const xOffset = i2 * xStride1 + j * xStride2 + beginK;
+      outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);
+      outOffset += size[2];
+    }
+  }
+}
+function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {
+  let outOffset = 0;
+  const beginI = begin[0];
+  const beginJ = begin[1];
+  const beginK = begin[2];
+  const endI = beginI + size[0];
+  const endJ = beginJ + size[1];
+  const endK = beginK + size[2];
+  const beginL = begin[3];
+  for (let i2 = beginI; i2 < endI; i2++) {
+    for (let j = beginJ; j < endJ; j++) {
+      for (let k = beginK; k < endK; k++) {
+        const xOffset = i2 * xStride1 + j * xStride2 + k * xStride3 + beginL;
+        outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);
+        outOffset += size[3];
+      }
+    }
+  }
+}
+var sliceConfig3 = {
+  kernelName: Slice,
+  backendName: "wasm",
+  kernelFunc: slice4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchToSpaceND.js
+function batchToSpaceND4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });
+  const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });
+  const result = slice4({
+    inputs: { x: xTransposedReshaped },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  backend2.disposeData(xReshaped.dataId);
+  backend2.disposeData(xTransposed.dataId);
+  backend2.disposeData(xReshaped.dataId);
+  return result;
+}
+var batchToSpaceNDConfig3 = {
+  kernelName: BatchToSpaceND,
+  backendName: "wasm",
+  kernelFunc: batchToSpaceND4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js
+function cast5(args) {
+  const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, dtype);
+  const inVals = backend2.typedArrayFromHeap(x);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.set(inVals);
+  return out;
+}
+var castConfig3 = {
+  kernelName: Cast,
+  backendName: "wasm",
+  kernelFunc: cast5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js
+var ceilConfig3 = createUnaryKernelConfig(Ceil);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js
+var wasmClip;
+function setup8(backend2) {
+  wasmClip = backend2.wasm.cwrap(ClipByValue, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function clip(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { clipValueMin, clipValueMax } = attrs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmClip(xId, clipValueMin, clipValueMax, outId);
+  return out;
+}
+var clipByValueConfig3 = {
+  kernelName: ClipByValue,
+  backendName: "wasm",
+  setupFunc: setup8,
+  kernelFunc: clip
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js
+function concat4(args) {
+  const { inputs, backend: backend2 } = args;
+  const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
+  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const out = backend2.makeOutput(outShape, inputs[0].dtype);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return out;
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, axis);
+  if ($inputs[0].dtype === "string") {
+    const inputs2D = $inputs.map((t2) => {
+      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+      const shape = [-1, innerSize];
+      return reshape5({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    });
+    const inputsValShapes = inputs2D.map((t2) => {
+      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    });
+    outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);
+    const simplyConcat = inputs2D[0].shape[0] === 1;
+    const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
+    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), axis);
+    out.shape = finalOutShape;
+    const outData = backend2.dataIdMap.get(out.dataId);
+    outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);
+    inputs2D.forEach((t2) => backend2.disposeData(t2.dataId));
+    return out;
+  }
+  const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));
+  let sumInnerDims = 0;
+  const innerDims = $inputs.map((input2) => {
+    const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));
+    sumInnerDims += innerDim;
+    return innerDim;
+  });
+  const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));
+  const outVals = backend2.typedArrayFromHeap(out);
+  for (let b = 0; b < batchDim; b++) {
+    let outOffset = b * sumInnerDims;
+    for (let i2 = 0; i2 < inVals.length; i2++) {
+      const innerDim = innerDims[i2];
+      const inOffset = b * innerDim;
+      const vals = inVals[i2].subarray(inOffset, inOffset + innerDim);
+      outVals.set(vals, outOffset);
+      outOffset += innerDim;
+    }
+  }
+  return out;
+}
+var concatConfig3 = {
+  kernelName: Concat,
+  backendName: "wasm",
+  kernelFunc: concat4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js
+var wasmConv2d;
+function setup9(backend2) {
+  wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function conv2d5(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const outputChannels = convInfo.outChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
+  return out;
+}
+var conv2DConfig3 = {
+  kernelName: Conv2D,
+  backendName: "wasm",
+  setupFunc: setup9,
+  kernelFunc: conv2d5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js
+var wasmConv2DBackpropInput;
+function setup10(backend2) {
+  wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function conv2DBackpropInput4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { dy, filter } = inputs;
+  const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs;
+  const dilations = 1;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
+  const topPad = filterHeight - 1 - convInfo.padInfo.top;
+  const leftPad = filterWidth - 1 - convInfo.padInfo.left;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const dxStrides = util_exports.computeStrides(convInfo.inShape);
+  const dyStrides = util_exports.computeStrides(dy.shape);
+  const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);
+  const xBatchStride = dxStrides[0];
+  const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];
+  const xColStride = isChannelsLast ? dxStrides[2] : 1;
+  const xChannelStride = isChannelsLast ? 1 : dxStrides[1];
+  const yBatchStride = dyStrides[0];
+  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
+  const yColStride = isChannelsLast ? dyStrides[2] : 1;
+  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
+  const out = backend2.makeOutput(convInfo.inShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const dyId = backend2.dataIdMap.get(dy.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);
+  return out;
+}
+var conv2DBackpropInputConfig3 = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "wasm",
+  setupFunc: setup10,
+  kernelFunc: conv2DBackpropInput4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js
+var cosConfig3 = createUnaryKernelConfig(Cos);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cosh.js
+var coshConfig3 = createUnaryKernelConfig(Cosh);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js
+var InterpolationMethod;
+(function(InterpolationMethod2) {
+  InterpolationMethod2[InterpolationMethod2["bilinear"] = 0] = "bilinear";
+  InterpolationMethod2[InterpolationMethod2["nearest"] = 1] = "nearest";
+})(InterpolationMethod || (InterpolationMethod = {}));
+var wasmCropAndResize;
+function setup11(backend2) {
+  wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function cropAndResize4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { method, extrapolationValue, cropSize } = attrs;
+  const { image: image2, boxes, boxInd } = inputs;
+  const numBoxes = boxes.shape[0];
+  const [cropHeight, cropWidth] = cropSize;
+  const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]];
+  let imagesData = backend2.dataIdMap.get(image2.dataId);
+  let castedData;
+  if (image2.dtype !== "float32") {
+    castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: "float32" } });
+    imagesData = backend2.dataIdMap.get(castedData.dataId);
+  }
+  const imagesId = imagesData.id;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;
+  const out = backend2.makeOutput(outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer);
+  wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);
+  if (castedData != null) {
+    backend2.disposeData(castedData.dataId);
+  }
+  return out;
+}
+var cropAndResizeConfig3 = {
+  kernelName: CropAndResize,
+  backendName: "wasm",
+  setupFunc: setup11,
+  kernelFunc: cropAndResize4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumprod.js
+var wasmCumprod;
+function setup12(backend2) {
+  wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function cumprod4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  const xRank = x.shape.length;
+  util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation !== null) {
+    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  backend_util_exports.assertAxesAreInnerMostDims("cumprod", [permutedAxis], xRank);
+  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);
+  const finalDim = permutedX.shape[permutedAxis];
+  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;
+  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;
+  wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
+  let out = permutedOut;
+  if (permutation !== null) {
+    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });
+    backend2.disposeData(permutedX.dataId);
+    backend2.disposeData(permutedOut.dataId);
+  }
+  return out;
+}
+var cumprodConfig3 = {
+  kernelName: Cumprod,
+  backendName: "wasm",
+  setupFunc: setup12,
+  kernelFunc: cumprod4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js
+var wasmCumsum;
+function setup13(backend2) {
+  wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function cumsum4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  const xRank = x.shape.length;
+  util_exports.assert(x.dtype === "float32" || x.dtype === "int32", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation !== null) {
+    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  backend_util_exports.assertAxesAreInnerMostDims("cumsum", [permutedAxis], xRank);
+  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);
+  const finalDim = permutedX.shape[permutedAxis];
+  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;
+  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;
+  wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);
+  let out = permutedOut;
+  if (permutation !== null) {
+    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });
+    backend2.disposeData(permutedX.dataId);
+    backend2.disposeData(permutedOut.dataId);
+  }
+  return out;
+}
+var cumsumConfig3 = {
+  kernelName: Cumsum,
+  backendName: "wasm",
+  setupFunc: setup13,
+  kernelFunc: cumsum4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js
+var wasmDepthToSpace;
+function setup14(backend2) {
+  wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function depthToSpace4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  const batchSize = x.shape[0];
+  const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
+  const out = backend2.makeOutput(outputShape, "float32");
+  const xData = backend2.dataIdMap.get(x.dataId);
+  const xId = xData.id;
+  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
+  const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);
+  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const channelsLast = dataFormat === "NHWC" ? 1 : 0;
+  wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);
+  return out;
+}
+var depthToSpaceConfig3 = {
+  kernelName: DepthToSpace,
+  backendName: "wasm",
+  setupFunc: setup14,
+  kernelFunc: depthToSpace4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js
+var wasmDepthwiseConv2d;
+function setup15(backend2) {
+  wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function depthwiseConv2d5(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const { strides, dilations, pad: pad3, dimRoundingMode } = attrs;
+  const $dilations = dilations == null ? [1, 1] : dilations;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const outputChannels = convInfo.outChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
+  return out;
+}
+var depthwiseConv2dNativeConfig3 = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "wasm",
+  setupFunc: setup15,
+  kernelFunc: depthwiseConv2d5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Elu.js
+var eluConfig3 = createUnaryKernelConfig(Elu);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js
+var supportsFullBroadcast2 = false;
+var equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js
+var expConfig3 = createUnaryKernelConfig(Exp, "float32");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js
+function expandDims5(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const { dim } = attrs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig3 = {
+  kernelName: ExpandDims,
+  backendName: "wasm",
+  kernelFunc: expandDims5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js
+function fill4(args) {
+  const { attrs: { shape, value, dtype }, backend: backend2 } = args;
+  const out = backend2.makeOutput(shape, dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.fill(value);
+  return out;
+}
+var fillConfig3 = {
+  kernelName: Fill,
+  backendName: "wasm",
+  kernelFunc: fill4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js
+var wasmFlipLeftRight;
+function setup16(backend2) {
+  wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function flipLeftRight2(args) {
+  const { inputs, backend: backend2 } = args;
+  const { image: image2 } = inputs;
+  const out = backend2.makeOutput(image2.shape, image2.dtype);
+  const imageId = backend2.dataIdMap.get(image2.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);
+  return out;
+}
+var flipLeftRightConfig3 = {
+  kernelName: FlipLeftRight,
+  backendName: "wasm",
+  kernelFunc: flipLeftRight2,
+  setupFunc: setup16
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js
+var floorConfig3 = createUnaryKernelConfig(Floor);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js
+var supportsFullBroadcast3 = false;
+var floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js
+var wasmBatchNorm;
+function setup17(backend2) {
+  wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, ["number", "number", "number", "number", "number", "number", "number"]);
+}
+function fusedBatchNorm(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { varianceEpsilon } = attrs;
+  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const meanId = backend2.dataIdMap.get(mean5.dataId).id;
+  const varianceId = backend2.dataIdMap.get(variance.dataId).id;
+  const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;
+  const scaleId = scale2 != null ? backend2.dataIdMap.get(scale2.dataId).id : 0;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    return out;
+  }
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);
+  return out;
+}
+var fusedBatchNormConfig = {
+  kernelName: FusedBatchNorm,
+  backendName: "wasm",
+  setupFunc: setup17,
+  kernelFunc: fusedBatchNorm
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js
+var wasmFusedConv2d;
+function setup18(backend2) {
+  wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function fusedConv2d2(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode);
+  const fusedActivation = FusableActivation[activation2];
+  if (fusedActivation == null) {
+    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);
+  }
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const outputChannels = convInfo.outChannels;
+  let biasId = 0;
+  if (bias != null) {
+    const biasData = backend2.dataIdMap.get(bias.dataId);
+    if (biasData.shape.length !== 1) {
+      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
+    }
+    if (biasData.shape[0] !== outputChannels) {
+      throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
+    }
+    biasId = biasData.id;
+  }
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  const batchSize = convInfo.batchSize;
+  const inHeight = convInfo.inHeight;
+  const inWidth = convInfo.inWidth;
+  if (dataFormat !== "NHWC") {
+    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
+  wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
+  return out;
+}
+var fusedConv2DConfig3 = {
+  kernelName: FusedConv2D,
+  backendName: "wasm",
+  setupFunc: setup18,
+  kernelFunc: fusedConv2d2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js
+var wasmFusedDepthwiseConv2d;
+function setup19(backend2) {
+  wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function fusedDepthwiseConv2d(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);
+  const fusedActivation = FusableActivation[activation2];
+  if (fusedActivation == null) {
+    throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
+  }
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const filterId = backend2.dataIdMap.get(filter.dataId).id;
+  const outputChannels = convInfo.outChannels;
+  let biasId = 0;
+  if (bias != null) {
+    const biasData = backend2.dataIdMap.get(bias.dataId);
+    if (biasData.shape.length !== 1) {
+      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);
+    }
+    if (biasData.shape[0] !== outputChannels) {
+      throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);
+    }
+    biasId = biasData.id;
+  }
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const isSamePad = convInfo.padInfo.type === "SAME" ? 1 : 0;
+  const batchSize = convInfo.batchSize;
+  const inHeight = convInfo.inHeight;
+  const inWidth = convInfo.inWidth;
+  if (dataFormat !== "NHWC") {
+    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;
+  wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);
+  return out;
+}
+var fusedDepthwiseConv2DConfig3 = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "wasm",
+  setupFunc: setup19,
+  kernelFunc: fusedDepthwiseConv2d
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js
+var wasmGatherNd;
+function setup20(backend2) {
+  wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number"
+  ]);
+}
+function gatherNd3(args) {
+  const { backend: backend2, inputs } = args;
+  const { params, indices } = inputs;
+  const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);
+  const out = backend2.makeOutput(resultShape, params.dtype);
+  if (numSlices === 0) {
+    return out;
+  }
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const xData = backend2.dataIdMap.get(params.dataId);
+  const xId = xData.id;
+  const indicesData = backend2.dataIdMap.get(indices.dataId);
+  const indicesId = indicesData.id;
+  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);
+  return out;
+}
+var gatherNdConfig3 = {
+  kernelName: GatherNd,
+  backendName: "wasm",
+  setupFunc: setup20,
+  kernelFunc: gatherNd3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js
+var wasmGather;
+function setup21(backend2) {
+  wasmGather = backend2.wasm.cwrap("Gather", null, [
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number"
+  ]);
+}
+function gatherV23(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const indicesVals = backend2.readSync(indices.dataId);
+  const axisDim = x.shape[parsedAxis];
+  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
+    const index = indicesVals[i2];
+    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
+  }
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+  const flattenX = reshape5({
+    inputs: { x },
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    },
+    backend: backend2
+  });
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const flattenIndex = reshape5({
+    inputs: { x: indices },
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] },
+    backend: backend2
+  });
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  const out = backend2.makeOutput(flattenOutputShape, x.dtype);
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    return out;
+  }
+  const stridesSize = flattenX.shape.length - 1;
+  const xData = backend2.dataIdMap.get(flattenX.dataId);
+  const xId = xData.id;
+  const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);
+  const indicesId = indicesData.id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);
+  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);
+  wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);
+  backend2.disposeData(flattenX.dataId);
+  backend2.disposeData(flattenIndex.dataId);
+  out.shape = shapeInfo.outputShape;
+  return out;
+}
+var gatherV2Config3 = {
+  kernelName: GatherV2,
+  backendName: "wasm",
+  setupFunc: setup21,
+  kernelFunc: gatherV23
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js
+var supportsFullBroadcast4 = false;
+var greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js
+var supportsFullBroadcast5 = false;
+var greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js
+var wasmFunc3;
+function setupFunc2(backend2) {
+  wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function leakyRelu4(args) {
+  const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, "float32");
+  if (util_exports.sizeFromShape(x.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmFunc3(xId, CppDType[x.dtype], alpha, outId);
+  }
+  return out;
+}
+var leakyReluConfig3 = {
+  kernelName: LeakyRelu,
+  backendName: "wasm",
+  setupFunc: setupFunc2,
+  kernelFunc: leakyRelu4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js
+var supportsFullBroadcast6 = false;
+var lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js
+var supportsFullBroadcast7 = false;
+var lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js
+var logConfig3 = createUnaryKernelConfig(Log);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js
+var supportsFullBroadcast8 = false;
+var logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js
+var wasmMax;
+function setup22(backend2) {
+  wasmMax = backend2.wasm.cwrap(Max, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function max5(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { reductionIndices: axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    input2 = transposed;
+    inputId = transposedId;
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("max", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, x.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var maxConfig3 = {
+  kernelName: Max,
+  backendName: "wasm",
+  setupFunc: setup22,
+  kernelFunc: max5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js
+var supportsFullBroadcast9 = false;
+var maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast9);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js
+var wasmMaxPool;
+function setup23(backend2) {
+  wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function maxPool4(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const x = inputs.x;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  util_exports.assert(x.dtype === "float32", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);
+  const filterHeight = convInfo.filterHeight;
+  const filterWidth = convInfo.filterWidth;
+  const padTop = convInfo.padInfo.top;
+  const padRight = convInfo.padInfo.right;
+  const padBottom = convInfo.padInfo.bottom;
+  const padLeft = convInfo.padInfo.left;
+  const dilationHeight = convInfo.dilationHeight;
+  const dilationWidth = convInfo.dilationWidth;
+  const strideHeight = convInfo.strideHeight;
+  const strideWidth = convInfo.strideWidth;
+  const inputChannels = convInfo.inChannels;
+  const outputChannels = convInfo.outChannels;
+  if (convInfo.dataFormat !== "channelsLast") {
+    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);
+  }
+  const out = backend2.makeOutput(convInfo.outShape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);
+  return out;
+}
+var maxPoolConfig3 = {
+  kernelName: MaxPool,
+  backendName: "wasm",
+  setupFunc: setup23,
+  kernelFunc: maxPool4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js
+var wasmMean;
+function setup24(backend2) {
+  wasmMean = backend2.wasm.cwrap(Mean, null, ["number, number, number"]);
+}
+function mean3(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  let reductionAxes = axes;
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
+    }
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("mean", reductionAxes, input2.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  let castedInput = input2;
+  if (input2.dtype !== "float32") {
+    castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: "float32" } });
+    inputId = backend2.dataIdMap.get(castedInput.dataId).id;
+  }
+  const out = backend2.makeOutput(outShape, "float32");
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmMean(inputId, reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  if (input2.dtype !== "float32") {
+    backend2.disposeData(castedInput.dataId);
+  }
+  return out;
+}
+var meanConfig3 = {
+  kernelName: Mean,
+  backendName: "wasm",
+  setupFunc: setup24,
+  kernelFunc: mean3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js
+var wasmMin;
+function setup25(backend2) {
+  wasmMin = backend2.wasm.cwrap(Min, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function min5(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+    }
+  }
+  const inputRank = input2.shape.length;
+  backend_util_exports.assertAxesAreInnerMostDims("min", axes, inputRank);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, input2.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var minConfig3 = {
+  kernelName: Min,
+  backendName: "wasm",
+  setupFunc: setup25,
+  kernelFunc: min5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js
+var supportsFullBroadcast10 = false;
+var minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast10);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MirrorPad.js
+var MirrorPaddingMode;
+(function(MirrorPaddingMode2) {
+  MirrorPaddingMode2[MirrorPaddingMode2["reflect"] = 0] = "reflect";
+  MirrorPaddingMode2[MirrorPaddingMode2["symmetric"] = 1] = "symmetric";
+})(MirrorPaddingMode || (MirrorPaddingMode = {}));
+var wasmMirrorPad;
+function setup26(backend2) {
+  wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function mirrorPad3(args) {
+  const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(outShape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);
+  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);
+  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
+  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
+  wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId);
+  return out;
+}
+var mirrorPadConfig3 = {
+  kernelName: MirrorPad,
+  backendName: "wasm",
+  kernelFunc: mirrorPad3,
+  setupFunc: setup26
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js
+var supportsFullBroadcast11 = true;
+var multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast11);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js
+var negConfig3 = createUnaryKernelConfig(Neg);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js
+function parseResultStruct(backend2, resOffset) {
+  const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);
+  const pSelectedIndices = result[0];
+  const selectedSize = result[1];
+  const pSelectedScores = result[2];
+  const pValidOutputs = result[3];
+  backend2.wasm._free(resOffset);
+  return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js
+var wasmFunc4;
+function setup27(backend2) {
+  wasmFunc4 = backend2.wasm.cwrap(NonMaxSuppressionV3, "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function kernelFunc(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;
+  const { boxes, scores } = inputs;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const scoresId = backend2.dataIdMap.get(scores.dataId).id;
+  const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);
+  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);
+  backend2.wasm._free(pSelectedScores);
+  backend2.wasm._free(pValidOutputs);
+  const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
+  return selectedIndicesTensor;
+}
+var nonMaxSuppressionV3Config3 = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "wasm",
+  setupFunc: setup27,
+  kernelFunc
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js
+var wasmFunc5;
+function setup28(backend2) {
+  wasmFunc5 = backend2.wasm.cwrap(NonMaxSuppressionV4, "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "bool"
+  ]);
+}
+function nonMaxSuppressionV43(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;
+  const { boxes, scores } = inputs;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const scoresId = backend2.dataIdMap.get(scores.dataId).id;
+  const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
+  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);
+  backend2.wasm._free(pSelectedScores);
+  const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
+  const validOutputsTensor = backend2.makeOutput([], "int32", pValidOutputs);
+  return [selectedIndicesTensor, validOutputsTensor];
+}
+var nonMaxSuppressionV4Config3 = {
+  kernelName: NonMaxSuppressionV4,
+  backendName: "wasm",
+  setupFunc: setup28,
+  kernelFunc: nonMaxSuppressionV43
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js
+var wasmFunc6;
+function setup29(backend2) {
+  wasmFunc6 = backend2.wasm.cwrap(NonMaxSuppressionV5, "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function kernelFunc2(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;
+  const { boxes, scores } = inputs;
+  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;
+  const scoresId = backend2.dataIdMap.get(scores.dataId).id;
+  const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
+  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);
+  backend2.wasm._free(pValidOutputs);
+  const selectedIndicesTensor = backend2.makeOutput([selectedSize], "int32", pSelectedIndices);
+  const selectedScoresTensor = backend2.makeOutput([selectedSize], "float32", pSelectedScores);
+  return [selectedIndicesTensor, selectedScoresTensor];
+}
+var nonMaxSuppressionV5Config3 = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "wasm",
+  setupFunc: setup29,
+  kernelFunc: kernelFunc2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js
+var supportsFullBroadcast12 = false;
+var notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast12, "bool");
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js
+var wasmOneHot;
+function setup30(backend2) {
+  wasmOneHot = backend2.wasm.cwrap(OneHot, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function oneHot4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices } = inputs;
+  const { depth, onValue, offValue } = attrs;
+  const out = backend2.makeOutput([...indices.shape, depth], "int32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const indicesData = backend2.dataIdMap.get(indices.dataId);
+  const indicesId = indicesData.id;
+  wasmOneHot(indicesId, depth, onValue, offValue, outId);
+  return out;
+}
+var oneHotConfig3 = {
+  kernelName: OneHot,
+  backendName: "wasm",
+  setupFunc: setup30,
+  kernelFunc: oneHot4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js
+function onesLike4(args) {
+  const { inputs: { x }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.fill(1);
+  return out;
+}
+var onesLikeConfig3 = {
+  kernelName: OnesLike,
+  backendName: "wasm",
+  kernelFunc: onesLike4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js
+function pack3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims5({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return result;
+}
+var packConfig3 = {
+  kernelName: Pack,
+  backendName: "wasm",
+  kernelFunc: pack3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js
+var wasmPadV2;
+function setup31(backend2) {
+  wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function pad2(args) {
+  const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;
+  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    return fill4({
+      backend: backend2,
+      attrs: { shape: outShape, value: constantValue, dtype: x.dtype }
+    });
+  }
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(outShape, x.dtype);
+  const outTensorData = backend2.dataIdMap.get(out.dataId);
+  const outId = outTensorData.id;
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);
+  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);
+  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);
+  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);
+  wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);
+  return out;
+}
+var padV2Config3 = {
+  kernelName: PadV2,
+  backendName: "wasm",
+  kernelFunc: pad2,
+  setupFunc: setup31
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js
+var supportsFullBroadcast13 = false;
+var powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast13);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js
+var wasmPrelu;
+function setup32(backend2) {
+  wasmPrelu = backend2.wasm.cwrap(Prelu, null, [
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function prelu5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const weightsId = backend2.dataIdMap.get(alpha.dataId).id;
+  let inputId = xId;
+  const input2 = x;
+  let castedInput = input2;
+  if (input2.dtype !== "float32") {
+    castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: "float32" } });
+    inputId = backend2.dataIdMap.get(castedInput.dataId).id;
+  }
+  const out = backend2.makeOutput(x.shape, "float32");
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmPrelu(inputId, weightsId, outId);
+  if (input2.dtype !== "float32") {
+    backend2.disposeData(castedInput.dataId);
+  }
+  return out;
+}
+var preluConfig3 = {
+  kernelName: Prelu,
+  backendName: "wasm",
+  setupFunc: setup32,
+  kernelFunc: prelu5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js
+var wasmProd;
+function setup33(backend2) {
+  wasmProd = backend2.wasm.cwrap(Prod, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function prod4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  let reductionAxes = axes;
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
+    }
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("prod", reductionAxes, input2.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, input2.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var prodConfig3 = {
+  kernelName: Prod,
+  backendName: "wasm",
+  setupFunc: setup33,
+  kernelFunc: prod4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js
+var range5 = (args) => {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, step: step5, dtype } = attrs;
+  const values = rangeImpl(start, stop, step5, dtype);
+  const out = backend2.makeOutput([values.length], dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.set(values);
+  return out;
+};
+var rangeConfig3 = {
+  kernelName: Range,
+  backendName: "wasm",
+  kernelFunc: range5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js
+var supportsFullBroadcast14 = true;
+var realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast14);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js
+var reluConfig3 = createUnaryKernelConfig(Relu);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js
+var relu6Config3 = createUnaryKernelConfig(Relu6);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js
+var wasmResizeBilinear;
+function setup34(backend2) {
+  wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function resizeBilinear4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size } = attrs;
+  const [newHeight, newWidth] = size;
+  const [batch, oldHeight, oldWidth, numChannels] = images.shape;
+  const outShape = [batch, newHeight, newWidth, numChannels];
+  let xData = backend2.dataIdMap.get(images.dataId);
+  let castedData;
+  if (xData.dtype !== "float32") {
+    castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: "float32" } });
+    xData = backend2.dataIdMap.get(castedData.dataId);
+  }
+  const xId = xData.id;
+  const out = backend2.makeOutput(outShape, "float32");
+  if (util_exports.sizeFromShape(images.shape) === 0) {
+    return out;
+  }
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);
+  if (castedData != null) {
+    backend2.disposeData(castedData.dataId);
+  }
+  return out;
+}
+var resizeBilinearConfig3 = {
+  kernelName: ResizeBilinear,
+  backendName: "wasm",
+  setupFunc: setup34,
+  kernelFunc: resizeBilinear4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js
+var wasmReverse;
+function setup35(backend2) {
+  wasmReverse = backend2.wasm.cwrap(Reverse, null, [
+    "number",
+    "array",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function reverse4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dims } = attrs;
+  const axes = util_exports.parseAxisParam(dims, x.shape);
+  if (x.shape.length === 0) {
+    return identity4({ inputs: { x }, backend: backend2 });
+  }
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const axesBytes = new Uint8Array(new Int32Array(axes).buffer);
+  const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);
+  const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 });
+  backend2.disposeData(out.dataId);
+  return reshaped;
+}
+var reverseConfig3 = {
+  kernelName: Reverse,
+  backendName: "wasm",
+  kernelFunc: reverse4,
+  setupFunc: setup35
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js
+var wasmRotate;
+function setup36(backend2) {
+  wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function rotateWithOffset2(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2 } = inputs;
+  const { radians, fillValue, center } = attrs;
+  const out = backend2.makeOutput(image2.shape, image2.dtype);
+  const imageId = backend2.dataIdMap.get(image2.dataId).id;
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
+  const fillIsBlack = fillValue === 0;
+  const fullOpacityValue = 255;
+  const fillValues2 = typeof fillValue === "number" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];
+  const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);
+  wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);
+  return out;
+}
+var rotateWithOffsetConfig3 = {
+  kernelName: RotateWithOffset,
+  backendName: "wasm",
+  kernelFunc: rotateWithOffset2,
+  setupFunc: setup36
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js
+var roundConfig3 = createUnaryKernelConfig(Round);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js
+var rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js
+var wasmScatterNd;
+function setup37(backend2) {
+  wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function scatterNd3(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const out = backend2.makeOutput(shape, updates.dtype);
+  if (util_exports.sizeFromShape(shape) === 0) {
+    return out;
+  }
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape);
+  const indicesData = backend2.dataIdMap.get(indices.dataId);
+  const indicesId = indicesData.id;
+  const updatesData = backend2.dataIdMap.get(updates.dataId);
+  const updatesId = updatesData.id;
+  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);
+  return out;
+}
+var scatterNdConfig3 = {
+  kernelName: ScatterNd,
+  backendName: "wasm",
+  setupFunc: setup37,
+  kernelFunc: scatterNd3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js
+var wasmSelect;
+function setup38(backend2) {
+  wasmSelect = backend2.wasm.cwrap("SelectV2", null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function select4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  const conditionId = backend2.dataIdMap.get(condition.dataId).id;
+  const tId = backend2.dataIdMap.get(t2.dataId).id;
+  const eId = backend2.dataIdMap.get(e2.dataId).id;
+  const out = backend2.makeOutput(t2.shape, t2.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const cRank = condition.shape.length;
+  const tRank = t2.shape.length;
+  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
+  wasmSelect(conditionId, tId, eId, offset, outId);
+  return out;
+}
+var selectConfig3 = {
+  kernelName: Select,
+  backendName: "wasm",
+  kernelFunc: select4,
+  setupFunc: setup38
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js
+var wasmFunc7;
+function setup39(backend2) {
+  wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, ["number", "number"]);
+}
+function sigmoid4(args) {
+  const { backend: backend2, inputs: { x } } = args;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  if (util_exports.sizeFromShape(out.shape) === 0) {
+    return out;
+  }
+  wasmFunc7(xId, outId);
+  return out;
+}
+var sigmoidConfig3 = {
+  kernelName: "Sigmoid",
+  backendName: "wasm",
+  setupFunc: setup39,
+  kernelFunc: sigmoid4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js
+var sinConfig3 = createUnaryKernelConfig(Sin);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js
+var wasmFunc8;
+function setup40(backend2) {
+  wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function softmax5(args) {
+  const { backend: backend2, inputs: { logits }, attrs: { dim } } = args;
+  const xId = backend2.dataIdMap.get(logits.dataId).id;
+  const out = backend2.makeOutput(logits.shape, logits.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const channels = logits.shape[dim];
+  const batch = util_exports.sizeFromShape(logits.shape) / channels;
+  if (util_exports.sizeFromShape(out.shape) === 0) {
+    return out;
+  }
+  wasmFunc8(xId, outId, channels, batch);
+  return out;
+}
+var softmaxConfig3 = {
+  kernelName: Softmax,
+  backendName: "wasm",
+  setupFunc: setup40,
+  kernelFunc: softmax5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SpaceToBatchND.js
+function spaceToBatchND4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  const prod6 = util_exports.sizeFromShape(blockShape);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const paddedX = padV2Config3.kernelFunc({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapeInputs = { x: paddedX };
+  const reshapeAttrs = { shape: reshapedPaddedShape };
+  const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });
+  const transposeInputs = { x: paddedXReshaped };
+  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };
+  const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });
+  const resultReshapeInputs = { x: paddedXT };
+  const resultReshapeAttrs = { shape: flattenShape };
+  const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });
+  backend2.disposeData(paddedX.dataId);
+  backend2.disposeData(paddedXReshaped.dataId);
+  backend2.disposeData(paddedXT.dataId);
+  return result;
+}
+var spaceToBatchNDConfig3 = {
+  kernelName: SpaceToBatchND,
+  backendName: "wasm",
+  kernelFunc: spaceToBatchND4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseFillEmptyRows.js
+var wasmSparseFillEmptyRows;
+function setup41(backend2) {
+  wasmSparseFillEmptyRows = backend2.wasm.cwrap("SparseFillEmptyRows", "number", [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sparseFillEmptyRows4(args) {
+  const { backend: backend2, inputs } = args;
+  const { indices, values, denseShape, defaultValue } = inputs;
+  const indicesCount = indices.shape[0];
+  const rank = indices.shape[1];
+  const denseRows = backend2.readSync(denseShape.dataId)[0];
+  const maxOutputIndicesShape = [indicesCount + denseRows, rank];
+  const indicesId = backend2.dataIdMap.get(indices.dataId).id;
+  const valuesId = backend2.dataIdMap.get(values.dataId).id;
+  const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;
+  const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype);
+  const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id;
+  const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);
+  const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id;
+  const emptyRowIndicator = backend2.makeOutput([denseRows], "bool");
+  const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id;
+  const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype);
+  const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id;
+  const exceptionValues = backend2.makeOutput([4], "int32");
+  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;
+  const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);
+  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);
+  let exceptionMessage;
+  switch (exceptionValuesArray[0]) {
+    case 1: {
+      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]);
+      break;
+    }
+    case 2: {
+      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    }
+    case 3:
+      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);
+      break;
+    default:
+      exceptionMessage = "";
+  }
+  backend2.disposeData(exceptionValues.dataId);
+  if (exceptionMessage) {
+    backend2.disposeData(outputIndices.dataId);
+    backend2.disposeData(outputValues.dataId);
+    backend2.disposeData(emptyRowIndicator.dataId);
+    backend2.disposeData(reverseIndexMap.dataId);
+    throw new Error(exceptionMessage);
+  }
+  let resizedIndices = outputIndices;
+  let resizedValues = outputValues;
+  if (outputRows !== maxOutputIndicesShape[0]) {
+    resizedIndices = slice4({
+      inputs: { x: outputIndices },
+      attrs: { begin: 0, size: [outputRows, rank] },
+      backend: backend2
+    });
+    resizedValues = slice4({
+      inputs: { x: outputValues },
+      attrs: { begin: 0, size: outputRows },
+      backend: backend2
+    });
+    backend2.disposeData(outputIndices.dataId);
+    backend2.disposeData(outputValues.dataId);
+  }
+  return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];
+}
+var sparseFillEmptyRowsConfig3 = {
+  kernelName: SparseFillEmptyRows,
+  backendName: "wasm",
+  setupFunc: setup41,
+  kernelFunc: sparseFillEmptyRows4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseReshape.js
+var wasmSparseReshape;
+function setup42(backend2) {
+  wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sparseReshape4(args) {
+  const { backend: backend2, inputs } = args;
+  const { inputIndices, inputShape, newShape } = inputs;
+  if (inputIndices.shape.length !== 2) {
+    throw new Error(`Input indices should be a matrix but received shape
+        ${inputIndices.shape}`);
+  }
+  if (inputShape.shape.length !== 1) {
+    throw new Error(`Input shape should be a vector but received shape
+        ${inputShape.shape}`);
+  }
+  if (newShape.shape.length !== 1) {
+    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
+  }
+  const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id;
+  const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id;
+  const newShapeId = backend2.dataIdMap.get(newShape.dataId).id;
+  const nnz = inputIndices.shape[0];
+  const outputRank = util_exports.sizeFromShape(newShape.shape);
+  const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype);
+  const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id;
+  const outputShape = backend2.makeOutput([outputRank], newShape.dtype);
+  const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id;
+  const exceptionValues = backend2.makeOutput([3], "int32");
+  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;
+  wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId);
+  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);
+  let exceptionMessage;
+  switch (exceptionValuesArray[0]) {
+    case 0: {
+      exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    }
+    case 1: {
+      exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    }
+    case 2:
+      exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
+      break;
+    case 3: {
+      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));
+      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues);
+      break;
+    }
+    case 4: {
+      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));
+      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues);
+      break;
+    }
+    default:
+      exceptionMessage = "";
+  }
+  backend2.disposeData(exceptionValues.dataId);
+  if (exceptionMessage) {
+    backend2.disposeData(newIndices.dataId);
+    backend2.disposeData(outputShape.dataId);
+    throw new Error(exceptionMessage);
+  }
+  return [newIndices, outputShape];
+}
+var sparseReshapeConfig3 = {
+  kernelName: SparseReshape,
+  backendName: "wasm",
+  setupFunc: setup42,
+  kernelFunc: sparseReshape4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentReduction.js
+var wasmSparseSegmentReduction;
+function setup43(backend2) {
+  wasmSparseSegmentReduction = backend2.wasm.cwrap("SparseSegmentReduction", null, [
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sparseSegmentReduction(args, isMean) {
+  const { backend: backend2, inputs } = args;
+  const { data, indices, segmentIds } = inputs;
+  const numIndices = indices.shape[0];
+  const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0];
+  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;
+  const outputRows = lastSegmentIdPlusOne;
+  if (outputRows < 0) {
+    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
+  }
+  const outputShape = data.shape.slice();
+  outputShape[0] = outputRows;
+  const dataId = backend2.dataIdMap.get(data.dataId).id;
+  const indicesId = backend2.dataIdMap.get(indices.dataId).id;
+  const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id;
+  const output = backend2.makeOutput(outputShape, data.dtype);
+  const outputId = backend2.dataIdMap.get(output.dataId).id;
+  const exceptionValues = backend2.makeOutput([4], "int32");
+  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;
+  wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0);
+  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);
+  let exceptionMessage;
+  switch (exceptionValuesArray[0]) {
+    case 0: {
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
+      break;
+    }
+    case 1: {
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
+      break;
+    }
+    case 2:
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);
+      break;
+    case 3:
+      exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);
+      break;
+    default:
+      exceptionMessage = "";
+  }
+  backend2.disposeData(exceptionValues.dataId);
+  if (exceptionMessage) {
+    backend2.disposeData(output.dataId);
+    throw new Error(exceptionMessage);
+  }
+  return output;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentMean.js
+function sparseSegmentMean4(args) {
+  return sparseSegmentReduction(args, true);
+}
+var sparseSegmentMeanConfig3 = {
+  kernelName: SparseSegmentMean,
+  backendName: "wasm",
+  setupFunc: setup43,
+  kernelFunc: sparseSegmentMean4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentSum.js
+function sparseSegmentSum4(args) {
+  return sparseSegmentReduction(args, false);
+}
+var sparseSegmentSumConfig3 = {
+  kernelName: SparseSegmentSum,
+  backendName: "wasm",
+  setupFunc: setup43,
+  kernelFunc: sparseSegmentSum4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js
+function splitV3(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const begin = new Array(x.shape.length).fill(0);
+  const size = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const xSliceSize = [...size];
+    xSliceSize[$axis] = s2;
+    const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });
+    begin[$axis] += s2;
+    return xSlice;
+  });
+}
+var splitVConfig3 = {
+  kernelName: SplitV,
+  backendName: "wasm",
+  kernelFunc: splitV3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js
+var sqrtConfig3 = createUnaryKernelConfig(Sqrt);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js
+var squareConfig3 = createUnaryKernelConfig(Square);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js
+var supportsFullBroadcast15 = true;
+var squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast15);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js
+var wasmStep;
+function setup44(backend2) {
+  wasmStep = backend2.wasm.cwrap(Step, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function step4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { alpha } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmStep(xId, alpha, CppDType[x.dtype], outId);
+  return out;
+}
+var stepConfig3 = {
+  kernelName: Step,
+  backendName: "wasm",
+  setupFunc: setup44,
+  kernelFunc: step4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js
+var wasmStridedSlice;
+function setup45(backend2) {
+  wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [
+    "number",
+    "array",
+    "number",
+    "array",
+    "array",
+    "array",
+    "array",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function stridedSlice4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
+    result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeData(sliced.dataId);
+  } else {
+    const out = backend2.makeOutput(finalShapeSparse, "float32");
+    const xId = backend2.dataIdMap.get(x.dataId).id;
+    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);
+    const beginBytes = new Uint8Array(new Int32Array($begin).buffer);
+    const endBytes = new Uint8Array(new Int32Array($end).buffer);
+    const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);
+    const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer);
+    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer);
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);
+    result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeData(out.dataId);
+  }
+  return result;
+}
+var stridedSliceConfig3 = {
+  kernelName: StridedSlice,
+  backendName: "wasm",
+  setupFunc: setup45,
+  kernelFunc: stridedSlice4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js
+var supportsFullBroadcast16 = true;
+var subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast16);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js
+var wasmSum;
+function setup46(backend2) {
+  wasmSum = backend2.wasm.cwrap(Sum, null, [
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function sum5(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { axis, keepDims } = attrs;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  let inputId = xId;
+  let input2 = x;
+  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);
+  let reductionAxes = axes;
+  if (inputWasTransposed) {
+    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;
+    if (transposedId !== xId) {
+      input2 = transposed;
+      inputId = transposedId;
+      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);
+    }
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, input2.shape.length);
+  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);
+  const reduceSize = util_exports.sizeFromShape(reduceShape);
+  const out = backend2.makeOutput(outShape, input2.dtype);
+  if (util_exports.sizeFromShape(input2.shape) !== 0) {
+    const outId = backend2.dataIdMap.get(out.dataId).id;
+    wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);
+  }
+  if (inputWasTransposed) {
+    backend2.disposeData(transposed.dataId);
+  }
+  if (keepDims) {
+    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);
+    out.shape = newShape;
+  }
+  return out;
+}
+var sumConfig3 = {
+  kernelName: Sum,
+  backendName: "wasm",
+  setupFunc: setup46,
+  kernelFunc: sum5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tan.js
+var tanConfig3 = createUnaryKernelConfig(Tan);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js
+var tanhConfig3 = createUnaryKernelConfig(Tanh);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js
+var wasmTile;
+function setup47(backend2) {
+  wasmTile = backend2.wasm.cwrap(Tile, null, [
+    "number",
+    "array",
+    "number",
+    "array",
+    "number",
+    "number"
+  ]);
+}
+function tile5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const { reps } = attrs;
+  const newShape = new Array(x.shape.length);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[i2] * reps[i2];
+  }
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
+  const out = backend2.makeOutput(newShape, x.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);
+  return out;
+}
+var tileConfig3 = {
+  kernelName: Tile,
+  backendName: "wasm",
+  setupFunc: setup47,
+  kernelFunc: tile5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js
+var wasmTopK;
+function setup48(backend2) {
+  wasmTopK = backend2.wasm.cwrap(TopK, null, [
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "bool",
+    "number",
+    "number"
+  ]);
+}
+var topk2 = ({ inputs, backend: backend2, attrs }) => {
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  const xId = backend2.dataIdMap.get(x.dataId).id;
+  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
+  const outputShape = x.shape.slice();
+  outputShape[outputShape.length - 1] = k;
+  const outValues = backend2.makeOutput(outputShape, x.dtype);
+  const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;
+  const outIndices = backend2.makeOutput(outputShape, "int32");
+  const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;
+  wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);
+  return [outValues, outIndices];
+};
+var topKConfig3 = {
+  kernelName: TopK,
+  backendName: "wasm",
+  setupFunc: setup48,
+  kernelFunc: topk2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transform.js
+var wasmTransform;
+function setup49(backend2) {
+  wasmTransform = backend2.wasm.cwrap(Transform, null, [
+    "number",
+    "number",
+    "bool",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number",
+    "array",
+    "number",
+    "number",
+    "number",
+    "number",
+    "number"
+  ]);
+}
+function transform4(args) {
+  const { backend: backend2, inputs, attrs } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [
+    batch,
+    outHeight,
+    outWidth,
+    numChannels
+  ];
+  const strides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer);
+  const out = backend2.makeOutput(outShape, image2.dtype);
+  const outId = backend2.dataIdMap.get(out.dataId).id;
+  const imageData = backend2.dataIdMap.get(image2.dataId);
+  const imageId = imageData.id;
+  const transformsData = backend2.dataIdMap.get(transforms.dataId);
+  const transformsId = transformsData.id;
+  const interpolationModeId = interpolation === "nearest" ? 1 : 2;
+  let fillModeId;
+  switch (fillMode) {
+    case "constant":
+      fillModeId = 1;
+      break;
+    case "reflect":
+      fillModeId = 2;
+      break;
+    case "wrap":
+      fillModeId = 3;
+      break;
+    case "nearest":
+      fillModeId = 4;
+      break;
+    default:
+      fillModeId = 1;
+      break;
+  }
+  wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, strides, image2.shape.length - 1, interpolationModeId, fillModeId, fillValue, outId);
+  return out;
+}
+var transformConfig3 = {
+  kernelName: Transform,
+  backendName: "wasm",
+  setupFunc: setup49,
+  kernelFunc: transform4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js
+function unpack3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const numOutputs = value.shape[axis];
+  const rank = value.shape.length;
+  const outShape = new Array(rank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < rank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = value.shape[i2];
+    }
+  }
+  const outs = new Array(numOutputs);
+  const begin = new Array(rank).fill(0);
+  const size = value.shape.slice();
+  size[axis] = 1;
+  for (let i2 = 0; i2 < outs.length; i2++) {
+    begin[axis] = i2;
+    outs[i2] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });
+  }
+  return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));
+}
+var unpackConfig3 = {
+  kernelName: Unpack,
+  backendName: "wasm",
+  kernelFunc: unpack3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js
+function zerosLike4(args) {
+  const { inputs: { x }, backend: backend2 } = args;
+  const out = backend2.makeOutput(x.shape, x.dtype);
+  const outVals = backend2.typedArrayFromHeap(out);
+  outVals.fill(0);
+  return out;
+}
+var zerosLikeConfig3 = {
+  kernelName: ZerosLike,
+  backendName: "wasm",
+  kernelFunc: zerosLike4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js
+var kernelConfigs3 = [
+  _fusedMatMulConfig3,
+  absConfig3,
+  addConfig3,
+  addNConfig3,
+  allConfig3,
+  anyConfig3,
+  argMaxConfig3,
+  avgPoolConfig3,
+  batchMatMulConfig3,
+  batchToSpaceNDConfig3,
+  castConfig3,
+  ceilConfig3,
+  clipByValueConfig3,
+  concatConfig3,
+  conv2DConfig3,
+  conv2DBackpropInputConfig3,
+  cosConfig3,
+  coshConfig3,
+  cropAndResizeConfig3,
+  cumprodConfig3,
+  cumsumConfig3,
+  depthToSpaceConfig3,
+  depthwiseConv2dNativeConfig3,
+  eluConfig3,
+  equalConfig3,
+  expConfig3,
+  expandDimsConfig3,
+  fillConfig3,
+  flipLeftRightConfig3,
+  floorConfig3,
+  floorDivConfig3,
+  fusedBatchNormConfig,
+  fusedConv2DConfig3,
+  fusedDepthwiseConv2DConfig3,
+  gatherNdConfig3,
+  gatherV2Config3,
+  greaterConfig3,
+  greaterEqualConfig3,
+  identityConfig3,
+  leakyReluConfig3,
+  lessConfig3,
+  lessEqualConfig3,
+  logConfig3,
+  logicalAndConfig3,
+  maxConfig3,
+  maximumConfig3,
+  maxPoolConfig3,
+  meanConfig3,
+  minConfig3,
+  minimumConfig3,
+  mirrorPadConfig3,
+  multiplyConfig3,
+  negConfig3,
+  nonMaxSuppressionV3Config3,
+  nonMaxSuppressionV4Config3,
+  nonMaxSuppressionV5Config3,
+  notEqualConfig3,
+  oneHotConfig3,
+  onesLikeConfig3,
+  packConfig3,
+  padV2Config3,
+  powConfig3,
+  preluConfig3,
+  prodConfig3,
+  rangeConfig3,
+  realDivConfig3,
+  reluConfig3,
+  relu6Config3,
+  reshapeConfig3,
+  resizeBilinearConfig3,
+  reverseConfig3,
+  rotateWithOffsetConfig3,
+  roundConfig3,
+  rsqrtConfig3,
+  scatterNdConfig3,
+  selectConfig3,
+  sigmoidConfig3,
+  sinConfig3,
+  sliceConfig3,
+  softmaxConfig3,
+  spaceToBatchNDConfig3,
+  sparseFillEmptyRowsConfig3,
+  sparseReshapeConfig3,
+  sparseSegmentMeanConfig3,
+  sparseSegmentSumConfig3,
+  splitVConfig3,
+  sqrtConfig3,
+  squareConfig3,
+  squaredDifferenceConfig3,
+  stepConfig3,
+  stridedSliceConfig3,
+  subConfig3,
+  sumConfig3,
+  tanConfig3,
+  tanhConfig3,
+  tileConfig3,
+  topKConfig3,
+  transformConfig3,
+  transposeConfig3,
+  unpackConfig3,
+  zerosLikeConfig3
+];
+for (const kernelConfig of kernelConfigs3) {
+  registerKernel(kernelConfig);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js
+var ENV6 = env();
+ENV6.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([
+  0,
+  97,
+  115,
+  109,
+  1,
+  0,
+  0,
+  0,
+  1,
+  4,
+  1,
+  96,
+  0,
+  0,
+  3,
+  2,
+  1,
+  0,
+  10,
+  9,
+  1,
+  7,
+  0,
+  65,
+  0,
+  253,
+  15,
+  26,
+  11
+])));
+ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
+  if (ENV6.get("IS_NODE")) {
+    return false;
+  }
+  try {
+    new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));
+    return WebAssembly.validate(new Uint8Array([
+      0,
+      97,
+      115,
+      109,
+      1,
+      0,
+      0,
+      0,
+      1,
+      4,
+      1,
+      96,
+      0,
+      0,
+      3,
+      2,
+      1,
+      0,
+      5,
+      4,
+      1,
+      3,
+      1,
+      1,
+      10,
+      11,
+      1,
+      9,
+      0,
+      65,
+      0,
+      254,
+      16,
+      2,
+      0,
+      26,
+      11
+    ]));
+  } catch (e2) {
+    return false;
+  }
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
+var import_tfjs_backend_wasm_threaded_simd = __toESM(require_tfjs_backend_wasm_threaded_simd());
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js
+var wasmWorkerContents = '"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"\n");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});';
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js
+var import_tfjs_backend_wasm = __toESM(require_tfjs_backend_wasm());
+var BackendWasm = class extends KernelBackend {
+  constructor(wasm) {
+    super();
+    this.wasm = wasm;
+    this.dataIdNextNumber = 1;
+    this.wasm.tfjs.initWithThreadsCount(threadsCount);
+    actualThreadsCount = this.wasm.tfjs.getThreadsCount();
+    this.dataIdMap = new DataStorage(this, engine());
+  }
+  write(values, shape, dtype) {
+    const dataId = { id: this.dataIdNextNumber++ };
+    this.move(dataId, values, shape, dtype, 1);
+    return dataId;
+  }
+  numDataIds() {
+    return this.dataIdMap.numDataIds();
+  }
+  async time(f) {
+    const start = util_exports.now();
+    f();
+    const kernelMs = util_exports.now() - start;
+    return { kernelMs };
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    const id = this.dataIdNextNumber++;
+    if (dtype === "string") {
+      const stringBytes = values;
+      this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount });
+      return;
+    }
+    const size = util_exports.sizeFromShape(shape);
+    const numBytes = size * util_exports.bytesPerElement(dtype);
+    const memoryOffset = this.wasm._malloc(numBytes);
+    this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount });
+    this.wasm.tfjs.registerTensor(id, size, memoryOffset);
+    if (values != null) {
+      this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);
+    }
+  }
+  async read(dataId) {
+    return this.readSync(dataId);
+  }
+  readSync(dataId, start, end) {
+    const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId);
+    if (dtype === "string") {
+      if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) {
+        return stringBytes;
+      }
+      return stringBytes.slice(start, end);
+    }
+    start = start || 0;
+    end = end || util_exports.sizeFromShape(shape);
+    const bytesPerElement2 = util_exports.bytesPerElement(dtype);
+    const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2);
+    return typedArrayFromBuffer(bytes.buffer, dtype);
+  }
+  disposeData(dataId, force = false) {
+    if (this.dataIdMap.has(dataId)) {
+      const data = this.dataIdMap.get(dataId);
+      data.refCount--;
+      if (!force && data.refCount > 0) {
+        return false;
+      }
+      this.wasm._free(data.memoryOffset);
+      this.wasm.tfjs.disposeData(data.id);
+      this.dataIdMap.delete(dataId);
+    }
+    return true;
+  }
+  refCount(dataId) {
+    if (this.dataIdMap.has(dataId)) {
+      const tensorData = this.dataIdMap.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const data = this.dataIdMap.get(dataId);
+    if (data != null) {
+      data.refCount++;
+    }
+  }
+  floatPrecision() {
+    return 32;
+  }
+  getMemoryOffset(dataId) {
+    return this.dataIdMap.get(dataId).memoryOffset;
+  }
+  dispose() {
+    this.wasm.tfjs.dispose();
+    if ("PThread" in this.wasm) {
+      this.wasm.PThread.terminateAllThreads();
+    }
+    this.wasm = null;
+  }
+  memory() {
+    return { unreliable: false };
+  }
+  makeOutput(shape, dtype, memoryOffset) {
+    let dataId;
+    if (memoryOffset == null) {
+      dataId = this.write(null, shape, dtype);
+    } else {
+      const id = this.dataIdNextNumber++;
+      dataId = { id };
+      this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 });
+      const size = util_exports.sizeFromShape(shape);
+      this.wasm.tfjs.registerTensor(id, size, memoryOffset);
+    }
+    return { dataId, shape, dtype };
+  }
+  typedArrayFromHeap({ shape, dtype, dataId }) {
+    const buffer2 = this.wasm.HEAPU8.buffer;
+    const { memoryOffset } = this.dataIdMap.get(dataId);
+    const size = util_exports.sizeFromShape(shape);
+    switch (dtype) {
+      case "float32":
+        return new Float32Array(buffer2, memoryOffset, size);
+      case "int32":
+        return new Int32Array(buffer2, memoryOffset, size);
+      case "bool":
+        return new Uint8Array(buffer2, memoryOffset, size);
+      default:
+        throw new Error(`Unknown dtype ${dtype}`);
+    }
+  }
+};
+function createInstantiateWasmFunc(path) {
+  return (imports, callback) => {
+    util_exports.fetch(path, { credentials: "same-origin" }).then((response) => {
+      if (!response["ok"]) {
+        imports.env.a(`failed to load wasm binary file at '${path}'`);
+      }
+      response.arrayBuffer().then((binary) => {
+        WebAssembly.instantiate(binary, imports).then((output) => {
+          callback(output.instance, output.module);
+        });
+      });
+    });
+    return {};
+  };
+}
+function getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {
+  if (wasmPath != null) {
+    return wasmPath;
+  }
+  let path = "tfjs-backend-wasm.wasm";
+  if (simdSupported && threadsSupported) {
+    path = "tfjs-backend-wasm-threaded-simd.wasm";
+  } else if (simdSupported) {
+    path = "tfjs-backend-wasm-simd.wasm";
+  }
+  if (wasmFileMap != null) {
+    if (wasmFileMap[path] != null) {
+      return wasmFileMap[path];
+    }
+  }
+  return wasmModuleFolder + path;
+}
+async function init() {
+  const [simdSupported, threadsSupported] = await Promise.all([
+    env().getAsync("WASM_HAS_SIMD_SUPPORT"),
+    env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")
+  ]);
+  return new Promise((resolve, reject) => {
+    const factoryConfig = {};
+    factoryConfig.locateFile = (path, prefix) => {
+      if (path.endsWith(".worker.js")) {
+        const response = wasmWorkerContents.replace(/\n/g, "\\n");
+        const blob = new Blob([response], { type: "application/javascript" });
+        return URL.createObjectURL(blob);
+      }
+      if (path.endsWith(".wasm")) {
+        return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);
+      }
+      return prefix + path;
+    };
+    if (customFetch) {
+      factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : ""));
+    }
+    let initialized = false;
+    factoryConfig.onAbort = () => {
+      if (initialized) {
+        return;
+      }
+      if (initAborted) {
+        return;
+      }
+      initAborted = true;
+      const rejectMsg = "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers";
+      reject({ message: rejectMsg });
+    };
+    let wasm;
+    if (threadsSupported && simdSupported && wasmPath == null) {
+      factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + import_tfjs_backend_wasm_threaded_simd.default.toString()], { type: "text/javascript" });
+      wasm = (0, import_tfjs_backend_wasm_threaded_simd.default)(factoryConfig);
+    } else {
+      wasm = (0, import_tfjs_backend_wasm.default)(factoryConfig);
+    }
+    wasm.then((module) => {
+      initialized = true;
+      initAborted = false;
+      const voidReturnType = null;
+      module.tfjs = {
+        init: module.cwrap("init", null, []),
+        initWithThreadsCount: module.cwrap("init_with_threads_count", null, ["number"]),
+        getThreadsCount: module.cwrap("get_threads_count", "number", []),
+        registerTensor: module.cwrap("register_tensor", null, [
+          "number",
+          "number",
+          "number"
+        ]),
+        disposeData: module.cwrap("dispose_data", voidReturnType, ["number"]),
+        dispose: module.cwrap("dispose", voidReturnType, [])
+      };
+      resolve({ wasm: module });
+    });
+  });
+}
+function typedArrayFromBuffer(buffer2, dtype) {
+  switch (dtype) {
+    case "float32":
+      return new Float32Array(buffer2);
+    case "int32":
+      return new Int32Array(buffer2);
+    case "bool":
+      return new Uint8Array(buffer2);
+    default:
+      throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+var wasmBinaryNames = [
+  "tfjs-backend-wasm.wasm",
+  "tfjs-backend-wasm-simd.wasm",
+  "tfjs-backend-wasm-threaded-simd.wasm"
+];
+var wasmPath = null;
+var wasmPathPrefix = null;
+var wasmFileMap = {};
+var initAborted = false;
+var customFetch = false;
+function setWasmPath(path, usePlatformFetch = false) {
+  deprecationWarn("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.");
+  if (initAborted) {
+    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
+  }
+  wasmPath = path;
+  customFetch = usePlatformFetch;
+}
+function setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {
+  if (initAborted) {
+    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
+  }
+  if (typeof prefixOrFileMap === "string") {
+    wasmPathPrefix = prefixOrFileMap;
+  } else {
+    wasmFileMap = prefixOrFileMap;
+    const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);
+    if (missingPaths.length > 0) {
+      throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
+    }
+  }
+  customFetch = usePlatformFetch;
+}
+var threadsCount = -1;
+var actualThreadsCount = -1;
+function setThreadsCount(numThreads) {
+  threadsCount = numThreads;
+}
+function getThreadsCount() {
+  if (actualThreadsCount === -1) {
+    throw new Error(`WASM backend not initialized.`);
+  }
+  return actualThreadsCount;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js
+var version8 = "3.18.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js
+var WASM_PRIORITY = 2;
+registerBackend("wasm", async () => {
+  const { wasm } = await init();
+  return new BackendWasm(wasm);
+}, WASM_PRIORITY);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flags_webgpu.js
+var ENV7 = env();
+ENV7.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
+ENV7.registerFlag("WEBGPU_CPU_FORWARD", () => true);
+ENV7.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
+ENV7.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
+ENV7.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
+ENV7.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
+ENV7.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
+ENV7.registerFlag("WEBGPU_USE_IMPORT", () => false);
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_util.js
+var BinaryOpType;
+(function(BinaryOpType2) {
+  BinaryOpType2[BinaryOpType2["MUL"] = 0] = "MUL";
+  BinaryOpType2[BinaryOpType2["ADD"] = 1] = "ADD";
+  BinaryOpType2[BinaryOpType2["SUB"] = 2] = "SUB";
+  BinaryOpType2[BinaryOpType2["DIV"] = 3] = "DIV";
+  BinaryOpType2[BinaryOpType2["EQUAL"] = 4] = "EQUAL";
+  BinaryOpType2[BinaryOpType2["GREATER"] = 5] = "GREATER";
+  BinaryOpType2[BinaryOpType2["GREATER_EQUAL"] = 6] = "GREATER_EQUAL";
+  BinaryOpType2[BinaryOpType2["LESS"] = 7] = "LESS";
+  BinaryOpType2[BinaryOpType2["LESS_EQUAL"] = 8] = "LESS_EQUAL";
+  BinaryOpType2[BinaryOpType2["LOGICAL_AND"] = 9] = "LOGICAL_AND";
+  BinaryOpType2[BinaryOpType2["NOT_EQUAL"] = 10] = "NOT_EQUAL";
+  BinaryOpType2[BinaryOpType2["SQUARED_DIFFERENCE"] = 11] = "SQUARED_DIFFERENCE";
+  BinaryOpType2[BinaryOpType2["INT_DIV"] = 12] = "INT_DIV";
+  BinaryOpType2[BinaryOpType2["POW"] = 13] = "POW";
+  BinaryOpType2[BinaryOpType2["PRELU"] = 14] = "PRELU";
+  BinaryOpType2[BinaryOpType2["MAX"] = 15] = "MAX";
+  BinaryOpType2[BinaryOpType2["MIN"] = 16] = "MIN";
+  BinaryOpType2[BinaryOpType2["COMPLEX_MULTIPLY_REAL"] = 17] = "COMPLEX_MULTIPLY_REAL";
+  BinaryOpType2[BinaryOpType2["COMPLEX_MULTIPLY_IMAG"] = 18] = "COMPLEX_MULTIPLY_IMAG";
+})(BinaryOpType || (BinaryOpType = {}));
+var ADD2 = "return a + b;";
+var COMPLEX_MULTIPLY_REAL = "return areal * breal - aimag * bimag;";
+var COMPLEX_MULTIPLY_IMAG = "return areal * bimag + aimag * breal;";
+var DIV2 = "return a / b;";
+var MUL2 = "return a * b;";
+var SQUARED_DIFFERENCE2 = "return (a - b) * (a - b);";
+var SUB2 = "return a - b;";
+var EQUAL2 = "return f32(a == b);";
+var EQUAL_VEC4 = "return vec4<f32>(a == b);";
+var GREATER2 = "return f32(a > b);";
+var GREATER_VEC4 = "return vec4<f32>(a > b);";
+var GREATER_EQUAL2 = "return f32(a >= b);";
+var GREATER_EQUAL_VEC4 = "return vec4<f32>(a >= b);";
+var LESS2 = "return f32(a < b);";
+var LESS_VEC4 = "return vec4<f32>(a < b);";
+var LESS_EQUAL2 = "return f32(a <= b);";
+var LESS_EQUAL_VEC4 = "return vec4<f32>(a <= b);";
+var LOGICAL_AND2 = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
+var LOGICAL_AND_VEC4 = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`;
+var CHECK_NAN_SNIPPET4 = `
+  if (isnan(a)) { return a; }
+  if (isnan(b)) { return b; }
+  `;
+var CHECK_NAN_SNIPPET_VEC4 = `
+  if (isNaN.r) {
+    resultTemp.r = uniforms.NAN;
+  }
+  if (isNaN.g) {
+    resultTemp.g = uniforms.NAN;
+  }
+  if (isNaN.b) {
+    resultTemp.b = uniforms.NAN;
+  }
+  if (isNaN.a) {
+    resultTemp.a = uniforms.NAN;
+  }
+  `;
+var INT_DIV2 = `
+  let s = sign(a) * sign(b);
+  let ia = i32(round(a));
+  let ib = i32(round(b));
+  return f32(idiv(ia, ib, s));
+  `;
+var INT_DIV_VEC4 = `
+  let ia = vec4<i32>(round(a));
+  let ib = vec4<i32>(round(b));
+  let cond = ib != vec4<i32>(0);
+  var resultTemp = vec4<i32>(0);
+  let s = sign(a) * sign(b);
+
+  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
+  if (cond[0]) {
+    resultTemp[0] = idiv(ia[0], ib[0], s[0]);
+  }
+  if (cond[1]) {
+    resultTemp[1] = idiv(ia[1], ib[1], s[1]);
+  }
+  if (cond[2]) {
+    resultTemp[2] = idiv(ia[2], ib[2], s[2]);
+  }
+  if (cond[3]) {
+    resultTemp[3] = idiv(ia[3], ib[3], s[3]);
+  }
+  return vec4<f32>(resultTemp);
+  `;
+var NOT_EQUAL2 = "return f32(a != b);";
+var NOT_EQUAL_VEC4 = "return vec4<f32>(a != b);";
+var POW2 = `
+  if(a < 0.0 && floor(b) < b) {
+    return uniforms.NAN;
+  }
+  if (b == 0.0) {
+    return 1.0;
+  }
+  if (round(abs(b) % 2.0) != 1.0) {
+    return pow(abs(a), b);
+  }
+  return sign(a) * pow(abs(a), b);
+  `;
+var POW_VEC4 = `
+  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
+  let isModRound1 = vec4<f32>(isModRound1Bool);
+  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
+  var resultTemp = multiplier * pow(abs(a), b);
+
+  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
+  let isExpZero = b == vec4<f32>(0.0);
+  if (isExpZero.r) {
+    resultTemp.r = 1.0;
+  }
+  if (isExpZero.g) {
+    resultTemp.g = 1.0;
+  }
+  if (isExpZero.b) {
+    resultTemp.b = 1.0;
+  }
+  if (isExpZero.a) {
+    resultTemp.a = 1.0;
+  }
+  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
+  ${CHECK_NAN_SNIPPET_VEC4}
+  return resultTemp;
+  `;
+var PRELU2 = `if (a < 0.0) { return b * a; }  return a;`;
+var PRELU_VEC4 = `
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+  `;
+function getMinMaxString(op2, useVec4) {
+  const checkNanSnippet = useVec4 ? CHECK_NAN_SNIPPET_VEC4 : CHECK_NAN_SNIPPET4;
+  return useVec4 ? `
+    var resultTemp = vec4<f32>(${op2}(a, b));
+    let isNaN = isnanVec4(a) | isnanVec4(b);
+    ` + checkNanSnippet + `
+    return resultTemp;
+  ` : checkNanSnippet + `
+    return ${op2}(a, b);
+  `;
+}
+function getBinaryOpString(type, useVec4) {
+  switch (type) {
+    case BinaryOpType.MUL:
+      return MUL2;
+    case BinaryOpType.ADD:
+      return ADD2;
+    case BinaryOpType.SUB:
+      return SUB2;
+    case BinaryOpType.DIV:
+      return DIV2;
+    case BinaryOpType.EQUAL:
+      return useVec4 ? EQUAL_VEC4 : EQUAL2;
+    case BinaryOpType.GREATER:
+      return useVec4 ? GREATER_VEC4 : GREATER2;
+    case BinaryOpType.GREATER_EQUAL:
+      return useVec4 ? GREATER_EQUAL_VEC4 : GREATER_EQUAL2;
+    case BinaryOpType.LESS:
+      return useVec4 ? LESS_VEC4 : LESS2;
+    case BinaryOpType.LESS_EQUAL:
+      return useVec4 ? LESS_EQUAL_VEC4 : LESS_EQUAL2;
+    case BinaryOpType.LOGICAL_AND:
+      return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND2;
+    case BinaryOpType.NOT_EQUAL:
+      return useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL2;
+    case BinaryOpType.SQUARED_DIFFERENCE:
+      return SQUARED_DIFFERENCE2;
+    case BinaryOpType.INT_DIV:
+      return useVec4 ? INT_DIV_VEC4 : INT_DIV2;
+    case BinaryOpType.PRELU:
+      return useVec4 ? PRELU_VEC4 : PRELU2;
+    case BinaryOpType.MAX:
+      return getMinMaxString("max", useVec4);
+    case BinaryOpType.MIN:
+      return getMinMaxString("min", useVec4);
+    case BinaryOpType.POW:
+      return useVec4 ? POW_VEC4 : POW2;
+    case BinaryOpType.COMPLEX_MULTIPLY_REAL:
+      return COMPLEX_MULTIPLY_REAL;
+    case BinaryOpType.COMPLEX_MULTIPLY_IMAG:
+      return COMPLEX_MULTIPLY_IMAG;
+    default:
+      throw new Error(`BinaryType ${type} is not implemented!`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_util.js
+var UnaryOpType;
+(function(UnaryOpType2) {
+  UnaryOpType2[UnaryOpType2["ABS"] = 0] = "ABS";
+  UnaryOpType2[UnaryOpType2["CEIL"] = 1] = "CEIL";
+  UnaryOpType2[UnaryOpType2["COS"] = 2] = "COS";
+  UnaryOpType2[UnaryOpType2["COSH"] = 3] = "COSH";
+  UnaryOpType2[UnaryOpType2["ELU"] = 4] = "ELU";
+  UnaryOpType2[UnaryOpType2["EXP"] = 5] = "EXP";
+  UnaryOpType2[UnaryOpType2["EXPM1"] = 6] = "EXPM1";
+  UnaryOpType2[UnaryOpType2["FLOOR"] = 7] = "FLOOR";
+  UnaryOpType2[UnaryOpType2["LINEAR"] = 8] = "LINEAR";
+  UnaryOpType2[UnaryOpType2["LOG"] = 9] = "LOG";
+  UnaryOpType2[UnaryOpType2["LOGICAL_NOT"] = 10] = "LOGICAL_NOT";
+  UnaryOpType2[UnaryOpType2["NEG"] = 11] = "NEG";
+  UnaryOpType2[UnaryOpType2["RELU"] = 12] = "RELU";
+  UnaryOpType2[UnaryOpType2["RELU6"] = 13] = "RELU6";
+  UnaryOpType2[UnaryOpType2["LEAKYRELU"] = 14] = "LEAKYRELU";
+  UnaryOpType2[UnaryOpType2["RSQRT"] = 15] = "RSQRT";
+  UnaryOpType2[UnaryOpType2["SIN"] = 16] = "SIN";
+  UnaryOpType2[UnaryOpType2["SINH"] = 17] = "SINH";
+  UnaryOpType2[UnaryOpType2["SIGMOID"] = 18] = "SIGMOID";
+  UnaryOpType2[UnaryOpType2["SQRT"] = 19] = "SQRT";
+  UnaryOpType2[UnaryOpType2["SQUARE"] = 20] = "SQUARE";
+  UnaryOpType2[UnaryOpType2["TANH"] = 21] = "TANH";
+  UnaryOpType2[UnaryOpType2["TO_INT"] = 22] = "TO_INT";
+})(UnaryOpType || (UnaryOpType = {}));
+var ABS3 = `return abs(a);`;
+var CEIL2 = `return ceil(a);`;
+var COS2 = `return cos(a);`;
+var COSH2 = `
+  let e2x = exp(-a);
+  return (e2x + 1.0 / e2x) / 2.0;
+`;
+var EXPM12 = `return exp(a) - 1.0;`;
+var ELU5 = `if (a >= 0.0) { return a; }  return (exp(a) - 1.0);`;
+var ELU_VEC4 = `
+  var resFloat = exp(a) - vec4<f32>(1.0);
+  if (a.r >= 0.0) {
+    resFloat.r = a.r;
+  }
+  if (a.g >= 0.0) {
+    resFloat.g = a.g;
+  }
+  if (a.b >= 0.0) {
+    resFloat.b = a.b;
+  }
+  if (a.a >= 0.0) {
+    resFloat.a = a.a;
+  }
+  return resFloat;
+`;
+var EXP2 = `return exp(a);`;
+var FLOOR2 = `return floor(a);`;
+var LINEAR3 = `return a;`;
+var LOG2 = `if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`;
+var LOGICAL_NOT2 = `return f32(!(a >= 1.0));`;
+var NEG2 = `return -a;`;
+var LEAKYRELU2 = `if (a < 0.0) { return uniforms.alpha * a; } return a;`;
+var LEAKYRELU_VEC4 = `
+  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
+  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
+`;
+var RELU4 = `return select(a, 0.0, a < 0.0);`;
+var RELU64 = "return clamp(a, 0.0, 6.0);";
+var RELU6_VEC4 = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
+var RELU_VEC4 = `
+  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
+`;
+var RSQRT2 = `return 1.0/sqrt(a);`;
+var SIGMOID4 = `return 1.0 / (1.0 + exp(-1.0 * a));`;
+var SIN2 = `return sin(a);`;
+var SINH2 = `
+  let e2x = exp(a);
+  return (e2x - 1.0 / e2x) / 2.0;
+`;
+var SQRT2 = `return sqrt(a);`;
+var SQUARE2 = `return a * a;`;
+var TANH2 = `
+  let e2x = exp(-2.0 * abs(a));
+  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
+`;
+var TO_INT2 = `return f32(i32((a)));`;
+function getUnaryOpString(type, useVec4) {
+  switch (type) {
+    case UnaryOpType.ABS:
+      return ABS3;
+    case UnaryOpType.COS:
+      return COS2;
+    case UnaryOpType.COSH:
+      return COSH2;
+    case UnaryOpType.CEIL:
+      return CEIL2;
+    case UnaryOpType.ELU:
+      return useVec4 ? ELU_VEC4 : ELU5;
+    case UnaryOpType.EXP:
+      return EXP2;
+    case UnaryOpType.EXPM1:
+      return EXPM12;
+    case UnaryOpType.FLOOR:
+      return FLOOR2;
+    case UnaryOpType.LINEAR:
+      return LINEAR3;
+    case UnaryOpType.LOG:
+      return LOG2;
+    case UnaryOpType.LOGICAL_NOT:
+      return LOGICAL_NOT2;
+    case UnaryOpType.NEG:
+      return NEG2;
+    case UnaryOpType.LEAKYRELU:
+      return useVec4 ? LEAKYRELU_VEC4 : LEAKYRELU2;
+    case UnaryOpType.RELU:
+      return useVec4 ? RELU_VEC4 : RELU4;
+    case UnaryOpType.RELU6:
+      return useVec4 ? RELU6_VEC4 : RELU64;
+    case UnaryOpType.RSQRT:
+      return RSQRT2;
+    case UnaryOpType.SIGMOID:
+      return SIGMOID4;
+    case UnaryOpType.SIN:
+      return SIN2;
+    case UnaryOpType.SINH:
+      return SINH2;
+    case UnaryOpType.SQRT:
+      return SQRT2;
+    case UnaryOpType.SQUARE:
+      return SQUARE2;
+    case UnaryOpType.TANH:
+      return TANH2;
+    case UnaryOpType.TO_INT:
+      return TO_INT2;
+    default:
+      throw new Error(`BinaryType ${type} is not implemented!`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/activation_util.js
+function mapActivationToShaderProgram2(activation2, packed = false) {
+  if (activation2 === null) {
+    return null;
+  } else if (activation2 === "linear") {
+    return getUnaryOpString(UnaryOpType.LINEAR);
+  } else if (activation2 === "relu") {
+    return getUnaryOpString(UnaryOpType.RELU, packed);
+  } else if (activation2 === "elu") {
+    return getUnaryOpString(UnaryOpType.ELU, packed);
+  } else if (activation2 === "relu6") {
+    return getUnaryOpString(UnaryOpType.RELU6, packed);
+  } else if (activation2 === "prelu") {
+    return getBinaryOpString(BinaryOpType.PRELU, packed);
+  } else if (activation2 === "sigmoid") {
+    return getUnaryOpString(UnaryOpType.SIGMOID, packed);
+  } else if (activation2 === "leakyrelu") {
+    return getUnaryOpString(UnaryOpType.LEAKYRELU, packed);
+  }
+  throw new Error(`Activation ${activation2} has not been implemented for the WebGPU backend.`);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/shader_util.js
+function symbolicallyComputeStrides2(indicesArr, variableName) {
+  if (Math.max(...indicesArr) > 3) {
+    throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
+  }
+  const numCoords = indicesArr.length;
+  const shape = indicesArr.map((d) => `${variableName}[${d}]`);
+  const strides = new Array(numCoords - 1);
+  strides[numCoords - 2] = shape[numCoords - 1];
+  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
+    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;
+  }
+  return strides;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/shader_preprocessor.js
+function getCoordsDataType2(rank) {
+  if (rank <= 1) {
+    return "i32";
+  } else if (rank === 2) {
+    return `vec2<i32>`;
+  } else if (rank === 3) {
+    return `vec3<i32>`;
+  } else if (rank === 4) {
+    return `vec4<i32>`;
+  } else if (rank === 5) {
+    return `vec5`;
+  } else if (rank === 6) {
+    return `vec6`;
+  } else {
+    throw Error(`GPU for rank ${rank} is not yet supported`);
+  }
+}
+function getCoordsXYZ(index) {
+  if (index === 0) {
+    return "x";
+  } else if (index === 1) {
+    return "y";
+  } else if (index === 2) {
+    return "z";
+  } else if (index === 3) {
+    return "w";
+  } else if (index === 4) {
+    return "u";
+  } else if (index === 5) {
+    return "v";
+  } else {
+    throw Error(`Index ${index} is not yet supported`);
+  }
+}
+function mapToWgslTypes(type, isVec4) {
+  if (type === "float32") {
+    return isVec4 ? "vec4<f32>" : "f32";
+  } else if (type === "int32") {
+    return isVec4 ? "vec4<i32>" : "i32";
+  } else if (type === "bool") {
+    return isVec4 ? "vec4<i32>" : "i32";
+  }
+  return type;
+}
+function getWorkGroupSizeString() {
+  return `
+  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
+`;
+}
+function getMainHeaderString() {
+  return `
+  ${getWorkGroupSizeString()}
+  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
+          @builtin(global_invocation_id) GlobalId : vec3<u32>,
+          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
+    localId = LocalId;
+    globalId = GlobalId;
+    numWorkgroups = NumWorkgroups;
+`;
+}
+function getMainHeaderAndGlobalIndexString() {
+  return `
+    ${getMainHeaderString()}
+      let index = getGlobalIndex();
+`;
+}
+function makeShader2(inputInfo, outputData, program, isFromPixel = false) {
+  const prefixSnippets = [];
+  prefixSnippets.push(`
+    let workGroupSizeX = ${program.workGroupSize[0]}u;
+    let workGroupSizeY = ${program.workGroupSize[1]}u;
+    let workGroupSizeZ = ${program.workGroupSize[2]}u;
+
+    var<private> localId: vec3<u32>;
+    var<private> globalId: vec3<u32>;
+    var<private> numWorkgroups: vec3<u32>;
+
+    // Only used when the y/z dimension of workgroup size is 1.
+    fn getGlobalIndex() -> i32 {
+      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
+        return i32(globalId.x);
+      }
+
+      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+          localId.y * workGroupSizeX + localId.x;
+      let workGroupID = (globalId - localId)/vec3<u32>(
+          workGroupSizeX, workGroupSizeY, workGroupSizeZ);
+
+      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
+        workGroupID.y * numWorkgroups.x + workGroupID.x) *
+        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
+        localInvocationIndex);
+    }
+  `);
+  if (isFromPixel === true) {
+    prefixSnippets.push(`
+      struct Uniform {
+        size            : i32,
+        numChannels     : i32,
+        outShapeStrides : vec2<i32>,
+        dispatchSize    : vec3<u32>,
+      };
+
+      @group(0) @binding(0) var<storage, write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;
+      @group(0) @binding(2) var<uniform> uniforms: Uniform;
+    `);
+    return [
+      commonSnippet,
+      prefixSnippets.join("\n"),
+      getCoordsFromIndexSnippet(outputData.shape),
+      program.getUserCode()
+    ].join("\n");
+  }
+  let preMemberIsStruct = false;
+  let currentMemberIsStruct = false;
+  let uniformDeclaration = "struct Uniforms { NAN : f32, ";
+  program.variableNames.forEach((x, i2) => {
+    const perDataType = getCoordsDataType2(inputInfo[i2].shape.length);
+    if (perDataType === "vec5" || perDataType === "vec6") {
+      currentMemberIsStruct = true;
+    }
+    if (preMemberIsStruct || currentMemberIsStruct) {
+      uniformDeclaration += `@align(16) `;
+    }
+    preMemberIsStruct = currentMemberIsStruct;
+    uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;
+  });
+  const outputDataType = getCoordsDataType2(outputData.shape.length);
+  currentMemberIsStruct = outputDataType === "vec5" || outputDataType === "vec6";
+  if (preMemberIsStruct || currentMemberIsStruct) {
+    uniformDeclaration += `@align(16) `;
+  }
+  preMemberIsStruct = currentMemberIsStruct;
+  uniformDeclaration += `outShape : ${outputDataType}, `;
+  const stridesLength = outputData.shape.length - 1;
+  const stridesDataType = getCoordsDataType2(stridesLength);
+  currentMemberIsStruct = stridesDataType === "vec5" || stridesDataType === "vec6";
+  if (preMemberIsStruct || currentMemberIsStruct) {
+    uniformDeclaration += `@align(16) `;
+  }
+  preMemberIsStruct = currentMemberIsStruct;
+  uniformDeclaration += `
+       outShapeStrides: ${stridesDataType}, `;
+  if (program.size) {
+    if (preMemberIsStruct) {
+      uniformDeclaration += `@align(16) `;
+    }
+    preMemberIsStruct = false;
+    uniformDeclaration += "size : i32, ";
+  }
+  if (program.uniforms) {
+    if (preMemberIsStruct) {
+      uniformDeclaration += `@align(16) `;
+    }
+    uniformDeclaration += program.uniforms;
+  }
+  uniformDeclaration += "};";
+  prefixSnippets.push(uniformDeclaration);
+  if (program.atomic) {
+    prefixSnippets.push(`
+    @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
+  `);
+  } else {
+    prefixSnippets.push(`
+    @group(0) @binding(0) var<storage, write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;
+  `);
+  }
+  program.variableNames.forEach((x, i2) => {
+    prefixSnippets.push(`
+    @group(0) @binding(${1 + i2}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i2] : mapToWgslTypes(inputInfo[i2].dtype, program.isVec4)}>;
+      `);
+  });
+  if (uniformDeclaration !== "") {
+    prefixSnippets.push(`
+    @group(0) @binding(${1 + program.variableNames.length}) var<uniform> uniforms: Uniforms;
+    `);
+  }
+  const [coordsSnippet, dispatchLayoutRank] = getOutputCoordsSnippet(outputData.shape, program.dispatchLayout);
+  const sources = [
+    commonSnippet,
+    prefixSnippets.join("\n"),
+    getCoordsFromIndexSnippet(outputData.shape),
+    coordsSnippet,
+    getOutputIndexFromCoordsSnippet(outputData.shape.length)
+  ];
+  if (!program.atomic) {
+    sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));
+  }
+  if (dispatchLayoutRank === outputData.shape.length) {
+    const inputSnippet = inputInfo.map((x, i2) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i2] === "vec4<f32>" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n");
+    sources.push(inputSnippet);
+  }
+  sources.push(program.getUserCode());
+  const source = sources.join("\n");
+  return source;
+}
+var commonSnippet = `
+  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
+  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
+
+  // Checks whether coordinates lie within the bounds of the shape.
+  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
+    return all(coord >= vec2<i32>(0)) && all(coord < shape);
+  }
+  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
+    return all(coord >= vec3<i32>(0)) && all(coord < shape);
+  }
+  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
+    return all(coord >= vec4<i32>(0)) && all(coord < shape);
+  }
+
+  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {
+    return coord;
+  }
+  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
+    return dot(coords, vec2<i32>(shape.y, 1));
+  }
+  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
+    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
+  }
+  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
+    return dot(coords, vec4<i32>(
+        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
+  }
+  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {
+    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);
+    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;
+  }
+  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {
+    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);
+    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;
+  }
+
+  fn idiv(a: i32, b: i32, sign: f32) -> i32 {
+    var res: i32 = a / b;
+    let mod: i32 = a % b;
+    if (sign < 0. && mod != 0) {
+      res = res - 1;
+    }
+    return res;
+  }
+
+  // NaN defination in IEEE 754-1985 is :
+  //   - sign = either 0 or 1.
+  //   - biased exponent = all 1 bits.
+  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
+  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
+  fn isnan(val: f32) -> bool {
+    let floatToUint: u32 = bitcast<u32>(val);
+    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
+  }
+  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
+    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
+  }
+`;
+function getOutputIndexFromCoordsSnippet(outRank) {
+  let snippet = "";
+  switch (outRank) {
+    case 0:
+    case 1:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : i32) -> i32 {
+          return coords;
+        }
+        `;
+      break;
+    case 2:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {
+          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
+        }
+        `;
+      break;
+    case 3:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {
+          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
+        }
+        `;
+      break;
+    case 4:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {
+          return dot(coords, vec4<i32>(
+            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
+        }
+        `;
+      break;
+    case 5:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec5) -> i32 {
+          return coords.x * uniforms.outShapeStrides.x +
+              coords.y * uniforms.outShapeStrides.y +
+              coords.z * uniforms.outShapeStrides.z +
+              coords.w * uniforms.outShapeStrides.w +
+              coords.u;
+        }
+        `;
+      break;
+    case 6:
+      snippet += `
+        fn getOutputIndexFromCoords(coords : vec6) -> i32 {
+          return coords.x * uniforms.outShapeStrides.x +
+              coords.y * uniforms.outShapeStrides.y +
+              coords.z * uniforms.outShapeStrides.z +
+              coords.w * uniforms.outShapeStrides.w +
+              coords.u * uniforms.outShapeStrides.u +
+              coords.v;
+        }
+        `;
+      break;
+    default:
+      util_exports.assert(false, () => `Unsupported ${outRank}D shape`);
+      break;
+  }
+  return snippet;
+}
+function setOutputSnippet(outShape, outBufferType, isVec4) {
+  const outRank = outShape.length;
+  const wgslType = mapToWgslTypes(outBufferType, isVec4);
+  let snippet;
+  if (isVec4) {
+    snippet = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+      result[flatIndex] = ${wgslType}(value);
+    }
+    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
+      result[flatIndex] = ${wgslType}(value);
+    }`;
+  } else {
+    snippet = `fn setOutputAtIndex(flatIndex : i32, value : f32) {
+      result[flatIndex] = ${wgslType}(value);
+    }
+    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
+      result[flatIndex] = ${wgslType}(value);
+    }`;
+  }
+  if (outRank >= 2) {
+    const dims = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, outRank);
+    const type = getCoordsDataType2(outRank);
+    if (isVec4) {
+      snippet += `
+      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(", ")}, value : vec4<f32>) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndex(flatIndex / 4, value);
+      }
+      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(", ")}, value : vec4<i32>) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndexI32(flatIndex / 4, value);
+      }
+    `;
+    } else {
+      snippet += `
+      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(", ")}, value : f32) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndex(flatIndex, value);
+      }
+      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(", ")}, value : i32) {
+        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(", ")}));
+        setOutputAtIndexI32(flatIndex, value);
+      }
+    `;
+    }
+  }
+  return snippet;
+}
+function getInputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {
+  let res = getInputAtCoordsSnippet(inputInfo, isVec4);
+  const inShape = inputInfo.shape;
+  if (inShape.length <= outShape.length) {
+    res += getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout);
+  }
+  return res;
+}
+function getInputAtCoordsSnippet(inputInfo, isVec4) {
+  const texName = inputInfo.name;
+  const rank = inputInfo.shape.length;
+  const type = getCoordsDataType2(rank);
+  const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
+  const dims = ["d0", "d1", "d2", "d3", "d4", "d5"].slice(0, rank);
+  const inputs = dims.map((d) => `${d} : i32`).join(", ");
+  if (rank < 1) {
+    if (isVec4) {
+      return `
+        fn ${funcName}() -> vec4<f32> {
+          return vec4<f32>(${texName}[0]);
+        }
+      `;
+    }
+    return `
+      fn ${funcName}() ->f32 {
+        return f32(${texName}[0]);
+      }
+    `;
+  }
+  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;
+  let rankStr = `${rank}D`;
+  if (rank === 0) {
+    rankStr = "1D";
+  }
+  if (isVec4) {
+    return `
+      fn ${funcName}(${inputs}) -> vec4<f32> {
+        return vec4<f32>(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(",")}),
+          ${shapeStr}) / 4]);
+      }
+      `;
+  }
+  return `
+    fn ${funcName}(${inputs}) -> f32 {
+      return f32(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(",")}),
+        ${shapeStr})]);
+    }
+   `;
+}
+function getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {
+  const texName = inputInfo.name;
+  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+  const funcName = "get" + texFuncSnippet + "ByOutput";
+  const inRank = inputInfo.shape.length;
+  const outRank = outShape.length;
+  const type = getCoordsDataType2(outRank);
+  if (util_exports.arraysEqual(inputInfo.shape, outShape) && isFlatDispatchLayout) {
+    if (isVec4) {
+      return `
+        fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {
+          return vec4<f32>(${texName}[globalIndex]);
+        }
+
+        fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {
+          return vec4<f32>(${texName}[${outRank > 1 ? "getOutputIndexFromCoords(coords)" : "coords"} / 4]);
+        }
+        `;
+    } else {
+      return `
+      fn ${funcName}Index(globalIndex : i32) -> f32 {
+        return f32(${texName}[globalIndex]);
+      }
+
+      fn ${funcName}Coords(coords : ${type}) -> f32 {
+        return f32(${texName}[${outRank > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}]);
+      }
+      `;
+    }
+  }
+  const broadcastDims = backend_util_exports.getBroadcastDims(inputInfo.shape, outShape);
+  const rankDiff = outRank - inRank;
+  let coordsSnippet = "";
+  if (inRank === 0) {
+    if (isVec4) {
+      return `
+      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {
+        return get${texFuncSnippet}();
+      }
+
+      fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {
+        return get${texFuncSnippet}();
+      }
+    `;
+    }
+    return `
+      fn ${funcName}Index(globalIndex : i32) -> f32{
+        return get${texFuncSnippet}();
+      }
+
+      fn ${funcName}Coords(coords : ${type}) -> f32{
+        return get${texFuncSnippet}();
+      }
+    `;
+  } else {
+    if (outRank < 2 && broadcastDims.length >= 1) {
+      coordsSnippet = "coords = 0;";
+    } else {
+      coordsSnippet = broadcastDims.map((d) => `coords.${getCoordsXYZ(d + rankDiff)} = 0;`).join("\n");
+    }
+  }
+  let unpackedCoordsSnippet = "";
+  if (outRank < 2 && inRank > 0) {
+    unpackedCoordsSnippet = "coords";
+  } else {
+    if (outRank > 1) {
+      const coordsType = getCoordsDataType2(inRank);
+      const coordsValues = inputInfo.shape.map((s2, i2) => `coords.${getCoordsXYZ(i2 + rankDiff)}`).join(", ");
+      unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;
+    } else {
+      unpackedCoordsSnippet = "coords";
+    }
+  }
+  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;
+  const rankStr = `${inRank}D`;
+  if (isVec4) {
+    return `
+      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {
+        var coords = getCoordsFromIndex(globalIndex);
+        ${coordsSnippet}
+        return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];
+      }
+
+      fn ${funcName}Coords(coordsIn : ${type}) -> vec4<f32> {
+        var coords = coordsIn;
+        ${coordsSnippet}
+        return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];
+      }
+    `;
+  }
+  return `
+    fn ${funcName}Index(globalIndex : i32) -> f32 {
+      var coords = getCoordsFromIndex(globalIndex);
+      ${coordsSnippet}
+      return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);
+    }
+
+    fn ${funcName}Coords(coordsIn : ${type}) -> f32 {
+      var coords = coordsIn;
+      ${coordsSnippet}
+      return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);
+    }
+  `;
+}
+function getOutputCoordsSnippet(outShape, dispatchLayout) {
+  const { x, y = [], z = [] } = dispatchLayout;
+  const outRank = outShape.length;
+  if (x.length === outRank) {
+    const dtype2 = getCoordsDataType2(outRank);
+    const snippet2 = `fn getOutputCoords() -> ${dtype2}{
+      let globalIndex = getGlobalIndex();
+      return getCoordsFromIndex(globalIndex);
+    }
+    `;
+    return [snippet2, outRank];
+  }
+  let gatherDimensionsStr = "";
+  const dims = [x, y, z];
+  let rank = 0;
+  for (let i2 = 0; i2 < dims.length; i2++) {
+    const arr = dims[i2];
+    if (arr.length === 0) {
+      continue;
+    }
+    rank += arr.length;
+    if (arr.length === 1) {
+      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i2}]);`;
+    } else {
+      const strides = symbolicallyComputeStrides2(arr, "uniforms.outShape");
+      gatherDimensionsStr += `var index${i2} = i32(globalId[${i2}]);`;
+      for (let j = 0; j < strides.length; j++) {
+        gatherDimensionsStr += `let d${arr[j]} = index${i2} / ${strides[j]};`;
+        if (j === strides.length - 1) {
+          gatherDimensionsStr += `let d${arr[j + 1]} = index${i2} - d${arr[j]} * ${strides[j]};`;
+        } else {
+          gatherDimensionsStr += `index${i2} = index${i2} - d${arr[j]} * ${strides[j]};`;
+        }
+      }
+    }
+  }
+  const dimensions = [];
+  for (let i2 = 0; i2 < rank; i2++) {
+    dimensions.push(`d${i2}`);
+  }
+  const dtype = getCoordsDataType2(rank);
+  let snippet = `fn getOutputCoords() -> ${dtype} {
+    ${gatherDimensionsStr}
+  `;
+  if (dimensions.length === 0) {
+    snippet += `return ${dtype}(0); }`;
+  } else {
+    snippet += `return ${dtype}(${dimensions.join(",")}); }`;
+  }
+  return [snippet, rank];
+}
+function getCoordsFromIndexSnippet(shape) {
+  const rank = shape.length;
+  if (rank <= 1) {
+    return `fn getCoordsFromIndex(index : i32) -> i32 { return index; }`;
+  }
+  const strides = util_exports.computeStrides(shape);
+  const dtype = getCoordsDataType2(rank);
+  const coords3 = [];
+  for (let i2 = 0; i2 < rank; i2++) {
+    coords3.push(`d${i2}`);
+  }
+  if (strides.length === 1) {
+    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
+      return vec2<i32>(d0, d1);
+    }`;
+  }
+  let snippet;
+  snippet = "var index2 = index;" + strides.map((_, i2) => {
+    const line1 = `let ${coords3[i2]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
+    const line2 = i2 === strides.length - 1 ? `let ${coords3[i2 + 1]} = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}` : `index2 = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
+    return `${line1}; ${line2};`;
+  }).join("");
+  return `
+    fn getCoordsFromIndex(index : i32) -> ${dtype} {
+      ${snippet}
+      return ${dtype}(${coords3.join(",")});
+    }
+  `;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_util.js
+var webgpu_util_exports = {};
+__export(webgpu_util_exports, {
+  ArrayBufferToTypedArray: () => ArrayBufferToTypedArray,
+  GPUBytesPerElement: () => GPUBytesPerElement,
+  computeDispatch: () => computeDispatch,
+  computeWorkGroupSizeForConv2d: () => computeWorkGroupSizeForConv2d,
+  computeWorkGroupSizeForMatMul: () => computeWorkGroupSizeForMatMul,
+  computeWorkPerThreadForConv2d: () => computeWorkPerThreadForConv2d,
+  flatDispatchLayout: () => flatDispatchLayout,
+  isWebGPUSupported: () => isWebGPUSupported,
+  tilesFitEvenlyIntoShape: () => tilesFitEvenlyIntoShape
+});
+var arrayProduct = (arr) => {
+  let product = 1;
+  for (let i2 = 0; i2 < arr.length; i2++) {
+    product *= arr[i2];
+  }
+  return product;
+};
+function tilesFitEvenlyIntoShape(tileSize, shape) {
+  if (tileSize.length !== shape.length) {
+    throw new Error(`Cannot compute whether rank ${tileSize.length} tiles fit evenly into rank ${shape.length} shape - ranks must match.`);
+  }
+  return shape.every((dim, dimIdx) => dim % tileSize[dimIdx] === 0);
+}
+function computeDispatch(layout, outputShape, workGroupSize = [1, 1, 1], elementsPerThread = [1, 1, 1]) {
+  const [dispatchX, dispatchY, dispatchZ] = [
+    Math.ceil(arrayProduct(layout.x.map((d) => outputShape[d])) / (workGroupSize[0] * elementsPerThread[0])),
+    layout.y ? Math.ceil(arrayProduct(layout.y.map((d) => outputShape[d])) / (workGroupSize[1] * elementsPerThread[1])) : 1,
+    layout.z ? Math.ceil(arrayProduct(layout.z.map((d) => outputShape[d])) / (workGroupSize[2] * elementsPerThread[2])) : 1
+  ];
+  return [dispatchX, dispatchY, dispatchZ];
+}
+function computeWorkGroupSizeForConv2d(layout, outputShape) {
+  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));
+  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));
+  if (dim0 <= 4) {
+    return [4, 16, 1];
+  }
+  if (dim1 <= 4) {
+    return [16, 4, 1];
+  }
+  return [16, 16, 1];
+}
+function computeWorkGroupSizeForMatMul(dimAOuter, dimInner, dimBOuter) {
+  if (dimAOuter === 1) {
+    return [32, 1, 1];
+  } else if (dimBOuter === 1) {
+    return [1, 32, 1];
+  }
+  return [8, 8, 1];
+}
+function computeWorkPerThreadForConv2d(layout, outputShape) {
+  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));
+  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));
+  if (dim0 <= 4) {
+    return [1, 2, 1];
+  }
+  if (dim1 <= 4) {
+    return [2, 1, 1];
+  }
+  return [2, 2, 1];
+}
+function flatDispatchLayout(shape) {
+  return { x: shape.map((d, i2) => i2) };
+}
+function GPUBytesPerElement(dtype) {
+  if (dtype === "float32" || dtype === "int32" || dtype === "bool" || dtype === "string") {
+    return 4;
+  } else if (dtype === "complex64") {
+    return 8;
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+function ArrayBufferToTypedArray(data, dtype) {
+  if (dtype === "float32") {
+    return new Float32Array(data);
+  } else if (dtype === "int32") {
+    return new Int32Array(data);
+  } else if (dtype === "bool" || dtype === "string") {
+    return Uint8Array.from(new Int32Array(data));
+  } else {
+    throw new Error(`Unknown dtype ${dtype}`);
+  }
+}
+function isWebGPUSupported() {
+  return (typeof window !== "undefined" || typeof WorkerGlobalScope !== "undefined") && !!navigator.gpu;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_packed_vec4_webgpu.js
+function makeMatMulPackedVec4Source(workPerThread, tileAOuter, tileBOuter, tileInner, innerElementSize = 4) {
+  util_exports.assert((tileInner % 4 === 0 || tileInner % 3 === 0) && workPerThread[0] === 4 && (innerElementSize === 3 || innerElementSize === 4), () => `tileInner must be divisible by 4|3. ColPerThread must be 4.
+           innerElementSize must be 3|4.`);
+  return `
+  var<workgroup> mm_Asub : array<array<vec${innerElementSize}<f32>, ${tileInner / innerElementSize}>, ${tileAOuter}>;
+  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${tileBOuter / workPerThread[0]}>, ${tileInner}>;
+
+  let RowPerThread = ${workPerThread[1]};
+  let ColPerThread = ${workPerThread[0]};
+  let InnerElementSize = ${innerElementSize};
+  let TileInner = ${tileInner};
+
+  ${getMainHeaderString()}
+
+    let tileRow = ${tileAOuter === 1 ? "0" : "i32(localId.y) * RowPerThread"};
+    let tileCol = i32(localId.x);
+
+    let globalRow = ${tileAOuter === 1 ? "0" : "i32(globalId.y) * RowPerThread"};
+    let globalCol = i32(globalId.x);
+    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
+
+    var acc: array<vec4<f32>, RowPerThread>;
+    var BCached : array<vec4<f32>, 4>;
+
+    // Loop over shared dimension.
+    var globalColA = tileCol;
+    let RowPerThreadB = TileInner / i32(workGroupSizeY);
+    let tileRowB = i32(localId.y) * RowPerThreadB;
+    for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileCol;
+            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
+        }
+        globalColA = globalColA + TileInner / InnerElementSize;
+
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol;
+            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {
+            BCached[0] = mm_Bsub[k * InnerElementSize][tileCol];
+            BCached[1] = mm_Bsub[k * InnerElementSize + 1][tileCol];
+            BCached[2] = mm_Bsub[k * InnerElementSize + 2][tileCol];
+            ${innerElementSize === 3 ? "" : "BCached[3] = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
+
+            for (var i = 0; i < RowPerThread; i = i + 1) {
+                let ACached = mm_Asub[tileRow + i][k];
+                acc[i] = BCached[0] * ACached.x + acc[i];
+                acc[i] = BCached[1] * ACached.y + acc[i];
+                acc[i] = BCached[2] * ACached.z + acc[i];
+                ${innerElementSize === 3 ? "" : "acc[i] = BCached[3] * ACached.w + acc[i];"}
+            }
+        }
+
+        workgroupBarrier();
+    }
+
+    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
+        mm_write(globalRow + innerRow,
+                 globalCol,
+                 acc[innerRow], globalId);
+    }
+  }`;
+}
+var MatMulPackedVec4Program = class {
+  constructor(aShape, outputShape, rowPerThread, batchAEqualOne, batchBEqualOne, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [8, 8, 1];
+    this.isVec4 = true;
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [2], y: [1], z: [0] };
+    if (outputShape[1] === 1) {
+      this.elementsPerThread = [4, 1, 1];
+    } else {
+      this.elementsPerThread = [4, 4, 1];
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    const addBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.tileAOuter = outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1];
+    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];
+    this.tileInner = this.tileBOuter;
+    this.aShape = aShape;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = batchAEqualOne;
+    this.batchBEqualOne = batchBEqualOne;
+    [this.fitA, this.fitB] = this.getShapeFit();
+    this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getShapeFit() {
+    const dimInner = this.aShape[2];
+    const dimBOuter = this.outputShape[2];
+    const bShape = [this.outputShape[0], dimInner, dimBOuter];
+    const tileSizeA = [this.tileAOuter, this.tileInner];
+    const tileSizeB = [this.tileInner, this.tileBOuter];
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)),
+      tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1))
+    ];
+  }
+  getUserCode() {
+    const sampleA = this.fitA ? `return A[batch * batchASize + row * uniforms.dimInner / 4 + col]` : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+            return A[batch * batchASize + row * uniforms.dimInner / 4 + col];
+        }
+        return vec4<f32>(0.0)`;
+    const sampleB = this.fitB ? `return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]` : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
+        }
+        return vec4<f32>(0.0)`;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${activationOp}
+                }`;
+      } else {
+        activationSnippet = `
+            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
+              ${activationOp}
+            }`;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchAEqualOne ? `
+          let batchASize = 0;
+          let batch = 0;
+        ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;
+          let batch = i32(globalId.z);
+        `}
+
+        ${sampleA};
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {
+        ${this.batchBEqualOne ? `
+          let batchBSize = 0;
+          let batch = 0;
+          ` : `
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;
+          let batch = i32(globalId.z);
+       `}
+        ${sampleB};
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
+        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
+        {
+          var value = valueIn;
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col * 4);
+          ${addBiasSnippet}
+          ${applyActivationSnippet}
+          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
+        }
+      }
+      ${makeMatMulPackedVec4Source(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_packed_webgpu.js
+function makeMatMulPackedSource(workPerThread, workGroupSize) {
+  const tileAOuter = workGroupSize[1] * workPerThread[1];
+  const tileBOuter = workGroupSize[0] * workPerThread[0];
+  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+  return `
+    var<workgroup> mm_Asub : array<array<f32, ${tileInner}>, ${tileAOuter}>;
+    var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;
+    ${getMainHeaderString()}
+      let tileRow = i32(localId.y) * ${workPerThread[1]};
+      let tileCol = i32(localId.x) * ${workPerThread[0]};
+
+      let globalRow = i32(globalId.y) * ${workPerThread[1]};
+      let globalCol = i32(globalId.x) * ${workPerThread[0]};
+
+      let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;
+
+      var acc : array<array<f32, ${workPerThread[0]}>, ${workPerThread[1]}>;
+      var ACached : f32;
+      var BCached : array<f32, ${workPerThread[0]}>;
+
+      // Without this initialization strange values show up in acc.
+      for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+        for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+          acc[innerRow][innerCol] = 0.0;
+        }
+      }
+
+      let ColPerThreadA = ${tileInner} / ${workGroupSize[0]};
+      let tileColA = i32(localId.x) * ColPerThreadA;
+      let RowPerThreadB = ${tileInner} / ${workGroupSize[1]};
+      let tileRowB = i32(localId.y) * RowPerThreadB;
+
+      // Loop over shared dimension.
+      for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
+            let inputRow = tileRow + innerRow;
+            let inputCol = tileColA + innerCol;
+
+            mm_Asub[inputRow][inputCol] = mm_readA(
+                globalRow + innerRow,
+                t * ${tileInner} + inputCol, globalId);
+          }
+        }
+        // Load one tile of B into local memory.
+        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
+          for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+            let inputRow = tileRowB + innerRow;
+            let inputCol = tileCol + innerCol;
+
+            mm_Bsub[inputRow][inputCol] = mm_readB(
+              t * ${tileInner} + inputRow,
+              globalCol + innerCol, globalId);
+          }
+        }
+
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < ${tileInner}; k = k + 1) {
+          for (var inner = 0; inner < ${workPerThread[0]}; inner = inner + 1) {
+            BCached[inner] = mm_Bsub[k][tileCol + inner];
+          }
+
+          for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+            ACached = mm_Asub[tileRow + innerRow][k];
+            for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
+            }
+          }
+        }
+
+        workgroupBarrier();
+      }
+
+      for (var innerRow = 0; innerRow < ${workPerThread[1]}; innerRow = innerRow + 1) {
+        for (var innerCol = 0; innerCol < ${workPerThread[0]}; innerCol = innerCol + 1) {
+
+          if ((globalCol + innerCol) < uniforms.dimBOuter &&
+              (globalRow + innerRow) < uniforms.dimAOuter) {
+            mm_write(globalRow + innerRow,
+                     globalCol + innerCol,
+                     acc[innerRow][innerCol], globalId);
+          }
+        }
+      }
+    }
+  `;
+}
+function makeMatMulVectorSource(workGroupSize) {
+  return `
+    let TileSize = ${workGroupSize[0] * 4};
+    var<workgroup> mm_Asub : array<vec4<f32>, ${workGroupSize[0]}>;
+
+    ${getMainHeaderString()}
+      let tileCol = i32(localId.x);
+      let globalCol = i32(globalId.x);
+      let globalRow = i32(globalId.y);
+
+      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
+
+      // Without this initialization strange values show up in acc.
+      var acc = 0.0;
+
+      // Loop over shared dimension.
+      for (var t = 0; t < numTiles; t = t + 1) {
+        // Load one tile of A into local memory.
+        let colA = t * TileSize + tileCol * 4;
+        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
+                                mm_readA(globalRow, colA + 1, globalId),
+                                mm_readA(globalRow, colA + 2, globalId),
+                                mm_readA(globalRow, colA + 3, globalId));
+        workgroupBarrier();
+
+        // Compute acc values for a single thread.
+        for (var k = 0; k < TileSize / 4; k = k + 1) {
+          let rowB = t * TileSize + k * 4;
+          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
+                              mm_readB(rowB + 1, globalCol, globalId),
+                              mm_readB(rowB + 2, globalCol, globalId),
+                              mm_readB(rowB + 3, globalCol, globalId));
+
+          let ACached = mm_Asub[k];
+          acc = acc + dot(ACached, BCached);
+        }
+
+        workgroupBarrier();
+      }
+
+      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
+        mm_write(globalRow, globalCol, acc, globalId);
+      }
+    }
+  `;
+}
+var MatMulPackedProgram2 = class {
+  constructor(aShape, outputShape, workPerThread, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [16, 16, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [2], y: [1], z: [0] };
+    const dimInner = transposeA ? aShape[1] : aShape[2];
+    this.workGroupSize = computeWorkGroupSizeForMatMul(outputShape[1], dimInner, outputShape[2]);
+    if (outputShape[1] === 1 || outputShape[2] === 1) {
+      workPerThread = 1;
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]);
+    if (util_exports.arraysEqual(this.dispatch, [1, 1, 1])) {
+      workPerThread = 1;
+      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [workPerThread, workPerThread, 1]);
+    }
+    const addBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.workPerThread = workPerThread;
+    this.aShape = aShape;
+    this.transposeA = transposeA;
+    this.transposeB = transposeB;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = batchAEqualOne;
+    this.batchBEqualOne = batchBEqualOne;
+    const dimBOuter = this.outputShape[2];
+    const bShape = this.transposeB ? [this.outputShape[0], dimBOuter, dimInner] : [this.outputShape[0], dimInner, dimBOuter];
+    [this.fitA, this.fitB] = this.getShapeFit(bShape);
+    this.shaderKey = `matMulPacked_${this.workPerThread}_${transposeA}_${transposeB}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getShapeFit(bShape) {
+    const tileAOuter = this.workGroupSize[1] * this.workPerThread;
+    const tileBOuter = this.workGroupSize[0] * this.workPerThread;
+    let tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+    if (this.outputShape[1] === 1) {
+      tileInner *= 4;
+    }
+    util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => `tileInner must be multiple of workgroupsize.x and workgroupsize.y`);
+    const tileSizeA = [tileAOuter, tileInner];
+    const tileSizeB = [tileInner, tileBOuter];
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, this.aShape.slice(1)),
+      tilesFitEvenlyIntoShape(tileSizeB, bShape.slice(1))
+    ];
+  }
+  getUserCode() {
+    let sampleA;
+    if (this.transposeA === false) {
+      sampleA = this.fitA ? `return A[batch * batchASize + row * uniforms.dimInner + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+             return A[batch * batchASize + row * uniforms.dimInner + col];
+           }
+           return 0.0;`;
+    } else {
+      sampleA = this.fitA ? `return A[batch * batchASize + col * uniforms.dimAOuter + row];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+             return A[batch* batchASize + col * uniforms.dimAOuter + row];
+           }
+           return 0.0;`;
+    }
+    let sampleB;
+    if (this.transposeB === false) {
+      sampleB = this.fitB ? `return B[batch * batchBSize + row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + row * uniforms.dimBOuter + col];
+           }
+           return 0.0;`;
+    } else {
+      sampleB = this.fitB ? `return B[batch * batchBSize + col * uniforms.dimInner + row];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+             return B[batch * batchBSize + col * uniforms.dimInner + row];
+           }
+           return 0.0;`;
+    }
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+               let b = getPreluActivationWeightsByOutputCoords(outCoord);
+               ${activationOp}
+            }`;
+      } else {
+        activationSnippet = `
+              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+                ${activationOp}
+              }
+            `;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchAEqualOne ? `
+        let batch = 0;
+        let batchASize = 0;
+        ` : `
+        let batch = i32(globalId.z);
+        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+        `}
+        ${sampleA}
+      }
+
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchBEqualOne ? `
+        let batch = 0;
+        let batchBSize = 0;
+        ` : `
+        let batch = i32(globalId.z);
+        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+        `}
+        ${sampleB}
+      }
+
+      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        var value = valueIn;
+        let batch = i32(globalId.z);
+        let outCoord = vec3<i32>(batch, row, col);
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutputAtCoords(batch, row, col, value);
+      }
+      ${this.outputShape[1] > 1 ? makeMatMulPackedSource([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : makeMatMulVectorSource(this.workGroupSize)}
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_reduce.js
+function makeMatMulReduceSource() {
+  return `
+    var<workgroup> sumValues : array<f32, workGroupSizeX>;
+    ${getMainHeaderString()}
+      let coords = getOutputCoords();
+      let batch = coords[0];
+      let row = coords[1];
+      let col = coords[2];
+      var sum = 0.0;
+      let Length = uniforms.dimInner;
+      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
+        let dataA = mm_readA(batch, row, k);
+        let dataB = mm_readB(batch, k, col);
+        sum = sum + dataA * dataB;
+      }
+      sumValues[localId.x] = sum;
+      workgroupBarrier();
+
+      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
+          currentSize = currentSize / 2u) {
+        if (localId.x < currentSize)
+        {
+          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
+        }
+        workgroupBarrier();
+      }
+
+      if (localId.x == 0u) {
+        sum = sumValues[0] + sumValues[1];
+        mm_write(batch, row, col, sum);
+      }
+    }
+  `;
+}
+var MatMulReduceProgram = class {
+  constructor(outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [256, 1, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [], y: [1, 2], z: [0] };
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    const addBias = bias != null;
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.transposeA = transposeA;
+    this.transposeB = transposeB;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = batchAEqualOne;
+    this.batchBEqualOne = batchBEqualOne;
+    this.shaderKey = `matMulReduce_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getUserCode() {
+    let sampleA;
+    if (this.transposeA === false) {
+      sampleA = `return f32(A[batch * batchASize + row * uniforms.dimInner + col]);`;
+    } else {
+      sampleA = `return f32(A[batch * batchASize + col * uniforms.dimAOuter + row]);`;
+    }
+    let sampleB;
+    if (this.transposeB === false) {
+      sampleB = `return f32(B[batch * batchBSize + row * uniforms.dimBOuter + col]);`;
+    } else {
+      sampleB = `return f32(B[batch * batchBSize + col * uniforms.dimInner + row]);`;
+    }
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+               let b = getPreluActivationWeightsByOutputCoords(outCoord);
+               ${activationOp}
+            }`;
+      } else {
+        activationSnippet = `
+              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+                ${activationOp}
+              }
+            `;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn mm_readA(batchIn: i32, row : i32, col : i32) -> f32 {
+        ${this.batchAEqualOne ? `
+          let batchASize = 0;
+          let batch = 0;
+          ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+          let batch = batchIn;
+          `}
+        ${sampleA}
+      }
+
+      fn mm_readB(batchIn: i32, row : i32, col : i32) -> f32 {
+        ${this.batchBEqualOne ? `
+          let batch = 0;
+          let batchBSize = 0;
+          ` : `
+          let batch = batchIn;
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+          `}
+        ${sampleB}
+      }
+
+      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
+        var value = valueIn;
+        let outCoord = vec3<i32>(batch, row, col);
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        setOutputAtCoords(batch, row, col, value);
+      }
+      ${makeMatMulReduceSource()}
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_small_output_size_webgpu.js
+function makeMatMulSmallOutputSizeSource(workGroupSize) {
+  const tileAOuter = workGroupSize[1] / 2;
+  const tileBOuter = workGroupSize[0];
+  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+  return `
+  var<workgroup> mm_Asub1 : array<array<f32, ${tileInner}>, ${tileAOuter}>;
+  var<workgroup> mm_Bsub1 : array<array<f32, ${tileBOuter}>, ${tileInner}>;
+  var<workgroup> mm_Asub2 : array<array<f32, ${tileInner}>, ${tileAOuter}>;
+  var<workgroup> mm_Bsub2 : array<array<f32, ${tileBOuter}>, ${tileInner}>;
+
+  // If the output size is small for matrix multiplication, avoid to use vec4
+  // and handle some elements per thread to optimally utilize the ALU.
+  // Introduces two shared memory buffers, some logical threads could handle
+  // arithmetic operations and others handle IO operations between barrier api,
+  // makes ALUs and load/store units work simultaneously, could improves
+  // the performance.
+  ${getMainHeaderString()}
+    let tileRow = i32(localId.y);
+    let tileCol = i32(localId.x);
+    let globalRow = i32(globalId.y);
+    let globalCol = i32(globalId.x);
+
+    // uniforms.dimInner should be greater than 0.
+    let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;
+    var acc = 0.0;
+
+    var globalColA = tileCol;
+    var globalRowB = tileRow;
+    for (var t = 0; t < numTiles; t = t + 1) {
+      if (t == 0) {
+        if (tileRow < ${tileAOuter}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub1[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${tileInner};
+          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${tileInner};
+        }
+      } else {
+        if (tileRow < ${tileAOuter}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub1[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${tileInner};
+          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${tileInner};
+        } else {
+          // Compute acc values for a single thread.
+          for (var k = 0; k < ${tileInner}; k = k + 1) {
+            let subRow = tileRow - ${tileAOuter};
+            if (subRow < 0) {
+              continue;
+            }
+            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
+          }
+        }
+      }
+      workgroupBarrier();
+      if (t != 0) {
+        t = t + 1;
+      }
+
+      if (t < numTiles) {
+        if (tileRow < ${tileAOuter}) {
+          // Load one tile of A and B into local memory.
+          // globalRow is always greater than or equal tileRow.
+          mm_Asub2[tileRow][tileCol] =
+              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
+          globalColA = globalColA + ${tileInner};
+          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
+          globalRowB = globalRowB + ${tileInner};
+        } else {
+          // Compute acc values for a single thread.
+          for (var k = 0; k < ${tileInner}; k = k + 1) {
+            let subRow = tileRow - ${tileAOuter};
+            if (subRow < 0) {
+              continue;
+            }
+            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
+          }
+        }
+      }
+      workgroupBarrier();
+    }
+    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${tileAOuter};
+    if (tileRow >= ${tileAOuter} && writeCol >= 0) {
+      mm_write(writeCol, globalCol, acc, globalId);
+    }
+  }
+  `;
+}
+var MatMulSmallOutputSizeProgram = class {
+  constructor(aShape, bShape, outputShape, bias = null, activation2 = null, preluActivationWeights = null) {
+    this.variableNames = ["A", "B"];
+    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [8, 16, 1];
+    util_exports.assert(aShape[1] <= 16 || bShape[2] <= 16, () => "This program can be only used when A width or B Height are small");
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [2], y: [1], z: [0] };
+    this.dispatch = [
+      Math.ceil(outputShape[2] / this.workGroupSize[0]),
+      Math.ceil(outputShape[1] * 2 / this.workGroupSize[1]),
+      outputShape[0]
+    ];
+    const addBias = bias != null;
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    const hasPreluActivationWeights = preluActivationWeights != null;
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.batchAEqualOne = aShape[0] === 1;
+    this.batchBEqualOne = bShape[0] === 1;
+    this.shaderKey = `matMulSmallOutputSize_${this.activation}_${this.batchAEqualOne}_${this.batchBEqualOne}`;
+  }
+  getUserCode() {
+    const sampleA = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+          return A[batch * batchASize + row * uniforms.dimInner + col];
+        }
+        return 0.0;`;
+    const sampleB = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return B[batch * batchBSize + row * uniforms.dimBOuter + col];
+         }
+         return 0.0;`;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+            let b = getPreluActivationWeightsByOutputCoords(outCoord);
+            ${activationOp}
+            }`;
+      } else {
+        activationSnippet = `fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
+            ${activationOp}
+        }`;
+      }
+      applyActivationSnippet = "value = activation(value, outCoord);";
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchAEqualOne ? `
+          let batch = 0;
+          let batchASize = 0;
+          ` : `
+          let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
+          let batch = i32(globalId.z);
+          `}
+        ${sampleA}
+      }
+      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
+        ${this.batchBEqualOne ? `
+          let batch = 0;
+          let batchBSize = 0;
+          ` : `
+          let batch = i32(globalId.z);
+          let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
+          `}
+        ${sampleB}
+      }
+      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
+        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
+          let batch = i32(globalId.z);
+          let outCoord = vec3<i32>(batch, row, col);
+          var value = valueIn;
+          ${addBiasSnippet}
+          ${applyActivationSnippet}
+          setOutputAtCoords(batch, row, col, value);
+        }
+      }
+      ${makeMatMulSmallOutputSizeSource(this.workGroupSize)}
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Reshape.js
+function reshape6(args) {
+  const { inputs, attrs } = args;
+  const { x } = inputs;
+  const { shape } = attrs;
+  const xSize = util_exports.sizeFromShape(x.shape);
+  const $shape = util_exports.inferFromImplicitShape(shape, xSize);
+  const $xSize = util_exports.sizeFromShape($shape);
+  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
+  args.backend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
+}
+var reshapeConfig4 = {
+  kernelName: Reshape,
+  backendName: "webgpu",
+  kernelFunc: reshape6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul_impl.js
+function batchMatMulImpl2({ a: a6, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const aRank = a6.shape.length;
+  const bRank = b.shape.length;
+  const innerShapeA = transposeA ? a6.shape[aRank - 2] : a6.shape[aRank - 1];
+  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
+  const outerShapeA = transposeA ? a6.shape[aRank - 1] : a6.shape[aRank - 2];
+  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
+  const outerDimsA = a6.shape.slice(0, -2);
+  const outerDimsB = b.shape.slice(0, -2);
+  const batchDimA = util_exports.sizeFromShape(outerDimsA);
+  const batchDimB = util_exports.sizeFromShape(outerDimsB);
+  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a6.shape.slice(0, -2), b.shape.slice(0, -2));
+  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
+  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a6.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
+  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];
+  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];
+  const a3d = reshape6({ inputs: { x: a6 }, backend: backend2, attrs: { shape: a3dShape } });
+  const b3d = reshape6({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
+  const intermediates = [a3d, b3d];
+  const batchDim = Math.max(batchDimA, batchDimB);
+  const batchAEqualOne = batchDimA === 1;
+  const batchBEqualOne = batchDimB === 1;
+  const useVec4 = innerShapeA % 4 === 0 && outerShapeB % 4 === 0 && !transposeA && !transposeB;
+  let program;
+  if (outerShapeA * outerShapeB <= 32) {
+    program = new MatMulReduceProgram([batchDim, outerShapeA, outerShapeB], batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);
+  } else if (!transposeA && !transposeB && (outerShapeA <= 16 && (outerShapeB <= 512 || innerShapeB >= 2 * outerShapeB) || outerShapeB <= 16 && (outerShapeA <= 512 || innerShapeA >= 2 * outerShapeA))) {
+    program = new MatMulSmallOutputSizeProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], bias, activation2, preluActivationWeights);
+  } else if (useVec4) {
+    program = new MatMulPackedVec4Program(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), batchAEqualOne, batchBEqualOne, bias, activation2, preluActivationWeights);
+  } else {
+    program = new MatMulPackedProgram2(a3dShape, [batchDim, outerShapeA, outerShapeB], env().get("WEBGPU_MATMUL_WORK_PER_THREAD"), batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);
+  }
+  const inputs = [a3d, b3d];
+  if (bias) {
+    inputs.push(bias);
+  }
+  if (preluActivationWeights) {
+    inputs.push(preluActivationWeights);
+  }
+  const dimensions = [
+    { type: "int32", data: [outerShapeA] },
+    { type: "int32", data: [outerShapeB] },
+    { type: "int32", data: [innerShapeA] }
+  ];
+  if (activation2 === "leakyrelu") {
+    dimensions.push({ type: "float32", data: [leakyreluAlpha] });
+    program.uniforms += " alpha : f32,";
+  }
+  const out = backend2.runWebGPUProgram(program, inputs, a6.dtype, dimensions);
+  const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
+  intermediates.push(out);
+  for (const i2 of intermediates) {
+    backend2.disposeData(i2.dataId);
+  }
+  return outReshaped;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/_FusedMatMul.js
+function _fusedMatMul3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b, bias, preluActivationWeights } = inputs;
+  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;
+  return batchMatMulImpl2({
+    a: a6,
+    b,
+    transposeA,
+    transposeB,
+    backend: backend2,
+    bias,
+    preluActivationWeights,
+    leakyreluAlpha,
+    activation: activation2
+  });
+}
+var _fusedMatMulConfig4 = {
+  kernelName: _FusedMatMul,
+  backendName: "webgpu",
+  kernelFunc: _fusedMatMul3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_complex_webgpu.js
+var BinaryOpComplexProgram2 = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["AReal", "AImag", "BReal", "BImag"];
+    this.workGroupSize = [128, 1, 1];
+    this.size = true;
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `binaryOpComplex_${op2}`;
+    this.op = op2;
+  }
+  getUserCode() {
+    const opStr = getBinaryOpString(this.op, false);
+    const userCode = `
+      fn binaryOpComplex(
+          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
+        ${opStr}
+      }
+
+      ${getMainHeaderAndGlobalIndexString()}
+        if(index < uniforms.size) {
+          let areal = getARealByOutputIndex(index);
+          let aimag = getAImagByOutputIndex(index);
+          let breal = getBRealByOutputIndex(index);
+          let bimag = getBImagByOutputIndex(index);
+          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_shared_webgpu.js
+var BinaryOpSharedProgram = class {
+  constructor(op2, aShape, bShape, useSharedMemoryWithB) {
+    this.variableNames = ["A", "B"];
+    this.size = true;
+    const workGroupSizeX = 256;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.lastDimensionSize = useSharedMemoryWithB ? bShape[0] : aShape[0];
+    if (this.lastDimensionSize < 256) {
+      this.workPerThread = 1;
+    } else if (this.lastDimensionSize < 512) {
+      this.workPerThread = 2;
+    } else {
+      this.workPerThread = 4;
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.useSharedMemoryWithB = useSharedMemoryWithB;
+    this.op = op2;
+    this.shaderKey = `binaryShared_${op2}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;
+  }
+  getUserCode() {
+    const sharedIndexSnippet = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : "0";
+    const accessDataSnippet = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);
+         let b = sharedBuf[${sharedIndexSnippet}];` : `let a = sharedBuf[${sharedIndexSnippet}];
+         let b = getBByOutputCoords(coords);`;
+    const opStr = getBinaryOpString(this.op, false);
+    const userCode = `
+        fn binaryOperation(a : f32, b : f32) -> f32 {
+          ${opStr}
+        }
+        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
+        ${getMainHeaderAndGlobalIndexString()}
+
+          // Fill in the shared memory buffer. Here we need a loop to make sure
+          // that all data in A|B are uploaded when |sharedMemorySize| is larger
+          // than work group size.
+          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
+            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? "B" : "A"}[localIndex]);
+          }
+          workgroupBarrier();
+
+          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+            let flatIndex = index * ${this.workPerThread} + i;
+            if(flatIndex < uniforms.size) {
+              let coords = getCoordsFromIndex(flatIndex);
+
+              ${accessDataSnippet}
+              setOutputAtIndex(flatIndex, binaryOperation(a, b));
+            }
+          }
+        }
+        `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_vec4_webgpu.js
+var BinaryOpVec4Program = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["A", "B"];
+    this.workPerThread = 4;
+    this.isVec4 = true;
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.op = op2;
+    this.shaderKey = `binaryVec4_${op2}`;
+  }
+  getUserCode() {
+    const opStr = getBinaryOpString(this.op, this.isVec4);
+    const userCode = `
+      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
+        ${opStr}
+      }
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          let b = getBByOutputIndex(index);
+          setOutputAtIndex(index, binaryOperation(a, b));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_webgpu.js
+var BinaryOpProgram2 = class {
+  constructor(op2, aShape, bShape) {
+    this.variableNames = ["A", "B"];
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `binary_${op2}`;
+    this.op = op2;
+  }
+  getUserCode() {
+    const opStr = getBinaryOpString(this.op, false);
+    const userCode = `
+      fn binaryOperation(a : f32, b : f32) -> f32 {
+        ${opStr}
+      }
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          let b = getBByOutputIndex(index);
+          setOutputAtIndex(index, binaryOperation(a, b));
+        }
+      }
+      `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_ops.js
+function getBinaryProgram(op2, aShape, bShape) {
+  const useVec4 = util_exports.arraysEqual(aShape, bShape) && util_exports.sizeFromShape(aShape) % 4 === 0;
+  if (useVec4) {
+    return new BinaryOpVec4Program(op2, aShape, bShape);
+  }
+  const useSharedMemoryWithA = aShape.length === 1 && bShape.length > 1 && aShape[0] < 1024;
+  const useSharedMemoryWithB = bShape.length === 1 && aShape.length > 1 && bShape[0] < 1024;
+  if (useSharedMemoryWithA || useSharedMemoryWithB) {
+    return new BinaryOpSharedProgram(op2, aShape, bShape, useSharedMemoryWithB);
+  } else {
+    return new BinaryOpProgram2(op2, aShape, bShape);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Identity.js
+function identity5(args) {
+  const { inputs } = args;
+  const { x } = inputs;
+  args.backend.incRef(x.dataId);
+  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
+}
+var identityConfig4 = {
+  kernelName: Identity,
+  backendName: "webgpu",
+  kernelFunc: identity5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Complex.js
+function complex4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { real: real5, imag: imag5 } = inputs;
+  const complexInfo = backend2.makeTensorInfo(real5.shape, "complex64");
+  const complex5 = backend2.tensorMap.get(complexInfo.dataId);
+  const realTensorInfo = identity5({ inputs: { x: real5 }, backend: backend2 });
+  const imagTensorInfo = identity5({ inputs: { x: imag5 }, backend: backend2 });
+  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };
+  return complexInfo;
+}
+var complexConfig3 = {
+  kernelName: Complex,
+  backendName: "webgpu",
+  kernelFunc: complex4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_webgpu.js
+var UnaryOpProgram2 = class {
+  constructor(outputShape, op2) {
+    this.variableNames = ["A"];
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.op = op2;
+    this.shaderKey = `unary_${op2}`;
+  }
+  getUserCode() {
+    return `
+      fn unaryOperation(a : f32) -> f32 {
+        ${getUnaryOpString(this.op, false)}
+      }
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let a = getAByOutputIndex(index);
+          setOutputAtIndex(index, unaryOperation(a));
+        }
+      }
+      `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/kernel_funcs_utils.js
+function unaryKernelFunc3({ opType, cpuKernelImpl, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const webgpuBackend = backend2;
+    const $dtype = dtype || x.dtype;
+    if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
+      const xData = webgpuBackend.tensorMap.get(x.dataId);
+      const outValues = cpuKernelImpl(xData.values, $dtype);
+      return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues);
+    }
+    const program = new UnaryOpProgram2(x.shape, opType);
+    return webgpuBackend.runWebGPUProgram(program, [x], $dtype);
+  };
+}
+function binaryKernelFunc3({ opSnippet, cpuKernelImpl, supportsComplex = false, dtype }) {
+  return ({ inputs, backend: backend2 }) => {
+    const { a: a6, b } = inputs;
+    const webgpuBackend = backend2;
+    if (supportsComplex && a6.dtype === "complex64") {
+      const aData = webgpuBackend.tensorMap.get(a6.dataId);
+      const bData = webgpuBackend.tensorMap.get(b.dataId);
+      let real5, imag5;
+      if (opSnippet !== BinaryOpType.MUL) {
+        [real5, imag5] = [
+          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],
+          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
+        ].map((complexParts) => {
+          const [aPart, bPart] = complexParts;
+          const aHandle = {
+            dataId: aPart.dataId,
+            dtype: aPart.dtype,
+            shape: a6.shape
+          };
+          const bHandle = {
+            dataId: bPart.dataId,
+            dtype: bPart.dtype,
+            shape: b.shape
+          };
+          const program2 = getBinaryProgram(opSnippet, a6.shape, b.shape);
+          return webgpuBackend.runWebGPUProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
+        });
+      } else {
+        const realProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_REAL, a6.shape, b.shape);
+        const imagProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_IMAG, a6.shape, b.shape);
+        const inputs2 = [
+          {
+            dataId: aData.complexTensorInfos.real.dataId,
+            dtype: aData.complexTensorInfos.real.dtype,
+            shape: a6.shape
+          },
+          {
+            dataId: aData.complexTensorInfos.imag.dataId,
+            dtype: aData.complexTensorInfos.imag.dtype,
+            shape: a6.shape
+          },
+          {
+            dataId: bData.complexTensorInfos.real.dataId,
+            dtype: bData.complexTensorInfos.real.dtype,
+            shape: b.shape
+          },
+          {
+            dataId: bData.complexTensorInfos.imag.dataId,
+            dtype: bData.complexTensorInfos.imag.dtype,
+            shape: b.shape
+          }
+        ];
+        real5 = webgpuBackend.runWebGPUProgram(realProgram, inputs2, "float32");
+        imag5 = webgpuBackend.runWebGPUProgram(imagProgram, inputs2, "float32");
+      }
+      const complexOutput = complex4({ inputs: { real: real5, imag: imag5 }, backend: webgpuBackend });
+      webgpuBackend.disposeData(real5.dataId);
+      webgpuBackend.disposeData(imag5.dataId);
+      return complexOutput;
+    }
+    const $dtype = dtype || upcastType(a6.dtype, b.dtype);
+    if ((a6.dtype === "string" || b.dtype === "string" || webgpuBackend.shouldExecuteOnCPU([a6, b])) && cpuKernelImpl != null) {
+      const aData = webgpuBackend.tensorMap.get(a6.dataId).values;
+      const bData = webgpuBackend.tensorMap.get(b.dataId).values;
+      const decodedAVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aData) : aData;
+      const decodedBVals = a6.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bData) : bData;
+      const [outValues, outShape] = cpuKernelImpl(a6.shape, b.shape, decodedAVals, decodedBVals, $dtype);
+      return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues);
+    }
+    const program = getBinaryProgram(opSnippet, a6.shape, b.shape);
+    return webgpuBackend.runWebGPUProgram(program, [a6, b], $dtype);
+  };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/shared.js
+var { addImpl: addImplCPU2, ceilImpl: ceilImplCPU2, concatImpl: concatImplCPU2, equalImpl: equalImplCPU2, expImpl: expImplCPU2, expm1Impl: expm1ImplCPU2, floorImpl: floorImplCPU2, gatherNdImpl: gatherNdImplCPU2, gatherV2Impl: gatherV2ImplCPU2, greaterEqualImpl: greaterEqualImplCPU2, greaterImpl: greaterImplCPU2, lessEqualImpl: lessEqualImplCPU2, lessImpl: lessImplCPU2, logImpl: logImplCPU2, maxImpl: maxImplCPU2, maximumImpl: maximumImplCPU2, minimumImpl: minimumImplCPU2, multiplyImpl: multiplyImplCPU2, negImpl: negImplCPU2, notEqualImpl: notEqualImplCPU2, prodImpl: prodImplCPU2, rangeImpl: rangeImplCPU2, rsqrtImpl: rsqrtImplCPU2, scatterImpl: scatterImplCPU2, simpleAbsImpl: simpleAbsImplCPU2, sliceImpl: sliceImplCPU2, stridedSliceImpl: stridedSliceImplCPU2, stringNGramsImpl: stringNGramsImplCPU2, subImpl: subImplCPU2, tileImpl: tileImplCPU2, topKImpl: topKImplCPU2, transposeImpl: transposeImplCPU2, uniqueImpl: uniqueImplCPU2 } = shared_exports;
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Abs.js
+var abs4 = unaryKernelFunc3({ opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU2 });
+var absConfig4 = {
+  kernelName: Abs,
+  backendName: "webgpu",
+  kernelFunc: abs4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Add.js
+var addKernelFunc2 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.ADD,
+  cpuKernelImpl: addImplCPU2,
+  supportsComplex: true
+});
+var addConfig4 = {
+  kernelName: Add,
+  backendName: "webgpu",
+  kernelFunc: addKernelFunc2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/addn_packed_webgpu.js
+var AddNPackedProgram2 = class {
+  constructor(shapes) {
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shapes[0];
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.shaderKey = "addN";
+  }
+  getUserCode() {
+    const snippets = [];
+    this.variableNames.forEach((variable2) => {
+      snippets.push(`let v${variable2} = get${variable2}ByOutputCoords(coords);`);
+    });
+    const operation = this.variableNames.map((variable2) => {
+      return `v${variable2}`;
+    }).join(" + ");
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if (flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndex);
+            ${snippets.join("\n        ")}
+            setOutputAtIndex(flatIndex, ${operation});
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AddN.js
+function addN4(args) {
+  const { inputs, backend: backend2 } = args;
+  const tensors = inputs;
+  if (tensors.length === 1) {
+    return identity5({ inputs: { x: tensors[0] }, backend: backend2 });
+  }
+  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));
+  const shapes = tensors.map((t2) => t2.shape);
+  const program = new AddNPackedProgram2(shapes);
+  return backend2.runWebGPUProgram(program, tensors, dtype);
+}
+var addNConfig4 = {
+  kernelName: AddN,
+  backendName: "webgpu",
+  kernelFunc: addN4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/argminmax_webgpu.js
+var ArgMinMaxProgram2 = class {
+  constructor(inputShape, axis, reduceType) {
+    this.workGroupSize = [64, 1, 1];
+    this.variableNames = ["x"];
+    this.uniforms = "infinityValue : f32,";
+    this.size = true;
+    const axes = [axis];
+    backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, inputShape.length);
+    this.op = reduceType === "min" ? "<" : ">";
+    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(inputShape, axes);
+    this.outputShape = outputShape.length === 0 ? [1] : outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);
+    this.inputShape = inputShape;
+    this.shaderKey = `argMinMax${this.op}`;
+  }
+  getUserCode() {
+    const sharedMemorySnippet = `
+      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
+      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
+    `;
+    const getInputShapeLastDim = () => {
+      if (this.inputShape.length === 1) {
+        return "uniforms.xShape";
+      } else {
+        return `uniforms.xShape.${getCoordsXYZ(this.inputShape.length - 1)}`;
+      }
+    };
+    const splitOutputCoords = () => {
+      let snippet = "";
+      if (this.outputShape.length === 1) {
+        if (this.inputShape.length !== 1) {
+          snippet += "outputCoords,";
+        }
+      } else {
+        for (let i2 = 0; i2 < this.outputShape.length; i2++) {
+          snippet += `outputCoords.${getCoordsXYZ(i2)},`;
+        }
+      }
+      return snippet;
+    };
+    const userCode = `
+      fn DIV_CEIL(a : u32, b : u32) -> u32 {
+        return ((a - 1u) / b + 1u);
+      }
+
+      ${sharedMemorySnippet}
+
+      ${getMainHeaderAndGlobalIndexString()}
+        let outputIndex = index / i32(workGroupSizeX);
+        let reduceLength = ${getInputShapeLastDim()};
+
+        var bestIndex = i32(localId.x);
+        var bestValue = uniforms.infinityValue;
+        let outputCoords = getCoordsFromIndex(outputIndex);
+        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;
+            k = k + i32(workGroupSizeX)) {
+          let candidate = getX(${splitOutputCoords()} k);
+          if (!isnan(candidate) && candidate ${this.op} bestValue) {
+            bestValue = candidate;
+            bestIndex = k;
+          }
+        }
+        xBestValues[localId.x] = bestValue;
+        xBestIndices[localId.x] = bestIndex;
+        workgroupBarrier();
+
+        var reduceSize = min(u32(reduceLength), workGroupSizeX);
+        for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+            currentSize = reduceSize / 2u) {
+          let interval = DIV_CEIL(reduceSize, 2u);
+          if (localId.x < currentSize) {
+            let candidate = xBestValues[localId.x + interval];
+            if (candidate ${this.op} bestValue) {
+              bestValue = candidate;
+              xBestValues[localId.x] = bestValue;
+              xBestIndices[localId.x] = xBestIndices[localId.x + interval];
+            }
+          }
+          reduceSize = interval;
+          workgroupBarrier();
+        }
+
+        if (localId.x == 0u && outputIndex < uniforms.size) {
+          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_shared_webgpu.js
+var TransposeSharedProgram = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    this.workGroupSize = [16, 16, 1];
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.dispatchLayout = { x: [0], y: [1] };
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]);
+    this.shaderKey = "transposeShared";
+  }
+  getUserCode() {
+    const userCode = `
+      let TILE_DIM = ${this.workGroupSize[0]};
+      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
+      ${getWorkGroupSizeString()}
+      fn main(@builtin(local_invocation_id) localId : vec3<u32>,
+              @builtin(workgroup_id) workgroupId : vec3<u32>) {
+        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
+        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
+        let width = uniforms.outShape[0];
+        let height = uniforms.outShape[1];
+        if (x < width && y < height) {
+          tile[localId.y][localId.x] = A[y * width + x];
+        }
+        workgroupBarrier();
+
+        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
+        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
+        if (x < height && y < width) {
+          setOutputAtIndex((y * height + x), tile[localId.x]
+            [localId.y]);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_webgpu.js
+var TransposeProgram2 = class {
+  constructor(aShape, newDim) {
+    this.variableNames = ["A"];
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[newDim[i2]];
+    }
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.newDim = newDim;
+    this.shaderKey = `transpose_${newDim}`;
+  }
+  getUserCode() {
+    const dtype = getCoordsDataType2(this.outputShape.length);
+    const switched = getSwitchedCoords2(this.newDim);
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+
+        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if(flatIndex < uniforms.size) {
+            let resRC = getCoordsFromIndex(flatIndex);
+            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(
+              ${dtype}(${switched}), uniforms.aShape)]);
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function getSwitchedCoords2(newDim) {
+  const rank = newDim.length;
+  if (rank > 6) {
+    throw Error(`Transpose for rank ${rank} is not yet supported`);
+  }
+  const switchedCoords = new Array(rank);
+  for (let i2 = 0; i2 < newDim.length; i2++) {
+    switchedCoords[newDim[i2]] = `resRC.${getCoordsXYZ(i2)}`;
+  }
+  return switchedCoords.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transpose.js
+function transpose5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { perm } = attrs;
+  const webgpuBackend = backend2;
+  const xRank = x.shape.length;
+  const newShape = new Array(xRank);
+  for (let i2 = 0; i2 < newShape.length; i2++) {
+    newShape[i2] = x.shape[perm[i2]];
+  }
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xData = webgpuBackend.tensorMap.get(x.dataId);
+    const values = xData.values;
+    const outValues = transposeImplCPU2(values, x.shape, x.dtype, perm, newShape);
+    return backend2.makeTensorInfo(newShape, x.dtype, outValues);
+  }
+  if (x.shape.length === 2 && util_exports.arraysEqual(perm, [1, 0])) {
+    const program2 = new TransposeSharedProgram(x.shape, perm);
+    return webgpuBackend.runWebGPUProgram(program2, [x], x.dtype);
+  }
+  const program = new TransposeProgram2(x.shape, perm);
+  return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);
+}
+var transposeConfig4 = {
+  kernelName: Transpose,
+  backendName: "webgpu",
+  kernelFunc: transpose5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMax.js
+function argMax4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
+  const program = new ArgMinMaxProgram2($x.shape, axes[0], "max");
+  const uniformData = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }];
+  const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData);
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return out;
+}
+var argMaxConfig4 = {
+  kernelName: ArgMax,
+  backendName: "webgpu",
+  kernelFunc: argMax4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMin.js
+function argMin4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis } = attrs;
+  let axes = util_exports.parseAxisParam(axis, x.shape);
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
+  let $x = x;
+  const intermediateTensorInfos = [];
+  if (permutedAxes != null) {
+    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
+    intermediateTensorInfos.push($x);
+    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
+  const program = new ArgMinMaxProgram2($x.shape, axes[0], "min");
+  const uniformData = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }];
+  const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData);
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return out;
+}
+var argMinConfig3 = {
+  kernelName: ArgMin,
+  backendName: "webgpu",
+  kernelFunc: argMin4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool2d_webgpu.js
+var Pool2DProgram2 = class {
+  constructor(convInfo, poolType) {
+    this.variableNames = ["x"];
+    this.uniforms = `stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,`;
+    this.workGroupSize = [128, 1, 1];
+    this.size = true;
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `pool2D_${poolType}`;
+    this.poolType = poolType;
+  }
+  getUserCode() {
+    let updateSnippet = `resultValue = max(value, resultValue);`;
+    if (this.poolType === "avg") {
+      updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`;
+    }
+    let returnValue = `resultValue`;
+    if (this.poolType === "avg") {
+      returnValue = `resultValue / count`;
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+      if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+          let batch = coords[0];
+          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
+          let xRCorner = xRCCorner.x;
+          let xCCorner = xRCCorner.y;
+
+          var resultValue = ${this.poolType === "avg" ? "0.0" : "-1.0 / pow(10.0, -20.0)"};
+          var count = 0.0;
+
+          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
+            let xR = xRCorner + wR;
+
+            if (xR < 0 || xR >= uniforms.convDims.x) {
+              continue;
+            }
+
+            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
+              let xC = xCCorner + wC;
+              if (xC < 0 || xC >= uniforms.convDims.y) {
+                continue;
+              }
+
+              let value = getX(batch, xR, xC, coords[3]);
+              ${updateSnippet}
+            }
+          }
+
+          setOutputAtIndex(index, ${returnValue});
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool_filtersizeone_webgpu.js
+var PoolWithFilterSizeEqualsOneProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x"];
+    this.uniforms = `stride : vec2<i32>,`;
+    this.workGroupSize = [256, 1, 1];
+    this.size = true;
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "poolWithFilterSizeEqualsOne";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let batch = coords[0];
+          let d = coords[3];
+
+          let xRCCorner = coords.yz * uniforms.stride;
+          let xRCorner = xRCCorner.x;
+          let xCCorner = xRCCorner.y;
+
+          let value = getX(batch, xRCorner, xCCorner, d);
+          setOutputAtIndex(index, value);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AvgPool.js
+function avgPool5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+    return identity5({ inputs: { x }, backend: backend2 });
+  }
+  let program;
+  const dimensions = [{ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }];
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {
+    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);
+  } else {
+    program = new Pool2DProgram2(convInfo, "avg");
+    dimensions.push({ type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {
+      type: "int32",
+      data: [convInfo.dilationHeight, convInfo.dilationWidth]
+    }, { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }, {
+      type: "int32",
+      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
+    });
+  }
+  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);
+}
+var avgPoolConfig4 = {
+  kernelName: AvgPool,
+  backendName: "webgpu",
+  kernelFunc: avgPool5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul.js
+function batchMatMul4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { a: a6, b } = inputs;
+  const { transposeA, transposeB } = attrs;
+  return batchMatMulImpl2({ a: a6, b, transposeA, transposeB, backend: backend2 });
+}
+var batchMatMulConfig4 = {
+  kernelName: BatchMatMul,
+  backendName: "webgpu",
+  kernelFunc: batchMatMul4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/slice_webgpu.js
+var SliceProgram2 = class {
+  constructor(start, destSize) {
+    this.variableNames = ["source"];
+    this.workPerThread = 1;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = destSize;
+    this.rank = destSize.length;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.start = start;
+    this.uniforms = `start : ${getCoordsDataType2(start.length)}, `;
+    this.shaderKey = "slice";
+  }
+  getUserCode() {
+    const dtype = getCoordsDataType2(this.rank);
+    const sourceCoords = getCoords3(this.rank);
+    let coordSum;
+    if (this.start.length === 1) {
+      coordSum = this.outputShape.map((_, i2) => {
+        return `sourceLoc = uniforms.start + coords;`;
+      });
+    } else {
+      coordSum = this.outputShape.map((_, i2) => {
+        return `sourceLoc.${coords2[i2]} = uniforms.start[${i2}] + coords.${coords2[i2]};`;
+      });
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          var sourceLoc : ${dtype};
+          let coords = getCoordsFromIndex(index);
+          ${coordSum.join("\n")}
+          setOutputAtIndex(index, getSource(${sourceCoords}));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+var coords2 = ["x", "y", "z", "w", "u", "v"];
+function getCoords3(rank) {
+  if (rank === 1) {
+    return "sourceLoc";
+  } else if (rank <= 6) {
+    return coords2.slice(0, rank).map((coord) => `sourceLoc.${coord}`).join(",");
+  } else {
+    throw Error(`Slicing for rank ${rank} is not yet supported`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Slice.js
+function slice5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, size } = attrs;
+  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
+  slice_util_exports.assertParamsValid(x, $begin, $size);
+  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") {
+    const xBufferInfo = backend2.tensorMap.get(x.dataId);
+    const outValues = sliceImplCPU2(xBufferInfo.values, $begin, $size, x.shape, x.dtype);
+    return backend2.makeTensorInfo($size, x.dtype, outValues);
+  }
+  if (util_exports.sizeFromShape($size) === 0) {
+    return backend2.makeTensorInfo($size, x.dtype, []);
+  }
+  const program = new SliceProgram2($begin, $size);
+  const uniformData = [{ type: "int32", data: $begin }];
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+}
+var sliceConfig4 = {
+  kernelName: Slice,
+  backendName: "webgpu",
+  kernelFunc: slice5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchToSpaceND.js
+var batchToSpaceND5 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, crops } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);
+  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
+  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);
+  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
+  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
+  const toDispose = [];
+  const reshapedIntermediate = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
+  const transposedIntermediate = transpose5({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });
+  const reshapedIntermediate2 = reshape6({
+    inputs: { x: transposedIntermediate },
+    backend: backend2,
+    attrs: { shape: reshapedPermuted }
+  });
+  const sliced = slice5({
+    inputs: { x: reshapedIntermediate2 },
+    backend: backend2,
+    attrs: { begin: sliceBeginCoords, size: sliceSize }
+  });
+  toDispose.push(reshapedIntermediate);
+  toDispose.push(transposedIntermediate);
+  toDispose.push(reshapedIntermediate2);
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return sliced;
+};
+var batchToSpaceNDConfig4 = {
+  kernelName: BatchToSpaceND,
+  backendName: "webgpu",
+  kernelFunc: batchToSpaceND5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NotEqual.js
+var notEqual4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.NOT_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: notEqualImplCPU2
+});
+var notEqualConfig4 = {
+  kernelName: NotEqual,
+  backendName: "webgpu",
+  kernelFunc: notEqual4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Real.js
+function real4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.tensorMap.get(input2.dataId);
+  return identity5({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });
+}
+var realConfig3 = {
+  kernelName: Real,
+  backendName: "webgpu",
+  kernelFunc: real4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/int.js
+function int2(input2, backend2) {
+  const program = new UnaryOpProgram2(input2.shape, UnaryOpType.TO_INT);
+  const output = backend2.runWebGPUProgram(program, [input2], "int32");
+  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cast.js
+function cast6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { dtype } = attrs;
+  if (dtype === "complex64") {
+    if (x.dtype === "complex64") {
+      return identity5({ inputs: { x }, backend: backend2 });
+    }
+    const zerosTensor = zeros(x.shape);
+    const floatX = cast6({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
+    const result = complex4({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });
+    zerosTensor.dispose();
+    backend2.disposeData(floatX.dataId);
+    return result;
+  }
+  if (x.dtype === "complex64") {
+    const realPart = real4({ inputs: { input: x }, backend: backend2 });
+    const result = cast6({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
+    backend2.disposeData(realPart.dataId);
+    return result;
+  }
+  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
+    const result = identity5({ inputs: { x }, backend: backend2 });
+    return { dataId: result.dataId, shape: result.shape, dtype };
+  }
+  if (dtype === "int32") {
+    return int2(x, backend2);
+  }
+  if (dtype === "bool") {
+    const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1));
+    const binaryInputs = { a: x, b: zerosTensorInfo };
+    const result = notEqual4({ inputs: binaryInputs, backend: backend2 });
+    backend2.disposeData(zerosTensorInfo.dataId);
+    return result;
+  }
+  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
+}
+var castConfig4 = {
+  kernelName: Cast,
+  backendName: "webgpu",
+  kernelFunc: cast6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Ceil.js
+var ceil4 = unaryKernelFunc3({ opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU2 });
+var ceilConfig4 = {
+  kernelName: Ceil,
+  backendName: "webgpu",
+  kernelFunc: ceil4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_vec4_webgpu.js
+var ClipVec4Program = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.uniforms = "minVal : f32, maxVal : f32,";
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.isVec4 = true;
+    this.size = true;
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.shaderKey = "clipVec4";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if(index < uniforms.size) {
+          let value = getAByOutputIndex(index);
+          var clampedValue : vec4<f32>;
+          for (var i = 0; i < 4; i = i + 1) {
+            if (isnan(value[i])) {
+              clampedValue[i] = value[i];
+            } else {
+              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
+            }
+          }
+
+          setOutputAtIndex(index, clampedValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_webgpu.js
+var ClipProgram2 = class {
+  constructor(outputShape) {
+    this.variableNames = ["A"];
+    this.uniforms = "minVal : f32, maxVal : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "clip";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if(index < uniforms.size) {
+          let value = getAByOutputIndex(index);
+          if (isnan(value)) {
+            setOutputAtIndex(index, value);
+            return;
+          }
+          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ClipByValue.js
+function clipByValue4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { clipValueMin, clipValueMax } = attrs;
+  let program;
+  const uniformData = [
+    { type: "float32", data: [clipValueMin] },
+    { type: "float32", data: [clipValueMax] }
+  ];
+  if (util_exports.sizeFromShape(x.shape) % 4 === 0) {
+    program = new ClipVec4Program(x.shape);
+  } else {
+    program = new ClipProgram2(x.shape);
+  }
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+}
+var clipByValueConfig4 = {
+  kernelName: ClipByValue,
+  backendName: "webgpu",
+  kernelFunc: clipByValue4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/concat_webgpu.js
+var ConcatProgram2 = class {
+  constructor(shapes) {
+    this.uniforms = "";
+    this.workPerThread = 4;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
+    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    this.offsetLength = shapes.length - 1;
+    for (let i2 = 0; i2 < this.offsetLength; i2++) {
+      this.uniforms += `offset${i2} : i32,`;
+    }
+    this.shaderKey = "concat";
+  }
+  getUserCode() {
+    const snippets = [];
+    if (this.offsetLength > 0) {
+      snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);
+      for (let i2 = 1; i2 < this.offsetLength; i2++) {
+        snippets.push(`else if (yC < uniforms.offset${[i2]}){ setOutputAtCoords(coords.x, coords.y, getT${i2}(yR, yC - uniforms.offset${i2 - 1})); }`);
+      }
+      const lastIndex = this.offsetLength;
+      const lastShiftIndex = this.offsetLength - 1;
+      snippets.push(`else { setOutputAtCoords(coords.x, coords.y, getT${lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`);
+    } else {
+      snippets.push(`setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));`);
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
+          let flatIndex = index * ${this.workPerThread} + i;
+          if(flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndex);
+            let yR = coords.x;
+            let yC = coords.y;
+
+            ${snippets.join("\n        ")}
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Imag.js
+function imag4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { input: input2 } = inputs;
+  const inputData = backend2.tensorMap.get(input2.dataId);
+  return identity5({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });
+}
+var imagConfig3 = {
+  kernelName: Imag,
+  backendName: "webgpu",
+  kernelFunc: imag4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat_impl.js
+function concatImpl3(inputs, axis, backend2) {
+  const dtype = inputs[0].dtype;
+  if (dtype === "complex64") {
+    const reals = inputs.map((t2) => real4({ inputs: { input: t2 }, backend: backend2 }));
+    const imags = inputs.map((t2) => imag4({ inputs: { input: t2 }, backend: backend2 }));
+    const realConcated = concatImpl3(reals, axis, backend2);
+    const imagConcated = concatImpl3(imags, axis, backend2);
+    const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
+    reals.forEach((r2) => backend2.disposeData(r2.dataId));
+    imags.forEach((i2) => backend2.disposeData(i2.dataId));
+    backend2.disposeData(realConcated.dataId);
+    backend2.disposeData(imagConcated.dataId);
+    return result;
+  }
+  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);
+  if (dtype === "string") {
+    runOnCpu = true;
+  }
+  if (runOnCpu) {
+    const tensors2D2 = inputs.map((t2) => {
+      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+      const shape = [-1, innerSize];
+      return reshape6({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    });
+    const inputsValShapes = tensors2D2.map((t2) => {
+      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    });
+    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);
+    const simplyConcat = tensors2D2[0].shape[0] === 1;
+    const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat);
+    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
+    tensors2D2.forEach((t2) => backend2.disposeData(t2.dataId));
+    return outInfo;
+  }
+  const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2);
+  const shapes = tensors2D.map((t2) => t2.shape);
+  const program = new ConcatProgram2(shapes);
+  const uniformData = [];
+  const offsets = new Array(shapes.length - 1);
+  if (offsets.length > 0) {
+    offsets[0] = shapes[0][1];
+    uniformData.push({ type: "int32", data: [offsets[0]] });
+    for (let i2 = 1; i2 < offsets.length; i2++) {
+      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
+      uniformData.push({ type: "int32", data: [offsets[i2]] });
+    }
+  }
+  const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);
+  tensors2D.forEach((r2) => backend2.disposeData(r2.dataId));
+  const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
+  backend2.disposeData(res.dataId);
+  return reshapedResult;
+}
+function computeTensors2D2(inputs, axis, backend2) {
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const tensors2D = inputs.map((t2) => reshape6({
+    inputs: { x: t2 },
+    backend: backend2,
+    attrs: {
+      shape: [
+        util_exports.sizeFromShape(t2.shape.slice(0, axis)),
+        util_exports.sizeFromShape(t2.shape.slice(axis))
+      ]
+    }
+  }));
+  return { tensors2D, outShape };
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat.js
+function concat5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  if (util_exports.sizeFromShape(outShape) === 0) {
+    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
+  }
+  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  if ($inputs.length === 1) {
+    return identity5({ inputs: { x: $inputs[0] }, backend: backend2 });
+  }
+  const shapes = $inputs.map((t2) => t2.shape);
+  backend_util_exports.assertParamsConsistent(shapes, $axis);
+  return concatImpl3($inputs, $axis, backend2);
+}
+var concatConfig4 = {
+  kernelName: Concat,
+  backendName: "webgpu",
+  kernelFunc: concat5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv2d_mm_vec4_webgpu.js
+var Conv2DMMVec4Program = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.workGroupSize = [8, 8, 1];
+    this.isVec4 = true;
+    this.outputShape = convInfo.outShape;
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };
+    if (this.outputShape[1] === 1) {
+      this.elementsPerThread = [4, 1, 1];
+    } else {
+      this.elementsPerThread = [4, 4, 1];
+    }
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    this.innerElementSize = this.convInfo.inChannels % 4 === 0 ? 4 : 3;
+    if (this.innerElementSize === 3) {
+      this.variableTypes = ["f32", "vec4<f32>"];
+    } else {
+      this.variableTypes = ["vec4<f32>", "vec4<f32>"];
+    }
+    if (this.addBias) {
+      this.variableNames.push("bias");
+      this.variableTypes.push("vec4<f32>");
+    }
+    if (this.hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+      this.variableTypes.push("vec4<f32>");
+    }
+    this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1];
+    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];
+    this.tileInner = this.workGroupSize[0] * this.innerElementSize;
+    [this.fitA, this.fitB] = this.getShapeFit();
+    this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}_${this.innerElementSize}`;
+  }
+  getShapeFit() {
+    const tileSizeA = [this.tileAOuter, this.tileInner];
+    const tileSizeB = [this.tileInner, this.tileBOuter];
+    const dimAOuter = this.outputShape[1] * this.outputShape[2];
+    const dimBOuter = this.outputShape[3];
+    const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]),
+      tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter])
+    ];
+  }
+  getUserCode() {
+    const matMulSource = makeMatMulPackedVec4Source(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner, this.innerElementSize);
+    const readASnippet = `let outRow = r / uniforms.outShape[2];
+        let outCol = r % uniforms.outShape[2];
+        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
+        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
+        let inChCoord = c % uniforms.xShape[3];
+        let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+        let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+
+        var resData = vec${this.innerElementSize}<f32>(0.0);
+        // The bounds checking is always needed since we use it to pad zero for
+        // the 'same' padding type.
+        if (xRow >= 0 && xRow < uniforms.xShape[1] && xCol >= 0 && xCol < uniforms.xShape[2]) {
+          var coord = vec4<i32>(
+            batch,
+            xRow,
+            xCol,
+            inChCoord);
+          let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);
+          ${this.innerElementSize === 3 ? "resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);" : "resData = x[xIndex / 4];"}
+        }
+        return resData;`;
+    const sampleA = this.fitA ? `${readASnippet}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
+          ${readASnippet}
+         }
+         return vec${this.innerElementSize}<f32>(0.0);
+        `;
+    const sampleB = this.fitB ? `return W[row * uniforms.dimBOuter / 4 + col];` : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return W[row * uniforms.dimBOuter / 4 + col];
+         }
+         return vec4<f32>(0.0);
+        `;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${activationOp}
+        }`;
+      } else {
+        activationSnippet = `
+        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          ${activationOp}
+        }`;
+      }
+      applyActivationSnippet = `value = activation(value, outCoord);`;
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+        ${activationSnippet}
+        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec${this.innerElementSize}<f32> {
+          let r = row;
+          let c = col * ${this.innerElementSize};
+          var batch = i32(globalId.z);
+          ${sampleA}
+        }
+
+        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
+          ${sampleB}
+        }
+
+        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
+          var batch = i32(globalId.z);
+          var value = valueInput;
+          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
+          {
+            let outCoord = vec4<i32>(
+              batch,
+              row / uniforms.outShape[2],
+              row % uniforms.outShape[2],
+              col * 4);
+            ${addBiasSnippet}
+            ${applyActivationSnippet}
+            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
+              value);
+          }
+        }
+        ${matMulSource}
+      `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv2d_mm_webgpu.js
+var Conv2DMMProgram = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;
+    this.outputShape = convInfo.outShape;
+    this.isChannelsLast = convInfo.dataFormat === "channelsLast";
+    this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [1], y: [2, 3], z: [0] };
+    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape);
+    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivationWeights) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivationWeights = hasPreluActivationWeights;
+    [this.fitA, this.fitB] = this.getShapeFit();
+    this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}_${this.isChannelsLast}`;
+  }
+  getShapeFit() {
+    const tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];
+    const tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];
+    const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;
+    util_exports.assert(tileInner % this.workGroupSize[0] === 0 && tileInner % this.workGroupSize[1] === 0, () => "tileInner must be multiple of workgroupsize.x and workgroupsize.y");
+    const tileSizeA = [tileAOuter, tileInner];
+    const tileSizeB = [tileInner, tileBOuter];
+    const dimAOuter = this.convInfo.outHeight * this.convInfo.outWidth;
+    const dimBOuter = this.convInfo.outChannels;
+    const dimInner = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
+    return [
+      tilesFitEvenlyIntoShape(tileSizeA, [dimAOuter, dimInner]),
+      tilesFitEvenlyIntoShape(tileSizeB, [dimInner, dimBOuter])
+    ];
+  }
+  getUserCode() {
+    const coordASnippet = this.isChannelsLast ? `
+    let coord = vec4<i32>(batch, xRow, xCol, col % inChannels);
+    ` : `
+    let coord = vec4<i32>(batch, col % inChannels, xRow, xCol);
+    `;
+    const coordResSnippet = this.isChannelsLast ? `
+    let outCoord = vec4<i32>(
+      batch,
+      row / outWidth,
+      row % outWidth,
+      col);
+    ` : `
+    let outCoord = vec4<i32>(
+      batch,
+      col,
+      row / outWidth,
+      row % outWidth);
+    `;
+    const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);
+    const readASnippet = `
+    let inChannels = uniforms.wShape[2];
+    let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
+    let outRow = row / outWidth;
+    let outCol = row % outWidth;
+
+    let WRow = col / (uniforms.filterDims[1] * inChannels);
+    let WCol = col / inChannels % uniforms.filterDims[1];
+    let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
+    let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
+    ${coordASnippet}
+    // The bounds checking is always needed since we use it to pad zero for the
+    // 'same' padding type.
+    if(coordsInBounds4D(coord, uniforms.xShape)) {
+      return x[getIndexFromCoords4D(coord, uniforms.xShape)];
+    }
+    return 0.0;`;
+    const sampleA = this.fitA ? `${readASnippet}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+      ${readASnippet}
+    }
+    return 0.0;
+    `;
+    const sampleB = this.fitB ? `return W[row * uniforms.dimBOuter + col];` : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
+           return W[row * uniforms.dimBOuter + col];
+	 }
+	 return 0.0;
+	 `;
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivationWeights) {
+        activationSnippet = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
+                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
+                  ${activationOp}
+                }`;
+      } else {
+        activationSnippet = `
+                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+                    ${activationOp}
+                  }
+                `;
+      }
+      applyActivationSnippet = `value = activation(value, outCoord);`;
+    }
+    const addBiasSnippet = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
+    const userCode = `
+    ${activationSnippet}
+    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      var batch = i32(globalId.z);
+      ${sampleA}
+    }
+
+    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      ${sampleB}
+    }
+
+    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      var batch = i32(globalId.z);
+      var value = valueInput;
+      let outWidth = ${this.isChannelsLast ? "uniforms.outShape[2]" : "uniforms.outShape[3]"};
+      ${coordResSnippet}
+      ${addBiasSnippet}
+      ${applyActivationSnippet}
+      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+    }
+    ${matMulSource}
+  `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D_impl.js
+function conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  const transposeA = isChannelsLast ? false : true;
+  const transposeB = false;
+  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === "VALID";
+  let xReshaped;
+  let filterReshaped;
+  if (sameSize) {
+    const sharedDim = convInfo.inHeight * convInfo.inWidth * convInfo.inChannels;
+    xReshaped = reshape6({
+      inputs: { x },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.batchSize, sharedDim] }
+    });
+    filterReshaped = reshape6({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, sharedDim, convInfo.outChannels] }
+    });
+  } else {
+    xReshaped = reshape6({
+      inputs: { x },
+      backend: backend2,
+      attrs: {
+        shape: isChannelsLast ? [
+          convInfo.batchSize,
+          convInfo.inHeight * convInfo.inWidth,
+          convInfo.inChannels
+        ] : [
+          convInfo.batchSize,
+          convInfo.inChannels,
+          convInfo.inHeight * convInfo.inWidth
+        ]
+      }
+    });
+    filterReshaped = reshape6({
+      inputs: { x: filter },
+      backend: backend2,
+      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
+    });
+  }
+  const result = batchMatMulImpl2({
+    a: isChannelsLast ? xReshaped : filterReshaped,
+    b: isChannelsLast ? filterReshaped : xReshaped,
+    transposeA,
+    transposeB,
+    backend: backend2,
+    bias,
+    activation: activation2,
+    preluActivationWeights,
+    leakyreluAlpha
+  });
+  const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });
+  backend2.disposeData(xReshaped.dataId);
+  backend2.disposeData(filterReshaped.dataId);
+  backend2.disposeData(result.dataId);
+  return out;
+}
+function conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  const isChannelsLast = convInfo.dataFormat === "channelsLast";
+  let program;
+  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === "VALID";
+  if (sameSize || convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
+    return conv2dByMatMul2({
+      x,
+      filter,
+      convInfo,
+      backend: backend2,
+      bias,
+      activation: activation2,
+      preluActivationWeights,
+      leakyreluAlpha
+    });
+  }
+  const useVec4 = (convInfo.inChannels % 4 === 0 || convInfo.inChannels % 3 === 0) && convInfo.outChannels % 4 === 0 && isChannelsLast;
+  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];
+  const dimensions = [
+    { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] },
+    { type: "int32", data: [...padInfo] },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] }
+  ];
+  if (useVec4) {
+    program = new Conv2DMMVec4Program(convInfo, hasBias, activation2, hasPreluActivationWeights);
+  } else {
+    program = new Conv2DMMProgram(convInfo, hasBias, activation2, hasPreluActivationWeights);
+  }
+  const dimAOuter = convInfo.outHeight * convInfo.outWidth;
+  const dimBOuter = convInfo.outChannels;
+  const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inChannels;
+  dimensions.push({ type: "int32", data: [dimAOuter] }, { type: "int32", data: [dimBOuter] }, { type: "int32", data: [dimInner] });
+  const inputVar = [x, filter];
+  if (hasBias) {
+    inputVar.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    inputVar.push(preluActivationWeights);
+  }
+  if (activation2 === "leakyrelu") {
+    dimensions.push({ type: "float32", data: [leakyreluAlpha] });
+    program.uniforms += " alpha : f32,";
+  }
+  return backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D.js
+function conv2d6(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  return conv2DImpl({ x, filter, convInfo, backend: backend2 });
+}
+var conv2DConfig4 = {
+  kernelName: Conv2D,
+  backendName: "webgpu",
+  kernelFunc: conv2d6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_mm_webgpu.js
+var Conv2DDerInputMMProgram = class {
+  constructor(convInfo) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,";
+    this.outputShape = convInfo.inShape;
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };
+    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape);
+    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);
+    this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
+  }
+  getUserCode() {
+    const matMulSource = makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);
+    const readASnippet = `
+    let outRow = row / uniforms.outShape[2];
+    let outCol = row % uniforms.outShape[2];
+
+    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
+    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
+    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
+    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
+    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
+      return 0.0;
+    }
+    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
+      return 0.0;
+    }
+    let coord = vec4<i32>(
+        batch,
+        i32(xR),
+        i32(xC),
+        col % uniforms.outBackprop[3]);
+    return x[getIndexFromCoords4D(coord, uniforms.xShape)];`;
+    const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
+      ${readASnippet}
+    }
+    return 0.0;`;
+    const userCode = `
+    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      var batch = i32(globalId.z);
+      ${sampleA}
+    }
+
+    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
+      let coordX = uniforms.filterDims.x - 1 -
+          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
+      let coordY = uniforms.filterDims.y - 1 -
+          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
+      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
+          coordX >= 0 && coordY >= 0) {
+        let coord = vec4<i32>(coordX, coordY, col,
+            row % uniforms.outBackprop[3]);
+        return W[getIndexFromCoords4D(coord, uniforms.wShape)];
+      }
+      return 0.0;
+    }
+
+    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
+      var batch = i32(globalId.z);
+      var value = valueInput;
+      let outCoord = vec4<i32>(
+          batch,
+          row / uniforms.outShape[2],
+          row % uniforms.outShape[2],
+          col);
+      result[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
+    }
+
+    ${matMulSource}
+  `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_webgpu.js
+var Conv2DDerInputProgram2 = class {
+  constructor(convInfo) {
+    this.variableNames = ["dy", "W"];
+    this.uniforms = "filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = convInfo.inShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.isChannelsLast = convInfo.dataFormat === "channelsLast";
+    this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;
+  }
+  getUserCode() {
+    const rowDim = this.isChannelsLast ? 1 : 2;
+    const colDim = this.isChannelsLast ? 2 : 3;
+    const channelDim = this.isChannelsLast ? 3 : 1;
+    return `
+    ${getMainHeaderAndGlobalIndexString()} {
+      if(index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+        let batch = coords[0];
+        let d1 = coords[${channelDim}];
+
+        let dyCorner = vec2<i32>(coords[${rowDim}]), coords[${colDim}]) - uniforms.pads;
+        let dyRCorner = dyCorner.x;
+        let dyCCorner = dyCorner.y;
+
+        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
+        // ? = to be determined. : = across all values in that axis.
+        var dotProd = 0.0;
+        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
+          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
+          let wRPerm = uniforms.filterDims.x - 1 - wR;
+          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
+              wRPerm < 0) {
+            continue;
+          }
+          let idyR = dyR;
+
+          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
+            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
+            let wCPerm = uniforms.filterDims.y - 1 - wC;
+            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
+                fract(dyC) > 0.0 || wCPerm < 0) {
+              continue;
+            }
+            let idyC = dyC;
+
+            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
+              if (${this.isChannelsLast}) {
+                let xValue = getDy(batch, idyR, idyC, d2);
+                let wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd = dotProd + xValue * wValue;
+              } else {
+                let xValue = getDy(batch, d2, idyR, idyC);
+                let wValue = getW(wRPerm, wCPerm, d1, d2);
+                dotProd = dotProd + xValue * wValue;
+              }
+
+            }
+          }
+        }
+        setOutputAtIndex(index, dotProd);
+      }
+    }
+  `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2DBackpropInput.js
+function conv2DBackpropInput5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { dy, filter } = inputs;
+  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);
+  const dimensions = [
+    { type: "int32", data: [convInfo.filterHeight, convInfo.filterWidth] },
+    {
+      type: "int32",
+      data: [
+        convInfo.filterHeight - 1 - convInfo.padInfo.top,
+        convInfo.filterWidth - 1 - convInfo.padInfo.left
+      ]
+    },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    {
+      type: "int32",
+      data: [
+        convInfo.batchSize,
+        convInfo.outHeight,
+        convInfo.outWidth,
+        convInfo.outChannels
+      ]
+    }
+  ];
+  let program;
+  if (env().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE")) {
+    program = new Conv2DDerInputProgram2(convInfo);
+  } else {
+    program = new Conv2DDerInputMMProgram(convInfo);
+    const dimAOuter = convInfo.inShape[1] * convInfo.inShape[2];
+    const dimBOuter = convInfo.inShape[3];
+    const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels;
+    dimensions.push({ type: "uint32", data: [dimAOuter] }, { type: "uint32", data: [dimBOuter] }, { type: "uint32", data: [dimInner] });
+  }
+  return backend2.runWebGPUProgram(program, [dy, filter], "float32", dimensions);
+}
+var conv2DBackpropInputConfig4 = {
+  kernelName: Conv2DBackpropInput,
+  backendName: "webgpu",
+  kernelFunc: conv2DBackpropInput5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cos.js
+var cos4 = unaryKernelFunc3({ opType: UnaryOpType.COS });
+var cosConfig4 = {
+  kernelName: Cos,
+  backendName: "webgpu",
+  kernelFunc: cos4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cosh.js
+var cosh4 = unaryKernelFunc3({ opType: UnaryOpType.COSH });
+var coshConfig4 = {
+  kernelName: Cosh,
+  backendName: "webgpu",
+  kernelFunc: cosh4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/crop_and_resize_webgpu.js
+var CropAndResizeProgram2 = class {
+  constructor(channnel, boxShape, cropSize, method) {
+    this.variableNames = ["Image", "Boxes", "BoxInd"];
+    this.uniforms = "extrapolationValue : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    const [numBoxes] = boxShape;
+    this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.methodId = method === "bilinear" ? 1 : 0;
+    this.cropHeightBiggerThan1 = this.outputShape[1] > 1;
+    this.cropWidthBiggerThan1 = this.outputShape[2] > 1;
+    this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;
+  }
+  getUserCode() {
+    const [inputHeightFloat, inputWidthFloat] = [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`];
+    const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ? [
+      `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`,
+      "(y2-y1) * height_ratio",
+      `y1*${inputHeightFloat} + f32(y)*(height_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (y1+y2) * ${inputHeightFloat}`
+    ];
+    const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ? [
+      `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`,
+      "(x2-x1) * width_ratio",
+      `x1*${inputWidthFloat} + f32(x)*(width_scale)`
+    ] : [
+      "0.0",
+      "0.0",
+      `0.5 * (x1+x2) * ${inputWidthFloat}`
+    ];
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+      if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+        let height_ratio = f32(${heightRatio});
+        let width_ratio = f32(${widthRatio});
+        let b = coords[0];
+        let y = coords[1];
+        let x = coords[2];
+        let d = coords[3];
+        // get box vals
+        let y1 = getBoxes(b, 0);
+        let x1 = getBoxes(b, 1);
+        let y2 = getBoxes(b, 2);
+        let x2 = getBoxes(b, 3);
+        // get image in batch index
+        let bInd = i32(round(getBoxInd(b)));
+        if(bInd < 0 || bInd >= uniforms.outShape[0]) {
+          return;
+        }
+        let height_scale = ${heightScale};
+        let width_scale = ${widthScale};
+        let in_y = ${inY};
+        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
+          setOutputAtIndex(index, uniforms.extrapolationValue);
+          return;
+        }
+        let in_x = ${inX};
+        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
+          setOutputAtIndex(index, uniforms.extrapolationValue);
+          return;
+        }
+        let sourceFracIndexCR = vec2<f32>(in_x,in_y);
+        if(${this.methodId} == 1) {
+          // Compute the four integer indices.
+          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
+          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
+          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
+          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
+          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
+          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
+          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
+          let top = topLeft + (topRight - topLeft) * fracCR.x;
+          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
+          let newValue = top + (bottom - top) * fracCR.y;
+          setOutputAtIndex(index, newValue);
+        } else {
+          // Compute the coordinators of nearest neighbor point.
+          let sourceNearestCR = vec2<i32>(floor(
+            sourceFracIndexCR + vec2<f32>(0.5,0.5)));
+          let newValue = getImage(
+            bInd, sourceNearestCR.y, sourceNearestCR.x, d);
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/CropAndResize.js
+var cropAndResize5 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, boxes, boxInd } = inputs;
+  const { cropSize, method, extrapolationValue } = attrs;
+  const program = new CropAndResizeProgram2(image2.shape[3], boxes.shape, cropSize, method);
+  const uniformData = [{ type: "float32", data: [extrapolationValue] }];
+  return backend2.runWebGPUProgram(program, [image2, boxes, boxInd], "float32", uniformData);
+};
+var cropAndResizeConfig4 = {
+  kernelName: CropAndResize,
+  backendName: "webgpu",
+  kernelFunc: cropAndResize5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/cum_webgpu.js
+var CumOpType2;
+(function(CumOpType3) {
+  CumOpType3["Prod"] = "*";
+  CumOpType3["Sum"] = "+";
+})(CumOpType2 || (CumOpType2 = {}));
+var CumProgram2 = class {
+  constructor(op2, shape, exclusive, reverse5) {
+    this.variableNames = ["x"];
+    this.uniforms = "index : f32,";
+    this.size = true;
+    const workGroupSizeX = 128;
+    this.workGroupSize = [workGroupSizeX, 1, 1];
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.exclusive = exclusive;
+    this.reverse = reverse5;
+    this.op = op2;
+    this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;
+  }
+  getUserCode() {
+    const rank = this.outputShape.length;
+    const initVal = this.op === CumOpType2.Prod ? "1.0" : "0.0";
+    const val = this.exclusive ? initVal : `getX(${getCoords4(rank, "coords", this.op)})`;
+    const length = this.outputShape[this.outputShape.length - 1];
+    let condition = "";
+    let idxString = "";
+    if (this.exclusive) {
+      condition = this.reverse ? `end != ${length - 1}` : "end != 0";
+      idxString = this.reverse ? "end + 1" : "end - 1";
+    } else {
+      condition = this.reverse ? `end + pow2 < ${length}` : "end >= pow2";
+      idxString = this.reverse ? "end + pow2" : "end - pow2";
+    }
+    return `
+      ${getMainHeaderAndGlobalIndexString()}
+       if (index < uniforms.size) {
+         var coords = getCoordsFromIndex(index);
+
+         let end = ${getFinalCoord2(rank, "coords", this.op)};
+         var val = ${val};
+         let pow2 = i32(pow(2.0, uniforms.index));
+         if (${condition}) {
+           let idx = ${idxString};
+           ${getFinalCoord2(rank, "coords", this.op)} = idx;
+           val ${this.op}= getX(${getCoords4(rank, "coords", this.op)});
+         }
+         setOutputAtIndex(index, val);
+       }
+      }
+    `;
+  }
+};
+function getCoords4(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.x, ${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.x, ${name}.y, ${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
+  } else {
+    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+function getFinalCoord2(rank, name, op2) {
+  if (rank === 1) {
+    return `${name}`;
+  } else if (rank === 2) {
+    return `${name}.y`;
+  } else if (rank === 3) {
+    return `${name}.z`;
+  } else if (rank === 4) {
+    return `${name}.w`;
+  } else {
+    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cum_impl.js
+function cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {
+  const xRank = x.shape.length;
+  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
+  let permutedX = x;
+  if (permutation != null) {
+    permutedX = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
+  }
+  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
+  if (permutedAxis !== xRank - 1) {
+    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);
+  }
+  const size = permutedX.shape[permutedAxis];
+  let result = identity5({ inputs: { x: permutedX }, backend: backend2 });
+  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {
+    const program = new CumProgram2(op2, permutedX.shape, false, reverse5);
+    const prevResult = result;
+    const uniformData = [{ type: "float32", data: [i2] }];
+    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);
+    backend2.disposeData(prevResult.dataId);
+  }
+  if (exclusive) {
+    const program = new CumProgram2(op2, permutedX.shape, exclusive, reverse5);
+    const prevResult = result;
+    const uniformData = [{ type: "float32", data: [0] }];
+    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);
+    backend2.disposeData(prevResult.dataId);
+  }
+  if (permutation != null) {
+    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
+    const reverseTransposedResult = transpose5({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
+    backend2.disposeData(result.dataId);
+    backend2.disposeData(permutedX.dataId);
+    return reverseTransposedResult;
+  }
+  return result;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumprod.js
+function cumprod5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl2(CumOpType2.Prod, x, backend2, axis, exclusive, reverse5);
+}
+var cumprodConfig4 = {
+  kernelName: Cumprod,
+  backendName: "webgpu",
+  kernelFunc: cumprod5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumsum.js
+function cumsum5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, exclusive, reverse: reverse5 } = attrs;
+  return cumImpl2(CumOpType2.Sum, x, backend2, axis, exclusive, reverse5);
+}
+var cumsumConfig4 = {
+  kernelName: Cumsum,
+  backendName: "webgpu",
+  kernelFunc: cumsum5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depth_to_space_webgpu.js
+var DepthToSpaceProgram2 = class {
+  constructor(outputShape, dataFormat) {
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.uniforms = "blockSize : i32,";
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `depthToSpace_${dataFormat}`;
+    this.dataFormat = dataFormat;
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let h = ${this.getHeightCoordString()};
+          let w = ${this.getWidthCoordString()};
+          let d = ${this.getDepthCoordString()};
+
+          let in_h = h / uniforms.blockSize;
+          let offset_h = h % uniforms.blockSize;
+          let in_w = w / uniforms.blockSize;
+          let offset_w = w % uniforms.blockSize;
+          let offset_d = (offset_h * uniforms.blockSize + offset_w) *
+            ${this.getOutputDepthSize()};
+          let in_d = d + offset_d;
+
+          let rlt = ${this.getInputSamplingString()};
+          setOutputAtIndex(index, rlt);
+        }
+      }`;
+    return userCode;
+  }
+  getHeightCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[1]`;
+    } else {
+      return `coords[2]`;
+    }
+  }
+  getWidthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[2]`;
+    } else {
+      return `coords[3]`;
+    }
+  }
+  getDepthCoordString() {
+    if (this.dataFormat === "NHWC") {
+      return `coords[3]`;
+    } else {
+      return `coords[1]`;
+    }
+  }
+  getOutputDepthSize() {
+    if (this.dataFormat === "NHWC") {
+      return `uniforms.outShape[3]`;
+    } else {
+      return `uniforms.outShape[1]`;
+    }
+  }
+  getInputSamplingString() {
+    if (this.dataFormat === "NHWC") {
+      return `getX(b, in_h, in_w, in_d)`;
+    } else {
+      return `getX(b, in_d, in_h, in_w)`;
+    }
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthToSpace.js
+function depthToSpace5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockSize, dataFormat } = attrs;
+  const batchSize = x.shape[0];
+  const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
+  const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
+  const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
+  const outputHeight = inputHeight * blockSize;
+  const outputWidth = inputWidth * blockSize;
+  const outputDepth = inputDepth / (blockSize * blockSize);
+  const outputShape = dataFormat === "NHWC" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];
+  const uniformData = [
+    { type: "int32", data: [blockSize] }
+  ];
+  const program = new DepthToSpaceProgram2(outputShape, dataFormat);
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+}
+var depthToSpaceConfig4 = {
+  kernelName: DepthToSpace,
+  backendName: "webgpu",
+  kernelFunc: depthToSpace5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_3x3_webgpu.js
+var DepthwiseConv2D3x3Program = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = "pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, inDims : vec2<i32>,";
+    this.workGroupSize = [4, 4, 4];
+    this.isVec4 = true;
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = { x: [0, 1], y: [2], z: [3] };
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]);
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivation = hasPreluActivation;
+    this.shaderKey = `depthwise3x3_${activation2}`;
+  }
+  getUserCode() {
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, this.isVec4);
+      if (this.hasPreluActivation) {
+        activationSnippet = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${activationOp}
+        }`;
+      } else {
+        activationSnippet = `
+        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+            ${activationOp}
+          }
+        `;
+      }
+      applyActivationSnippet = `dotProd[i] = activation(dotProd[i], coords);`;
+    }
+    const addBiasSnippet = this.addBias ? "dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      ${getWorkGroupSizeString()}
+      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
+        let batch = 0;
+        let r = i32(globalId.x);
+        let c = i32(globalId.y) * 4;
+        let d2 = i32(globalId.z) * 4;
+        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
+        let d1 = d2;
+        let q = 0;
+
+        let xRCorner = xRCCorner.x;
+        let xCCorner = xRCCorner.y;
+
+        var wVals : array<vec4<f32>, 9>;
+        wVals[0] = getW(0, 0, d1, q);
+        wVals[1] = getW(0, 1, d1, q);
+        wVals[2] = getW(0, 2, d1, q);
+        wVals[3] = getW(1, 0, d1, q);
+        wVals[4] = getW(1, 1, d1, q);
+        wVals[5] = getW(1, 2, d1, q);
+        wVals[6] = getW(2, 0, d1, q);
+        wVals[7] = getW(2, 1, d1, q);
+        wVals[8] = getW(2, 2, d1, q);
+
+        var xVals : array<array<vec4<f32>, 6>, 3>;
+        for (var wR = 0; wR < 3; wR = wR + 1) {
+          let xR = xRCorner + wR * uniforms.dilation[0];
+          for (var wC = 0; wC < 6; wC = wC + 1) {
+            let xC = xCCorner + wC * uniforms.dilation[1];
+            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
+              xVals[wR][wC] = vec4<f32>(0.0);
+            } else {
+              xVals[wR][wC] = getX(batch, xR, xC, d1);
+            }
+          }
+        }
+
+        var dotProd : array<vec4<f32>, 4>;
+        dotProd[0] = vec4<f32>(0.0);
+        dotProd[1] = vec4<f32>(0.0);
+        dotProd[2] = vec4<f32>(0.0);
+        dotProd[3] = vec4<f32>(0.0);
+
+        for (var wR = 0; wR < 3; wR = wR + 1) {
+          for (var wC = 0; wC < 3; wC = wC + 1) {
+            let indexW = wR * 3 + wC;
+            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
+            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
+            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
+            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
+          }
+        }
+
+        for (var i = 0; i < 4; i = i + 1) {
+          let coords = vec4<i32>(batch, r, c + i, d2);
+          if (coordsInBounds4D(coords, uniforms.outShape)) {
+            ${addBiasSnippet}
+            ${applyActivationSnippet}
+            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_webgpu.js
+var DepthwiseConv2DProgram2 = class {
+  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {
+    this.variableNames = ["x", "W"];
+    this.uniforms = `pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>,
+      inDims : vec2<i32>, filterHeight : i32, filterWidth : i32,
+      channelMul : i32,`;
+    this.workGroupSize = [256, 1, 1];
+    this.outputShape = convInfo.outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    util_exports.assert(convInfo.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented");
+    if (addBias) {
+      this.variableNames.push("bias");
+    }
+    if (hasPreluActivation) {
+      this.variableNames.push("preluActivationWeights");
+    }
+    this.convInfo = convInfo;
+    this.addBias = addBias;
+    this.activation = activation2;
+    this.hasPreluActivation = hasPreluActivation;
+    this.shaderKey = `depthwise_${this.activation}`;
+  }
+  getUserCode() {
+    let activationSnippet = "", applyActivationSnippet = "";
+    if (this.activation) {
+      const activationOp = mapActivationToShaderProgram2(this.activation, false);
+      if (this.hasPreluActivation) {
+        activationSnippet = `fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+          let b = getPreluActivationWeightsByOutputCoords(outCoord);
+          ${activationOp}
+        }`;
+      } else {
+        activationSnippet = `
+          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
+            ${activationOp}
+          }
+        `;
+      }
+      applyActivationSnippet = `dotProd = activation(dotProd, coords);`;
+    }
+    const addBiasSnippet = this.addBias ? "dotProd = dotProd + getBiasByOutputCoords(coords);" : "";
+    const userCode = `
+      ${activationSnippet}
+
+      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
+          value : f32) {
+        let coord = vec4<i32>(batch, row, col, chan);
+        if (coordsInBounds4D(coord, uniforms.outShape)) {
+          setOutputAtCoords(batch, row, col, chan, value);
+        }
+      }
+
+      ${getMainHeaderString()}
+        let coords = getOutputCoords();
+        let batch = coords[0];
+        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
+        let d2 = coords[3];
+        let d1 = d2 / uniforms.channelMul;
+        let q = d2 - d1 * uniforms.channelMul;
+
+        let inputRowStart = xRCCorner.x;
+        let inputColStart = xRCCorner.y;
+        let inputRowEnd = inputRowStart + uniforms.filterHeight *
+            uniforms.dilation[0];
+        let inputColEnd = inputColStart + uniforms.filterWidth *
+            uniforms.dilation[1];
+
+        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
+        // ? = to be determined. : = across all values in that axis.
+        var dotProd = 0.0;
+
+        // Extract if checking out of for loop for performance.
+        if (inputRowStart >= 0 && inputColStart >= 0 &&
+          inputRowEnd < uniforms.inDims[0] &&
+              inputColEnd < uniforms.inDims[1]) {
+            // Here using a constant value |this.convInfo.filterHeight| instead
+            // of uniform value is in order to loop unrolling.
+            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
+              let xR = inputRowStart + wR * uniforms.dilation[0];
+
+              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
+                let xC = inputColStart + wC * uniforms.dilation[1];
+
+                let xVal = getX(batch, xR, xC, d1);
+                let wVal = getW(wR, wC, d1, q);
+                dotProd = dotProd + xVal * wVal;
+              }
+            }
+          } else {
+            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
+              let xR = inputRowStart + wR * uniforms.dilation[0];
+
+              if (xR < 0 || xR >= uniforms.inDims[0]) {
+                continue;
+              }
+
+              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
+                let xC = inputColStart + wC * uniforms.dilation[1];
+
+                if (xC < 0 || xC >= uniforms.inDims[1]) {
+                  continue;
+                }
+
+                let xVal = getX(batch, xR, xC, d1);
+                let wVal = getW(wR, wC, d1, q);
+                dotProd = dotProd + xVal * wVal;
+              }
+            }
+          }
+
+        ${addBiasSnippet}
+        ${applyActivationSnippet}
+        writeResult(batch, coords[1], coords[2], d2, dotProd);
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthwiseConv2dNative.js
+function depthwiseConv2dNative3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter } = inputs;
+  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
+  const dimensions = [
+    { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] },
+    { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }
+  ];
+  let program;
+  if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) {
+    program = new DepthwiseConv2D3x3Program(convInfo);
+  } else {
+    program = new DepthwiseConv2DProgram2(convInfo);
+    dimensions.push({ type: "int32", data: [convInfo.filterHeight] }, { type: "int32", data: [convInfo.filterWidth] }, { type: "int32", data: [convInfo.outChannels / convInfo.inChannels] });
+  }
+  return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions);
+}
+var depthwiseConv2dNativeConfig4 = {
+  kernelName: DepthwiseConv2dNative,
+  backendName: "webgpu",
+  kernelFunc: depthwiseConv2dNative3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Multiply.js
+var multiplyKernelFunc = binaryKernelFunc3({
+  opSnippet: BinaryOpType.MUL,
+  cpuKernelImpl: multiplyImplCPU2,
+  supportsComplex: true
+});
+var multiplyConfig4 = {
+  kernelName: Multiply,
+  backendName: "webgpu",
+  kernelFunc: multiplyKernelFunc
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/reduce_webgpu.js
+var ReduceProgram2 = class {
+  constructor(reduceInfo, reduceType) {
+    this.workGroupSize = [64, 1, 1];
+    this.variableNames = ["x"];
+    this.uniforms = "reduceSize : i32,";
+    this.size = true;
+    this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize];
+    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(this.inputShape, [1]);
+    this.outputShape = outputShape.length === 0 ? [1] : outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);
+    this.reduceType = reduceType;
+    this.shaderKey = `reduce_${reduceType}`;
+  }
+  getUserCode() {
+    let reduceOp = ``;
+    let initValue = "0.0";
+    if (this.reduceType === "min" || this.reduceType === "max") {
+      reduceOp = `
+         if (isnan(candidate)) {
+          bestValue = uniforms.NAN;
+         } else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue)
+           {  bestValue = candidate; }`;
+      initValue = "f32(x[offset])";
+    } else if (this.reduceType === "sum" || this.reduceType === "mean") {
+      reduceOp = " bestValue = bestValue + candidate; ";
+    } else if (this.reduceType === "prod") {
+      reduceOp = " bestValue = bestValue * candidate; ";
+      initValue = "1.0";
+    }
+    const outputSnippet = this.reduceType === "mean" ? `setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));` : `setOutputAtIndex(outputIndex, bestValue);`;
+    const sharedMemorySnippet = `
+         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
+       `;
+    const userCode = `
+       fn DIV_CEIL(a : u32, b : u32) -> u32 {
+        return ((a - 1u) / b + 1u);
+       }
+
+       ${sharedMemorySnippet}
+       fn getOffset(outputIndex : i32) -> i32 {
+         let outputCoords = getCoordsFromIndex(outputIndex);
+         let offset = ${this.outputShape.length === 1 ? "outputCoords" : "outputCoords[0]"} * uniforms.reduceSize;
+          return offset;
+       }
+       ${getMainHeaderAndGlobalIndexString()}
+         let outputIndex = index / i32(workGroupSizeX);
+         let offset = getOffset(outputIndex);
+         var bestValue = ${initValue};
+         let Length = uniforms.reduceSize;
+         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
+         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
+             k = k + i32(workGroupSizeX)) {
+           let candidate = f32(x[offset + k]);
+           ${reduceOp}
+         }
+         xBestValues[localId.x] = bestValue;
+         workgroupBarrier();
+
+         var reduceSize = min(u32(Length), workGroupSizeX);
+         for (var currentSize = reduceSize / 2u; reduceSize > 1u;
+             currentSize = reduceSize / 2u) {
+           let interval = DIV_CEIL(reduceSize, 2u);
+           if (localId.x < currentSize) {
+            let candidate = xBestValues[localId.x + interval];
+            ${reduceOp}
+            xBestValues[localId.x] = bestValue;
+           }
+           reduceSize = interval;
+           workgroupBarrier();
+         }
+
+         if (localId.x == 0u && outputIndex < uniforms.size) {
+          ${outputSnippet}
+        }
+       }
+     `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/reduce.js
+function reduce2(x, axis, keepDims, reduceType, backend2) {
+  const xRank = x.shape.length;
+  const toDispose = [];
+  const origAxes = util_exports.parseAxisParam(axis, x.shape);
+  let axes = origAxes;
+  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
+  let input2 = x;
+  if (permutedAxes != null) {
+    input2 = transpose5({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });
+    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
+    toDispose.push(input2);
+  }
+  backend_util_exports.assertAxesAreInnerMostDims(reduceType, axes, xRank);
+  const [reduceOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);
+  let resOutShape = reduceOutShape;
+  if (keepDims) {
+    resOutShape = backend_util_exports.expandShapeToKeepDim(reduceOutShape, origAxes);
+  }
+  let res;
+  if ((reduceType === "max" || reduceType === "prod") && backend2.shouldExecuteOnCPU([input2])) {
+    const xVals = backend2.tensorMap.get(input2.dataId).values;
+    switch (reduceType) {
+      case "max":
+        const outValues = maxImplCPU2(xVals, util_exports.sizeFromShape(reduceShape), resOutShape, x.dtype);
+        res = backend2.makeTensorInfo(resOutShape, x.dtype, outValues);
+        break;
+      case "prod":
+        const { outVals, outShape, outDtype } = prodImplCPU2(input2.shape, input2.dtype, xVals, axes);
+        res = backend2.makeTensorInfo(outShape, outDtype, outVals);
+        break;
+      default:
+        throw new Error(`${reduceType} CPU implementation is not yet supported.`);
+    }
+  } else {
+    const inSize = util_exports.sizeFromShape(reduceShape);
+    const xSize = util_exports.sizeFromShape(input2.shape);
+    const batchSize = xSize / inSize;
+    const reduceInfo = { windowSize: inSize, inSize, batchSize, outSize: 1 };
+    const dtype = reduceType === "mean" ? "float32" : sumOutType(x.dtype);
+    const uniformData = [
+      { type: "int32", data: [inSize] }
+    ];
+    const program = new ReduceProgram2(reduceInfo, reduceType);
+    const reduced = backend2.runWebGPUProgram(program, [input2], dtype, uniformData);
+    toDispose.push(reduced);
+    res = reshape6({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 });
+  }
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return res;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sum.js
+function sum6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return reduce2(x, axis, keepDims, "sum", backend2);
+}
+var sumConfig4 = {
+  kernelName: Sum,
+  backendName: "webgpu",
+  kernelFunc: sum6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Einsum.js
+function einsum4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { equation } = attrs;
+  const tensors = inputs;
+  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
+  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
+  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
+  const nSteps = steps.length;
+  let out = null;
+  let numDimsRemaining = allDims.length;
+  const tensorsToDispose = [];
+  for (let i2 = 0; i2 < nSteps; ++i2) {
+    for (const idTerm of steps[i2]) {
+      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
+      let x;
+      if (backend_util_exports.isIdentityPermutation(perm)) {
+        x = tensors[idTerm];
+      } else {
+        x = transpose5({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
+        tensorsToDispose.push(x);
+      }
+      const targetShape = x.shape.slice();
+      for (let k = 0; k < dimsToExpand.length; ++k) {
+        targetShape.splice(dimsToExpand[k], 0, 1);
+      }
+      if (!util_exports.arraysEqual(x.shape, targetShape)) {
+        x = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
+        tensorsToDispose.push(x);
+      }
+      if (out === null) {
+        out = x;
+      } else {
+        out = multiplyKernelFunc({ inputs: { a: x, b: out }, backend: backend2 });
+        tensorsToDispose.push(out);
+      }
+    }
+    if (i2 < nSteps - 1) {
+      if (path[i2] >= 0) {
+        out = sum6({
+          inputs: { x: out },
+          backend: backend2,
+          attrs: {
+            axis: path[i2] - (allDims.length - numDimsRemaining),
+            keepDims: false
+          }
+        });
+        tensorsToDispose.push(out);
+      }
+      numDimsRemaining--;
+    }
+  }
+  for (const tensorInfo of tensorsToDispose) {
+    if (tensorInfo === out) {
+      continue;
+    }
+    backend2.disposeData(tensorInfo.dataId);
+  }
+  return out;
+}
+var einsumConfig3 = {
+  kernelName: Einsum,
+  backendName: "webgpu",
+  kernelFunc: einsum4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Elu.js
+var elu6 = unaryKernelFunc3({ opType: UnaryOpType.ELU });
+var eluConfig4 = {
+  kernelName: Elu,
+  backendName: "webgpu",
+  kernelFunc: elu6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Equal.js
+var equal4 = binaryKernelFunc3({ opSnippet: BinaryOpType.EQUAL, dtype: "bool", cpuKernelImpl: equalImplCPU2 });
+var equalConfig4 = {
+  kernelName: Equal,
+  backendName: "webgpu",
+  kernelFunc: equal4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Exp.js
+var exp4 = unaryKernelFunc3({
+  opType: UnaryOpType.EXP,
+  cpuKernelImpl: expImplCPU2,
+  dtype: "float32"
+});
+var expConfig4 = {
+  kernelName: Exp,
+  backendName: "webgpu",
+  kernelFunc: exp4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ExpandDims.js
+function expandDims6(args) {
+  const { inputs, attrs, backend: backend2 } = args;
+  const { dim } = attrs;
+  const { input: input2 } = inputs;
+  const inputRank = input2.shape.length;
+  const newShape = input2.shape.slice();
+  let $dim = dim;
+  if (dim < 0) {
+    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
+    $dim = inputRank + dim + 1;
+  }
+  newShape.splice($dim, 0, 1);
+  return reshape6({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
+}
+var expandDimsConfig4 = {
+  kernelName: ExpandDims,
+  backendName: "webgpu",
+  kernelFunc: expandDims6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Expm1.js
+var expm14 = unaryKernelFunc3({ opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU2 });
+var expm1Config3 = {
+  kernelName: Expm1,
+  backendName: "webgpu",
+  kernelFunc: expm14
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/fill_webgpu.js
+var FillProgram2 = class {
+  constructor(shape) {
+    this.variableNames = [];
+    this.outputShape = [];
+    this.uniforms = "value : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "fill";
+  }
+  getUserCode() {
+    const userCode = `
+    ${getMainHeaderAndGlobalIndexString()}
+      if (index < uniforms.size) {
+        setOutputAtIndex(index, uniforms.value);
+      }
+    }
+  `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Fill.js
+function fill5(args) {
+  const { backend: backend2, attrs } = args;
+  const { shape, value } = attrs;
+  let { dtype } = attrs;
+  dtype = dtype || util_exports.inferDtype(value);
+  if (dtype === "string") {
+    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));
+    values.fill(value);
+    return backend2.makeTensorInfo(shape, dtype, values);
+  } else {
+    const program = new FillProgram2(shape);
+    const uniformData = [{ type: "float32", data: [value] }];
+    return backend2.runWebGPUProgram(program, [], dtype, uniformData);
+  }
+}
+var fillConfig4 = {
+  kernelName: Fill,
+  backendName: "webgpu",
+  kernelFunc: fill5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flip_left_right_webgpu.js
+var FlipLeftRightProgram2 = class {
+  constructor(imageShape) {
+    this.outputShape = [];
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = imageShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "flipLeftRight";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let coordX = uniforms.xShape[2] - coords[2] - 1;
+          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
+          setOutputAtIndex(index, outputValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FlipLeftRight.js
+var flipLeftRightConfig4 = {
+  kernelName: FlipLeftRight,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const webgpuBackend = backend2;
+    const program = new FlipLeftRightProgram2(image2.shape);
+    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype);
+    return output;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Floor.js
+var floor4 = unaryKernelFunc3({ opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU2 });
+var floorConfig4 = {
+  kernelName: Floor,
+  backendName: "webgpu",
+  kernelFunc: floor4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FloorDiv.js
+var floorDiv4 = binaryKernelFunc3({ opSnippet: BinaryOpType.INT_DIV, dtype: "int32" });
+var floorDivConfig4 = {
+  kernelName: FloorDiv,
+  backendName: "webgpu",
+  kernelFunc: floorDiv4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/from_pixels_webgpu.js
+var FromPixelsProgram2 = class {
+  constructor(outputShape, useImport = false) {
+    this.outputShape = [0];
+    this.variableNames = [];
+    this.workGroupSize = [256, 1, 1];
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.useImport = useImport;
+    this.shaderKey = `fromPixels_${this.useImport}`;
+  }
+  getUserCode() {
+    const textureLoad = this.useImport ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
+    const textureType = this.useImport ? "texture_external" : "texture_2d<f32>";
+    return `
+      @binding(1) @group(0) var src: ${textureType};
+
+      ${getMainHeaderAndGlobalIndexString()}
+        let flatIndexBase = index * uniforms.numChannels;
+        for (var i = 0; i < uniforms.numChannels; i = i + 1) {
+          let flatIndex = flatIndexBase + i;
+          if (flatIndex < uniforms.size) {
+            let coords = getCoordsFromIndex(flatIndexBase);
+            let values = ${textureLoad};
+            result[flatIndex] = i32(floor(255.0 * values[i]));
+          }
+        }
+      }
+  `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FromPixels.js
+var fromPixelsConfig2 = {
+  kernelName: FromPixels,
+  backendName: "webgpu",
+  kernelFunc: fromPixels3
+};
+var fromPixels2DContext3;
+function fromPixels3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  let { pixels } = inputs;
+  const { numChannels } = attrs;
+  if (pixels == null) {
+    throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
+  }
+  const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
+  const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
+  const isCanvas = typeof HTMLCanvasElement !== "undefined" && pixels instanceof HTMLCanvasElement || typeof OffscreenCanvas !== "undefined" && pixels instanceof OffscreenCanvas;
+  const isImageBitmap = typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap;
+  const [width, height] = isVideo ? [
+    pixels.videoWidth,
+    pixels.videoHeight
+  ] : [pixels.width, pixels.height];
+  const outShape = [height, width, numChannels];
+  if (env().getBool("WEBGPU_USE_IMPORT")) {
+    if (isVideo) {
+      return fromPixelsExternalImage({
+        externalImage: pixels,
+        backend: backend2,
+        attrs,
+        outShape,
+        useImport: true
+      });
+    }
+  }
+  if (isVideo || isImage) {
+    if (fromPixels2DContext3 == null) {
+      fromPixels2DContext3 = document.createElement("canvas").getContext("2d");
+    }
+    fromPixels2DContext3.canvas.width = width;
+    fromPixels2DContext3.canvas.height = height;
+    fromPixels2DContext3.drawImage(pixels, 0, 0, width, height);
+    pixels = fromPixels2DContext3.canvas;
+  }
+  if (isImageBitmap || isCanvas || isVideo || isImage) {
+    return fromPixelsExternalImage({
+      externalImage: pixels,
+      backend: backend2,
+      attrs,
+      outShape,
+      useImport: false
+    });
+  }
+  const imageData = pixels.data;
+  let pixelArray = imageData;
+  if (numChannels != null && numChannels !== 4) {
+    pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);
+    const dataLength = imageData.length;
+    let j = 0;
+    for (let i2 = 0; i2 < dataLength; i2++) {
+      if (i2 % 4 < numChannels) {
+        pixelArray[j++] = imageData[i2];
+      }
+    }
+  }
+  const output = backend2.makeTensorInfo(outShape, "int32");
+  const info = backend2.tensorMap.get(output.dataId);
+  info.values = new Int32Array(pixelArray);
+  backend2.maybeReleaseBuffer(output.dataId);
+  backend2.uploadToGPU(output.dataId);
+  return output;
+}
+function fromPixelsExternalImage(args) {
+  const { externalImage, backend: backend2, attrs, outShape, useImport } = args;
+  const { numChannels } = attrs;
+  const size = util_exports.sizeFromShape(outShape);
+  const strides = util_exports.computeStrides(outShape);
+  const program = new FromPixelsProgram2(outShape, useImport);
+  const uniformData = [
+    { type: "uint32", data: [size] },
+    { type: "uint32", data: [numChannels] },
+    { type: "uint32", data: [...strides] },
+    { type: "uint32", data: [...program.dispatch] }
+  ];
+  const output = backend2.runFromPixelsProgram(program, outShape, uniformData, useImport, externalImage);
+  return output;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/batchnorm_webgpu.js
+var BatchNormProgram2 = class {
+  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape) {
+    this.uniforms = "varianceEpsilon : f32,";
+    this.workGroupSize = [128, 1, 1];
+    this.size = true;
+    this.variableNames = ["x", "mean", "variance"];
+    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
+    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
+    this.outputShape = xShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    if (offsetShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
+      this.variableNames.push("offset");
+    }
+    if (scaleShape != null) {
+      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
+      this.variableNames.push("scale");
+    }
+    this.offsetShape = offsetShape;
+    this.scaleShape = scaleShape;
+    this.shaderKey = "batchNorm";
+  }
+  getUserCode() {
+    let offsetSnippet = "0.0";
+    if (this.offsetShape != null) {
+      offsetSnippet = "getOffsetByOutputIndex(index)";
+    }
+    let scaleSnippet = "1.0";
+    if (this.scaleShape != null) {
+      scaleSnippet = "getScaleByOutputIndex(index)";
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size)
+        {
+          let xValue = getXByOutputIndex(index);
+          let meanValue = getMeanByOutputIndex(index);
+          let varianValue = getVarianceByOutputIndex(index);
+          let offsetValue = ${offsetSnippet};
+          let scaleValue = ${scaleSnippet};
+          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
+          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
+        }
+      }
+  `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedBatchNorm.js
+var fusedBatchNormConfig2 = {
+  kernelName: FusedBatchNorm,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x, scale: scale2, offset, mean: mean5, variance } = inputs;
+    const { varianceEpsilon } = attrs;
+    const webGPUBackend = backend2;
+    const batchNormInputs = [x, mean5, variance];
+    let offsetShape = null;
+    if (offset != null) {
+      offsetShape = offset.shape;
+      batchNormInputs.push(offset);
+    }
+    let scaleShape = null;
+    if (scale2 != null) {
+      scaleShape = scale2.shape;
+      batchNormInputs.push(scale2);
+    }
+    const program = new BatchNormProgram2(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape);
+    const uniformData = [{ type: "float32", data: [varianceEpsilon] }];
+    return webGPUBackend.runWebGPUProgram(program, batchNormInputs, x.dtype, uniformData);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedConv2D.js
+function fusedConv2d3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  if (dataFormat !== "NHWC") {
+    throw new Error(`WebGPU backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);
+  }
+  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);
+  return conv2DImpl({
+    x,
+    filter,
+    convInfo,
+    backend: backend2,
+    bias,
+    preluActivationWeights,
+    leakyreluAlpha,
+    activation: activation2
+  });
+}
+var fusedConv2DConfig4 = {
+  kernelName: FusedConv2D,
+  backendName: "webgpu",
+  kernelFunc: fusedConv2d3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedDepthwiseConv2D.js
+function fusedDepthwiseConv2D3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, filter, bias, preluActivationWeights } = inputs;
+  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;
+  let $dilations = dilations;
+  if ($dilations == null) {
+    $dilations = [1, 1];
+  }
+  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
+  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);
+  const programInputs = [x, filter];
+  const hasBias = bias != null;
+  const hasPreluActivationWeights = preluActivationWeights != null;
+  if (hasBias) {
+    programInputs.push(bias);
+  }
+  if (hasPreluActivationWeights) {
+    programInputs.push(preluActivationWeights);
+  }
+  const dimensions = [
+    { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] },
+    { type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] },
+    { type: "int32", data: [convInfo.dilationHeight, convInfo.dilationWidth] },
+    { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }
+  ];
+  let program;
+  if (convInfo.batchSize === 1 && convInfo.inHeight === convInfo.outHeight && convInfo.inWidth === convInfo.outWidth && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.filterHeight === convInfo.filterWidth && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.filterHeight === 3 && convInfo.inChannels % 4 === 0) {
+    program = new DepthwiseConv2D3x3Program(convInfo, hasBias, activation2, hasPreluActivationWeights);
+  } else {
+    program = new DepthwiseConv2DProgram2(convInfo, hasBias, activation2, hasPreluActivationWeights);
+    dimensions.push({ type: "int32", data: [convInfo.filterHeight] }, { type: "int32", data: [convInfo.filterWidth] }, { type: "int32", data: [convInfo.outChannels / convInfo.inChannels] });
+  }
+  if (activation2 === "leakyrelu") {
+    dimensions.push({ type: "float32", data: [leakyreluAlpha] });
+    program.uniforms += " alpha : f32,";
+  }
+  const result = backend2.runWebGPUProgram(program, programInputs, "float32", dimensions);
+  return result;
+}
+var fusedDepthwiseConv2DConfig4 = {
+  kernelName: FusedDepthwiseConv2D,
+  backendName: "webgpu",
+  kernelFunc: fusedDepthwiseConv2D3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_nd_webgpu.js
+var GatherNDProgram2 = class {
+  constructor(sliceDim, shape) {
+    this.variableNames = ["A", "indices"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `gathernd_${sliceDim}`;
+    this.sliceDim = sliceDim;
+    this.uniforms = `sliceDim : i32, strides : ${getCoordsDataType2(sliceDim)},`;
+  }
+  getUserCode() {
+    let strideString;
+    if (this.sliceDim > 1) {
+      strideString = "uniforms.strides[j]";
+    } else {
+      strideString = "uniforms.strides";
+    }
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          var flattenIndex = 0;
+          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+            let indexTemp = i32(round(getIndices(coords[0], j)));
+            let strideNum = ${strideString};
+            flattenIndex = flattenIndex + indexTemp * strideNum;
+          }
+
+          setOutputAtIndex(index, getA(flattenIndex, coords[1]));
+        }
+      }
+      `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherNd.js
+function gatherNd4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { params, indices } = inputs;
+  const indicesShape = indices.shape;
+  const sliceRank = indicesShape[indicesShape.length - 1];
+  const paramsSize = util_exports.sizeFromShape(params.shape);
+  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
+  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });
+  const flattenX = reshape6({
+    inputs: { x: params },
+    backend: backend2,
+    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }
+  });
+  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") {
+    const indicesData = backend2.readSync(indices.dataId);
+    const paramsBuf = backend2.bufferSync(params);
+    const outValue = gatherNdImplCPU2(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
+    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);
+  }
+  const program = new GatherNDProgram2(sliceRank, [numSlices, sliceSize]);
+  const uniformData = [{ type: "int32", data: [sliceRank] }, { type: "int32", data: strides }];
+  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], flattenX.dtype, uniformData);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });
+  backend2.disposeData(flattenIndices.dataId);
+  backend2.disposeData(flattenX.dataId);
+  backend2.disposeData(res.dataId);
+  return reshaped;
+}
+var gatherNdConfig4 = {
+  kernelName: GatherNd,
+  backendName: "webgpu",
+  kernelFunc: gatherNd4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_webgpu.js
+var GatherProgram2 = class {
+  constructor(aShape, outputShape) {
+    this.variableNames = ["A", "indices"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = aShape.slice();
+    this.aShape = aShape;
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `gather`;
+  }
+  getUserCode() {
+    const sourceCoords = getSourceCoords4(this.aShape);
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          let indexZ = i32(getIndices(resRC.x, resRC.z));
+          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);
+          setOutputAtIndex(index, inBounds * getA(${sourceCoords}));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function getSourceCoords4(aShape) {
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < aShape.length; i2++) {
+    if (i2 === 2) {
+      sourceCoords.push("indexZ");
+    } else {
+      sourceCoords.push(`${currentCoords[i2]}`);
+    }
+  }
+  return sourceCoords.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherV2.js
+function gatherV24(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x, indices } = inputs;
+  const { axis, batchDims } = attrs;
+  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
+  const indicesSize = util_exports.sizeFromShape(indices.shape);
+  const toDispose = [];
+  const flattenX = reshape6({
+    inputs: { x },
+    backend: backend2,
+    attrs: {
+      shape: [
+        shapeInfo.batchSize,
+        shapeInfo.outerSize,
+        shapeInfo.dimSize,
+        shapeInfo.sliceSize
+      ]
+    }
+  });
+  const flattenIndex = reshape6({
+    inputs: { x: indices },
+    backend: backend2,
+    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
+  });
+  toDispose.push(flattenX);
+  toDispose.push(flattenIndex);
+  const flattenOutputShape = [
+    shapeInfo.batchSize,
+    shapeInfo.outerSize,
+    indicesSize / shapeInfo.batchSize,
+    shapeInfo.sliceSize
+  ];
+  if (backend2.shouldExecuteOnCPU([x, indices])) {
+    const indicesBufferInfo = backend2.tensorMap.get(flattenIndex.dataId);
+    const indicesValues = indicesBufferInfo.values;
+    const indicesBuf = buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues);
+    const xBufferInfo = backend2.tensorMap.get(flattenX.dataId);
+    const xValues = xBufferInfo.values;
+    const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues);
+    const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape);
+    toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
+  }
+  const program = new GatherProgram2(flattenX.shape, flattenOutputShape);
+  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype);
+  toDispose.push(res);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return reshaped;
+}
+var gatherV2Config4 = {
+  kernelName: GatherV2,
+  backendName: "webgpu",
+  kernelFunc: gatherV24
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Greater.js
+var greater5 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.GREATER,
+  cpuKernelImpl: greaterImplCPU2,
+  dtype: "bool"
+});
+var greaterConfig4 = {
+  kernelName: Greater,
+  backendName: "webgpu",
+  kernelFunc: greater5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GreaterEqual.js
+var greaterEqual4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.GREATER_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: greaterEqualImplCPU2
+});
+var greaterEqualConfig4 = {
+  kernelName: GreaterEqual,
+  backendName: "webgpu",
+  kernelFunc: greaterEqual4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LeakyRelu.js
+function leakyRelu5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { alpha } = attrs;
+  const uniformData = [{ type: "float32", data: [alpha] }];
+  const program = new UnaryOpProgram2(x.shape, UnaryOpType.LEAKYRELU);
+  program.uniforms = "alpha : f32,";
+  return backend2.runWebGPUProgram(program, [x], "float32", uniformData);
+}
+var leakyReluConfig4 = {
+  kernelName: LeakyRelu,
+  backendName: "webgpu",
+  kernelFunc: leakyRelu5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Less.js
+var less5 = binaryKernelFunc3({ opSnippet: BinaryOpType.LESS, dtype: "bool", cpuKernelImpl: lessImplCPU2 });
+var lessConfig4 = {
+  kernelName: Less,
+  backendName: "webgpu",
+  kernelFunc: less5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LessEqual.js
+var lessEqual4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.LESS_EQUAL,
+  dtype: "bool",
+  cpuKernelImpl: lessEqualImplCPU2
+});
+var lessEqualConfig4 = {
+  kernelName: LessEqual,
+  backendName: "webgpu",
+  kernelFunc: lessEqual4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Log.js
+var log5 = unaryKernelFunc3({ opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU2 });
+var logConfig4 = {
+  kernelName: Log,
+  backendName: "webgpu",
+  kernelFunc: log5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalAnd.js
+var logicalAnd4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.LOGICAL_AND,
+  dtype: "bool"
+});
+var logicalAndConfig4 = {
+  kernelName: LogicalAnd,
+  backendName: "webgpu",
+  kernelFunc: logicalAnd4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalNot.js
+var logicalNot4 = unaryKernelFunc3({ opType: UnaryOpType.LOGICAL_NOT });
+var logicalNotConfig3 = {
+  kernelName: LogicalNot,
+  backendName: "webgpu",
+  kernelFunc: logicalNot4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Max.js
+function max6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { reductionIndices, keepDims } = attrs;
+  return reduce2(x, reductionIndices, keepDims, "max", backend2);
+}
+var maxConfig4 = {
+  kernelName: Max,
+  backendName: "webgpu",
+  kernelFunc: max6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Maximum.js
+var maximum5 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.MAX,
+  cpuKernelImpl: maximumImplCPU2
+});
+var maximumConfig4 = {
+  kernelName: Maximum,
+  backendName: "webgpu",
+  kernelFunc: maximum5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MaxPool.js
+function maxPool5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;
+  const dilations = 1;
+  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);
+  let program;
+  const dimensions = [];
+  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {
+    if (util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
+      return identity5({ inputs: { x }, backend: backend2 });
+    }
+    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);
+    dimensions.push({ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] });
+  } else {
+    program = new Pool2DProgram2(convInfo, "max");
+    dimensions.push({ type: "int32", data: [convInfo.strideHeight, convInfo.strideWidth] }, { type: "int32", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {
+      type: "int32",
+      data: [convInfo.dilationHeight, convInfo.dilationWidth]
+    }, { type: "int32", data: [convInfo.inHeight, convInfo.inWidth] }, {
+      type: "int32",
+      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]
+    });
+  }
+  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);
+}
+var maxPoolConfig4 = {
+  kernelName: MaxPool,
+  backendName: "webgpu",
+  kernelFunc: maxPool5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Mean.js
+function mean4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { keepDims, axis } = attrs;
+  return reduce2(x, axis, keepDims, "mean", backend2);
+}
+var meanConfig4 = {
+  kernelName: Mean,
+  backendName: "webgpu",
+  kernelFunc: mean4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Min.js
+function min6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return reduce2(x, axis, keepDims, "min", backend2);
+}
+var minConfig4 = {
+  kernelName: Min,
+  backendName: "webgpu",
+  kernelFunc: min6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Minimum.js
+var minimum5 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.MIN,
+  cpuKernelImpl: minimumImplCPU2
+});
+var minimumConfig4 = {
+  kernelName: Minimum,
+  backendName: "webgpu",
+  kernelFunc: minimum5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/mirror_pad_webgpu.js
+var MirrorPadProgram2 = class {
+  constructor(xShape, paddings, mode) {
+    this.uniforms = "";
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.xShape = xShape;
+    paddings.map((_, i2) => {
+      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    });
+    this.offset = mode === "reflect" ? 0 : 1;
+    this.shaderKey = `mirrorPad_${mode}`;
+  }
+  getUserCode() {
+    const rank = this.xShape.length;
+    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
+    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const shaderStart = rank === 1 ? "start" : "start[i]";
+    const shaderEnd = rank === 1 ? "end" : "end[i]";
+    const shaderOutC = rank === 1 ? "outC" : "outC[i]";
+    const dtype = getCoordsDataType2(rank);
+    const unpackedCoords = rank > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank) : "coords";
+    return `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let start = ${dtype}(${start});
+          let end = ${dtype}(${end});
+          var outC = getCoordsFromIndex(index);
+          for (var i = 0; i < ${rank}; i = i + 1) {
+            if (${shaderOutC} < ${shaderStart}) {
+              ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${this.offset};
+            } else if(${shaderOutC} >= ${shaderEnd}) {
+              ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${this.offset};
+            }
+          }
+          let coords = outC - start;
+          setOutputAtIndex(index, getX(${unpackedCoords}));
+        }
+      }
+    `;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MirrorPad.js
+var mirrorPadConfig4 = {
+  kernelName: MirrorPad,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { x } = inputs;
+    const { paddings, mode } = attrs;
+    const webGPUBackend = backend2;
+    const uniformData = paddings.map((p2) => {
+      return { type: "int32", data: [p2[0], p2[1]] };
+    });
+    const program = new MirrorPadProgram2(x.shape, paddings, mode);
+    const output = webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData);
+    return output;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Neg.js
+function neg4(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xData = backend2.tensorMap.get(x.dataId);
+    const [outValues, newShape] = negImplCPU2(xData.values, x.shape, x.dtype);
+    return backend2.makeTensorInfo(newShape, x.dtype, outValues);
+  }
+  const program = new UnaryOpProgram2(x.shape, UnaryOpType.NEG);
+  return backend2.runWebGPUProgram(program, [x], x.dtype);
+}
+var negConfig4 = {
+  kernelName: Neg,
+  backendName: "webgpu",
+  kernelFunc: neg4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV3.js
+function nonMaxSuppressionV33(args) {
+  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const { selectedIndices } = kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+  return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
+}
+var nonMaxSuppressionV3Config4 = {
+  kernelName: NonMaxSuppressionV3,
+  backendName: "webgpu",
+  kernelFunc: nonMaxSuppressionV33
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV5.js
+function nonMaxSuppressionV53(args) {
+  console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
+  const { inputs, backend: backend2, attrs } = args;
+  const { boxes, scores } = inputs;
+  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
+  const boxesVals = backend2.readSync(boxes.dataId);
+  const scoresVals = backend2.readSync(scores.dataId);
+  const maxOutputSizeVal = maxOutputSize;
+  const iouThresholdVal = iouThreshold;
+  const scoreThresholdVal = scoreThreshold;
+  const softNmsSigmaVal = softNmsSigma;
+  const { selectedIndices, selectedScores } = kernel_impls_exports.nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
+  return [
+    backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
+    backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
+  ];
+}
+var nonMaxSuppressionV5Config4 = {
+  kernelName: NonMaxSuppressionV5,
+  backendName: "webgpu",
+  kernelFunc: nonMaxSuppressionV53
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ZerosLike.js
+function zerosLike5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "complex64") {
+    const realPart = real4({ inputs: { input: x }, backend: backend2 });
+    const r2 = zerosLike5({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeData(realPart.dataId);
+    backend2.disposeData(r2.dataId);
+    backend2.disposeData(imagPart.dataId);
+    backend2.disposeData(i2.dataId);
+    return result;
+  } else {
+    return fill5({
+      attrs: {
+        shape: x.shape,
+        dtype: x.dtype,
+        value: x.dtype === "string" ? "" : 0
+      },
+      backend: backend2
+    });
+  }
+}
+var zerosLikeConfig4 = {
+  kernelName: ZerosLike,
+  backendName: "webgpu",
+  kernelFunc: zerosLike5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/OnesLike.js
+function onesLike5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x } = inputs;
+  if (x.dtype === "string") {
+    throw new Error("onesLike is not supported under string dtype");
+  } else if (x.dtype === "complex64") {
+    const realPart = real4({ inputs: { input: x }, backend: backend2 });
+    const r2 = onesLike5({ inputs: { x: realPart }, backend: backend2 });
+    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
+    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    backend2.disposeData(realPart.dataId);
+    backend2.disposeData(r2.dataId);
+    backend2.disposeData(imagPart.dataId);
+    backend2.disposeData(i2.dataId);
+    return result;
+  } else {
+    return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
+  }
+}
+var onesLikeConfig4 = {
+  kernelName: OnesLike,
+  backendName: "webgpu",
+  kernelFunc: onesLike5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pack.js
+function pack4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { axis } = attrs;
+  if (inputs.length === 1) {
+    return expandDims6({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
+  }
+  const shape = inputs[0].shape;
+  const dtype = inputs[0].dtype;
+  inputs.forEach((t2) => {
+    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  });
+  const intermediateTensorInfos = [];
+  const expandedTensors = inputs.map((t2) => {
+    const expandedT = expandDims6({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+    intermediateTensorInfos.push(expandedT);
+    return expandedT;
+  });
+  const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
+  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  return result;
+}
+var packConfig4 = {
+  kernelName: Pack,
+  backendName: "webgpu",
+  kernelFunc: pack4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pad_webgpu.js
+var PadProgram2 = class {
+  constructor(xShape, paddings) {
+    this.variableNames = ["x"];
+    this.uniforms = "constantValue : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    paddings.map((_, i2) => {
+      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    });
+    this.xShape = xShape;
+    this.shaderKey = "pad";
+  }
+  getUserCode() {
+    const rank = this.xShape.length;
+    const type = getCoordsDataType2(rank);
+    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
+    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const startValue = rank > 1 ? `${type}(${start})` : `${start}`;
+    const endValue = rank > 1 ? `${type}(${end})` : `${end}`;
+    const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;
+    const rightPadCondition = rank > 1 ? `any(outC >= end)` : `outC >= end`;
+    const unpackedCoords = rank > 1 ? ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank) : "coords";
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let start = ${startValue};
+          let end = ${endValue};
+          let outC = getCoordsFromIndex(index);
+
+          if (${leftPadCondition} || ${rightPadCondition}) {
+            setOutputAtIndex(index, uniforms.constantValue);
+          } else {
+            let coords = outC - start;
+            setOutputAtIndex(index, getX(${unpackedCoords}));
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/PadV2.js
+var padV23 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { paddings, constantValue } = attrs;
+  if (paddings.every((p2) => util_exports.arraysEqual(p2, [0, 0]))) {
+    return identity5({ inputs: { x }, backend: backend2 });
+  }
+  if (util_exports.sizeFromShape(x.shape) === 0) {
+    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    return fill5({
+      backend: backend2,
+      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
+    });
+  }
+  const uniformData = [{ type: "float32", data: [constantValue] }];
+  paddings.map((p2) => uniformData.push({ type: "int32", data: [p2[0], p2[1]] }));
+  const program = new PadProgram2(x.shape, paddings);
+  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+};
+var padV2Config4 = {
+  kernelName: PadV2,
+  backendName: "webgpu",
+  kernelFunc: padV23
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pow.js
+var pow4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.POW
+});
+var powConfig4 = {
+  kernelName: Pow,
+  backendName: "webgpu",
+  kernelFunc: pow4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prelu.js
+function prelu6(args) {
+  const { inputs, backend: backend2 } = args;
+  const { x, alpha } = inputs;
+  const program = new BinaryOpProgram2(BinaryOpType.PRELU, x.shape, alpha.shape);
+  return backend2.runWebGPUProgram(program, [x, alpha], "float32");
+}
+var preluConfig4 = {
+  kernelName: Prelu,
+  backendName: "webgpu",
+  kernelFunc: prelu6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prod.js
+function prod5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { axis, keepDims } = attrs;
+  return reduce2(x, axis, keepDims, "prod", backend2);
+}
+var prodConfig4 = {
+  kernelName: Prod,
+  backendName: "webgpu",
+  kernelFunc: prod5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Range.js
+var range6 = (args) => {
+  const { backend: backend2, attrs } = args;
+  const { start, stop, step: step5, dtype } = attrs;
+  const values = rangeImplCPU2(start, stop, step5, dtype);
+  return backend2.makeTensorInfo([values.length], dtype, values);
+};
+var rangeConfig4 = {
+  kernelName: Range,
+  backendName: "webgpu",
+  kernelFunc: range6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RealDiv.js
+var realDiv2 = binaryKernelFunc3({ opSnippet: BinaryOpType.DIV });
+var realDivConfig4 = {
+  kernelName: RealDiv,
+  backendName: "webgpu",
+  kernelFunc: realDiv2
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu.js
+var relu4 = unaryKernelFunc3({ opType: UnaryOpType.RELU });
+var reluConfig4 = {
+  kernelName: Relu,
+  backendName: "webgpu",
+  kernelFunc: relu4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu6.js
+var relu64 = unaryKernelFunc3({ opType: UnaryOpType.RELU6 });
+var relu6Config4 = {
+  kernelName: Relu6,
+  backendName: "webgpu",
+  kernelFunc: relu64
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_bilinear_webgpu.js
+var ResizeBilinearProgram2 = class {
+  constructor(inputShape, newHeight, newWidth) {
+    this.variableNames = ["x"];
+    this.uniforms = "adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = `resizeBilinear`;
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+        let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let d = coords[3];
+          let rc = coords.yz;
+
+          let effectiveInSize = vec2<f32>(
+            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveOutSize = vec2<f32>(
+            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveInputOverOutputRatioRC =
+              effectiveInSize / effectiveOutSize;
+
+          // Fractional source index
+          let sourceFracIndexRC =
+            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
+            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
+
+          // Compute the four integer indices.
+          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
+          let sourceCeilRC = vec2<i32>(
+            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
+
+          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
+          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
+          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
+          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
+
+          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
+
+          let top = topLeft + (topRight - topLeft) * fracRC.y;
+          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
+          let newValue = top + (bottom - top) * fracRC.x;
+
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeBilinear.js
+function resizeBilinear5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, size, halfPixelCenters } = attrs;
+  const [newHeight, newWidth] = size;
+  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;
+  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;
+  const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0;
+  const uniformData = [
+    { type: "float32", data: [adjustHeight, adjustWidth] },
+    { type: "float32", data: [halfPixelCentersValue] }
+  ];
+  const program = new ResizeBilinearProgram2(images.shape, newHeight, newWidth);
+  return backend2.runWebGPUProgram(program, [images], "float32", uniformData);
+}
+var resizeBilinearConfig4 = {
+  kernelName: ResizeBilinear,
+  backendName: "webgpu",
+  kernelFunc: resizeBilinear5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_nearest_neighbor_webgpu.js
+var ResizeNearestNeighborProgram2 = class {
+  constructor(inputShape, newHeight, newWidth, halfPixelCenters) {
+    this.variableNames = ["x"];
+    this.uniforms = "adjustHeightWidth : vec2<f32>, roundBase : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.halfPixelCenters = halfPixelCenters;
+    this.shaderKey = `resizeNearest_${halfPixelCenters}`;
+  }
+  getUserCode() {
+    let sourceFracIndexRC;
+    if (this.halfPixelCenters) {
+      sourceFracIndexRC = `max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))`;
+    } else {
+      sourceFracIndexRC = `vec2<f32>(rc) * effectiveInputOverOutputRatioRC`;
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let coords = getCoordsFromIndex(index);
+          let b = coords[0];
+          let d = coords[3];
+          let rc = coords.yz;
+
+          let effectiveInSize = vec2<f32>(
+            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveOutSize = vec2<f32>(
+            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
+            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
+
+          let effectiveInputOverOutputRatioRC =
+              effectiveInSize / effectiveOutSize;
+
+          // Fractional source index
+          let sourceFracIndexRC = ${sourceFracIndexRC};
+
+          // Compute the coordinators of nearest neighbor point.
+          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
+          let sourceNearestRC = vec2<i32>(
+            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
+          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
+
+          setOutputAtIndex(index, newValue);
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeNearestNeighbor.js
+function resizeNearestNeighbor4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { images } = inputs;
+  const { alignCorners, halfPixelCenters, size } = attrs;
+  const [newHeight, newWidth] = size;
+  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;
+  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;
+  const roundBase = alignCorners ? 0.5 : 0;
+  const uniformData = [
+    { type: "float32", data: [adjustHeight, adjustWidth] },
+    { type: "float32", data: [roundBase] }
+  ];
+  const program = new ResizeNearestNeighborProgram2(images.shape, newHeight, newWidth, halfPixelCenters);
+  return backend2.runWebGPUProgram(program, [images], images.dtype, uniformData);
+}
+var resizeNearestNeighborConfig3 = {
+  kernelName: ResizeNearestNeighbor,
+  backendName: "webgpu",
+  kernelFunc: resizeNearestNeighbor4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/rotate_webgpu.js
+var RotateProgram2 = class {
+  constructor(imageShape, fillValue) {
+    this.outputShape = [];
+    this.variableNames = ["x"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = imageShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,
+          cosRadians : f32,`;
+    this.shaderKey = "rotate";
+    this.outputShape = imageShape;
+    if (typeof fillValue === "number") {
+      this.uniforms += ` fillValue : f32,`;
+      this.fillSnippet = `var outputValue = uniforms.fillValue;`;
+      this.shaderKey += "_float";
+    } else {
+      this.uniforms += ` fillValue : vec3<f32>,`;
+      this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`;
+      this.shaderKey += "_vec3";
+    }
+  }
+  getUserCode() {
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+
+          if (index < uniforms.size) {
+            let coords = getCoordsFromIndex(index);
+            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
+                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
+                uniforms.sinRadians;
+            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
+                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
+                uniforms.cosRadians;
+            let coordX = i32(round(coordXFloat + uniforms.centerX));
+            let coordY = i32(round(coordYFloat + uniforms.centerY));
+            ${this.fillSnippet}
+            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
+                coordY < uniforms.xShape[1]) {
+              outputValue = getX(coords[0], coordY, coordX, coords[3]);
+            }
+            setOutputAtIndex(index, outputValue);
+          }
+        }
+      `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RotateWithOffset.js
+var rotateWithOffsetConfig4 = {
+  kernelName: RotateWithOffset,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
+    const { image: image2 } = inputs;
+    const { radians, fillValue, center } = attrs;
+    const webgpuBackend = backend2;
+    const program = new RotateProgram2(image2.shape, fillValue);
+    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);
+    const uniformData = [
+      { type: "float32", data: [centerX] },
+      { type: "float32", data: [centerY] },
+      { type: "float32", data: [Math.sin(radians)] },
+      { type: "float32", data: [Math.cos(radians)] }
+    ];
+    if (typeof fillValue === "number") {
+      uniformData.push({ type: "float32", data: [Number.parseFloat(fillValue.toFixed(2))] });
+    } else {
+      uniformData.push({ type: "float32", data: fillValue });
+    }
+    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype, uniformData);
+    return output;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Rsqrt.js
+var rsqrt4 = unaryKernelFunc3({ opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU2 });
+var rsqrtConfig4 = {
+  kernelName: Rsqrt,
+  backendName: "webgpu",
+  kernelFunc: rsqrt4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/scatter_optimized_webgpu.js
+var ScatterOptimizedProgram = class {
+  constructor(flattenXShape, sliceDim, indicesRank, updatesRank, strides, shape, outputDtype) {
+    this.variableNames = ["updates", "indices"];
+    this.workGroupSize = [64, 1, 1];
+    this.atomic = true;
+    this.outputShape = shape;
+    this.type = outputDtype;
+    this.dispatchLayout = flatDispatchLayout(flattenXShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, flattenXShape, this.workGroupSize);
+    this.sliceDimGreaterThanOne = sliceDim > 1;
+    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${outputDtype}`;
+    const stridesType = getCoordsDataType2(strides.length);
+    this.uniforms = `sliceDim : i32, strides: ${stridesType}, size: i32,`;
+    this.updatesRank = updatesRank;
+    this.indicesRank = indicesRank;
+  }
+  getUserCode() {
+    let indicesString = "";
+    if (this.indicesRank === 1) {
+      indicesString = "coords[0]";
+    } else if (this.indicesRank === 2) {
+      indicesString = "coords[0], j";
+    }
+    const indicesSnippet = `getIndices(${indicesString})`;
+    const strideString = this.sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides";
+    let updatesString = "";
+    let outCoordsString = "";
+    let getUpdatesCoordsFromFlatIndex = "";
+    if (this.updatesRank === 1) {
+      updatesString = "coords[0]";
+      outCoordsString = "flattenedIndex";
+      getUpdatesCoordsFromFlatIndex = `
+      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
+        return index;
+      }
+      `;
+    } else if (this.updatesRank === 2) {
+      updatesString = "coords[0], coords[1]";
+      outCoordsString = "vec2<i32>(flattenedIndex, coords[1])";
+      getUpdatesCoordsFromFlatIndex = `
+      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
+        let d0 = index / uniforms.updatesShape[1];
+        let d1 = index - d0 * uniforms.updatesShape[1];
+        return vec2<i32>(d0, d1);
+      }
+      `;
+    }
+    const updatesSnippet = `getUpdates(${updatesString})`;
+    const atomicAddSnippet = this.type === "int32" ? `atomicAdd(&(result[flatIndex]), i32(updateValue));` : `
+     var assumed = atomicLoad(&(result[flatIndex]));
+     var success = 0;
+     for (; success == 0;) {
+       let new = bitcast<f32>(assumed) + updateValue;
+       let newI32 = bitcast<i32>(new);
+       let resValue = atomicCompareExchangeWeak(&(result[flatIndex]), assumed, newI32);
+       assumed = resValue[0];
+       success = resValue[1];
+     }
+     `;
+    const userCode = `
+    ${getUpdatesCoordsFromFlatIndex}
+
+      ${getMainHeaderAndGlobalIndexString()}
+
+        if (index < uniforms.size) {
+          let coords = getUpdatesCoordsFromFlatIndex(index);
+          var flattenedIndex = 0;
+          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+            let indexInside = i32(round(${indicesSnippet}));
+            flattenedIndex = flattenedIndex + indexInside * ${strideString};
+          }
+          let updateValue = ${updatesSnippet};
+          let flatIndex = getOutputIndexFromCoords(${outCoordsString});
+
+         ${atomicAddSnippet}
+        }
+      }`;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ScatterNd.js
+function scatterNd4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { indices, updates } = inputs;
+  const { shape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);
+  const flattenShape = [outputSize / sliceSize, sliceSize];
+  if (outputSize === 0) {
+    return backend2.makeTensorInfo(shape, indices.dtype);
+  }
+  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });
+  const flattenX = reshape6({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });
+  const type = flattenX.dtype;
+  const output = fill5({ backend: backend2, attrs: { shape: flattenShape, value: 0, dtype: type } });
+  const size = util_exports.sizeFromShape(flattenX.shape);
+  const uniformData = [
+    { type: "int32", data: [sliceRank] },
+    { type: "int32", data: strides },
+    { type: "int32", data: [size] }
+  ];
+  const program = new ScatterOptimizedProgram(flattenX.shape, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, type);
+  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], type, uniformData, output);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape } });
+  backend2.disposeData(flattenIndices.dataId);
+  backend2.disposeData(flattenX.dataId);
+  backend2.disposeData(res.dataId);
+  return reshaped;
+}
+var scatterNdConfig4 = {
+  kernelName: ScatterNd,
+  backendName: "webgpu",
+  kernelFunc: scatterNd4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/select_webgpu.js
+var SelectProgram2 = class {
+  constructor(cRank, shape, rank) {
+    this.variableNames = ["c", "a", "b"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.cRank = cRank;
+    this.rank = rank;
+    this.shaderKey = "select";
+  }
+  getUserCode() {
+    let cCoords;
+    let abCoords;
+    if (this.rank > 4) {
+      throw Error(`Where for rank ${this.rank} is not yet supported`);
+    }
+    if (this.rank === 1) {
+      abCoords = `resRC`;
+      cCoords = `resRC`;
+    } else {
+      const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+      const cCoordVars = [];
+      const abCoordVars = [];
+      for (let i2 = 0; i2 < this.outputShape.length; i2++) {
+        abCoordVars.push(`${currentCoords[i2]}`);
+        if (i2 < this.cRank) {
+          cCoordVars.push(`${currentCoords[i2]}`);
+        }
+      }
+      cCoords = cCoordVars.join();
+      abCoords = abCoordVars.join();
+    }
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          let cVal = getC(${cCoords});
+          if (cVal >= 1.0) {
+            setOutputAtIndex(index, getA(${abCoords}));
+          } else {
+            setOutputAtIndex(index, getB(${abCoords}));
+          }
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Select.js
+function select5(args) {
+  const { inputs, backend: backend2 } = args;
+  const { condition, t: t2, e: e2 } = inputs;
+  const program = new SelectProgram2(condition.shape.length, t2.shape, t2.shape.length);
+  return backend2.runWebGPUProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+}
+var selectConfig4 = {
+  kernelName: Select,
+  backendName: "webgpu",
+  kernelFunc: select5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sigmoid.js
+var sigmoid5 = unaryKernelFunc3({ opType: UnaryOpType.SIGMOID });
+var sigmoidConfig4 = {
+  kernelName: Sigmoid,
+  backendName: "webgpu",
+  kernelFunc: sigmoid5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sin.js
+var sin4 = unaryKernelFunc3({ opType: UnaryOpType.SIN });
+var sinConfig4 = {
+  kernelName: Sin,
+  backendName: "webgpu",
+  kernelFunc: sin4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sinh.js
+var sinh4 = unaryKernelFunc3({ opType: UnaryOpType.SINH });
+var sinhConfig3 = {
+  kernelName: Sinh,
+  backendName: "webgpu",
+  kernelFunc: sinh4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sub.js
+var sub4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.SUB,
+  cpuKernelImpl: subImplCPU2,
+  supportsComplex: true
+});
+var subConfig4 = {
+  kernelName: Sub,
+  backendName: "webgpu",
+  kernelFunc: sub4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Softmax.js
+function softmax6(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { logits } = inputs;
+  const { dim } = attrs;
+  const axes = util_exports.parseAxisParam([dim], logits.shape);
+  const maxLogit = max6({
+    inputs: { x: logits },
+    backend: backend2,
+    attrs: { reductionIndices: axes, keepDims: false }
+  });
+  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
+  const maxLogitsReshaped = reshape6({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
+  const a6 = sub4({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });
+  const b = exp4({ inputs: { x: a6 }, backend: backend2 });
+  const sumExp = sum6({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
+  const sumExpReshaped = reshape6({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
+  const res = realDiv2({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });
+  backend2.disposeData(maxLogit.dataId);
+  backend2.disposeData(maxLogitsReshaped.dataId);
+  backend2.disposeData(a6.dataId);
+  backend2.disposeData(b.dataId);
+  backend2.disposeData(sumExp.dataId);
+  backend2.disposeData(sumExpReshaped.dataId);
+  return res;
+}
+var softmaxConfig4 = {
+  kernelName: Softmax,
+  backendName: "webgpu",
+  kernelFunc: softmax6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SpaceToBatchND.js
+var spaceToBatchND5 = (args) => {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { blockShape, paddings } = attrs;
+  util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");
+  const prod6 = blockShape.reduce((a6, b) => a6 * b);
+  const completePaddings = [[0, 0]];
+  completePaddings.push(...paddings);
+  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+    completePaddings.push([0, 0]);
+  }
+  const toDispose = [];
+  const paddedX = padV23({
+    inputs: { x },
+    backend: backend2,
+    attrs: { paddings: completePaddings, constantValue: 0 }
+  });
+  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);
+  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);
+  const reshapedPaddedX = reshape6({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });
+  const paddedXT = transpose5({
+    inputs: { x: reshapedPaddedX },
+    backend: backend2,
+    attrs: { perm: permutedReshapedPaddedPermutation }
+  });
+  const result = reshape6({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });
+  toDispose.push(paddedX);
+  toDispose.push(reshapedPaddedX);
+  toDispose.push(paddedXT);
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return result;
+};
+var spaceToBatchNDConfig4 = {
+  kernelName: SpaceToBatchND,
+  backendName: "webgpu",
+  kernelFunc: spaceToBatchND5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/scatter_webgpu.js
+var ScatterProgram2 = class {
+  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {
+    this.variableNames = ["updates", "indices", "defaultValue"];
+    this.workGroupSize = [64, 1, 1];
+    this.workPerThread = 4;
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    const sliceDimGreaterThanOne = sliceDim > 1;
+    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${sliceDimGreaterThanOne}`;
+    const stridesType = getCoordsDataType2(strides.length);
+    this.uniforms = `updateSize : i32, sliceDim : i32, strides: ${stridesType},`;
+    let indicesString = "";
+    if (indicesRank === 1) {
+      indicesString = "i";
+    } else if (indicesRank === 2) {
+      indicesString = "i, j";
+    }
+    this.indicesSnippet = `getIndices(${indicesString})`;
+    let updatesString = "";
+    if (updatesRank === 1) {
+      updatesString = "i";
+    } else if (updatesRank === 2) {
+      updatesString = "i, coords[1]";
+    }
+    this.updatesSnippet = `getUpdates(${updatesString})`;
+    this.strideString = sliceDimGreaterThanOne ? "uniforms.strides[j]" : "uniforms.strides";
+  }
+  getUserCode() {
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+
+        let globalIndex = index * ${this.workPerThread};
+        if (globalIndex < uniforms.size) {
+          var sum = vec4<f32>(0.0);
+          var found = vec4<bool>(false);
+          for (var i = 0; i < uniforms.updateSize; i = i + 1) {
+            var flattenedIndex = 0;
+            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
+              let indexInside = i32(round(${this.indicesSnippet}));
+              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
+            }
+            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
+              let curIndex = globalIndex + innerIndex;
+              let coords = getCoordsFromIndex(curIndex);
+              if (flattenedIndex == coords[0]) {
+                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
+                found[innerIndex] = true;
+              }
+            }
+          }
+          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
+            let curIndex = globalIndex + innerIndex;
+            if (curIndex < uniforms.size)
+            {
+              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
+            }
+          }
+        }
+      }`;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SparseToDense.js
+function sparseToDense4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { sparseIndices, sparseValues, defaultValue } = inputs;
+  const { outputShape } = attrs;
+  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
+  const sumDupeIndices = false;
+  if (sparseValues.dtype === "string") {
+    const indicesBuf = backend2.bufferSync(sparseIndices);
+    const updatesBuf = backend2.bufferSync(sparseValues);
+    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);
+    const outBuf = scatterImplCPU2(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
+    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
+  }
+  const uniformData = [
+    { type: "int32", data: [numUpdates] },
+    { type: "int32", data: [sliceRank] },
+    { type: "int32", data: strides }
+  ];
+  const program = new ScatterProgram2(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);
+  const res = backend2.runWebGPUProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype, uniformData);
+  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });
+  backend2.disposeData(res.dataId);
+  return reshaped;
+}
+var sparseToDenseConfig3 = {
+  kernelName: SparseToDense,
+  backendName: "webgpu",
+  kernelFunc: sparseToDense4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SplitV.js
+function splitV4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { numOrSizeSplits, axis } = attrs;
+  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];
+  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
+  const xRank = x.shape.length;
+  const begin = new Array(xRank).fill(0);
+  const size = x.shape.slice();
+  return splitSizes.map((s2) => {
+    const sliceSize = [...size];
+    sliceSize[$axis] = s2;
+    const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
+    begin[$axis] += s2;
+    return sliceT;
+  });
+}
+var splitVConfig4 = {
+  kernelName: SplitV,
+  backendName: "webgpu",
+  kernelFunc: splitV4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sqrt.js
+var sqrt4 = unaryKernelFunc3({ opType: UnaryOpType.SQRT });
+var sqrtConfig4 = {
+  kernelName: Sqrt,
+  backendName: "webgpu",
+  kernelFunc: sqrt4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Square.js
+var squareConfig4 = {
+  kernelName: Square,
+  backendName: "webgpu",
+  kernelFunc: ({ inputs, backend: backend2 }) => {
+    const { x } = inputs;
+    const webGPUBackend = backend2;
+    const program = new UnaryOpProgram2(x.shape, UnaryOpType.SQUARE);
+    return webGPUBackend.runWebGPUProgram(program, [x], x.dtype);
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SquaredDifference.js
+var squaredDifference4 = binaryKernelFunc3({
+  opSnippet: BinaryOpType.SQUARED_DIFFERENCE
+});
+var squaredDifferenceConfig4 = {
+  kernelName: SquaredDifference,
+  backendName: "webgpu",
+  kernelFunc: squaredDifference4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/strided_slice_webgpu.js
+var StridedSliceProgram2 = class {
+  constructor(destSize) {
+    this.variableNames = ["x"];
+    this.workPerThread = 1;
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = destSize;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
+    const dtype = getCoordsDataType2(this.outputShape.length);
+    this.uniforms = `begin : ${dtype},  strides : ${dtype}, `;
+    this.shaderKey = "stridedSlice";
+  }
+  getUserCode() {
+    const rank = this.outputShape.length;
+    let newCoords = "";
+    if (rank === 1) {
+      newCoords = "coords * uniforms.strides + uniforms.begin";
+    } else {
+      let outputAxis = 0;
+      newCoords = this.outputShape.map((_, i2) => {
+        outputAxis++;
+        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i2}] + uniforms.begin[${i2}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i2}] + uniforms.begin[${i2}]`;
+      }).join(",");
+    }
+    const userCode = `
+       ${getMainHeaderAndGlobalIndexString()}
+         if (index < uniforms.size) {
+           let coords = getCoordsFromIndex(index);
+           setOutputAtIndex(index, getX(${newCoords}));
+         }
+       }
+     `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StridedSlice.js
+function stridedSlice5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
+  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+  let result;
+  if (isIdentity) {
+    result = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
+  } else if (sliceDim0 || isSimpleSlice) {
+    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
+    const size = slice_util_exports.computeOutShape($begin, $end, $strides);
+    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
+    result = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
+    backend2.disposeData(sliced.dataId);
+  } else {
+    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
+    if (shouldExecuteOnCPU) {
+      const values = backend2.readSync(x.dataId);
+      const xBuf = buffer(x.shape, x.dtype, values);
+      const resultValues = stridedSliceImplCPU2(finalShapeSparse, xBuf, $strides, $begin);
+      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);
+    } else {
+      const program = new StridedSliceProgram2(finalShapeSparse);
+      const uniformData = [{ type: "int32", data: $begin }, { type: "int32", data: $strides }];
+      const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+      result = reshape6({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } });
+      backend2.disposeData(resultValues.dataId);
+    }
+  }
+  return result;
+}
+var stridedSliceConfig4 = {
+  kernelName: StridedSlice,
+  backendName: "webgpu",
+  kernelFunc: stridedSlice5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StringNGrams.js
+function stringNGrams4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
+  const { data, dataSplits } = inputs;
+  const $data = backend2.readSync(data.dataId);
+  const $dataSplits = backend2.readSync(dataSplits.dataId);
+  const [nGrams, nGramsSplits] = stringNGramsImplCPU2($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
+  return [
+    backend2.makeTensorInfo([nGrams.length], "string", nGrams),
+    backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
+  ];
+}
+var stringNGramsConfig3 = {
+  kernelName: StringNGrams,
+  backendName: "webgpu",
+  kernelFunc: stringNGrams4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tanh.js
+var tanh5 = unaryKernelFunc3({ opType: UnaryOpType.TANH });
+var tanhConfig4 = {
+  kernelName: Tanh,
+  backendName: "webgpu",
+  kernelFunc: tanh5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/tile_webgpu.js
+var TileProgram2 = class {
+  constructor(aShape, reps) {
+    this.variableNames = ["A"];
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    const outputShape = new Array(aShape.length);
+    for (let i2 = 0; i2 < outputShape.length; i2++) {
+      outputShape[i2] = aShape[i2] * reps[i2];
+    }
+    this.outputShape = outputShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.rank = this.outputShape.length;
+    this.shaderKey = "tile";
+  }
+  getUserCode() {
+    const sourceCoords = getSourceCoords5(this.rank, "uniforms.");
+    const userCode = `
+      ${getMainHeaderAndGlobalIndexString()}
+        if (index < uniforms.size) {
+          let resRC = getCoordsFromIndex(index);
+          setOutputAtIndex(index, getA(${sourceCoords}));
+        }
+      }
+    `;
+    return userCode;
+  }
+};
+function getSourceCoords5(rank, uniformPrefix = "") {
+  if (rank >= 5) {
+    throw Error(`Tile for rank ${rank} is not yet supported`);
+  }
+  if (rank === 1) {
+    return `(resRC % ${uniformPrefix}aShape)`;
+  }
+  const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
+  const sourceCoords = [];
+  for (let i2 = 0; i2 < rank; i2++) {
+    sourceCoords.push(`(${currentCoords[i2]} % ${uniformPrefix}aShape[${i2}])`);
+  }
+  return sourceCoords.join();
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tile.js
+function tile6(params) {
+  const { inputs, backend: backend2, attrs } = params;
+  const { x } = inputs;
+  const { reps } = attrs;
+  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string" || x.shape.length >= 5) {
+    const data = backend2.readSync(x.dataId);
+    const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data;
+    const buf = buffer(x.shape, x.dtype, value);
+    const outBuf = tileImplCPU2(buf, reps);
+    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
+  }
+  const program = new TileProgram2(x.shape, reps);
+  const output = backend2.runWebGPUProgram(program, [x], x.dtype);
+  return output;
+}
+var tileConfig4 = {
+  kernelName: Tile,
+  backendName: "webgpu",
+  kernelFunc: tile6
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/top_k_webgpu.js
+var SwapProgram2 = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.workGroupSize = [256, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,
+        dir : i32, inc : i32,`;
+    this.shaderKey = "swap";
+  }
+  getUserCode() {
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+          if (index < uniforms.size) {
+            let outC = getCoordsFromIndex(index);
+            let batch = outC[0];
+            let elemIdx = outC[1];
+            // We compare elements pair-wise within a group of size 2 * inc.
+            // The comparing rule for each group alternates between ascending
+            // and descending. Within each group, we compare each pair at
+            // positions i and i+inc. To decide whether an element at position i
+            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
+            // inc, it is in the first half of the group, we denote it as x0,
+            // otherwise we denote it as x1.
+            // For example, as shown in the Bitonic top K paper referenced
+            // above, Figure5(a) shows that element[1] is in the second half of
+            // the group when group size is 2, but it is in the first half of
+            // the group when group size is 4.
+            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
+            var i = 0;
+            if (isFirstInPair) {
+              i = elemIdx;
+            } else {
+              i = elemIdx - uniforms.inc;
+            }
+
+            var i0 = 0;
+            if (uniforms.firstPass == 1) {
+              i0 = i;
+            } else {
+              i0 = i32(getIndices(batch, i));
+            }
+
+            var i1 = 0;
+            if (uniforms.firstPass == 1) {
+              i1 = i + uniforms.inc;
+            } else {
+              i1 = i32(getIndices(batch, i + uniforms.inc));
+            }
+
+            var x0 = f32(0.0);
+            var x1 = f32(0.0);
+            if (i0 < uniforms.inputSize) {
+              x0 = getX(batch, i0);
+            } else {
+              x0 = uniforms.negativeInf;
+            }
+            if (i1 < uniforms.inputSize) {
+              x1 = getX(batch, i1);
+            } else {
+              x1 = uniforms.negativeInf;
+            }
+
+            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
+            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
+            if (reverse == isGreater) {
+              // Elements in opposite order of direction
+              let iTemp = i0;
+              i0 = i1;
+              i1 = iTemp;
+            }
+            if (isFirstInPair) {
+              setOutputAtIndex(index, f32(i0));
+            } else {
+              setOutputAtIndex(index, f32(i1));
+            }
+          }
+        }
+      `;
+    return userCode;
+  }
+};
+var MergeProgram2 = class {
+  constructor(shape) {
+    this.variableNames = ["x", "indices"];
+    this.workGroupSize = [256, 1, 1];
+    this.size = true;
+    this.outputShape = shape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.uniforms = `inputSize : i32, firstPass : i32, k : i32,`;
+    this.shaderKey = "merge";
+  }
+  getUserCode() {
+    const userCode = `
+        ${getMainHeaderAndGlobalIndexString()}
+          if (index < uniforms.size) {
+            let outC = getCoordsFromIndex(index);
+            let batch = outC[0];
+            let elemIdx = outC[1];
+            // The output size is half of the previous size.
+            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _
+            // (k=4), we only need to output the indices at positions |, the
+            // indices at positions _ can be thrown away, see Figure5(b) After
+            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced
+            // above.
+            // For example, the paper shows we only need to output the orange
+            // bars. The output sequence should look like this | | | | | | | |.
+            // Because the sequence is halved, to map the output index back to
+            // the previous sequence to find the corresponding value, we need
+            // to double the index. When we double the index, we basically
+            // interpolate a position, so 2i looks like
+            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
+            // position of each 2k positions by - elemIdx % k. E.g. for output
+            // at index 4,5,6,7, we want to get the corresponding element at
+            // original index 8,9,10,11, for output at index 8,9,10,11,
+            // we want to get the corresponding element at original index
+            // 16,17,18,19, so on and so forth.
+
+            var i = 0;
+            if (elemIdx < uniforms.k) {
+              i = elemIdx;
+            } else {
+              i = elemIdx * 2 - elemIdx % uniforms.k;
+            }
+            var i0 = 0;
+            if (uniforms.firstPass == 1) {
+              i0 = i;
+            } else {
+              i0 = i32(getIndices(batch, i));
+            }
+            var i1 = 0;
+            if (uniforms.firstPass == 1) {
+              i1 = i + uniforms.k;
+            } else {
+              i1 = i32(getIndices(batch, i + uniforms.k));
+            }
+
+            let x0 = getX(batch, i0);
+            var x1 = f32(0.0);
+            if (i1 < uniforms.inputSize) {
+              x1 = getX(batch, i1);
+            } else {
+              x1 = x0;
+            }
+
+            if (x0 >= x1) {
+              setOutputAtIndex(index, f32(i0));
+            } else {
+              setOutputAtIndex(index, f32(i1));
+            }
+          }
+        }
+      `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/TopK.js
+function disposeIntermediateTensorInfoOrNull2(backend2, tensorInfo) {
+  if (tensorInfo !== null) {
+    backend2.disposeData(tensorInfo.dataId);
+  }
+}
+function roundUpToPow22(num) {
+  let pow22 = 1;
+  while (pow22 < num) {
+    pow22 *= 2;
+  }
+  return pow22;
+}
+function topK3(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { x } = inputs;
+  const { k, sorted } = attrs;
+  const xShape = x.shape;
+  const lastDim = xShape[xShape.length - 1];
+  if (backend2.shouldExecuteOnCPU([x])) {
+    const xVals = backend2.readSync(x.dataId);
+    const [allTopKVals, allTopKIndices] = topKImplCPU2(xVals, xShape, x.dtype, k, sorted);
+    return [
+      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
+      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
+    ];
+  }
+  if (k === 0) {
+    xShape[xShape.length - 1] = 0;
+    return [
+      backend2.makeTensorInfo(xShape, x.dtype, []),
+      backend2.makeTensorInfo(xShape, "int32", [])
+    ];
+  }
+  if (lastDim === 1) {
+    return [
+      x,
+      fill5({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 })
+    ];
+  }
+  const xSize = util_exports.sizeFromShape(xShape);
+  const batch = xSize / lastDim;
+  const x2D = reshape6({ inputs: { x }, attrs: { shape: [batch, lastDim] }, backend: backend2 });
+  const kPow2 = roundUpToPow22(k);
+  const lastDimPow2 = roundUpToPow22(lastDim);
+  let indices = null;
+  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
+  const runSwap = (dir, inc, shape) => {
+    const inputs2 = getInputs();
+    const program = new SwapProgram2(shape);
+    const firstPass = indices === null ? 1 : 0;
+    const uniformDataSwap = [
+      { type: "int32", data: [lastDim] },
+      { type: "int32", data: [firstPass] },
+      { type: "float32", data: [Number.NEGATIVE_INFINITY] },
+      { type: "int32", data: [dir] },
+      { type: "int32", data: [inc] }
+    ];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGPUProgram(program, inputs2, "int32", uniformDataSwap);
+    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);
+  };
+  for (let len = 1; len < kPow2; len *= 2) {
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, [batch, lastDimPow2]);
+    }
+  }
+  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
+    const inputs2 = getInputs();
+    const mergeProgram = new MergeProgram2([batch, indicesSize / 2]);
+    const firstPass = indices === null ? 1 : 0;
+    const uniformDataMerge = [
+      { type: "int32", data: [lastDim] },
+      { type: "int32", data: [firstPass] },
+      { type: "int32", data: [kPow2] }
+    ];
+    const prevIndices2 = indices;
+    indices = backend2.runWebGPUProgram(mergeProgram, inputs2, "int32", uniformDataMerge);
+    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);
+    const len = kPow2 / 2;
+    const dir = len * 2;
+    for (let inc = len; inc >= 1; inc /= 2) {
+      runSwap(dir, inc, indices.shape);
+    }
+  }
+  let prevIndices = indices;
+  indices = slice5({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });
+  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);
+  let values = gatherV24({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });
+  disposeIntermediateTensorInfoOrNull2(backend2, x2D);
+  const newShape = xShape.slice(0, -1);
+  newShape.push(k);
+  prevIndices = indices;
+  indices = reshape6({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);
+  const prevValues = values;
+  values = reshape6({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });
+  disposeIntermediateTensorInfoOrNull2(backend2, prevValues);
+  return [values, indices];
+}
+var topKConfig4 = {
+  kernelName: TopK,
+  backendName: "webgpu",
+  kernelFunc: topK3
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transform_webgpu.js
+var TransformProgram2 = class {
+  constructor(outShape) {
+    this.variableNames = ["Image", "Transforms"];
+    this.uniforms = "interpolationModeId : i32, fillModeId : i32, fillValue : f32,";
+    this.workGroupSize = [64, 1, 1];
+    this.size = true;
+    this.outputShape = outShape;
+    this.dispatchLayout = flatDispatchLayout(this.outputShape);
+    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
+    this.shaderKey = "transform";
+  }
+  getUserCode() {
+    const userCode = `
+          fn mapCoord(outCoord : f32, len : f32) -> f32{
+            var inCoord = outCoord;
+            if(uniforms.fillModeId == 2) {
+              if (inCoord < 0.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz2 = 2.0 * len;
+                  if (inCoord < sz2) {
+                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
+                    inCoord;
+                  }
+                  if (inCoord < -len) {
+                    inCoord = inCoord + sz2;
+                  } else {
+                    inCoord = -inCoord - 1.0;
+                  }
+                }
+              } else if (inCoord > len - 1.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz2 = 2.0 * len;
+                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
+                  if (inCoord >= len) {
+                    inCoord = sz2 - inCoord - 1.0;
+                  }
+                }
+              }
+              return clamp(inCoord, 0.0, len - 1.0);
+            } else if (uniforms.fillModeId == 3) {
+              if (inCoord < 0.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz = len - 1.0;
+                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
+                }
+              } else if (inCoord > len - 1.0) {
+                if (len <= 1.0) {
+                  inCoord = 0.0;
+                } else {
+                  let sz = len - 1.0;
+                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
+                }
+              }
+              return clamp(inCoord, 0.0, len - 1.0);
+            } else if (uniforms.fillModeId == 4) {
+              return clamp(outCoord, 0.0, len - 1.0);
+            }
+            return outCoord;
+          }
+          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
+            channel : i32) -> f32 {
+            var outputValue : f32;
+            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
+                outputValue = getImage(batch, coordY, coordX, channel);
+            } else {
+              outputValue = uniforms.fillValue;
+            }
+            return outputValue;
+          }
+
+          ${getMainHeaderAndGlobalIndexString()}
+            if (index < uniforms.size) {
+              let coords = getCoordsFromIndex(index);
+              var outputValue : f32;
+              let batch = coords[0];
+              let x = coords[2];
+              let y = coords[1];
+              let channel = coords[3];
+              let xf = f32(x);
+              let yf = f32(y);
+              let a1 = getTransforms(batch, 0);
+              let a2 = getTransforms(batch, 1);
+              let a3 = getTransforms(batch, 2);
+              let b1 = getTransforms(batch, 3);
+              let b2 = getTransforms(batch, 4);
+              let b3 = getTransforms(batch, 5);
+              let c1 = getTransforms(batch, 6);
+              let c2 = getTransforms(batch, 7);
+              let projection = c1 * xf + c2 * yf + 1.0;
+              if (projection == 0.0) {
+                outputValue = uniforms.fillValue;
+              } else {
+                let inX = (a1 * xf + a2 * yf + a3) / projection;
+                let inY = (b1 * xf + b2 * yf + b3) / projection;
+                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
+                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
+
+                if (uniforms.interpolationModeId == 1) {
+                  let coordY = i32(round(mapY));
+                  let coordX = i32(round(mapX));
+                  outputValue = readWithFillValue(batch, coordY, coordX,
+                    channel);
+                } else {
+                  let yFloor = floor(mapY);
+                  let xFloor = floor(mapX);
+                  let yCeil = yFloor + 1.0;
+                  let xCeil = xFloor + 1.0;
+                  let valueYFloor = (xCeil - mapX) *
+                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
+                  let valueYCeil = (xCeil - mapX) *
+                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
+                  (mapX - xFloor) *
+                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
+                  outputValue = (yCeil - mapY) * valueYFloor +
+                  (mapY - yFloor) * valueYCeil;
+                }
+              }
+              setOutputAtIndex(index, outputValue);
+            }
+          }
+        `;
+    return userCode;
+  }
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transform.js
+function transform5(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { image: image2, transforms } = inputs;
+  const { interpolation, fillMode, fillValue, outputShape } = attrs;
+  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;
+  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
+  const outShape = [
+    batch,
+    outHeight,
+    outWidth,
+    numChannels
+  ];
+  const program = new TransformProgram2(outShape);
+  const interpolationModeId = interpolation === "nearest" ? 1 : 2;
+  let fillModeId;
+  switch (fillMode) {
+    case "constant":
+      fillModeId = 1;
+      break;
+    case "reflect":
+      fillModeId = 2;
+      break;
+    case "wrap":
+      fillModeId = 3;
+      break;
+    case "nearest":
+      fillModeId = 4;
+      break;
+    default:
+      fillModeId = 1;
+      break;
+  }
+  const uniformData = [
+    { type: "int32", data: [interpolationModeId] },
+    { type: "int32", data: [fillModeId] },
+    { type: "float32", data: [fillValue] }
+  ];
+  return backend2.runWebGPUProgram(program, [image2, transforms], "float32", uniformData);
+}
+var transformConfig4 = {
+  kernelName: Transform,
+  backendName: "webgpu",
+  kernelFunc: transform5
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Unpack.js
+function unpack4(args) {
+  const { inputs, backend: backend2, attrs } = args;
+  const { value } = inputs;
+  let { axis } = attrs;
+  if (axis < 0) {
+    axis += value.shape.length;
+  }
+  const x = value;
+  const xRank = x.shape.length;
+  const num = value.shape[axis];
+  const outShape = new Array(xRank - 1);
+  let outIndex = 0;
+  for (let i2 = 0; i2 < xRank; i2++) {
+    if (i2 !== axis) {
+      outShape[outIndex++] = x.shape[i2];
+    }
+  }
+  const toDispose = [];
+  const begin = new Array(xRank).fill(0);
+  const size = x.shape.slice();
+  size[axis] = 1;
+  const res = new Array(num);
+  for (let i2 = 0; i2 < res.length; i2++) {
+    begin[axis] = i2;
+    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size } });
+    const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
+    res[i2] = reshaped;
+    toDispose.push(sliced);
+  }
+  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  return res;
+}
+var unpackConfig4 = {
+  kernelName: Unpack,
+  backendName: "webgpu",
+  kernelFunc: unpack4
+};
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/register_all_kernels.js
+var kernelConfigs4 = [
+  _fusedMatMulConfig4,
+  absConfig4,
+  addConfig4,
+  addNConfig4,
+  argMaxConfig4,
+  argMinConfig3,
+  avgPoolConfig4,
+  batchMatMulConfig4,
+  batchToSpaceNDConfig4,
+  castConfig4,
+  ceilConfig4,
+  clipByValueConfig4,
+  complexConfig3,
+  concatConfig4,
+  conv2DConfig4,
+  conv2DBackpropInputConfig4,
+  cosConfig4,
+  coshConfig4,
+  cropAndResizeConfig4,
+  cumprodConfig4,
+  cumsumConfig4,
+  depthToSpaceConfig4,
+  depthwiseConv2dNativeConfig4,
+  einsumConfig3,
+  eluConfig4,
+  equalConfig4,
+  expConfig4,
+  expandDimsConfig4,
+  expm1Config3,
+  fillConfig4,
+  flipLeftRightConfig4,
+  fromPixelsConfig2,
+  floorConfig4,
+  floorDivConfig4,
+  fusedBatchNormConfig2,
+  fusedConv2DConfig4,
+  fusedDepthwiseConv2DConfig4,
+  gatherNdConfig4,
+  gatherV2Config4,
+  greaterConfig4,
+  greaterEqualConfig4,
+  identityConfig4,
+  imagConfig3,
+  leakyReluConfig4,
+  lessConfig4,
+  lessEqualConfig4,
+  logConfig4,
+  logicalAndConfig4,
+  logicalNotConfig3,
+  maxConfig4,
+  maximumConfig4,
+  maxPoolConfig4,
+  meanConfig4,
+  minConfig4,
+  minimumConfig4,
+  mirrorPadConfig4,
+  multiplyConfig4,
+  negConfig4,
+  nonMaxSuppressionV3Config4,
+  nonMaxSuppressionV5Config4,
+  notEqualConfig4,
+  onesLikeConfig4,
+  packConfig4,
+  padV2Config4,
+  powConfig4,
+  preluConfig4,
+  prodConfig4,
+  rangeConfig4,
+  realConfig3,
+  realDivConfig4,
+  reluConfig4,
+  relu6Config4,
+  reshapeConfig4,
+  resizeBilinearConfig4,
+  resizeNearestNeighborConfig3,
+  rotateWithOffsetConfig4,
+  rsqrtConfig4,
+  scatterNdConfig4,
+  selectConfig4,
+  sigmoidConfig4,
+  sinConfig4,
+  sinhConfig3,
+  sliceConfig4,
+  stridedSliceConfig4,
+  stringNGramsConfig3,
+  softmaxConfig4,
+  spaceToBatchNDConfig4,
+  sparseToDenseConfig3,
+  splitVConfig4,
+  sqrtConfig4,
+  squareConfig4,
+  squaredDifferenceConfig4,
+  subConfig4,
+  sumConfig4,
+  tanhConfig4,
+  tileConfig4,
+  topKConfig4,
+  transformConfig4,
+  transposeConfig4,
+  unpackConfig4,
+  zerosLikeConfig4
+];
+for (const kernelConfig of kernelConfigs4) {
+  registerKernel(kernelConfig);
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/buffer_manager.js
+var BufferManager = class {
+  constructor(device) {
+    this.device = device;
+    this.numUsedBuffers = 0;
+    this.numFreeBuffers = 0;
+    this.freeBuffers = /* @__PURE__ */ new Map();
+    this.usedBuffers = /* @__PURE__ */ new Map();
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+  acquireUploadBuffer(byteSize, usage) {
+    return this.acquireBuffer(byteSize, usage, true);
+  }
+  acquireBuffer(byteSize, usage, mappedAtCreation = false) {
+    const key = getBufferKey(byteSize, usage);
+    if (!this.freeBuffers.has(key)) {
+      this.freeBuffers.set(key, []);
+    }
+    if (!this.usedBuffers.has(key)) {
+      this.usedBuffers.set(key, []);
+    }
+    this.numBytesUsed += byteSize;
+    this.numUsedBuffers++;
+    if (this.freeBuffers.get(key).length > 0) {
+      this.numFreeBuffers--;
+      const newBuffer2 = this.freeBuffers.get(key).shift();
+      this.usedBuffers.get(key).push(newBuffer2);
+      return newBuffer2;
+    }
+    this.numBytesAllocated += byteSize;
+    const newBuffer = this.device.createBuffer({ mappedAtCreation, size: byteSize, usage });
+    this.usedBuffers.get(key).push(newBuffer);
+    return newBuffer;
+  }
+  releaseBuffer(buffer2, byteSize, usage) {
+    if (this.freeBuffers.size === 0) {
+      return;
+    }
+    const key = getBufferKey(byteSize, usage);
+    if (!this.freeBuffers.has(key)) {
+      this.freeBuffers.set(key, []);
+    }
+    this.freeBuffers.get(key).push(buffer2);
+    this.numFreeBuffers++;
+    this.numUsedBuffers--;
+    const bufferList = this.usedBuffers.get(key);
+    const bufferIndex = bufferList.indexOf(buffer2);
+    if (bufferIndex < 0) {
+      throw new Error("Cannot release a buffer that was never provided by this buffer manager");
+    }
+    bufferList.splice(bufferIndex, 1);
+    this.numBytesUsed -= byteSize;
+  }
+  releaseUploadBuffer(buffer2, byteSize, usage) {
+    buffer2.mapAsync(GPUMapMode.WRITE).then(() => {
+      this.releaseBuffer(buffer2, byteSize, usage);
+    }, (err) => {
+    });
+  }
+  getNumUsedBuffers() {
+    return this.numUsedBuffers;
+  }
+  getNumFreeBuffers() {
+    return this.numFreeBuffers;
+  }
+  dispose() {
+    this.freeBuffers.forEach((buffers, key) => {
+      buffers.forEach((buff) => {
+        buff.destroy();
+      });
+    });
+    this.usedBuffers.forEach((buffers, key) => {
+      buffers.forEach((buff) => {
+        buff.destroy();
+      });
+    });
+    this.freeBuffers = /* @__PURE__ */ new Map();
+    this.usedBuffers = /* @__PURE__ */ new Map();
+    this.numUsedBuffers = 0;
+    this.numFreeBuffers = 0;
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+};
+function getBufferKey(byteSize, usage) {
+  return `${byteSize}_${usage}`;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/texture_manager.js
+var TextureManager2 = class {
+  constructor(device) {
+    this.device = device;
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this.freeTextures = /* @__PURE__ */ new Map();
+    this.usedTextures = /* @__PURE__ */ new Map();
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+  acquireTexture(width, height, format, usage) {
+    const bytesPerElement2 = getBytesPerElement(format);
+    const byteSize = width * height * bytesPerElement2;
+    const key = getTextureKey(width, height, format, usage);
+    if (!this.freeTextures.has(key)) {
+      this.freeTextures.set(key, []);
+    }
+    if (!this.usedTextures.has(key)) {
+      this.usedTextures.set(key, []);
+    }
+    this.numBytesUsed += byteSize;
+    this.numUsedTextures++;
+    if (this.freeTextures.get(key).length > 0) {
+      this.numFreeTextures--;
+      const newTexture2 = this.freeTextures.get(key).shift();
+      this.usedTextures.get(key).push(newTexture2);
+      return newTexture2;
+    }
+    this.numBytesAllocated += byteSize;
+    const newTexture = this.device.createTexture({
+      size: [width, height],
+      format,
+      usage
+    });
+    this.usedTextures.get(key).push(newTexture);
+    return newTexture;
+  }
+  releaseTexture(texture, width, height, format, usage) {
+    if (this.freeTextures.size === 0) {
+      return;
+    }
+    const key = getTextureKey(width, height, format, usage);
+    if (!this.freeTextures.has(key)) {
+      this.freeTextures.set(key, []);
+    }
+    this.freeTextures.get(key).push(texture);
+    this.numFreeTextures++;
+    this.numUsedTextures--;
+    const textureList = this.usedTextures.get(key);
+    const textureIndex = textureList.indexOf(texture);
+    if (textureIndex < 0) {
+      throw new Error("Cannot release a texture that was never provided by this texture manager");
+    }
+    textureList.splice(textureIndex, 1);
+    const bytesPerElement2 = getBytesPerElement(format);
+    const byteSize = width * height * bytesPerElement2;
+    this.numBytesUsed -= byteSize;
+  }
+  getNumUsedTextures() {
+    return this.numUsedTextures;
+  }
+  getNumFreeTextures() {
+    return this.numFreeTextures;
+  }
+  dispose() {
+    this.freeTextures.forEach((textures, key) => {
+      textures.forEach((texture) => {
+        texture.destroy();
+      });
+    });
+    this.usedTextures.forEach((textures, key) => {
+      textures.forEach((texture) => {
+        texture.destroy();
+      });
+    });
+    this.freeTextures = /* @__PURE__ */ new Map();
+    this.usedTextures = /* @__PURE__ */ new Map();
+    this.numUsedTextures = 0;
+    this.numFreeTextures = 0;
+    this.numBytesUsed = 0;
+    this.numBytesAllocated = 0;
+  }
+};
+function getTextureKey(width, height, format, usage) {
+  return `${width}_${height}_${format}_${usage}`;
+}
+function getBytesPerElement(format) {
+  if (format === "rgba8unorm") {
+    return 16;
+  } else {
+    throw new Error(`${format} is not supported!`);
+  }
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_program.js
+var makeBindGroup = (device, bindGroupLayout, inputs, output, uniforms) => {
+  const bindings = [output, ...inputs];
+  if (uniforms) {
+    bindings.push(uniforms);
+  }
+  return device.createBindGroup({
+    layout: bindGroupLayout,
+    entries: bindings.map((b, i2) => ({ binding: i2, resource: b }))
+  });
+};
+var compileProgram2 = (device, program, pipelineLayout, inputsData, output, isFromPixel = false) => {
+  const outputData = { dtype: output.dtype, shape: output.shape };
+  const source = makeShader2(inputsData, outputData, program, isFromPixel);
+  const module = device.createShaderModule({ code: source, label: program.constructor.name });
+  const pipeline = device.createComputePipeline({
+    layout: pipelineLayout,
+    compute: { module, entryPoint: "main" },
+    label: program.constructor.name
+  });
+  return pipeline;
+};
+function makeShaderKey2(program, shapes, types = [], broadcastDimsKey = "", inputShapesEqualsOutShape = "") {
+  const key = program.shaderKey + "_" + (program.workGroupSize ? program.workGroupSize.join(",") : "") + shapes.map((shape) => shape.length).join(",") + types.join(",") + program.variableNames.join(",") + broadcastDimsKey + inputShapesEqualsOutShape;
+  return key;
+}
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/backend_webgpu.js
+var CPU_HANDOFF_SIZE_THRESHOLD2 = env().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
+var reshapeDispatch = (device, program) => {
+  const MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE = device.limits.maxComputeWorkgroupsPerDimension;
+  const layout = program["dispatchLayout"];
+  const dispatch = program["dispatch"];
+  if (dispatch.every((d) => d <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE)) {
+    return dispatch;
+  }
+  util_exports.assert(dispatch[0] > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && layout.y === void 0 && layout.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
+  let dispatchAverage = Math.ceil(Math.sqrt(dispatch[0]));
+  if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) {
+    dispatchAverage = Math.ceil(Math.cbrt(dispatch[0]));
+    util_exports.assert(dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE, () => "Total dispatch size exceeds WebGPU maximum.");
+    return [dispatchAverage, dispatchAverage, dispatchAverage];
+  } else {
+    return [dispatchAverage, dispatchAverage, 1];
+  }
+};
+var WebGPUBackend = class extends KernelBackend {
+  constructor(device, supportTimeQuery = false) {
+    super();
+    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();
+    this.tensorDisposalQueue = [];
+    this.uniformDisposalQueue = [];
+    this.stagingDisposalQueue = [];
+    this.textureDisposalQueue = [];
+    this.disposed = false;
+    this.uploadWaitMs = 0;
+    this.downloadWaitMs = 0;
+    this.dispatchNumberInEncoder = 0;
+    this.fromPixelTextureLayout = null;
+    this.fromPixelImportTextureLayout = null;
+    if (!isWebGPUSupported()) {
+      throw new Error("WebGPU is not supported on this device");
+    }
+    this.layoutCache = {};
+    this.pipelineCache = {};
+    this.device = device;
+    this.queue = device.queue;
+    this.currentCommandEncoder = null;
+    this.currentComputePass = null;
+    this.supportTimeQuery = supportTimeQuery;
+    this.bufferManager = new BufferManager(this.device);
+    this.textureManager = new TextureManager2(this.device);
+    this.tensorMap = new DataStorage(this, engine());
+    if (this.supportTimeQuery) {
+      this.querySet = this.device.createQuerySet({
+        type: "timestamp",
+        count: 2
+      });
+    }
+    if (env().getBool("WEBGPU_USE_PROFILE_TOOL")) {
+      this.dummyCanvas = document.createElement("canvas");
+      this.dummyCanvas.width = 1;
+      this.dummyCanvas.height = 1;
+      this.dummyContext = this.dummyCanvas.getContext("webgpu");
+      this.dummyContext.configure({
+        device,
+        format: "bgra8unorm"
+      });
+      document.body.appendChild(this.dummyCanvas);
+    }
+  }
+  nextDataId() {
+    return WebGPUBackend.nextDataId++;
+  }
+  floatPrecision() {
+    return 32;
+  }
+  defaultGpuBufferUsage() {
+    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;
+  }
+  flushDisposalQueue() {
+    this.tensorDisposalQueue.forEach((d) => {
+      this.maybeReleaseBuffer(d);
+      this.tensorMap.delete(d);
+    });
+    this.uniformDisposalQueue.forEach((d) => this.bufferManager.releaseBuffer(d.buffer, d.byteSize, d.usage));
+    this.stagingDisposalQueue.forEach((d) => this.bufferManager.releaseUploadBuffer(d.buffer, d.byteSize, d.usage));
+    this.textureDisposalQueue.forEach((d) => this.textureManager.releaseTexture(d.texture, d.width, d.height, d.format, d.usage));
+    this.tensorDisposalQueue = [];
+    this.uniformDisposalQueue = [];
+    this.stagingDisposalQueue = [];
+    this.textureDisposalQueue = [];
+  }
+  disposeData(dataId, force = false) {
+    if (this.tensorMap.has(dataId)) {
+      const data = this.tensorMap.get(dataId);
+      data.refCount--;
+      if (!force && data.refCount > 0) {
+        return false;
+      }
+      if (this.commandQueueOwnedIds.has(dataId)) {
+        this.tensorDisposalQueue.push(dataId);
+        return false;
+      } else {
+        this.maybeReleaseBuffer(dataId);
+      }
+      const { complexTensorInfos } = this.tensorMap.get(dataId);
+      if (complexTensorInfos != null) {
+        this.disposeData(complexTensorInfos.real.dataId, true);
+        this.disposeData(complexTensorInfos.imag.dataId, true);
+      }
+      this.tensorMap.delete(dataId);
+    }
+    return true;
+  }
+  memory() {
+    return {
+      numBytesInGPU: this.bufferManager.numBytesUsed,
+      numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated,
+      unreliable: false
+    };
+  }
+  getBufferManager() {
+    return this.bufferManager;
+  }
+  getTextureManager() {
+    return this.textureManager;
+  }
+  acquireBuffer(byteSize, usage = this.defaultGpuBufferUsage()) {
+    return this.bufferManager.acquireBuffer(byteSize, usage);
+  }
+  maybeReleaseBuffer(dataId) {
+    const info = this.tensorMap.get(dataId);
+    if (info != null && info.bufferInfo.buffer != null) {
+      this.bufferManager.releaseBuffer(info.bufferInfo.buffer, info.bufferInfo.byteSize, info.bufferInfo.usage);
+      info.bufferInfo.buffer = null;
+    }
+  }
+  refCount(dataId) {
+    if (this.tensorMap.has(dataId)) {
+      const tensorData = this.tensorMap.get(dataId);
+      return tensorData.refCount;
+    }
+    return 0;
+  }
+  incRef(dataId) {
+    const tensorData = this.tensorMap.get(dataId);
+    tensorData.refCount++;
+  }
+  decRef(dataId) {
+    if (this.tensorMap.has(dataId)) {
+      const tensorData = this.tensorMap.get(dataId);
+      tensorData.refCount--;
+    }
+  }
+  write(values, shape, dtype) {
+    if (dtype === "complex64" && values != null) {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    const dataId = { id: this.nextDataId() };
+    const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);
+    this.tensorMap.set(dataId, {
+      dtype,
+      shape,
+      values,
+      bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() },
+      refCount: 1
+    });
+    return dataId;
+  }
+  move(dataId, values, shape, dtype, refCount) {
+    if (dtype === "complex64") {
+      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
+    }
+    const byteSize = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);
+    this.tensorMap.set(dataId, {
+      dtype,
+      shape,
+      values,
+      bufferInfo: { byteSize, usage: this.defaultGpuBufferUsage() },
+      refCount
+    });
+  }
+  submitQueue() {
+    this.ensureComputePassEnded();
+    this.queue.submit([this.currentCommandEncoder.finish()]);
+    this.currentCommandEncoder = null;
+    this.dispatchNumberInEncoder = 0;
+    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();
+    this.flushDisposalQueue();
+  }
+  getBuffer(dataId) {
+    this.uploadToGPU(dataId);
+    return this.tensorMap.get(dataId).bufferInfo.buffer;
+  }
+  ensureCommandEncoderReady() {
+    if (!this.currentCommandEncoder) {
+      this.currentCommandEncoder = this.device.createCommandEncoder();
+    }
+  }
+  ensureComputePassEnded() {
+    if (this.currentComputePass) {
+      this.currentComputePass.end();
+      this.currentComputePass = null;
+    }
+  }
+  getComputePass() {
+    if (!this.currentComputePass) {
+      this.currentComputePass = this.currentCommandEncoder.beginComputePass();
+    }
+    return this.currentComputePass;
+  }
+  async getBufferData(buffer2, size) {
+    const staging = this.acquireBuffer(size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
+    this.ensureCommandEncoderReady();
+    this.ensureComputePassEnded();
+    this.currentCommandEncoder.copyBufferToBuffer(buffer2, 0, staging, 0, size);
+    this.submitQueue();
+    await staging.mapAsync(GPUMapMode.READ);
+    const values = staging.getMappedRange().slice(0);
+    staging.unmap();
+    if (staging != null) {
+      this.bufferManager.releaseBuffer(staging, size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
+    }
+    if (env().getBool("WEBGPU_USE_PROFILE_TOOL")) {
+      util_exports.assert(this.dummyContext !== void 0, () => `Fail to get context for profiling tool`);
+      this.dummyContext.getCurrentTexture();
+    }
+    return values;
+  }
+  convertAndCacheOnCPU(dataId, data) {
+    const info = this.tensorMap.get(dataId);
+    this.maybeReleaseBuffer(dataId);
+    info.values = data;
+    return info.values;
+  }
+  readSync(dataId) {
+    const texData = this.tensorMap.get(dataId);
+    const { values } = texData;
+    if (values == null) {
+      throw new Error("WebGPU readSync is only available for CPU-resident tensors.");
+    }
+    return values;
+  }
+  async read(dataId) {
+    if (!this.tensorMap.has(dataId)) {
+      throw new Error(`Tensor ${dataId} was not registered!`);
+    }
+    const info = this.tensorMap.get(dataId);
+    const { values } = info;
+    if (values != null) {
+      return this.convertAndCacheOnCPU(dataId, values);
+    }
+    let vals;
+    if (info.dtype === "complex64") {
+      const ps = await Promise.all([
+        this.read(info.complexTensorInfos.real.dataId),
+        this.read(info.complexTensorInfos.imag.dataId)
+      ]);
+      const realValues = ps[0];
+      const imagValues = ps[1];
+      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
+    } else {
+      const data = info.values != null ? info.values : await this.getBufferData(info.bufferInfo.buffer, info.bufferInfo.byteSize);
+      vals = ArrayBufferToTypedArray(data, info.dtype);
+    }
+    this.convertAndCacheOnCPU(dataId, vals);
+    return vals;
+  }
+  readToGPU(dataId) {
+    const srcData = this.tensorMap.get(dataId);
+    const { values, dtype, shape, bufferInfo } = srcData;
+    if (dtype === "complex64") {
+      throw new Error("Does not support reading buffer for complex64 dtype.");
+    }
+    if (bufferInfo.buffer == null) {
+      if (values != null) {
+        throw new Error("Data is not on GPU but on CPU.");
+      } else {
+        throw new Error("There is no data on GPU or CPU.");
+      }
+    }
+    const size = util_exports.sizeFromShape(shape) * GPUBytesPerElement(dtype);
+    const resBuffer = this.acquireBuffer(size);
+    this.ensureCommandEncoderReady();
+    this.ensureComputePassEnded();
+    this.currentCommandEncoder.copyBufferToBuffer(bufferInfo.buffer, 0, resBuffer, 0, size);
+    this.submitQueue();
+    const tensorInfo = this.makeTensorInfo(shape, dtype);
+    const tensorRef = engine().makeTensorFromTensorInfo(tensorInfo);
+    const info = this.tensorMap.get(tensorInfo.dataId);
+    info.bufferInfo.buffer = resBuffer;
+    return { tensorRef, buffer: resBuffer, bufSize: size };
+  }
+  bufferSync(t2) {
+    const data = this.readSync(t2.dataId);
+    if (t2.dtype === "string") {
+      try {
+        const strings = data.map((d) => util_exports.decodeString(d));
+        return buffer(t2.shape, t2.dtype, strings);
+      } catch (_a) {
+        throw new Error("Failed to decode encoded string bytes into utf-8");
+      }
+    }
+    return buffer(t2.shape, t2.dtype, data);
+  }
+  async time(f) {
+    const oldActiveTimers = this.activeTimers;
+    const newActiveTimers = [];
+    let outerMostTime = false;
+    if (this.programTimersStack == null) {
+      this.programTimersStack = newActiveTimers;
+      outerMostTime = true;
+    } else {
+      this.activeTimers.push(newActiveTimers);
+    }
+    this.activeTimers = newActiveTimers;
+    f();
+    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);
+    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);
+    this.activeTimers = oldActiveTimers;
+    if (outerMostTime) {
+      this.programTimersStack = null;
+    }
+    const res = {
+      uploadWaitMs: this.uploadWaitMs,
+      downloadWaitMs: this.downloadWaitMs,
+      kernelMs: null,
+      wallMs: null
+    };
+    const kernelMs = await Promise.all(flattenedActiveTimerQueries);
+    res["kernelMs"] = util_exports.sum(kernelMs);
+    res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+    this.uploadWaitMs = 0;
+    this.downloadWaitMs = 0;
+    return res;
+  }
+  getAndSavePipeline(key, getPipeline) {
+    if (!(key in this.pipelineCache)) {
+      this.pipelineCache[key] = getPipeline();
+    }
+    return this.pipelineCache[key];
+  }
+  makeTensorInfo(shape, dtype, values) {
+    let dataId;
+    if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
+      const encodedValues = values.map((d) => util_exports.encodeString(d));
+      dataId = this.write(encodedValues, shape, dtype);
+    } else {
+      dataId = this.write(values, shape, dtype);
+    }
+    return { dataId, shape, dtype };
+  }
+  tensorToBinding(tensor2) {
+    if (!tensor2) {
+      return null;
+    }
+    const tensorData = this.tensorMap.get(tensor2.dataId);
+    return {
+      offset: 0,
+      size: tensorData.bufferInfo.byteSize,
+      buffer: tensorData.bufferInfo.buffer
+    };
+  }
+  async getQueryTime(query) {
+    if (this.supportTimeQuery) {
+      return this.getTimeFromQuerySet(query);
+    } else {
+      return 0;
+    }
+  }
+  uploadToGPU(dataId) {
+    const info = this.tensorMap.get(dataId);
+    if (info.bufferInfo.buffer != null) {
+      return;
+    }
+    info.bufferInfo.buffer = this.acquireBuffer(info.bufferInfo.byteSize);
+    if (info.values) {
+      const stagingBuffer = this.bufferManager.acquireUploadBuffer(info.bufferInfo.byteSize, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC);
+      const arrayBuffer = stagingBuffer.getMappedRange();
+      if (info.dtype === "int32" || info.dtype === "bool") {
+        new Int32Array(arrayBuffer).set(info.values);
+      } else {
+        new Float32Array(arrayBuffer).set(info.values);
+      }
+      stagingBuffer.unmap();
+      this.ensureCommandEncoderReady();
+      this.ensureComputePassEnded();
+      this.currentCommandEncoder.copyBufferToBuffer(stagingBuffer, 0, info.bufferInfo.buffer, 0, info.bufferInfo.byteSize);
+      const stagingInfo = {
+        byteSize: info.bufferInfo.byteSize,
+        usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC,
+        buffer: stagingBuffer
+      };
+      this.stagingDisposalQueue.push(stagingInfo);
+    }
+  }
+  makeUniforms(uniformsWithType) {
+    let currentOffset = 0;
+    let preLength = 0;
+    const offsets = [];
+    uniformsWithType.forEach((d) => {
+      if (d.data.length === 0) {
+        d.data = [1];
+      }
+      let baseAlignment;
+      switch (d.data.length) {
+        case 1:
+          baseAlignment = 4;
+          break;
+        case 2:
+          baseAlignment = 8;
+          break;
+        case 3:
+          baseAlignment = 16;
+          break;
+        case 4:
+          baseAlignment = 16;
+          break;
+        case 5:
+          baseAlignment = 16;
+          break;
+        case 6:
+          baseAlignment = 16;
+          break;
+        default:
+          util_exports.assert(false, () => `Unsupported ${d.data.length}D shape`);
+      }
+      if (preLength === 5 || preLength === 6) {
+        baseAlignment = 16;
+      }
+      currentOffset = Math.ceil(currentOffset / baseAlignment) * baseAlignment;
+      preLength = d.data.length;
+      offsets.push(currentOffset);
+      currentOffset += d.data.length * 4;
+    });
+    const arrayBuffer = new ArrayBuffer(currentOffset);
+    uniformsWithType.forEach((d, i2) => {
+      const offset = offsets[i2];
+      if (d.type === "int32") {
+        new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);
+      } else if (d.type === "uint32") {
+        new Uint32Array(arrayBuffer, offset, d.data.length).set(d.data);
+      } else {
+        new Float32Array(arrayBuffer, offset, d.data.length).set(d.data);
+      }
+    });
+    const uniformBuffer = this.acquireBuffer(currentOffset, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
+    this.queue.writeBuffer(uniformBuffer, 0, arrayBuffer, 0, currentOffset);
+    const uniformInfo = {
+      byteSize: currentOffset,
+      usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM,
+      buffer: uniformBuffer
+    };
+    this.uniformDisposalQueue.push(uniformInfo);
+    return { offset: 0, size: currentOffset, buffer: uniformBuffer };
+  }
+  createLayout(inputEntrySize) {
+    const bindGroupLayoutEntries = [];
+    bindGroupLayoutEntries.push({
+      binding: 0,
+      visibility: GPUShaderStage.COMPUTE,
+      buffer: { type: "storage" }
+    });
+    for (let i2 = 0; i2 < inputEntrySize; i2++) {
+      bindGroupLayoutEntries.push({
+        binding: i2 + 1,
+        visibility: GPUShaderStage.COMPUTE,
+        buffer: { type: "read-only-storage" }
+      });
+    }
+    bindGroupLayoutEntries.push({
+      binding: inputEntrySize + 1,
+      visibility: GPUShaderStage.COMPUTE,
+      buffer: { type: "uniform" }
+    });
+    const bindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries });
+    const pipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [bindGroupLayout] });
+    return { bindGroupLayout, pipelineLayout };
+  }
+  getCachedOrCreateLayout(inputEntrySize) {
+    if (!(inputEntrySize in this.layoutCache)) {
+      this.layoutCache[inputEntrySize] = this.createLayout(inputEntrySize);
+    }
+    return this.layoutCache[inputEntrySize];
+  }
+  runWebGPUProgram(program, inputs, outputDtype, programUniforms, output) {
+    if (!output) {
+      output = this.makeTensorInfo(program.outputShape, outputDtype);
+      if (util_exports.sizeFromShape(output.shape) === 0) {
+        const outData = this.tensorMap.get(output.dataId);
+        outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
+        return output;
+      }
+      this.uploadToGPU(output.dataId);
+    }
+    program.dispatch = reshapeDispatch(this.device, program);
+    let uniformsWithType = [{ type: "float32", data: [NaN] }];
+    const bufferShapes = inputs.concat(output).map((d) => d.shape);
+    const uniformsType = "int32";
+    bufferShapes.map((d) => {
+      uniformsWithType.push({ type: uniformsType, data: d });
+    });
+    const strides = util_exports.computeStrides(output.shape);
+    uniformsWithType.push({ type: uniformsType, data: strides });
+    if (program.size) {
+      const size = util_exports.sizeFromShape(program.outputShape);
+      uniformsWithType.push({ type: uniformsType, data: [program.isVec4 ? size / 4 : size] });
+    }
+    if (programUniforms) {
+      uniformsWithType = [...uniformsWithType, ...programUniforms];
+    }
+    const uniforms = this.makeUniforms(uniformsWithType);
+    const inputsData = inputs.map((input2, i2) => {
+      if (input2.dtype === "complex64") {
+        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
+      }
+      this.uploadToGPU(input2.dataId);
+      return {
+        dtype: this.tensorMap.get(input2.dataId).dtype,
+        shape: input2.shape,
+        name: program.variableNames[i2]
+      };
+    });
+    const bufferTypes = inputsData.map((d) => d.dtype).concat(output.dtype);
+    const broadcastDims = inputsData.map((d) => backend_util_exports.getBroadcastDims(d.shape, output.shape));
+    const inputShapesEqualsOutShape = inputsData.map((d) => util_exports.arraysEqual(d.shape, output.shape)).join("_");
+    const broadcastDimsKey = broadcastDims.map((d) => d.join("_")).join(";");
+    const key = makeShaderKey2(program, bufferShapes, bufferTypes, broadcastDimsKey, inputShapesEqualsOutShape);
+    const { bindGroupLayout, pipelineLayout } = this.getCachedOrCreateLayout(program.variableNames.length);
+    const pipeline = this.getAndSavePipeline(key, () => {
+      return compileProgram2(this.device, program, pipelineLayout, inputsData, output);
+    });
+    const shouldTimeProgram = this.activeTimers != null;
+    const bg = makeBindGroup(this.device, bindGroupLayout, inputs.map((t2) => this.tensorToBinding(t2)), this.tensorToBinding(output), uniforms);
+    this.ensureCommandEncoderReady();
+    const pass = this.getComputePass();
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 0);
+      }
+    }
+    pass.setPipeline(pipeline);
+    pass.setBindGroup(0, bg);
+    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 1);
+      }
+    }
+    this.dispatchNumberInEncoder++;
+    inputs.forEach((input2) => {
+      this.commandQueueOwnedIds.add(input2.dataId);
+    });
+    this.commandQueueOwnedIds.add(output.dataId);
+    if (env().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder) {
+      this.submitQueue();
+    }
+    if (shouldTimeProgram) {
+      this.activeTimers.push({
+        name: program.constructor.name,
+        query: this.getQueryTime(this.querySet)
+      });
+    }
+    return output;
+  }
+  getFromPixelTextureLayout(useImport) {
+    if (useImport) {
+      if (this.fromPixelImportTextureLayout === null) {
+        this.fromPixelImportTextureLayout = this.createFromPixelTextureLayout(true);
+      }
+      return this.fromPixelImportTextureLayout;
+    }
+    if (this.fromPixelTextureLayout === null) {
+      this.fromPixelTextureLayout = this.createFromPixelTextureLayout(false);
+    }
+    return this.fromPixelTextureLayout;
+  }
+  createFromPixelTextureLayout(useImport) {
+    const bindGroupLayoutEntries = [];
+    bindGroupLayoutEntries.push({
+      binding: 0,
+      visibility: GPUShaderStage.COMPUTE,
+      buffer: { type: "storage" }
+    });
+    if (useImport) {
+      bindGroupLayoutEntries.push({
+        binding: 1,
+        visibility: GPUShaderStage.COMPUTE,
+        externalTexture: {}
+      });
+    } else {
+      bindGroupLayoutEntries.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, texture: {} });
+    }
+    bindGroupLayoutEntries.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} });
+    const fromPixelBindGroupLayout = this.device.createBindGroupLayout({ entries: bindGroupLayoutEntries });
+    const fromPixelPipelineLayout = this.device.createPipelineLayout({ bindGroupLayouts: [fromPixelBindGroupLayout] });
+    return {
+      bindGroupLayout: fromPixelBindGroupLayout,
+      pipelineLayout: fromPixelPipelineLayout
+    };
+  }
+  copyExternalImageToTexture(externalImage, outShape) {
+    const textureUsage = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING;
+    const textureFormat = "rgba8unorm";
+    const texture = this.textureManager.acquireTexture(outShape[1], outShape[0], textureFormat, textureUsage);
+    const externalResource = texture.createView();
+    this.queue.copyExternalImageToTexture({ source: externalImage }, { texture }, [outShape[1], outShape[0]]);
+    const textureInfo = {
+      width: outShape[1],
+      height: outShape[0],
+      format: textureFormat,
+      usage: textureUsage,
+      texture
+    };
+    this.textureDisposalQueue.push(textureInfo);
+    return externalResource;
+  }
+  runFromPixelsProgram(program, outShape, programUniforms, useImport, externalImage) {
+    program.dispatch = reshapeDispatch(this.device, program);
+    const output = this.makeTensorInfo(outShape, "int32");
+    if (util_exports.sizeFromShape(output.shape) === 0) {
+      const outData = this.tensorMap.get(output.dataId);
+      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
+      return output;
+    }
+    this.uploadToGPU(output.dataId);
+    const key = makeShaderKey2(program, [output.shape]);
+    const layout = this.getFromPixelTextureLayout(useImport);
+    const pipeline = this.getAndSavePipeline(key, () => {
+      return compileProgram2(this.device, program, layout.pipelineLayout, [], output, true);
+    });
+    let externalResource;
+    if (useImport) {
+      const externalTextureDescriptor = {
+        source: externalImage
+      };
+      externalResource = this.device.importExternalTexture(externalTextureDescriptor);
+    } else {
+      externalResource = this.copyExternalImageToTexture(externalImage, output.shape);
+    }
+    const binding = this.tensorToBinding(output);
+    const uniforms = this.makeUniforms(programUniforms);
+    const bindGroup = this.device.createBindGroup({
+      layout: layout.bindGroupLayout,
+      entries: [
+        {
+          binding: 0,
+          resource: {
+            buffer: binding.buffer
+          }
+        },
+        {
+          binding: 1,
+          resource: externalResource
+        },
+        {
+          binding: 2,
+          resource: {
+            buffer: uniforms.buffer
+          }
+        }
+      ]
+    });
+    this.ensureCommandEncoderReady();
+    const pass = this.getComputePass();
+    const shouldTimeProgram = this.activeTimers != null;
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 0);
+      }
+    }
+    pass.setPipeline(pipeline);
+    pass.setBindGroup(0, bindGroup);
+    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);
+    if (shouldTimeProgram) {
+      if (this.supportTimeQuery) {
+        pass.writeTimestamp(this.querySet, 1);
+      }
+    }
+    this.commandQueueOwnedIds.add(output.dataId);
+    this.dispatchNumberInEncoder++;
+    if (env().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder) {
+      this.submitQueue();
+    }
+    if (shouldTimeProgram) {
+      this.activeTimers.push({
+        name: program.constructor.name,
+        query: this.getQueryTime(this.querySet)
+      });
+    }
+    return output;
+  }
+  async getTimeFromQuerySet(querySet) {
+    const queryBuffer = this.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);
+    const dst = this.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
+    this.ensureCommandEncoderReady();
+    this.ensureComputePassEnded();
+    this.currentCommandEncoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0);
+    this.currentCommandEncoder.copyBufferToBuffer(queryBuffer, 0, dst, 0, 16);
+    this.submitQueue();
+    await dst.mapAsync(GPUMapMode.READ);
+    const arrayBuf = new BigUint64Array(dst.getMappedRange());
+    const timeElapsedNanos = Number(arrayBuf[1] - arrayBuf[0]);
+    dst.unmap();
+    this.bufferManager.releaseBuffer(dst, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
+    this.bufferManager.releaseBuffer(queryBuffer, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);
+    return timeElapsedNanos / 1e6;
+  }
+  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD2) {
+    return env().getBool("WEBGPU_CPU_FORWARD") && inputs.every((input2) => this.tensorMap.get(input2.dataId).bufferInfo.buffer == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);
+  }
+  numDataIds() {
+    return this.tensorMap.numDataIds() - this.tensorDisposalQueue.length;
+  }
+  dispose() {
+    if (this.disposed) {
+      return;
+    }
+    this.bufferManager.dispose();
+    this.textureManager.dispose();
+    this.disposed = true;
+  }
+};
+WebGPUBackend.nextDataId = 0;
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu.js
+var webgpu_exports = {};
+__export(webgpu_exports, {
+  WebGPUBackend: () => WebGPUBackend,
+  webgpu_util: () => webgpu_util_exports
+});
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.11_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-backend-webgpu/dist/index.js
+if (isWebGPUSupported()) {
+  registerBackend("webgpu", async () => {
+    env().set("CHECK_COMPUTATION_FOR_ERRORS", false);
+    const gpuDescriptor = {
+      powerPreference: env().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance"
+    };
+    const adapter = await navigator.gpu.requestAdapter(gpuDescriptor);
+    const adapterLimits = adapter.limits;
+    const deviceDescriptor = {};
+    const supportTimeQuery = adapter.features.has("timestamp-query");
+    deviceDescriptor.requiredLimits = {
+      "maxComputeWorkgroupStorageSize": adapterLimits.maxComputeWorkgroupStorageSize,
+      "maxComputeWorkgroupsPerDimension": adapterLimits.maxComputeWorkgroupsPerDimension
+    };
+    if (supportTimeQuery) {
+      deviceDescriptor.requiredFeatures = ["timestamp-query"];
+    } else {
+      console.warn(`This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.`);
+    }
+    const device = await adapter.requestDevice(deviceDescriptor);
+    return new WebGPUBackend(device, supportTimeQuery);
+  }, 3);
+}
+
+// dist/tfjs.version.js
+var e = "3.18.0";
+var s = "3.18.0";
+var t = "3.18.0";
+var r = "3.18.0";
+var l = "3.18.0";
+var i = "3.18.0";
+var a = "3.18.0";
+var V = { tfjs: e, "tfjs-core": s, "tfjs-data": t, "tfjs-layers": r, "tfjs-converter": l, "tfjs-backend-webgl": i, "tfjs-backend-wasm": a };
+export {
+  Abs,
+  Acos,
+  Acosh,
+  AdadeltaOptimizer,
+  AdagradOptimizer,
+  AdamOptimizer,
+  AdamaxOptimizer,
+  Add,
+  AddN,
+  All,
+  Any,
+  ArgMax,
+  ArgMin,
+  Asin,
+  Asinh,
+  Atan,
+  Atan2,
+  Atanh,
+  AvgPool,
+  AvgPool3D,
+  AvgPool3DGrad,
+  AvgPoolGrad,
+  BackendWasm,
+  BatchMatMul,
+  BatchToSpaceND,
+  Bincount,
+  BroadcastArgs,
+  BroadcastTo,
+  Callback,
+  CallbackList,
+  Cast,
+  Ceil,
+  ClipByValue,
+  Complex,
+  ComplexAbs,
+  Concat,
+  Conv2D,
+  Conv2DBackpropFilter,
+  Conv2DBackpropInput,
+  Conv3D,
+  Conv3DBackpropFilterV2,
+  Conv3DBackpropInputV2,
+  Cos,
+  Cosh,
+  CropAndResize,
+  Cumprod,
+  Cumsum,
+  CustomCallback,
+  DataStorage,
+  DenseBincount,
+  DepthToSpace,
+  DepthwiseConv2dNative,
+  DepthwiseConv2dNativeBackpropFilter,
+  DepthwiseConv2dNativeBackpropInput,
+  Diag,
+  Dilation2D,
+  Dilation2DBackpropFilter,
+  Dilation2DBackpropInput,
+  ENV,
+  EarlyStopping,
+  Einsum,
+  Elu,
+  EluGrad,
+  Environment,
+  Equal,
+  Erf,
+  Exp,
+  ExpandDims,
+  Expm1,
+  FFT,
+  Fill,
+  FlipLeftRight,
+  Floor,
+  FloorDiv,
+  FromPixels,
+  FusedBatchNorm,
+  FusedConv2D,
+  FusedDepthwiseConv2D,
+  GPGPUContext,
+  GatherNd,
+  GatherV2,
+  GraphModel,
+  Greater,
+  GreaterEqual,
+  History,
+  IFFT,
+  Identity,
+  Imag,
+  InputSpec,
+  IsFinite,
+  IsInf,
+  IsNan,
+  KernelBackend,
+  LRN,
+  LRNGrad,
+  LayerVariable,
+  LayersModel,
+  LeakyRelu,
+  Less,
+  LessEqual,
+  LinSpace,
+  Log,
+  Log1p,
+  LogSoftmax,
+  LogicalAnd,
+  LogicalNot,
+  LogicalOr,
+  LowerBound,
+  MathBackendWebGL,
+  Max,
+  MaxPool,
+  MaxPool3D,
+  MaxPool3DGrad,
+  MaxPoolGrad,
+  MaxPoolWithArgmax,
+  Maximum,
+  Mean,
+  Min,
+  Minimum,
+  MirrorPad,
+  Mod,
+  MomentumOptimizer,
+  Multinomial,
+  Multiply,
+  Neg,
+  NonMaxSuppressionV3,
+  NonMaxSuppressionV4,
+  NonMaxSuppressionV5,
+  NotEqual,
+  OP_SCOPE_SUFFIX,
+  OneHot,
+  OnesLike,
+  Optimizer,
+  OptimizerConstructors,
+  Pack,
+  PadV2,
+  Pool,
+  Pow,
+  Prelu,
+  Prod,
+  RMSPropOptimizer,
+  RNN,
+  Range,
+  Rank,
+  Real,
+  RealDiv,
+  Reciprocal,
+  Reduction,
+  Relu,
+  Relu6,
+  Reshape,
+  ResizeBilinear,
+  ResizeBilinearGrad,
+  ResizeNearestNeighbor,
+  ResizeNearestNeighborGrad,
+  Reverse,
+  RotateWithOffset,
+  Round,
+  Rsqrt,
+  SGDOptimizer,
+  ScatterNd,
+  SearchSorted,
+  Select,
+  Selu,
+  Sequential,
+  Sigmoid,
+  Sign,
+  Sin,
+  Sinh,
+  Slice,
+  Softmax,
+  Softplus,
+  SpaceToBatchND,
+  SparseFillEmptyRows,
+  SparseReshape,
+  SparseSegmentMean,
+  SparseSegmentSum,
+  SparseToDense,
+  SplitV,
+  Sqrt,
+  Square,
+  SquaredDifference,
+  Step,
+  StridedSlice,
+  StringNGrams,
+  StringSplit,
+  StringToHashBucketFast,
+  Sub,
+  Sum,
+  SymbolicTensor,
+  Tan,
+  Tanh,
+  Tensor,
+  TensorBuffer,
+  Tile,
+  TopK,
+  Transform,
+  Transpose,
+  Unique,
+  Unpack,
+  UnsortedSegmentSum,
+  UpperBound,
+  Variable,
+  ZerosLike,
+  _FusedMatMul,
+  abs,
+  acos,
+  acosh,
+  add2 as add,
+  addN,
+  all,
+  any,
+  argMax,
+  argMin,
+  asin,
+  asinh,
+  atan,
+  atan2,
+  atanh,
+  avgPool,
+  avgPool3d,
+  backend,
+  backend_util_exports as backend_util,
+  basicLSTMCell,
+  batchNorm,
+  batchNorm2d,
+  batchNorm3d,
+  batchNorm4d,
+  batchToSpaceND,
+  bincount,
+  booleanMaskAsync,
+  broadcastArgs,
+  broadcastTo,
+  broadcast_util_exports as broadcast_util,
+  browser_exports as browser,
+  buffer,
+  callbacks,
+  cast,
+  ceil,
+  clipByValue,
+  clone,
+  complex,
+  concat,
+  concat1d,
+  concat2d,
+  concat3d,
+  concat4d,
+  exports_constraints_exports as constraints,
+  conv1d,
+  conv2d,
+  conv2dTranspose,
+  conv3d,
+  conv3dTranspose,
+  copyRegisteredKernels,
+  cos,
+  cosh,
+  cosineWindow,
+  cumprod,
+  cumsum,
+  customGrad,
+  dist_exports2 as data,
+  denseBincount,
+  deprecationWarn,
+  depthToSpace,
+  depthwiseConv2d,
+  deregisterOp,
+  device_util_exports as device_util,
+  diag,
+  dilation2d,
+  disableDeprecationWarnings,
+  dispose,
+  disposeVariables,
+  div,
+  divNoNan,
+  dot,
+  dropout,
+  einsum,
+  elu,
+  enableDebugMode,
+  enableProdMode,
+  enclosingPowerOfTwo,
+  engine,
+  env,
+  equal,
+  erf,
+  euclideanNorm,
+  exp,
+  expandDims,
+  expm1,
+  eye,
+  fft,
+  fill,
+  findBackend,
+  findBackendFactory,
+  floor,
+  floorDiv,
+  forceHalfFloat,
+  fused_ops_exports as fused,
+  gather,
+  gatherND,
+  gather_nd_util_exports as gather_util,
+  getBackend,
+  getGradient,
+  getKernel,
+  getKernelsForBackend,
+  getThreadsCount,
+  gpgpu_util_exports as gpgpu_util,
+  grad,
+  grads,
+  greater,
+  greaterEqual,
+  ifft,
+  imag,
+  image,
+  inTopKAsync,
+  exports_initializers_exports as initializers,
+  input,
+  io_exports as io,
+  irfft,
+  isFinite2 as isFinite,
+  isInf,
+  isNaN2 as isNaN,
+  keep,
+  kernel_impls_exports as kernel_impls,
+  exports_layers_exports as layers,
+  leakyRelu,
+  less,
+  lessEqual,
+  linalg,
+  linspace,
+  loadGraphModel,
+  loadGraphModelSync,
+  loadLayersModel,
+  localResponseNormalization,
+  log2 as log,
+  log1p,
+  logSigmoid,
+  logSoftmax,
+  logSumExp,
+  logicalAnd,
+  logicalNot,
+  logicalOr,
+  logicalXor,
+  losses,
+  lowerBound,
+  matMul,
+  math_exports as math,
+  max,
+  maxPool,
+  maxPool3d,
+  maxPoolWithArgmax,
+  maximum,
+  mean,
+  memory,
+  meshgrid,
+  exports_metrics_exports as metrics,
+  min,
+  minimum,
+  mirrorPad,
+  mod,
+  model,
+  exports_models_exports as models,
+  moments,
+  movingAverage,
+  mul,
+  multiRNNCell,
+  multinomial,
+  neg,
+  nextFrame,
+  norm,
+  notEqual,
+  oneHot,
+  ones2 as ones,
+  onesLike,
+  op,
+  outerProduct,
+  pad,
+  pad1d,
+  pad2d,
+  pad3d,
+  pad4d,
+  pool,
+  pow,
+  prelu,
+  print,
+  prod,
+  profile,
+  rand,
+  randomGamma,
+  randomNormal,
+  randomUniform,
+  range,
+  ready,
+  real,
+  reciprocal,
+  registerBackend,
+  registerCallbackConstructor,
+  registerGradient,
+  registerKernel,
+  registerOp,
+  exports_regularizers_exports as regularizers,
+  relu,
+  relu6,
+  removeBackend,
+  reshape,
+  reverse,
+  reverse1d,
+  reverse2d,
+  reverse3d,
+  reverse4d,
+  rfft,
+  round2 as round,
+  rsqrt,
+  scalar,
+  scatterND,
+  scatter_nd_util_exports as scatter_util,
+  searchSorted,
+  selu,
+  separableConv2d,
+  sequential,
+  serialization_exports as serialization,
+  setBackend,
+  setPlatform,
+  setThreadsCount,
+  setWasmPath,
+  setWasmPaths,
+  setWebGLContext,
+  setdiff1dAsync,
+  sigmoid,
+  sign,
+  signal,
+  sin,
+  sinh,
+  slice,
+  slice1d,
+  slice2d,
+  slice3d,
+  slice4d,
+  slice_util_exports as slice_util,
+  softmax,
+  softplus,
+  spaceToBatchND,
+  sparse,
+  sparseToDense,
+  spectral,
+  split,
+  sqrt,
+  square,
+  squaredDifference,
+  squeeze,
+  stack,
+  step,
+  stridedSlice,
+  string,
+  sub,
+  sum2 as sum,
+  sumOutType,
+  tan,
+  tanh2 as tanh,
+  tensor,
+  tensor1d,
+  tensor2d,
+  tensor3d,
+  tensor4d,
+  tensor5d,
+  tensor6d,
+  tensor_util_exports as tensor_util,
+  test_util_exports as test_util,
+  tidy,
+  tile,
+  time,
+  topk,
+  train,
+  transpose,
+  truncatedNormal,
+  unique,
+  unregisterGradient,
+  unregisterKernel,
+  unsortedSegmentSum,
+  unstack,
+  upcastType,
+  upperBound,
+  util_exports as util,
+  valueAndGrad,
+  valueAndGrads,
+  variable,
+  variableGrads,
+  V as version,
+  version3 as version_converter,
+  version as version_core,
+  version2 as version_layers,
+  version8 as version_wasm,
+  version6 as version_webgl,
+  webgl,
+  webgl_util_exports as webgl_util,
+  webgpu_exports as webgpu,
+  where,
+  whereAsync,
+  zeros,
+  zerosLike
+};
+/**
+ * @license
+ * Copyright 2017 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2019 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use backend file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2020 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the License);
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2021 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the License);
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google Inc. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC
+ *
+ * Use of this source code is governed by an MIT-style
+ * license that can be found in the LICENSE file or at
+ * https://opensource.org/licenses/MIT.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+ * @license
+ * Copyright 2022 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the 'License');
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an 'AS IS' BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+/**
+* @license
+* Copyright 2018 Google LLC. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+* http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+* =============================================================================
+*/
+/** @license See the LICENSE file. */
diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js
new file mode 100644
index 00000000..9aad8af0
--- /dev/null
+++ b/dist/tfjs.version.js
@@ -0,0 +1,7 @@
+/*
+  Human
+  homepage: <https://github.com/vladmandic/human>
+  author: <https://github.com/vladmandic>'
+*/
+
+var e="3.18.0";var s="3.18.0";var t="3.18.0";var r="3.18.0";var l="3.18.0";var i="3.18.0";var a="3.18.0";var V={tfjs:e,"tfjs-core":s,"tfjs-data":t,"tfjs-layers":r,"tfjs-converter":l,"tfjs-backend-webgl":i,"tfjs-backend-wasm":a};export{V as version};
diff --git a/test/build.log b/test/build.log
index 2d8be428..c32a64ba 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,32 +1,24 @@
-2022-07-21 12:50:35 INFO:  Application: {"name":"@vladmandic/human","version":"2.9.0"} 
-2022-07-21 12:50:35 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-07-21 12:50:35 INFO:  Toolchain: {"build":"0.7.7","esbuild":"0.14.49","typescript":"4.7.4","typedoc":"0.23.8","eslint":"8.20.0"} 
-2022-07-21 12:50:35 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2022-07-21 12:50:35 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":608} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":74,"inputBytes":647896,"outputBytes":304014} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":612} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":74,"inputBytes":647900,"outputBytes":304018} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":664} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":74,"inputBytes":647952,"outputBytes":304068} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1069,"outputBytes":358} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1032,"outputBytes":583} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":74,"inputBytes":647871,"outputBytes":302893} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":11,"inputBytes":1271,"outputBytes":2799617} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":74,"inputBytes":3446905,"outputBytes":1678594} 
-2022-07-21 12:50:36 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":74,"inputBytes":3446905,"outputBytes":3078417} 
-2022-07-21 12:50:43 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":30} 
-2022-07-21 12:50:45 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":76,"generated":true} 
-2022-07-21 12:50:45 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6414,"outputBytes":3094} 
-2022-07-21 12:50:45 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
-2022-07-21 12:50:55 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":107,"errors":0,"warnings":0} 
-2022-07-21 12:50:55 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2022-07-21 12:50:55 INFO:  Done... 
-2022-07-21 12:52:39 INFO:  @vladmandic/human version 2.9.0 
-2022-07-21 12:52:39 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.15.0 
-2022-07-21 12:52:39 INFO:  Application: {"name":"@vladmandic/human","version":"2.9.0"} 
-2022-07-21 12:52:39 INFO:  Environment: {"profile":"clean","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-07-21 12:52:39 INFO:  Toolchain: {"build":"0.7.7","esbuild":"0.14.49","typescript":"4.7.4","typedoc":"0.23.8","eslint":"8.20.0"} 
-2022-07-21 12:52:39 INFO:  Build: {"profile":"clean","steps":["clean"]} 
-2022-07-21 12:52:39 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
-2022-07-21 12:52:39 INFO:  Done... 
+2022-07-21 13:00:58 INFO:  Application: {"name":"@vladmandic/human","version":"2.9.0"} 
+2022-07-21 13:00:58 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-07-21 13:00:58 INFO:  Toolchain: {"build":"0.7.7","esbuild":"0.14.49","typescript":"4.7.4","typedoc":"0.23.8","eslint":"8.20.0"} 
+2022-07-21 13:00:58 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2022-07-21 13:00:58 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":608} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":74,"inputBytes":647934,"outputBytes":304014} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":612} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":74,"inputBytes":647938,"outputBytes":304018} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":664} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":74,"inputBytes":647990,"outputBytes":304068} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1069,"outputBytes":358} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1032,"outputBytes":583} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":74,"inputBytes":647909,"outputBytes":302893} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":11,"inputBytes":1271,"outputBytes":2799617} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":74,"inputBytes":3446943,"outputBytes":1678594} 
+2022-07-21 13:00:58 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":74,"inputBytes":3446943,"outputBytes":3078417} 
+2022-07-21 13:01:06 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":30} 
+2022-07-21 13:01:08 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":76,"generated":true} 
+2022-07-21 13:01:08 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6414,"outputBytes":3094} 
+2022-07-21 13:01:08 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
+2022-07-21 13:01:20 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":107,"errors":0,"warnings":0} 
+2022-07-21 13:01:20 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2022-07-21 13:01:20 INFO:  Done... 
diff --git a/test/test.log b/test/test.log
index ad0a768e..9a72b2d9 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,684 +1,684 @@
-2022-07-16 09:06:08 INFO:  @vladmandic/human version 2.8.1 
-2022-07-16 09:06:08 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.15.0 
-2022-07-16 09:06:08 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
-2022-07-16 09:06:08 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
-2022-07-16 09:06:08 INFO:  
-2022-07-16 09:06:08 INFO:  test-node.js start 
-2022-07-16 09:06:08 INFO:  test-node.js test: configuration validation 
-2022-07-16 09:06:08 STATE: test-node.js passed: configuration default validation [] 
-2022-07-16 09:06:08 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-07-16 09:06:08 INFO:  test-node.js test: model load 
-2022-07-16 09:06:08 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
-2022-07-16 09:06:08 INFO:  test-node.js test: warmup 
-2022-07-16 09:06:08 STATE: test-node.js passed: create human 
-2022-07-16 09:06:08 INFO:  test-node.js human version: 2.8.1 
-2022-07-16 09:06:08 INFO:  test-node.js platform: linux x64 agent: NodeJS v16.15.0 
-2022-07-16 09:06:08 INFO:  test-node.js tfjs version: 3.18.0 
-2022-07-16 09:06:08 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
-2022-07-16 09:06:08 STATE: test-node.js passed: set backend: tensorflow 
-2022-07-16 09:06:08 STATE: test-node.js tensors 1921 
-2022-07-16 09:06:08 STATE: test-node.js passed: load models 
-2022-07-16 09:06:08 STATE: test-node.js  result: defined models: 22 loaded models: 12 
-2022-07-16 09:06:08 STATE: test-node.js passed: warmup: none default 
-2022-07-16 09:06:08 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-07-16 09:06:08 DATA:  test-node.js  result: performance: load: null total: null 
-2022-07-16 09:06:08 STATE: test-node.js passed: warmup none result match 
-2022-07-16 09:06:08 STATE: test-node.js event: image 
-2022-07-16 09:06:09 STATE: test-node.js event: detect 
-2022-07-16 09:06:09 STATE: test-node.js event: warmup 
-2022-07-16 09:06:09 STATE: test-node.js passed: warmup: face default 
-2022-07-16 09:06:09 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-07-16 09:06:09 DATA:  test-node.js  result: performance: load: null total: 371 
-2022-07-16 09:06:09 STATE: test-node.js passed: warmup face result match 
-2022-07-16 09:06:09 STATE: test-node.js event: image 
-2022-07-16 09:06:09 STATE: test-node.js event: detect 
-2022-07-16 09:06:09 STATE: test-node.js event: warmup 
-2022-07-16 09:06:09 STATE: test-node.js passed: warmup: body default 
-2022-07-16 09:06:09 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:09 DATA:  test-node.js  result: performance: load: null total: 279 
-2022-07-16 09:06:09 STATE: test-node.js passed: warmup body result match 
-2022-07-16 09:06:09 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-07-16 09:06:09 INFO:  test-node.js test: details verification 
-2022-07-16 09:06:09 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:09 STATE: test-node.js event: image 
-2022-07-16 09:06:10 STATE: test-node.js event: detect 
-2022-07-16 09:06:10 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:10 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:10 DATA:  test-node.js  result: performance: load: null total: 263 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face length 1 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face score 1 0.93 1 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face arrays 4 478 1024 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face anti-spoofing 0.79 
-2022-07-16 09:06:10 STATE: test-node.js passed: details face liveness 0.83 
-2022-07-16 09:06:10 STATE: test-node.js passed: details body length 1 
-2022-07-16 09:06:10 STATE: test-node.js passed: details body 0.92 17 6 
-2022-07-16 09:06:10 STATE: test-node.js passed: details hand length 1 
-2022-07-16 09:06:10 STATE: test-node.js passed: details hand 0.51 0.73 point 
-2022-07-16 09:06:10 STATE: test-node.js passed: details hand arrays 21 5 7 
-2022-07-16 09:06:10 STATE: test-node.js passed: details gesture length 7 
-2022-07-16 09:06:10 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-07-16 09:06:10 STATE: test-node.js passed: details object length 1 
-2022-07-16 09:06:10 STATE: test-node.js passed: details object 0.72 person 
-2022-07-16 09:06:10 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-07-16 09:06:10 STATE: test-node.js event: image 
-2022-07-16 09:06:10 STATE: test-node.js event: detect 
-2022-07-16 09:06:10 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-07-16 09:06:10 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-07-16 09:06:10 STATE: test-node.js event: image 
-2022-07-16 09:06:10 STATE: test-node.js event: detect 
-2022-07-16 09:06:10 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-07-16 09:06:11 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:11 STATE: test-node.js event: image 
-2022-07-16 09:06:11 STATE: test-node.js event: detect 
-2022-07-16 09:06:11 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-07-16 09:06:11 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:11 STATE: test-node.js event: image 
-2022-07-16 09:06:11 STATE: test-node.js event: detect 
-2022-07-16 09:06:11 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-07-16 09:06:11 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-07-16 09:06:11 STATE: test-node.js event: image 
-2022-07-16 09:06:12 STATE: test-node.js event: detect 
-2022-07-16 09:06:12 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-07-16 09:06:12 INFO:  test-node.js test default 
-2022-07-16 09:06:12 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:12 STATE: test-node.js event: image 
-2022-07-16 09:06:12 STATE: test-node.js event: detect 
-2022-07-16 09:06:12 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:12 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:12 DATA:  test-node.js  result: performance: load: null total: 248 
-2022-07-16 09:06:12 STATE: test-node.js passed: default result face match 1 female 0.97 
-2022-07-16 09:06:12 INFO:  test-node.js test sync 
-2022-07-16 09:06:12 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:12 STATE: test-node.js event: image 
-2022-07-16 09:06:12 STATE: test-node.js event: detect 
-2022-07-16 09:06:12 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:12 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:12 DATA:  test-node.js  result: performance: load: null total: 234 
-2022-07-16 09:06:12 STATE: test-node.js passed: default sync 1 female 0.97 
-2022-07-16 09:06:12 INFO:  test-node.js test: image process 
-2022-07-16 09:06:12 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:12 STATE: test-node.js passed: image input null [1,256,256,3] 
-2022-07-16 09:06:12 INFO:  test-node.js test: image null 
-2022-07-16 09:06:12 STATE: test-node.js passed: invalid input could not convert input to tensor 
-2022-07-16 09:06:12 INFO:  test-node.js test face similarity 
-2022-07-16 09:06:12 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:12 STATE: test-node.js event: image 
-2022-07-16 09:06:13 STATE: test-node.js event: detect 
-2022-07-16 09:06:13 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-07-16 09:06:13 DATA:  test-node.js  result: performance: load: null total: 231 
-2022-07-16 09:06:13 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:13 STATE: test-node.js event: image 
-2022-07-16 09:06:13 STATE: test-node.js event: detect 
-2022-07-16 09:06:13 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:13 DATA:  test-node.js  result: performance: load: null total: 242 
-2022-07-16 09:06:13 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-07-16 09:06:13 STATE: test-node.js event: image 
-2022-07-16 09:06:13 STATE: test-node.js event: detect 
-2022-07-16 09:06:13 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-07-16 09:06:13 DATA:  test-node.js  result: performance: load: null total: 213 
-2022-07-16 09:06:13 STATE: test-node.js passed: face descriptor 
-2022-07-16 09:06:13 STATE: test-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
-2022-07-16 09:06:13 INFO:  test-node.js test face matching 
-2022-07-16 09:06:13 STATE: test-node.js passed: face database 40 
-2022-07-16 09:06:13 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
-2022-07-16 09:06:13 INFO:  test-node.js test object 
-2022-07-16 09:06:14 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:14 STATE: test-node.js event: image 
-2022-07-16 09:06:14 STATE: test-node.js event: detect 
-2022-07-16 09:06:14 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:14 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:14 DATA:  test-node.js  result: performance: load: null total: 232 
-2022-07-16 09:06:14 STATE: test-node.js passed: object result match 
-2022-07-16 09:06:14 INFO:  test-node.js test sensitive 
-2022-07-16 09:06:14 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:14 STATE: test-node.js event: image 
-2022-07-16 09:06:14 STATE: test-node.js event: detect 
-2022-07-16 09:06:14 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:14 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:14 DATA:  test-node.js  result: performance: load: null total: 256 
-2022-07-16 09:06:14 STATE: test-node.js passed: sensitive result match 
-2022-07-16 09:06:14 STATE: test-node.js passed: sensitive face result match 
-2022-07-16 09:06:14 STATE: test-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-07-16 09:06:14 STATE: test-node.js passed: sensitive body result match 
-2022-07-16 09:06:14 STATE: test-node.js passed: sensitive hand result match 
-2022-07-16 09:06:14 INFO:  test-node.js test detectors 
-2022-07-16 09:06:14 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:14 STATE: test-node.js event: image 
-2022-07-16 09:06:15 STATE: test-node.js event: detect 
-2022-07-16 09:06:15 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:15 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:15 DATA:  test-node.js  result: performance: load: null total: 168 
-2022-07-16 09:06:15 STATE: test-node.js passed: detector result face match 
-2022-07-16 09:06:15 STATE: test-node.js passed: detector result hand match 
-2022-07-16 09:06:15 INFO:  test-node.js test: multi-instance 
-2022-07-16 09:06:15 STATE: test-node.js event: image 
-2022-07-16 09:06:15 STATE: test-node.js event: detect 
-2022-07-16 09:06:15 STATE: test-node.js passed: detect: random default 
-2022-07-16 09:06:15 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-07-16 09:06:15 DATA:  test-node.js  result: performance: load: null total: 164 
-2022-07-16 09:06:15 INFO:  test-node.js test: first instance 
-2022-07-16 09:06:15 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-07-16 09:06:15 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:15 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:15 DATA:  test-node.js  result: performance: load: null total: 173 
-2022-07-16 09:06:15 INFO:  test-node.js test: second instance 
-2022-07-16 09:06:15 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-07-16 09:06:15 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:15 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:15 DATA:  test-node.js  result: performance: load: null total: 163 
-2022-07-16 09:06:15 INFO:  test-node.js test: concurrent 
-2022-07-16 09:06:15 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:15 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:16 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-07-16 09:06:16 STATE: test-node.js event: image 
-2022-07-16 09:06:16 STATE: test-node.js event: image 
-2022-07-16 09:06:16 STATE: test-node.js event: image 
-2022-07-16 09:06:17 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:17 DATA:  test-node.js  result: performance: load: null total: 1597 
-2022-07-16 09:06:17 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:17 DATA:  test-node.js  result: performance: load: null total: 1597 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1732 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1732 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1732 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1732 
-2022-07-16 09:06:18 STATE: test-node.js event: detect 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1475 
-2022-07-16 09:06:18 STATE: test-node.js event: detect 
-2022-07-16 09:06:18 STATE: test-node.js event: detect 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1475 
-2022-07-16 09:06:18 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:18 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:18 DATA:  test-node.js  result: performance: load: null total: 1475 
-2022-07-16 09:06:18 INFO:  test-node.js test: monkey-patch 
-2022-07-16 09:06:18 STATE: test-node.js event: image 
-2022-07-16 09:06:18 STATE: test-node.js event: detect 
-2022-07-16 09:06:18 STATE: test-node.js passed: monkey patch 
-2022-07-16 09:06:18 STATE: test-node.js passed: segmentation [65536] 
-2022-07-16 09:06:18 STATE: test-node.js passeed: equal usage 
-2022-07-16 09:06:18 INFO:  test-node.js test: input compare 
-2022-07-16 09:06:18 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-07-16 09:06:18 STATE: test-node.js passed: image compare 0 23.275441687091504 
-2022-07-16 09:06:18 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
-2022-07-16 09:06:18 INFO:  test-node.js tensors 1927 
-2022-07-16 09:06:18 INFO:  test-node.js test complete: 9740 ms 
-2022-07-16 09:06:18 INFO:  
-2022-07-16 09:06:18 INFO:  test-node-gpu.js start 
-2022-07-16 09:06:19 INFO:  test-node-gpu.js test: configuration validation 
-2022-07-16 09:06:19 STATE: test-node-gpu.js passed: configuration default validation [] 
-2022-07-16 09:06:19 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-07-16 09:06:19 INFO:  test-node-gpu.js test: model load 
-2022-07-16 09:06:20 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
-2022-07-16 09:06:20 INFO:  test-node-gpu.js test: warmup 
-2022-07-16 09:06:20 STATE: test-node-gpu.js passed: create human 
-2022-07-16 09:06:20 INFO:  test-node-gpu.js human version: 2.8.1 
-2022-07-16 09:06:20 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v16.15.0 
-2022-07-16 09:06:20 INFO:  test-node-gpu.js tfjs version: 3.18.0 
-2022-07-16 09:06:20 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
-2022-07-16 09:06:20 STATE: test-node-gpu.js passed: set backend: tensorflow 
-2022-07-16 09:06:20 STATE: test-node-gpu.js tensors 1921 
-2022-07-16 09:06:20 STATE: test-node-gpu.js passed: load models 
-2022-07-16 09:06:20 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
-2022-07-16 09:06:20 STATE: test-node-gpu.js passed: warmup: none default 
-2022-07-16 09:06:20 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-07-16 09:06:20 DATA:  test-node-gpu.js  result: performance: load: null total: null 
-2022-07-16 09:06:20 STATE: test-node-gpu.js passed: warmup none result match 
-2022-07-16 09:06:21 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:25 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:25 STATE: test-node-gpu.js event: warmup 
-2022-07-16 09:06:25 STATE: test-node-gpu.js passed: warmup: face default 
-2022-07-16 09:06:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-07-16 09:06:25 DATA:  test-node-gpu.js  result: performance: load: null total: 4353 
-2022-07-16 09:06:25 STATE: test-node-gpu.js passed: warmup face result match 
-2022-07-16 09:06:25 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:25 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:25 STATE: test-node-gpu.js event: warmup 
-2022-07-16 09:06:25 STATE: test-node-gpu.js passed: warmup: body default 
-2022-07-16 09:06:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:25 DATA:  test-node-gpu.js  result: performance: load: null total: 204 
-2022-07-16 09:06:25 STATE: test-node-gpu.js passed: warmup body result match 
-2022-07-16 09:06:25 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-07-16 09:06:25 INFO:  test-node-gpu.js test: details verification 
-2022-07-16 09:06:25 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:25 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:26 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:26 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:26 DATA:  test-node-gpu.js  result: performance: load: null total: 172 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face length 1 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face anti-spoofing 0.79 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details face liveness 0.83 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details body length 1 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details body 0.92 17 6 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details hand length 1 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details gesture length 7 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details object length 1 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: details object 0.72 person 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-07-16 09:06:26 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:26 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-07-16 09:06:26 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:26 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:26 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-07-16 09:06:27 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:27 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:27 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:27 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-07-16 09:06:27 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:27 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:27 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:27 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-07-16 09:06:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-07-16 09:06:28 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:28 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:28 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-07-16 09:06:28 INFO:  test-node-gpu.js test default 
-2022-07-16 09:06:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:28 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:28 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:28 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:28 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:28 DATA:  test-node-gpu.js  result: performance: load: null total: 202 
-2022-07-16 09:06:28 STATE: test-node-gpu.js passed: default result face match 1 female 0.97 
-2022-07-16 09:06:28 INFO:  test-node-gpu.js test sync 
-2022-07-16 09:06:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:28 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: performance: load: null total: 199 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: default sync 1 female 0.97 
-2022-07-16 09:06:29 INFO:  test-node-gpu.js test: image process 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
-2022-07-16 09:06:29 INFO:  test-node-gpu.js test: image null 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
-2022-07-16 09:06:29 INFO:  test-node-gpu.js test face similarity 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: performance: load: null total: 159 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: performance: load: null total: 187 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:29 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-07-16 09:06:29 DATA:  test-node-gpu.js  result: performance: load: null total: 166 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: face descriptor 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
-2022-07-16 09:06:29 INFO:  test-node-gpu.js test face matching 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: face database 40 
-2022-07-16 09:06:29 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
-2022-07-16 09:06:29 INFO:  test-node-gpu.js test object 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:30 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:30 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:30 DATA:  test-node-gpu.js  result: performance: load: null total: 185 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: object result match 
-2022-07-16 09:06:30 INFO:  test-node-gpu.js test sensitive 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:30 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:30 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:30 DATA:  test-node-gpu.js  result: performance: load: null total: 164 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: sensitive result match 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: sensitive face result match 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: sensitive body result match 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: sensitive hand result match 
-2022-07-16 09:06:30 INFO:  test-node-gpu.js test detectors 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:30 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:30 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:30 DATA:  test-node-gpu.js  result: performance: load: null total: 107 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: detector result face match 
-2022-07-16 09:06:30 STATE: test-node-gpu.js passed: detector result hand match 
-2022-07-16 09:06:30 INFO:  test-node-gpu.js test: multi-instance 
-2022-07-16 09:06:31 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:31 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: detect: random default 
-2022-07-16 09:06:31 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-07-16 09:06:31 DATA:  test-node-gpu.js  result: performance: load: null total: 101 
-2022-07-16 09:06:31 INFO:  test-node-gpu.js test: first instance 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:31 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:31 DATA:  test-node-gpu.js  result: performance: load: null total: 111 
-2022-07-16 09:06:31 INFO:  test-node-gpu.js test: second instance 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:31 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:31 DATA:  test-node-gpu.js  result: performance: load: null total: 105 
-2022-07-16 09:06:31 INFO:  test-node-gpu.js test: concurrent 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:31 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-07-16 09:06:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-07-16 09:06:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:32 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-07-16 09:06:32 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:32 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:32 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:32 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:32 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:32 DATA:  test-node-gpu.js  result: performance: load: null total: 954 
-2022-07-16 09:06:32 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:32 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:32 DATA:  test-node-gpu.js  result: performance: load: null total: 954 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 1008 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 1008 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 1008 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 1008 
-2022-07-16 09:06:33 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 768 
-2022-07-16 09:06:33 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:33 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 768 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:33 DATA:  test-node-gpu.js  result: performance: load: null total: 768 
-2022-07-16 09:06:33 INFO:  test-node-gpu.js test: monkey-patch 
-2022-07-16 09:06:33 STATE: test-node-gpu.js event: image 
-2022-07-16 09:06:33 STATE: test-node-gpu.js event: detect 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: monkey patch 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: segmentation [65536] 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passeed: equal usage 
-2022-07-16 09:06:33 INFO:  test-node-gpu.js test: input compare 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-07-16 09:06:33 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
-2022-07-16 09:06:33 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
-2022-07-16 09:06:33 INFO:  test-node-gpu.js tensors 1927 
-2022-07-16 09:06:33 INFO:  test-node-gpu.js test complete: 13806 ms 
-2022-07-16 09:06:34 INFO:  
-2022-07-16 09:06:34 INFO:  test-node-wasm.js start 
-2022-07-16 09:06:34 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
-2022-07-16 09:06:34 INFO:  test-node-wasm.js test: configuration validation 
-2022-07-16 09:06:34 STATE: test-node-wasm.js passed: configuration default validation [] 
-2022-07-16 09:06:34 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-07-16 09:06:34 INFO:  test-node-wasm.js test: model load 
-2022-07-16 09:06:35 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"https://vladmandic.github.io/human/models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"https://vladmandic.github.io/human/models/emotion.json"},{"name":"facedetect","loaded":true,"url":"https://vladmandic.github.io/human/models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"https://vladmandic.github.io/human/models/iris.json"},{"name":"facemesh","loaded":true,"url":"https://vladmandic.github.io/human/models/facemesh.json"},{"name":"faceres","loaded":true,"url":"https://vladmandic.github.io/human/models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"https://vladmandic.github.io/human/models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"https://vladmandic.github.io/human/models/handtrack.json"},{"name":"liveness","loaded":true,"url":"https://vladmandic.github.io/human/models/liveness.json"},{"name":"movenet","loaded":true,"url":"https://vladmandic.github.io/human/models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"https://vladmandic.github.io/human/models/selfie.json"},{"name":"antispoof","loaded":true,"url":"https://vladmandic.github.io/human/models/antispoof.json"}] 
-2022-07-16 09:06:35 INFO:  test-node-wasm.js test: warmup 
-2022-07-16 09:06:35 STATE: test-node-wasm.js passed: create human 
-2022-07-16 09:06:35 INFO:  test-node-wasm.js human version: 2.8.1 
-2022-07-16 09:06:35 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v16.15.0 
-2022-07-16 09:06:35 INFO:  test-node-wasm.js tfjs version: 3.18.0 
-2022-07-16 09:06:35 STATE: test-node-wasm.js passed: set backend: wasm 
-2022-07-16 09:06:35 STATE: test-node-wasm.js tensors 1921 
-2022-07-16 09:06:35 STATE: test-node-wasm.js passed: load models 
-2022-07-16 09:06:35 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
-2022-07-16 09:06:35 STATE: test-node-wasm.js passed: warmup: none default 
-2022-07-16 09:06:35 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-07-16 09:06:35 DATA:  test-node-wasm.js  result: performance: load: null total: null 
-2022-07-16 09:06:35 STATE: test-node-wasm.js passed: warmup none result match 
-2022-07-16 09:06:35 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:36 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:36 STATE: test-node-wasm.js event: warmup 
-2022-07-16 09:06:36 STATE: test-node-wasm.js passed: warmup: face default 
-2022-07-16 09:06:36 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-07-16 09:06:36 DATA:  test-node-wasm.js  result: performance: load: null total: 514 
-2022-07-16 09:06:36 STATE: test-node-wasm.js passed: warmup face result match 
-2022-07-16 09:06:36 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:36 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:36 STATE: test-node-wasm.js event: warmup 
-2022-07-16 09:06:36 STATE: test-node-wasm.js passed: warmup: body default 
-2022-07-16 09:06:36 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:36 DATA:  test-node-wasm.js  result: performance: load: null total: 369 
-2022-07-16 09:06:36 STATE: test-node-wasm.js passed: warmup body result match 
-2022-07-16 09:06:36 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-07-16 09:06:36 INFO:  test-node-wasm.js test: details verification 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:37 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:37 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:37 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:37 DATA:  test-node-wasm.js  result: performance: load: null total: 345 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face length 1 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face anti-spoofing 0.79 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details face liveness 0.83 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details body length 1 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details body 0.92 17 6 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details hand length 1 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details gesture length 7 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details object length 1 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: details object 0.72 person 
-2022-07-16 09:06:37 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2022-07-16 09:06:37 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:38 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:38 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-07-16 09:06:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2022-07-16 09:06:38 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:38 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:38 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-07-16 09:06:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:38 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:39 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:39 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-07-16 09:06:39 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:39 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:39 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:39 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-07-16 09:06:40 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-07-16 09:06:40 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:40 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:40 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-07-16 09:06:40 INFO:  test-node-wasm.js test default 
-2022-07-16 09:06:40 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:40 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:41 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:41 DATA:  test-node-wasm.js  result: performance: load: null total: 374 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: default result face match 1 female 0.97 
-2022-07-16 09:06:41 INFO:  test-node-wasm.js test sync 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:41 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:41 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:41 DATA:  test-node-wasm.js  result: performance: load: null total: 330 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: default sync 1 female 0.97 
-2022-07-16 09:06:41 INFO:  test-node-wasm.js test: image process 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
-2022-07-16 09:06:41 INFO:  test-node-wasm.js test: image null 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
-2022-07-16 09:06:41 INFO:  test-node-wasm.js test face similarity 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-07-16 09:06:41 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:41 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:41 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-07-16 09:06:41 DATA:  test-node-wasm.js  result: performance: load: null total: 292 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:42 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:42 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:42 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:42 DATA:  test-node-wasm.js  result: performance: load: null total: 323 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-07-16 09:06:42 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:42 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:42 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-07-16 09:06:42 DATA:  test-node-wasm.js  result: performance: load: null total: 299 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: face descriptor 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
-2022-07-16 09:06:42 INFO:  test-node-wasm.js test face matching 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: face database 40 
-2022-07-16 09:06:42 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
-2022-07-16 09:06:42 INFO:  test-node-wasm.js test object 
-2022-07-16 09:06:43 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:43 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:43 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:43 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:43 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:43 DATA:  test-node-wasm.js  result: performance: load: null total: 331 
-2022-07-16 09:06:43 STATE: test-node-wasm.js passed: object result match 
-2022-07-16 09:06:43 INFO:  test-node-wasm.js test sensitive 
-2022-07-16 09:06:43 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:43 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:44 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:44 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:44 DATA:  test-node-wasm.js  result: performance: load: null total: 360 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: sensitive result match 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: sensitive face result match 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: sensitive body result match 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: sensitive hand result match 
-2022-07-16 09:06:44 INFO:  test-node-wasm.js test detectors 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:44 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:44 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:44 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:44 DATA:  test-node-wasm.js  result: performance: load: null total: 226 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: detector result face match 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: detector result hand match 
-2022-07-16 09:06:44 INFO:  test-node-wasm.js test: multi-instance 
-2022-07-16 09:06:44 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:44 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: detect: random default 
-2022-07-16 09:06:44 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-07-16 09:06:44 DATA:  test-node-wasm.js  result: performance: load: null total: 200 
-2022-07-16 09:06:44 INFO:  test-node-wasm.js test: first instance 
-2022-07-16 09:06:44 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:45 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:45 DATA:  test-node-wasm.js  result: performance: load: null total: 225 
-2022-07-16 09:06:45 INFO:  test-node-wasm.js test: second instance 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:45 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:45 DATA:  test-node-wasm.js  result: performance: load: null total: 218 
-2022-07-16 09:06:45 INFO:  test-node-wasm.js test: concurrent 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-07-16 09:06:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-07-16 09:06:46 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:46 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-07-16 09:06:46 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:46 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:46 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2044 
-2022-07-16 09:06:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2044 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 2267 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 2267 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 2267 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 2267 
-2022-07-16 09:06:48 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 1965 
-2022-07-16 09:06:48 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:48 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 1966 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-07-16 09:06:48 DATA:  test-node-wasm.js  result: performance: load: null total: 1966 
-2022-07-16 09:06:48 INFO:  test-node-wasm.js test: monkey-patch 
-2022-07-16 09:06:48 STATE: test-node-wasm.js event: image 
-2022-07-16 09:06:48 STATE: test-node-wasm.js event: detect 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: monkey patch 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: segmentation [65536] 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passeed: equal usage 
-2022-07-16 09:06:48 INFO:  test-node-wasm.js test: input compare 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-07-16 09:06:48 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
-2022-07-16 09:06:48 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
-2022-07-16 09:06:48 INFO:  test-node-wasm.js tensors 1929 
-2022-07-16 09:06:48 INFO:  test-node-wasm.js test complete: 14023 ms 
-2022-07-16 09:06:48 INFO:  all tests complete 
-2022-07-16 09:06:48 INFO:  failed: {"count":0,"messages":[]} 
-2022-07-16 09:06:48 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
-2022-07-16 09:06:48 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
-2022-07-16 09:06:48 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
+2022-07-21 13:01:40 INFO:  @vladmandic/human version 2.9.0 
+2022-07-21 13:01:40 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.15.0 
+2022-07-21 13:01:40 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
+2022-07-21 13:01:40 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
+2022-07-21 13:01:40 INFO:  
+2022-07-21 13:01:40 INFO:  test-node.js start 
+2022-07-21 13:01:40 INFO:  test-node.js test: configuration validation 
+2022-07-21 13:01:40 STATE: test-node.js passed: configuration default validation [] 
+2022-07-21 13:01:40 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-07-21 13:01:40 INFO:  test-node.js test: model load 
+2022-07-21 13:01:41 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
+2022-07-21 13:01:41 INFO:  test-node.js test: warmup 
+2022-07-21 13:01:41 STATE: test-node.js passed: create human 
+2022-07-21 13:01:41 INFO:  test-node.js human version: 2.9.0 
+2022-07-21 13:01:41 INFO:  test-node.js platform: linux x64 agent: NodeJS v16.15.0 
+2022-07-21 13:01:41 INFO:  test-node.js tfjs version: 3.18.0 
+2022-07-21 13:01:41 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
+2022-07-21 13:01:41 STATE: test-node.js passed: set backend: tensorflow 
+2022-07-21 13:01:41 STATE: test-node.js tensors 1921 
+2022-07-21 13:01:41 STATE: test-node.js passed: load models 
+2022-07-21 13:01:41 STATE: test-node.js  result: defined models: 22 loaded models: 12 
+2022-07-21 13:01:41 STATE: test-node.js passed: warmup: none default 
+2022-07-21 13:01:41 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-07-21 13:01:41 DATA:  test-node.js  result: performance: load: null total: null 
+2022-07-21 13:01:41 STATE: test-node.js passed: warmup none result match 
+2022-07-21 13:01:41 STATE: test-node.js event: image 
+2022-07-21 13:01:41 STATE: test-node.js event: detect 
+2022-07-21 13:01:41 STATE: test-node.js event: warmup 
+2022-07-21 13:01:41 STATE: test-node.js passed: warmup: face default 
+2022-07-21 13:01:41 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-07-21 13:01:41 DATA:  test-node.js  result: performance: load: null total: 475 
+2022-07-21 13:01:41 STATE: test-node.js passed: warmup face result match 
+2022-07-21 13:01:41 STATE: test-node.js event: image 
+2022-07-21 13:01:41 STATE: test-node.js event: detect 
+2022-07-21 13:01:41 STATE: test-node.js event: warmup 
+2022-07-21 13:01:41 STATE: test-node.js passed: warmup: body default 
+2022-07-21 13:01:41 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:41 DATA:  test-node.js  result: performance: load: null total: 318 
+2022-07-21 13:01:41 STATE: test-node.js passed: warmup body result match 
+2022-07-21 13:01:41 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-07-21 13:01:41 INFO:  test-node.js test: details verification 
+2022-07-21 13:01:42 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:42 STATE: test-node.js event: image 
+2022-07-21 13:01:42 STATE: test-node.js event: detect 
+2022-07-21 13:01:42 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:42 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:42 DATA:  test-node.js  result: performance: load: null total: 303 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face length 1 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face score 1 0.93 1 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face arrays 4 478 1024 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face anti-spoofing 0.79 
+2022-07-21 13:01:42 STATE: test-node.js passed: details face liveness 0.83 
+2022-07-21 13:01:42 STATE: test-node.js passed: details body length 1 
+2022-07-21 13:01:42 STATE: test-node.js passed: details body 0.92 17 6 
+2022-07-21 13:01:42 STATE: test-node.js passed: details hand length 1 
+2022-07-21 13:01:42 STATE: test-node.js passed: details hand 0.51 0.73 point 
+2022-07-21 13:01:42 STATE: test-node.js passed: details hand arrays 21 5 7 
+2022-07-21 13:01:42 STATE: test-node.js passed: details gesture length 7 
+2022-07-21 13:01:42 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-07-21 13:01:42 STATE: test-node.js passed: details object length 1 
+2022-07-21 13:01:42 STATE: test-node.js passed: details object 0.72 person 
+2022-07-21 13:01:42 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-07-21 13:01:42 STATE: test-node.js event: image 
+2022-07-21 13:01:42 STATE: test-node.js event: detect 
+2022-07-21 13:01:42 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-07-21 13:01:43 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-07-21 13:01:43 STATE: test-node.js event: image 
+2022-07-21 13:01:43 STATE: test-node.js event: detect 
+2022-07-21 13:01:43 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-07-21 13:01:43 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:43 STATE: test-node.js event: image 
+2022-07-21 13:01:43 STATE: test-node.js event: detect 
+2022-07-21 13:01:43 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-07-21 13:01:44 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:44 STATE: test-node.js event: image 
+2022-07-21 13:01:44 STATE: test-node.js event: detect 
+2022-07-21 13:01:44 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-07-21 13:01:44 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-07-21 13:01:44 STATE: test-node.js event: image 
+2022-07-21 13:01:44 STATE: test-node.js event: detect 
+2022-07-21 13:01:44 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-07-21 13:01:44 INFO:  test-node.js test default 
+2022-07-21 13:01:45 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:45 STATE: test-node.js event: image 
+2022-07-21 13:01:45 STATE: test-node.js event: detect 
+2022-07-21 13:01:45 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:45 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:45 DATA:  test-node.js  result: performance: load: null total: 253 
+2022-07-21 13:01:45 STATE: test-node.js passed: default result face match 1 female 0.97 
+2022-07-21 13:01:45 INFO:  test-node.js test sync 
+2022-07-21 13:01:45 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:45 STATE: test-node.js event: image 
+2022-07-21 13:01:45 STATE: test-node.js event: detect 
+2022-07-21 13:01:45 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:45 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:45 DATA:  test-node.js  result: performance: load: null total: 280 
+2022-07-21 13:01:45 STATE: test-node.js passed: default sync 1 female 0.97 
+2022-07-21 13:01:45 INFO:  test-node.js test: image process 
+2022-07-21 13:01:45 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:01:45 STATE: test-node.js passed: image input null [1,256,256,3] 
+2022-07-21 13:01:45 INFO:  test-node.js test: image null 
+2022-07-21 13:01:45 STATE: test-node.js passed: invalid input could not convert input to tensor 
+2022-07-21 13:01:45 INFO:  test-node.js test face similarity 
+2022-07-21 13:01:45 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:01:45 STATE: test-node.js event: image 
+2022-07-21 13:01:46 STATE: test-node.js event: detect 
+2022-07-21 13:01:46 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:01:46 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-07-21 13:01:46 DATA:  test-node.js  result: performance: load: null total: 241 
+2022-07-21 13:01:46 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:46 STATE: test-node.js event: image 
+2022-07-21 13:01:46 STATE: test-node.js event: detect 
+2022-07-21 13:01:46 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:46 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:46 DATA:  test-node.js  result: performance: load: null total: 248 
+2022-07-21 13:01:46 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-07-21 13:01:46 STATE: test-node.js event: image 
+2022-07-21 13:01:46 STATE: test-node.js event: detect 
+2022-07-21 13:01:46 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:01:46 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-07-21 13:01:46 DATA:  test-node.js  result: performance: load: null total: 230 
+2022-07-21 13:01:46 STATE: test-node.js passed: face descriptor 
+2022-07-21 13:01:46 STATE: test-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
+2022-07-21 13:01:46 INFO:  test-node.js test face matching 
+2022-07-21 13:01:46 STATE: test-node.js passed: face database 40 
+2022-07-21 13:01:46 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
+2022-07-21 13:01:46 INFO:  test-node.js test object 
+2022-07-21 13:01:46 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:46 STATE: test-node.js event: image 
+2022-07-21 13:01:47 STATE: test-node.js event: detect 
+2022-07-21 13:01:47 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:47 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:47 DATA:  test-node.js  result: performance: load: null total: 242 
+2022-07-21 13:01:47 STATE: test-node.js passed: object result match 
+2022-07-21 13:01:47 INFO:  test-node.js test sensitive 
+2022-07-21 13:01:47 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:47 STATE: test-node.js event: image 
+2022-07-21 13:01:47 STATE: test-node.js event: detect 
+2022-07-21 13:01:47 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:47 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:47 DATA:  test-node.js  result: performance: load: null total: 265 
+2022-07-21 13:01:47 STATE: test-node.js passed: sensitive result match 
+2022-07-21 13:01:47 STATE: test-node.js passed: sensitive face result match 
+2022-07-21 13:01:47 STATE: test-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2022-07-21 13:01:47 STATE: test-node.js passed: sensitive body result match 
+2022-07-21 13:01:47 STATE: test-node.js passed: sensitive hand result match 
+2022-07-21 13:01:47 INFO:  test-node.js test detectors 
+2022-07-21 13:01:47 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:47 STATE: test-node.js event: image 
+2022-07-21 13:01:48 STATE: test-node.js event: detect 
+2022-07-21 13:01:48 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:48 DATA:  test-node.js  result: performance: load: null total: 166 
+2022-07-21 13:01:48 STATE: test-node.js passed: detector result face match 
+2022-07-21 13:01:48 STATE: test-node.js passed: detector result hand match 
+2022-07-21 13:01:48 INFO:  test-node.js test: multi-instance 
+2022-07-21 13:01:48 STATE: test-node.js event: image 
+2022-07-21 13:01:48 STATE: test-node.js event: detect 
+2022-07-21 13:01:48 STATE: test-node.js passed: detect: random default 
+2022-07-21 13:01:48 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-07-21 13:01:48 DATA:  test-node.js  result: performance: load: null total: 164 
+2022-07-21 13:01:48 INFO:  test-node.js test: first instance 
+2022-07-21 13:01:48 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-07-21 13:01:48 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:01:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:01:48 DATA:  test-node.js  result: performance: load: null total: 167 
+2022-07-21 13:01:48 INFO:  test-node.js test: second instance 
+2022-07-21 13:01:48 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-07-21 13:01:48 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:01:48 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:01:48 DATA:  test-node.js  result: performance: load: null total: 165 
+2022-07-21 13:01:48 INFO:  test-node.js test: concurrent 
+2022-07-21 13:01:48 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:01:48 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:01:48 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:49 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:49 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-07-21 13:01:49 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-07-21 13:01:49 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:01:49 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:49 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-07-21 13:01:49 STATE: test-node.js event: image 
+2022-07-21 13:01:49 STATE: test-node.js event: image 
+2022-07-21 13:01:49 STATE: test-node.js event: image 
+2022-07-21 13:01:50 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:01:50 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:01:50 DATA:  test-node.js  result: performance: load: null total: 1593 
+2022-07-21 13:01:50 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:01:50 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:01:50 DATA:  test-node.js  result: performance: load: null total: 1593 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1741 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1741 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1741 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1741 
+2022-07-21 13:01:51 STATE: test-node.js event: detect 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1495 
+2022-07-21 13:01:51 STATE: test-node.js event: detect 
+2022-07-21 13:01:51 STATE: test-node.js event: detect 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1496 
+2022-07-21 13:01:51 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:51 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:51 DATA:  test-node.js  result: performance: load: null total: 1496 
+2022-07-21 13:01:51 INFO:  test-node.js test: monkey-patch 
+2022-07-21 13:01:51 STATE: test-node.js event: image 
+2022-07-21 13:01:51 STATE: test-node.js event: detect 
+2022-07-21 13:01:51 STATE: test-node.js passed: monkey patch 
+2022-07-21 13:01:51 STATE: test-node.js passed: segmentation [65536] 
+2022-07-21 13:01:51 STATE: test-node.js passeed: equal usage 
+2022-07-21 13:01:51 INFO:  test-node.js test: input compare 
+2022-07-21 13:01:51 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:01:51 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-07-21 13:01:51 STATE: test-node.js passed: image compare 0 23.275441687091504 
+2022-07-21 13:01:51 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
+2022-07-21 13:01:51 INFO:  test-node.js tensors 1927 
+2022-07-21 13:01:51 INFO:  test-node.js test complete: 10593 ms 
+2022-07-21 13:01:51 INFO:  
+2022-07-21 13:01:51 INFO:  test-node-gpu.js start 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js test: configuration validation 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: configuration default validation [] 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js test: model load 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js test: warmup 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: create human 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js human version: 2.9.0 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v16.15.0 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js tfjs version: 3.18.0 
+2022-07-21 13:01:53 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: set backend: tensorflow 
+2022-07-21 13:01:53 STATE: test-node-gpu.js tensors 1921 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: load models 
+2022-07-21 13:01:53 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: warmup: none default 
+2022-07-21 13:01:53 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-07-21 13:01:53 DATA:  test-node-gpu.js  result: performance: load: null total: null 
+2022-07-21 13:01:53 STATE: test-node-gpu.js passed: warmup none result match 
+2022-07-21 13:01:54 STATE: test-node-gpu.js event: image 
+2022-07-21 13:01:58 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:01:58 STATE: test-node-gpu.js event: warmup 
+2022-07-21 13:01:58 STATE: test-node-gpu.js passed: warmup: face default 
+2022-07-21 13:01:58 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-07-21 13:01:58 DATA:  test-node-gpu.js  result: performance: load: null total: 3883 
+2022-07-21 13:01:58 STATE: test-node-gpu.js passed: warmup face result match 
+2022-07-21 13:01:58 STATE: test-node-gpu.js event: image 
+2022-07-21 13:01:58 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:01:58 STATE: test-node-gpu.js event: warmup 
+2022-07-21 13:01:58 STATE: test-node-gpu.js passed: warmup: body default 
+2022-07-21 13:01:58 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:58 DATA:  test-node-gpu.js  result: performance: load: null total: 194 
+2022-07-21 13:01:58 STATE: test-node-gpu.js passed: warmup body result match 
+2022-07-21 13:01:58 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-07-21 13:01:58 INFO:  test-node-gpu.js test: details verification 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:01:59 STATE: test-node-gpu.js event: image 
+2022-07-21 13:01:59 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:01:59 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:01:59 DATA:  test-node-gpu.js  result: performance: load: null total: 190 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face length 1 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face anti-spoofing 0.79 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details face liveness 0.83 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details body length 1 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details body 0.92 17 6 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details hand length 1 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details gesture length 7 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details object length 1 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: details object 0.72 person 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-07-21 13:01:59 STATE: test-node-gpu.js event: image 
+2022-07-21 13:01:59 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-07-21 13:01:59 STATE: test-node-gpu.js event: image 
+2022-07-21 13:01:59 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:01:59 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-07-21 13:02:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:00 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:00 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:00 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-07-21 13:02:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:00 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:00 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:00 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-07-21 13:02:00 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-07-21 13:02:00 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:01 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-07-21 13:02:01 INFO:  test-node-gpu.js test default 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:01 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:01 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:01 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:01 DATA:  test-node-gpu.js  result: performance: load: null total: 177 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: default result face match 1 female 0.97 
+2022-07-21 13:02:01 INFO:  test-node-gpu.js test sync 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:01 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:01 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:01 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:01 DATA:  test-node-gpu.js  result: performance: load: null total: 211 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: default sync 1 female 0.97 
+2022-07-21 13:02:01 INFO:  test-node-gpu.js test: image process 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
+2022-07-21 13:02:01 INFO:  test-node-gpu.js test: image null 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
+2022-07-21 13:02:01 INFO:  test-node-gpu.js test face similarity 
+2022-07-21 13:02:01 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:02:01 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:02 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-07-21 13:02:02 DATA:  test-node-gpu.js  result: performance: load: null total: 190 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:02 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:02 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:02 DATA:  test-node-gpu.js  result: performance: load: null total: 214 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-07-21 13:02:02 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:02 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:02 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-07-21 13:02:02 DATA:  test-node-gpu.js  result: performance: load: null total: 180 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: face descriptor 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
+2022-07-21 13:02:02 INFO:  test-node-gpu.js test face matching 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: face database 40 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
+2022-07-21 13:02:02 INFO:  test-node-gpu.js test object 
+2022-07-21 13:02:02 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:02 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: performance: load: null total: 168 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: object result match 
+2022-07-21 13:02:03 INFO:  test-node-gpu.js test sensitive 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: performance: load: null total: 187 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: sensitive result match 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: sensitive face result match 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: sensitive body result match 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: sensitive hand result match 
+2022-07-21 13:02:03 INFO:  test-node-gpu.js test detectors 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: performance: load: null total: 84 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: detector result face match 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: detector result hand match 
+2022-07-21 13:02:03 INFO:  test-node-gpu.js test: multi-instance 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:03 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: detect: random default 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-07-21 13:02:03 DATA:  test-node-gpu.js  result: performance: load: null total: 87 
+2022-07-21 13:02:03 INFO:  test-node-gpu.js test: first instance 
+2022-07-21 13:02:03 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:04 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:04 DATA:  test-node-gpu.js  result: performance: load: null total: 91 
+2022-07-21 13:02:04 INFO:  test-node-gpu.js test: second instance 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:04 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:04 DATA:  test-node-gpu.js  result: performance: load: null total: 90 
+2022-07-21 13:02:04 INFO:  test-node-gpu.js test: concurrent 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:02:04 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 962 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 962 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 1032 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 1032 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 1032 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 1032 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 773 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 773 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:05 DATA:  test-node-gpu.js  result: performance: load: null total: 773 
+2022-07-21 13:02:05 INFO:  test-node-gpu.js test: monkey-patch 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: image 
+2022-07-21 13:02:05 STATE: test-node-gpu.js event: detect 
+2022-07-21 13:02:05 STATE: test-node-gpu.js passed: monkey patch 
+2022-07-21 13:02:06 STATE: test-node-gpu.js passed: segmentation [65536] 
+2022-07-21 13:02:06 STATE: test-node-gpu.js passeed: equal usage 
+2022-07-21 13:02:06 INFO:  test-node-gpu.js test: input compare 
+2022-07-21 13:02:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-07-21 13:02:06 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-07-21 13:02:06 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
+2022-07-21 13:02:06 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
+2022-07-21 13:02:06 INFO:  test-node-gpu.js tensors 1927 
+2022-07-21 13:02:06 INFO:  test-node-gpu.js test complete: 13303 ms 
+2022-07-21 13:02:07 INFO:  
+2022-07-21 13:02:07 INFO:  test-node-wasm.js start 
+2022-07-21 13:02:07 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
+2022-07-21 13:02:07 INFO:  test-node-wasm.js test: configuration validation 
+2022-07-21 13:02:07 STATE: test-node-wasm.js passed: configuration default validation [] 
+2022-07-21 13:02:07 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-07-21 13:02:07 INFO:  test-node-wasm.js test: model load 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"https://vladmandic.github.io/human/models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"https://vladmandic.github.io/human/models/emotion.json"},{"name":"facedetect","loaded":true,"url":"https://vladmandic.github.io/human/models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"https://vladmandic.github.io/human/models/iris.json"},{"name":"facemesh","loaded":true,"url":"https://vladmandic.github.io/human/models/facemesh.json"},{"name":"faceres","loaded":true,"url":"https://vladmandic.github.io/human/models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"https://vladmandic.github.io/human/models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"https://vladmandic.github.io/human/models/handtrack.json"},{"name":"liveness","loaded":true,"url":"https://vladmandic.github.io/human/models/liveness.json"},{"name":"movenet","loaded":true,"url":"https://vladmandic.github.io/human/models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"https://vladmandic.github.io/human/models/selfie.json"},{"name":"antispoof","loaded":true,"url":"https://vladmandic.github.io/human/models/antispoof.json"}] 
+2022-07-21 13:02:09 INFO:  test-node-wasm.js test: warmup 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: create human 
+2022-07-21 13:02:09 INFO:  test-node-wasm.js human version: 2.9.0 
+2022-07-21 13:02:09 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v16.15.0 
+2022-07-21 13:02:09 INFO:  test-node-wasm.js tfjs version: 3.18.0 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: set backend: wasm 
+2022-07-21 13:02:09 STATE: test-node-wasm.js tensors 1921 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: load models 
+2022-07-21 13:02:09 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: warmup: none default 
+2022-07-21 13:02:09 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-07-21 13:02:09 DATA:  test-node-wasm.js  result: performance: load: null total: null 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: warmup none result match 
+2022-07-21 13:02:09 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:09 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:09 STATE: test-node-wasm.js event: warmup 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: warmup: face default 
+2022-07-21 13:02:09 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-07-21 13:02:09 DATA:  test-node-wasm.js  result: performance: load: null total: 556 
+2022-07-21 13:02:09 STATE: test-node-wasm.js passed: warmup face result match 
+2022-07-21 13:02:09 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:10 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:10 STATE: test-node-wasm.js event: warmup 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: warmup: body default 
+2022-07-21 13:02:10 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:10 DATA:  test-node-wasm.js  result: performance: load: null total: 402 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: warmup body result match 
+2022-07-21 13:02:10 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-07-21 13:02:10 INFO:  test-node-wasm.js test: details verification 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:10 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:10 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:10 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:10 DATA:  test-node-wasm.js  result: performance: load: null total: 369 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face length 1 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face anti-spoofing 0.79 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details face liveness 0.83 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details body length 1 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details body 0.92 17 6 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details hand length 1 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details gesture length 7 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details object length 1 
+2022-07-21 13:02:10 STATE: test-node-wasm.js passed: details object 0.72 person 
+2022-07-21 13:02:11 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
+2022-07-21 13:02:11 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:11 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:11 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-07-21 13:02:11 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
+2022-07-21 13:02:11 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:12 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:12 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-07-21 13:02:12 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:12 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:12 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:12 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-07-21 13:02:12 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:12 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:13 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:13 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-07-21 13:02:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-07-21 13:02:13 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:13 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:13 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-07-21 13:02:13 INFO:  test-node-wasm.js test default 
+2022-07-21 13:02:13 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:13 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:14 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:14 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:14 DATA:  test-node-wasm.js  result: performance: load: null total: 334 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: default result face match 1 female 0.97 
+2022-07-21 13:02:14 INFO:  test-node-wasm.js test sync 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:14 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:14 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:14 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:14 DATA:  test-node-wasm.js  result: performance: load: null total: 337 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: default sync 1 female 0.97 
+2022-07-21 13:02:14 INFO:  test-node-wasm.js test: image process 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
+2022-07-21 13:02:14 INFO:  test-node-wasm.js test: image null 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
+2022-07-21 13:02:14 INFO:  test-node-wasm.js test face similarity 
+2022-07-21 13:02:14 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-07-21 13:02:14 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:15 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:15 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:15 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-07-21 13:02:15 DATA:  test-node-wasm.js  result: performance: load: null total: 302 
+2022-07-21 13:02:15 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:15 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:15 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:15 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:15 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:15 DATA:  test-node-wasm.js  result: performance: load: null total: 362 
+2022-07-21 13:02:15 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-07-21 13:02:15 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:16 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:16 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-07-21 13:02:16 DATA:  test-node-wasm.js  result: performance: load: null total: 307 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: face descriptor 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
+2022-07-21 13:02:16 INFO:  test-node-wasm.js test face matching 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: face database 40 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
+2022-07-21 13:02:16 INFO:  test-node-wasm.js test object 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:16 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:16 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:16 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:16 DATA:  test-node-wasm.js  result: performance: load: null total: 342 
+2022-07-21 13:02:16 STATE: test-node-wasm.js passed: object result match 
+2022-07-21 13:02:16 INFO:  test-node-wasm.js test sensitive 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:17 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:17 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:17 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:17 DATA:  test-node-wasm.js  result: performance: load: null total: 353 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: sensitive result match 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: sensitive face result match 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: sensitive body result match 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: sensitive hand result match 
+2022-07-21 13:02:17 INFO:  test-node-wasm.js test detectors 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:17 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:17 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:17 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:17 DATA:  test-node-wasm.js  result: performance: load: null total: 230 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: detector result face match 
+2022-07-21 13:02:17 STATE: test-node-wasm.js passed: detector result hand match 
+2022-07-21 13:02:17 INFO:  test-node-wasm.js test: multi-instance 
+2022-07-21 13:02:17 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:18 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: detect: random default 
+2022-07-21 13:02:18 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
+2022-07-21 13:02:18 DATA:  test-node-wasm.js  result: performance: load: null total: 216 
+2022-07-21 13:02:18 INFO:  test-node-wasm.js test: first instance 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:18 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:18 DATA:  test-node-wasm.js  result: performance: load: null total: 222 
+2022-07-21 13:02:18 INFO:  test-node-wasm.js test: second instance 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:18 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:18 DATA:  test-node-wasm.js  result: performance: load: null total: 233 
+2022-07-21 13:02:18 INFO:  test-node-wasm.js test: concurrent 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-07-21 13:02:18 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-07-21 13:02:19 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:19 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:19 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2257 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2257 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2478 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2478 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2478 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2478 
+2022-07-21 13:02:21 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2157 
+2022-07-21 13:02:21 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:21 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2157 
+2022-07-21 13:02:21 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-07-21 13:02:21 DATA:  test-node-wasm.js  result: performance: load: null total: 2157 
+2022-07-21 13:02:21 INFO:  test-node-wasm.js test: monkey-patch 
+2022-07-21 13:02:21 STATE: test-node-wasm.js event: image 
+2022-07-21 13:02:22 STATE: test-node-wasm.js event: detect 
+2022-07-21 13:02:22 STATE: test-node-wasm.js passed: monkey patch 
+2022-07-21 13:02:22 STATE: test-node-wasm.js passed: segmentation [65536] 
+2022-07-21 13:02:22 STATE: test-node-wasm.js passeed: equal usage 
+2022-07-21 13:02:22 INFO:  test-node-wasm.js test: input compare 
+2022-07-21 13:02:22 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-07-21 13:02:22 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-07-21 13:02:22 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
+2022-07-21 13:02:22 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
+2022-07-21 13:02:22 INFO:  test-node-wasm.js tensors 1929 
+2022-07-21 13:02:22 INFO:  test-node-wasm.js test complete: 14781 ms 
+2022-07-21 13:02:22 INFO:  all tests complete 
+2022-07-21 13:02:22 INFO:  failed: {"count":0,"messages":[]} 
+2022-07-21 13:02:22 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
+2022-07-21 13:02:22 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
+2022-07-21 13:02:22 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
diff --git a/typedoc/assets/highlight.css b/typedoc/assets/highlight.css
new file mode 100644
index 00000000..70096219
--- /dev/null
+++ b/typedoc/assets/highlight.css
@@ -0,0 +1,85 @@
+:root {
+    --light-hl-0: #0000FF;
+    --dark-hl-0: #569CD6;
+    --light-hl-1: #000000;
+    --dark-hl-1: #D4D4D4;
+    --light-hl-2: #0070C1;
+    --dark-hl-2: #4FC1FF;
+    --light-hl-3: #A31515;
+    --dark-hl-3: #CE9178;
+    --light-hl-4: #AF00DB;
+    --dark-hl-4: #C586C0;
+    --light-hl-5: #001080;
+    --dark-hl-5: #9CDCFE;
+    --light-hl-6: #795E26;
+    --dark-hl-6: #DCDCAA;
+    --light-hl-7: #098658;
+    --dark-hl-7: #B5CEA8;
+    --light-hl-8: #000000;
+    --dark-hl-8: #C8C8C8;
+    --light-code-background: #FFFFFF;
+    --dark-code-background: #1E1E1E;
+}
+
+@media (prefers-color-scheme: light) { :root {
+    --hl-0: var(--light-hl-0);
+    --hl-1: var(--light-hl-1);
+    --hl-2: var(--light-hl-2);
+    --hl-3: var(--light-hl-3);
+    --hl-4: var(--light-hl-4);
+    --hl-5: var(--light-hl-5);
+    --hl-6: var(--light-hl-6);
+    --hl-7: var(--light-hl-7);
+    --hl-8: var(--light-hl-8);
+    --code-background: var(--light-code-background);
+} }
+
+@media (prefers-color-scheme: dark) { :root {
+    --hl-0: var(--dark-hl-0);
+    --hl-1: var(--dark-hl-1);
+    --hl-2: var(--dark-hl-2);
+    --hl-3: var(--dark-hl-3);
+    --hl-4: var(--dark-hl-4);
+    --hl-5: var(--dark-hl-5);
+    --hl-6: var(--dark-hl-6);
+    --hl-7: var(--dark-hl-7);
+    --hl-8: var(--dark-hl-8);
+    --code-background: var(--dark-code-background);
+} }
+
+:root[data-theme='light'] {
+    --hl-0: var(--light-hl-0);
+    --hl-1: var(--light-hl-1);
+    --hl-2: var(--light-hl-2);
+    --hl-3: var(--light-hl-3);
+    --hl-4: var(--light-hl-4);
+    --hl-5: var(--light-hl-5);
+    --hl-6: var(--light-hl-6);
+    --hl-7: var(--light-hl-7);
+    --hl-8: var(--light-hl-8);
+    --code-background: var(--light-code-background);
+}
+
+:root[data-theme='dark'] {
+    --hl-0: var(--dark-hl-0);
+    --hl-1: var(--dark-hl-1);
+    --hl-2: var(--dark-hl-2);
+    --hl-3: var(--dark-hl-3);
+    --hl-4: var(--dark-hl-4);
+    --hl-5: var(--dark-hl-5);
+    --hl-6: var(--dark-hl-6);
+    --hl-7: var(--dark-hl-7);
+    --hl-8: var(--dark-hl-8);
+    --code-background: var(--dark-code-background);
+}
+
+.hl-0 { color: var(--hl-0); }
+.hl-1 { color: var(--hl-1); }
+.hl-2 { color: var(--hl-2); }
+.hl-3 { color: var(--hl-3); }
+.hl-4 { color: var(--hl-4); }
+.hl-5 { color: var(--hl-5); }
+.hl-6 { color: var(--hl-6); }
+.hl-7 { color: var(--hl-7); }
+.hl-8 { color: var(--hl-8); }
+pre, code { background: var(--code-background); }
diff --git a/typedoc/assets/main.js b/typedoc/assets/main.js
new file mode 100644
index 00000000..c815b334
--- /dev/null
+++ b/typedoc/assets/main.js
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RegExp("^"+s+i+"[^aeiouwxy]$"),A=/^(.+?[^aeiou])y$/,q=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,$=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,V=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,W=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,U=/ll$/,G=new RegExp("^"+s+i+"[^aeiouwxy]$"),N=function(c){var g,C,T,d,x,O,D;if(c.length<3)return c;if(T=c.substr(0,1),T=="y"&&(c=T.toUpperCase()+c.substr(1)),d=E,x=f,d.test(c)?c=c.replace(d,"$1$2"):x.test(c)&&(c=c.replace(x,"$1$2")),d=p,x=w,d.test(c)){var L=d.exec(c);d=m,d.test(L[1])&&(d=S,c=c.replace(d,""))}else if(x.test(c)){var L=x.exec(c);g=L[1],x=y,x.test(g)&&(c=g,x=I,O=_,D=z,x.test(c)?c=c+"e":O.test(c)?(d=S,c=c.replace(d,"")):D.test(c)&&(c=c+"e"))}if(d=A,d.test(c)){var L=d.exec(c);g=L[1],c=g+"i"}if(d=q,d.test(c)){var L=d.exec(c);g=L[1],C=L[2],d=m,d.test(g)&&(c=g+e[C])}if(d=$,d.test(c)){var L=d.exec(c);g=L[1],C=L[2],d=m,d.test(g)&&(c=g+n[C])}if(d=V,x=W,d.test(c)){var L=d.exec(c);g=L[1],d=v,d.test(g)&&(c=g)}else if(x.test(c)){var L=x.exec(c);g=L[1]+L[2],x=v,x.test(g)&&(c=g)}if(d=P,d.test(c)){var L=d.exec(c);g=L[1],d=v,x=b,O=G,(d.test(g)||x.test(g)&&!O.test(g))&&(c=g)}return d=U,x=v,d.test(c)&&x.test(c)&&(d=S,c=c.replace(d,"")),T=="y"&&(c=T.toLowerCase()+c.substr(1)),c};return function(M){return M.update(N)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");t.generateStopWordFilter=function(e){var n=e.reduce(function(r,i){return r[i]=i,r},{});return function(r){if(r&&n[r.toString()]!==r.toString())return r}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");t.trimmer=function(e){return e.update(function(n){return n.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var n=new t.TokenSet.Builder,r=0,i=e.length;r<i;r++)n.insert(e[r]);return n.finish(),n.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,n){for(var r=new t.TokenSet,i=[{node:r,editsRemaining:n,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var l=s.node.edges["*"];else{var l=new t.TokenSet;s.node.edges["*"]=l}s.str.length==1&&(l.final=!0),i.push({node:l,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var h=s.str.charAt(0),m=s.str.charAt(1),v;m in s.node.edges?v=s.node.edges[m]:(v=new t.TokenSet,s.node.edges[m]=v),s.str.length==1&&(v.final=!0),i.push({node:v,editsRemaining:s.editsRemaining-1,str:h+s.str.slice(2)})}}}return r},t.TokenSet.fromString=function(e){for(var n=new t.TokenSet,r=n,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")n.edges[o]=n,n.final=a;else{var u=new t.TokenSet;u.final=a,n.edges[o]=u,n=u}}return r},t.TokenSet.prototype.toArray=function(){for(var e=[],n=[{prefix:"",node:this}];n.length;){var r=n.pop(),i=Object.keys(r.node.edges),s=i.length;r.node.final&&(r.prefix.charAt(0),e.push(r.prefix));for(var o=0;o<s;o++){var a=i[o];n.push({prefix:r.prefix.concat(a),node:r.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",n=Object.keys(this.edges).sort(),r=n.length,i=0;i<r;i++){var s=n[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var n=new t.TokenSet,r=void 0,i=[{qNode:e,output:n,node:this}];i.length;){r=i.pop();for(var s=Object.keys(r.qNode.edges),o=s.length,a=Object.keys(r.node.edges),u=a.length,l=0;l<o;l++)for(var h=s[l],m=0;m<u;m++){var v=a[m];if(v==h||h=="*"){var b=r.node.edges[v],y=r.qNode.edges[h],E=b.final&&y.final,f=void 0;v in r.output.edges?(f=r.output.edges[v],f.final=f.final||E):(f=new t.TokenSet,f.final=E,r.output.edges[v]=f),i.push({qNode:y,output:f,node:b})}}}return n},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var n,r=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)r++;this.minimize(r),this.uncheckedNodes.length==0?n=this.root:n=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=r;i<e.length;i++){var s=new t.TokenSet,o=e[i];n.edges[o]=s,this.uncheckedNodes.push({parent:n,char:o,child:s}),n=s}n.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var n=this.uncheckedNodes.length-1;n>=e;n--){var r=this.uncheckedNodes[n],i=r.child.toString();i in this.minimizedNodes?r.parent.edges[r.char]=this.minimizedNodes[i]:(r.child._str=i,this.minimizedNodes[i]=r.child),this.uncheckedNodes.pop()}};t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(n){var r=new t.QueryParser(e,n);r.parse()})},t.Index.prototype.query=function(e){for(var n=new t.Query(this.fields),r=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(n,n);for(var u=0;u<n.clauses.length;u++){var l=n.clauses[u],h=null,m=t.Set.empty;l.usePipeline?h=this.pipeline.runString(l.term,{fields:l.fields}):h=[l.term];for(var v=0;v<h.length;v++){var b=h[v];l.term=b;var y=t.TokenSet.fromClause(l),E=this.tokenSet.intersect(y).toArray();if(E.length===0&&l.presence===t.Query.presence.REQUIRED){for(var f=0;f<l.fields.length;f++){var p=l.fields[f];o[p]=t.Set.empty}break}for(var w=0;w<E.length;w++)for(var S=E[w],I=this.invertedIndex[S],_=I._index,f=0;f<l.fields.length;f++){var p=l.fields[f],z=I[p],A=Object.keys(z),q=S+"/"+p,$=new t.Set(A);if(l.presence==t.Query.presence.REQUIRED&&(m=m.union($),o[p]===void 0&&(o[p]=t.Set.complete)),l.presence==t.Query.presence.PROHIBITED){a[p]===void 0&&(a[p]=t.Set.empty),a[p]=a[p].union($);continue}if(i[p].upsert(_,l.boost,function(ke,Ie){return ke+Ie}),!s[q]){for(var V=0;V<A.length;V++){var W=A[V],P=new t.FieldRef(W,p),U=z[W],G;(G=r[P])===void 0?r[P]=new t.MatchData(S,p,U):G.add(S,p,U)}s[q]=!0}}}if(l.presence===t.Query.presence.REQUIRED)for(var f=0;f<l.fields.length;f++){var p=l.fields[f];o[p]=o[p].intersect(m)}}for(var N=t.Set.complete,M=t.Set.empty,u=0;u<this.fields.length;u++){var p=this.fields[u];o[p]&&(N=N.intersect(o[p])),a[p]&&(M=M.union(a[p]))}var c=Object.keys(r),g=[],C=Object.create(null);if(n.isNegated()){c=Object.keys(this.fieldVectors);for(var u=0;u<c.length;u++){var P=c[u],T=t.FieldRef.fromString(P);r[P]=new t.MatchData}}for(var u=0;u<c.length;u++){var T=t.FieldRef.fromString(c[u]),d=T.docRef;if(!!N.contains(d)&&!M.contains(d)){var x=this.fieldVectors[T],O=i[T.fieldName].similarity(x),D;if((D=C[d])!==void 0)D.score+=O,D.matchData.combine(r[T]);else{var L={ref:d,score:O,matchData:r[T]};C[d]=L,g.push(L)}}}return g.sort(function(we,Te){return Te.score-we.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(r){return[r,this.invertedIndex[r]]},this),n=Object.keys(this.fieldVectors).map(function(r){return[r,this.fieldVectors[r].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:n,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var n={},r={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var l=0;l<i.length;l++){var h=i[l],m=h[0],v=h[1];r[m]=new t.Vector(v)}for(var l=0;l<o.length;l++){var h=o[l],b=h[0],y=h[1];a.insert(b),s[b]=y}return a.finish(),n.fields=e.fields,n.fieldVectors=r,n.invertedIndex=s,n.tokenSet=a.root,n.pipeline=u,new t.Index(n)};t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,n){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=n||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,n){var r=e[this._ref],i=Object.keys(this._fields);this._documents[r]=n||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],l=this.tokenizer(u,{fields:[o]}),h=this.pipeline.run(l),m=new t.FieldRef(r,o),v=Object.create(null);this.fieldTermFrequencies[m]=v,this.fieldLengths[m]=0,this.fieldLengths[m]+=h.length;for(var b=0;b<h.length;b++){var y=h[b];if(v[y]==null&&(v[y]=0),v[y]+=1,this.invertedIndex[y]==null){var E=Object.create(null);E._index=this.termIndex,this.termIndex+=1;for(var f=0;f<i.length;f++)E[i[f]]=Object.create(null);this.invertedIndex[y]=E}this.invertedIndex[y][o][r]==null&&(this.invertedIndex[y][o][r]=Object.create(null));for(var p=0;p<this.metadataWhitelist.length;p++){var w=this.metadataWhitelist[p],S=y.metadata[w];this.invertedIndex[y][o][r][w]==null&&(this.invertedIndex[y][o][r][w]=[]),this.invertedIndex[y][o][r][w].push(S)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),n=e.length,r={},i={},s=0;s<n;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,r[a]||(r[a]=0),r[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var l=u[s];r[l]=r[l]/i[l]}this.averageFieldLength=r},t.Builder.prototype.createFieldVectors=function(){for(var e={},n=Object.keys(this.fieldTermFrequencies),r=n.length,i=Object.create(null),s=0;s<r;s++){for(var o=t.FieldRef.fromString(n[s]),a=o.fieldName,u=this.fieldLengths[o],l=new t.Vector,h=this.fieldTermFrequencies[o],m=Object.keys(h),v=m.length,b=this._fields[a].boost||1,y=this._documents[o.docRef].boost||1,E=0;E<v;E++){var f=m[E],p=h[f],w=this.invertedIndex[f]._index,S,I,_;i[f]===void 0?(S=t.idf(this.invertedIndex[f],this.documentCount),i[f]=S):S=i[f],I=S*((this._k1+1)*p)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+p),I*=b,I*=y,_=Math.round(I*1e3)/1e3,l.insert(w,_)}e[o]=l}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var n=Array.prototype.slice.call(arguments,1);n.unshift(this),e.apply(this,n)},t.MatchData=function(e,n,r){for(var i=Object.create(null),s=Object.keys(r||{}),o=0;o<s.length;o++){var a=s[o];i[a]=r[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][n]=i)},t.MatchData.prototype.combine=function(e){for(var n=Object.keys(e.metadata),r=0;r<n.length;r++){var i=n[r],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var l=0;l<u.length;l++){var h=u[l];this.metadata[i][a][h]==null?this.metadata[i][a][h]=e.metadata[i][a][h]:this.metadata[i][a][h]=this.metadata[i][a][h].concat(e.metadata[i][a][h])}}}},t.MatchData.prototype.add=function(e,n,r){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][n]=r;return}if(!(n in this.metadata[e])){this.metadata[e][n]=r;return}for(var i=Object.keys(r),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][n]?this.metadata[e][n][o]=this.metadata[e][n][o].concat(r[o]):this.metadata[e][n][o]=r[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,n){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(n))},this),this;var r=n||{};return r.term=e.toString(),this.clause(r),this},t.QueryParseError=function(e,n,r){this.name="QueryParseError",this.message=e,this.start=n,this.end=r},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],n=this.start,r=this.pos,i=0;i<this.escapeCharPositions.length;i++)r=this.escapeCharPositions[i],e.push(this.str.slice(n,r)),n=r+1;return e.push(this.str.slice(n,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,n;do e=this.next(),n=e.charCodeAt(0);while(n>47&&n<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var n=e.next();if(n==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(n.charCodeAt(0)==92){e.escapeCharacter();continue}if(n==":")return t.QueryLexer.lexField;if(n=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(n=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(n=="+"&&e.width()===1||n=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(n.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,n){this.lexer=new t.QueryLexer(e),this.query=n,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var 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+/*!
+ * lunr.Builder
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.Index
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.Pipeline
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.Set
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.TokenSet
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.Vector
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.stemmer
+ * Copyright (C) 2020 Oliver Nightingale
+ * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
+ */
+/*!
+ * lunr.stopWordFilter
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.tokenizer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.trimmer
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/*!
+ * lunr.utils
+ * Copyright (C) 2020 Oliver Nightingale
+ */
+/**
+ * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
+ * Copyright (C) 2020 Oliver Nightingale
+ * @license MIT
+ */
diff --git a/typedoc/assets/search.js b/typedoc/assets/search.js
new file mode 100644
index 00000000..51c1dad6
--- /dev/null
+++ b/typedoc/assets/search.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/typedoc/assets/style.css b/typedoc/assets/style.css
new file mode 100644
index 00000000..8f6ed2c4
--- /dev/null
+++ b/typedoc/assets/style.css
@@ -0,0 +1,1224 @@
+:root {
+    /* Light */
+    --light-color-background: #f2f4f8;
+    --light-color-background-secondary: #eff0f1;
+    --light-color-icon-background: var(--light-color-background);
+    --light-color-accent: #c5c7c9;
+    --light-color-text: #222;
+    --light-color-text-aside: #707070;
+    --light-color-link: #4da6ff;
+    --light-color-ts: #db1373;
+    --light-color-ts-interface: #139d2c;
+    --light-color-ts-enum: #9c891a;
+    --light-color-ts-class: #2484e5;
+    --light-color-ts-function: #572be7;
+    --light-color-ts-namespace: #b111c9;
+    --light-color-ts-private: #707070;
+    --light-color-ts-variable: #4d68ff;
+    --light-external-icon: url("data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 100 100' width='10' height='10'><path fill-opacity='0' stroke='%23000' stroke-width='10' d='m43,35H5v60h60V57M45,5v10l10,10-30,30 20,20 30-30 10,10h10V5z'/></svg>");
+    --light-color-scheme: light;
+
+    /* Dark */
+    --dark-color-background: #2b2e33;
+    --dark-color-background-secondary: #1e2024;
+    --dark-color-icon-background: var(--dark-color-background-secondary);
+    --dark-color-accent: #9096a2;
+    --dark-color-text: #f5f5f5;
+    --dark-color-text-aside: #dddddd;
+    --dark-color-link: #00aff4;
+    --dark-color-ts: #ff6492;
+    --dark-color-ts-interface: #6cff87;
+    --dark-color-ts-enum: #f4d93e;
+    --dark-color-ts-class: #61b0ff;
+    --dark-color-ts-function: #9772ff;
+    --dark-color-ts-namespace: #e14dff;
+    --dark-color-ts-private: #e2e2e2;
+    --dark-color-ts-variable: #4d68ff;
+    --dark-external-icon: url("data:image/svg+xml;utf8,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 100 100' width='10' height='10'><path fill-opacity='0' stroke='%23fff' stroke-width='10' d='m43,35H5v60h60V57M45,5v10l10,10-30,30 20,20 30-30 10,10h10V5z'/></svg>");
+    --dark-color-scheme: dark;
+}
+
+@media (prefers-color-scheme: light) {
+    :root {
+        --color-background: var(--light-color-background);
+        --color-background-secondary: var(--light-color-background-secondary);
+        --color-icon-background: var(--light-color-icon-background);
+        --color-accent: var(--light-color-accent);
+        --color-text: var(--light-color-text);
+        --color-text-aside: var(--light-color-text-aside);
+        --color-link: var(--light-color-link);
+        --color-ts: var(--light-color-ts);
+        --color-ts-interface: var(--light-color-ts-interface);
+        --color-ts-enum: var(--light-color-ts-enum);
+        --color-ts-class: var(--light-color-ts-class);
+        --color-ts-function: var(--light-color-ts-function);
+        --color-ts-namespace: var(--light-color-ts-namespace);
+        --color-ts-private: var(--light-color-ts-private);
+        --color-ts-variable: var(--light-color-ts-variable);
+        --external-icon: var(--light-external-icon);
+        --color-scheme: var(--light-color-scheme);
+    }
+}
+
+@media (prefers-color-scheme: dark) {
+    :root {
+        --color-background: var(--dark-color-background);
+        --color-background-secondary: var(--dark-color-background-secondary);
+        --color-icon-background: var(--dark-color-icon-background);
+        --color-accent: var(--dark-color-accent);
+        --color-text: var(--dark-color-text);
+        --color-text-aside: var(--dark-color-text-aside);
+        --color-link: var(--dark-color-link);
+        --color-ts: var(--dark-color-ts);
+        --color-ts-interface: var(--dark-color-ts-interface);
+        --color-ts-enum: var(--dark-color-ts-enum);
+        --color-ts-class: var(--dark-color-ts-class);
+        --color-ts-function: var(--dark-color-ts-function);
+        --color-ts-namespace: var(--dark-color-ts-namespace);
+        --color-ts-private: var(--dark-color-ts-private);
+        --color-ts-variable: var(--dark-color-ts-variable);
+        --external-icon: var(--dark-external-icon);
+        --color-scheme: var(--dark-color-scheme);
+    }
+}
+
+html {
+    color-scheme: var(--color-scheme);
+}
+
+body {
+    margin: 0;
+}
+
+:root[data-theme="light"] {
+    --color-background: var(--light-color-background);
+    --color-background-secondary: var(--light-color-background-secondary);
+    --color-icon-background: var(--light-color-icon-background);
+    --color-accent: var(--light-color-accent);
+    --color-text: var(--light-color-text);
+    --color-text-aside: var(--light-color-text-aside);
+    --color-link: var(--light-color-link);
+    --color-ts: var(--light-color-ts);
+    --color-ts-interface: var(--light-color-ts-interface);
+    --color-ts-enum: var(--light-color-ts-enum);
+    --color-ts-class: var(--light-color-ts-class);
+    --color-ts-function: var(--light-color-ts-function);
+    --color-ts-namespace: var(--light-color-ts-namespace);
+    --color-ts-private: var(--light-color-ts-private);
+    --color-ts-variable: var(--light-color-ts-variable);
+    --external-icon: var(--light-external-icon);
+    --color-scheme: var(--light-color-scheme);
+}
+
+:root[data-theme="dark"] {
+    --color-background: var(--dark-color-background);
+    --color-background-secondary: var(--dark-color-background-secondary);
+    --color-icon-background: var(--dark-color-icon-background);
+    --color-accent: var(--dark-color-accent);
+    --color-text: var(--dark-color-text);
+    --color-text-aside: var(--dark-color-text-aside);
+    --color-link: var(--dark-color-link);
+    --color-ts: var(--dark-color-ts);
+    --color-ts-interface: var(--dark-color-ts-interface);
+    --color-ts-enum: var(--dark-color-ts-enum);
+    --color-ts-class: var(--dark-color-ts-class);
+    --color-ts-function: var(--dark-color-ts-function);
+    --color-ts-namespace: var(--dark-color-ts-namespace);
+    --color-ts-private: var(--dark-color-ts-private);
+    --color-ts-variable: var(--dark-color-ts-variable);
+    --external-icon: var(--dark-external-icon);
+    --color-scheme: var(--dark-color-scheme);
+}
+
+h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+    line-height: 1.2;
+}
+
+h1 {
+    font-size: 1.875rem;
+    margin: 0.67rem 0;
+}
+
+h2 {
+    font-size: 1.5rem;
+    margin: 0.83rem 0;
+}
+
+h3 {
+    font-size: 1.25rem;
+    margin: 1rem 0;
+}
+
+h4 {
+    font-size: 1.05rem;
+    margin: 1.33rem 0;
+}
+
+h5 {
+    font-size: 1rem;
+    margin: 1.5rem 0;
+}
+
+h6 {
+    font-size: 0.875rem;
+    margin: 2.33rem 0;
+}
+
+.uppercase {
+    text-transform: uppercase;
+}
+
+pre {
+    white-space: pre;
+    white-space: pre-wrap;
+    word-wrap: break-word;
+}
+
+dl,
+menu,
+ol,
+ul {
+    margin: 1em 0;
+}
+
+dd {
+    margin: 0 0 0 40px;
+}
+
+.container {
+    max-width: 1600px;
+    padding: 0 2rem;
+}
+
+@media (min-width: 640px) {
+    .container {
+        padding: 0 4rem;
+    }
+}
+@media (min-width: 1200px) {
+    .container {
+        padding: 0 8rem;
+    }
+}
+@media (min-width: 1600px) {
+    .container {
+        padding: 0 12rem;
+    }
+}
+
+/* Footer */
+.tsd-generator {
+    border-top: 1px solid var(--color-accent);
+    padding-top: 1rem;
+    padding-bottom: 1rem;
+    max-height: 3.5rem;
+}
+
+.tsd-generator > p {
+    margin-top: 0;
+    margin-bottom: 0;
+    padding: 0 1rem;
+}
+
+.container-main {
+    display: flex;
+    justify-content: space-between;
+    position: relative;
+    margin: 0 auto;
+}
+
+.col-4,
+.col-8 {
+    box-sizing: border-box;
+    float: left;
+    padding: 2rem 1rem;
+}
+
+.col-4 {
+    flex: 0 0 25%;
+}
+.col-8 {
+    flex: 1 0;
+    flex-wrap: wrap;
+    padding-left: 0;
+}
+
+@keyframes fade-in {
+    from {
+        opacity: 0;
+    }
+    to {
+        opacity: 1;
+    }
+}
+@keyframes fade-out {
+    from {
+        opacity: 1;
+        visibility: visible;
+    }
+    to {
+        opacity: 0;
+    }
+}
+@keyframes fade-in-delayed {
+    0% {
+        opacity: 0;
+    }
+    33% {
+        opacity: 0;
+    }
+    100% {
+        opacity: 1;
+    }
+}
+@keyframes fade-out-delayed {
+    0% {
+        opacity: 1;
+        visibility: visible;
+    }
+    66% {
+        opacity: 0;
+    }
+    100% {
+        opacity: 0;
+    }
+}
+@keyframes shift-to-left {
+    from {
+        transform: translate(0, 0);
+    }
+    to {
+        transform: translate(-25%, 0);
+    }
+}
+@keyframes unshift-to-left {
+    from {
+        transform: translate(-25%, 0);
+    }
+    to {
+        transform: translate(0, 0);
+    }
+}
+@keyframes pop-in-from-right {
+    from {
+        transform: translate(100%, 0);
+    }
+    to {
+        transform: translate(0, 0);
+    }
+}
+@keyframes pop-out-to-right {
+    from {
+        transform: translate(0, 0);
+        visibility: visible;
+    }
+    to {
+        transform: translate(100%, 0);
+    }
+}
+body {
+    background: var(--color-background);
+    font-family: "Segoe UI", sans-serif;
+    font-size: 16px;
+    color: var(--color-text);
+}
+
+a {
+    color: var(--color-link);
+    text-decoration: none;
+}
+a:hover {
+    text-decoration: underline;
+}
+a.external[target="_blank"] {
+    background-image: var(--external-icon);
+    background-position: top 3px right;
+    background-repeat: no-repeat;
+    padding-right: 13px;
+}
+
+code,
+pre {
+    font-family: Menlo, Monaco, Consolas, "Courier New", monospace;
+    padding: 0.2em;
+    margin: 0;
+    font-size: 0.875rem;
+    border-radius: 0.8em;
+}
+
+pre {
+    padding: 10px;
+    border: 0.1em solid var(--color-accent);
+}
+pre code {
+    padding: 0;
+    font-size: 100%;
+}
+
+blockquote {
+    margin: 1em 0;
+    padding-left: 1em;
+    border-left: 4px solid gray;
+}
+
+.tsd-typography {
+    line-height: 1.333em;
+}
+.tsd-typography ul {
+    list-style: square;
+    padding: 0 0 0 20px;
+    margin: 0;
+}
+.tsd-typography h4,
+.tsd-typography .tsd-index-panel h3,
+.tsd-index-panel .tsd-typography h3,
+.tsd-typography h5,
+.tsd-typography h6 {
+    font-size: 1em;
+    margin: 0;
+}
+.tsd-typography h5,
+.tsd-typography h6 {
+    font-weight: normal;
+}
+.tsd-typography p,
+.tsd-typography ul,
+.tsd-typography ol {
+    margin: 1em 0;
+}
+
+@media (max-width: 1024px) {
+    html .col-content {
+        float: none;
+        max-width: 100%;
+        width: 100%;
+        padding-top: 3rem;
+    }
+    html .col-menu {
+        position: fixed !important;
+        overflow-y: auto;
+        -webkit-overflow-scrolling: touch;
+        z-index: 1024;
+        top: 0 !important;
+        bottom: 0 !important;
+        left: auto !important;
+        right: 0 !important;
+        padding: 1.5rem 1.5rem 0 0;
+        max-width: 25rem;
+        visibility: hidden;
+        background-color: var(--color-background);
+        transform: translate(100%, 0);
+    }
+    html .col-menu > *:last-child {
+        padding-bottom: 20px;
+    }
+    html .overlay {
+        content: "";
+        display: block;
+        position: fixed;
+        z-index: 1023;
+        top: 0;
+        left: 0;
+        right: 0;
+        bottom: 0;
+        background-color: rgba(0, 0, 0, 0.75);
+        visibility: hidden;
+    }
+
+    .to-has-menu .overlay {
+        animation: fade-in 0.4s;
+    }
+
+    .to-has-menu :is(header, footer, .col-content) {
+        animation: shift-to-left 0.4s;
+    }
+
+    .to-has-menu .col-menu {
+        animation: pop-in-from-right 0.4s;
+    }
+
+    .from-has-menu .overlay {
+        animation: fade-out 0.4s;
+    }
+
+    .from-has-menu :is(header, footer, .col-content) {
+        animation: unshift-to-left 0.4s;
+    }
+
+    .from-has-menu .col-menu {
+        animation: pop-out-to-right 0.4s;
+    }
+
+    .has-menu body {
+        overflow: hidden;
+    }
+    .has-menu .overlay {
+        visibility: visible;
+    }
+    .has-menu :is(header, footer, .col-content) {
+        transform: translate(-25%, 0);
+    }
+    .has-menu .col-menu {
+        visibility: visible;
+        transform: translate(0, 0);
+        display: grid;
+        align-items: center;
+        grid-template-rows: auto 1fr;
+        grid-gap: 1.5rem;
+        max-height: 100vh;
+        padding: 1rem 2rem;
+    }
+    .has-menu .tsd-navigation {
+        max-height: 100%;
+    }
+}
+
+.tsd-breadcrumb {
+    margin: 0;
+    padding: 0;
+    color: var(--color-text-aside);
+}
+.tsd-breadcrumb a {
+    color: var(--color-text-aside);
+    text-decoration: none;
+}
+.tsd-breadcrumb a:hover {
+    text-decoration: underline;
+}
+.tsd-breadcrumb li {
+    display: inline;
+}
+.tsd-breadcrumb li:after {
+    content: " / ";
+}
+
+.tsd-comment-tags {
+    display: flex;
+    flex-direction: column;
+}
+dl.tsd-comment-tag-group {
+    display: flex;
+    align-items: center;
+    overflow: hidden;
+    margin: 0.5em 0;
+}
+dl.tsd-comment-tag-group dt {
+    display: flex;
+    margin-right: 0.5em;
+    font-size: 0.875em;
+    font-weight: normal;
+}
+dl.tsd-comment-tag-group dd {
+    margin: 0;
+}
+code.tsd-tag {
+    padding: 0.25em 0.4em;
+    border: 0.1em solid var(--color-accent);
+    margin-right: 0.25em;
+    font-size: 70%;
+}
+h1 code.tsd-tag:first-of-type {
+    margin-left: 0.25em;
+}
+
+dl.tsd-comment-tag-group dd:before,
+dl.tsd-comment-tag-group dd:after {
+    content: " ";
+}
+dl.tsd-comment-tag-group dd pre,
+dl.tsd-comment-tag-group dd:after {
+    clear: both;
+}
+dl.tsd-comment-tag-group p {
+    margin: 0;
+}
+
+.tsd-panel.tsd-comment .lead {
+    font-size: 1.1em;
+    line-height: 1.333em;
+    margin-bottom: 2em;
+}
+.tsd-panel.tsd-comment .lead:last-child {
+    margin-bottom: 0;
+}
+
+.tsd-filter-visibility h4 {
+    font-size: 1rem;
+    padding-top: 0.75rem;
+    padding-bottom: 0.5rem;
+    margin: 0;
+}
+.tsd-filter-item:not(:last-child) {
+    margin-bottom: 0.5rem;
+}
+.tsd-filter-input {
+    display: flex;
+    width: fit-content;
+    width: -moz-fit-content;
+    align-items: center;
+    user-select: none;
+    -webkit-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    cursor: pointer;
+}
+.tsd-filter-input input[type="checkbox"] {
+    cursor: pointer;
+    position: absolute;
+    width: 1.5em;
+    height: 1.5em;
+    opacity: 0;
+}
+.tsd-filter-input input[type="checkbox"]:disabled {
+    pointer-events: none;
+}
+.tsd-filter-input svg {
+    cursor: pointer;
+    width: 1.5em;
+    height: 1.5em;
+    margin-right: 0.5em;
+    border-radius: 0.33em;
+    /* Leaving this at full opacity breaks event listeners on Firefox.
+    Don't remove unless you know what you're doing. */
+    opacity: 0.99;
+}
+.tsd-filter-input input[type="checkbox"]:focus + svg {
+    transform: scale(0.95);
+}
+.tsd-filter-input input[type="checkbox"]:focus:not(:focus-visible) + svg {
+    transform: scale(1);
+}
+.tsd-checkbox-background {
+    fill: var(--color-accent);
+}
+input[type="checkbox"]:checked ~ svg .tsd-checkbox-checkmark {
+    stroke: var(--color-text);
+}
+.tsd-filter-input input:disabled ~ svg > .tsd-checkbox-background {
+    fill: var(--color-background);
+    stroke: var(--color-accent);
+    stroke-width: 0.25rem;
+}
+.tsd-filter-input input:disabled ~ svg > .tsd-checkbox-checkmark {
+    stroke: var(--color-accent);
+}
+
+.tsd-theme-toggle {
+    padding-top: 0.75rem;
+}
+.tsd-theme-toggle > h4 {
+    display: inline;
+    vertical-align: middle;
+    margin-right: 0.75rem;
+}
+
+.tsd-hierarchy {
+    list-style: square;
+    margin: 0;
+}
+.tsd-hierarchy .target {
+    font-weight: bold;
+}
+
+.tsd-panel-group.tsd-index-group {
+    margin-bottom: 0;
+}
+.tsd-index-panel .tsd-index-list {
+    list-style: none;
+    line-height: 1.333em;
+    margin: 0;
+    padding: 0.25rem 0 0 0;
+    overflow: hidden;
+    display: grid;
+    grid-template-columns: repeat(3, 1fr);
+    column-gap: 1rem;
+    grid-template-rows: auto;
+}
+@media (max-width: 1024px) {
+    .tsd-index-panel .tsd-index-list {
+        grid-template-columns: repeat(2, 1fr);
+    }
+}
+@media (max-width: 768px) {
+    .tsd-index-panel .tsd-index-list {
+        grid-template-columns: repeat(1, 1fr);
+    }
+}
+.tsd-index-panel .tsd-index-list li {
+    -webkit-page-break-inside: avoid;
+    -moz-page-break-inside: avoid;
+    -ms-page-break-inside: avoid;
+    -o-page-break-inside: avoid;
+    page-break-inside: avoid;
+}
+.tsd-index-panel a,
+.tsd-index-panel a.tsd-parent-kind-module {
+    color: var(--color-ts);
+}
+.tsd-index-panel a.tsd-parent-kind-interface {
+    color: var(--color-ts-interface);
+}
+.tsd-index-panel a.tsd-parent-kind-enum {
+    color: var(--color-ts-enum);
+}
+.tsd-index-panel a.tsd-parent-kind-class {
+    color: var(--color-ts-class);
+}
+.tsd-index-panel a.tsd-kind-module {
+    color: var(--color-ts-namespace);
+}
+.tsd-index-panel a.tsd-kind-interface {
+    color: var(--color-ts-interface);
+}
+.tsd-index-panel a.tsd-kind-enum {
+    color: var(--color-ts-enum);
+}
+.tsd-index-panel a.tsd-kind-class {
+    color: var(--color-ts-class);
+}
+.tsd-index-panel a.tsd-kind-function {
+    color: var(--color-ts-function);
+}
+.tsd-index-panel a.tsd-kind-namespace {
+    color: var(--color-ts-namespace);
+}
+.tsd-index-panel a.tsd-kind-variable {
+    color: var(--color-ts-variable);
+}
+.tsd-index-panel a.tsd-is-private {
+    color: var(--color-ts-private);
+}
+
+.tsd-flag {
+    display: inline-block;
+    padding: 0.25em 0.4em;
+    border-radius: 4px;
+    color: var(--color-comment-tag-text);
+    background-color: var(--color-comment-tag);
+    text-indent: 0;
+    font-size: 75%;
+    line-height: 1;
+    font-weight: normal;
+}
+
+.tsd-anchor {
+    position: absolute;
+    top: -100px;
+}
+
+.tsd-member {
+    position: relative;
+}
+.tsd-member .tsd-anchor + h3 {
+    display: flex;
+    align-items: center;
+    margin-top: 0;
+    margin-bottom: 0;
+    border-bottom: none;
+}
+.tsd-member [data-tsd-kind] {
+    color: var(--color-ts);
+}
+.tsd-member [data-tsd-kind="Interface"] {
+    color: var(--color-ts-interface);
+}
+.tsd-member [data-tsd-kind="Enum"] {
+    color: var(--color-ts-enum);
+}
+.tsd-member [data-tsd-kind="Class"] {
+    color: var(--color-ts-class);
+}
+.tsd-member [data-tsd-kind="Private"] {
+    color: var(--color-ts-private);
+}
+
+.tsd-navigation a {
+    display: block;
+    margin: 0.4rem 0;
+    border-left: 2px solid transparent;
+    color: var(--color-text);
+    text-decoration: none;
+    transition: border-left-color 0.1s;
+}
+.tsd-navigation a:hover {
+    text-decoration: underline;
+}
+.tsd-navigation ul {
+    margin: 0;
+    padding: 0;
+    list-style: none;
+}
+.tsd-navigation li {
+    padding: 0;
+}
+
+.tsd-navigation.primary .tsd-accordion-details > ul {
+    margin-top: 0.75rem;
+}
+.tsd-navigation.primary a {
+    padding: 0.75rem 0.5rem;
+    margin: 0;
+}
+.tsd-navigation.primary ul li a {
+    margin-left: 0.5rem;
+}
+.tsd-navigation.primary ul li li a {
+    margin-left: 1.5rem;
+}
+.tsd-navigation.primary ul li li li a {
+    margin-left: 2.5rem;
+}
+.tsd-navigation.primary ul li li li li a {
+    margin-left: 3.5rem;
+}
+.tsd-navigation.primary ul li li li li li a {
+    margin-left: 4.5rem;
+}
+.tsd-navigation.primary ul li li li li li li a {
+    margin-left: 5.5rem;
+}
+.tsd-navigation.primary li.current > a {
+    border-left: 0.15rem var(--color-text) solid;
+}
+.tsd-navigation.primary li.selected > a {
+    font-weight: bold;
+    border-left: 0.2rem var(--color-text) solid;
+}
+.tsd-navigation.primary ul li a:hover {
+    border-left: 0.2rem var(--color-text-aside) solid;
+}
+.tsd-navigation.primary li.globals + li > span,
+.tsd-navigation.primary li.globals + li > a {
+    padding-top: 20px;
+}
+
+.tsd-navigation.secondary.tsd-navigation--toolbar-hide {
+    max-height: calc(100vh - 1rem);
+    top: 0.5rem;
+}
+.tsd-navigation.secondary > ul {
+    display: inline;
+    padding-right: 0.5rem;
+    transition: opacity 0.2s;
+}
+.tsd-navigation.secondary ul li a {
+    padding-left: 0;
+}
+.tsd-navigation.secondary ul li li a {
+    padding-left: 1.1rem;
+}
+.tsd-navigation.secondary ul li li li a {
+    padding-left: 2.2rem;
+}
+.tsd-navigation.secondary ul li li li li a {
+    padding-left: 3.3rem;
+}
+.tsd-navigation.secondary ul li li li li li a {
+    padding-left: 4.4rem;
+}
+.tsd-navigation.secondary ul li li li li li li a {
+    padding-left: 5.5rem;
+}
+
+a.tsd-index-link {
+    margin: 0.25rem 0;
+    font-size: 1rem;
+    line-height: 1.25rem;
+    display: inline-flex;
+    align-items: center;
+}
+.tsd-accordion-summary > h1,
+.tsd-accordion-summary > h2,
+.tsd-accordion-summary > h3,
+.tsd-accordion-summary > h4,
+.tsd-accordion-summary > h5 {
+    display: inline-flex;
+    align-items: center;
+    vertical-align: middle;
+    margin-bottom: 0;
+    user-select: none;
+    -moz-user-select: none;
+    -webkit-user-select: none;
+    -ms-user-select: none;
+}
+.tsd-accordion-summary {
+    display: block;
+    cursor: pointer;
+}
+.tsd-accordion-summary > * {
+    margin-top: 0;
+    margin-bottom: 0;
+    padding-top: 0;
+    padding-bottom: 0;
+}
+.tsd-accordion-summary::-webkit-details-marker {
+    display: none;
+}
+.tsd-index-accordion .tsd-accordion-summary svg {
+    margin-right: 0.25rem;
+}
+.tsd-index-content > :not(:first-child) {
+    margin-top: 0.75rem;
+}
+.tsd-index-heading {
+    margin-top: 1.5rem;
+    margin-bottom: 0.75rem;
+}
+
+.tsd-kind-icon {
+    margin-right: 0.5rem;
+    width: 1.25rem;
+    height: 1.25rem;
+    min-width: 1.25rem;
+    min-height: 1.25rem;
+}
+.tsd-kind-icon path {
+    transform-origin: center;
+    transform: scale(1.1);
+}
+.tsd-signature > .tsd-kind-icon {
+    margin-right: 0.8rem;
+}
+
+@media (min-width: 1024px) {
+    .col-content {
+        margin: 2rem auto;
+    }
+
+    .menu-sticky-wrap {
+        position: sticky;
+        height: calc(100vh - 2rem);
+        top: 4rem;
+        right: 0;
+        padding: 0 1.5rem;
+        padding-top: 1rem;
+        margin-top: 3rem;
+        transition: 0.3s ease-in-out;
+        transition-property: top, padding-top, padding, height;
+        overflow-y: auto;
+    }
+    .col-menu {
+        border-left: 1px solid var(--color-accent);
+    }
+    .col-menu--hide {
+        top: 1rem;
+    }
+    .col-menu .tsd-navigation:not(:last-child) {
+        padding-bottom: 1.75rem;
+    }
+}
+
+.tsd-panel {
+    margin-bottom: 2.5rem;
+}
+.tsd-panel.tsd-member {
+    margin-bottom: 4rem;
+}
+.tsd-panel:empty {
+    display: none;
+}
+.tsd-panel > h1,
+.tsd-panel > h2,
+.tsd-panel > h3 {
+    margin: 1.5rem -1.5rem 0.75rem -1.5rem;
+    padding: 0 1.5rem 0.75rem 1.5rem;
+}
+.tsd-panel > h1.tsd-before-signature,
+.tsd-panel > h2.tsd-before-signature,
+.tsd-panel > h3.tsd-before-signature {
+    margin-bottom: 0;
+    border-bottom: none;
+}
+
+.tsd-panel-group {
+    margin: 4rem 0;
+}
+.tsd-panel-group.tsd-index-group {
+    margin: 2rem 0;
+}
+.tsd-panel-group.tsd-index-group details {
+    margin: 2rem 0;
+}
+
+#tsd-search {
+    transition: background-color 0.2s;
+}
+#tsd-search .title {
+    position: relative;
+    z-index: 2;
+}
+#tsd-search .field {
+    position: absolute;
+    left: 0;
+    top: 0;
+    right: 2.5rem;
+    height: 100%;
+}
+#tsd-search .field input {
+    box-sizing: border-box;
+    position: relative;
+    top: -50px;
+    z-index: 1;
+    width: 100%;
+    padding: 0 10px;
+    opacity: 0;
+    outline: 0;
+    border: 0;
+    background: transparent;
+    color: var(--color-text);
+}
+#tsd-search .field label {
+    position: absolute;
+    overflow: hidden;
+    right: -40px;
+}
+#tsd-search .field input,
+#tsd-search .title {
+    transition: opacity 0.2s;
+}
+#tsd-search .results {
+    position: absolute;
+    visibility: hidden;
+    top: 40px;
+    width: 100%;
+    margin: 0;
+    padding: 0;
+    list-style: none;
+    box-shadow: 0 0 4px rgba(0, 0, 0, 0.25);
+}
+#tsd-search .results li {
+    padding: 0 10px;
+    background-color: var(--color-background);
+}
+#tsd-search .results li:nth-child(even) {
+    background-color: var(--color-background-secondary);
+}
+#tsd-search .results li.state {
+    display: none;
+}
+#tsd-search .results li.current,
+#tsd-search .results li:hover {
+    background-color: var(--color-accent);
+}
+#tsd-search .results a {
+    display: block;
+}
+#tsd-search .results a:before {
+    top: 10px;
+}
+#tsd-search .results span.parent {
+    color: var(--color-text-aside);
+    font-weight: normal;
+}
+#tsd-search.has-focus {
+    background-color: var(--color-accent);
+}
+#tsd-search.has-focus .field input {
+    top: 0;
+    opacity: 1;
+}
+#tsd-search.has-focus .title {
+    z-index: 0;
+    opacity: 0;
+}
+#tsd-search.has-focus .results {
+    visibility: visible;
+}
+#tsd-search.loading .results li.state.loading {
+    display: block;
+}
+#tsd-search.failure .results li.state.failure {
+    display: block;
+}
+
+.tsd-signature {
+    margin: 0 0 1rem 0;
+    padding: 1rem 0.5rem;
+    border: 1px solid var(--color-accent);
+    font-family: Menlo, Monaco, Consolas, "Courier New", monospace;
+    font-size: 14px;
+    overflow-x: auto;
+}
+
+.tsd-signature-symbol {
+    color: var(--color-text-aside);
+    font-weight: normal;
+}
+
+.tsd-signature-type {
+    font-style: italic;
+    font-weight: normal;
+}
+
+.tsd-signatures {
+    padding: 0;
+    margin: 0 0 1em 0;
+    list-style-type: none;
+}
+.tsd-signatures .tsd-signature {
+    margin: 0;
+    border-color: var(--color-accent);
+    border-width: 1px 0;
+    transition: background-color 0.1s;
+}
+.tsd-description .tsd-signatures .tsd-signature {
+    border-width: 1px;
+}
+
+ul.tsd-parameter-list,
+ul.tsd-type-parameter-list {
+    list-style: square;
+    margin: 0;
+    padding-left: 20px;
+}
+ul.tsd-parameter-list > li.tsd-parameter-signature,
+ul.tsd-type-parameter-list > li.tsd-parameter-signature {
+    list-style: none;
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Env.html">Env</a></li></ul>
+<h1>Class Env</h1></div>
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+<div class="tsd-comment tsd-typography"><p>Env class that holds detected capabilities</p>
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+<div class="tsd-signature">agent<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &#39;&#39;</span></div>
+<div class="tsd-comment tsd-typography"><p>Detected agent</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L15">src/util/env.ts:15</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="backends" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>backends</span><a href="#backends" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">backends<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> = []</span></div>
+<div class="tsd-comment tsd-typography"><p>List of supported backends</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L17">src/util/env.ts:17</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="browser" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>browser</span><a href="#browser" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">browser<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Running in Browser</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L7">src/util/env.ts:7</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="cpu" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>cpu</span><a href="#cpu" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">cpu<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>flags<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">; </span>model<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> = ...</span></div>
+<div class="tsd-comment tsd-typography"><p>CPU info</p>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>flags<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h5></li>
+<li class="tsd-parameter">
+<h5>model<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L65">src/util/env.ts:65</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="filter" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>filter</span><a href="#filter" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">filter<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Are image filters supported?</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L21">src/util/env.ts:21</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="initial" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>initial</span><a href="#initial" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">initial<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Has any work been performed so far</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L19">src/util/env.ts:19</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="kernels" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>kernels</span><a href="#kernels" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">kernels<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> = []</span></div>
+<div class="tsd-comment tsd-typography"><p>List of supported kernels for current backend</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L73">src/util/env.ts:73</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="node" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>node</span><a href="#node" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">node<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Running in NodeJS</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L9">src/util/env.ts:9</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="offscreen" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>offscreen</span><a href="#offscreen" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">offscreen<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Is offscreenCanvas supported?</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L27">src/util/env.ts:27</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="perfadd" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>perfadd</span><a href="#perfadd" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">perfadd<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = false</span></div>
+<div class="tsd-comment tsd-typography"><p>Are performance counter instant values or additive</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L29">src/util/env.ts:29</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="platform" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>platform</span><a href="#platform" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">platform<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> = &#39;&#39;</span></div>
+<div class="tsd-comment tsd-typography"><p>Detected platform</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L13">src/util/env.ts:13</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="tfjs" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>tfjs</span><a href="#tfjs" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">tfjs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>version<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-comment tsd-typography"><p>TFJS instance details</p>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>version<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L23">src/util/env.ts:23</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="wasm" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>wasm</span><a href="#wasm" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">wasm<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>backend<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>multithread<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>simd<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>supported<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> = ...</span></div>
+<div class="tsd-comment tsd-typography"><p>WASM detected capabilities</p>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>backend<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li>
+<li class="tsd-parameter">
+<h5>multithread<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li>
+<li class="tsd-parameter">
+<h5>simd<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li>
+<li class="tsd-parameter">
+<h5>supported<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L31">src/util/env.ts:31</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="webgl" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>webgl</span><a href="#webgl" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">webgl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>backend<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>renderer<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>supported<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>version<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> = ...</span></div>
+<div class="tsd-comment tsd-typography"><p>WebGL detected capabilities</p>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>backend<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li>
+<li class="tsd-parameter">
+<h5>renderer<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span></h5></li>
+<li class="tsd-parameter">
+<h5>supported<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li>
+<li class="tsd-parameter">
+<h5>version<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L43">src/util/env.ts:43</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="webgpu" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>webgpu</span><a href="#webgpu" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">webgpu<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>adapter<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>backend<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>supported<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> = ...</span></div>
+<div class="tsd-comment tsd-typography"><p>WebGPU detected capabilities</p>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>adapter<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span></h5></li>
+<li class="tsd-parameter">
+<h5>backend<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li>
+<li class="tsd-parameter">
+<h5>supported<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L55">src/util/env.ts:55</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="worker" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>worker</span><a href="#worker" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">worker<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Running in WebWorker thread</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L11">src/util/env.ts:11</a></li></ul></aside></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Methods</h2>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="updateBackend" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>update<wbr/>Backend</span><a href="#updateBackend" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="updateBackend.updateBackend-1">update<wbr/>Backend<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><a href="#updateBackend.updateBackend-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>update backend information</p>
+</div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L114">src/util/env.ts:114</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="updateCPU" class="tsd-anchor"></a>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GraphModel | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li><a href="GraphModel.html">GraphModel</a></li></ul>
+<h1>Class GraphModel&lt;ModelURL&gt;</h1></div>
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+<div class="tsd-comment tsd-typography"><p>A <code>tf.GraphModel</code> is a directed, acyclic graph built from a
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+the command line converter tool and loaded via <code>tf.loadGraphModel</code>.</p>
+
+<h3>Doc</h3></div></section>
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+<h4>Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>ModelURL<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">Url</span> = <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">io.IOHandler</span></h4></li></ul></section>
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+<h4>Hierarchy</h4>
+<ul class="tsd-hierarchy">
+<li><span class="target">GraphModel</span></li></ul></section>
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+<h4>Implements</h4>
+<ul class="tsd-hierarchy">
+<li><span class="tsd-signature-type">InferenceModel</span></li></ul></section><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:34</li></ul></aside>
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+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Methods</h3>
+<div class="tsd-index-list"><a href="GraphModel.html#dispose" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4DB8" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-2048-path"></rect><path d="M9.162 16V7.24H10.578L11.514 10.072C11.602 10.328 11.674 10.584 11.73 10.84C11.794 11.088 11.842 11.28 11.874 11.416C11.906 11.28 11.954 11.088 12.018 10.84C12.082 10.584 12.154 10.324 12.234 10.06L13.122 7.24H14.538V16H13.482V12.82C13.482 12.468 13.49 12.068 13.506 11.62C13.53 11.172 13.558 10.716 13.59 10.252C13.622 9.78 13.654 9.332 13.686 8.908C13.726 8.476 13.762 8.1 13.794 7.78L12.366 12.16H11.334L9.894 7.78C9.934 8.092 9.97 8.456 10.002 8.872C10.042 9.28 10.078 9.716 10.11 10.18C10.142 10.636 10.166 11.092 10.182 11.548C10.206 12.004 10.218 12.428 10.218 12.82V16H9.162Z" fill="var(--color-text)" id="icon-2048-text"></path></svg><span>dispose</span></a>
+<a href="GraphModel.html#disposeIntermediateTensors" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>dispose<wbr/>Intermediate<wbr/>Tensors</span></a>
+<a href="GraphModel.html#execute" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>execute</span></a>
+<a href="GraphModel.html#executeAsync" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>execute<wbr/>Async</span></a>
+<a href="GraphModel.html#getIntermediateTensors" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>get<wbr/>Intermediate<wbr/>Tensors</span></a>
+<a href="GraphModel.html#load" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>load</span></a>
+<a href="GraphModel.html#loadSync" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>load<wbr/>Sync</span></a>
+<a href="GraphModel.html#predict" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>predict</span></a>
+<a href="GraphModel.html#save" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>save</span></a>
+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Constructors</h2>
+<section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class"><a id="constructor" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>constructor</span><a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="constructor.new_GraphModel">new <wbr/>Graph<wbr/>Model<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>modelUrl<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span>, loadOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">LoadOptions</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span><a href="#constructor.new_GraphModel" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>ModelURL<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">Url</span> = <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span></h4></li></ul></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>modelUrl: <span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span></h5>
+<div class="tsd-comment tsd-typography"><p>url for the model, or an <code>io.IOHandler</code>.</p>
+</div></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> loadOptions: <span class="tsd-signature-type">LoadOptions</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:61</li></ul></aside></li></ul></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputNodes" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>input<wbr/>Nodes</span><a href="#inputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">input<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:45</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputs" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>inputs</span><a href="#inputs" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">inputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
+<p>Implementation of InferenceModel.inputs</p>
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:47</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="metadata" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>metadata</span><a href="#metadata" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">metadata<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters"></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:50</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelSignature" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>model<wbr/>Signature</span><a href="#modelSignature" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">model<wbr/>Signature<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters"></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:51</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelVersion" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>model<wbr/>Version</span><a href="#modelVersion" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">model<wbr/>Version<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:44</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputNodes" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>output<wbr/>Nodes</span><a href="#outputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">output<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:46</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputs" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>outputs</span><a href="#outputs" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">outputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources">
+<p>Implementation of InferenceModel.outputs</p>
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:48</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="weights" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>weights</span><a href="#weights" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">weights<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">NamedTensorsMap</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:49</li></ul></aside></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Methods</h2>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="dispose.dispose-1">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#dispose.dispose-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Releases the memory used by the weight tensors and resourceManager.</p>
+
+<h3>Doc</h3></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:215</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="disposeIntermediateTensors" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>dispose<wbr/>Intermediate<wbr/>Tensors</span><a href="#disposeIntermediateTensors" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="disposeIntermediateTensors.disposeIntermediateTensors-1">dispose<wbr/>Intermediate<wbr/>Tensors<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#disposeIntermediateTensors.disposeIntermediateTensors-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Dispose intermediate tensors for model debugging mode (flag
+KEEP_INTERMEDIATE_TENSORS is true).</p>
+
+<h3>Doc</h3></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:208</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="execute" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>execute</span><a href="#execute" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="execute.execute-1">execute<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, outputs<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><a href="#execute.execute-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Executes inference for the model for given input tensors.</p>
+
+<h3>Returns</h3><p>A single tensor if provided with a single output or no outputs
+are provided and there is only one default output, otherwise return a
+tensor array. The order of the tensor array is the same as the outputs
+if provided, otherwise the order of outputNodes attribute of the model.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inputs: <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h5>
+<div class="tsd-comment tsd-typography"><p>tensor, tensor array or tensor map of the inputs for the
+model, keyed by the input node names.</p>
+</div></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> outputs: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h5>
+<div class="tsd-comment tsd-typography"><p>output node name from the Tensorflow model, if no
+outputs are specified, the default outputs of the model would be used.
+You can inspect intermediate nodes of the model by adding them to the
+outputs array.</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span></h4><aside class="tsd-sources">
+<p>Implementation of InferenceModel.execute</p>
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:177</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="executeAsync" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>execute<wbr/>Async</span><a href="#executeAsync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="executeAsync.executeAsync-1">execute<wbr/>Async<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, outputs<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span><a href="#executeAsync.executeAsync-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Executes inference for the model for given input tensors in async
+fashion, use this method when your model contains control flow ops.</p>
+
+<h3>Returns</h3><p>A Promise of single tensor if provided with a single output or
+no outputs are provided and there is only one default output, otherwise
+return a tensor map.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inputs: <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h5>
+<div class="tsd-comment tsd-typography"><p>tensor, tensor array or tensor map of the inputs for the
+model, keyed by the input node names.</p>
+</div></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> outputs: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h5>
+<div class="tsd-comment tsd-typography"><p>output node name from the Tensorflow model, if no outputs
+are specified, the default outputs of the model would be used. You can
+inspect intermediate nodes of the model by adding them to the outputs
+array.</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:194</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="getIntermediateTensors" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>get<wbr/>Intermediate<wbr/>Tensors</span><a href="#getIntermediateTensors" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="getIntermediateTensors.getIntermediateTensors-1">get<wbr/>Intermediate<wbr/>Tensors<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">NamedTensorsMap</span><a href="#getIntermediateTensors.getIntermediateTensors-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Get intermediate tensors for model debugging mode (flag
+KEEP_INTERMEDIATE_TENSORS is true).</p>
+
+<h3>Doc</h3></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">NamedTensorsMap</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:201</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="load" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>load</span><a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="load.load-1">load<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">UrlIOHandler</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">IOHandlerSync</span><span class="tsd-signature-symbol"> ? </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> : </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span><a href="#load.load-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Loads the model and weight files, construct the in memory weight map and
+compile the inference graph.</p>
+</div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">UrlIOHandler</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">ModelURL</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-type">IOHandlerSync</span><span class="tsd-signature-symbol"> ? </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> : </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:67</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="loadSync" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>load<wbr/>Sync</span><a href="#loadSync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="loadSync.loadSync-1">load<wbr/>Sync<span class="tsd-signature-symbol">(</span>artifacts<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ModelArtifacts</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><a href="#loadSync.loadSync-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Synchronously construct the in memory weight map and
+compile the inference graph. Also initialize hashtable if any.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>artifacts: <span class="tsd-signature-type">ModelArtifacts</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">boolean</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:74</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="predict" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>predict</span><a href="#predict" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="predict.predict-1">predict<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, config<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">ModelPredictConfig</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span><a href="#predict.predict-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Execute the inference for the input tensors.</p>
+
+<h3>See</h3><p><a href="GraphModel.html#inputNodes">inputNodes</a></p>
+<p>You can also feed any intermediate nodes using the NamedTensorMap as the
+input type. For example, given the graph
+   InputNode =&gt; Intermediate =&gt; OutputNode,
+you can execute the subgraph Intermediate =&gt; OutputNode by calling
+   model.execute(&#39;IntermediateNode&#39; : tf.tensor(...));</p>
+<p>This is useful for models that uses tf.dynamic_rnn, where the intermediate
+state needs to be fed manually.</p>
+<p>For batch inference execution, the tensors for each input need to be
+concatenated together. For example with mobilenet, the required input shape
+is [1, 244, 244, 3], which represents the [batch, height, width, channel].
+If we are provide a batched data of 100 images, the input tensor should be
+in the shape of [100, 244, 244, 3].</p>
+
+<h3>Returns</h3><p>Inference result tensors. The output would be single <code>tf.Tensor</code>
+if model has single output node, otherwise Tensor[] or NamedTensorMap[]
+will be returned for model with multiple outputs.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inputs: <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> config: <span class="tsd-signature-type">ModelPredictConfig</span></h5>
+<div class="tsd-comment tsd-typography"><p>Prediction configuration for specifying the batch size and
+output node names. Currently the batch size option is ignored for graph
+model.</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h4><aside class="tsd-sources">
+<p>Implementation of InferenceModel.predict</p>
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:158</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="save" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>save</span><a href="#save" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="save.save-1">save<span class="tsd-signature-symbol">(</span>handlerOrURL<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span>, config<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">SaveConfig</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">SaveResult</span><span class="tsd-signature-symbol">&gt;</span><a href="#save.save-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Save the configuration and/or weights of the GraphModel.</p>
+<p>An <code>IOHandler</code> is an object that has a <code>save</code> method of the proper
+signature defined. The <code>save</code> method manages the storing or
+transmission of serialized data (&quot;artifacts&quot;) that represent the
+model&#39;s topology and weights onto or via a specific medium, such as
+file downloads, local storage, IndexedDB in the web browser and HTTP
+requests to a server. TensorFlow.js provides <code>IOHandler</code>
+implementations for a number of frequently used saving mediums, such as
+<code>tf.io.browserDownloads</code> and <code>tf.io.browserLocalStorage</code>. See <code>tf.io</code>
+for more details.</p>
+<p>This method also allows you to refer to certain types of <code>IOHandler</code>s
+as URL-like string shortcuts, such as &#39;localstorage://&#39; and
+&#39;indexeddb://&#39;.</p>
+<p>Example 1: Save <code>model</code>&#39;s topology and weights to browser <a href="https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage">local
+storage</a>;
+then load it back.</p>
+<pre><code class="language-js"><span class="hl-0">const</span><span class="hl-1"> </span><span class="hl-2">modelUrl</span><span class="hl-1"> =</span><br/><span class="hl-1">   </span><span class="hl-3">&#39;https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json&#39;</span><span class="hl-1">;</span><br/><span class="hl-0">const</span><span class="hl-1"> </span><span class="hl-2">model</span><span class="hl-1"> = </span><span class="hl-4">await</span><span class="hl-1"> </span><span class="hl-5">tf</span><span class="hl-1">.</span><span class="hl-6">loadGraphModel</span><span class="hl-1">(</span><span class="hl-5">modelUrl</span><span class="hl-1">);</span><br/><span class="hl-0">const</span><span class="hl-1"> </span><span class="hl-2">zeros</span><span class="hl-1"> = </span><span class="hl-5">tf</span><span class="hl-1">.</span><span class="hl-6">zeros</span><span class="hl-1">([</span><span class="hl-7">1</span><span class="hl-1">, </span><span class="hl-7">224</span><span class="hl-1">, </span><span class="hl-7">224</span><span class="hl-1">, </span><span class="hl-7">3</span><span class="hl-1">]);</span><br/><span class="hl-5">model</span><span class="hl-1">.</span><span class="hl-6">predict</span><span class="hl-1">(</span><span class="hl-5">zeros</span><span class="hl-1">).</span><span class="hl-6">print</span><span class="hl-1">();</span><br/><br/><span class="hl-0">const</span><span class="hl-1"> </span><span class="hl-2">saveResults</span><span class="hl-1"> = </span><span class="hl-4">await</span><span class="hl-1"> </span><span class="hl-5">model</span><span class="hl-1">.</span><span class="hl-6">save</span><span class="hl-1">(</span><span class="hl-3">&#39;localstorage://my-model-1&#39;</span><span class="hl-1">);</span><br/><br/><span class="hl-0">const</span><span class="hl-1"> </span><span class="hl-2">loadedModel</span><span class="hl-1"> = </span><span class="hl-4">await</span><span class="hl-1"> </span><span class="hl-5">tf</span><span class="hl-1">.</span><span class="hl-6">loadGraphModel</span><span class="hl-1">(</span><span class="hl-3">&#39;localstorage://my-model-1&#39;</span><span class="hl-1">);</span><br/><span class="hl-5">console</span><span class="hl-1">.</span><span class="hl-6">log</span><span class="hl-1">(</span><span class="hl-3">&#39;Prediction from loaded model:&#39;</span><span class="hl-1">);</span><br/><span class="hl-5">model</span><span class="hl-1">.</span><span class="hl-6">predict</span><span class="hl-1">(</span><span class="hl-5">zeros</span><span class="hl-1">).</span><span class="hl-6">print</span><span class="hl-1">();</span>
+</code></pre>
+
+<h3>Returns</h3><p>A <code>Promise</code> of <code>SaveResult</code>, which summarizes the result of
+the saving, such as byte sizes of the saved artifacts for the model&#39;s
+  topology and weight values.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>handlerOrURL: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span></h5>
+<div class="tsd-comment tsd-typography"><p>An instance of <code>IOHandler</code> or a URL-like,
+scheme-based string shortcut for <code>IOHandler</code>.</p>
+</div></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> config: <span class="tsd-signature-type">SaveConfig</span></h5>
+<div class="tsd-comment tsd-typography"><p>Options for saving the model.</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">SaveResult</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.18.0_br26fteayl44zj43fz4bazb7oq/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:119</li></ul></aside></li></ul></section></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="current tsd-kind-class"><a href="GraphModel.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg><span>Graph<wbr/>Model</span></a>
+<ul>
+<li class="tsd-kind-constructor tsd-parent-kind-class"><a href="GraphModel.html#constructor" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-512-path"></use><use href="#icon-512-text"></use></svg>constructor</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputNodes" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>input<wbr/>Nodes</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputs" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>inputs</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#metadata" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>metadata</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelSignature" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>model<wbr/>Signature</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelVersion" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>model<wbr/>Version</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputNodes" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>output<wbr/>Nodes</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputs" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>outputs</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#weights" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>weights</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#dispose" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>dispose</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#disposeIntermediateTensors" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>dispose<wbr/>Intermediate<wbr/>Tensors</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#execute" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>execute</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#executeAsync" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>execute<wbr/>Async</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#getIntermediateTensors" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>get<wbr/>Intermediate<wbr/>Tensors</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#load" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>load</a></li>
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+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#predict" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>predict</a></li>
+<li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#save" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg>save</a></li></ul></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html
new file mode 100644
index 00000000..c921d718
--- /dev/null
+++ b/typedoc/classes/Human.html
@@ -0,0 +1,855 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Human | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Human.html">Human</a></li></ul>
+<h1>Class Human</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p><em>Human</em>* library main class</p>
+<p>All methods and properties are available only as members of Human class</p>
+<ul>
+<li>Configuration object definition: <a href="../interfaces/Config.html">Config</a></li>
+<li>Results object definition: <a href="../interfaces/Result.html">Result</a></li>
+<li>Possible inputs: <a href="../types/Input.html">Input</a></li>
+</ul>
+
+<h3>Param</h3><p><a href="../interfaces/Config.html">Config</a></p>
+
+<h3>Returns</h3><p>instance of <a href="Human.html">Human</a></p>
+</div></section>
+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
+<ul class="tsd-hierarchy">
+<li><span class="target">Human</span></li></ul></section><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L55">src/human.ts:55</a></li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
+<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
+<h5 class="tsd-index-heading uppercase" role="button" aria-expanded="false" tabIndex=0><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M1.5 5.50969L8 11.6609L14.5 5.50969L12.5466 3.66086L8 7.96494L3.45341 3.66086L1.5 5.50969Z" fill="var(--color-text)"></path></svg> Index</h5></summary>
+<div class="tsd-accordion-details">
+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Constructors</h3>
+<div class="tsd-index-list"><a href="Human.html#constructor" class="tsd-index-link tsd-kind-constructor tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#4D7FFF" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-512-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-512-text"></path></svg><span>constructor</span></a>
+</div></section>
+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Properties</h3>
+<div class="tsd-index-list"><a href="Human.html#config" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF984D" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-1024-path"></rect><path d="M9.354 16V7.24H12.174C12.99 7.24 13.638 7.476 14.118 7.948C14.606 8.412 14.85 9.036 14.85 9.82C14.85 10.604 14.606 11.232 14.118 11.704C13.638 12.168 12.99 12.4 12.174 12.4H10.434V16H9.354ZM10.434 11.428H12.174C12.646 11.428 13.022 11.284 13.302 10.996C13.59 10.7 13.734 10.308 13.734 9.82C13.734 9.324 13.59 8.932 13.302 8.644C13.022 8.356 12.646 8.212 12.174 8.212H10.434V11.428Z" fill="var(--color-text)" id="icon-1024-text"></path></svg><span>config</span></a>
+<a href="Human.html#distance" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>distance</span></a>
+<a href="Human.html#draw" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>draw</span></a>
+<a href="Human.html#env" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>env</span></a>
+<a href="Human.html#events" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>events</span></a>
+<a href="Human.html#faceTriangulation" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>face<wbr/>Triangulation</span></a>
+<a href="Human.html#faceUVMap" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>faceUVMap</span></a>
+<a href="Human.html#gl" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>gl</span></a>
+<a href="Human.html#match" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>match</span></a>
+<a href="Human.html#performance" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>performance</span></a>
+<a href="Human.html#process" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>process</span></a>
+<a href="Human.html#result" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>result</span></a>
+<a href="Human.html#similarity-1" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>similarity</span></a>
+<a href="Human.html#state" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>state</span></a>
+<a href="Human.html#tf" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>tf</span></a>
+<a href="Human.html#version" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>version</span></a>
+</div></section>
+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Methods</h3>
+<div class="tsd-index-list"><a href="Human.html#analyze" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4DB8" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-2048-path"></rect><path d="M9.162 16V7.24H10.578L11.514 10.072C11.602 10.328 11.674 10.584 11.73 10.84C11.794 11.088 11.842 11.28 11.874 11.416C11.906 11.28 11.954 11.088 12.018 10.84C12.082 10.584 12.154 10.324 12.234 10.06L13.122 7.24H14.538V16H13.482V12.82C13.482 12.468 13.49 12.068 13.506 11.62C13.53 11.172 13.558 10.716 13.59 10.252C13.622 9.78 13.654 9.332 13.686 8.908C13.726 8.476 13.762 8.1 13.794 7.78L12.366 12.16H11.334L9.894 7.78C9.934 8.092 9.97 8.456 10.002 8.872C10.042 9.28 10.078 9.716 10.11 10.18C10.142 10.636 10.166 11.092 10.182 11.548C10.206 12.004 10.218 12.428 10.218 12.82V16H9.162Z" fill="var(--color-text)" id="icon-2048-text"></path></svg><span>analyze</span></a>
+<a href="Human.html#compare" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>compare</span></a>
+<a href="Human.html#detect" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>detect</span></a>
+<a href="Human.html#emit" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>emit</span></a>
+<a href="Human.html#enhance" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>enhance</span></a>
+<a href="Human.html#getModelStats" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>get<wbr/>Model<wbr/>Stats</span></a>
+<a href="Human.html#image" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>image</span></a>
+<a href="Human.html#init" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>init</span></a>
+<a href="Human.html#load" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>load</span></a>
+<a href="Human.html#next" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>next</span></a>
+<a href="Human.html#now" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>now</span></a>
+<a href="Human.html#profile" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>profile</span></a>
+<a href="Human.html#reset" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>reset</span></a>
+<a href="Human.html#segmentation" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>segmentation</span></a>
+<a href="Human.html#validate" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>validate</span></a>
+<a href="Human.html#warmup" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>warmup</span></a>
+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Constructors</h2>
+<section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class"><a id="constructor" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>constructor</span><a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="constructor.new_Human">new <wbr/>Human<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a><a href="#constructor.new_Human" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Constructor for <strong>Human</strong> library that is futher used for all operations</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5>
+<div class="tsd-comment tsd-typography"><p>user configuration object <a href="../interfaces/Config.html">Config</a></p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L126">src/human.ts:126</a></li></ul></aside></li></ul></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="config" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>config</span><a href="#config" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">config<span class="tsd-signature-symbol">:</span> <a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a></div>
+<div class="tsd-comment tsd-typography"><p>Current configuration</p>
+<ul>
+<li>Defaults: <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">config</a></li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L62">src/human.ts:62</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="distance" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>distance</span><a href="#distance" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">distance<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> = match.distance</span></div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class">
+<li class="tsd-signature" id="__type.__type-1"><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Calculates distance between two descriptors</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>descriptor1: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>descriptor2: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>options: <a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p>calculation options</p>
+<ul>
+<li>order - algorithm to use
+Euclidean distance if <code>order</code> is 2 (default), Minkowski distance algorithm of nth order if <code>order</code> is higher than 2</li>
+<li>multiplier - by how much to enhance difference analysis in range of 1..100
+default is 20 which normalizes results to similarity above 0.5 can be considered a match</li>
+</ul>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4></li></ul></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L216">src/human.ts:216</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="draw" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>draw</span><a href="#draw" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">draw<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>all<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>gesture<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../types/GestureResult.html" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>object<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">; </span>options<span class="tsd-signature-symbol">: </span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">; </span>person<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-comment tsd-typography"><p>Draw helper classes that can draw detected objects on canvas using specified draw</p>
+<ul>
+<li>canvas: draws input to canvas</li>
+<li>options: are global settings for all draw operations, can be overriden for each draw method <a href="../types/DrawOptions.html">DrawOptions</a></li>
+<li>face, body, hand, gesture, object, person: draws detected results as overlays on canvas</li>
+</ul>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>all<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-3.__type-4"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>meta-function that performs draw for: canvas, face, body, hand</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-5.__type-6"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw detected bodies</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-7.__type-8"><span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span>, output<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw processed canvas</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>input: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">HTMLVideoElement</span></h5></li>
+<li>
+<h5>output: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-9.__type-10"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw detected faces</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>gesture<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../types/GestureResult.html" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-11.__type-12"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../types/GestureResult.html" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw detected gestures</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../types/GestureResult.html" class="tsd-signature-type" data-tsd-kind="Type alias">GestureResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-13.__type-14"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw detected hands</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/HandResult.html" class="tsd-signature-type" data-tsd-kind="Interface">HandResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>object<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-15.__type-16"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw detected objects</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/ObjectResult.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li>
+<li class="tsd-parameter">
+<h5>options<span class="tsd-signature-symbol">: </span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a></h5></li>
+<li class="tsd-parameter">
+<h5>person<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span></h5>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-type-literal">
+<li class="tsd-signature" id="__type-2.__type-17.__type-18"><span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw combined person results instead of individual detection result objects</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></li></ul></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L92">src/human.ts:92</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="env" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>env</span><a href="#env" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">env<span class="tsd-signature-symbol">:</span> <a href="Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a></div>
+<div class="tsd-comment tsd-typography"><p>Object containing environment information used for diagnostics</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L85">src/human.ts:85</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="events" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>events</span><a href="#events" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">events<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">EventTarget</span></div>
+<div class="tsd-comment tsd-typography"><p>Container for events dispatched by Human
+Possible events:</p>
+<ul>
+<li><code>create</code>: triggered when Human object is instantiated</li>
+<li><code>load</code>: triggered when models are loaded (explicitly or on-demand)</li>
+<li><code>image</code>: triggered when input image is processed</li>
+<li><code>result</code>: triggered when detection is complete</li>
+<li><code>warmup</code>: triggered when warmup is complete</li>
+<li><code>error</code>: triggered on some errors</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L109">src/human.ts:109</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="faceTriangulation" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>face<wbr/>Triangulation</span><a href="#faceTriangulation" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">face<wbr/>Triangulation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>Reference face triangualtion array of 468 points, used for triangle references between points</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L111">src/human.ts:111</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="faceUVMap" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>faceUVMap</span><a href="#faceUVMap" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">faceUVMap<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>Refernce UV map of 468 values, used for 3D mapping of the face mesh</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L113">src/human.ts:113</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="gl" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>gl</span><a href="#gl" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">gl<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">unknown</span><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>WebGL debug info</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L120">src/human.ts:120</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="match" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>match</span><a href="#match" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">match<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> = match.match</span></div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class">
+<li class="tsd-signature" id="__type-19.__type-20"><span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Matches given descriptor to a closest entry in array of descriptors</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>descriptor: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5>
+<div class="tsd-comment tsd-typography"><p>face descriptor</p>
+</div></li>
+<li>
+<h5>descriptors: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span></h5>
+<div class="tsd-comment tsd-typography"><p>array of face descriptors to commpare given descriptor to</p>
+</div></li>
+<li>
+<h5>options: <a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p>see <code>similarity</code> method for options description
+Returns</p>
+<ul>
+<li><code>index</code> index array index where best match was found or -1 if no matches</li>
+<li><code>distance</code> calculated <code>distance</code> of given descriptor to the best match</li>
+<li><code>similarity</code> calculated normalized <code>similarity</code> of given descriptor to the best match</li>
+</ul>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li></ul></li></ul></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L218">src/human.ts:218</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="performance" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>performance</span><a href="#performance" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">performance<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Performance object that contains values for all recently performed operations</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L115">src/human.ts:115</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="process" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>process</span><a href="#process" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">process<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-comment tsd-typography"><p>currenty processed image tensor and canvas</p>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li class="tsd-parameter">
+<h5>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L76">src/human.ts:76</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="result" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>result</span><a href="#result" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">result<span class="tsd-signature-symbol">:</span> <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></div>
+<div class="tsd-comment tsd-typography"><p>Last known result of detect run</p>
+<ul>
+<li>Can be accessed anytime after initial detection</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L67">src/human.ts:67</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="similarity-1" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>similarity</span><a href="#similarity-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">similarity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> =&gt; </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol"> = match.similarity</span></div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter-signature">
+<ul class="tsd-signatures tsd-kind-type-literal tsd-parent-kind-class">
+<li class="tsd-signature" id="__type-23.__type-24"><span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Calculates normalized similarity between two face descriptors based on their <code>distance</code></p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>descriptor1: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>descriptor2: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>options: <a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p>calculation options</p>
+<ul>
+<li>order - algorithm to use
+Euclidean distance if <code>order</code> is 2 (default), Minkowski distance algorithm of nth order if <code>order</code> is higher than 2</li>
+<li>multiplier - by how much to enhance difference analysis in range of 1..100
+default is 20 which normalizes results to similarity above 0.5 can be considered a match</li>
+<li>min - normalize similarity result to a given range</li>
+<li>max - normalzie similarity resutl to a given range
+default is 0.2...0.8
+Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity</li>
+</ul>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4></li></ul></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L214">src/human.ts:214</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="state" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>state</span><a href="#state" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">state<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
+<div class="tsd-comment tsd-typography"><p>Current state of Human library</p>
+<ul>
+<li>Can be polled to determine operations that are currently executed</li>
+<li>Progresses through: &#39;config&#39;, &#39;check&#39;, &#39;backend&#39;, &#39;load&#39;, &#39;run:<model>&#39;, &#39;idle&#39;</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L73">src/human.ts:73</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="tf" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>tf</span><a href="#tf" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">tf<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">any</span></div>
+<div class="tsd-comment tsd-typography"><p>Instance of TensorFlow/JS used by Human</p>
+<ul>
+<li>Can be embedded or externally provided
+<a href="https://js.tensorflow.org/api/latest/">TFJS API</a></li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L82">src/human.ts:82</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="version" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>version</span><a href="#version" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">version<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
+<div class="tsd-comment tsd-typography"><p>Current version of Human library in <em>semver</em> format</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L57">src/human.ts:57</a></li></ul></aside></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Methods</h2>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="analyze" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>analyze</span><a href="#analyze" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="analyze.analyze-1">analyze<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">...</span>msg<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#analyze.analyze-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>internal function to measure tensor leaks</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagRest">Rest</code> <span class="tsd-signature-symbol">...</span>msg: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L179">src/human.ts:179</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="compare" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>compare</span><a href="#compare" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="compare.compare-1">compare<span class="tsd-signature-symbol">(</span>firstImageTensor<span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span>, secondImageTensor<span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><a href="#compare.compare-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Compare two input tensors for pixel simmilarity</p>
+<ul>
+<li>use <code>human.image</code> to process any valid input and get a tensor that can be used for compare</li>
+<li>when passing manually generated tensors:</li>
+<li>both input tensors must be in format [1, height, width, 3]</li>
+<li>if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor</li>
+<li>return value is pixel similarity score normalized by input resolution and rgb channels</li>
+</ul>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>firstImageTensor: <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li>
+<li>
+<h5>secondImageTensor: <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L266">src/human.ts:266</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="detect" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>detect</span><a href="#detect" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="detect.detect-1">detect<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span><a href="#detect.detect-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Main detection method</p>
+<ul>
+<li>Analyze configuration: <a href="../interfaces/Config.html">Config</a></li>
+<li>Pre-process input: <a href="../types/Input.html">Input</a></li>
+<li>Run inference for all configured models</li>
+<li>Process and return result: <a href="../interfaces/Result.html">Result</a></li>
+</ul>
+
+<h3>Returns</h3><p>result - <a href="../interfaces/Result.html">Result</a></p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>input: <a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5>
+<div class="tsd-comment tsd-typography"><p><a href="../types/Input.html">Input</a></p>
+</div></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5>
+<div class="tsd-comment tsd-typography"><p><a href="../interfaces/Config.html">Config</a></p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L379">src/human.ts:379</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="emit" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>emit</span><a href="#emit" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="emit.emit-1">emit<span class="tsd-signature-symbol">(</span>event<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#emit.emit-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>emit event</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>event: <span class="tsd-signature-type">string</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L318">src/human.ts:318</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="enhance" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>enhance</span><a href="#enhance" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="enhance.enhance-1">enhance<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><a href="#enhance.enhance-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Enhance method performs additional enhacements to face image previously detected for futher processing</p>
+
+<h3>Returns</h3><p>Tensor</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>input: <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h5>
+<div class="tsd-comment tsd-typography"><p>Tensor as provided in human.result.face[n].tensor</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L255">src/human.ts:255</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="getModelStats" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>get<wbr/>Model<wbr/>Stats</span><a href="#getModelStats" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="getModelStats.getModelStats-1">get<wbr/>Model<wbr/>Stats<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="../types/models.ModelStats.html" class="tsd-signature-type" data-tsd-kind="Type alias">ModelStats</a><a href="#getModelStats.getModelStats-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>get model loading/loaded stats</p>
+</div>
+<h4 class="tsd-returns-title">Returns <a href="../types/models.ModelStats.html" class="tsd-signature-type" data-tsd-kind="Type alias">ModelStats</a></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L333">src/human.ts:333</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="image" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>image</span><a href="#image" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="image.image-1">image<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, getTensor<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span><a href="#image.image-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Process input as return canvas and tensor</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>input: <a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5>
+<div class="tsd-comment tsd-typography"><p>any input <a href="../types/Input.html">Input</a></p>
+</div></li>
+<li>
+<h5>getTensor: <span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> = true</span></h5>
+<div class="tsd-comment tsd-typography"><p>should image processing also return tensor or just canvas
+Returns object with <code>tensor</code> and <code>canvas</code></p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>tensor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L231">src/human.ts:231</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="init" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>init</span><a href="#init" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="init.init-1">init<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><a href="#init.init-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Explicit backend initialization</p>
+<ul>
+<li>Normally done implicitly during initial load phase</li>
+<li>Call to explictly register and initialize TFJS backend without any other operations</li>
+<li>Use when changing backend during runtime</li>
+</ul>
+</div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L275">src/human.ts:275</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="load" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>load</span><a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="load.load-1">load<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><a href="#load.load-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Load method preloads all configured models on-demand</p>
+<ul>
+<li>Not explicitly required as any required model is load implicitly on it&#39;s first run</li>
+</ul>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5>
+<div class="tsd-comment tsd-typography"><p><a href="../interfaces/Config.html">Config</a></p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L285">src/human.ts:285</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="next" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>next</span><a href="#next" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="next.next-1">next<span class="tsd-signature-symbol">(</span>result<span class="tsd-signature-symbol">?: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><a href="#next.next-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Runs interpolation using last known result and returns smoothened result
+Interpolation is based on time since last known result so can be called independently</p>
+
+<h3>Returns</h3><p>result - <a href="../interfaces/Result.html">Result</a></p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>result: <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p><a href="../interfaces/Result.html">Result</a> optional use specific result set to run interpolation on</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L328">src/human.ts:328</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="now" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>now</span><a href="#now" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="now.now-1">now<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><a href="#now.now-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Utility wrapper for performance.now()</p>
+</div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L221">src/human.ts:221</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="profile" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>profile</span><a href="#profile" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="profile.profile-1">profile<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><a href="#profile.profile-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Run detect with tensorflow profiling</p>
+<ul>
+<li>result object will contain total exeuction time information for top-20 kernels</li>
+<li>actual detection object can be accessed via <code>human.result</code></li>
+</ul>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>input: <a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> userConfig: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L353">src/human.ts:353</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="reset" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>reset</span><a href="#reset" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="reset.reset-1">reset<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#reset.reset-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Reset configuration to default values</p>
+</div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/human.ts#L202">src/human.ts:202</a></li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="segmentation" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>segmentation</span><a href="#segmentation" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="segmentation.segmentation-1">segmentation<span class="tsd-signature-symbol">(</span>input<span class="tsd-signature-symbol">: </span><a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a>, background<span class="tsd-signature-symbol">?: </span><a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-symbol">{ </span>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>canvas<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a><span class="tsd-signature-symbol">; </span>data<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">&gt;</span><a href="#segmentation.segmentation-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Segmentation method takes any input and returns processed canvas with body segmentation</p>
+<ul>
+<li>Segmentation is not triggered as part of detect process</li>
+</ul>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>input: <a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5>
+<div class="tsd-comment tsd-typography"><p><a href="../types/Input.html">Input</a></p>
+</div></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> background: <a href="../types/Input.html" class="tsd-signature-type" data-tsd-kind="Type alias">Input</a></h5>
+<div class="tsd-comment tsd-typography"><p><a href="../types/Input.html">Input</a></p>
+<ul>
+<li>Optional parameter background is used to fill the background with specific input
+ Returns:</li>
+<li><code>data</code> as raw data array with per-pixel segmentation values</li>
+<li><code>canvas</code> as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging</li>
+<li><code>alpha</code> as grayscale canvas that represents segmentation alpha values</li>
+</ul>
+</div></li></ul></div>
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+<li class="tsd-signature tsd-anchor-link" id="validate.validate-1">validate<span class="tsd-signature-symbol">(</span>userConfig<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>expected<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>reason<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>where<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span><a href="#validate.validate-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
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+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Tensor-1.html">Tensor</a></li></ul>
+<h1>Class Tensor&lt;R&gt;</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>A <code>tf.Tensor</code> object represents an immutable, multidimensional array of
+numbers that has a shape and a data type.</p>
+<p>For performance reasons, functions that create tensors do not necessarily
+perform a copy of the data passed to them (e.g. if the data is passed as a
+<code>Float32Array</code>), and changes to the data will change the tensor. This is not
+a feature and is not supported. To avoid this behavior, use the tensor before
+changing the input data or create a copy with <code>copy = tf.add(yourTensor, 0)</code>.</p>
+<p>See <code>tf.tensor</code> for details on how to create a <code>tf.Tensor</code>.</p>
+
+<h3>Doc</h3></div></section>
+<section class="tsd-panel tsd-type-parameters">
+<h4>Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>R<span class="tsd-signature-symbol"> extends </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul></section>
+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
+<ul class="tsd-hierarchy">
+<li><span class="target">Tensor</span></li></ul></section><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:124</li>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:139</li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
+<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
+<h5 class="tsd-index-heading uppercase" role="button" aria-expanded="false" tabIndex=0><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M1.5 5.50969L8 11.6609L14.5 5.50969L12.5466 3.66086L8 7.96494L3.45341 3.66086L1.5 5.50969Z" fill="var(--color-text)"></path></svg> Index</h5></summary>
+<div class="tsd-accordion-details">
+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Constructors</h3>
+<div class="tsd-index-list"><a href="Tensor-1.html#constructor" class="tsd-index-link tsd-kind-constructor tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#4D7FFF" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-512-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-512-text"></path></svg><span>constructor</span></a>
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+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Properties</h3>
+<div class="tsd-index-list"><a href="Tensor-1.html#dataId" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF984D" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-1024-path"></rect><path d="M9.354 16V7.24H12.174C12.99 7.24 13.638 7.476 14.118 7.948C14.606 8.412 14.85 9.036 14.85 9.82C14.85 10.604 14.606 11.232 14.118 11.704C13.638 12.168 12.99 12.4 12.174 12.4H10.434V16H9.354ZM10.434 11.428H12.174C12.646 11.428 13.022 11.284 13.302 10.996C13.59 10.7 13.734 10.308 13.734 9.82C13.734 9.324 13.59 8.932 13.302 8.644C13.022 8.356 12.646 8.212 12.174 8.212H10.434V11.428Z" fill="var(--color-text)" id="icon-1024-text"></path></svg><span>data<wbr/>Id</span></a>
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+<a href="Tensor-1.html#strides" class="tsd-index-link tsd-kind-property tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>strides</span></a>
+</div></section>
+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Methods</h3>
+<div class="tsd-index-list"><a href="Tensor-1.html#array" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4DB8" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-2048-path"></rect><path d="M9.162 16V7.24H10.578L11.514 10.072C11.602 10.328 11.674 10.584 11.73 10.84C11.794 11.088 11.842 11.28 11.874 11.416C11.906 11.28 11.954 11.088 12.018 10.84C12.082 10.584 12.154 10.324 12.234 10.06L13.122 7.24H14.538V16H13.482V12.82C13.482 12.468 13.49 12.068 13.506 11.62C13.53 11.172 13.558 10.716 13.59 10.252C13.622 9.78 13.654 9.332 13.686 8.908C13.726 8.476 13.762 8.1 13.794 7.78L12.366 12.16H11.334L9.894 7.78C9.934 8.092 9.97 8.456 10.002 8.872C10.042 9.28 10.078 9.716 10.11 10.18C10.142 10.636 10.166 11.092 10.182 11.548C10.206 12.004 10.218 12.428 10.218 12.82V16H9.162Z" fill="var(--color-text)" id="icon-2048-text"></path></svg><span>array</span></a>
+<a href="Tensor-1.html#arraySync" class="tsd-index-link tsd-kind-method tsd-parent-kind-class"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-2048-path"></use><use href="#icon-2048-text"></use></svg><span>array<wbr/>Sync</span></a>
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+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Constructors</h2>
+<section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class"><a id="constructor" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>constructor</span><a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="constructor.new_Tensor">new <wbr/>Tensor<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span>, dtype<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span>, dataId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">object</span>, id<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><a href="#constructor.new_Tensor" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>R<span class="tsd-signature-symbol"> extends </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>shape: <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h5></li>
+<li>
+<h5>dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li>
+<li>
+<h5>dataId: <span class="tsd-signature-type">object</span></h5></li>
+<li>
+<h5>id: <span class="tsd-signature-type">number</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:165</li></ul></aside></li></ul></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dataId" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>data<wbr/>Id</span><a href="#dataId" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">data<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div>
+<div class="tsd-comment tsd-typography"><p>Id of the bucket holding the data for this tensor. Multiple arrays can
+point to the same bucket (e.g. when calling array.reshape()).</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:146</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dtype" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>dtype</span><a href="#dtype" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></div>
+<div class="tsd-comment tsd-typography"><p>The data type for the array.</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:152</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="id" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>id</span><a href="#id" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>Unique id of this tensor.</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:141</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="isDisposed" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>is<wbr/>Disposed</span><a href="#isDisposed" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">is<wbr/>Disposed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:252</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="kept" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>kept</span><a href="#kept" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">kept<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Whether this tensor has been globally kept.</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:156</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="rank" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>rank</span><a href="#rank" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">rank<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:166</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="rankType" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>rank<wbr/>Type</span><a href="#rankType" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">rank<wbr/>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span></div>
+<div class="tsd-comment tsd-typography"><p>The rank type for the array (see <code>Rank</code> enum).</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:154</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="scopeId" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>scope<wbr/>Id</span><a href="#scopeId" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">scope<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>The id of the scope this tensor is being tracked in.</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:158</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="shape" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>shape</span><a href="#shape" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">shape<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></div>
+<div class="tsd-comment tsd-typography"><p>The shape of the tensor.</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:148</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="size" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>size</span><a href="#size" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">size<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>Number of elements in the tensor.</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:150</li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="strides" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagReadonly">Readonly</code> <span>strides</span><a href="#strides" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">strides<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>Number of elements to skip in each dimension when indexing. See
+<a href="https://docs.scipy.org/doc/numpy/reference/generated/%5C">https://docs.scipy.org/doc/numpy/reference/generated/\</a>
+numpy.ndarray.strides.html</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:164</li></ul></aside></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Methods</h2>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="array" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>array</span><a href="#array" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="array.array-1">array<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><a href="#array.array-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns the tensor data as a nested array. The transfer of data is done
+asynchronously.</p>
+
+<h3>Doc</h3></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:184</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="arraySync" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>array<wbr/>Sync</span><a href="#arraySync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="arraySync.arraySync-1">array<wbr/>Sync<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><a href="#arraySync.arraySync-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns the tensor data as a nested array. The transfer of data is done
+synchronously.</p>
+
+<h3>Doc</h3></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:191</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="buffer" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>buffer</span><a href="#buffer" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="buffer.buffer-1">buffer<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><a href="#buffer.buffer-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns a promise of <code>tf.TensorBuffer</code> that holds the underlying data.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:172</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="bufferSync" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>buffer<wbr/>Sync</span><a href="#bufferSync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="bufferSync.bufferSync-1">buffer<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><a href="#bufferSync.bufferSync-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns a <code>tf.TensorBuffer</code> that holds the underlying data.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:177</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="bytes" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>bytes</span><a href="#bytes" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="bytes.bytes-1">bytes<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span><a href="#bytes.bytes-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns the underlying bytes of the tensor&#39;s data.</p>
+</div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:244</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="clone" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>clone</span><a href="#clone" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="clone.clone-1">clone<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>this<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><a href="#clone.clone-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns a copy of the tensor. See <code>tf.clone</code> for details.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>T<span class="tsd-signature-symbol"> extends </span><a href="Tensor-1.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>this: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:267</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="data" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>data</span><a href="#data" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="data.data-1">data<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><a href="#data.data-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Asynchronously downloads the values from the <code>tf.Tensor</code>. Returns a
+promise of <code>TypedArray</code> that resolves when the computation has finished.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:198</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dataSync" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>data<wbr/>Sync</span><a href="#dataSync" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="dataSync.dataSync-1">data<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><a href="#dataSync.dataSync-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Synchronously downloads the values from the <code>tf.Tensor</code>. This blocks the
+UI thread until the values are ready, which can cause performance issues.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-type-parameters">
+<h4 class="tsd-type-parameters-title">Type Parameters</h4>
+<ul class="tsd-type-parameters">
+<li>
+<h4>D<span class="tsd-signature-symbol"> extends </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:242</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dataToGPU" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>data<wbr/>ToGPU</span><a href="#dataToGPU" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="dataToGPU.dataToGPU-1">data<wbr/>ToGPU<span class="tsd-signature-symbol">(</span>options<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">DataToGPUWebGLOption</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">GPUData</span><a href="#dataToGPU.dataToGPU-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Copy the tensor&#39;s data to a new GPU resource. Comparing to the <code>dataSync()</code>
+and <code>data()</code>, this method prevents data from being downloaded to CPU.</p>
+<p>For WebGL backend, the data will be stored on a densely packed texture.
+This means that the texture will use the RGBA channels to store value.</p>
+<p>For WebGPU backend, the data will be stored on a buffer. There is no
+parameter, so can not use an user defined size to create the buffer.</p>
+
+<h3>Returns</h3><p>For WebGL backend, a GPUData contains the new texture and
+    its information.
+    {
+       tensorRef: The tensor that is associated with this texture,
+       texture: WebGLTexture,
+       texShape: [number, number] // [height, width]
+    }</p>
+<pre><code><span class="hl-5">For</span><span class="hl-1"> </span><span class="hl-5">WebGPU</span><span class="hl-1"> </span><span class="hl-5">backend</span><span class="hl-1">, </span><span class="hl-5">a</span><span class="hl-1"> </span><span class="hl-5">GPUData</span><span class="hl-1"> </span><span class="hl-5">contains</span><span class="hl-1"> </span><span class="hl-5">the</span><span class="hl-1"> </span><span class="hl-0">new</span><span class="hl-1"> </span><span class="hl-5">buffer</span><span class="hl-1"> </span><span class="hl-5">and</span><br/><span class="hl-5">its</span><span class="hl-1"> </span><span class="hl-5">information</span><span class="hl-1">.</span><br/><span class="hl-1">{</span><br/><span class="hl-1">   </span><span class="hl-8">tensorRef</span><span class="hl-1">: </span><span class="hl-5">The</span><span class="hl-1"> </span><span class="hl-5">tensor</span><span class="hl-1"> </span><span class="hl-5">that</span><span class="hl-1"> </span><span class="hl-5">is</span><span class="hl-1"> </span><span class="hl-5">associated</span><span class="hl-1"> </span><span class="hl-4">with</span><span class="hl-1"> </span><span class="hl-0">this</span><span class="hl-1"> </span><span class="hl-5">buffer</span><span class="hl-1">,</span><br/><span class="hl-1">   </span><span class="hl-8">buffer</span><span class="hl-1">: </span><span class="hl-5">GPUBuffer</span><span class="hl-1">,</span><br/><span class="hl-1">   </span><span class="hl-8">bufSize</span><span class="hl-1">: </span><span class="hl-5">number</span><br/><span class="hl-1">}</span><br/><br/><span class="hl-5">Remember</span><span class="hl-1"> </span><span class="hl-5">to</span><span class="hl-1"> </span><span class="hl-5">dispose</span><span class="hl-1"> </span><span class="hl-5">the</span><span class="hl-1"> </span><span class="hl-5">GPUData</span><span class="hl-1"> </span><span class="hl-5">after</span><span class="hl-1"> </span><span class="hl-5">it</span><span class="hl-1"> </span><span class="hl-5">is</span><span class="hl-1"> </span><span class="hl-5">used</span><span class="hl-1"> </span><span class="hl-5">by</span><br/><span class="hl-3">`res.tensorRef.dispose()`</span><span class="hl-1">.</span>
+</code></pre>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> options: <span class="tsd-signature-type">DataToGPUWebGLOption</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">GPUData</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:235</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>dispose</span><a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="dispose.dispose-1">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#dispose.dispose-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Disposes <code>tf.Tensor</code> from memory.</p>
+
+<h3>Doc</h3></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:250</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="print" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>print</span><a href="#print" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="print.print-1">print<span class="tsd-signature-symbol">(</span>verbose<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#print.print-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Prints the <code>tf.Tensor</code>. See <code>tf.print</code> for details.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> verbose: <span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>Whether to print verbose information about the tensor,
+   including dtype and size.</p>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:262</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="throwIfDisposed" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>throw<wbr/>If<wbr/>Disposed</span><a href="#throwIfDisposed" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="throwIfDisposed.throwIfDisposed-1">throw<wbr/>If<wbr/>Disposed<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span><a href="#throwIfDisposed.throwIfDisposed-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:253</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="toString" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>to<wbr/>String</span><a href="#toString" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="toString.toString-1">to<wbr/>String<span class="tsd-signature-symbol">(</span>verbose<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><a href="#toString.toString-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Returns a human-readable description of the tensor. Useful for logging.</p>
+
+<h3>Doc</h3></div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> verbose: <span class="tsd-signature-type">boolean</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:273</li></ul></aside></li></ul></section>
+<section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="variable" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>variable</span><a href="#variable" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class">
+<li class="tsd-signature tsd-anchor-link" id="variable.variable-1">variable<span class="tsd-signature-symbol">(</span>trainable<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span>, name<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span>, dtype<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Variable</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><a href="#variable.variable-1" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> trainable: <span class="tsd-signature-type">boolean</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> name: <span class="tsd-signature-type">string</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Variable</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.18.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:274</li></ul></aside></li></ul></section></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
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+<div class="tsd-accordion-details">
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+<li class="selected tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Models | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="tsd-page-title">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/models.html">models</a></li>
+<li><a href="models.Models.html">Models</a></li></ul>
+<h1>Class Models</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Instances of all possible TFJS Graph Models used by Human</p>
+<ul>
+<li>loaded as needed based on configuration</li>
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+<li>initialized implicity on first call to <code>human.detect()</code></li>
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+<h3 class="tsd-index-heading">Constructors</h3>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="antispoof" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>antispoof</span><a href="#antispoof" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">antispoof<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L59">src/models.ts:59</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="blazepose" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>blazepose</span><a href="#blazepose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">blazepose<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L41">src/models.ts:41</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="blazeposedetect" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>blazeposedetect</span><a href="#blazeposedetect" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">blazeposedetect<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L40">src/models.ts:40</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="centernet" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>centernet</span><a href="#centernet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">centernet<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L42">src/models.ts:42</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="efficientpose" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>efficientpose</span><a href="#efficientpose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">efficientpose<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L43">src/models.ts:43</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="emotion" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>emotion</span><a href="#emotion" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">emotion<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L45">src/models.ts:45</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="facedetect" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>facedetect</span><a href="#facedetect" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">facedetect<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L46">src/models.ts:46</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="faceiris" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>faceiris</span><a href="#faceiris" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">faceiris<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L47">src/models.ts:47</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="facemesh" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>facemesh</span><a href="#facemesh" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">facemesh<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L48">src/models.ts:48</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="faceres" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>faceres</span><a href="#faceres" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">faceres<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L49">src/models.ts:49</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="gear" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>gear</span><a href="#gear" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">gear<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L39">src/models.ts:39</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="handpose" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>handpose</span><a href="#handpose" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">handpose<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L51">src/models.ts:51</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="handskeleton" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>handskeleton</span><a href="#handskeleton" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">handskeleton<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L52">src/models.ts:52</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="handtrack" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>handtrack</span><a href="#handtrack" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">handtrack<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L53">src/models.ts:53</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="liveness" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>liveness</span><a href="#liveness" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">liveness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L54">src/models.ts:54</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="mobilefacenet" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>mobilefacenet</span><a href="#mobilefacenet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">mobilefacenet<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L44">src/models.ts:44</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="movenet" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>movenet</span><a href="#movenet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">movenet<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L55">src/models.ts:55</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="nanodet" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>nanodet</span><a href="#nanodet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">nanodet<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L56">src/models.ts:56</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="posenet" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>posenet</span><a href="#posenet" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">posenet<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L57">src/models.ts:57</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="segmentation" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>segmentation</span><a href="#segmentation" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">segmentation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L58">src/models.ts:58</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="ssrnetage" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>ssrnetage</span><a href="#ssrnetage" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">ssrnetage<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L38">src/models.ts:38</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="ssrnetgender" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>ssrnetgender</span><a href="#ssrnetgender" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">ssrnetgender<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> = null</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L50">src/models.ts:50</a></li></ul></aside></section></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
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+<div class="tsd-accordion-details">
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+<div class="tsd-accordion-details">
+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<ul>
+<li class="current tsd-kind-class tsd-parent-kind-namespace"><a href="models.Models.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg><span>Models</span></a>
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+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#blazepose" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>blazepose</a></li>
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+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#emotion" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>emotion</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#facedetect" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>facedetect</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#faceiris" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>faceiris</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#facemesh" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>facemesh</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#faceres" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>faceres</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#gear" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>gear</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#handpose" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>handpose</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#handskeleton" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>handskeleton</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#handtrack" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>handtrack</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#liveness" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>liveness</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#mobilefacenet" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>mobilefacenet</a></li>
+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#movenet" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>movenet</a></li>
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+<li class="tsd-kind-property tsd-parent-kind-class"><a href="models.Models.html#posenet" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>posenet</a></li>
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+<li class="tsd-signature tsd-anchor-link" id="all">all<span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/Result.html" class="tsd-signature-type" data-tsd-kind="Interface">Result</a>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span><a href="#all" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>meta-function that performs draw for: canvas, face, body, hand</p>
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+<ul class="tsd-parameter-list">
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+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/draw/draw.ts#L63">src/draw/draw.ts:63</a></li></ul></aside></li></ul></section></div>
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+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
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+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
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+<ul>
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+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="draw.canvas.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>canvas</a></li>
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+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/draw.canvas.html b/typedoc/functions/draw.canvas.html
new file mode 100644
index 00000000..ece16093
--- /dev/null
+++ b/typedoc/functions/draw.canvas.html
@@ -0,0 +1,66 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>canvas | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
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+<li><a href="draw.canvas.html">canvas</a></li></ul>
+<h1>Function canvas</h1></div>
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+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
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+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw processed canvas</p>
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+<div class="tsd-parameters">
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+<h5>output: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li></ul></div>
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/draw/draw.ts#L55">src/draw/draw.ts:55</a></li></ul></aside></li></ul></section></div>
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+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<li class="tsd-kind-reference tsd-parent-kind-namespace"><a href="../modules/draw.html#hand" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>hand</a></li>
+<li class="tsd-kind-reference tsd-parent-kind-namespace"><a href="../modules/draw.html#object" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>object</a></li>
+<li class="tsd-kind-variable tsd-parent-kind-namespace"><a href="../variables/draw.options.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-variable)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-32-path"></rect><path d="M11.106 16L8.85 7.24H9.966L11.454 13.192C11.558 13.608 11.646 13.996 11.718 14.356C11.79 14.708 11.842 14.976 11.874 15.16C11.906 14.976 11.954 14.708 12.018 14.356C12.09 13.996 12.178 13.608 12.282 13.192L13.758 7.24H14.85L12.582 16H11.106Z" fill="var(--color-text)" id="icon-32-text"></path></svg>options</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="draw.all.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>all</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="draw.canvas.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>canvas</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="draw.person.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>person</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/draw.person.html b/typedoc/functions/draw.person.html
new file mode 100644
index 00000000..5c5caff8
--- /dev/null
+++ b/typedoc/functions/draw.person.html
@@ -0,0 +1,68 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>person | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/draw.html">draw</a></li>
+<li><a href="draw.person.html">person</a></li></ul>
+<h1>Function person</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
+<li class="tsd-signature tsd-anchor-link" id="person">person<span class="tsd-signature-symbol">(</span>inCanvas<span class="tsd-signature-symbol">: </span><a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a>, result<span class="tsd-signature-symbol">: </span><a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span>, drawOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><a href="#person" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>draw combined person results instead of individual detection result objects</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>inCanvas: <a href="../types/AnyCanvas.html" class="tsd-signature-type" data-tsd-kind="Type alias">AnyCanvas</a></h5></li>
+<li>
+<h5>result: <a href="../interfaces/PersonResult.html" class="tsd-signature-type" data-tsd-kind="Interface">PersonResult</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li>
+<h5><code class="tsd-tag ts-flagOptional">Optional</code> drawOptions: <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol">&gt;</span></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/draw/draw.ts#L27">src/draw/draw.ts:27</a></li></ul></aside></li></ul></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<ul>
+<li class="tsd-kind-reference tsd-parent-kind-namespace"><a href="../modules/draw.html#body" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4D82" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-8388608-path"></rect><path d="M10.354 17V8.24H13.066C13.586 8.24 14.042 8.348 14.434 8.564C14.826 8.772 15.13 9.064 15.346 9.44C15.562 9.816 15.67 10.256 15.67 10.76C15.67 11.352 15.514 11.86 15.202 12.284C14.898 12.708 14.482 13 13.954 13.16L15.79 17H14.518L12.838 13.28H11.434V17H10.354ZM11.434 12.308H13.066C13.514 12.308 13.874 12.168 14.146 11.888C14.418 11.6 14.554 11.224 14.554 10.76C14.554 10.288 14.418 9.912 14.146 9.632C13.874 9.352 13.514 9.212 13.066 9.212H11.434V12.308Z" fill="var(--color-text)" id="icon-8388608-text"></path></svg>body</a></li>
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+<li class="tsd-kind-reference tsd-parent-kind-namespace"><a href="../modules/draw.html#gesture" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>gesture</a></li>
+<li class="tsd-kind-reference tsd-parent-kind-namespace"><a href="../modules/draw.html#hand" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>hand</a></li>
+<li class="tsd-kind-reference tsd-parent-kind-namespace"><a href="../modules/draw.html#object" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>object</a></li>
+<li class="tsd-kind-variable tsd-parent-kind-namespace"><a href="../variables/draw.options.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-variable)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-32-path"></rect><path d="M11.106 16L8.85 7.24H9.966L11.454 13.192C11.558 13.608 11.646 13.996 11.718 14.356C11.79 14.708 11.842 14.976 11.874 15.16C11.906 14.976 11.954 14.708 12.018 14.356C12.09 13.996 12.178 13.608 12.282 13.192L13.758 7.24H14.85L12.582 16H11.106Z" fill="var(--color-text)" id="icon-32-text"></path></svg>options</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="draw.all.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>all</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="draw.canvas.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>canvas</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="draw.person.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>person</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/match.distance.html b/typedoc/functions/match.distance.html
new file mode 100644
index 00000000..11616772
--- /dev/null
+++ b/typedoc/functions/match.distance.html
@@ -0,0 +1,72 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>distance | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/match.html">match</a></li>
+<li><a href="match.distance.html">distance</a></li></ul>
+<h1>Function distance</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
+<li class="tsd-signature tsd-anchor-link" id="distance">distance<span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><a href="#distance" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Calculates distance between two descriptors</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>descriptor1: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>descriptor2: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>options: <a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p>calculation options</p>
+<ul>
+<li>order - algorithm to use
+Euclidean distance if <code>order</code> is 2 (default), Minkowski distance algorithm of nth order if <code>order</code> is higher than 2</li>
+<li>multiplier - by how much to enhance difference analysis in range of 1..100
+default is 20 which normalizes results to similarity above 0.5 can be considered a match</li>
+</ul>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L12">src/face/match.ts:12</a></li></ul></aside></li></ul></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/match.Descriptor.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Descriptor</a></li>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/match.MatchOptions.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Match<wbr/>Options</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="match.distance.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>distance</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.match.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>match</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.similarity.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>similarity</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/match.match.html b/typedoc/functions/match.match.html
new file mode 100644
index 00000000..9be7be27
--- /dev/null
+++ b/typedoc/functions/match.match.html
@@ -0,0 +1,83 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>match | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/match.html">match</a></li>
+<li><a href="match.match.html">match</a></li></ul>
+<h1>Function match</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
+<li class="tsd-signature tsd-anchor-link" id="match">match<span class="tsd-signature-symbol">(</span>descriptor<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptors<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><a href="#match" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Matches given descriptor to a closest entry in array of descriptors</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>descriptor: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5>
+<div class="tsd-comment tsd-typography"><p>face descriptor</p>
+</div></li>
+<li>
+<h5>descriptors: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a><span class="tsd-signature-symbol">[]</span></h5>
+<div class="tsd-comment tsd-typography"><p>array of face descriptors to commpare given descriptor to</p>
+</div></li>
+<li>
+<h5>options: <a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p>see <code>similarity</code> method for options description
+Returns</p>
+<ul>
+<li><code>index</code> index array index where best match was found or -1 if no matches</li>
+<li><code>distance</code> calculated <code>distance</code> of given descriptor to the best match</li>
+<li><code>similarity</code> calculated normalized <code>similarity</code> of given descriptor to the best match</li>
+</ul>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-symbol">{ </span>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>distance<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>index<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>similarity<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li></ul><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L56">src/face/match.ts:56</a></li></ul></aside></li></ul></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="current selected tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/match.Descriptor.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Descriptor</a></li>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/match.MatchOptions.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Match<wbr/>Options</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.distance.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>distance</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="match.match.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>match</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.similarity.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>similarity</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/match.similarity.html b/typedoc/functions/match.similarity.html
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index 00000000..deafe7f4
--- /dev/null
+++ b/typedoc/functions/match.similarity.html
@@ -0,0 +1,76 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>similarity | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/match.html">match</a></li>
+<li><a href="match.similarity.html">similarity</a></li></ul>
+<h1>Function similarity</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
+<li class="tsd-signature tsd-anchor-link" id="similarity">similarity<span class="tsd-signature-symbol">(</span>descriptor1<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, descriptor2<span class="tsd-signature-symbol">: </span><a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a>, options<span class="tsd-signature-symbol">?: </span><a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><a href="#similarity" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Calculates normalized similarity between two face descriptors based on their <code>distance</code></p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>descriptor1: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>descriptor2: <a href="../types/match.Descriptor.html" class="tsd-signature-type" data-tsd-kind="Type alias">Descriptor</a></h5></li>
+<li>
+<h5>options: <a href="../types/match.MatchOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">MatchOptions</a><span class="tsd-signature-symbol"> = ...</span></h5>
+<div class="tsd-comment tsd-typography"><p>calculation options</p>
+<ul>
+<li>order - algorithm to use
+Euclidean distance if <code>order</code> is 2 (default), Minkowski distance algorithm of nth order if <code>order</code> is higher than 2</li>
+<li>multiplier - by how much to enhance difference analysis in range of 1..100
+default is 20 which normalizes results to similarity above 0.5 can be considered a match</li>
+<li>min - normalize similarity result to a given range</li>
+<li>max - normalzie similarity resutl to a given range
+default is 0.2...0.8
+Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity</li>
+</ul>
+</div></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">number</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/face/match.ts#L42">src/face/match.ts:42</a></li></ul></aside></li></ul></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/match.Descriptor.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Descriptor</a></li>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/match.MatchOptions.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Match<wbr/>Options</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.distance.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>distance</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="match.match.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>match</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="match.similarity.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>similarity</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/models.getModelStats.html b/typedoc/functions/models.getModelStats.html
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+++ b/typedoc/functions/models.getModelStats.html
@@ -0,0 +1,59 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>getModelStats | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/models.html">models</a></li>
+<li><a href="models.getModelStats.html">getModelStats</a></li></ul>
+<h1>Function getModelStats</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
+<li class="tsd-signature tsd-anchor-link" id="getModelStats">get<wbr/>Model<wbr/>Stats<span class="tsd-signature-symbol">(</span>instance<span class="tsd-signature-symbol">: </span><a href="../classes/Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="../types/models.ModelStats.html" class="tsd-signature-type" data-tsd-kind="Type alias">ModelStats</a><a href="#getModelStats" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>instance: <a href="../classes/Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <a href="../types/models.ModelStats.html" class="tsd-signature-type" data-tsd-kind="Type alias">ModelStats</a></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L74">src/models.ts:74</a></li></ul></aside></li></ul></section></div>
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+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
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+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-class tsd-parent-kind-namespace"><a href="../classes/models.Models.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg>Models</a></li>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/models.ModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Model<wbr/>Stats</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="models.getModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>get<wbr/>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.load.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>load</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.reset.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>reset</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.validate.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>validate</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/models.load.html b/typedoc/functions/models.load.html
new file mode 100644
index 00000000..1a9a612e
--- /dev/null
+++ b/typedoc/functions/models.load.html
@@ -0,0 +1,61 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>load | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/models.html">models</a></li>
+<li><a href="models.load.html">load</a></li></ul>
+<h1>Function load</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
+<li class="tsd-signature tsd-anchor-link" id="load">load<span class="tsd-signature-symbol">(</span>instance<span class="tsd-signature-symbol">: </span><a href="../classes/Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span><a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></li>
+<li class="tsd-description">
+<div class="tsd-comment tsd-typography"><p>Load method preloads all instance.configured models on-demand</p>
+</div>
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
+<li>
+<h5>instance: <a href="../classes/Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L103">src/models.ts:103</a></li></ul></aside></li></ul></section></div>
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+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
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+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
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+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
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+<li class="current tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<ul>
+<li class="tsd-kind-class tsd-parent-kind-namespace"><a href="../classes/models.Models.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg>Models</a></li>
+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/models.ModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.getModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>get<wbr/>Model<wbr/>Stats</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="models.load.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>load</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.reset.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>reset</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.validate.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>validate</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/functions/models.reset.html b/typedoc/functions/models.reset.html
new file mode 100644
index 00000000..88550e4a
--- /dev/null
+++ b/typedoc/functions/models.reset.html
@@ -0,0 +1,59 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>reset | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
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+<li class="state loading">Preparing search index...</li>
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+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/models.html">models</a></li>
+<li><a href="models.reset.html">reset</a></li></ul>
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+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
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+<li class="current tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<ul>
+<li class="tsd-kind-class tsd-parent-kind-namespace"><a href="../classes/models.Models.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg>Models</a></li>
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+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="models.reset.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>reset</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.validate.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>validate</a></li></ul></nav></div></div>
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\ No newline at end of file
diff --git a/typedoc/functions/models.validate.html b/typedoc/functions/models.validate.html
new file mode 100644
index 00000000..1724db8b
--- /dev/null
+++ b/typedoc/functions/models.validate.html
@@ -0,0 +1,59 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>validate | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
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+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/models.html">models</a></li>
+<li><a href="models.validate.html">validate</a></li></ul>
+<h1>Function validate</h1></div>
+<section class="tsd-panel">
+<ul class="tsd-signatures tsd-kind-function tsd-parent-kind-namespace">
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+<li class="tsd-description">
+<div class="tsd-parameters">
+<h4 class="tsd-parameters-title">Parameters</h4>
+<ul class="tsd-parameter-list">
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+<h5>instance: <a href="../classes/Human.html" class="tsd-signature-type" data-tsd-kind="Class">Human</a></h5></li></ul></div>
+<h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">void</span><span class="tsd-signature-symbol">&gt;</span></h4><aside class="tsd-sources">
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L148">src/models.ts:148</a></li></ul></aside></li></ul></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
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+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
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+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<li class="tsd-kind-type-alias tsd-parent-kind-namespace"><a href="../types/models.ModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.getModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>get<wbr/>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.load.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>load</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="models.reset.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>reset</a></li>
+<li class="current tsd-kind-function tsd-parent-kind-namespace"><a href="models.validate.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>validate</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/index.html b/typedoc/index.html
new file mode 100644
index 00000000..7dd80044
--- /dev/null
+++ b/typedoc/index.html
@@ -0,0 +1,221 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>@vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="assets/style.css"/><link rel="stylesheet" href="assets/highlight.css"/><script async src="assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
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+<h1>Interface BodyConfig</h1></div>
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+<li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="BodyConfig.html#skipTime" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>skip<wbr/>Time</a></li></ul></li></ul></nav></div></div>
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\ No newline at end of file
diff --git a/typedoc/interfaces/BodyKeypoint.html b/typedoc/interfaces/BodyKeypoint.html
new file mode 100644
index 00000000..09dfef3a
--- /dev/null
+++ b/typedoc/interfaces/BodyKeypoint.html
@@ -0,0 +1,106 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyKeypoint | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyKeypoint.html">BodyKeypoint</a></li></ul>
+<h1>Interface BodyKeypoint</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Body Result keypoints</p>
+</div></section>
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+<h4>Hierarchy</h4>
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+<li><span class="target">BodyKeypoint</span></li></ul></section><aside class="tsd-sources">
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="score" class="tsd-anchor"></a>
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+<div class="tsd-accordion-details">
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+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
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+<ul>
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+<li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyKeypoint.html#score" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>score</a></li></ul></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/interfaces/BodyResult.html b/typedoc/interfaces/BodyResult.html
new file mode 100644
index 00000000..55668ff0
--- /dev/null
+++ b/typedoc/interfaces/BodyResult.html
@@ -0,0 +1,115 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyResult | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<div class="table-cell" id="tsd-search" data-base="..">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
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+<h1>Interface BodyResult</h1></div>
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+<h3 class="tsd-index-heading">Properties</h3>
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\ No newline at end of file
diff --git a/typedoc/interfaces/Config.html b/typedoc/interfaces/Config.html
new file mode 100644
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--- /dev/null
+++ b/typedoc/interfaces/Config.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Config | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Config.html">Config</a></li></ul>
+<h1>Interface Config</h1></div>
+<section class="tsd-panel tsd-comment">
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+<a href="Config.html#deallocate" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>deallocate</span></a>
+<a href="Config.html#debug" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>debug</span></a>
+<a href="Config.html#face" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>face</span></a>
+<a href="Config.html#filter" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>filter</span></a>
+<a href="Config.html#gesture" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>gesture</span></a>
+<a href="Config.html#hand" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>hand</span></a>
+<a href="Config.html#modelBasePath" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>model<wbr/>Base<wbr/>Path</span></a>
+<a href="Config.html#object" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>object</span></a>
+<a href="Config.html#segmentation" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>segmentation</span></a>
+<a href="Config.html#skipAllowed" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>skip<wbr/>Allowed</span></a>
+<a href="Config.html#warmup" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>warmup</span></a>
+<a href="Config.html#wasmPath" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>wasm<wbr/>Path</span></a>
+<a href="Config.html#wasmPlatformFetch" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>wasm<wbr/>Platform<wbr/>Fetch</span></a>
+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="async" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>async</span><a href="#async" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<div class="tsd-signature">async<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Perform model loading and inference concurrently or sequentially</p>
+<p>default: <code>true</code></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L254">src/config.ts:254</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="backend" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>backend</span><a href="#backend" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">backend<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cpu&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;wasm&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;webgl&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;humangl&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tensorflow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;webgpu&quot;</span></div>
+<div class="tsd-comment tsd-typography"><p>Backend used for TFJS operations
+valid build-in backends are:</p>
+<ul>
+<li>Browser: <code>cpu</code>, <code>wasm</code>, <code>webgl</code>, <code>humangl</code>, <code>webgpu</code></li>
+<li>NodeJS: <code>cpu</code>, <code>wasm</code>, <code>tensorflow</code>
+default: <code>humangl</code> for browser and <code>tensorflow</code> for nodejs</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L230">src/config.ts:230</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>body</span><a href="#body" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">body<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="BodyConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Body config <a href="BodyConfig.html">BodyConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L300">src/config.ts:300</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="cacheModels" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>cache<wbr/>Models</span><a href="#cacheModels" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">cache<wbr/>Models<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Cache models in IndexDB on first sucessfull load
+default: true if indexdb is available (browsers), false if its not (nodejs)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L274">src/config.ts:274</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="cacheSensitivity" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>cache<wbr/>Sensitivity</span><a href="#cacheSensitivity" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">cache<wbr/>Sensitivity<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>Cache sensitivity</p>
+<ul>
+<li>values 0..1 where 0.01 means reset cache if input changed more than 1%</li>
+<li>set to 0 to disable caching</li>
+</ul>
+<p>default: 0.7</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L282">src/config.ts:282</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="deallocate" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>deallocate</span><a href="#deallocate" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">deallocate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Perform immediate garbage collection on deallocated tensors instead of caching them</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L285">src/config.ts:285</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="debug" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>debug</span><a href="#debug" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">debug<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Print debug statements to console</p>
+<p>default: <code>true</code></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L248">src/config.ts:248</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>face</span><a href="#face" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">face<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Face config <a href="FaceConfig.html">FaceConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L297">src/config.ts:297</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="filter" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>filter</span><a href="#filter" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">filter<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FilterConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FilterConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Filter config <a href="FilterConfig.html">FilterConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L291">src/config.ts:291</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gesture" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>gesture</span><a href="#gesture" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">gesture<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="GestureConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GestureConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Gesture config <a href="GestureConfig.html">GestureConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L294">src/config.ts:294</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hand" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>hand</span><a href="#hand" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">hand<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="HandConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">HandConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Hand config <a href="HandConfig.html">HandConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L303">src/config.ts:303</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="modelBasePath" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>model<wbr/>Base<wbr/>Path</span><a href="#modelBasePath" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">model<wbr/>Base<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
+<div class="tsd-comment tsd-typography"><p>Base model path (typically starting with file://, http:// or https://) for all models</p>
+<ul>
+<li>individual modelPath values are relative to this path</li>
+</ul>
+<p>default: <code>../models/</code> for browsers and <code>file://models/</code> for nodejs</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L269">src/config.ts:269</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="object" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>object</span><a href="#object" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">object<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="ObjectConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Object config <a href="ObjectConfig.html">ObjectConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L306">src/config.ts:306</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="segmentation" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>segmentation</span><a href="#segmentation" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">segmentation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="SegmentationConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">SegmentationConfig</a><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>Segmentation config <a href="SegmentationConfig.html">SegmentationConfig</a></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L309">src/config.ts:309</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="skipAllowed" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>skip<wbr/>Allowed</span><a href="#skipAllowed" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">skip<wbr/>Allowed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>Internal Variable</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L288">src/config.ts:288</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="warmup" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>warmup</span><a href="#warmup" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">warmup<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;none&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;face&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;full&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;body&quot;</span></div>
+<div class="tsd-comment tsd-typography"><p>What to use for <code>human.warmup()</code></p>
+<ul>
+<li>warmup pre-initializes all models for faster inference but can take significant time on startup</li>
+<li>used by <code>webgl</code>, <code>humangl</code> and <code>webgpu</code> backends</li>
+</ul>
+<p>default: <code>full</code></p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">src/config.ts:262</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="wasmPath" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>wasm<wbr/>Path</span><a href="#wasmPath" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">wasm<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
+<div class="tsd-comment tsd-typography"><p>Path to *.wasm files if backend is set to <code>wasm</code></p>
+<p>default: auto-detects to link to CDN <code>jsdelivr</code> when running in browser</p>
+</div><aside class="tsd-sources">
+<ul>
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+++ b/typedoc/interfaces/FaceAntiSpoofConfig.html
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
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+<h1>Interface FaceAntiSpoofConfig</h1></div>
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diff --git a/typedoc/interfaces/FaceAttentionConfig.html b/typedoc/interfaces/FaceAttentionConfig.html
new file mode 100644
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--- /dev/null
+++ b/typedoc/interfaces/FaceAttentionConfig.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceAttentionConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="col-8 col-content">
+<div class="tsd-page-title">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceAttentionConfig.html">FaceAttentionConfig</a></li></ul>
+<h1>Interface FaceAttentionConfig</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Attention part of face configuration</p>
+</div></section>
+<section class="tsd-panel tsd-hierarchy">
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L47">src/config.ts:47</a></li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
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+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Properties</h3>
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+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipTime" class="tsd-anchor"></a>
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+<div class="tsd-signature">skip<wbr/>Time<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>how many max milliseconds to go without re-running model if cached results are acceptable
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+</div><aside class="tsd-sources">
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+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<ul>
+<li class="current tsd-kind-interface"><a href="FaceAttentionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg><span>Face<wbr/>Attention<wbr/>Config</span></a>
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\ No newline at end of file
diff --git a/typedoc/interfaces/FaceConfig.html b/typedoc/interfaces/FaceConfig.html
new file mode 100644
index 00000000..924bd8e1
--- /dev/null
+++ b/typedoc/interfaces/FaceConfig.html
@@ -0,0 +1,168 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceConfig.html">FaceConfig</a></li></ul>
+<h1>Interface FaceConfig</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description</p>
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+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
+<ul class="tsd-hierarchy">
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+<section class="tsd-panel-group tsd-index-group">
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+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Properties</h3>
+<div class="tsd-index-list"><a href="FaceConfig.html#antispoof" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF984D" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-1024-path"></rect><path d="M9.354 16V7.24H12.174C12.99 7.24 13.638 7.476 14.118 7.948C14.606 8.412 14.85 9.036 14.85 9.82C14.85 10.604 14.606 11.232 14.118 11.704C13.638 12.168 12.99 12.4 12.174 12.4H10.434V16H9.354ZM10.434 11.428H12.174C12.646 11.428 13.022 11.284 13.302 10.996C13.59 10.7 13.734 10.308 13.734 9.82C13.734 9.324 13.59 8.932 13.302 8.644C13.022 8.356 12.646 8.212 12.174 8.212H10.434V11.428Z" fill="var(--color-text)" id="icon-1024-text"></path></svg><span>antispoof</span></a>
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+<a href="FaceConfig.html#description" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>description</span></a>
+<a href="FaceConfig.html#detector" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>detector</span></a>
+<a href="FaceConfig.html#emotion" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>emotion</span></a>
+<a href="FaceConfig.html#enabled" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>enabled</span></a>
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+<a href="FaceConfig.html#iris" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>iris</span></a>
+<a href="FaceConfig.html#liveness" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>liveness</span></a>
+<a href="FaceConfig.html#mesh" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>mesh</span></a>
+<a href="FaceConfig.html#modelPath" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>model<wbr/>Path</span></a>
+<a href="FaceConfig.html#skipFrames" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>skip<wbr/>Frames</span></a>
+<a href="FaceConfig.html#skipTime" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>skip<wbr/>Time</span></a>
+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="antispoof" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>antispoof</span><a href="#antispoof" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<div class="tsd-signature">antispoof<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceAntiSpoofConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceAntiSpoofConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L83">src/config.ts:83</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="attention" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>attention</span><a href="#attention" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">attention<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceAttentionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceAttentionConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L79">src/config.ts:79</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="description" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>description</span><a href="#description" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">description<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceDescriptionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceDescriptionConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L81">src/config.ts:81</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="detector" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>detector</span><a href="#detector" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">detector<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceDetectorConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceDetectorConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L77">src/config.ts:77</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="emotion" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>emotion</span><a href="#emotion" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">emotion<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceEmotionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceEmotionConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L82">src/config.ts:82</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>enabled</span><a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>is module enabled?</p>
+</div><aside class="tsd-sources">
+<p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p>
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gear" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>gear</span><a href="#gear" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">gear<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceGearConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceGearConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L85">src/config.ts:85</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="iris" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>iris</span><a href="#iris" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">iris<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceIrisConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceIrisConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L80">src/config.ts:80</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="liveness" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>liveness</span><a href="#liveness" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">liveness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceLivenessConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceLivenessConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L84">src/config.ts:84</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="mesh" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>mesh</span><a href="#mesh" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">mesh<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceMeshConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceMeshConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L78">src/config.ts:78</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="modelPath" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>model<wbr/>Path</span><a href="#modelPath" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">model<wbr/>Path<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div>
+<div class="tsd-comment tsd-typography"><p>path to model json file (relative to <code>modelBasePath</code></p>
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+<p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#modelPath">modelPath</a></p>
+<ul>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipFrames" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>skip<wbr/>Frames</span><a href="#skipFrames" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">skip<wbr/>Frames<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>how many max frames to go without re-running model if cached results are acceptable
+for two-phase models such as face and hand caching applies to bounding boxes detection only</p>
+</div><aside class="tsd-sources">
+<p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#skipFrames">skipFrames</a></p>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipTime" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>skip<wbr/>Time</span><a href="#skipTime" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">skip<wbr/>Time<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>how many max milliseconds to go without re-running model if cached results are acceptable
+for two-phase models such as face and hand caching applies to bounding boxes detection only</p>
+</div><aside class="tsd-sources">
+<p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#skipTime">skipTime</a></p>
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L15">src/config.ts:15</a></li></ul></aside></section></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
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+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
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+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="current tsd-kind-interface"><a href="FaceConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg><span>Face<wbr/>Config</span></a>
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+<li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceConfig.html#skipTime" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>skip<wbr/>Time</a></li></ul></li></ul></nav></div></div>
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diff --git a/typedoc/interfaces/FaceDescriptionConfig.html b/typedoc/interfaces/FaceDescriptionConfig.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceDescriptionConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceDescriptionConfig.html">FaceDescriptionConfig</a></li></ul>
+<h1>Interface FaceDescriptionConfig</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Description or face embedding part of face configuration</p>
+<ul>
+<li>also used by age and gender detection</li>
+</ul>
+</div></section>
+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
+<ul class="tsd-hierarchy">
+<li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a>
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L52">src/config.ts:52</a></li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
+<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
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+<h3 class="tsd-index-heading">Properties</h3>
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+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="minConfidence" class="tsd-anchor"></a>
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+</div><aside class="tsd-sources">
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L54">src/config.ts:54</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="modelPath" class="tsd-anchor"></a>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipFrames" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>skip<wbr/>Frames</span><a href="#skipFrames" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">skip<wbr/>Frames<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>how many max frames to go without re-running model if cached results are acceptable
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+<p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#skipFrames">skipFrames</a></p>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipTime" class="tsd-anchor"></a>
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+<div class="tsd-comment tsd-typography"><p>how many max milliseconds to go without re-running model if cached results are acceptable
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+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li class="current tsd-kind-interface"><a href="FaceDescriptionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg><span>Face<wbr/>Description<wbr/>Config</span></a>
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\ No newline at end of file
diff --git a/typedoc/interfaces/FaceDetectorConfig.html b/typedoc/interfaces/FaceDetectorConfig.html
new file mode 100644
index 00000000..82ffe204
--- /dev/null
+++ b/typedoc/interfaces/FaceDetectorConfig.html
@@ -0,0 +1,161 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceDetectorConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceDetectorConfig.html">FaceDetectorConfig</a></li></ul>
+<h1>Interface FaceDetectorConfig</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Detector part of face configuration</p>
+</div></section>
+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L19">src/config.ts:19</a></li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
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+<h3 class="tsd-index-heading">Properties</h3>
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+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>enabled</span><a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="iouThreshold" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>iou<wbr/>Threshold</span><a href="#iouThreshold" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">iou<wbr/>Threshold<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>minimum overlap between two detected faces before one is discarded</p>
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+<ul>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="mask" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>mask</span><a href="#mask" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">mask<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>should child models perform on masked image of a face</p>
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L31">src/config.ts:31</a></li></ul></aside></section>
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L25">src/config.ts:25</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="minConfidence" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>min<wbr/>Confidence</span><a href="#minConfidence" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="modelPath" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>model<wbr/>Path</span><a href="#modelPath" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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+<div class="tsd-comment tsd-typography"><p>path to model json file (relative to <code>modelBasePath</code></p>
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+if enabled it must be manually deallocated to avoid memory leak</p>
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L34">src/config.ts:34</a></li></ul></aside></section>
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+<ul>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipFrames" class="tsd-anchor"></a>
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+<div class="tsd-comment tsd-typography"><p>how many max frames to go without re-running model if cached results are acceptable
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\ No newline at end of file
diff --git a/typedoc/interfaces/FaceEmotionConfig.html b/typedoc/interfaces/FaceEmotionConfig.html
new file mode 100644
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--- /dev/null
+++ b/typedoc/interfaces/FaceEmotionConfig.html
@@ -0,0 +1,114 @@
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+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
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+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceEmotionConfig.html">FaceEmotionConfig</a></li></ul>
+<h1>Interface FaceEmotionConfig</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Emotion part of face configuration</p>
+</div></section>
+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L58">src/config.ts:58</a></li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
+<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
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+<h3 class="tsd-index-heading">Properties</h3>
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\ No newline at end of file
diff --git a/typedoc/interfaces/FaceGearConfig.html b/typedoc/interfaces/FaceGearConfig.html
new file mode 100644
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--- /dev/null
+++ b/typedoc/interfaces/FaceGearConfig.html
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+<div class="tsd-page-title">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceGearConfig.html">FaceGearConfig</a></li></ul>
+<h1>Interface FaceGearConfig</h1></div>
+<section class="tsd-panel tsd-comment">
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new file mode 100644
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+<h1>Interface FaceIrisConfig</h1></div>
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diff --git a/typedoc/interfaces/FaceLivenessConfig.html b/typedoc/interfaces/FaceLivenessConfig.html
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
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+<h1>Interface FaceLivenessConfig</h1></div>
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new file mode 100644
index 00000000..0d157693
--- /dev/null
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\ No newline at end of file
diff --git a/typedoc/interfaces/FaceResult.html b/typedoc/interfaces/FaceResult.html
new file mode 100644
index 00000000..3f80ab71
--- /dev/null
+++ b/typedoc/interfaces/FaceResult.html
@@ -0,0 +1,245 @@
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+<li><a href="FaceResult.html">FaceResult</a></li></ul>
+<h1>Interface FaceResult</h1></div>
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+<a href="FaceResult.html#id" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>id</span></a>
+<a href="FaceResult.html#iris" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>iris</span></a>
+<a href="FaceResult.html#live" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>live</span></a>
+<a href="FaceResult.html#mesh" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>mesh</span></a>
+<a href="FaceResult.html#meshRaw" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>mesh<wbr/>Raw</span></a>
+<a href="FaceResult.html#race" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>race</span></a>
+<a href="FaceResult.html#real" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>real</span></a>
+<a href="FaceResult.html#rotation" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>rotation</span></a>
+<a href="FaceResult.html#score" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>score</span></a>
+<a href="FaceResult.html#tensor" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>tensor</span></a>
+</div></section></div></details></section></section>
+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="age" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>age</span><a href="#age" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<div class="tsd-signature">age<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>detected age</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L51">src/result.ts:51</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="annotations" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>annotations</span><a href="#annotations" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">annotations<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/FaceLandmark.html" class="tsd-signature-type" data-tsd-kind="Type alias">FaceLandmark</a><span class="tsd-signature-symbol">, </span><a href="../types/Point.html" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>mesh keypoints combined into annotated results</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L49">src/result.ts:49</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="box" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>box</span><a href="#box" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">box<span class="tsd-signature-symbol">:</span> <a href="../types/Box.html" class="tsd-signature-type" data-tsd-kind="Type alias">Box</a></div>
+<div class="tsd-comment tsd-typography"><p>detected face box</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L37">src/result.ts:37</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="boxRaw" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>box<wbr/>Raw</span><a href="#boxRaw" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">box<wbr/>Raw<span class="tsd-signature-symbol">:</span> <a href="../types/Box.html" class="tsd-signature-type" data-tsd-kind="Type alias">Box</a></div>
+<div class="tsd-comment tsd-typography"><p>detected face box normalized to 0..1</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L39">src/result.ts:39</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="boxScore" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>box<wbr/>Score</span><a href="#boxScore" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">box<wbr/>Score<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>detection score</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L33">src/result.ts:33</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="embedding" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>embedding</span><a href="#embedding" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">embedding<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>face descriptor</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L61">src/result.ts:61</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="emotion" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>emotion</span><a href="#emotion" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">emotion<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-symbol">{ </span>emotion<span class="tsd-signature-symbol">: </span><a href="../types/Emotion.html" class="tsd-signature-type" data-tsd-kind="Type alias">Emotion</a><span class="tsd-signature-symbol">; </span>score<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>detected emotions</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L57">src/result.ts:57</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="faceScore" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>face<wbr/>Score</span><a href="#faceScore" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">face<wbr/>Score<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>mesh score</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L35">src/result.ts:35</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="gender" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>gender</span><a href="#gender" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">gender<span class="tsd-signature-symbol">?:</span> <a href="../types/Gender.html" class="tsd-signature-type" data-tsd-kind="Type alias">Gender</a></div>
+<div class="tsd-comment tsd-typography"><p>detected gender</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L53">src/result.ts:53</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="genderScore" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>gender<wbr/>Score</span><a href="#genderScore" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">gender<wbr/>Score<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>gender detection score</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L55">src/result.ts:55</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="id" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>id</span><a href="#id" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>face id</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L29">src/result.ts:29</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="iris" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>iris</span><a href="#iris" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">iris<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>face iris distance from camera</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L63">src/result.ts:63</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="live" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>live</span><a href="#live" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">live<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>face liveness result confidence</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L67">src/result.ts:67</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="mesh" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>mesh</span><a href="#mesh" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">mesh<span class="tsd-signature-symbol">:</span> <a href="../types/Point.html" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>detected face mesh</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L41">src/result.ts:41</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="meshRaw" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>mesh<wbr/>Raw</span><a href="#meshRaw" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">mesh<wbr/>Raw<span class="tsd-signature-symbol">:</span> <a href="../types/Point.html" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>detected face mesh normalized to 0..1</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L43">src/result.ts:43</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="race" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>race</span><a href="#race" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">race<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-symbol">{ </span>race<span class="tsd-signature-symbol">: </span><a href="../types/Race.html" class="tsd-signature-type" data-tsd-kind="Type alias">Race</a><span class="tsd-signature-symbol">; </span>score<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">[]</span></div>
+<div class="tsd-comment tsd-typography"><p>detected race</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L59">src/result.ts:59</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="real" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>real</span><a href="#real" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">real<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>face anti-spoofing result confidence</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L65">src/result.ts:65</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="rotation" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>rotation</span><a href="#rotation" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">rotation<span class="tsd-signature-symbol">?:</span> <span class="tsd-signature-type">null</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">{ </span>angle<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>pitch<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>roll<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>yaw<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">; </span>gaze<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">{ </span>bearing<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>strength<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol">; </span>matrix<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-comment tsd-typography"><p>face rotation details</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L69">src/result.ts:69</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="score" class="tsd-anchor"></a>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
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+<h1>Interface FilterConfig</h1></div>
+<section class="tsd-panel tsd-comment">
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+<ul>
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+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="blur" class="tsd-anchor"></a>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="brightness" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>brightness</span><a href="#brightness" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">brightness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>range: -1 (darken) to 1 (lighten)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L180">src/config.ts:180</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="contrast" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>contrast</span><a href="#contrast" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">contrast<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>range: -1 (reduce contrast) to 1 (increase contrast)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L182">src/config.ts:182</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="enabled" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>enabled</span><a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>are image filters enabled?</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L158">src/config.ts:158</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="equalization" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>equalization</span><a href="#equalization" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">equalization<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>perform image histogram equalization</p>
+<ul>
+<li>equalization is performed on input as a whole and detected face before its passed for further analysis</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L162">src/config.ts:162</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="flip" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>flip</span><a href="#flip" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">flip<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>flip input as mirror image</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L178">src/config.ts:178</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="height" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>height</span><a href="#height" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">height<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>resize input height</p>
+<ul>
+<li>if both width and height are set to 0, there is no resizing</li>
+<li>if just one is set, second one is scaled automatically</li>
+<li>if both are set, values are used as-is</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L174">src/config.ts:174</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="hue" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>hue</span><a href="#hue" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">hue<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>range: 0 (no change) to 360 (hue rotation in degrees)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L190">src/config.ts:190</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="kodachrome" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>kodachrome</span><a href="#kodachrome" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">kodachrome<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>image kodachrome colors</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L198">src/config.ts:198</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="negative" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>negative</span><a href="#negative" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">negative<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>image negative</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L192">src/config.ts:192</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="pixelate" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>pixelate</span><a href="#pixelate" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">pixelate<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>range: 0 (no pixelate) to N (number of pixels to pixelate)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L204">src/config.ts:204</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="polaroid" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>polaroid</span><a href="#polaroid" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">polaroid<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>image polaroid camera effect</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L202">src/config.ts:202</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="return" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>return</span><a href="#return" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">return<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>return processed canvas imagedata in result</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L176">src/config.ts:176</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="saturation" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>saturation</span><a href="#saturation" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">saturation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>range: -1 (reduce saturation) to 1 (increase saturation)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L188">src/config.ts:188</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="sepia" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>sepia</span><a href="#sepia" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">sepia<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>image sepia colors</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L194">src/config.ts:194</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="sharpness" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>sharpness</span><a href="#sharpness" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">sharpness<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>range: 0 (no sharpening) to 1 (maximum sharpening)</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L184">src/config.ts:184</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="technicolor" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>technicolor</span><a href="#technicolor" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">technicolor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>image technicolor colors</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L200">src/config.ts:200</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="vintage" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>vintage</span><a href="#vintage" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">vintage<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
+<div class="tsd-comment tsd-typography"><p>image vintage colors</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L196">src/config.ts:196</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="width" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>width</span><a href="#width" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">width<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>resize input width</p>
+<ul>
+<li>if both width and height are set to 0, there is no resizing</li>
+<li>if just one is set, second one is scaled automatically</li>
+<li>if both are set, values are used as-is</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L168">src/config.ts:168</a></li></ul></aside></section></section></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
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+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
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+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="current tsd-kind-interface"><a href="FilterConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg><span>Filter<wbr/>Config</span></a>
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+<li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#blur" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>blur</a></li>
+<li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#brightness" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>brightness</a></li>
+<li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#contrast" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>contrast</a></li>
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+<li class="tsd-kind-property tsd-parent-kind-interface"><a href="FilterConfig.html#kodachrome" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg>kodachrome</a></li>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GenericConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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new file mode 100644
index 00000000..30e5fad6
--- /dev/null
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>HandConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<h1>Interface HandConfig</h1></div>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="modelPath-1" class="tsd-anchor"></a>
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+<div class="tsd-signature">rotation<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div>
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+<ul class="tsd-parameters">
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="skipFrames" class="tsd-anchor"></a>
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+<div class="tsd-comment tsd-typography"><p>how many max milliseconds to go without re-running model if cached results are acceptable
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\ No newline at end of file
diff --git a/typedoc/interfaces/HandResult.html b/typedoc/interfaces/HandResult.html
new file mode 100644
index 00000000..1f86daaa
--- /dev/null
+++ b/typedoc/interfaces/HandResult.html
@@ -0,0 +1,151 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>HandResult | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<div class="table-cell" id="tsd-search" data-base="..">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="HandResult.html">HandResult</a></li></ul>
+<h1>Interface HandResult</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Hand results</p>
+</div></section>
+<section class="tsd-panel tsd-hierarchy">
+<h4>Hierarchy</h4>
+<ul class="tsd-hierarchy">
+<li><span class="target">HandResult</span></li></ul></section><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L125">src/result.ts:125</a></li></ul></aside>
+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
+<details class="tsd-index-content tsd-index-accordion" open><summary class="tsd-accordion-summary tsd-index-summary">
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+<section class="tsd-index-section">
+<h3 class="tsd-index-heading">Properties</h3>
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+<a href="HandResult.html#label" class="tsd-index-link tsd-kind-property tsd-parent-kind-interface"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-1024-path"></use><use href="#icon-1024-text"></use></svg><span>label</span></a>
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+<section class="tsd-panel-group tsd-member-group">
+<h2>Properties</h2>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="annotations" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>annotations</span><a href="#annotations" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>
+<div class="tsd-signature">annotations<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><a href="../types/Finger.html" class="tsd-signature-type" data-tsd-kind="Type alias">Finger</a><span class="tsd-signature-symbol">, </span><a href="../types/Point.html" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></div>
+<div class="tsd-comment tsd-typography"><p>detected hand keypoints combined into annotated parts</p>
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L143">src/result.ts:143</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="box" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>box</span><a href="#box" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">box<span class="tsd-signature-symbol">:</span> <a href="../types/Box.html" class="tsd-signature-type" data-tsd-kind="Type alias">Box</a></div>
+<div class="tsd-comment tsd-typography"><p>detected hand box</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L135">src/result.ts:135</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="boxRaw" class="tsd-anchor"></a>
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+<div class="tsd-signature">box<wbr/>Raw<span class="tsd-signature-symbol">:</span> <a href="../types/Box.html" class="tsd-signature-type" data-tsd-kind="Type alias">Box</a></div>
+<div class="tsd-comment tsd-typography"><p>detected hand box normalized to 0..1</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L137">src/result.ts:137</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="boxScore" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>box<wbr/>Score</span><a href="#boxScore" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">box<wbr/>Score<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
+<div class="tsd-comment tsd-typography"><p>hand detection score</p>
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+<h1>Interface ObjectConfig</h1></div>
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diff --git a/typedoc/interfaces/PersonResult.html b/typedoc/interfaces/PersonResult.html
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+++ b/typedoc/interfaces/PersonResult.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>PersonResult | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="PersonResult.html">PersonResult</a></li></ul>
+<h1>Interface PersonResult</h1></div>
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+<ul>
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+<section class="tsd-panel-group tsd-member-group">
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a>
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+<ul>
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+<h3 class="tsd-anchor-link"><code class="tsd-tag ts-flagOptional">Optional</code> <span>box<wbr/>Raw</span><a href="#boxRaw" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
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+<div class="tsd-comment tsd-typography"><p>box that defines the person normalized to 0..1</p>
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+<ul>
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a>
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+<div class="tsd-signature">face<span class="tsd-signature-symbol">:</span> <a href="FaceResult.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceResult</a></div>
+<div class="tsd-comment tsd-typography"><p>face result that belongs to this person</p>
+</div><aside class="tsd-sources">
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+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L193">src/result.ts:193</a></li></ul></aside></section>
+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="id" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>id</span><a href="#id" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><use href="#icon-anchor-a"></use><use href="#icon-anchor-b"></use><use href="#icon-anchor-c"></use></svg></a></h3>
+<div class="tsd-signature">id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div>
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L187">src/result.ts:187</a></li></ul></aside></section></section></div>
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+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<ul>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Result | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Result.html">Result</a></li></ul>
+<h1>Interface Result</h1></div>
+<section class="tsd-panel tsd-comment">
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+<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="face" class="tsd-anchor"></a>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>SegmentationConfig | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
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+<div class="col-8 col-content">
+<div class="tsd-page-title">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
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+<h1>Interface SegmentationConfig</h1></div>
+<section class="tsd-panel tsd-comment">
+<div class="tsd-comment tsd-typography"><p>Configures all body segmentation module
+removes background from input containing person
+if segmentation is enabled it will run as preprocessing task before any other model
+alternatively leave it disabled and use it on-demand using human.segmentation method which can
+remove background or replace it with user-provided background</p>
+</div></section>
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+<section class="tsd-panel-group tsd-index-group">
+<section class="tsd-panel tsd-index-panel">
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new file mode 100644
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diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html
new file mode 100644
index 00000000..d7b1905f
--- /dev/null
+++ b/typedoc/modules/draw.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>draw | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="draw.html">draw</a></li></ul>
+<h1>Namespace draw</h1></div>
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+<h3 class="tsd-index-heading">References</h3>
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+<div class="tsd-index-list"><a href="../variables/draw.options.html" class="tsd-index-link tsd-kind-variable tsd-parent-kind-namespace"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-variable)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-32-path"></rect><path d="M11.106 16L8.85 7.24H9.966L11.454 13.192C11.558 13.608 11.646 13.996 11.718 14.356C11.79 14.708 11.842 14.976 11.874 15.16C11.906 14.976 11.954 14.708 12.018 14.356C12.09 13.996 12.178 13.608 12.282 13.192L13.758 7.24H14.85L12.582 16H11.106Z" fill="var(--color-text)" id="icon-32-text"></path></svg><span>options</span></a>
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+<div class="tsd-index-list"><a href="../functions/draw.all.html" class="tsd-index-link tsd-kind-function tsd-parent-kind-namespace"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg><span>all</span></a>
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+<section class="tsd-panel-group tsd-member-group">
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+<section class="tsd-panel tsd-member tsd-kind-reference tsd-parent-kind-namespace"><a id="body" class="tsd-anchor"></a>
+<h3 class="tsd-anchor-link"><span>body</span><a href="#body" aria-label="Permalink" class="tsd-anchor-icon"><svg class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none" id="icon-anchor-a"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5" id="icon-anchor-b"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5" id="icon-anchor-c"></path></svg></a></h3>Renames and re-exports <a href="../classes/Human.html#__type-2.__type-5">__type</a></section>
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+<section class="tsd-panel tsd-member tsd-kind-reference tsd-parent-kind-namespace"><a id="gesture" class="tsd-anchor"></a>
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+<div class="tsd-navigation settings">
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\ No newline at end of file
diff --git a/typedoc/modules/match.html b/typedoc/modules/match.html
new file mode 100644
index 00000000..0359ac69
--- /dev/null
+++ b/typedoc/modules/match.html
@@ -0,0 +1,64 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>match | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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diff --git a/typedoc/modules/models.html b/typedoc/modules/models.html
new file mode 100644
index 00000000..f2a32f47
--- /dev/null
+++ b/typedoc/modules/models.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>models | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="models.html">models</a></li></ul>
+<h1>Namespace models</h1></div>
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\ No newline at end of file
diff --git a/typedoc/types/AnyCanvas.html b/typedoc/types/AnyCanvas.html
new file mode 100644
index 00000000..0ebd45ea
--- /dev/null
+++ b/typedoc/types/AnyCanvas.html
@@ -0,0 +1,117 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>AnyCanvas | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyAnnotationBlazePose.html">BodyAnnotationBlazePose</a></li></ul>
+<h1>Type alias BodyAnnotationBlazePose</h1></div>
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+<ul>
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+<div class="tsd-accordion-details">
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+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
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+<li class="tsd-kind-class"><a href="../classes/Human.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Human</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/BodyConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg>Body<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/BodyKeypoint.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Body<wbr/>Keypoint</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/FaceDescriptionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Description<wbr/>Config</a></li>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyAnnotationEfficientPose.html">BodyAnnotationEfficientPose</a></li></ul>
+<h1>Type alias BodyAnnotationEfficientPose</h1></div>
+<div class="tsd-signature">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;leftLeg&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightLeg&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;torso&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftArm&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightArm&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;head&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L86">src/result.ts:86</a></li></ul></aside></div>
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+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyGesture | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyGesture.html">BodyGesture</a></li></ul>
+<h1>Type alias BodyGesture</h1></div>
+<div class="tsd-signature">Body<wbr/>Gesture<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">`</span><span class="tsd-signature-type">leaning </span><span class="tsd-signature-symbol">${</span><span class="tsd-signature-type">&quot;left&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;right&quot;</span><span class="tsd-signature-symbol">}</span><span class="tsd-signature-symbol">`</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">`</span><span class="tsd-signature-type">raise </span><span class="tsd-signature-symbol">${</span><span class="tsd-signature-type">&quot;left&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;right&quot;</span><span class="tsd-signature-symbol">}</span><span class="tsd-signature-type"> hand</span><span class="tsd-signature-symbol">`</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;i give up&quot;</span></div>
+<div class="tsd-comment tsd-typography"><p>body gesture type</p>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/gesture/gesture.ts#L22">src/gesture/gesture.ts:22</a></li></ul></aside></div>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyLandmarkBlazePose | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyLandmarkBlazePose.html">BodyLandmarkBlazePose</a></li></ul>
+<h1>Type alias BodyLandmarkBlazePose</h1></div>
+<div class="tsd-signature">Body<wbr/>Landmark<wbr/>Blaze<wbr/>Pose<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;nose&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeInside&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeOutside&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeInside&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeOutside&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftMouth&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightMouth&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftPinky&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightPinky&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftIndex&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightIndex&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftThumb&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightThumb&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftAnkle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightAnkle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftHeel&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightHeel&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftFoot&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightFoot&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bodyCenter&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bodyTop&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftPalm&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftHand&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightPalm&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightHand&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L81">src/result.ts:81</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
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+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyLandmarkEfficientNet | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<div class="tsd-signature">Body<wbr/>Landmark<wbr/>Efficient<wbr/>Net<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;head&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;neck&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;chest&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bodyCenter&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightAnkle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftAnkle&quot;</span></div><aside class="tsd-sources">
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L80">src/result.ts:80</a></li></ul></aside></div>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyLandmarkMoveNet.html">BodyLandmarkMoveNet</a></li></ul>
+<h1>Type alias BodyLandmarkMoveNet</h1></div>
+<div class="tsd-signature">Body<wbr/>Landmark<wbr/>Move<wbr/>Net<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;nose&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftAnkle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightAnkle&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L79">src/result.ts:79</a></li></ul></aside></div>
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+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyLandmarkPoseNet | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="BodyLandmarkPoseNet.html">BodyLandmarkPoseNet</a></li></ul>
+<h1>Type alias BodyLandmarkPoseNet</h1></div>
+<div class="tsd-signature">Body<wbr/>Landmark<wbr/>Pose<wbr/>Net<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;nose&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightShoulder&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightElbow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightWrist&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightHip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightKnee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftAnkle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightAnkle&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L78">src/result.ts:78</a></li></ul></aside></div>
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+<li class="tsd-kind-type-alias"><a href="ModelInfo.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Model<wbr/>Info</a></li>
+<li class="tsd-kind-type-alias"><a href="ObjectType.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Object<wbr/>Type</a></li>
+<li class="tsd-kind-type-alias"><a href="Point.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Point</a></li>
+<li class="tsd-kind-type-alias"><a href="Race.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Race</a></li>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>DrawOptions | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="DrawOptions.html">DrawOptions</a></li></ul>
+<h1>Type alias DrawOptions</h1></div>
+<div class="tsd-signature">Draw<wbr/>Options<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>color<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>drawAttention<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>drawBoxes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>drawGaze<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>drawGestures<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>drawLabels<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>drawPoints<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>drawPolygons<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>fillPolygons<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>font<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>labelColor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>lineHeight<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>lineWidth<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>pointSize<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>roundRect<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>shadowColor<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">; </span>useCurves<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">; </span>useDepth<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-comment tsd-typography"><p>Draw Options</p>
+<ul>
+<li>Accessed via <code>human.draw.options</code> or provided per each draw method as the drawOptions optional parameter</li>
+</ul>
+</div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>alpha<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5>
+<div class="tsd-comment tsd-typography"><p>alpha value used for lines</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>color<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></h5>
+<div class="tsd-comment tsd-typography"><p>draw line color</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Attention<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should face attention keypoints be highlighted</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Boxes<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should draw boxes around detection results?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Gaze<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should draw gaze arrows?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Gestures<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should detected gestures be drawn?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Labels<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should labels be drawn?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Points<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should points be drawn?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>draw<wbr/>Polygons<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should draw polygons from detection points?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>fill<wbr/>Polygons<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should fill polygons?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>font<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></h5>
+<div class="tsd-comment tsd-typography"><p>label font</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>label<wbr/>Color<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></h5>
+<div class="tsd-comment tsd-typography"><p>label color</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>line<wbr/>Height<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5>
+<div class="tsd-comment tsd-typography"><p>line spacing between labels</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>line<wbr/>Width<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5>
+<div class="tsd-comment tsd-typography"><p>line width for drawn lines</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>point<wbr/>Size<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5>
+<div class="tsd-comment tsd-typography"><p>size of drawn points</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>round<wbr/>Rect<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5>
+<div class="tsd-comment tsd-typography"><p>draw rounded boxes by n pixels</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>shadow<wbr/>Color<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></h5>
+<div class="tsd-comment tsd-typography"><p>label shadow color</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>use<wbr/>Curves<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>should lines be curved?</p>
+</div></li>
+<li class="tsd-parameter">
+<h5>use<wbr/>Depth<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></h5>
+<div class="tsd-comment tsd-typography"><p>use z-coordinate when available</p>
+</div></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/draw/options.ts#L4">src/draw/options.ts:4</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Emotion | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Emotion.html">Emotion</a></li></ul>
+<h1>Type alias Emotion</h1></div>
+<div class="tsd-signature">Emotion<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;angry&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;disgust&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;fear&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;happy&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sad&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;surprise&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;neutral&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L14">src/result.ts:14</a></li></ul></aside></div>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="Events.html">Events</a></li></ul>
+<h1>Type alias Events</h1></div>
+<div class="tsd-signature">Events<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;create&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;load&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;image&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;result&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;warmup&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;error&quot;</span></div>
+<div class="tsd-comment tsd-typography"><p>Events dispatched by <code>human.events</code></p>
+<ul>
+<li><code>create</code>: triggered when Human object is instantiated</li>
+<li><code>load</code>: triggered when models are loaded (explicitly or on-demand)</li>
+<li><code>image</code>: triggered when input image is processed</li>
+<li><code>result</code>: triggered when detection is complete</li>
+<li><code>warmup</code>: triggered when warmup is complete</li>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceLandmark | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="tsd-page-title">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="FaceLandmark.html">FaceLandmark</a></li></ul>
+<h1>Type alias FaceLandmark</h1></div>
+<div class="tsd-signature">Face<wbr/>Landmark<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;leftEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEye&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;nose&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;mouth&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEar&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;symmetryLine&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;silhouette&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;lipsUpperOuter&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;lipsLowerOuter&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;lipsUpperInner&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;lipsLowerInner&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeUpper0&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeLower0&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeUpper1&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeLower1&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeUpper2&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeLower2&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeLower3&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyebrowUpper&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyebrowLower&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightEyeIris&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeUpper0&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeLower0&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeUpper1&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeLower1&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeUpper2&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeLower2&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeLower3&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyebrowUpper&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyebrowLower&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftEyeIris&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;midwayBetweenEyes&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;noseTip&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;noseBottom&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;noseRightCorner&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;noseLeftCorner&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;rightCheek&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;leftCheek&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L17">src/result.ts:17</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
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+<h4 class="uppercase">Member Visibility</h4><form>
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+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-reference"><a href="../index.html#Descriptor" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4D82" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-8388608-path"></rect><path d="M10.354 17V8.24H13.066C13.586 8.24 14.042 8.348 14.434 8.564C14.826 8.772 15.13 9.064 15.346 9.44C15.562 9.816 15.67 10.256 15.67 10.76C15.67 11.352 15.514 11.86 15.202 12.284C14.898 12.708 14.482 13 13.954 13.16L15.79 17H14.518L12.838 13.28H11.434V17H10.354ZM11.434 12.308H13.066C13.514 12.308 13.874 12.168 14.146 11.888C14.418 11.6 14.554 11.224 14.554 10.76C14.554 10.288 14.418 9.912 14.146 9.632C13.874 9.352 13.514 9.212 13.066 9.212H11.434V12.308Z" fill="var(--color-text)" id="icon-8388608-text"></path></svg>Descriptor</a></li>
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+<li class="tsd-kind-enum"><a href="../enums/Rank.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-enum)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-8-path"></rect><path d="M9.45 16V7.24H14.49V8.224H10.518V10.936H14.07V11.908H10.518V15.016H14.49V16H9.45Z" fill="var(--color-text)" id="icon-8-text"></path></svg>Rank</a></li>
+<li class="tsd-kind-class"><a href="../classes/Env.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg>Env</a></li>
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+<li class="tsd-kind-class"><a href="../classes/Human.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Human</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/Config.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Config</a></li>
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+<h1>Type alias FingerDirection</h1></div>
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+<li class="state loading">Preparing search index...</li>
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+<div class="container container-main">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="GestureResult.html">GestureResult</a></li></ul>
+<h1>Type alias GestureResult</h1></div>
+<div class="tsd-signature">Gesture<wbr/>Result<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>face<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>gesture<span class="tsd-signature-symbol">: </span><a href="FaceGesture.html" class="tsd-signature-type" data-tsd-kind="Type alias">FaceGesture</a><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">{ </span>gesture<span class="tsd-signature-symbol">: </span><a href="IrisGesture.html" class="tsd-signature-type" data-tsd-kind="Type alias">IrisGesture</a><span class="tsd-signature-symbol">; </span>iris<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">{ </span>body<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>gesture<span class="tsd-signature-symbol">: </span><a href="BodyGesture.html" class="tsd-signature-type" data-tsd-kind="Type alias">BodyGesture</a><span class="tsd-signature-symbol"> }</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">{ </span>gesture<span class="tsd-signature-symbol">: </span><a href="HandGesture.html" class="tsd-signature-type" data-tsd-kind="Type alias">HandGesture</a><span class="tsd-signature-symbol">; </span>hand<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-comment tsd-typography"><p>Gesture combined results
+Each result has:</p>
+<ul>
+<li>part: part name and number where gesture was detected: <code>face</code>, <code>iris</code>, <code>body</code>, <code>hand</code></li>
+<li>gesture: gesture detected</li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L176">src/result.ts:176</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
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+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
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+<div class="tsd-theme-toggle">
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+<div class="tsd-accordion-details">
+<ul>
+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-reference"><a href="../index.html#Descriptor" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4D82" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-8388608-path"></rect><path d="M10.354 17V8.24H13.066C13.586 8.24 14.042 8.348 14.434 8.564C14.826 8.772 15.13 9.064 15.346 9.44C15.562 9.816 15.67 10.256 15.67 10.76C15.67 11.352 15.514 11.86 15.202 12.284C14.898 12.708 14.482 13 13.954 13.16L15.79 17H14.518L12.838 13.28H11.434V17H10.354ZM11.434 12.308H13.066C13.514 12.308 13.874 12.168 14.146 11.888C14.418 11.6 14.554 11.224 14.554 10.76C14.554 10.288 14.418 9.912 14.146 9.632C13.874 9.352 13.514 9.212 13.066 9.212H11.434V12.308Z" fill="var(--color-text)" id="icon-8388608-text"></path></svg>Descriptor</a></li>
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+<li class="tsd-kind-reference"><a href="../index.html#Models" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>Models</a></li>
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+<li class="tsd-kind-class"><a href="../classes/Env.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg>Env</a></li>
+<li class="tsd-kind-class"><a href="../classes/GraphModel.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Graph<wbr/>Model</a></li>
+<li class="tsd-kind-class"><a href="../classes/Human.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Human</a></li>
+<li class="tsd-kind-class"><a href="../classes/Tensor-1.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Tensor</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/BodyConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg>Body<wbr/>Config</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/FaceAntiSpoofConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceAttentionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Attention<wbr/>Config</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/FaceDetectorConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Detector<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceEmotionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Emotion<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceGearConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Gear<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceIrisConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Iris<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceLivenessConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Liveness<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceMeshConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Mesh<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceResult.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Result</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/Result.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Result</a></li>
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+<li class="tsd-kind-type-alias"><a href="BodyGesture.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Body<wbr/>Gesture</a></li>
+<li class="tsd-kind-type-alias"><a href="BodyLandmark.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Body<wbr/>Landmark</a></li>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
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+<li class="tsd-kind-type-alias"><a href="IrisGesture.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Iris<wbr/>Gesture</a></li>
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+<li class="tsd-kind-type-alias"><a href="ObjectType.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Object<wbr/>Type</a></li>
+<li class="tsd-kind-type-alias"><a href="Point.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-4194304-path"></use><use href="#icon-4194304-text"></use></svg>Point</a></li>
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>ObjectType | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="ObjectType.html">ObjectType</a></li></ul>
+<h1>Type alias ObjectType</h1></div>
+<div class="tsd-signature">Object<wbr/>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;person&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bicycle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;car&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;motorcycle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;airplane&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bus&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;train&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;truck&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;boat&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;traffic light&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;fire hydrant&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;stop sign&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;parking meter&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bench&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bird&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cat&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;dog&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;horse&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sheep&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;elephant&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bear&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;zebra&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;giraffe&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;backpack&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;umbrella&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;handbag&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tie&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;suitcase&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;frisbee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;skis&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;snowboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sports ball&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;kite&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;baseball bat&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;baseball glove&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;skateboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;surfboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tennis racket&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bottle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;wine glass&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cup&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;fork&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;knife&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;spoon&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bowl&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;banana&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;apple&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sandwich&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;orange&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;broccoli&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;carrot&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;hot dog&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;pizza&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;donut&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cake&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;chair&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;couch&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;potted plant&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bed&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;dining table&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;toilet&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tv&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;laptop&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;mouse&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;remote&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;keyboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cell phone&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;microwave&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;oven&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;toaster&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sink&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;refrigerator&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;book&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;clock&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;vase&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;scissors&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;teddy bear&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;hair drier&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;toothbrush&quot;</span></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L148">src/result.ts:148</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
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+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
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+<li class="tsd-kind-reference"><a href="../index.html#ModelStats" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>Model<wbr/>Stats</a></li>
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diff --git a/typedoc/types/match.Descriptor.html b/typedoc/types/match.Descriptor.html
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diff --git a/typedoc/types/match.MatchOptions.html b/typedoc/types/match.MatchOptions.html
new file mode 100644
index 00000000..f30dca18
--- /dev/null
+++ b/typedoc/types/match.MatchOptions.html
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+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>MatchOptions | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
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+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
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+<li><a href="../modules/match.html">match</a></li>
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\ No newline at end of file
diff --git a/typedoc/types/models.ModelStats.html b/typedoc/types/models.ModelStats.html
new file mode 100644
index 00000000..14c9e44e
--- /dev/null
+++ b/typedoc/types/models.ModelStats.html
@@ -0,0 +1,71 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>ModelStats | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/models.html">models</a></li>
+<li><a href="models.ModelStats.html">ModelStats</a></li></ul>
+<h1>Type alias ModelStats</h1></div>
+<div class="tsd-signature">Model<wbr/>Stats<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>modelStats<span class="tsd-signature-symbol">: </span><a href="ModelInfo.html" class="tsd-signature-type" data-tsd-kind="Type alias">ModelInfo</a><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">; </span>numDefinedModels<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>numEnabledModels<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol">; </span>numLoadedModels<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>percentageLoaded<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>totalSizeEnabled<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span><span class="tsd-signature-symbol">; </span>totalSizeFromManifest<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>totalSizeLoading<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">; </span>totalSizeWeights<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> }</span></div>
+<div class="tsd-type-declaration">
+<h4>Type declaration</h4>
+<ul class="tsd-parameters">
+<li class="tsd-parameter">
+<h5>model<wbr/>Stats<span class="tsd-signature-symbol">: </span><a href="ModelInfo.html" class="tsd-signature-type" data-tsd-kind="Type alias">ModelInfo</a><span class="tsd-signature-symbol">[]</span></h5></li>
+<li class="tsd-parameter">
+<h5>num<wbr/>Defined<wbr/>Models<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>num<wbr/>Enabled<wbr/>Models<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span></h5></li>
+<li class="tsd-parameter">
+<h5>num<wbr/>Loaded<wbr/>Models<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>percentage<wbr/>Loaded<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>total<wbr/>Size<wbr/>Enabled<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">undefined</span></h5></li>
+<li class="tsd-parameter">
+<h5>total<wbr/>Size<wbr/>From<wbr/>Manifest<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>total<wbr/>Size<wbr/>Loading<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li>
+<li class="tsd-parameter">
+<h5>total<wbr/>Size<wbr/>Weights<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span></h5></li></ul></div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/models.ts#L62">src/models.ts:62</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li class="current"><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="current tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-class tsd-parent-kind-namespace"><a href="../classes/models.Models.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-class)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-128-path"></rect><path d="M11.898 16.1201C11.098 16.1201 10.466 15.8961 10.002 15.4481C9.53803 15.0001 9.30603 14.3841 9.30603 13.6001V9.64012C9.30603 8.85612 9.53803 8.24012 10.002 7.79212C10.466 7.34412 11.098 7.12012 11.898 7.12012C12.682 7.12012 13.306 7.34812 13.77 7.80412C14.234 8.25212 14.466 8.86412 14.466 9.64012H13.386C13.386 9.14412 13.254 8.76412 12.99 8.50012C12.734 8.22812 12.37 8.09212 11.898 8.09212C11.426 8.09212 11.054 8.22412 10.782 8.48812C10.518 8.75212 10.386 9.13212 10.386 9.62812V13.6001C10.386 14.0961 10.518 14.4801 10.782 14.7521C11.054 15.0161 11.426 15.1481 11.898 15.1481C12.37 15.1481 12.734 15.0161 12.99 14.7521C13.254 14.4801 13.386 14.0961 13.386 13.6001H14.466C14.466 14.3761 14.234 14.9921 13.77 15.4481C13.306 15.8961 12.682 16.1201 11.898 16.1201Z" fill="var(--color-text)" id="icon-128-text"></path></svg>Models</a></li>
+<li class="current tsd-kind-type-alias tsd-parent-kind-namespace"><a href="models.ModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-4194304-path"></rect><path d="M11.31 16V8.224H8.91V7.24H14.79V8.224H12.39V16H11.31Z" fill="var(--color-text)" id="icon-4194304-text"></path></svg>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="../functions/models.getModelStats.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-function)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-64-path"></rect><path d="M9.39 16V7.24H14.55V8.224H10.446V11.128H14.238V12.112H10.47V16H9.39Z" fill="var(--color-text)" id="icon-64-text"></path></svg>get<wbr/>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="../functions/models.load.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>load</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="../functions/models.reset.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>reset</a></li>
+<li class="tsd-kind-function tsd-parent-kind-namespace"><a href="../functions/models.validate.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-64-path"></use><use href="#icon-64-text"></use></svg>validate</a></li></ul></nav></div></div>
+<div class="overlay"></div><script src="../assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/variables/defaults.html b/typedoc/variables/defaults.html
new file mode 100644
index 00000000..d894104f
--- /dev/null
+++ b/typedoc/variables/defaults.html
@@ -0,0 +1,119 @@
+<!DOCTYPE html><html class="default" lang="en"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>defaults | @vladmandic/human - v2.9.0</title><meta name="description" content="Documentation for @vladmandic/human - v2.9.0"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.documentElement.dataset.theme = localStorage.getItem("tsd-theme") || "os"</script><header class="tsd-page-toolbar">
+<div class="tsd-toolbar-contents container">
+<div class="table-cell" id="tsd-search" data-base="..">
+<div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><path d="M15.7824 13.833L12.6666 10.7177C12.5259 10.5771 12.3353 10.499 12.1353 10.499H11.6259C12.4884 9.39596 13.001 8.00859 13.001 6.49937C13.001 2.90909 10.0914 0 6.50048 0C2.90959 0 0 2.90909 0 6.49937C0 10.0896 2.90959 12.9987 6.50048 12.9987C8.00996 12.9987 9.39756 12.4863 10.5008 11.6239V12.1332C10.5008 12.3332 10.5789 12.5238 10.7195 12.6644L13.8354 15.7797C14.1292 16.0734 14.6042 16.0734 14.8948 15.7797L15.7793 14.8954C16.0731 14.6017 16.0731 14.1267 15.7824 13.833ZM6.50048 10.499C4.29094 10.499 2.50018 8.71165 2.50018 6.49937C2.50018 4.29021 4.28781 2.49976 6.50048 2.49976C8.71001 2.49976 10.5008 4.28708 10.5008 6.49937C10.5008 8.70852 8.71314 10.499 6.50048 10.499Z" fill="var(--color-text)"></path></svg></label><input type="text" id="tsd-search-field" aria-label="Search"/></div>
+<ul class="results">
+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
+<div class="container container-main">
+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="defaults.html">defaults</a></li></ul>
+<h1>Variable defaults<code class="tsd-tag ts-flagConst">Const</code> </h1></div>
+<div class="tsd-signature">defaults<span class="tsd-signature-symbol">:</span> <a href="../interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div>
+<div class="tsd-comment tsd-typography"><ul>
+<li><a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">See all default Config values...</a></li>
+</ul>
+</div><aside class="tsd-sources">
+<ul>
+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L313">src/config.ts:313</a></li></ul></aside></div>
+<div class="col-4 col-menu menu-sticky-wrap menu-highlight">
+<div class="tsd-navigation settings">
+<details class="tsd-index-accordion"><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Settings</h3></summary>
+<div class="tsd-accordion-details">
+<div class="tsd-filter-visibility">
+<h4 class="uppercase">Member Visibility</h4><form>
+<ul id="tsd-filter-options">
+<li class="tsd-filter-item"><label class="tsd-filter-input"><input type="checkbox" id="tsd-filter-inherited" name="inherited" checked/><svg width="32" height="32" viewBox="0 0 32 32" aria-hidden="true"><rect class="tsd-checkbox-background" width="30" height="30" x="1" y="1" rx="6" fill="none"></rect><path class="tsd-checkbox-checkmark" d="M8.35422 16.8214L13.2143 21.75L24.6458 10.25" stroke="none" stroke-width="3.5" stroke-linejoin="round" fill="none"></path></svg><span>Inherited</span></label></li></ul></form></div>
+<div class="tsd-theme-toggle">
+<h4 class="uppercase">Theme</h4><select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></div></div></details></div>
+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
+<ul>
+<li><a href="../index.html">@vladmandic/human -<wbr/> v2.9.0</a>
+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
+<nav class="tsd-navigation secondary menu-sticky">
+<ul>
+<li class="tsd-kind-reference"><a href="../index.html#Descriptor" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="#FF4D82" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="12" id="icon-8388608-path"></rect><path d="M10.354 17V8.24H13.066C13.586 8.24 14.042 8.348 14.434 8.564C14.826 8.772 15.13 9.064 15.346 9.44C15.562 9.816 15.67 10.256 15.67 10.76C15.67 11.352 15.514 11.86 15.202 12.284C14.898 12.708 14.482 13 13.954 13.16L15.79 17H14.518L12.838 13.28H11.434V17H10.354ZM11.434 12.308H13.066C13.514 12.308 13.874 12.168 14.146 11.888C14.418 11.6 14.554 11.224 14.554 10.76C14.554 10.288 14.418 9.912 14.146 9.632C13.874 9.352 13.514 9.212 13.066 9.212H11.434V12.308Z" fill="var(--color-text)" id="icon-8388608-text"></path></svg>Descriptor</a></li>
+<li class="tsd-kind-reference"><a href="../index.html#ModelStats" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>Model<wbr/>Stats</a></li>
+<li class="tsd-kind-reference"><a href="../index.html#Models" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>Models</a></li>
+<li class="tsd-kind-reference"><a href="../index.html#default" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-8388608-path"></use><use href="#icon-8388608-text"></use></svg>default</a></li>
+<li class="tsd-kind-enum"><a href="../enums/Rank.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-enum)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-8-path"></rect><path d="M9.45 16V7.24H14.49V8.224H10.518V10.936H14.07V11.908H10.518V15.016H14.49V16H9.45Z" fill="var(--color-text)" id="icon-8-text"></path></svg>Rank</a></li>
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+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="col-8 col-content">
+<div class="tsd-page-title">
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+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="../modules/draw.html">draw</a></li>
+<li><a href="draw.options.html">options</a></li></ul>
+<h1>Variable options<code class="tsd-tag ts-flagConst">Const</code> </h1></div>
+<div class="tsd-signature">options<span class="tsd-signature-symbol">:</span> <a href="../types/DrawOptions.html" class="tsd-signature-type" data-tsd-kind="Type alias">DrawOptions</a><span class="tsd-signature-symbol"> = ...</span></div>
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\ No newline at end of file
diff --git a/typedoc/variables/env.html b/typedoc/variables/env.html
new file mode 100644
index 00000000..471fe08d
--- /dev/null
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+<li class="state loading">Preparing search index...</li>
+<li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.9.0</a></div>
+<div class="table-cell" id="tsd-widgets"><a href="#" class="tsd-widget menu no-caption" data-toggle="menu" aria-label="Menu"><svg width="16" height="16" viewBox="0 0 16 16" fill="none"><rect x="1" y="3" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="7" width="14" height="2" fill="var(--color-text)"></rect><rect x="1" y="11" width="14" height="2" fill="var(--color-text)"></rect></svg></a></div></div></header>
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+<div class="col-8 col-content">
+<div class="tsd-page-title">
+<ul class="tsd-breadcrumb">
+<li><a href="../index.html">@vladmandic/human - v2.9.0</a></li>
+<li><a href="env.html">env</a></li></ul>
+<h1>Variable env<code class="tsd-tag ts-flagConst">Const</code> </h1></div>
+<div class="tsd-signature">env<span class="tsd-signature-symbol">:</span> <a href="../classes/Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources">
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+<li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L170">src/util/env.ts:170</a></li></ul></aside></div>
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+<nav class="tsd-navigation primary">
+<details class="tsd-index-accordion" open><summary class="tsd-accordion-summary">
+<h3><svg width="20" height="20" viewBox="0 0 24 24" fill="none"><path d="M4.93896 8.531L12 15.591L19.061 8.531L16.939 6.409L12 11.349L7.06098 6.409L4.93896 8.531Z" fill="var(--color-text)"></path></svg> Modules</h3></summary>
+<div class="tsd-accordion-details">
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+<ul>
+<li class="tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/draw.html">draw</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/match.html">match</a></li>
+<li class="tsd-kind-namespace"><a href="../modules/models.html">models</a></li></ul></li></ul></div></details></nav>
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+<ul>
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+<li class="tsd-kind-class"><a href="../classes/GraphModel.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Graph<wbr/>Model</a></li>
+<li class="tsd-kind-class"><a href="../classes/Human.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Human</a></li>
+<li class="tsd-kind-class"><a href="../classes/Tensor-1.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-128-path"></use><use href="#icon-128-text"></use></svg>Tensor</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/BodyConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><rect fill="var(--color-icon-background)" stroke="var(--color-ts-interface)" stroke-width="1.5" x="1" y="1" width="22" height="22" rx="6" id="icon-256-path"></rect><path d="M9.51 16V15.016H11.298V8.224H9.51V7.24H14.19V8.224H12.402V15.016H14.19V16H9.51Z" fill="var(--color-text)" id="icon-256-text"></path></svg>Body<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/BodyKeypoint.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Body<wbr/>Keypoint</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/BodyResult.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Body<wbr/>Result</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/Config.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Config</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/FaceAttentionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Attention<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceDescriptionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Description<wbr/>Config</a></li>
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+<li class="tsd-kind-interface"><a href="../interfaces/FaceEmotionConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Emotion<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceGearConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Gear<wbr/>Config</a></li>
+<li class="tsd-kind-interface"><a href="../interfaces/FaceIrisConfig.html" class="tsd-index-link"><svg class="tsd-kind-icon" width="24" height="24" viewBox="0 0 24 24"><use href="#icon-256-path"></use><use href="#icon-256-text"></use></svg>Face<wbr/>Iris<wbr/>Config</a></li>
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